Data Asset Disclosure and Stock Price Crash Risk: A Double Machine Learning Study of Chinese A Share Firms

Junzuo Zhou, Zhaoyang Zhu, Huimeng Wang, Yuki Gong, Yuge Zhang () and Frank Li ()
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Junzuo Zhou: Academic Affairs Office, Guangdong University of Finance and Economics, Guangzhou 510320, China
Zhaoyang Zhu: School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Huimeng Wang: College of Arts and Social Sciences, Australian National University, Canberra, ACT 2601, Australia
Yuki Gong: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
Yuge Zhang: College of Economics, Ocean University of China, Qingdao 266100, China
Frank Li: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA

IJFS, 2025, vol. 13, issue 4, 1-33

Abstract: In the digital economy, data assets have become key drivers of firm competitiveness and market stability. This study examines the association between data asset information disclosure and stock price crash risk. Using annual reports of Chinese A-share listed firms from 2010 to 2023, we construct a Data Asset Information Disclosure Index through textual analysis. A double machine learning framework is employed to flexibly control for high-dimensional confounders, and the results indicate that greater disclosure is associated with lower crash risk across multiple specifications. Generalized random forest analysis further highlights heterogeneous relationships, with disclosures on both internally used and transactional data assets showing stronger negative associations with crash risk. Mechanism evidence suggests that disclosure may facilitate information dissemination, strengthen investor confidence, and improve analyst forecast accuracy. The association is more pronounced in firms with weaker corporate governance, higher reporting transparency, more competitive industries, and in regions with less developed digital economies. An industry spillover pattern is also observed, whereby one firm’s disclosure is linked to reduced crash risk among peers. Overall, this study contributes to the literature on data asset disclosure and corporate risk management by providing empirical evidence from a major emerging market and by highlighting the potential relevance of enhanced transparency for digital governance and capital market resilience.

Keywords: data asset information disclosure; stock price crash risk; double machine learning (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2025
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