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.


PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16031/

Who’s Being Naïve, Kay?


Satan: Dream other dreams, and better!

The Mysterious Stranger (1916)

Twain spent 11 years writing his final novel, “The Mysterious Stranger”, but never finished it. The book exists in three large fragments and is Twain’s darkest and least funny work. It’s also my personal favorite.


Stanley:   I thought you were called Lucifer.

George:   I know. “The Bringer of the Light” it used to be. Sounded a bit poofy to me. Everything I’ve ever told you has been a lie. Including that.

Stanley :   Including what?

George :   That everything I’ve ever told has been a lie. That’s not true.

Stanley :   I don’t know WHAT to believe.

George :   Not me, Stanley, believe me!

Bedazzled (1967)

A must-see movie, and I don’t mean the 2000 abomination with Brendan Fraser, but the genius 1967 version by Peter Cook and Dudley Moore. Plus Raquel Welch as Lust. Yes, please.


Henry Hill:   Ladies and gentlemen, either you are closing your eyes to a situation you do not wish to acknowledge, or you are not aware of the caliber of disaster indicated by the presence of a pool table in your community!

The Music Man (1962)

The Pied Piper legend, originally a horrific tale of murder, finds its source in the earliest written records of the German town of Hamelin (1384).

The story begins: “it is 100 years since our children left.”


As Tolstoy famously said, there are only two stories in all of literature: either a man goes on a journey, or a stranger comes to town. Of the two, we are far more familiar and comfortable with the first in the world of markets and investing, because it’s the subjectively perceived narrative of our individual lives. We learn. We experience. We overcome adversity. We get better. Or so we tell ourselves.

But when the story of our investment age is told many years from now, it won’t be remembered as a Hero’s Journey, but as a classic tale of a Mysterious Stranger. It’s a story as old as humanity itself, and it always ends with the same realization by the Stranger’s foil: what was I thinking when I signed that contract or fell for that line? Why was I so naïve?

The Mysterious Stranger today, of course, is not a single person but is the central banking Mafia apparatus in the US, Europe, Japan, and China. The leaders of these central banks may not be as charismatic as Robert Preston in The Music Man, but they hold us investors in equal rapture. The Music Man uses communication policy and forward guidance to get the good folks of River City to buy band instruments. Central bankers use communication policy and forward guidance to get investors large and small to buy financial assets. It’s a difference in degree and scale, not in kind.

epsilon-theory-whos-being-naive-kay-may-24-2016-music-man

The Mysterious Stranger is NOT a simple or single-dimensional fraud. No, the Mysterious Stranger is a liar, to be sure, but he’s a proper villain, as the Brits would say, and typically he’s quite upfront about his goals and his use of clever words to accomplish those goals. I mean, it’s not like Kay doesn’t know what she’s getting herself into when she marries into the Corleone family. Michael is crystal clear with her, right from the start. But she wants to believe so badly in what Michael is telling her when he suddenly reappears in her life, that she suspends her disbelief in his words and embraces the Narrative of legitimacy he presents. I think Michael actually believed his own words, too, that he would in fact be able to move the Family out of organized crime entirely, just as I’m sure that Yellen believed her own words of tightening and light-at-the-end-of-the-tunnel in the summer of 2014. Ah, well. Events doth make liars of us all.

epsilon-theory-whos-being-naive-kay-may-24-2016-godfather-2
epsilon-theory-whos-being-naive-kay-may-24-2016-godfather-3

Draw your own comparisons to this story arc of The Godfather, with investors playing the role of Kay and the Fed playing the role of Michael Corleone. I think it’s a pretty neat fit. It ends poorly for Kay, of course (and not so great for Michael). Let’s see if we can avoid her fate.

