This study investigates the effect of AI disclosure on the value relevance of earnings using Chinese listed companies from 2013 to 2022. We find that AI disclosure significantly enhances earnings’ value relevance. Mechanism tests show that AI disclosure increases firms’ earnings quality and boosts investor attention, thus elevating the value relevance of earnings. Heterogeneity analysis further reveals that this effect is more pronounced in firms in advanced manufacturing industries, firms in highly competitive industries and non-SOEs. Overall, this study highlights the important role of AI disclosure in the price discovery of both fundamental and unexpected earnings, and contributes to a deeper understanding of the underlying mechanisms through which AI affects the value relevance of earnings.
In this study, we analyze the network structure of institutional investor cliques and their effect on corporate innovation in a large sample of Chinese firms. Clique ownership is positively associated with innovation input and output, particularly among firms characterized by non-state ownership, more severe agency problems and CEO duality, suggesting that governance environment matters. This baseline effect is strengthened by product and capital market pressures, captured respectively by product market competitiveness and stock liquidity. To ameliorate endogeneity concerns, we show that our inferences are robust to more granular fixed effects, an instrumental variable approach, propensity score matching and Heckman correction. Using the Mainland China–Hong Kong Stock Connect program as a quasi-natural experiment and a difference-in-differences design, we find that clique ownership has a more salient effect after firms enter the Connect program. Mechanism tests reveal that cliques improve innovation incentives by increasing information transparency and reducing forced CEO turnovers and enhance innovation capability by increasing innovation investment efficiency and employees’ innovation productivity. Institutional site visit evidence is consistent with more active institutional monitoring of firms with higher clique ownership.
According to upper echelons theory, top management team (TMT) characteristics significantly influence corporate strategic decision-making. This study examines the impact of TMT openness on corporate innovation using a sample of Chinese A-share listed firms from 2007 to 2022. We employ a large language model (GLM-4) to measure TMT personality traits based on Q&A transcripts from annual performance briefings, offering a novel approach to capturing managerial personalities. Our findings demonstrate that TMT openness significantly enhances both explorative and exploitative innovation. Moreover, our extended analysis reveals that TMT openness exhibits differential effects across innovation types, with a more pronounced enhancing effect on digital technology innovation than non-digital technology innovation. Mechanism analyses reveal that TMTs with higher openness exhibit stronger innovation awareness, greater tolerance for uncertainties and reduced managerial myopia. Cross-sectional analyses show that the positive effect is larger among firms facing higher performance pressure, greater institutional ownership and more intense competition. Further analyses demonstrate that TMT openness is positively associated with R&D expenditure and personnel allocation. Moreover, TMT openness increases not only innovation quantity but innovation quality and efficiency. This study contributes to the literature by highlighting the role of TMT personality in corporate innovation and introducing an innovative measurement approach using large language models, providing methodological insights for future empirical research.
