Stock Return Prediction

This lecture discusses stock return prediction using supervised learning techniques. It covers challenges such as low signal-to-noise ratio, few observations, and the moving target problem. The goal is to approximate conditional expected returns by mapping observable asset-level characteristics into expectations of returns. The lecture also explains the use of ridge regression with leave-one-year-out cross-validation to predict stock returns. Important coefficients for past returns, squared past returns, and third power of past returns are analyzed. The lecture concludes with remarks on the predictive performance of the model and challenges ahead in maximizing portfolio performance.