The Society of Convergence Knowledge TransactionsISSN:2287-8920(Print)융복합지식학회

In the traditional portfolio theory based on H.Markowitz's mean-variance framework, the objective function in fund structuring is the minimization of risk, the constraints become the target return at a certain level, the stocks incorporated into the fund are given in advance, and only the proportion of stocks' inclusion is determined. However, in terms of stock selection, the fund structuring comes down to the combinatorial optimization problem. In other words, it is ideal to choose the optimal solution by enumerating all solutions in the combinatorial optimization problem. In reality, with the scale of the problem, the number of solutions to be investigated increases dramatically, so it is impossible for calculators to find a rigorous optimal solution. Fund structuring is a combinatorial optimization problem and genetic algorithms are used as a way to search for the optimal solution. The genetic algorithm is an effective method for combinatorial optimization problem among the optimization problems by executing the crossover, selection and mutation of the natural systems by computer operations. Many conventional multi-objective optimization techniques have solved the problem by one-dimensional optimization of the objective function and repetition of the single-objective optimization. In this study, however, the pareto optimal solution group is directly obtained by using the genetic algorithm constituting a plurality of solution candidate groups is different. It is also possible to rebalancing existing funds using genetic algorithms. Furthermore, by improving the method of changing or re-adjusting the evaluation function, more advanced results can be obtained.