PERBANDINGAN OPTIMISASI PORTOFOLIO METODE MEAN- VARIANCE DENGAN METODE MEAN-SEMIVARIANCE - , SEPTI WAHYUNI ([Yogyakarta] : Universitas Gadjah Mada, 2013)
In 1952 Markowitz pioneered the use of Mean-Variance method for portfolio optimization problems, for which Mean-Variance method is very popular to use. However, Mean-Variance method has drawbacks that the return data should be normal distributed. In fact, it is very difficult to get data that has normal distributed return. Markowitz (1959) argued that âanalysis based on semivariance tend to produce better portfolios than those based on varianceâ. However, why is the analysis of the portfolio with Mean-Variance more often used than Mean-Semivariance? This is because, unlike covariance matrix that is symmetric and exogenous, semicovariance matrix is asymmetric and endogenous. Thus in calculating the weights, numerical algorithms must be used that is rarely used by practitioners and academics. Therefore, heuristic approach used which fuction to change semicovariance matrix to be symmetric and exogenous. So calculating the weights of Mean-Semivariance portfolio could use the same with Mean-Variance portfolio. Portfolio optimization using Mean-Semivariance does not require any distribution assumptions, making it much easier to use than Mean-Variance. The calculations are easy and with heuristic approach obtained semicovariance matrix which has the same form and finishing with covariance matrix of Mean-Variance. In this thesis the empirical comparison will be made between Mean-Semivariance portfolio optimization with Mean-Variance portfolio optimization. Then in case studies portfolio formation carried out Mean-Variance portfolio and Mean- Semivariance portfolio with combination of multiple financial assets.