Failing to Foresee the Updating of the Reference Point Leads to Time-Inconsistent Investment

Moris S. Strub () and Duan Li ()
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Moris S. Strub: Business School, Southern University of Science and Technology, Shenzhen, 518055 Guangdong, China
Duan Li: School of Data Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong

Operations Research, 2020, vol. 68, issue 1, 199-213

Abstract: The current literature on behavioral portfolio optimization with reference point updating assumes that the decision maker foresees how the reference point will evolve and thus solves a time-consistent problem formulation. Empirical findings, however, suggest that decision makers often fail to foresee the updating of the reference point and consequently make time-inconsistent decisions. We analyze and compare the optimal investment strategies for a discrete time behavioral portfolio optimization problem with loss-aversion and time-varying reference points under both the time-consistent and time-inconsistent framework and for different updating rules for the reference point. There is only one framework predicting realistic investment behavior: the decision maker fails to foresee the updating of the reference point and thus faces a time-inconsistent problem, solves for a dynamically optimal strategy, and updates the reference point in a nonrecursive manner.

Keywords: reference-dependent preferences; reference point updating; time-inconsistency; stochastic control; portfolio selection; prediction bias (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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