Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting factors more directly related to stock market dynamics, providing clear explanations for complex temporal changes. Our framework directs the LLMs to create background knowledge through a fill-in-the-blank strategy and then discerns potential factors affecting stock prices from related news. Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. An extensive evaluation of the LLMFactor framework across four benchmark datasets from both the U.S. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its effectiveness in financial time-series forecasting.

翻译：近年来，大型语言模型（LLMs）因其在广泛任务中（尤其是文本分析领域）的卓越表现而受到极大关注。然而，金融领域因其依赖时间序列数据进行复杂预测任务而构成独特挑战。本研究提出了一种名为LLMFactor的新型框架，该框架采用序列知识引导提示（Sequential Knowledge-Guided Prompting, SKGP）技术，利用LLMs识别影响股票走势的因子。与以往依赖关键词或情感分析的方法不同，本方法侧重于提取与股票市场动态更直接相关的因子，为复杂的时序变化提供清晰解释。我们的框架引导LLMs通过填空策略构建背景知识，继而从相关新闻中辨识影响股价的潜在因子。在背景知识和已识别因子的引导下，我们利用文本格式的历史股价数据预测股票走势。基于美国和中文股票市场的四个基准数据集对LLMFactor框架进行的广泛评估表明，其性能优于现有最先进方法，并在金融时间序列预测中展现出显著有效性。