How Gold Lost Its Luster, How the All-Weather Fund Got Wet, and Other Just-So Stories


Gold is money. Everything else is credit.
– John Pierpont Morgan

The relationships of asset performance to growth and inflation are reliable – indeed, timeless and universal – and knowable, rooted in the durations and sources of variability of the assets’ cash flows.
– Bob Prince, Co-Chief Investment Officer, Bridgewater Associates

Like every middle-aged white guy I know, I am a big fan of Rudyard Kipling. I grew up on his Just-So Stories and as an adult found that his novels and poems spoke to me, as they did to my father and his father before that. Kipling writes simply, directly, and evocatively. Whether it’s a poem, a short story, or a novel, the man knows how to tell a story. He was the youngest winner of the Nobel Prize in Literature (as well as the first English-language recipient), and after a too-long period of disfavor in the academy he now enjoys a well-deserved renaissance of interest and acclaim.


But there is also no doubt that what Kipling wrote was used by political and economic entities of his day to support their own self-interests. As George Orwell said, with wildly popular poems like “The White Man’s Burden” he was the “prophet of British imperialism.” Was he a simplistic rah-rah tout for the rewards of Empire? Not in the least. There is tension, nuance, and respect for the human condition in everything Kipling wrote, at least that I’m aware of. And this is exactly why he was such an effective prophet, such an effective Narrator for mainstream British policy in the first three decades of the 20thcentury. Kipling’s skill as an author allowed British citizens to feel good about themselves and to support their government’s policies without requiring them to check their brains or their scruples at the door.

These are the hallmarks of effective Narratives – they have an intrinsic ring of truth (“truthiness”, to use Stephen Colbert’s wonderful phrase) that speaks to us on an intellectual and emotional level AND they coincide with the goals and preferences of powerful political and economic entities. Neither of these qualities is inherently a bad thing, whatever “bad” means.  Nor is the content of a Narrative necessarily less truthful because it helps serve broader interests, whatever “truthful” means. Questions of truth and falsehood, good and bad, are impossible to assess from the informational content of the Narrative itself and are only meaningful in the broader context of human society at some given point in time. Kipling’s work gave a voice to the orthodoxy of foreign policy Common Knowledge in 1905 and the anti-orthodoxy of foreign policy Common Knowledge in 1965, even though Kipling’s words themselves never changed. In both eras, the Narrative of Imperialism – pro in 1905, anti in 1965 – was highly relevant to political and economic entities, which gave the world a public lens to interpret Kipling’s work. Today no one cares about the Narrative of Imperialism. It is a dead Narrative, like Manifest Destiny or Cultural Revolution. What Kipling wrote 100 years ago is largely irrelevant for the Narratives that shape our world today, which is probably what allows his work to be better appreciated for how it moves us on a personal level.

The Narrative of Gold was relevant 100 years ago and, unlike the Narrative of Imperialism, it remains relevant today. But like the Imperialism Narrative in 1965, it has morphed from a centerpiece of Common Knowledge orthodoxy into the foil or antithesis for a more modern, ascendant Narrative. Just as the Narrative of Imperialism was supplanted by the Narrative of Self-Determination, so has the Narrative of Gold been supplanted by the Narrative of Central Banker Omnipotence.

So long as the Narrative of Self-Determination was useful to powerful political and economic entities, the Narrative of Imperialism was relevant as well. It’s much easier to make an argument for something when you have something to argue against. But when the Narrative of Self-Determination lost its usefulness (not coincidentally with the end of the Cold War), so did the Narrative of Imperialism fade away. Today the Narrative of Central Banker Omnipotence is extremely useful to powerful political and economic entities, which means that the Narrative of Gold is important, too. Gold is just not important in the same way that it was important 100 years ago, and that shift in meaning makes all the difference in understanding the price of gold.

What “Jupiter” Morgan said about the primacy of gold above all other stores of value rang true to almost everyone when he said it. Did his public statements in support of the gold standard also serve his own self-interest? Absolutely. The US Treasury bought 3.5 million ounces of gold in 1895 from the House of Rothschild and … J.P. Morgan, using funds from a massive (for the time) 30-year bond issue syndicated by … J.P. Morgan. In a highly unusual (i.e. unconstitutional) move, this bond issue was carried out by the White House without any Congressional approval, under the authority of a forgotten Civil War era statute that was identified by … J.P. Morgan.


But while there’s no question that the bond sale and subsequent gold purchase lined Morgan’s pockets and served the interests of the rich and powerful considerably, there is also no question that these moves effectively ended the Panic of 1893. Why? Because everyone knew that everyone knew that gold was money. And now the US Treasury had lots of it. Huzzah! Confidence is restored as the Republic is saved by Grover Cleveland and Jupiter Morgan.

To be sure, the Narrative of Gold as told by J.P. Morgan was not accepted at the time by everyone as good or wise policy, any more than the Narrative of Imperialism as told by Kipling was accepted by everyone as good or wise policy at the time. Political oratory being what it was back then, in fact, William Jennings Bryan famously compared the imposition of the gold standard as the equivalent of “crucifying mankind on a cross of gold” at the Democratic national convention of 1896 and was nominated by acclamation as his party’s Presidential candidate. This despite Bryan being only 36 years old (the youngest Presidential candidate in American history) and despite Grover Cleveland, the outgoing President who bought the gold from Morgan and Rothschild, being a Democrat and the standard-bearer of the party. Clearly that must have been one hell of a speech!

epsilon-theory-how-gold-lost-its-luster-june-30-2013-speech epsilon-theory-how-gold-lost-its-luster-june-30-2013-bryan

But Bryan and his Free Silver Democrats weren’t opposed to the gold standard because they disagreed with the notion that gold was money; they just thought that silver should be money, too. Whatever you thought about the policy implications, the Common Knowledge about the meaning of gold in 1895 was clear: it was money, and the behavior of market participants in buying and selling gold reflected this meaning.

Now imagine if the current head of the House of Morgan, Jamie Dimon, made the same statement as Jupiter Morgan did, equating gold with money. People would think it was a joke. Everyone knows that everyone knows that gold does not mean the same thing to Jamie Dimon that it did to J.P. Morgan.

To market participants in 2013 gold means lack of confidence in money, and their behavior in buying and selling gold similarly reflects this meaning. Buying gold today is a statement that you believe that global economic events may spiral out of the control of Central Bankers. It is insurance against some sort of massive monetary policy mistake that cannot be fixed without re-conceptualizing the global economic regime – hyperinflation in a developed nation, the collapse of the Euro, something like that – not an expression of a commonly shared belief in some inherent value of gold.

The source of gold’s meaning, whether you are a market participant in 1895 or 2013, comes from the Common Knowledge regarding gold. J.P. Morgan said that gold is money, and he was right, but only because at the time he said it everyone believed that everyone believed that gold is money. Today that same statement is wrong, but only because no one believes that everyone believes that gold is money.

You may privately believe that J.P. Morgan is still right, that gold has meaning as a store of value. But if you participate in the market on the basis of that belief, then you will buy and sell gold in an incredibly inefficient manner. You would be a smart gold investor in 1895, but a poor gold investor today. Or let’s say that you privately believe gold to be a “barbarous relic” and that it’s ridiculous for gold to have any ascribed value at all other than what jewelry demand would bring. You, too, will buy and sell gold in an incredibly inefficient manner. In fact, you would be a poor gold investor in both 1895 and today.

In some periods of history gold is money. In other periods of history gold is not. But gold is always something, and that something is defined by the Common Knowledge of the day. To be an efficient gold investor in any period, I believe it’s crucial to identify and measure the relevant Narrative that is driving the Common Knowledge regarding gold. Only then can one construct an informational surface that predicts how the equilibrium price of gold will respond to new information (see “Through the Looking Glass” for a description of this game theoretic methodology).

There is no stand-alone Narrative regarding gold today, as there was in 1895. Today gold is understood from a Common Knowledge perspective only as a shadow or reflection of a powerful stand-alone Narrative regarding central banks, particularly the Fed … what I will call the Narrative of Central Banker Omnipotence. Like all effective Narratives it’s simple: central bank policy WILL determine market outcomes. There is no political or fundamental economic issue impacting markets that cannot be addressed by central banks. Not only are central banks the ultimate back-stop for market stability (although that is an entirely separate Narrative), but also they are the immediate arbiters of market outcomes. Whether the market goes up or down depends on whether central bank policy is positive or negative for markets. The Narrative of Central Banker Omnipotence does NOT imply that the market will always go up or that central bank policy will always support the market. It connotes that whatever the central bank policy might be, it will drive a market outcome; whatever the market outcome, it was driven by a central bank policy.

Like all effective Narratives it has a great deal of “truthiness” … it rings true to our intellect even as it appeals to our emotions. How comforting to believe that there is a reason why markets go up and go down, and that this reason is clearly identifiable and attributable to the decisions of a few Wise Men and Women, as opposed to the much scarier notion that the world (and markets) are adrift on a sea of chaotic events and hidden currents. And like all effective Narratives it serves the interests of the world’s most powerful political and economic entities … not that there’s anything wrong with that.

The strength of the Narrative of Central Banker Omnipotence has nothing to do with whether people believe that central bank policy is wise or foolish, good or bad. To predict market behavior it really doesn’t matter if QE is the balm of Gilead or the work of the Devil, any more than it mattered in 1895 whether the gold standard saved the Republic or crucified mankind. The only thing that matters from an Epsilon Theory perspective is whether everyone believes that everyone believes that central bank policy determines market outcomes.

The stronger the Narrative of Central Banker Omnipotence, the more likely it is that the price of gold goes down. The weaker the Narrative – the less established the Common Knowledge that central bank policy determines market outcomes – the more likely it is that the price of gold will go up.  In other words, it’s not central bank policy per se that makes the price of gold go up or down, it’s Common Knowledge regarding the ability of central banks to control economic outcomes that makes the price of gold go up or down.

Look below at the price chart for gold over the past year. Gold peaked in late September and early October 2012, immediately after the Fed announced its open-ended QE program (red line). From the perspective of traditional macroeconomics, this makes no sense at all. The Fed had just announced its most aggressive monetary easing policy in history. Not only were they announcing yet another balance sheet expansion, but this time they were telling you that they weren’t going to stop at any pre-determined level, but were going to keep going for as long as it took to satisfy their full employment mandate. This is an inflation engine, pure and simple, and gold should go up, up, and away in price if the standard macroeconomic correlation between the price of gold and monetary easing held true.


London Gold Market Fixing Price, June 30, 2012 – June 30, 2013 (Source: Bloomberg L.P.)

