Python for Finance - Second Edition

Tests of normality

In finance, knowledge about normal distribution is very important for two reasons. First, stock returns are assumed to follow a normal distribution. Second, the error terms from a good econometric model should follow a normal distribution with a zero mean. However, in the real world, this might not be true for stocks. On the other hand, whether stocks or portfolios follow a normal distribution could be tested by various so-called normality tests. The Shapiro-Wilk test is one of them. For the first example, random numbers are drawn from a normal distribution. As a consequence, the test should confirm that those observations follow a normal distribution:

from scipy import stats import scipy as sp sp.random.seed(12345) mean=0.1 ...