Modeling and simulation of the open-end equity mutual fund market in Taiwan by using self-organizing map

Abstract

This research applies artificial intelligence (AI) of unsupervised learning self-organizing map neural network (SOM-NN) to establish a model to select the superior funds. This research period is from year 2000 to 2010 and picks 100 domestic equity mutual funds as study object. This research used 30 days prior to the beginning of each month's prior 30 days, 60 days, 90 days on fund's net asset value and the Taiwan Weighted Stock Index (TAIEX) return as the fund's relative performance evaluation indicators classified by month. Finally, based on the superior rate or the average return rate, this research select the superior funds and simulate investment transactions according to this model. The empirical results show that using the mutual fund's net asset value and the TAIEX's relative return as SOM-NN input variables not only finds out the superior fund but also has a good predictive ability. Applying this model to simulate investment transactions will be better than the random trading model and market. The experiments also found that the investment simulation of a three-month interval has the highest profitability. The model operation suggests that it is more suitable for short-term and medium-term investment. This research can assist investors in making the right investment decisions while facing rapid financial environment changes. (C) 2013 Elsevier B.V. All rights reserved.