Letter From a Birmingham Museum

As regular readers of Epsilon Theory know, I may make my home in the wilds of Connecticut today, doing my best Eddie Albert / Green Acres impersonation here on Little River Farm, but I grew up just outside of Birmingham, Alabama. My father spent his entire adult working life as an ER doc at Lloyd Noland Hospital in Fairfield, Alabama (trust me, about as far from Fairfield, Connecticut as the Earth is from Mars), starting back before emergency medicine was even a thing. My mother kept their two sons from getting into too much trouble and created a wonderful home from a (quite) modest house in an unincorporated area that’s now part of Hoover.

Lloyd Noland Hospital itself is an interesting story for a brief Epsilon Theory aside. It was the old Tennessee Coal & Iron employees hospital, dating back to 1919, acquired by US Steel when it bought TCI in the 1950s, then immediately spun off as a nonprofit foundation. The Foundation sold its assets to Tenet Healthcare in 1996, and the senior Foundation executives made a fortune. A lot of the staff, both doctors and nurses, were fired. Funny how that works. Tenet flipped the hospital to HealthSouth just three years later in a deal backed by public money. Funny how that works, too. In 2004, HealthSouth imploded in one of the largest accounting frauds in American history, and Lloyd Noland Hospital was shuttered for good. Funny how that … ah, who am I kidding … none of this is funny at all.

At least the HealthSouth CEO, Richard Scrushy, went to prison for a few years. A few. He’s found Jesus now, of course, and if you’re looking for “a dynamic risk taking entrepreneur with a powerful track record”, he’s available to speak at your next corporate retreat. Maybe you’ll catch him on Fox Business or CNBC. Or you could buy his book. Barf.

Anyway, my wife and I took three of our daughters down to Birmingham last week to visit their cousins and their Nana, and we decided to take a morning and go see the museum at the Birmingham Civil Rights Institute. It’s been open since 1992, and I’ve only heard rave reviews. But I had never been to the museum. It’s been open for 25 YEARS, and I had never been. Why not? As my father would say, Ben, you have plenty of excuses, but not a single reason.

Well, that’s not exactly true. I had a reason, just not a good one. My bad reason: I didn’t want to be lectured on civil rights. I didn’t want to be served a heaping dish of cold spinach and feel like it was my social duty to smile wanly and say “why, thank you, that was delicious. May I have some more?” What I told myself, and this is the excuse part, is that I’m a modern, educated man. I told myself that I already knew pretty much everything that needed knowing about the civil rights movement.

NARRATOR:    He did not know.

Nope, not even close.

I wasn’t lectured. I wasn’t put down. I was uplifted.

Yes, it’s spinach. Yes, I walked through half of the exhibits with a lump in my throat. Yes, I was ashamed for only coming now, 25 years late. And you know what? That’s okay. I deserve that feeling of shame. I welcome that feeling of shame, because if you don’t feel shame you’re a creature of the flock, not a creature of the pack. Frankly, we need a lot more shame in the world, not as a permanent scarlet letter or as a bureaucratic tool of the Nudging State, but as a catalyst for the gut check that we all need from time to time. The gut check that requires you to come to grips with the painful past or the painful present and DEAL WITH IT as honestly as you can. The gut check that MUST be passed if you’re ever going to succeed or move forward with ANYTHING.

That’s what the Birmingham Civil Rights museum gives you. A gut check.

What makes the museum so effective in communicating a difficult story well? Just that. They present it as a story, as a narrative. Not a cartoon story of Superheroes, although it’s impossible to avoid some degree of hagiography when it comes to this stuff, and not a cartoon story of Social Justice™, either, although here, too, it’s impossible to eliminate completely the heavy-handed nudging of the Smileyface State. No, it’s mostly a story of … people. Of the actual lives of actual people. It’s immersive and it’s real. It creates a compelling narrative arc, but not in a way that feels scripted or forced. What do I mean? I mean that the very last exhibit of the museum is a gigantic room, filled only with photographic portraits of African Americans who endured the civil rights struggles of Birmingham in the 1960s. Not activists, necessarily, just people. No one famous. No one with a statue somewhere. A chemistry teacher. A church deacon. A housewife. Not photographs of heroic actions back in the day, but a simple portrait of how they look today. Which is … old. Weathered. But oh my god … PROUD.

And that brings me to the point of all this. Because my gut check wasn’t just an examination of the shame I felt in coming to this museum 25 years too late. There was another gut check, too. Where was my family in all of this? Because unlike the people in those photographs, I wasn’t feeling particularly proud.

I was born in 1964 at St. Vincent’s Hospital, on the edge of downtown Birmingham. I think that’s where almost everyone of my cohort and my race was born in Birmingham in those days. And unlike Lloyd Noland Hospital, St. Vincent’s is still around. Looks like it’s going strong, in fact. I understand that lots of babies, white and black, are born there every day.

Eight months before I was born, not two miles distant from St. Vincent’s Hospital, these four girls were killed in the dynamite bombing of the 16th Street Baptist Church, right across the street from where the museum stands today. It took 14 years to bring one of the killers to justice, 38 years to convict two more.

The girls’ names are (left to right) Carol Denise McNair, Carole Robertson, Addie Mae Collins, and Cynthia Wesley. I’d like for us to remember these names and not the killers’ names.

Twelve months before I was born, even closer to St. Vincent’s Hospital, Bull Connor sicced dogs on civil rights marchers and ordered the Birmingham Fire Department to attack with high-pressure hoses.

   

You’ve probably seen these photographs before. They’re pretty famous. Or infamous, I guess. What you might not know, however, is that most of the people in these photographs are children.

   

Yes, black children were intentionally attacked and detained by Bull Connor’s Police and Fire Departments, specifically because they wanted “to send a message”, something that seems particularly poignant given the “deterrence” rationale given by today’s White House in defense of its immigration policy, where brown children have been intentionally separated from their parents and detained indefinitely.

What’s also true, of course, is that there was nothing accidental about the Birmingham Childrens Crusade of 1963. Children didn’t march in some organic display of civil rights awareness. They were intentionally deployed by march organizers – “used”, if you will – in order to galvanize national public opinion against segregationist policies and political leaders. That, too, seems particularly relevant given what’s happening with our immigration policy today and the Fiat News constructed both in favor and in opposition to those policies.

But my question remains. Where was my family in all of this? How is it possible that all of this was happening just down the street from where I was born, just a few miles from where I would live my entire pre-adult life, and I NEVER got a glimpse or heard a word about ANY of this? How is it possible that I would grow up without these events touching my life in any way, shape, or form? Because they didn’t. At all. More directly, why didn’t my father do something … no, scratch that … why didn’t my father do ANYTHING to support the civil rights movement happening in his backyard? Because he didn’t. At all.

To be clear, my father wasn’t a Bull Connor or George Wallace supporter. He thought they were thugs. He definitely wasn’t a segregationist or an avowed racist, and – quite the rarity – he wasn’t an unavowed racist, either, the sort of man who mutters the n-word under his breath and laughs uproariously at the “jokes”. I mean, I’m not going to say something stupid like “he didn’t have a racist bone in his body”, because I don’t think you could say that about any white person born in America in 1934, like my father. Hell, you couldn’t say that about anyone born in 1964, like me. But I’ll say this. For his day and his place, my father was as colorblind and as woke in his personal and professional life as anyone I’ve ever known. I’ve got a hundred memories of watching my father act with grace and humanity and camaraderie in interracial social settings, and not one – not ONE – of hostility or a mean-spirit. But in his political life – in his life as a citizen – he was AWOL from the defining struggle of his day. Why?

I think I found the answer to that question at the Birmingham Civil Rights museum, and I’ll use the Montgomery bus boycott of 1955 – 1956 to illustrate.

We’re all familiar with Rosa Parks, the seamstress who refused to give up her seat on a Montgomery bus to a white man, and was duly arrested, tried, and fined for breaking this prototypical Jim Crow law. What we’re less familiar with, however, are the politics and the NARRATIVE of the civil rights protest that followed in the wake of Parks’ arrest.

First, it wasn’t just Rosa Parks who refused to give up her seat, and several of those arrested were children.

Look at the charges filed against this 15-year-old girl – assault and battery for refusing to give up a bus seat. Look at the sentence here – the girl is declared a ward of the state, legally and permanently separated from her parents. This happened nine months before Rosa Parks was arrested.

Like the Childrens Crusade of 1963, it was no accident that a 15-year-old was on the front lines of a civil rights battle. The girl in this case – Claudette Colvin – was a member of the NAACP Youth Council, and her mentor – Rosa Parks – was the secretary of the NAACP Montgomery Chapter. Like the Birmingham children eight years later, Colvin was intentionally placed in harm’s way with the explicit goal of becoming a cause celebre that would be sympathetic to a national audience.

And it worked. National media coverage of the Montgomery bus boycott was highly critical of the arrests, particularly Colvin’s. In fact, the Colvin case – much more so than Rosa Parks’ own case – was the backbone of the Supreme Court decision in Browder v. Gayle, which struck down the Montogomery bus segregation laws as unconstitutional.

But Alabama media coverage – the media coverage that my father would have seen – focused entirely on the agency of the NAACP in breaking the law. There was zero assessment or discussion of the law itself. There was enormous assessment of the de facto illegality of the acts and the intentional use of children to perform illegal acts. In fact, E.D. Nixon, the head of the NAACP in Alabama during this span, decided not to proceed with a boycott of the Montgomery bus system after Colvin’s arrest precisely because – as effective as the Colvin Narrative might be on the national stage – he thought the child-used-by-NAACP Narrative would undermine the boycott’s effectiveness on the ground in the Montgomery area. Instead, he wanted an adult to be the face of the event, and that’s why Rosa Parks, arrested nine months later, is on a postage stamp but Claudette Colvin is not.

This War of Narratives, one acting nationally and one acting locally, escalated dramatically as the Rosa Parks arrest catalyzed a full-scale boycott of the Montgomery bus system in December 1955. Just as he had chosen Rosa Parks as the public face of the arrest, Nixon chose Martin Luther King, Jr., then a 26-year-old minister new to the Montgomery area, as the public face of this largescale protest action, MLK’s first. As with the choice of Parks, Nixon’s choice of MLK was brilliant from a Narrative construction and delivery perspective. E.D. Nixon played one hell of a metagame!

The white Narrative response was pretty effective, too, though. Rather than fight the boycott on the “merits” of segregation and Jim Crow laws, the status quo Narrative effort focused almost entirely on the illegality of the boycott. Yes, I know this sounds bizarre to the modern ear, but calling for a boycott of a commercial service used to be illegal. I’m not making this up.

Let me say this again, with emphasis: only a few decades ago, you would be arrested if you said out loud that people should stop going to Starbucks or Walmart or Amazon or SeaWorld or Chick-fil-A or Exxon or Red Hen or whatever.

This wasn’t just an Alabama thing and it wasn’t just a segregationist South thing. It was an anti-Labor thing across the country. It was a status quo political thing.

The Montgomery bus boycott was defined as illegal, which allowed the construction of a VERY effective Narrative that the organizers were, by definition, criminals. That MLK mug shot at the start of this note … that’s not from his Birmingham arrest, where he wrote his masterpiece “Letter From a Birmingham Jail”, but from his Montgomery arrest, where a grand jury indicted him and close to 100 others on felony charges of “conspiracy” against a business enterprise. MLK was sentenced to a $500 fine or a YEAR in the state penitentiary. No joke. More than a year, actually. He spent two weeks in jail before the fine was paid. For his words. For the criminal harm done by his “hate speech”, as it was defined then.

THAT’S the Narrative that my father heard. THAT’S the Narrative that moderate whites all over the South heard. It didn’t turn my father into a segregationist or a racist. But that was never the intent. The intent was to take my father off the political board. By constructing a dominant and immersive Narrative where opposing the status quo was defined as criminal, status quo institutions made it impossible for my father to actively support the civil rights movement. Why? Because to act in that way would mean self-identifying as a criminal, and that’s something my father would never do. It’s not that my father was oh-so concerned about the State seeing him as a criminal, although yeah, there’s that. My father’s pack was his family, and he wasn’t about to do anything that might draw the gaze of the State, which he distrusted immensely, onto his family. The bigger issue, though, was that my father could not abide seeing HIMSELF as a criminal, and that was the meaning of civil rights activism in the Narrative ocean in which 1960s Alabama white people swam: civil rights activism = criminality.

This is the awesome power of effective Narratives and the Common Knowledge Game. They don’t control us directly, like high-pressure fire hoses and billy clubs. No, they’re much more effective than that. Narratives and the Common Knowledge Game drive us to control OURSELVES.

The goal of Narrative creation by status quo Missionaries like politicians and oligarchs is rarely to change your mind. It’s rarely to try and switch you from one side to the other side. It’s rarely to get you to vote FOR them or to buy FROM them. Because you already do.

The goal of most Narrative creation is to take you off the board.

The goal of most Narrative creation is to convince you to sit down and shut up.

In our investment lives, we are told to sit down and shut up when it comes to industrially necessary eggs, investment products like ETFs and passive index funds. We are told by trillion dollar asset managers, who just happen to dominate the market in ETFs and passive index funds, that our fiduciary fitness is defined by our opposition to “high fees”. We are told that we are acting against our client’s best interests – i.e. we must self-identify as bad guys if not outright criminals – if we don’t focus on investment fees as our be-all-and-end-all consideration. None of this will turn independent-thinking financial advisors into outright Vanguard-indexing pod people. But it will absolutely make independent-thinking financial advisors doubt themselves and their own virtue if they start to question the party line. You’re not one of those bad guys trying to screw over your clients by putting them into actively managed funds, are you? No, of course you’re not.

In our political lives, we are told to sit down and shut up when it comes to law-breaking Others, like child-using MS-13 gangbangers or Muslim-country-originating ISIS terrorists or … on the other side … statue-protecting Charlottesville Nazis or Putin-loving White House traitors. We are told by trillion dollar political/media machines that our patriotic fitness is defined by our opposition to these cartoon foes. None of this will convince independent-thinking Republicans to vote Democrat or independent-thinking Democrats to vote Republican. But it will absolutely make both independent-thinking Republicans and independent-thinking Democrats doubt themselves and their own virtue if they start to question the (literally) party line. You’re not one of those bad guys trying to screw over America by supporting the criminals/terrorists/Nazis/traitors, are you? No, of course you’re not.

Last summer I wrote a note – Always Go To the Funeral – to introduce the social and game theory dynamics in play with all of this. At the time I didn’t see how our Narrative shock collars could possibly get any stronger.

And yet here we are. The shock collars are zapping us harder and harder. Our respective yards encompassed by our respective invisible fences are getting smaller and smaller.

Red Hen … ZAP! Child prisons … ZAP! Supreme Court … ZAP! MS-13 … ZAP! Russia … ZAP!

I’m not saying that you should fight the Man, whatever that means to you.

I’m saying that the Man is very, very active in these Narrative efforts to take you off the board, to convince you to sit down and shut up as an investor or as a voter. I’m saying that once you start looking for these efforts, you will see them everywhere.

I’m saying that the Man is very, very skilled at defining your choices in ways that don’t seem at all like they’ve been defined for you. In ways that seem like common sense. In ways that seem like common decency. In ways that make you believe that YOU are the bad guy if you question the Narrative.

I’m saying that’s not true. I’m saying that you’re not a bad person for questioning the party line. I’m saying that you may still make the choice to take yourself off the board, but make it a choice. I’m saying that the sense of shame you may feel when you wrestle with these issues isn’t a sign of weakness, but a sign of strength. I’m saying that you may feel alone and besieged and full of self-doubt as you wrestle with these issues, but only because that’s the way that your social animal brain is hard-wired. Not because you are truly alone.

If I could go back in time and tell my father, gone more than 20 years now, ONE thing it would be that. You are not alone. Because I suspect he felt pretty darn lonely as he wrestled with all this. I think it would have meant the world to him to talk this through with a member of his pack, to try and figure it out together.

And that’s why I write Epsilon Theory. This is the blessing it has given me. To connect me with other free-thinking and truth-seeking human beings, from all over the world and from every walk of life, who are wrestling with this most basic question: how do we make our way in a fallen world without losing ourselves in the process?

I never had a chance to talk with my father about that. Not directly, anyway. But I can talk with you.

We are not alone.

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The Acrobat and the Fly

No, nothing makes sense, nothing seems to fit
I know you’d hit out if you only knew who to hit
And I’d join the movement
If there was one I could believe in.
Yeah, I’d break bread and wine
If there was a church I could receive in,
Cause I need it now
To take the cup
To fill it up, to drink it slow
I can’t let you go
And I must be an acrobat
To talk like this and act like that,
And you can dream, so dream out loud,
And don’t let the bastards grind you down.

— U2, Achtung Baby, “Acrobat” (1991)

It’s no secret that a conscience can sometimes be a pest
It’s no secret ambition bites the nails of success
Every artist is a cannibal, every poet is a thief
All kill their inspiration and sing about their grief
Over love
A man will rise
A man will fall
From the sheer face of love
Like a fly from a wall
It’s no secret at all

— U2, Achtung Baby, “The Fly” (1991)


Maybe because of their popular appeal, or the fact that our society can’t abide a person like Bono with unapologetic earnestness about his beliefs, or because of the band’s retreat into musical weirdness and emergence into arena bombast, U2 has been treated rather uncharitably by modern commentators. But at their best, U2 were mesmerizing. Stylistically, I prefer Unforgettable Fire or War, and for sheer songwriting genius, Joshua Tree remains one of the greatest albums ever recorded.

But where art about making art (e.g., La La Land, Birdman) can sometimes veer toward self-indulgence, Achtung Baby reaches a different kind of peak. It is raw and self-critical, with no attempt at final redemption. I mean, it is melodramatic as all hell, which is kind of the concept of the whole album, but if its arc carries any absolution for the artist, it is that hypocrisy is the universal result of art and not some unique moral failing. Every artist is a cannibal, every poet is a thief.

But in the end, neither the artist’s cannibalism nor the poet’s thievery invalidate their art. You can dream, so dream out loud, and don’t let the bastards grind you down. There’s a narrow lesson in this that goes like, “You can still read Ender’s Game even though Orson Scott Card once ate a Chick-fil-A sandwich.” But there’s a bigger lesson, too: if you go around looking for hypocrisy in your enemies, you’ll always find it. Doing so will always feel good. Doing so will rarely get you closer to truth, beauty or love.


I was recently explaining to a friend and former colleague what I write about on Epsilon Theory. They asked me if it was a behavioral investing blog, and I wasn’t sure how to answer.

In a sense, yes, of course Epsilon Theory is a behavioral investing blog. We believe that humans and the stories they tell heavily influence, and sometimes determine asset prices. And we write about that. But when most people say “behavioral economics” or discuss investment strategies that account for investor behaviors, what they usually mean is “cognitive biases.”  Yes, we write about those things, too.

But except in the way that all human activities are influenced bv the way that our brains evolved to process information, Epsilon Theory isn’t really about cognitive biases. That isn’t because we don’t believe in those biases. Quite the contrary. Instead, it is a recognition that our biased brains are riding on meat puppets that spend most of their time interacting with other meat puppets. Our brains are rarely tasked with drawing conclusions from raw data. Most of the things that matter to us and our lives are social. That means that the stimuli that reach us, the basis for our judgments and opinions, are usually the outputs of other compromised brains, processed through established cultural and social structures.

It is intuitive that understanding and mastering our own biases should mean not only being aware of innate evolutionary impulses, but also understanding how they manifest in social behavior. This is what Ben meant when he wrote about acknowledging our own vulnerabilities to the introduction of memes and Narrative in This is Why We Can’t Have Nice Things. We like to think that we operate from internally coherent, epistemologically sound ethical, social and political frameworks. You don’t. I don’t. We don’t. We’re making it up as we go along and we all know it.

It is rarely possible to divorce ourselves completely from the ways in which our human brains are wired to respond to society that is increasingly aware of the ways in which other human brains are wired to respond. We cannot pretend that it doesn’t change anything that companies like Tesla and Salesforce now seek to foster rabid audiences and stabilize their stock price through targeted social media engagement strategies — what we write about on these pages as Missionary activity. We cannot pretend that it doesn’t change the priors that drive how we build investment portfolios that standing governments consider markets to be utilities for maintaining public order and assent, and actively employ communications strategies to establish Narrative around fiscal, monetary and trade policy.

And so it is that in our investing lives, and in our public, political lives, it is very difficult to refuse to play the game. Ours is a Narrative-driven world, and surviving in it means doing more than understanding how our biology predisposes us to cognitive biases. It means understanding how our engagement with social structures and with one another creates new biases and pitfalls altogether, a kind of special susceptibility to brutal logical fallacies. Many of these are so-called ecological fallacies, discussed in an ET note from 2013 that still reads very well.

Ben wrote recently that the memeification of information — the transformation of Emails into Emails! or Lyme disease into Lyme disease! — is a big part of why we can’t have nice things. I want to suggest to you that it is possible to have nice things again. Real conversations with other people that result in real outcomes. Perhaps even a move back in the direction of Cooperative Games from the Competitive Game we are in today. I argued in my essay from last August that this would require a critical mass of well-intentioned people willing to give up on, to lose, non-existential battles. I also warned that you wouldn’t like my advice. In the interest of providing you with yet more unsolicited advice that you won’t like, allow me to outline the four Competitive Game equilibrium-enforcing strategies I think we must give up if we’re going to make ourselves and our thinking less vulnerable to memes, abstractions, tribalism and Narrative.

Know in advance that in a Competitive Game, each of these four is a dominant strategy. To wit:

  • The People Who Disagree with Me are Hypocrites
  • The People Who Disagree with Me are Stupid
  • The People Who Disagree with Me are Evil
  • The People Who Disagree with Me are Controlled

If you give up using these strategies as I will recommend to you, you will lose. You will lose credibility. You will lose standing. You will lose popularity. People will believe you are losing arguments. People will believe you are less intelligent. People may believe you are less committed to ethics, morality and justice.

Wondering where you can sign up yet? Good. We’re going to lose so much, you’re going to be so sick and tired of #losing.

You’re Too Biased to Measure the Impact of Hypocrisy on Credibility

Before we get too far, however, let’s get one thing out of the way: the people who disagree with you really are hypocrites.

I don’t know the people who disagree with you and I don’t need to know. Hypocrisy has been the human condition since Adam proclaimed his holiness by blaming the apple eating on his wife (I mean, it was kind of her fault, if you think about it). But the game of find-the-hypocrite isn’t really about finding gaps between the behaviors people condemn in others and the actions those people take themselves. We all know those exist, and I hope you came here for meatier arguments than, “We’re all hypocrites, so live and let live, amirite?” No, the game is about how we go about quantifying that gap. Who is the bigger fraud, the bigger phony? It’s also about why we seek out hypocrisy in others.

You might think that this strategy would be played out by first looking for the worst actions and then aligning them with incongruous statements of condemnation. Turns out that isn’t exactly the case. Four Yale researchers in psychology published a fascinating study in 2017 on this topic. It’s a well-written, very digestible bit of research based on cleverly formulated questions. A rarity for such papers, I recommend reading the whole thing. It holds a few interesting insights:

  • People attribute more moral value to condemnations of bad acts than to claims of good acts.
  • People will forgive admitted actions that don’t jive with values, but they won’t forgive bad acts that conflict with condemnations of bad acts.

In other words, what people hate about hypocrisy isn’t the immoral act, or even the gap between values and actions. It’s the intentionally false signal from moralizing about the act. And while the paper doesn’t suggest this directly, it is my belief that this aversion is one reason why excessively strong signaling or moral condemnation, when coupled with even suspicions that someone may be acting in conflict with those signals, is so distasteful to many of us. You’ve heard of virtue signaling, I presume.

The gulf between a false signal and simple conflict between values and action may seem like a distinction without a difference, but it isn’t. It matters that our anger about hypocrisy is not the response to a moral failure, but to a failure in ideological signaling. That means that it is an opportunity to assault the credibility of those signaling.

It just so happens, of course, that credibility is one of the most important social signals we send, and one of the ones that matters most in Narrative-driven political, financial and other social and civic markets. The mechanisms of credibility within social capital are so pivotal to influence, wealth generation, capital formation, new lead generation and popularity in general that signaling “I am a credible person” becomes for many of us an objective unto itself. We may complain about Missionaries and their attempts to influence us, but we would all be Missionaries in a heartbeat if we could. For those who have read my piece exhorting us to Make America Good Again (and to stop worrying about being great), you won’t be surprised to learn where I come out on this issue. Those who have built on the sands of cringeworthy credibility signaling may come to a different conclusion.

One of our most potent weapons for winning the credibility game — or so we perceive — is seeking out and identifying hypocrisy in others. We are attracted to assaulting hypocrisy for two reasons. First, it acts as a credibility signal for us. It tells others that we are players in the great game. It tells others that we care about logical consistency and other Good Things. Second, it acts as a credibility reducer for our opponent. It challenges and reduces their believability and standing, and seeks to insinuate that they care less about intellectual honesty and logical and moral consistency. In effect, it is a force multiplier for our arguments, because once we establish that another party has made hypocritical statements, we can summon that spectre again and again to relieve us of the need to dispute further arguments on their merits.

There’s just one problem with this: we are hopelessly prone to bias in our assessments of others’ hypocrisy. Why? Because our anger about hypocrisy doesn’t begin with systematic, objective observation of moral failures or flawed reasoning under our value system. It begins with our selective observation of moral, philosophical or intellectual condemnations made by others — and guess what? We tend to pay a little more attention when someone condemns someone we like or something we believe in. In other words, when someone expresses a criticism of us, our friends, our allies and their behaviors or actions, we are simultaneously inspired to diminish that person’s credibility to protect our ego, and to search for actions that conflict with their condemnation. It’s like handing a three-year old a club and telling him that other boy over there took his favorite toy.

It’s an overwhelming bias that seems so obvious and non-partisan in its pervasiveness when you step back to view it with as much dispassion as any of us can muster. It’s why the political right quickly finds every example of a preening Hollywood numbskull moralizing about some progressive social justice issue right before they end up in TMZ for abetting the abuse of young actors and actresses.  It’s why the political left is lying in wait for any Bible-thumping family values Republican politician to get caught in an ethics scandal. It’s why there are millions of people still penning gotcha pieces on the hypocrisy of Bill Clinton supporters who criticized the moral failings of Donald Trump and why millions of people are still writing pieces on the hypocrisy of Donald Trump supporters who had criticized the moral failings of Bill Clinton. Claims of hypocrisy aren’t about morality. Claims of hypocrisy are about ideology.

But Hypocrisy! the meme isn’t about either of those things. It’s about credibility. And Hypocrisy! the meme is warm, wet garbage. In those rare moments when we are honest with ourselves, we know that the reason we accuse others of hypocrisy rarely has anything to do with a good-faith belief that it justifies devaluation of their opinions or arguments which would often stand on their own merits. Likewise, research tells us it has next to nothing to do with any moral objection on our part. No, we do it because we know that those we disagree with will use this same technique at every opportunity to devalue us and those we agree with. We know that not responding in kind makes us vulnerable.

I saw a lovely anecdote recently from Ethics and Public Policy Center fellow Pascal-Emmanuel Gobry recently, which expresses a similar idea somewhat more succinctly:

My high school best friend’s dad was one of the most talented jazz guitarists of his generation. When my friend was a kid, he asked his dad if he could teach him to play guitar. The dad was of course thrilled. “I’d love nothing more in the world, he said. But first, you’ll have to learn music notation and music theory and chords. Then I’ll teach you to play.”

My friend, being a wiseass, retorted, “Paul McCartney never learned any of that stuff, and it didn’t stop him.” My friend’s dad, being a wise man, replied, “Yeah, but you’re not Paul McCartney.”

Yeah, in the Bible Jesus calls people broods of vipers and whitewashed tombs. And Paul, and the Prophets, and saints, used salty language. Yes, there are times when such language is called for. But the reality of original sin means the odds of you using this language out of pride overwhelm the odds of using it because of the necessities of speaking love in truth.

You’re not Paul McCartney.

Whatever social structure or biological impulse evolved in us to make us respond the way we do to hypocrisy makes us uniquely unsuited to routinely rely on our detection of it as an indicator of anything other than our own bias. Neither you nor I are Paul McCartney (unless you are Paul McCartney, in which case, hello, thank you for reading and what is the weird chord in the second half of the third verse of “Let It Be” when you sing “Mother Mary” because I’ve been trying to figure out what’s happening there for 20 years).

