Early 2017 Reading Pile

The following books will be on my reading pile for early 2017:


  1. Sheelah Kolhatkar’s Black Edge: Inside Information, Dirty Money, and the Quest to Bring Down the Most Wanted Man on Wall Street (New York: Random House, 2017). Kolhatkar is a staff writer at The New Yorker. I followed the insider trading case against Steve A. Cohen and his hedge fund SAC Capital for several years. I thought about writing a PhD chapter on it — but getting access to the court records was going to be expensive and it was out-of-scope to my main focus. Kolhatkar has saved me the trouble — and illustrates why investigative journalism is important.
  2. Ed Thorp’s A Man For All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market (New York: Random House, 2017). Thorp is a giant in quantitative investing and card counting in poker. There’s a lengthy interview with Thorp in Jack D. Schwager’s book Hedge Fund Market Wizards, and this book promises more revelations. Features a foreward by Nassim Nicholas Taleb.
  3. Andrew W. Lo’s Adaptive Markets: Financial Evolution at the Speed of Thought (Princeton, NJ: Princeton University Press, 2017). Lo is the Charles E. and Susan T. Harris Professor, a Professor in Finance, and the Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. This book outlines Lo’s Adaptive Markets Hypothesis – a challenger to the Efficient Markets Hypothesis – and offers a conceptual basis for why some hedge fund trading works.
  4. Siva Vaidyanathan’s Intellectual Property: A Very Short Introduction (New York: Oxford University Press, 2017). Vaidyanathan is Professor of Media Studies at the University of Virginia. Intellectual Property (IP) is an intangible asset class that includes copyrights (works of creative expression), trademarks (logos and symbols that differentiate a company in the marketplace), patents (know how and processes), and trade secrets (confidential and secret information). Vaidyanathan explains how IP works and examines its legal / cultural debates. A good primer for content creators.

On Jim Simons, String Theory, and Quantitative Hedge Funds

Renaissance Technologies founder and mathematics professor Jim Simons is an enigma in quantitative hedge funds.


Simons rarely gives interviews. One of the best is an Institutional Investor interview he gave in 2000 (PDF). One insight is that Renaissance makes trades in specific time periods using pattern recognition to model volatility.


Simons has done important work in differential geometry and the theoretical physics subdiscipline of string theory. I recently looked at some academic journal articles by Lars Brink (Sweden’s Chalmers University of Technology) and Leonard Susskind (Stanford University) to try and understand how Simons views financial markets.


String theory proposes one-dimensional objects called strings as particle-like objects that have quantum states. String theory and cosmology has progressed over the past 35 years to describe this phenomena but still lacks some key insights.


How might Simons use string theory to understand financial markets? Two possibilities:


(1) The mathematical language of couplings, phase transitions, perturbations, rotational states, and supersymmetries provides a scientific way to describe financial market  data and price time-series. It does so in a different way to fundamental analysis, technical analysis, and behavioural finance: Simons uses string theory to understand the structure of information in financial markets. (Ed Thorp pursued a similar insight with Claude Shannon using probability theory.) String theory-oriented trading may be falsifiable in Karl Popper’s philosophy of science.


(2) String theory provides a topological model that can be applied to money flows between mutual funds, hedge funds, and bank trading desks over short periods of time. This might enable Simons’ traders to forecast the likely catalysts for changes in stock prices in the short-term and to trade accordingly. This might involve using string theory to forecast how price trajectories might change if portfolio managers at other funds alter their portfolio weights for a stock. In doing so, Simons is trading in a similar way to SAC’s Steve Cohen (who uses game theory) and D.E. Shaw’s David Shaw but uses different methods of pattern recognition to do so.


I have made a list of popular science books and Springer academic monographs to keep an eye on string theory. Simons’ success also illustrates how insights from one knowledge domain (string theory, astrophysics, computational linguistics, and voice recognition) can be transferred to another domain (financial markets trading).