Retrieval-augmented Large Language Models for Financial Time Series
  Forecasting
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.
67aab4a98642da4d695cf045
Mengxi Xiao
claimed_verified
2025-02-18T09:34:50.108Z
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Mengxi Xiao
user
ElsaShaw
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Zihao Jiang
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Lingfei Qian
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2025-06-11T20:21:35.233Z
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Lingfei Qian
user
lfqian
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Zhengyu Chen
67aab4a98642da4d695cf049
Yueru He
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2025-06-18T16:16:02.860Z
65bd14e8ce846f8aa94db1d1
/avatars/76eaad15bf32eba75271f3dc315527c2.svg
Yueru He
user
Yueru1
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Yijing Xu
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Yuecheng Jiang
67aab4a98642da4d695cf04c
Dong Li
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Ruey-Ling Weng
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Min Peng
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Jimin Huang
claimed_verified
2025-06-19T09:11:12.012Z
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Jimin Huang
user
jiminHuang
67aab4a98642da4d695cf050
Sophia Ananiadou
67aab4a98642da4d695cf051
Qianqian Xie
claimed_verified
2025-02-13T08:25:37.116Z
6479f4317c18dca75e9a9324
/avatars/9aa709230b057f57ee4415c04a622c63.svg
Xie
user
QianqianXie1994
Retrieval-augmented Large Language Models for Financial Time Series
  Forecasting
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.
A retrieval-augmented generation framework enhances stock prediction accuracy by fine-tuning a large language model and integrating it with a novel retrieval method on specialized datasets.
retrieval-augmented generation fine-tuned large language model candidate selection training objective similarity retriever FinSeer financial forecasting BIGDATA22