LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage

ArXiv ID: 2412.09394 “View on arXiv”

Authors: Unknown

Abstract

Recently, LLMs (Large Language Models) have been adapted for time series prediction with significant success in pattern recognition. However, the common belief is that these models are not suitable for predicting financial market returns, which are known to be almost random. We aim to challenge this misconception through a counterexample. Specifically, we utilized the Chronos model from Ansari et al.(2024) and tested both pretrained configurations and fine-tuned supervised forecasts on the largest American single stocks using data from Guijarro-Ordonnez et al.(2022). We constructed a long/short portfolio, and the performance simulation indicates that LLMs can in reality handle time series that are nearly indistinguishable from noise, demonstrating an ability to identify inefficiencies amidst randomness and generate alpha. Finally, we compared these results with those of specialized models and smaller deep learning models, highlighting significant room for improvement in LLM performance to further enhance their predictive capabilities.

Keywords: Large Language Models (LLMs), Time Series Prediction, Alpha Generation, Portfolio Construction, Chronos Model, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (LLMs/Transformers with >11M parameters) and sophisticated financial modeling (residual returns, long/short portfolios), reflecting high mathematical complexity. It also features substantial empirical work using large-scale US stock data, pre-trained/fine-tuned models, and performance simulations, indicating strong data and implementation rigor.

  flowchart TD
    Goal["Research Goal:<br/>Challenge belief that LLMs<br/>cannot predict financial returns<br/>(quasi-random data)"] --> Method["Methodology:<br/>Utilize Chronos Model<br/>(Pretrained & Fine-tuned)"]
    Method --> Data["Data Input:<br/>Largest American Single Stocks<br/>(Guijarro-Ordonnez et al., 2022)"]
    Data --> Process["Computational Process:<br/>1. Time Series Prediction<br/>2. Generate Trading Signals<br/>3. Construct Long/Short Portfolio"]
    Process --> Findings["Key Findings & Outcomes:<br/>1. LLMs identify inefficiencies in noise<br/>2. Generate Alpha (Excess Returns)<br/>3. Compare with specialized/smaller DL models<br/>4. Room for performance improvement"]