FinGPT has an industry-leading multimodal financial data processing system that can simultaneously parse heterogeneous information such as text, time-series data, and structured reports. Its original Cross-Modal Attention mechanism can establish a correlation model between news and public opinion and stock price fluctuations, and achieve a quarterly frequency return correlation coefficient of 0.87 in the forecast of NASDAQ-100 index constituents. The professional data pipeline built into the platform supports real-time access to more than 20 data sources such as Yahoo Finance and Tushare, automatically completing the conversion of unstructured data to quantitative.
Typical use cases include predicting earnings volatility by merging SEC filing text with options implied volatility data; and constructing cryptocurrency trading signals by combining Twitter sentiment analysis with order book data. This multi-dimensional information fusion capability enabled FinGPT to improve the predictive accuracy of its multimodal model by 411 TP3T over a unimodal baseline in the Bloomberg Industry Research test.
This answer comes from the articleFinGPT: Open Source Financial Big Language Modeling Platform for Financial Analytics and PredictionThe
