Active Portfolio Management Modeling with VaR and Genetic Algorithms



Active portfolio management is a famous strategy in capital markets. A problem with this strategy is that it ignores the overall portfolio risk. For solving this problem, we examine the impact of adding a new Value at Risk (VaR) constraint to the active management model. In this study, we use Genetic Algorithms for optimization of this model, Sharp Ratio and Return to VaR Ratio for performance measurement of the models.
Results show that the new model, in comparison to active model without VaR constraint, has significantly better performance.