China has introduced its inaugural artificial intelligence system specifically engineered to evaluate meteorological influences on stock market performance, representing a significant advancement in climate-informed financial analytics. The innovative model, named Shangji (Stock), emerged from a collaborative initiative between Fudan University in Shanghai and the National Meteorological Information Centre.
This groundbreaking technology leverages global meteorological datasets combined with historical stock trading information to predict short-term returns for most stocks within China’s A-share market. The system demonstrates particular effectiveness in identifying weather-sensitive sectors including renewable energy, traditional petrochemicals, construction, and agricultural industries.
According to Zhao Yanxia, lead developer and director of the CMA Key Open Laboratory for Financial Meteorology, validation testing confirms the model’s capacity to accurately pinpoint industries vulnerable to meteorological conditions. Back-testing of investment strategies derived from the model’s predictions has demonstrated consistent positive returns across multiple historical periods, indicating substantial practical applicability.
Professor Li Hao from Fudan University’s Artificial Intelligence Innovation and Incubation Institute highlighted the technology’s diverse financial applications. Commercial enterprises in climate-vulnerable sectors can employ the system for enhanced risk management, while financial institutions including banks and insurance companies can utilize it to mitigate risks in equity-based transactions and develop climate-responsive financing products.
Beyond immediate commercial applications, the model offers academic researchers new methodologies for testing and refining asset pricing theories. The development team has announced plans to expand the model’s capabilities to include bonds and futures markets, with commitments to continuous updates ensuring the technology remains aligned with evolving market dynamics.