| Reference: | Abu-Mostafa Y. S. and Atiya A. F., 1996. Introduction to Financial Forecasting, Applied Intelligence, 6: 205-213.
Yoon Youngohc., Swales, George, 1991. Predicting Stock Price Performance: A Neural Network Approach, Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System.
Kamijo, K. and Tanigawa, T., 1993. Stock price pattern recognition: a recurrent neural network approach, in Trippi, R. and Truban, E. (eds), Neural Networks in Finance and Investing.
Kuan, C. M. White, H., 1994. Artificial neural networks: An econometric perspective, Econometric Reviews, 13, 1-91.
Wood, Douglas and Bhaskar.Dasqupta, 1994. Modelling and Index of the French Capital Market, Economic and Financial Computing, Autumn/Winter, pp.119-136
En Tzu Li, 2011. TAIEX Option Trading by using EEMD-based Neural Network Learning Paradigm. Master Thesis of Graduate Institute of Applied Physics, College of Science NCCU.
Zheng-Hsiu Chu, 2004. On Study of The Relationship between Taiwan Stock Market and The International Stock Markets. Master Thesis of Department of Statistics, College of management NCKU.
D. E. Rumelhart and J. L. McClelland, 1986. Parallel Distributed Processing：Explorations in the Microstructure of Cognition, Vol. 1, MA: MITPress.
H. A. Rowley, S. Baluja, and T. Kanade, 1998. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), January 1998.
Kaastra, I., Boyd, M., 1996. Designing a neural network for forecasting fi nancial and economic time series. Neurocomputing, 10, 215-236.
M. T. Hagan, H. B. Demuth, Neural Network Design, Thomson Learning, 1996.
Stent, G. S., 1973. A Physiological Mechanism for Hebb’s Postulate of Learning. PNAS 70 (4), 997–1001.
Hush, D. and Horne, B.1993. Progress in supervised neural networks. IEEE Signal Processing Magazine, 10(1):8-39.
Gately, E., 1996.Neural Networks for Financial Forecasting. John Wiley, New York
Martin F. and Aguado J.A., 2003.Wavelet-based ANN approach for transmission line protection, IEEE Trans. Power Delivery, vol.18, no.4, pp.1572-1574.
Nayak, P.C., Sudheer, K.P., Rangan, D.M. and Ramasastri, K.S., 2004. Aneuro-fuzzy computing technique for modeling hydrological time series, Journal of Hydrology, 291(1-2): 52-66.
Joseph E. Granville, 1960. A Strategy of Daily Stock Market Timing for Maximum Profit. Englewood Cliffs, N. J.: Prentice-Hall, p.155.
Kitchin, Joseph., 1923. Cycles and Trends in Economic Factors, Review of Economics and Statistics, The MIT Press, 5 (1), pp. 10-16.
Harry S. Dent Jr., 2008. The Great Depression Ahead: How to Prosper in the Crash
Following the Greatest Boom in History (New York: Free Press, 2008), at17-39.
Siegel, J.J., "Stocks for the Long Run", 4nd ed., New York: McGraw-Hill, 1998.
Chang-Hsu Liu, 2010. Calendar Anomalies: A Comparative Study of International Equity Markets.
Jasemi, M., Kimiagari, A. M. and Memariani, A., 2011. A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick. Expert Systems with Applications 38, 3884–3890. |
