In the cutting-edge field of financial and meteorological innovation, China has achieved a major breakthrough. On January 11th, the country's first AI large model specifically developed for financial meteorology, "Entropy Machine" (stock), was officially launched in Guangzhou. The model was jointly developed by Fudan University and the National Meteorological Information Center of China, marking the deep integration of meteorological data into financial asset pricing and risk management.

The core competitiveness of "Entropy Machine" lies in its strong cross-disciplinary data processing capabilities. According to AIbase, the model can make forward-looking predictions on the short-term returns of most A-share stocks by integrating global meteorological reanalysis data with stock market volume and price data. In practical verification, the model has shown high industry sensitivity, particularly in meteorologically sensitive industries such as new energy power generation, petrochemicals, construction, and agriculture, with its identification results highly consistent with internationally recognized meteorological risk management standards.

The application scenarios of this model are very extensive. For listed companies, it can assist in climate risk management and maintaining market value; for banks, insurance companies, and other institutions, it is a powerful tool to enhance equity pledge risk control and explore climate-related financing businesses. Additionally, ordinary investors can also use it as an auxiliary reference tool for quantitative investment. With the promotion of national policies on the coordinated development of financial and meteorological fields, AIbase believes that the release of "Entropy Machine" will provide key technical support for China to build a full-chain financial meteorological service system.

Key points:

  • 🌡️ New Benchmark for Cross-Disciplinary Integration: "Entropy Machine," as China's first financial meteorological AI model, realizes the deep connection between global meteorological data and A-share volume and price data.

  • 📈 Precise Prediction of Short-Term Returns: The model can predict the short-term returns of most A-share stocks and has demonstrated stable positive return potential in historical backtesting.

  • 🛡️ Empowering Multiple Financial Entities: Widely applied in corporate risk control, banking and insurance business innovation, and quantitative investment assistance, helping to cope with economic fluctuations caused by climate change.