Mean–variance portfolio selection with dynamic attention behavior in a hidden Markov model

Yu Zhang, Zhuo Jin, Jiaqin Wei*, George Yin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we study closed-loop equilibrium strategies for mean–variance portfolio selection problems in a hidden Markov model with dynamic attention behavior. In addition to the investment strategy, the investor's attention to news is introduced as a control of the accuracy of the news signal process. The objective is to find equilibrium strategies by numerically solving an extended HJB equation by using Markov chain approximation method. An iterative algorithm is constructed and its convergence is established. Numerical examples are provided to illustrate the results.

Original languageEnglish
Article number110629
Pages (from-to)1-8
Number of pages8
JournalAutomatica
Volume146
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Dynamic attention behavior
  • Extended HJB equation
  • Hidden Markov model
  • Markov chain approximation
  • Mean–variance portfolio selection

Fingerprint

Dive into the research topics of 'Mean–variance portfolio selection with dynamic attention behavior in a hidden Markov model'. Together they form a unique fingerprint.

Cite this