Tuesday, October 3rd @ 11:00-12:30 PM
The Bayes-Stein model is widely used to tackle parameter uncertainty in the classical Markowitz mean-variance portfolio optimization framework. In practice, however, it suffers from estimation errors and often fails to outperform the naive 1/N asset allocation rule. To address this, we develop a generalized counterpart that leverages machine learning (ML) techniques to estimate some core model parameters. Specifically, we propose a time-dependent weighted Elastic Net (TW-ENet) approach predicting expected asset returns, a hybrid double selective clustering combination (HDS-CC) strategy calibrating shrinkage factors, and a graphical adaptive Elastic Net (GA-ENet) algorithm estimating the inverse covariance matrix. Extensive empirical studies show that the ML-augmented model leads to significant and persistent out-of-sample gains over the 1/N strategy. More broadly, our work demonstrates how machine learning can be leveraged to overcome longstanding limitations and unlock value in conventional finance models.