The family of linear models represents one of the most useful hypothesis classes. Many learning algorithms that are widely applied in algorithmic trading rely on linear predictors because they can be efficiently trained in many cases, they are relatively robust to noisy financial data, and they have strong links to the theory of finance. Linear predictors are also intuitive, easy to interpret, and often fit the data reasonably well or at least provide a good baseline.
Linear regression has been known for over 200 years when Legendre and Gauss applied it to astronomy and began to analyze its statistical properties. Numerous extensions have since adapted the linear regression model and the baseline ordinary least squares (OLS) method to learn its parameters:
- Generalized linear models (GLM) expand the scope of applications by allowing for response variables that imply...
