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| title: FinText-TSFM | |
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| [](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5770562) | |
| [](https://www.arxiv.org/abs/2511.18578) | |
| [](https://www.researchgate.net/publication/397872068_ReVisiting_Time_Series_Foundation_Models_in_Finance) | |
| [](https://fintext.ai) | |
| [](https://github.com/DeepIntoStreams/TSFM_Finance) | |
| ## 🎤 Podcast | |
| You can now listen to the accompanying podcast here: https://soundcloud.com/eghbal-rahimikia/revisiting-time-series-foundation-models-in-finance | |
| ## 🆕 GitHub Model Loading Support (NEW) | |
| All models can now be loaded directly from GitHub. The repository includes utilities and setup instructions. | |
| 🔗 **https://github.com/DeepIntoStreams/TSFM_Finance** | |
| ## 🚀 TSFMs Release | |
| We are pleased to introduce **FinText-TSFM**, a comprehensive suite of **time series foundation models (TSFMs)** with 613 models pre-trained for quantitative finance. This release accompanies the paper : | |
| **[*Re(Visiting) Time Series Foundation Models in Finance*](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5770562)** by *Eghbal Rahimikia, Hao Ni, and Weiguan Wang (2025)*. | |
| ### 💡 Key Highlights | |
| - **Finance-Native Pre-training:** | |
| Models are pre-trained **from scratch** on large-scale financial time series datasets — including daily excess returns across **89 markets** and **over 2 billion observations** — to ensure full temporal and domain alignment. | |
| - **Bias-Free Design:** | |
| Pre-training strictly follows a **chronological expanding-window setup**, avoiding any **look-ahead bias** or **information leakage**.<br> | |
| Each variation includes 23 separately pre-trained models, corresponding to each year from **2000** to **2023**, with data starting in 1990. | |
| - **Model Families:** | |
| This release includes variants of **Chronos** and **TimesFM** architectures adapted for financial time series: | |
| - Chronos-Tiny (8M) / Mini (20M) / Small (46M) | |
| - TimesFM-8M / 20M | |
| - **Model Collections:** | |
| - U.S.: Covers **U.S.** market-wide excess returns from 2000 to 2023, with one pre-trained model per year. | |
| - Global: Covers excess returns across **94 global markets** from 2000 to 2023, with one pre-trained model for each year. | |
| - Augmented: Extends the global data with **augmented factors** from 2000 to 2023, with one pre-trained model for each year. | |
| - The remaining **253 pre-trained models** are available for download via the [**FinText.ai Portal**](https://fintext.ai). These include models pre-trained with varying **hyperparameter configurations** for extended experimentation and performance comparison. | |
| - **Performance Insights:** | |
| Our findings show that **off-the-shelf TSFMs** underperform in zero-shot forecasting, while **finance-pretrained models** achieve large gains in both predictive accuracy and portfolio performance. | |
| - **Evaluation Scope:** | |
| Models are benchmarked across **U.S. and seven international markets**, using rolling windows of **5, 21, 252, and 512 days**, with over **18 million out-of-sample forecasts** spanning **22 years (2001–2023)** of daily excess returns, evaluated at both the **statistical** and **economic performance** levels. | |
| ### 🧠 Technical Overview | |
| - **Architecture:** Transformer-based TSFMs (Chronos & TimesFM) | |
| - **Compute:** 50,000 GPU hours on NVIDIA GH200 Grace Hopper clusters | |
| ### 📚 Citation | |
| Please cite the accompanying paper if you use these models: | |
| > **Re(Visiting) Time Series Foundation Models in Finance.** | |
| > **Rahimikia, Eghbal; Ni, Hao; Wang, Weiguan.** | |
| > SSRN: [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5770562) | |
| ### 🔋 Acknowledgments | |
| This project was made possible through computational and institutional support from: | |
| - **UK Research and Innovation (UKRI)** | |
| - **Isambard-AI National AI Research Resource (AIRR)** | |
| - **Alliance Manchester Business School (AMBS), University of Manchester** | |
| - **N8 Centre of Excellence in Computationally Intensive Research (N8 CIR)** | |
| - **The University of Manchester** (Research IT & Computational Shared Facility) | |
| - **University College London (UCL)** | |
| - **The Alan Turing Institute** | |
| - **Shanghai University** | |
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| <p style="font-weight:bold; font-size:1.1em; margin:4px 0;">Developed by:</p> | |
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| <img src="https://fintext.ai/UoM-logo.svg" alt="University of Manchester Logo" width="210" style="display:block; margin:0;"> | |
| <img src="https://fintext.ai/UCL-logo.jpg" alt="UCL Logo" width="100" style="display:block; margin:0;"> | |
| </div> | |
| <p style="font-size:0.8em; margin-top:0; line-height:1.3;"> | |
| Alliance Manchester Business School, University of Manchester<br> | |
| Department of Mathematics, University College London (UCL) | |
| </p> | |
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| <!-- Powered by --> | |
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| <p style="font-weight:bold; font-size:1.1em; margin:4px 0;">Powered by:</p> | |
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| <img src="https://fintext.ai/BriCS-logo.png" alt="BriCS Logo" width="180" style="display:block; margin:0;"> | |
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| Isambard-AI, Bristol Centre for Supercomputing (BriCS)<br> | |
| The Bede Supercomputer | |
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