Title :
Multi-objective indicator based evolutionary algorithm for portfolio optimization
Author :
Bhagavatula, Sowmya Sree ; Sanjeevi, Sriram G. ; Kumar, Dinesh ; Yadav, Chitranjan Kumar
Author_Institution :
Dept. of Comput. Sci. & Eng., N.I.T. Warangal, Warangal, India
Abstract :
Portfolio optimization is a standard problem in the financial world for making investment decisions which involve investing into a variety of assets with the aim of maximizing yield and minimizing risk. Modern portfolio theory is a mathematical approach to the problem that endeavors to accomplish a plausive portfolio by giving best weighting of the assets. In this study, an indicator based evolutionary algorithm (IBEA) has been compared with two well known evolutionary algorithms-Non-dominated Sorting Genetic Algorithm II( NSGA- II) and Strength Pareto Evolutionary Algorithm (SPEA-II).The results reveal that IBEA outperforms the other two algorithms in terms of its closeness to the true pareto front. Also, a diversity enhanced version of IBEA (IBEA-D) is proposed, which is found to be providing more diverse solutions than IBEA.
Keywords :
Pareto optimisation; decision making; economic indicators; genetic algorithms; investment; risk management; sorting; IBEA-D; NSGA- II; Pareto front; SPEA-II; assets; diversity enhanced version; financial world; investment decision making; mathematical approach; multiobjective indicator based evolutionary algorithm; nondominated sorting genetic algorithm II; portfolio optimization; portfolio theory; risk minimization; strength Pareto evolutionary algorithm; yield maximization; Evolutionary computation; Measurement; Optimization; Portfolios; Sociology; Sorting; Statistics; crowded comparison; evolutionary algorithm; hypervolume indicator; mating; multiobjective; portfolio optimization; survivor selection;
Conference_Titel :
Advance Computing Conference (IACC), 2014 IEEE International
Conference_Location :
Gurgaon
Print_ISBN :
978-1-4799-2571-1
DOI :
10.1109/IAdCC.2014.6779499
