We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the...

AI/ML models are used for many financial applications ranging from portfolio selection to efficient credit allocation. However, the drawback to applying these models in practice is that performance (i.e., predictive power) is generally inversely related to model complexity. In this chapter, we...