Machine learning portfolio allocation

Journal of Finance and Data Science

David Ruppert¹  Michael Pinelis² 

[1] Corresponding author. 222 East 39th Street, New York, NY 10016, USA.;Department of Economics, Cornell University, 404 Uris Hall, Ithaca, NY 14853, USA;

关键词: Portfolio allocation;    Finance;    Machine learning;    Random forest;    Market timing;    Reward-risk timing;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown