Kronos: A Foundation Model for the Language of Financial Markets

Yu Shi, Zongliang Fu, Shuo Chen and others propose Kronos, a unified and scalable pre-training framework for financial K-line modeling; its dedicated tokenizer discretizes continuous market information into token sequences to preserve price dynamics and trading activity patterns. The authors state that the model was pre-trained with an autoregressive objective on a multi-market corpus of more than 12 billion K-line records from 45 global exchanges. According to the paper, under a zero-shot setting, Kronos improves RankIC for price series forecasting by 93% over leading time-series foundation models and by 87% over the best non-pre-trained baseline on benchmark datasets, reduces MAE for volatility forecasting by 9%, and improves the fidelity of synthetic K-line sequence generation by 22%. The pre-trained model has been made public on GitHub.