LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage (2412.09394v1)

Abstract: Recently, LLMs 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.

• The paper demonstrates that LLMs can uncover market inefficiencies and generate alpha in financial time series prediction.
• The methodology uses zero-shot evaluations and fine-tuning with PCA, IPCA, and Fama-French factors to mitigate overfitting in stock return forecasting.
• Empirical analysis shows competitive Sharpe ratios and enhanced forecasting efficiency through volatility-adjusted weights despite trading cost challenges.

Overview of Applying LLMs to Time Series Prediction in Finance

The paper "LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage" by Sebastien Valeyre and Sofiane Aboura embarks on an intriguing exploration of adapting LLMs for financial time series prediction, especially targeting single stock returns and statistical arbitrage. Conventionally, financial market returns are regarded as nearly random, posing a significant challenge for predictive modeling. This paper challenges the common belief by employing LLMs to identify inefficiencies and generate alpha in financial markets.

Methodology

The authors employed the Chronos model, building on work by Ansari et al., to forecast daily returns for large American stocks. This paper uniquely applies zero-shot evaluations, using a model trained on non-financial series, to mitigate overfitting concerns. Moreover, the paper emphasizes fine-tuning LLMs on financial datasets to adjust model parameters tailored for financial time series that are identified through historical residual returns after removing common market factors utilizing Principal Component Analysis (PCA), Instrumental Principal Component Analysis (IPCA), and Fama-French factors.

Empirical Analysis

Results indicate a sparse yet promising outperformance of pre-trained LLMs over traditional methods in identifying market opportunities, achieving substantial Sharpe ratios in a zero-shot context, particularly using PCA-tuned datasets. The paper reveals that while the pre-trained model adeptly identifies opportunities before 2008, a decline follows, possibly due to evolving market efficiencies or reduced autocorrelation of returns post-financial crisis. Although trading costs erode profit margins, thereby highlighting the limitations for practical TCIs, the results demonstrate an innovative stride toward integrating AI in signal extraction from noise-laden financial data.

Furthermore, tuned models, albeit with partial retention of their original predictive prowess, delivered competitive performance against standard quantitative strategies such as the Short Term Reversal (STR), suggesting the potential complexities LLMs can capture, surpassing linear financial signals with minimal computational depths. The usage of resized weights inversely proportional to volatility also exhibited improved forecasting efficiency, suggesting the benefit of volatility adjustments in portfolio allocations derived from LLM predictions.

Implications and Future Prospects

The implications of this research are manifold. Practically, if trading costs decrease or alpha generation increases through enhanced model architectures or denser data feeds, LLMs could contribute significantly to systematic trading strategies, particularly in short-term forecasting domains where traditional econometric models may falter. Theoretically, the adaptation of LLMs presents opportunities for refining stochastic modeling of financial markets through AI, enhancing the robustness of anomaly detection and pattern recognition beyond straightforward autoregressive approaches.

Looking forward, future AI developments, particularly in areas tailoring architectures to handle time series-specific correlations and heteroscedasticity, may bridge the current limitations seen in trading applications. The potential for hybrid models combining LLM efficiency with econometric robustness warrants further attention, as do techniques for controlling overfitting while adapting LLMs' weights to diverse financial environments.

In conclusion, this paper not only challenges preconceived notions about LLMs in finance but also opens new avenues for AI integration into financial time series analysis, encouraging further exploration and innovation in this rapidly evolving interdisciplinary field.