Alpha Go Everywhere: Machine Learning and International Stock Returns

57 Pages Posted: 5 Dec 2019 Last revised: 17 Mar 2020

See all articles by Darwin Choi

Darwin Choi

The Chinese University of Hong Kong (CUHK) - CUHK Business School

Wenxi Jiang

CUHK Business School, The Chinese University of Hong Kong

Chao Zhang

University of Oxford - Department of Statistics

Date Written: March 15, 2020

Abstract

We apply machine learning techniques and use stock characteristics to predict the cross-section of stock returns in 33 international markets. We conduct a stringent out-of-sample test to allay concerns about overfitting: the models are trained with past U.S. data and used to predict international stock returns. With fewer variables (based on past returns, size, volume, and accounting information) as inputs, we arrive at a conclusion similar to that in previous studies predicting U.S. stock returns with hundreds of stock characteristics and macroeconomic variables; complex methods outperform linear models in terms of both predicting returns and generating profits. We achieve even stronger results when the models are trained separately for each market, allowing for country-specific return-characteristic relationships. In most markets, we obtain out-of-sample R^2 and Sharpe ratios that are comparable to those in previous studies. Neural network models, which can handle complicated interactions among the predictors, produce the best results.

Keywords: Machine Learning, Cross-section of Stock Returns, International Markets, Neural Networks

JEL Classification: G12, G15

Suggested Citation

Choi, Darwin and Jiang, Wenxi and Zhang, Chao, Alpha Go Everywhere: Machine Learning and International Stock Returns (March 15, 2020). Available at SSRN: https://ssrn.com/abstract=3489679 or http://dx.doi.org/10.2139/ssrn.3489679

Darwin Choi (Contact Author)

The Chinese University of Hong Kong (CUHK) - CUHK Business School ( email )

Cheng Yu Tung Building
12 Chak Cheung Street
Shatin, N.T.
Hong Kong

Wenxi Jiang

CUHK Business School, The Chinese University of Hong Kong ( email )

Room 1250, Cheng Yu Tung Building
Chinese University of Hong Kong
Shatin, NT 06520
Hong Kong

Chao Zhang

University of Oxford - Department of Statistics ( email )

24-29 St Giles
Oxford
United Kingdom

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