metadata

dataset_info:
  - config_name: earnings_forecast
    features:
      - name: meta
        dtype: string
      - name: summary
        dtype: string
      - name: bs
        dtype: string
      - name: pl
        dtype: string
      - name: cf
        dtype: string
      - name: text
        dtype: string
      - name: label
        dtype: int64
      - name: naive_prediction
        dtype: int64
      - name: edinet_code
        dtype: string
      - name: doc_id
        dtype: string
      - name: previous_year_file_path
        dtype: string
      - name: current_year_file_path
        dtype: string
    splits:
      - name: train
        num_bytes: 59126713
        num_examples: 549
      - name: test
        num_bytes: 43577804
        num_examples: 451
    download_size: 42722742
    dataset_size: 102704517
  - config_name: fraud_detection
    features:
      - name: meta
        dtype: string
      - name: summary
        dtype: string
      - name: bs
        dtype: string
      - name: pl
        dtype: string
      - name: cf
        dtype: string
      - name: text
        dtype: string
      - name: label
        dtype: int64
      - name: explanation
        dtype: string
      - name: edinet_code
        dtype: string
      - name: ammended_doc_id
        dtype: string
      - name: doc_id
        dtype: string
      - name: file_path
        dtype: string
    splits:
      - name: train
        num_bytes: 91248130
        num_examples: 865
      - name: test
        num_bytes: 24178157
        num_examples: 224
    download_size: 47624977
    dataset_size: 115426287
  - config_name: industry_prediction
    features:
      - name: meta
        dtype: string
      - name: summary
        dtype: string
      - name: bs
        dtype: string
      - name: pl
        dtype: string
      - name: cf
        dtype: string
      - name: text
        dtype: string
      - name: industry
        dtype: string
      - name: edinet_code
        dtype: string
      - name: doc_id
        dtype: string
      - name: file_path
        dtype: string
    splits:
      - name: train
        num_bytes: 52393541
        num_examples: 496
    download_size: 21997966
    dataset_size: 52393541
configs:
  - config_name: earnings_forecast
    data_files:
      - split: train
        path: earnings_forecast/train-*
      - split: test
        path: earnings_forecast/test-*
  - config_name: fraud_detection
    data_files:
      - split: train
        path: fraud_detection/train-*
      - split: test
        path: fraud_detection/test-*
  - config_name: industry_prediction
    data_files:
      - split: train
        path: industry_prediction/train-*
task_categories:
  - text-classification
language:
  - ja
tags:
  - finance
  - accounting
size_categories:
  - 1K<n<10K
license: other

EDINET-Bench

📚 Paper | 📝 Blog | 🧑‍💻 Code

EDINET-Bench is a Japanese financial benchmark designed to evaluate the performance of LLMs on challenging financial tasks including accounting fraud detection, earnings forecasting, and industry prediction. This dataset is built leveraging EDINET, a platform managed by the Financial Services Agency (FSA) of Japan that provides access to disclosure documents such as securities reports.

Notice

June 9, 2025: This dataset was originally released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Although section 1.7.3 of the Public Domain License (PDL) 1.0 states that it is compatible with CC BY 4.0, we have relicensed the dataset under PDL 1.0 to ensure strict consistency with the original licensing terms of the source data.

Resources

📃Paper: Read our paper for detailed dataset construction pipeline and evaluation results at https://arxiv.org/abs/2506.08762
🏗️Counstruction Code: Create a new benchmark dataset at https://github.com/SakanaAI/edinet2dataset
📊Evaluation Code: Evaluate the performance of models on EDINET-Bench at https://github.com/SakanaAI/EDINET-Bench

Dataset Construction Pipeline

EDINET-Bench is built by downloading the past 10 years of annual reports of Japanese listed companies via EDINET-API and automatically annotating labels for each task. For detailed information, please read our paper and code.

How to Use

Acounting fraud detection

This task is a binary classification problem aimed at predicting whether a given annual report is fraudulent. The label is either fraud (1) or non-fraud (0). The explanation includes the reasons why the LLM determined that the contents of the amended report are related to accounting fraud.

