The assumptions made by PCA

PCA makes several assumptions that are important to keep in mind. These include the following:

  • High variance implies a high signal-to-noise ratio
  • The data is standardized so that the variance is comparable across features
  • Linear transformations capture the relevant aspects of the data
  • Higher-order statistics beyond the first and second moment do not matter, which implies that the data has a normal distribution

The emphasis on the first and second moments aligns with standard risk/return metrics, but the normality assumption may conflict with the characteristics of market data.

..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
18.212.102.174