Characterising Companies Based on Financial Metrics During Covid19

Characterising Companies Based on Financial Metrics During Covid19

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.

principal-component data-science stock-market factor-analysis finance

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