I should be clear about the narrow point I am making, and the point I am certainly not making. From a moral and ethical perspective, there is no particular reason why being biased should prevent us from holding one another accountable for dishonesty, hypocrisy and other flaws. If we only spoke up about injustice and error when we had no dog in the fight, we would comprise an ugly society indeed. But I hope that you can see the difference between the impact of bias on the justifiable use of it as an argumentation technique and the justifiable reference to it in good faith efforts to improve our own behavior or of those who we love, trust and want to grow us with as humans.

Being a Hypocrite Doesn’t Make You Wrong

Even if we can play a mean left-handed bass and believe that we are capable of being even-handed in using accusations of hypocrisy as an element of our political and social engagement, it doesn’t take long to recognize that doing so is frequently counterproductive to the whole point of that engagement in the first place. It’s pretty simple. If what you care about is being considered right and winning those arguments, then the hypocrisy! meme is the right tool for the job. If your objective is to get to a better policy or portfolio outcome, then it isn’t.

The next time you’re looking to bring this tool out in an argument or disagreement, ask yourself: does this person’s false signaling really devalue the argument he or she is making? The data he or she is using to support it? Or is it just a tool I would use to discredit this person so that I don’t have to bother with the whole debate? It’s a bad, biased heuristic.

Consider Warren Buffett, the investing world’s moralizer-in-chief. Here he is on leverage.

Once having profited from its wonders, very few people retreat to more conservative practices. And as we all learned in third grade — and some relearned in 2008 — any series of positive numbers, however impressive the numbers may be, evaporates when multiplied by a single zero. History tells us that leverage all too often produces zeroes, even when it is employed by very smart people.

Here he is in 2003 on derivatives:

No matter how financially sophisticated you are, you can’t possibly learn from reading the disclosure documents of a derivatives-intensive company what risks lurk in its positions. Indeed, the more you know about derivatives, the less you will feel you can learn from the disclosures normally proffered you. In Darwin’s words, “Ignorance more frequently begets confidence than does knowledge.”

Guess who sold protection on a bunch of munis starting in 2007, not entirely different in scope, although admittedly in scale, from similar trades that sunk AIG around the same time? Guess who, according to research from AQR, has historically generated his returns through effective leverage of 1.6-to-1?

For someone like me, who is convinced that randomness would almost certainly produce a Buffett or two through sheer chance rather than skill, applying the hypocrisy! meme is tempting. I am envious of his reputation, and I hold the good-faith belief that people who follow what Buffett does are focusing on things that don’t matter. I believe that the people who follow what he says about index funds place too much emphasis on costs and too little emphasis on getting the right level and sources of investment risk. It is so easy for me to justify why it isn’t just correct, it’s the right and moral thing to do to throw this guy under the bus for hypocrisy, to try to reduce his influence.

Except he really is an incredibly thoughtful investor with innumerable traits I wish I had, wisdom our world would be worse without, and perhaps the keenest insight into the role of temperament in the success of the investor we’ve seen in the last 50 years. The Competitive Game strategy says to seek to diminish him — to make ourselves the Fly. To kill our inspiration to sing about our grief.

But that’s just the meme talking. The fact that Buffett’s views on leverage and derivatives are insanely hypocritical don’t change the fact that he has a tremendous amount of investment wisdom to share.

Letting Ourselves Off the Hook

Maybe the worst harm this tick has in store for us, however, is the doubt it sows in us. You and I are both hypocrites. There’s a fine balance between internalizing the moral importance of honesty, consistency and forthrightness on the one hand, and not internalizing the hypocrisy! meme in ways that would cause us not to champion causes and values we believe in simply because we know we can’t live up to them on the other. This is a real danger.

In many cases, our hypocrisy is just growth. When I was 23, I put myself at odds with some genuinely nice and thoughtful people I worked with and for. Why? Because I was an arrogant ass who knew that no one could build and code a model as quickly and efficiently as I could, and because I knew that my skills in this area were creating all the company’s value. Except that wasn’t true. Of course it wasn’t true. I was a stupid kid with no concept of the value of different people and skills. Should I let this moral failure keep me from teaching young analysts today that modeling is a commodity skill? That their real value in an organization will come from cultivating trust, honing temperament, identifying business drivers that matter and becoming better communicators?

In some cases, what looks like hypocrisy is just the reality of a world of contradictions. I’ve written and, yes, moralized about the things investors waste time on, and the things they should focus on more. In these pages, I’ve condemned bad behaviors, like focusing too much time on picking stocks, on picking funds, on fees over other costs. And yet, like many who agree with me on these topics, I still spend far too much time doing each. I’ve spent days trying to figure out if my largely systematic framework for selecting U.S. stocks for our wealth management business is missing something on consumer brands. I’ve spent more time thinking about General Mills and Colgate-Palmolive than I have about things that I know will have greater long-term impact on financial markets and investor outcomes. But I know that these things are important to my clients, too. I know that it matters to them to understand what they own, and why, in a very qualitative sense. And if it matters to them, it matters to me. The hypocrisy that seems so clear in others is not always so cut and dry when we apply it to ourselves with all the details. Our life and work are complicated.

We are complicated, too. Today I relish the trappings of my Texas identity, but it wasn’t always that way. It took me five seconds to decide where I would go to college when the opportunity to escape a small town in southeast Texas presented itself more than 20 years ago. While I can’t imagine harboring that sentiment now, there’s a part of me that can’t figure how much of that refound identity is affectation, a resistance to things I didn’t like about living in the northeast, or an authentic expression of my values. We’re all complicated, conflicted, growing and changing, and there’s no nobility in allowing the hypocrisy! meme to cause us to withdraw from figuring out our own small issues, or helping our communities and societies figure out the big issues.

This isn’t some weird attempt to present hypocrisy as moral, or something we should be more or less prone to forgive or criticize. None of that. It’s awful and you should stamp it out wherever you see it whenever you have the standing with someone to be influential. It is about how you will respond to the tick, the meta-meme of hypocrisy! that seeks to shut off people you would learn from, deepen your falsely held belief in your tribe’s moral superiority, and short-circuit your own brilliance out of false-humble feelings driven by knowledge of your own hypocrisy.


Now because by reason of those daily sins of which I have spoken, it is necessary for you to say, in that daily prayer of cleansing as it were, “Forgive us our debts, as we also forgive our debtors;” what will ye do? Ye have enemies. For who can live on this earth without them? Take heed to yourselves, love them. In no way can thine enemy so hurt thee by his violence, as thou dost hurt thyself if thou love him not… In this he is as thou art: thou hast a soul, and so hath he. Thou hast a body, and so hath he. He is of the same substance as thou art; ye were made both out of the same earth, and quickened by the same Lord. In all this he is as thou art. Acknowledge in him then thy brother.

— Saint Augustine of Hippo, St. Matthew’s Gospel, “Sermon on the Lord’s Prayer”


The most painful realization of all in a world awash with Narrative, of course, is that the people who disagree with us are not especially hypocritical or contradictory. It is that they are our brother. Our sister. Made out of the same earth. And probably every bit as smart, upstanding, independent-minded and, yes, flawed as we are.

When we stop telling lies about why we disagree and start telling this truth, we can grapple with the uncomfortable fact that our brothers and sisters saw the same facts and came to different conclusions. As Narratives force us into ever narrower bands of acceptable views on markets and politics, the speech we must tolerate becomes more uncomfortable, and will feel more extreme. It will also feel more contradictory. Friends, if you would end the Competitive Game, if you would triumph over tribalism, you must learn to tolerate some hypocrisy — in yourself and in others. You must embrace the Acrobat and not the Fly. How?

Dream out loud, and don’t let the bastards grind you down.

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The Fundamentals Are Sound

Cobb: What do you want?
Saito: Inception. Is it possible?
Arthur: Of course not.
Saito:  If you can steal an idea, why can’t you plant one there instead?
Arthur: Okay, this is me, planting an idea in your mind. I say: don’t think about elephants. What are you thinking about?
Saito: Elephants?
Arthur: Right, but it’s not your idea. The dreamer can always remember the genesis of the idea. True inspiration is impossible to fake.
Cobb: No, it’s not.
— Inception (2010)

Cobb is right. It’s not impossible. When we are deep in our element as analysts of economies and issuers, we are supremely confident. We know the critical assumptions in our portfolios, models and projections cold. But when we apply ourselves to assessing what others’ views may be, with understanding what is ‘priced in’, we begin to doubt. Deeper into the hole, where we grapple with what other price-setters are treating as the consensus of yet other investors, our models break. More importantly, our confidence evaporates. Our vulnerability to those stories explodes. So how do you feel about your positioning today?

— Randall Munroe, “Night Sky”, XKCD

The dream of retreat to a world where we can win by understanding what’s really happening underneath the hood is a siren call. We remember the first time we figured out how to identify potentially unpriced optionality in a business model. When we absolutely pegged that fatally flawed assumption in the new management team’s cost reduction plan that no one else saw. You know. The good ol’ days.

There are brief flashes in which central bank or inflation narratives, fiscal policy angles, next-thing rotation pitches from the sell-side and “cash coming off the sidelines” think pieces seem to fade to the background and we see daylight again. And sure enough, it’s another head fake. That long-awaited rotation back to value unwinds after two or three sessions, and we start grumbling about “algos” and passive investors and volatility-targeted strategies and extravagant tech multiples and cryptocurrency excesses and are there mountain lions around here?

Arthur: So, once we’ve made the plant, how do we go out? Hope you have something more elegant in mind than shooting me in the head.
Cobb: A kick.
Ariadne: What’s a kick?
Eames: This, Ariadne, would be a kick.
— Inception (2010)

It’s easy, if a bit heartbreaking at times, to move on from a fundamental investment thesis. Most good ones have a list of sell disciplines describing exactly how they fail, anyway. It’s slightly tougher to change our perspectives on how other investors are likely to behave. But once we acknowledge that everybody knows that everybody knows something, it is almost impossible to know what sort of evidence we can rely on to reject it. That has a lot of important implications, but one more than any other: Narratives tend to influence prices far, far longer than we expect.


“I don’t know half of you half as well as I should like; and I like less than half of you half as well as you deserve.”

This was unexpected and rather difficult. There was some scattered clapping, but most of them were trying to work it out and see if it came out to a compliment.

— J.R.R. Tolkien, The Lord of the Rings, Speech from Bilbo’s 111th Birthday Party

From time to time I speak to and run seminars with students at colleges in Texas, usually with business school students or participants in student-run investment funds. Like any instructor, I have a go-to challenge question. It is a question to spark inquiry, to raise a skeptical eye to the priors with which we approach many of the fundamental questions of investing. It’s also an asshole question. Because, like most instructors, I am an asshole.

“What”, I ask the students, “is the most important single driver of today’s price of ExxonMobil stock?”

It’s the worst kind of question, because I’m obviously asking it for the sole purpose of telling everyone they’re wrong. Still, it’s fun to watch the arguments between very bright students. “Value” is always among the first two or three responses. “Well, what do you mean by value?” I prod, usually yielding a response about multiples. “Value may influence your returns going forward, but a multiple IS the price, so that can’t be it,” a student usually responds, before the discussion descends into bickering and debate over fundamental data which may drive pricing. Earnings? EBITDA? Cash Flow? Oil Price? No, future expectations for oil prices!

It’s yesterday’s price, I tell them.

It feels like a throwaway, the sort of dad joke enjoyed only by middle-aged professionals in tweed playing at being a professor. But for investors trained by schools, banks or long-only shops in the various churches of fundamental stock-picking, it is a necessary and important reminder. Most approaches to security analysis inherently view each day as a tabula rasa. We wake up and decide to evaluate all available information about companies and their securities, determine that the appropriate price either has or hasn’t changed and send our updated limits to the desk. Except that isn’t how this works at all. Like almost anything else in public and political spheres, prices are always determined around the margin.

Consider the tax cut debate the U.S. just endured, and the language used by politicians and media to discuss the issue. Each tax plan is presented as either a cut or a hike, and good or evil on that basis (or on how said cut or hike disproportionately favors one class or another). Did you hear a single analyst discuss what absolute level for a particular income category would generate the most revenue? What would be the fairest on either an objective or subjective basis? Stimulate the most consumption or investment? A politician who never said a word about a static 20% tax rate might be furious with the idea of taking it from 15% to 16%, for example. This is true across every kind of policy issue, and across budget issues for every corporation and household in America. We rarely, if ever, discuss and debate policy issues or investment decisions on an absolute, aggregated basis. Our evaluations are always, always, always on the margin.

This is doubly true for financial markets, where these marginal determinations are made daily. That means that exogenously influenced, random and economically sensible drivers of variations in prices, and, most importantly, the narratives built around them, all become part of the accepted structure of a security’s price going into the next trading day. Strong efficient-markets hypothesis adherents would say that this is wrong, and that any trading not reflective of currently available information would be quickly stamped out and the price returned to an appropriate representation of all available facts (whatever those are). Strong EMH adherents are also too busy being served negative calorie donuts glazed with a 1937 Chateau D’Yquem reduction from a polished unicorn’s horn, so be grateful that the rest of us can have a serious conversation about investing in peace.

That said, the basic idea isn’t wrong, is it? Over enough time, securities prices can diverge enough from the price of comparable investments in ways that influence enough investors to abandon the idea that the accumulated information contained in yesterday’s price is right. EMH assumes that this happens insanely quickly, and the rest of us sane people recognize that it takes some time. In fact, I’d say the world today largely falls into three camps: (1) rare EMH holdovers in academia, (2) kinda-sorta efficient market folks that believe information just propagates slowly, and sentiment…er..something something Brownian motion, and (3) those who believe that prices reflect a shifting mix of fundamental financial data, investor preferences, objective functions and attempts to guess the preferences and objective functions of others.

Some would characterize these differences as a simple question of time horizon.

But are they?

Dick Thaler’s Party Trick

If you’ve ever had a professional dinner with Dick Thaler (maybe personal dinners with him go this way too, but I have never been invited), you’ve probably heard him give his telling of the Keynesian Beauty Contest that Ben has written about several times.

In Keynes’s version of the contest, you win by correctly picking the woman from a series of pictures in a newspaper that you think will be voted as the most beautiful by everyone participating. First-degree thinking, in Keynes’s parlance, is to pick the woman you believe is the most beautiful. Second-degree thinking is picking the woman that you believe the other participants will believe is the most beautiful. Degrees above that require thinking less about beauty or what others will think is beautiful, and more about what the contestants are likely to think about one another. There is no neat solution to this illustration, of course, because we don’t really know what others find beautiful. We are even less certain about what others will believe about their peers’ ability to judge beauty. This uncertainty makes it particularly apt as an analogy to the practice of investment management, but Thaler’s version has the added feature of applying simple mathematics in the place of subjective determinations. That’s useful because it allows us to quantify consistent behavioral tendencies in the game.

Thaler’s version is a little different, and goes something like this:

Everyone at the table must pick a number between 0 and 100. The winner will be the person who chooses the number that is closest to 2/3 of the average.

0th Degree 50.00
1st Degree 33.33
2nd Degree 22.22
3rd Degree 14.81
4th Degree 9.88
5th Degree 6.58
6th Degree 4.39
7th Degree 2.93
8th Degree 1.95
9th Degree 1.30
10th Degree 0.87
11th Degree 0.58
12th Degree 0.39
13th Degree 0.26
14th Degree 0.17
15th Degree 0.11
16th Degree 0.08
17th Degree 0.05
18th Degree 0.03
19th Degree 0.02
20th Degree 0.02
21st Degree 0.01
22nd Degree 0.01
23rd Degree 0.00

Because there are multiple calculations that a person might ignore or fail at, I’m taking some liberty of interpretation, but I think the first-degree answer to this question is 33. The player will realize that he has no information to guide his first step within the 0-to-100 range, so he concludes that the average of 50 is the only sensible place to start. We’ll give him credit for realizing that he must be 2/3 of that number, and thus arrives at 33.

Unlike Keynes’s contest, Thaler’s also has a ‘real’ solution. You’ve seen it replicated (albeit in a flawed format that isn’t Pareto-optimal) in the movie A Beautiful Mind. You know, the bar scene with the blonde? Also, why is every example of game theory a creepy story about old male economists picking beautiful women? Anyway, Thaler’s problem has a single solution that is a Nash Equilibrium: zero. If everyone can calculate 33, then surely they’ll figure out 22, 15, 10, and all the way down. By the time you’re playing 23rd-Degree Dinner with Dick, you’ve already gotten down to two digits of zero. A computer would tell you this instantly. But then, a computer would also assume that all the people playing understood AND remembered limits from their first week of calculus. There’s no shame if you don’t. I mean, there is, but it’s politer to say that there isn’t.

When we have played this game with clients, audiences, classrooms and colleagues, my experience is that the winning guess consistently falls between 15 and 22, usually closer to 22. I expect, but don’t know, that Thaler would give you a similar value.

What does this mean? Or at the least, what does it imply?

First, it should be obvious that every sufficiently large iteration of this game will include some people who don’t understand it at all. Some won’t have a natural grasp of expected value and won’t start from 50, but from some other number they expect will be popular. These people will tend to increase the average winning point total somewhat, since they aren’t following the averaging and iterative mathematical process that forces all the numbers downward. If you want some real-world examples of what this person looks like, Google “Bitcoin Price Target.”

The second group of participants — usually a small group — are those who understand the basic principles of the problem but think that everyone else is a moron who doesn’t. They bet on 33. These are your first-degree thinkers. This is basically every graduate of every business school in the world until he has to manage an actual P&L for the first time.

The third group of participants — usually larger than the second — understand the math all too well, and assume that everyone else can, too. They provide the real solution of zero, or if they have a modicum of wisdom to pair with that beautiful brain and neckbeard combo, add a couple points to catch the stragglers who are too slow to catch on. They drag down the winning score. Ben wrote about these people earlier this week in Too Clever by Half. They’re the coyotes.

The bulk of participants, however, answer between 22 and 33. They understand that the principle is to recognize that you want to be 2/3 of the answer everyone will guess. Since the most basic answer without getting into guessing others’ behavior is 33, they go one layer deeper and judge it to be sufficient. This is second-degree Keynes. In this way, Keynes’s example is much more like financial markets, because it incorporates compounding uncertainty at every level. We know what we think. We have a pretty good guess at what others think. But building a mental model of what others think others will think is an order of magnitude more challenging, because it requires perspective not only on the underlying — a woman’s beauty — but on others’ prejudices and biases about the other judges!

Playing a third-degree game is too daunting a task to consider for most, and so curiously, even in the mathematically deterministic version of the game that has a Nash equilibrial ‘correct’ answer, the takeaway is the same as in the beauty contest: you usually win by guessing that others are playing a mix of one to two degrees of the Common Knowledge Game. Some people buy and sell on fundamentals, and some on how they think people will react to them.

But as Ben discussed in The Three-Body Problem, we think that this is changing. We think it has changed. We think that the violent expansion of communications policy by global central banks and the accompanying expansion of always-on media has meant more participants shifting to third-degree thinking. The reason we talk about Narrative so much is that we find it a useful meta-expression of and proxy for exactly the kind of mental model a third-degree participant must construct. When we refer to Narrative, we mean it as an expression of what everyone knows that everyone knows.

If you accept that Narrative is exerting greater influence on asset prices, you will lose if you play the traditional strategy. You will lose if you assume that others are playing one- or two-degree strategies.

The Fundamentals are Sound

So what did everybody know that everybody knows over the last couple weeks? And when you looked at the game unfolding, what strategy were you playing?

I’ve written about the silliness of trying to ascribe specific causes to market action, but I’m willing to stand on this as probably, approximately correct. Let me tell you what I think happened. Then let me tell you what I think other people think happened. And if you’ll bear with me, let me tell you what I think markets will ultimately decide everybody knows that everybody knows happened.

I think that there was already an emerging Inflation Narrative coming into 2018, although not much actual inflation to show for it. Ben has written credibly about this on several occasions. Torsten Slok at Deutsche Bank put out a nice chart highlighting breakevens leading into the events of last week (don’t get too cynical about the forced perspective of sell-side axis ninjas, please).

Source: Deutsche Bank 2018

I think that a roaring start for risk assets in early January gave tactical allocators, macro shops and hedge funds an opportunity to bank early returns (and incentive fees) by taking off risk. I say “think,” but “know” would be nearer the truth. I have the receipts, as it were.

I think these funds thought that the emerging Inflation Narrative warranted pulling back some of that risk not just in risky assets but across their book, including in rates (sovereign debt). I think this accelerated and compounded confidence in the Inflation Narrative.

I think that many market participants thought that the focal point of the event through the end of January was not inflationary expectations, but frothiness of equity markets. I think they thought this because that is where their focus had been as a result of the remarkable returns of 2017 and 2018 and the length of time since the last S&P decline of any significance. I think media bears this out, but it’s story, not fact.

I think that the resulting spike in volatility on February 2nd and into February 5th confirmed and exacerbated what most people thought about the proximate cause of the correction. As a result, the weight of market behaviors shifted from response to a rate shock or rise in inflationary expectations to a classic risk-off trade.

I think that with the relaxation in volatility since the events of late January into February 5th many investors think that the event was an equity and volatility event. A moment of irrational pessimism brought on by blow-ups in vol-selling and vol-targeting.

I think that more large institutional allocators today than at any point since the early 1980s know that their peers know that inflation, if and when it comes, will fundamentally change how they must build, allocate and manage portfolios.

I think that instead of focusing on this, other investors are comforting themselves with an age-old mantra: “The Fundamentals are Sound.”

“The Fundamentals are Sound” on the U.S. economy. On stocks. This was just a correction that we needed after things got a little frothy. It was short-term sentiment. It was risk parity and vol-targeting funds driving markets lower for no reason after a jump in vol. If you loved the Dow at 26,000, you ought to really love it now.

“The Fundamentals are Sound” on cryptocurrencies. The price action doesn’t matter. It’s the technology that matters. As long as you research and understand the technology and what it has the potential to do to overcome overcentralized, centrally planned banking and transactional systems, you won’t lose. All the smartest people, all the people who have really done their research on this technology, the people who get it, are not sweating these price moves.

Amazing. Every word of what I just said is wrong.

Well, it isn’t that the statement isn’t factual. It may be.

It’s that we have no idea if and when it is going to matter. You can argue all you want that it’s a random walk to a known destination, but as the walk gets longer, that distinction becomes less meaningful.

Sure, it serves a useful purpose to use this language with some clients, in that it keeps them from taking rash actions to change their asset allocation without a real basis for doing so. If you’re a financial advisor and telling your client this fact helps to keep them from dumping all of their risky assets, then you have my blessing and more. But we must be honest with ourselves. If we believe that “Fundamentals are Sound” is necessarily a relevant statement after a correction like this, we must acknowledge that it also carries two embedded assumptions that are so extreme that it’s worth taking a step back to truly unpack them.

  1. It requires us to believe that yesterday’s price was the right one.
  2. It requires us to believe that non-fundamental influences on price (second-degree or third-degree issues) have not changed either, or that they will revert soon.

The silliness of the first ought to be self-explanatory. The “Fundamentals are Sound” relative to what? Relative to how they manifested in prices yesterday? Last week? How they would have manifested over the last 30 years? Absolute pronouncements of appropriate valuation and marginal thinking about price changes are a risky combination.

Understanding the second is a bit nearer to my purpose here. After events like this, it is appropriate to ask: do I think that the decision-making processes of other investors have changed? Do I think that those investors’ views of other investors’ positioning and decision-making has changed? Furthermore, do I think that any of the broad Narratives reflective of how investors are responding to one another have changed, or that they have strengthened or weakened?

Now, my confidence about the mechanics I’m describing here is high, but I don’t judge my ability to evaluate using these mechanics to be higher than any of yours. In fact, many of you are probably shrewder investors than I. But I think a lot of investors will be coming out of the last two weeks saying that nothing has changed, or focusing on how long it will take to bounce back from a couple weeks of fear-driven market behavior. I think that may be a mistake. Why?

Because it takes much longer to unwind third-degree thinking. Narratives last.

Think about the Keynes game again. Imagine that I drew a feature on one of the men or women from the beauty contest to make them most distinctive, and perhaps more polarizing. Let’s say a diamond nose stud, or a face tattoo. How long does it take you to figure out how your first-degree thinking about the game changes? Second? Third? How much more data would you need to conclude that there was a change, and how would that differ for thinking at each degree? How many more events to give you insight into responses? There’s a reason Narrative-driven markets last far longer than we expect them to. Time passes more slowly in a dream-within-a-dream than it does in a single dream alone.

Again, I think investors who look at risky or speculative assets and say, “I like this just as much, and I don’t really see why it should have gone down this much,” may well be right. I think that they’d be justified in having some expectation that volatility will fall, and that some of the correction would be recaptured over coming weeks and months as people forgot why they felt the need to go risk-off for three days in February.

But I think it’s not the Friday and Monday sell-offs and whether they were “justified” that will end up mattering. It’s what happened the week before that we should be paying attention to.

If the events of that week did anything, it was to further convince me that market participants have bought into the Inflation Narrative — even well in advance of strong data on actual inflation. So while I don’t have any valuable short-term positioning thoughts (and I never will, so don’t ask), I think that the surprising strength and persistence of this Narrative — and Narratives in general — has real implications for us as asset allocators and alpha-seekers. Even alpha-seekers in the Craftsmanship Alpha mode.

We all talk a big game about diversification, and rightfully so. Look, I wrote a piece that called it the second most important thing in investing. But how big a part do TIPS (Treasury Inflation Protection Securities) play in your portfolios? Commodities? Other real assets? Many of these have been such abominable relative investment opportunities over the last 35 years that they frequently aren’t even considered as asset classes. In some generous cases they’re called alternatives or diversifiers, but few investors today consider them in the same context as stocks and bonds.

As the Inflation Narrative heats up, I believe asset allocators will have to seriously evaluate the extent to which this tacit assumption is still appropriate. They will have to grapple with whether nominal bonds have the same crisis risk aversion and diversification characteristics that they have over the last couple decades. But here’s the rub. They will have to do so in a prospective, long-term way that may not have the benefit of a recent high-confidence in-sample and out-of-sample period for their backtests. Are you ready to tell your committees that you think sovereign bonds may not be the same safe asset in certain types of major equity drawdowns? Are you ready to suggest what to do about that? Are you prepared to stake your career on it?

It’s not uncharted territory. There is nothing new under the sun, after all. But it’s territory that few of us have trod during our careers. And if you’re staring at the ground, trying to convince yourself that it’s solid before every step, you may be missing where we’re headed.

We have to be humble, too. If you’ve been talking about an emerging Inflation Narrative for a few months, we know enough about behavioral biases to recognize that you’ll start seeing ‘evidence’ of it everywhere you look. But that’s kind of how Narrative works in the first place, y’all. In the end, we don’t have all the answers, but we do think we know how to think about these questions.

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The Myth of Market In-Itself: Things That Matter #3, Pt. 2

Nothing at all. No figures. Only a blank.

“What’s it mean?” Reinhart muttered, dazed.

“It’s fantastic. We didn’t think this could—”

“What’s happened?”

“The machines aren’t able to handle the item. No reading can come. It’s data they can’t integrate. They can’t use it for prediction material, and it throws off all their other figures.”

“Why?”

“It’s—it’s a variable.” Kaplan was shaking, white-lipped and pale. “Something from which no inference can be made. The man from the past. The machines can’t deal with him. The variable man!”

Philip K. Dick, The Variable Man (1953)

This science fiction classic imagines a future world where specialization and technology have made versatility, adaptability and ingenuity obsolete. The unwitting introduction of a man from the past Thomas Cole capable of solving practical (and mundane) problems of this world throws off the models they use to predict the outcomes of government and military action.

Thomas Cole breaks the models because his foreignness allows him to see problems outside the confines of specialized taxonomy. He isn’t too dumb to see the tribes and archetypes of the future. He transcends them, and can’t be controlled by them. The successful navigator of policy-controlled, narrative-driven markets must be Thomas Cole. He must be The Variable Man.

When someone shows you who they are, believe them the first time.

Maya Angelou, as told by Oprah Winfrey

I have given them Your word; and the world has hated them because they are not of the world, just as I am not of the world. I do not pray that You should take them out of the world, but that You should keep them from the evil one. They are not of the world, just as I am not of the world. Sanctify them by Your truth. Your word is truth. As You sent Me into the world, I also have sent them into the world.

The Bible, The Gospel of John 17:14-18

One of the most powerful consistent themes of many religious texts is the battle between the adherent’s role in the spiritual world and in the physical one. The approach Jesus describes here in the Gospel of John is to be in the world, but not of it. It’s a consistent message for the man who dined with gamblers and prostitutes.

