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研究生:余松庭
研究生(外文):YU, SONGTING
論文名稱(外文):Trend Prediction based on SVM with Nonlinear Feature Selection
指導教授:李政軒李政軒引用關係
指導教授(外文):LI, CHEN-HSUAN
口試委員:郭伯臣黃孝雲李政軒
口試委員(外文):KUO, BOR-CHENHUANG, HSIAO-YUNLI, CHEN-HSUAN
口試日期:2017-06-21
學位類別:碩士
校院名稱:國立臺中教育大學
系所名稱:教育資訊與測驗統計研究所
學門:教育學門
學類:教育測驗評量學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:44
外文關鍵詞:Stock market prediction, Support vector machine, Kernel method, Nonlinear feature selection method, Automatic parameter selection method
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Prediction of the stock market is one of the popular research issues in recent years. The stock market trend in Taiwan is highly related to the industry. There are many factors considered as the main effects of fluctuation of the stock's price such as political factors, economic factors and the monetary policies. Stockholders and investors used lots of technical indicators to estimate the flow of stock market. Moreover, many researchers have applied Support Vector Machine (SVM) to predict the fluctuation of stock trend.
In this study, the nonlinear kernel-based feature selection method was applied to determine an appropriate subset (i.e., suitable indicators) with the largest nonlinear class separability. Furthermore, an automatic SVM based on the automatic parameter selection method was used to predict the stock trend (Raise or Down). The examination on the stock price of TSMC (Taiwan Semiconductor Manufacturing Company), Hon Hai Precision Industry Company Ltd and United Microelectronics Corporation (UMC) from 23 June 2008 to 4 October 2016 showed that the prediction accuracy is close to 0.885, 0.933 and 0.957 respectively based on selected technical analysis indicators.
The Coh-Metrix education data set related to reading ability is also our target to analyze. When we utilize both indices such as Content word frequency and Age of acquisition, the accuracy appears to be 0.862 that is similar to the highest accuracy 0.865 based on about twenty three selected indices.

Keywords: Stock market prediction, Support vector machine, Kernel method, Nonlinear feature selection method, Automatic parameter selection method


Acknowledgments II
Abstract III
Table of Contents IV
List of Tables V
List of Figures VI
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 LITERATURE REVIEW 3
2.1 Stock forecasting 3
2.2 Stock price prediction with machine learning 4
2.3 Support vector machine 6
2.4 An automatic kernel parameter selection 7
2.5 Kernel-based feature selection 9
CHAPTER 3 RESEARCH METHOD 13
3.1 Stock data collection 13
3.2 Technical indicators 15
3.3 Coh-metrix education data set 20
3.4 Research flow chart 21
CHAPTER 4 RESEARCH RESULTS 23
4.1 Stock prediction of TSMC (2330) 23
4.2 Stock prediction of Hon Hai Precision Industry Company Ltd (2317) 26
4.3 Stock prediction of UMC (2303) 30
4.4 Results of coh-metrix education data set 34
CHAPTER 5 CONCLUSIONS 36
REFERENCES 38





