A clustering-based investment strategy for high-dimensional portfolio optimization

This paper introduces an investment management strategy that integrates clustering techniques with advanced portfolio optimization models to tackle the limitations of traditional approaches in high-dimensional and highly correlated financial environments. The proposed framework consists of two key components: a clustering algorithm that defines stock similarity based on autocorrelation coefficients, and a general expected-utility portfolio optimization model equipped with an elastic-net regularizer that combines l1 and l2 norms to promote sparse yet stable allocations in high-dimensional and highly correlated settings. Applying the clustering algorithm, the framework groups stocks into clusters with low internal similarity, thereby increasing portfolio diversity. To reduce portfolio complexity, two decision rules are introduced to select representative stocks from each cluster, ensuring that the final investment portfolio remains compact without sacrificing heterogeneity. We employ a shrinkage-based optimization approach for portfolio weight allocation using a linear combination of l1 and l2 norms. This design eliminates the reliance on direct estimation of the covariance matrix, which is often unstable in sparse settings, and allows the model to accommodate strong inter-stock correlations better. Empirical studies conducted on historical S&P 500 data demonstrate that the proposed method enhances average returns and improves the overall stability and robustness of the investment portfolio, outperforming both the unclustered baseline and other benchmark portfolio strategies.