Investment Portfolio Optimization by Applying a Genetic Algorithm-Based Approach
Dubinskas Petras () and
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Urbšienė Laimutė: Businesss School, Vilnius University, Saulėtekio Ave. 22, LT-10225,Vilnius, Lithuania
Ekonomika (Economics), 2017, vol. 96, issue 2, 66-78
The investment portfolio optimization issues have been widely discussed by scholars for more than 60 years. One of the key issues that emerge for researchers is to clarify which optimization approach helps to build the most efficient portfolio (in this case, the efficiency refers to the minimization of the investment risk and the maximization of the return). The objective of the study is to assess the fitness of a genetic algorithm approach in optimizing the investment portfolio. The paper analyzes the theoretical aspects of applying a genetic algorithm-based approach, then it adapts them to practical research. To build an investment portfolio, four Lithuanian enterprises listed on the OMX Baltics Stock Exchange Official List were selected in accordance with the chosen criteria. Then, by applying a genetic algorithm-based approach and using MatLab software, the optimum investment portfolio was constructed from the selected enterprises. The research results showed that the genetic algorithm-based portfolio in 2013 reached a better risk-return ratio than the portfolio optimized by the deterministic and stochastic programing methods. Also, better outcomes were achieved in comparison with the OMX Baltic Market Index. As a result, the hypothesis of the superiority of a portfolio, optimized on the basis of a genetic algorithm, is not rejected. However, it should be noted that in seeking for more reliable conclusions, further research should include more trial periods as the current study examined a period of one year. In this context, the operation of the approach in the context of a market downturn could be of particular interest.
Keywords: artificial intelligence; genetic algorithm; stochastic programming; investment portfolio optimization (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ekonom:v:96:y:2017:i:2:p:66-78:n:5
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