chakki Financial Report Corpus
Project description
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
# Download raw file version dataset of 2014.
chafic download --kind F --year 2014
# Extract business.overview_of_result part of TIS.Inc (sec code=3626).
chafic parse business.overview_of_result --sec_code 3626
# Tokenize text by Janome (Janome or Sudachi is supported).
pip install janome
chafic parse business.overview_of_result --sec_code 3626
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 andopen
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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<p>Example: NER</p>
<pre lang="py"><code>
Project details
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