But like Kay, for now we are married to the Mob … err, I mean, the Fed and competitive monetary policies, as reflected in the relative value of the dollar and other currencies. The cold hard fact is that since the summer of 2014 there has been a powerful negative correlation between the trade-weighted dollar and oil, between the trade-weighted dollar and emerging markets, and between the trade-weighted dollar and industrial, manufacturing, and energy stocks. Here’s an example near and dear to the hearts of any energy investor, the trade-weighted dollar shown in green versus the inverted Alerian MLP index (ticker AMZ), a set of 43 midstream energy companies, principally pipelines and infrastructure, shown in blue.

epsilon-theory-whos-being-naive-kay-may-24-2016-bloomberg-1

This is a -94% correlation, remarkably strong for any two securities, much less two – pipelines and the dollar – that are not obviously connected in any fundamental or real economy sort of way. But this is always what happens when the Mysterious Stranger comes to town: our traditional behavioral rules (i.e., correlations) go out the window and are replaced with new behavioral rules and correlations as we give ourselves over to his smooth words and promises. Because that’s what a Mysterious Stranger DOES – tell compelling stories, stories that stick fast to whatever it is in our collective brains that craves Narrative and Belief.

There’s nothing particularly new about this phenomenon in markets, as there have always been “story stocks”, especially in the technology, media, and telecom (TMT) sector where you have more than your fair share of charismatic management storytellers and valuation multiples that depend on their efforts.

My favorite example of a “story stock” is Salesforce.com (ticker CRM), a $55 billion market cap technology company with 19,000 employees and about $6.5 billion in revenues. I’m pretty sure that Salesforce.com has never had a single penny of GAAP earnings in its existence (in FY 2016 the company lost $0.07 per share on a GAAP basis). Instead, the company is valued on the basis of non-GAAP earnings, but even there it trades at about an 80x multiple (!) of FY 2017 company guidance of $1.00 per share. Salesforce.com is blessed with a master story-teller in its CEO, Marc Benioff, who – if you’ve ever heard him speak – puts forth a pretty compelling case for why his company should be valued on the basis of bookings growth and other such metrics. Of course, the skeptic in me might note that it is perhaps no great feat to sell more and more of a software service at a loss, particularly when your salespeople are compensated on bookings growth, and the cynic in me might also note that for the past 10+ years Benioff has sold between 12,500 and 20,000 shares of CRM stock every day through a series of 10b5-1 programs. But hey, that’s why he’s the multi-billionaire (and a liquid multi-billionaire, to boot) and I’m not. Here’s the 5-year chart for CRM:

epsilon-theory-whos-being-naive-kay-may-24-2016-bloomberg-2

Not bad. Up 138% over the past five years. A few ups and downs, particularly here at the start of 2016, although the stock has certainly come roaring back. But when you dig a little deeper …

There are 1,272 trading days that comprise this 5-year chart. 21 of those trading days, less than 2% of the total, represent the Thursday after Salesforce.com reports quarterly earnings (always on a Wednesday after the market close). If you take out those 21 trading days, Salesforce.com stock is up only 35% over the past five years. How does this work? What’s the causal process? Every Wednesday night after the earnings release, for the past umpteen years, Benioff appears on Mad Money, where Cramer’s verdict is always an enthusiastic “Buy, buy, buy!” Every Thursday morning after the earnings release, the two or three sell-side analyst “axes” on the stock publish their glowing assessment of the quarterly results before trading begins. It’s not that every investor on Thursday believes what Cramer or the sell-side analysts are saying, particularly anyone who’s short the stock (CRM always has a high short interest). But in a perfect example of the Common Knowledge Game, if you ARE short the stock, you know that everyone else has heard what Cramer and the sell-side analysts (the Missionaries, in game theory lingo) have said, and you have to assume that everyone else will act on this Common Knowledge (what everyone knows that everyone knows). The only logical thing for you to do is cover your short before everyone else covers their short, resulting in a classic short squeeze and a big up day. Now to be sure, this isn’t the story of every earnings announcement … sometimes even Marc Benioff and his lackeys can’t turn a pig’s ear of a quarter into a silk purse … but it’s an incredibly consistent behavioral result over time and one of the best examples I know of the Common Knowledge Game in action.

