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

2. Portfolio Optimization without Transaction Costs
Data Description
Experimental Designs
Experimental Results

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

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

5. 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.

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

7. Portfolio Optimization with Transaction Costs
Data Description
Experimental Designs
Experimental Results

8. GA in Portfolio Optimization: Data

9. GA in Portfolio Optimization: Experimental Designs

10. GA in Portfolio Optimization: Experimental Results
Role of Turnover Rate

14. Jan., June, 1995 (30 Months)
March, Aug., 1995 (30 Months)

15. Capacity, kappa = 2

22. 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.

23. Figure A B C D A