Financial asset allocation strategies using statistical and Machine Learning Models: Evidence from comprehensive scenario testing

Abstract

Accurate return and risk forecasts are critical for asset allocation; however, traditional models such as Mean-Variance (MV) and Risk Parity (RP) suffer from significant estimation errors and sensitivity to noise. We address these challenges by comparing six asset allocation strategies—four MV configurations and two RP-based approaches—against an equally weighted benchmark, using 111 stocks from the NASDAQ-100 and NASDAQ Financial-100 indices over 2000–2019. Two of the MV strategies, one of which we introduce, combine both econometric and Machine Learning (ML) forecasts for returns (via Facebook Prophet) and volatility (via GARCH), while another established ML variation of RP uses Hierarchical Risk Parity (HRP). The proposed hybrid MV strategy combines interpretable, regulatory-compliant methods with ML methodology. Our hypothesis was that ML strategies would significantly outperform their simpler counterparts, and that our proposed MV approach would be particularly competitive. Scenario testing was performed to assess the generalizability of the strategies. Rigorous scenario testing—varying stock sets, training periods, and hyperparameter configurations—reveals that: (i) our ML-enhanced Maximum Sharpe Ratio (MSR) strategy achieves up to 1490% higher Return on Investment (ROI) than the benchmark and 1390%–1909% higher than alternative strategies; (ii) Prophet’s competitive Normalized Mean-Square Error (NMSE) values confirm its robustness in forecasting noisy data; (iii) ML approaches exhibit sensitivity to training data, with compound annual returns declining by up to 5.24% under alternative training periods, reflecting macroeconomic regime-switching effects; and (iv) while ML methods often produce higher absolute returns, they do not consistently yield improved risk-adjusted performance, with non-ML strategies sometimes matching or surpassing ML Sharpe Ratios (SR). Notably, HRP outperformed naïve RP in all scenarios, consistently delivering higher SR. Overall, while ML methods show strong potential, their effectiveness is contingent on data selection and regime stability—underscoring the need for robust scenario analyses such as the one presented.