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題名 基於 EEMD 與類神經網路方法進行台指期貨高頻交易研究
A Study of TAIEX Futures High-frequency Trading by using EEMD-based Neural Network Learning Paradigms
作者 黃仕豪
Huang, Sven Shih Hao
貢獻者 蕭又新
Shiau, Yuo Hsien
黃仕豪
Huang, Sven Shih Hao
關鍵詞 類神經網路方法
燭型圖(K線圖)
自回歸滑動平均模型
集合經驗模態分解法
高頻交易
平行運算
時間序列分析
大型數據處理
Artificial Neural Networks
Candlestick Charts
Autoregressive Moving Average model
Ensemble Empirical Mode Decomposition
High-Frequency Trading
Parallel Computing
Time series analysis
Big Data Processing
日期 2013
上傳時間 25-Aug-2014 15:22:46 (UTC+8)
摘要 金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。
Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.
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描述 碩士
國立政治大學
應用物理研究所
100755005
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100755005
資料類型 thesis
dc.contributor.advisor 蕭又新zh_TW
dc.contributor.advisor Shiau, Yuo Hsienen_US
dc.contributor.author (Authors) 黃仕豪zh_TW
dc.contributor.author (Authors) Huang, Sven Shih Haoen_US
dc.creator (作者) 黃仕豪zh_TW
dc.creator (作者) Huang, Sven Shih Haoen_US
dc.date (日期) 2013en_US
dc.date.accessioned 25-Aug-2014 15:22:46 (UTC+8)-
dc.date.available 25-Aug-2014 15:22:46 (UTC+8)-
dc.date.issued (上傳時間) 25-Aug-2014 15:22:46 (UTC+8)-
dc.identifier (Other Identifiers) G0100755005en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/69232-
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) 100755005zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。zh_TW
dc.description.abstract (摘要) Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.en_US
dc.description.tableofcontents 口試委員會審定書 i
Acknowledgments iii
中文摘要 v
Abstract vii
Contents ix
List of Figures xiii
List of Tables xvii
1 Introduction 1
1.1 Overview of The Development Track of Quantitative Analysis, Econophysics and High-Frequency Trading 1
1.2 EEMD and ANN in Forecasting 6
2 Methodology 9
2.1 Empirical Mode Decomposition (EMD) 9
2.1.1 Ensemble Empirical Mode Decomposition (EEMD) 11
2.2 The Artificial Neural Networks (ANNs) 17
2.2.1 Operation of Back Propagation Neuron 21
2.3 Autoregressive Moving Average model (ARMA) 27
2.3.1 The Autoregressive Model (AR) 27
2.3.2 The Moving-Average Model (MA) 27
2.3.3 ARMA(p,q) Model 28
2.4 High Frequency Trading, Big Data Processing and Parallel computing 29
2.4.1 High Frequency Data 29
2.4.2 Parallel Computing for high-frequency trading backtesting 29
2.5 Candlestick Charts 32
2.5.1 Supply and Demand Principle with Candlestick Chart 33
3 Market Efficiency and Physical Mean of IMFs 35
3.1 The Efficient-Market Hypothesis(EMH) 35
3.2 Market Inefficiency and High Frequency Trading 36
3.3 The Randomness of Data 38
3.4 Physical Meaning of IMFs in High Frequency Data 41
3.4.1 Long-Short Position 42
3.4.2 Volume 44
3.4.3 MSCI Taiwan 51
3.4.4 Gold Futures 54
3.4.5 TWD/USD Currency Rate 57
4 Hybrid Algorithmic Trading Model 61
4.1 Constructing Model 61
4.1.1 The Moving Window Method 61
4.1.2 The construction models 62
4.2 Performance 70
4.2.1 Evaluation Indexes 70
4.2.2 Performance Analysis 72
5 Conclusion 79
5.1 Summary 79
5.2 Future Works 80
Appendix A Data tables 83
Bibliography 87		zh_TW
dc.format.extent 1986452 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/#G0100755005en_US
dc.subject (關鍵詞) 類神經網路方法zh_TW
dc.subject (關鍵詞) 燭型圖(K線圖)zh_TW
dc.subject (關鍵詞) 自回歸滑動平均模型zh_TW
dc.subject (關鍵詞) 集合經驗模態分解法zh_TW
dc.subject (關鍵詞) 高頻交易zh_TW
dc.subject (關鍵詞) 平行運算zh_TW
dc.subject (關鍵詞) 時間序列分析zh_TW
dc.subject (關鍵詞) 大型數據處理zh_TW
dc.subject (關鍵詞) Artificial Neural Networksen_US
dc.subject (關鍵詞) Candlestick Chartsen_US
dc.subject (關鍵詞) Autoregressive Moving Average modelen_US
dc.subject (關鍵詞) Ensemble Empirical Mode Decompositionen_US
dc.subject (關鍵詞) High-Frequency Tradingen_US
dc.subject (關鍵詞) Parallel Computingen_US
dc.subject (關鍵詞) Time series analysisen_US
dc.subject (關鍵詞) Big Data Processingen_US
dc.title (題名) 基於 EEMD 與類神經網路方法進行台指期貨高頻交易研究zh_TW
dc.title (題名) A Study of TAIEX Futures High-frequency Trading by using EEMD-based Neural Network Learning Paradigmsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] B. B. Mandelbrot, “A multifractalwalkdown,” Scientific American, p. 71, 1999.
