Intelligent Risk Processing and Opportunity Formation in Financial Markets: The Superior Performance of the HERC Algorithm in Efficient Portfolio Construction

Hierarchical portfolio optimization methods, particularly the Hierarchical Equal Risk Contribution (HERC) approach, have become increasingly prominent in financial research due to their effectiveness in balancing risk and enhancing diversification. Unlike traditional methods such as Equal-Weight (EW) and Inverse Volatility (IV), which rely on oversimplified assumptions and often underperform in volatile markets, HERC allocates capital by distributing risk more efficiently across assets. This study examines the performance of the HERC model relative to EW and IV to determine its ability to convert risk into investment opportunities under fluctuating market conditions. The methodology follows a structured process that includes deriving variables from multiple data sources, conducting thorough data cleaning and normalization, and implementing traditional allocation models as benchmarks. Advanced hierarchical clustering techniques are then applied to provide a more innovative allocation framework. Rigorous hypothesis testing is used to validate the results, and portfolio performance is evaluated using established statistical metrics. Findings reveal that HERC—especially its single linkage and average linkage versions—delivers substantially higher risk-adjusted returns, as measured by the Sharpe and Sortino ratios, compared to EW and IV. The proposed methodology not only improves overall investment outcomes but also enables more effective risk and return management, making it a strong alternative to conventional portfolio construction and risk evaluation approaches.

کلیدواژه ها:

Asset Allocation ، Hierarchical Equal Risk Contribution (HERC) ، Risk Management ، Unsupervised Learning