On the prediction of stock price crash risk using textual sentiment of management statement
China Finance Review International
ISSN: 2044-1398
Article publication date: 25 September 2023
Issue publication date: 7 June 2024
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
Acknowledgements
The authors would like to thank for the support from the Fundamental Research Funds for the Central Universities, China [JBK2103013]. This work was also supported by Financial Innovation Centre (Project No. 2022A0011) at Southwestern University of Finance and Economics, China. The authors thank the editors and the anonymous reviewers for their comments to improve this paper.
Citation
Yao, X., Wu, D., Li, Z. and Xu, H. (2024), "On the prediction of stock price crash risk using textual sentiment of management statement", China Finance Review International, Vol. 14 No. 2, pp. 310-331. https://doi.org/10.1108/CFRI-12-2022-0250
Publisher
:Emerald Publishing Limited
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