Genetic Algorithm: Application to Portfolio Optimization

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Presentation transcript:

Genetic Algorithm: Application to Portfolio Optimization Review on Portfolio Optimization Portfolio Optimization without Transaction Costs Portfolio Optimization with Transaction Costs

Portfolio Optimization without Transaction Costs Data Description Experimental Designs Experimental Results

GA in Portfolio Optimization: Data 10 stocks from the list of 48 stocks are used to construct a portfolio. Data Period: Jan., 1993 -- August, 1995

GA in Portfolio Optimization: Experimental Designs The portfolio optimization is done on the basis of 30 monthly returns. Parameters Setting

GA in Portfolio Optimization: Experimental Results The different portfolios with similar objective function values, which are solutions obtained from the final population of genetic algorithms, are shown in Figure 6.

GA in Portfolio Optimization: The Last Generation Subpopulations and Portfolios Analysis Subpopulations Transient Subpopulations Dynamics of Subpopluations Examinations on a Few Portfolios

Portfolio Optimization with Transaction Costs Data Description Experimental Designs Experimental Results

GA in Portfolio Optimization: Data

GA in Portfolio Optimization: Experimental Designs

GA in Portfolio Optimization: Experimental Results Role of Turnover Rate

Jan., 1993--June, 1995 (30 Months) March, 1993--Aug., 1995 (30 Months)

Capacity, kappa = 2

If transaction cost incurred by the change of portfolio is taken into account, the most similar portfolio with near optimal objective function value will be the choice.

Figure A B C D A