Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Dynamic proportion portfolio insurance using genetic programming with principal component analysis
Expert Systems with Applications: An International Journal
Automatic stock decision support system based on box theory and SVM algorithm
Expert Systems with Applications: An International Journal
Financial market trading system with a hierarchical coevolutionary fuzzy predictive model
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Genetic algorithms for the investment of the mutual fund with global trend indicator
Expert Systems with Applications: An International Journal
Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks
Expert Systems with Applications: An International Journal
Evolving accurate and compact classification rules with gene expression programming
IEEE Transactions on Evolutionary Computation
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The aim of this paper is to combine several techniques together to provide one systematic method for guiding the investment in mutual funds. Many researches focus on the prediction of a single asset time series, or focus on portfolio management to diversify the investment risk, but they do not generate explicit trading rules. Only a few researches combine these two concepts together, but they adjust trading rules manually. Our method combines the techniques for generating observable and profitable trading rules, managing portfolio and allocating capital. First, the buying timing and selling timing are decided by the trading rules generated by gene expression programming. The trading rules are suitable for the constantly changing market. Second, the funds with higher Sortino ratios are selected into the portfolio. Third, there are two models for capital allocation, one allocates the capital equally (EQ) and the other allocates the capital with the mean variance (MV) model. Also, we perform superior predictive ability test to ensure that our method can earn positive returns without data snooping. To evaluate the return performance of our method, we simulate the investment on mutual funds from January 1999 to September 2012. The training duration is from 1999/1/1 to 2003/12/31, while the testing duration is from 2004/1/1 to 2012/9/11. The best annualized return of our method with EQ and MV capital allocation models are 12.08% and 12.85%, respectively. The latter also lowers the investment risk. To compare with the method proposed by Tsai et al., we also perform testing from January 2004 to December 2008. The experimental results show that our method can earn annualized return 9.07% and 11.27%, which are better than the annualized return 6.89% of Tsai et al.