Industry return predictability: A machine learning approach

• Main Authors: RAPACH, David E., STRAUSS, Jack K., TU, Jun, ZHOU, Guofu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Big data/machine learning; analysis of individual factors/risk premia; portfolio construction; performance measurement; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7672; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8671/viewcontent/Industry_Return_Predictability_A_Machine.pdf
• Institution: Singapore Management University

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.