Chapter 17 Further Reading: Portfolio Construction and Risk Management

Foundational Portfolio Theory

Modern Portfolio Theory

1. Markowitz, H. (1952). "Portfolio Selection." Journal of Finance, 7(1), 77-91. - The foundational paper that launched modern portfolio theory. Markowitz introduced the mean-variance optimization framework, demonstrating mathematically that diversification reduces risk without proportionally reducing expected return. While developed for continuous-return assets, the core insight --- that correlation structure determines portfolio risk --- applies directly to prediction market portfolios. Every concept in Section 17.5 (Diversification Strategies) traces back to this paper.

2. Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis. 9th Edition. Wiley. - The standard graduate textbook on portfolio theory. Covers mean-variance optimization, the efficient frontier, factor models, and performance measurement in comprehensive detail. Chapters on portfolio construction translate to prediction markets with the substitution of binary outcome distributions for continuous returns. The discussion of estimation error in covariance matrices is particularly relevant for Chapter 17.

The Kelly Criterion and Bankroll Management

3. Kelly, J. L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917-926. - The original paper deriving the Kelly Criterion from information theory. Kelly showed that betting a fraction of your bankroll equal to your edge divided by the odds maximizes the expected logarithmic growth rate. While the paper considers single bets, it provides the theoretical foundation for the portfolio Kelly extension in Section 17.3.

4. Thorp, E. O. (2006). "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market." In S. A. Zenios & W. T. Ziemba (Eds.), Handbook of Asset and Liability Management, Vol. 1, pp. 385-428. North-Holland. - Thorp, the mathematician who brought Kelly to practical finance, provides a masterful survey of the criterion's application across domains. The sections on simultaneous Kelly betting with multiple wagers are directly relevant to Section 17.3. Includes practical guidance on fractional Kelly and the tradeoff between growth rate and drawdown risk.

5. MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. - The definitive collected volume on Kelly criterion theory and applications. Contains reprints of seminal papers plus new contributions on multi-asset Kelly optimization, fractional Kelly strategies, and the practical challenges of implementing Kelly in real portfolios. Chapter 17's treatment of portfolio Kelly draws heavily on the multi-asset extensions presented here.

6. Poundstone, W. (2005). Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street. Hill and Wang. - An accessible narrative history of the Kelly Criterion, from Claude Shannon's information theory to Ed Thorp's blackjack exploits to the application in financial markets. Provides intuitive understanding of why Kelly works, why fractional Kelly is safer, and the real-world consequences of over-betting. Excellent for building the intuition behind Sections 17.3 and 17.9.

Risk Management

Risk Metrics and Measurement

7. Jorion, P. (2006). Value at Risk: The New Benchmark for Managing Financial Risk. 3rd Edition. McGraw-Hill. - The standard reference on Value at Risk methodology. Covers parametric, historical simulation, and Monte Carlo approaches to VaR estimation. While focused on continuous-return portfolios, the Monte Carlo methods translate directly to binary outcome portfolios. The discussion of VaR limitations and the case for Expected Shortfall (CVaR) motivates the risk metric adaptations in Section 17.6.

8. McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques and Tools. Revised Edition. Princeton University Press. - A rigorous mathematical treatment of risk measurement, including copula models for dependence, extreme value theory for tail risk, and coherent risk measures. The chapter on copulas is essential background for understanding the Gaussian copula approach used in Section 17.3 and 17.7 to model correlated binary outcomes.

Tail Risk and Black Swans

9. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House. - Taleb's influential argument that rare, high-impact events dominate outcomes in complex systems. Essential reading for prediction market traders because it challenges the assumption that historical distributions capture future risk. Case Study 2 (Stress Testing Through a Black Swan Event) is directly informed by Taleb's framework. His emphasis on robustness over optimization resonates with the chapter's theme that survival precedes success.

10. Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. Random House.

    • Extends the Black Swan framework to argue that the best systems do not merely survive shocks but improve from them. The concept of antifragility has direct application to prediction market portfolio management: a well-managed portfolio with reserve capital and drawdown rules can exploit crises (through recovery trades and new opportunities) rather than merely enduring them.

