Sustainable Portfolio Rebalancing Under Uncertainty: A Multi-Objective Framework with Interval Analysis and Behavioral Strategies

This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices.