Linear Algebra Quant Interview Guide
Linear algebra quant interview guide covering vectors, matrices, covariance, eigenvalues, PCA, least squares, numerical issues, and examples.
Candidates reviewing the matrix and numerical foundations behind quant modeling questions.
Linear algebra gives models shape
Vectors represent observations, positions, or features, while matrices represent transformations, exposures, covariance, and systems of equations. Always name what rows and columns mean.
Dimensions prevent many errors
Before multiplying or decomposing anything, check dimensions. A formula that is algebraically familiar can be meaningless if the matrix orientation is wrong.
Concrete example
A factor model can write asset returns as factor exposures times factor returns plus residuals. The dimensions identify assets, factors, and time periods.
Numerical caveats matter
Estimated covariance, collinearity, scaling, and finite precision can make clean formulas fragile. Interview answers should mention validation and conditioning.
Common mistakes
Candidates often memorize matrix formulas without interpreting them. Strong answers connect the operation to risk, regression, PCA, or portfolio construction.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.