EPSILON THEORY

I have recently discovered Ben Hunt and his web site Epsilon Theory. Ben uses game theory, history and behavioral analysis to assess investment risk. Using a poker game analogy,

Game theoretic analysis 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.

In financial markets, we refer to the monitoring of changes in conventional wisdom. Understanding, evaluating, and anticipating the investment decisions of other investors is paramount to investment success. A cheap stock, or a cheap market, can stay cheap for a long time until the narrative changes. Ben goes on:

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. (…)

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

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. (…)

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 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.

Ben has decided to soon make his web site available only on a subscription basis and I wish him well. If you can afford it, you should consider subscribing.

What struck me when I discovered Epsilon Theory was that, in a different and very personal and humble way, News-to-Use is built as as a layman’s version of epsilon theory. My monitoring, choosing, structuring and presenting of the news for the daily New$ & View$ is meant to help me, and hopefully you as well, understand the narratives and their evolution and how common knowledge is likely to evolve. All the “ ***.WATCH” segments I create from time to time are meant to help us monitor how a particular narrative is changing and likely to change and how these changes might eventually change common knowledge. Being ahead of the curve in order to anticipate changes in the curve.

All this in the context of the evolving risk/reward profiles for various financial assets.

Overlaying the narratives, their evolution and the anticipated changes thereon over the risk/reward relationships of various financial instruments is the essence of New$-To-Use.

That said, I have no plans and no intentions to copy Ben into a subscription site. I cherish my sovereignty too much for even thinking about considering that, even if my readership were to grow exponentially. New$-To-Use is growing nicely and I find it very rewarding that, being into its fifth year, many of you have been reading me regularly for quite some time meaning that NTU fills a need for many people across the world.

Some readers even gratify NTU with donations. Some are small, others are larger, but all are extremely appreciated and totally reinvested in research material. The reality is that many info sources are doing like Ben and require payments. Donations allow me to afford subscribing to some of these.

This post was meant to introduce you to Ben Hunt’s web site and his approach to investing because it is good stuff that is worth some of your time. If you can’t subscribe, you should at least visit his site and read some of his articles while still freely available.

His most recent email is very interesting in the present context (my emphasis):

Epsilon Theory: Increased Instability in US Markets

The Credit Suisse Equity Derivatives group put out this chart in their weekly note today, and I thought it was worth forwarding to the Epsilon Theory list.

The chart shows the spread between the 1M implied volatility (taken from 1-month forward option prices) and the current realized volatility (taken from historical price changes) in the S&P 500. The greater the spread, the more expensive options are on the SPX relative to the actual volatility that is occurring. Basically, it’s an indication that expectations or fears of bad things happening in the short term future have increased, even if those bad things haven’t actually materialized yet.

Chart: S&P 1M Implied-Realized Volatility Spread Widens to 90th Percentile High

hunt_thumb[1]Source: CS Equity Derivatives Strategy

My interest is less in whether the spread is at the 99th percentile high or the 1st percentile low (both of which have occurred in the past 12 months), but in the violence and rapidity of moves between very high percentile spreads and very low percentile spreads. This is what an unstable market looks like from an information or game theoretic perspective. Realized volatility is relatively stable, but expectations of short-term volatility swing from pillar to post. In the absence of a strong Common Knowledge structure for the US market, investor behavior becomes unmoored, and that’s what we’re seeing here. An unstable market is like a marble on a glass table … it takes very little “news” to make the marble roll for a long way in *either* direction.

The Common Knowledge structure around the US market has weakened in the past few weeks. That’s NOT the same thing as saying that sentiment has grown more negative, although in terms of the US growth outlook it happens to be true. What I’m saying is that a few weeks ago everyone knew that everyone knew that the US economy was on a self-sustaining growth trajectory. Even if you believed privately that growth was likely to be weak, you believed that everyone else thought otherwise because there was a monolithic media Narrative telling you that everyone else thought otherwise.

But then the Fed talked down growth after their July 30-31 meeting (without talking down the Taper), and since then more and more opinion leaders have joined the chorus on slower-than-expected growth. I have no idea what the “truth” is regarding US growth, and my private opinion is not particularly useful in any event. But I do think it’s useful to be aware of increased instability in the US markets, because it makes the risk/reward assessment of ALL exposures — both long and short — less certain.

 

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