I had in mind to cluster stocks based on some risk indicators such as VaR, sharpe ratio or variance. In a first instance I was thinking to cluster those data points and analyze the results, because the idea was to segmentate stocks by risk levels (clusters) assuming that if all variables were related to risk I would be able to assess if cluster 1 corresponds to very low risk, 2 medium-low risk etc... and therefore asign stocks to different risk-acceptance users.
All those thoughts were before realizing that I need to run PCA and data normalization. Here are my questions:
- Should I run PCA before the clustering due to risk indicators are high-correlated, or should I first cluster and finally perform PCA to visualize the clusters?
- I performed PCA to my data before clustering and here is the plot I got: After seeing this my first thought was: ok, if I cluster the data now, how could I analyze the results and see if each cluster represents different risks levels? I do not have the risk indicators anymore instead I have PC1 and PC2. I am somehow convinced that if all data is related to risk, according to distance clustering, the clusters should contain risk related stocks but how to know which risk level have each cluster. This is critical because the main idea is that if the user wants low risk stocks I provide low risk stocks and no any other stock.