@article {Rapach9, author = {David E. Rapach and Jack K. Strauss and Jun Tu and Guofu Zhou}, title = {Industry Return Predictability: A Machine Learning Approach}, volume = {1}, number = {3}, pages = {9--28}, year = {2019}, doi = {10.3905/jfds.2019.1.3.009}, publisher = {Institutional Investor Journals Umbrella}, 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.TOPICS: Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement}, issn = {2640-3943}, URL = {https://jfds.pm-research.com/content/1/3/9}, eprint = {https://jfds.pm-research.com/content/1/3/9.full.pdf}, journal = {The Journal of Financial Data Science} }