Abstract
In investment management, multifactor risk modeling is the most common application of financial modeling. Multifactor risk models, or simply factor models, are linear regressions over a number of variables called factors. Factors can be exogenous variables or abstract variables formed by portfolios. Exogenous factors (or known factors) can be identified from traditional fundamental analysis or economic theory from macroeconomic factors. Abstract factors, also called unidentified or latent factors, can be determined with factor analysis or principal component analysis. Principal component analysis identifies the largest eigenvalues of the variance-covariance matrix or the correlation matrix. The largest eigenvalues correspond to eigenvectors that identify the entire market and sectors that correspond to industry classification. Factor analysis can be used to identify the structure of the latent factors. Keywords: Factor models; Linear factor models; factor loadings; static models; dynamic; normal factor model; Principal components analysis; principal components; factor analysis; expectation maximization
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