What is a possible drawback of using R2 for comparing different models with the same dependent variable and different number of explanatory variables? Why using adjusted R2 for model evaluation may be better?
R-squared does not measure goodness of fit. R-squared does not measure predictive error. R-squared does not allow you to compare models using transformed responses. R-squared does not measure how one variable explains another.
Many investors prefer adjusted R-squared because adjusted R-squared can provide a more precise view of the correlation by also taking into account how many independent variables are added to a particular model against which the stock index is measured.
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