A 'Plug-and-Play' Goal-Based Dynamic Asset Allocation Model with Genetic Algorithms

This work aims to reconcile theory and practice, proposing a dynamic model of asset allocation that allows the creation, with the help of genetic algorithms, of reasonable portfolios for investors chasing a variety of objectives over multiple time horizons. Our results are interesting and confirm the efficiency of the model due to its good convergence towards the space of the best solutions in a limited computation time. The methodology may be applied: i) in the field of wealth management to define approaches useful for investors whose wealth must be invested in order to offset future liabilities; and ii) in the field of pension funds to define in a personalized way - based on individual pension needs - the investment dynamics useful for meeting retirement needs