The Effect of Annual Report Complexity on Market Reaction: An Analysis Using ChatGPT - Mulyadi, Dhiki; Suryani, Ani Wilujeng; Hasmi, Hazreel; Setiaji, Yongky Teguh (Jurnal Akuntansi Aktual, 2026)
Purpose: The purpose of this study is to examine the effect of annual report complexity on market reactions. Method: The sample consists of 587 annual reports from companies listed on the Indonesia Stock Exchange (IDX) in 2023. The complexity scores, which become the independent variable, were assessed from Management Discussion and Analysis section using pre-trained ChatGPT-4. For the dependent variable, we used the market model to identify abnormal returns. Finally, regression was performed to investigate the impact of complexity on the abnormal returns. Findings: We found that the complexity has a positive impact on the abnormal returns. This indicates that more complex annual reports are associated with an increase in abnormal returns, and managers utilized strategic narratives and complex report structures to shape positive perceptions of the company. Originality/Value: To the researchers' knowledge, no previous research has explored the utility of artificial intelligence (AI) tools like ChatGPT in conducting textual analysis of annual reports of in Indonesia. The findings also demonstrate how AI-based tools can enhance capital market text analytics and support financial disclosure analysis in emerging markets.