Artificial Bee Colony Algorithms for Portfolio Optimization Problems

• Main Authors: Chia-Chien Liu, 劉佳倩
• Other Authors: Yun-Chia Liang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/04193596577989134590

碩士 === 元智大學 === 工業工程與管理學系 === 99 === As global economy of today is slowdown and meager salary and interest of term deposit can’t adjust to the impact of inflation, financial investment has become an important focus of public concern. Artificial Bee Colony (ABC), one of metaheuristic algorithms, has attracted lots of attention in optimization field in recent years. ABC, employing the idea of swarm intelligence, simulates the principle of bee foraging behavior in the nature. Thus, this study adopts ABC to solve Portfolio Optimization Problem, and to provide effective portfolio as a reference basis of investment for investors. This study, based on Markowitz’s famous Mean-Variance Portfolio Model (M-V Model), aims at considering Downside Standard Deviation (DSD) and Upside Standard Deviation (USD) respectively and modifying the M-V model to fit the demands of different types of investors. Several well-known stock market indexes over different periods of time are tested to verify the modified models and the performance of the proposed ABC algorithms. The results are also compared with the ones obtained by Variable Neighborhood Search (VNS), Simulated Annealing (SA) and Tabu Search (TS) algorithms in the literatures. The results show that ABC performs better in terms of diversity, convergence, and effectiveness, particularly in ABC II when a Pareto front selection strategy is considered. Thus, ABC in this study is suitable for solving Portfolio Optimization Problem, and is able to provide valuable portfolio for investors with different attributes.