Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization

변동성 지수기반 유전자 알고리즘을 활용한 계층구조 포트폴리오 최적화에 관한 연구

  • Byun, Hyun-Woo (Department of Information and Industial Engineering, Yonsei University) ;
  • Song, Chi-Woo (Department of Information and Industial Engineering, Yonsei University) ;
  • Han, Sung-Kwon (Department of Information and Industial Engineering, Yonsei University) ;
  • Lee, Tae-Kyu (Department of Information and Industial Engineering, Yonsei University) ;
  • Oh, Kyong-Joo (Department of Information and Industial Engineering, Yonsei University)
  • 변현우 (연세대학교 정보산업공학과) ;
  • 송치우 (연세대학교 정보산업공학과) ;
  • 한성권 (연세대학교 정보산업공학과) ;
  • 이태규 (연세대학교 정보산업공학과) ;
  • 오경주 (연세대학교 정보산업공학과)
  • Published : 2009.11.30


The expansion of volatility in Korean Stock Market made it more difficult for the individual to invest directly and increased the weight of indirect investment through a fund. The purpose of this study is to construct the EIF(enhanced index fund) model achieves an excessive return among several types of fund. For this purpose, this paper propose portfolio optimization model to manage an index fund by using GA(genetic algorithm), and apply the trading amount and the closing price of standard index to earn an excessive return add to index fund return. The result of the empirical analysis of this study suggested that the proposed model is well represented the trend of KOSPI 200 and the new investment strategies using this can make higher returns than Buy-and-Hold strategy by an index fund, if an appropriate number of stocks included.


  1. Adeli, H. and Hung, S. (1995). Machine learning: neural networks, genetic algorithms, and fuzzy systems, John Wiley & Sons, New York.
  2. Bogle, J. C. (1998). The implications of style analysis for mutual fund performance. Journal of Portfolio Management, 24, 34-42.
  3. Clarke, R. C., Krase, S. and Statman, M. (1994). TEs, regret, and tactical asset allocation. Journal of Portfolio Management, 20, 16-24.
  4. Fama, E. F. (1970). Multiperiod consumption-investment decisions. American Economic Review, 60, 163-174.
  5. Gerard, C. and Reha, T. (2007) Optimization methods in finance, Cambridge University Press.
  6. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley Publishing Company, Inc., New York.
  7. Hakansson, N. (1970). Optimal investment and consumption strategies under risk for a class of utility functions. Econometrica, 38, 587-607.
  8. Hakansson, N. (1974). Convergence in multiperiod portfolio choice. Journal of Financial Economics, 1, 201-224.
  9. Hallerbach, W. and Pouchkarev, I. (2005). A relative view on tracking error, ERIM Report Series Research in Management, Erasmus University, Rotterdam.
  10. Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence, University of Michigan Press.
  11. Kim. K. K., Cho. M. H. and Park. E. S. (2008). Forecasting the volatility of KOSPI 200 using data mining. Korean Data and Information Science Society, 19, 1305-1325.
  12. Kim K. S. and Lee. C. S. (2003). A study of data mining optimization model for the credit evaluation. Korean Data and Information Science Society, 14, 825-836.
  13. Konno, H. and Yamazaki, H. (1991). Mean absolute deviation portfolio optimization model and its application to Tokyo stock market. Management Science, 37, 519-531.
  14. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7, 77-91.
  15. Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments, John Wiley & Sons, Inc., New York.
  16. Merton, R. C. (1990). Continuous time finance, Basil Blackwell, Oxford.
  17. Mossin, J. (1969). Optimal multiperiod portfolio policies. Journal of Business, 41, 215-229.
  18. Roll, R. (1992). A mean/variance analysis of TE. Journal of Portfolio Management, 18, 13-22.
  19. Sharpe, W. F. (1971). A linear programming approximation for the general portfolio analysis problem. Journal of Financial and Quantitative Analysis, 6, 1263-1275.
  20. Wong, F. and Tan, C. (1994). Hybrid neural, genetic, and fuzzy systems, in: G.J. Deboeck, Ed., In Trading on the edge, 243-261, John Wiley & Sons, Inc., New York.