FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools based on Real-Time Database
Paper presents FinSphere, an innovative conversational agent integrating real-time data, Stocksis dataset, and AnalyScore framework for professional stock analysis.
What it examines
The paper presents FinSphere, a conversational stock analysis agent that overcomes LLM limitations by integrating real-time financial data, quantitative tools, domain-specific instructions, and a curated dataset (Stocksis) with an evaluation framework (AnalyScore). It aims to deliver professional-grade, accessible stock analysis.
What it concludes
FinSphere outperforms traditional LLMs and agent systems by combining real-time data, quantitative tools, Stocksis, and AnalyScore. Its reliable stock analyses can aid both retail and institutional investors. Future work may further automate expert evaluations and expand its use in financial decision-making.
Evidence objects
FinSphere revolutionizes financial analysis by integrating real-time market data, advanced quantitative tools, and the expert-curated Stocksis dataset, remarkably delivering superior stock analysis that outperforms both general-purpose and domain-specific language models.
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Innovative contributions include the novel AnalyScore evaluation framework and chain-of-thought reasoning for task decomposition, revealing impressive non-linear performance improvements and a balanced methodology that integrates technical, fundamental, and news-based analysis.
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Overall, the research democratizes professional financial analysis by effectively addressing large language model weaknesses, while noting scalability limitations in volatile markets and calling for further validation in diverse market scenarios.
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This novel paper overcomes persistent limitations in financial machine learning by integrating real-time databases, advanced quantitative tools, and instruction-tuned language models to generate professional-grade stock analysis reports. It introduces unique constructs, $Stocksis$ and $AnalyScore$, which dramatically amplify originality, novelty, and impact while extending established boundaries in advanced global financial analytics.
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Raw abstract and provenance
Abstract: 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… ▽ More 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. △ Less
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