Monte Carlo Methods

11. Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer.

    • The definitive technical reference on Monte Carlo simulation for financial applications. Covers random number generation, variance reduction techniques, simulation of correlated processes, and confidence interval estimation. The methods in Section 17.7 are simplified versions of the techniques presented here. The chapters on efficiency improvement (antithetic variates, control variates) are valuable for speeding up the portfolio simulations.

12. Kroese, D. P., Taimre, T., & Botev, Z. I. (2011). Handbook of Monte Carlo Methods. Wiley.

    • A broader treatment of Monte Carlo methods beyond finance, covering the mathematical foundations, convergence properties, and advanced sampling techniques. Useful for understanding how many simulations are needed for reliable estimates of tail statistics (the table in Section 17.7) and for implementing more efficient simulation algorithms.

Prediction Markets and Portfolio Applications

Prediction Market Efficiency and Risk

13. Snowberg, E., Wolfers, J., & Zitzewitz, E. (2013). "Prediction Markets for Economic Forecasting." In G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting, Vol. 2, pp. 657-687. Elsevier.

    • A comprehensive survey of prediction markets' ability to aggregate information for economic forecasting. Relevant to Chapter 17 because it discusses the correlation structure between economic prediction markets and the conditions under which markets are efficient or biased. Understanding these structural correlations is essential for building the correlation matrices in Section 17.2.

14. Page, L., & Clemen, R. T. (2013). "Do Prediction Markets Produce Well-Calibrated Probability Forecasts?" Economic Journal, 123(568), 491-513.

    • Examines whether prediction market prices correspond to actual outcome frequencies. Finds systematic miscalibration at the extremes (prices near 0 and 1). This has direct implications for portfolio construction: if markets systematically misprice extreme probabilities, there may be persistent edge categories, but the correlation structure of these mispricings matters for portfolio-level risk.

Behavioral Finance and Drawdown Psychology

15. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

    • The foundational work on cognitive biases relevant to trading psychology. Loss aversion (losses feel twice as painful as equivalent gains feel good) explains why drawdowns are psychologically devastating even when mathematically manageable. The discussion of overconfidence is directly relevant to why fractional Kelly is necessary: traders systematically overestimate their edge. Section 17.8 on drawdown psychology draws on Kahneman's framework.

16. Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown.

    • Based on the Good Judgment Project, this book identifies techniques used by the best forecasters. The emphasis on calibration, updating in response to evidence, and intellectual humility maps directly to the probability estimation skills needed for prediction market portfolio management. Superforecasters' practice of assigning precise probabilities is the foundation of the edge estimation that feeds into Kelly sizing.

Practical Trading and Risk Management

17. De Prado, M. L. (2018). Advances in Financial Machine Learning. Wiley.

    • While focused on traditional financial markets, several chapters are directly applicable: the treatment of hierarchical risk parity (an alternative to mean-variance optimization), the discussion of backtest overfitting, and the methods for estimating covariance matrices from noisy data. The hierarchical clustering approach to portfolio construction could be an interesting extension of the methods in this chapter.

18. Chan, E. P. (2013). Algorithmic Trading: Winning Strategies and Their Rationale. Wiley.

    • Practical guidance on implementing quantitative trading strategies, including position sizing, risk management, and portfolio construction. The chapter on Kelly criterion implementation is particularly useful, with worked examples showing how estimation error affects optimal bet sizing. The discussion of drawdown-based position adjustment is consistent with the framework in Section 17.8.

Online Resources

19. Quantopian Lecture Series on Portfolio Optimization (archived at various educational sites).

    • A series of Python-based tutorials covering mean-variance optimization, risk factor models, and portfolio rebalancing. While the Quantopian platform has closed, the educational content remains available and provides excellent hands-on practice with the numerical methods used in this chapter.

20. Polymarket (https://polymarket.com/) and Kalshi (https://kalshi.com/).

    • Active prediction market platforms where the portfolio construction techniques from this chapter can be applied. Studying the correlation structure of live markets --- for example, how multiple political markets move together during major news events --- provides real-world data for calibrating the correlation models discussed in Section 17.2.