股票价格崩盘风险的机器学习方法

IF 4.5 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Abdullah Karasan, Ozge Sezgin Alp, Gerhard-Wilhelm Weber
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引用次数: 0

摘要

在这项研究中,我们提出了一种新的基于机器学习的股票价格崩溃风险度量方法,利用最小协方差行列式方法。利用这一新引入的因变量,我们通过横断面回归分析预测股价崩盘风险。研究结果证实,所提出的方法有效地捕获了股价崩溃风险,模型在统计显著性和经济相关性方面都表现出很强的性能。此外,利用新开发的公司特定投资者情绪指数,分析发现股价崩溃风险与公司特定投资者情绪之间存在正相关关系。具体来说,较高的情绪水平与股票价格崩溃风险的可能性增加有关。这种关系在不同公司规模和使用特定公司投资者情绪指数的强化版本时保持稳健,进一步验证了所提出方法的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning approach to stock price crash risk

In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach.

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来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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