Multiobjective financial portfolio design: a hybrid evolutionary approach

—A principal challenge in modern computational finance is efficient portfolio design – portfolio optimization followed by decision-making. Optimization based on even the widely used Markowitz two-objective mean-variance approach becomes computationally challenging for real-life portfolios. Practical portfolio design introduces further complexity as it requires the optimization of multiple return and risk measures subject to a variety of risk and regulatory constraints. Further, some of these measures may be nonlinear and nonconvex, presenting a daunting challenge to conventional optimization approaches. We introduce a powerful hybrid multiobjective optimization approach that combines evolutionary computation with linear programming to simultaneously maximize these return measures, minimize these risk measures, and identify the efficient frontier of portfolios that satisfy all constraints. We also present a novel interactive graphical decision-making method that allows the decision-...

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Raj Subbu, Piero P. Bonissone, Neil Eklund, Srinivas Bollapragada, Kete Charles Chalermkraivuth
• Keywords: Artificial Intelligence, CEC 2005, Efficient Portfolios, Real-life Portfolios, Risk Measures