Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

TL;DR

This paper explores using Large Language Models like GPT-4 and Open LLaMA for explainable financial time series forecasting, demonstrating their ability to reason over multi-modal data and outperform traditional models in certain scenarios.

Contribution

It introduces a novel approach leveraging LLMs for financial forecasting, combining textual news and price data, and shows how fine-tuning improves interpretability and performance.

Findings

LLMs can reason over multi-modal financial data.

Fine-tuned open LLaMA can generate explainable forecasts.

LLMs outperform classical models like ARMA-GARCH in experiments.

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.