p-values and coefficients
You can use the .pvalues attribute on a fitted smf.ols regression model to retrieve the p-values for each coefficient.
Normally, p-values less than 0.05 are considered statistically significant.
Coefficients can be extracted from the fitted regression object using the .params attribute.
In this example, a statistically significant negative SMB ('Small Minus Big') coefficient would signify a factor exposure to large cap stocks, while a positive coefficient would signify an exposure to small cap stocks.
The fitted regression model FamaFrench_fit from the previous exercise is available in your workspace.
This is a part of the course
“Introduction to Portfolio Risk Management in Python”
Exercise instructions
- Extract the p-value for
'SMB'. - Extract the regression coefficient for
'SMB'.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Extract the p-value of the SMB factor
smb_pval = FamaFrench_fit.____
# If the p-value is significant, print significant
if smb_pval < 0.05:
significant_msg = 'significant'
else:
significant_msg = 'not significant'
# Print the SMB coefficient
smb_coeff = FamaFrench_fit.____
print("The SMB coefficient is ", smb_coeff, " and is ", significant_msg)