In this chapter, we introduced the first 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 look at the important topic of linear time series models that are designed to capture serial correlation patterns in the univariate and multivariate case. We will also learn about new trading strategies as we explore pairs trading based on the concept of cointegration that captures dynamic correlation among two stock price series.