Multi-objective Portfolio Optimization Via Gradient Descent
This provides a practical tool for researchers and practitioners dealing with complex, real-world portfolio optimization problems, though it is incremental as it builds on existing gradient descent methods.
The paper tackles the challenges of scalability and flexibility in portfolio optimization by introducing a gradient descent framework with automatic differentiation for multi-objective problems, achieving competitive performance compared to standard solvers like CVXPY and SKFOLIO across six experimental scenarios.
Traditional approaches to portfolio optimization, often rooted in Modern Portfolio Theory and solved via quadratic programming or evolutionary algorithms, struggle with scalability or flexibility, especially in scenarios involving complex constraints, large datasets and/or multiple conflicting objectives. To address these challenges, we introduce a benchmark framework for multi-objective portfolio optimization (MPO) using gradient descent with automatic differentiation. Our method supports any optimization objective, such as minimizing risk measures (e.g., CVaR) or maximizing Sharpe ratio, along with realistic constraints, such as tracking error limits, UCITS regulations, or asset group restrictions. We have evaluated our framework across six experimental scenarios, from single-objective setups to complex multi-objective cases, and have compared its performance against standard solvers like CVXPY and SKFOLIO. Our results show that our method achieves competitive performance while offering enhanced flexibility for modeling multiple objectives and constraints. We aim to provide a practical and extensible tool for researchers and practitioners exploring advanced portfolio optimization problems in real-world conditions.