পদ্ধতির তুলনা করুন

	Principal Component Risk Factors	ফ্যাক্টর অ্যানালাইসিস (Factor Analysis)
ক্ষেত্র	অর্থায়ন	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	1991	1931
প্রবর্তক	Litterman & Scheinkman (bond-return factors); Connor & Korajczyk (statistical APT factors)	Louis Leon Thurstone
ধরন	Statistical factor model (dimension reduction)	Method
মৌলিক উৎস	Litterman, R. & Scheinkman, J. (1991). Common Factors Affecting Bond Returns. Journal of Fixed Income, 1(1), 54-61.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.
অপর নাম	risk factor PCA, return covariance decomposition, statistical factor model, Risk Faktörü PCA (Getiri Kovaryans Ayrışımı)	EFA, CFA, latent variable modeling
সম্পর্কিত	5	3
সারসংক্ষেপ	Risk Factor PCA is a dimension-reduction method that decomposes the return covariance matrix of many assets into a small set of orthogonal principal components interpreted as systematic risk factors. Litterman and Scheinkman (1991) used it to show that bond returns are driven by a few common factors, and Connor and Korajczyk (1988) developed the statistical-factor interpretation for the APT.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.