FinTexTS: 基于语义和多层级配对的金融文本-时间序列数据集
FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
AI总结：
提出基于语义和多层级配对的框架，从SEC文件和新闻中提取并匹配多层级文本信息，构建大规模文本配对的股票价格数据集FinTexTS，提升股价预测性能。
摘要：
金融领域涉及多种重要的时间序列问题。近年来，联合利用文本和数值信息的时间序列分析方法越来越受到关注。因此，人们做出了大量努力来构建金融领域中的文本配对时间序列数据集。然而，金融市场具有复杂的相互依赖性，一家公司的股票价格不仅受公司特定事件的影响，还受其他公司事件和更广泛的宏观经济因素的影响。现有的基于简单关键词匹配的文本与金融时间序列数据配对方法往往无法捕捉这种复杂关系。为了解决这一局限性，我们提出了一种基于语义和多层级的配对框架。具体来说，我们从SEC文件中提取目标公司的特定上下文，并应用基于嵌入的匹配机制，根据该上下文检索语义相关的新闻文章。此外，我们使用大语言模型（LLMs）将新闻文章分为四个层级（宏观层级、行业层级、相关公司层级和目标公司层级），实现新闻文章与目标公司的多层级配对。将该框架应用于公开可用的新闻数据集，我们构建了FinTexTS，这是一个新的大规模文本配对的股票价格数据集。在FinTexTS上的实验结果表明，我们的基于语义和多层级的配对策略在股价预测中是有效的。除了FinTexTS所依赖的公开新闻外，我们还表明，将我们的方法应用于专有但精心策划的新闻源，可以产生更高质量的配对数据，并提高股价预测性能。
英文摘要：
The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and numerical information have gained increasing attention. Accordingly, numerous efforts have been made to construct text-paired time-series datasets in the financial domain. However, financial markets are characterized by complex interdependencies, in which a company's stock price is influenced not only by company-specific events but also by events in other companies and broader macroeconomic factors. Existing approaches that pair text with financial time-series data based on simple keyword matching often fail to capture such complex relationships. To address this limitation, we propose a semantic-based and multi-level pairing framework. Specifically, we extract company-specific context for the target company from SEC filings and apply an embedding-based matching mechanism to retrieve semantically relevant news articles based on this context. Furthermore, we classify news articles into four levels (macro-level, sector-level, related company-level, and target company-level) using large language models (LLMs), enabling multi-level pairing of news articles with the target company. Applying this framework to publicly-available news datasets, we construct FinTexTS, a new large-scale text-paired stock price dataset. Experimental results on FinTexTS demonstrate the effectiveness of our semantic-based and multi-level pairing strategy in stock price forecasting. In addition to publicly-available news underlying FinTexTS, we show that applying our method to proprietary yet carefully curated news sources leads to higher-quality paired data and improved stock price forecasting performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
