Portfolio Optimization with Multi-Objective Optimization Algorithms
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Keywords:
Modern Portfolio Theory, Markowitz Mean-Variance, Mean Semi Variance, Mean Absolute Deviation, Conditional Value at Risk, Portfolio Optimization, Particle Swarm Optimization, Non-Dominated Sorting Genetic Algorithm-IIAbstract
One of the critical issues in financial management is the investment decision-making process,
and one of the main goals of investment management is optimal stock portfolio selection. In this context,
there are various criteria and methods for optimal stock portfolio selection in the literature. This article
first calculates investment return and investment risk using data from 6 companies such as Amazon,
Yahoo, Microsoft, IBM, Apple, and Google for a one-month period from January 2014 to November
2014. Investment is calculated by 4 classical methods (mean-variance, mean-semi variance, mean
absolute deviation, conditional value at risk). As a result of these calculations, 0.05706, 0.028409,
0.028871, and 0.032995 with maximum ROI (0.0142 respectively) and Risk are calculated for each
classical method. Then, meta-heuristic methods (PSO, NSGA-II) are used for optimal selection of the
portfolio. As a result of the calculations, it can be seen that the NSGA-II meta-heuristic algorithm tends to
achieve the highest return on investment with a lower risk. These results suggest that the integration of
advanced computational methods, such as multi-objective optimization algorithms, may be important to
improve the precision and efficiency of portfolio selection in financial management. This can provide
valuable insights for investors and financial analysts.
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