Machine Learning Portfolio Allocation
40 Pages Posted: 6 Mar 2020 Last revised: 9 Jul 2020
Date Written: March 2, 2020
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
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