Retrieval-augmented Large Language Models for Financial Time Series
  Forecasting
Authors: name: Mengxi Xiao; name: Zihao Jiang; name: Lingfei Qian; name: Zhengyu Chen; name: Yueru He; name: Yijing Xu; name: Yuecheng Jiang; name: Dong Li; name: Ruey-Ling Weng; name: Min Peng; name: Jimin Huang; name: Sophia Ananiadou; name: Qianqian Xie
Published: 2025-02-09 12:26:05

Abstract: 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.