Alexandru. C., & Caragea.N. (2015). The capital markets research based on the financial quantitative models. Eco-Economics Review, 1(1), 3-16.
Alsing, O., & Bahceci, O. (2015). Stock market prediction using social media analysis (Bachelors Thesis, KTH Royal Institute of Technology). Retrieved from http://kth.diva-portal.org/smash/get/diva2:811087/FULLTEXT01.pdf
Arafat, J., Habib, M. A., Hossain, R. (2013) Analyzing public emotion and predicting stock market using social media. American Journal of Engineering Research, 2(9), 265-275.
Arasu, B. S., Jeevananthan, M., Thamaraiselvan, N., & Janarthanan, B. (2014). Performances of data mining techniques in forecasting stock index - evidence from India and US. Journal of the National Science Foundation of Sri Lanka, 42(2), 177-191. doi: 10.4038/jnsfsr.v42i2.6989
Benediktsson, J. A., Palmason, A. J., & Sveinsson, J. R. (2005). Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 55, 229-243.
Ceviz, E. (2011). Risk quantification of mixed portfolios containing bonds and stocks (Master's thesis, Istanbul Commerce University). Retrieved from http://www.ie.boun.edu.tr/~hormannw/BounQuantitiveFinance/Thesis/EmreCeviz_Thesis.pdf
Cook, R.D., & Weisberg, S. (1999). Applied Regression Including Computing and Graphics. New York: John Wiley & Sons.
Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273–297.
Dale, E., & Chall, J. S. (1949). The concept of readability. Elementary English, 26, 19-26.
Dell’Acqua, F., Gamba, P., & Ferrari, A. (2003). Exploiting spectral and spatial information for classifying hyperspectral data in urban areas. Proceedings of IGARSS, 1, 464-466.
Earnforex. (2017). Technical analysis: how to read the price action. Retrieved from https://www.earnforex.com/guides/technical-analysis-how-to-read-the-price-action/
Falinouss, P. (2007). Stock trend prediction using news articles a text mining approach (Master's thesis, Luleå University of Technology). Retrieved from http://www.diva-portal.org/smash/get/diva2:1019373/FULLTEXT01.pdf
Farrahi. S., & Heydarizadeh. A. (2013). Application of hierarchical structure in stock classification and portfolio construction.(Bachelors Thesis, Mälardalen University College). Retrieved from http://econ.esy.es/econ/edu/cup/reports/2013/cluster.pdf
Foxconn Electronics Inc. (2013). Group profile. Retrieved from http://www.foxconn.com/GroupProfile_En/GroupProfile.html
Giuliani, M. (2011). Event study analysis of share price and stock market index data (Master's thesis, University of Stirling). Retrieved from http://www.cs.stir.ac.uk/courses/ITNP96/PastDissertations/2010-2011/Abstracts/GiulianiM.pdf
Graesser, A. C., McNamara, D. S., & Kulikowich, J. M. (2011). Coh-Metrix: Providing Multilevel Analyses of TextCharacteristics. Educational Researcher, 40, 223-234.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
Hargreaves, C. A., Dixit, P., & Solanki, A. (2013). Stock portfolio selection using data mining approach. IOSR Journal of Engineering, 3(1), 42-48.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer-Verlag.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer-Verlag
http://www.umc.com/English/about/index.asp
Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522. doi: 10.1016/j.cor.2004.03.016
IC Insights. (2017). TSMC will remain on top in 2016. Retrieved from http://technews.co/2016/08/31/ic-insights-tsmc-will-remain-on-top-in-2016/
Investopedia. (2017). Technical indicators. Retrieved from http://www.investopedia.com/active-trading/technical-indicators/
Kalyvas, E. (2001). Using Neural Networks And Genetic Algorithms To Predict Stock Market Returns (Master's thesis, University of Manchester). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.22.1625&rep=rep1&type=pdf
Klare, G. R. (2000). The measurement of Readability: Useful information for communicators. ACM Journal of Computer Documentation, 24, 107-121.
Lahmiri, S. (2014). Entropy-Based Technical Analysis Indicators Selection for International Stock Markets Fluctuations Prediction Using Support Vector Machines. Fluctuation and Noise Letters, 13(2). doi: 10.1142/s02194775 14500138
Landgrebe, D. A. (2005) Multispectral land sensing: where from, where to? IEEE Transactions on Geoscience and Remote Sensing, (43), 414-421.
Li, C. H., Ho, H. H., Liu, Y. L., Lin, C. T., Kuo, B. C., & Taur, J. S.(2012). An automatic method for selecting the parameter of the normalized kernel function to support vector machines. Journal of Information Science and Engineering, 28(1), 1-15.
Li, C. H., Lin, C. T., Kuo, B. C., & Chu, H. S. (2010). An automatic method for selecting the parameter of the RBF kernel function to support vector machines. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 836-839.
McLaughlin, G. H. (1969). SMOG grading: A new readability formula. Journal of Reading, 12, 639-646.
Mingyue, Q. (2014). A study on prediction of stock market index and portfolio selection (Doctoral dissertation, Fukuoka Institute of Technology) Retrieved from http://repository.lib.fit.ac.jp/bitstream/11478/145/1/DC_Ko_k_39.pdf
Ou, P., & Wang, H. S. (2012). Applications of Support Vector Machine in modeling and forecasting stock market volatility. Information-an International Interdisciplinary Journal, 15(8), 3365-3376.
Pehlivanli, A. C., Asikgil, B., & Gulay, G. (2016). Indicator selection with committee decision of filter methods for stock market price trend in ISE, Applied Soft Computing, 49, 792-800. doi: 10.1016/j.asoc.2016.09.004
QuantShare. (2017). DV Intermediate Oscillator (DVI). Retrieved from https://www.quantshare.com/item-1107-dv-intermediate-oscillator-dvi
Ruxanda, G., & Badea, L. M. (2014). Configuring artificial neural networks for stock market predictions. Technological and Economic Development of Economy, 20(1), 116-132. doi: 10.3846/20294913.2014.889051
Shah, V. H. (2007). Machine learning techniques for stock prediction. Retrieved from http://www.vatsals.com/essays/machinelearningtechniquesforstockprediction.pdf
Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for Pattern Analysis. New York: Cambridge University Press.
Sugumar, R., Rengarajan, A., & Jayakumar, C. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks. Computing and Informatics, 33(5), 992-1024.
Taiwan Semiconductor Manufacturing Company Limited. (2017). About TSMC. Retrieved from http://www.tsmc.com/english/investorRelations/index.htm
Thenmozhi, M., & Chand, G. S. (2016). Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Computing & Applications, 27(4), 805-824. doi: 10.1007/s00521-015-1897-9
Trading Technologies International. (2017). List of technical indicators. Retrieved from https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/list-of-technical-indicators/
TradingView. (2017). Technical analysis. Retrieved from https://www.tradingview.com/chart/technicalanalysis/
United Microelectronics Corporation. (2017). UMC overview. Retrieved from
Wang, Y. (2014). Stock price direction prediction by directly using prices data: an empiricalstudy on the kospi and hsi, International Journal of Business Intelligence and Data Mining, 9(2), 145-160.
Wu, B. H., & Duan, T. T. (2017). A performance comparison of neural networks in forecasting stock price trend. International Journal of Computational Intelligence Systems, 10(1), 336-346.
Yang, S. J. (1970). A readability formula for Chinese language. University of Wisconsin--Madison
Yu, L., Chen, H., Wang, S., & Lai, K. K. (2009). Evolving Least Squares Support Vector Machines for Stock Market Trend Mining. IEEE Transactions on Evolutionary Computation, 13(1), 87-102. doi: 10.1109/tevc.2008.928176
Zimbra, D., & Chen, H. C. (2011). A Stakeholder Approach to Stock Prediction Using Finance Social Media. IEEE Intelligent Systems, 26(6), 88-92.

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