Characterising Companies Based on Financial Metrics During Covid19. Here, we will dive deep at the first 5 PCs/factors and their respective underlying features.
_Note: All codes are available in the Repo: [https://quanp.readthedocs.io/en/latest/tutorials.html_](https://quanp.readthedocs.io/en/latest/tutorials.html)
Previously, the author performed principle component analysis on the financial metrics for all the S&P500 companies and found that the first 5 PCs carried most of the variance ratio. This article does not intend to replicate the previous work. It is recommended to first read through the previous article before proceeding.
A principal component (dimension) from PCA can be considered as a factor that consists of a space that made up of a set of features. Fundamentally, PCA or a similar analysis, Factor analysis (FA), allows variables that are correlated with one another but largely independent of other subsets of variables to combine as components/ factors. Both PCA and FA summarise patterns of correlations among observed variables and reduce a large number of observed variables (features/dimensions) to a smaller number of components/factors. Frequently, these factors/components analysis produces an operational definition for an underlying processes by using correlation/contributions (loadings) of observed variable in a factor/component (Tabachnick & Fidell, 2013).
Here, we will dive deep at the first 5 PCs/factors and their respective underlying features.
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