Sparse Predictive Regressions: Statistical Performance and Economic Significance

30 Pages Posted: 29 Oct 2019 Last revised: 31 Oct 2019

See all articles by Daniele Bianchi

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London

Andrea Tamoni

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick

Date Written: October 20, 2019

Abstract

We propose and evaluate a variety of penalized regression methods for forecasting and economic decision making in a data-rich environment under parameter uncertainty. Empirically, we explore the statistical and economic performance across different asset classes such as stocks, bonds, and currencies, and alternative strategies within an asset class (e.g., momentum and value in the space of equity). The main result shows that penalties that both shrinkage the model space and regularize the remaining regression parameters, e.g. elastic net penalty, tend to outperform competing sparse and dense methodologies, both statistically and economically.

Keywords: Return Predictability, Empirical Asset Pricing, Machine Learning, Bayesian Methods, Penalized Regressions.

JEL Classification: C38, C45, C53, E43, G12, G17.

Suggested Citation

Bianchi, Daniele and Tamoni, Andrea, Sparse Predictive Regressions: Statistical Performance and Economic Significance (October 20, 2019). Available at SSRN: https://ssrn.com/abstract=3472622

Daniele Bianchi (Contact Author)

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

Mile End Rd
Mile End Road
London, London E1 4NS
United Kingdom

HOME PAGE: http://whitesphd.com

Andrea Tamoni

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick ( email )

1 Washington Park
Newark, NJ 07102
United States

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