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題名 基於 EEMD 之類神經網路預測方法進行台指選擇權交易策略
TAIEX option trading by using EEMD-based neural network learning paradigm
作者 李恩慈
Li, En Tzu
貢獻者 蕭又新<br>廖四郎
Shiau, Yuo Hsien<br>Liao, Szu Lang
李恩慈
Li, En Tzu
關鍵詞 EEMD
ANN
交易策略
FK 指標
EEMD
ANN
Forecasting
FK Indicator
日期 2010
上傳時間 4-Sep-2013 15:27:39 (UTC+8)
摘要 金融市場瞬息萬變，幾乎所有商品價格都是非線性的動態過程，如何預測價格一直都是倍受討論和研究的議題。隨著電腦科技的不斷進步,許多財務學者以市場上的歷史交易資料作為研究對象，希望能夠預測出有效的結果。本研究利用 EEMD 法拆解原始加權指數訊號，建立類神經網路模型，並預測出未來市場之價格後，利用 FK 值當作交易門檻，帶回台指選擇權做交易測試並計算報酬。由於不同神經元個數會配適出不同的預測結果，本研究希望能夠找到較適合使用在指數預測的網路架構。
The financial market forecasting is characterized by data intensity, noise, non-stationary, high degree of uncertainty, and hidden relationships. Investors are concerned about the forecasting market price. Throughout the development of computational technology, researchers have been involved in data mining on historical trading enabling them to have a more accurate data. This research uses Ensemble Empirical Mode Decomposition-based Artificial Neural Networks (ANNs) learning paradigm to provide different ways to analyze the stock market. In our research, we used the ANN method to obtain our prediction of the stock price. First, the previous day’s stock price needs to be decomposed in order to see the various variables, that is, the numerous IMFs seen on the graphs. Acquiring the information, it is inserted into the ANN method to get a prediction. Following that, the prediction can then be transformed into a simpler result via the Forward Calculator % K indicator. As a result, the FK value can display a signal if to buy or sell, and confirm trading time, and make buy or sell Call-Put decisions on TAIEX options. In summary,we found different neuron numbers in the hidden layers that may affect the result of prediction.
參考文獻 Abu-mostafa Y. S. and Atiya A. F., Introduction to Financial Forecasting, Applied Intelligence, 1996, 6: 205-213.

Black F. and Scholes M.,The Pricing of Options and Corporate Liabilities, The Journal of Political Economy,1973, vol. 81,No. 3:637-654.

Chan M. C., Wong C. C., Lam C. C., Financial time series forecasting by neural network using conjugate gradient learning algorithm and multiple linear regression Weight Initialization, 2000, Citeseer.

Djennas Me., Benbouziane M. and Djennas Mu., An Approach of Combining Empirical Mode Decomposition and NeuralNetwork Learning for Currency Crisis

Forecasting, Politics and Economic Development, ERF 17th annual conference, 2011,Turkey.

Huang N.E., Shen Z., and Long S. R., The empirical mode decomposition and the hilbert spectrum for onlinear and non-stationary time series analysis, Process of the Royal Society of London, 1998, A454: 903–995.

Huang N. E., Wu M. L. Qu W. D., Long S. R., Shen S. P.and Zhang J. E., Applications of Hilbert–Huang transform to non-stationary financial time series analysis, Appl. Stochastic Models Bus. Ind., 2003: 245:268

Hamid S. A. and Iqbal Z., Using neural networks for forecasting volatility of S&P 500 Index futures prices, Journal of Business Research, 2004, 57: 1116-1125

Kaastra I., Boyd M., Designing a neural network for forecasting financial and economic time series, Neurocomputing, 1996, 10: 215-236

Klevecka, I., Lelis J., Pre-Processing of Input Data of Neural Networks: The Case of Forecasting Telecommunication Network Traffic, Telenor ASA, 2008

Lin, T. W. and Yu, C. C., Forecasting stock market with neural networks, SSRN Working Paper, 2009

Mendelsohn L., Preprocessing data for Neural Networks, Tech Anal Stocks Commod, 1993:52-58

Wu, Z. and Huang, N.E., Ensemble empirical mode decomposition: a noise-assisted data analysis method, Centre for Ocean Land Atmosphere Studies. Technical Report, 2004, 193: 51

Yu. L., Wang, S. Y. and Lai, K. K., Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm, Energy Economics,2008, 30: 2623-2635

Yu L., Wang S. Y., Lai K. K., Wen F. H., A multiscale neural network learning paradigm for financial crisis forecasting, Neuro computing, 2010, 73:716-725

