(untitled) (The Journal of Theoretical Accounting Research, 2026)
Traditional portfolio optimization models, such as the mean–variance framework, often fall short in addressing uncertainty, parameter estimation errors, and nonlinear investor objectives. This study provides a unified mathematical framework incorporating convex, stochastic, robust, and multi-objective optimization methods to model financial portfolios with greater theoretical rigor. The paper derives analytical conditions for the existence, uniqueness, and sensitivity of optimal solutions across formulations. A simplified symbolic example with three assets demonstrates the practical implications: increasing the risk aversion coefficient from 1 to 10 resulted in a 40% reduction in portfolio volatility, accompanied by a 15% decrease in expected return. This shift reflects a predictable yet mathematically tractable trade-off between risk minimization and return sacrifice, as governed by parameter tuning within each optimization model. The study underscores the value of mathematical generalization for improving the realism and robustness of portfolio models without relying on empirical backtesting. The comparison of model behaviors provides deep insight into the structural flexibility needed for informed financial decision-making under uncertainty. Theoretical contributions include formal derivations, comparative complexity analysis, and guidance for future integration with intelligent adaptive systems and algorithmic frameworks.