Third-party cyber evaluations involving OpenAI models
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AI Research
arXiv CS.AI·Jun 10·5 min read
FinTradeBench: A Financial Reasoning Benchmark for LLMs
TL;DR
Financial decision-making often struggles with integrating diverse data signals, such as company fundamentals and trading signals. FinTradeBench was developed as a benchmark to evaluate financial reasoning in Large Language Models (LLMs) using 1,400 questions based on NASDAQ-100 companies.
✦ Why It Matters
Engineers can leverage FinTradeBench to improve LLMs' financial reasoning capabilities and address specific performance gaps.
Key Takeaways
- Utilize FinTradeBench to evaluate LLMs in financial contexts.
- Focus on integrating fundamentals and trading signals for better performance.
- Explore numerical and time-series reasoning challenges in LLMs.
- Adopt a calibration-then-scaling framework for reliable benchmarking.
Full Summary
Financial decision-making requires analyzing various data signals, including company fundamentals from regulatory filings and trading signals from market price movements. To address the limitations of existing benchmarks that focus mainly on balance sheet data, FinTradeBench was created to evaluate financial reasoning in Large Language Models (LLMs).
This benchmark includes 1,400 questions covering three reasoning categories: fundamentals-focused, trading-signal-focused, and hybrid questions that require integrating both types of signals. A calibration-then-scaling framework was employed, which involved generating expert seed questions, multi-model responses, and human-LLM judge alignment.
Evaluating 14 LLMs under zero-shot prompting and retrieval-augmented settings revealed a clear performance gap, with retrieval improving reasoning for textual fundamentals but offering limited benefits for trading-signal reasoning. These findings underscore the need for further research into enhancing numerical and time-series reasoning capabilities in LLMs.