RMSE-triggered rebalancing for deep learning-guided portfolio optimization

This paper addresses the gap between improved forecasting accuracy from machine learning and deep learning models and their limited translation into realized portfolio performance under transaction-cost constraints. To bridge this gap, the study proposes a root mean square error (RMSE)-triggered rebalancing framework that links forecast reliability with portfolio adjustment decisions. Using daily data from VN-100 stock sample, the study first evaluates N-BEATS against a conventional ARIMA benchmark for return forecasting. The results show that N-BEATS provides more accurate and stable forecasts than ARIMA, and its predictions are therefore incorporated into Mean-Variance with Forecasting (MVF) and Mean Absolute Deviation with Forecasting (MADF) portfolio optimization models. The proposed framework uses RMSE as a predefined threshold-based trigger: portfolios are re-optimized only when realized forecast errors exceed the selected threshold. Portfolio performance is evaluated under transaction-cost settings using multiple return, risk, turnover, and drawdown metrics. The empirical findings indicate that RMSE-triggered rebalancing can improve net portfolio performance by reducing unnecessary portfolio adjustments while remaining responsive to material forecast deviations. MVF benefits most from moderate RMSE thresholds that balance return responsiveness with transaction-cost control, whereas MADF shows greater robustness under less frequent rebalancing. The study is limited by its focus on RMSE, a single proportional transaction-cost assumption, and a restricted asset universe. Future research should examine alternative error measures, richer cost structures, and broader asset classes. The study contributes by using RMSE as both a diagnostic of forecast reliability and an operational trigger for cost-aware rebalancing.