Further Reading: Chapter 18

Foundational Works in Behavioral Economics

Kahneman and Tversky

  • Kahneman, D. & Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-291. The foundational paper introducing prospect theory, including the value function (reference dependence, loss aversion, diminishing sensitivity) and the probability weighting function. Essential reading for understanding why people systematically misjudge probabilities and misprice risk.

  • Tversky, A. & Kahneman, D. (1974). "Judgment under Uncertainty: Heuristics and Biases." Science, 185(4157), 1124-1131. The seminal paper introducing the anchoring, availability, and representativeness heuristics. One of the most cited papers in all of social science.

  • Kahneman, D. & Tversky, A. (1992). "Advances in Prospect Theory: Cumulative Representation of Uncertainty." Journal of Risk and Uncertainty, 5(4), 297-323. The updated version of prospect theory (cumulative prospect theory) that handles multi-outcome gambles and provides refined parameter estimates for the value and weighting functions.

  • Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. A comprehensive, accessible summary of decades of research on cognitive biases and dual-process theory (System 1 vs. System 2). An essential book for any prediction market trader who wants to understand how human cognition produces systematic errors.

Thaler

  • Thaler, R.H. (2015). Misbehaving: The Making of Behavioral Economics. W.W. Norton. An engaging narrative history of behavioral economics by one of its founders. Covers mental accounting, the endowment effect, and the limits to arbitrage, all of which are directly relevant to prediction market trading.

  • Thaler, R.H. (1985). "Mental Accounting and Consumer Choice." Marketing Science, 4(3), 199-214. Introduces the concept of mental accounting — how people categorize and evaluate economic outcomes — which explains many of the position management errors traders make.

  • Thaler, R.H. (1999). "The End of Behavioral Finance." Financial Analysts Journal, 55(6), 12-17. Argues that behavioral finance should not be a separate subfield but rather the foundation of all financial theory. Includes a useful summary of the key behavioral findings relevant to markets.

Ariely

  • Ariely, D. (2008). Predictably Irrational: The Hidden Forces That Shape Our Decisions. HarperCollins. A highly accessible introduction to behavioral economics with vivid experiments demonstrating anchoring, the power of "free," and other biases. Good entry point for readers new to the field.

  • Ariely, D. (2010). The Upside of Irrationality. HarperCollins. Explores how irrationality can sometimes be beneficial and how understanding it can improve decision-making.

Behavioral Finance and Markets

Classic Papers

  • Shefrin, H. & Statman, M. (1985). "The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence." Journal of Finance, 40(3), 777-790. The paper that named and documented the disposition effect. Shows that investors are approximately 50% more likely to sell winning positions than losing ones, consistent with prospect theory.

  • Odean, T. (1998). "Are Investors Reluctant to Realize Their Losses?" Journal of Finance, 53(5), 1775-1798. Definitive empirical study of the disposition effect using brokerage account data. Finds that individual investors demonstrate a strong disposition effect and that it costs them money.

  • Barber, B.M. & Odean, T. (2001). "Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment." Quarterly Journal of Economics, 116(1), 261-292. Documents that overconfident traders trade more frequently and earn lower returns. Provides quantitative evidence on the cost of overconfidence.

  • De Bondt, W.F.M. & Thaler, R.H. (1985). "Does the Stock Market Overreact?" Journal of Finance, 40(3), 793-805. Foundational study showing that stocks that have performed well in the past tend to underperform in the future (and vice versa), consistent with overreaction driven by behavioral biases.

  • Shiller, R.J. (2000). Irrational Exuberance. Princeton University Press. Analysis of speculative bubbles driven by narrative, herding, and overconfidence. The framework applies directly to prediction markets where narratives can drive prices away from fundamentals.

Information Cascades and Herding

  • Banerjee, A.V. (1992). "A Simple Model of Herd Behavior." Quarterly Journal of Economics, 107(3), 797-817. Introduces the theoretical model of rational herding where agents ignore their private information and follow the crowd.

  • Bikhchandani, S., Hirshleifer, D. & Welch, I. (1992). "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades." Journal of Political Economy, 100(5), 992-1026. Develops the information cascade model showing how sequential decision-making can produce fragile consensus, even when the cascade is wrong.

  • Cipriani, M. & Guarino, A. (2009). "Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals." Journal of the European Economic Association, 7(1), 206-233. Experimental evidence of herding among financial professionals, showing that even experts are susceptible.

The Favorite-Longshot Bias

  • Griffith, R.M. (1949). "Odds Adjustments by American Horse-Race Bettors." American Journal of Psychology, 62(2), 290-294. The first systematic documentation of the favorite-longshot bias in horse racing.

  • Snowberg, E. & Wolfers, J. (2010). "Explaining the Favorite-Long Shot Bias: Is It Risk-Love or Misperceptions?" Journal of Political Economy, 118(4), 723-746. Distinguishes between risk-love and probability misperception as explanations for the FLB, using data from horse racing and prediction markets. Finds that misperception of probabilities (consistent with prospect theory) is the primary driver.

