Multicollinearity

What is 'Multicollinearity'

Multicollinearity refers to a situation where a number of independent variables in a multiple regression model are closely correlated to one another. Multicollinearity can lead to skewed or misleading results when a researcher or analyst is attempting to determine how well each one of a number of individual independent variables can most effectively be utilized 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.

BREAKING DOWN 'Multicollinearity'

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 also sometimes referred to as the outcome, target or criterion variable. Multicollinearity in a multiple regression model indicates that the collinear independent variables are related in some fashion, although the relationship may or may not be a causal relationship.

One of the most common ways of eliminating the problem of multicollinearity in a study is to first identify collinear independent variables and remove all collinear variables until there is only one remaining. It is also sometimes 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.

Multicollinearity in Investing

For investing, multicollinearity is a common consideration in doing technical analysis to predict probable future price movements of a security, such as a stock or commodity future. Market analysts want to avoid using technical indicators that are collinear in that they are based on very similar or related inputs, so they tend to reveal similar predictions regarding the dependent variable of price movement. Instead, they want to do 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 potential multicollinearity problems would be doing 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.