Year In Review

We’ve had a heckuva busy year at Epsilon Theory, so to ring out 2017 I thought it might be helpful to distribute a master list of our publications over the past 12 months. We’re long essay writers trying to make our way in a TLDR world, so even the most avid follower may well need a map!

It’s also a good opportunity to give thanks where thanks are due.

First, a heartfelt thank you to my partners at Salient for contributing a ton of resources to make Epsilon Theory happen, never once asking me to sell product, and allowing me the leeway to speak my mind with a strong voice that would make a less courageous firm blanch. Epsilon Theory isn’t charity, and it’s the smart move for a firm playing the long game, but no less rare for all that.

Second, an equally heartfelt thank you to the hundreds of thousands of readers who have contributed their most precious resource – their time and attention – to the Epsilon Theory effort. We live in a world that is simultaneously shattered and connected, where we are relentlessly encouraged to mistrust our fellow citizens IRL but to engage with complete strangers on social media. It’s an atomized and polarized existence, which works really well for the Nudging State and the Nudging Oligarchy, less well for everyone else. The lasting impact of Epsilon Theory won’t be in what we publish, but in how we’re able to bring together truth-seekers of all stripes and persuasions, because it’s your engagement with the ideas presented here that will change the world. I know that sounds corny, but it’s happening.

Now on to the 2017 publishing map.

Our big initiative for this year was to publish two coherent sets of long-form notes, one by yours truly and one by my partner Rusty Guinn.

My series of essays is called Notes From the Field. As many long-time readers know, I’m originally from Alabama but now live out in the wilds of Fairfield County, Connecticut, on a “farm” of 44 acres. I put that word in quotations because although we have horses and sheep and goats and chickens and bees, my grandfather – who owned a pre-electrification, pre-refrigeration, pre-pasteurization dairy farm in the 1930s – would surely enjoy a good belly laugh at my calling this a farm. Still, I’ve learned a few things over the years from the farm and its animals, and they’ve helped me to become a better investor.

  1. Notes From the Field: The eponymous note has two essays: “Fingernail Clean”, introducing the concept of the Industrially Necessary Egg – something we take for granted as proper and “natural” when it’s anything but, and “Structure is a Cruel Master”, introducing the genius of both humans and bees – our ability to build complex societies with simple algorithms.
  2. The Goldfinch in Winter: What can a bird teach us about value investing? To everything there is a season.
  3. Horsepower: The horse and horse collar revolutionized European agriculture in the 10th and 11th centuries, a revolution that lives on in words like “horsepower” and changed the course of human civilization. Today we are struggling with a productivity devolution, not revolution, and there is nothing more important for our investments and our politics and our future than understanding its causes and remedies.
  4. The Arborist: We are overrun with Oriental Bittersweet, privet, and kudzu — or as I like to call them, monetary policy, the regulatory state, and fiat news — invasive species that crowd out the small-l liberal virtues of free markets and free elections. What to do about it? Well, that’s citizenship, and I’ve got some ideas.
  5. Always Go To the Funeral: Going to the funeral is part of the personal obligation that we have to others, obligation that doesn’t fit neatly or at all into our bizarro world of crystalized self-interest, where scale and mass distribution are ends in themselves, where the supercilious State knows what’s best for you and your family, where communication policy and fiat news shout down authenticity, where rapacious, know-nothing narcissism is celebrated as leadership even as civility, expertise, and service are mocked as cuckery. Going to the funeral is at the heart of playing the meta-game – the game behind the game – of social systems like markets and elections, and it’s something we all need to understand so that we’re not played for fools.
  6. Sheep Logic: We think we are wolves, living by the logic of the pack. In truth we are sheep, living by the logic of the flock. In both markets and politics, our human intelligences are being trained to be sheep intelligences. Why? Because that’s how you transform capital markets into a political utility, which is just about the greatest gift status quo political institutions can imagine.
  7. Clever Hans: You don’t break a wild horse by crushing its spirit. You nudge it into willingly surrendering its autonomy. Because once you’re trained to welcome the saddle, you’re going to take the bit. We are Clever Hans, dutifully hanging on every word or signal from the Nudging Fed and the Nudging Street as we stomp out our investment behavior.
  8. Pecking Order: The pecking order is a social system designed to preserve economic inequality: inequality of food for chickens, inequality of wealth for humans. We are trained and told by Team Elite that the pecking order is not a real and brutal thing in the human species, but this is a lie. It is an intentional lie, formed by two powerful Narratives: trickle-down monetary policy and massive consumer debt financing.

The Three-Body Problem: What if I told you that the dominant strategies for human investing are, without exception, algorithms and derivatives? I don’t mean computer-driven investing, I mean good old-fashioned human investing … stock-picking and the like. And what if I told you that these algorithms and derivatives might all be broken today?

Rusty’s series of essays, Things that Matter (and Things that Don’t), connects to mine with his just published The Three-Body Portfolio. It’s a wonderful piece on its own (I can’t believe I didn’t think of the Soylent Green reference – Epsilon is people!) and is a great segue to his 2017 serial opus. In chronological order:

  1. With A Man Must Have a Code, Rusty begins the conversation about why we think that all investors ought to have a consistent way of approaching their major investment decisions.
  2. In I am Spartacus, Rusty writes that the passive-active debate doesn’t matter, and that the premise itself is fraudulent.
  3. In What a Good-Looking Question, Rusty writes that trying to pick stocks doesn’t matter, and is largely a waste of time for the majority of investors.
  4. In Break the Wheel, Rusty argues that fund picking doesn’t matter either, and he takes on the cyclical, mean-reverting patterns by which we evaluate fund managers.
  5. In And they Did Live by Watchfires, Rusty highlights how whatever skill we think we have in timing and trading (which is probably none) doesn’t matter anyway.
  6. In Chili P is My Signature, Rusty writes that the typical half-hearted tilts, even to legitimate factors like value and momentum, don’t matter either.
  7. In Whom Fortune Favors (Part 2 here), Rusty writes that quantity of risk matters more than anything else (and that most investors probably aren’t taking enough).
  8. In You Still Have Made a Choice, Rusty writes that maximizing the benefits of diversification matters more than the vast majority of views we may have on one market over another.
  9. In The Myth of Market In-Itself (Part 2 here), Rusty writes that investor behavior matters, and he spends a lot of electrons on the idea that returns are always a reflection of human behavior and emotion.
  10. In Wall Street’s Merry Pranks, Rusty acknowledges that costs matter, but he emphasizes that trading costs, taxes and indirect costs from bad buy/sell behaviors nearly always matter more than the far more frequently maligned advisory and fund management expenses.

But wait, there’s more!

You’ve got two more essays from Rusty:

  1. Before and After the Storm
  2. Gandalf, GZA and Granovetter

You’ve got 10 more essays from me:

  1. Harvey Weinstein and the Common Knowledge Game
  2. Mailbag! Fall 2017 Edition
  3. Mailbag! Midsummer 2017 Edition
  4. Gradually and Then Suddenly
  5. Tell My Horse
  6. Westworld
  7. The Horse in Motion
  8. Mailbag! Life in Trumpland
  9. The Evolution of Competition
  10. Fiat Money, Fiat News

Oh yeah, and you’ve got eleven 2017 podcasts here.

So there’s your 2017 Epsilon Theory map. 2018 will be even better.

The Three-Body Portfolio

Bernard: If knowledge isn’t self-knowledge, it isn’t doing much, mate. Is the universe expanding? Is it contracting? Is it standing on one leg and singing ‘When Father Painted the Parlour’? Leave me out. I can expand my universe without you.

‘She walks in beauty, like the night of cloudless climes and starry skies, and all that’s best of dark and bright meet in her aspect and her eyes.”

  Arcadia, Tom Stoppard

It is a romantic thought, that we might divorce our personal universe from the universe around us. For us investors, maybe that means to hide in a room building an elegant model to work out the true value of a thing. I mean, by itself it’s a complete waste of time…but so romantic!

