Most traders say that they have a knack for identifying patterns in market signals. It doesn’t matter whether they look for those signals in price, volume, volatility, spreads or any other aspect of a market; traders have an internalized pattern recognition system that they say gives them a persistent edge. Most of them are wrong.
Any trading strategy based on naïve induction is certain to have zero edge. Naïve induction refers to selecting a strategy based solely on the econometric fit of a time series data matrix to some market outcome like price change. It’s a trading strategy that works because it works. There’s no “why?” answered here, and as a result, the strategy is certain to be derivative, non-robust and arbitraged away. Whatever purely inductive trading strategy you think gives you an edge is already being used by thousands of non-human intelligences — powerful inference machines that fall under the rubric of Big Data — and they’re using the strategy far more effectively. To the degree a naïve induction strategy works at all, you’re just tagging along behind the non-human intelligences, picking up their crumbs.
The human brain does a lot of things pretty well, and pattern recognition is one of them. Even today, one of the popular myths associated with computer science is that non-human intelligences are “brute force” machines and inferior to humans at tasks like pattern recognition. In truth, a massively parallel processor cluster with in-line memory — something you can access today for less money than a junior analyst’s salary — is far better at pattern recognition than any human.
Much has been made about how pervasive robot technologies are replacing low-end industrial and service jobs. More important for anyone reading this column is how pervasive non-human intelligences are replacing high-end symbol manipulation jobs, like trading.
What trading strategies have even a theoretical possibility of edge or alpha? Here are two.
Example 1: Find a market niche where your counterparties are non-economic or differently economic market participants — like a giant, lumbering integrated oil company seeking to hedge production in crude oil, or a sovereign wealth fund looking for inflation protection — to scalp a few dimes by taking advantage of their very different preference functions. Traders who exploit these players’ market flows have a name in biological systems: Parasites. They are beautiful parasites because they offer the purest source of trading alpha.
Example 2: Find a market niche where you understand the impact of exogenous signals like news reports or policy statements on the behavioral tendencies of human participants, in exactly the same way that a good poker player “plays the player” as much as the cards. These market niches tend to be sectors or assets that are driven less by fundamentals than by stories, although here in the golden age of the central banker it’s hard to find any corner of the capital markets that’s not driven by policy and narrative. The game that these traders have internalized isn’t poker, but is some variant of what modern game theorists call “The Common Knowledge Game,” and what old-school game theorists like John Maynard Keynes called “The Newspaper Beauty Contest.”
What do these two examples of potentially alpha-generating trading strategies have in common? They operate in a world that an inference machine can’t figure out. Today’s effective alpha-generating trading strategies are based on a game (in the technical sense of the word) — meaning a strategic interaction between humans where my decisions depend on your decisions, and vice versa — that can have very different outcomes from one trade to another, even if the external, measurable characteristics of the trades are identical. This is the hallmark of games with more than one equilibrium solution, which simply means that there are multiple stable outcomes of the game that can arise from a single matrix of descriptive data.
It means that you can’t predict the outcome of a multi-equilibrium game just by knowing the externally visible attributes of the players. It means that the pattern of outcomes can’t be recognized with naïve (or sophisticated) induction techniques. It means that traders who successfully internalize the pattern recognition of strategic behaviors rather than the pattern recognition of time series data have a chance of not just surviving, but thriving in a market jungle where non-human intelligences are the new apex predator. It’s not so bad being a beautiful parasite.
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