Dynamic Portfolio Rebalancing with Derivatives : A Machine Learning-Based Approach for Enhanced Risk-Adjusted Returns

In the realm of financial investment, dynamic portfolio rebalancing has emerged as a critical strategy for optimizing returns while managing risks, particularly through the integration of derivatives. This study evaluates the performance of derivatives in dynamic portfolio rebalancing using machine learning predictive models and Monte Carlo simulations. By employing a random forest regressor for volatility forecasting and incorporating regime detection via clustering, we simulate various strategies including dynamic derivatives, buy-hold, fixed rebalance, and moving average rebalance. Comparative experiments with alternative models such as XGBoost, LSTM, and GARCH confirm the superiority of random forests, with OOS R² improvements of 8-15% over competitors. The results demonstrate that the dynamic derivatives strategy achieves a Sharpe ratio of 1.45, outperforming baselines by 15-30% in risk-adjusted returns, alongside enhanced tail risk metrics (CVaR of -0.12). The out-of-sample R² of 0.52 indicates robust predictive accuracy, with low mean squared error supporting the model's reliability. Statistical tests confirm significant improvements, with p-values below 0.05 for comparisons against baselines. The efficiency frontier analysis highlights optimal hedge weights around 0.6, reducing risk to 0.15 while maintaining expected returns of 0.12. Sensitivity analysis to transaction costs (0.01-0.05% per trade) shows only marginal degradation in Sharpe (to 1.38 at 0.05%). Greeks time series reveal stable delta near 0.9 and decaying gamma, underscoring the strategy's adaptability. Clustering regimes align with macroeconomic indicators like VIX spikes (¿20) for high-volatility states, enhancing economic interpretability. Overall, this research underscores the value of derivatives in enhancing portfolio stability and performance in volatile markets, providing practical insights for investors seeking sustainable growth.