Data Management

Risk Factors And Distributions From Long-Short Trading Strategies: A Monte Carlo Approach

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Executive Summary

The authors propose a simulation-based methodology for evaluating the significance of returns to long-short trading strategies and for investigating the effects of various risk adjustments and other return characteristics. As an example, they apply the approach to three widely studied long-short trading strategies: momentum, value and size. They generate empirical distributions for various moments of strategy returns and examine the distributions of Sharpe ratios, skewness and kurtosis, as well as factor-model alphas and betas. They find that if investors demand a premium for holding portfolios with high skewness/kurtosis, a significant fraction of long-short trading profits can be explained.

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