StockTime represents a paradigm shift in applying Large Language Models (LLMs) to financial time series prediction. Unlike existing Financial LLMs (FinLLMs) that focus primarily on textual analysis and interpretation, StockTime is specifically designed for stock price time series data. The framework leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, while extracting textual information such as stock correlations, statistical trends, and timestamps directly from the stock price data itself.
The core innovation of StockTime lies in its unique approach to bridging the modality gap between numerical time series data and natural language processing. Traditional FinLLMs require extensive fine-tuning on textual financial data and often struggle with the continuous nature of stock prices. StockTime addresses this by:
StockTime consists of four main components that work together to transform stock price prediction into a next-token prediction task:
The framework segments historical stock prices into consecutive, non-overlapping patches. Each patch of length l is treated as a basic token, reducing computational burden while capturing temporal patterns. The stock prices undergo reversible instance normalization to ensure proper scaling across different stocks.
Unlike traditional approaches that rely solely on transformer architectures, StockTime employs an LSTM-based autoregressive encoder to capture sequential dependencies in stock price data. This design choice acknowledges that recurrent architectures are particularly well-suited for modeling temporal sequences, especially in financial time series where long-term dependencies are crucial.
The most innovative aspect of StockTime is its ability to generate textual information directly from stock price data. The framework creates textual templates that include:
This textual information is processed through the frozen LLM and fused with the stock price embeddings in the latent space, enabling the model to understand both numerical patterns and contextual relationships.
By leveraging the LLM's inherent next-token prediction capability, StockTime can generate forecasts of arbitrary lengths. The training objective focuses on predicting the next tokens (price segments) based on historical sequences, with each predicted patch supervised by token-wise ground truth using Mean Squared Error loss.
The evaluation encompasses multiple real-world datasets spanning different frequencies and time periods:
StockTime demonstrates superior performance across multiple metrics:
Comparison with FinLLMs and General LLMs: - Achieves up to 5% improvement in stock movement prediction accuracy - Outperforms specialized FinLLMs like FinMA on most datasets - Shows consistent improvements over general LLMs (LLaMA3-8B, GPT-4o mini)
Time Series Model Comparison: - Outperforms LLM-based time series methods (Times-LLM, AutoTimes, FPT) - Superior performance on both MSE and Information Coefficient (IC) metrics - Demonstrates effectiveness for both daily and hourly frequency trading
Efficiency Gains: - Significantly reduced training time compared to fine-tuning approaches - Lower memory usage while maintaining high prediction accuracy - Enables quick adaptation without extensive computational resources
The comprehensive ablation studies reveal several key insights:
StockTime outperforms traditional autoregressive models (RNN, LSTM, Attention LSTM) on the same datasets, validating the benefits of the LLM architecture for financial time series prediction. This demonstrates that the framework's success stems not just from increased model capacity, but from the effective integration of temporal modeling with language understanding capabilities.
StockTime opens new possibilities for financial applications by demonstrating that LLMs can be effectively adapted for numerical time series without extensive fine-tuning. This approach could be particularly valuable for:
The success of StockTime suggests broader applications beyond finance:
StockTime represents a significant advancement in applying LLMs to financial time series prediction. By treating stock prices as tokens and generating meaningful textual representations from numerical data, the framework successfully bridges the gap between natural language processing and quantitative finance. The approach demonstrates that specialized architectures can outperform general-purpose FinLLMs while being more computationally efficient.
The framework's success challenges the conventional wisdom that financial LLMs must rely heavily on textual data and extensive fine-tuning. Instead, StockTime shows that the inherent structure and patterns in financial time series, when properly represented, can be effectively captured by frozen LLM architectures with minimal additional training.
For practitioners in quantitative finance and machine learning, StockTime offers a practical and efficient approach to leveraging state-of-the-art language models for time series prediction. The framework's ability to maintain high performance while reducing computational requirements makes it particularly attractive for real-world deployment in financial institutions and trading systems.
Created 2025-06-11T17:09:05-07:00