Investment Portfolio and Optimization Based on the Recent Popular Stocks in the US Stock Market

The US stock market remains a focal point for both institutional and individual investors, with portfolio optimization being a critical need. Traditional approaches to asset allocation often rely on single models or incremental improvements, which struggle to address the complexity and instability of financial markets. Hence, this study adopts a model-based analytical framework to address these limitations. First, theoretical models are collected and derived to establish the research foundation. Second, the financial market is analyzed using quantitative data from the Yahoo Finance database. Third, econometric models are constructed, parameters are estimated, and models are optimized. Fourth, data visualization techniques are employed to generate analytical charts. Finally, model results are evaluated and compared to provide investment strategy recommendations. Advanced models, including the mean-variance model, Black-Litterman model, and genetic algorithm, are introduced to enhance adaptability to market complexity. The mean-variance model provides intuitive risk-return insights but relies heavily on historical data. The Black-Litterman model incorporates investor views but overlooks asset-specific risks. The genetic algorithm offers flexibility in handling constraints but requires significant computational resources. While these models provide valuable insights, investors should integrate their own market understanding to optimize portfolio decisions. This study highlights the importance of combining model-based analysis with investor expertise to navigate financial market uncertainties effectively.