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Not really. PCA extract components (or factors) using individual items (in this case, each stock). OP points out that those 4 stocks strongly load (large amount
by pacbard 8y ago
Not really. PCA extract components (or factors) using individual items (in this case, each stock). OP points out that those 4 stocks strongly load (large amount of their variation used to calculate the component) on the PCA first component. I would interpret those variances explained more like a way to make sense of the component (maybe stocks of tech companies load on it) rather than a “quality” of extractoin measure. Usually, correlation matrix eigenvalues can be used to get a sense of how well the PCA is performing and how many dimensions are present in the raw data.
- wjn0 8y agoThe idea (ranking stocks by sum of the absolute value of the coefficients) is valid, but that line of code (variance explained by each component) doesn't do that.
- efavdb 8y agoI think your criticism is right. In that line I was thinking about the feature selector, which does pull out 4 of the individual stocks -- these capture more than 50% of the variance in the full set. As you pointed out, that description isn't quite right for the PCA line, which uses four hybrid components.