Summary

In this chapter, we explored unsupervised learning methods that allow us to extract valuable signals from our data without relying on the help of outcome information provided by labels.

We learned how to use linear dimensionality reduction methods like PCA and ICA to extract uncorrelated or independent components from data that can serve as risk factors or portfolio weights. We also covered advanced nonlinear manifold learning techniques that produce state-of-the-art visualizations of complex, alternative datasets. In the second part of the chapter, we covered several clustering methods that produce data-driven groupings under various assumptions. These groupings can be useful, for example, to construct portfolios that apply risk-parity principles to assets that have been clustered hierarchically.

In the next three chapters, we will learn about various machine learning techniques for a key source of alternative data, namely natural language processing for text documents...