Maximizing the Sharpe Ratio: A Genetic Programming Approach

55 Pages Posted: 13 Jan 2021

See all articles by Yang Liu

Yang Liu

Tsinghua University - School of Economics & Management

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Yingzi Zhu

Tsinghua University - School of Economics & Management

Date Written: November 7, 2020

Abstract

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to popular regression-based learning tools and the neural network, GP can double their performance in the US, and outperform them internationally. We find that, while the economic objective plays a role, GP captures nonlinearity in comparison with methods like the Lasso, and it requires smaller sample size than the neural network.

Keywords: Machine Learning, Genetic Programming, Cross-sectional Returns, Predictability

JEL Classification: G12, G14, G15

Suggested Citation

Liu, Yang and Zhou, Guofu and Zhu, Yingzi, Maximizing the Sharpe Ratio: A Genetic Programming Approach (November 7, 2020). Available at SSRN: https://ssrn.com/abstract=3726609 or http://dx.doi.org/10.2139/ssrn.3726609

Yang Liu (Contact Author)

Tsinghua University - School of Economics & Management ( email )

Beijing
China

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

Yingzi Zhu

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China
+86-10-62786041 (Phone)

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