Multicollinearity

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other. In finance, this is a common issue because many market indicators, such as moving averages or volatility measures, often move in tandem.

When multicollinearity is present, it becomes difficult for the model to isolate the individual effect of each variable on the target outcome. This can lead to unstable coefficient estimates and unreliable predictions.

To fix this, traders often use regularization or dimensionality reduction to remove redundant variables. Managing multicollinearity is essential for maintaining the stability and reliability of quantitative trading strategies.