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

Yu Zhang; Zhuo Jin; Jiaqin Wei; George Yin

doi:10.1016/j.automatica.2022.110629

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

School of Statistics

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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 language	English
Article number	110629
Journal	Automatica
Volume	146
DOIs	https://doi.org/10.1016/j.automatica.2022.110629
State	Published - Dec 2022

Keywords

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

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10.1016/j.automatica.2022.110629

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title = "Mean–variance portfolio selection with dynamic attention behavior in a hidden Markov model",

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.",

keywords = "Dynamic attention behavior, Extended HJB equation, Hidden Markov model, Markov chain approximation, Mean–variance portfolio selection",

author = "Yu Zhang and Zhuo Jin and Jiaqin Wei and George Yin",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

year = "2022",

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AU - Zhang, Yu

AU - Jin, Zhuo

AU - Wei, Jiaqin

AU - Yin, George

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

KW - Dynamic attention behavior

KW - Extended HJB equation

KW - Hidden Markov model

KW - Markov chain approximation

KW - Mean–variance portfolio selection

UR - https://www.scopus.com/pages/publications/85138463110

U2 - 10.1016/j.automatica.2022.110629

DO - 10.1016/j.automatica.2022.110629

M3 - 文章

AN - SCOPUS:85138463110

SN - 0005-1098

VL - 146

JO - Automatica

JF - Automatica

M1 - 110629

ER -

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

Abstract

Keywords

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