An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index |
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| Institution: | 1. Department of Computer Science and Engineering, United International University, House 80, Road 8A, Dhanmondi, Dhaka-1209, Bangladesh;2. Institute for Integrated and Intelligent Systems, Griffith University, Australia;3. School of Engineering and Physics, University of the South Pacific, Fiji;4. Department of Computer Science, School of Computer, Mathematical, and Natural Sciences, Morgan State University, United States;5. RIKEN Center for Integrative Medical Sciences, Japan;1. Laboratorio de Bioespectroscopía, CINDEFI-UNLP-CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, calle 47 y 115, CP: B1900ASH La Plata, Argentina;2. Instituto de Microbiología y Zoología Agrícola (IMYZA), Instituto Nacional de Tecnología Agropecuaria (INTA), Los Reseros y Las Cabañas s/n, B1712WAA Hurlingham, Argentina;3. Centro de Investigación y Desarrollo en Criotecnología de los Alimentos (CIDCA), UNLP-CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, calle 47 y 115, CP: B1900AS La Plata, Argentina;4. Centro de Investigación y Desarrollo en Fermentaciones Industriales (CINDEFI), UNLP; CCT-La Plata, CONICET. Facultad de Ciencias Exactas. Calle 47 y 115, (B1900ASH) Universidad Nacional de La Plata, Argentina |
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| Abstract: | We introduce a method for combining template matching, from pattern recognition, and the feed-forward neural network, from artificial intelligence, to forecast stock market activity. We evaluate the effectiveness of the method for forecasting increases in the New York Stock Exchange Composite Index at a 5 trading day horizon. Results indicate that the technique is capable of returning results that are superior to those attained by random choice. |
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