Fit a Fama-MacBeth (1973) two-pass cross-sectional regression: asset_tickers is treated as the cross-section of test assets, factor_tickers as the risk factor(s) whose risk premia are estimated – the standard procedure for testing whether a proposed risk factor is actually priced.

Also known as: two-pass regression, Fama-MacBeth procedure.

For more information about the method, see fama_macbeth_model.get_fama_macbeth_regression.

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Calculate the Fama-MacBeth Regression in Python

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

pip install financetoolkit -U

Then call get_fama_macbeth_regression as shown below.

from financetoolkit import Toolkit

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

# Benchmark (the default factor) is the single risk factor; AAPL and MSFT
# are the test assets. add_constant=False since only 2 assets are available.
toolkit.econometrics.get_fama_macbeth_regression(
    period="weekly", add_constant=False
)

Which returns:

  Risk Premium Std. Error t-Statistic P-Value
Benchmark 0.0032 0.0016 1.9798 0.0486

Parameters

get_fama_macbeth_regression accepts the following parameters:

  • factor_tickers (str | list[str] | None, optional): The ticker(s) whose returns are used as the risk factor(s) (e.g. “Benchmark” for a single-factor/CAPM-style test). Defaults to None, meaning ["Benchmark"].
  • asset_tickers (str | list[str] | None, optional): The ticker(s) forming the cross-section of test assets. Defaults to None, meaning every Toolkit ticker (including “Benchmark”) not already used as a factor.
  • period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
  • column (str, optional): The historical data column to regress on. Defaults to “Return”.
  • add_constant (bool, optional): Whether to include an intercept in the second-pass cross-sectional regression. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Ordinary Least Squares (OLS) Weighted Least Squares (WLS) Generalised Least Squares (GLS) Logistic Regression Probit Regression Quantile Regression

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

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