归一化期望效用-熵投资决策模型及其在选股中的应用

Jiping Yang, Lijian Zhang, Xiaoxuan Chen
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引用次数: 3

摘要

我们首先引入归一化期望效用-熵(EU-E)决策模型,它是归一化期望效用和信息熵的加权线性平均。在规范化EU-E决策模型的基础上,建立了规范化EU-E投资决策模型。然后将该模型应用于深成指40只样本股票的选股。结果表明,采用标准化EU-E模型选择的权衡系数λ较大的4只股票的投资组合比采用相对一般效用函数选择权衡系数λ较小的4只股票的投资组合效率更高。因此,这表明我们不仅要考虑风险行为本身的预期效用,还要考虑信息熵来衡量自然状态的不确定性,这进一步验证了信息熵的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Normalized Expected Utility-Entropy investment decision model and its application in stock selection
We first introduce the normalized Expected Utility-Entropy (EU-E) decision model, which is a weighted linear average of normalized expected utility and information entropy. Based on the normalized EU-E decision model, we establish a normalized EU-E investment decision model. Then we apply the model to stock selection when we invest in the 40 sample stocks of Shenzhen component index. It has concluded that portfolios of 4 stocks selected by normalized EU-E model with larger tradeoff coefficient λ are more efficient than that of those selected with smaller tradeoff coefficient λ with relative general utility function. Thus, this has demonstrated that we should not only take the expected utility of a risky action itself into account but also the information entropy to measure the uncertainty of the state of nature, which further verified the usefulness of the information entropy.
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