{"id":335,"date":"2024-09-15T19:06:48","date_gmt":"2024-09-15T19:06:48","guid":{"rendered":"https:\/\/fintext.ai\/?page_id=335"},"modified":"2025-11-19T12:12:39","modified_gmt":"2025-11-19T12:12:39","slug":"review","status":"publish","type":"page","link":"https:\/\/fintext.ai\/?page_id=335","title":{"rendered":"Review"},"content":{"rendered":"

Built on the study \u201cRe(Visiting) Time Series Foundation Models in Finance\u201d (Rahimikia, Ni, and Wang, 2025)<\/a>, FinText advances the frontiers of AI-driven financial forecasting through large-scale, domain-specific Time Series Foundation Models (TSFMs). These large scale mdoels models are pre-trained from scratch on massive datasets\u2014spanning over two billion daily excess return observations across 89 markets\u2014to capture the complex temporal dynamics of global finance.<\/p>\n

This study shows that generic foundation models fail to generalize effectively to financial data, whereas finance-native pre-training leads to significant improvements in predictive accuracy and portfolio performance. Each model is chronologically pre-trained to prevent look-ahead bias, ensuring realistic, time-consistent forecasting. The resulting FinText-TSFM suite includes hundreds of Chronos and TimesFM variants aligned with different years, markets, and datasets empowering researchers and practitioners with transparent, bias-free, and reproducible forecasting tools.<\/p>\n

Developed through collaboration between the University of Manchester, University College London, Shanghai University, and partners including, UKRI, Isambard-AI, and N8 Bede, FinText embodies the integration of financial economics, machine learning, and high-performance computing. Together, these efforts establish a new foundation for reliable, interpretable, and scalable AI in finance.<\/p>\n