Elastic Net

Elastic Net is a regularized regression method that linearly combines the L1 and L2 penalties of Lasso and Ridge methods. This hybrid approach allows for both feature selection and coefficient shrinkage, making it extremely versatile.

It is particularly effective when there are multiple correlated features in a dataset, which often occurs in market microstructure data. By balancing the strengths of both methods, Elastic Net provides a more robust model that can handle complex relationships in financial data.

It is a standard tool for building stable trading signals.