The EigenCovarMat3.lng Model

Eigenvalues/vectors of a covariance matrix

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Compute the eigenvalues/vectors of a covariance matrix.(EigenCovarMat3.lng)
Alternatively, do Principal Components Analysis.
If there is a single large eigenvalue for the covariance
matrix, then this suggests that there is a single factor,
e.g., "the market" that explains all the variability;

Some things to note,

1)In general, given a square matrix A, then we try to find
an eigenvalue, lambda, and its associated eigenvector X,
to satisfy the matrix equation:

A*X = lambda*X.

2) the sum of the eigenvalues = sum of the terms on the diagonal
of the original matrix, the variances if it is a covariance
matrix.

3) the product of the eigenvalues = determinant of the original matrix.

4) A positive definite matrix has all positive eigenvalues;

Keywords:

Principal Components Analysis | PCA | Eigenvalue | Covariance | Singular Value Decomposition |