PT  - JOURNAL ARTICLE
AU  - David E. Rapach
AU  - Jack K. Strauss
AU  - Jun Tu
AU  - Guofu Zhou
TI  - Industry Return Predictability: <em>A Machine Learning Approach</em>
AID  - 10.3905/jfds.2019.1.3.009
DP  - 2019 Jul 31
TA  - The Journal of Financial Data Science
PG  - 9--28
VI  - 1
IP  - 3
4099  - https://pm-research.com/content/1/3/9.short
4100  - https://pm-research.com/content/1/3/9.full
AB  - 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.TOPICS: Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement