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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?

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    $\begingroup$ the use of returns instead of price data is already a normalization. $\endgroup$ – DataAdventurer Oct 1 '18 at 7:37

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