Chapter 13 Further Reading

Foundational Works

The Kelly Criterion

  • Kelly, J.L. Jr. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917-926. The original paper introducing the Kelly criterion. Framed in terms of information theory and gambling, it establishes the mathematical foundation for optimal bet sizing. While the notation is dated, the core insight -- maximize expected log-wealth -- remains fundamental.

  • Thorp, E.O. (2006). "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market." In Handbook of Asset and Liability Management. Elsevier. Ed Thorp, who famously applied the Kelly criterion to blackjack and later to financial markets, provides a comprehensive and accessible treatment. Covers derivation, practical considerations, and extensions to portfolios.

  • Poundstone, W. (2005). Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street. Hill and Wang. A popular account of the Kelly criterion's history, from Claude Shannon and John Kelly at Bell Labs to Ed Thorp's adventures in casinos and financial markets. Excellent for understanding the human story behind the mathematics.

Expected Value and Decision Theory

  • Von Neumann, J. and Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press. The foundational text on expected utility theory. While dense, it establishes the framework for rational decision-making under uncertainty that underpins all of prediction market trading.

  • Kahneman, D. and Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-292. The seminal paper on how humans actually make decisions, as opposed to how rational agents should. Understanding prospect theory helps you identify behavioral edges in prediction markets.

Prediction Markets and Forecasting

Market Efficiency and Edge

  • Manski, C.F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. A careful analysis of what prediction market prices actually mean and when they may deviate from true probabilities. Important for understanding the conditions under which edge can exist.

  • Wolfers, J. and Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. A comprehensive survey of prediction markets, including evidence on their accuracy and the mechanisms through which they aggregate information. Good context for understanding when markets are efficient and when they are not.

  • Arrow, K.J. et al. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. A brief but influential letter signed by prominent economists advocating for prediction markets and summarizing evidence on their effectiveness.

Calibration and Forecasting Skill

  • Tetlock, P.E. (2005). Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press. Philip Tetlock's landmark study showing that most expert forecasters are poorly calibrated and often no better than simple base rate models. Essential reading for anyone who wants to understand what good forecasting looks like -- and how rare it is.

  • Tetlock, P.E. and Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers. Building on the Good Judgment Project, this book identifies the traits and techniques of elite forecasters ("superforecasters"). Practical advice on how to improve your calibration and accuracy. Directly applicable to prediction market trading.

  • Brier, G.W. (1950). "Verification of Forecasts Expressed in Terms of Probability." Monthly Weather Review, 78(1), 1-3. The original paper introducing the Brier score, now a standard metric for evaluating probabilistic forecasts.

Position Sizing and Risk Management

Advanced Kelly Theory

  • MacLean, L.C., Thorp, E.O., and Ziemba, W.T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. The definitive academic volume on the Kelly criterion. Contains the original paper plus dozens of extensions, applications, and critiques. Covers multi-asset Kelly, continuous-time Kelly, fractional Kelly, and empirical applications.

  • MacLean, L.C., Ziemba, W.T., and Blazenko, G. (1992). "Growth versus Security in Dynamic Investment Analysis." Management Science, 38(11), 1562-1585. Analyzes the trade-off between growth rate and downside risk in Kelly-type strategies. Provides the mathematical foundation for fractional Kelly and why it is often preferable to full Kelly.

Bankroll Management for Bettors

  • Haigh, J. (2003). Taking Chances: Winning with Probability. Oxford University Press. An accessible introduction to probability applied to gambling and betting. Good chapters on bankroll management and the mathematics of ruin.

  • Mallios, W. (2011). Sports Betting: The Mathematics Behind the Wagers. BookSurge Publishing. While focused on sports betting, the position sizing and bankroll management concepts translate directly to prediction markets.

Behavioral Finance and Biases

Cognitive Biases in Markets

  • Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. The definitive popular treatment of cognitive biases and dual-process theory. Understanding System 1 and System 2 thinking is essential for both avoiding your own biases and exploiting others'.

  • Thaler, R.H. (2015). Misbehaving: The Making of Behavioral Economics. W.W. Norton. Richard Thaler's account of the behavioral economics revolution. Particularly relevant chapters on the favorite-longshot bias, mental accounting, and how markets can remain irrational for extended periods.

Favorite-Longshot Bias

  • Snowberg, E. and Wolfers, J. (2010). "Explaining the Favorite-Long Shot Bias: Is It Risk-Love or Misperceptions?" Journal of Political Economy, 118(4), 723-746. A rigorous analysis of one of the most persistent biases in prediction and betting markets. Understanding this bias is a practical source of behavioral edge.

Quantitative Methods

Statistical Models for Forecasting

  • Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. 2nd ed. Springer. The standard reference for statistical learning methods. Chapters on logistic regression, ensemble methods, and cross-validation are directly applicable to building prediction models.

  • Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail -- but Some Don't. Penguin Press. Nate Silver's accessible introduction to forecasting across domains including elections, weather, economics, and sports. Good practical advice on model building and calibration.

Bayesian Methods

  • Gelman, A. et al. (2013). Bayesian Data Analysis. 3rd ed. CRC Press. The standard textbook on Bayesian statistics. Relevant for building models that properly quantify uncertainty in probability estimates, which feeds directly into Kelly sizing.

Online Resources

Forecasting Communities

  • Metaculus (metaculus.com) -- A forecasting platform where you can practice making calibrated predictions and track your performance over time. Excellent training ground for prediction market trading.

  • Good Judgment Open (gjopen.com) -- The public version of the Good Judgment Project. Participate in forecasting questions and compare your performance to other forecasters.

Technical References

  • Prediction Market Resource List -- Various academic and practitioner resources on prediction market design, efficiency, and trading strategies. Many university economics departments maintain curated lists.

  • Quantitative Finance Stack Exchange (quant.stackexchange.com) -- Active community discussing Kelly criterion, position sizing, and quantitative trading strategies. Many threads directly applicable to prediction markets.

Data and Tools

  • Python Libraries: NumPy, SciPy, pandas, scikit-learn, and matplotlib are the essential tools for implementing the quantitative methods in this chapter. The code examples use these libraries extensively.

For readers new to the topics in this chapter, we suggest the following order:

  1. Superforecasting (Tetlock and Gardner) -- Learn what good forecasting looks like
  2. Thinking, Fast and Slow (Kahneman) -- Understand cognitive biases
  3. Fortune's Formula (Poundstone) -- Grasp the Kelly criterion intuitively
  4. The Signal and the Noise (Silver) -- Learn practical model building
  5. The Kelly, Thorp, and MacLean academic papers -- Deepen your mathematical understanding
  6. Expert Political Judgment (Tetlock) -- Appreciate how hard forecasting really is

This progression moves from accessible narrative to deeper technical understanding, building intuition before formalism.