Multicollinearity Explained: Impact and Solutions for Accurate Analysis

What Is Multicollinearity?

Multicollinearity poses challenges when multiple regression models have independent variables that are highly correlated. This can skew analysis and produce unreliable results. Detecting multicollinearity involves using methods like the Variance Inflation Factor (VIF), which helps ensure more accurate statistical analysis and informed investment decisions.

Understanding the Basics of 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 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 relate to market capitalization. Well-performing companies often boost investor confidence, leading to higher demand and increased market value.

Impact of Multicollinearity on Regression Models

While multicollinearity doesn't change regression estimates, it makes them vague and unreliable, complicating the assessment of individual variable effects and inflating standard errors.

Detecting Multicollinearity in Data Sets  

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.

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.

Factors Leading to Multicollinearity in Regression

Multicollinearity occurs when independent variables are highly correlated or when derived variables yield similar 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.

Types of Multicollinearity Explained 

Perfect Multicollinearity

Perfect multicollinearity is when variables have an exact linear relationship, shown by data points on a regression line. In technical analysis, it appears when using identical indicators, like volume, with no distinguishable difference.

High Multicollinearity

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

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.

Data-Based Multicollinearity

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's Impact on Investment Strategies

For 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.

Analysts should use different indicators to ensure independent market analysis. For example, while momentum and trend indicators use similar data, they don't show perfect multicollinearity and yield different results due to varied data manipulation.

Effective Solutions for Multicollinearity Challenges

One of the most common ways of eliminating the problem of multicollinearity is first to identify collinear independent predictors and then remove one or more of them. Generally, in statistics, a variance inflation factor calculation is run to determine the degree of multicollinearity. An alternative method for fixing multicollinearity is to collect more data under different conditions.

In Investment Analysis

Noted technical analyst John Bollinger, creator of the Bollinger Bands indicator, wrote that a "cardinal rule for the successful use of technical analysis requires avoiding multicollinearity amid indicators." To solve the problem, 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 a separate analysis using a different type of indicator, such as a trend indicator.

For example, stochastics, the relative strength index (RSI), and Williams %R (Wm%R) are all momentum indicators that rely on similar inputs and are likely to produce similar results. In the image above, the stochastics and Wm%R are the same, so using them together doesn't reveal much. In this case, it is better to remove one of the indicators and use one that isn't tracking momentum. In the image below, stochastics show price momentum, and the Bollinger Band Width shows price consolidation before price movement.

The Bottom Line

Multicollinearity is a common issue in regression models when independent variables are highly correlated, which can lead to unreliable statistical inferences. To identify and address multicollinearity, analysts can use the variance inflation factor to pinpoint and remove redundant variables that inflate the variance. In technical analysis, to avoid the pitfalls of multicollinearity, it's crucial to use diverse indicators that capture different trends and don't duplicate data representation. Employing strategies such as removing collinear variables or using alternative regression models like ridge regression enhances the accuracy and reliability of the analysis, leading to more informed decision-making in finance and investment.