We’re presented with the same challenge. Behavior exists. Tribes exist. Taxonomies exist. “Communications Policy” exists. Rejecting them doesn’t mean rejecting their existence, and it absolutely doesn’t mean that we ought not to invest and trade with awareness of how they impact markets. Being as shrewd as snakes and as innocent as doves means a willingness to know about tribal thinking even when we reject it in ourselves.

The Most Interesting Man in the World: “I have no idea what this is.”

Although, truth be told, there are some things it’s worth being content knowing nothing about.

We will live in this world, which for us has all the disquieting strangeness of the desert and of the simulacrum, with all the veracity of living phantoms, of wandering and simulating animals that capital, that the death of capital has made of us—because the desert of cities is equal to the desert of sand—the jungle of signs is equal to that of the forests—the vertigo of simulacra is equal to that of nature—only the vertiginous seduction of a dying system remains, in which work buries work, in which value buries value—leaving a virgin, sacred space without pathways.

— Jean Baudrillard, Simulacra and Simulations (1981)

If anything describes the feeling I get about the market, it is disquieting strangeness. Sound familiar to you? As Baudrillard pointed out, this is the vertigo we get from a world of things that are not things in-themselves, but socially constructed amalgams of symbols and proxies for those things. With every Narrative, every bit of fiat news, the vertigo for those who seek after the truth of something increases. There is no cure, but the only treatment is to try to really, truly understand the simulacra of reality for what they are.


We live in a world awash with archetypes.

A personality test once told me that I’m an INTJ. When I play(ed) Dungeons and Dragons my alignment was Chaotic Good, and I usually roleplayed a Half-Elf Bard. I’m a #NeverTrumper on the libertarian wing of the Republican Party. I attend a Presbyterian Church, but I’ve always identified as Non-Denominational, which is, of course, a denomination that takes its denominational identity from not belonging to a denomination. I’ve been a WASP all my life, and a non-POC cishet who was coercively assigned the male gender at birth for about 2 ½ years since society decided that the sentence I just wrote is not at all horrifying and makes any kind of sense. I am of Scots-Irish extraction, a Libra or a Virgo depending on the calendar, and Buzzfeed tells me I would be Faramir[1] in the Lord of the Rings Universe, Jon Snow in Game of Thrones and Miranda in Sex and the City. Apparently, if I were admitted to Hogwarts the sorting hat would put me in Ravenclaw.

Over the last few months Ben and I have written a lot about archetypes like this, along with tribes and symbols, and the way that they are used. In Gandalf, GZA and Granovetter I argued that when symbols are used as allegories as tools to divide and dominate they have the effect of either (1) causing people to shift their beliefs and actions to match up with the symbol or tribe they identify with or (2) causing people to treat others as if their beliefs and values align with the symbol. Or, in Ben’s terminology, the (1) obedience collar and the (2) dog whistle. In that note, I took particular issue with the latter, with the idea that anyone gets to determine our intent as citizens or investors.

Here, as we continue the exploration of why investor behavior is one of the Things that Matter in our Code, I want to expand on the first: the tendency for the temperament and behaviors of investors to coalesce around archetypes. Because while we believe we ought to fight to ensure that we are all treated like principals, we also believe that when someone shows us who they are, we ought to believe them. And investors show us an awful lot about who they are. Archetypes are everywhere in markets, and if you have the patience to understand and observe them, you will understand what we think is one of the Things That Matter for all investors.

Notes from a Much More Boring Field

I grew up running through corn fields in Minooka, Illinois, but I don’t have it in me to be a gentleman farmer like Ben.

No, my notes from the field are much more dull as regular readers will know, my prior field was an institutional allocator. And people who were and are in my position bear a lot of responsibility for the archetypes in markets. You see, picking fund managers is hard, usually a waste of time, and basically everybody sucks at it. Fund evaluators have very little visibility into what causes a manager to generate returns that produce outperformance or a higher-than-expected risk-adjusted return. And so, instead of focusing on a few “things that matter” to identifying strategies and approaches that could even conceivably have an edge, the emphasis of nearly every fund allocator is exclusively on process.

Here’s the problem with that: process is a necessary but insufficient condition for consistently beating the market.

The fund allocator’s toolkit is full of ways to tell if a manager is following his process. He looks at tracking error. Rolling correlations to all sorts of indices. Cash positions over time. Factor exposures over time. Risk contributions from factor exposures, country bets, all sorts of things. These are the things that fund managers are expected to discuss, and they are often the right things to discuss. But if you have no justifiable idea whether the process itself should or will lead to outperformance, what the hell are you actually measuring? We have built an entire industry on accurately measuring whether someone followed the recipe, without knowing if the recipe tastes like hot garbage.

As a consequence, the conventions of our industry are exactly the same as the conventions of our political reality: we evaluate participants’ consistency with an archetype that is vapor, a construct, a simulacrum. In so doing, we create strong forces to drive them toward consistently behaving in that very particular way, toward incentives and responses to stimuli that are repeatable mostly because we reward them for being repeatable! It’s not really even a Pavlovian response, because the reward is usually crappy performance.

Managers of institutional pools of capital are one of the largest influences on markets, and so it is critical to understand the languages that coalesce around these archetypes. Others form around the conventions of retail gatekeepers (Howdy, Morningstar… or Lipper for the mutual fund managers who didn’t like their Morningstar rating), around sell-side research providers, around the styles of well-respected investors (e.g. Buffett) or around insufferable gasbaskets (e.g. Cramer). Others form around the self-reinforcing conventions of esoteric worlds like FinTwitter, which end up driving far more of something like USD/BTC than anything fundamental about cryptocurrency.

Returns are anywhere and everywhere a behavioral phenomenon. Dick Thaler likes to quote Herb Simon’s characterization of “behavioral economics” as a pleonasm, but talking about a behavioral approach to markets is just as redundant. It is impossible for a non-behavioral analysis of market returns to be useful. If we are ever to understand why prices move and why our investments generate returns for the portfolios we build for ourselves and our clients, we must at least develop some understanding of how and why blocs of investors form, how they buy and sell securities, how and when they change their stripes, and how that results in changes in the prices of the investments we own. We’re going to do a lot of generalizing, so caveat the below however you deem appropriate. This isn’t a precise science or at least it isn’t yet.

The Value Archetype

It’s easy enough to introduce what it is we’re talking about with a “style” that most investors are familiar with. Well, sort of, anyway.

The language of value is familiar—buy cheap things. The investor who has adopted it is rarely a news-responder. In many cases he fancies himself a bettor on things that are out-of-favor or forgotten. In the market voting machine, he casts his ballots and crosses the [actual and proverbial] spread for things with bad tape, with bad narratives, with problems. Don’t mistake the language for the style. Graham and Dodd, Buffett and their “intrinsic value” ilk are value investors in the way that everyone is a value investor – in that they want to buy something they think will be worth more in the future than it is today. They aren’t who we’re talking about here.

We are talking about the investor who believes that investors pay too much for quality, for growth, for sex appeal, and that it will harm their returns. These days, most of these value investors are quants. Some of them are financial advisors selling a package of contrarian ideas, of differentiated thinking. Many more of them are fundamental shops, folks that focus on multiples-based analysis and build fancy models after the fact to justify the things they buy on the basis of multiples, not that there’s anything wrong with that.

So how do these value investors impact prices and returns?

Visualize the order book from Part 1, and again, think about it in long-horizon terms. Members of the Value Archetype form a big part of the willingness of the market to buy things that most think are unattractive. They form the corpus of the out-of-the-money bid for any security or market, and like their counterparts in the Mean-Reversion Archetype we’ll read about shortly, that’s when they tend to participate in the marginal price-setting process. That, and on the ask side, where they tend to be the sellers of gains. When a lot of people are rallying at this banner, it can be a pretty meaningful force to constrain upward movement in prices.

When there aren’t as many, the Value Archetype plays a much smaller part in the price-setting process. Consider: who is selling a stock that goes from trading at 45x earnings to 50x earnings? It ain’t the Value guy. He sold it a long time ago, and the next guy couldn’t care less.

The Growth Archetype

We tend to think of “growth” as being the opposite of “value,” but that isn’t strictly true. For most of the indexes that track these styles, it is kinda true, although in their vernacular, “growth” is really just “anti-value.” In other words, when you see a growth index, in most cases it isn’t sorting companies by how quickly they grew or are expected to grow, but by how expensive they are. That’s not what we’re talking about.

There may be a few investors out there who are actively looking to buy things because they are expensive, I suppose, but there are plenty who don’t care all that much if it has what they are looking for. What many of them are looking for is growth, or at a minimum the narrative of growth. That narrative may be favoring one stock over a peer. It may be in favoring technology securities over the retail sector. It may be in favoring emerging markets investments over developed markets. There are some investors at certain times and under certain conditions who see valuations as temporary phenomena and growth narratives as the only relevant focus.

Some of these individuals actively choose this posture. They believe the narratives, they buy, and they cross the spread to do it. Prices rise.

Some under this banner have no choice. They have asset-liability issues that require them to seek out growth. They are pushed by falling yields in alternative asset classes precipitated by central bank action. They, too, must buy and cross the figurative spread to do it. Prices rise.

We’ll come back to this, because it’s important.

The Momentum Archetype

Quantitative investors do this. Traders do this. In a way, of course, these are people responding to the Epsilon that represents a portion of market returns. In most cases, they do it because it generally works. Winners tend to keep winning and losers tend to keep losing. Many investors who coalesce around this archetype do so very willingly (pictured right), while others would be mortified to think that they would be tarred with a “technical” investor brush. And so they are focused on consistent improvement in earnings, or in guidance from management, or in an improving story. Narrative momentum rather than price momentum, but momentum all the same.

In the end, what matters is that these individuals ‘cross the spread’ to support continued movement in the price of securities. Some can be long/short, and so this can happen in both directions. But it’s generally long, and getting longer.

The Mean-Reversion Archetype

I’m abstracting a lot from time horizons here, and I’m doing so intentionally. Part of my story is that in a non-ergodic world, the idea that the long-term can be considered fully independently from the path that begins in shorter horizons is madness. And so, while I fully recognize that there are many, many funds that pursue strategies that happily encompass each of value, momentum and mean-reversion strategies, I’m not talking about strategies. I’m talking about frameworks of thinking and talking about investments that color the decisions that investors make across the board.

And on this dimension, while mean-reversion has a specific meaning within the context of, say, CTA and statistical arbitrage strategies, what I’m really talking about is the consciously contrarian asset allocator. Only instead of looking for unloved companies, this is the falling-knife catcher. The one looking for the turn, the top, the bottom, the inflection point.

Some demonstrate this trait consciously, but far more do so passively through policies called “rebalancing,” most of which have a negative expected return. After all, momentum works. But these people are volatility reducers. They step in to provide the bid when the longs are screaming bloody murder and the ask when the shorts are getting crushed.

The Others

Look, there are all sorts of taxonomies people rally around. We could talk about some nebulous definition of “quality” guys or the nothing-land that is most “GARP” investing. We could talk about investors who are students of more arcane technical trading approaches, or about those who invest based on macroeconomic data or news. But it’s the four things above that matter.

Except that there is a rapidly growing fifth category, a sort of Nihilistic Archetype. It’s the passive investor. Except inasmuch as he adheres to another archetype in his cross-asset allocation decisions (which he frequently does), the passive investor expresses no opinion whatsoever with respect to the pricing of individual securities. He doesn’t participate in relative price-setting.

He is out of the game.

Where Does This Put Us?

Can you tell that I’m going somewhere with this? To better understand why I think it’s important for all investors to think about the behaviors of their fellow-travelers in markets, let’s walk through what I think is happening right now:

  • The Value Archetype is dead: No one is rallying around this banner. Read the sell-side language. No one is pitching value-oriented research, because they’d have no one to sell it to. Even the old stalwarts, the quants, have evolved toward either risk premia-based or Value+Momentum+Quality mandates that dampen the emphasis on value alone. Sure, you’ll get the occasional bank strategist calling for a rotation into financials (they’ve got to be early calling the new thing), but of the people setting prices, very few of them are speaking this language. I’m not saying I don’t believe in value. I do! But the market’s belief in it is nothing more than lip service right now.
  • The Mean-Reversion Archetype guys in CTA and Global Macro Land are bleeding out: Selling winners and buying losers has rarely been a more painful trade. I’ve talked to a few FAs who are sticking with long vol trades or defensive positions because, well, at this point, you might as well stick to your guns. But other than that, this is a dead language, folks. If you expect someone to bail you out of a short squeeze, you’re barking up the wrong tree.
  • Passive Investing is levitating broad markets but allowing intra-market volatility: Investors, allocators and fund managers alike have piled into the Growth train, in part because they want to, and in part because retirees and pension plans with unfunded future liabilities have no other choice. Since they are doing so through broad market instruments and are not about to sell into weaker growth prospects, there is continued upward pressure on prices. Within markets, the decline in participants who are actively participating on individual securities is allowing continued spread potential between sectors, styles, etc.

The combined effect? Everything is levitating. With value and mean-reversion as lingua non grata, the people setting prices are (1) Growth investors, (2) Momentum investors and (3) Passive investors adhering to those archetypes. There is no one left to sell, because there is no one left who cares nearly enough about valuation or is confident enough in their ability to time a top in markets to sell into strength. The result is in Information Surface terms a market that has tremendous difficulty generating any price volatility to the negative.

What Does This Mean for Investors?

We can be in the market and be long. We can be not of this market and be ready for the move to the downside. Or we can be in the market, but not of it, by incorporating the behaviors of others into our thinking about markets AND retaining our ability to think independently about possible outcomes. How?

  1. With the core of your portfolio, you don’t fight it. This is most of what being aware of investor behaviors and the complete hegemony they have over market movements means.
  2. You think more specifically about how other investors are thinking about this market. Why they’re buying. Why they’re buying what they’re buying. You think about their motivations. And you think about how a change in their motivations would change in response to various market influences. Is a shooting war in the Middle East going to materially change investors’ view of and preference for growth? (Probably not) Is a material change in language coming from all Central Banks going to shift it? (Maybe, as Ben has written)
  3. You prepare your portfolio or at least your framework for what happens when that informational bowling ball climbs the wall to the downside, because when it does, volatility can return in a big damned hurry.

Thomas Cole wasn’t a genius. He succeeded because he was capable of acknowledging the existence and influence of archetypes without succumbing to them in his own behavior and actions. If you would navigate this market, your Code should allow you to do the same.

[1]Hopefully it’s book Faramir, and not the movie Faramir that Peter Jackson made into a spineless clone of Boromir because Jackson lacks any understanding of plot or character.

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The Myth of Market In-Itself: Things That Matter #3, Pt. 1

That space and time are only forms of sensible intuition, and hence are only conditions of the existence of things as phenomena; that, moreover, we have no conceptions of the understanding, and, consequently, no elements for the cognition of things, except in so far as a corresponding intuition can be given to these conceptions; that, accordingly, we can have no cognition of an object, as a thing in itself, but only as an object of sensible intuition, that is, as phenomenon — all this is proved in the analytical part of the Critique; and from this the limitation of all possible speculative cognition to the mere objects of experience, follows as a necessary result.

Immanuel Kant, The Critique of Pure Reason (1781)

I know, diving right into 18th-century German metaphysics is a real crowd-pleaser. But this is just a bunch of fancy words to say that we can never know the fundamental truth of a thing independently from our perceptions and experience. It’s the realization that makes Kant probably the most indispensable of the great thinkers. Doubly so for Epsilon Theory. We desperately want to believe that with enough information and analysis, we can know the true value of something. There is an almost mythological belief in the market as the mechanism through which we uncover that truth. The rest of the world will realize that we are right, and then we will make money. But the ‘true value’ of a thing — the market in-itself — isn’t something we can know. We observe value only through price, a measure based on our collective subjective experience.

Is there any hiding of the character of an apple-tree or of a geranium, or of an ore, or of a horse, or of a man? A man is known by the books he reads, by the company he keeps, by the praise he gives, by his dress, by his tastes, by his distastes, by the stories he tells, by his gait, by the motion of his eye, by the look of his house, of his chamber; for nothing on earth is solitary, but everything hath affinities infinite.

— Journals of Ralph Waldo Emerson, June 7, 1860

Still, just because there isn’t a knowable intrinsic value to an investment, no investment in-itself, it doesn’t mean we can’t know anything about it. The people who form the market and apply their sensible tuition to these things have affinities infinite. Some of those affinities can be observed or inferred. This is the soul of the Narrative Machine writ large.

Run a BBW Tumblr blog and forget the password

I may be speaking too soon but this is a disaster

Like old people in modern sneakers

I saw Book of Mormon with a congregation of true believers

milo, “In Gaol”, a toothpaste suburb (2014)

As investors, it is very tempting to get so caught up in our own tribe of investing — our style, our philosophy — that we sit in a state of constant bemusement of other investors, sure that everyone is going to come around to our point of view on the value of something eventually. That congregation of true believers we can’t believe are watching a parody of their beliefs can stick around for a long, long time, folks. Over a sufficiently long period, being wrong about value but right about price can become indistinguishable from being right about value.

Empathy, evidently, existed only within the human community, whereas intelligence to some degree could be found throughout every phylum and order including the arachnida.

Philip K. Dick, Do Androids Dream of Electric Sheep (1968)

I’ll admit it. At any given time around our Houston office, there are four TVs tuned to CNBC. Don’t ask me why. Or at least don’t expect a satisfactory answer.

If pressed, I would tell you that it’s important to know what a voice that speaks through thousands of televisions in similar offices around the world is saying, even if it’s a meal of empty calories. After all, Epsilon Theory is about stories. Stories, those who tell them, and those who, in listening, respond. Some of those stories are powerful myths, timeless and universal, others virtuously or nefariously cultivated for a singular place in space and time. And some of them — including most of what you hear on financial television — are vapid and worthless.

A story linking six months of a presidency to the returns of a stock market at the same time. A story linking August returns to calendar years that end with the number 7. A story that “stocks slipped on the news” of a development in investigations of Russian collusion with no evidence of relationship other than that the two things happened on the same day. Oh look, oil fell on “profit-taking.” Linking a down day in U.S. markets to one of a million macro factors that moved that day. We say the stock moved “on this news” or “on that news” when, if we’re really being honest with ourselves and each other, we know that all of these stories are stupid and wrong. Deep down we know that we have no earthly clue why our investments go up and down every day, much less moment to moment, and we’re just grasping for answers. Stories. And boy, are people ready to give us some.

It’s a problem. And it’s a problem we can’t ignore, because our investment decisions communicate views about our expectations even if we don’t intend it. We don’t get to say that “it’s OK” not to understand what moves markets, because every day we are all making bets that say we do. Sure, we have all sorts of explanations for why investments should rise in value. We should be paid for taking risk. We should own something valuable if it continues to grow its earnings. We should be able to trust the fact that risky investable assets have produced positive returns over almost any long-term horizon for the last several centuries.

But the distinction between understanding why we ought to be paid for owning something and understanding how that manifests itself in changes in securities prices is not just academic. It is fundamental, and it sits squarely in Epsilon Theory’s wheelhouse. In the same way that Ben bastardized Gresham’s Law, I’m going to steal from Friedman:

Investment returns are always and everywhere a behavioral phenomenon.

That’s why Investor Behavior is #3 on our list of Things that Matter.

Knowledge and Information

I know we all know this, but from time to time it bears repeating: until it defaults, matures or is called, the price of every security in the world is ultimately driven by two — and only two — things:

  1. Who is willing to pay the most to buy the thing, and
  2. Who is willing to accept the least to sell the thing.

That’s it. A lot of applied behavioral economics IS flawed and less rigorous than it ought to be, but at the risk of giving Taleb the vapors, any argument for how prices are determined and, thus, how returns are generated that ignores investor behavior isn’t just weak. It’s objectively wrong.

Now, while there really aren’t any strong-form efficient market guys out there with skin in the game (i.e., outside of academia) anymore, there are still a lot who think about markets as being generally efficient, by which they mean that the market generally does a good job of pricing available information. This is actually a pretty fair point of view. To believe otherwise is to take a dim view of the value of markets as a mechanism for expressing the aggregated will of individuals. That ain’t me. I was and will always be a Hayekian at heart. Since we’ve decided it’s now acceptable to terminate employees for expressing wrongthink, I’ve started firing anyone who doesn’t see my copy of The Road to Serfdom and slam it down on the conference room table, shouting, “THIS is what we believe!” à la Maggie Thatcher.

The problem with most interpretations of information, however, is that fundamental data alone isn’t information in any real sense that matters. Facts about a company only become information when they are passed through the perceptions and preferences of the people who are participating in determining the security’s price. There is no objectively ‘right price’ for a security based on the available information about its business, its assets, its prospects or its profitability, because there is no objective sense in which changes in any of those things ought to result in changes in prices. There is only a price which reflects how that information is viewed through the collective lens of individuals or groups of individuals who participate in that market.

Now while two people functionally determine the current price of any security, the movement of prices in that security from that level are also influenced by a much larger group who are willing to buy for a little bit less than the guy setting the bid price, and those who are willing to sell for a little bit more than the guy setting the ask price. Those who’ve made those views explicit are part of the so-called order book, an actual group of people willing to buy and sell at certain prices. Greater is the group of individuals who haven’t explicitly put a line in the sand at all, but who do have a view that they have an interest in expressing. They are paying attention to the stock. Far, far larger still is the universe of investors and assets who are paying no attention to the security at all and play little to no role in its pricing, even if they own the thing. You can imagine it looking something like the below — a little bit of money is willing to trade close to the current bid-ask spread, and increasing amounts if you’re willing to sacrifice.

Source: Salient 2017. For illustrative purposes only

Again, excluding terminal events for a security — like its default, retirement, maturity or being called away — there are only two ways for a security to change in price:

  1. Someone who had an explicit or implicit view in the “order book” — a blue or red bar from the above chart — changes their mind about the price at which they would buy or sell.
  2. Someone who didn’t have a view before decides to express a view.

Many of the things we do to trade, like a market order or most common trading algorithms, cross the spread in order to find a trading partner. In other words, as the day wears on, a lot of the people who thought they’d only sell for $75.10 — but need to sell — end up saying that they’d take less. Those folks are making the price move by changing their mind about the price at which they’d buy or sell. In other situations, maybe we get a call from a client who needs money for a down payment for house. It’s a big, liquid stock, so we put in a market order. We take $74.90 for our shares despite having not expressed a view on price before that, and everyone else in the market tries to figure out why.

So why do these people change their mind? Are they, in fact, responding to the stimuli that financial TV suggest? Did a barrel of oil really just trade up by 15 cents because investors changed what they were willing to pay as a result of North Korean sabre rattling? Other than major sources of observable volatility — earnings, corporate actions and the like, and often even then — if anyone tells you they know, they are probably lying to you. All we know, because it is a tautology, is that it is absolutely a reflection of human behavior (which includes, mind you, the behaviors incorporated on the front-end of a systematic trading strategy or implicit in a trade execution algorithm). That doesn’t mean that people can’t make money off price movements over this horizon — plenty of stat arb and high-frequency trading firms do exactly that, albeit in different ways. But over the very short run, the drivers of market movement are noisy and overdetermined, meaning that there are more factors driving that noise than there is noise. They are also nearly impossible to generalize, other than to say that they are reflections of the behaviors of the individuals who caused them.

The great investor Benjamin Graham famously characterized his views on the matter in this way. “In the short run,” Graham said, “the market is a voting machine, but in the long run, it is a weighing machine.” This is a popular view. But with respect due to Mr. Graham, it is also wrong. Since I’ve bastardized and restated the words of one financial genius already, let’s make it two:

In the very short run, the market is a voting machine.

In the short run, the market is a voting machine.

In the long run, the market is a voting machine.

The Long-Run Voting Machine

There’s a contingent of people reading this who are probably saying to themselves, “Wait a minute. What about a bond? Every time I receive my coupon I book some return. Every day I get closer to maturity, and I can predict pretty accurately how a bond trading at a premium or discount is going to converge to par.” The implication of that argument is that while fundamental characteristics of an investment may only technically manifest themselves in some terminal event, they are effectively still very predictive because we can have a high degree of confidence around them for some types of investments. In other words, maybe sentiment is a bigger predictor for risky securities than it is for securities where the return is coming from predictable cash flows.

This is true.

If you intend to hold something to maturity, or if you hold an investment that is reliably paying enough cash flow to repay you over a reasonable length of time, all else being equal, the variability in the price attached to your investment and its returns, and the behaviorally driven component of those returns, ought to be lower. This is one of the reasons why we tend to buy and hold bonds in our client accounts to maturity. Yet, even here, your compound returns are going to be influenced by investor behaviors outside of that bond — you have to reinvest those coupons at rates determined by individual actors influencing prevailing interest rates, after all.

The other, more common argument — this is the Graham argument — is that these behaviorally driven features of markets are, even for investments in riskier parts of the capital structure like credit or equity, temporary noise on the path toward convergence of the investment’s price with its value. Investors have historically found comfort in the idea that the voting machine will someday converge to the weighing machine — that one day, everyone else will come to the conclusion that I have about this company and value it like I do.

This forms part of the story for a vast range of investment styles. For the investor who speaks the language of growth, it is indispensable. He is saying, explicitly or implicitly, “I believe this company will grow faster than other investors expect. When I’m right, the price will converge to the value implied by the higher earnings.” For the intrinsic value or quality investor (I’m talking to you, too, Holt, EVA, CFROI wonks), it is an even stronger impulse. He believes that a company’s ability to deploy its current assets and reinvest at higher rates than the market expects (or for a longer time than the market expects) forms a value that is essentially the stock-in-itself. That’s kind of what intrinsic means, isn’t it? Frankly, it is the multiples-driven value investor who approaches the question with the keenest awareness of behavioral influences on prices. He’s comfortable implying that investors tend to do a bad job of knowing which companies ought to be worth a lower multiple of their earnings/assets/cash flow, and that enough time will cause the outperformance of the cheap company to be recognized and rerated. Or maybe just the increase in earnings will cause investors to apply the same multiple to create a higher value. There is, at least, the self-awareness of behavior’s fickle influence.

In each of these cases, the investors recognize that the market is a noisy, behaviorally complicated voting machine in the short run. This is why when you meet with a fund manager, they will always always always tell you that the rest of the market is looking at the next quarter’s earnings, while they stand alone at the top of the mountain, summoning the courage to weather short-term storms in favor of long-term outcomes. They’re very brave. Lots of people have been talking about it. But in each of these cases, the reality is that other than significant sources of real cash flow distributions (i.e., not stock buy-backs or debt pay-downs, for fans of the “Shareholder Yield” concept), the convergence of the voting machine to the weighing machine can take a very, very, very long time. And it may never happen, for the forces that will cause it to take place are themselves behavioral in nature! Somebody’s gotta say they’re willing to buy your stock at that price, and that somebody is either a person or a computer programmed by a person.

If the market wants to convince itself that Amazon can and will someday raise its prices to generate actual profits, and that they will then use those profits to bestow untold trillions of dollars (or maybe bitcoins, by the time this actually happens) in dividends on its loyal investor base, it can do it for a very, very, very long time. If you do not think Amazon can manage to preach this narrative to its investor base for another 10, 25 or even 50 years, you are dead wrong.

Do you think I’m arguing against value investing? Against fundamental research? Because I’m not. Not even a little bit. OK, maybe a little bit in the case of most fundamental research. What I am arguing is that when these approaches work, they still work because of the lens of preferences and experience that those who participate in the pricing of the investment bring to the table with them. ANY criticism of “behavioral” methods of investing must also be a criticism of fundamental ones, because they both include assumptions about how humans will respond to something.

This has a lot of implications:

For asset owners and allocators: How much time and effort do you spend thinking about who else owns the investment? Who else might want to own it if some bigger thing happens in the world? To that investor’s situation? To the investment or company itself? Compare that to the amount of time you spend sifting through macro data, research reports and constructing models. If you’re like most of us, you’re spending <5% of your time and resources on the former and 95% on the latter. That’s a mistake.

For fund selectors: Spend more time developing theses about managers who — through intuitive or quantitative techniques — seek to understand what drives the behaviors of other investors (or non-investor influencers of securities prices), rather than simple security-based or macroeconomic analysis.

