PCA Linear Algebra Quant Interview Guide
PCA linear algebra quant interview guide for centering, covariance, eigenvectors, components, explained variance, factors, and caveats.
Candidates explaining principal components in risk, returns, and machine learning contexts.
PCA finds variance directions
PCA identifies orthogonal directions that explain variance in centered data. In returns, those directions can resemble common movements but need interpretation.
Centering and scaling matter
PCA results depend on preprocessing. Centering, standardization, universe selection, and time window can materially change components.
Concrete example
Applying PCA to yield changes may produce components resembling level, slope, and curvature, but the labels are interpretations rather than guaranteed truths.
Explained variance is not causality
A component can explain variance without representing an economic cause. Stability and out-of-sample usefulness should be checked.
Common mistakes
Candidates often call the first principal component the market factor automatically. A stronger answer explains the math and then validates the interpretation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.