To make a prairie it takes a clover and one bee,
One clover, and a bee.
And revery.
The revery alone will do,
If bees are few.
To make a prairie”, Emily Dickinson

Since I’m bogarting Ben’s title, I might as well steal his best literary reference, too. The market isn’t necessarily tied to ‘fundamentals’ any more than a prairie is to bees. Revery alone will do, and sometimes it will do for a very, very, very long time.

A true German can’t abide the French,
But he’ll gladly drink their wine.
— Faust, Johann Wolfgang von Goethe

You don’t have to be French to drink their wine, y’all. Being part of the Epsilon Theory pack doesn’t mean buying into narratives. It means understanding that in a market, if it matters to someone, it should matter to everyone. And narratives matter to a whole lot of someones.


Epsilon Theory started from a pretty simple idea. Ben observed that no financial or econometric model can ever fully explain the returns or volatility of financial markets. I don’t think he’ll be too mad if I point out that this wasn’t a particularly novel observation. After all, every statistical model in the world has an error term that basically accounts for this — epsilon.

β + α + ε

Said less vaguely, epsilon is the way in which — as people — investors respond to both financial and non-financial stimuli in various non-random ways. It is an observation that a not insignificant portion of the systematic (i.e. not diversifiable) risk and return in your portfolio is completely divorced from the risks faced by economies and businesses. It is a feature only of the markets and the people who comprise them[1].

Some regard changing perceptions, sentiment and shifting narratives as a source of short-term volatility in securities prices, and little more. Indeed, that is the implication of the old Benjamin Graham trope I disputed in The Myth of Market In-Itself — that the market is a short-run voting machine, but a long-run weighing machine. Sure, sentiment may matter in the short run, but eventually truth will out! I did a fair job, I think, of identifying my issues with that point of view, but as per usual, it was Ben that really got to the heart of the issue. In his latest note, he characterizes the occasional sharp rise in unpredictability of market outcomes — not to be mistaken for volatility — as the result of a Three-Body Problem. You do yourself a disservice if you haven’t read the piece, and probably a greater disservice if you haven’t read the Liu Cixin book of the same title recommended in it. But in short, a three-body problem refers to a system that is solvable not through elegant algorithm, but only through brute-force computation. There is no closed-form solution to predict the future locations of a set of three planetary bodies in a vacuum like, say, the Earth, the sun and the moon.

In most environments, where the purpose of markets is to efficiently and accurately price risk of various uses of capital, those markets tend to behave more or less like two-body systems. The interaction of Planet A (which we’ll call ‘fundamental data’) and Planet B (which we’ll call ‘prices’) is generally predictable. Oh sure, there’s volatility. Remember, not everyone agrees on the starting point and velocity of Planet A — at least, not since Reg FD, anyway. Information takes time to propagate. But we also know that Planet C (let’s call it ‘epsilon’) is sitting out there somewhere. Yet it’s far enough away that its gravity can’t do more than induce short- and medium term distortions in the relationship between A and B. If you knew the truth about the starting positions and velocities of Planet A and Planet B, however, you could develop a formula that would tell you within a pretty fair margin where prices would be down the line.

In this typical state of the world, being a better investor has meant getting better at uncovering the truth about Planet A so that you can predict Planet B’s future location. It’s no wonder that a generation of investors grew up learning about traditional security analysis, the only way investment management is taught in every business school in the world.

Still, everyone from the most well-respected market commentators to the staunchest Graham and Dodd-quoting undergrad recognizes the existence of markets in which Planet C — epsilon — contributes its gravity to the system. Among those periods in which our ability to make predictions on the basis of relationships between fundamental data and securities prices is especially poor, are those we know as bubbles and manias. William Bernstein characterizes these periods as those typified by the “flood of new investors who swallow plausible stories in place of doing the hard math.” He goes on to quote Templeton, admonishing investors, “The four most expensive words in the English language are ‘This time it’s different.’”

Well, guess what? Roll your eyes at the expression to your heart’s content, but I’m telling you what Ben has been telling you for years now:

This Time It’s Different.

It’s not different because people really got it right this time (in ways they missed every other time) about some new technology that’s going to Change The World! Electric cars, cryptocurrency, AI and automation, these may all be fabulous things, and they may well prove to be game-changers for productivity and returns on capital down the line, but if you think any of those things explain current valuations, you’re nuts. You’re also wrong.

It’s different because financial markets are no longer a mechanism for price discovery and the pricing of risk of capital allocation decisions.

Markets have been made into a utility. More to the point, they have been made into a political utility, a tool for ensuring wealth and stability of our political structures. The easing tools we dabbled in to stabilize prior business cycles were brought to bear instead as tools for propping up and expanding financial asset prices. Beyond the direct marginal price impact of the easing itself, central bankers tailored communications policies to create Pavlovian responses to every narrative. Our President tweets about the policy implications when the S&P 500 hits new highs, for God’s sake[2]. This isn’t a secret, y’all. The singular intent of every central banker in the world is to keep the prices of financial assets from going down, and the singular intent of every government that puts those central bankers in power is to ensure that they do so, in order to retain social stability. Sure, there’s a dual mandate. But the mandates aren’t employment and price stability. They’re (1) expanding financial asset prices and (2) effectively marketing the idea of corresponding wealth effects to the public.

Markets have also rapidly become a social utility, an inextricable part of every contract between governments and the governed. Underfunded pensions and undersized boomer 401(k) accounts mean that ownership of risky assets is not a choice driven by diversification or relative return expectations, but by the fact that it is the only asset they can buy that has any potential of meeting the returns they would need to be adequately funded. Let’s say that you are running a state pension plan that is 65% funded. Your legislature is telling you that no help is coming from the state budget. You and every member of your agency will be fired if you even suggest cutting benefits, if you even have that authority. Your consultant or internal staff just did their new mean reversion-based capital markets return projections, and higher valuations mean projected returns on everything are lower. What’s worse, your funded status assumes returns that are higher than anything on their sheet. You are being presented with a Hobson’s Choice — behind Door #1, you get fired, and behind Door #2, you lever up your stock exposure with an increased private equity allocation. This a brutal position to be in.

And sure, like most markets with bubble-like characteristics, this one has become a utility for psychic value as well. Investors buy Bitcoin on the narrative-driven belief that it is an ‘investment’ in the technology, a way to participate in shifting the economy toward privately negotiated and settled transactions. It isn’t. We’ve all seen the absurd stock charts of companies who did nothing more than add “blockchain” to their names. We’ve observed TSLA, NFLX and CRM continue to trade on earnings reports that provide zero incremental data on business direction or momentum but heroic narratives that the sell side dutifully push out to the masses looking for good stories. If you must own risky assets and those assets don’t have growth, then revery alone will do, if bees are few.

It may comfort us to say that “The market has been divorced from fundamentals for so long, but eventually it must swing back.” And it will. The point of this note, and the point of The Three Body-Problem isn’t to say that it won’t. Planet C will drift away again, and outcomes will look more like what economic and business fundamentals would predict. More like our historical analysis of what drives good, high quality investments. But too many investors are comforting themselves with the stories of the 1990s, of the Nifty Fifty, and the idea that non-fundamentally-driven markets mean return to sanity after five to ten years. But they don’t have to. And because of the utilitization of markets, because of the exit of passive-oriented investors from the price-setting margin of markets, it’s possible that they won’t for a very long time.

This Three-Body Problem isn’t going anywhere for a while.

