Multicollinearity

Multicollinearity is the occurrence of high intercorrelations among independent variables in a multiple regression model.  Multicollinearity can lead to skewed or misleading results when a researcher or analyst attempts to determine how well each independent variable can be used most effectively to predict or understand the dependent variable in a statistical model. In general, multicollinearity can lead to wider confidence intervals and less reliable probability values (P values) for the independent variables.

Statistical analysts use multiple regression models to predict the value of a specified dependent variable based on the values of two or more independent variables. The dependent variable is sometimes referred to as the outcome, target, or criterion variable. Multicollinearity in a multiple regression model indicates that collinear independent variables are related in some fashion, although the relationship may or may not be casual.

One of the most common ways of eliminating the problem of multicollinearity in a study is to first identify collinear independent variables and then remove all but one. It is also possible to eliminate multicollinearity by combining two or more collinear variables into a single variable. Statistical analysis can then be conducted to study the relationship between the specified dependent variable and only a single independent variable.

For investing, multicollinearity is a common consideration when performing a technical analysis to predict probable future price movements of a security, such as a stock or a commodity future. Market analysts want to avoid using technical indicators that are collinear in that they are based on very similar or related inputs; they tend to reveal similar predictions regarding the dependent variable of price movement. Instead, they want to perform a market analysis based on markedly different independent variables that refer to various technical indicators to ensure that they analyze the market from different independent analytical viewpoints.

Noted technical analyst John Bollinger, creator of the Bollinger Bands indicator, notes that "a cardinal rule for the successful use of technical analysis requires avoiding multicollinearity amid indicators."

To avoid the problem of multicollinearity, analysts avoid using two or more technical indicators of the same type. Instead, they analyze a security using one type of indicator, such as a momentum indicator, and then do separate analysis using a different type of indicator, such as a trend indicator. An example of a potential multicollinearity problem is performing technical analysis only using several similar indicators, such as stochastics, the relative strength index (RSI), and Williams %R, which are all momentum indicators that rely on similar inputs and are likely to produce similar results.