But what was more relevant for the price of gold was the strengthening of the Narrative of Central Banker Omnipotence after the open-ended QE announcement. When you examine the public statements in major media outlets in the weeks following this announcement through a Common Knowledge methodology – both direct statements from Fed governors and “analysis” statements from prominent journalists, investors, and politicians – there was remarkably little opinion-leading or Narrative effort devoted to the direct economic or market implications of the new QE program. There was a one-day spike in inflation expectations and a few public comments to quell the “Oh my God, this means rampant inflation” crowd in the first day or so, but very little else. Instead, the focus of the mainstream Narrative effort moved almost entirely towards what open-ended QE signaled for the Fed’s ability and resolve to create a self-sustaining economic recovery in the US. And it won’t surprise you to learn that this Narrative effort was overwhelmingly supportive of the notion that the Fed could and would succeed in this effort, that the Fed’s policies had proven their effectiveness at lifting the stock market and would now prove their effectiveness at repairing the labor market. Huzzah for the Fed!

Within a week or so, however, opinion-leading voices from other prominent journalists, investors, and politicians joined the fray to say that this congratulatory viewpoint of the Fed’s new policy was entirely misplaced. There was absolutely no evidence showing that further expansion of the Fed’s balance sheet would have any impact whatsoever on US labor conditions, and that to claim otherwise was simply magical thinking. Moreover, according to this counter-argument, there was clearly a declining economic utility to more and more QE, so this latest program was a bridge too far.

But here’s the crucial point … whether these opinion-leaders and Narrative creators thought open-ended QE was a wonderful thing or a terrible thing, they ALL agreed that Fed policy had been responsible for the current stock market level. It was J.P. Morgan and William Jennings Bryan all over again, just arguing the merits of more QE versus less QE instead of the merits of the gold standard versus the gold + silver standard. But just as the debate over Free Silver only intensified the Common Knowledge that gold was money in 1896, so did this debate over the merits of open-ended QE only intensify the Common Knowledge that Fed policy was responsible for market outcomes in 2012. This was a positive informational inflection point in the Narrative of Central Banker Omnipotence, and as a result the price of gold has not had a good day since.

Gold is the most pronounced example of an asset with a mutable behavioral foundation because for all practical purposes there is no practical use for gold. It’s pretty and shiny and relatively rare, but so are a lot of things. For gold, at least, there is no “timeless and universal” relationship between it and economic constructs like inflation and growth, or monetary policy constructs like easing and tightening. There is a relationship, to be sure, but the nature of that relationship changes over time as the Common Knowledge regarding the meaning of gold changes.

The same is true of every other symbolized asset, which is to say every cash flow or fractional ownership interest or thing that is securitized and traded. 

Not to the same extent as gold … there’s a continuum to this perspective, with securities representing gold and other precious metals at one end, then securities representing foreign exchange, then securities representing industrial metals and other commodities, then securities representing publicly traded stocks and bonds, and finally securities representing privately traded equity and debt at the other end of the spectrum. Within each of these categories, the more symbolic the security the more fragile the correlation between it and real-world economic factors (so, for example, an aggregation of stock symbols via an ETF is more prone to game-playing than an individual stock.) Put slightly differently, the more clearly identifiable and directly attributable the cash flow foundation of an asset, the less the impact of the Common Knowledge game. Still, the assignment of value to any symbolized asset is inherently a social construction and will inevitably change over time, occasionally in sharp and traumatic fashion.

The notion that the preference function of market participants may change over time has been around for a long time, particularly in the study of commodity markets. Ben Inker at GMO recently wrote an excellent paper on this topic (“We Have Met the Enemy, and He Is Us”) that I highly recommend if you want to dig into the gory details, but here’s the basic idea:

Back in the 1930’s Keynes proposed an idea called “normal backwardation” to explain how commodity futures markets could support a profit for traders who specialized in those markets. In this theory, commodity producers like farmers were typically risk averse when it came to market risk, and so would be willing to accept a forward contract guaranteeing a lower future price for their crop than a straightforward projection of the current spot price would suggest. The difference between this agreed upon futures price and the projected futures price was the risk premium required by the specialized commodity trader to take the other side of the trade. As Keynes pointed out, the risk premium would have to be pretty high for the commodity trader to engage in this trade (and thus push the futures price down) because, obviously, the commodity trader’s entire livelihood was based on making a profit on these trades.

But now let’s fast-forward 80 years to a world where anyone can be a commodity trader, or rather, anyone can make commodity trades without being a specialized commodity trader. In fact, the notion of an entire market being made by specialists seems terribly quaint today. More importantly, the meaningof a commodity futures contract has changed since Keynes proposed his theory, in the same way that the meaning of gold has changed since J.P. Morgan smoked his last cigar. Pretend you’re a giant pension fund with several hundred billion dollars worth of current assets and future liabilities. Do you think about owning a commodity futures contract because you’re interested in making a small profit in the difference between a farmer’s hedge and a projected forward spot price? Are you agonizing over a few basis points like a specialized commodity trader? Of course not. The only reason you are interested in owning a commodity futures contract is because you’re worried about inflation within the context of your portfolio of assets and liabilities. It’s your preference function regarding inflation that will drive your behavior, not a preference function regarding the intricacies and competitive risk premium associated with this particular commodity.

Whatever historical correlations and patterns existed in this commodity market when it was limited to specialty traders have to be tossed out the window when the pension funds and other enormous asset managers get involved. It’s like playing poker at a table with five penny-pinching off-duty Vegas dealers, and then moving to a table with five rich doctors in town for the weekend. If you don’t change the way you play your cards, even if you’re dealt exactly the same cards from one table to another, then you’re a fool.

This transformation in the composition and goals of market participants is by no means limited to commodity markets. Over the past decade there has been a sea change in the structure of global debt and equity markets, as well. Multiple papers by Simon Emrich and Charles Crow at Morgan Stanley lay out the structural transformation in equity markets in fantastic detail, most recently “Trading Strategies for 2013 – Optimal Responses to Current Market Structure” (March 18, 2013), but here are the two most striking findings from a game theoretic perspective:

1)      Over the past 10 years, institutional management of equity portfolios has increased from 54% to 81%.

2)      Over the same period, the share of what Emrich and Crow call “real institutional trading” has declined from 47% of trading volume to 29%.

There are far fewer market participants today than just ten years ago, managing much larger portfolios across more asset classes, and using much less trading. In future letters I’ll lay out in detail how this structural shift has large and specific consequences for the nature of game-playing in markets, but for the balance of this letter I just want to make a simple, and I hope obvious, point: structural change in any social environment wreaks havoc on historically observed correlations and patterns within that environment.

Unfortunately this sort of structural change is effectively invisible to econometric modeling of portfolios, and as a result understates the risks inherent in portfolios that rely heavily on historical correlation patterns. In a market undergoing structural change, all of the “timeless and universal” relationships that form the backbone of Risk Parity funds like Bridgewater’s All-Weather Fund and similar offerings by Invesco and AQR are much less certain than their econometric justifications would suggest. The underperformance of these strategies in recent weeks and months (“Fashionable ‘Risk Parity’ Funds Hit Hard”, Wall Street Journal, June 27, 2013) takes on new meaning when seen in this light.

There’s nothing wrong with the math of the correlation exercises that underpin Risk Parity funds, any more than there was anything wrong with the math of the correlation exercises that ratings agencies like Moody’s and S&P used to grade Residential Mortgage-Backed Securities (RMBS). But in both cases there is an assumption about market behavior – the relationship of asset performance under varying conditions of growth and inflation for Risk Parity funds; the role of geographical diversity in mitigating the risk profile of mortgage portfolios for RMBS ratings – that is exogenous to the calculation of the projected returns. In both cases, the standard portfolio model of y = α + β + Ɛ, where Epsilon is treated as an error term and the preference functions of market participants are assumed, gives a very compelling result: Risk Parity funds demonstrate an excellent risk-adjusted return profile, and trillions of dollars worth of RMBS deserve a AAA rating. But if you are wrong in your exogenous assumptions – if, for example, there is a nation-wide decline in US home prices for the first time since the 1930’s and geographical diversity provides no protection for a mortgage portfolio – then all the Gaussian cupolas and other econometric legerdemain in the world won’t save your AAA-rated security.

Risk Parity funds are a more broadly conceived, less levered version of Long-Term Capital Management. I mean that as a compliment, because it was the narrow conception and over-use of leverage at LTCM that ruined a solid investment premise and made it impossible for that firm to survive even a small disruption in patterns of market participant preferences – in LTCM’s case, the strong historical preference of major sovereign nations not to default on their debt obligations and the strong historical preference of major bond investors not to pay non-economic prices for the safety of US sovereign debt. The investment premise of LTCM was to identify small arbitrage opportunities between securities on the basis of historical correlations and to lever up those opportunities to generate nice returns. If you can take that premise and improve it significantly by expanding the scope and depth of the arbitrage opportunities and by shrinking the leverage turns required for acceptable returns … well, that seems like a really great idea to me. And I have zero doubt that investment giants like Ray Dalio, Bob Prince, and Cliff Asness can design a complex levered bond portfolio that is both safer and more rewarding than a simple unlevered stock and bond portfolio under most conditions. But I get VERY nervous when I am told that the reason these complex levered bond portfolios work so well is that a socially constructed behavior such as the assignment of value to highly symbolic securities is “timeless and universal”, particularly when the composition and preference functions of major market participants are clearly shifting, particularly when monetary policy is both massively sized and highly experimental, particularly when political fragmentation is rampant within and between every nation on earth.

Timing is everything with levered bond arbitrage, just ask Jon Corzine. Five years ago, this is a guy for whom there was a plausible path to become President of the United States. This is why he left the US Senate to become Governor of New Jersey, so that he could more easily and more effectively run for President. Losing his re-election bid in 2009 to Chris Christie closed that door, but only temporarily, as F. Scott Fitzgerald’s line that “there are no second acts in American lives” was probably wrong when he wrote it and is clearly not applicable to American society today.

So MF Global came along after the gubernatorial defeat and gave him a place to hang his hat for a few years, maybe refill the personal coffers that had been depleted by his phenomenally expensive campaigns. Within a year of taking the MF Global reins in March 2010, Corzine transformed the firm from a poorly managed commodities brokerage plagued by rogue traders and seemingly constant regulatory fines into a significant capital markets and prop trading player under his direct control. The culmination of this transformation was MF Global’s approval by the New York Fed as a primary dealer in February 2011, allowing the firm to fund itself as cheaply as any major investment bank. From that moment on Corzine – who started his career at Goldman Sachs as a sovereign bond trader – began to build a levered position in distressed European peripheral sovereign debt. By levering MF Global’s capital to the hilt in order to borrow dollars at historically low rates and buy, say, Portuguese 5-year paper with a 10% current yield in April 2011, Corzine stood to make an absolute killing as soon as the Europeans got their act together. After all, this is the sovereign nation of Portugal we’re talking about here, a full-fledged member of the European Union with a currency backstopped by the ECB and Germany, and it’s trading like a distressed corporate bond? Time to back up the truck. In fact, why don’t we put a little bit of duration risk into the mix to juice the returns even more. What could possibly go wrong?


