基本特征、机器学习和股价暴跌风险

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Financial Markets Pub Date : 2024-04-05 DOI:10.1016/j.finmar.2024.100908

Fuwei Jiang , Tian Ma , Feifei Zhu

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引用次数: 0

摘要

我们研究了机器学习算法在预测股价暴跌风险中的应用，采用了一组中国股市的特定公司特征。结果表明，机器学习技术在捕捉股价暴跌风险的细微差别方面具有优势，特别是通过盈利能力和价值与增长特征。这些技术在国有企业内部和经济政策不确定性较低的时期表现良好，预测性见解主要来自行业内部动态。此外，我们还对机器学习的可预测性提供了基于公司财务和金融市场的解释，以及对其关键决定因素的全面理解。

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Fundamental characteristics, machine learning, and stock price crash risk

We investigate the application of machine learning algorithms for predicting stock price crash risks by employing a set of firm-specific characteristics of the Chinese stock market. The results suggest that machine learning techniques are superior in capturing the nuances of stock price crash risk, particularly through profitability and value versus growth features. These techniques perform well within state-owned enterprises and during periods of low economic policy uncertainty, and predictive insights primarily originate from intra-industry dynamics. In addition, we offer corporate finance- and financial market-based interpretations of machine learning's predictability, as well as a comprehensive understanding of its key determinants.

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来源期刊

Journal of Financial Markets BUSINESS, FINANCE-

CiteScore

3.40

自引率

3.60%

发文量

期刊介绍： The Journal of Financial Markets publishes high quality original research on applied and theoretical issues related to securities trading and pricing. Area of coverage includes the analysis and design of trading mechanisms, optimal order placement strategies, the role of information in securities markets, financial intermediation as it relates to securities investments - for example, the structure of brokerage and mutual fund industries, and analyses of short and long run horizon price behaviour. The journal strives to maintain a balance between theoretical and empirical work, and aims to provide prompt and constructive reviews to paper submitters.