when analyzing stocks you can detect multicollinearity by noting whether the indicators graph the same for instance choosing two momentum indicators on a trading chart will generally create trend lines that indicate the same momentum 
reasons for multicollinearitymulticollinearity can exist when two independent variables are highly correlated it can also happen if an independent variable is computed from other variables in the data set or if two independent variables provide similar and repetitive results again if you re using the same data to create two or three of the same type of trading indicators the outcomes will be multicollinear because the data and its manipulation to create the indicators are very similar the statistical inferences from a model that contains multicollinearity may not be dependable types of multicollinearityperfect multicollinearity demonstrates a linear relationship that is exact between multiple independent variables this is usually seen on a chart where the data points fall along the regression line in technical analysis it can be seen when you use two indicators that measure the same thing such as volume if you overlaid one on top of the other there would be no difference between them high multicollinearity demonstrates a correlation between multiple independent variables but it is not as tight as in perfect multicollinearity not all data points fall on the regression line but it still signifies data is too tightly correlated to be used in technical analysis indicators with high multicollinearity have very similar outcomes structural multicollinearity occurs when you use data to create new features for instance if you collected data and then used it to perform other calculations and ran a regression on the results the outcomes will be correlated because they are derived from each other this is the type of multicollinearity seen in investment analysis because the same data is used to create different indicators a poorly designed experiment or data collection process such as using observational data generally results in data based multicollinearity where data is correlated due to the nature of the way it was collected some or all of the variables are correlated stock data used to create indicators is generally collected from historical prices and trading volume so the chances of it being multicollinear due to a poor collection method are small multicollinearity in investingfor investing multicollinearity is a common consideration when performing 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 the inputs referred to here are not the data itself but how it was manipulated to achieve the outcome instead the analysis must be based on markedly different indicators to ensure that the market is analyzed from independent analytical viewpoints for example momentum and trend indicators share the same data but they will not be perfectly multicollinear or even demonstrate high multicollinearity these two indicators have different outcomes based on how the data was manipulated most investors won t worry about the data and techniques behind the indicator calculations it s enough to understand what multicollinearity is and how it can affect an analysis 