政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/56891

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/56891

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政大典藏 > College of Science > Graduate Institute of Applied Physics > Theses > Item 140.119/56891

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/56891

Title:	基於EEMD與類神經網路建構台指期貨交易策略 A study of Trading Strategies of TAIEX Futures by using EEMD-based Neural Network Learning Paradigms
Authors:	陳原孝 Chen,Yuan Hsiao
Contributors:	蕭又新陳原孝 Chen,Yuan Hsiao
Keywords:	總體經驗模態類神經網路自回歸移動平均模型交易策略預測模型 Ensemble Empirical Mode Decomposition Artificial Neural Network ARMA Trading strategy Forecasting model
Date:	2012
Issue Date:	2013-02-01 16:55:52 (UTC+8)
Abstract:	金融市場瞬息萬變，股價漲跌似乎沒有顯著的規則，這意味著股價的行為特徵是不可精確預知和不確定的，為了在市場上增加收益和減少投資風險，研究人員不得不試圖建立一個有效預測金融市場的模型，它可以估算這種不確定性的影響，很可惜的，至今仍然沒有一個模型接近成功的。沒有成功的模型並不代表它是不存在的，相反的，研究人員需要建立更多的預測模型，以提供市場判斷的經驗法則。我們使用ARMA與兩種不同形式的EEMD-ANN去對台灣加權指數期貨做預測值的精確度比較，我們比較了兩種不同的行情：趨勢與震盪。此外，預測出未來市場之價格後，我們使用2種交易策略去做績效測試，本研究希望能夠找到較適合使用在指數預測的預測模型。另外在本文中，我們也分析影響TAIEX價格波動的因素，透過EEMD，我們可以將其拆解成數具有不同物理意義的本徵模態函數(IMF)，再藉由統計值選出較重要的IMF並分析其意義。 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.
Description:	碩士國立政治大學應用物理研究所 99755005 101
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0997550051
Data Type:	thesis
Appears in Collections:	[Graduate Institute of Applied Physics] Theses

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