Mining The Performance Of Mutual Funds Using Neural Networks And Multivariate Adaptive Regression Splines

• Main Authors: Ming-Hui Huang, 黃明輝
• Other Authors: Yueh-Jen Shao
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/84835063760647101602

碩士 === 輔仁大學 === 金融研究所 === 90 === Data mining is useful tools to discovery valid, novel, useful, new patterns or unknown relations in the data set or database. We also use data mining technology for finding out rules within database over the induction. Due to its applications to information systems, decision making, fraud detection, business failure prediction, database marketing, and lots of other applications, it has drawn serious attention from both academic researchers and practitioners. The purpose of this research is to investigate the performance of mutual fund bond using two commonly used data mining tools, artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS). Several variables that may affect the performance mentioned in the literature, like the age of the bond fund, the portfolio of the bond fund, the scale of the bond fund, will be used to “predict” whether a particular bond will have the timing ability or not. In order to evaluate the classification capability of ANNs and MARS in mining the timing capability of mutual funds, historical date of 34 Taiwan bond funds from July 1999 to June 2001 will be used in this study. Analytic results demonstrate that MARS has better out-of-sample forecasts than ANNS in terms of average correct classification rates.