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<p>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 the financial analysis capabilities of large language models (LLMs). Building on Stocksis, together with a novel integration framework and quantitative tools, we develop FinSphere, an artificial intelligence (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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Shijie Han,
  • Jingshu Zhang,
  • Yiqing Shen,
  • Kaiyuan Yan,
  • Hongguang Li

摘要

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 the financial analysis capabilities of large language models (LLMs). Building on Stocksis, together with a novel integration framework and quantitative tools, we develop FinSphere, an artificial intelligence (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.