Financial Fine-tuning a Large Time Series Model

ArXiv ID: 2412.09880 “View on arXiv”

Authors: Unknown

Abstract

Large models have shown unprecedented capabilities in natural language processing, image generation, and most recently, time series forecasting. This leads us to ask the question: treating market prices as a time series, can large models be used to predict the market? In this paper, we answer this by evaluating the performance of the latest time series foundation model TimesFM on price prediction. We find that due to the irregular nature of price data, directly applying TimesFM gives unsatisfactory results and propose to fine-tune TimeFM on financial data for the task of price prediction. This is done by continual pre-training of the latest time series foundation model TimesFM on price data containing 100 million time points, spanning a range of financial instruments spanning hourly and daily granularities. The fine-tuned model demonstrates higher price prediction accuracy than the baseline model. We conduct mock trading for our model in various financial markets and show that it outperforms various benchmarks in terms of returns, sharpe ratio, max drawdown and trading cost.

Keywords: Foundation Models, Time Series Forecasting, Continual Pre-training, Quantitative Trading, Price Prediction, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper primarily relies on applying an existing foundation model with minor modifications (log transform, masking) rather than introducing dense theoretical mathematics, but demonstrates high empirical rigor through extensive dataset curation (100M points), backtesting across multiple markets, and published code/model weights for reproducibility.

  flowchart TD
    A["Research Goal: Can large time series models predict financial markets?"] --> B["Methodology: Continual Pre-training"]
    B --> C["Data: 100M time points<br>(Hourly & Daily prices)"]
    C --> D["Model: TimesFM<br>(Fine-tuned on Financial Data)"]
    D --> E["Evaluation: Mock Trading"]
    E --> F["Outcome: Outperformed Benchmarks<br>(Returns, Sharpe Ratio, Max Drawdown)"]
    D --> G["Comparison: Baseline Model<br>(Direct Application)"]
    G --> H["Result: Unsatisfactory Accuracy<br>(Irregular price data)"]
    F & H --> I["Conclusion: Financial Fine-tuning Essential for Price Prediction"]