This study delves into how statistical moments shape return patterns, refine risk assessment models, and support moment-based trading strategies. It explores investment allocation approaches that integrate moments, illustrating their accuracy and flexibility through adjustments in moment parameters. The results indicate that moments provide a more precise reflection of investor inclinations. By employing Mhiri and Prigent’s (2010) valuation framework, the study evaluates investment styles, revealing a significant link between market reversals and investment dynamics. In asset distribution, equities exhibit lower efficiency when merged with other asset types, with ideal portfolios prioritizing short-term fixed income, agricultural commodities, and gold. Additionally, the study examines moment-based trading to determine if risk-neutral distributions anticipate actual market behaviors and whether financial markets overreact to external influences. Findings suggest limited predictability, with varied responses to market events, and R2 values peaking at 25% for intraday moment shifts. Finally, Machine Learning (ML)-driven stress tests are employed to uncover intricate data patterns. While these methods reinforce core insights, they offer only marginal gains in forecasting accuracy.