Publication Type
Journal Article
Version
submittedVersion
Publication Date
6-2019
Abstract
In this article, the authors use machine learning tools to analyze industry return predictability based on the information in lagged industry returns. Controlling for post-selection inference and multiple testing, they find 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 multifactor 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
Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement
Discipline
Finance and Financial Management
Research Areas
Finance
Publication
The Journal of Financial Data Science
Volume
3
First Page
9
Last Page
28
Identifier
10.3905/jfds.2019.1.3.009
Embargo Period
2-11-2025
Citation
RAPACH, David E.; STRAUSS, Jack K.; TU, Jun; and ZHOU, Guofu.
Industry return predictability: A machine learning approach. (2019). The Journal of Financial Data Science. 3, 9-28.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7672
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3905/jfds.2019.1.3.009
