I am looking to use a multi-factor model to make target-return predictions. Since the factor-returns come from different scales I need to normalize first.
There are different ways to normalize returns, to mention a few: subtract mean and divide by standard deviation(assumes non-zero drift), simply divide by standard deviation, divide by euclidian norm, divide by the standard deviation of highs/lows.
My question: is there a documented comparison of the different methods, on noisy time-series data such as market returns, stating the pros/cons of different methods? Empirically do you have any suggestions and remarks?