Abstract
Using expectations regarding utilities to make decisions in a risk environment hides a paradox, which is called the expected utility enigma. Moreover, the mystery has not been solved yet; an imagined utility function on the risk-return plane has been applied to establish the mean-variance model, but this hypothetical utility function not only lacks foundation, it also holds an internal contradiction. This paper studies these basic problems. Through risk preference VNM condition is proposed to solve the expected utility enigma. How can a utility function satisfy the VNM condition? This is a basic problem that is hard to deal with. Fortunately, it is found in this paper that the VNM utility function can have some concrete forms when individuals have constant relative risk aversion. Furthermore, in order to explore the basis of mean-variance utility, an MV function is founded that is based on the VNM utility function and rooted in underlying investment activities. It is shown that the MV function is just the investor’s utility function on the risk-return plane and that it has normal properties. Finally, the MV function is used to analyze the laws of investment activities in a systematic risk environment. In doing so, a tool, TRR, is used to measure risk aversion tendencies and to weigh risk and return.
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References
Andrew P, Matthew W. An empirical evaluation of sensitivity bounds for mean-variance Portfolio optimization. Finance Research Letters, 2022, 44: 102065
Berk C, Tutari B. The effect of systematic risk and previous period returns on portfolio selection. Applied Finance and Accounting, 2020, 7(1): 1–9
Bianconia M, Hua X X, Tan C M. Determinants of systemic risk and information dissemination. International Review of Economics & Finance, 2015, 38: 352–368
Brown M J. A mean-variance serial replacement decision model: the correlated case. The Engineering Economist, 2007, 38(3): 237–247
Coyle B T. Risk aversion and yield uncertainty in duality models of production: a mean-variance approach. American Journal of Agricultural Economics, 1999, 81(3): 553–567
Dai M, Jin H Q, Kou S, Xu Y H. A dynamic mean-variance analysis for log returns. Management Science Volume, 2020, 67(2): 1093–1108
Kassimatis K. Mean-variance versus utility maximization revisited: The case of constant relative risk aversion. International Review of Financial Analysis, 2021, 78: Art 101932
Khashanah K, Simaan M, Simaan Y. Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process. International Review of Financial Analysis, 2022, 81: 102068
Lai T, Xing H, Chen Z. Mean-variance portfolio optimization when means and covariances are unknown. The Annals of Applied Statistics, 2011, 5(2): 798–823
Li, X, Sinem U A, John M M. Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks. European Journal of Operational Research, 2022, 299(3): 1158–1176
Liu Y. The validity of mean-variance model in A-share market. World Scientific Research Journal, 2022, 8(3): 309–315
Marianil F, Polinesil G, Recchioni M C. A tail-revisited Markowitz mean-variance approach and a portfolio network centrality. Computational Management Science, 2022, 19: 425–455
Markovitz H. Portfolio selection. The Journal of Finance, 1952, 7(1): 77–91
Pratt J. Risk aversion in the small and in the large. Econometrica, 1964, 32(1): 22–36
Rainer S. Heuristic mean-variance optimization in Markov decision processes using state-dependent risk aversion. IMA Journal of Management Mathematics, 2022, 33(2): 181–199
Van Staden P M, Dang D M, Forsyth P A. The surprising robustness of dynamic mean-variance portfolio optimization to model misspecification errors. European Journal of Operational Research, 2021, 289(2): 774–792
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Dedicated to Professor Banghe LI on the Occasion of his 80th birthday
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Wu, K. Utility basis of consumption and investment decisions in a risk environment. Acta Math Sci 42, 2377–2398 (2022). https://doi.org/10.1007/s10473-022-0611-0
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DOI: https://doi.org/10.1007/s10473-022-0611-0