Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
Xinli Yu Zheng Chen Yuan Ling Shujing Dong Zongyi Liu Yanbin Lu
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.