Machine Learning Portfolio Allocation

40 Pages Posted: 6 Mar 2020 Last revised: 9 Jul 2020

See all articles by Michael Pinelis

Michael Pinelis

Department of Economics, Cornell University

D. Ruppert

Cornell University

Date Written: March 2, 2020

Abstract

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sign probabilities of the excess return with payout yields. The second is used to construct an optimized volatility estimate. Reward-risk timing with machine learning provides substantial improvements over the buy-and-hold in utility, risk-adjusted returns, and maximum drawdowns. This paper presents a new theoretical basis and unifying framework for machine learning applied to both return- and volatility-timing.

Keywords: Portfolio Allocation, Finance, Machine Learning, Random Forest, Market Timing, Reward-risk Timing, Volatility Estimation, Equity Return Predictability

JEL Classification: G11, G12, C13

Suggested Citation

Pinelis, Michael and Ruppert, D., Machine Learning Portfolio Allocation (March 2, 2020). Available at SSRN: https://ssrn.com/abstract=3546294 or http://dx.doi.org/10.2139/ssrn.3546294

Michael Pinelis (Contact Author)

Department of Economics, Cornell University ( email )

Ithaca, NY
United States

D. Ruppert

Cornell University ( email )

School of Operations Research and Industrial Engineering
Ithaca, NY 14853
United States
607-255-9136 (Phone)

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

Paper statistics

Downloads
532
Abstract Views
2,144
rank
62,095
PlumX Metrics