Divide and Conquer: Financial Ratios and Industry Returns Predictability

70 Pages Posted: 8 Mar 2018 Last revised: 10 May 2021

See all articles by Daniele Bianchi

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London

Ken McAlinn

Temple University, Fox School of Business

Date Written: May 25, 2020

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.

Suggested Citation

Bianchi, Daniele and McAlinn, Kenichiro, Divide and Conquer: Financial Ratios and Industry Returns Predictability (May 25, 2020). Available at SSRN: https://ssrn.com/abstract=3136368 or http://dx.doi.org/10.2139/ssrn.3136368

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London ( email )

Mile End Road
London, London E1 4NS
United Kingdom

HOME PAGE: http://whitesphd.com

Kenichiro McAlinn (Contact Author)

Temple University, Fox School of Business ( email )

Philadelphia, PA 19122
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
740
Abstract Views
3,397
rank
42,070
PlumX Metrics