Background: I'm working with log returns for about 400 tech stocks. I want to use PCA to reduce these into principal components (Internet companies, software developers, circuit board manufacturers, etc), which then are to be used as sector-related indices. Parallel analysis is my method of determining how many factors to extract in the first place. Rotation is done via Oblimin with Kaiser Normalization in SPSS.

Questions: How do I determine which variables need to be trimmed? Those with low communality? Or should I look at the pattern matrix and start cutting variables which happen to load on multiple factors? After that, can I use the figures in the pattern matrix to weight each stock's contribution to each component's variance? For example, if the rotation yields .71 for Stock A and .10 for Stock B, I could multiply the return for each stock in each period, T, by its respective weight

Background: I'm working with log returns for about 400 tech stocks. I want to use PCA to reduce these into principal components (Internet companies, software developers, circuit board manufacturers, etc), which then are to be used as sector-related indices. Parallel analysis is my method of determining how many factors to extract in the first place. Rotation is done via Oblimin with Kaiser Normalization in SPSS.

Questions: How do I determine which variables need to be trimmed? Those with low communality? Or should I look at the pattern matrix and start cutting variables which happen to load on multiple factors? After that, can I use the figures in the pattern matrix to weight each stock's contribution to each component's variance? For example, if the rotation yields .71 for Stock A and .10 for Stock B, I could multiply the return for each stock in each period, T, by its respective weight.