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The interactive quality of earnings communication conferences and post-earnings-announcement drift

Sihai Li et al.

China Journal of Accounting Studies2025https://doi.org/10.1080/21697213.2025.2473999article
AJG 1ABDC B
Weight
0.50

Abstract

Delivering information through regular or temporary reports is not the final step in disclosure. Using the Word2Vec model, we assess the interaction quality of earnings communication conferences, and find that high interaction quality mitigates the post-earnings-announcement drift (PEAD). Interaction quality on performance-related questions, especially performance interpretation questions, plays a crucial role. Mechanism test reveals that investors interpret annual report information more comprehensively and consistently after earnings communication conference. The mitigating effect is more pronounced in firms with longer interval between the conference and the annual report, firms not involved in margin trading and short selling, and those reporting negative unexpected earnings. The disclosure of first quarter reports after earnings communication conferences can offset this effect. We provide empirical evidence, from the perspective of interaction quality, to validate the ‘quality of content’ in the earnings communication conference and its contribution to enhancing information efficiency in the Chinese capital market.

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https://doi.org/https://doi.org/10.1080/21697213.2025.2473999

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@article{sihai2025,
  title        = {{The interactive quality of earnings communication conferences and post-earnings-announcement drift}},
  author       = {Sihai Li et al.},
  journal      = {China Journal of Accounting Studies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/21697213.2025.2473999},
}

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The interactive quality of earnings communication conferences and post-earnings-announcement drift

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.