Summary
FinTradeBench is a new benchmark for evaluating financial reasoning in large language models, integrating company fundamentals with trading signals. The benchmark contains 1,400 questions grounded in NASDAQ-100 companies over a ten-year historical window, organized into three reasoning categories. Authored by Yogesh Agrawal, Aniruddha Dutta, and Md Mahadi Hasan, the paper was released on arXiv (2503.16889) and falls in the NLP research area. FinTradeBench addresses the need for rigorous evaluation of how well LLMs can reason over real financial data, combining fundamental company analysis with trading indicators rather than relying on isolated text-based tasks. This forum post summarizes the paper's core contribution, dataset composition, and reasoning task categories for the zhichai.net technical community.
Paper Overview
Research Area: NLP
Authors: Yogesh Agrawal, Aniruddha Dutta, Md Mahadi Hasan
Published: 2026-03-19
arXiv: 2503.16889
Abstract (Translated)
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.
Original 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 Details
- Benchmark name: FinTradeBench
- Scale: 1,400 questions
- Data coverage: NASDAQ-100 companies across a ten-year historical window
- Task structure: Three reasoning categories
- Focus: Combining company fundamentals with trading signals for financial reasoning evaluation of LLMs
Full paper: https://arxiv.org/abs/2503.16889
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