Thursday 23 January 2025
Portfolio managers have long been searching for ways to optimize investment returns while minimizing risk. A new approach combines clustering techniques, which group similar assets together, with a Sharpe ratio-based optimization model that balances returns against volatility.
The researchers used K-Means clustering to segment 10 major stocks listed on the S&P 500 index into three distinct clusters based on their historical log-returns. The resulting clusters showed significant differences in terms of their mean and standard deviation, indicating varying levels of risk and return.
Next, they applied a Sharpe ratio-based optimization model to each cluster to derive optimal weights that maximize risk-adjusted returns. This approach differs from traditional mean-variance optimization methods by explicitly considering the trade-off between returns and volatility.
The researchers then constructed portfolios for each cluster using the optimized weights and evaluated their performance over a backtesting period spanning 2020-2024. The results showed that the portfolio built on Cluster 2, which consisted of AAPL, NVDA, META, and UNH, outperformed the benchmark portfolio by a significant margin.
The selected portfolio achieved a total return of 140.98%, with an annualized return of 24.67% and a Sharpe ratio of 0.84. In contrast, the benchmark portfolio generated a total return of 107.59%, with an annualized return of 20.09% and a Sharpe ratio of 0.73.
The study’s findings suggest that combining clustering techniques with Sharpe ratio-based optimization can lead to superior portfolio performance while maintaining a reasonable level of risk. The approach also allows for the identification of high-performing asset clusters, which can be used to inform investment decisions.
While this research provides promising results, further exploration is needed to refine and extend its applicability to real-world financial markets. Future studies could investigate the use of advanced machine learning models, dynamic rebalancing strategies, and sector-based analysis to enhance predictive accuracy and robustness.
Cite this article: “Optimizing Portfolio Performance through Clustering and Sharpe Ratio-Based Optimization”, The Science Archive, 2025.
Portfolio Optimization, Clustering Techniques, Sharpe Ratio, K-Means, S&P 500, Risk-Adjusted Returns, Mean-Variance Optimization, Backtesting, Portfolio Performance, Machine Learning Models