For all investors: Always keep in mind how prices are determined when you think about how certain trends and events may impact markets and your portfolio. Think about how regulation-driven moves toward passive instruments may change price-to-value convergence. Think about how an increase in private equity dollars may influence or change price-to-value convergence in public markets. Think about what behaviors a low global growth environment could induce on the part of financial advisors, institutions and individuals as they participate in the price-setting process.

OK, so how do we do all this? If the Market In-Itself is a myth, how do we adjust our thinking?

There is no mathematical proof that solves this conundrum for us, because we can’t know people’s full motivations, preferences and exogenous influences. We do not know what investors or traders are paying attention to, except by observing the results after-the-fact and coming up with stories to attach to those analyses. Even if we could, many of these behaviors are emergent properties of the market in the aggregate, meaning that the way people behave isn’t nearly as independent of the path or state of the overall market as we’d like it to be. The market is a complex system.

What we can do is recognize what we recognize about every other aspect of society: that these motivations, preferences and exogenous influences on our behavior are reflected in the tribes we select and the language we speak. We may not be able to observe specific behaviors in action, but we can understand a lot about investors by observing, for example, the sell side. Not because they have anything useful to say (sorry), and not even because we think that they somehow reflect the consensus about a fundamental fact about a company. Because the sell side is telling us who their customers are. The feedback mechanisms of industry conventions, of style boxes, of terminology and language that our fellow investors adopt — these, too, all tell us a great deal about investor behaviors. They can also give us insight — incomplete insight, but insight nonetheless — into things like sustained low volatility, limited liquidity, the rationale for the existence of behavioral premia like momentum, value and low volatility, and how they go through sustained periods of weak or strong performance.

And that’s exactly where we’re going in Part II: the languages and tribes of investing and how they can help us understand the behavioral drivers of the Long-Run Voting Machine.

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A Taxonomy of Humans, Evolution and Aliens (by Silly Rabbit)

Netflix recommendation system

High level, but still interesting, overview of how Netflix recommendation system works from Wired. Short answer: “The three legs of this stool would be Netflix members; taggers who understand everything about the content; and our machine learning algorithms that take all of the data and put things together.” The tagging piece is probably the most interesting (“dozens of in-house and freelance staff who watch every minute or every show on Netflix and tag it. The tags they use range massively from how cerebral the piece is, to whether it has an ensemble cast, is set in space, or stars a corrupt cop”) and point to the continued need for ‘human-in-the-loop’ content tagging for machine learning systems.

A taxonomy of humans according to Twitter

Sam Levine, an artist and programmer from Brooklyn, scraped Twitter’s ad creation page to produce a full list of all user segments, their names, descriptions and user count: a taxonomy of human beings according to Twitter and its data brokers. My favorite tag: “Buyers of deli bulk meat.”

Emergent physics behind evolution

Fascinating interview with Nigel Goldenfeld, Director of the NASA Astrobiology Institute for Universal Biology, on Seeing Emergent Physics Behind Evolution:

“People tend to think about evolution as being synonymous with population genetics. I think that’s fine, as far as it goes. But it doesn’t go far enough. Evolution was going on before genes even existed, and that can’t possibly be explained by the statistical models of population genetics alone. There are collective modes of evolution that one needs to take seriously, too. Processes like horizontal gene transfer, for example.”

The aliens on Earth

Continuing on the theme of evolution, this is a fascinating piece on ctenophores as aliens on earth:

“Leonid Moroz has spent two decades trying to wrap his head around a mind-boggling idea: even as scientists start to look for alien life in other planets, there might already be aliens, with surprisingly different biology and brains, right here on Earth. Those aliens have hidden in plain sight for millennia. They have plenty to teach us about the nature of evolution, and what to expect when we finally discover life on other worlds.”

Chinese science fiction

And finally, on the subject of aliens, I can not recommend strongly enough Liu Cixin’s The Three-Body Problem ( 三体 ) science fiction trilogy, which recently won a Hugo Award. Deeply intelligent and expansive science fiction on the scale of Asimov’s Foundation series. Without giving too much of a spoiler, my favorite quote is from the second book in the trilogy (The Dark Forest) which turns Johann Wolfgang von Goethe‘s “If I love you, what business is it of yours?” into “If I destroy you, what business is it of yours?”

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Before and After the Storm or: Make America Good Again


Thanks for being part of the Epsilon Theory community. One of the other communities that matters to us is Brazoria County, a rural county south of Houston that is experiencing heavy floods in the wake of Hurricane Harvey. The United Way of Brazoria County is a charity focused on recovery for this heavily impacted region.


Mr. Advocate, the rotten tree-trunk, until the very moment when the storm-blast breaks it in two, has all the appearance of might it ever had. The storm-blast whistles through the branches of the Empire even now. Listen with the ears of psychohistory, and you will hear the creaking.
— Isaac Asimov, Foundation (1951)

Do you hear the creaking?

I don’t. It’s not that I don’t see what’s going on in America or that I’m not pained by an increasingly bi-polar distribution of political, social and ethical views. After all, the belief in narrative-driven politics and narrative-driven markets isn’t a belief in their virtue, only their existence. I also don’t know how we get out of this cycle, but I believe that we will. This is not a Seldon Crisis, and Trump is not the Mule.

That Nature smiles at the union of freedom and equality in our utopias. For freedom and equality are sworn and everlasting enemies, and when one prevails the other dies. Leave men free, and their natural inequalities will multiply almost geometrically, as in England and America in the nineteenth century under laissez-faire. To check the growth of inequality, liberty must be sacrificed, as in Russia after 1917. Even when repressed, inequality grows; only the man who is below the average in economic ability desires equality; those who are conscious of superior ability desire freedom, and in the end superior ability has its way.
— Will and Ariel Durant, The Lessons of History, 1968

Cersei Lannister: You should have taken the realm for yourself. Jaime told me about the day King’s Landing fell. He was sitting in the Iron Throne and you made him give it up. All you needed to do was climb the steps yourself. Such a sad mistake.
Ned Stark: I’ve made many mistakes in my life, but that wasn’t one of them.
Cersei: Oh, but it was. When you play the Game of Thrones, you win or you die. There is no middle ground.
Game of Thrones, Season 1, Episode 7

Perhaps the cause of our contemporary pessimism is our tendency to view history as a turbulent stream of conflicts — between individuals in economic life, between groups in politics, between creeds in religion, between states in war…but if we turn from that Mississippi of strife, hot with hate and dark with blood, to look upon the banks of the stream, we find quieter but more inspiring scenes: women rearing children, men building homes, peasants drawing food from the soil, artisans making the conveniences of life, statesmen sometimes organizing peace instead of war, teachers forming savages into citizens, musicians taming our hearts with harmony and rhythm, scientists patiently accumulating knowledge, philosophers groping for truth, saints suggesting the wisdom of love. History has been too often a picture of the bloody stream. The history of civilization is a record of what happened on the banks.
— Will Durant

  Unidentified man/hero/Texan

Reporter:  You guys going to jump in and help out?
Unidentified Man:  Yes, sir.
Reporter:  Where you coming from?
Unidentified Man:  Texas City.
Reporter:  What…what are you going to do?
Unidentified Man:  I’m going to try to go save some lives.

“Val”, said Father, “we don’t expect you to understand this, but some of the things that make Peter…difficult…are the very things that might also make him great someday.”
“What about me?” asked Valentine. “As long as you’re telling fortunes.”
“Oh, Val,” said Father. “All you have to do is live your life, and everyone around you will be happier.”
“No greatness, then.”
“Val,” said Mother. “goodness trumps greatness any day.”
“Not in the history books,” said Valentine.
“Then the wrong people are writing history, aren’t they?” said Father.
Orson Scott Card, Ender in Exile, (2008)

Damn right, they are.

It’s hard to stay focused on a lot of things in the face of human tragedy. Including markets.

I’m writing this on Tuesday, August 29 from my home office in Memorial, a village on the west side of Houston. We’ve gotten more than 30 inches of rain through this morning, we can still do our jobs, and we’re doing fine. The people to the west of us in Katy aren’t. Waters from rains upstream have led to overflowing reservoirs that will be released over time, keeping flood waters high. People to the east of us aren’t, either. Many of Houston’s most populated areas are under water. We have colleagues that have been evacuated from houses they evacuated to, and clients and friends who haven’t been able to leave their second floors for a week.

My little hometown in Brazoria, Texas, some 60 miles to the south, is about to have the screws put to it next. It sits between two rivers. One is a stream called the San Bernard River. The other is a Big, Nasty River called the Brazos. It puts nine times as much water through it as the Rio Grande. Come later this week when this piece is published, it will be putting through 45-60 times as much water — at my hometown maybe some 70-80,000 cubic feet per second. If extrapolations from this NWS projection are to be believed, it could be more like 120-140,000 cubic feet per second. As you can see from the missing right axis, it is both literally and figuratively an unfathomable amount of water — an Olympic-sized swimming pool flowing every 3 seconds through a channel where it usually takes two minutes.

We tend to think big thoughts when big things like this happen, and there’s been a lot of that going on. For me, those thoughts have turned local, but I know a great many people outside of the Greater Houston area are focused on other things that are going on: Charlottesville, the Trump presidency, Berkeley, Eclipses, Nazis. It’s a lot to take, and Ben has accurately predicted and is now observing how some of these issues are manifesting themselves in Competitive Games that force us all into positions where we must either fight or lose. He was absolutely right that the aftermath of the Trump presidency would break us, that it would destroy any chance at productive political, social — hell, even investment dialogue. Was the event that broke us irrevocable? How do we get out of this Competitive Game? Can we?

These questions form the central context for one of the greatest works of science fiction ever written: Foundation, by Isaac Asimov. Spoilers follow, but frankly if you haven’t read it, you should stop reading this note and read it instead. It’s better. The story of Foundation is the story of a massive multi-planetary civilization and the development of a robust, flexible system for understanding and modeling the sociopolitical trends of its very large societies: psychohistory. The main champion of this system, a generational genius named Hari Seldon, identifies the inevitable fall of the prevailing government and its devastating aftermath. While the collapse is unavoidable, he determines, not all subsequent outcomes are equivalent. He devises a plan to plant seeds of the civilization that would survive in two corners of the galaxy, predicting that the evolution of those societies over future generations would lead to the maximum possible peace and stability. The system of psychohistory hinges on the behaviors of very large groups of humans and the simplifying assumption that no individual could possibly have the influence or power to break these models.

There are two kinks in Hari Seldon’s system. The first is the idea that Foundation — but really, any civilization — will reach inflection points from time to time where one set of actions will break the path back to peace and harmony, and one set of actions will maintain it. These events require active intervention outside of the normal behaviors that those in power would otherwise pursue. These are Seldon Crises. The second kink is different in that it is unpredictable, or at least was unpredicted. It is the existence of a single individual who does reach the level of power — in this case through the development of abilities to influence the emotions and judgments of those he encounters — to change the inevitability of Seldon’s map of history. The Mule, as he is called, nearly breaks the Seldon model, until those who rediscovered psychohistory rebuild the models and determine the appropriate strategy to ensure that the Foundation civilization gets back on its long-cycle path back toward peace and stability.

This is fiction and there is nothing in political science , economics or sociology that approaches psychohistory’s fictional robust stochastic framework for predicting the ebbs and flows of history. But there is truth here. The long cycles of history do have repeating features, which have never been better described in a non-fictional sense than by Will and Ariel Durant. Despite already having recommended one book, I think very few books are truly “must-reads.” Still, every human should own and read The Lessons of History as well. Among many other lessons, the Durants present a framework in which the path of history swings between liberty and freedom on the one hand, and equality through social control on the other. That control may extend from a government, from the seat of a priest, spiritualist or imam, from a military strongman or warlord, or from a particularly influential social structure.

In the days and weeks since Charlottesville, I think that a lot of people are starting to see President Trump’s election as a sort of Seldon Crisis. The language people used — the language *I* used when I left the GOP to be a #NeverTrumper — was the language of statistical distributions. “Sure, Hillary Clinton has a lower mean, but Trump has a fat left tail” was the particular phrase I used to sound smart and inoffensive to friends and family who either supported or opposed him. In a lot of ways, this is the language of a Seldon Crisis, because it begins to characterize the threats to society posed by an event or person as existential. I don’t know exactly how to communicate to you that existential language is now our lingua franca, but do I really need to?

Source: Google 2017

A lot of people see the president as The Mule now, too, I think, by which they imply that Trump was both unpredictable and capable of disproportionately large influence on the direction of society relative to what we would have expected from the ordinary ebbs and flows of history. Of course, the Voxsplainer types would be happy to provide you with their latest patronizing explanation for why and how Trump was elected. They’ll also follow it up with a series of snide sub-tweets to give themselves ironic cover. But the many on the left who cannot understand his election or his continued support often have difficulty fathoming that his base did not form as the result of Mule-style manipulation of some sort of another. It’s a backhanded compliment for a big slice of humanity: they couldn’t possibly be this stupid. Of course, it’s also condescending as hell.

The truth is even more condescending. Trump is not a Seldon Crisis. Trump is not the Mule. Sorry. The rotation between equality and liberty continues unabated, peacefully or otherwise, over the centuries. And it’s all happening again. Except it is different this time. It is happening faster. Much faster. Not because of the existence of a Mule character like, say, Hitler, whose individual influence thwarts the ability of the psychohistorians like Hari Seldon or Will Durant to predict paths. And it’s not because of Trump, as much as many want to paint him with that brush.

It’s because of the internet.

Taxonomy of Tribalism

“All politics is local.”
— Tip O’Neill, Jr.

It wasn’t that long ago that Speaker O’Neill was right in saying that politics was local. Politics and civics were largely formed in a household, shaped by a local community and then influenced by a largely regional experience. Most people shared party affiliations with their parents, and if they shed them, it was a ritualistic shedding of those affiliations in favor of another held by a similar group — think Woodstock or Haight-Ashbury. Diversity of belief was protected by general isolation from other groups. You knew what the politics and civics of a small town in Oklahoma with one Baptist church would be. You knew what politics a union town in Ohio with a steel mill would adopt. The meeting at the community center in a poor district of a big city held few secrets. Our towns, our families, our communities were our echo chambers.

I come to bury this notion, not to praise it!

These structures fostered social stability, which was often a boon to those communities. People had structures for emotional and material support, people who would be there to keep an eye on their home when they traveled. People who would stop by with food after a funeral (which they always went to). People who provided accountability and comfort and resources to empower productive risk-taking. They show themselves in the wake of tragedies like Hurricane Harvey in huge quantity because — and I genuinely believe this — people are generally good. But as much as I sobbed like a baby watching the good-ol-boys of the Cajun Navy roll in from New Orleans, Lafayette and Baton Rouge, I’m not naïve, Kay. I know this won’t last forever. In a few weeks, maybe a couple months, we’ll be back to business as usual. A lot of people (these are not the generally good people I was talking about earlier, in case you were wondering) have already jumped the gun, trying to decide which political stance they want to justify through use of the disaster. If history is any guide, the rest of us will follow.

If Charlottesville and Berkeley are a reminder of anything, however, it’s that our community echo chambers were often vile, too. When a community jointly agreed that racism was acceptable, that a socialist revolution was imminent, that communists were under every bed, or that southerners were all provincial rubes, the forces compelling change in those views were few. Oh, sure, some bold ones would stand up from within the community to speak truth to power. These were virtuous men and women, those who accelerated the necessary conversations. People moved, television and radio and newspapers still communicated narratives, and thoughts still flowed through the country. But slowly. And slowly but surely change took place in gradual, predictable ways. For centuries, it was a conservative America, not in the modern issue-based political sense but in the more traditional Buckleyan sense of standing athwart history yelling, “Stop!” It wasn’t slow because of some strong political force, but because the force required to change the inertia of a geographically massive country with relatively low population density was not there. Politics instead followed the patterns of linguistic dialects, where isolation and proximity drove deviations in diction, syntax and grammar, and where the things that caused interaction like trade, diplomacy, television, culture and politics, led to their convergence.

Both virtue and vileness notwithstanding, everyone was generally still playing a Collaborative Game. Not because of any special virtue of the parties involved, but because there were so many pockets of difference in experience that any kind of engagement required identifying commonalities and finding compromise. Of course there was conflict. But these were (figuratively) isolated populations coming together to discuss radically different world views, which generally required explanation, empathy and patience. Going Competitive meant true isolation, because the other side didn’t have to play our game, not really. Politics were local. In the same way that people coming together who speak different languages had to find a means of communication to proceed to rubrics and translations, there was a natural need for collaboration — and the occasional threat of conflict bred out of mistranslation! But after any negotiation, there was a home to return to. The Competitive Game didn’t work, because people had the option to leave that game and join another. You couldn’t force people to play in your game and lose, because they could take their ball to their community and go home.

The internet broke that.

It didn’t happen immediately, in part because of the pace of adoption of the technology itself, but more because the forms that constant, broad communication would take took some time to settle on. The message board begat the chat room begat the personal webpage begat the blog begat closed social media networks begat open social media networks. That was the singularity. The open social media network — Twitter and, increasingly, Facebook — replaced the community. Even for those who weren’t active participants in the networks themselves, a critical mass of other of society’s structures became connected to it, its language and its norms. The media, corporate executives, politicians — even sports leagues — cannot escape the influence of the norms promoted by these networks.

You could argue that churches, community groups, neighbors, extended families, political action groups, and other causes still act as anchors for cultural values, but for the most part, you’d be wrong. The average child may spend 6-8 hours a day on social media. The average adult spends two. How many hours does the average American spend in Church/Temple/Mosque? Reading his Bible/Torah/Koran? Outside of a natural disaster, how often does he really talk to his neighbors? Add to this the network effect of other media that are inextricable from the ways in which news is consumed, evaluated and parsed, and it becomes clear that there is no community to run to. Choose your box, because the game has changed, and you can’t leave the table.

So what’s the big deal? The big deal is that this has driven much more rapid propagation, acceptance and incorporation of new ideas. In the same way that a meme is already the subject of meta-jokes about cynical responses to the original meme by the time that half the country is just seeing it, dizzying new social values emerge almost daily. It took 396 years for America to decide that it probably doesn’t make sense to criminalize being born as a gay person. It took 12 years after that for America to recognize that the world isn’t going to come crashing down around us if we recognize that gay people who love each other ought to be able to get married. It took 2 years after that for social media to decide that there are 183 shades of human sexuality, and read the sticky post on the top of the forum for the acceptable terms to use for each of them, because the old terms you used yesterday are now hateful. The world is moving very, very quickly.

The social liberal looks at this state of affairs and says, “Hell yes!” Maybe we overshoot sometimes, but that overshooting is overstated. If moving quickly and pissing people off along the way is the cost of taking away the safe places for bigots, racist and sexists, and starting the process of taking away oppressive systems put in place by rich white men, then it’s worth it. Look, I hear you. A lot of good people think this way.

The social conservative looks at this and is puzzled. We’ve transitioned from a society that cared what you did, to a society that cared what you said, to a society that cared what you thought, he says. I’m kind to my family, to my friends, and to strangers. I really do try to improve myself, and I know I’m not perfect. I really do care about what happens to people, and I’ll drive 300 miles with my pick-up truck, a boat and some hip waders, and I’ll work myself to exhaustion for a week for people I don’t know and will never see again. But I also have values and beliefs I grew up with, and they’re values that have worked for hundreds of years. I’m not ready to throw them away on a whim. I hear you, too. A lot of good people think this way.

Good or not, neither of these people can take his ball and go home anymore, because there is no home. If they would be a part of the process of making social, cultural and political decisions at all, they must play, whether it is a Collaborative Game or a Competitive Game. The steering wheel has been ripped away from them, but to make the game of chicken complete, someone must point the cars at each other and set the stakes. Those who would marshal these forces find an easy tool to achieve this, whether intentionally or subconsciously: convince people they’re part of a tribe, and tell them they’re under attack.

What I’m talking about here isn’t just applying names to things we or others attach ourselves to. It isn’t just saying that “You’re a democrat so you’ll think this” or “You’re a black/white/Hispanic man, so this must be your view on this topic.” No, what we are talking about is the scorched earth tactic that treats every defining issue as an existential one. It’s us or them. You win or you die.

This dynamic isn’t out of character with the path of history, some aberration caused by an unduly influential Mule. It is an emergent property of a society undergoing too-rapid change.

Manufactured Existential Crises

The forces that seek to manipulate the political right do so through the creation of wholly imaginary ideals that are assumed to be in need of defending. Since they are imaginary, to conjure threats against them is purely a matter of narrative creation of the sort that has graced these pages for years. Consider the white race or white culture. It is a myth — it doesn’t exist. Racially, admixture analysis finds a tremendous amount of diversity within Europe. Mediterranean populations often have more in common with those of the Levant than with Northern Europe. Modern and ancient DNA archetypes found within Scandinavia, Ireland and the Balkans are extraordinarily different. I belong to a Y-DNA sub-clade called A738, a relatively recent off-shoot of M-222 that includes a narrow set of names: Guinn, Egan, Keegan, Morgan, Goggins, Larkin. And I am more likely to share a direct male line ancestor with a man from N’Djamena than a man from Nuremberg or Nizhny Novgorod. The below is the spread today of the R1b haplogroup, which is even further up the chain.

The Lost Cause vision of the Confederacy is a myth. I say this as someone who will defend almost any cemetery installation celebrating the simple bravery and honor of the individual soldier, and as someone who thinks Robert E. Lee was sufficiently brilliant as a tactician to merit historical remembrance. But anyone who says the largely disposable plaques and generic statues churned out by a generic factory to celebrate the “spirit of the Confederate Cause” are those kinds of monuments to history is defending an imaginary construct. It is vapor, but useful vapor to those who would divide us. It’s forced us into a world where people who don’t know Paul Johnson from Paul Blart have become self-appointed defenders of history, and where people who learned about the Federalist Papers in a Broadway musical are deeply concerned about celebrating treason. Please.

The forces motivating and influencing the political left in America have cultivated an even more perfect, self-reinforcing tool for division, I think. The post-modern sensibilities of the movement are utterly Foucauldian. In a rather clever sleight-of-hand from the intent-, conviction- and character-driven views that drove the Civil Rights movement, the manipulators of the American left now fully embrace the language of the Panopticon. By presenting society as citizens operating within a controlled and monitored system, the left can argue at any juncture that those who oppose their arguments are simply agents of an oppressive system. Can’t find data to support your statement? Can’t develop a logical path to support your conclusions? You need only say that your opponent argues from a place of privilege or status within an oppressive system, and the argument is over. This kind of language that automatically asserts the pervasive existence of oppression as an argument-ender, whether it exists or not, is just another way to promote the constant existential crisis.

If after reading one of the prior three paragraphs we think to ourselves, “Yes, but ____ is a fake existential crisis. Mine is real, and here’s why,” then we have to consider whether we’re part of the problem. All of these things, and the politicians we elect to promote our narrow view of them, are natural patterns in the swing of the pendulum toward equality-motivated control.

So what do we do?

It is time now for us to rise from sleep.
— Benedict of Nursia

What does the path of history tell us? What does the aftermath of one of America’s greatest natural disasters and human tragedies tell us? What can we do to survive and escape a Competitive Game that doesn’t allow us to pull away from the table? If you’re reading this, you’re probably in the investment industry, or at least have an interest in financial markets. If you’re in the investment industry or in the financial markets, you like to win. So you’re not going to like my answer.

We play. And we lose.

The story of history, I think, is that the only way to defuse a Competitive Game is to win by eliminating your competition, or to choose to play a Collaborative strategy even when you know it is sub-optimal.

There is a time for war, and that is usually our instinct. But there is a time for sacrifice, too. In 529 A.D., Benedict of Nursia chose sacrifice. At a time where the Competitive Game had so gone off the rails that Rome fell into ruin, Benedict and his adherents isolated themselves from society and devoted themselves to service, industry and memory. The result of their efforts was isolation, poverty and celibacy. It was also the preservation and creation of much that was and is good about European culture and society. They preserved and practiced techniques for making foods and wines. They preserved writing, language, literature and histories. Agricultural methods and metallurgy. They were the Foundation during the collapse of the Empire.

What about us? What can we do?

We can start by laying down our right to take offense. We can be unfailingly committed not only to the principles of freedom of speech, but to the value of free expression and exchange of ideas. In other words, by not pursuing the counterproductive, obstructive aims of the worst cartoon the otherwise brilliant Randall Munroe ever made. We can be vulnerable, we can let our opponents assign us identities and titles we would never adopt for ourselves without complaint. We can believe the best about people, even if we know it may cause us harm. We can give up our right to be right.

This is true in our businesses and lives as investors as well, because most of you know as well as I do that the cynicism that pervades politics has invaded our world as well. So what can we do? We can be unfailingly honest with our clients, our families. We can hold loosely to the things we think about markets and our portfolios by focusing on a narrow group of things that matter. We can engage with our clients and build portfolios that will allow them to focus on the things that happen on the banks, and not in the bloody river. We can do all in our power to destroy the agency issues and career risk dynamics that influence decisions and cause harm to the people who put their trust in us. We can gas up the boat and try to save some lives.

In short, we can choose goodness over greatness. It only works if we do it together.

Join us!

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Revenge of the Humans, Emojis & Mushrooms (by Silly Rabbit)

Revenge of the Humans Part II

This is a really terrific (long) piece of writing: Revenge of the Humans Part II: A New Blueprint For Discretionary Management:

The meat really starts to kick in the section ‘There Are No Shortcuts’ and reaches peak lucidity in the section ‘Organizational Structure’. Excellent work by Leigh Drogan, Founder and CEO at Estimize, laying out what I really do believe is the blueprint for success with ‘next gen’ strategies that are foundationally systematic and substantially software-encoded:

Portfolio Manager — Of all the roles this is where I think things really need to change in terms of who sits in this seat. It can no longer be hedge fund bros, they simply won’t survive here. Nor will the pure gunslingers and tape readers, gone. And you certainly don’t want the pure quants sitting in this seat. PMs of the future are going to be far more interpersonal and process driven…. This is a cross functional role, and one that needs to be based on the behavioral attributes of the person more than anything else. An MBA may be useful here, but I would even say that having experience working at the early stages of a startup as a CEO can add a lot. I’m waiting for someone to develop a firm to leverage psychometric testing for different investment strategies so that we can identify people tuned for momentum vs value. You’re talking about a completely different psychology between those two people and it’s imperative you choose the person correctly … PMs should have some training in statistical and quantitative methods in order for them to talk intelligently with the quants and trust the factor models. Without that trust, there’s simply no point in having them and you’ll only gain that by understanding how they are built. Should a PM know how to code, no. Should they understand what the code does and why, absolutely. Basic data science classes can provide this knowledge. Quantitative research methods 101 in college is a requirement … I believe that compensation structures for the PM need to change. This is no longer “his book”. He is another player on the team, who has a specific role, to coordinate the dance. But in many ways, he will have less impact on the alpha generated by the book than the analysts or the quants who create the factor models. The PM is now the offensive coordination calling the plays, not the quarterback on the field scrambling around and throwing touchdowns. We can now compensate analysts accurately for the efficacy of their calls, and the PM for how much alpha she adds above them. The rest of the team should be bonused out based on the performance of the book.

DeepMoji

Neat work by the good folks at MIT Media Lab:

Our basic idea with our DeepMoji project is that if the model is able to predict which emoji was included with a given sentence, then it has an understanding of the emotional content of that sentence. We are training our model to predict emojis on a dataset of 1.2B tweets (filtered from 55B tweets). We can then transfer this knowledge to a target task by doing just a little bit of additional training on top with the target dataset. With this approach, we beat the state-of-the-art across benchmarks for sentiment, emotion, and sarcasm detection.

Check out the online demo here, more detailed write-up here, and full technical paper here.

Useful skills like VR, NLP and… econometrics?

This list of fastest-growing freelancer skills compiled by Upwork, a job site that matches freelancers with employers, is just so odd I feel there is either some deep pattern coded in there that explains everything, or else some intern at Upwork is having a laugh.

Growth in VR and NLP makes total sense given the relative lack of experienced talent vs growth in demand, especially for VR developers. Neural network and Docker development for the same reasons. Adobe Photoshop freelancers — sure, I guess Photoshop is still operated by a priesthood although it’s unclear why the journeyman priesthood is growing rapidly.

But then Econometrics, really??!!? — never, ever, in my life have I thought “what I really need to do is to hire a random econometrician over the internet”, and for sure that thought has not been exponentially increasing of late.

And Asana work tracking, which had only around 20,000 paying customers a year ago?!!? — that’s like having ’Tesla car polisher’ on the list.

