 Explain or conceal? Causal language intensity in annual report and stock price crash riskDongmin Kong, Lu Shi and Fan Zhang Economic Modelling, 2021, vol. 94, issue C, 715-725 Abstract: Based on textual analysis technology, this paper investigates the relationship between causal language characteristics in annual financial reports and stock price crash risk. We find that the causal language intensity is positively associated with future stock price crash risk, which reveals that managers manipulate the causal language to successfully hide adverse information, then leading to higher stock price crash risk. Our results also hold after addressing the potential endogenous problems and robustness problems. Moreover, based on the mediation effect of information asymmetry, we demonstrate that the use of causal language can make information more complex to hide and mix adverse information, thus increasing information asymmetry. In addition, the positive effect of causal language intensity is stronger in the disclosure of bad news than good news. Overall, strategic information disclosure behavior that we reveal, has clear policy implications for regulators to strengthen the supervision firms’ information disclosures. Keywords: Causal language intensity; Stock price risk; Textual analysis; Information hoarding (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:94:y:2021:i:c:p:715-725 DOI: 10.1016/j.econmod.2020.02.013 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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