Multicollinearity Explained: Impact and Solutions for Accurate Analysis

Key takeaways

• Multicollinearity occurs when two or more independent variables in a regression are highly correlated, making it hard to isolate each variable’s effect.
• The Variance Inflation Factor (VIF) is a common diagnostic: VIF ≈ 1 (no correlation), 1–5 (moderate), >5 (high multicollinearity).
• Solutions include removing or transforming redundant predictors, collecting more diverse data, or using regression methods designed for correlated predictors (ridge regression, principal component regression, partial least squares).
• In investment analysis, avoid using multiple indicators that are derived from the same data/manipulations (e.g., several momentum indicators) to reduce misleading or unstable results.

What is multicollinearity?

In multiple regression, multicollinearity refers to strong linear relationships among independent variables. When predictors are not truly independent, coefficient estimates remain unbiased but become imprecise (large standard errors), reducing statistical significance and interpretability.

Simple example: predicting stock returns using price-to-earnings ratio, market capitalization and past performance. If past performance correlates strongly with market cap, it’s difficult to determine the individual contribution of each predictor.

Why multicollinearity matters

• Inflated variances: Coefficient estimates have larger standard errors, widening confidence intervals.
• Unstable estimates: Small data changes can produce large swings in estimated coefficients.
• Misleading inference: Variables that truly matter may appear insignificant, and sign or magnitude of coefficients may be unreliable.
• For investors, it can produce false confidence in strategies built on redundant indicators.

Types of multicollinearity

• Perfect multicollinearity — exact linear relationship between predictors (correlation ±1). This makes the model non-identifiable.
• High multicollinearity — strong but not exact correlation; estimates are still unstable.
• Structural multicollinearity — arises when new features are derived from the same original data (common in engineered financial indicators).
• Data-based multicollinearity — results from how data were collected or the sample composition (less common with well-collected market data).

How to detect multicollinearity

• Variance Inflation Factor (VIF): compute VIF for each predictor. Interpret generally as:
• VIF ≈ 1: negligible correlation
• 1 < VIF ≤ 5: moderate correlation
• VIF > 5: high multicollinearity (possible concern)
• Correlation matrix: large pairwise correlations point to potential problems.
• Condition number / eigenvalue diagnostics: identify near-linear dependencies among predictors.
• In technical analysis, visually spot indicators that move almost identically (e.g., two momentum indicators based on the same inputs).

Common causes

• Using multiple indicators derived from the same raw data (e.g., several momentum measures from price history).
• Including variables that are mathematical functions of one another.
• Poor study design or limited sample variation.
• Feature engineering that creates redundant predictors.

Impact on investment strategies

• Technical analysts should avoid combining indicators that effectively measure the same thing (e.g., RSI, stochastics, and Williams %R are all momentum indicators and often highly correlated).
• Using redundant indicators can create a false sense of confirmation and reduce the robustness of signals.
• Better practice: combine indicators that capture different dimensions (momentum, trend, volatility, volume) or test analyses separately.

How to address multicollinearity

1. Variable selection
2. Remove the most collinear predictors (based on VIF or domain knowledge).
3. Prefer the variable with clearer interpretability or better theoretical justification.
4. Transform or combine variables
5. Create composite scores or averages, or use ratios that capture unique information.
6. Apply orthogonal transformations (e.g., principal components).
7. Gather more or different data
8. Increasing sample size or adding observations with different conditions can reduce multicollinearity.
9. Use specialized regression methods
10. Ridge regression (L2 penalization): shrinks coefficients and reduces variance from multicollinearity.
11. Principal component regression: regress on principal components rather than original correlated variables.
12. Partial least squares: finds components that explain both predictors and outcome.
13. Re-specify the model
14. Rethink feature construction to avoid deriving multiple predictors from identical inputs.

Practical checklist for analysts and investors

• Before modeling, examine pairwise correlations and compute VIFs.
• Avoid using several indicators of the same type simultaneously in trading rules.
• Prefer parsimonious models—include only predictors that add distinct information.
• If multicollinearity remains, use ridge or principal-component approaches and report that coefficients may be attenuated.
• Validate models out of sample to ensure stability of estimates and signals.

Bottom line

Multicollinearity does not bias coefficient estimates, but it undermines their precision and interpretability. Detect it early (VIF, correlation checks), reduce redundancy in predictors, or apply regression techniques built for correlated inputs. In finance and trading, use diverse indicators and parsimonious models to keep analysis robust and decisions better grounded.