The Markowitz portfolio framework is broadly used to find out static asset weights, whereas Merton’s dynamic method permits allocations to regulate with altering market situations however is mathematically difficult and fewer sensible. We handle this hole by making use of machine studying to dynamic portfolio optimization within the spirit of Merton, incorporating financial regimes outlined by the VIX volatility index. A man-made neural community is skilled to study optimum allocation insurance policies throughout regime-switching environments and is in contrast with classical regime-agnostic and theoretical regime-switching Merton methods. On artificial knowledge with life like constraints prohibiting borrowing and quick promoting, the machine studying technique outperforms conventional benchmarks. Two empirical backtests—utilizing month-to-month knowledge from 1990 to 2025 and annual knowledge from 1928 to 2025—present that accounting for regimes enhances efficiency and robustness.