The Three-Body Portfolio

When I began this Code series at the beginning of 2017, I kicked it off with A Man Must Have a Code, a conversation about why we think that all investors ought to have a consistent way of approaching their major investment decisions. I posited that a code ought to consist of a concise list of Things that Matter, Things that Don’t Matter and Things that Don’t Always Matter (But Do Now). And so my notes have focused on investing principles that I think of as generalized solutions. These are things that I believe are true in both Two-Body and Three-Body Markets:

  1. In I am Spartacus, I wrote that the passive-active debate doesn’t matter, and that the premise itself is fraudulent.
  2. In What a Good-Looking Question, I wrote that trying to pick stocks doesn’t matter, and is largely a waste of time for the majority of investors.
  3. In Break the Wheel, I argued that fund picking doesn’t matter either, and took on the cyclical, mean-reverting patterns by which we evaluate fund managers.
  4. And They Did Live by Watchfires highlighted how whatever skill we think we have in timing and trading (which is probably none) doesn’t matter anyway.
  5. In Chili P is My Signature, I wrote that the typical half-hearted tilts, even to legitimate factors like value and momentum, don’t matter either.
  6. In Whom Fortune Favors (Part 2 here), I wrote that quantity of risk matters more than anything else (and that most investors probably aren’t taking enough).
  7. In You Still Have Made a Choice, I wrote that maximizing the benefits of diversification matters more than the vast majority of views we may have on one market over another.
  8. In The Myth of Market In-Itself (Part 2 here), I wrote that investor behavior matters, and spent a lot of electrons on the idea that returns are always a reflection of human behavior and emotion.
  9. In Wall Street’s Merry Pranks, I acknowledged that costs matter, but emphasized that trading costs, taxes and indirect costs from bad buy/sell behaviors nearly always matter more than the far more frequently maligned advisory and fund management expenses.

In all of these, you’ve gotten a healthy dose of emphasis on getting beta — how we get exposure to financial markets — right. But you’ve seen precious little on alpha — uncorrelated sources of non-systematic, incremental return. Where we dealt with the usual ways in which investors seek out alpha, I have been critical. Maybe even derisive. Sorry, not sorry. There’s a reason:

Even in normal environments, alpha is hard.

Alpha is hard because it’s hard to measure. It’s hard to know if what we’re doing is actually something that adds value, or if we’re being fooled by randomness. We may even just be layering on some other systematic factor, or beta, that is just compensating us for taking additional risk. Every couple of years, someone rediscovers that bond managers tend to “have more persistent alpha”, and a couple of weeks later, someone rediscovers that bond managers just layer on more corporate credit risk than the benchmark. Every couple years, stock-picking strategies go back into vogue — it’s a stock-pickers’ market, they say! Fundamentals matter again! No, they don’t. You’re structurally smaller cap and loaded up on higher volatility names, and when those factors work you feel smart.

Alpha is hard because randomness is an insanely powerful force in the universe and within our industry. Any time I hear a fund manager or financial advisor say, “Look, a 10-year track record like that doesn’t just happen by accident,” every part of me wants to scream, “Yes, it bloody well does, and if there’s anything true and good about mathematics at all, it will happen by accident ALL THE TIME.” I’m not saying that Warren Buffett’s success is an accident, but I am absolutely saying that with as many investors as there are, it is improbable that history would not create Warren Buffett’s track record. We are all looking for a way to increase returns that doesn’t involve taking more risk, and once we’ve gotten all we can out of diversification, believing in alpha is our only choice.

In a three-body market where epsilon exerts its gravity, alpha is not just hard. Finding it emphasizes entirely different types of data and analysis. The whole point of recognizing an increasingly chaotic system is that our confidence in the causal relationships over time and between assets at a point in time drops significantly. Given that tools relying on temporal and cross-asset relationships are the ones we most often use to evaluate investment strategies, it puts us in a bit of a pickle! Sure, there are some exogenous strategies like high-frequency trading that are pretty effective across environments for mechanical reasons. But many of the “time-tested” strategies that work in Two-Body Markets can stop working for a very long time, and the “new” strategies we identify in our in-sample periods can start looking like hot garbage the second we drop them out-of-sample. The search for alpha in this kind of environment — even when it occasionally bears fruit — is time-consuming, expensive, and often leads to unintended risk, cost and diversification decisions that more than offset any positive that they generate. We get a stock pick right, but it is dwarfed by sector effects. We pick the right country to invest in, but get killed on currency. For many investors – for most investors — this means that trying to beat “the market” on an intra-asset class level or at on an inter-asset class level through tactical asset allocation should not be part of their playbook. For these people, I hope the Code to this point is a useful tool. Thanks for reading.

But for those who know they won’t be content with that very adequate outcome, I’ll do my best to talk about alpha strategies that I think can work in a Three-Body Market. That means identifying the likelihood of success of various analytical security and asset class selection strategies. It also means giving you my perspective on what edge (ugh, I know) would even conceivably look like in a fund manager. As we close the door on 2017, I also close my series on The Things That Matter and The Things that Don’t Matter. As we open the door on 2018, look forward to a new series on the Things that Don’t Always Matter (But Do Now).

Together, I think they’ll provide a pretty good roadmap for the Three-Body Portfolio. But I mean, even if they don’t, at worst you’ll get to read horror stories of terrible fund managers, so what have you really got to lose?

From Texas, my best wishes for a prosperous New Year to you and yours.

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/15758/

[1] I know some will pipe in that this is just part of beta. This is mathematically true in that systematic sources of risk are what we mean when we say ‘betas’, but anyone reading this who pretends not to understand that what we’re talking about is a specific component of systematic risk that tends to be understated in favor of econometric and financial variables is a filthy pedant.

[2] Actually, he tweets about the Dow hitting new highs, because of course he refers to the Dow.

The Three-Body Problem

As much as I dislike the chickens on our farm, I love my bees. Do they sting? Of course they sting. The swarm is a wild animal. But after a few painful years I’m no longer a ham-handed goofball with my hives, and a morning spent in sync with this amazing animal is never a bad morning. Not only are bees low maintenance, not only do they pay a wonderful rent, but they demonstrate a genius and an optimism — there’s just no other word for it — that makes me feel more creative and alive.

The Connecticut winters are tough, though. I do what I can to support the bees, which is mostly just building a wind break with bales of straw, making sure that the hive stays ventilated enough to prevent water vapor condensation, and preventing mice from taking up residence. That and avoiding original sins like poor hive placement or collecting too much rent. But ultimately it’s a battle between the animal and Mother Nature. It’s up to them to survive. Or not.

Honeybees don’t hibernate (bumblebees do, but hive colony bees don’t), and they can’t fly south for the winter. To survive a Northern winter, bees change the composition of the swarm by shrinking the overall population, caulking the hive, getting rid of the deadweight males (i.e., ALL of the males), and laying just enough eggs to preserve a minimal survivable population through the winter and into spring. They cluster together in the center of the hive, keeping the queen in the center, shivering their wings to create kinetic energy, occasionally sending out suicide squads to retrieve honey stores from the outer combs. They lower their metabolism by creating a cloud of carbon dioxide in the hive. Yes, a carbon dioxide cloud.

All of this preparation takes time. To survive winter, the swarm starts to change its behaviors — from brood patterns to pollen collection to comb creation — not when the weather starts getting cold, but in the middle of summer when the dog days of August are still in front of us. And not just on some random date, but on a completely predictable day.

In 2018 my bees will begin to prepare for winter on Friday, June 22nd.

Why? Because bees can measure the angle of the sun’s rays. They can remember this from one day to the next. When today’s midday sun is ever so slightly lower in the sky than yesterday’s midday sun, a bee will know it. And the entire colony will begin to change.

Bees recognize the freakin’ summer solstice with as much accuracy as any human civilization ever did.

See? Genius. But we’re just getting started.

When bees act on their awareness of the summer solstice, they are trading a derivative. And they expertly manage the basis risk of that trade.

Huh? Time out, Ben. What are you talking about?

A derivative, in the broadest sense of the word, is something that’s related to something else you care about (the “underlying”), but for whatever reason you choose to interact with the derivative-something rather than the underlying-something. For humans, you might care about the stock price of company XYZ, so that’s the underlying, but you think something momentous is going to happen to the company three months from now, so you interact with a derivative on the stock, in this case a three-month option contract. For bees, the thing they truly care about is how cold it gets, so from their perspective the temperature is the underlying and the sunlight angles are the derivative thing that they analyze and interact with. In truth, of course, it’s the tilt of the Earth’s axis and the resultant sunlight angles that cause seasonality and temperature changes, so a curmudgeonly reader might accurately say that actually, it’s the temperature that’s the derivative here, but I trust we’re all open-minded enough to take a bee’s eye view of the world for the duration of this note.

