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Algorithmic trading, systematic tail risk, and corporate transparency

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  • Liu, Jianxiang
  • Shi, Haofang
  • Yi, Wenyu

Abstract

This paper takes Chinese A-share listed companies from 2010 to 2023 as the research sample to empirically examine the impact and mechanism of algorithmic trading on corporate transparency. The results show that algorithmic trading forces enterprises to improve transparency by increasing systematic tail risks of stock prices. Further research indicates that algorithmic trading can effectively reduce stock price synchronicity and analysts' forecast divergence, while improving the quality of accounting information disclosure, thus providing additional empirical evidence for the conclusion that algorithmic trading enhances corporate transparency. This study enriches the relevant literature on the economic consequences of algorithmic trading and the influencing factors of corporate transparency, and also provides a reference for capital market supervision and corporate information disclosure decisions.

Suggested Citation

  • Liu, Jianxiang & Shi, Haofang & Yi, Wenyu, 2026. "Algorithmic trading, systematic tail risk, and corporate transparency," Finance Research Letters, Elsevier, vol. 100(C).
    • Handle: RePEc:eee:finlet:v:100:y:2026:i:c:s1544612326005441
    DOI: 10.1016/j.frl.2026.110015
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