Summary

In this chapter, we introduced the first of our machine learning models using the important baseline case of linear models for regression and classification. We explored the formulation of the objective functions for both tasks, learned about various training methods, and learned how to use the model for both inference and prediction.

We applied these new machine learning techniques to estimate linear factor models that are very useful to manage risks, assess new alpha factors, and attribute performance. We also applied linear regression and classification to accomplish the first predictive task of predicting stock returns in absolute and directional terms.

In the next chapter, we will put together what we have covered so far in the form of the machine learning for trading workflow. This process starts with sourcing and preparing the data about a specific investment universe and the computation of useful features, continues with the design and evaluation of machine...