Maximizing the Sharpe Ratio: A Genetic Programming Approach

50 Pages Posted: 13 Jan 2021 Last revised: 10 Oct 2022

See all articles by Yang Liu

Yang Liu

Hunan University - College of Finance and Statistics

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 such as LASSO 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. Economic constraints can also be easily imposed on GP trading strategies. Moreover, we find adding a GP factor to the market can explain all anomalous returns from other machine learning methods.

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)

Hunan University - College of Finance and Statistics ( email )

Changsha
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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