But wait, there’s more. Now let’s add the Fed’s storytelling and its Common Knowledge Game to Benioff’s storytelling and his Common Knowledge Game. Over the past five years there have been 43 days where the FOMC made a formal statement. If you owned Salesforce.com stock for only the 43 FOMC announcement days and the 21 earnings announcement days over the past five years, you would be UP 167%. If you owned Salesforce.com stock for the other 1,208 trading days, you would be DOWN 8%.

epsilon-theory-whos-being-naive-kay-may-24-2016-salesforc

Okay, Ben, how about other stocks? How about entire indices? Well, let’s look again at that Alerian MLP index. Over the past five years, if you had owned the AMZ for only the 43 FOMC announcement days over that span, you would be UP 28%. If you owned it for the other 1,229 trading days you would be DOWN 39%. Over the past two years, if you had owned the AMZ for only the 16 FOMC announcement days over that span, you would be UP 18%. If you owned it for the other 487 trading days you would be DOWN 48%. Addition by subtraction to a degree that would make Lao Tzu proud.

epsilon-theory-whos-being-naive-kay-may-24-2016-alerian

I’ll repeat what I wrote in Optical Illusion / Optical Reality … it’s hard to believe that MLP investors should be paying a lot more attention to G-7 meetings and reading the Fed governor tea leaves than to gas field depletion schedules and rig counts, but I gotta call ‘em like I see ‘em. In fact, if there’s a core sub-text to Epsilon Theory it’s this: call things by their proper names. That’s a profoundly subversive act. Maybe the only subversive act that really changes things. So here goes. Today there are vast swaths of the market, like emerging markets and commodity markets and industrial/energy stocks, that we should call by their proper name: a derivative expression of FOMC policy. Used to be that only tech stocks were “story stocks”. Today, almost all stocks are “story stocks”, and the Common Knowledge Game is more applicable to helping us understand market behaviors and price action than ever before.

You see this phenomenon clearly in the entire S&P 500, as well, although not as starkly with a complete plus/minus reversal in performance between FOMC announcement days and all other days. Over the past five years, if you had owned the SPX for only the 43 FOMC announcement days over that span, you would be UP 17%. If you owned it for the other 1,229 trading days you would be UP 28%. Over the past two years, if you had owned the SPX for only the 16 FOMC announcement days over that span, you would be UP 5%. If you owned it for the other 487 trading days you would be UP 2%.

epsilon-theory-whos-being-naive-kay-may-24-2016-s-p-500

What do I take from eyeballing these charts? The Narrative effect and the impact of the Common Knowledge Game have accelerated over the past two years (ever since Draghi and Yellen launched the Great Monetary Policy Schism of June 2014); they’re particularly impactful during periods when stock prices are otherwise declining, and they’re spreading to broader equity indices. That’s what it looks like to me, at least.

So what does an investor do with these observations? Two things, I think, one a practical course of action and one a shift in perspective. The former being more fun but the latter more important.

First, there really is a viable research program here, and what I’ve tried to show in this brief note is that there really are practical implementations of the Common Knowledge Game that can support investment strategies dealing with story stocks. I want to encourage anyone who’s intrigued by this research program to take the data baton and try this on your favorite stock or mutual fund or index. You can get the FOMC announcement dates straight from the Federal Reserve website. This doesn’t require an advanced degree in econometrics to explore.

I don’t know where this research program ends up, but it’s my commitment to do this in plain sight through Epsilon Theory. Think of it as the equivalent of open source software development, just in the investment world. I suspect it’s hard to turn the Common Knowledge Game into a standalone investment strategy because you’re promising that you’ll do absolutely nothing for 98 out of 100 trading days. Good luck raising money on that. But it’s a great perspective to add to our current standalone strategies, especially actively managed funds. Stock-pickers today are being dealt one dull, low-conviction hand after another here in the Grand Central Bank Casino, and the hardest thing in the world for any smart investor, regardless of strategy, is to sit on his hands and do nothing, even though that’s almost always the right thing to do. Incorporating an awareness of the Common Knowledge Game and its highly punctuated impact makes it easier to do the right thing – usually nothing – in our current investment strategies.

And that gets us to the second take-away from this note. The most important thing to know about any Mysterious Stranger story is that the Stranger is the protagonist. There is no Hero! When you meet a Mysterious Stranger, your goal should be simple: survive the encounter.

This is an insanely difficult perspective to adopt, that we (either individually or collectively) are not the protagonist of the investing age in which we live. It’s difficult because we are creatures of ego. We all star in our own personal movie and we all hear the anthems of our own personal soundtrack. But the Mysterious Stranger is not an obstacle to be heroically overcome, as if we were Liam Neeson setting off (again! and again!) to rescue a kidnapped daughter in yet another “Taken” sequel. At some point this sort of heroism is just a reflection of bad parenting in the case of Liam Neeson, and a reflection of bad investing in the case of stock pickers and other clingers to the correlations and investment meanings of yesterday.

