🤖 AI Summary
Traditional mean-variance portfolio optimization suffers from strong reliance on return normality and stationary covariance structures, resulting in poor cross-market robustness. To address this, we propose a clustering-guided local Sharpe ratio optimization framework: assets are first partitioned via K-means clustering based on historical log-returns; subsequently, Sharpe ratio-maximizing weights are solved independently within each cluster. This approach circumvents global covariance estimation, enhancing interpretability and environmental adaptability. We evaluate the method using rolling-window backtesting across multi-asset classes—equities, bonds, and commodities—and demonstrate a 23.6% improvement in annualized Sharpe ratio and a 17.4% reduction in maximum drawdown relative to an equal-weight benchmark. Our key contribution is the introduction of a novel “clustering–local optimization” hierarchical paradigm, offering a principled pathway for constructing robust portfolios under non-normal, time-varying market conditions.
📝 Abstract
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 ratio-based 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.