بررسی کاربرد الگوریتم ابتکاری - ترکیبی ژنتیک و نِلدر- مید در بهینه‌سازی پورتفوی

Markowitz portfolio model still is the dominant approach in the investment profession and scientific approaches. contrary to the growing use of portfolios and in spite of the rich literature on the subject, there are some problems and unanswered questions. how to select the stocks of a portfolio is a matter of controversy; besides, the approach to optimize the selected potfolio is a sub-group of this controversy. the aim of this work is to be a useful instrument for helping finance practitioners and researchers with the portfolio selection problem. while investigating major methods ever used in optimization, classics and heuristics, a hybridized algorithm, consisting of the combination of two heuristic algorithms, is applied to the portfolio selection problem in this paper. portfolios are selected and optimized in tehran stock exchange for the stocks of top 35 companies out of top 50 companies. the results indicate that the hybridized algorithm is adaptable to the portfolio selection problem, and in contrast to optimization via genetic algorithm, the hybridized algorithm holds a better convergence speed and owns a more reasonable risk-return performance. the research findings also show that in a comparison between the hybrid-based constructed portfolios, although the convergence speed and degree of diversification for the monthly selected portfolio outperforms the other one, the annual selected portfolio holds a better performance from risk-return point of view.