用于金融时间序列预测的检索增强型大型语言模型

摘要

股票走势预测是金融时间序列预测中的基本任务，需要从大量时间序列数据中识别和检索关键影响因素。然而，现有基于文本训练或数字相似度的检索方法在处理复杂的金融分析时存在不足。为解决这一问题，我们提出了第一个用于金融时间序列预测的检索增强生成（RAG）框架，具有三个关键创新点：以精调的10亿参数大型语言模型（StockLLM）为支撑，利用LLM反馈的新型候选选择方法，以及最大化查询与历史重要序列之间相似性的训练目标。这使得我们的检索器FinSeer能够发现有意义的模式，同时最大程度地减少复杂金融数据中的噪音。我们还构建了整合金融指标和历史股价的新数据集，用于训练FinSeer并确保稳健评估。实验结果表明，我们的RAG框架优于单独的StockLLM和随机检索，突显了其有效性，而FinSeer超越了现有的检索方法，在BIGDATA22上准确率提高了8％，并检索到更具影响力的序列。这项工作强调了金融预测中定制检索模型的重要性，并为未来研究提供了一个新颖的框架。

English

Stock movement prediction, a fundamental task in financial time-series forecasting, requires identifying and retrieving critical influencing factors from vast amounts of time-series data. However, existing text-trained or numeric similarity-based retrieval methods fall short in handling complex financial analysis. To address this, we propose the first retrieval-augmented generation (RAG) framework for financial time-series forecasting, featuring three key innovations: a fine-tuned 1B parameter large language model (StockLLM) as the backbone, a novel candidate selection method leveraging LLM feedback, and a training objective that maximizes similarity between queries and historically significant sequences. This enables our retriever, FinSeer, to uncover meaningful patterns while minimizing noise in complex financial data. We also construct new datasets integrating financial indicators and historical stock prices to train FinSeer and ensure robust evaluation. Experimental results demonstrate that our RAG framework outperforms bare StockLLM and random retrieval, highlighting its effectiveness, while FinSeer surpasses existing retrieval methods, achieving an 8\% higher accuracy on BIGDATA22 and retrieving more impactful sequences. This work underscores the importance of tailored retrieval models in financial forecasting and provides a novel framework for future research.

用于金融时间序列预测的检索增强型大型语言模型

Retrieval-augmented Large Language Models for Financial Time Series Forecasting

摘要

Summary

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