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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Deni Saepudin
School of Computing, Telkom University, Bandung, 40257, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Adaptive Portfolio Optimization Using MVF with Machine Learning Forecasting and Regime Switching: Evidence from LQ45 Stocks Fadly Ramdhani; Deni Saepudin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29447

Abstract

This study proposes an adaptive portfolio optimization framework that integrates Random Forest(RF)-based return forecasting into a Mean-Variance-Forecast Error (MVF) model, augmented by a Hidden Markov Model (HMM) for market regime identification. Using weekly historical return data from 40 LQ45-listed stocks spanning January 2014 to January 2025, the framework dynamically adjusts portfolio allocations in response to bull and bear market conditions detected by a two-state HMM. The primary methodological contribution lies in addressing the static limitation of conventional MVF under shifting market regimes. Out-of-sample evaluation over a 138-week test period demonstrates that regime-switching MVF achieves Sharpe ratios above 1.30, substantially lower maximum drawdowns than the MVF-only portfolio, and cumulative returns of 291.96%. Bootstrap-validated 95% confidence intervals confirm the statistical robustness of these improvements. Nevertheless, portfolio turnover remains high during active reallocations. These findings indicate that combining machine-learning-based predictive modelling with adaptive, regime-driven allocation enhances portfolio stability, mitigates extreme losses, and improves risk-return efficiency under dynamic emerging-market conditions.
Co-Authors Fadly Ramdhani