What is multicollinearity?

In a multi-regression model, it is possible to have high inter-correlations among multiple independent variables. We call it “multicollinearity.” Analysts try their best to give the best ideas, and investors try to look for the best pieces of advice. But the best advice does not always come along. Sometimes, researchers and analysts try their best to know how they can effectively use an independent variable to predict or understand dependent variables in a statistic model. But unfortunately, all of these may lead to misleading results. But all in all, multicollinearity can end up in broad confidence intervals that make probabilities that may be less reliable in terms of independent variables’ effect in a model. Furthermore, it may not be a good idea to always depend on statistical interference from models with multicollinearity.

Tell me more about it.

We mentioned analysts earlier. Specifically, statistical analysts try to predict a specified dependent variable’s value based on the values of multiple independent variables. On the other hand, some may think about outcomes, targets, or variable criteria when we say dependent variable. For instance, multivariate regression models try to anticipate shock returns depending on some elements like price-to-earnings ratios or P/E ratios, history, market capitalization, and more. The returns of the stock are the dependent variable. On the other hand, the different bits and pieces of financial data are the independent variables.

If we see multicollinearity in various multiple regression models tells us that collinear independent variables are somehow related. But if this is the case, the relationship can either be casual or not. For instance, market capitalization can involve history or past performance because good-performing stocks in the past already increased in market values. In a sense, we can encounter multicollinearity if two independent variables are extremely correlated. It may exist when one independent variable’s computation is from other variables in the data. It can also be there when two independent variables produce the same results.

How to remove multicollinearity problems

If the question is eliminating multicollinearity problems, the most common answer might be identifying independent variables. When we remove, we remove all but one. Another way is combining two or multiple collinear variables into one variable. One can conduct statistical analysis to know the relationship between the dependent variable and one independent variable.

Let us cite an example.

When we say multicollinearity and investing, we can always think about technical analysis together with them. If we perform technical analysis, we should always consider multicollinearity to predict the potential future price movement of a security. These securities can either be a stock, commodity future and more.

In another perspective, market analysts try their best to avoid collinear technical indicators because they are based on similar related inputs. Furthermore, the tendency is to reveal similar predictions about the price movement’s dependent variable. Instead, the market analysis should depend on markedly independent variables to rest assured that the market analysis is from various independent analytical viewpoints.