Why do bees take their behavioral cues from sunlight angles rather than temperature change directly? Because the algorithm for predicting seasonality:

IF (maximum incident angle of sunlight today is less than the maximum incident angle of sunlight yesterday)

THEN (prepare for winter)

is enormously simpler, more predictive, more timely, and less volatile than any sort of temperature time-series analysis, or at least any temperature time-series analysis available to bees and pre-weather satellite humans. The genius (and fatal flaw) of bees and humans is their ability to create complex social systems on the basis of simple algorithms like this. Modern computing systems of the Big Data sort have a very different type of genius.

Hold that thought.

But first let’s make sure we understand what basis risk means, and why it’s The Most Important Thing to understand when you’re dealing with derivatives. “Basis” is the relationship between the derivative and the underlying, and so basis risk is how bad things could get if the relationship between derivative and underlying isn’t as tight as you thought it was. For bees, basis risk takes the form of cold weather coming sooner or later than normal. Shrinking the colony like clockwork based on the summer solstice works great if the first big freeze comes in November, not so well if you get a big snow in mid-October.

The key to managing basis risk is to keep your risk antennae (literally antennae when it comes to bees) focused on how well the derivative thing is tracking with the underlying thing. You need to watch the correlation. So to manage their basis risk, bees are also sensitive to temperature (the underlying) and all of the other derivative things related to changing temperature, like flower bloom patterns or prevailing winds. Nothing will totally override the summer solstice trade (even tropical bees make some small colony adjustments based on seasonality), but bees are adaptive investors, able to accelerate their winter preparation if cold weather comes early or delay it if cold weather comes late. Efficient management of basis risk is a balancing act between sticking with the original trade and adapting your behavior to changing correlations (you don’t want to mistake an Indian Summer for spring!), but that’s the beauty of evolution — billions of bee colonies over millions of years have lived and died and reproduced to naturally select the combination of hard-wired nervous system algorithms that allows honeybee species to thrive across a wide range of ecosystems and a wide range of seasonal weather variations.

But it’s only a range. Bees can’t live in as wide a range of ecosystems and weather variations as, say, ants. I doubt there’s a bee colony on Earth that can survive six months straight of sub-50 degree weather. If you’re a bee colony and you’ve moved that far north and that’s the magnitude of your downside basis risk, it really doesn’t matter how amazing you are in your solar declination calculations … you’re not going to make it. Maybe you get lucky for a couple of years, but if it’s possible that you could have four or five months of harshly cold weather, then sooner or later that severe basis risk catches up with you. This is a basis risk that you can’t insure against, that you can’t hedge against with extra preparation or precaution. It’s an unmanageable basis risk. For most of North America, though, even pretty far up into Canada, cold weather is a manageable basis risk, particularly if you’ve got a beekeeper able to lend a helping hand. Sometimes the bees will get a bad roll of the weather dice and you’ll lose a hive to basis risk, but it doesn’t threaten the species.

Species risk comes into play when you get a major climactic event that lasts for a long time in terms of a colony’s lifespan but not long at all in terms of evolution, genetic mutation, and natural selection. Like, say, what if spring no longer followed winter? What if it snowed in August and flowers bloomed in January? What if winter disappeared for a decade? What if it lasted that long? What if your weather basis risk was unknowable, as in Game of Thrones? Even a short Westerosi winter of a couple of years would kill every bee colony on the continent, which is why I don’t think I’ve ever seen a bee hive on Game of Thrones. [Hmm … I’ve just been informed by Grand Maester Guinn that “one of the Baratheon vassal houses of the Reach is House Beesbury, with a family seat of Honeyholt and a family motto of Beware Our Sting.” Sigh. You see what I have to put up with? Okay, we’ll stipulate that Dornish latitudes are safe. But The North is no place for bees when winter comes!]

This is basis uncertainty, where you’re not even sure that any basis exists at all, as opposed to mere basis risk. Basis uncertainty is an unknowable basis risk, which is much more damaging to species development than the occasional bout of severe basis risk.

[Long parenthetical: understanding the distinction between risk and uncertainty is crucial in every aspect of life. A risky decision is when you have a pretty good sense of the odds and the pay-offs. It lends itself to statistical analysis and econometrics, particularly if it’s a decision you will have the opportunity to make multiple times. An uncertain decision is when you don’t have a good sense of odds and pay-offs. Here, statistical analysis may very well kill you, particularly if you’re not going to get many cracks at the game, or if you don’t know how many times you’ll get to make a choice. You need game theory to make sense of decisions made under uncertainty.]

Basis uncertainty is the core problem facing every investor today.

It’s not just that we endure large basis risks here in the Hollow Market, unmanageable for many. It’s not just that all of our old signposts and moorings for navigating markets aren’t working very well. It’s not just difficult to identify predictive/derivative patterns in today’s markets. There is a non-trivial chance that structural changes in our social worlds of politics and markets have made it impossible to identify predictive/derivative patterns. THIS is basis uncertainty, and it’s as problematic for humans facing markets that don’t make sense as it is for bees facing weather patterns that don’t make sense.

Well, that’s just crazy talk, Ben. What do you mean that it might be impossible to identify predictive/derivative patterns? What do you mean that basis might not exist at all? Of course there’s a pattern to markets and everything else. Of course spring follows winter.

Nope. This is the Three-Body Problem.

Or rather, the Three-Body Problem is a famous example of a system which has no derivative pattern with any predictive power, no applicable algorithm that a human (or a bee) could discover to adapt successfully and turn basis uncertainty into basis risk. In the lingo, there is no “general closed-form solution” to the Three-Body Problem. (It’s also the title of the best science fiction book I’ve read in the past 20 years, by Cixin Liu. Truly a masterpiece. Life and perspective-changing, in fact, both in its depiction of China and its depiction of the game theory of civilization.)

What is the “problem”? Imagine three massive objects in space … stars, planets, something like that. They’re in the same system, meaning that they can’t entirely escape each other’s gravitational pull. You know the position, mass, speed, and direction of travel for each of the objects. You know how gravity works, so you know precisely how each object is acting on the other two objects. Now predict for me, using a formula, where the objects will be at some point in the future.

Answer: you can’t. In 1887, Henri Poincaré proved that the motion of the three objects, with the exception of a few special starting cases, is non-repeating. This is a chaotic system, meaning that the historical pattern of object positions has ZERO predictive power in figuring out where these objects will be in the future. There is no algorithm that a human can possibly discover to solve this problem. It does not exist.

To visualize the Three-Body Problem, here’s a simulation of the orbits of green, blue, and red objects with random starting conditions, each exerting a gravitational pull on the others. What Poincaré proved is that there is no formula where you can plug in the initial information and get the right answer for where any of the objects will be at any future point in time. No human can predict the future of this system.

But a computer can. Not by using an algorithm, which is how biological brains — human and bee alike — evolved to make sense of the world, but by brute force calculations. Remember, you know everything about these three objects … none of the physics here is a mystery. If you can do the calculation quickly enough, you can compute where all three objects will be one second from now. And one second from then. And one second from then. And so on and so on. With enough processing power (and this can require a LOT of processing power) you can calculate where the three objects will be 100 years from now, even though it is impossible to solve for this outcome.

It’s a hard concept to wrap your head around, this difference between calculating the future and predicting the future, but it will change the way you see the world. And your place in it.  

Now here’s an observation that I can’t emphasize strongly enough, although I’ll try:

THIS IS NOT HOW WE USE COMPUTERS IN OUR INVESTING STRATEGIES TODAY

The way that computers can calculate an answer to the Three-Body Problem is straightforward — they can be programmed with the physics rules for how one object influences another object, so they can simulate where each object will go next. There is ZERO examination of where the objects have been in the past. This is entirely forward looking.

