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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...

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Detalles Bibliográficos
Autores principales: Mercurio, Peter Joseph, Wu, Yuehua, Xie, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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