用大模型融合财报与新闻，预测股票涨跌。
Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models
- 将财报数据与新闻文本结合，通过大模型统一处理。
- 3个月和6个月预测准确率F1达58.5%和59.1%。
- 适合量化交易、金融AI研究者参考。
预测金融市场和股票价格波动需综合分析公司业绩、历史股价、行业事件及人类行为因素（如社交媒体与媒体报道）。本文认为财务报告（如损益表、资产负债表、现金流量表）、历史股价数据与近期新闻文章可共同体现上述因素。我们结合表格形式的财务数据与文本新闻，采用预训练大语言模型（LLM）进行市场走势预测。近期研究表明，大语言模型能同时处理表格与文本分类任务，因此被选为多模态数据分类的核心模型。通过检索增强技术，将相关新闻片段与公司财务指标关联，并在零样本、两样本和四样本设置下提示模型。数据集涵盖20家不同行业中交易量最高的公司，包含多源新闻、历史股价及财务报表。实验使用GPT-3、GPT-4、LLaMA-2与LLaMA-3等最新语言模型。所提出的基于大模型的分类器可融合结构化与非结构化数据。结果显示，在3个月和6个月周期内，预测股票价格变动的加权F1分数分别为58.5%和59.1%，马修斯相关系数为0.175。
原文摘要 · Abstract (English)
Predicting financial markets and stock price movements requires analyzing a company's performance, historic price movements, industry-specific events alongside the influence of human factors such as social media and press coverage. We assume that financial reports (such as income statements, balance sheets, and cash flow statements), historical price data, and recent news articles can collectively represent aforementioned factors. We combine financial data in tabular format with textual news articles and employ pre-trained Large Language Models (LLMs) to predict market movements. Recent research in LLMs has demonstrated that they are able to perform both tabular and text classification tasks, making them our primary model to classify the multi-modal data. We utilize retrieval augmentation techniques to retrieve and attach relevant chunks of news articles to financial metrics related to a company and prompt the LLMs in zero, two, and four-shot settings. Our dataset contains news articles collected from different sources, historic stock price, and financial report data for 20 companies with the highest trading volume across different industries in the stock market. We utilized recently released language models for our LLM-based classifier, including GPT- 3 and 4, and LLaMA- 2 and 3 models. We introduce an LLM-based classifier capable of performing classification tasks using combination of tabular (structured) and textual (unstructured) data. By using this model, we predicted the movement of a given stock's price in our dataset with a weighted F1-score of 58.5% and 59.1% and Matthews Correlation Coefficient of 0.175 for both 3-month and 6-month periods.
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