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一种新的自适应熵投资组合选择模型。

A New Adaptive Entropy Portfolio Selection Model.

作者信息

Song Ruidi, Chan Yue

机构信息

Institute for Advanced Study, Shenzhen University, Shenzhen 518060, Guangdong, China.

出版信息

Entropy (Basel). 2020 Aug 28;22(9):951. doi: 10.3390/e22090951.

DOI:10.3390/e22090951
PMID:33286720
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7597235/
Abstract

In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz's mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors' expectations. Our model applies equally well to portfolio optimizations for other financial markets.

摘要

在本文中，我们提出了一种自适应熵模型（AEM），该模型将熵度量和适应性纳入传统的马科维茨均值 - 方差模型（MVM）中。我们使用五年期上海证券交易所50指数（SSE50）成分股数据集，基于几个投资组合绩效指标评估了AEM的性能。我们的结果表明，与传统的投资组合选择模型相比，AEM倾向于使我们的投资更加分散，从而有助于抵消非系统性风险。由于自适应的存在，AEM被证明更能适应市场波动，并有助于在分散投资和集中投资之间保持平衡，以满足投资者的期望。我们的模型同样适用于其他金融市场的投资组合优化。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a306/7597235/916bbe6eda2f/entropy-22-00951-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a306/7597235/1cd26374267f/entropy-22-00951-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a306/7597235/eeb6932d3373/entropy-22-00951-g012.jpg

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