FinTradeBench: A Financial Reasoning Benchmark for LLMs

2026-03-19 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, FinTech & Digital Financial Services · Depth: Expert, quick

Summary

To address the limitations of existing financial question answering benchmarks for Large Language Models (LLMs), which primarily focus on company balance sheet data and neglect market trading signals, researchers introduce FinTradeBench. This new benchmark integrates company fundamentals and trading signals, comprising 1,400 questions grounded in NASDAQ-100 companies over a ten-year historical window, organized into fundamentals-focused, trading-signal-focused, and hybrid reasoning categories. A robust "calibration-then-scaling" framework, involving expert seed questions, multi-model response generation, and human-LLM judge alignment, ensures its reliability. Evaluation of 14 LLMs under zero-shot and retrieval-augmented settings revealed a clear performance gap, with retrieval substantially improving reasoning over textual fundamentals but providing limited benefit for trading-signal reasoning. These findings underscore fundamental challenges in LLMs' numerical and time-series reasoning capabilities, motivating future research in financial intelligence.

Key takeaway

FinTradeBench introduces a new benchmark with 1,400 questions on NASDAQ-100 company fundamentals and trading signals to evaluate LLM financial reasoning. Initial evaluations show retrieval substantially improves textual fundamental reasoning but offers limited benefit for trading-signal reasoning. This exposes critical challenges in current LLMs' numerical and time-series reasoning capabilities for financial intelligence.

Topics

• Financial Reasoning
• Large Language Models
• Financial Benchmarks
• Retrieval-Augmented Generation
• Time-Series Reasoning

Best for: Research Scientist, AI Researcher, AI Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.