Data mining technology in mutual funds evaluation

There are many Mutual Funds rating companies in the market. Rating companies have different standards to evaluate Mutual Funds such as risks, incomes and some referable indexes. The study is aim at setting a score criterion to discuss the performance of equity fund, ETF and Index Fund. The objects of this study include eight domestic funds which are set up more than one year. We collect the data which are set up before August, 2008. The study uses Text Mining to choose proper variables. By Cluster Analysis, the similar characteristics of risk and return can be distributed into same group. We analyze the performance of the Mutual Funds for each group. Furthermore, each fund gets scores through the score criterion and tries to compare the study with Morningstar, Lipper, and Fund-Watch. Finally, Discriminant Analysis can determine that new data may be distributed into which cluster. The main conclusions are: 1. According to risk and return, Cluster Analysis can divide Large-sized Equity Fund into three clusters. ETF and Index Fund can also divide into three clusters by the same way. Besides, there are obvious differences between each cluster. 2. On the basis of the result, JF (TAIWAN) Taiwan Fund is the best performance in Large-sized Equity Fund. Polaris Taiwan Top 50 Tracker Fund is the best in ETF and Index Fund. 3. The accuracy is about 96.03% in Large-sized Equity Funds and 97.35% in Index Funds by Discriminant Analysis. In term of the study, it is immediate to find out the result of cluster and performance evaluation for new data. 4. Morningstar, Lipper and Fund-Watch are common rating companies in the market, but the rating of Large-sized Equity Fund is different. In business recession, they can evaluate a better fund relatively, but the evaluated fund can't increase profit. In this study, the way of rating bases on the outcome of Cluster Analysis and score of the funds. It can not only reduce the loss and risk but also provide objective and useful information to investors.