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

作　　者：Xiang-Yu Cui Duan Li Xiao Qiao Moris S.Strub 

机构地区：[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

出　　处：《Journal of the Operations Research Society of China》2022年第3期529-558,共30页中国运筹学会会刊（英文）

基　　金：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);partially supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(No.CityU 11200219);partially supported by the National Natural Science Foundation of China(No.72050410356).

摘　　要：We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency 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.

关 键 词：Mean-risk optimization MEAN-VARIANCE Expected utility maximization Portfolio choice RISK POTENTIAL Robo-advising FinTech 

分 类 号：O17[理学—数学]