The way that computers can NOT calculate an answer to the Three-Body Problem is by examining the historical data of where the objects have been. In a chaotic system, it doesn’t matter how hard or how fast or how deeply you look at the historical data. There is NO predictive pattern, NO secret algorithm hiding in the data. And yet this is exactly what we all have our computers doing … examining historical data to look for patterns that will give us the magic algorithm for predicting what’s next! The only thing that the past gives you in a chaotic system is inertia, which can look like a pattern or an algorithm for some period of time, depending on how all the objects are aligned. But it’s a mirage. It will not last. Examining the past of a chaotic system can give you lots of little answers, like sparks off a bonfire, none lasting more than a few seconds. And certainly if you’re efficient with your inertia-identifying spark-capturing effort, you can make some money using computers this way. But this examination of the past through naïve induction will never give you The Answer. Because The Answer does not exist in the past. The Answer — which is another word for algorithm, which is another word for “general closed-end solution” — doesn’t exist at all in a chaotic Three-Body System.

But we can approximate The Answer. We can calculate the future in small computational chunks even if we can’t predict the future in one big algorithmic swoop, but only if we can program the computer with the “physics” of how “gravity” works in social systems like markets. What’s our financial world equivalent of a theory of gravity? I think it’s a theory of narrative. This, to me, is a more interesting research program than identifying small inertias or capturing brief sparks. But it’s not where our computing resources are being allocated, because there’s no money in it. Yet.

Exploring a theory of narrative, what I’ve called the Narrative Machine, is basic research. Like all basic research, it’s not immediately remunerative and thus is difficult to fund. But that’s not the biggest obstacle. No, the biggest obstacle to basic research in computational finance is that humans are hard-wired to look for algorithms and have a really hard time imagining that it’s even possible to pursue a non-anthropomorphic (how about that for a $10 word) research design that doesn’t pore through historical data looking for predictive algorithms at every turn. We can’t help ourselves!

What if I told you that algorithms and derivatives are as much at the heart of how humans prepare for their financial future as they are for bees preparing for their seasonal future? What if I told you that the dominant strategies for human discretionary investing are, without exception, algorithms and derivatives? And what if I told you that these algorithms and derivatives were perhaps “evolved” under a “benign” configuration of the Three-Body Problem that not only might never repeat, but in fact is certain to never repeat because it is a chaotic system?

I’ll give you two examples of influential investment algorithms/derivatives. There are many more.

GOOD COMPANIES => GOOD STOCKS

GOOD COUNTRIES => GOOD GOVERNMENT BONDS

These are the central tenets of stock-picking and sovereign bond-picking, respectively. In both cases, goodness (like beauty) is in the eye of the beholder, so I’m not saying that there is some single standard for what makes a “good” company or what makes a “good” set of macroeconomic policies. What I’m saying is that everyone reading this note (including me!) believes that there is a direct relationship between the quality of a company or an economy (however you define quality) and the future price of whatever stocks or bonds are connected to that company or economy. What I’m saying is that everyone reading this note believes that tracking the measurable quality of a company or an economy (the derivative) according to some standardized and repeatable process (the algorithm) will, over time, have a predictive correlation with the future price of the related stock and bond securities (the underlying).

What stocks do we want to own? Why, the stocks of high quality companies, of course … companies with stellar management teams, fortress balance sheets, and wonderful products or services that everyone wants to buy. Ditto for government bonds and currencies and broad market indices and the like. Maybe it will take some time for this faith in Quality to pay off, but we all believe that it WILL pay off. It’s only natural, right? As natural as spring following winter. As natural as flowers blooming in May and snow falling in December. Maybe the flowers will bloom a few weeks late and maybe the snows will fall a few weeks early, but that’s just basis risk, and we can manage for that.

But what if spring doesn’t follow winter anymore?

Look, I’m not asking us to abandon our faith in Quality. One of the key corrolaries of the Three-Body Problem is that we don’t have to reject our belief that Objects 1 and 2 exist. We don’t have to deny our faith that the Quality-of-Companies is an actual thing and that it has a big gravitational pull on the price of stocks. We don’t have to deny our faith that the Quality-of-Governments is an actual thing and that it has a big gravitational pull on the price of government bonds.

What we have to accept is that there is an Object 3 that has moved into a position such that its gravity absolutely swamps the impact of Objects 1 and 2. This Object 3, of course, is extraordinary monetary policy, specifically the purchase of $20 TRILLION worth of financial assets by the Big 4 central banks — the Fed, the ECB, the BOJ, and the PBOC.

$20 trillion is a lot of mass. $20 trillion is a lot of gravity.

Here’s the impact of all that gravity on the Quality-of-Companies derivative investment strategy.

The green line below is the S&P 500 index. The white line below is a Quality Index sponsored by Deutsche Bank. They look at 1,000 global large cap companies and evaluate them for return on equity, return on invested capital, and accounting accruals … quantifiable proxies for the most common ways that investors think about quality. Because the goal is to isolate the Quality factor, the index is long in equal amounts the top 20% of measured  companies and short the bottom 20% (so market neutral), and has equal amounts invested long and short in the component sectors of the market (so sector neutral). The chart begins on March 9, 2009, when the Fed launched its first QE program.

Over the past eight and a half years, Quality has been absolutely useless as an investment derivative. You’ve made a grand total of not quite 3% on your investment, while the S&P 500 is up almost 300%.

This is not a typo.

Have the Quality stocks in your portfolio gone up over the past eight and a half years? Sure, but it’s not because of the Quality-ness of the companies. It’s because ALL stocks have gone up ever since Object 3, the balance sheets of central banks, started exerting its massive gravity on everything BUT Quality. That’s not an accident, by the way. Central banks don’t care about rewarding “good” companies. In fact, if they care about anything on this dimension, they care about keeping “bad” companies from going under.

This is what it looks like when spring does not follow winter.

And here’s the impact of all that gravity on the Quality-of-Countries derivative investment strategy.

The gold line below is the spread (difference) between Portugal’s 10-year bond yield and the U.S. 10-year bond yield, and the blue line is the spread between Italy’s 10-year note yield and the U.S. equivalent. In “normal” times, a country with a weaker set of macroeconomic characteristics (high levels of national debt, say, or maybe low productivity) will have to offer investors a higher rate of interest to borrow their money than a country with a stronger set of macroeconomic characteristics. So in the summer of 2012, when Portugal and Italy were both looking like deadbeat countries, they had to pay investors a much higher rate of interest than the U.S. did to attract the investment … about 9% more (this is per year, mind you) for Portugal and 4% more for Italy. Those are enormous spreads in the world of sovereign debt!

This chart begins in the summer of 2012, when the ECB announced its intentions to prop up the European sovereign debt market directly. Since that announcement — even though both Portugal and Italy have higher debt-to-GDP ratios today than in 2012 — the spread versus U.S. interest rates has done nothing but decline. Driven by the commitment of the ECB to “do whatever it takes” and to be not only a last-resort buyer but also a first-in-line buyer of Portuguese and Italian debt, it now costs LESS for these countries to borrow money for 10 years than the U.S.

This is nuts. It’s an understandable nuts when you consider that the German 10-year bond yield is currently about 30 basis points, and was actually negative (meaning that you had to pay the German government for the privilege of lending them money for the next 10 years) for about six months in 2016. Meaning that at least with Italian and Portuguese debt you’re being paid something (a little less than 2% per year). It’s an understandable nuts when you consider that the Swiss 10-year bond still sports a negative interest rate and has been negative for the past two and a half years. There’s about $10 trillion worth of negative yielding sovereign bonds out there today, something that is IMPOSSIBLE under a [good country => good bond] derivative algorithm. No country is that good! But it’s entirely possible under the immense gravitational force of massive central bank asset purchases.

Here’s the kicker. Below is the spread between Greek 10-year sovereign bonds and U.S. 10-year notes. In 2012 you were paid 24% more to lend money to Greece. Per year! Today you are paid less than 2% more to lend money to Greece rather than the United States. For ten years. To Greece.

Again, I’m not saying that the Quality derivative doesn’t exist as a real thing or that it isn’t an important factor in the history of successful stock-picking or bond-picking. What I’m saying is that the Quality derivative hasn’t mattered for eight and a half years with stocks and five years with sovereign debt. What I’m saying is that it might not matter for another eighty years. Or it might matter again in eight months. A Three-Body System is a chaotic system. As the boilerplate says, past performance is not a guarantee of future results. In fact, the only thing I can promise you is that past performance will NEVER give you a predictive algorithm for future results in a chaotic system.

