Robust portfolio management: A novel multi-task learning model fusing predicted returns and residual data under the framework of Mean-VaR

Qingyun He, Chuanyang Hong, Liang Xu and Ling Li

Journal of the Operational Research Society, 2025, vol. 76, issue 7, 1466-1480

Abstract: We investigate how to build a robust portfolio by introducing a novel multi-task learning model that fuses predicted returns and residual data to assess the portfolio risk under the decision-making framework of Mean-VaR. A common way to build a portfolio is to predict the return of assets and then allocate weights according to the predicted return and corresponding risk. However, predicting asset returns accurately in financial markets remains a challenge. To improve prediction accuracy and, more importantly, effectively reduce risk in the portfolio, we adopt the multi-task learning anomaly detection (MTLAD). In this model, predicting asset returns using deep learning model (long short-term memory, LSTM) is the main task, and anomaly detection is the auxiliary task. We then combine the predicted returns and residual data to evaluate the risk measure when allocating the asset weights. Furthermore, we perform an extensive numerical investigation based on data in the Chinese financial market. Results obtained show that our robust portfolio management approach has great potential compared with multiple benchmarks.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2438333 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:76:y:2025:i:7:p:1466-1480

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2438333

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().