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Leverage manipulation and strategic disclosure: evidence from non-financial information in annual reports based on multimodal machine learning

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  • Jianhua Tan
  • Yipu Li
  • Tiancheng Gao
  • Jun Pan

Abstract

This study adopts multi-modal machine learning to deeply interpret corporate non-financial information and explore the influence of corporate leverage manipulation on non-financial information disclosure. It is found that enterprises will strategically disclose non-financial information when implementing leverage manipulation, that is, the emotion of non-financial information text is more positive, farther away and more difficult. Based on the motivation of leverage manipulation, when enterprises face greater pressure of ‘deleveraging’ policy and stronger financing constraints, enterprises are more inclined to disclose non-financial information strategically when implementing leverage manipulation. Further, the dual motives of leverage manipulation are explored deeply. For the samples with high ‘deleveraging’ policy pressure such as state-owned enterprises and high media attention, and the samples with strong financing constraints such as high short-term debt repayment pressure and high debt financing cost, the positive relationship between leverage manipulation and strategic disclosure of non-financial information is more significant.

Suggested Citation

  • Jianhua Tan & Yipu Li & Tiancheng Gao & Jun Pan, 2024. "Leverage manipulation and strategic disclosure: evidence from non-financial information in annual reports based on multimodal machine learning," China Journal of Accounting Studies, Taylor & Francis Journals, vol. 12(3), pages 550-590, July.
    • Handle: RePEc:taf:rcjaxx:v:12:y:2024:i:3:p:550-590
    DOI: 10.1080/21697213.2023.2300296
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