Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods

37 Pages Posted: 1 Aug 2019

See all articles by David Rapach

David Rapach

Saint Louis University; Washington University in St. Louis

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School; China Academy of Financial Research (CAFR)

Date Written: July 27, 2019

Abstract

This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series context. The methods use the elastic net to refine the simple combination return forecast from Rapach et al. (2010). In a time-series application focused on forecasting the US market excess return using a large number of potential predictors, we find that the elastic net refinement substantively improves the simple combination forecast, thereby providing one of the best market excess return forecasts to date. We also discuss the cross-sectional return forecasts developed in Han et al. (2019), highlighting how machine learning methods can be used to improve combination forecasts in both the time-series and cross-sectional dimensions. Overall, because many important questions in finance are related to time-series or cross-sectional return forecasts, the machine learning methods discussed in this paper should provide valuable tools to researchers and practitioners alike.

Keywords: Expected stock returns, Time series, Cross section, Forecast combination, Shrinkage, Elastic net

JEL Classification: C53, G11, G12

Suggested Citation

Rapach, David and Zhou, Guofu, Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods (July 27, 2019). Available at SSRN: https://ssrn.com/abstract=3428095 or http://dx.doi.org/10.2139/ssrn.3428095

David Rapach (Contact Author)

Saint Louis University ( email )

3674 Lindell Blvd
St. Louis, MO 63108-3397
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

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/

China Academy of Financial Research (CAFR)

Shanghai Advanced Institute of Finance
Shanghai P.R.China, 200030
China

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