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.

翻译：本文提出了一项创新研究，旨在利用大语言模型卓越的知识与推理能力进行可解释的金融时间序列预测。将机器学习模型应用于金融时间序列面临若干挑战，包括跨序列推理与推断的困难、整合历史新闻与金融知识图谱等多模态信号的障碍，以及对模型结果进行解释与阐明的问题。本文以纳斯达克100指数股票为研究对象，利用公开可获取的历史股票价格数据、公司元数据及历史经济/金融新闻，通过实验展示了大语言模型在统一应对上述挑战方面的潜力。我们的实验包括：尝试使用GPT-4进行零样本/少样本推断，以及使用公开大语言模型Open LLaMA进行基于指令的微调。实验结果表明，我们的方法优于若干基线模型，包括广泛应用的经典ARMA-GARCH模型和梯度提升树模型。通过性能对比结果及若干案例，我们发现大语言模型能够通过对文本新闻与价格时间序列信息进行推理并提取洞见、利用跨序列信息以及调用其内在嵌入知识，从而做出深思熟虑的决策。此外，我们还表明，经过微调的公开大语言模型（如Open-LLaMA）能够理解指令以生成可解释的预测，并取得合理性能，尽管其效果相较于GPT-4仍显不足。