Divide and Conquer: Financial Ratios and Industry Returns Predictability
70 Pages Posted: 8 Mar 2018 Last revised: 10 May 2021
Date Written: May 25, 2020
We propose a novel approach for forecasting the equity premium within a data-rich environment based on ensembling small-scale linear models. The economic nature of the predictors is exploited to efficiently retain all of the information available without assuming a priori that some predictor might be irrelevant or easily reducible to a latent factor. Empirically, our results lend strong support for transparent linear predictive models and the use of accounting-based information when forecasting both industry and aggregate stock market excess returns: positive statistical and economic out-of-sample performance compared to sparse predictive regressions, forecast combination strategies and complex non-linear machine learning algorithms.
Keywords: Financial ratios, returns predictability, data-rich models, industry portfolios, equity premium, machine learning, ensemble learning.
JEL Classification: G11, G17, C55, C58.
Suggested Citation: Suggested Citation