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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2500414


FinSphere: a real-time stock analysis agent with instruction-tuned LLMs and domain-specific tool integration


Author(s):  Shijie HAN1, 3, Jingshu ZHANG1, 3, Yiqing SHEN3, 4, Kaiyuan YAN3, , Hongguang LI3,

Affiliation(s):  1Department of Industrial Engineering and Operations Research, Columbia University, New York 10027, USA; more

Corresponding email(s):   sh4460@columbia.edu, zhangjingshu@mail.shufe.edu.cn, yshen92@jhu.edu, yankaiyuani@163.com, harvey2@mail.ustc.edu.cn

Key Words:  Instruction-tuned financial LLM, Real-time stock analysis, Evaluation framework & dataset


Shijie HAN1,3, Jingshu ZHANG1,3, Yiqing SHEN3,4, Kaiyuan YAN3,, Hongguang LI3,. FinSphere: a real-time stock analysis agent with instruction-tuned LLMs and domain-specific tool integration[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
Current financial large language models (FinLLMs) exhibit two major limitations: the absence of standardized evaluation metrics for stock analysis quality and insufficient analytical depth. We address these limitations with two contributions. First, we introduce AnalyScore, a systematic framework for evaluating the quality of stock analysis. Second, we construct Stocksis, an expert-curated dataset designed to enhance LLMs'financial analysis capabilities. Building on Stocksis, together with a novel integration framework and quantitative tools, we develop FinSphere, an AI agent that generates professional-grade stock analysis reports. Evaluations with AnalyScore show that FinSphere consistently surpasses general-purpose LLMs, domain-specific FinLLMs, and existing agent-based systems, even when the latter are enhanced with real-time data access and few-shot guidance. The findings highlight FinSphere's significant advantages in analytical quality and real-world applicability.

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