Multicollinearity is a Statistical Term for a Problem that is Common in Technical Analysis

Multicollinearity is a statistical term for a problem that is common in technical analysis. That is, when one unknowingly uses the same type of information more than once. Analysts need to be careful and not utilize technical indicators that reveal the same type of information.

Here is how John Bollinger states it: “A cardinal rule for the successful use of technical analysis requires avoiding Multicollinearity amid indicators. Multicollinearity is simply the multiple counting of the same information. The use of four different indicators all derived from the same series of closing prices to confirm each other is a perfect example.”

The issue of Multicollinearity is a serious issue in technical analysis when your money is at stake. It is a problem because collinear variables contribute redundant information and can cause other variables to appear to be less important than they really are. One of the real problems is that sometimes Multicollinearity is difficult to even spot.

Technical indicators should be arranged in categories to keep from using too many from the same category.
Here is a table that categorizes the indicators:

Bottom Line: If you are randomly selecting indicators to support your analysis, you will more than likely fall into the multicollinearity trap of using multiple indicators that are all saying the same thing. They are not giving you any additional information; in fact, they are restricting your overall view of the market. Don’t search for supporting information among collinear indicators, it is just misleading.

Be very careful while picking up your charts for Technical Analysis.

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Tagged as: Multicollinearity, Technical Analysis, Technical Indicators