Can public data availability affect stock price crash risk? Evidence from China

Rui Ma, Fei Guo and Dongdong Li

International Review of Financial Analysis, 2024, vol. 94, issue C

Abstract: In the era of the digital economy, data has emerged as a critical factor of production. The exploration of methods to unlock the economic value inherent in the vast reservoirs of public data held by the government has now begun to draw the attention of scholars. However, little research has considered this issue from the perspective of financial market stability. Leveraging the staggered establishment of public data open platforms (PDOPs) across different cities of China and employing the staggered difference-in-differences (DID) model, we find that the establishment of PDOPs significantly decrease local firms' stock price crash risk (SPCR). Our results remain valid after various robustness tests, such as placebo tests, entropy balancing analysis, alternative measures of SPCR, as well as alternative difference-in-differences (DID) estimators. Furthermore, additional evidence suggests that the observed effects are driven by two mechanisms: improved operating performance and enhanced external monitoring. The former mechanism reduces the generation of bad news within firms, while the latter restricts managers' ability to hoard bad news. Further research findings show that the motivation of management to withhold bad news and the nature of firms' property rights are important factors affecting the relationship between public data availability and SPCR. Overall, our findings suggest that government can help improve capital market resource allocation efficiency by supplying public data to market participants.

Keywords: Public data availability; Stock price crash risk; Operating performance; External monitoring (search for similar items in EconPapers)
JEL-codes: D80 G20 G38 H41 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:94:y:2024:i:c:s1057521924002023

DOI: 10.1016/j.irfa.2024.103270

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