期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3183891
关键词
Market research; Portfolios; Optimization; Reactive power; Estimation; Task analysis; Information science; Conditional value at risk (CVaR); interior point method; multiple trends; portfolio optimization (PO)
类别
资金
- National Natural Science Foundation of China [62176103, 61703182, 61972179, 62077028, 61877029]
- Science and Technology Planning Project of Guangzhou, China [202102021173, 202102080307, 202206030007]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515011476, 2021A1515011873]
- Science and Technology Planning Project of Guangdong [2021A1515011873, 2019A050510024, 2020B0909030005, 2020B1212030003, 2020ZDZX3013]
- Specially Creative University Project for Guangdong [2019KTSCX010]
- Project of Guangxi Key Laboratory of Trusted Software [kx202007]
In this study, a novel multitrend conditional value at risk (MT-CVaR) method is proposed for portfolio optimization, incorporating multiple trends and their influences to achieve state-of-the-art investing performance and risk management.
Trend representation has been attracting more and more attention recently in portfolio optimization (PO) via machine learning methods. It adopts concepts and phenomena from the field of empirical and behavioral finance when little prior knowledge is obtained or strict statistical assumptions cannot be guaranteed. It is used mostly in estimating the expected asset returns, but hardly in measuring risk. To fill this gap, we propose a novel multitrend conditional value at risk (MT-CVaR), which embeds multiple trends and their influences in CVaR. Besides, we propose a novel PO model with this MT-CVaR as the risk metric and then design a solving algorithm based on the interior point method to compute the portfolio. Extensive experiments on six benchmark datasets from diverse financial markets with different frequencies show that MT-CVaR achieves the state-of-the-art investing performance and risk management.
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