Optimizing Portfolio Performance through Clustering and Sharpe Ratio-Based Optimization: A Comparative Backtesting Approach

Optimizing portfolio performance is a fundamental challenge in financial modeling, requiring the integration of advanced clustering techniques and data-driven optimization strategies. This paper introduces a comparative backtesting approach that combines clustering-based portfolio segmentation and Sharpe ratiobased optimization to enhance investment decision-making. First, we segment a diverse set of financial assets into clusters based on their historical log-returns using K-Means clustering. This segmentation enables the grouping of assets with similar return characteristics, facilitating targeted portfolio construction. Next, for each cluster, we apply a Sharpe ratio-based optimization model to derive optimal weights that maximize risk-adjusted returns. Unlike traditional mean-variance optimization, this approach directly incorporates the trade-off between returns and volatility, resulting in a more balanced allocation of resources within each cluster. The proposed framework is evaluated through a backtesting study using historical data spanning multiple asset classes. Optimized portfolios for each cluster are constructed and their cumulative returns are compared over time against a traditional equal-weighted benchmark portfolio. Our results demonstrate the effectiveness of combining clustering and Sharpe ratio-based optimization in identifying high-performing asset groups and allocating resources accordingly. The selected cluster portfolio achieved a total return of 140.98%, with an annualized return of 24.67%, significantly outperforming the benchmark portfolio, which achieved a total return of 107.59% and an annualized return of 20.09%. The Sharpe ratio of the selected portfolio (0.84) further highlights its superior risk-adjusted performance compared to the benchmark portfolio (0.73). This methodology provides a systematic and data-driven approach to portfolio optimization, achieving superior returns while maintaining a reasonable level of risk.