We all know the rest of the story. One day you’re at the pinnacle of business and politics, poised to make a billion or two on a killer trade; the next day you’re testifying before Congress about misuse of client funds and considering taking the Fifth; tomorrow there may be a perp walk. The irony, of course, is that if Corzine had put this trade on 6 to 9 months later, we would today be talking about the brilliance of Jon Corzine, Lion of Wall Street, and how he had created a new Goldman Sachs.

The lesson? Pride may goeth before a fall, but so does leverage, bad timing, and poorly examined assumptions. A dislocation in the price of gold may be the least of our worries in a market and world undergoing structural change.

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2 Fast 2 Furious


We are all impaled on the crook of conditioning.
– James Dean (1931 – 1955)

This note is a sequel to my letter from two weeks ago, What We’ve Got Here Is … Failure to Communicate, a sequel made necessary by the market fall-out from the FOMC announcement on Wednesday. The Fed’s communications to the market are clearly not having the effect intended by Bernanke et al., and the problem remains that the Fed is clueless about the game-playing that dominates this market. The car-driving analogy used by Bernanke in Wednesday’s press conference (to paraphrase, “we are not putting our foot on the brake, we are taking our foot off the accelerator”), intended to soothe and placate, is a perfect example of the Fed’s tone-deafness. From a game-playing perspective, taking your foot off the accelerator is more important than putting your foot on the brake. It is an informational inflection point that absolutely changes game-playing behavior in potentially extreme ways.


Rebel Without a Cause (1955)

We’re all familiar with the classic game of Chicken as depicted in popular narrative: two hot-headed teenagers race their cars toward a cliff’s edge; the first to brake or swerve is the Chicken who loses the game and the girl. Cue Natalie Wood to drop the white handkerchief and start the race …

Now put yourself in the shoes of one of the drivers. Let’s assume that you want to win the game but you also don’t want to die. How do you play this game?

There are two well-known strategies to win a game of Chicken. The first is to signal your opponent convincingly that you really don’t care about living past this race, that you prefer to die young and leave a pretty corpse. The second is to signal convincingly that you have no control over your ability to stop the race or swerve out of the way once you begin … rip the steering wheel out of your car or something like that. The problem with these strategies is that they require effective signaling prior to the race’s start. It really doesn’t do you much good if you remove your steering wheel and pre-commit yourself to driving off the cliff if your opponent doesn’t see you do it! Also, if you make these signals and your opponent still goes forward with the race, then you’ve already lost. Why? Well, if you’re signaling strongly that you’d rather die than lose the race, but your opponent decides to race anyway, what does that signal about him? In poker terms, your all-in bluff was just called.

Here’s why Chicken is so hard to play if the race begins and you’re heading towards the cliff … given the extreme consequences of going off the cliff, your rational decision is to stop your car and let the other guy win. But that logic applies to your opponent, too, and you know it. The rational decision for your opponent is to stop his car and let you win. Both of you want to stop your car, but both of you know that both of you want to stop your car. Why shouldn’t he stop his car first instead of you? Of course, he’s thinking the same thing, and the clock is ticking on both of you going off the cliff.

In formal terms, the game of Chicken has two pure strategy equilibria, and that’s what makes for its extreme instability. Below on the left is a classic two-player Prisoner’s Dilemma game with cardinal expected utility pay-offs as per a customary 2×2 matrix representation. Both you and James Dean have only two decision choices – Stop and Drive – with the joint pay-off structures shown as (you , James Dean) and the twin equilibrium outcomes (Drive , Stop) and  (Stop , Drive) shaded in light blue. With this informational structure, there is absolutely no way to predict which equilibrium will end up occurring, or whether any equilibrium will result.


So you’re still in the race, you’ve still got the pedal to the metal, and you’re starting to freak out. Should you stop the race and let James Dean win? That cliff edge is looming closer and closer, and you are suddenly struck by the realization that your corpse will not be so pretty when pulled out of the wreckage. But then you notice something … your car is starting to pull ahead of James Dean’s car. You know that your car isn’t faster than his, so the only explanation is that James Dean has let up on the accelerator. He’s not putting on the brake, but the informational value of this reduction in acceleration is HUGE. Now you know that James Dean is wavering more than you are. And you know that James Dean knows he is wavering more than you are. Once an unstable game like Chicken tips towards one equilibrium or the other, it moves inexorably towards that equilibrium, faster and faster. Once James Dean starts to waver, both of you know that the next move is for him to waver more and you to waver less. You have won this game, and both of you know it, well before James Dean actually puts his foot on the brake.

Now to be clear, I’m not saying that the game-playing that occurs in markets is a straightforward corollary of Chicken. It’s much more aptly described as a Common Knowledge game. But what the current Common Knowledge market game shares with Chicken is that it is extremely unstable (see Through the Looking Glass, or … This is the Red Pill). And in unstable games, a change in the change of a critical data function – what’s called the second derivative – is incredibly influential on game-playing behavior.

The critical data function in your Chicken Run with James Dean is the position of the two cars. The speed of the cars (change in position over time) is the first derivative of this function, and the acceleration of the cars (change over time in the change in position over time) is the second derivative. If you were to draw a line on a graph to mark the position of the cars over time, the speed of the cars is the slope of that line at any given point in time (more speed = steeper slope = more distance per unit of time) and the acceleration of the cars is the curvature of that line (the slope of the slope). When acceleration stops, the slope of the line is still steep (the cars are still going really fast) but it’s no longer curving upwards. This is what’s called a negative inflection point. It’s the point where the marginal change in your position over time stops getting better. And that’s a really big deal for any rational decision-maker in any strategic environment, whether it’s high school in the 1950’s or the market in 2013.

For all the reasons I’ve laid out in prior work (What We’ve Got Here Is … Failure to Communicate), the critical data function in the market’s expectations of future Fed policy is the unemployment rate, and what the Fed said on Wednesday is that the unemployment rate is improving faster than they previously thought it would. Everyone knows that everyone knows that the unemployment rate is going down (the equivalent of our cars moving forward at a nice speed). The new information from the Fed is that this improvement is accelerating. The second derivative of the unemployment rate, the curve of the unemployment rate over time, is changing in a positive direction for the economy.

So why isn’t that a good thing for the market? Isn’t this good news for the fundamental health of the US economy, and thus good news for corporate earnings and revenue growth? Hasn’t the Fed been crystal-clear that it has no intention of actually tightening (putting its foot on the brake), but is going to remain historically accommodative (the car will continue to go fast) even as conditions improve?

Unfortunately, the Fed has also been crystal-clear that it is taking its foot off the accelerator. They announced an inflection point, which has enormous repercussions for game-playing behavior in an unstable game. This is the point where the marginal improvement in the Fed’s support for the market stops getting better and starts getting worse. And that shift in Fed policy is more than enough to trump whatever organic improvement we are seeing in the US economy.

What we are witnessing today is the opposite of the “green shoots” Narrative of 2009. On Sunday March 15, 2009 Bernanke created the “green shoots” Narrative with a “60 Minutes” television interview (his first) and announced a positive inflection point: from this moment onwards, the marginal improvement in the Fed’s support for the market would increase. This was, of course, accompanied by the Fed’s first Large Scale Asset Purchase (LSAP) program, also known as QE1, and the rest is history. Here’s a chart of the S&P 500 from March 16, 2009 through the rest of the year – an inexorable march upwards for a 48% increase in this broad market index, one of the most ferocious rallies in history – even as the fundamental health of the US economy remained (to be charitable) challenged.


S&P 500, March 16, 2009 – December 31, 2009 (source: Bloomberg)

In exactly the same way that the market went up sharply in 2009 even as the real economy got worse, so today can the market go down sharply even as the real economy gets better.

How far down? I have no idea. It all depends on how the Narrative is shaped from here. Narrative formation can be tricky thing, and we will see over the coming days and weeks how the Powers That Be and talking heads respond with their public statements to support the market.

This past Friday, for example, at 6:08 AM St. Louis Fed President James Bullard released a statement detailing why he dissented from Wednesday’s FOMC decision on dovish grounds. You can find the full text on the St. Louis Fed’s website ( and judge for yourself, but what’s notable to me is the act of publishing a formal dissent as well as the stridency of Bullard’s language within the usually staid context of Fed-speak. He “found much to disagree with in this decision” and even invoked the C-word – credibility – in his criticism. To suggest that the FOMC might have a problem in its efforts “to maintain credibility” with Wednesday’s announcement is the Fed-speak equivalent of going nuclear.

Bloomberg picked up the release of the Bullard’s statement (the only FOMC member to make a statement since Wednesday) but didn’t give it much attention. The Wall Street Journal even less. But then the market went down from the opening bell, continuing the losses from the prior two days. So much for the “Rebound in Stocks” promised by the Wall Street Journal before the open, a story which became “Stocks Give Back Gains” soon afterwards.


S&P 500 intraday chart, June 21, 2013 (source: Bloomberg)

The response from major financial print media:

  • At 10:15 AM the Financial Times published an article about Bullard’s statement titled: “Bernanke decision ‘inappropriately timed’, says St. Louis Fed”.
  • At 11:20 AM Bullard gave a telephone interview to Bloomberg. Naturally, Bloomberg gave this interview a lot more space than the earlier statement, and kept a story titled “Bullard Says Fed May Need to Boost Asset Buying If Inflation Slows Further” on its Top Stories list throughout the rest of the day.
  • At 11:57 AM the Wall Street Journal published an article titled “Bullard’s Unusual Dissent” in its MoneyBeat section.

The market at least stopped going down after Bloomberg and the Wall Street Journal trumpeted the Bullard interview, hitting its lows for the day at 11:31 AM, but the march up didn’t begin until about 12:45 PM in anticipation of an influential Wall Street Journal article (the Wall Street Journal typically publishes its major Opinion-Leading-Masquerading-As-Analysis pieces at 1 PM).

Sure enough, at 1:01 PM the Wall Street Journal published an article by Jon Hilsenrath titled “Analysis: Markets Might Be Misreading Fed’s Messages” and the market completed its resuscitation immediately after this article came out. “Stocks Give Back Gains” becomes “Stocks Try to Regain Footing.”

How can a Wall Street Journal writer move the market so much more than the St. Louis Fed President? Because everyone knows that everyone knows that Hilsenrath is the Fed’s favorite print media mouthpiece. This is the market’s Common Knowledge about how Fed intentions are revealed. In the Bizarro-market that we must all endure, divining Fed intentions third-hand through Hilsenrath’s “analysis” is more informationally influential than hearing the St. Louis Fed President’s beliefs directly!

