Machine Learning Portfolio Allocation

Michael Pinelis and David Ruppert

Papers from arXiv.org

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.

Date: 2020-03, Revised 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk, nep-rmg and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2003.00656

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