The Application of Meta-Heuristic Algorithms in the Optimization of Investments Portfolio

• Main Authors: Chou, Yu-Wen, 周郁文
• Other Authors: Chen, Ming-Hua
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/23313165239165328462

碩士 === 嶺東科技大學 === 資訊管理與應用研究所 === 99 === This study attempts to investigate the optimization solution of investments portfolio in terms of the appropriate investments weights. It is to find the feasible solution of this optimization by using Meta-Heuristic Algorithms (MHA) including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Quantum-based Genetic Algorithms (QGA) as well. This research is firstly to test MHA by the Knapsack Problem from which we could select five better Algorithms containing QGA, with which tries to find the best investment weights for each asset in the portfolio. The research sample includes randomly selecting 50 listed firms in Taiwan Stock Exchange (TSE) every month during January 2007 to March 2008. It ends up with 15 months during sample period which the first twelve months are used for training sample and last three months are considered as for testing sample. The object function of the investment portfolio is to attain the minimum risk of 15 stocks under certain level of return by applying the appropriate MHA for searching the best investment weights in the portfolio. The results show that the return of chosen investment weights under MHA is superior to the return under mean-variance model, and it also beats the return of market portfolio. The result could provide constructive reference for the market investors, especially for the fund managers. To compare these methods of MHA, we also interestingly find that the PSO is less efficiency in speed and QGA is the best in selecting the investment weights.