Interpretable Machine Learning for Diversified Portfolio Construction

In this article, the authors construct a pipeline to benchmark hierarchical risk parity (HRP) relative to equal risk contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage (volatility target). The authors use interpretable machine learning concepts (explainable AI) to compare the robustness of the strategies and to back out implicit rules for decision-making. The empirical dataset consists of 17 equity index, government bond, and commodity futures markets across 20 years. The two strategies are back tested for the empirical dataset and for about 100,000 bootstrapped datasets. XGBoost is used to regress the Calmar ratio spread between the two strategies against features of the bootstrapped datasets. Compared to ERC, HRP shows higher Calmar ratios and better matches the volatility target. Using Shapley values, the Calmar ratio spread can be attributed especially to univariate drawdown measures of the asset classes. TOPICS:Quantitative methods, statistical methods, big data/machine learning, portfolio construction, performance measurement Key Findings ▪ The authors introduce a procedure to benchmark rule-based investment strategies and to explain the differences in path-dependent risk-adjusted performance measures using interpretable machine learning. ▪ They apply the procedure to the Calmar ratio spread between hierarchical risk parity (HRP) and equal risk contribution (ERC) allocations of a multi-asset futures portfolio and find HRP to have superior risk-adjusted performance. ▪ The authors regress the Calmar ratio spread against statistical features of bootstrapped futures return datasets using XGBoost and apply the SHAP framework by Lundberg and Lee (2017) to discuss the local and global feature importance.