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In order to explain systematic risk we use risk factors and I've learned that since they try to explain 'systematic' risk, risk factors are relatively well-known. However, what happens if the risk factors I got from PCA (Statistical Risk Model) turn out to be really different from other PM's risk factors who also used PCA to get risk factors.

Depending on which data to use, and for which period to analyze, it seems likely to have different risk factors according to individual PM.

It seems that if different risk factors try to capture the same concept 'systematic risk', it no longer captures systematic risk anymore since they use 'different' factors.

Is there any part I misunderstand?

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  • $\begingroup$ I think I am not able to follow your question. Could you please clarify and add a bit more detail on what you did and how it differs from others? $\endgroup$ Mar 26, 2021 at 18:21

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There are two kinds of factors.

Named or defined factors are related to observable economic or financial variables, such as FamaFrench HMB, or the market factor or an oil price factor.

Unnamed or statistically identified factors are the result of a PCA using only stock prices. Although the first PCA factor is usually close the Market factor mentioned above (i.e. overall movement of all stocks), the other factors are not easy to describe, and they are sensitive to the time period studied. Basically the PCA algorithm is picking up that some stocks are moving together during this period but cannot tell us why, and in another period the same comovement may not occur. To many, that is a disadvantage of using PCA factors in stock research.

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If I understand correctly your question is:

Question

If I ran PCA and someone else runs PCA and the result is completely different then what good is PCA? Does it make sense to use it for risk management?

Answer

If you would get a different outcome due to small changes in the data it would be bad measure. However, what people (hopefully) found that use PCA in their work is that the method is robust to small changes in the data they use.

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