title: Retrieval-augmented Large Language Models for Financial Time Series
    Forecasting
summary: 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.
paper_authors._id: 67aab4a98642da4d695cf045
paper_authors.name: Mengxi Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:34:50.108Z
paper_authors._id: 663adb42e14047f710dc1d29
paper_authors.avatarUrl: /avatars/7ca49d67a4a8b4cf0ee896e07646715f.svg
paper_authors.fullname: Mengxi Xiao
paper_authors.type: user
paper_authors.user: ElsaShaw
paper_authors._id: 67aab4a98642da4d695cf046
paper_authors.name: Zihao Jiang
paper_authors._id: 67aab4a98642da4d695cf047
paper_authors.name: Lingfei Qian
paper_authors._id: 67aab4a98642da4d695cf048
paper_authors.name: Zhengyu Chen
paper_authors._id: 67aab4a98642da4d695cf049
paper_authors.name: Yueru He
paper_authors._id: 67aab4a98642da4d695cf04a
paper_authors.name: Yijing Xu
paper_authors._id: 67aab4a98642da4d695cf04b
paper_authors.name: Yuecheng Jiang
paper_authors._id: 67aab4a98642da4d695cf04c
paper_authors.name: Dong Li
paper_authors._id: 67aab4a98642da4d695cf04d
paper_authors.name: Ruey-Ling Weng
paper_authors._id: 67aab4a98642da4d695cf04e
paper_authors.name: Min Peng
paper_authors._id: 67aab4a98642da4d695cf04f
paper_authors.name: Jimin Huang
paper_authors._id: 67aab4a98642da4d695cf050
paper_authors.name: Sophia Ananiadou
paper_authors._id: 67aab4a98642da4d695cf051
paper_authors.name: Qianqian Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:25:37.116Z
paper_authors._id: 6479f4317c18dca75e9a9324
paper_authors.avatarUrl: /avatars/9aa709230b057f57ee4415c04a622c63.svg
paper_authors.fullname: Xie
paper_authors.type: user
paper_authors.user: QianqianXie1994
paper_title: Retrieval-augmented Large Language Models for Financial Time Series
    Forecasting
paper_summary: 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.
paper_ai_keywords: retrieval-augmented generation (RAG) fine-tuned large language model (LLM) candidate selection method training objective similarity between queries historically significant sequences FinSeer financial indicators historical stock prices BIGDATA22