But it’s not easy to reshape a Narrative as firmly entrenched as “the Fed will reduce monetary accommodation proportionally to the decline in the unemployment rate.” For more than four years now, the market has been trained (and by “the market” I mean both human investors and trading algorithms) to take Bernanke communications as the single most influential signal in determining investment decisions. As James Dean said, “We are all impaled on the crook of conditioning,” and no group of individuals or computer programs is more intensively conditioned than market participants.

The only person with enough informational “juice” to undo the inflection point that occurred on Wednesday is Bernanke himself. The only other signal emitters that even come close in their informational influence are Draghi and Merkel. Everyone else – and that includes Obama, much less other FOMC members or any journalist – are an order of magnitude less important from an Information Theory perspective. The Hilsenrath’s and the Bullard’s of the world can stop the bleeding for a day or two, but they can’t change the underlying Common Knowledge structure.

Even someone as informationally powerful as Bernanke is not omnipotent. He must operate under both the institutional constraints of being a lame-duck Fed Chairman and the personal constraints of wanting to cement a legacy … both of which are very powerful and inherently risk-averse forces.  To convince the market that the Wednesday announcement meant something different from its plain-faced interpretation would require a wholesale dismantling of prior communications linking unemployment thresholds to QE tapering, and that’s something Bernanke will be extremely loathe to do unless he has the formal backing of the FOMC and/or things get a whole lot worse.

Keep in mind, too, that Bernanke’s signals are not communicated to us directly, but are mediated by a host of self-serving entities, from political institutions to individuals (including Bernanke himself) to corporations large and small. In the absence of Bernanke making a public mea culpa on tying Fed monetary accommodation to the unemployment rate, the best thing for diminishing this market-negative Common Knowledge informational structure would be for these signal mediators to reduce the attention and meaning attached to the unemployment rate. But that ain’t happening.

The “good news” of a declining unemployment rate serves too many institutional and personal self-interests for this Narrative to weaken, no matter how weak the broader measures of US labor conditions might be.

For example, listen to what David Axelrod says about the unemployment rate in a panel discussion organized by Axelrod’s Institute of Politics: Campaign Strategists: 2012 Explained. It’s a long video, but for anyone interested in US politics it’s a must-see. Why did Obama win in November? Because the unemployment rate went down in the months leading up to the election.  It wasn’t the Obama campaign’s use of Big Data. It wasn’t any failing in the Romney message or strategy. The economy got better, as evidenced and interpreted by the unemployment rate, and that swung a lot of undecided voters. That’s what won the election.

Or look at the ratings for CNBC on Jobs Friday versus any other day of the month … it’s not even close. Nuanced discussions of US labor conditions are for Charlie Rose, not Jim Cramer, which is why the former is seen by a handful of people on PBS and the latter is laughing all the way to the bank.

Everyone knows that viewing US labor conditions solely through this single constructed number is simplistic and kinda stupid. But so what? Everyone also knows that everyone knows that this number moves the market. Unless Bernanke reverses course and tells us otherwise, everyone knows that everyone knows that this is a crucial number for the Fed. And all signal mediators – from the White House to CNBC to everyone in-between – have a vested interest in keeping the Narrative of a “healing US labor market” intact. As a result, from an informational perspective it is now easier for this market to go down than to go up. Be careful out there.

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The Narrative Battle is Joined


A review of Narrative formation efforts on June 21st to support the market.

epsilon-theory-the-narrative-battle-is-joined-june-21-2013.pdf (227KB)


Failure to Communicate, Part Deux


A review of Narrative formation immediately after the June 19th FOMC announcement.

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Through the Looking Glass, or … This is the Red Pill


There are unwritten rules for almost all social phenomena, from investing to writing epic poetry. Not that these weekly notes aspire to Homeric levels (although they do get pretty lengthy), but Epsilon Theory does follow one unwritten rule taken from the Iliad, the Aeneid, etc. in that I began this story in media res– in the middle of things. Last week’s note looked at two very current issues – the jobs report on Friday June 7th and the Fed’s market communication policy – through the lens of game theory to develop what I think are some non-intuitive results … namely that the market’s Narrative around US labor conditions is fundamentally at odds with the Fed’s communications, creating a major source of instability for global markets.  And if you look at the archived notes I’ve posted, they are almost all focused on some specific event or issue.

But at some point it’s important to step back and show in a more general way how game theory works to shape markets. This may strike some readers as too academic, but c’est la vie. We will return to our regularly scheduled entertainment next week. This week I want to lift the hood a bit on Epsilon Theory and show you how the engine works, or at least that there is an engine, so that you will trust me enough to get in the car and let me drive you to this or that destination. Epsilon Theory is not a collection of musings, and it’s not a blog. There’s significant intellectual property here, and you deserve to be convinced of that before you invest more of your time reading what I’ve got to say.

Along the way, though, there will be payoffs for the patient reader.  I’ll show you the difference between volatility and instability, why it’s the latter we are suffering through today, and why the difference is critically important for your portfolio. Also, I can give you an answer to the lead story in the JP Morgan Market Intelligence note from this Wednesday, June 12th:

Market Update – “why are the futures up?” – as has been the case on most mornings for the last few weeks, the futures are making a large move for no apparent reason. On the whole it was a quiet night as far as incremental news is concerned and as a result people had a lot of time to contemplate some of the big recent themes driving trading (bond weakness, EM rout, Fed tapering, whether stocks are whistling past the graveyard, etc).

A game theoretic perspective reveals the all too real dynamics behind the “no apparent reason” for these market swings, and there’s nothing academic about that.

An unwritten rule is also called a Convention, and both are just alternative names for Common Knowledge. The best example of a Convention is … language. There’s no inherent reason why we should call a rabbit by the name “rabbit” instead of some other word; it’s just a behavioral Convention that English-speaking people have developed over centuries in order to improve their mutual lot in life. There was no Saxon chieftain that commanded people to start calling the long-eared rodent that jumps around a lot a “rabbit” instead of a “gavagai”, to use Quine’s famous example. Instead, over time it somehow became clear to this group of people that everyone knows that everyone knows that a long-eared rodent that jumps around a lot is called a “rabbit”. A behavioral equilibrium to call a rabbit a “rabbit” developed without coercion, and as a result hunting long-eared rodents that jump around a lot got a whole lot easier for the group of people who shared this Convention. If you lived in this group but didn’t share the Convention – if you insisted on calling a rabbit a “gavagai” and had your own words for lots of other things – well, you probably didn’t last very long in this group. Similarly, if you’re an investor and you don’t share the Conventions of the market (“don’t fight the Fed” and its like) – well, you’re probably not going to last very long, either.


WVO Quine, photograph by Steve Pyke (1990)

The best game theoretic work on Common Knowledge comes from linguists like Brian Skyrms and evolutionary biologists like Edward O. Wilson, not economists. There’s an enormous intellectual depth to these fields that I can’t do justice to here, but for our purposes of applying Common Knowledge game theory to markets I want to highlight a few ideas that underpin modern linguistic and biological studies of Convention.

Conventions evolve over time – whether we are talking about Conventions governing language or market behavior or any other social behavior – which is why it’s so important to know something about history in order to understand behavior. There is nothing eternal or written in stone about any behavioral Convention, and even the most socially entrenched Convention can change with amazing speed. For example, dueling and slavery as state-sponsored Conventions were considered part of the “natural order of things” for thousands of years; within a span of about 80 years in the 19th century they were wiped out globally. That said, it’s very hard to see Conventions changing when you’re living in one. John Lennon wrote that “it’s easy if you try” to imagine a future with alternative sets of Conventions, but for most of us it is nearly impossible. Of course, that doesn’t mean that change isn’t coming. It always does.

Conventions are formed by communication. Language is an obvious form of communication, but so is buying 100 shares of Apple. Any behavior, if made publicly, is a communication of sorts, whether communication was intended or not. It is a signal.

As social animals our brains are hard-wired to look constantly for communication signals and respond to them. As social animals we train each other from birth to look constantly for communication signals and respond to them. We can no more ignore a speech delivered by Bernanke than ants can ignore a pheromone emitted by their queen. At first blush this might seem like a weakness, as something to be avoided or at least mitigated. But it is precisely this heightened sensitivity to signals that makes us, like ants, such a successful species! Human behavior in response to signals – what is more commonly called decision-making – is not chaotic or illogical or counterproductive. On the contrary, it’s the finely honed product of millions of years of biological evolution and hundreds of thousands of years of social reinforcement. It’s why there are 8 billion of us on the planet today.

The insight of evolutionary studies of linguistic Convention is that because we have been socially organized as a certain type of social animal for millennia and because the wiring of our brains for social success hasn’t changed in a lot longer than that, there is an identifiable pattern to our behavior around signals. There is an underlying behavioral logic at work in humans. The parameters of that behavior – the Convention itself – may be socially constructed and constantly changing (i.e., there’s no natural reason to call a rabbit a “rabbit”, or to value gold more highly than peacock feathers), but the logic and pattern of strategic human decision-making are constant over time.

If you can measure the signals that investors are biologically and socially wired to respond to, and if you can map out the likely behavioral pattern of those responses … then you can predict how markets will respond to new signals.

That’s what I’m trying to do with Epsilon Theory.

There is a methodology for measuring and analyzing signals. It’s called Information Theory.  To conceptualize how signals and patterns of strategic decision-making work together to create predictable market outcomes, I have developed what I believe is a novel way of depicting the informational structure of markets. This is the intellectual heart of Epsilon Theory. And not to get all Matrix-y, but once you start to see the market in terms of its informational structure, that in fact, the market IS an informational structure, nothing more and nothing less, then you will have a very difficult time going back to seeing it as you once did.


Defining the strength of a signal as the degree to which it changes assessments of future states of the world dates back to Claude Shannon’s seminal work in 1948, and in a fundamental way back to the work of Thomas Bayes in the 1700’s.  Here’s the central insight of this work: information is measured by how much it changes your mind. In fact, if a signal doesn’t make you see the world differently, then it has zero information. As a corollary, the more confident you are in a certain view of the world, the more new information is required to make you have the opposite view of the world and the less information is required to confirm your initial view. There’s no inherent “truth” to any signal, no need to make a distinction between (or even think of) this signal as having true information and that signal as having false information. Information is neither true nor false. It is only more or less useful in our decision-making, and that’s a function of how much it makes us see the world differently. As a result, the informational strength of any signal is relative. The same signal may make a big difference in my assessment of the future but a tiny difference in yours. In that case, we are hearing the same message, but it has a lot of information to me and very little to you.

