Statistical tests for normality
In order to truly be confident in your judgement of the normality of the stock's return distribution, you will want to use a true statistical test rather than simply examining the kurtosis or skewness.
You can use the shapiro() function from scipy.stats to run a Shapiro-Wilk test of normality on the stock returns. The function will return two values in a list. The first value is the t-stat of the test, and the second value is the p-value. You can use the p-value to make a judgement about the normality of the data. If the p-value is less than or equal to 0.05, you can safely reject the null hypothesis of normality and assume that the data are non-normally distributed.
clean_returns 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
- Import
shapirofromscipy.stats. - Run the Shapiro-Wilk test on
clean_returns. - Extract the p-value from the
shapiro_resultstuple.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import shapiro from scipy.stats
from ____ import ____
# Run the Shapiro-Wilk test on the stock returns
shapiro_results = ____
print("Shapiro results:", shapiro_results)
# Extract the p-value from the shapiro_results
p_value = ____
print("P-value: ", p_value)This exercise is part of the course
Introduction to Portfolio Risk Management in Python
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Learn about the fundamentals of investment risk and financial return distributions.
Exercise 1: Financial returnsExercise 2: Financial timeseries dataExercise 3: Calculating financial returnsExercise 4: Return distributionsExercise 5: Mean, variance, and normal distributionExercise 6: First moment: MuExercise 7: Second moment: VarianceExercise 8: Annualizing varianceExercise 9: Skewness and kurtosisExercise 10: Third moment: SkewnessExercise 11: Fourth moment: KurtosisExercise 12: Statistical tests for normalityWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.