From Prediction to Decision: Leakage-Controlled Machine Learning with Uncertainty-Calibrated Black-Litterman Allocation

Machine-learning (ML) portfolio studies often emphasize forecast accuracy, although economic value depends on how forecasts propagate through uncertainty, constraints, turnover, and transaction costs. We evaluate a leakage-controlled decision-support pipeline combining weekly Elastic Net and Random Forest excess-return forecasts with uncertainty-calibrated Black–Litterman (BL) allocation across five ETFs. Data from 2021–2023 form the training sample, 2024 is used for walk-forward selection and confidence calibration, and 2025 is a final 53-week holdout; model coefficients may update with newly observed data, but the configuration is frozen before the holdout. Neither pooled ML model beats the zero or historical-mean forecasts on the pre-specified Mean NRMSE. BL–Elastic Net obtains a net Sharpe ratio of 2.762 with 0.86% average weekly turnover and a −3.11% maximum drawdown; descriptively, its Sharpe exceeds 1/N (2.425) and mean–variance (2.324) but not GMV (3.069). However, circular block-bootstrap 95% confidence intervals for all BL–Elastic Net and BL–Random Forest Sharpe differences versus 1/N, GMV, and mean–variance include zero. Sensitivity, regime, and implementation analyses therefore support a bounded conclusion: forecast ranking and portfolio ranking can diverge, but the available holdout does not establish statistically significant risk-adjusted dominance.