Go to ACL ARR 2025 February homepageFinSphere: A Financial Question-Answering Agent for Real-Time Stock Comprehensive Analysis
Abstract: Current financial Large Language Models (LLMs) 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 conversational 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.
Paper Type: Long
Research Area: Question Answering
Research Area Keywords: Financial LLM, Financial Question-Answering System, Stock Analysis
Contribution Types: NLP engineering experiment, Data resources, Data analysis
Languages Studied: English, Chinese
Submission Number: 3723
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