A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural network performance on the bankruptcy classification problem
Proceedings of the 15th annual conference on Computers and industrial engineering
Self organizing neural networks for financial diagnosis
Decision Support Systems
Neural networks in applied statistics
Technometrics
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
FANNC: a fast adaptive neural network classifier
Knowledge and Information Systems
Decision Support Systems - Special issue: Data mining for financial decision making
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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.