Principal Component Analysis (PCA) on Stock Returns in R
Principal Component Analysis is a statistical process that distills measurement variation into vectors with greater ability to predict outcomes utilizing a process of scaling, covariance, and eigendecomposition.
MS Azure Notebook
The work for this is done in the following notebook, Principal Component Analysis (PCA) on Stock Returns in R
, with detailed code, output, and charts. An outline of the notebook contents are below.
Overview of Demonstration
- Supporting Material
- Load Data: Format Data & Sort
- Prep Data: Create Returns
- Eigen Decomposition and Scree Plot
- Create Principal Components
- FVX using PCA versus Logistic Regression
- Alternative Libraries: Psych for the Social Sciences