Lasso Regression

Lasso regression is a linear model that performs both variable selection and regularization to enhance prediction accuracy. It adds a penalty equivalent to the absolute value of the magnitude of coefficients, which can shrink some coefficients to exactly zero.

This effectively performs feature selection, identifying the most important drivers of asset price movement. In complex derivative markets, this helps simplify models by removing irrelevant or noisy variables.

It is highly effective for high-dimensional datasets where many potential indicators exist.