標題: Using fast adaptive neural network classifier for mutual fund performance evaluation
作者: Wang, Kehluh
Huang, Szuwei
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Neural network;Mutual fund;Performance evaluation;Mutual fund forecasting
公開日期: 1-Aug-2010
摘要: 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. (C) 2010 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2010.02.003
http://hdl.handle.net/11536/32361
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2010.02.003
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 37
Issue: 8
起始頁: 6007
結束頁: 6011
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