Anyway, I leave you to ponder. It certainly is an intriguing list — perhaps what we need is an econometric hireling to make sense of it for us…

Mushrooms

And finally and frivolously, we have this article which is pretty much a total waste of storage space as it is a 700-word, not-very-good takedown of a new not-very-good mushroom-identifying mobile app with sub-par mushroom image recognition. However, it warrants inclusion in this week’s Rabbit Hole for the one immortal line:

There’s a saying in the mushroom-picking community that all mushrooms are edible but some mushrooms are only edible once.

Surely the apothegm of the week!

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Data Access Battles, Creative Thinking & Full Script AI (by Silly Rabbit)

Data access battles

A couple of weeks back I shared a link to the story of ImageNet and the importance of data to developing algorithms. Ars Technica reports on two ‘at the coalface’ battles over data access with HiQ and Power Ventures fighting with LinkedIn and Facebook over data access. I’m not advocating a position on this but, to be sure, small — and currently obscure — court cases like these will, cumulatively, end up setting the precedents which will have a significant impact on the evolution and ownership of powerful algorithms that are increasingly driving behavior and economics.

Creative thinking

This speech from Claude Shannon at Bell Labs in 1952 has been circulating online for the past couple of weeks. It is a timeless, pragmatic speech on creative thinking which remains, 65 years later, fully relevant for developing novel computational strategies:

Sometimes I have had the experience of designing computing machines of various sorts in which I wanted to compute certain numbers out of certain given quantities. This happened to be a machine that played the game of nim and it turned out that it seemed to be quite difficult. It took quite a number of relays to do this particular calculation although it could be done. But then I got the idea that if I inverted the problem, it would have been very easy to do — if the given and required results had been interchanged; and that idea led to a way of doing it which was far simpler than the first design. The way of doing it was doing it by feedback; that is, you start with the required result and run it back until — run it through its value until it matches the given input. So the machine itself was worked backward putting range S over the numbers until it had the number that you actually had and, at that point, until it reached the number such that P shows you the correct way.

Facebook shuts down robots after they invent their own language

Facebook shuts down robots after they invent their own language has become a widely reported and wildly commentated story over the past month, referencing a story on ’Tricky chatbots’ linked here a couple of months back. For melodramatic illustrative effect, I like switching a couple of words in the Facebook headline so that it reads ‘Lehman (doesn’t) shuts down traders after they invent their own language’ as it illustrates that, in general, if you: put a bunch of agents (human or machine) together and set up a narrowly defined, adversarial, multi-player game with a strong reward function then the agents will develop their own task-specific language and protocols, keep adding complexity, lie to each other (yes, the FB bots also learnt to do that), be tempted to obfuscate behavior in order to reduce interference and maximize the reward function, and develop models which are positive for near-term reward maximization but do not necessarily deal with longer-term consequence or long tail events, and so become very hard for human overseers to truly assess…

DICK FULD (2008): 
I wake up every single night wondering what I could have done differently — this is a pain that will stay with me the rest of my life

FACEBOOK (2017):
Hold my beer

AI: From partial to full script

Thinking more broadly about the longer-term evolution of AI (and the nature of money and contracts, per Ethereum link last week), it has been interesting to re-read Sapiens: A Brief History of Humankind by Yuval Noah Harari which charts the rise to dominance of us Sapiens with especially interesting chapters on the development of written language and money. A concept which particularly grabbed me was that written language was initially developed as ‘partial script’ technology for narrow tasks such as tax accounting, and then evolved to be full script and so capable of much more than it was originally conceived for.

The history of writing is almost certainly a wonderful historical premonition of the trajectory of AI, except with the evolution being much faster and the warning that likely “the AI is more powerful than pen.”

Relevant excerpt from Sapiens:

Full script is a system of material signs that can represent spoken language more or less completely. It can therefore express everything people can say, including poetry. Partial script, on the other hand, is a system of material signs that can represent only particular types of information, belonging to a limited field of activity … It didn’t disturb the Sumerians (who invented the script) that their script was ill-suited for writing poetry. They didn’t invent it in order to copy spoken language, but rather to do things that spoken language failed at … Between 3000 BC and 2500 BC more and more signs were added to the Sumerian system, gradually transforming it into a full script that we today call cuneiform. By 2500 BC, kings were using cuneiform to issue decrees, priests were using it to record oracles, and less-exalted citizens were using it to write personal letters.

The beautiful mathematical explorations of Maryam Mirzakhani

And finally, at the risk of turning into The Economist, we conclude this week’s Rabbit Hole with a touching obituary of the Tehran-born, Fields Medal-winning mathematician Maryam Mirzakhani:

A bit more than a decade ago when the mathematical world started hearing about Maryam Mirzakhani, it was hard not to mispronounce her then-unfamiliar name. The strength and beauty of her work made us learn it. It is heartbreaking not to have Maryam among us any longer. It is also hard to believe: The intensity of her mind made me feel that she would be shielded from death.

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You Still Have Made a Choice: Things that Matter #2

Drummers are really nothing more than time-keepers. They’re the time of the band. I don’t consider I should have as much recognition as say a brilliant guitar player. I think the best thing a drummer can have is restraint when he’s playing — and so few have today. They think playing loud is playing best. Of course, I don’t think I’ve reached my best yet. The day I don’t move on I stop playing. I don’t practice ever. I can only play with other people, I need to feel them around me.

— Ginger Baker (founder of Cream), from a 1970 interview with Disc Magazine

La cuisine, c’est quand les choses ont le goût de ce qu’elles sont.
(Good cooking is when things taste of what they are.)

— Maurice Edmond Sailland (Curnonsky) — 1872-1956

There are those who think that life
Has nothing left to chance
A host of holy horrors
To direct our aimless dance

A planet of playthings
We dance on the strings
Of powers we cannot perceive
The stars aren’t aligned
Or the gods are malign
Blame is better to give than receive

You can choose a ready guide
In some celestial voice
If you choose not to decide
You still have made a choice

 — Rush, “Freewill”, Permanent Waves (1980)

For the kingdom of heaven is like a man traveling to a far country, who called his own servants and delivered his goods to them. And to one he gave five talents, to another two, and to another one, to each according to his own ability; and immediately he went on a journey. Then he who had received the five talents went and traded with them, and made another five talents. And likewise, he who had received two gained two more also. But he who had received one went and dug in the ground, and hid his lord’s money. After a long time the lord of those servants came and settled accounts with them.

So he who had received five talents came and brought five other talents, saying, ‘Lord, you delivered to me five talents; look, I have gained five more talents besides them.’ His lord said to him, ‘Well done, good and faithful servant; you were faithful over a few things, I will make you ruler over many things. Enter into the joy of your lord.’ He also who had received two talents came and said, ‘Lord, you delivered to me two talents; look, I have gained two more talents besides them.’ His lord said to him, ‘Well done, good and faithful servant; you have been faithful over a few things, I will make you ruler over many things. Enter into the joy of your lord.’

Then he who had received the one talent came and said, ‘Lord, I knew you to be a hard man, reaping where you have not sown, and gathering where you have not scattered seed. And I was afraid, and went and hid your talent in the ground. Look, there you have what is yours.’

But his lord answered and said to him, ‘You wicked and lazy servant, you knew that I reap where I have not sown, and gather where I have not scattered seed. So you ought to have deposited my money with the bankers, and at my coming I would have received back my own with interest. Therefore, take the talent from him, and give it to him who has ten talents.

For to everyone who has, more will be given, and he will have abundance; but from him who does not have, even what he has will be taken away. And cast the unprofitable servant into the outer darkness. There will be weeping and gnashing of teeth.

The Bible, The Gospel of Matthew 25:14-30

This note was featured in Meb Faber’s book The Best Investment Writing – Volume 2, alongside another Epsilon Theory note from Ben Hunt. Click here to get a copy.

I will never understand why more people don’t revere Rush.

With the possible exception of Led Zeppelin[1], I’m not sure there has been another band with such extraordinary instrumentalists across the board, such synergy between those members and their musical style and such a consistent approach to both lyrical and melodic construction. And yet they were only inducted into the Rock & Roll Hall of Fame in 2013. A short list of bands and singers the selection committee thought were more deserving: ABBA, Madonna, Jackson Browne, the Moonglows, Run DMC. At least they got in when Randy Newman did. I remember the first time I heard YYZ, the Rush tune named after the IATA airport code for Toronto’s Pearson International Airport, pronounced “Why Why Zed” in the charming manner of the Commonwealth. It was then that I decided I would be a drummer. I did play for a while, and reached what I would describe as just above a baseline threshold of competence.

That’s not a throwaway line.

There’s a clear, explicit line that every drummer (hopefully) crosses at one point. A step-change in his understanding of the role of the instrument. The true novice drummer always picks up the sticks and plays the same thing. Common time. Somewhere between 90-100 beats per minute. Eighth note closed hi-hat throughout. Bass drum on the down and upbeat of the first beat. Snare on second down beat. And then it’s all jazzy up-beat doodling on the snare for the rest of that bar until the down beat of four. Same thing for three measures, and on the fourth measure it’s time for that awesome fill he’s been practicing. I don’t know how many subscribers are drummers, but I assure you, literally couples of you are nodding your heads.

The fills and off-beat snare hits are all superfluous and not necessary to the principal role of a drummer in rock and roll: to keep the damned beat. But there are a number of reasons why every neophyte does these same things. Mimicry of more advanced players who can do the creative and interesting things without losing the beat, for one. We see Tony Williams, John Bonham, or Bill Bruford and do what it is we think they are doing to make the music sound good. The amateur often also thinks that these are the necessary things to be perceived as a more advanced player, for another. He doesn’t just imagine that his mimicry will make him sound more like the excellent players, but imagines himself looking like them to others. More than anything, the amateur does these things because he hasn’t quite figured out that keeping a good beat is so much more important than anything else he will do that he’s willing to sacrifice it for what he thinks is impressive.

This thought process dominates so many other fields as well. Consider the number of amateur cooks who hit every sauce or piece of meat with a handful of garlic powder, onion powder, oregano, salt, pepper and cayenne, when the simplicity of salt as seasoning dominates most of the world’s great cuisine. There is an instinct to think that complexity and depth must come from a huge range of ingredients[2] or from complexity in preparation, but most extraordinary cooking begins from an understanding of a small number of methods for heating, seasoning and establishing bases for sauces. Inventiveness, creativity and passion can take cuisine in millions of directions from there, but many home cooks see the celebrity chef’s flamboyant recipe and internalize that the creative flourishes are what matters to the dish, and not the fact that he cooked a high-quality piece of meat at the right heat for the right amount of time.

If you’re not much of a cook, consider instead the 30-handicap golfer who wouldn’t be caught dead without a full complement of four lob wedges in his bag. You know, so that he can address every possible situation on the course. The trilling singer of the national anthem who can’t hold a pitch but sees every word of the song as an opportunity to sing an entire scale’s worth of notes. The karate novice who addresses his opponent with a convoluted stance. The writer who doesn’t know when to stop giving examples to an audience who understood what he was getting at half-way through the one about cooking.

I’m guessing at least one of these things pisses you off, or at the very least makes you do an internal eye roll. And yet, as investors we are guilty of doing this kind of thing all the time, any time the topic of diversification comes up.

It comes from a good place. We know from what we’ve been taught (and from watching the experts) that we should diversify, but we don’t have a particularly good way of knowing what that means. And so we fill our portfolios with multiple flavors of funds, accounts and individual securities. Three international equity funds with different strategies. Multiple different styles in emerging markets. Some value. Some growth. Some minimum volatility. Some call writing strategies. Some sector funds. Maybe some long/short hedge funds. Some passively managed index funds, some actively managed funds. Definitely some sexy stock picks. And in the end, the portfolio that we end up with looks very much like the global equity market, maybe with a tilt here or there to express uniqueness — that flashy extra little hit on the snare drum to look impressive.

This piece isn’t about the time we waste on these things. I already wrote a piece about that a few weeks ago. This is about the harm we do to our portfolios when we play at diversifying instead of actually doing it.

The Parable of the Two FA’s

So what does actually diversifying look like?

There are lot of not-very-useful definitions out there. The eggs-in-one-basket definition we’re all familiar with benefits from simplicity, which is not nothing. In addition, it does work if people have a good concept of what the basket is in the analogy. Most people don’t. Say you have $100, and you decide that a basket is an advisor or a fund. So you split the money between the two, and they invest in the same thing. You have not diversified[3]. The other definitions for diversification tend to be more complicated, more quantitative in nature. That doesn’t make them bad, and we’ll be leaning on some of them. But we need a rule of thumb, some heuristic for describing what diversification ought to look like so that we know it when we see it. For the overwhelming majority of investors, that rule of thumb should go something like this:

Diversification is reducing how much you expect to lose when risky assets do poorly or very poorly without necessarily reducing how much total return you expect to generate.

Now, this is not exactly true, and it’s very obviously not the whole definition. But by and large it is the part of the definition that matters most. The more nuanced way to think about diversification, of course, is to describe it as all the benefits you get from the fact that things in your portfolio don’t always move together, even if they’re both generally going up in value. But most investors are so concentrated in general exposure to risky assets — securities whose value rises and falls with the fortunes and profitability of companies, and how other investors perceive those fortunes — that this distinction is mostly an academic one. Investors live and die by home country equity risk. Period. Most investors understand this to one degree or another, but the way they respond in their portfolios doesn’t reflect it.

I want to describe this to you in a parable.

There was once a rich lord who held $10 million in a S&P 500 ETF. He knew that he would be occupied with his growing business over the next year. Before he left, he met with his two financial advisors and gave them $1 million of his wealth and told them to “diversify his holdings.”

He returned after a year and came before the first financial advisor. “My lord, I put the $1 million you gave me in a Russell 1000 Value ETF. Here is your $1.1 million.” The rich man replied, “Dude, that’s almost exactly what my other ETF did over the same period. What if the market had crashed? I wasn’t diversified at all!” And the financial advisor was ashamed.

Furious and frustrated, the rich man then summoned his second financial advisor. “Sir, I put your $1 million in a Short-Duration Fixed Income mutual fund of impeccable reputation. Here’s your $1 million back.”

“Oh my God,” the lord replied, “Are you being serious right now? If I wanted to reduce my risk by stuffing my money in a mattress I could have done that without paying you a 65bp wrap fee. How do you sleep at night? I’m going to open a robo-advisor account.”

Most of us know we shouldn’t just hold a local equity index. We usually buy something else to diversify, because that’s what you do. But what we usually do falls short either because (1) the thing we buy to diversify isn’t actually all that different from what we already owned, or (2) the thing we buy to diversify reduces our risk and our return, which defeats the purpose. There’s nothing novel in what I’m saying here. Modern portfolio theory’s fundamental formula helps us to isolate how much of the variation in our portfolio’s returns comes from the riskiness of the stuff we invested in vs. the fact that this stuff doesn’t always move together.

Source: Salient 2017 For illustrative purposes only.

The Free Lunch Effect

So assuming we didn’t have any special knowledge about what assets would generate the highest risk-adjusted returns over the year our rich client was away on business, what answer would have made us the good guy in the parable? Maximizing how much benefit we get from that second expression above — the fact that this stuff doesn’t always move together.

Before we jump into the math on this, it’s important to reinforce the caveat above: we’re assuming we don’t have any knowledge about risk-adjusted returns, which isn’t always true. Stay with me, because we will get back to that. For the time being, however, let’s take as a given that we don’t know what the future holds. Let’s also assume that, like the Parable of the Two FA’s, our client holds $10 million in S&P 500 ETFs. Also like the parable, we have been asked to reallocate $1 million of those assets to what will be most diversifying. In other words, it’s a marginal analysis.

The measure we’re looking to maximize is the Free Lunch Effect, which we define as the difference between the portfolio’s volatility after our change at the margin and the raw weighted average volatility of the underlying components. If the two assets both had volatility of 10%, for example, and the resulting portfolio volatility was 9%, the Free Lunch Effect would be 1%.

If maximizing the Free Lunch Effect is the goal, here’s the relative attractiveness of various things the two FA’s could have allocated to (based on characteristics of these markets between January 2000 and July 2017).

Volatility Reduction from Diversification — Adding 10% to a Portfolio of S&P 500

Source: Salient 2017. For illustrative purposes only. Past performance is not indicative of how the index will perform in the future. The index reflects the reinvestment of dividends and income and does not reflect deductions for fees, expenses or taxes. The index is unmanaged and is not available for direct investment.

The two FA’s failed for two different reasons. The first failed because he selected an asset which was too similar. The second failed because he selected an asset which was not risky enough for its differentness to matter. The first concept is intuitive to most of us, but the second is a bit more esoteric. I think it’s best thought of by considering how much the risk of a portfolio is reduced by adding an asset with varying levels of correlation and volatility. To stop playing at diversification, this is where you start.

Volatility Reduction by Correlation and Volatility of Diversifying Asset

Source: Salient 2017. For illustrative purposes only. Past performance is not indicative of how the index will perform in the future. The index reflects the reinvestment of dividends and income and does not reflect deductions for fees, expenses or taxes. The index is unmanaged and is not available for direct investment.

If You Choose not to Decide

If there are some complaints that can be leveled against this approach, two of them, I think, are valid and worthy of exploration.

The first is that diversification cannot be fully captured in measures of correlation. If you read Whom Fortune Favors, you’ll know that our code recognizes that we live in a behaviorally-influenced, non-ergodic world. While I think we’d all recognize that U.S. value stocks are almost always going to be a poor diversifier against global equities (and vice versa), clearly there are events outside of the historical record or what we know today that could completely change that. And so the proper reading of this should always be in context of an adaptive portfolio management process.

The second complaint, as I alluded to earlier, is the fact that we are not always indifferent in our risk-adjusted return expectations for different assets. I’m sure many of you looked at the above chart and said to yourself, “Yeah, I’m not piling into commodities.” I don’t blame you (I’m still not satisfied with explanations for why I ought to be paid for being long contracts on many commodities), but that is the point. Not owning commodities or MLPs because you don’t get them isn’t the same as not expressing an opinion. If you choose not to decide, you still have made a choice.

When investors choose to forgo diversification, on any basis, they are implicitly betting that decisions that they make will outperform what diversification would have yielded them. It may not be optimal to own the most diversified portfolio you can possibly own, because anti-diversifying decisions might, in fact, be worth it. But it is exactly that thought process that must become part of our code as investors. It’s OK to turn down a free lunch, but you’d damn well better know that what you’re going to spend your money on is better.

So how do you quantify that implicit bet? Again, the Free Lunch Effect gives us our easiest answer. Consider the following case: let’s assume we had two investment options, both with similar risk of around 15%. For simplicity’s sake we’ll start from our naïve assumption that our assets produce, say, 0.5 units of return for every unit of risk we take. If the two assets are perfectly uncorrelated, how much more return would we need to demand from Asset 1 vs. Asset 2 to own more of it than the other? To own 100% Asset 1?

Well, the chart below shows it. In the case above, if you invest 100% of your portfolio in Asset 1, an investor who thinks about his portfolio in risk-adjusted terms is implicitly betting that Asset 1 will generate more than 3% more return per year, or an incremental 0.21 in return/risk units. If the assets are less similar, this implicit view grows exponentially.

Implied Incremental Return Expectation from Overweighted Asset

Source: Salient 2017. For illustrative purposes only. Past performance is not indicative of how the index will perform in the future. The index reflects the reinvestment of dividends and income and does not reflect deductions for fees, expenses or taxes. The index is unmanaged and is not available for direct investment.

A Chain of Linked Engagements

If we do not learn to regard a war, and the separate campaigns of which it is composed, as a chain of linked engagements each leading to the next, but instead succumb to the idea that the capture of certain geographical points or the seizure of undefended provinces are of value in themselves, we are liable to regard them as windfall profits.

— On War, Carl von Clausewitz

The point of this note isn’t to try to convince you to focus your portfolio construction efforts on higher volatility diversifiers like those highlighted earlier (although many of you should). It’s also not to argue that maximizing diversification should be your first objective (although most of us are so far from the optimum that moving in this direction wouldn’t hurt). It is to emphasize that portfolio construction and the decisions we make are a chain of linked engagements. It is to give you pause when you or your client asks for a ‘best new investment idea’. If your experiences are like mine, the question is nearly always expressed in isolation — recommend me a stock, a mutual fund, a hedge fund. These questions can never be answered in isolation. If you really must tinker with your allocation, sure, I can give you my view, but only if I know what else you own, and only if I know what you intend to sell in order to buy the thing.

Anyone who will make a recommendation to you without knowing those things is an idiot, a charlatan, or both.

Most of us, whether we are entrenched in financial markets or not, think about our decisions not in a vacuum but in terms of opportunity cost. If we buy A, we’re giving up B. If we invest in A, we’re giving up on B. If we do A, we won’t have time for B. Opportunity cost is fundamental to thinking about nearly every aspect of human endeavor but for some reason is completely absent from the way many investors typically think about building portfolios.

Look, if you didn’t completely follow where I was going with Whom Fortune Favors, I get it. Telling you to think about risk and diversification separately is more than a little bit arcane. But here’s where it comes together: an investor can only make wise decisions about asset allocation, about selecting fund managers, about tactical bets and about individual investments when he has an objective opportunity cost to assess those decisions against that allows him to make his portfolio decisions intentionally, not implicitly. That opportunity cost is the free lunch provided by diversification.

If we take this way of thinking to its natural extreme, we must recognize that we can, at any point, identify the portfolio that would have provided the maximum diversification, at least using the tools we’ve outlined here. For most periods, if you run through that analysis, you are very likely to find that a portfolio of those assets in which every investment contributes a comparable amount of risk to the whole — a risk parity portfolio, in other words — typically provides something near to that maximum level of diversification. I am not suggesting that your portfolio be the maximum diversification portfolio or risk parity. But I am suggesting that a risk parity portfolio of your investable universe is an excellent place to use as an anchor for this necessary analysis.

If you don’t favor it for various reasons (e.g. using volatility as a proxy for risk is the devil, it’s just levered bonds, etc.), then find your home portfolio that accomplishes similar goals in a way that is rules-based and sensible. Maybe it’s the true market portfolio we highlight in I am Spartacus. If you’re conservative, maybe it’s the tangency portfolio from the efficient frontier. And if you’re more aggressive, maybe it is something closer to the Kelly Optimal portfolio we discussed in Whom Fortune Favors. From there, your portfolio construction exercise becomes relatively simple: does the benefit I expect from this action exceed its diversification opportunity cost?

How do you measure it? If you have capital markets assumptions or projections, feel free to use them. Perhaps simpler, assume a particular Sharpe Ratio, say 0.25 or 0.30, and multiply it times the drop in diversification impact from the action you’re taking. Are you confident that the change you’re making to the portfolio is going to have more of an impact than that? That’s…really it. Now the shrewd among you might be saying, “Rusty, isn’t that kind of like what a mean-variance optimization model would do?” It isn’t kind of like that, it’s literally that. And so what? We’re not reinventing portfolio science here, we’re trying to unpack it so that we can use it more effectively as investors.

Recognize that this isn’t just a relevant approach to scenarios where you’re changing things around because you think it will improve returns dramatically. This is also a useful construct for understanding whether all the shenanigans in search of diversification, all that Chili P you’re adding, are really worth the headache. Is that fifth emerging markets manager really adding something? Is sub-dividing your regions to add country managers really worth the time?

In the end, it’s all about being intentional. With as many decisions as we have to manage, the worst thing we can do is let our portfolios make our decisions for us. Given the benefits of diversification, investors ought to put the burden of proof on anything that makes a portfolio less diversified. In doing so, they will recognize why this code recognizes the intentional pursuit of real diversification as the #2 Thing that Matters.


[1] I don’t want to hear it from the “but they stole people’s music and weren’t super nice about it” crowd. Zep played better rock and roll music than anyone before or after, and it’s not even close.

[2] And it can. Pueblan and Oaxacan cuisine feature moles with extraordinary complexity that does come from the melding of a range of seasonings and ingredients. Traditional American chilis, South Asian curries and soups from around the world often do as well. Dishes en croute (e.g. pate en croute, coulibiac, etc.) are notoriously tricky, too.

[3] Cue the fund-of-funds due diligence analyst pointing out that we would have, in fact, diversified our fraud risk. Die on that hill if you want to, friend.


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AI BS Detectors & the Origins of Life (by Silly Rabbit)

Confidence levels for the Social and Behavioral Sciences

DARPA recently put out an RFI:

…requesting information on new ideas and approaches for creating (semi)automated capabilities to assign ‘Confidence Levels’ to specific studies, claims, hypotheses, conclusions, models, and/or theories found in social and behavioral science research (and) help experts and non-experts separate scientific wheat from wrongheaded chaff using machine reading, natural language processing, automated meta-analyses, statistics-checking algorithms, sentiment analytics, crowdsourcing tools, data sharing and archiving platforms, network analytics, etc.

A visionary and high value RFI. Wired article on the same, enticingly titled, DARPA Wants to Build a BS Detector for Science.

Claude Berrou on turbo codes and informational neuroscience

Fascinating short interview with Claude Berrou, a French computer and electronics engineer who has done important work on turbo codes for telecom transmissions and is now working on informational neuroscience. Berrou describes his work through the lens of information and graph theory:

My starting point is still information, but this time in the brain. The human cerebral cortex can be compared to a graph, with billions of nodes and thousands of billions of edges. There are specific modules, and between the modules are lines of communication. I am convinced that the mental information, carried by the cortex, is binary. Conventional theories hypothesize that information is stored by the synaptic weights, the weights on the edges of the graph. I propose a different hypothesis. In my opinion, there is too much noise in the brain; it is too fragile, inconsistent, and unstable; pieces of information cannot be carried by weights, but rather by assemblies of nodes. These nodes form a clique, in the geometric sense of the word, meaning they are all connected two by two. This becomes digital information…

Thermodynamics in far-from-equilibrium systems

I’m a sucker for methods to try to understand and explain complex systems such as this story by Quanta (the publishing arm of the Simons Foundation — as in Jim Simons or Renaissance Technologies fame) of Jeremy England, a young MIT associate professor, using non-equilibrium statistical mechanics to poke at the origins of life.

Game theory

And finally, check out this neat little game theory simulator which explores how trust develops in society. It’s a really sweet little application with fun interactive graphics framed around the historical 1914 No Man’s Land Ceasefire. Check out more fascinating and deeply educational games from creator Nicky Case here.

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Programmable Money & Auto Public Offerings (by Silly Rabbit)

Programmable money

I’ve recently — perhaps belatedly — developed an interest in blockchain, and particularly in Ethereum. Not so much in trading crypto-currencies, but more in the realm of the type of ‘Smart Token’ protocols being developed by Bancor. As I start to process the implications of smart contracts I’m convinced that we are currently at Day Zero of a massive disruption. To quote Mike Goldin on one dimension of this disruption: “What blockchains give us, fundamentally, is programmable money. When you can program money, you can program incentives. When you can program incentives, you can kind of program people’s behavior.”

Another week, another set of ‘human’ skills which algorithms are mastering: Google demonstrates both an algorithm for tastefully selecting landscape photography, which is almost as good as a pro photographer, and, from the DeepMind division, “a new family of approaches for imagination-based planning (and) architectures which provide new ways for agents to learn and construct plans to maximize the efficiency of a task.”

Rough translation: AI which has the rudimentary ability to consider potential consequences of an action (‘imagine’) and plan ahead result in a higher success rate than AIs without this ability.

ImageNet: the data that changed AI research

Long, terrific overview of the history and impact of the ImageNet data set: “One thing ImageNet changed in the field of AI is suddenly people realized the thankless work of making a dataset was at the core of AI research. People really recognize the importance — the dataset is front and center in the research as much as algorithms.”

Auto Public Offering

Generally, ‘automation of white collar work’ is such an obviously disruptive category of AI — and near-term economic earthquake for many industries — that there is not much to say about it. However, this short piece by Bloomberg a few weeks back caught my eye: Apparently Goldman has automated (or at least mapped out how to automate) half the tasks needed to prepare for an IPO, thus replacing the work previously done by associates earning $326,000 a year. As Bill Gates famously said: “Be nice to nerds. Chances are you’ll end up working for one.”

The paradox of historical knowledge

And finally, I shared a pretty hefty quote from “Homo Deus: A Brief History of Tomorrow” by Yuval Noah Harari last week related to algorithms and self. On a completely different topic, the book also contains a fantastic quote on the paradox of historical knowledge: “This is the paradox of historical knowledge: Knowledge that does not change behavior is useless. But knowledge that changes behavior quickly loses its relevance. The more data we have and the better we understand history, the faster history alters its course, and the faster our knowledge becomes outdated.”