Zhang, X., Lai, K.K., and Wang, S. Y., A new approach for crude oil price analysis based on Empirical Mode Decomposition, Energy Economics, 2008, 30: 905-918
描述 碩士
國立政治大學
應用物理研究所
98755003
99
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098755003
資料類型 thesis
dc.contributor.advisor 蕭又新<br>廖四郎zh_TW
dc.contributor.advisor Shiau, Yuo Hsien<br>Liao, Szu Langen_US
dc.contributor.author (Authors) 李恩慈zh_TW
dc.contributor.author (Authors) Li, En Tzuen_US
dc.creator (作者) 李恩慈zh_TW
dc.creator (作者) Li, En Tzuen_US
dc.date (日期) 2010en_US
dc.date.accessioned 4-Sep-2013 15:27:39 (UTC+8)-
dc.date.available 4-Sep-2013 15:27:39 (UTC+8)-
dc.date.issued (上傳時間) 4-Sep-2013 15:27:39 (UTC+8)-
dc.identifier (Other Identifiers) G0098755003en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60093-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用物理研究所zh_TW
dc.description (描述) 98755003zh_TW
dc.description (描述) 99zh_TW
dc.description.abstract (摘要) 金融市場瞬息萬變，幾乎所有商品價格都是非線性的動態過程，如何預測價格一直都是倍受討論和研究的議題。隨著電腦科技的不斷進步,許多財務學者以市場上的歷史交易資料作為研究對象，希望能夠預測出有效的結果。本研究利用 EEMD 法拆解原始加權指數訊號，建立類神經網路模型，並預測出未來市場之價格後，利用 FK 值當作交易門檻，帶回台指選擇權做交易測試並計算報酬。由於不同神經元個數會配適出不同的預測結果，本研究希望能夠找到較適合使用在指數預測的網路架構。zh_TW
dc.description.abstract (摘要) The financial market forecasting is characterized by data intensity, noise, non-stationary, high degree of uncertainty, and hidden relationships. Investors are concerned about the forecasting market price. Throughout the development of computational technology, researchers have been involved in data mining on historical trading enabling them to have a more accurate data. This research uses Ensemble Empirical Mode Decomposition-based Artificial Neural Networks (ANNs) learning paradigm to provide different ways to analyze the stock market. In our research, we used the ANN method to obtain our prediction of the stock price. First, the previous day’s stock price needs to be decomposed in order to see the various variables, that is, the numerous IMFs seen on the graphs. Acquiring the information, it is inserted into the ANN method to get a prediction. Following that, the prediction can then be transformed into a simpler result via the Forward Calculator % K indicator. As a result, the FK value can display a signal if to buy or sell, and confirm trading time, and make buy or sell Call-Put decisions on TAIEX options. In summary,we found different neuron numbers in the hidden layers that may affect the result of prediction.en_US
dc.description.tableofcontents 1. Introduction ............................................................................................................... 7

2. Methodology ........................................................................................................... 12

2.1 The Ensemble Empirical Mode Decomposition (EEMD) ........................ 12

2.2 The Artificial Neural Networks (ANNs) .................................................. 17

2.3 EEMD-Based Neural Network Learning paradigm .................................. 22

3. Index Options .......................................................................................................... 24

4. Algorithmic Trading ............................................................................................... 31

4.1 Moving FK Indicator ................................................................................ 31

4.2 Process ...................................................................................................... 33

4.3 Performance .............................................................................................. 36

5. Conclusion .............................................................................................................. 39

APPENDIX A. ............................................................................................................ 40

APPENDIX B. ............................................................................................................ 41

APPENDIX C. ............................................................................................................ 43

Reference .................................................................................................................... 44		zh_TW
dc.format.extent 1089284 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098755003en_US
dc.subject (關鍵詞) EEMDzh_TW
dc.subject (關鍵詞) ANNzh_TW
dc.subject (關鍵詞) 交易策略zh_TW
dc.subject (關鍵詞) FK 指標zh_TW
dc.subject (關鍵詞) EEMDen_US
dc.subject (關鍵詞) ANNen_US
dc.subject (關鍵詞) Forecastingen_US
dc.subject (關鍵詞) FK Indicatoren_US
dc.title (題名) 基於 EEMD 之類神經網路預測方法進行台指選擇權交易策略zh_TW
dc.title (題名) TAIEX option trading by using EEMD-based neural network learning paradigmen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Abu-mostafa Y. S. and Atiya A. F., Introduction to Financial Forecasting, Applied Intelligence, 1996, 6: 205-213.

Black F. and Scholes M.,The Pricing of Options and Corporate Liabilities, The Journal of Political Economy,1973, vol. 81,No. 3:637-654.

Chan M. C., Wong C. C., Lam C. C., Financial time series forecasting by neural network using conjugate gradient learning algorithm and multiple linear regression Weight Initialization, 2000, Citeseer.

Djennas Me., Benbouziane M. and Djennas Mu., An Approach of Combining Empirical Mode Decomposition and NeuralNetwork Learning for Currency Crisis

Forecasting, Politics and Economic Development, ERF 17th annual conference, 2011,Turkey.

Huang N.E., Shen Z., and Long S. R., The empirical mode decomposition and the hilbert spectrum for onlinear and non-stationary time series analysis, Process of the Royal Society of London, 1998, A454: 903–995.

Huang N. E., Wu M. L. Qu W. D., Long S. R., Shen S. P.and Zhang J. E., Applications of Hilbert–Huang transform to non-stationary financial time series analysis, Appl. Stochastic Models Bus. Ind., 2003: 245:268

Hamid S. A. and Iqbal Z., Using neural networks for forecasting volatility of S&P 500 Index futures prices, Journal of Business Research, 2004, 57: 1116-1125

Kaastra I., Boyd M., Designing a neural network for forecasting financial and economic time series, Neurocomputing, 1996, 10: 215-236

Klevecka, I., Lelis J., Pre-Processing of Input Data of Neural Networks: The Case of Forecasting Telecommunication Network Traffic, Telenor ASA, 2008

Lin, T. W. and Yu, C. C., Forecasting stock market with neural networks, SSRN Working Paper, 2009

Mendelsohn L., Preprocessing data for Neural Networks, Tech Anal Stocks Commod, 1993:52-58

Wu, Z. and Huang, N.E., Ensemble empirical mode decomposition: a noise-assisted data analysis method, Centre for Ocean Land Atmosphere Studies. Technical Report, 2004, 193: 51

Yu. L., Wang, S. Y. and Lai, K. K., Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm, Energy Economics,2008, 30: 2623-2635

Yu L., Wang S. Y., Lai K. K., Wen F. H., A multiscale neural network learning paradigm for financial crisis forecasting, Neuro computing, 2010, 73:716-725

Zhang, X., Lai, K.K., and Wang, S. Y., A new approach for crude oil price analysis based on Empirical Mode Decomposition, Energy Economics, 2008, 30: 905-918		zh_TW