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An Entropy-Based Approach to Portfolio Optimization
This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. R...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516790/ https://www.ncbi.nlm.nih.gov/pubmed/33286106 http://dx.doi.org/10.3390/e22030332 |
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author | Mercurio, Peter Joseph Wu, Yuehua Xie, Hong |
author_facet | Mercurio, Peter Joseph Wu, Yuehua Xie, Hong |
author_sort | Mercurio, Peter Joseph |
collection | PubMed |
description | This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases. |
format | Online Article Text |
id | pubmed-7516790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75167902020-11-09 An Entropy-Based Approach to Portfolio Optimization Mercurio, Peter Joseph Wu, Yuehua Xie, Hong Entropy (Basel) Article This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases. MDPI 2020-03-14 /pmc/articles/PMC7516790/ /pubmed/33286106 http://dx.doi.org/10.3390/e22030332 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mercurio, Peter Joseph Wu, Yuehua Xie, Hong An Entropy-Based Approach to Portfolio Optimization |
title | An Entropy-Based Approach to Portfolio Optimization |
title_full | An Entropy-Based Approach to Portfolio Optimization |
title_fullStr | An Entropy-Based Approach to Portfolio Optimization |
title_full_unstemmed | An Entropy-Based Approach to Portfolio Optimization |
title_short | An Entropy-Based Approach to Portfolio Optimization |
title_sort | entropy-based approach to portfolio optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516790/ https://www.ncbi.nlm.nih.gov/pubmed/33286106 http://dx.doi.org/10.3390/e22030332 |
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