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Whom Fortune Favors: Things that Matter #1, Pt. 2


Click here to read Part 1 of Whom Fortune Favors


Fook: There really is an answer?

Deep Thought: Yes. There really is one.

Fook: Oh!

Lunkwill: Can you tell us what it is?

Deep Thought: Yes. Though I don’t think you’re going to like it.

Fook: Doesn’t matter! We must know it!

Deep Thought: You’re really not going to like it!

Fook: Tell us!

Deep Thought: Alright. The answer to the ultimate question…of Life, the Universe, and Everything…is… “42”. I checked it thoroughly. It would have been simpler, of course, to have known what the actual question was.

— Douglas Adams, Hitchhiker’s Guide to the Galaxy

As investors, our process is usually to start from the answer and work our way back to the question. Unfortunately, the answers we are provided are usually pre-baked products, vehicle types or persistent industry conventions, which means that the answers we get when we actually focus on the questions that matter may be counterintuitive and jarring. The entire point of developing a personal code for investing is knowing which questions matter and ought to be asked first, before a single product, vehicle or style box gets thrown into the mix.

The purpose you undertake is dangerous.’ Why, that’s certain. ‘Tis dangerous to take a cold, to sleep, to drink; but I tell you, my lord fool, out of this nettle, danger, we pluck this flower, safety.

William Shakespeare, Henry IV, Part 1, Act 2, Scene 3, Hotspur

Thomasina: When you stir your rice pudding, Septimus, the spoonful of jam spreads itself round making red trails like the picture of a meteor in my astronomical atlas. But if you stir backwards, the jam will not come together again. Indeed, the pudding does not notice and continues to turn pink just as before. Do you think this is odd?

Septimus: No.

Thomasina: Well, I do. You cannot stir things apart.

Septimus: No more you can, time must needs run backward, and since it will not, we must stir our way onward mixing as we go, disorder out of disorder into disorder until pink is complete, unchanging and unchangeable, and we are done with it forever. This is known as free will or self-determination.

Thomasina: Septimus, do you think God is a Newtonian?

Septimus: An Etonian? Almost certainly, I’m afraid. We must ask your brother to make it his first enquiry.

Thomasina: No, Septimus, a Newtonian. Septimus! Am I the first person to have thought of this?

Septimus: No.

Thomasina: I have not said yet.

Septimus: “If everything from the furthest planet to the smallest atom of our brain acts according to Newton’s law of motion, what becomes of free will?”

Thomasina: No.

Septimus: God’s will.

Thomasina: No

Septimus: Sin.

Thomasina (derisively): No!

Septimus: Very well.

Thomasina: If you could stop every atom in its position and direction, and if your mind could comprehend all the actions thus suspended, then if you were really, really good at algebra you could write the formula for all the future; and although nobody can be so clever as to do it, the formula must exist just as if one could.

Septimus (after a pause): Yes. Yes, as far as I know, you are the first person to have thought of this.

— Tom Stoppard, Arcadia, (1993)

On this most important question of risk, we and our advisors often default to approaches which rely on the expectation that the past and present give us profound and utterly reliable insights into what we ought to expect going forward. As a result, we end up with portfolios and, more importantly, portfolio construction frameworks which don’t respect the way in which capital actually grows over time and can’t adapt to changing environments. That’s not good enough.

Most of these notes tend to stand on their own, but this one (being a Part 2) borrows a lot from the thinking in Part 1. If you’re going to get the most out of this note, I recommend you start there. But if you’re pressed for time or just lazy, I wanted you to take away two basic ideas:

  • That the risk decision dominates all other decisions you make.
  • That the risk decision is not exactly the same as the asset class decision.

Children of a Lazier God

Before I dive into the weeds on those ideas, however, I want to tell you about a dream I have. It’s a recurring dream. In this dream, I have discovered the secret to making the most possible money with the least possible effort.

Hey, I never said it was a unique dream.

It is, however, a unique investing case. Imagine for a moment that we had perfect omniscience into returns, but also that we were profoundly lazy – a sort of Jeffersonian version of God. We live in a world of stocks, bonds and commodities, and we want to set a fixed proportion of our wealth to invest in each of those assets. We want to hold that portfolio for 50+ years, sit on a beach watching dolphins or whatever it is people do on beach vacations, and maximize our returns. What do we hold? The portfolio only needs to satisfy one explicit and one implicit objective. The explicit objective is to maximize how much money we have at the end of the period. The implicit objective is the small matter of not going bankrupt in the process.

This rather curious portfolio is noteworthy for another reason, too: it is a static and rather cheeky case of an optimal portfolio under the Kelly Criterion. Named after John Kelly, Jr., a Bell Labs researcher in the 1950s, the eponymous criterion was formally proposed in 1956 before being expanded and given its name by Edward O. Thorp in the 1960s. As applied by Thorp and many others, the Kelly Criterion is a mechanism for translating assessments about risk and edge into both trading and betting decisions.

Thorp himself has written several must-reads for any investor. Beat the Dealer, Beat the Market and A Man for All Markets are all on my team’s mandatory reading list. His story and that of the Kelly Criterion were updated and expanded in William Poundstone’s similarly excellent 2005 book, Fortune’s Formula: The Untold Story of the Scientific Betting System that Beat the Casinos and Wall Street.  The criterion itself has long been part of the parlance of the professional and would-be professional gambler, and has also been the subject of various finance papers for the better part of 60 years. For the less prone to the twin vices of gambling and authoring finance papers, Kelly translates those assessments about risk and edge into position sizes. In other words, it’s a guide to sizing bets. The objective is to maximize the geometric growth rate of your bankroll — or the expected value of your final bankroll — but with zero probability of going broke along the way. It is popular because it is simple and because, when applied to games with known payoffs, it works.

When we moonlight as non-deities and seek to determine how much we ought to bet/invest, Kelly requires knowing only three facts: the size of your bankroll, your odds of winning and the payout of a winning and losing bet. For the simplest kind of friendly bet, where a wager of $1 wins $1, the calculation is simple: Kelly says that you should bet the difference between your odds of winning and your odds of losing. If you have a 55-to-45 edge against your friend, you should bet 10% of your bankroll. Your expected compounded return of doing so is provably optimal once you have bet against him enough to prove out the stated edge — although should you manage to reach this point, you are a provably suboptimal friend.

Most of the finance papers that apply this thinking to markets have focused on individual trades that look more or less like bets we’d make at a casino. These are usually things with at least a kinda-sorta knowable payoff and a discrete event where that payoff is determined: a single hand of blackjack, an exercise of an option, or a predicted corporate action taking place (or not taking place). It’s a lot harder to get your head around what “bet” we’re making and what “edge” we have when we, say, buy an S&P 500 ETF instead of holding cash. Unless you really are omniscient or carry around a copy of Grays Sports Almanac, you’re going to find estimating the range of potential outcomes for an investment or portfolio of investments pretty tricky. Not that it stops anyone from trying.

Since I don’t want to assume that any of us is quite so good at algebra as to write the formula for all the future, at a minimum what I’m trying to do is get us to think about risk unanchored to the arbitrarily determined characteristics and traits of asset classes. In other words, I want to establish an outside bound on the amount of risk a person could theoretically take in a portfolio if his only goal was maximizing return. Doing that requires us to think in geometric space, which is just a fancy way of saying that we want to know how the realization of returns over time ends up differing from a more abstract return assumption. It’s easy enough to get a feel for this yourself by opening Excel and calculating what the return would be if your portfolio went up 5% in one year and down 5% in the next (works for any such pair of numbers). Your simple average will always be zero, but your geometric mean will always be less than zero, by an increasing amount as the volatility increases.

So, if we knew exactly what stocks, bond and commodities would do between 1961 and 2016, what portfolio would we have bought? The blend of assets if we went Full Kelly would have looked like this:

Source: Salient 2017. For illustrative purposes only.

Only there’s a catch. Yes, we would have bought this portfolio, but we would have bought it more than six times. With perfect information about odds and payoffs, the optimal bet would have been to buy a portfolio with 634% (!) exposure, consisting of $2.00 in stocks, $3.21 in bonds and $1.13 in commodities for every dollar in capital we had. After all was said and done, if we looked back on the annualized volatility of this portfolio over those 50 years, what would we have found? What was the answer to life, the universe and everything?

44. Sorry, Deep Thought, you were off by two.

Perhaps the only characteristic of this portfolio more prominent than its rather remarkable level of exposure and leverage, is its hale and hearty annualized volatility of 44.1%. This result means if all you cared about was having the most money over a 50+ year period that ended last year, you would have bought a portfolio of stocks, bonds and commodities that had annualized volatility of 44.1%, roughly three times the long-term average for most equity markets[1], and probably five times that of the typical HNW investor’s portfolio.

And before you go running off to tell my lovely, charming, well-dressed and distressingly unsusceptible-to-flattery compliance officer that I told you to buy a 44% volatility super-portfolio, allow me to acknowledge that this requires some… uh… qualification. Most of these qualifications are pretty self-explanatory, since the whole exercise isn’t intended to tell you what you should buy going forward, or even the right amount of risk for you. This portfolio, this leverage and that level of risk worked over the last 50 years. Would they be optimal over the next 50?

Of course not. In real life, we’re not omniscient. Whereas a skilled card counter can estimate his mathematical edge fairly readily, it’s a lot harder for those of us in markets who are deciding what our asset allocation ought to look like. Largely for this reason, even Thorp himself advised betting “half-Kelly” or less, whether at the blackjack table or in the market. When asked why, Thorp told Jack Schwager in Hedge Fund Market Wizards, “We are not able to calculate exact probabilities… there are things that are going on that are not part of one’s knowledge at the time that affect the probabilities. So you need to scale back to a certain extent.”

Said another way, going Full Kelly on a presumption of precise certainty about outcomes in markets is a surefire way to over-bet, potentially leading to a complete loss of capital. Now, scaling back is easy if we are starting from an explicit calculation of our edge as in a game of blackjack. It’s not as easy to think about scaling down to, say, a Half Kelly portfolio. There is, however, another fascinating (but intuitive) feature of the Kelly Optimal Portfolio that allows us to scale back this portfolio in a way that may be more familiar: the Kelly Optimal Portfolio can be generalized as the highest return case of a set of portfolios generating geometric returns that are most efficient relative to the risk they take[2].

This may sound familiar. In a way, it’s very much like a presentation of Markowitz’s efficient frontier. Markowitz plots the portfolios that generate the most return for a given unit of risk, but his is a single-period calculation. It isn’t a geometric approach like Kelly, but rather reflects a return expectation that doesn’t incorporate how volatility and non-linearities impact the path and the resulting compound return. There have been a variety of academic pieces over the years covering the application of geometric returns to this framework, but most have focused on either identifying a single optimal geometric portfolio or on utility. Bernstein and Wilkinson went a bit further, developing a geometric efficient frontier.

All of these analyses are instructive and useful to the investor who wants to take path into account, but because the efficient frontier is heavily constrained by the assumed constraint on leverage, it’s not as useful for us. What we want is to take the most efficient portfolio in geometric terms, and take up or down the risk of that portfolio to reflect our tolerance for capital loss. In other words, we want a geometric capital market line. The intuitive outcome of doing this is that we can plot the highest point on this line as the Full Kelly portfolio. The second, and perhaps more satisfying outcome, is that we can retrospectively identify that scaling back from Full Kelly just looks like delevering on this geometric capital market line.

The below figure plots each of these items, including a Half Kelly portfolio that defines ruin as any scenario in the path in which losses exceed 50%, rather than full bankruptcy. The Half Kelly portfolio delivers the highest total return over this period without ever experiencing a drawdown of 50%.

Source: Salient, as of December 31, 2016. For illustrative purposes only.

When we de-lever from the Full Kelly to Half Kelly portfolio, we drop from a terrifying 44% annualized volatility number (which experiences an 80% drawdown at one point) to 18.5%, closer to but still materially higher in risk than most aggressive portfolios available from financial advisors or institutional investors.

This can be thought of in drawdown space as well for investors or advisors who have difficulty thinking in more arcane volatility terms. The below exhibit maps annualized volatility to maximum loss of capital over the analysis period. As mentioned, the 50% maximum drawdown portfolio historically looks like about 18.5% in volatility units.

Source: Salient, as of December 31, 2016. For illustrative purposes only.

For many investors, their true risk tolerance and investment horizon makes this whole discussion irrelevant. Traditional methods of thinking about risk and return are probably serving more conservative investors quite well. And there are some realities that anyone thinking about taking more risk needs to come to terms with, a lot of which I’m going to talk about in a moment — there’s a reason we wanted to talk about this in geometric terms, and it’s all about risk. But for those with a 30, 40 or 50-year horizon, for the permanent institutions with limited cash flow needs, it’s reasonable to ask the question: is the amount of risk in the S&P 500 Index or in a blend of that with the Bloomberg Barclays Aggregate Bond Index the right amount of risk to take? Or can we be taking more? Should we be taking more?

Did you think that was rhetorical? Nope.

Many investors can – and if they are acting as fiduciaries probably ought to — take more risk.

If every hedge fund manager jumped off a bridge…

This may not be a message you hear every day, but I’m not telling you anything novel. Don’t just listen to what your advisors, fund managers and institutional peers are telling you. They’re as motivated and influenced by career risk concerns as the rest of us. Instead, look at what they’re doing.

The next time you have a conversation with a sophisticated money manager you work with, ask them where they typically put their money. Yes, many of them will invest alongside you because that is right and appropriate (and also expected of them). But many more, when they are being honest, will tell you that they have a personal account or an internal-only strategy operated for staff, that operates at a significantly higher level of risk than almost anything they offer to clients. Vehicles with 20%, 25% or even 30% volatility are not uncommon. Yes, some of this is hubris, but some of it is also the realization on the part of professional investors that maximizing portfolio returns — if that is indeed your objective — can only be done if we strip back the conventions that tell us that the natural amount of risk in an unlevered investment in broad asset classes is always the right amount of risk.

Same thing with the widely admired investors, entrepreneurs and business operators. The individual stocks that represent their wealth are risky in a way that dwarfs most of what we would be willing to tolerate in individual portfolios. We explain it away with the notion that they are very skilled, or that they have control over the outcomes of the company — which may be true in doses — but in reality, they are typically equally subject to many of the uncontrollable whims that drive broader macroeconomic and financial market outcomes.

Then observe your institutional peers who are increasing their allocations to private equity and private real estate. They’re not just increasing because hedge funds have had lower absolute returns in a strong equity environment, although that is one very stupid reason why this is happening. It’s also happening because institutions are increasingly aware that they have limited alternatives to meet their target returns. While few will admit it explicitly, they use private equity because it’s the easiest way to lever their portfolios in a way that won’t look like leverage. In a true sense of uncertainty or portfolio level risk, when the risk of private portfolios is appropriately accounted for, I believe many pools of institutional capital are taking risk well beyond that of traditional equity benchmarks.

Many of the investors we all respect the most are already taking more risk than they let on, but explain it away because it’s not considered “right thinking.”

To Whom Much is Given

When we make the decision to take more risk, however, our tools and frameworks for managing uncertainty must occupy more of the stage. This isn’t only about our inability to build accurate forecasts, or even our inability to build mostly accurate stochastic frameworks based on return and volatility, like the Monte Carlo simulations many of us build for clients to simulate their growth in wealth over time. It’s also because the kinds of portfolios that a Full Kelly framework will lead you to are usually pretty risky. Their risk constraint is avoiding complete bankruptcy, and that’s not a very high bar. The things we have to do to capture such a high level of risk and return also usually disproportionately increase our exposure to big, unpredictable events. If you increase the risk of a portfolio by 20%, most of the ways you would do so will increase the exposure to these kinds of events by a lot more than 20%.

Taken together, all these things create that famous gap between our realized experience and what we expected going in. This is a because most financial and economic models assume that the world is ergodic. And it ain’t. I know that’s a ten-dollar word, but it’s important. My favorite explanation of ergodicity comes from Nassim Nicholas Taleb, who claims to have stolen it from mathematician Yakov Sinai, who in turns claims to have stolen it from Israel Gelfand:

Suppose you want to buy a pair of shoes and you live in a house that has a shoe store. There are two different strategies: one is that you go to the store in your house every day to check out the shoes and eventually you find the best pair; another is to take your car and to spend a whole day searching for footwear all over town to find a place where they have the best shoes and you buy them immediately. The system is ergodic if the result of these two strategies is the same.

There are infinite examples of investors making this mistake. My mind wanders to the fund manager who offers up the fashionable but not-very-practical “permanent loss of capital” definition of risk, a stupid definition that is the last refuge of the fund manager with lousy long-term performance. “Sure, it’s down 65%, but that’s a non-permanent impairment!” Invariably, the PM will grumble and call this a 7-standard deviation event because he assumed a world of ergodicity. Because of the impact of a loss like this on the path of our wealth, we’ll now have to vastly exceed the average expected return we put in our scenario models in Excel just to break even on it.


“It’s not a permanent impairment of capital!”

It matters what path our portfolios follow through time. It matters that our big gains and losses may come all at once. It matters to how we should bet and it matters to how we invest. You cannot stir things apart!

So if you’ve decided to take risk as an investor, how we do avoid this pitfall? Consider again the case of the entrepreneur.

The entrepreneur’s portfolio is concentrated, which means that much of his risk has not been diversified away. A lot of that is going to be reflected in the risk and return measures we would use if we were to plot him on the efficient frontier. That doesn’t necessarily mean his risk of ruin will appear high, and his analysis might, in fact, inform the entrepreneur that he ought to borrow and hold this business as his sole investment. He’s done the work, performed business plan SWOT analyses, competitor analyses, etc., and concluded that he has a pretty good grasp of what his range of outcomes and risks look like.

In an ergodic world, this makes us feel all warm and fuzzy, and we give ourselves due diligence gold stars for asking all the right questions. In a non-ergodic world, the guy dies using his own product. A competitor comes out of nowhere with a product that immediately invalidates his business model. A bigger player in a related industry decides they want to dominate his industry, too. And these are just your usual tail events, not even caused the complexity of a system we can’t understand but by sheer happenstance. For the entrepreneur, all sorts of non-tail events over time may materially and permanently change any probabilistic assessment going forward. How do we address this?

The first line of defense as we take more risk must be diversification. After all, there is a reason why the Kelly Portfolios distribute the risk fairly evenly across the constituent asset classes.[3]

Even that isn’t enough. Consider also the case of the leveraged investor in multiple investments with some measure of diversification, for example a risk parity investor, Berkshire Hathaway[4], or the guy who went Full Kelly per our earlier example, but without the whole perfect information thing. This investor has taken the opposite approach, which is to diversify heavily across different asset classes and/or company investments. His return expectation is driven not so much by his ability to create an outcome but by the exploitation of diversification. As he increases his leverage, his sensitivity to the correctness of his point-in-time probabilistic estimates of risk, return and correlations between his holdings will increase as well. In an ergodic world, this is fine and dandy. In a non-ergodic world, while he has largely mitigated the risk of idiosyncratic tails, he is relying on relationships which are based on a complex system and human behaviors that can change rapidly.

Thus, the second line of defense as we take more risk must be adaptive investing. Sometimes the only answer to a complex system is not to play the game, or at least to play less of it. Frameworks which adapt to changing relationships between markets and changing levels of risk are critical. But even they can only do so much.

Liquidity, leverage and concentration limits are your rearguard. These three things are also the only three ways you’ll be able to take more risk than asset classes give you. They are also the three horsemen of the apocalypse. They must be monitored and tightly managed if you want to have an investment program that takes more risk.

It’s not my intent to end on a fearful note, because that isn’t the point at all. More than asset class selection, more than diversification, more than fees, more than any source of alpha you believe in, nothing will matter to your portfolio and the returns it generates more than risk. And the more you take, the more it must occupy your attention. That doesn’t mean that we as investors ought to cower in fear.

On the contrary, my friends, fortune favors the bold.


[1] Back in 1989, Grauer and Hakansson undertook a somewhat similar analysis on a finite, pre-determined set of weightings among different assets with directionally similar results. Over most windows the optimal backward-looking levered portfolio tends to come out with a mid-30s level of annualized volatility.

[2] For this and the other exhibits and simulations presented here, I’m very grateful to my brilliant colleague and our head of quantitative strategies at Salient, Dr. Roberto Croce.

[3] And that reason isn’t just “we’re at the end of a 30-year bond rally,” if you’re thinking about being that guy.

[4] One suspects Mr. Buffett would be less than thrilled by the company we’re assigning him, but to misquote Milton Friedman, we are all levered derivatives users now.


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Algorithmic Complexes, Alpha Male Brains, and Winnie the Pooh (by Silly Rabbit)

Massively complex complexes of algorithms

Let me come straight out with it and state, for the record, that I believe the best current truth we have is that we humans, along with all other living beings, are simply massively complex complexes of algorithms. What do I mean by that? Well, let’s take a passage from the terrific Homo Deus by Yuval Noah Harari, which describes this concept at length and in detail:

In recent decades life scientists have demonstrated that emotions are not some mysterious spiritual phenomenon that is useful just for writing poetry and composing symphonies. Rather, emotions are biochemical algorithms that are vital for the survival and reproduction of all mammals. What does this mean? Well, let’s begin by explaining what an algorithm is, because the 21st Century will be dominated by algorithms. ‘Algorithm’ is arguably the single most important concept in our world. If we want to understand our life and our future, we should make every effort to understand what an algorithm is and how algorithms are connected with emotions. An algorithm is a methodical set of steps that can be used to make calculations, resolve problems and reach decisions. An algorithm isn’t a particular calculation but the method followed when making the calculation.

Consider, for example, the following survival problem: a baboon needs to take into account a lot of data. How far am I from the bananas? How far away is the lion? How fast can I run? How fast can the lion run? Is the lion awake or asleep? Does the lion seem to be hungry or satiated? How many bananas are there? Are they big or small? Green or ripe? In addition to these external data, the baboon must also consider information about conditions within his own body. If he is starving, it makes sense to risk everything for those bananas, no matter the odds. In contrast, if he has just eaten, and the bananas are mere greed, why take any risks at all? In order to weigh and balance all these variables and probabilities, the baboon requires far more complicated algorithms than the ones controlling automatic vending machines. The prize for making correct calculations is correspondingly greater. The prize is the very survival of the baboon. A timid baboon — one whose algorithms overestimate dangers — will starve to death, and the genes that shaped these cowardly algorithms will perish with him. A rash baboon —one whose algorithms underestimate dangers — will fall prey to the lion, and his reckless genes will also fail to make it to the next generation. These algorithms undergo constant quality control by natural selection. Only animals that calculate probabilities correctly leave offspring behind. Yet this is all very abstract. How exactly does a baboon calculate probabilities? He certainly doesn’t draw a pencil from behind his ear, a notebook from a back pocket, and start computing running speeds and energy levels with a calculator. Rather, the baboon’s entire body is the calculator. What we call sensations and emotions are in fact algorithms. The baboon feels hunger, he feels fear and trembling at the sight of the lion, and he feels his mouth watering at the sight of the bananas. Within a split second, he experiences a storm of sensations, emotions and desires, which is nothing but the process of calculation. The result will appear as a feeling: the baboon will suddenly feel his spirit rising, his hairs standing on end, his muscles tensing, his chest expanding, and he will inhale a big breath, and ‘Forward! I can do it! To the bananas!’ Alternatively, he may be overcome by fear, his shoulders will droop, his stomach will turn, his legs will give way, and ‘Mama! A lion! Help!’ Sometimes the probabilities match so evenly that it is hard to decide. This too will manifest itself as a feeling. The baboon will feel confused and indecisive. ‘Yes . . . No . . . Yes . . . No . . . Damn! I don’t know what to do!’

Why does this matter? I think understanding and accepting this point is absolutely critical to being able to construct certain classes of novel and interesting algorithms. “But what about consciousness?” you may ask, “Does this not distinguish humans and raise us above all other animals, or at least machines?”

There is likely no better explanation, or succinct quote, to deal with the question of consciousness than Douglas Hofstadter’s in I Am a Strange Loop:

“In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference.”

Let’s accept Hofstadter’s explanation (which is — to paraphrase and oversimplify terribly — that, at a certain point of algorithmic complexity, consciousness emerges due to self-referencing feedback loops) and now hand the mic back to Harari to finish his practical thought:

“This raises a novel question: which of the two is really important, intelligence or consciousness? As long as they went hand in hand, debating their relative value was just an amusing pastime for philosophers, but now humans are in danger of losing their economic value because intelligence is decoupling from consciousness.”

Or, to put it another way: if what I need is an intelligent algorithm to read, parse and tag language in certain reports based on whether humans with a certain background would perceive the report as more ‘growth-y’ vs ‘value-y’ in its tone and tenor, why do I need to discriminate whether the algorithm performing this action has consciousness or not, or which parts of the algorithms have consciousness (assuming that the action can be equally parallelized either way)?

AI vs. human performance

Electronic Frontier Foundation have done magnificent work pulling together problems and metrics/datasets from the AI research literature in order to see how things are progressing in specific subfields or AI/machine learning as a whole. Very interesting charts on AI versus human performance in image recognition, chess, book comprehension, and speech recognition (keep scrolling down; it’s a very long page with lots of charts).

Alpha male brain switch

Researchers led by Prof Hailan Hu, a neuroscientist at Zhejiang University in Hangzhou, China have demonstrated activating the dorsal medial prefrontal cortex (dmPFC) brain circuit in mice to flip the neural switch for becoming an alpha male. This turned the timid mice bold after their ‘alpha’ circuit was stimulated.  Results also show that the ‘winner effect’ lingers on and that the mechanism may be similar in humans. Profound and fascinating work.

Explaining vs. understanding

And finally, generally I find @nntaleb’s tweets pretty obnoxious and low value (unlike his books, which I find pretty obnoxious and tremendously high value), but this tweet really captured me: “Society is increasingly run by those who are better at explaining than understanding.” I pondered last week on how allocators and Funds of Funds are going to allocate to ‘AI’ (or ‘ALIS’). This quote succinctly sums up and generalizes that concern.

And finally, finally, this has nothing to do with Big Compute, AI, or investment strategies, but it is just irresistible: Winnie the Pooh blacklisted by China’s online censors: “Social media ban for fictional bear follows comparisons with Xi Jinping.” Original FT article here (possibly pay-walled) and lower resolution derivative article (not pay-walled) by inUth here. As Pooh says “Sometimes I sits and thinks, and sometimes I just sits…”

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One Model to Learn Them All and AI Is/Isn’t Taking Over the World (by Silly Rabbit)

One model to learn them all

The Google corporation recently shared this technical paper: “One model to learn them all” (less technical write up here by VentureBeat). While the model in and of itself is not transformational, the approach is a pretty big deal as it lays out a template for how to create a single machine learning model that can address multiple tasks well.

And in other Google machine learning news, Google and Carnegie Mellon University ran an experiment using ‘enormous data,’ taking an unprecedentedly huge collection of 300 million labeled images (rather than a more typical one million images) to test whether it’s possible to get more accurate image recognition not by tweaking the design of existing algorithms but by feeding them much, much more data. The answer, unsurprisingly, is yes, you get better-trained models using enormous data sets and having fifty powerful GPUs grind on the data for two months solid.

Semantic scholar

Semantic Scholar is a neat search engine for scientific papers, which has been gaining traction with Microsoft, Google and Baidu joining the Open Academic Search working group.

Quote by Oren Etzioni, the CEO of the Allen Institute for AI (AI2), who produce Semantic Scholar: “What if a cure for an intractable cancer is hidden within the tedious reports on thousands of clinical studies? In 20 years’ time, AI will be able to read — and more importantly, understand — scientific text. These AI readers will be able to connect the dots between disparate studies to identify novel hypotheses and to suggest experiments that would otherwise be missed. AI-based discovery engines will help find the answers to science’s thorniest problem.”

AI is/isn’t taking over the world

Depending who you ask, AI is either just about to take over the world or is embryonic and trivial in its achievements to date.

In the taking-over-the-world corner, we have this canonical article titled “How AI is taking over the global economy in one chart.” The absolute comparisons of R&D budget sizes in this article (and the oversimplified social conclusions) seem pretty dubious, but the point is most likely directionally correct on the relative size of R&D spending of ‘the big eight’ compared to smaller industrialized nations, as well as the fact that the ability to fund R&D is going to be very decisive for both companies and nations over the next few decades.

For illustrative purposes only. Source: Axios 2017.

