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

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 this https URL. STQA thus serves as a rigorous testbed for future research on trustworthy, tool-augmented financial agents.

9 pages of main text, 15 pages of appendices, 19 figures. Accepted to Findings of EMNLP 2026

Computation and Language (cs.CL)

arXiv:2609.06117 [cs.CL]

(or arXiv:2609.06117v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.06117 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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