On the prediction of stock price crash risk using textual sentiment of management statement

Xiao Yao, Dongxiao Wu, Zhiyong Li and Haoxiang Xu

China Finance Review International, 2023, vol. 14, issue 2, 310-331

Abstract: Purpose - Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction. Design/methodology/approach - Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques. Findings - The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL). Research limitations/implications - It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies. Originality/value - The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Keywords: Stock market prediction; Stock price crash risk; Textual sentiment; Machine learning techniques; Management statement (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:cfripp:cfri-12-2022-0250

DOI: 10.1108/CFRI-12-2022-0250

Access Statistics for this article

China Finance Review International is currently edited by Professor Chongfeng Wu and Professor Haitao Li

More articles in China Finance Review International from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().