Loss Aversion Robust Optimization Model Under Distribution and Mean Return Ambiguity

Jia Wang, Meng Chu Zhou, Xiwang Guo, Liang Qi, Xu Wang

Research output: Contribution to journalArticlepeer-review

Abstract

From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown. Then, under the premise that the mean returns of risky assets belong to an ellipsoidal uncertainty set, a model under joint ambiguity in distribution and mean returns is constructed. This study solves both robust models and derives their analytical solutions, respectively. Moreover, the effect of ambiguity aversion and loss aversion on robust optimal portfolio returns is studied. The results show that ambiguity-neutral investors who do not know the return distribution obtain more robust optimal portfolio returns than ambiguity-averse investors who are unaware of both the distribution and mean return. The difference between them decreases with the increase of loss aversion coefficients and increases with ambiguity aversion coefficients. Both loss aversion and ambiguity aversion play important roles in investors’ behavioral portfolio selection.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Computational Social Systems
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

Keywords

  • Ambiguity aversion
  • Analytical models
  • Computational modeling
  • Investment
  • Loss measurement
  • Portfolios
  • Symbols
  • Uncertainty
  • behavioral finance
  • human behavior
  • loss aversion
  • robust optimization

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