Ernest Chan Presents Conditional Portfolio Optimization for Regime-Aware Allocation

Ernest Chan presented Conditional Portfolio Optimization (CPO), a machine-learning method that trains a neural network to predict portfolio performance based on market features and then solves a constrained optimization to allocate capital accordingly. Backtests reported by PredictNow.ai and in an accompanying SSRN paper showed CPO outperforming traditional unconditional optimization across multiple markets and portfolio sets. The approach, detailed in a blog post and an NYU Mathematical Finance seminar, represents a formal method for regime-aware allocation that conditions portfolio weights on prevailing market conditions.