Momentum-Driven Mean-Variance Optimization Strategy for Large-Cap U.S. Stocks

This study investigates the effectiveness of combining momentum investing with Mean-Variance Optimization (MVO) to improve portfolio performance in large-cap U.S. equities. The core problem is addressing the challenge of systematically enhancing returns and controlling risks compared to passive investment strategies. This research is critical as it contributes to portfolio management by evaluating whether a tactical momentum approach, when integrated with MVO, can offer sustainable risk-adjusted returns superior to standard benchmarks. The methodological approach includes calculating weekly momentum scores across 50 large-cap stocks from diverse sectors over a ten-year period (2015-2025). The top-performing momentum stocks are then weighted weekly using an MVO algorithm that balances risk and return. The strategy performance is evaluated through metrics including annualized returns, volatility, Sharpe ratio, and maximum drawdown, benchmarked against the S&P 500 and an equal-weight portfolio. Results demonstrate significant outperformance of the momentum-MVO strategy relative to benchmarks, achieving consistently higher annualized returns and Sharpe ratios. The strategy effectively reduces maximum drawdowns and provides more stable risk-adjusted returns during volatile market periods. Overall, it indicates robustness in enhancing portfolio efficiency across diverse market conditions. This research suggests significant practical implications by demonstrating that combining momentum with MVO enhances portfolio management effectiveness and can potentially reshape strategic asset allocation practices.