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.

本文研究了利用大型语言模型在可解释金融时间序列预测中的应用，使用股票价格数据、公司元数据和历史新闻等多模态信号，在 NASDAQ-100 股票上进行实验，结果显示与一些基准模型相比，使用 GPT-4 和 Open LLaMA 这类新型模型进行零样本 / 少样本推断并通过基于指令的微调生成可解释预测，优于经典的 ARMA-GARCH 模型和梯度提升树模型。