Let’s say that you are thinking about Apple stock but you are totally up in the air about whether the stock is going up or down over whatever your investment horizon might be, say 1 year. Your initial estimation of the future price of Apple stock is a coin toss … 50% likelihood to be higher a year from now, 50% likelihood to be lower a year from now. So you do nothing. But you start reading analyst reports about Apple or you build a cash-flow model … whatever it is that you typically do to gather information about a potential investment decision.

The graph below shows how Information Theory would represent the amount of signal information (generically represented as bits) required to change your initial assessment of a 50% likelihood of Apple stock going up over the next year to a post-signaling assessment of some new percentage likelihood. These are logarithmic curves, so even relatively small amounts of information (a small fraction of a generic bit) will change your mind about Apple pretty significantly, but more and more information is required to move your assessment closer and closer to certainty (either a 0% or a 100% perceived likelihood of the stock going up).


Of course, your assessment of Apple is not a single event and does not take place at a single point in time. As an investor you are constantly updating your opinion about every potential investment decision, and you are constantly taking in new signals. Each new update becomes the starting point for the next, ad infinitum, and as a result all of your prior assessments become part of the current assessment and influence the informational impact of any new signal.

Let’s say that your initial signals regarding Apple were mildly positive, enough to give you a new view that the likelihood of Apple stock going up in the next year is 60%. The graph below shows how Information Theory represents the amount of information required to change your mind from here. The curves are still logarithmic, but because your starting point is different it now only requires 80% of the information as before to get you to 100% certainty that Apple stock will go up in the next year (0.8 generic bits versus 1.0 generic bits with a 50% starting estimation). Conversely, it requires almost 140% of the same negative information as before to move you to certainty that Apple stock is going down.


What these graphs are showing is the information surface of your non-strategic (i.e., without consideration of others) decision-making regarding Apple stock at any given point in time.  Your current assessment is the lowest point on the curve, the bottom of the informational “trough”, and the height of each trough “wall” is proportional to the information required to move you to a new assessment of the future probabilities. The higher the wall, the more information required in any given signal to get you to change your mind in a big way about Apple.

Now let’s marry Information Theory with Game Theory. What does an information surface look like for strategic decision-making, where your estimations of the future state of the world are contingent on the decisions you think others will make, and where everyone knows that everyone is being strategic?

I’m assuming we’re all familiar with the basic play of the Prisoner’s Dilemma, and if you’re not just watch any episode of Law and Order. Two criminals are placed in separate rooms for questioning by the police, and while they are both better off if they both keep silent, each is individually much better off if he rats his partner out while the partner remains silent. Unfortunately, in this scenario the silent partner takes the fall all by himself, resulting in what is called the “sucker pay-off”. Because both players know that this pay-off structure exists (and are always told that it exists by the police), the logical behavior for each player is to rat out his buddy for fear of being the sucker.

Below on the left is a classic two-player Prisoner’s Dilemma game with cardinal expected utility pay-offs as per a customary 2×2 matrix representation. Both the Row player and the Column player have only two decision choices – Rat and Silence – with the joint pay-off structures shown as (Row , Column) and the equilibrium outcome (Rat , Rat) shaded in light blue.

The same equilibrium outcome is shown below on the right as an informational surface, where both the Row and the Column player face an expected utility hurdle of 5 units to move from a decision of Rat to a decision of Silence. For a move to occur, new information must change the current Rat pay-off and/or the potential Silence pay-off for either the Row or the Column player in order to eliminate or overcome the hurdle. The shape of the informational surface indicates the relative stability of the equilibrium as the depth of the equilibrium trough, or conversely the height of the informational walls that comprise the trough, is a direct representation of the informational content required to change the conditional pay-offs of the game and allow the ball (the initial decision point) to “roll” to a new equilibrium position. In this case we have a deep informational trough, reflecting the stability of the (Rat , Rat) equilibrium in a Prisoner’s Dilemma game.



Now let’s imagine that new information is presented to the Row player such that it improves the expected utility pay-off of a future (Silence, Rat) position from -10 to -6. Maybe he hears that prison isn’t all that bad so long as he’s not a Rat. As a result the informational hurdle required by the Row player to change decisions from Rat to Silence is reduced from +5 to +1.



The (Rat , Rat) outcome is still an equilibrium outcome because neither player believes that there is a higher pay-off associated with changing his mind, but this is a much less stable equilibrium from the Row player’s perspective (and thus for the overall game) than the original equilibrium.

With this less stable equilibrium framework, even relatively weak new information that changes the Row player’s assessment of the current position utility may be enough to move the decision outcome to a new equilibrium. Below, new information of 2 units changes the perceived utility of the current Rat decision for the Row player from -5 to -7. Maybe he hears from his lawyer that the Mob intends to break his legs if he stays a Rat. This is the equivalent of “pushing” the decision outcome over the +1 informational hurdle on the Row player’s side of the (Rat , Rat) trough, and it is reflected in both representations as a new equilibrium outcome of (Silence , Rat).



This new (Silence , Rat) outcome is an equilibrium because neither the Row player nor the Column player perceives a higher expected utility outcome by changing decisions. It is still a weak equilibrium because the informational hurdle to return to (Rat , Rat) is only 1 informational unit, but all the same it generates a new behavior by the Row player: instead of ratting out his partner, he now keeps his mouth shut.

The Column player never changed decisions, but moving from a (Rat , Rat) equilibrium to a (Silence , Rat) equilibrium in this two time-period example resulted in an increase of utility from -5 to +10 (and for the Row Player a decrease from -5 to -6). This change in utility pay-offs over time can be mapped as:


Replace the words “Column Utility” with “AAPL stock price” and you’ll see what I’m going for. The Column player bought the police interrogation at -5 and sold it at +10. By mapping horizontal movement on a game’s informational surface to utility outcomes over time we can link game theoretic market behavior to market price level changes.

Below are two generic examples of a symmetric informational structure for the S&P 500 and a new positive signal hitting the market. New signals will “push” any decision outcome in the direction of the new information. But only if the new signal is sufficiently large (whatever that means in the context of a specific game) will the decision outcome move to a new equilibrium and result in stable behavioral change.



In the first structure, there is enough informational strength to the signal to overcome the upside informational wall and push the market to a higher and stable price equilibrium.



In the second structure, while the signal moves the market price higher briefly, there is not enough strength to the signal to change the minds of market participants to a degree that a new stable equilibrium behavior emerges.

All market behaviors – from “Risk-On/Risk-Off” to “climbing a wall of worry” to “buying the effin’ dip” to “going up on bad news” – can be described with this informational structure methodology. 

For example, here’s how “going up on bad news” works. First, the market receives a negative Event signal – a poor Manufacturing ISM report, for example – that is bad enough to move the market down but not so terrible as to change everyone’s mind about what everyone knows that everyone knows about the health of the US economy and thus move the market index to a new, lower equilibrium level.



Following this negative event, however, the market then receives a set of public media signals – a Narrative – asserting that in response to this bad ISM number the Fed is more likely to launch additional easing measures. This Narrative signal is repeated widely enough and credibly enough that it changes Common Knowledge about future Fed policy and moves the market to a new, higher, and stable level.



So what is the current informational structure for the S&P500? Well, it looks something like this:


The market equilibrium today is like a marble sitting on a glass table. It is an extremely unstable equilibrium because the informational barriers that keep the marble from rolling a long way in either direction are as low as they have been in the past five years. Even a very weak signal is enough to push the marble a long way in one direction, only to have another weak signal push it right back. This is how you get big price movements “for no apparent reason”.

Why are the informational barriers to equilibrium shifts so low today? Because levels of Common Knowledge regarding future central bank policy decisions are so low today. The Narratives on both sides of the collective decision to buy or sell this market are extremely weak. What does everyone know that everyone knows about Abenomics? Very little. What does everyone know that everyone knows about Fed tapering? Very little. What does everyone know that everyone knows about the current state of global growth? Very little. I’m not saying that there’s a lack of communication on these subjects or that there’s a lack of opinion about these subjects or that there’s a lack of knowledge about these subjects. I’m saying that there’s a lack of Common Knowledge on these subjects, and that’s what determines the informational structure of a market.

The unstable market informational structure today is NOT a volatile informational structure, at least not as “volatility” is defined and measured by today’s market Conventions. Here’s what a volatile market structure looks like:


This is an asymmetric informational structure where the signal barriers for the market to go down are much lower than the signal barriers for the market to go up. This structure does not mean that the market will definitely go down; it simply means that the market can go down, and will go down, with “ordinary” bad news on either increased macroeconomic stress or reduced policy support. The market could still go up, but it would take extremely positive signals on either the macro or policy front to overcome the high informational barrier. Given anything close to a normally distributed set of new market signals, a market with this informational structure is much more likely to go down than to go up, which will be reflected by higher market volatility measures.

I’m a big believer in calling things by their proper names. Why? Because if you make the mistake of conflating instability with volatility, and then you try to hedge your portfolio today with volatility “protection” – VIX futures, one of the VIX ETF’s, S&P 500 puts, etc. – you are throwing your money away. You are buying insurance for a danger that doesn’t exist right now, and you are leaving yourself unprotected against the danger that is staring you in the face. 

So if you’re reading Epsilon Theory for specific trade ideas, here’s one: short VXX every time it pops its head up on a big down day as investors rush to buy “volatility protection”. Hedge it out with some sort of long straddle on the S&P 500 or short positions on underlying stocks if you want to be cautious, but you really don’t need to. So long as most investors mistake instability for volatility (and unless Epsilon Theory gets a LOT more distribution I think it’s safe to say that will persist for a looooong time) this is an archetypal behavioral trade. I’ll let you know if the informational structure shifts so that volatility really does raise its ugly head.

On that note … I need to ‘fess up about something. The informational structures I’m showing in this note are a rudimentary version of what I’m using in my current research, in at least four ways.

  • There’s a fractal structure to game-playing, where the same patterns occur on different time frames and different market aggregation levels. Read anything by Benoit Mandelbrot if you don’t know what I’m talking about.
  • There’s a meaningful distinction between backward-looking Event signals, like the release of macroeconomic data, and forward-looking Narrative signals, like the communications of central bankers and politicians.
  • There’s also a Market signal – what George Soros calls “reflexivity” – that plays an important role in market game-playing. If you’ve ever watched a stock drop violently without any news showing up on Bloomberg or your traders hearing anything on chat, and then you’ve sold the stock because “somebody must know something” … that’s reflexivity.
  • There’s a dimension of time to all this, so that an information surface is three-dimensional (much like a volatility surface in options trading), not two-dimensional as shown in this note. Actually, the information surface is four-dimensional when you add uncertainty and what game theorists call “the shadow of the future” into the algorithms.

And you’ll notice I’m not saying anything about the methodology for actually measuring any of this.

