README.md

---
title: FinText-TSFM
emoji: 📈
colorFrom: gray
colorTo: blue
sdk: static
pinned: false
---
<div style="display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; margin-top: 10px; margin-bottom: 10px;">
  <img src="https://fintext.ai/FinText_logo.webp" alt="FinText Logo" width="140" style="display: block; margin: 0 auto 6px auto;">
  <h1 style="font-size: 1.9em; font-weight: bold; margin: 4px 0;">Time Series Foundation Models for Finance</h1>
</div>


## 🚀 TSFMs Release

We are pleased to introduce **FinText-TSFM**, a comprehensive suite of **time series foundation models (TSFMs)** with 565 models developed for financial forecasting and quantitative research. This release accompanies the paper :
**[*Re(Visiting) Time Series Foundation Models in Finance*](https://ssrn.com/abstract=4963618)** 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 **89 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 **220 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 excell 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://ssrn.com/abstract=4963618](https://ssrn.com/abstract=4963618)  
> DOI: [10.2139/ssrn.4963618](http://dx.doi.org/10.2139/ssrn.4963618)

### 🔋 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**


---
<div style="text-align:center; margin:auto; max-width:800px;">
  <!-- Developed by -->
  <div style="margin-bottom:12px;">
    <p style="font-weight:bold; font-size:1.1em; margin:4px 0;">Developed by:</p>
    <div style="display:flex; justify-content:center; align-items:center; gap:20px; flex-wrap:wrap; margin-bottom:15px;">
      <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>
  </div>

  <!-- Powered by -->
  <div>
    <p style="font-weight:bold; font-size:1.1em; margin:4px 0;">Powered by:</p>
    <div style="display:flex; justify-content:center; align-items:center; gap:20px; flex-wrap:wrap; margin-bottom:10px;">
      <img src="https://fintext.ai/BriCS-logo.png" alt="BriCS Logo" width="180" style="display:block; margin:0;">
      <img src="https://fintext.ai/N8_bede_logo.webp" alt="N8 Bede Logo" width="140" style="display:block; margin:0;">
    </div>
    <p style="font-size:0.8em; margin-top:0; line-height:1.3;">
      Isambard-AI, Bristol Centre for Supercomputing (BriCS)<br>
      The Bede Supercomputer
    </p>
  </div>
</div>