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|Title: ||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
Huang, Sven Shih Hao
Shiau, Yuo Hsien
Huang, Sven Shih Hao
Artificial Neural Networks
Autoregressive Moving Average model
Ensemble Empirical Mode Decomposition
Time series analysis
Big Data Processing
|Issue Date: ||2014-08-25 15:22:46 (UTC+8)|
|Abstract: ||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.
|Reference: || B. B. Mandelbrot, “A multifractalwalkdown,” Scientific American, p. 71, 1999.|
 F. Black and M. Scholes, “The pricing of options and corporate liabilities,” The jour-
nal of political economy, pp. 637–654, 1973.
 J. L. Treynor, “How to rate management of investment funds,” Harvard business
review, vol. 43, no. 1, pp. 63–75, 1965.
 R. N. Mantegna, H. E. Stanley, et al., An introduction to econophysics: correlations
and complexity in finance, vol. 9. Cambridge university press Cambridge, 2000.
 A. J. Frost and R. R. Prechter, Elliott wave principle: key to market behavior. Elliott
Wave International, 2005.
 R. N. Elliott, “The wave principle,” New York, 1938.
 H. Kleinert, Path integrals in quantum mechanics, statistics, polymer physics, and
financial markets. World Scientific, 2009.
 M. Chlistalla, B. Speyer, S. Kaiser, and T. Mayer, “High-frequency trading,”
Deutsche Bank Research, pp. 1–19, 2011.
 B. Biais and P. Woolley, “High frequency trading,” Manuscript, Toulouse University,
 S.-S. Chern and J. Simons, “Characteristic forms and geometric invariants,” Annals
of Mathematics, pp. 48–69, 1974.
 I. Aldridge, High-frequency trading: a practical guide to algorithmic strategies and
trading systems. John Wiley & Sons, 2013.
 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.
 Y. S. Abu-Mostafa and A. F. Atiya, “Introduction to financial forecasting,” Applied
Intelligence, vol. 6, no. 3, pp. 205–213, 1996.
 羅華強 and 通信工程, 類神經網路: MATLAB 的應用. 高立, 2011.
 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.
 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–
 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.
 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.
 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.
 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.
 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.
 H. Demuth and M. Beale, “Neural network toolbox for use with matlab,” 1993.
 K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are
universal approximators,” Neural networks, vol. 2, no. 5, pp. 359–366, 1989.
 M. T. Hagan, H. B. Demuth, M. H. Beale, et al., Neural network design, vol. 1. Pws
 E. M. Azoff, Neural network time series forecasting of financial markets. John Wiley
& Sons, Inc., 1994.
 王奕鈞, “神經網路應用於地籍坐標轉換之研究,” 2005.
 陈明, MATLAB 神经网络原理与实例精解. 清华大学出版社, 2013.
 P. Whitle, Hypothesis testing in time series analysis, vol. 4. Almqvist & Wiksells,
 G. E. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: forecasting and
control. John Wiley & Sons, 2013.
 J. D. Hamilton, Time series analysis, vol. 2. Princeton university press Princeton,
 G. E. Box and D. A. Pierce, “Distribution of residual autocorrelations in
autoregressive-integrated moving average time series models,” Journal of the Amer-
ican statistical Association, vol. 65, no. 332, pp. 1509–1526, 1970.
 R. S. Tsay, Analysis of financial time series, vol. 543. John Wiley & Sons, 2005.
 A. Pole, Statistical arbitrage: algorithmic trading insights and techniques, vol. 411.
John Wiley & Sons, 2008.
 V. Menon and A. E. Trefethen, “Multimatlab integrating matlab with high per-
formance parallel computing,” in Supercomputing, ACM/IEEE 1997 Conference,
pp. 30–30, IEEE, 1997.
 B. Barney et al., “Introduction to parallel computing,” Lawrence Livermore National
Laboratory, vol. 6, no. 13, p. 10, 2010.
 T. Hendershott and R. Riordan, “Algorithmic trading and the market for liquidity,”
Journal of Financial and Quantitative Analysis, vol. 48, no. 04, pp. 1001–1024,
 M. Schaden, “Quantum finance,” Physica A: Statistical Mechanics and its Applica-
tions, vol. 316, no. 1, pp. 511–538, 2002.
 K. Lee and G. Jo, “Expert system for predicting stock market timing using a candle-
stick chart,” Expert Systems with Applications, vol. 16, no. 4, pp. 357–364, 1999.
