Probit Regression

Fit a Probit Regression of whether dependent_ticker’s return is positive on independent_tickers.

Also known as: probit model.

For more information about the method, see regression_model.get_probit_regression.

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Calculate the Probit Regression in Python

The Probit Regression is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_probit_regression as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

# AAPL (the first ticker) is dependent; MSFT and Benchmark are independent
toolkit.econometrics.get_probit_regression(
    independent_tickers=["MSFT", "Benchmark"], period="weekly"
)

Which returns:

Parameters

get_probit_regression accepts the following parameters:

• dependent_ticker (str | None, optional): The dependent asset (whose up/down direction is predicted). Defaults to None, meaning the Toolkit instance’s first ticker.
• independent_tickers (str | list[str] | None, optional): The independent (predictor) asset(s). Defaults to None, meaning every other ticker in the Toolkit instance besides dependent_ticker.
• include_benchmark (bool, optional): Whether to include “Benchmark” in the default independent ticker(s) (has no effect when independent_tickers is given explicitly). Defaults to False.
• period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
• column (str, optional): The historical data column to derive returns from. Defaults to “Return”.
• add_constant (bool, optional): Whether to include an intercept. Defaults to True.
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Regression

The Econometrics module page introduces the module, and the sidebar lists all of its functions.