Uses of Variance and Lower Partial Moment Measures for Portfolio Optimization
Portfolio optimization is mainly a multi-objective optimization problem that aims to maximize expected return while minimizing risk. It is important to define the meaning of these parameters accurately, in terms of validity that is acquired by the solution of the problem. In this study, portfolio optimization is implemented through two downside risk measures, semi-variance and lower partial moment, which are stated by researchers to be better representation for investors risk perception. Genetic algorithms, which are among the heuristic computational methods, are used to achieve pareto-efficient portfolios. The implementation is tested by historical data of the shares that are authorized to Istanbul Stock Exchange (ISE) 100 Index, and it is observed that the efficient portfolios achieved by the implementation are consistent with expected results.
Volume (Year): 4 (2010)
Issue (Month): 1 ()
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