This paper provides descriptive evidence on how stock market participants use Generative Artificial Intelligence (GenAI) to process investment-related information. Using a data set of 1.7 million stock-related queries from one of China's largest GenAI platforms during the first half of 2024, we document that user queries address a wide range of topics and tasks and vary systematically with usage intensity and financial sophistication. Query activity increases around corporate disclosure events, but these increases largely track contemporaneous media coverage. We also find evidence consistent with a substitution between the informativeness of voluntary managerial disclosures and investors' reliance on GenAI. Continued platform engagement more likely follows answers that are concise and contain directionally accurate trading signals. Over time, users' subsequent queries increasingly reflect the specificity and financial terminology present in earlier GenAI answers. At the market level, GenAI usage is associated with higher measures of informed trading and lower liquidity, while aggregated sentiment in GenAI-generated answers correlates with same-day abnormal returns, particularly when user feedback is positive. Overall, our findings offer insights into early-stage GenAI adoption by retail investors and inform discussions on how GenAI shapes information processing in financial markets.

This paper provides descriptive evidence on how stock market participants use Generative Artificial Intelligence (GenAI) to process investment-related information. Using a data set of 1.7 million stock-related queries from one of China's largest GenAI platforms during the first half of 2024, we document that user queries address a wide range of topics and tasks and vary systematically with usage intensity and financial sophistication. Query activity increases around corporate disclosure events, but these increases largely track contemporaneous media coverage. We also find evidence consistent with a substitution between the informativeness of voluntary managerial disclosures and investors' reliance on GenAI. Continued platform engagement more likely follows answers that are concise and contain directionally accurate trading signals. Over time, users' subsequent queries increasingly reflect the specificity and financial terminology present in earlier GenAI answers. At the market level, GenAI usage is associated with higher measures of informed trading and lower liquidity, while aggregated sentiment in GenAI-generated answers correlates with same-day abnormal returns, particularly when user feedback is positive. Overall, our findings offer insights into early-stage GenAI adoption by retail investors and inform discussions on how GenAI shapes information processing in financial markets.