This project explores applying dynamic clustering techniques in conjunction with Hierarchical Principal Component Analysis (HPCA) for sector-based equity portfolio management. Building upon the work of Avellaneda and Serur (2020), we focus on sector breakdowns and evaluate the performance of two dynamic clustering methods: the statistical clustering approach proposed in the original paper and the K-means clustering algorithm. By applying these techniques to a universe of stocks, we aim to identify homogeneous clusters of stocks that share common risk factors and analyze their potential for portfolio construction and risk management. Our findings suggest that dynamic clustering enhances the adaptability of HPCA to changing market conditions, allowing for more effective sector-based portfolio strategies. The report provides insights into implementing these methods and their impact on portfolio performance, offering valuable guidance for quantitative asset managers seeking to optimize their investment strategies in the face of evolving market dynamics.