Chapter 16: Further Reading

Foundational Papers on Arbitrage Theory

  • de Finetti, B. (1937). "La prevision: ses lois logiques, ses sources subjectives." Annales de l'Institut Henri Poincare, 7, 1--68. (English translation: "Foresight: Its Logical Laws, Its Subjective Sources.") The original Dutch book argument establishing that coherent subjective probabilities must obey the axioms of probability. If a bookmaker's prices allow a Dutch book (guaranteed loss for the bookmaker), the prices are incoherent. This is the theoretical foundation for all arbitrage detection in prediction markets.

  • Ross, S.A. (1976). "The Arbitrage Theory of Capital Asset Pricing." Journal of Economic Theory, 13(3), 341--360. Introduces the Arbitrage Pricing Theory (APT), which formalizes the idea that assets must be priced consistently to prevent arbitrage. While developed for financial assets, the principles apply directly to prediction market contracts.

  • Harrison, J.M. & Kreps, D.M. (1979). "Martingales and Arbitrage in Multiperiod Securities Markets." Journal of Economic Theory, 20(3), 381--408. Establishes the fundamental theorem of asset pricing: the absence of arbitrage is equivalent to the existence of a risk-neutral probability measure. This provides the mathematical backbone for understanding why prediction market prices should behave as probabilities.

Prediction Market Efficiency and Arbitrage

  • Wolfers, J. & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107--126. A widely cited survey of prediction markets, discussing their efficiency properties and the conditions under which prices reflect true probabilities. Includes discussion of arbitrage as an equilibrating mechanism.

  • Wolfers, J. & Zitzewitz, E. (2006). "Interpreting Prediction Market Prices as Probabilities." NBER Working Paper No. 12200. Examines when prediction market prices can (and cannot) be interpreted as probabilities. Discusses how transaction costs and risk aversion create arbitrage bounds within which prices can deviate from true probabilities without being exploitable.

  • Rothschild, D. (2009). "Forecasting Elections: Comparing Prediction Markets, Polls, and Their Biases." Public Opinion Quarterly, 73(5), 895--916. Analyzes prediction market accuracy for elections, documenting systematic biases (including the favorite-longshot bias) that create quasi-arbitrage opportunities for informed traders.

  • Page, L. & Clemen, R.T. (2013). "Do Prediction Markets Produce Well-Calibrated Probability Forecasts?" The Economic Journal, 123(568), 491--513. Empirically tests whether prediction market prices are well-calibrated probabilities. Finds systematic miscalibration patterns that represent exploitable inefficiencies for arbitrageurs.

Cross-Platform Arbitrage

  • Tetlock, P.C. (2008). "Liquidity and Prediction Market Efficiency." Working paper. Examines how liquidity differences across prediction markets affect pricing efficiency, providing direct evidence for the market fragmentation that creates cross-platform arbitrage opportunities.

  • Dreber, A., Pfeiffer, T., Almenberg, J., Isaksson, S., Wilson, B., Chen, Y., Nosek, B.A., & Johannesson, M. (2015). "Using Prediction Markets to Estimate the Reproducibility of Scientific Research." Proceedings of the National Academy of Sciences, 112(50), 15343--15347. While focused on scientific replication, this paper documents price discrepancies between related prediction markets on different platforms, providing evidence that cross-platform arbitrage opportunities exist in practice.

  • Gjerstad, S. (2004). "Risk Aversion, Beliefs, and Prediction Market Equilibrium." Working paper, University of Arizona. Develops a model of prediction market equilibrium that accounts for trader heterogeneity and risk aversion. Shows how different trader populations on different platforms can lead to persistent price differences.

Execution and Market Microstructure

  • Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. The definitive textbook on market microstructure. While focused on equities, the chapters on order book dynamics, slippage, and execution costs are directly applicable to understanding arbitrage execution challenges in prediction markets.

  • Othman, A. & Sandholm, T. (2010). "Automated Market Making in the Large." Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Analyzes automated market makers in prediction markets, including how AMM mechanics affect slippage and create different arbitrage dynamics compared to order-book markets.

  • Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107--119. Discusses market design for multi-outcome and combinatorial prediction markets, including how the structure creates or prevents arbitrage opportunities across related markets.

Statistical Arbitrage and Pairs Trading

  • Gatev, E., Goetzmann, W.N., & Rouwenhorst, K.G. (2006). "Pairs Trading: Performance of a Relative-Value Arbitrage Rule." Review of Financial Studies, 19(3), 797--827. The foundational empirical study of pairs trading in equities. Documents that the strategy generates statistically significant returns, providing a template for applying similar techniques to correlated prediction markets.

  • Vidyamurthy, G. (2004). Pairs Trading: Quantitative Methods and Analysis. John Wiley & Sons. A practitioner-oriented book on implementing pairs trading strategies. Covers cointegration testing, spread modeling, and signal generation -- all applicable to statistical arbitrage in prediction markets.

  • Avellaneda, M. & Lee, J.H. (2010). "Statistical Arbitrage in the US Equities Market." Quantitative Finance, 10(7), 761--782. Develops a comprehensive statistical arbitrage framework using principal component analysis and mean-reversion models. The methodology can be adapted to detect correlated mispricings across related prediction markets.

Platform-Specific References

  • 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 letter signed by 22 leading economists advocating for prediction markets and discussing their efficiency properties. Provides context for the regulatory environment that shapes cross-platform arbitrage opportunities.

  • Berg, J.E., Nelson, F.D., & Rietz, T.A. (2008). "Prediction Market Accuracy in the Long Run." International Journal of Forecasting, 24(2), 285--300. Analyzes the Iowa Electronic Markets (IEM) over 16 years. Documents pricing patterns and inefficiencies that would have been exploitable through systematic arbitrage strategies.

Books

  • Shleifer, A. (2000). Inefficient Markets: An Introduction to Behavioral Finance. Oxford University Press. Explores the limits of arbitrage in financial markets -- why mispricings persist despite the presence of arbitrageurs. The concepts of noise trader risk, fundamental risk, and implementation costs directly apply to prediction markets.

  • Thorp, E.O. (2017). A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market. Random House. An autobiographical account by one of history's greatest arbitrageurs. While focused on traditional markets, the chapter on convertible arbitrage and the general framework for identifying and exploiting mispricings provides invaluable practical wisdom.

  • Vaughan Williams, L. (Ed.) (2011). Prediction Markets: Theory and Applications. Routledge. A comprehensive academic overview of prediction markets, with chapters covering market efficiency, arbitrage, and the relationship between prices and probabilities.

  • Hausch, D.B., Lo, V.S.Y., & Ziemba, W.T. (Eds.) (2008). Efficiency of Racetrack Betting Markets. World Scientific. A collection of papers on betting market efficiency, including extensive discussion of Dutch book arguments, cross-market arbitrage in parimutuel systems, and the practical limits of exploiting mispricings.

Online Resources

  • Polymarket Documentation. https://docs.polymarket.com Official API documentation for Polymarket, including order placement, market data retrieval, and fee structure details. Essential reference for implementing cross-platform scanners.

  • Kalshi API Reference. https://trading-api.readme.io Official API documentation for Kalshi's regulated exchange, including REST and WebSocket endpoints, order types, and fee schedules.

  • Metaculus FAQ and API. https://www.metaculus.com/help/faq/ While Metaculus does not support real-money trading, its forecasting data can be used as a reference price when evaluating whether prediction market prices deviate from informed consensus.