Review: The Physics of Wall Street: A Brief History of Predicting the Unpredictable

The Physics of Wall Street: A Brief History of Predicting the Unpredictable
The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

My rating: 5 of 5 stars

Accessible history of physicists’ influence on economics and finance. Contains some interesting and unorthodox characters. Clearly written and like Weatherall writes in the epilogue, he did not “try to force the pieces into an overarching narrative” (p208); but there is coherence.

From the French Bachelier who figured out a lot of statistics on random walks at the beginning of the 20th century, only to be forgotten for decades, via Osborne who argued that rates of return were normally distributed, not stock prices, to Mandelbrot (of fractal-fame) who stressed fat-tails and non-normal distributions (but Cauchy / Lévy stable distributions).

Also includes Ed Thorp (of Beat the Dealer: A Winning Strategy for the Game of Twenty-One fame), who started one of the first hedge funds; Fischer Black (Black-Scholes formula to price options); Packard and Doyne Farmer (The prediction company [later bought by the enigmatic and secretive O’Connor and Associates, using chaos theory, black box modeling and genetic algorithms, both worked at Santa Fe institute); Sornette who used prediction models for earthquakes to predict large financial crashes (and made 400% putting his money where his mouth was in the 1997 crash).

“The stories in this book show the [scientific/physics] methodology in action: one uses simplifying assumptions to make a problem tractable and solve it. Then, once you see how your solution works, you can double back and begin asking what happens when you play with your assumptions” (p209)

financial modeling is an evolving process, one that proceeds in iterative fashion. (…) Models fail. Sometimes we can anticipate when they will fail (…) in other cases, we figure out what went wrong only as we are trying to put the pieces back together. (…) since mathematical modeling in finance is an evolving process, we should fully expect that new methods can be developed that will begin to solve the problems that have plagued the models that have gotten us to where we are today. (p128-129)

Data outclass theory (p.155)

If you continue to trade based on a model whose assumptions have ceased to be met by the market, and you lose money, it’s hardly a failure of the model. It’s like attaching a car engine to a plane and being disappointed when it doesn’t fly (p47)

no matter how good the theoretical backing for your (non-black box) model, you ultimately need to evaluate it on the basis of how well it performs. Even the most transparent models need to be constantly tested by just the same kind of statistical methods that are used to evaluate black box models. (p155-156)

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