Industry Return Predictability: A Machine Learning Approach
Posted: 17 Feb 2018 Last revised: 21 May 2019
Date Written: October 8, 2018
We use machine learning tools to analyze industry return predictability based on the information in lagged industry returns from across the entire economy. Controlling for post-selection inference and multiple testing, we nd 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 multi- factor 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: Predictive regression; LASSO; Post-selection inference; Network analysis; Industry-rotation portfolio; Multifactor model; Gradual information dffusion
JEL Classification: C22, C58, G11, G12, G14
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