In this chapter, we were introduced to PCA as a dimension reduction technique in portfolio modeling. By breaking down the movement of asset prices of a portfolio into its principal components, or common factors, the most useful factors can be kept, and portfolio analysis can be greatly simplified without compromising on computational time and space complexity. In applying PCA to the Dow and its thirty components using the KernelPCA function of the sklearn.decomposition module, we obtained eigenvectors and eigenvalues, which we used to reconstruct the Dow with five components.
In the statistical analysis of time series data, the data is considered as either stationary or non-stationary. Stationary time series data is data whose statistical properties are constant over time. Non-stationary time series data has its statistical properties change over time, most likely due...
