frds 0.3.1

Financial Research Data Services

FRDS - Financial Research Data Services

frds is a Python framework that aims to provide the simplest way to compute a collection of major academic measures used in the finance literature, one-click with a Graphical User Interface (GUI).

Installation & Configuration

frds requires Python3.8 or higher. To install using pip:

$ pip install frds

After installation, a folder frds will be created under your user's home directory, which contains a data folder, a result folder and a default configuration file config.ini:

[Paths]
base_dir: ~/frds
data_dir: ${base_dir}/data
result_dir: ${base_dir}/result

[Login]
wrds_username: 
wrds_password: 

You need to enter your WRDS username and password under the login section.

Usage

To start estimating various measures, run frds as a module:

$ python -m frds.gui.run

Alternatively, run without GUI to estimate all measures with default parameters:

$ python -m frds.run

You can also use the following example to use frds programmatically.

from frds import Professor
from frds.measures import AccountingRestatement, ROA, FirmSize

measures = [
    AccountingRestatement(years=1),
    AccountingRestatement(years=2),
    ROA(),
    FirmSize(),
]

config = dict(
        wrds_username="your_wrds_username",
        wrds_password="you_wrds_password",
        result_dir="path/to/where/you/want/to/store/the/result/",
        data_dir="path/to/where/you/want/to/store/the/data/",
    )

if __name__ == "__main__":
    with Professor(config=config) as prof:
        prof.calculate(measures)

The output data will be saved as STATA .dta file in the result folder.

For example, below is a screenshot of the output for frds.measures.AccountingRestatement.

Supported Measures

The built-in measures currently supported by frds are as below. New measures will be added to frds gradually and can be easily developed following a template shown in Developing New Measures.

Measure	Description	Datasets Used
Accounting Restatement	Number of various accounting restatements during the fiscal year	wrds.comp.funda, wrds.audit.auditnonreli
Asset Tangibility	Property, Plant and Equipment (Net) scaled by Assets (Total)	wrds.comp.funda
Board Independence	Board size and independence measured as the ratio of independent board members to board size	wrds.funda, wrds.boardex.na_wrds_company_profile, wrds.boardex.na_wrds_org_composition
Book Leverage	(Long-term Debt + Debt in Current Liabilities) / (Long-term Debt + Debt in Current Liabilities + Common Equity)	wrds.comp.funda
Capital Expenditure	Capital Expenditures scaled by Assets (Total)	wrds.comp.funda
Executive Ownership	Executive stock ownership	wrds.comp.funda, wrds.execcomp.anncomp
Firm Size	Natural logarithm of total assets	wrds.comp.funda
Market-to-Book ratio	Market Value of Common Equity to Book Common Equity	wrds.comp.funda
ROA	Income Before Extraordinary Items scaled by Assets (Total)	wrds.comp.funda
ROE	Income Before Extraordinary Items scaled by Common Equity (Total)	wrds.comp.funda

Developing New Measures

New measures can be easily added by subclassing frds.measures.Measure and implement the estimate function, as shown in the template below. The best working example would be frds.measures.ROA.

from typing import List
import numpy as np
import pandas as pd
from frds.measures import Measure
from frds.data import Dataset

DATASETS_REQUIRED = [
    Dataset(source='wrds',
            library="comp",
            table="funda",
            vars=[
                "datadate",
                "gvkey",
                "at",
                "ib",
            ],
            date_vars=["datadate"],
    )
]
VARIABLE_LABELS = {}

class NewMeasure(Measure):
    
    def __init__(self):
        # Note: `name` will be used to name the result dataset. 
        # So this will lead to a `New Measure.dta` in the `result` folder.
        # If the new measure contains custom parameters, please also implement
        # the `__str__()` function to differentiate
        super().__init__(name="New Measure", datasets_required=DATASETS_REQUIRED)

    def estimate(self, nparrays: List[np.recarray]):
        # do something with nparrays and produce a `result` pandas DataFrame
        # ...
        assert isinstance(result, pd.DataFrame)
        return result, VARIABLE_LABELS

Then to estimate NewMeasure:

from frds import Professor

measures = [
    NewMeasure(),
]

config = dict(
        wrds_username="your_wrds_username",
        wrds_password="you_wrds_password",
        result_dir="path/to/where/you/want/to/store/the/result/",
        data_dir="path/to/where/you/want/to/store/the/data/",
    )

if __name__ == "__main__":
    with Professor(config=config) as prof:
        prof.calculate(measures)