
actually I want to do nearly the same. My idea is to cluster the whole set for example with the k-medoids algorithm.The only problem is to determine the right number of clusters. But there is a very strong method, it's called jumpmethod. The underlying idea is the shannon information. You can read for example"mining the stock market: which measure is best", Gavrilov,...and about the jump method:"finding the number of clusters in a data set - an information theoretic approach", Sugar and James.
Is that something, you're looking for? 
regardkonrad

> Date: Fri, 4 Dec 2009 10:16:37 -0500
> From: jorge.nieves@moorecap.com
> To: r-sig-finance@stat.math.ethz.ch
> Subject: [R-SIG-Finance] Portfolio optimization & PCA
> 
> Hi,
> 
> I have a portfolio of securities that I like to determine the optimal
> asset allocation using something simple as Markowitz (Mean-Variance). I
> have the problem that some of the securities in the portfolio are highly
> correlated. I would like to reduce my security set, or covariance matrix
> before running the optimization, using something like PCA or clustering.
> I was wondering if someone has done any work that might shed some like
> on the process.? Is there piece of literature that some one can
> recommend?
> 
> Thanks,
> 
> Jorge Nieves
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-SIG-Finance@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only.
> -- If you want to post, subscribe first.
 		 	   		  
_________________________________________________________________

Nie mehr um Weihnachtsgrüße verlegen: mit tollen Weihnachts-E-Mails 
	[[alternative HTML version deleted]]