  • Rothschild, D. (2009). "Forecasting Elections: Comparing Prediction Markets, Polls, and Their Biases." Public Opinion Quarterly, 73(5), 895-916. Analyzes biases in prediction markets for elections, documenting the FLB and other systematic patterns.

  • Ottaviani, M. & Sorensen, P.N. (2008). "The Favorite-Longshot Bias: An Overview of the Main Explanations." In Handbook of Sports and Lottery Markets, North-Holland, pp. 83-101. Comprehensive review of theoretical explanations for the FLB, including risk preferences, asymmetric information, and market structure effects.

  • Ziemba, W.T. & Hausch, D.B. (1986). Betting at the Racetrack. Dr. Z Investments. Classic practitioner-oriented work on exploiting the FLB in horse racing, with practical strategies and quantitative analysis.

Prediction Market Specific Research

  • Wolfers, J. & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. Excellent overview of prediction markets, their properties, and their biases. Discusses how behavioral biases affect prediction market prices.

  • Manski, C.F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. Important paper on how to interpret prediction market prices, noting that prices do not directly equal probabilities due to risk preferences and other behavioral factors.

  • Page, L. & Clemen, R.T. (2013). "Do Prediction Markets Produce Well-Calibrated Probability Forecasts?" Economic Journal, 123(568), 491-513. Empirical analysis of calibration in prediction markets, finding evidence of miscalibration consistent with behavioral biases.

  • Arrow, K.J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J.O., Levmore, S., Litan, R., Milgrom, P., Nelson, F.D., Neumann, G.R., Ottaviani, M., Schelling, T.C., Shiller, R.J., Smith, V.L., Snowberg, E., Sunstein, C.R., Tetlock, P.C., Tetlock, P.E., Varian, H.R., Wolfers, J. & Zitzewitz, E. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. A collective statement by leading economists on the value and limitations of prediction markets.

Debiasing and Calibration

  • Tetlock, P.E. (2005). Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press. Landmark study of forecasting accuracy among political experts. Finds that most experts are poorly calibrated and that simple statistical models often outperform them. Introduces the "fox vs. hedgehog" framework for forecasting style.

  • Tetlock, P.E. & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers. Describes the findings of the Good Judgment Project, which identified "superforecasters" who consistently outperform prediction markets. Key debiasing techniques include calibration training, belief updating, and cognitive self-awareness.

  • Klein, G. (2007). "Performing a Project Premortem." Harvard Business Review, 85(9), 18-19. Introduces the pre-mortem technique for identifying risks before they materialize. A concise, practical paper that every trader should read.

  • Gawande, A. (2009). The Checklist Manifesto: How to Get Things Right. Metropolitan Books. Makes the case for checklists as a tool for reducing errors in complex decision-making. The principles apply directly to trading.

  • Fischhoff, B. (1982). "Debiasing." In D. Kahneman, P. Slovic & A. Tversky (Eds.), Judgment Under Uncertainty: Heuristics and Biases, Cambridge University Press, pp. 422-444. Early systematic treatment of debiasing techniques. Reviews evidence on what works and what does not for reducing cognitive biases.

  • Larrick, R.P. (2004). "Debiasing." In D.J. Koehler & N. Harvey (Eds.), Blackwell Handbook of Judgment and Decision Making, Blackwell, pp. 316-337. Updated review of debiasing research. Categorizes techniques into motivational, cognitive, and technological approaches.

Narrative and Recency Effects

  • Taleb, N.N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House. Discusses the narrative fallacy, our blindness to randomness, and the tendency to construct post-hoc explanations for random events. Highly relevant to understanding how narratives drive prediction market mispricing.

  • Taleb, N.N. (2004). Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Random House. Explores how humans systematically misperceive randomness, mistake luck for skill, and construct narratives to explain noise. Essential reading for prediction market traders.

  • Barberis, N., Shleifer, A. & Vishny, R. (1998). "A Model of Investor Sentiment." Journal of Financial Economics, 49(3), 307-343. Formal model of how conservatism (under-reaction) and representativeness (over-reaction) combine to produce the patterns observed in financial markets.

Advanced and Supplementary Reading

  • Camerer, C.F. (2003). Behavioral Game Theory: Experiments in Strategic Interaction. Princeton University Press. Comprehensive treatment of how behavioral biases affect strategic decision-making, relevant to understanding how traders interact in prediction markets.

  • Hastie, R. & Dawes, R.M. (2010). Rational Choice in an Uncertain World. SAGE Publications. Excellent textbook on judgment and decision-making under uncertainty. Covers all major heuristics and biases with rigorous but accessible treatment.

  • Gigerenzer, G. (2008). Rationality for Mortals: How People Cope with Uncertainty. Oxford University Press. Presents an alternative view to Kahneman-Tversky, arguing that heuristics can be adaptive in the right environment. Useful for understanding when biases might actually help rather than hurt in prediction markets.

  • Sunstein, C.R. & Hastie, R. (2015). Wiser: Getting Beyond Groupthink to Make Groups Smarter. Harvard Business Review Press. Examines how group dynamics amplify individual biases, directly relevant to understanding echo chambers in prediction market communities.