This is basis uncertainty. This is the biggest concern that every investor should have, that the signals (derivatives) and processes (algorithms) that we ALL use to make sense of the investing world are no longer connected to security prices.

… Okay, Ben, you’ve exhausted me. It’s a weird and strange way of looking at the world, but let’s go with it for a minute. What’s the pay-off here? What do we DO in a chaotic system? What does that even mean, to say that we are investors in a chaotic system?

Four suggestions.

First, I think we should adopt a philosophy of what I’ve called profound agnosticism when it comes to investing, where we don’t just embrace the notion that no one has a crystal ball in this system, but we actually get kinda annoyed with those who insist they do. I think that risk balancing strategies make a ton of sense in a chaotic system, so that we think first about budgeting our risk agnostically across geographies and asset classes and sectors, and secondarily think about budgeting our dollars.

Second, and relatedly, I think we should adopt a classic game theory strategy for dealing with uncertain systems — minimax regret. The idea is simple, but the implications profound: instead of seeking to maximize returns, we seek to minimize our maximum regret. Keep in mind that our maximum regret may not be ruinous loss! I know plenty of people whose maximum regret is not keeping up with the Joneses. In fact, from a business model perspective, that’s more common than not. Or if you’ve bought into Bitcoin north of $15,000 per coin, I think you know what I’m talking about, too. The point being that we need to be painfully honest with ourselves about our sources of regret and target our investments accordingly. If we can be this honest with ourselves, it’s a VERY powerful strategy.

Third, I think we should reconsider our approach to computer-directed investment strategies. Using computers in an anthropomorphic way, where we treat them like a smarter, faster human, set loose in a vast field of historical data to search for patterns and algorithms … it’s a snipe hunt. Or at least I think we’ve squeezed just about all the juice out of this inductive orange that we’re likely to get. With the massive processing power at our fingertips today, not to mention the orders-of-magnitude-greater processing power that quantum computing will bring to bear in the future, there’s much bigger game afoot with computational approaches that take a more deductive, forward-looking strategy.

Fourth, and perhaps most importantly, I think we need to accept that we’re never going to fully understand the reality of a chaotic system, but that it’s never been more important to try. The brains of both bees and humans are hard-wired for algorithms. Both species see patterns even when patterns don’t exist, and both species tend to do poorly in environments where derivative signals are plagued by basis uncertainty rather than mere basis risk. Every bee in the world will follow its hard-wired algorithms even unto death. And most humans will, too. But humans have the capacity to think beyond their biological and cultural programming … if they work at it.

Where do we lose good people? When they convince themselves that they’ve found The Answer — either in the form of a charismatic person or, more dangerously still, a charismatic idea — in a chaotic system where no Answer exists. A chaotic system like markets, yes, but also a chaotic system like politics.

The Answer is, by nature, totalitarian. Why? Because it’s a general closed-form solution. That’s the technical definition of The Answer, and that’s the practical definition of totalitarian thought. We’re hard-wired to want the all-encompassing algorithm, which is why it’s so difficult to resist. But if we care about liberty. If we care about justice. If we care about liberty and justice for all … we have to resist The Answer.

Because we’ve lost enough good people.

As wise as serpents, as harmless as doves …

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/15775/

Wall Street’s Merry Pranks: Things that Matter #4

Lisa Simpson: Get out! Get out!
Bart Simpson: OK! But on my way, I’m going to be doing this. And if you get hit, it’s your own fault!
Lisa: OK, then I’m gonna start kicking air like this. And if any part of you should fill that air, it’s your own fault.
Marge Simpson: Hmm. I better go check that out. Now Homer…don’t. You. Eat. This. Pie!
Homer Simpson: Okaaaay. Alright pie, I’m just going to do this. And if you get eaten, it’s your own fault!
 The Simpsons, Season 6, Episode 8 “Lisa on Ice”

So much of the nudging from our smiley-faced authoritarian overlords on Wall Street sounds like it is coming from a mid-windmill Bart Simpson. I’m going to give you low-cost tools. I’m going to create ways to encourage you to trade them (frequently) at close to no cost (that you can see, anyway). And if you happen to trade so much that you create short-term gains everywhere, if you end up with an underdiversified portfolio that blows up, if you end up selling at the bottom and holding cash through half of the recovery…well… it’s your own fault.

Verbal: Who is Keyzer Söze?

He is supposed to be Turkish. Some say his father was German. Nobody believed he was real. Nobody ever saw him or knew anybody that ever worked directly for him, but to hear Kobayashi tell it, anybody could have worked for Soze. You never knew. That was his power. The greatest trick the Devil ever pulled was convincing the world he didn’t exist. One story the guys told me — the story I believe — was from his days in Turkey. There was a petty gang of Hungarians that wanted their own mob. They realized that to be in power you didn’t need guns or money or even numbers. You just needed the will to do what the other guy wouldn’t. After a while they come to power.

 The Usual Suspects (1995)

I originally intended to include nothing but Spacey quotes that feel much creepier and weirder than they did six months ago. I decided against it but had to keep the classic Söze line.

Why? Because the greatest trick any nudger, any libertarian paternalist ever pulls is always to convince the world they don’t exist. Their new business model, their new sales pitch, is all about the customer. They are just conveying it to you, and it’s really your decision whether you’re going to take advantage of it. But don’t mistake value for virtue — the magic of free markets is that they empower value-seeking behaviors to generate societally virtuous outcomes. The low-cost investing revolution and its sister — the movement to empower individual investors — has been a generational boon for investors. They created immense value. But never forget that this is a business and that these products are created by profit maximizers, not virtue maximizers. When we see branding and advertising that convey psychic or moral value from do-it-yourself and low-cost investing, we must know that we’re being nudged toward other less virtuous, riskier behaviors. 

Well, when all the people arrived in church, Eulenspiegel mounted the pulpit, said something from the Old Testament, tossed in the New as well, with Noah’s Ark and the Golden Bucket, in which the bread of Heaven Lay — and said, moreover, that all this stuff was the greatest holiness. Then at the same time he began to speak of the head of Saint Brendan, who had been a holy man. He had Brendan’s head right there — and it had been commanded of him that he use it to collect for the building of a new church, and to do so with purest goodness, never (on pain of death) accepting any offerings from any woman who might be an adulteress.

“And whoever here may be such women, let them stand back. For if they offer me something — those who are guilty of adultery — I won’t take it, and they will be revealed in shame unto me! So — know yourselves!”

Till Eulenspiegel: His Adventures, No. 31, Paul Oppenheimer (Editor/Translator)

The Söze-style penchant for creating havoc and disappearing into the background — and avoiding responsibility — is hardly new. It’s straight out of the playbook for the Trickster that exists within every major mythology. Till Eulenspiegel, a late middle ages version, is well known to Germans but almost completely unknown in America, which is a shame. This story is the best example I know of a trickster’s need to adapt his schemes to be more palatable when the world gets wise to him. Sound like any industries you know?


Some people always knew they wanted to be investors. They read Barron’s as a teenager and had their parents create an account for them to buy and sell stocks when they were 13.

Not me.

I wanted to be a composer. I would have settled for being an operatic tenor. If I couldn’t do that, I’d be willing to try my hand at being a musician in an orchestra. Short of that, I’d settle for being a drummer in a rock band. I supposed, if I couldn’t do any of those things, finance might be interesting enough. When I started the process of looking at universities, I considered pursuing each of these five things. I ultimately concluded that, if I were good enough at any of the first four, it wouldn’t matter if I had a degree saying that I was. So I studied finance. It’s a good thing, too, because it turns out I wasn’t nearly good enough at any of the others. I auditioned with some regional orchestras and opera companies, and got enough alternate work to pay for food and rent in college. It never would have gone any further than that.

