what is multicollinearity 
multicollinearity is the occurrence of high intercorrelations among two or more 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 that produce less reliable probabilities in terms of the effect of independent variables in a model in technical analysis multicollinearity can lead to incorrect assumptions about an investment it generally occurs because multiple indicators of the same type have been used to analyze a stock understanding multicollinearitystatistical 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 called the outcome target or criterion variable an example is a multivariate regression model that attempts to anticipate stock returns based on metrics such as the price to earnings ratio p e ratios market capitalization or other data the stock return is the dependent variable the outcome and the various bits of financial data are the independent variables multicollinearity in a multiple regression model indicates that collinear independent variables are not truly independent for example past performance might be related to market capitalization the stocks of businesses that have performed well experience investor confidence increasing demand for that company s stock which increases its market value although multicollinearity does not affect the regression estimates it makes them vague imprecise and unreliable thus it can be hard to determine how the independent variables influence the dependent variable individually this inflates the standard errors of some or all of the regression coefficients a statistical technique called the variance inflation factor vif can detect and measure the amount of collinearity in a multiple regression model vif measures how much the variance of the estimated regression coefficients is inflated as compared to when the predictor variables are not linearly related a vif of 1 will mean that the variables are not correlated a vif between 1 and 5 shows that variables are moderately correlated and a vif between 5 and 10 will mean that variables are highly correlated 