The correlations and investment meanings of today are inextricably entwined with central bankers and their storytelling. To be investment survivors in the low-return and policy-controlled world of the Silver Age of the Central Banker, we need to recognize the impact of their words and incorporate that into our existing investment strategies, while never accepting those words naïvely in our hearts.


PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16271/

Optical Illusion / Optical Truth

epsilon-theory-optical-illusion-optical-truth-may-4-2016-tufte

epsilon-theory-optical-illusion-optical-truth-may-4-2016-cholera-2Portion of original dot map by Dr. John Snow, the founding father of epidemiology, showing the clusters of cholera cases in the London epidemic of 1854. The visual representation of Snow’s data analysis convinced local authorities to shut down the contaminated public well at ground zero of the cholera outbreak, although it would be another 20 years before Snow’s arguments in favor of germ theory and a direct connection between cholera and fecal contamination of water supply would be widely accepted.

John Snow, “On the Mode of Communication of Cholera” (1855)

Anscombe’s Quartet: four datasets that appear identical using summary statistical methods (mean, variance, correlation, linear regression), but are completely different in meaning and composition – a difference that is clearly revealed through visual inspection.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-quartet

Frank Anscombe, “Graphs in Statistical Analysis” American Statistician v.27 no.1 (1973), drawing by Schutz 

epsilon-theory-optical-illusion-optical-truth-may-4-2016-minard-map

Charles Joseph Minard, “Carte Figurative” of Napoleon’s 1812 Russian Campaign (1869)

The Minard Map: a map of Napoleon’s disastrous invasion of Russia in 1812, showing six distinct data dimensions (troop strength, temperature, distance marched, geographic latitude and longitude, direction of travel, location at event dates) in 2-dimensional form.

Mephistopheles:Here too it’s masquerade, I find:
As everywhere, the dance of mind.
I grasped a lovely masked procession,
And caught things from a horror show…
I’d gladly settle for a false impression,
If it would last a little longer, though.
epsilon-theory-optical-illusion-optical-truth-may-4-2016-mephistopheles

Edouard de Reszke as Mephistopheles
in Gounod’s opera “Faust” (c. 1880)

So, so you think you can tell
Heaven from Hell,
Blue skies from pain.
Can you tell a green field
From a cold steel rail?
A smile from a veil?
Do you think you can tell?

– Roger Waters, “Wish You Were Here” (1975)

A great deal of intelligence can be invested in ignorance when the need for illusion is deep.

– Saul Bellow, “To Jerusalem and Back” (1976)

It is difficult to get a man to understand something, when his salary depends on his not understanding it.

– Upton Sinclair, “I, Candidate for Governor: And How I Got Licked” (1935)

Knowledge kills action; action requires the veils of illusion.

– Friedrich Nietzsche, “The Birth of Tragedy” (1872)

To find out if she really loved me, I hooked her up to a lie detector. And just as I suspected, my machine was broken.

– Jarod Kintz, “Love Quotes for the Ages. Specifically Ages 19-91” (2013)

Edward Tufte is a personal and professional hero of mine. Professionally, he’s best known for his magisterial work in data visualization and data communication through such classics as The Visual Display of Quantitative Information (1983) and its follow-on volumes, but less well-known is his outstanding academic work in econometrics and statistical analysis. His 1974 book Data Analysis for Politics and Policy remains the single best book I’ve ever read in terms of teaching the power and pitfalls of statistical analysis. If you’re fluent in the language of econometrics (this is not a book for the uninitiated) and now you want to say something meaningful and true using that language, you should read this book (available for $2 in Kindle form on Tufte’s website). Personally, Tufte is a hero to me for escaping the ivory tower, pioneering what we know today as self-publishing, making a lot of money in the process, and becoming an interesting sculptor and artist. That’s my dream. That one day when the Great Central Bank Wars of the 21st century are over, I will be allowed to return, Cincinnatus-like, to my Connecticut farm where I will write short stories and weld monumental sculptures in peace. That and beekeeping.

