Stock portfolio optimization using multi-objective genetic algorithm (NSGA II) and maximum Sharp ratio
Subject Areas : Financial engineering
1 - Department of Financial Mathematics, Faculty of Basic Sciences, Univercity of Ayatollah Boroujerdi, Boroujerd, Iran
Keywords: Multi-objective genetic algorithm, Stock Portfolio, Sharp ratio, Conditional Value at Risk, Efficient Frontier, Markowitz, Capital Market Line,
Abstract :
One of the most important issues in finance is how to choose an investment portfolio. Activists in this field are seeking to select a portfolio that controls risk with high return. Due to the increasing limitations of the capital market, the efficiency of classical methods has been discussed. Hence, researchers have turned their attention to metaheuristic algorithms. The aim of this study is to determine the optimal portfolio of pharmaceutical companies accepted in the Tehran Stock Exchange by two methods of multi-objective genetic algorithm (NSGA-II) and maximum Sharp ratio. In this study, the multi-objective genetic algorithm (NSGA-II) is under Conditional Value at Risk criterion. Also, the data of 13 companies in the period of 90-97 were used to form the portfolio. The results show that in the multi-objective genetic algorithm (NSGA-II) method, the stock with the lowest Value at Risk gains the most weight in the optimal portfolio. Also, the optimized portfolio by multi-objective genetic algorithm is more return and at the same time less risky.
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