A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment

Zhenlong Jiang, Ran Ji and Kuo-Chu Chang
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Zhenlong Jiang: Department of Systems Engineering and Operations Research, George Mason University, 4400 University Dr., MS 4A6, Fairfax, VA 22030, USA
Ran Ji: Department of Systems Engineering and Operations Research, George Mason University, 4400 University Dr., MS 4A6, Fairfax, VA 22030, USA
Kuo-Chu Chang: Department of Systems Engineering and Operations Research, George Mason University, 4400 University Dr., MS 4A6, Fairfax, VA 22030, USA

JRFM, 2020, vol. 13, issue 7, 1-20

Abstract: We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

Keywords: portfolio optimization; Mean-Gini model; risk-aversion coefficient; machine learning models; technical indicators; information fusion (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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