I mention all this, not to be coy, but because I want to make clear that there is a depth to Epsilon Theory beyond some interesting but abstract perspective on markets. I mean for Epsilon Theory to have a strong practical application to active investment management, and to that end, I think that I’m pretty far along in developing the necessary tools and instruments.

Of course, I also mean to signal that there’s a lot more to Epsilon Theory than what I am distributing publicly! I don’t want Epsilon Theory to be a black box, not because I have anything against black boxes, but because I think the Convention of trusting a black box had been dying for a long time before Madoff put the final nail into that coffin. I’m not sure what the black box Convention has evolved into, but I’m trying to find out.

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What We’ve Got Here Is … Failure to Communicate


epsilon-theory-what-weve-got-here-is-a-failure-to-communicate-june-9-2013-cool-hand-lukeFrom the classic Paul Newman movie, Cool Hand Luke, as the Captain administers Luke’s punishment in the prison yard for yet another escape attempt:

 Captain: You gonna get used to wearing those chains after a while, Luke. Don’t you never stop listening to them clinking, ‘cause they gonna remind you what I been saying for your own good.

Luke: I wish you’d stop being so good to me, Cap’n.

Captain: Don’t you ever talk that way to me. NEVER! NEVER! [Captain hits Luke, who rolls down the hill to the other prisoners] What we’ve got here is … failure to communicate. Some men you just can’t reach. So you get what we had here last week, which is the way he wants it. Well, he gets it. I don’t like it any more than you men.

There are plenty of great cinematic scenes of the Common Knowledge game in action, but this is one of my favorites. The “failed” communication of the Captain to Luke is the basis for the successful communication of the Captain to the prisoners: subvert my rules and you will be crushed. The brutal message is made in public, not so that all the prisoners can see what happens to Luke, but so that all the prisoners can see all the prisoners seeing what happens to Luke.

In environments like prisons (and capital markets!), behavioral decisions based on private information (“I saw Luke beaten down for breaking the rules. If I break the rules I might get beaten, too.”) are almost always weaker than behavioral decisions based on Common Knowledge (“Everyone knows that if you break the rules like Luke you will be beaten down. Why would I even think about breaking the rules?”). The latter is a more stable equilibrium because, in effect, the prisoners themselves end up enforcing the warden’s rules. Even if you privately believe that you and your fellow prisoners could make a break for it, so long as you believe that “everyone knows” that you will be punished for breaking the rules, then you do not believe that you will receive any support from your fellow prisoners. It is irrational to even raise the subject with your fellow prisoners, as you will mark yourself as someone who is either too stupid or too dangerous not to recognize what everyone else knows that everyone else knows. And because everyone is making a similar calculation, no one ever makes an escape attempt and the Common Knowledge grows stronger over time, as does the no-escaping behavioral equilibrium. This is why the Captain goes to such lengths not just to punish Luke for his escape attempts, but to break Luke, and not just to break Luke, but to break Luke as publicly as possible.

Because of the Common Knowledge game, there is enormous power in making a Public Spectacle out of information, which is why coronations and executions alike have traditionally been carried out in front of large crowds. This lesson in behavioral influence – the crowd doesn’t just need to see the event, the crowd needs to see the crowd seeing the event – is why so many of our modern social institutions – from political campaigns to American Idol – are staged in front of live audiences. When you sit in front of your TV set and watch, say, a national political convention, you are infinitely more engaged with the event when you see a crowd than when you don’t. You can’t help yourself. It doesn’t even matter if the live audience is faked and we know that the audience is faked … have you ever listened to a sitcom without a laugh track? It’s just not as funny. The fact is that humans are social animals. We are hard-wired to look for and respond to Common Knowledge, and smart people – from political leaders to religious leaders to business leaders – have taken advantage of this for thousands of years.

So with that as introduction … ladies and gentlemen, I give you Jobs Friday™, brought to you by your friends at CNBC and Bloomberg and CNN and MSNBC and Fox and the WSJ and the FT and the NYT and every other financial media outlet.

There is an enormous difference between an unemployment number released in the context of Jobs Friday™ versus that same number released in the absence of Public Spectacle. The employment data today is imbued with so much more Meaning than it was even a few years ago … far too much Meaning … more than the numbers themselves can bear. And all of us, including the media creators of Jobs Friday™, know this to be true.

We all know that whether the unemployment rate is 7.5% today or 7.4% or 7.7% really makes no difference whatsoever for Fed policy decisions. We all know that whether there were 175,000 jobs added last month or 165,000 jobs added or whether it beat or missed expectations by 3,000 jobs makes even less difference. We all know that whatever the numbers are this month they will be revised next month. We all know that we are collectively paying too much attention to these numbers, but because we believe that everyone else is paying a lot of attention to these numbers, because the Fed has told us how important these numbers are, because we see the crowd of people participating in breathless commentary over the importance of Jobs Friday™, we can’t help ourselves.

We play the Common Knowledge game around the monthly jobs number, even though none of us believe privately that the numbers from month to month are very important. In fact, it would be irrational not to play the Common Knowledge game under the circumstances of Jobs Friday™. The jobs report, as mediated and narrated by talking heads through public statements, moves the market a lot. That’s an incontrovertible fact. So burying your head in the sand and refusing to play the game is not really an option for most active investment managers.

But apart from the frustration of investors ill-prepared to play a game that they are forced to play, here’s the bigger problem. While one monthly jobs number versus another may not make any difference in shifting Fed policy, the game-playing around the jobs numbers certainly does. The Fed’s communication policy is fundamentally flawed because it does not take into account game theory. This is a huge problem for the Fed, and as a result, it’s a huge problem for the stability of global capital markets.

The problem for the Fed is that their interpretation of US labor conditions does not match the market Narrative. The Fed, as you would expect, looks at lots of data in determining the “true” state of US labor conditions. The Narrative does not.

The Narrative around US labor conditions is formed almost exclusively around two numbers: the unemployment rate and the month-over-month change in non-farm payrolls. Both of these numbers are social constructions, which at first blush may sound weird. These numbers are based on actual counting of actual people. There may be mistakes made in the counting, but in what sense are they “constructed”? They are constructions because the government’s choice of what people to count and how to define employment is only one choice among many as to how to represent this social phenomenon. They are constructions because the choice of which systematic mistakes in the collection of labor data are adjusted for and which are not is enough to make a difference in the construction and perception of the US labor Narrative. I’ve written in the past about the ways in which the construction of the weekly unemployment claims number has been molded to support a particular Narrative (“10-29-12 Jack Welch Was Right”), so you can take a look at that piece if you’re interested, but that’s not what I want to explore today.

The point today is that the limitation of the US labor Narrative to the unemployment rate and the non-farm payroll number – a limitation that is encouraged, promulgated, and reinforced by the mediators of Jobs Friday™ and creates the informational structure of market game-playing around labor announcements – undermines the Fed’s market communications in a predictable and significant manner. Here’s the crux of the problem: the numbers that go into the labor Narrative are good, but the broader labor numbers that the Fed looks at aren’t so good. Unfortunately, the Fed has explicitly communicated their QE policy in terms of the Narrative, not the broader conditions, which means that Fed communications are increasingly at odds with Fed intentions.

The reason that the unemployment rate and the non-farm payrolls number don’t mean what they used to mean in terms of a broad view of US labor conditions, even though they are the sole sources of Meaning for the Narrative, is that the US labor market has changed structurally in terms of trend employment growth. Trend employment growth is the number of jobs that need to be created each month to keep the unemployment rate flat. Anything above-trend will push the unemployment rate down over time, and conversely, below-trend job creation will push the unemployment rate up. Back in the 1980’s and 1990’s it took close to 200,000 new jobs every month just to keep the unemployment rate flat. Today trend employment growth is only 80,000 jobs, and that number will continue to go down for the foreseeable future. As a result, even if we only continue to average, say, 160,000 new jobs per month, which historically speaking is no great feat, the unemployment rate will inexorably fall. It will continue to go down even if US growth remains anemic. It will continue to go down well past the 6.5% threshold communicated by the Fed – not because of open-ended QE – but because above-trend employment growth is so easy to achieve today.

This is not some fringe view; nor is it original to me (which is often, but not always, a fringe view). This is the view of the Chicago Fed. Here’s the link to the paper: (“Chicago Fed Letter, July 2013″). It’s an important piece of work and you should read it. I would only add that I think the authors are too conservative in their views of how quickly the unemployment rate will fall, because in addition to structural change in trend employment growth we are impacted by three policy-driven changes in measurements of unemployment: elimination of unemployment benefits after 99 weeks (moving more people from the unemployed category to the no-longer-looking-for-work category), expansion of student loans (full-time students do not count as unemployed), and hiring at shorter work-weeks to avoid Federal health insurance mandates (so hiring more people to do the same amount of aggregate work).

Even a cursory look at the unemployment rate over the past five years shows an unmistakable trend downwards. I’ve marked the open-ended QE decision with a green line just to show how immaterial this Fed policy is to the unemployment rate trend, even though its scope and its very existence are tied directly to the unemployment rate. In some future note I’ll discuss the concept of linkage from a game theoretic perspective, particularly the strategy of linking things that aren’t really meant to be linked together. For now let me just say that this is a very, very risky thing to do.


Epsilon Theory Manifesto


Our times require an investment and risk management perspective that is fluent in econometrics but is equally grounded in game theory, history, and behavioral analysis. Epsilon Theory is my attempt to lay the foundation for such a perspective.

The name comes from the fundamental regression equation of modern portfolio management: y = α + β+ ε where the return of a security (y) is equal to its idiosyncratic factors (alpha) plus its co-movement with relevant market indices (beta) plus everything else (epsilon).

The language of professional investment is dominated by this simple econometric formulation, and the most fundamental questions regarding active portfolio management – does an investment strategy work? how does an investment strategy work? – are now entirely framed in terms of alpha and beta, even if these words are not used explicitly. When investors ask a portfolio manager “what’s your edge?” they are asking about the set of alpha factors that can differentiate the performance of an actively managed portfolio from a passively managed portfolio. Even a response as non-systematic as “I know everything about the semiconductor industry and I have a keen sense of when these stocks are over-valued or under-valued” is really a statement about alpha factors. It is a claim that there is a historical pattern to security price movements in the semiconductor industry, that these movements are linked to certain characteristics of semiconductor companies, and that the manager can predict the future state of security prices in this industry better than by chance alone by recognizing and extrapolating this historical pattern.

This notion – that observed characteristics of securities, companies, or the world determine to a large extent the future prices of securities – is so ingrained in the active investment management consciousness that it is hard to imagine an alternative. If the observed characteristics of a security or its underlying economic entity or its relevant events are not responsible for making the price of the security go up or down, then what is? If market co-movement (beta) is the answer, then the passive investment crowd is right and we should just put our equity allocations into broad market ETF’s and call it a day.

