Creating Portfolio Insights by a Practical Multimethod Optimization Approach
Bart J.A. Willigers, F.. Weis, F.. Majou · SPE Economics & Management · 2013
Summary This study demonstrates how portfolio insights can be created by combining well-known optimization methods that are generally used individually—genetic algorithm (GA), linear programming (LP), and portfolio filtering (PF). An integrated optimization approach combines the advantages of individual methods while mitigating their shortcomings. Effective portfolio management requires a comprehensive understanding of the tradeoffs between different portfolio choices. Given a set of constraints, portfolio-optimization techniques based on LP and GAs can be applied to identify an optimal portfolio. However, this optimal portfolio might not be the preferred portfolio. Decision makers have to understand the tradeoffs between generally conflicting objectives and constraints before one portfolio can be identified as the preferred option. Such assessment of the overall search space is not made with LP and GAs when used to identify a single best solution, and many portfolio options will have been eliminated before an understanding of these alternatives has been developed. Markowitz's mean-variance (M-V) approach and the traditional “rank and cut” approach are used typically to establish a relationship between a portfolio's value and its variance or associated development cost. Although these methods enable decision makers to compare and contrast different options, the optimization is limited to the portfolio value measure and a single other metric. This latter limitation is overcome by the more recently developed PF approach. This method is practical and transparent and allows for a quick development of strategic portfolio alternatives while considering a large number of portfolio attributes. Its main drawback is that the analyzed set of portfolios generally represents a subset of the total search space. Thus, as the number of feasible portfolio options increases, so does the chance that the optimal portfolio is not present in the population of sampled portfolios.