Multiportfolio Optimization: A Fairness-Aware TargetOriented Model

• 成果类型: Article
• 署名作者: Cai, Xiaoqiang; Long, Daniel Zhuoyu; Yu, Gen; Zhang, Lianmin
• 署名单位: The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; Chinese University of Hong Kong; University of Zurich; The Chinese University of Hong Kong, Shenzhen
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2021.0363
• 发表日期: 2024
• 关键词: multiportfolio; market impact costs; satisficing; distributionally robust; financial service

摘要

Problem definition: We consider a multiportfolio optimization problem in which nonlinear market impact costs result in a strong dependency of one account's performance on the trading activities of the other accounts. Methodology/results: We develop a novel target -oriented model that jointly optimizes the rebalancing trades and the split of market impact costs. The key advantages of our proposed model include the consideration of clients' targets on investment returns and the incorporation of distributional uncertainty. The former helps fund managers to circumvent the difficulty in identifying clients' utility functions or risk parameters, whereas the latter addresses a practical challenge that the probability distribution of risky asset returns cannot be fully observed. Specifically, to evaluate the quality of multiple portfolios' investment payoffs in achieving targets, we propose a new class of performance measures, called fairness -aware multiparticipant satisficing (FMS) criteria. These criteria can be extended to encompass distributional uncertainty and have the salient feature of addressing the fairness issue with the collective satisficing level as determined by the least satisfied participant. We find that, structurally, the FMS criteria have a dual connection with a set of risk measures. For multiportfolio optimization, we consider the FMS criterion with conditional value -at -risk being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although nonconvex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. Managerial implications: For the multiportfolio optimization problem, the numerical study shows that our approach outperforms utility -based models in achieving targets and in out -of -sample performance. More generally, the proposed FMS criteria provide a new decision framework for operational problems in which the decision makers are target -oriented rather than being utility maximizers and issues of fairness and ambiguity should be considered.