[2] F. Black and M. Scholes, “The pricing of options and corporate liabilities,” The jour-
nal of political economy, pp. 637–654, 1973.
[3] J. L. Treynor, “How to rate management of investment funds,” Harvard business
review, vol. 43, no. 1, pp. 63–75, 1965.
[4] R. N. Mantegna, H. E. Stanley, et al., An introduction to econophysics: correlations
and complexity in finance, vol. 9. Cambridge university press Cambridge, 2000.
[5] A. J. Frost and R. R. Prechter, Elliott wave principle: key to market behavior. Elliott
Wave International, 2005.
[6] R. N. Elliott, “The wave principle,” New York, 1938.
[7] H. Kleinert, Path integrals in quantum mechanics, statistics, polymer physics, and
financial markets. World Scientific, 2009.
[8] M. Chlistalla, B. Speyer, S. Kaiser, and T. Mayer, “High-frequency trading,”
Deutsche Bank Research, pp. 1–19, 2011.
[9] B. Biais and P. Woolley, “High frequency trading,” Manuscript, Toulouse University,
IDEI, 2011.
[10] S.-S. Chern and J. Simons, “Characteristic forms and geometric invariants,” Annals
of Mathematics, pp. 48–69, 1974.
[11] I. Aldridge, High-frequency trading: a practical guide to algorithmic strategies and
trading systems. John Wiley & Sons, 2013.
[12] C. A. Goodhart and M. O’Hara, “High frequency data in financial markets: Issues
and applications,” Journal of Empirical Finance, vol. 4, no. 2, pp. 73–114, 1997.
[13] Y. S. Abu-Mostafa and A. F. Atiya, “Introduction to financial forecasting,” Applied
Intelligence, vol. 6, no. 3, pp. 205–213, 1996.
[14] 羅華強 and 通信工程, 類神經網路: MATLAB 的應用. 高立, 2011.
[15] 蘇木春, 張孝德, et al., 機器學習: 類神經網路, 模糊系統以及基因演算法則. 臺
北市: 全華科技圖書股份有限公司, 1997.
[16] S. A. Hamid and Z. Iqbal, “Using neural networks for forecasting volatility of s&p
500 index futures prices,” Journal of Business Research, vol. 57, no. 10, pp. 1116–
1125, 2004.
[17] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C.
Tung, and H. H. Liu, “The empirical mode decomposition and the hilbert spectrum
for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Soci-
ety of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454,
no. 1971, pp. 903–995, 1998.
[18] Z. Wu and N. E. Huang, “A study of the characteristics of white noise using the
empirical mode decomposition method,” Proceedings of the Royal Society of Lon-
don. Series A: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046,
pp. 1597–1611, 2004.
[19] I. Kļevecka and J. Lelis, “Pre-processing of input data of neural networks: the case of
forecasting telecommunication network traffic,” publication. editionName, vol. 104,
pp. 168–178, 2008.
[20] Y. C. Tsai, “Forecasting electricity consumption as well as gold price by using an
eemd-based back-propagation neural network learning paradigm,” Master’s thesis,
National Chengchi University, Taiwan, 2011.
[21] Y.-H. Wang, C.-H. Yeh, H.-W. V. Young, K. Hu, and M.-T. Lo, “On the computational
complexity of the empirical mode decomposition algorithm,” Physica A: Statistical
Mechanics and its Applications, vol. 400, pp. 159–167, 2014.
[22] H. Demuth and M. Beale, “Neural network toolbox for use with matlab,” 1993.
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