Now, orchestral auditions are especially funny. These days, you’re called into the room and you sit in front of a screen. You are asked to play excerpts from the standard repertoire. I was a hornist. The horn is among the most versatile instruments, and so the point of the audition is to demonstrate your ability to perform all the things the horn is called upon to do. You need to display technical prowess, and so they ask you to play the famous horn call from Richard Wagner’s “Siegfried”. You need to display facility throughout the range of the instrument, so you are called to play the opening from “Ein Heldenleben”, by Richard Strauss. You must have a capacity for lyricism, for which you are asked to play the solo theme from the Poco Allegretto movement of Brahms’ Third Symphony, which has been shamelessly stolen by everyone from Sinatra to a weird and creepy Santana-Dave Matthews duo. Then they ask you to show you can do all of those things at once by playing the corno obbligato solo from Mahler’s Symphony No. 5.

But there’s one piece that shows up every time, even though it doesn’t get played by orchestras too terribly often: “Till Eulenspiegel’s Merry Pranks”. It’s another Strauss tone poem, and it is the Tower of Babel to every richly scored Merrie Melodies and Looney Tunes cartoon. Seriously, listen to the whole piece with your eyes closed, and your mind will fill in just where Bugs Bunny tricks Elmer Fudd into shooting his shotgun into a rabbit hole that sends it back through a funnel into Fudd’s face. The piece itself is a musical imagining of Germany’s version of the classic trickster character. Of course, it’s Germany, so the pranks are pretty dark, and most are scatological. As with many such stories, however, they also effectively highlight some of the absurdities of language and human behavior.

My favorite Till Eulenspiegel tale is the 31st of those that may have been assembled by Hermann Bote in the late 15th century. Paul Oppenheimer at CCNY did a lovely and sadly underbought translation, if you’re looking for an off-the-beaten-path and entirely inappropriate bedtime storybook. In this story, Eulenspiegel has become a bit too well-known to play his usual characters. A sort of Sacha Baron Cohen of the German late middle ages, if you will. He needs to find a new way to make money, and so he decides to pass himself off as a dealer in religious relics. He takes on a priest’s cassock and goes from town to town in Pomerania with a certain scam in mind. After procuring a skull from a graveyard, he inlays it with silver and asks the local priests — who are, naturally, universally corrupt and drunk — to allow him to raise money to build a new church in honor of the saint whose skull he carried. Which is, of course, just the remains of some dead blacksmith, but no matter. There is, however, a catch: our heroic church-building protector of holy relics is sworn never to accept donations from any woman who has ever committed adultery, and he will know if they are lying.

So who comes up to kiss the skull and drop some money in the pan?

Every woman in the church, of course. As soon as the first pious busybody makes her way up to demonstrate her faithfulness and purity (and to wait for the fireworks from her more libidinous sisters), all the other faithful wives don’t really have a choice. If she is going to go up there, then it’s not like they can avoid giving to the cause and proclaiming their virtue. Now, the guilty ones, their decision is a bit more complicated. Their bet is either that Eulenspiegel is a fraud or that he’s not. The alternative, not going up, results in the same outcome for them: being outed as an adulteress. So, for them, too, the best strategy is to kiss the skull and drop a coin or two in the basket. They were doomed to this roll of the dice as soon as the first woman walked to the front.

With every adulteress who comes to this conclusion, it’s easier for all the rest, because it’s more likely they’ve seen someone they know or suspect to be guilty come away blameless. At a certain point, despite Eulenspiegel’s bold words celebrating the holiness of their parish, most people have gotten wise. But who cares? We like the eggs. It’s a useful exercise for everyone. Eulenspiegel and the priest get paid. The pious confirm their piety. The guilty get a bit of public absolution, and even more license to continue their sin.

OK, great, so what does this have to do with finance, markets and politics? Well, “Till Eulenspiegel’s” is a story of those who would deceive us by exploiting our need to appear virtuous and good. It’s a story of the good among us proclaiming virtue and forcing the hands of others to proclaim it as well (whether they are virtuous or not). And when the non-virtuous join in the proclamations, it is a story of how they lean on their newly-found virtue to get themselves into some real trouble. Of course, by that time, Till Eulenspiegel has already gone full Keyzer Söze. He never even existed.

What virtue are we called upon to signal in finance, then? Well, is there any investing behavior more widely pursued, more universally lauded than low-cost, passive investing? Is there any trait more prized than empowerment of the individual investor to use those tools to take his investments into his own hands? When the industry comes to us, silver skull in hand and asking us to pledge ourselves to these virtues, most of us do so willingly. After all, we recognize that stock-picking is usually a waste of time. We recognize that spending a bunch of time fussing about which fund manager to pick is usually a waste of time.  And we don’t like to pay fees that don’t buy us anything.

This pound-the-table religion about active vs. passive management is all well and good for those of us in the business, who mostly live and breathe the Things that Matter. But for a universe of individual investors (and rather distressingly, probably some institutional investors, too), the message of empowerment is anything but. It’s a nudge into terrible portfolios, terrible costs and terrible outcomes. But hey, at least they got religion and implemented those terrible, far-too-actively-traded portfolios with ETFs!

I recognize that this is an odd way to start a note that’s going to be about the importance of costs in investing. And that really is what this note is about. It’s an especially odd way to start a note that’s going to characterize those costs as one of the big Things that Matter in investing. But while we walk through just how indispensable low-cost investment tools must be to any modern portfolio, what I really want to emphasize is that the order of this Code was not an accident. By and large, the decisions you make about risk, diversification and behavior are all going to impact your portfolio more than the expenses you are paying on funds or to your financial advisor.

Perhaps more controversially, I also want to observe that even among costs, those direct expenses will often fall short of other costs that get short shrift in most solutions offered: especially taxes, transaction and market impact costs, and the indirect costs imposed by buy/sell behaviors.

More to the point, I fear the empowerment of low-cost investing tools is making adulteresses of many of us.  Given license by a perception that buying a bunch of index funds makes us passive (it doesn’t), we actively trade those positions in ways that impose far more costs than we ever would have borne directly. We build portfolios that are excessively risky (or too defensive), underdiversified or dependent on single factors that produce long-term risk and return impacts that dwarf the costs of advice on portfolio construction. We trade portfolios for no cost without recognizing the terrible execution we are getting. We fire the only people holding us accountable for our behavior as investors — our financial advisors — to chart out our own course.

And the Pranksters and Priests of Wall Street love every minute of it.

The Most Important Development in Finance Since 1952

So now that I’m done negging on low-cost investing, let me give you a slightly more positive take: the availability of low-cost indexed vehicles to access the world’s financial markets is the single most important development in finance since at least the development of the 401(k) in 1978, and probably since Markowitz in 1952. It’s difficult to really measure the impact that indexing has had, because it has taken a variety of forms. Most people think of the direct impact of funds flowing from expensive actively managed mutual funds to indexed mutual funds and ETFs with a lower cost. This alone has had a multi-hundred-billion-dollar impact, retaining wealth in the hands of individuals that would otherwise have gone to fill the coffers of fund management companies. There has also been a direct shift from ownership of individual securities to index funds, which has had the effect of reducing commissions paid to brokers by an amount that is probably somewhat less. I haven’t seen a detailed study on the matter, but my back-of-the-envelope math says investors have accumulated something on the lower end of hundreds of billions here as well.

Indirect benefits have been significant, too. Because of prevalence of low-cost index fund options, active funds and other strategies have been under pressure to lower their costs in response. This kind of thing is hard to quantify, but as the guy running a fund management business, trust me… it’s a lot. And while all this was going on in retail land, we’ve observed financial futures contracts becoming more common, more liquid and among the most cost-effective means of accessing global financial markets for institutions. We have also seen hedge fund fees drop steadily, incentive fees shift toward a recognition of underlying market betas, and private equity managers begin to move away from fees on uncalled commitments. In a 2016 article, Bloomberg estimated the cumulative impact for asset owners at a cool trillion, give or take. I think this number is probably a bit light once the indirect impact is considered fully.

I argued in “You Still Have Made a Choice” that investors should think constantly about opportunity costs and the implicit bets they make in their portfolios. There, my focus was on the implicit bets we make on one asset class or investment to outperform another when we under diversify. One could just as easily apply that kind of thinking to the explicit expenses we pay. And they set a very high bar. If you think that you have a good chance of selecting a manager in a major asset class that is likely to consistently outperform the 70-80bps per year you typically save these days by selecting a comparable passive option, you are suffering from hubris, delusion or both.

