A machine learning integrated portfolio rebalance framework with risk-aversion adjustment

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 13 ; Year: 2020 ; Issue: 7 ; Pages: 1-20 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: information fusion ; machine learning models ; Mean-Gini model ; portfolio optimization ; risk-aversion coefficient ; technical indicators

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Jiang, Zhenlong ; Ji, Ran ; Chang, Kuo-Chu
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2020

Further information

• DOI: doi:10.3390/jrfm13070155
• Handle: http://hdl.handle.net/10419/239243
• Last update: 10.03.2025, 11:44 AM CET