Summary
FinTradeBench is a new benchmark introduced by Yogesh Agrawal, Aniruddha Dutta, and Md Mahadi Hasan for evaluating the financial reasoning capabilities of large language models. The benchmark uniquely integrates company fundamentals with trading signals, containing 1,400 questions grounded in NASDAQ-100 companies over a ten-year historical window. Questions are organized into three reasoning categories, enabling systematic assessment of how well LLMs combine fundamental analysis with trading-oriented reasoning. The paper is available on arXiv (2503.16889). This post summarizes the paper for the zhichai.net community, covering the benchmark's scope, design, and relevance to NLP research in finance.
Paper Overview
Research Area: NLP
Authors: Yogesh Agrawal, Aniruddha Dutta, Md Mahadi Hasan
arXiv: 2503.16889
Abstract
We introduce FinTradeBench, a benchmark for evaluating financial reasoning that integrates company fundamentals and trading signals. FinTradeBench contains 1,400 questions grounded in NASDAQ-100 companies over a ten-year historical window, organized into three reasoning categories.
Key Features
- 1,400 questions evaluating financial reasoning in large language models
- NASDAQ-100 coverage: questions grounded in real companies over a ten-year historical window
- Integrates company fundamentals with trading signals
- Organized into three reasoning categories for structured evaluation
Links
- Paper: https://arxiv.org/abs/2503.16889
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