Temporal Data Meets LLM – Explainable Financial Time Series Forecasting

ArXiv ID: 2306.11025 “View on arXiv”

Authors: Unknown

Abstract

This paper presents a novel study on harnessing Large Language Models’ (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results. In this paper, we focus on NASDAQ-100 stocks, making use of publicly accessible historical stock price data, company metadata, and historical economic/financial news. We conduct experiments to illustrate the potential of LLMs in offering a unified solution to the aforementioned challenges. Our experiments include trying zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with a public LLM model Open LLaMA. We demonstrate our approach outperforms a few baselines, including the widely applied classic ARMA-GARCH model and a gradient-boosting tree model. Through the performance comparison results and a few examples, we find LLMs can make a well-thought decision by reasoning over information from both textual news and price time series and extracting insights, leveraging cross-sequence information, and utilizing the inherent knowledge embedded within the LLM. Additionally, we show that a publicly available LLM such as Open-LLaMA, after fine-tuning, can comprehend the instruction to generate explainable forecasts and achieve reasonable performance, albeit relatively inferior in comparison to GPT-4.

Keywords: Large Language Models (LLMs), Time Series Forecasting, Multi-Modal Signals, Explainable AI, Financial Knowledge Graphs, Equity (Stocks)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper relies on high-level machine learning concepts and LLM architectures rather than dense mathematical derivations, but features a rigorous empirical setup with real-world NASDAQ-100 data, multiple baselines (ARMA-GARCH, gradient boosting), and detailed performance comparisons.

  flowchart TD
    A["Research Goal<br>Use LLMs for Explainable<br>Financial Time Series Forecasting"] --> B["Methodology<br>LLM Inference & Fine-tuning<br>(GPT-4 & Open-LLaMA)"]
    
    B --> C["Input Data<br>Historical Prices +<br>News & Knowledge Graphs"]
    
    C --> D["Processing<br>Unified Analysis<br>(Multi-modal Reasoning)"]
    
    D --> E["Comparative Analysis<br>vs. Baselines<br>(ARMA-GARCH, GBM)"]
    
    E --> F["Key Findings<br>• LLMs outperform baselines<br>• Unified solution for multi-modal data<br>• Explainable reasoning process"]