[2603.19225] FinTradeBench: A Financial Reasoning Benchmark for LLMs

Computer Science > Computational Engineering, Finance, and Science arXiv:2603.19225 (cs) [Submitted on 19 Mar 2026 (v1), last revised 20 Mar 2026 (this version, v2)] Title:FinTradeBench: A Financial Reasoning Benchmark for LLMs Authors:Yogesh Agrawal, Aniruddha Dutta, Md Mahadi Hasan, Santu Karmaker, Aritra Dutta View a PDF of the paper titled FinTradeBench: A Financial Reasoning Benchmark for LLMs, by Yogesh Agrawal and 4 other authors View PDF HTML (experimental) Abstract:Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals computed from price dynamics. Recently, with the advancement of Large Language Models (LLMs), financial analysts have begun to use them for financial decision-making tasks. However, existing financial question answering benchmarks for testing these models primarily focus on company balance sheet data and rarely evaluate reasoning over how company stocks trade in the market or their interactions with fundamentals. To take advantage of the strengths of both approaches, 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. The benchmark is organized into three reasoning categories: fundamentals-focused, trading-signal-focused, and...