Industry Return Predictability: A Machine Learning Approach

Posted: 17 Feb 2018 Last revised: 21 May 2019

See all articles by David Rapach

David Rapach

Saint Louis University; Washington University in St. Louis

Jack Strauss

University of Denver - Reiman School of Finance; University of Denver

Jun Tu

Singapore Management University - Lee Kong Chian School of Business

Guofu Zhou

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

Date Written: October 8, 2018

Abstract

We use machine learning tools to analyze industry return predictability based on the information in lagged industry returns from across the entire economy. Controlling for post-selection inference and multiple testing, we nd significant in-sample evidence of industry return predictability. Lagged returns for the financial sector and commodity- and material-producing industries exhibit widespread predictive ability, consistent with the gradual diffusion of information across economically linked industries. Out-of- sample industry return forecasts that incorporate the information in lagged industry returns are economically valuable: controlling for systematic risk using leading multi- factor models from the literature, an industry-rotation portfolio that goes long (short) industries with the highest (lowest) forecasted returns delivers an annualized alpha of over 8%. The industry-rotation portfolio also generates substantial gains during economic downturns, including the Great Recession.

Keywords: Predictive regression; LASSO; Post-selection inference; Network analysis; Industry-rotation portfolio; Multifactor model; Gradual information dffusion

JEL Classification: C22, C58, G11, G12, G14

Suggested Citation

Rapach, David and Strauss, Jack and Tu, Jun and Zhou, Guofu, Industry Return Predictability: A Machine Learning Approach (October 8, 2018). Available at SSRN: https://ssrn.com/abstract=3120110 or http://dx.doi.org/10.2139/ssrn.3120110

David Rapach

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

Jack Strauss

University of Denver - Reiman School of Finance ( email )

2101 S. University Blvd
Denver, CO COLORADO 80126
United States
314 602 7265 (Phone)

University of Denver ( email )

2201 S. Gaylord St
Denver, CO 80208-2685
United States

Jun Tu

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
#04-01
Singapore, 178899
Singapore

Guofu Zhou (Contact Author)

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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