Optimal Portfolio Selection for Index Investing Based on Immune Memory Clonal Algorithm
Lin Sun · Yunchou yu guanli · 2009
In order to exploit the optimizing strategy for indexed portfolio selection,an Immune Memory Clonal Algorithm for indexing investment is put forward and applied to optimal indexed portfolio selection based on the clonal selection theory and mechanisms of biological immune response.Indexed portfolio selection with multi-objective is modeled according to the index investing practice.Extra return maximization is included in the model as an objective function.The algorithm design includes antigen,antibody,fitness function,clonal selection operator and immune memory operator.The algorithm effectively overcomes the flaws in the traditional Genetic Algorithm,such as less of result diversity,prematurity and low convergent speed.Meanwhile,a heuristics is designed to limit the number of stocks in the portfolio.Strategy is tested by the historical data of 6 main stock indexes and their component stocks in the world.The results show that:(1)the new evolutionary strategy is capable of improving the search performance significantly both in convergent speed and precision;(2)the indexing portfolio selection model are rational and effective;(3)indexing portfolio selection based on Immune Memory Clonal Algorithm is very helpful in investing practice.