Divide and Conquer: Financial Ratios and Industry Returns Predictability

Abstract

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.