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 advances in 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 about how company stocks trade in the market or their interactions with fundamentals. To leverage 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，需要基于异质信号进行推理，这些信号包括从监管文件中得出的公司基本面数据以及从价格动态计算出的交易信号。近年来，随着大型语言模型（LLM）的发展，金融分析师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侧重于公司资产负债表数据，很少评估有关公司股票如何在市场中交易或与基本面的交互推理。为充分利用两种方法的优势，我们提出了FinTradeBench，一个评估融合公司基本面与交易信号的金融推理能力的基准。FinTradeBench包含1400个问题，基于纳斯达克100指数成分股公司十年历史窗口的数据。该基准分为三类推理问题：基本面聚焦型、交易信号聚焦型以及需要跨信号推理的混合型问题。为确保大规模可靠性，我们采用了一种先校准后扩展的框架，该框架结合了专家种子问题、多模型响应生成、模型内自过滤、数值审计以及人-大模型判断对齐。我们在零样本提示和检索增强设置下评估了14种大语言模型，并观察到明显的性能差距。检索显著提高了对文本基本面的推理能力，但对交易信号推理的帮助有限。这些发现凸显了当前大语言模型在数值推理和时间序列推理方面面临的根本性挑战，并为未来金融智能研究提供了动力。