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I have been searching for quite a while on how singular value decomposition is used in analyzing stock price behavior. I know how to perform it on a matrix of stock prices, have the results in python but have no idea on how to interpret the results. Tried searching all of youtube and the net and cant find any explanations.

Note that I am not looking for an explanation of the SVD concept, but how its interpreted for its finance applications.

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    $\begingroup$ Hi, good question. Are you performing SVD on a matrix of stock prices ($N\times K$, N timestamps by K ticker symbols), or are you performing the SVD on stock returns ($N\times K$) or are you performing the SVD on some covariance matrix ($K\times K$) $\endgroup$ Feb 3 at 16:01
  • $\begingroup$ Hey I am performing it on a square matrix that I got by multipying a (N x K) matrix with N timestamps and K ticker symbols by its transpose. Would appreciate any help. $\endgroup$
    – atastix
    Feb 5 at 5:47
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    $\begingroup$ Ah, it is truly a question like this that sends me on a scavenger hunt to learn more... Let me upvote the question just to attract some more scholars who may weigh in onthis... $\endgroup$ Feb 5 at 7:09
  • $\begingroup$ Thanks a ton, much appreciated $\endgroup$
    – atastix
    Feb 5 at 7:11
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An example of typical use of SVD:

Suppose you have a square matrix. You would like to apply Cholesky decomposition. But Cholesky complains that the matrix is not positive definite. So you call SVD instead.

You can then see whether you can tweak the matrix to obtain a positive definite matrix not materially different from your original matrix, and use Cholesky.

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  • $\begingroup$ Thanks for your response. Like I mentioned, I understand how SVD works but I dont know how to interpret the results of SVD performed on a matrix of stock prices/ $\endgroup$
    – atastix
    Feb 5 at 5:49

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