We construct a daily Chinese Housing Market Sentiment Index by applying GPT-4o to Chinese news articles. Our method outperforms traditional models in several validation tests, including a test based on a suite of machine learning models. Applying this index to household-level data, we find that after monetary easing, an important group of homebuyers (who have a college degree and are aged between 30 and 50) in cities with more optimistic housing sentiment have lower responses in non-housing consumption, whereas for homebuyers in other age-education groups, such a pattern does not exist. This suggests that current monetary easing might be more effective in boosting non-housing consumption than in the past for China due to weaker crowding-out effects from pessimistic housing sentiment. The paper also highlights the need for complementary structural reforms to enhance monetary policy transmission in China, a lesson relevant for other similar countries. Methodologically, it offers a tool for monitoring housing sentiment and lays out some principles for applying generative AI models, adaptable to other studies globally.

Our study explored the rise of public companies competing to launch large language models (LLMs) in the Chinese stock market after ChatGPTs' success. We analyzed 25 companies listed on the Chinese Stock Exchange and discovered that the cumulative abnormal return (CAR) was high up to 3% before LLMs' release, indicating a positive view from insiders. However, CAR dropped to around 1.5% after their release. Early LLM releases had better market reactions, especially those focused on customer service, design, and education. Conversely, LLMs dedicated to IT and civil service received negative feedback.