When Harry Markowitz developed a mean-variance framework for modern portfolio optimization more than 50 years ago, simplicity was a key part of its appeal. However, the adoption of more sophisticated risk measures—for example, value at risk—and constraints, including restrictions on the maximum number of different assets in a portfolio and minimum holding size, has made it all but impossible to optimize portfolios with classical techniques.
Using MATLAB®, Parallel Computing Toolbox™, and MATLAB Distributed Computing Server™, researchers at the University of Geneva have developed a multipurpose, data-driven optimization heuristic that addresses the challenges of more sophisticated risk measures and practical portfolio constraints.
"Financial analysts have been talking about downside risk for years, but few have attempted to use it for portfolio selection because the resultant optimization problem is difficult to solve," says Professor Manfred Gilli of the Department of Econometrics at the University of Geneva. "Using heuristic methods and MathWorks tools, we have developed a system that any analyst can use to arrive at optimal solutions very quickly."