Re(Visiting) Time Series Foundation Models in Finance

• URL: http://arxiv.org/abs/2511.18578v1
• Date: Sun, 23 Nov 2025 18:44:19 GMT
• Title: Re(Visiting) Time Series Foundation Models in Finance
• Authors: Eghbal Rahimikia, Hao Ni, Weiguan Wang,
• Abstract summary: Financial time series forecasting is central to trading, portfolio optimization, and risk management.<n>Recent advances in time series foundation models (TSFMs) offer a new paradigm for learning generalizable temporal representations from large and diverse datasets.<n>This paper presents the first comprehensive empirical study of TSFMs in global financial markets.
• Score: 3.295157175236371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial time series forecasting is central to trading, portfolio optimization, and risk management, yet it remains challenging due to noisy, non-stationary, and heterogeneous data. Recent advances in time series foundation models (TSFMs), inspired by large language models, offer a new paradigm for learning generalizable temporal representations from large and diverse datasets. This paper presents the first comprehensive empirical study of TSFMs in global financial markets. Using a large-scale dataset of daily excess returns across diverse markets, we evaluate zero-shot inference, fine-tuning, and pre-training from scratch against strong benchmark models. We find that off-the-shelf pre-trained TSFMs perform poorly in zero-shot and fine-tuning settings, whereas models pre-trained from scratch on financial data achieve substantial forecasting and economic improvements, underscoring the value of domain-specific adaptation. Increasing the dataset size, incorporating synthetic data augmentation, and applying hyperparameter tuning further enhance performance.

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