StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction

ArXiv ID: 2409.08281 “View on arXiv”

Authors: Unknown

Abstract

The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist in stock price forecasting. Firstly, effectively integrating the modalities of time series data and natural language to fully leverage these capabilities remains complex. Secondly, FinLLMs focus more on analysis and interpretability, which can overlook the essential features of time series data. Moreover, due to the abundance of false and redundant information in financial markets, models often produce less accurate predictions when faced with such input data. In this paper, we introduce StockTime, a novel LLM-based architecture designed specifically for stock price data. Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. StockTime then integrates both textual and time series data into the embedding space. By fusing this multimodal data, StockTime effectively predicts stock prices across arbitrary look-back periods. Our experiments demonstrate that StockTime outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs.

Keywords: Large Language Models (LLMs), Multimodal Integration, Stock Price Time Series, Next Token Prediction, Embedding Space, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced deep learning architectures (LLM adaptation, autoregressive encoders, multimodal fusion) with significant mathematical modeling, but the empirical rigor is limited to experimental claims without providing code, datasets, or detailed statistical backtesting metrics.

  flowchart TD
    A["Research Goal: Design specialized LLM for stock price prediction"] --> B["Core Methodology: Treat stock prices as consecutive tokens"]
    B --> C["Data Inputs: Time series data & textual correlations"]
    C --> D["Multimodal Integration: Fuse data into embedding space"]
    D --> E["Computational Process: LLM next token prediction"]
    E --> F["Key Findings: Improved accuracy, reduced memory/runtime"]