Using fast adaptive neural network classifier for mutual fund performance evaluation

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 its performance in classification and forecasting with those from a backpropagation neural network (BPN) model. FANNC is a newly-developed model which combines features of adaptive resonance theory and field theory. 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. Consequently, an on-line evaluation system can be established to provide real-time mutual fund performance for investors.