Summary
In this chapter, we introduced how unsupervised learning leverages deep learning. Autoencoders learn sophisticated, nonlinear feature representations that are capable of significantly compressing complex data while losing little information. As a result, they are very useful to counter the curse of dimensionality associated with rich datasets that have many features, especially common datasets with alternative data. We also saw how to implement various types of autoencoders using TensorFlow 2.
Most importantly, we implemented recent academic research that extracts data-driven risk factors from data to predict returns. Different from our linear approach to this challenge in Chapter 13, Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning, autoencoders capture nonlinear relationships. Moreover, the flexibility of deep learning allowed us to incorporate numerous key asset characteristics to model more sensitive factors that helped predict returns.
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