This expository post lined up the 6 stocks and computed the SVD of the time history of all 6 together. This shows how the 6 stocks correlate. You can do it an

This expository post lined up the 6 stocks and computed the SVD of the time history of all 6 together. This shows how the 6 stocks correlate. You can do it another way. Run a sliding window across one single stock, line up all the resulting vectors, and then take the SVD of (err...apply PCA to) that. That is, if you started with a single-stock time history: x1, x2, x3... then form: z1 = [x1 x2 x3] z2 = [x2 x3 x4] z3 = [x3 x4 x5] etc., and use PCA on the z's instead of the x's. (In practice, you'd make the z's much longer.) This will extract seasonable variability (on all kinds of scales -- not just annual). One name for it is Singular Spectrum Analysis (http://en.wikipedia.org/wiki/Singular_spectrum_analysis http://en.wikipedia.org/wiki/Singular_spectrum_analysis)