Textual analysis on SEC filings from EDGAR
Project description
edgar-analyzer - Textual Analysis with EDGAR filings
edgar-analyzer is a CLI tool to download SEC filings from EDGAR and perform textual analyses.
Installation
pip install edgar-analyzer
Workflow
Setup
Download index files, which contain the firm CIK, name, filing date, type, and URL of the filing.
edgar-analyzer download_index --user_agent "MyCompany name@mycompany.com" --output "./index"
Build a database of the previously download index files for more efficient queries.
edgar-analyzer build_database --inputdir "./index" --database "edgar-idx.sqlite3"
Download filings, only filings in the database but not downloaded yet will be downloaded. Download speed will be auto throttled as per SEC's fair use policy.
edgar-analyzer download_filings --user_agent "MyCompany name@mycompany.com" --output "./output" --database "edgar-idx.sqlite3" --file_type "8-K" -t 4
Run specific jobs
These tasks can be executed once the database of filings is built.
Find event date
❯ edgar-analyzer find_event_date -h
usage: edgar-analyzer [OPTION]... find_event_date [-h] -d data_directory --file_type file_type [-db databsae] [-t threads]
Find event date from filings from header data
options:
-h, --help show this help message and exit
-t threads, --threads threads
number of processes to use
required named arguments:
-d data_directory, --data_dir data_directory
directory of filings
--file_type file_type
type of filing
-db databsae, --database databsae
sqlite database to store results
Find reported items
❯ edgar-analyzer find_reported_items -h
usage: edgar-analyzer [OPTION]... find_reported_items [-h] -d data_directory --file_type file_type [-db databsae] [-t threads]
Find reported items from filings from header data
options:
-h, --help show this help message and exit
-t threads, --threads threads
number of processes to use
required named arguments:
-d data_directory, --data_dir data_directory
directory of filings
--file_type file_type
type of filing
-db databsae, --database databsae
sqlite database to store results
more to be integrated
Example
Just a simple example of the job find_event_date. Based on the 1,491,368 8K filings (2004-2022), the table below shows the reporting lags (date of filing minus date of event).
We can find that most filings are filed on the same day as the event reported, and that over 99.99% of filings are filed within 4 calendar days (SEC requires 4 business days).
| Filing lag (calendar days) | Frequency | Percentage | Cumulative |
|---|---|---|---|
| 0 | 1470089 | 98.57% | 98.57% |
| 1 | 20761 | 1.39% | 99.97% |
| 2 | 285 | 0.02% | 99.98% |
| 3 | 89 | 0.01% | 99.99% |
| 4 | 47 | 0.00% | 99.99% |
| 5 | 26 | 0.00% | 100.00% |
| 6 | 14 | 0.00% | 100.00% |
| 7 | 6 | 0.00% | 100.00% |
| 8 | 4 | 0.00% | 100.00% |
| 9 | 3 | 0.00% | 100.00% |
| 10 or more | 44 | 0.00% | 100.00% |
Note
This tool is a work in progress and breaking changes may be expected.
Contact
If you identify any issue, please feel free to contact me at mingze.gao@sydney.edu.au.
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