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The Journal of Financial Data Science

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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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The Journal of Financial Data Science: 1 (3)
The Journal of Financial Data Science
Vol. 1, Issue 3
Summer 2019
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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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    • PREDICTIVE REGRESSION FRAMEWORK
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