AlphaFin：基于检索增强的股票链框架的金融分析基准测试
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
AI总结：
针对金融分析中预测缺乏可解释性和实时信息整合不足的问题，本文提出AlphaFin数据集和Stock-Chain框架，结合检索增强生成技术，通过实验验证其在股票趋势预测和金融问答任务上的有效性。
摘要：
金融分析任务主要涵盖两个关键领域：股票趋势预测和相应的金融问答。目前，机器学习和深度学习算法（ML&DL）已被广泛应用于股票趋势预测，并取得了显著进展。然而，这些方法无法提供预测的理由，缺乏可解释性和推理过程。同时，它们也无法整合金融新闻或报告等文本信息。与此同时，大型语言模型（LLMs）具有卓越的文本理解和生成能力。但由于金融训练数据集的稀缺以及与实时知识的整合有限，LLMs仍然存在幻觉问题，且无法跟上最新信息。为应对这些挑战，我们首先发布了AlphaFin数据集，该数据集结合了传统研究数据集、实时金融数据和手写的思维链（CoT）数据。这对训练LLMs完成金融分析具有积极影响。随后，我们使用AlphaFin数据集对一种最先进的方法——称为Stock-Chain——进行基准测试，该方法有效应对金融分析任务，并整合了检索增强生成（RAG）技术。我们进行了大量实验，以证明我们的框架在金融分析上的有效性。
英文摘要：
The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering. Currently, machine learning and deep learning algorithms (ML&DL) have been widely applied for stock trend predictions, leading to significant progress. However, these methods fail to provide reasons for predictions, lacking interpretability and reasoning processes. Also, they can not integrate textual information such as financial news or reports. Meanwhile, large language models (LLMs) have remarkable textual understanding and generation ability. But due to the scarcity of financial training datasets and limited integration with real-time knowledge, LLMs still suffer from hallucinations and are unable to keep up with the latest information. To tackle these challenges, we first release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. It has a positive impact on training LLMs for completing financial analysis. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task, which integrates retrieval-augmented generation (RAG) techniques. Extensive experiments are conducted to demonstrate the effectiveness of our framework on financial analysis.
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。
