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Government disclosure specificity and stock price synchronicity: Evidence from local government work reports in China

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  • Gong, Manning
  • Cao, Chunfang
  • Zhang, Yuheng

Abstract

Using machine learning methods to conduct a textual analysis of city government work reports in China, we measure the specificity of government disclosures and examine whether this specificity affects stock pricing efficiency. We find that more specific forward-looking information disclosed by local governments is associated with lower stock price synchronicity among local listed companies. This effect is more pronounced for companies operating in environments with high economic policy uncertainty.

Suggested Citation

  • Gong, Manning & Cao, Chunfang & Zhang, Yuheng, 2025. "Government disclosure specificity and stock price synchronicity: Evidence from local government work reports in China," Finance Research Letters, Elsevier, vol. 71(C).
  • Handle: RePEc:eee:finlet:v:71:y:2025:i:c:s1544612324014922
    DOI: 10.1016/j.frl.2024.106463
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    References listed on IDEAS

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    1. Jiaming Liu & Xuemei Zhang & Haitao Xiong, 2024. "Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1625-1660, August.
    2. Zhi Su & Xuanye Cai & You Wu, 2023. "Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 30(15), pages 2052-2059, September.
    3. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    4. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    6. Ole-Kristian Hope & Danqi Hu & Hai Lu, 2016. "The benefits of specific risk-factor disclosures," Review of Accounting Studies, Springer, vol. 21(4), pages 1005-1045, December.
    7. Gul, Ferdinand A. & Kim, Jeong-Bon & Qiu, Annie A., 2010. "Ownership concentration, foreign shareholding, audit quality, and stock price synchronicity: Evidence from China," Journal of Financial Economics, Elsevier, vol. 95(3), pages 425-442, March.
    8. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    More about this item

    Keywords

    Government disclosure; Information specificity; Stock price synchronicity; Stock pricing efficiency;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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