Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for April 2024 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for April 2024 (Final)
1st ETF 2nd ETF 3rd ETF

A Few Notes on The Physics of Wall Street

| | Posted in: Big Ideas

James Weatherall, physicist, mathematician and philosopher, introduces his 2012 book, The Physics of Wall Street: A Brief History of Predicting the Unpredictable, by stating: “This book tells the story of physicists in finance. …It is about how the quants came to be, and about how to understand the ‘complex mathematical models’ that have become central to modern finance.” Tracing the historical stream of key contributions by physicists and mathematicians to finance, he concludes that:

From Chapter 1, “Primordial Seeds” (Page 22): “Bachelier offered no clear insight into how to incorporate his options pricing model in a trading strategy [about 1900]. This was one reason why Bachelier’s options pricing model got less attention than his random walk model, even after his thesis was rediscovered…”

From Chapter 2, “Swimming Upstream” (Page 46): “In the mid-sixties, Osborne and a collaborator showed that at any instant, the chances that a stock will go up are not necessarily the same as the chances that a stock will go down. …Osborne showed that if a stock went up by a little bit, its next motion was much more likely to be a move back down than another move up. Likewise, if a stock went down, it was much more likely to go up in value in its next change. …If a stock moved in the same direction twice, it was much more likely to continue in that direction than if it had moved in a given direction only once.”

From Chapter 3, “From Coastlines to Cotton Prices” (Page 74): “Extreme events occur far more often than Bachelier and Osborne believed they would, and markets are wilder places than normal distributions can describe. To fully understand markets, and to model them as safely as possible, these facts must be accounted for. And Mandelbrot is singularly responsible for discovering the shortcomings of the Bachelier-Osborne approach, and for developing the mathematics necessary to study them.”

From Chapter 4, “Beating the Dealer” (Pages 102, 104): “Using such [delta hedging] strategies, Thorp was able to consistently make 20% per year…for about 45 years. He’s still doing it–indeed, 2008 was one of his worst years ever, and he made 18%. …Thorp had ushered in a ‘switch in money management’ to quantitative, computer-driven methods. It’s amazing what a little information theory can do.”

From Chapter 5, “Physics Hits the Street” (Pages 128-129): “In order to make complicated financial markets tractable, Bachelier, Osborne, Thorp, Black and even Mandelbrot introduced idealization and often strong assumptions about how markets work. …the models that resulted were only as good as the assumptions that went in. Sometimes assumptions that are usually excellent quickly become lousy as market conditions change. …we should fully expect that new methods can be developed that will begin to solve the problems… One part of this process has involved modifying the ideas that Black and Scholes introduced to financial practice to better accommodate Mandelbrot’s observations about extreme events.”

From Chapter 6, “The Prediction Company” (Pages 149, 156, 158): “…the fifteen years Farmer and Packard spent working on chaos theory gave them unprecedented (by 1991 standards) understanding of how complex systems work…how regular patterns–patterns with real predictive power–could be masked by the appearance of randomness. …They were at ease with the statistical properties of fat-tailed distributions and wild randomness…the Prediction Company, and dozens of other black box trading groups that have sprung up subsequently, purports to predict how the market will behave, over short periods of time and under special circumstances…the Prediction Company succeeded by figuring out how to be the most sophisticated investor as often as possible.”

From Chapter 7, “Tyranny of the Dragon King” (Pages 174-175): “…the central idea behind Sornette’s market-crash-as-critical-event hypothesis involves collective action, or herding behavior. …The question, then, is why under some circumstances herding seems to take over. …Sornette does not have an answer to this question, though he has developed some models that predict which circumstances will lead herding effects to become particularly strong. What Sornette can do is identify when herding effects have taken over. This amounts to identifying when a speculative bubble has taken hold in a particular market and to predicting the probability that the bubble will pop before a certain fixed time (the critical point). …If you look at Mandelbrot’s work as a revision to the early accounts of random markets, pointing out why they fail and how, then Sornette’s proposal is a second revision. It is a way of saying that, even if markets are wildly random and extreme events occur all the time, at least some extreme events can be anticipated…”

In summary, the steady migration of scientists and mathematicians into financial markets, as described in The Physics of Wall Street, offers investors context on the the inefficiency and exploitability of these markets (and the level of competition).

Cautions regarding approach/conclusions include:

  • This book is an historical survey of ideas. It is not a source of tools for investing.
  • Investors may be impatient with some of the ground-laying digressions from financial markets, or may find ties to other fields interesting.
  • Chapter 8 is not about “Wall Street,” and the book’s epilogue is advocacy for devoting “enormous resources to staving off economic calamity” via U.S. government and international agencies or “some new research organization devoted to interdisciplinary economic research.” Readers attracted by the book’s title may be uninterested in these sections.

See also the following four items from source material by Emanuel Derman:

Daily Email Updates
Filter Research
  • Research Categories (select one or more)