Key points are not available for this paper at this time.
This study examines how firms’ strategic attention to generative artificial intelligence (GenAI) is associated with three forms of earnings management. Using 25,208 firm-year observations for Chinese A-share firms from 2011 to 2023, we measure firm-level GenAI-related strategic attention with a term frequency-inverse document frequency (TF-IDF) index constructed from Management Discussion and Analysis (MD&A) disclosures. Empirical results show that higher GenAI-related attention is associated with lower real earnings management (REM), no significant change in accrual-based earnings management (AEM), and higher textual earnings management (TEM), measured by abnormal optimistic tone. The results remain robust to alternative measures, the 2015–2023 subsample, high-dimensional fixed effects, and an industry-peer instrumental-variable specification. Mediation analysis shows that the administrative expense ratio partially mediates the positive association between GenAI-related attention and TEM: greater GenAI-related attention is associated with a lower administrative expense ratio, which in turn is associated with higher TEM. Heterogeneity analysis shows that the negative REM relation is concentrated among less diversified, domestic, and non-outsourcing firms. The positive TEM relation appears in both diversification groups but only in domestic and non-outsourcing firms. Given the significant positive association between GenAI-related attention and TEM, we further examine whether TEM has capital-market consequences. The results show that TEM predicts higher stock price crash risk in the following year. Overall, the findings are consistent with a shift in managerial discretion from operational actions toward narrative disclosures rather than a uniform improvement in reporting quality.
Zhaodong Li (Sat,) studied this question.