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Portfolio Optimization with a Mean-Entropy-Mutual Information Model

التفاصيل البيبلوغرافية
العنوان: Portfolio Optimization with a Mean-Entropy-Mutual Information Model
المؤلفون: Rodrigo Gonçalves Novais, Peter Wanke, Jorge Antunes, Yong Tan
المصدر: Entropy, Vol 24, Iss 3, p 369 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Science
LCC:Astrophysics
LCC:Physics
مصطلحات موضوعية: portfolio optimization, entropy, mutual information, variance and covariance, Science, Astrophysics, QB460-466, Physics, QC1-999
الوصف: This paper describes a new model for portfolio optimization (PO), using entropy and mutual information instead of variance and covariance as measurements of risk. We also compare the performance in and out of sample of the original Markowitz model against the proposed model and against other state of the art shrinkage methods. It was found that ME (mean-entropy) models do not always outperform their MV (mean-variance) and robust counterparts, although presenting an edge in terms of portfolio diversity measures, especially for portfolio weight entropy. It further shows that when increasing return constraints on portfolio optimization, ME models were more stable overall, showing dampened responses in cumulative returns and Sharpe indexes in comparison to MV and robust methods, but concentrated their portfolios more rapidly as they were more evenly spread initially. Finally, the results suggest that it was also shown that, depending on the market, increasing return constraints may have positive or negative impacts on the out-of-sample performance.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1099-4300
Relation: https://www.mdpi.com/1099-4300/24/3/369; https://doaj.org/toc/1099-4300
DOI: 10.3390/e24030369
URL الوصول: https://doaj.org/article/2436a33693e147648b21cf6d4febd340
رقم الأكسشن: edsdoj.2436a33693e147648b21cf6d4febd340
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10994300
DOI:10.3390/e24030369