Regimes Count: Regime-Based Dynamic Asset Allocation Using Neural Networks

Thomas M. Menino Convention & Exhibition Center 

We study the dynamic optimal portfolio allocation problem in a market responsive to macroeconomic regimes, as characterized by VIX. Unlike the traditional approach, we train an artificial neural network (ANN) to learn the optimal allocation as a feedback function. Our regime-specific strategy, benchmarked against the classical Merton portfolio, shows superior performance subject to realistic diversification constraints with borrowing/short selling excluded. A 35-year backtest (1990-2024), including 17 out-of-sample years, on a diversified portfolio of twelve assets plus cash, reveals that accounting for regime shifts improves both expected utility and average returns. Our approach can be applied to other types of regime models, as well as other models generally that strive to optimize investors' utility. It can also be used to optimize other metrics such as the Sharpe ratio or Sortino ratio, which are favored by many practitioners.

Merton's problem

portfolio optimization

market regimes

deep learning

artificial neural network

multi-layer perceptron 

Business and Economic Statistics Section