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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;