{"ID":2887622,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02739","arxiv_id":"2508.02739","title":"Kronos: A Foundation Model for the Language of Financial Markets","abstract":"The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis. Our pre-trained model is publicly available at https://github.com/shiyu-coder/Kronos.","short_abstract":"The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing...","url_abs":"https://arxiv.org/abs/2508.02739","url_pdf":"https://arxiv.org/pdf/2508.02739v1","authors":"[\"Yu Shi\",\"Zongliang Fu\",\"Shuo Chen\",\"Bohan Zhao\",\"Wei Xu\",\"Changshui Zhang\",\"Jian Li\"]","published":"2025-08-02T13:15:59Z","proceeding":"q-fin.ST","tasks":"[\"q-fin.ST\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":611452,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2887622,"paper_url":"https://arxiv.org/abs/2508.02739","paper_title":"Kronos: A Foundation Model for the Language of Financial Markets","repo_url":"https://github.com/shiyu-coder/Kronos","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}