The predictability of cross-sectional returns in high frequency

Authors

  • Yifan Wang Princeton University

DOI:

https://doi.org/10.12660/rbfin.v20n1.2022.84399

Keywords:

Machine learning, Forecasting returns

Abstract

Stock return forecast is of great importance to trading, hedging, and portfolio management. In this article, we apply LASSO and random forest to make rolling one-minute-ahead return forecasts of Dow Jones stocks, using the cross-section of lagged returns of S&P 500 components as candidate predictors. Although the number of candidate variables is large, the negative out-of-sample R2 suggests that the predictions from LASSO and random forest give larger mean-squared error than the historical average. So, there is no evidence of predictability in the cross-sectional returns of large stocks in high frequency. The predictability presented by Chinco et al. (2019) might be due to the interaction between large and small stocks.

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Published

04/09/2022 — Updated on 05/07/2022

Issue

Vol. 20 No. 1 (2022): January-March

Section

Articles

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