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 hybrid questions requiring cross-signal reasoning. To ensure reliability at scale, we adopt a calibration-then-scaling framework that combines expert seed questions, multi-model response generation, intra-model self-filtering, numerical auditing, and human-LLM judge alignment. We evaluate 14 LLMs under zero-shot prompting and retrieval-augmented settings and witness a clear performance gap. Retrieval substantially improves reasoning over textual fundamentals, but provides limited benefit for trading-signal reasoning. These findings highlight fundamental challenges in the numerical and time-series reasoning for current LLMs and motivate future research in financial intelligence.

翻译：现实世界中的金融决策是一项具有挑战性的问题，需要对异质信号进行推理，包括从监管文件中提取的公司基本面信息以及从价格动态中计算得出的交易信号。近年来，随着大语言模型（LLMs）的进步，金融分析师已开始将其应用于金融决策任务。然而，现有用于测试这些模型的金融问答基准主要聚焦于公司资产负债表数据，极少评估模型对公司股票市场交易行为或基本面与交易互动的推理能力。为融合两种方法的优势，我们提出FinTradeBench——一个整合公司基本面与交易信号的金融推理评估基准。该基准包含基于纳斯达克100指数成分股十年历史窗口的1,400个问题，按三类推理主题组织：基本面聚焦型、交易信号聚焦型及需跨信号推理的混合型问题。为确保大规模可靠性，我们采用"校准-缩放"框架，结合专家种子问题、多模型响应生成、模型内自过滤、数值审计及人机判据对齐。我们在零样本提示和检索增强设置下评估了14个大语言模型，观察到显著的性能差距。检索显著提升了文本基本面推理能力，但对交易信号推理的改进有限。这些发现凸显当前大语言模型在数值与时间序列推理方面的根本挑战，并为金融智能领域的未来研究提供了方向。