README.md

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title: FinText-TSFM
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[![SSRN](https://img.shields.io/badge/SSRN-5770562-1a5dab?logo=ssrn&logoColor=white)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5770562)
[![arXiv](https://img.shields.io/badge/arXiv-2511.18578-b31b1b?logo=arxiv&logoColor=white)](https://www.arxiv.org/abs/2511.18578)
[![ResearchGate](https://img.shields.io/badge/ResearchGate-Paper-00CCBB?logo=researchgate&logoColor=white)](https://www.researchgate.net/publication/397872068_ReVisiting_Time_Series_Foundation_Models_in_Finance)
[![Website - FinText.ai](https://img.shields.io/badge/Website-FinText.ai-0A66C2?logo=google-chrome&logoColor=white)](https://fintext.ai)
[![GitHub - FinText.ai](https://img.shields.io/badge/GitHub-FinText.ai-181717?logo=github&logoColor=white)](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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