The “Other” Direct Costs of Investing

OK, so the amount of ink spilled on saving the 70-80bps previously paid to financial advisors and fund managers is justifiably voluminous. But here’s where Eulenspiegel takes us for a ride — and where those of us jumping up to kiss the skull act as their accomplices: In celebrating this victory, we seem to assume that everyone else is using their newfound freedom from a financial advisor and an infinite array of index funds the same way that we are. We assume that they are long-term, diversified, generally buy-and-hold investors in command of their behaviors that are reveling in the synergy of that approach with an arsenal of low-cost solutions.

It is a pleasant fiction, isn’t it?

In a prior piece about the Things that Don’t Matter — “And They Did Live by Watchfires” — I referenced some of the data showing just how fictional that is, at least as it pertains to ETFs. Most of the data on average holding periods you can find pretty easily, something I encourage everyone to do. The data and reports you’ll find split the world into two pieces. On the one hand, you’ll find a lot of studies showing that average holding periods for ETFs are somewhat less than for other vehicles — usually around 1 ½ to 2 years — but still on the longish side. On the other hand, mean holding periods of the instruments themselves end up being shorter. Much shorter. In some cases, these periods can be measured in weeks. So what gives?

What gives is that you’ve got a very bipolar universe of users. Most of the world — especially people with financial advisors — see low-cost index strategies, whether they come in mutual funds, ETFs or futures contracts, as tools to develop an efficient portfolio. All hail us, the faithful wives. But you’ve also got a smaller universe of (mostly individual) investors who have rallied around the message of low-cost investing to execute strategies that are anything but. Witness the march of the adulteress army to the altar. How do I know these people exist, other than the fact that the data show that they exist?

Because I meet them every day. Because financial advisors tell me about them and ask me what to do about them every day. The amount of bitcoin day-trading, TVIX-flipping, SPY-to-QQQ-to-IWD swapping that goes on in some individual investor accounts is shocking. And to a one, these investors are painfully cost-conscious. They wouldn’t dream of paying a management fee to a fund manager. And paying a financial advisor? Forget about it!

And yet.

The Petajisto paper referenced in “Watchfires” covers trading costs reasonably well, although increases in size and volume of the market have almost certainly narrowed some of the spreads since it was originally published. Still, trading costs of any actively traded strategy have the potential to be as large as the average difference between a passive and active fund. All investors should be thinking about trading costs as a Thing that Matters every bit as much as direct advisory costs, and based on my conversations with advisors and individual investors, I don’t think most investors give it much of a thought at all.

Separately, and potentially far more importantly, the kind of activity implied by the increased turnover of this special class of investors in low-cost vehicles can have a massive impact on taxes. In the chart below, I show the equivalent advisory fee you would pay in order to equal the impact of different levels of annual short-term taxable turnover.

Source: Salient 2017. For illustrative purposes only.

Let’s unpack that. Assume, for example, that you had a 20-year horizon. Now assume that every year you turned over 40% of your portfolio, and that you did so with positions that produced short-term gains. This is fairly typical of the activity I see even from goody two-shoes virtue-signaling investors and advisors like me, whose average holding periods are in the 18-to-24-month range. Even in this case, the impact from taxes is the same as paying a 75bp advisory fee to a fund manager or financial advisor. Now imagine (or just look above to see) what a more actively traded approach is going to look like. It’s not pretty.

Now, I’ve made a lot of fuss about how some misguided investors are misusing index funds and ETFs, but this isn’t really about that. Frankly, this is a point that needs to be heard by investors in any commingled vehicle, mutual funds and hedge funds perhaps more than any, so I want to say this as clearly as I can: if you are a taxable investor, taxes matter more than fees, and you’re not paying enough attention to them.

No, Virginia, You’re Not a Passive Investor

The “vehicle-centric” view of investing guides investors toward do-it-yourself solutions where the only important consideration is whether you’re paying an explicit fee. The resultant behaviors for a portion of those investors lead to disproportionate tax impacts, and they lead to transaction costs. There’s a reason why I’ve written that Investor Behavior Matters. But those behaviors matter most when they impact the two biggest decisions that investors must make: how much risk to take and where to take it (diversification).  So, unsurprisingly, I do want to take another opportunity to remind everyone that they aren’t passive investors just because they bought a bunch of index funds. In “I am Spartacus”, I put this in context of a global portfolio of financial assets. Here, I want to take a different tack.

Let’s assume that the world of index funds (here, I use ETFs, but could just as easily be mutual funds) consisted of the 100 largest such vehicles as they exist today. Now consider an exercise in which we decided we wanted to buy 10 of those funds at random, assigning them 10 different portfolio weights (also at random). If we did this random selection, say, 100,000 times, how often would we have gotten different risk and return outcomes[1]? How much would they really differ from one another? Basically, what I’m trying to answer is, “If I had absolutely no insight into asset allocation, but was being told I needed to keep costs down and do the investing myself, what is the range and likelihood of different portfolios I could end up with using those instruments.” The table below shows the answer.

Source: Salient 2017. For illustrative purposes only.

First, how do we interpret this? In short, the difference between good and bad asset allocation decisions is big. It’s bigger than taxes. It’s bigger than transaction costs. It’s bigger than advisory fees. And if you are assuming that you don’t really have a lot of ex ante insights, that’s kind of a big deal.

A nice portion of the portfolios cluster around a 6-8% return with 12-14% volatility, but they’re only about 17% of the total. More than 43% of the portfolios have returns that differ materially (by 2-4%). And if you’re comparing the best 20% to the worst 20% of portfolios we could have selected, you’re talking as much as a 6% annualized difference in returns, with correspondingly large differences in the amount of risk you were taking to achieve it. And this is just including the Top 100 ETFs, which are largely confined to large, liquid, broad market instruments, and not the more esoteric options that begin to fill up the portfolios of many practitioners.

To many readers this is certainly intuitive, and so I don’t pretend I’m making a novel point here by saying that asset allocation matters. Other readers might criticize this by saying, “Surely we should expect more from folks than just a random allocation to funds.” This is the point in the conversation where I would stare at you intently, but quietly, until you amended your statement to something less stupid so that we could continue our erstwhile cordial discussion.

More novel, however, is the observation that — in rough numbers — a better than random approach to asset allocation can matter to the tune of 2-6x the impact of a typical active fund’s fees.

In Defense of the Financial Advisor

So yes, Costs Matter. And if you asked me how much each of these direct and indirect costs matter, my very generalized, not-at-all-personalized for you list would look something like this:

  1. Indirect Behavioral Costs on Asset Allocation Decisions
  2. Taxes
  3. Advisory Fees (Tie) / Transaction Costs (Tie)

One of the responses Ben and I invariably get from readers is, “Include more actionable ideas!”, so I’m ready for the chorus of, “OK, I get it. What do I do?” Here’s what you do:

Hire and pay a financial advisor.

Before anyone jumps in, I’m not telling you to pay a bunch of high fees for actively managed strategies in markets where they don’t have a prayer of producing sustained outperformance after those fees. God knows homo marketus can find just as many perfectly good ways to create taxable gains and transaction costs in actively managed solutions as well.

What I AM telling you is that you should find someone who you trust. Find someone who is looking out for you on taxes. Find someone who is looking out for you on transaction costs. Find someone who tells you the truth about how much you’re paying them. Find someone who is going to save you from your fear when you want to sell low, and who will protect you from your greed when you want to buy high. Find someone who will keep you diversified when you want to take stupid risks in markets and securities where you don’t have an edge (which is all of them — sorry). Find someone who isn’t trying to get you to trade in and out of new ideas all the time. Find someone who doesn’t sell to you with Eulenspiegel-style virtue signals. Find someone who doesn’t disappear like Keyzer Söze when things go wrong.

Find someone who will treat you like a partner, and pay them.

But not too much.

[1] Here, we present this as a historical analysis. No resampling, etc.

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/15781/