And in the embryonic-and-trivial corner, Evolutionary biologist Phil Madgwick points out that, “Artificial intelligence does not mimic natural intelligence, and it is not clear that there have been significant developments toward anything with rabbit-like intelligence, let alone human-like intelligence.”

My view: both of these things are simultaneously true in that while we are far from human-level machines, woe betide companies and countries which are currently under-investing in applied AI R&D.

ALIS

MOV37, a Fund of Funds (FoF) put out their thesis/manifesto for ALIS (Autonomous Learning Investment Strategies), which, as well as being a handy anthology of every known AI trope in the last 12 months, is also, in my opinion, a pretty accurate perspective on the next wave of AI-driven investing (except for the ‘two people and a laptop’ bit, which just doesn’t jibe with anything we’re seeing in any other machine learning field, per the ‘enormous data’ link above).

The real question this piece left me with is: who is going to decide which ALIS funds to invest in? Here in the Valley, ‘Deep tech’ investors are typically ex-tech entrepreneurs with deep engineering backgrounds, so they somewhat understand what they’re investing in. What’s unclear is how the majority of FoFs and allocators are going to arrange themselves to invest in ALIS machine learning strategies without any actual experience in developing ALIS-type machine learning strategies. Perhaps the FoF strategy will be more the Consumer-VC strategy of ‘just seed a bunch of small things with limited discrimination, let most die, and wait until a couple become scaled breakouts like Instagram/Pinterest/Snapchat and return the fund.’

Time will tell.

Kai Fu Lee, Commence!

And finally, as a genre, I really like commencement speeches. Speakers seem to push themselves to ‘tell their best truth’ as well as address the meaning of their achievements (while keeping it short and accessible).

Here is a great commencement speech to the Engineering School of Columbia University by legendary engineer Kai Fu Lee (of Apple, Microsoft and Google fame).

Enjoy!

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Whom Fortune Favors: Things that Matter #1, Pt. 1

President Camacho

Still, brave Turnus did not lose hope of seizing the shore first,
and driving the approaching enemy away from land.
And he raised his men’s spirits as well, and chided them:
‘What you asked for in prayer is here, to break through
with the sword. Mars himself empowers your hands, men!
Now let each remember his wife and home, now recall
the great actions, the glories of our fathers. And let’s
meet them in the waves, while they’re unsure and
their first steps falter as they land. Fortune favors the brave.’
So he spoke, and asked himself whom to lead in attack
and whom he could trust the siege of the walls.

— Virgil, The Aeneid, 10. 270-28

I had to take a verbal physical. A bunch of yes or no questions. But they were strangely worded, like, “Have you ever tried sugar… or PCP?”

— Mitch Hedberg

Imani: Am I not all you dreamed I would be?
Akeem: You’re fine. Beautiful! But if we’re going to be married, we should talk and get to know each other.
Imani: Ever since I was born, I have been trained to serve you.
Akeem: I know, but I’d like to know about you. What do you like to do?
Imani: Whatever you like.
Akeem: What kind of music do you like?
Imani: Whatever kind of music you like.
Akeem: I know what I like, and you know what I like, ’cause you were trained to know, but I would like to know what you like. Do you have a favorite food? Good! What is your favorite food?
Imani: Whatever food you like.
Akeem: This is impossible. I command you not to obey me.
— Coming to America (1988)

Natural selection, the process by which the strongest, the smartest, the fastest reproduced in greater number than the rest, a process which had once favored the noblest traits of man now began to favor different traits. While most science fiction of the day predicted a future that was more civilized and more intelligent, all signs indicated that the human race was heading in the opposite direction: a dumbing down. How did this happen? Evolution does not make moral judgments. Evolution does not necessarily reward that which is good or beautiful. It simply rewards those who reproduce the most.

— Opening Narration, Idiocracy (2006)

Lepidus: What manner o’ thing is your crocodile?
Antony: It is shap’d, sir, like itself, and it is as broad as it hath breadth; it is just as high as it is, and moves with its own organs. It lives by that which nourisheth it, and the elements once out of it, it transmigrates.
Lepidus: What colour is it of?
Antony: Of its own colour too.
Lepidus: ‘Tis a strange serpent.
Antony: ‘Tis so. And the tears of it are wet.
— William Shakespeare, Antony and Cleopatra, Act 2, Scene 7
He ended frowning, and his look denounc’d
Desperate revenge, and Battel dangerous
To less then Gods. On th’ other side up rose
Belial, in act more graceful and humane;
A fairer person lost not Heav’n; he seemd
For dignity compos’d and high exploit:
But all was false and hollow; though his Tongue
Dropt Manna, and could make the worse appear
The better reason, to perplex and dash
Maturest Counsels: for his thoughts were low;
To vice industrious, but to Nobler deeds
Timorous and slothful: yet he pleas’d the ear,
And with perswasive accent thus began.
— John Milton, Paradise Lost (1667)

For the whole earth is the tomb of famous men; not only are they commemorated by columns and inscriptions in their own country, but in foreign lands there dwells also an unwritten memorial of them, graven not on stone but in the hearts of men. Make them your examples, esteeming courage to be freedom and freedom to be happiness.

— Thucydides, Funeral Oration for Pericles

They don’t think it be like it is, but it do.

— Career journeyman Oscar Gamble, when asked about the New York Yankees clubhouse

The reality show president and the High King of Ireland

If you’ve seen the film, you know why it has become so fashionable to talk about Idiocracy’s prescience. If you haven’t, a brief synopsis: the film tells the story of humanity many years in the future. In this future, humans are very stupid. The biggest celebrity is the resilient star of a hit reality show about a man subjected to repeated groin injuries. Farmers water their fields with an electrolyte-laden sports drink since, after all, as the Brawndo company clearly states, “it’s got what plants crave.” Plus, with that kind of television programming available, it’s not like you’re going to have time to read debates among historians about whether Scipio Africanus truly ordered the salting of Carthaginian fields.

Well, all that and they elected a wrestling and adult film star as president.

Don’t worry. I’m not going where you think I’m going with this, although I will admit that even though I threatened to write in President Dwayne Elizondo Mountain Dew Herbert Camacho in two prior elections, when he actually appeared on the ballot I found it a bit more difficult to pull the lever.

So how did this happen (the movie plot, not Trump)? Well, the proximate cause proferred by the narrator is that all the smart, creative people saw overcrowding and a dangerous world and decided not to have kids. So it’s a gene pool argument. Underneath this purely genetic argument, however, lie truths about both evolution and social structures that form around and because of some of the trappings of genetics and lineage. From an evolutionary perspective, we are presented with the asymmetric potential of humanity that has solved most of its existential problems. If intelligence and creativity have little-to-no bearing on survival (more accurately, on a given human’s potential to procreate), what is the catalyst for the development of positive traits? Should procreation become associated with long-run maladaptive traits, however, the bigger issue becomes: how quickly do social power structures develop around and entrench those traits? How effectively do those structures prevent the emergence of adaptive traits when we need them again (e.g., knowing that you should probably just use water)?

You’re reading a note on a website long published with the header, “Politics trumps economics every time,” so I expect you won’t be surprised to learn that I think that over short periods of time, the pressure of the social structures is by far the stronger of these two dynamics. After all, the driving force behind the Idiocracy scenario is not entirely fictional. If you’ve participated in any sort of foray into genetic genealogy, you’ve seen the effect in action.

A few years back, a group of researchers from Trinity College Dublin identified that there was a strong relationship between certain genetic haplotypes and surnames that matched published lineages of a certain quasi-historical Irish king: the wonderfully named Niall of the Nine Hostages. Researchers found in subsequent testing of individuals with those surnames that many shared a mutation in their Y chromosome. At a location called 14902414 (don’t ask), where they expected to find guanine, which is what they’d find in researching any other human male they’d ever come across, they found adenine instead. We call this kind of mutation a “single nucleotide polymorphism.” These mutations are one of the most important ways we map the branching of lineages in male genetic history. Stable SNPs are passed down like a scar from generation to generation in a path-dependent chain.

Once this was discovered, we were off to the races in the usual ways. One of the largest DNA testing companies wasted no time in creating a special logo that was applied like “flair” to user accounts certifying them as a Descendant of Niall of the Nine Hostages! If you’re one of the few million men who would test positive for this mutation, you can still scrape a bit of your cheek into a vial, send it in and then download and print a certificate attesting to this, although they’ve softened the language somewhat. As always, lineage and genetics are far more complicated than they appear on the surface, and subsequent research made it clear that the mutation happened centuries before this man would have lived, probably in Cornwall and not Ireland, and included all sorts of other lineages as well.

Even if the specifics were a bit off, there was a kernel of truth in this mode of thinking: in general, rich people with swords who could afford food had more children that didn’t die early, and their children had more children who didn’t die early. In addition to really bad genealogical practices, this is why everyone you meet who has done any research into their family has found some super-famous king or viscount or third earl of something-somewhere from whom they’re descended. It’s also why when a particular common lineage seems to spring out of a place and time, we are drawn to the notion of the fecund king, whether it’s Niall or, say, Genghis Khan. A 13th century peasant farmer probably didn’t have healthy kids, and if he did he probably didn’t keep exquisite written records of them. But in the short run, evolution is a fickle, funny, random thing. Does the success of the line of someone like Niall mean that it had some significant, genetically heritable trait that made its members more likely to thrive? It’s possible, of course, and in many cases throughout history it is certainly true — evolution is a thing, after all — but over shorter horizons it is natural variation and randomness that dominate.

Yes, Niall himself may have successfully overcome his opponents because he was predisposed to carry more muscle mass and greater range of motion in his arms, and your 12th great-grandfather, the Marquis of Accepting Internet Strangers’ Shoddy Research, may have risen to his position from obscurity because of his stunning intellect. But power structures like nobility and primogeniture1 aren’t necessary to protect the remarkable. They are necessary to protect the weaker links in the chain that come as a result of even more remarkable genetic variation, and the resilience of the line over time is functionally the strength of the power structure that supports it — and must support it in order to endure such variation. In short, those power structures — the ideas of nobility, genetic superiority and divine right — are just narratives. Very, very strong ones.

1Or at least patrilineality. Unlike a lot of Germanic cultures, Irish (and later Scottish) traditions favored Tanistry, under which a sept could allow any male descendant of a chosen accepted ancestor to become the Tanist, the heir apparent. Often it was simply the King’s eldest son, but not always.

From the very beginning of Epsilon Theory — but reaching its zenith with When Does the Story Break — these pages and our thinking have focused on the almost-shocking resilience of the stories we tell ourselves and each other about markets and investing. In that piece, we placed the focus squarely on the inflection point: what does it look like when the narrative changes? When do gentlemen stop wearing the wigs they wore for 150 years? When and why do they stop wearing hats? When will we all stop knowing that we all know that markets are policy-controlled? When will Mike Judge’s future humanity accumulate enough negative results from maladaptive traits that marginally superior traits become relevant to reproduction again (so that we don’t die out as a result of malnourishment and repetitive concussive injuries to the groin)?

For such a narrative to break, our private knowledge — a collective state of understanding of something so agreed-upon as to be considered fact — must be influenced by new public knowledge. When it’s a pervasive idea, it resolves to a strong equilibrium, like the information surfaces we talked about in Through the Looking Glass. And it requires an awful lot of information for a narrative like this to break.

It’s true for High Kings of Ireland and it’s true for investing.

What manner o’thing is your manna

Let me tell you about an especially stupid investing idea that has managed to survive for a very long time.

Since we’ve covered dystopian fantasies, let’s imagine this stupid idea in context of something wonderful: let’s assume that we are 22 years old again, right out of college and talking to our first financial advisor about our 401(k) allocation. Now, it doesn’t matter if you’re a financial advisor yourself, an institutional allocator, an individual or a professional investor, you know what’s coming next. The book says 100% stocks. Maybe the home office dropped in some higher risk/return styles into their mean/variance model and so we probably get a dash of Chili P in the form of emerging markets and small caps too. All stocks, mostly U.S., with a bit more international, emerging markets and small cap than the average client. Sound about right?

Let’s unpack this advice. The financial advisor in this scenario is essentially telling his client the following:

I’m happy to inform you that the trillions of business decisions of billions of employees and managers of companies around the world, combined with the decisions of bankers who determined whether and how much to lend to those companies, the decisions of individuals who chose whether to buy or sell that company’s products, global weather phenomena, collective actions of terrorist groups, trillions of trading decisions made by computers and individuals alike on a microsecond-by-microsecond basis, the general pace of technological growth, the changing risk appetites of a dozen different classes of investors, the state of rule of law in various countries around the world, the changing policies of governments and central banks governing trade, commerce and financial markets, the current level of prices and valuations, and the way in which billions of individuals will perceive and estimate the outcomes of all of the above — that all these things together have conspired together to create an entity we call a stock, which, when taken in combination with a more or less arbitrarily determined number of other stocks and all of their differing characteristics, will create a stock market that just happens to have exactly the right amount of risk for you!

What a bunch of superstitious hogwash.

We treat asset classes like manna from heaven, preordained structures that were designed to meet our every need, in which the lowest-risk major asset class has just the right amount of risk for a retired person and the highest-risk major asset class is perfect for the most risk-seeking individual. The very idea pleases the ear because it asks little of us. You’ll eat your manna and like it! But be honest, can you think of anything else where the universe conspires so beautifully and elegantly to meet our needs?

Fortunately, at this point many investors at least pay lip service to the preeminence of asset allocation, but we often think of it in terms that commingle the types of risk we are taking and the amount of risk we take. We see this commingling — a thing we call asset classes, like broad definitions of stocks and bonds — as manna from heaven because we tend to inextricably link the concepts of asset classes with risk and return. We are trained by the investment industry to see our asset class decisions as a proxy for risk decisions. They aren’t, and the distinction matters.

It’s easy to get caught up in terminology and semantics here, so intead, think about the act of investing in its most fundamental sense. Strip away products, market conventions, regulation and structures like exchanges, even corporations. Investing is the act of using capital to buy an asset or pay expenses to support it. We invest so that we will either (1) produce income from the asset or (2) cause the asset to become more valuable in the eyes of other investors. In this sense we can think of our risk as the range of outcomes from (1) and (2) after considering the (3) nature of our claim on both. This is true for any investment.

What, then, is an asset class? Well, it’s a mostly sensible, if subjective, way to generalize how some investments are more like other investments. Asset classes define that similarity mostly in how their characteristics (1) and (2) above respond to the same stimuli. So ignoring that the right answer is, “because it’s just what we do”, why do we consider U.S. large cap stocks an asset class? Well, generally speaking, it should be because the things that cause risks to a company’s ability to generate earnings are pretty similar, and (rather self-prophesyingly) because the fact that it is considered an asset class influences how other investors are likely to respond similarly when they assess the value of all the other underlying constituents of the asset class.

In practice, however, the factors that influence the viability and the value of our claims on enterprises we invest in (i.e., companies, governments, properties, projects, etc.), and especially the magnitude of sensitivity to those factors, can be hugely variable within asset classes. TSLA and T theoretically have exposure to some of the same drivers of variability, but how much, really? Do people scale back their texting and phone plans during a recession? Eh, maybe. Do they stop buying $90,000 rolling batteries? Oh yeah. And yet, more often than not, investments like this move in sympathy. What is so fundamental about and shared within these asset classes that they can be aggregated like this? This is a critical thing to understand if you spend any time assessing risk or building portfolios:

The real reason that many investments behave like each other at all is that they are grouped into asset classes that most investors trade together.

It’s the sort of tautological, Schrodingeresque yarn that should be familiar to any Epsilon Theory reader: asset classes behave like asset classes because we treat them like asset classes. No matter how much we grouse about fundamentals not mattering, no matter how much we may wish this weren’t the case, it is. And it’s becoming truer as passive investing and indexing become more dominant. We may not think it be like it is, but it do.

If you find this dissatisfying, join the club. The cementing of this kind of mechanic is a big part of the hollow, petty, transactional, voodoo wasp-infested investing world we live in. I’m not asking you to pretend that it isn’t a thing. What I am asking you to do is consider whether it is right to anchor the way we think about portfolios and appropriate levels of risk for ourselves and our clients on the independent and recursively derived characteristics of “asset classes.”

In behavioral finance and cognitive psychology, this is a classic example of both the availability and anchoring heuristics. In the absence of a clear framework to assess how much risk we ought to take in our portfolios, we instead look at the continuum of risk/reward opportunities as expressed through these asset classes, whose risk characteristics are readily apparent — and available. We anchor on the “most risky” and “least risky” of those asset classes, and treat every individual as a relative or marginal analysis against those anchors. Thus, we arrive at all the variants of 60/40, 70/30 and 50/50 portfolios consisting of varying percentages of stocks and bonds. It’s a Coming to America conversation with every advisor: “How much risk is appropriate for a high-risk investor? Why, however much risk a broad market stock market index has.” “How much for a moderate risk investor? I don’t know, let’s add some bonds to whatever we just sold the last guy.”

The conflation of the types and amount of risk has other effects as well. A portfolio that is 80% bonds isn’t just less risky than a portfolio that is 80% stocks — it is also exposed to really different drivers of returns for what it holds. Thus, even in an asset class-conscious framework, the narrative holds. And it is a strong one.

Its missionaries take many forms: practitioners, econometricians, academics, and even regulators, who conduct all sorts of other analyses to support these conclusions. They anchor us to conventional definitions and groupings like asset classes, style boxes and the like, they take for granted assumptions (e.g., no leverage) that create massive bias in their conclusions, and they focus unerringly on improving utility theory to better understand what investors will do instead of identifying what they should do. In so doing they unwittingly conspire to force us into a set of investment options that reflect a sad mix of human behavioral tendencies, conclusions biased by massive abstractions and absurd faith in coincidences.

Have you ever tried sugar…or PCP?

I think it’s pretty unlikely this narrative goes anywhere any time soon. Its assumptions are too convenient, too perswasive, its conventions too embedded in product structure and regulation. Think Target Date funds, balanced funds and ’40 Act limitations. It’s also true that it can be pretty useful. As these pages have made clear, we have a pretty dim view of spending a lot of time sitting around talking stocks and we’re not in the business of wasting time on window dressing or fiddling. When we build portfolios, we use a lot of index-linked instruments — ETFs, futures, swaps — because they do a pretty good job of delivering many of the core sources of risk and return we want.

But believing in and using low-cost vehicles doesn’t require you to calibrate your whole framework of thinking around the characteristics of the indexes they track. So what is our framework? What will be robust to changing levels of risk and changing sentiment? What has a true north even when the drivers of asset classes are shifting? What allows us to answer something other than “Yes” or “No” when someone asks us whether we’ve ever tried sugar or PCP?

How much risk you take is probably the most important decision you will make as an investor. It is certainly the first decision you should make.

This is a deceptively simple point, but it matters. I am saying that before you spend a minute thinking about or designing an asset allocation, your complete focus should be on the quantity of risk you’re willing to take.

In some cases — decisions among similar asset classes — the risk decision is very obviously more important. This is most easily understood by example. Below we examine the risk and return of five different portfolios since January 2001 and rebalanced monthly:

  1. A portfolio invested 100% in the MSCI All Country World Index (“ACWI”)
  2. A portfolio invested 90% in ACWI and 10% in the S&P 500
  3. A portfolio invested 90% in ACWI and 10% in the MSCI Japan Index
  4. A portfolio invested 90% in ACWI and 10% in the MSCI Europe Index
  5. A portfolio invested 90% in ACWI and 10% in nothing (under a mattress)

Think of Portfolio 1 as our control. Portfolios 2, 3 and 4 represent — for the most part — an isolation of the “asset” dimension and an abstraction from risk. Portfolio 5 represents an isolation of the risk dimension. If we chose to overweight the U.S., Europe or Japan by 10% against a global market cap weighted index, the average difference in annualized return between the 10% overweight bets and the ACWI over this period was about 8 basis points. By contrast, taking off 10% of our risk took away about 30bp of return. Intuitively this is a function of the relative Sharpe ratios of various asset classes and how they differ, and so over different periods — such as ones in which the broad market was down — this analysis might have different signs. But over most of history and across most markets the magnitude, the importance of this decision, would be like what we show here.

Source: Salient Partners, L.P., as of 12/31/16. For illustrative purposes only. Past performance is no guarantee of future results. Certain performance information shown is compared to broad-based securities market indices. Broad-based securities indices are unmanaged and are not subject to fees and expenses typically associated with managed accounts or investment funds. Investments cannot be made directly in an index.

You could think about this in risk space as well. The volatility of the ACWI over this period was just under 16%, coincidentally not that far from what you would observe over many other long-term windows. In contrast, the volatility of the excess return between each individual market and the broad market — their tracking error — was always lower. On average using the U.S., Europe and Japan, the average tracking error is about 7.6%, less than half of the volatility of the market itself.

This analysis becomes a bit more convoluted if you’re comparing decisions across assets that tend to have very different amounts and types of risk — say, U.S. large caps and Treasurys. If we were able to achieve a similar level of risk from government debt, we’d see that the impact of the different types of risk becomes as significant as the risk decision itself. But even in this case, to get to a place where we are thinking in those terms, quantity of risk is our starting place. How much you own usually matters more than what you own.

For many investors, this is counterintuitive. It presents a strong contrast to the way in which many investors have taken advice from people like Peter Lynch and Warren Buffett, whose letters and books highlight the extent to which focusing on simple businesses they can understand or can “sketch with a crayon” has led to their own success. Much to Mr. Lynch’s dismay, for example, investors have often understood this to mean buying Procter & Gamble stock because they personally use a lot of Crest toothpaste and feel strongly that it’s a superior product to Colgate. I would extend this to include even the more sensible-sounding notion that a senior IT professional has some edge that should allow him to successfully manage a JNPR/CSCO pairs trade. Please.

Buffett and some others are probably an exception to our rule of thumb here, although only marginally so. Not because of his talent, but because of his extreme concentration. The idiosyncratic characteristics of the portfolio of companies in which he chooses to invest may sometimes be more different from those of other companies than the difference between holding the portfolio and holding cash. But that level of concentration is so extraordinarily rare among investors that I think it’s probably approximately correct to consider it irrelevant for our discussion.

So what am I saying to the “quality” and “buy what you know” investors? I am saying that unless you have a portfolio that is very concentrated in individual securities — by which I mean that more than 6 or 7% of your total net worth or investable assets are invested in an average stock or bond position — if you think that the unique characteristics of what you own are going to drive your success more than how much market risk you’re taking, you are wrong.

Measurement will be important as we walk down this road, but I don’t have a lot of interest in spilling more ink/electrons debating the best way to measure risk. We’ll get into it more in Part 2, but regardless of what measure for risk we choose, by and large, how much exposure we have to financial market risk will have more impact on our portfolio results than any other factor.

Primum non nocere

What does all this mean for the code-driven investor?

It means that anything lower in the priority must be considered in context of its impact on risk. This seems intuitive, but is extremely poorly understood. Take a look at this article from the world’s leading newspaper covering financial markets. Without tongue firmly planted in cheek, this author undertakes to compare hedge fund returns to private equity returns as part of explaining why private equity funds are raising so much more money. This is really stupid.

The first reason it is stupid is because the comparison is terrible. Most private equity — large buyout funds, anyway — is just levered stocks with high fees and a PM who calls himself a “deal guy” and wears Brioni instead of your long-only guy’s Brooks Brothers. It’s the exact same type of risk as mid-cap equities, and if it were in a constantly marked structure, it would demonstrate more risk than your average mid-cap equity benchmark. Not to be too on-the-nose about this, but hedge funds are usually hedged. Most try to avoid equity sources of risk, and almost universally avoid taking as much risk as traditional strategies. Evaluating and comparing absolute returns of these two assets because they’re both “alternatives” is like the guy with the butter-laden tomahawk ribeye gloating when I order the petit filet. Yes, we all saw the 26-ounce steak on the menu, guy.

The second and more disquieting reason it is stupid is because it’s kind of true. People and funds really are making this exact decision: to sell their hedge funds to fund private equity. At other times (usually after PE disappoints) they do the opposite. But I see decisions like this all the time. I see advisors trying to improve a client’s yield by swapping stocks for high yield. Selling their equity index fund to go into an unlevered low volatility equity fund. I see them going to cash because U.S. stocks feel expensive. I see them rotating from market-neutral hedge funds to high volatility CTAs and managed futures funds, or visa versa.  There are always good decisions why we don’t like Asset X and maybe some good reasons why we like Asset Y. But because our frameworks often don’t first think about the baseline expectations for risk and return for these assets, these decisions often fall victim to the pitfalls we highlighted in And They Did Live by Watchfires, where our temptation to tweak leads us to make small changes that have big unintended consequences. In a huge majority of cases, risk differences between assets will dominate the expected edge we have on views of the relative attractiveness of different types of return. More on this to come.

The other implication — and chief benefit — of starting the portfolio construction process with a risk target is that it frees us from the anchoring biases of a framework that begins from the arbitrarily determined characteristics of asset classes. That does place some onus on us to develop a view of the right amount of risk to take, of course. And while some of the techniques for developing such a view are standard fare, they also usually either revert to boundary constraints driven by asset classes and vehicles, or else focus on an exercise where the expected portfolio return just meets a return target or theoretically minimizes the probability of not reaching some horrifying outcome.

So, while I believe that your quantity of risk is the most important decision you can make, I can’t tell you how much risk you can tolerate. I can, however, generalize what I know many professional investors do in their personal portfolios:

  1. They take a lot more risk than you.
  2. They concentrate a lot more than you.
  3. The fact that they offer lower-risk products reflects their assessment of business risk, not investment merits.
  4. Tail risk becomes a much bigger consideration as we do more of #1 and #2.

I’ll be the first to say that the notion of “smart money” is mostly a myth, but there’s a reason why your fund managers behave like this. The notion that bonds are manna for conservative investors turns out to be just about right. Go figure. The idea that equities are manna for risk-seeking investors turns out to be pretty far off. For those of us in the risk-seeking camp, we need to start over on the question of the right amount of risk to take. For that, you’ll have to wait for Part 2.

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AI & Video Games, Tricky Chatbots and More… (by Silly Rabbit)

AI and video games (again)

Vicarious (a buzzy Silicon Valley company developing AI for robots) say they have a new and crazy-good AI technique called Schema Networks. The Allen Institute for Artificial Intelligence and others seem pretty skeptical and demand a throw-down challenge with AlphaGo (or, failing that, some peer-reviewed papers with commonly used terms and a broader set of tests).

In other AI video game news, Microsoft released a video of their AI winning at Ms. Pacman, with an instructive voiceover of how the system works.

Tricky chatbots

I recently stumbled upon Carl Icahn’s Twitter feed which has the tag line: “Some people get rich studying artificial intelligence. Me, I make money studying natural stupidity.” Me, I think in 2017 this dichotomy is starting to sound pretty quaint. See: Overview of recent FAIR (Facebook Artificial Intelligence Research division) study teaching chatbots how to negotiate, including the bots self-discovery of the strategy of pretending to care about an item to which they actually give little or no value, just so they can later give up that item to seem to have made a compromise. Apparently, while they were at it, the Facebook bots also unexpectedly created their own language.

The quantum age has officially arrived

I’ve been jabbering on and pointing to links about quantum computing and the types of intractable problems it can solve for some time here, here and here, but now Bloomberg has written a long piece on quantum we can officially declare “The quantum age has officially arrived, hurrah!”. Very good overview piece on quantum computing from Bloomberg Markets here.

Your high dimensional brain

We tend to view ourselves (our ‘selfs’) through the lens of the technology of the day: in the Victorian ‘Mechanical age’ we were (and partly are) bellows and pumps, and now we are, by mass imagination, a collection of algorithms and processors, and possibly living in a VR simulation. While this ‘Silicon Age’ view is probably not entirely inaccurate it is also, probably, in the grand scheme of things, nearly as naive and incomplete as the Victorian view was. Blowing up some of the reductions of current models, this new (very interesting, pretty dense, somewhat contested) paper points towards brain structure in 11 dimensions. Shorter and easier explainer here by Wired or even more concisely by the NY Post“If the brain is actually working in 11 dimensions, looking at a 3D functional MRI and saying that it explains brain activity would be like looking at the shadow of a head of a pin and saying that it explains the entire universe, plus a multitude of other dimensions.”

