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

Keon Vin Park

TL;DR

The paper tackles portfolio optimization by marrying K-Means clustering of historical log-returns with cluster-specific Sharpe ratio-based optimization to improve risk-adjusted performance. It employs SLSQP to solve the constrained optimization problem, ensuring full investment and no short selling, with a backtest framework spanning 2010–2024 on ten S&P 500 stocks. Results show a clearly superior cluster-based portfolio (notably Cluster 2) achieving higher Total and Annualized returns and a higher Sharpe ratio than an equal-weighted benchmark, despite accepting similar or modestly higher volatility. This approach demonstrates a practical pathway to exploit inter-asset return structure via segmentation and risk-aware optimization for dynamic 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.

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

TL;DR

The paper tackles portfolio optimization by marrying K-Means clustering of historical log-returns with cluster-specific Sharpe ratio-based optimization to improve risk-adjusted performance. It employs SLSQP to solve the constrained optimization problem, ensuring full investment and no short selling, with a backtest framework spanning 2010–2024 on ten S&P 500 stocks. Results show a clearly superior cluster-based portfolio (notably Cluster 2) achieving higher Total and Annualized returns and a higher Sharpe ratio than an equal-weighted benchmark, despite accepting similar or modestly higher volatility. This approach demonstrates a practical pathway to exploit inter-asset return structure via segmentation and risk-aware optimization for dynamic 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.
Paper Structure (10 sections, 2 equations, 3 figures, 4 tables)

This paper contains 10 sections, 2 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Data
  4. Results
  5. Clustering Results
  6. Correlation Matrix Heatmap
  7. Portfolio Optimization Results
  8. Backtesting Performance
  9. Performance Metrics
  10. Conclusion and Future Work

Figures (3)

  • Figure 1: Correlation matrix heatmap for selected stocks.
  • Figure 2: Cumulative Returns: Clusters vs. Benchmark Portfolio.
  • Figure 3: Cumulative Returns: Best Cluster (Cluster 2) vs. Benchmark Portfolio.