Predictive Regressions: A Machine Learning Perspective

46 Pages Posted: 10 Nov 2020 Last revised: 11 Nov 2020

Date Written: November 10, 2020

Abstract

In this article, we quantify the forecasting efficiency of the OLS estimator in uni-variate predictive regressions. We link the prediction accuracy to three key quantities: the persistence of the underlying series, the forecasting horizon, and the sample size. We find that high auto-correlation in the dependent variable is required to reach reasonably low levels of mean squared errors. In this case, we identify two configurations which generate positive out-of-sample R-squared: short term forecasting with small samples and long horizon predictions with very deep samples. Two examples of such configurations can easily be found in financial economics: the short term volatility and the long term equity premium. We confirm our results via an empirical study on the SP 500 with a series of 15 popular predictors used in the literature.

Keywords: Asset Pricing, Predictive Regression, Machine Learning, Estimator Efficiency

JEL Classification: G11, G12, C53, C22

Suggested Citation

Coqueret, Guillaume and Deguest, Romain, Predictive Regressions: A Machine Learning Perspective (November 10, 2020). Available at SSRN: https://ssrn.com/abstract=3709412 or http://dx.doi.org/10.2139/ssrn.3709412

Guillaume Coqueret (Contact Author)

EMLYON Business School ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

Romain Deguest

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

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