First post, hope I'm explaining things sufficiently clearly. I want to take a universe of potential, trade-able instruments and allocate them to portfolio managers. Traditionally, this is done using a sector classification, such as GICS. When PMs submitted their owned proposed universes they obviously had their own sector classifications which differed from GICS which made me think: 1. Is GICS the best way of organizing things (which I've always concluded that it's not ideal but better than all the other methods)? 2. Is there a statistical way to take all the instruments within our universe and do a cluster analysis and make a dendrogram and then compare the clustering to both GICS and the PM's personally chosen subsets? This would also help for instances of overlap of chosen subsets, as it would help to make the decision as to who should get the overlapping instruments. 3. I created a correlation matrix, and converted it to distances using d^2 = 2(1-|r|) I'm trying to figure out the next step and I'm having a few issues. Euclidean distances are comparable for each node calculation, but it seems like each subsequent node needs a new matrix and new distances as the combination of a prior node represents an additional row/column to add to the original matrix. Since distances are not additive due to lack of orthogonality.
Any ideas and code suggestions for doing something in python would be greatly appreciated.