Principal Components Analysis (SPSS): Stock Price Discount/Premiums

In my experience, most researchers are swimming in enormous amounts of data, and the biggest challenge they face is to reduce both the number of variables and the number of analyses into a coherent and relatively concise story. Analyses such as principal component's analysis and factor analysis (PCA and FA) are great techniques for just such a purpose. They help reduce the data into a smaller and arguably more meaningful set of components/factors. Then, with these components/factors, hypotheses can be tested, as they can be used as either independent variables and/or dependent variables (i.e., if component/factor scores are generated).

I recently applied PCA to a set of variables, which consisted of the percentage discount/premium associated with nine listed investment companies. Nine variables is not exactly swimming in data, I know, but PCA/FA can still be a very useful tool to help uncover patterns within a correlation matrix. Essentially, I wanted to know whether the discount/premium associated with these nine LICs could be accounted for by a single component. Based on the results of the analyses, the answer was a clear 'No'. There appears to be two nearly totally orthogonal (uncorrelated) components. I found this interesting for a variety of reasons. One of the most obvious is that despite the fact that two LICs may have very similar underlying portfolios, their share price may not necessarily correlate with each other very strongly. This implies that selecting a LIC from each of the two components may be considered a more diversified portfolio than selecting two LICs that load onto the same component. 

Why it is that these two components exist remains an interesting question. I've got some speculative hypotheses. Ultimately, though, the results appear to be relatively robust and may help serve to generate some interesting trading strategies (i.e., for retail traders; these stocks are relatively too illiquid for any large scale trading).