Latest Research Papers
2025-01-08
arXiv
FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools based on Real-Time Database
This paper introduces FinSphere, a conversational stock analysis agent, along with a curated dataset and an evaluation framework to improve the quality of stock analysis. The system demonstrates superior performance compared to existing LLMs and agent-based systems.
Current financial Large Language Models (LLMs) struggle with two critical
limitations: a lack of depth in stock analysis, which impedes their ability to
generate professional-grade insights, and the absence of objective evaluation
metrics to assess the quality of stock analysis reports. To address these
challenges, this paper introduces FinSphere, a conversational stock analysis
agent, along with three major contributions: (1) Stocksis, a dataset curated by
industry experts to enhance LLMs' stock analysis capabilities, (2) AnalyScore,
a systematic evaluation framework for assessing stock analysis quality, 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.