But until that happy day, I am inspired in my war-fighting efforts by Tufte’s skepticism and truth-seeking. The former is summed up well in an anecdote Tufte found in a medical journal and cites in Data Analysis:

One day when I was a junior medical student, a very important Boston surgeon visited the school and delivered a great treatise on a large number of patients who had undergone successful operations for vascular reconstruction. At the end of the lecture, a young student at the back of the room timidly asked, “Do you have any controls?” Well, the great surgeon drew himself up to his full height, hit the desk, and said, “Do you mean did I not operate on half of the patients?” The hall grew very quiet then. The voice at the back of the room very hesitantly replied, “Yes, that’s what I had in mind.” Then the visitor’s fist really came down as he thundered, “Of course not. That would have doomed half of them to their death.” God, it was quiet then, and one could scarcely hear the small voice ask, “Which half?”

‘Nuff said.

The latter quality — truth-seeking — takes on many forms in Tufte’s work, but most noticeably in his constant admonitions to LOOK at the data for hints and clues on asking the right questions of the data. This is the flip-side of the coin for which Tufte is best known, that good/bad visual representations of data communicate useful/useless answers to questions that we have about the world. Or to put it another way, an information-rich data visualization is not only the most powerful way to communicate our answers as to how the world really works, but it is also the most powerful way to design our questions as to how the world really works. Here’s a quick example of what I mean, using a famous data set known as “Anscombe’s Quartet”.

Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

In this original example (developed by hand by Frank Anscombe in 1973; today there’s an app for generating all the Anscombe sets you could want) Roman numerals I – IV refer to four data sets of 11 (x,y) coordinates, in other words 11 points on a simple 2-dimensional area. If you were comparing these four sets of numbers using traditional statistical methods, you might well think that they were four separate data measurements of exactly the same phenomenon. After all, the mean of x is exactly the same in each set of measurements (9), the mean of y is the same in each set of measurements to two decimal places (7.50), the variance of x is exactly the same in each set (11), the variance of y is the same in each set to two decimal places (4.12), the correlation between x and y is the same in each set to three decimal places (0.816), and if you run a linear regression on each data set you get the same line plotted through the observations (y = 3.00 + 0.500x).

But when you LOOK at these four data sets, they are totally alien to each other, with essentially no similarity in meaning or probable causal mechanism. Of the four, linear regression and our typical summary statistical efforts make sense for only the upper left data set. For the other three, applying our standard toolkit makes absolutely no sense. But we’d never know that — we’d never know how to ask the right questions about our data — if we didn’t eyeball it first.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-anscombes-quartet

Okay, you might say, duly noted. From now on we will certainly look at a visual plot of our data before doing things like forcing a line through it and reporting summary statistics like r-squared and standard deviation as if they were trumpets of angels from on high. But how do you “see” multi-variate datasets? It’s one thing to imagine a line through a set of points on a plane, quite another to visualize a plane through a set of points in space, and impossible to imagine a cubic solid through a set of points in hyperspace. And how do you “see” embedded or invisible data dimensions, whether it’s an invisible market dimension like volatility or an invisible measurement dimension like time aggregation or an invisible statistical dimension like the underlying distribution of errors?

The fact is that looking at data is an art, not a science. There’s no single process, no single toolkit for success. It requires years of practice on top of an innate artist’s eye before you have a chance of being good at this, and it’s something that I’ve never seen a non-human intelligence accomplish successfully (I can’t tell you how happy I am to write that sentence). But just because it’s hard, just because it doesn’t come easily or naturally to people and machines alike … well, that doesn’t mean it’s not the most important thing in data-based truth-seeking.

Why is it so important to SEE data relationships? Because we’re human beings. Because we are biologically evolved and culturally trained to process information in this manner. Because — and this is the Tufte-inspired market axiom that I can’t emphasize strongly enough — the only investable ideas are visible ideas. If you can’t physically see it in the data, then it will never move you strongly enough to overcome the pleasant fictions that dominate our workaday lives, what Faust’s Tempter, the demon Mephistopheles, calls the “masquerade” and “the dance of mind.” Our similarity to Faust (who was a really smart guy, a man of Science with a capital S) is not that the Devil may soon pay us a visit and tempt us with all manner of magical wonders, but that we have already succumbed to the blandishments of easy answers and magical thinking. I mean, don’t get me started on Part Two, Act 1 of Goethe’s magnum opus, where the Devil introduces massive quantities of paper money to encourage inflationary pressures under a false promise of recovery in the real economy. No, I’m not making this up. That is the actual, non-allegorical plot of one of the best, smartest books in human history, now almost 200 years old.

