chafic 0.1.6

chakki Financial Report Corpus

chaFiC: chakki Financial Report Corpus

We organized Japanese financial reports to encourage applying NLP techniques to financial analytics.

Dataset

You can download dataset by command line tool.

pip install chafic

chafic download --kind F --year 2014

Please refer the usage by --.

chafic --

Raw dataset file

The corpora are separated to each financial years.

fiscal_year	Raw file version (F)	Text extracted version (E)
2014	.zip (9.3GB)	.zip (270.8MB)
2015	.zip (9.8GB)	.zip (291.1MB)
2016	.zip (10.2GB)	.zip (334.7MB)
2017	.zip (9.1GB)	.zip (310.2MB)
2018	.zip (10.5GB)	.zip (260.9MB)

Statistics

fiscal_year	number_of_reports	has_csr_reports	has_financial_data	has_stock_data
2014	3,724	92	3,583	3,595
2015	3,870	96	3,725	3,751
2016	4,066	97	3,924	3,941
2017	3,578	89	3,441	3,472
2018	3,513	70	2,893	3,413

• financial data is from 決算短信情報.
  • We use non-cosolidated data if it exist.
• stock data is from 月間相場表（内国株式）.
  • close is fiscal period end and open is 1 year before of it.

Content

Raw file version

The structure of dataset is following.

chakki_esg_financial_{year}.zip
└──{year}
     ├── documents.csv
     └── docs/

docs includes XBRL and PDF file.

• XBRL file of annual reports (files are retrieved from [EDINET]).
• PDF file of CSR reports (additional content).

documents.csv has metadata like following.

• edinet_code: E0000X
• filer_name: XXX株式会社
• fiscal_year: 201X
• fiscal_period: FY
• doc_path: docs/S000000X.xbrl
• csr_path: docs/E0000X_201X_JP_36.pdf

Text extracted version

Text extracted version includes txt files that match each part of an annual report.
The extracted parts are defined at edinet-python.

chakki_esg_financial_{year}_extracted.zip
└──{year}
     ├── documents.csv
     └── docs/

Utilize Data for NLP

We offer the parser for the financial documents based on GiNZA. Please refer the ficser to use this feature.

Example: Parse

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