Mutual Fund Performance Evaluation System Using Fast Adaptive Neural Network Classifier

Abstract

Application of financial information systems requires instant and fast response for continually changing market conditions. The purpose of this paper is to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier (FANNC), and to compare our results with those from a backpropagation neural networks (BPN) model. In our experiment, the FANNC approach requires much less time than the BPN approach to evaluate mutual fund performance. RMS is also superior for FANNC. These results hold for both classification problems and for prediction problems, making FANNC ideal for financial applications which require massive volumes of data and routine updates.