The 5-factor model
In 2015, Fama and French extended their previous 3-factor model, adding two additional factors:
- RMW: Profitability
- CMA: Investment
The RMW factor represents the returns of companies with high operating profitability versus those with low operating profitability, and the CMA factor represents the returns of companies with aggressive investments versus those who are more conservative.
The FamaFrenchData object is available in your workspace and contains the RMW and CMA factors in addition to the previous factors.
Diese Übung ist Teil des Kurses
Introduction to Portfolio Risk Management in Python
Anleitung zur Übung
- Use what you've learned from the previous exercises to define the
FamaFrench5_modelregression model forPortfolio_Excessagainst the original 3 Fama-French factors (Market_Excess,SMB,HML) in addition to the two new factors (RMW,CMA). - Fit the regression model and store the results in
FamaFrench5_fit. - Extract the adjusted r-squared value and assign it to
regression_adj_rsq.
Interaktive Übung zum Anfassen
Probieren Sie diese Übung aus, indem Sie diesen Beispielcode ausführen.
# Import statsmodels.formula.api
import statsmodels.formula.api as smf
# Define the regression formula
FamaFrench5_model = smf.ols(formula='Portfolio_Excess ~ Market_Excess + SMB + HML ____ ', data=FamaFrenchData)
# Fit the regression
FamaFrench5_fit = ____
# Extract the adjusted r-squared
regression_adj_rsq = ____
print(regression_adj_rsq)