Genetic Programming for the Investment of the Mutual Fund with Sortino Ratio and Mean Variance Model

• Main Authors: Hung-Hsin Chen, 陳紘昕
• Other Authors: Chang-Biau Yang
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/90090776257976174693

碩士 === 國立中山大學 === 資訊工程學系研究所 === 98 === In this thesis, we propose two genetic-programming-based models that improve the trading strategies for mutual funds. These two models can help investors get returns and reduce risks. The first model increases the return by selecting funds with high Sortino ratios and allocates the capital equally, achieving the best annualized return. The second model also selects funds with high Sortino ratios, but reduces the risk by allocating the capital with the mean variance model. Most importantly, our model utilizes the genetic programming to generate feasible trading strategies to gain return, which is suitable for the market that changes anytime. To verify our models, we simulate the investment for mutual funds from January 1999 to December 2009 (11 years in total). The experimental results show that our first model can gain return from 2004/1/1 to 2008/12/31, achieving the best annualized return 9.11%, which is better than the annualized return 6.89% of previous approaches. In addition, our second model with smaller downside volatility can achieve almost the same return as previous results.