I built risk models using cluster analysis in a previous life. Years ago I learned about principal component analysis and I've often wondered whether that would have been more appropriate. What are the pros and cons of using PCA as opposed to clustering to derive risk factors?
If it makes a difference, I'm using the risk models for portfolio optimization, not performance attribution.
Answer
I've played around with both schemes, but not for portfolio optimization.
I used PCA on some interest rate models. That turned into a Partial Least Squares scheme, then into some non-linear thing. I wasn't impressed with the results.
My Cluster Analysis scheme morphed into a classification scheme, and it turned out that the K-Nearest-Neighbor method worked just as well, and possibly better. Again, this wasn't for portfolio optimization, so it may not apply to your situation.
From what I've seen, if you're depending on the computational method to find excess returns (or lower risk), you'll probably be disappointed. On the other hand, it is common for various methods to highlight some problems that weren't originally obvious. For instance, bootstrapping your portfolio(s) to determine just how good they are compared to luck. I've dumped a lot of ideas because of that issue.
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