arbiter
JournalsAccount

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

Jianhua Tan et al.

China Journal of Accounting Studies2024https://doi.org/10.1080/21697213.2023.2300296article
AJG 1ABDC B
Weight
0.57

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 nonfinancial 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 stateowned 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.

4 citations

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1080/21697213.2023.2300296

Or copy a formatted citation

@article{jianhua2024,
  title        = {{Leverage manipulation and strategic disclosure: evidence from non-financial information in annual reports based on multimodal machine learning}},
  author       = {Jianhua Tan et al.},
  journal      = {China Journal of Accounting Studies},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1080/21697213.2023.2300296},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

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

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.57

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.65 × 0.4 = 0.26
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.