一种利用新闻量和情绪进行投资组合选择的新方法

IF 1.8 4区经济学 Q2 BUSINESS, FINANCE

International Review of Finance Pub Date : 2023-08-08 DOI:10.1111/irfi.12427

Kin-Yip Ho, Kun Tracy Wang, Wanbin Walter Wang

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引用次数: 0

摘要

在本研究中，我们开发了一种新颖的方法，通过将新闻量和情绪信息与k近邻(kNN)算法相结合来实现投资组合多样化。实证分析表明，高新闻量增加了投资组合风险，而新闻情绪增加了投资组合收益。基于这些发现，我们提出了一种kNN算法用于投资组合选择。我们的样本内和样本外测试表明，所提出的kNN投资组合选择方法优于基准指数投资组合。总体而言，我们表明将新闻量和情绪纳入投资组合选择可以通过提高回报和降低风险来提高投资组合绩效。

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A novel approach to portfolio selection using news volume and sentiment

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A novel approach to portfolio selection using news volume and sentiment

In this study, we develop a novel approach to portfolio diversification by integrating information on news volume and sentiment with the k-nearest neighbors (kNN) algorithm. Our empirical analysis indicates that high news volume contributes to portfolio risk, whereas news sentiment contributes to portfolio return. Based on these findings, we propose a kNN algorithm for portfolio selection. Our in-sample and out-of-sample tests suggest that the proposed kNN portfolio selection approach outperforms the benchmark index portfolio. Overall, we show that incorporating news volume and sentiment into portfolio selection can enhance portfolio performance by improving returns and reducing risk.

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来源期刊

International Review of Finance BUSINESS, FINANCE-

CiteScore

3.30

自引率

5.90%

发文量

期刊介绍： The International Review of Finance (IRF) publishes high-quality research on all aspects of financial economics, including traditional areas such as asset pricing, corporate finance, market microstructure, financial intermediation and regulation, financial econometrics, financial engineering and risk management, as well as new areas such as markets and institutions of emerging market economies, especially those in the Asia-Pacific region. In addition, the Letters Section in IRF is a premium outlet of letter-length research in all fields of finance. The length of the articles in the Letters Section is limited to a maximum of eight journal pages.