Industry Return Predictability: A Machine Learning Approach

David E. Rapach; Jack K. Strauss; Jun Tu; Guofu Zhou

doi:10.3905/jfds.2019.1.3.009

Industry Return Predictability: A Machine Learning Approach

David E. Rapach, Jack K. Strauss, Jun Tu and Guofu Zhou

The Journal of Financial Data Science Summer 2019, 1 (3) 9-28; DOI: https://doi.org/10.3905/jfds.2019.1.3.009

David E. Rapach

is a professor of economics and the John Simon Endowed Chair in Economics at the Saint Louis University Chaifetz School of Business in St. Louis, MO

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For correspondence: david.rapach@slu.edu

Jack K. Strauss

is a professor of economics and the Miller Chair of Applied Economics at the University of Denver Daniels School of Business in Denver, CO

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For correspondence: jack.strauss@du.edu

Jun Tu

is an associate professor of finance at the Singapore Management University Lee Kong Chian School of Business in Singapore

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For correspondence: tujun@smu.edu.sg

Guofu Zhou

is the Frederick Bierman and James E. Spears Professor of Finance at Washington University in the St. Louis Olin Business School in St. Louis, MO

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For correspondence: zhou@wustl.edu

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Industry Return Predictability: A Machine Learning Approach

David E. Rapach, Jack K. Strauss, Jun Tu, Guofu Zhou

The Journal of Financial Data Science Jul 2019, 1 (3) 9-28; DOI: 10.3905/jfds.2019.1.3.009

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Industry Return Predictability: A Machine Learning Approach

David E. Rapach, Jack K. Strauss, Jun Tu, Guofu Zhou

The Journal of Financial Data Science Jul 2019, 1 (3) 9-28; DOI: 10.3905/jfds.2019.1.3.009

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