Sample Explanation:

この訂正有価証券報告書は明らかに会計不正に関連しています。提出理由の部分に「当社の元従業員が、複数年度に亘って、商品の不正持ち出し転売するなどの行為を行っていた事実が判明」と記載されており、「第三者委員会」を設置して調査を行ったことが明記されています。さらに「不適切な会計処理を訂正」という表現も使用されています。この不正行為により、連結財務諸表および財務諸表の数値に変更が生じており、訂正箇所として貸借対照表、損益計算書、連結キャッシュ・フロー計算書など財務諸表の主要部分が挙げられています。これは単なる記載ミスではなく、元従業員による不正行為に起因する重大な会計上の問題であることが明確です。
(The amended securities report is clearly related to accounting fraud. In the section stating the reason for the amendment, it is noted that "it was discovered that a former employee of the company had, over multiple fiscal years, engaged in misconduct such as unlawfully removing and reselling products." It is also clearly stated that a "third-party committee" was established to investigate the matter. Furthermore, the report uses expressions such as "correction of inappropriate accounting treatment." As a result of this misconduct, changes have been made to figures in both the consolidated financial statements and the individual financial statements. The corrected sections include major parts of the financial statements, such as the balance sheet, income statement, and consolidated cash flow statement. This is not merely a clerical error, but rather a serious accounting issue stemming from fraudulent actions by a former employee.)

>>> from datasets import load_dataset
>>> ds = load_dataset("SakanaAI/EDINET-Bench", "fraud_detection")
>>> ds
DatasetDict({
    train: Dataset({
        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'explanation', 'edinet_code', 'ammended_doc_id', 'doc_id', 'file_path'],
        num_rows: 865
    })
    test: Dataset({
        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'explanation', 'edinet_code', 'ammended_doc_id', 'doc_id', 'file_path'],
        num_rows: 224
    })
})

Earnings forecast

This task is a binary classification problem that predicts whether a company's earnings will increase or decrease in the next fiscal year based on its current annual report. The label is either increase (1) or not (0).

>>> from datasets import load_dataset
>>> ds = load_dataset("SakanaAI/EDINET-Bench", "earnings_forecast")
>>> ds
DatasetDict({
    train: Dataset({
        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'naive_prediction', 'edinet_code', 'doc_id', 'previous_year_file_path', 'current_year_file_path'],
        num_rows: 549
    })
    test: Dataset({
        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'naive_prediction', 'edinet_code', 'doc_id', 'previous_year_file_path', 'current_year_file_path'],
        num_rows: 451
    })
})

Industry prediction

This task is a multi-class classification problem that predicts a company's industry type (e.g., Banking) based on its current annual report. Each label (in this case, the industry column) represents one of 16 possible industry types.

>>> from datasets import load_dataset
>>> ds = load_dataset("SakanaAI/EDINET-Bench", "industry_prediction")
>>> ds
DatasetDict({
    train: Dataset({
        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'industry', 'edinet_code', 'doc_id', 'file_path'],
        num_rows: 496
    })
})

Limitation

Mislabeling: When constructing the benchmark dataset for the accounting fraud detection task, we assume that only cases explicitly reported as fraudulent are labeled as such, while all others are considered non-fraudulent. However, there may be undiscovered fraud cases that remain unreported, introducing potential label noise into the dataset. Additionally, our fraud examples are constructed by having the LLM read the contents of the amended reports and determine whether they are related to fraudulent activities. Due to the hallucination problem inherent in LLMs and lack of instruction following abilities, there is a risk that some cases may be incorrectly identified as fraudulent.
Intrinsic difficulty: Among the tasks in our benchmark, the fraud detection and earnings forecasting tasks may be intrinsically challenging with a performance upper bound, as the LLM relies solely on information from a single annual report for its predictions. Future research directions could explore the development of benchmark task designs that enable the model to utilize information beyond the annual report with novel agentic pipelines.

LICENSE

EDINET-Bench is licensed under the PDL 1.0 in accordance with EDINET's Terms of Use.

⚠️ Warnings

EDINET-Bench is intended solely for advancing LLM applications in finance and must not be used to target or harm any real companies included in the dataset.

Citation

@misc{sugiura2025edinet,
  author={Issa Sugiura and Takashi Ishida and Taro Makino and Chieko Tazuke and Takanori Nakagawa and Kosuke Nakago and David Ha},
  title={{EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements}},
  year={2025},
  eprint={2506.08762},
  archivePrefix={arXiv},
  primaryClass={q-fin.ST},
  url={https://arxiv.org/abs/2506.08762}, 
}