Does corporate digital transformation affect investor trading behaviour? – A perspective based on investor divergence

ABSTRACT

This paper takes A-share listed companies during 2010–2021 as the research object, and finds that the digital transformation (DT) of enterprises affects investors’ trading behaviours; more specifically, the digital transformation of enterprises increases investor divergence. This paper also finds that external governance factors mitigate the impact of digital transformation on investor divergence; specifically, analyst tracking mitigates the impact of digital transformation on investor divergence by alleviating information asymmetry. As more professional investors, institutional investors have stronger information mining and analysis capabilities, so the impact of digital transformation on investor divergence could also be mitigated with the increase in the proportion of institutional investors. The intermediary mechanism test suggests that corporate digital transformation triggers investor divergence by increasing surplus volatility. Further research finds that, firstly, different approaches to digital transformation have different impacts, with digital transformation through self-developed technology being more likely to trigger investor divergence. Secondly, different firm natures have different impacts, with the digital transformation of non-high-tech firms having relatively greater impacts on investor divergence. Finally, the digital transformation of non-state-owned enterprises has greater impact on investor analysis.

KEYWORDS:

• Digital transformation
• investor behaviour
• investor divergence
• institutional investors
• analyst tracking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Data from China Internet Development Report 2022.

2 Data from McKinsey's report Unlocking success in digital transformations, published in 2018.

3 We defined DT-related keywords as follows: artificial intelligence, blockchain, cloud computing, big data, business intelligence, image understanding, investment decision support system, intelligent data analysis, intelligent robot, machine learning, deep learning, semantic search, biometric technology, face recognition, speech recognition, identity verification, autonomous driving, natural language processing, etc. The complete list of feature words is available from the author.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: grant number [71962035]; Yunnan Provincial Department of Education New Community Governance Science and Technology Innovation Team; Yunnan Philosophy Social Science Project: grant number [ZD202212] and Yunnan Provincial Department of Education Science Research Fund Project: grant number [2024J1592].

Notes on contributors

Xiaojun Li

Xiaojun Li is a professor of Yunnan University of Finance and Economics, and president of the Zhonghua Vocational College of Yunnan University of Finance and Economics. His research interests include corporate governance.

Hongjing Pu

Hongjing Pu is an assistant of Yunnan Tourism College. His research interests include corporate governance.

Mengyun Zhang

Mengyun Zhang is the corresponding author, a Yunnan University of Finance and Economics doctoral student. Her research interests include corporate governance and finance.