Lebron, The Laws of Trading

Augustin Lebron, formerly a trader and researcher at Jane Street Capital and now head of a consulting firm on decision-making, has written a refreshingly cerebral book, The Laws of Trading: A Trader’s Guide to Better Decision-making for Everyone (Wiley, 2019). Although the book’s pitch is that “mental tools, forged in the competitive fire of financial markets, help us make good decisions in all other areas of life,” the book is first and foremost about the principles underlying sophisticated, professional trading. A reader who knows nothing about the financial markets, the “everyone” of the subtitle, would be lost in the thicket of options models and hedge optimization. On the other hand, a trader who picks up this book will be drawn into a fascinating meta-analysis of the tools he does or should use and will probably have little interest in applying them to the world outside of trading. This is not a criticism of the book, just of its marketing.

The book is, in fact, terrific. In eleven chapters it covers motivation, adverse selection, risk, liquidity, edge, models, costs and capacity, possibility, alignment, technology, and adaptation.

Take the ever-vexing issue of edge. Lebron sets out the fundamental axiom of edge: “all trades that have edge are profitable because there is some fact about the world that you understand and can act on that the marginal participant in the market doesn’t understand or can’t act on.” The marginal trader is the only one who matters, and he is (if you’re the buyer) “the most aggressive seller who isn’t aggressive enough to hit a bid himself.” So, to do profitable trading, you don’t need to be the best, just better than the trader of the median share. Alas, that trader is both sophisticated and skilled. “In any mature market, the really bad traders have either left or trade very small, and good traders (ones with edge) are everywhere, because they’re the ones that survived.”

What kinds of models should traders build? Should they be “descriptive of a true underlying structure of the world (a generative model)” or is “describing some observed regularity (a phenomenological model)” good enough? Both kinds of models are scary. With generative models, it is all to easy to mistake the map for the territory. With phenomenological models, it’s very difficult to know when the perceived regularity will stop existing.

Lebron also addresses the role of the inductive hypothesis (that if one has seen some regularity in the past, it makes sense that it should continue in the future) in financial markets. Because markets are both stochastic and self-organized feedback systems, relying on the inductive hypothesis is problematic. Which leads to the rule: “Just because something has never happened doesn’t mean it can’t. Corollary: Enough people relying on something being true makes it false.”

The Laws of Trading is, to my mind, essential reading for traders who want not only to survive but to flourish. It certainly won’t guarantee success (or continued success), but it will prompt the trader to reflect on his biases and perhaps even his ignorance. And it will provide him with new ways to think about framing trades.