Abstract
ABSTRACT Stock price crash risk represents a considerable concern due to its potential for extreme volatility and crisis contagion within the stock market, attracting substantial interest from both academia and the industry. This study focuses on A-shares listed on the Shanghai and Shenzhen Stock Exchanges from January 2012 to April 2021. We construct qualitative news indicators through text analysis techniques using Python programming to extract news texts from the China Stock Exchange Network. Our objective is to examine the influence of qualitative news on stock price crash risk. The findings reveal a significant correlation between qualitative news and increased stock price crash risk. Further, applying the Sobel test for intermediary effects confirms that differences in opinion act as a conduit for qualitative news to impact stock price crash risk. This research offers a novel perspective on analyzing stock price crash risk, aiding investors in understanding the effects and pathways through which various types of news influence the securities market. It provides valuable insights for mitigating the risk of stock price crashes.
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