So what I’m going to ask of you, dear reader, is to look at some pictures of market data, with the hope that seeing will indeed spark believing. Not as a temptation, but as a talisman against the same. Because when I tell you that the statistical correlation between the US dollar and the price of oil since Janet Yellen and Mario Draghi launched competitive monetary policies in mid-June of 2014 is -0.96 I can hear the yawns. I can also hear my own brain start to pose negative questions, because I’ve experienced way too many instances of statistical “evidence” that, like the Anscombe data sets, proved to be misleading at best. But when I show you what that correlation looks like …

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-1

© Bloomberg Finance L.P., for illustrative purposes only

I can hear you lean forward in your seat. I can hear my own brain start to whir with positive questions and ideas about how to explore this data further. This is what a -96% correlation looks like.

What you’re looking at in the green line is the Fed’s favored measure of what the US dollar buys around the world. It’s an index where the components are the exchange rates of all the US trading partners (hence a “broad dollar” index) and where the individual components are proportionally magnified/minimized by the size of that trading relationship (hence a “trade-weighted” index). That index is measured by the left hand vertical axis, starting with a value of about 102 on June 18, 2014 when Janet Yellen announced a tightening bias for US monetary policy and a renewed focus on the full employment half of the Fed’s dual mandate, peaking in late January and declining to a current value of about 119 as first Japan and Europe called off the negative rate dogs (making their currencies go up against the dollar) and then Yellen completely back-tracked on raising rates this year (making the dollar go down against all currencies). Monetary policy divergence with a hawkish Fed and a dovish rest-of-world makes the dollar go up. Monetary policy convergence with everyone a dove makes the dollar go down.

What you’re looking at in the magenta line is the upside-down price of West Texas Intermediate crude oil over the same time span, as measured by the right hand vertical axis. So on June 18, 2014 the spot price of WTI crude oil was over $100/barrel. That bottomed in the high $20s just as the trade-weighted broad dollar index peaked this year, and it’s been roaring back higher (lower in the inverse depiction) ever since. Now correlation may not imply causation, but as Ed Tufte is fond of saying, it’s a mighty big hint. I can SEE the consistent relationship between change in the dollar and change in oil prices, and that makes for a coherent, believable story about a causal relationship between monetary policy and oil prices.

What is that causal narrative? It’s not just the mechanistic aspects of pricing, such that the inherent exchange value of things priced in dollars — whether it’s a barrel of oil or a Caterpillar earthmover — must by definition go down as the exchange value of the dollar itself goes up. More impactful, I think, is that for the past seven years investors have been well and truly trained to see every market outcome as the result of central bank policy, a training program administered by central bankers who now routinely and intentionally use forward guidance and placebo words to act on “the dance of mind” in classic Mephistophelean fashion. In effect, the causal relationship between monetary policy and oil prices is a self-fulfilling prophecy (or in the jargon du jour, a self-reinforcing behavioral equilibrium), a meta-example of what George Soros calls reflexivity and what a game theorist calls the Common Knowledge Game.

The causal relationship of the dollar, i.e. monetary policy, to the price of oil is a reflection of the Narrative of Central Bank Omnipotence, nothing more and nothing less. And today that narrative is everything.

Here’s something smart that I read about this relationship between oil prices and monetary policy back in November 2014 when oil was north of $70/barrel:

I think that this monetary policy divergence is a very significant risk to markets, as there’s no direct martingale on how far monetary policy can diverge and how strong the dollar can get. As a result I think there’s a non-trivial chance that the price of oil could have a $30 or $40 handle at some point over the next 6 months, even though the global growth and supply/demand models would say that’s impossible. But I also think the likely duration of that heavily depressed price is pretty short. Why? Because the Fed and China will not take this lying down. They will respond to the stronger dollar and stronger yuan (China’s currency is effectively tied to the dollar) and they will prevail, which will push oil prices back close to what global growth says the price should be. The danger, of course, is that if they wait too long to respond (and they usually do), then the response will itself be highly damaging to global growth and market confidence and we’ll bounce back, but only after a near-recession in the US or a near-hard landing in China.

