关键词: 投资组合优化, 交易成本, 引力搜索算法, 粒子群优化算法

Abstract: This research focuses on the portfolio optimization model considering transaction costs. Value-at-Risk(VAR)and Sharpe-Ratio(SR)are used respectively as the risk evaluation index and efficiency evaluation index for a portfolio. To solve the problem effectively, a hybrid optimization algorithm named IN-GSA-PSO is proposed by combining the Gravitational Search Algorithm(GSA)and the Particle Swarm Optimization(PSO)algorithm. The “global best” and “personal best” positions in PSO are integrated with the “acceleration” mechanism in GSA to determine the search direction. As a result, the hybrid algorithm is able to fully utilize the exploration and exploitation abilities of each individual algorithm. With fine-tuned parameters, the hybrid algorithm can achieve an effective balance between global search and local search and thus converges fast to high-quality solutions. We adopt the data of 50 stocks in the Shanghai index during 126 trading days in the second half of 2014 for simulation experiments using Matlab software. Experimental results show that the portfolio model considering transaction costs can guarantee higher yields for the investors. Computational results also suggest that the hybrid algorithm based on PSO and GSA outperforms individual algorithms when solving the portfolio optimization problem, leading to more satisfactory solutions.

Key words: portfolio optimization, transaction costs, gravitational search algorithm, particle swarm optimization