Please use this identifier to cite or link to this item:
A study of Trading Strategies of TAIEX Futures by using EEMD-based Neural Network Learning Paradigms
Ensemble Empirical Mode Decomposition
Artificial Neural Network
|Issue Date: ||2013-02-01 16:55:52 (UTC+8)|
Financial market changes constantly and Stock Price Volatility (SPV) seems to be no significant rules. This means behavioral characteristic of the stock price cannot foresee and uncertain accurately. In order to increase revenue and reduce investment risk in the market, researchers had to try to establish an effective prediction model of financial markets. It can estimate the impact of this uncertainty. It's a great pity that there is not a model that is close successfully yet. That does not represent it does not exist successful model. Instead, researchers need to establish more predictive models to offer the market to judge the rule of thumb.
The forecasting results of TAIEX Index futures by ARMA Model and two types of EEMD-ANN Models were compared in two kinds of markets – trend and fluctuation. In addition, two trading strategies were tested after the future prices are forecasted. The study attempted to identify a suitable forecasting model.
Moreover, the factors for price fluctuation of TAIEX were also analyzed in the study. Through EEMD, they could be decomposed to IMFs with various physical meanings and more important IMFs were selected to be analyzed in accordance with the statistic value.
|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.
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0997550051|
|Data Type: ||thesis|
|Appears in Collections:||[應用物理研究所 ] 學位論文|
Files in This Item:
All items in 政大典藏 are protected by copyright, with all rights reserved.