FinSphere improves LLM stock analysis by combining real-time data, quantitative tools, and a specialized dataset. This addresses limitations in existing financial LLMs, which lack depth in analysis and objective evaluation metrics.
-----
Paper - https://arxiv.org/abs/2501.12399
Original Problem 🤔:
→ Existing financial LLMs lack depth in stock analysis and objective evaluation metrics.
→ They struggle to generate professional-grade insights.
-----
Solution in this Paper 💡:
→ FinSphere integrates real-time financial databases, specialized quantitative tools, and an instruction-tuned LLM.
→ Stocksis, a dataset curated by industry experts, enhances the LLM’s stock analysis capabilities.
→ AnalyScore provides a systematic evaluation framework to assess the quality of stock analysis reports.
→ FinSphere decomposes user queries into subtasks, utilizes quantitative tools to analyze real-time data, and synthesizes the results into a comprehensive report.
-----
Key Insights from this Paper 🗝️:
→ Combining real-time data, quantitative tools, and a specialized dataset enhances LLM stock analysis capabilities.
→ Stocksis and AnalyScore address the limitations of existing financial LLMs.
→ FinSphere's integrated approach outperforms general and domain-specific LLMs and agent-based systems.
-----
Results 💯:
→ FinSphere achieves a total score of 70.88 out of 100 using the AnalyScore framework.
→ FinMem and GPT-40 score 67.55 and 66.61, respectively.
→ Specialized agent-based systems generally outperform standalone language models.
Share this post