Supervised Learning in Asset Pricing

This lecture covers the application of supervised learning in asset pricing, focusing on stock return prediction. It discusses challenges such as low signal-to-noise ratio and few observations, and explains the goal of approximating conditional expected returns using linear functions. The lecture also explores the use of ridge regression with cross-validation and analyzes coefficients for past returns. It delves into the importance of model assessment, the predictive performance of the model, and the comparison of different classification methods like logistic regression and decision trees.