Multi-Hypothesis Prediction for Portfolio Optimization: A Structured
Ensemble Learning Approach to Risk Diversification
Multi-Hypothesis Prediction for Portfolio Optimization: A Structured
Ensemble Learning Approach to Risk Diversification
A framework for portfolio allocation based on multiple hypotheses prediction using structured ensemble models is presented. Portfolio optimization is formulated as an ensemble learning problem, where each predictor focuses on a specific asset or hypothesis. The portfolio weights are determined by optimizing the ensemble's parameters, using an equal-weighted portfolio as …