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한국 주식시장에서 마코위츠 포트폴리오 선정 모형의 입력 변수의 정확도에 따른 투자 성과 연구

Investment Performance of Markowitz's Portfolio Selection Model over the Accuracy of the Input Parameters in the Korean Stock Market

  • 김홍선 (연세대학교 경영대학 경영학과) ;
  • 정종빈 (연세대학교 경영대학 경영학과) ;
  • 김성문 (연세대학교 경영대학 경영학과)
  • 투고 : 2013.09.29
  • 심사 : 2013.12.02
  • 발행 : 2013.12.31

초록

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

키워드

참고문헌

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