Innovations in financial decision-making: unveiling insights through a novel approach to almost stochastic dominance

Traditional stochastic dominance approaches are often too restrictive for practical portfolio selection, as strict dominance conditions rarely hold in real-world financial data. This study addresses this limitation by proposing a novel framework that integrates p-values into the Almost Stochastic Dominance (ASD) approach, interpreting them as preference-based tolerance parameters for flexible and risk-sensitive asset selection. Using U.S. energy sector equities classified into Mega Cap, Large Cap, and Mid Cap groups, we evaluate dominance relationships under first-, second-, and third-order stochastic dominance (FSD, SSD, and TSD) across different p-value thresholds. Unlike the strict non-parametric dominance rule, the proposed framework interprets p-value thresholds as tolerance parameters that control the acceptance of dominance under statistical uncertainty. Empirically, the analysis is between 2021–2025 period and further strengthened through regime-based robustness tests and a 30-day rolling-window Sharpe maximization framework. The results indicate that TSD-based portfolios generally achieve stronger risk-adjusted performance and exhibit improved downside-risk characteristics relative to FSD- and SSD-based portfolios. Tail-risk measures such as Value-at-Risk, Expected Shortfall, and Maximum Drawdown indicate that dominance-based screening becomes more informative when combined with dynamic optimization rather than equal-weighted allocation. The findings also suggest that the optimal p-value threshold is regime-dependent: stricter thresholds perform better in more turbulent periods, whereas intermediate thresholds may improve performance in more stable environments. Overall, the ASD–p-value framework provides a flexible and practically implementable decision rule for risk-sensitive portfolio selection.