Machine Learning Portfolio Allocation

42 Pages Posted: 6 Mar 2020 Last revised: 3 Jun 2021

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 monthly excess returns with macroeconomic factors including payout yields. The second is used to estimate the prevailing volatility. 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 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)

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