STQA: A Benchmark for Stock-Focused Tabular Question Answering over Historical and Forecasted Data

2026-09-05

AI Summary

为解决股票市场分析中历史数据和未来预测的综合推理问题，提出STQA基准及SQFRS框架，通过自然语言问答评估模型性能。

Abstract

Stock market analysis inherently requires composite reasoning over historical records and future projections, yet existing benchmarks remain fragmented across isolated tasks. We introduce STQA (Stock-focused Tabular Question Answering), an end-to-end benchmark designed to systematically evaluate natural-language question answering over historical data, numerical forecasts, and forecast-based reasoning. Built on a large-scale financial dataset, STQA covers 4,417 stocks and contains 31,400 question-answer pairs derived from expert-crafted templates, accompanied by fine-grained intent and slot annotations. To operationalize this benchmark, we present SQFRS (Stock Query-Forecast-Reasoning System), an agent-based unified framework that orchestrates SQL retrieval and time-series forecasting tools. Experiments demonstrate that while current large language models perform well on historical queries, forecast-based reasoning poses a substantial challenge, revealing critical bottlenecks in tool coordination and reasoning under uncertainty. The dataset and code are available at https://github.com/xuxubaobaoan/STQA_Project. STQA thus serves as a rigorous testbed for future research on trustworthy, tool-augmented financial agents.

Problem

• Stock Market Analysis
• Tabular Question Answering
• Historical Data
• Forecasted Data
• Benchmark

Innovation

• Stock-focused Tabular Question Answering
• Forecast-based Reasoning
• Unified Framework
• Tool Coordination