Maximizing the Sharpe Ratio: A Genetic Programming Approach

Abstract

While existing studies focus on minimizing model fitting errors, we maximize directly the Sharpe ratio of spread portfolios with a genetic programming (GP) approach. We find that the GP approach can double the performance in the US and outperform internationally, compared with other approaches under examination. We also apply the GP to maximize the Sharpe ratio of investing in all the underlying stocks, which amounts to searching for the stochastic discount factor that prices all the assets. We find that the Sharpe ratio is almost 70% greater than before, indicating the loss of relying on spread portfolios for investing and pricing can be substantial.

Keywords: JEL Classification: G12, G14, G15 Machine Learning, Genetic Programming, Cross-sectional Returns, Portfolio Optimization

JEL Classification: G12, G14, G15