Unfortunately for the active management community, alpha factors have not done terribly well in recent years, regardless of asset class, investment strategy, geography, etc. I’m not saying that active managers as a group have not had positive performance. I’m saying that on both a risk-adjusted and non-risk-adjusted basis the population of active managers today has underperformed prior populations of active managers to a significant degree. Are there individual exceptions to this general observation? Of course. I am making an observation about a population, not any individual member of the population. But I don’t think it’s a particularly contentious statement to say that the enterprise of active investment management has been challenged over the past five years.

One way to improve the efficacy of active management is to do better with alpha identification … to identify new historical patterns (including the pattern of pattern change), to measure the characteristics of securities and companies and events more accurately, etc. It seems to me, though, that this sort of effort, where we seek to add one more term to the list of alpha variables or improve the list we already have, is inevitably an exercise in diminishing returns, and a crowded exercise at that. I think it is ultimately a dead end, particularly in an era of Big Data technology and strong regulatory proscriptions against private information regarding public companies.

Instead, I think we should be looking outside the confines of factor-based investment analysis. We can’t squeeze much more juice out of the alpha fruit, and we know that beta gives no sustenance to the active investor. But what about epsilon? What about ε? We pejoratively call this an “error” term, and the goal in any applied econometric exercise is to make this term as small and inconsequential as possible. That makes perfect sense if we are trying to predict the future states of, say, decaying nuclear particles, where it seems unlikely that there is any agency or causal process outside of the particles themselves (i.e., outside the physical universe). But it makes no sense at all if we are investigating a social phenomenon such as a financial market, where strategic human behavior and decision making play a crucial role, but a role largely exogenous to the observed characteristics of the financial universe.

It’s the epsilon term that I want to explore, because it includes anything that cannot be expressed easily in econometric terms – things like strategic decision-making and shifting behavioral preferences. Modern portfolio theory ignores these dynamic behavioral characteristics by assumption, as the epsilon term is defined as residual and random information from the perspective of the static factors defined within the alpha and beta terms. Because decision-making and behavioral characteristics cannot easily be expressed in the language of factors and regression, they are essentially invisible to the econometric eye.


Roy Lichtenstein, “I Can See The Whole Room …” (1961)

Fortunately, there is both a language and a lens available to analyze these human behavioral patterns in a rigorous fashion: game theory. Game theory and its close cousin, information theory, allow us to extract non-random, non-residual information from the epsilon term, which in turn allows us to predict or understand the likely return of a security more accurately.

To take a recent example, consider the recent plunge in the price of gold. Here’s how Alen Mattich “explained” the sharp drop in the MoneyBeat column of the Wall Street Journal on May 20th:

But just as there wasn’t any real logic needed to keep prices advancing when everyone was caught up in the euphoria, there’s no need for logic to intrude in the fall either. These things take on a life of their own. Especially when there’s so little rational basis on which to price these assets in the first place.

If you’re looking solely through the lens of factor analysis, Mattich is right: what’s happened recently in gold prices makes no sense. But through the lens of game theory, there is absolutely a logic and a rational basis for this market behavior. Game theory is explicitly designed to help explain events that otherwise appear to have “taken on a life of their own”, and my goal in Epsilon Theory is to elucidate and communicate that explanatory perspective to as broad an audience as possible.

I believe that we are witnessing a structural change in markets, brought on by a witches’ brew of global debt crisis, new technology, and new regulatory regimes. By structural change I mean a fundamental shift in the market’s relationship to society and politics, as well as a sea change in the behavioral preferences of market participants. Modern portfolio theory takes both of these terms – market rules and market participant preferences – as constants, and as a result it is impossible to see the impact of structural change by looking solely through the lens of alpha and beta factor analysis. We need another lens.

Here’s an easy example of what I mean … in modern portfolio theory Risk-On/Risk-Off does not exist. We all know that it’s out there, and we can even see some its impact on measurable alpha and beta factors, sort of a Risk-On/Risk-Off effect by proxy. But there is nothing in any alpha or beta factor that explains or predicts Risk-On/Risk-Off. It’s like trying to see Dark Matter with a telescope. We know that Dark Matter is out there in the universe, but a telescope detects photons, which is pretty good for most astronomical tasks, but not if you’re trying to see something that doesn’t interact at all with light. Why can’t factor analysis “see” Risk-On/Risk-Off? Because Risk-On/Risk-Off is neither an attribute of a security nor a discrete event; it is a behavior that emerges from a strategic decision-making structure, and factor analysis simply cannot detect behaviors.

My intent is not to rain on the econometric parade. My intent is to show its limitations and suggest an additional methodology for improving the efficacy of active investment management. From an econometric perspective, strategic human behavior and decision-making may reside in epsilon, the “error term”, where it is, by definition, largely impervious to econometric tools. But that does not mean that these strategic human behaviors are unpredictable or unknowable. It simply means that we need an entirely different tool kit, and that’s what game theory is.

Game theory is only useful for social phenomena. It is a methodology for understanding strategic decision making within informational constraints. I say “strategic” because, like the tango, it takes (at least) two decision makers to play a game, and each player’s decisions are made in the context of expectations regarding the other player’s decision-making process. Game theory does not see the world in terms of factors and historical correlations. It sees the world in terms of equilibria, as decision-making balance points where strategically-aware players have no incentive to make alternative decisions. Movement from one equilibrium to another is determined entirely by changes in the perceived pay-offs of the possible decisions, which is another way of saying that behavioral change is determined entirely by a change in the information available to the players regarding future probabilities of future states of the world.

The game of poker provides an instructive corollary for evaluating the relative strengths of game theoretic and econometric analysis. Econometric or factor analysis is the equivalent of “playing the cards”, where decisions are based on the odds of this card or that card appearing relative to the revealed strengths of other players’ hands and the potential stakes to be won or lost. Game theoretic analysis, on the other hand, is the equivalent of “playing the player”, where decisions are based on a strategic assessment of the likely behavior of other players relative to the informational signals provided by bets. If you want a tool kit to evaluate the static factors that describe the structure of a poker game or a capital market, then econometrics is the right choice. Game theory, on the other hand, is the right choice if you want to evaluate the dynamic interactions that emerge from the structure of a poker game or a capital market.


Cassius Coolidge, “A Friend in Need” (ca. 1908)

The need for this combined perspective has never been greater. As Mohamed El-Erian writes with his customary clarity:

The recent volatility [in gold] speaks to a dynamic that has played out elsewhere and, more importantly, underpins the gradually widening phenomenon of western market-based systems that have been operating with artificial pricing for an unusually prolonged period.

… think of the underlying dynamic as one of a powerful brand where valuation has become completely divorced from the intrinsic attributes of the product – thus rendering it vulnerable to any change in conventional wisdom (or what economists would characterize as a stable disequilibrium).

… Essentially, today’s global economy is in the midst of its own stable disequilibrium; and markets have outpaced fundamentals on the expectation that western central banks, together with a more functional political system, will deliver higher growth.

– Financial Times, “Markets Insight: We should listen to what gold is really telling us,” May 20, 2013

What El-Erian calls “change in conventional wisdom” is exactly the phenomenon that game theory perceives well. Conversely, it is exactly the phenomenon that factor analysis perceives poorly.

My only quibble with El-Erian is that the disjuncture between security prices of all sorts and fundamentals is not only a function of central bank policy designed specifically to create that disjuncture, but is also a function of new technology and regulatory regime shifts. Simply reversing the extraordinary measures taken by the Fed and its acolytes won’t put the Big Data genie back in the bottle, or reverse the impact of Reg FD and Reg NFM. I’ll have a lot more to say about the structural impact of technology and regulatory policy change in future letters, but here’s the bottom line: all of these changes create significant challenges for active investment management. The new policy regimes make it much more difficult for any investment firm to acquire private information about a public company legally, and the new technology ensures that any investment advantage gleaned from public information will be arbitraged away almost immediately. The result is alpha scarcity and beta dominance, a poisonous environment for active investment managers of all stripes, as well as the Wall Street firms with business models designed to support active investment management. Welcome to the New Normal.

But here’s the thing … this has all happened before. The New Normal turns out to be an Old Normal, or at least an Intermittent Normal, and history provides a crucial lesson for active investors seeking to ride out the current storm. Risk-On/Risk-Off behavior is nothing new, you just have to look back before World War II. Risk-On/Risk-Off was an accepted fact of market life in the U.S. for 100 years, from at least the 1850’s all the way through the 1940’s, because the conditions that create a structure where Risk-On/Risk-Off behavior emerges – global financial crisis + new technology + regulatory regime change – were so commonplace. We think that the Internet has changed the way we make investment decisions … imagine what the telegraph and the telephone did. We worry about central bank decisions to expand their balance sheets … imagine the concern over the creation of fiat currency and the outlawing of gold ownership. The New York Stock Exchange survived a Civil War and two World Wars quite nicely, thank you, and there were actual human investors who thrived during these decades, all without the benefit of Modern Portfolio Theory. It might behoove us to learn a thing or two from these men.

Here’s the big lesson I’ve gleaned from reading first-hand accounts of pre-World War II investors – they were all game players. Understanding, evaluating, and anticipating the investment decisions of other investors was at least as important to investment success as understanding, evaluating, and anticipating the future cash flows of corporations. To men like Andrew Carnegie, Jay Gould, and Cornelius Vanderbilt – just to name three of the more famous investors of this time – the notion that they would make any investment without strategically considering the decision-making process of other investors would be laughable. In fact, most of their public investments were driven by the strategic calculus of “corners”, “bulges”, and “points”. These men played the player, not the cards, in almost everything they did.

epsilon-theory-manifesto-june-1-2013-carnegieAndrew Carnegie

epsilon-theory-manifesto-june-1-2013-gouldJay Gould

epsilon-theory-manifesto-june-1-2013-vanderbiltCornelius Vanderbilt

To take one of literally hundreds of examples, it wasn’t some great secret that Jay Gould and James Fisk were trying to corner the gold market in the summer of 1869. They were buying in the open market and clearly communicating their intentions to every market participant, big and small. What they didn’t communicate was that they had a mole in the Grant Administration, someone who would tip them off to any government gold sale. Some investors figured out Gould’s game and avoided the original Black Friday, September 24th 1869, when the Grant Administration sold $4 million worth of gold in the open market and crushed the corner. Other investors (including some in Gould’s inner circle) were themselves crushed. The point is that everyone involved in the capital markets in 1869 was trying to figure out the behavioral intentions of a few very public figures, and investment success or failure in any security depended mightily on this strategic assessment. There was no hand-wringing and moaning about the “divergence of prices from their fundamentals”. It was just an accepted fact of life that yes, fundamentals mattered, but game-playing mattered a lot, too, and often it was the only thing that really mattered.

