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 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.

Key words: Large language model (LLM); Instruction-tuned financial LLM; Real-time stock analysis; Evaluation framework and dataset

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