[2506.08762] EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements

Quantitative Finance > Statistical Finance arXiv:2506.08762 (q-fin) [Submitted on 10 Jun 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements Authors:Issa Sugiura, Takashi Ishida, Taro Makino, Chieko Tazuke, Takanori Nakagawa, Kosuke Nakago, David Ha View a PDF of the paper titled EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements, by Issa Sugiura and Takashi Ishida and Taro Makino and Chieko Tazuke and Takanori Nakagawa and Kosuke Nakago and David Ha View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have made remarkable progress, surpassing human performance on several benchmarks in domains such as mathematics and coding. A key driver of this progress has been the development of benchmark datasets. In contrast, the financial domain poses higher entry barriers due to its demand for specialized expertise, and benchmarks remain relatively scarce compared to those in mathematics or coding. We introduce EDINET-Bench, an open-source Japanese financial benchmark designed to evaluate LLMs on challenging tasks such as accounting fraud detection, earnings forecasting, and industry classification. EDINET-Bench is constructed from ten years of annual reports filed by Japanese companies. These tasks require models to process entire annual reports and integrate information across multiple tables and textual sections, demanding e...