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

In this paper, we propose two geneticprogramming-based models that improve the trading strategy for mutual funds. These two models can get better 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 signals to gain return, which is suitable for the market that changes anytime. To verify our model, 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 the previous approach. In addition, our second model with smaller downside volatility can achieve almost the same return as previous results. Keywords-fund; global trend indicator; monitoring indicator; Sortino ratio; genetic programming; annualized return.