Are the subjects of game-playing in markets different in 2013 than they were in 1913? Sure. The days of “corners” are largely over, or at least illegal, just as the days of cozying up to management for non-public fundamental information are now largely over, or at least illegal. But the nature of game-playing hasn’t changed, and the centrality of game-playing to successful investment, particularly during periods of global economic stress, hasn’t changed at all.

The secret of effective market game-playing, whether you were an investor 100 years ago or you are an investor today, is to recognize that the market game hinges on the Narrative, on the strength of the public statements that create Common Knowledge. These are the core concepts of Epsilon Theory.


Rene Magritte, “The Treachery of Images” (1929)

The concept of Narrative is a thoroughly post-modern idea. What I mean by this is that Narrative is a social construction, a malleable public representation of malleable public statements that lacks any inherent Truth with a capital T. In fact, the public statements that go into the construction of a Narrative are often intentionally untrue.

As Jean Claude Juncker (far left in photo), Luxembourg PM and former Eurogroup Council President, famously said in reference to market communications, “When it becomes serious, you have to lie.”


And even if the information behind a Narrative is not intentionally a lie, it may have zero causal or correlative relationship to the Narrative. Randall Munroe captured this idea well in a cartoon on the statistical analysis (or lack thereof) underlying the business of sports commentary, and precisely the same critique can be made of the business of financial market commentary.

The financial news media has to say something, and they have to be saying something all the time. So they will.


There’s nothing evil or immoral about this. It is what it is. But it’s critical to recognize a Narrative for what it is and not imbue it with superfluous attributes, such as Truth. To be effective, it is only important for a Narrative to sound truthful (this is what Stephen Colbert calls “truthiness”, which is actually a very interesting concept, not to mention a great word), not that it be truthful. A Narrative may in fact be quite truthful, but this is an accident, neither a necessary nor a sufficient condition of its existence.

My goal with Epsilon Theory is not to somehow expose a Narrative for being demonstrably untrue or disconnected from facts (although sometimes I just can’t help myself). And while it can be personally satisfying to indulge one’s righteous indignation by asking cui bono – who benefits? – from some particularly egregious repre-sentation of the world, that, too, is really here nor there. Demanding some arbitrary degree of truthfulness from a Narrative is a categorization error, pure and simple, and something of a conceit, to boot. No, I want to use a proper conception of Narrative, which has no inherent notion of truthfulness and is simply a public representation of a set of public statements made by influential people about the world, because I think that this can help me predict market behaviors that are not easily predictable by factor-based or econometric analysis. To that end, my goal with Epsilon Theory is to identify Narratives, measure their strength, and assess their likely impact on security prices through an application of game theory and information theory.


Mark Tansey, “Constructing the Grand Canyon” (1990)

Human history is littered with the corpses of dead Narratives, from the ancient myths of Greece or Rome to more modern concepts such as Manifest Destiny or Cultural Revolution. By definition, the verdict of history (which itself is a socially constructed representation of actual historical facts) has not been kind to dead Narratives, in that we see them now as myths, which is to say, as Narratives whose constituent public statements have lost whatever power they once had to move us.

epsilon-theory-manifesto-june-1-2013-american-progress epsilon-theory-manifesto-june-1-2013-zedong
John Gast, “American Progress” (1872) Mao Zedong Thought poster (ca. 1970)

It’s all too easy either to chuckle or raise a disapproving eyebrow at these more modern myths, wondering how anyone could be swayed or motivated by what seems to be obvious propaganda, which is to say, public media messages that no longer create Common Knowledge. But back in their respective days, these Narratives were powerful, indeed.

Many older Narratives have kept their potency, some for centuries. For example, the Narrative of the American Founding Fathers is just as powerful today as it was 100 years ago, maybe more so. There is no inherent expiration date on a Narrative, and it can survive as a meaningful driver of behavior so long as it regenerates itself by sparking influential public statements that create widespread Common Knowledge. This is certainly the case with the ongoing representations of public statements made by Washington, Madison, Jefferson, etc. over 200 years ago. Not only are their public statements still retransmitted and remediated in a positive light, but they are widely referenced by current influential speakers with new public statements on a daily basis.

It’s the new Narratives, though, that I am most interested in. How do they emerge? How do they sustain themselves? How do they manifest themselves in predictable patterns of behavior?


Time, December 16, 2009

 For example, the current Narrative associated with Federal Reserve policy is just as powerful and just as real as any historical Narrative I am aware of, including the Narratives of global religions and major nationalities. Fifty years from now, will we look back on Central Bank Omnipotence as a dead myth, as something akin to Manifest Destiny, or will it continue to shape our expectations and behaviors as the Founding Fathers

Narrative does? The answer to this question will almost certainly not depend on the actual efficacy of Federal Reserve policy! Narratives tend to die with a whimper, not a bang, and even successful Narratives from a policy perspective (as Manifest Destiny surely was) can wither as they are supplanted by new interpretations and representations of the world that better serve the interests of the economic and political entities that promulgate Narratives.

For a current Battle Royale between two competing Narratives, look no further than Europe. On the one hand, we have the Narrative of European Union, which is a potent and vibrant public representation of an active set of public statements by extremely influential people advocating shared notions of identity and sovereignty across national European borders. This Narrative serves the interests of a large mandarin class of bureaucrats, as well as the economic interests of most European companies.


il Giornale, August 3, 2012

And on the other hand, we have the Narrative of German Hegemonic Desires, advocating political resistance to Germany’s imposition of its preferred economic policies through EU mechanisms. This Narrative serves the interests of Opposition political parties and is particularly strong in Italy under the aegis of Berlusconi’s media empire. Neither of these competing European Narratives is going away anytime soon, if ever. But the waxing and waning of one versus the other has investable consequences for market behaviors, and it is this assessment of the Narrative battlefield, if you will, where the Epsilon Theory perspective can provide direct benefits.

The link between Narrative and behavior is Common Knowledge, which is defined as what everyone knows that everyone knows. This is actually a trickier concept than it might appear at first blush, because as investors we are very accustomed to evaluating the consensus (what everyone knows), and it’s easy to fall into the trap of conflating the two concepts, or believing that Common Knowledge is somehow related to your private evaluation of the consensus. It’s not. Your opinion of whether the consensus view is right or wrong has absolutely nothing to do with Common Knowledge, and the consensus view, no matter how accurately measured or widely surveyed, is never the same thing as Common Knowledge.

Common Knowledge is, in effect, a second-order consensus (the consensus view of the consensus view), and it is extremely difficult to measure by traditional means. You might think that if a survey measures a consensus, then all we need to do is have a survey about the survey to measure a consensus view of the consensus view and hence Common Knowledge, but you would be wrong. What would the second survey ask? Whether or not the second-survey individuals agree with the first-survey individuals? Common Knowledge has nothing to do with whether the second-survey individuals think the original consensus view is right or wrong … that would just be an adjustment of the original survey. What you’re trying to figure out is the degree to which everyone believes that everyone else is relying on the original survey as an accurate view of the world, which has nothing to do with whether the original survey does in fact have an accurate view of the world. It has everything to do, however, with how widely promulgated that original survey was. It has everything to do with how many influential people – famous investors, famous journalists, politicians, etc. – made a public statement in support of the original survey. It has everything to do with the strength and scope of the Narrative around that original survey, and this is what you need to evaluate in order to infer the level of Common Knowledge in play regarding the original survey.

Now obviously it’s unlikely for a powerful Narrative like Central Bank Omnipotence to emerge around a survey, but replace the words “original survey” with “consensus view that the Fed has got your back” and you’ll see how this works.

The more Common Knowledge in play at any given time, the more that market behaviors will be driven by the rules and logic of the Common Knowledge Game than by fundamentals or traditional factors. I’ve written extensively about the CK Game in my prior letters, so I won’t repeat all of that here (for a collection of this work see the Epsilon Theory Archives). Suffice it to say that you’ll be reading a lot more about specific applications of the CK Game in future letters. You’ll also be reading a lot more about pre-World War II investing in future letters, as I find that matching examples of successful game-playing in the past with opportunities for game-playing today is a very effective way of communicating the power and relevance of Epsilon Theory.

On that note, I want to conclude with an extended passage from John Maynard Keynes, writing in the 1930’s about the game-playing he saw and experienced on a daily basis with his personal investing.


Thus the professional investor is forced to concern himself with the anticipation of impending changes, in the news or in the atmosphere, of the kind by which experience shows that the mass psychology of the market is most influenced.

This battle of wits to anticipate the basis of conventional valuation a few months hence, rather than the prospective yield of an investment over a long term of years, does not even require gulls amongst the public to feed the maws of the professional; it can be played by professionals amongst themselves. Nor is it necessary that anyone should keep his simple faith in the conventional basis of valuation having any genuine long-term validity. For it is, so to speak of, a game of Snap, of Old Maid, of Musical Chairs – a pastime in which he is victor who says Snap neither too soon nor too late, who passes the Old Maid to his neighbour before the game is over, who secures a chair for himself when the music stops. …

Or, to change the metaphor slightly, professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preference of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point view. It is not a case of choosing those which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest.

We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees. – The General Theory of Employment, Interest, and Money (1935)

Any investment manager who has watched market indices tick-by-tick after an FOMC announcement knows the truth of what Keynes wrote 80 years ago. It clearly doesn’t matter what you think about the Fed statement itself. And you quickly learn that it doesn’t matter what you think about whether expectations of the Fed statement were met or not, because as often as not the market will go in the opposite direction that you surmised.

What you want to know is what everyone thinks that everyone thinks about the Fed statement, and you can’t find that in the Fed statement, nor in any private information or belief. You can only find it in the Narrative that emerges after the Fed statement is released. So you wait for the talking heads and famous economists and famous investors to tell you how to interpret the Fed statement, but not because you can’t do the interpreting yourself and not because you think the talking heads are smarter than you are. You wait because you know that everyone else is also waiting. You are playing a game, in the formal sense of the word. You wait because it is the act of making public statements that creates Common Knowledge, and until those public statements are made you don’t know what move to make in the game.

As Keynes wrote, you are devoting your intelligence to anticipating what average opinion expects the average opinion to be. And there is nothing – absolutely nothing – in the standard model of modern portfolio theory or the fundamentals of the market or any alpha or beta factor that can help you with this effort. It’s not that the standard model is wrong. It’s just incomplete, both on its own terms (we have precious little alpha or beta factor data from prior periods of global financial crisis) and, more importantly, in that it was never intended to answer questions of strategic behavior. You need an additional tool kit, one designed from the outset to answer these questions. That’s what Epsilon Theory is intended to be, and I hope you will join me in its development.

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