And in other interesting-brain-related news:

Taming the “Black Dog”

And finally, three different but complimentary technology-enabled approaches to diagnosing and fighting depression:

  • basic algorithm with limited data has shown to be 80-90 percent accurate when predicting whether someone will attempt suicide within the next two years, and 92 percent accurate in predicting whether someone will attempt suicide within the next week.
  • In a different predictive approach, researchers fed facial images of three groups of people (those with suicidal ideation, depressed patients, and a medical control group) into a machine-learning algorithm that looked for correlations between different gestures. The results: individuals displaying a non-Duchenne smile (which doesn’t involve the eyes in the smile) were far more likely to possess suicidal ideation.
  • On the treatment-side, researchers have developed a potentially revolutionary treatment that pulses magnetic waves into the brain, treating depression by changing neurological structures, not its chemical balance.

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Long Short-Term Memory, Algorithms for Social Justice, and External Cognition (by Silly Rabbit)

DARPA funds graph analytics processor

Last week I posted a bunch of links pointing towards quantum computing. However, there are also other compute initiatives which also offer significant potential for “redefining intractable” for problems such as graph comparison, for example, DARPA’s HIVE which aims to create a 1000x improvement in processing speed (and at much lower power) on this problem. Write-up on EE Times of the DARPA HIVE program here.

Exploring long short-term memory networks

Nice explainer on LSTMs by Edwin Chen: “The first time I learned about LSTMs, my eyes glazed over. Not in a good, jelly donut kind of way. It turns out LSTMs are a fairly simple extension to neural networks, and they’re behind a lot of the amazing achievements deep learning has made in the past few years.” (Long, detailed and interesting blog post, but even if you just read the first few page scrolls still quite worthwhile for the intuition of the value and function of LSTMs.)

FairML: Auditing black box predictive models

Machine learning models are used for important decisions like determining who has access to bail. The aim is to increase efficiency and spot patterns in data that humans would otherwise miss. But how do we know if a machine learning model is fair? And what does fairness in machine learning mean? Paper exploring these questions using FairML, a new Python library that audits black-box predictive models.

Fast iteration wins prizes

Great Quora answer on “Why has Keras been so successful lately at Kaggle competitions?” (By the author of Keras, an open source neural net library designed to enable fast experimentation). Key quote: ”You don’t lose to people who are smarter than you, you lose to people who have iterated through more experiments than you did, refining their models a little bit each time. If you ranked teams on Kaggle by how many experiments they ran, I’m sure you would see a very strong correlation with the final competition leaderboard.” 

Language from police body camera footage shows racial disparities in officer respect

This paper presents a systematic analysis of officer body-worn camera footage, using computational linguistic techniques to automatically measure the respect level that officers display to community members.

External cognition

Large-scale brainlike systems are possible with existing technology — if we’re willing to spend the money — proposes Jennifer Hassler in A Road Map for the Artificial Brain.

Pretty well re-tweeted and shared already, but interesting nonetheless: External cognition: The Thoughts of a Spiderweb.

And related somewhat related (or at least a really nice AR UX for controlling synthesizers), a demonstration of “prosthetic knowledge” — check out the two minute video with sound at the bottom of the page – awesome stuff!

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The Summer Reading List (by Jeremy Radcliffe)

It’s that time of year, when the kids get out of school and somehow you’re supposed to have more time to spend reading. I’m going to share a few of my current, hopefully off-the-beaten-path favorites with you. These recommendations are going to focus on good old-fashioned free email subscriptions, kind of like Epsilon Theory. If you want to read great literature, please check out the McSweeney’s store, where the books are as beautiful on the outside as the words are on the inside. And if you want the list of finance-related classics, well, Ben’s already done that work for you here (I can’t recommend Fortune’s Formula highly enough!). So, on to my email list recommendations:

Bob Lefsetz

Ostensibly, Bob writes about music and the music business, so this is certainly most applicable for those with an interest in music and the music scene, but Bob’s near-daily communiques are about so much more than music. I’ve been reading Bob for about three years now and his advice for artists is applicable to business leaders as well — primarily to focus on being authentic and not to worry about appearing vulnerable, which is actually humanizing and allows others to bond with you.  http://lefsetz.com/wordpress/

Scott Galloway

I don’t know where I first came across Scott’s blog/newsletter, which is nominally about digital marketing strategy, but it’s now a weekly blessing. He’s a professor at NYU Stern and just sold his consulting business L2, but he’s continued to publish notes that are very much in the Lefsetz vein. Scott’s an expert in his field, and he also understands that transparency and authenticity drive the connection with the reader. His tagline or motto is “life is so rich,” and it is, especially when you’re reading his smart, beautiful, and brutally honest stuff.  https://www.l2inc.com/

Scott Belsky

When it comes to technology and the VC world, my go-to used to be Bill Gurley of Benchmark Capital and his wonderful Above the Crowd (great name; Bill’s super-tall); however, Bill is down to about a post a year of late, so don’t expect much on a regular basis, but consider signing up because when he does post, it’s a must-read. However, his friend and Benchmark venture partner, Scott Belsky has started doing a monthly-ish collection of his thoughts and links to interesting content in the technology and design arena which he is calling Positive Slope, and I highly recommend it.  http://digest.scottbelsky.com/

 Tim Urban

Tim’s WaitButWhy blog is tech-focused also, but his specialty seems to be explaining Elon Musk’s ambitions in relatively plain but plentiful (like 40,000 words at a time) English for those of us who aren’t engineers, using low-tech stick figure diagrams and clip art.  http://waitbutwhy.com/

Lacy Hunt & Van Hoisington

OK, so this is a more straightforward investment management letter, but if you want to understand why interest rates are so stubbornly low in the face of unprecedented “money printing” by central banks around the world (spoiler alert: velocity of money!), you should be reading whatever Lacy and his partner Van Hoisington of Hoisington Asset Management in Austin, Texas are writing. Yes, they run a long-dated Treasury fund and are “talking their book,” but they’ve been so right for so long while almost everybody else in our business has used every 20-basis-point backup in rates as an excuse to call for the Death of the Bond Bull Market.  http://www.hoisingtonmgt.com/newsletter

Eknath Easwaran

I learned to meditate a few years ago using a simple technique called passage meditation pioneered (or documented!) by Blue Mountain Center of Meditation founder, Eknath Easwaran. You can sign up for a daily dose of wisdom, taken from his book Words to Live By and delivered via email.  https://www.bmcm.org/subscribe/

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Chili P is My Signature: Things that Don’t Matter #5

Jesse After His Chili P Phase

Walter: Did you learn nothing from my chemistry class?
Jesse: No. You flunked me, remember, you prick? Now let me tell you something else. This ain’t chemistry — this is art. Cooking is art. And the shit I cook is the bomb, so don’t be telling me…
Walter: The shit you cook is shit. I saw your setup. Ridiculous. You and I will not make garbage. We will produce a chemically pure and stable product that performs as advertised. No adulterants. No baby formula. No chili powder.
Jesse: No, no, chili P is my signature!
Walter: Not anymore.
Breaking Bad, Season 1, Episode 1

“There was only one decline in church attendance, and that was in the late 1960s, when the Vatican said it was not a sin to miss Mass. They said Catholics could act like Protestants, and so they did.“
— Rodney Stark, Ph.D.

She should have died hereafter;
There would have been a time for such a word.
To-morrow, and to-morrow, and to-morrow,
Creeps in this petty pace from day to day
To the last syllable of recorded time,
And all our yesterdays have lighted fools
The way to dusty death. Out, out, brief candle!
Life’s but a walking shadow, a poor player
That struts and frets his hour upon the stage
And then is heard no more: it is a tale
Told by an idiot, full of sound and fury,
Signifying nothing.
— William Shakespeare, Macbeth, Act 5, Scene 5

“I can’t do it if I think about it. I would fall down, especially if I’m wearing street shoes,” he said, laughing. “It wasn’t something I did because I wanted to. I didn’t even know I did that until someone showed me a video.”
— Fernando Valenzuela about his unique windup to the LA Times (2011)

Fernando-mania

Baseball was in the midst of a crisis in 1981.

In the years prior, competition for talent in larger markets had driven player salaries higher and higher. This caused owners to seek increasing restrictions on free agency. The players’ union went on strike in June, right in the middle of the season. Fans were furious, and mostly with the owners, as is the usual way of things. We still hate millionaires, of course, but we positively loathe billionaires. While the strike ended by the All-Star break in early August, work stoppages and disputes of this sort have often been the signposts of baseball’s long, slow march to obscurity against the rising juggernaut of American football and the sneaky, if uneven, popularity of basketball. It was not a riskless gamble for either party, and as future strikes taught us, the aftermath could have gone very badly.

But not this time. You see, baseball had a secret weapon to quickly bring fans back after the 1981 strike: a “short fat dark guy with a bad haircut.” His name was Fernando Valenzuela.

Fernando was an anomaly in another long, slow march — that of baseball’s transition from a pastime to something more clinical, more analytical, more athletic. We were at a midpoint in the shift from the everyman-made-myth that was Babe Ruth or the straight-from-the-storybook folk hero like Joe DiMaggio to the brilliant, polished finished products of baseball academies today. Only a few years after 1981, we would see the birth of the new generation of uberathletes in Bo Jackson, a man who many still consider among the most gifted natural athletes in history. Only a decade earlier, the top prospect in baseball was one Greg Luzinski. The two weighed about the same. Their body composition was just a little bit different.

Fernando was certainly a physical throwback of the Luzinski variety, but so much more. He was a little pudgy. His hair was, long, shaggy and unkempt. More to the point, everything he did was inefficient, out of line with trends in the league. His windup was long and tortured, with a high leg kick that reached shoulder level in his early years and chest level in his older, slightly chubbier years. It featured an unnecessary vertical jerk of his glove straight upward near the end, and most uniquely, a glance to the heavens that became a signature of Fernando-mania. To stretch the inefficiency to its natural limits, his most effective pitch was a filthy screwball, a pitch that had been popular for decades but had already significantly waned by the early 1980s. Fathers and coaches taught their sons that it would hurt their arms (which a properly thrown screwball does not do), and by the late 1990s the pitch that ran inside on same-handed batters was all but extinct, except in Japan, where a very similar pitch called the shuuto continued to find adherents.

There were many reasons he captured the national imagination. He was a gifted Mexican pitcher in Los Angeles, a city full of baseball-obsessed Mexican-Americans and migrant workers. He was also truly marvelous as a 20-year old rookie in 1981. His stretch of eight games between April 9th and May 14th still ranks as one of the most dominant in history. Eight wins. Eight complete games. Five shutouts. Sixty-eight strikeouts. And that was how he started his career!(1)

But more than anything, I think, it was the pageantry and the spectacle of it all. The chubby, mop-top everyman who came out of nowhere with a corny sense of humor, who threw from a windup out of a cartoon, who threw a pitch that nobody else threw anymore. It was inefficient and ornamental and just so unnecessary — and we loved it. I still do. It was even how I was taught to pitch growing up. My father told me and instructed me to throw with “reckless abandon”, and so in my windup I would rotate my hips and point my left toe at second base before kicking it in a 180-degree arc at a shoulder level, nearly falling to the ground from the violent shift in weight after every pitch.

Alas, the efficiency buffs who disdained such extravagances were and are mostly right. While Valenzuela had a long and decent career, the greatest pitchers of the modern era — Roger Clemens, Pedro Martinez and especially Greg Maddux — all thrived on efficient mechanics and a focus on a smaller number of high quality pitches.(2) While a screwball is nice, and in many ways unique, it also isn’t particularly effective as a strikeout pitch in comparison to pitches with more vertical movement like, say, curveballs, split-finger fastballs or change-ups, or pitches that can accommodate lateral movement AND velocity, like sliders and cut-fastballs.

There’s a lesson in this.

As humans, especially humans in an increasingly crowded world where we can be instantly connected to billions of other people, the urge to stand out, to carve out a different path, can be irresistible. This influences our behavior in a couple of ways. First, it drives us to cynicism. Think back on the #covfefe absurdity. If you’re active on social media, by the time you thought of a funny #covfefe joke, your feed was probably already filled with an equal number of posts that decided that the meme was over, using the opportunity to skewer the latecomers to the game. Those, too, were late to the real game, which had by that time transitioned to new ironic uses of the nonsense word. A clever idea that is shared by too many quickly becomes an idea worthy of derision. And so the equilibrium — or at least the dominant game theory strategy — is to be immediately critical of everything.

It also makes us inexorably prone to affectation. We must add our own signature, that thing that distinguishes us or our product; the figurative chili-powder-in-the-meth of whatever our form of productive output happens to be.  Since we are all writers of one sort or another now, we feel this acutely in how we communicate. When part of what you want to be is authentic in your communication, our introspection becomes a very meta thing — we can talk ourselves into circles about whether we’re being authentic or trying inauthentically to appear authentic. But we’re always selling, and while our need for a unique message has exaggerated this tendency, at its core it clearly isn’t a novel impulse. People have been selling narratives forever. But if there’s a lesson in Epsilon Theory, surely it is that successful investors will be those who recognize, survive and maybe even capitalize on narrative-driven markets — not necessarily those whose success is only a function of their ability to push substance-less narratives of their own.

Perhaps most perniciously, our urge to stand out is also an urge to belong to a Tribe — to find that small niche of other humans that afford us some measure of human interaction while still permitting us to define ourselves as a Thing Set Apart. The screwball, the chili powder, the fancy windup, the obscure quotes about Catholicism from sociology Ph.D.s in your investing think-piece — instead of a barbaric yawp, it becomes a signal to your tribe. When pressed, our willingness to rip off the steering wheel and adopt a competitive strategy becomes dominant, a necessity. Lingering in the back of our heads as we go all-in on our tribe is the knowledge that our tribal leaders, no matter who they are, will sell us down the river every time.

In our investing lives, when we build portfolios, we know full well how many options our clients or constituents have, so these three competing impulses drive our behaviors: cynicism, affectation and tribalism. The cynical, nihilistic impulse shouts at us that nothing matters enough to justify risking being fired, and so we end up choosing the solution that looks most like what everyone else has done. That’s the ultimate equilibrium play we’re all headed toward anyway, right? The affectation impulse requires that we add a little something to distinguish us from our peers. A dash of chili powder. A screwball here or there, or an outlandish delivery to delight and astonish. Our tribal impulse compels us toward the right-sounding idea that makes us part of a group (I’m looking at you, Bogleheads). More frequently, we’re motivated by a combination of all three of these things in one convoluted, ennui-laden bit of arbitrary decision-making.

The real kick in the teeth of all this is that many of the things we are compelled to do by these impulses are actually good and important things, even Things that Matter. But because of the complex rationale by which we arrive at them (and other biases besides), we often implement the decisions at such a halfhearted scale that they become irrelevant. In other, worse cases, the decisions function like the tinkering we discussed in And They Did Live by Watchfires, potentially creating portfolio damage in service of a more compelling marketing message or to satisfy one of these impulses. In both cases, these flourishes and tilts are too often full of sound and fury, signifying nothing.

Too Little of a Good Thing

What, exactly, are we talking about? Well, how about value investing, for starters?

I think this one pops up most often as a form of the tribal impulse, although clearly many advisors and allocators use it as a way to add a dash of differentiation as well. Now, most of us are believers in at least a few investing tribes, each with its own taxonomy, rituals, acolytes and list of other tribes we’re supposed to hate in order to belong. But none can boast the membership rolls of the Value Tribe (except maybe the Momentum Tribe or the Passive Tribe). And for good reason! Unlike most investment strategies and approaches devised, buying things that are less expensive and buying things that have recently gone up in price can both be defended empirically and arrived at deductively based on observations of human behavior. The cases where science is really being applied to investing are very, very rare, and this is one of them. Rather than pour more ink into something I rather suppose everyone reading this believes to one extent or another, I’d instead direct you to read the splendid gospel from brothers Asness, Moskowitz and Pedersen on the subject. Or, you know, if you’re convinced non-linearities within a population’s conditioning to sustained depressing corporate results and lower levels of expected growth mean that such observations are only useful for analysis of the actions of an individual human and can’t possibly be generalized or synthesized into a hypothesis underpinning the existence of the value premium as an expression of market behavior, then don’t read it. Radical freedom!

What is shocking is how ubiquitous this belief is when I talk to investors, and how little investors demonstrate that belief in their portfolios. We adhere to the tribe’s religion, but now that it’s not a sin to skip out, we only attend its church on Christmas and Easter. And maybe after we did something bad for which we need to atone.

Value is the more socially acceptable tribe (let’s be honest, momentum has always had a bit of a culty, San Diego vibe), so let’s use that as our case study. Since I’m worried I’m leaving out those for whom cynicism is the chosen neurosis, let’s use robo-advisors to illustrate that case study. They’re instructive as a general case as well, since they, by definition, seek to be an industry-standard approach at a lower price point. Now, of the two most well-advertised robos, one — Wealthfront — mostly ignores value except in context of income generation. The other — Betterment — embraces it in a pretty significant way. I went to their very fine website and asked WOPR what a handsome young investment writer ought to invest in to retire around 2045. Here is what they recommended:

Source: Betterment 2017. For illustrative purposes only.

Pretty vanilla, but then, that’s kind of the idea of the robo-advisor. But I see a lot of registered investment advisors and this is also straight out of their playbook. It’s tough to find an anchor for the question “I know I want/need value, but how much?” As a result, one of the most common landing spots I see is exactly what our robot overlords have recommended: half of our large cap equities in core, and the other half in value. We signal/yawp a bit further: we can probably also afford to do it in the smaller chunks of the portfolios, too. Lets just do all of our small cap and mid cap equities in a value flavor. As for international and emerging equities, we don’t want to scare the client with any more line items or pie slices invested in foreign markets than we need, so let’s just do one big core allocation there.

I’m putting words in a lot of our mouths here, but if you’re an advisor or investor who works with clients and this line of thinking doesn’t feel familiar to you, I’d really like to hear about it. Because this is exactly the kind of rule of thumb I see driving portfolio decisions with so many allocators that I speak to. But how do we actually get to a portfolio like this? If you think there’s a realistic optimization or non-rule-of-thumb-driven investment process that’s going to get you here, let’s disabuse ourselves of that notion.

Could plugging historical volatility figures and capital markets expectations into a mean/variance optimizer get you to this split on value vs. core? In short? No. No, we know that this is an impossible optimizer solution because the diversification potential at the portfolio level — what we call the Free Lunch Effect in this piece — would continue to rise as we allocated more and more of our large cap allocation to a value style (and less and less to core). In other words, while the intuition might be that having both a core and value allocation is more diversifying (more pie slices!), that just isn’t true. In a purely quantitative sense, you’d be most diversified at the portfolio level with no core allocation at all!

Free Lunch Effect of Various Allocations to Large Cap Value vs. Large Cap Core in Example Portfolio

Source: Salient 2017. For illustrative purposes only.

If your instinct is to say that doesn’t look like much diversification, however, you’d be right as well. Swinging our large cap portfolio from no value to nothing but value reduces our portfolio risk by around 8bp without reducing return (i.e., the Free Lunch). That’s not nothing, but it’s damn near. The reason is that the difference between the Russell 1000 Value Index and the Russell 1000 Index or the S&P 500, or the difference between your average large cap value mutual fund and your average large cap blend mutual fund, is not a whole lot in context of how most things within a diversified portfolio interact. Said another way, the correlation is low, but the volatility is even lower, which means it has very little capacity to impact the portfolio. Take a look below at how much that value spread contributes to portfolio volatility. The below is presented in context of total portfolio volatility, so you should read this as “If I invested all 32% of the large cap portion of this portfolio in a value index and none in a core index, the value vs. core spread itself would account for about 0.1% of portfolio volatility.”

Percentage of Portfolio Volatility Contributed by LC Value-Core Spread

Source: Salient 2017. For illustrative purposes only.

Fellow tribesmen, does this reflect your conviction in value as a source of return? Some of you may quibble, “Well, this is just in some weird risk space. I think about my portfolios in terms of return.” Fine, I guess, but that just tells the same story. Consider how most value indices are constructed, which is to say a capitalization weighted splitting of “above average” vs. “below average” stocks on some measure (e.g., Russell) or multiple measures (e.g., MSCI) of value. We may have in our heads some of the excellent research on the value premium, but those are almost always expressed as regression alphas or as spread between high and low quintiles or deciles (Fama/French) or tertiles (Asness et al). In most cases they are also based on long/short or market neutral portfolios, or using methodologies that directly or indirectly size positions based on the strength of the value signal rather than the market capitalization of the stock. There are strategies based on these approaches that do capitalize on the long-term edge of behavioral factors like value. But that’s not really what you’re getting when you buy most of these indices or the many products based on them.(3)

So what are you getting? For long-only stock indices globally, probably around 80bp(4) and that assumes no erosion in the premium vs. long-term average. Most other research echoes this – the top 5 value-weighted deciles of Fama/French get you about 1.1% annualized over the average since 1972, and comparable amounts if you go back even further. Using the former figure, if you swung from 0% value to 32% value in your expression of your large cap allocation — frankly a pretty huge move for most investors and allocators — we’re talking about a 26bp difference in expected portfolio returns. Again, not nothing, but if our portfolio return expectations are, say, 8%, that’s a 3.2% contributor to our portfolio returns under fairly extreme assumptions.

Does this reflect your conviction in value as a source of return? No matter how we slice it, the ways we implement even fundamental, widely understood and generally well-supported sources of return like value seem to be a bit long on the sound and fury, but unable to really drive portfolio risk or return. Why is this so hard? Why do we end up with arbitrary solutions like splitting an asset class between core and value exposure like some sort of half-hearted genuflection in the general direction of value?

Because we have no anchor. We believe in value, but deep down we struggle to make it tangible. We don’t know how much of it we have, we don’t even know how much of it we want. We struggle even to define what “how much” means, and so we end up picking some amount that will allow us to sound sage and measured to the people who put their trust in us to sound sage and measured.

I’m going to spend a good bit of time talking about how I think about the powerful diversifying and return-amplifying role of behavioral sources of return like value as we transition our series to the Things that Matter, so I’ll beg both your patience and indulgence for leaving this as a bit of a resolutionless diatribe. I’ll also beg your pardon if it looks like I’ve been excessively critical of the fine folks who put together the portfolio that has been our case study. In truth, that portfolio goes much further along the path than most.

The point is that for various behavioral reasons, our style tilts like value, momentum or quality occupy a significant amount of our time, marketing and conversations with clients, and — by and large — signify practically nothing in terms of portfolio results. In case I wasn’t clear, yes, I am saying that value investing — at least the way most of us pursue it — doesn’t matter.

The Magically Disappearing Diversifier

The time we spend fussing around with miniscule style tilts, however, often pales in comparison to the labor we sink into our flourishes in alternatives, especially hedge funds. Some of this time is well-spent, and well-constructed hedge fund allocations can play an important role in a portfolio. When I’m asked to look at investors’ hedge fund portfolios, there are usually two warning signs to me that the portfolios are serving a signaling/tribal purpose and not some real portfolio objective:

  1. Low volatility hedge funds inside of high volatility portfolios that aren’t using leverage
  2. Hedge fund portfolios replacing Treasury or fixed income allocations

Because of the general sexiness (still, after all these years!) of hedge fund allocations to many clients or constituents, the first category tends to be the result of our affectation impulse. We want to add that low-vol, market-neutral hedge fund, or the fixed income RV fund that might have been taking some real risk back in 2006 when they could lever it up a bajillion times, not because of some worthwhile portfolio construction insight, but perhaps because it allows us to sell the notion that we are smart enough to understand the strategies and important enough to have access to them. Not everyone can get you that Chili P, after all. In some cases, sure — we are signaling to others that we are also part of that smart and sophisticated enough crowd that invests in things like this. In the institutional world, where it’s more perfunctory to do this, it’s probably closer to cynicism: “Look, I know I’m going to have a portfolio of low-vol hedge funds, so let’s just get this over with.”

For many clients and plans — specifically those where assets and liabilities are mostly in line and the portfolio can be positioned conservatively, say <10% long-term volatility — that’s completely fine. But for more aggressive allocations, there is going to be so much equity risk, so much volatility throughout the portfolio, that the notion that these portfolios will serve any diversification role whatsoever is absurd. They’re just taking down risk, and almost certainly portfolio expected returns along with it. Unless you feel supremely confident that you’ve got a manager, maybe a high frequency or quality stat arb fund, that can run at a 2 or 3 Sharpe, it is almost impossible to justify a place for a <4% volatility hedge fund in a >10% target risk portfolio. They just won’t move the needle, and there are better ways to improve portfolio diversification, returns or risk-adjusted returns.

The second category starts to veer out of “Things that Don’t Matter” territory into “Things that Do Matter, but in a Bad Way.” More and more over the last two years, as I’ve talked to investors their primary concern isn’t equity valuations, global demographics, policy-controlled markets, deflationary pressures, competitive currency crises, protectionism, or even fees! It’s their bond portfolio. The bleeding hedge fund industry has been looking for a hook since their lousy 2008 and their lousier 2009, and by God, they found it: sell hedge funds against bond portfolios! Absolute return is basically just like an income stream! There seems to be such a strong consensus for this that it may have become that cynical equilibrium.

No. Just no.

It’s impossible to overstate the importance of a bond/deflation allocation for almost any portfolio. This is an environment that prevails with meaningful frequency that has allowed the strong performance of one asset historically: bonds, especially government bonds (I see you with your hands raised in the back, CTAs, but I’m not taking questions until the end). The absolute last thing any allocator should be thinking about if they have any interest in maintaining a diversified portfolio, is reducing their strategic allocation to bonds. I’ll be the first to admit that when inflationary regimes do arrive, they can be long and persistent, during which the ability of duration to diversify has historically been squashed. The negative correlation we assume for bonds today is by no means static or certain, which is one of the reason I favor using more adaptive asset allocation schemes like risk parity that will dynamically reflect those changes in relationship. But even in that context, the dominance and ubiquity of equity-like sources of risk means that almost every investor I see is still probably vastly underweight duration.

Now many of us do have leverage limitations that start to create constraints, and so I won’t dismiss that there are scenarios where that constraint forces a rational investor not to maximize risk-adjusted returns, but absolute returns. I’m also willing to consider that on a more tactical basis, you may be smarter than I am, and have a better sense of the near-term direction of bond markets. In those cases, reducing bond exposure, potentially in favor of absolute return allocations, may be the right call. But if you have the ability to invest in higher volatility risk parity and managed futures, or if you have a mandate to run with some measure of true or derivatives-induced leverage, my strong suspicion is that you’ll find no cause to sell your bond portfolios in favor of absolute return.

Ultimately, it’s hard to be too prescriptive about all this, because our constraints and objective functions really may be quite different. To me, that means that the solution here isn’t to advise you to do this or not to do that, except to recommend this:

Make an honest assessment of your portfolio, of the tilts you’ve put on, and each of your allocations. Do they all matter? Are you including them because of a good faith and supportable belief that they will move the portfolio closer to its objective?

If we don’t feel confident that the answer is yes, it’s time to question whether we’re being influenced by the sorts of behavioral impulses that drive us elsewhere in our lives: cynicism, affectation and tribalism. In the end, the answer may be that we will continue to do those things because they feel right to us and our clients. And that may be just fine. A little bit of marketing isn’t a sin, and if your processes that have served you well over a career of investing are expressed in context of a particular posture, there’s a lot to be said for not fixing what ain’t broken. There’s nothing wrong with an impressive-looking windup, after all, until it adversely impacts the velocity and control of our pitches.

What is a sin, however, is when a half-hearted value tilt causes us to be comfortable not taking advantage of the full potential of the value premium in our portfolios. When the desire to get cute with low-vol hedge funds causes us to undershoot our portfolio risk and return targets. Perhaps most of all, when we spend our most precious resource — time — designing these affectations. We will be most successful when we reserve our resources and focus for the Things that Matter.


(1) Please – no letters about his relief starts in 1980. If MLB called him a rookie, imma call him a rookie.

(2) Probably the only exception in this conversation is Randy Johnson, who, while mostly vanilla in his mechanics, would probably get feedback from a coach today about his arm angle, his hip rotation and a whole bunch of other things that didn’t keep him from striking out almost 5,000 batters.

(3) As much as marketing professionals at some of the firms with products in this area would like to disagree and call their own product substantially different, they all just operate on a continuum expressed by the shifting of weightings toward cheaper stocks. Moving from left to right as we exaggerate the weighting scheme toward value, the continuum basically looks like this: Value Indices -> Fundamental Indexing -> Long-Only Quant Equity -> Factor Portfolios

(4) Simplistically, we’re just averaging the P2 and half of the P3 returns from the Individual Stock Portfolios Panel of Value and Momentum Everywhere, less the average of the full universe. An imperfect approach, but in broad strokes it replicates the general half growth/half value methodology for the construction of most indices in the space.


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