用真实数据+专业工具打造能生成高质量股票分析的AI助手
FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools
- 结合实时数据与量化工具，让LLM更懂金融分析
- 在专家标注数据集上表现超越通用和领域模型
- 适合金融从业者快速生成专业级投资报告
当前金融大语言模型存在两大缺陷：缺乏客观评估股票分析报告质量的指标，以及分析深度不足，难以生成专业级洞察。本文提出FinSphere——一个股票分析智能体，包含三大贡献：(1) AnalyScore，一套系统化评估股票分析质量的框架；(2) Stocksis，由行业专家构建的数据集，用于提升LLM的分析能力；(3) FinSphere，能够根据用户查询生成高质量股票分析报告的AI代理。实验表明，即使在引入实时数据和少量示例引导的情况下，FinSphere仍显著优于通用与领域专用模型，以及现有基于代理的系统。融合实时数据流、量化工具与指令微调的大语言模型，大幅提升了分析质量和实际应用价值。
原文摘要 · Abstract (English)
Current financial large language models (FinLLMs) struggle with two critical limitations: the absence of objective evaluation metrics to assess the quality of stock analysis reports and a lack of depth in stock analysis, which impedes their ability to generate professional-grade insights. To address these challenges, this paper introduces FinSphere, a stock analysis agent, along with three major contributions: (1) AnalyScore, a systematic evaluation framework for assessing stock analysis quality, (2) Stocksis, a dataset curated by industry experts to enhance LLMs' stock analysis capabilities, and (3) FinSphere, an AI agent that can generate high-quality stock analysis reports in response to user queries. Experiments demonstrate that FinSphere achieves superior performance compared to both general and domain-specific LLMs, as well as existing agent-based systems, even when they are enhanced with real-time data access and few-shot guidance. The integrated framework, which combines real-time data feeds, quantitative tools, and an instruction-tuned LLM, yields substantial improvements in both analytical quality and practical applicability for real-world stock analysis.
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