Journal of the Operations Research Society of China ›› 2022, Vol. 10 ›› Issue (3): 529-558.doi: 10.1007/s40305-022-00400-0

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  • 收稿日期:2021-02-26 修回日期:2022-02-15 出版日期:2022-09-30 发布日期:2022-09-06

Risk and Potential:An Asset Allocation Framework with Applications to Robo-Advising

Xiang-Yu Cui1, Duan Li2, Xiao Qiao3, Moris S. Strub4   

  1. 1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200437, China;
    2. School of Data Science, City University of Hong Kong, Hong Kong, China;
    3. School of Data Science and Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong, China;
    4. Department of Information Systems and Management Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
  • Received:2021-02-26 Revised:2022-02-15 Online:2022-09-30 Published:2022-09-06
  • Contact: Moris S.Strub,Xiang-Yu Cui,Duan Li,Xiao Qiao E-mail:strub@sustech.edu.cn;cui.xiangyu@mail.shufe.edu.cn;dli@se.cuhk.edu.hk;xiaoqiao@cityu.edu.hk
  • Supported by:
    Xiang-Yu Cui was partially supported by the National Natural Science Foundation of China (Nos.71671106 and 72171138),by the Shanghai Institute of International Finance and Economics,and by the Program for Innovative Research Team of Shanghai University of Finance and Economics (No.2020110930).Duan Li and Xiao Qiao were partially supported by the Research Grants Council of the Hong Kong Special Administrative Region,China (No.CityU 11200219).Moris Strub was partially supported by the National Natural Science Foundation of China (No.72050410356).

Abstract: We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and timeinconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.

Key words: Mean-risk optimization, Mean-variance, Expected utility maximization, Portfolio choice, Risk, Potential, Robo-advising, FinTech

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