How can Principal Component Analysis (PCA) enhance financial forecasting accuracy in a dataset with multiple correlated variables?

PCA turns many correlated financial variables into a few independent components, so the data become simpler to model. Because the new components are uncorrelated, forecasting models can focus on real signals instead of being misled by multicollinearity. The first few components usually capture most of the variation, letting you use fewer variables while keeping the essential information. For example, if a portfolio has daily returns for ten stocks that move together, PCA might produce one component that represents the overall market trend and another that captures sector‑specific movement. Using these components instead of the original ten stock returns in a forecast reduces overfitting and improves prediction accuracy.