Multi-objective Evolutionary Approach for Portfolio Optimization

• Main Authors: Chester Marvin C. So, 蘇天恩
• Other Authors: James Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/26391726474759170963

碩士 === 中華大學 === 資訊工程學系碩士班 === 103 === Allocating one’s assets optimally in a portfolio is the goal of every investor. Much of the theory of portfolio optimization is detailed in Markowitz. Investors need to balance the objective of maximizing the return on their investment with the constraint of minimizing the risk involved. It’s generally accepted that the greater the expected return, the greater the risk. Portfolio optimization problems can be shown to be very hard and heuristic algorithms are a typical way to address them. Typically these approaches aim to maximize the return with the constraint that the risk should not be above a given level. In this thesis, a portfolio evolutionary optimization system is proposed, which uses a multi-objective genetic algorithm (MOGA) as a major optimization algorithm to optimization the investigated problem. Several experiments are conducted using this approach with different strategies for portfolio optimization.The experimental results indicatedthat the approach can be profitable; it doesn’t necessarily give the optimal portfoliobutdoesn’t give the worst portfolio either. Keywords: Portfolio optimization, genetic algorithms, multi-objective evolutionary algorithms, multi-objective genetic algorithm.