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：现实世界的金融决策是一项极具挑战性的任务，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根据价格动态计算出的交易信号。近年来，随着大语言模型（LLM）的进步，金融分析师已开始将其用于金融决策任务。然而，现有用于测试这些模型的金融问答基准主要关注公司资产负债表数据，极少评估模型对公司股票在市场上的交易行为或其与基本面的交互作用进行推理的能力。为融合两种方法的优势，我们提出了FinTradeBench——一个整合公司基本面与交易信号的金融推理评估基准。FinTradeBench包含1,400个基于纳斯达克100成分股公司十年历史窗口的题目。该基准按推理类别分为三类：基本面聚焦、交易信号聚焦以及需要跨信号推理的混合型题目。为确保在大规模应用中的可靠性，我们采用了一种"校准-缩放"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、数值审计以及人类-语言模型评判对齐。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，并观察到显著的性能差距。检索显著提升了对文本基本面信息的推理能力，但对交易信号推理的改善有限。这些发现揭示了当前大语言模型在数值推理和时间序列推理方面的根本性挑战，并推动了金融智能领域的未来研究。