 J. H. Fock, C. Klein, and B. Zwergel, “Performance of candlestick analysis on intra-
day futures data,” The Journal of Derivatives, vol. 13, no. 1, pp. 28–40, 2005.
 S. Nison, Japanese candlestick charting techniques: a contemporary guide to the
ancient investment techniques of the Far East. Penguin, 2001.
 DayTradingCoach, “Candlestick chart course.” http://www.
 T. Chordia, R. Roll, and A. Subrahmanyam, “Liquidity and market efficiency,” Jour-
nal of Financial Economics, vol. 87, no. 2, pp. 249–268, 2008.
 J. Brogaard, “High frequency trading and its impact on market quality,” Northwest-
ern University Kellogg School of Management Working Paper, p. 66, 2010.
 W. Hoeffding, “A non-parametric test of independence,” The Annals of Mathemati-
cal Statistics, pp. 546–557, 1948.
 L. Bachelier, “Théorie de la spéculation,” in Annales scientifiques de l’École Nor-
male Supérieure, vol. 17, pp. 21–86, Société mathématique de France, 1900.
 J. M. Karpoff, “The relation between price changes and trading volume: A survey,”
Journal of Financial and quantitative Analysis, vol. 22, no. 01, pp. 109–126, 1987.
 G. E. Tauchen and M. Pitts, “The price variability-volume relationship on speculative
markets,” Econometrica: Journal of the Econometric Society, pp. 485–505, 1983.
 S.-Y. Chen, C.-C. Lin, P.-H. Chou, and D.-Y. Hwang, “A comparison of hedge ef-
fectiveness and price discovery between taifex taiex index futures and sgx msci tai-
wan index futures,” Review of Pacific Basin Financial Markets and Policies, vol. 5,
no. 02, pp. 277–300, 2002.
 MSCI, “Msci taiwan.” http://www.msci.com/products/indexes/
 C. Wang and S. Sern Low, “Hedging with foreign currency denominated stock index
futures: evidence from the msci taiwan index futures market,” Journal of Multina-
tional Financial Management, vol. 13, no. 1, pp. 1–17, 2003.
 H.-P. Spahn, From Gold to Euro: On monetary theory and the history of currency
systems. Springer, 2001.
 G. Grudnitski and L. Osburn, “Forecasting s&p and gold futures prices: an applica-
tion of neural networks,” Journal of Futures Markets, vol. 13, no. 6, pp. 631–643,
 T. G. Andersen and T. Bollerslev, “Intraday periodicity and volatility persistence in
financial markets,” Journal of empirical finance, vol. 4, no. 2, pp. 115–158, 1997.
 I. S. Abdalla and V. Murinde, “Exchange rate and stock price interactions in emerging
financial markets: evidence on india, korea, pakistan and the philippines,” Applied
financial economics, vol. 7, no. 1, pp. 25–35, 1997.
 C. K. Ma and G. W. Kao, “On exchange rate changes and stock price reactions,”
Journal of Business Finance & Accounting, vol. 17, no. 3, pp. 441–449, 1990.
 A. Lendasse, E. de Bodt, V. Wertz, M. Verleysen, et al., “Non-linear financial time
series forecasting-application to the bel 20 stock market index,” European Journal
of Economic and Social Systems, vol. 14, no. 1, pp. 81–92, 2000.
 E. T. Li, “Taiex option trading by using eemd-based neural network learning
paradigm,” Master’s thesis, National Chengchi University, Taiwan, 2011.
 Y. H. Chen, “A study of trading strategies of taiex futures by using eemd-based neural
network learning paradigms,” Master’s thesis, National Chengchi University, Tai-
 KaplanSchweser, ed., SCHWESERNOTES 2014 CFA LEVEL I BOOK 1: ETHI-
CAL AND PROFESSIONAL STANDARDS AND QUANTITATIVE METHODS. Ka-
 D. Kirk, “Nvidia cuda software and gpu parallel computing architecture,” in ISMM,
vol. 7, pp. 103–104, 2007.
 M. Fatica and W.-K. Jeong, “Accelerating matlab with cuda,” in The High Perfor-
mance Embedded Computing Workshop, 2007.
 D. Agrawal, S. Das, and A. El Abbadi, “Big data and cloud computing: current state
and future opportunities,” in Proceedings of the 14th International Conference on
Extending Database Technology, pp. 530–533, ACM, 2011.
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0100755005|
|Data Type: ||thesis|
|Appears in Collections:||[應用物理研究所 ] 學位論文|
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