Chapter 14 Further Reading: Binary Outcome Trading Strategies

Foundational Texts

Prediction Markets and Information Aggregation

  1. Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. - The seminal survey of prediction markets. Provides the intellectual foundation for why prediction market prices reflect probability estimates and under what conditions they are efficient. Essential background for understanding why edges exist and how they are eroded.

  2. Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. - A critical examination of whether prediction market prices truly equal probabilities. Important for fundamental analysts who must understand the difference between market prices and true probabilities, especially when risk-neutral and real-world probabilities diverge.

  3. Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., ... & Zitzewitz, E. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. - A concise argument for prediction markets by leading economists. Useful for understanding the theoretical basis for market efficiency and the conditions under which strategies can find edges.

Probability and Decision Making

  1. Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail --- But Some Don't. Penguin Press. - Covers the practical challenges of probabilistic forecasting across domains (elections, weather, sports, economics). Directly relevant to building fundamental models for prediction markets. The sections on polling aggregation are particularly useful for election market trading.

  2. Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown. - Based on the Good Judgment Project, which identified individuals who consistently outperform prediction markets. Contains practical techniques for probability estimation that map directly to the fundamental analysis strategies in this chapter.

  3. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. - The definitive work on cognitive biases that create trading opportunities. Chapters on overconfidence, anchoring, and availability heuristic are directly relevant to contrarian strategies and understanding why markets overreact.

Strategy Development

Quantitative Trading and Backtesting

  1. De Prado, M. L. (2018). Advances in Financial Machine Learning. Wiley. - Although focused on traditional financial markets, the chapters on backtesting methodology (walk-forward testing, purged cross-validation, meta-labeling) are directly applicable to binary market backtesting. The discussion of backtest overfitting is essential reading.

  2. Chan, E. P. (2009). Quantitative Trading: How to Build Your Own Algorithmic Trading Business. Wiley. - Practical guide to developing and testing trading strategies. The chapters on mean reversion, momentum, and strategy evaluation translate well to prediction markets with appropriate modifications.

  3. Thorp, E. O. (2017). A Man for All Markets. Random House. - Memoir of the mathematician who developed the Kelly Criterion for practical use. Provides deep intuition for position sizing, bankroll management, and the relationship between edge and bet size --- core concepts for binary market trading.

Mean Reversion and Momentum

  1. Poterba, J. M., & Summers, L. H. (1988). "Mean Reversion in Stock Prices: Evidence and Implications." Journal of Financial Economics, 22(1), 27-59.

    • Classic paper establishing the statistical framework for mean reversion. The methodology for testing whether price reversals are statistically significant applies directly to binary market mean-reversion strategies.

  2. Jegadeesh, N., & Titman, S. (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." Journal of Finance, 48(1), 65-91.

    • The foundational paper on momentum strategies. While focused on equities, the basic insight that past price movements predict future price movements has been found to hold (weakly) in prediction markets as well.

Event-Driven Strategies

  1. Snowberg, E., Wolfers, J., & Zitzewitz, E. (2007). "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections." Quarterly Journal of Economics, 122(2), 807-829.

    • Demonstrates how prediction markets respond to political events. Useful for calibrating expected market reactions to various types of catalysts.

  2. Rothschild, D. (2009). "Forecasting Elections: Comparing Prediction Markets, Polls, and Their Biases." Public Opinion Quarterly, 73(5), 895-916.

    • Compares the accuracy of prediction markets versus polls for election forecasting. Essential reading for building fundamental models that combine multiple information sources.

Binary Options and Exotic Markets

  1. Natenberg, S. (2015). Option Volatility and Pricing. 2nd Edition. McGraw-Hill.

    • The standard text on option pricing and trading strategies. Binary options (digital options) are covered in the context of exotic options. The discussion of volatility, time decay, and delta applies to binary prediction markets with appropriate reinterpretation.

  2. Haug, E. G. (2007). The Complete Guide to Option Pricing Formulas. McGraw-Hill.

    • Reference for the mathematical pricing of binary options. The formulas for digital call and put options provide the theoretical framework for understanding how binary contract prices should behave as a function of underlying probability and time to expiry.

Behavioral Finance and Market Microstructure

  1. Shiller, R. J. (2015). Irrational Exuberance. 3rd Edition. Princeton University Press.

    • Explores how psychological and social forces create market bubbles and crashes. The mechanisms described (herding, feedback loops, overconfidence) are directly observable in prediction markets and create the opportunities exploited by contrarian strategies.

  2. Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.

    • Comprehensive treatment of how markets work at the microstructure level: order books, market makers, price discovery, and execution. Essential for understanding the execution challenges discussed in Section 14.11 (backtesting assumptions about fills, slippage, and market impact).

  3. Page, L. (2012). "It Ain't Over Till It's Over: Yogi Berra Bias on Prediction Markets." Applied Economics, 44(1), 81-92.

    • Demonstrates that prediction markets systematically underreact to late-breaking information (prices do not move to extremes fast enough). This bias is the foundation of the closing-the-gap strategy.

Risk Management

  1. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.

    • Essential reading for understanding tail risk in prediction markets. The closing-the-gap strategy is particularly vulnerable to black swan events, where "near-certain" outcomes do not materialize.

  2. Kelly, J. L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917-926.

    • The original Kelly Criterion paper. Mathematically dense but foundational for understanding optimal position sizing in binary outcome games.

Online Resources and Platforms

Research and Data

  1. PredictIt Research Archive (https://www.predictit.org/)

    • Historical price data from PredictIt markets. Useful for backtesting strategies on real prediction market data.

  2. Polymarket (https://polymarket.com/)

    • Active prediction market platform. Provides real-time data for studying market behavior and testing strategies.

  3. Metaculus (https://www.metaculus.com/)

    • Forecasting platform with calibration scoring. Not a trading platform, but useful for developing probability estimation skills that translate to fundamental analysis.

  4. Good Judgment Open (https://www.gjopen.com/)

    • Public forecasting platform based on the research described in Superforecasting. Provides practice in the probabilistic reasoning that underlies fundamental analysis strategies.

Technical References

  1. Scikit-learn Documentation (https://scikit-learn.org/)

    • Machine learning library useful for building more sophisticated fundamental models. The classification algorithms (logistic regression, random forests) can be adapted for binary outcome prediction.

  2. Statsmodels Documentation (https://www.statsmodels.org/)

    • Statistical modeling library for Python. Useful for time series analysis (mean reversion testing), regression (fundamental models), and statistical tests (significance of backtest results).

Academic Papers on Prediction Market Strategies

  1. Leigh, A., & Wolfers, J. (2006). "Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets." Economic Record, 82(258), 325-340.

    • Compares the accuracy of different forecasting approaches for elections. Provides evidence on whether fundamental models or market prices are more accurate, directly informing strategy development.

  2. Berg, J., Forsythe, R., Nelson, F., & Rietz, T. (2008). "Results from a Dozen Years of Election Futures Markets Research." Handbook of Experimental Economic Results, 1, 742-751.

    • Summarizes decades of research on the Iowa Electronic Markets. Contains evidence on market efficiency, biases, and the conditions under which prediction market prices deviate from true probabilities.

  3. Gjerstad, S. (2005). "Risk Aversion, Beliefs, and Prediction Market Equilibrium." Working Paper.

    • Analyzes how risk aversion affects prediction market prices. Important for understanding why market prices may systematically differ from true probabilities, creating opportunities for strategies that account for risk premium.

  4. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107-119.

    • Describes automated market maker designs that are used in many prediction market platforms. Understanding the market maker mechanism is important for execution strategy and understanding price dynamics.