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

TL;DR

This paper develops a model for mean-variance portfolio optimization considering an investor's dynamic attention to news within a hidden Markov framework, using numerical methods to find equilibrium strategies.

Contribution

It introduces a novel approach incorporating attention control into a hidden Markov model for portfolio selection, solved via an extended HJB equation and Markov chain approximation.

Findings

Numerical algorithms successfully find equilibrium strategies.

The model captures the impact of attention dynamics on investment decisions.

Convergence of the proposed iterative algorithm is established.

Abstract

In this paper, we study closed-loop equilibrium strategies for mean-variance portfolio selection problem 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 also provided to illustrate the results.