how can one deal with multicollinearity 
to reduce the amount of multicollinearity found in a statistical model one can remove the specific variables identified as the most collinear you can also try to combine or transform the offending variables to lower their correlation if that does not work or is unattainable there are modified regression models that better deal with multicollinearity such as ridge regression principal component regression or partial least squares regression in stock analysis the best method is to choose different types of indicators 