A Generalized Entropy Approach to Portfolio Selection under a Hidden Markov Model

Leonard MacLean, Lijun Yu and Yonggan Zhao
Additional contact information
Leonard MacLean: Rowe School of Business, Dalhousie University, 6100 University Avenue, Suite 2010, Halifax, NS B3H 4R2, Canada
Lijun Yu: Rowe School of Business, Dalhousie University, 6100 University Avenue, Suite 2010, Halifax, NS B3H 4R2, Canada
Yonggan Zhao: Rowe School of Business, Dalhousie University, 6100 University Avenue, Suite 2010, Halifax, NS B3H 4R2, Canada

JRFM, 2022, vol. 15, issue 8, 1-25

Abstract: This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models.

Keywords: hidden Markov model; entropy; dynamic portfolio optimization; Bayesian analysis; Sharpe ratio; return to entropy ratio; kernel density estimation (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1911-8074/15/8/337/pdf (application/pdf)
https://www.mdpi.com/1911-8074/15/8/337/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:15:y:2022:i:8:p:337-:d:876199

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().