The summary is available in the notebook to save some space due to the large number of variables. The diagnostic statistics show that, given the high p-value on the Jarque—Bera statistic, the hypothesis that the residuals are normally distributed cannot be rejected.

However, the Durbin—Watson statistic is low at 1.5 so we can reject the null hypothesis of no autocorrelation comfortably at the 5% level. Hence, the standard errors are likely positively correlated. If our goal were to understand which factors are significantly associated with forward returns, we would need to rerun the regression using robust standard errors (a parameter in statsmodels .fit() method), or use a different method altogether such as a panel model that allows for more complex error covariance.