Oh wait, I wrote that. Good stuff.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-economist

But that was a voice in the wilderness in 2014, as the dominant narrative for the causal factors driving oil pricing was all OPEC all the time. So what about that, Ben? What about the steel cage death match within OPEC between Saudi Arabia and Iran and outside of OPEC between Saudi Arabia and US frackers? What about supply and demand? Where is that in your price chart of oil? Sorry, but I don’t see it in the data. Doesn’t mean it’s not really there. Doesn’t mean it’s not a statistically significant data relationship. What it means is that the relationship between oil supply and oil prices in a policy-controlled market is not an investable relationship. I’m sure it used to be, which is why so many people believe that it’s so important to follow and fret over. But today it’s an essentially useless exercise in data analytics. Not wrong, but useless … there’s a difference!

Of course, crude oil isn’t the only place where fundamental supply and demand factors are invisible in the data and hence essentially useless as an investable attribute. Here’s the dollar and something near and dear to the hearts of anyone in Houston, the Alerian MLP index, with an astounding -94% correlation:

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-2

© Bloomberg Finance L.P., for illustrative purposes only

Interestingly, the correlation between the Alerian MLP index and oil is noticeably less at -88%. Hard to believe that MLP investors should be paying more attention to Bank of Japan press conferences than to gas field depletion schedules, but I gotta call ‘em like I see ‘em.

And here’s the dollar and EEM, the dominant emerging market ETF, with a -89% correlation:

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-3

© Bloomberg Finance L.P., for illustrative purposes only

There’s only one question that matters about Emerging Markets as an asset class, and it’s the subject of one of my first (and most popular) Epsilon Theory notes, “It Was Barzini All Along”: are Emerging Market growth rates a function of something (anything!) particular to Emerging Markets, or are they simply a derivative function of Developed Market central bank liquidity measures and monetary policy? Certainly this chart suggests a rather definitive answer to that question!

And finally, here’s the dollar and the US Manufacturing PMI survey of real-world corporate purchasing managers, probably the most respected measure of US manufacturing sector health. This data relationship clocks in at a -92% correlation. I mean … this is nuts.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-bloomberg-4

© Bloomberg Finance L.P., for illustrative purposes only

Here’s what I wrote last summer about the inexorable spread of monetary policy contagion.

Monetary policy divergence manifests itself first in currencies, because currencies aren’t an asset class at all, but a political construction that represents and symbolizes monetary policy. Then the divergence manifests itself in those asset classes, like commodities, that have no internal dynamics or cash flows and are thus only slightly removed in their construction and meaning from however they’re priced in this currency or that. From there the divergence spreads like a cancer (or like a cure for cancer, depending on your perspective) into commodity-sensitive real-world companies and national economies. Eventually – and this is the Big Point – the divergence spreads into everything, everywhere.

I think this is still the only story that matters for markets.

The good Lord giveth and the good Lord taketh away. Right now the good Lord’s name is Janet Yellen, and she’s in a giving mood. It won’t last. It never does. But it does give us time to prepare our portfolios for a return to competitive monetary policy actions, and it gives us insight into what to look for as catalysts for that taketh away part of the equation.

epsilon-theory-optical-illusion-optical-truth-may-4-2016-cholera

Most importantly, though, I hope that this exercise in truth-seeking inoculates you from the Big Narrative Lie coming soon to a status quo media megaphone near you, that this resurgence in risk assets is caused by a resurgence in fundamental real-world economic factors. I know you want to believe this is true. I do, too! It’s unpleasant personally and bad for business in 2016 to accept the reality that we are mired in a policy-controlled market, just as it was unpleasant personally and bad for business in 1854 to accept the reality that cholera is transmitted through fecal contamination of drinking water. But when you SEE John Snow’s dot map of death you can’t ignore the Broad Street water pump smack-dab in the middle of disease outcomes. When you SEE a Bloomberg correlation map of prices you can’t ignore the trade-weighted broad dollar index smack-dab in the middle of market outcomes. Or at least you can’t ignore it completely. It took another 20 years and a lot more cholera deaths before Snow’s ideas were widely accepted. It took the development of a new intellectual foundation: germ theory. I figure it will take another 20 years and the further development of game theory before we get widespread acceptance of the ideas I’m talking about in Epsilon Theory. That’s okay. The bees can wait.

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16276/