Chapter 15: Further Reading

Foundational Papers

Shin, H.S. (1991). "Optimal Betting Odds Against Insider Traders." The Economic Journal, 101(408), 1179--1185. The foundational paper on Shin's method for extracting true probabilities from overround markets. Derives the bookmaker's optimal pricing strategy in the presence of informed traders.
Shin, H.S. (1993). "Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims." The Economic Journal, 103(420), 1141--1153. Extends the 1991 paper with empirical applications to horse racing markets. Provides the framework for estimating the proportion of informed traders from market prices.
Clarke, S.R., Kovalchik, S., & Ingram, M. (2017). "Adjusting Bookmaker's Odds to Allow for Overround." American Journal of Sports Analytics, 3(2), 97--114. Comprehensive comparison of overround removal methods (multiplicative, additive, Shin, power) with empirical evaluation on sports betting data.
Jullien, B. & Salanie, B. (2000). "Estimating Preferences Under Risk: The Case of Racetrack Bettors." Journal of Political Economy, 108(3), 503--530. Uses structural econometric methods to separate risk preferences from probability distortions in multi-outcome betting markets.

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 the connection between coherent beliefs and probability axioms.
Wolfers, J. & Zitzewitz, E. (2006). "Interpreting Prediction Market Prices as Probabilities." NBER Working Paper No. 12200. Discusses when and how prediction market prices can be interpreted as probabilities, including the role of risk aversion and transaction costs.

Kelly, J.L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917--926. The original Kelly criterion paper. While focused on binary bets, the information-theoretic framework extends naturally to multi-outcome settings.
Smoczynski, P. & Tomkins, D. (2010). "An Explicit Solution to the Problem of Optimizing the Allocations of a Bettor's Wealth When Wagering on Horse Races." Mathematical Scientist, 35(1), 10--17. Derives analytical properties of the Kelly criterion for mutually exclusive outcomes in parimutuel markets.
Busseti, E., Ryu, E.K., & Boyd, S. (2016). "Risk-Constrained Kelly Gambling." Journal of Investing, 25(3), 118--134. Extends the Kelly framework with explicit risk constraints, particularly relevant for multi-outcome markets where tail risk is significant.

Hausch, D.B., Lo, V.S.Y., & Ziemba, W.T. (Eds.) (2008). Efficiency of Racetrack Betting Markets. World Scientific. A comprehensive collection of papers on multi-outcome market efficiency, with a focus on horse racing. Covers overround, favorite-longshot bias, and optimal betting strategies.
MacLean, L.C., Thorp, E.O., & Ziemba, W.T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. The definitive reference on Kelly criterion applications, including multi-asset and multi-outcome extensions.
Vaughan Williams, L. (Ed.) (2011). Prediction Markets: Theory and Applications. Routledge. Academic overview of prediction markets, with chapters on market design, pricing, and applications to multi-outcome events.
Hanson, R. (2007). "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation." Journal of Prediction Markets, 1(1), 3--15. The foundational paper on the LMSR automated market maker, widely used in multi-outcome prediction markets.

Chen, Y. & Pennock, D.M. (2007). "A Utility Framework for Bounded-Loss Market Makers." Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI). Generalizes the LMSR to a broader class of cost-function-based market makers for multi-outcome events.
Abernethy, J., Chen, Y., & Vaughan, J.W. (2013). "Efficient Market Making via Convex Optimization, and a Connection to Online Learning." ACM Transactions on Economics and Computation, 1(2), Article 12. Connects multi-outcome market making to convex optimization and online learning theory.
Othman, A. & Sandholm, T. (2010). "Decision Rules and Decision Markets." Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Discusses market design for multi-outcome prediction markets with a focus on computational aspects.

Wolfers, J. & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107--126. Overview of prediction markets including discussion of scalar and index markets.
Berg, J. & Rietz, T. (2019). "Longshots, Overconfidence and Efficiency on the Iowa Electronic Markets." International Journal of Forecasting, 35(1), 271--287. Empirical study of multi-outcome political prediction markets, with evidence on pricing efficiency and bias.

Snowberg, E. & Wolfers, J. (2010). "Explaining the Favorite-Longshot Bias: Is it Risk-Love or Misperceptions?" Journal of Political Economy, 118(4), 723--746. Disentangles the favorite-longshot bias into risk preference and probability misperception components using multi-outcome prediction market data.
Thaler, R.H. & Ziemba, W.T. (1988). "Anomalies: Parimutuel Betting Markets --- Racetracks and Lotteries." Journal of Economic Perspectives, 2(2), 161--174. Classic paper documenting systematic mispricings in multi-outcome parimutuel markets.

Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107--119. Introduction to combinatorial prediction markets where traders can bet on combinations of outcomes across multiple events.
Chen, Y., Fortnow, L., Lambert, N., Pennock, D.M., & Wortman, J. (2008). "Complexity of Combinatorial Market Makers." Proceedings of the 9th ACM Conference on Electronic Commerce (EC). Analyzes the computational complexity of pricing in combinatorial prediction markets.

Metaculus (metaculus.com): Active prediction platform with many multi-outcome and continuous (scalar) questions. Excellent source for practicing the techniques in this chapter.
Polymarket (polymarket.com): Major crypto-based prediction market with multi-outcome political and economic markets. Real-time data for analysis.
PredictIt (predictit.org): Political prediction market with multi-candidate markets. Historical data available for backtesting.
Kalshi (kalshi.com): CFTC-regulated prediction market with bracket-style (scalar) markets for economic indicators.

SciPy (scipy.org): Python library used extensively in this chapter for optimization (scipy.optimize.minimize, scipy.optimize.brentq) and distribution fitting (scipy.stats).
CVXPY (cvxpy.org): Python convex optimization library useful for more complex portfolio optimization problems in multi-outcome markets.
Stan (mc-stan.org): Bayesian inference framework useful for building probability models that generate independent probability estimates for multi-outcome events.