A critical feature extraction by kernel PCA in stock trading model

详细信息    查看全文

• 作者：Pei-Chann Chang (1)
  Jheng-Long Wu (1)
  
  1. Innovation Center of Big Data & Digital Convergence and Department of Information Management ; Yuan Ze University ; Taoyuan ; Taiwan
  
• 关键词：Kernel PCA ; Feature extraction ; Dimensionality reduction ; Stock trading model ; Financial forecasting
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：19
• 期：5
• 页码：1393-1408
• 全文大小：1,841 KB
• 参考文献：1. Achelis B (2000) Technical analysis from A to Z, 4th edn. McGraw-Hill, New York
  2. Chang, PC, Liao, TW, Lin, JJ, Fan, CY (2011) A dynamic threshold decision system for stock trading signals detection. Appl Soft Comput 1: pp. 3998-4010 CrossRef
  3. Chang, PC, Lin, JJ, Hsieh, JC (2012) Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models. Appl Soft Comput 12: pp. 3165-3175 CrossRef
  4. Comon, P (1994) Independent component analysis, a new concept?. Signal Process 36: pp. 287-314 CrossRef
  5. Dai, J, Xu, Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13: pp. 211-221 CrossRef
  6. Derrac, J, Verbiest, N, Garc铆a, S, Cornelis, C, Herrera, F (2013) On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection. Soft Comput 17: pp. 223-238 CrossRef
  7. Diamantaras, KI, Kung, SY (1996) Principal component neural networks. Wiley, New York
  8. Ding C, He X, Zha H, Simon HD (2003) Adaptive dimension reduction for clustering high dimensional data. In: Proceedings of IEEE international conference on data mining, pp 147鈥?54
  9. Draper N, Smith H (1981) Applied regression analysis, 2nd edn. Wiley, New York
  10. Ekbal, A, Saha, S (2013) Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition. Soft Comput 17: pp. 1-16 CrossRef
  11. Fan, TH, Cheng, KF (2007) Tests and variables selection on regression analysis for massive datasets. Data Knowl Eng 63: pp. 811-819 CrossRef
  12. Guo, L, Rivero, D, Dorado, J, Munteanu, CR, Pazos, A (2011) Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst Appl 38: pp. 10425-10436 CrossRef
  13. Guo, Z, Wang, H, Liu, Q (2013) Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17: pp. 805-818 CrossRef
  14. Hoffmann, H (2007) Kernel PCA for novelty detection. Pattern Recognit 40: pp. 863-874 CrossRef
  15. Hoyer, PO, Hyv盲rinen, A (2000) Independent component analysis applied to feature extraction from colour and stereo images. Network 11: pp. 191-210 CrossRef
  16. Hoyer, PO, Hyv盲rinen, A, Yamamoto, R (2012) Intraday technical analysis of individual stocks on the Tokyo Stock Exchange. J Bank Financ 36: pp. 3033-3047
  17. Jolliffe, IT (2002) Principal component analysis. Springer, New York
  18. Li W, Liu Z (2011) A method of SVM with normalization in intrusion detection. Procedia Environ Sci 11(A): 256鈥?62
  19. Lin, X, Yang, Z, Song, Y (2011) Intelligent stock trading system based on improved technical analysis and echo state network. Expert Syst Appl 38: pp. 11347-11354 CrossRef
  20. Luna, I, Ballini, R (2011) Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting. Int J Forecast 27: pp. 708-724 CrossRef
  21. Mika S, Sch枚lkopf B, Smola A, M眉ller KR, Scholz M, R盲tsch G (1998) Kernel PCA and de-noising in feature spaces. In: Proceeding of the 1998 conference on advances in neural information processing system II, pp 536鈥?42
  22. Mitchell, TM (1997) Machine learning. McGraw-Hill, New York
  23. Samet, H (2006) Foundations of multidimensional and metric data structures. Morgan Kaufmann, San Francisco
  24. Scholkopf B, Smola A, Muller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299鈥?319
  25. Scholkopf, B, Mika, S, Burges, CJC, Knirsch, P, Muller, KR, Ratsch, G, Smola, A (1999) Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw 10: pp. 1000-1017 CrossRef
  26. Sch枚lkopf B, Smola A, Muller KR (1999) Kernel principal component analysis. In: Sch枚lkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods-support vector learning. MIT Press, Cambridge, pp 327鈥?52
  27. Scholz, M, Kaplan, F, Guy, CL, Kopka, J, Selbig, J (2005) Non-linear PCA: a missing data approach. Bioinformatics 21: pp. 3887-3895 CrossRef
  28. Smola, A, Sch枚lkopf, B (2004) A tutorial on support vector regression. J Stat Comput 14: pp. 199-222 CrossRef
  29. Ssegane H, Tollner EW, Mohamoud YM, Rasmussen TC, Dowd JF (2012) Advances in variable selection methods I: causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships. J Hydrol 438鈥?39:16鈥?5
  30. Tan F, Fu X, Zhang Y, Bourgeois AG (2006) A genetic algorithm-based method for feature subset selection. Soft Comput 12(2):111鈥?20
  31. Tsai, CF, Hsiao, YC (2010) Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis Support Syst 50: pp. 258-269 CrossRef
  32. Wu JL, Chang PC (2012) A trend-based segmentation method and the support vector regression for financial time series forecasting. Math Probl Eng, 20 pp. Article ID 615152
  33. Wu JL, Chang PC, Chang KT, Zhang L (2011) A collaborative trading model by support vector regression and TS fuzzy rule for daily stock turning points detection. In: Proceedings of the 2011 3rd international conference on computer engineering and technology, pp 185鈥?90
  34. Wu, JL, Yu, LC, Chang, PC (2011) Emotion classification by removal of the overlap from incremental association language features. J Chin Inst Eng 34: pp. 947-955 CrossRef
  35. Zhang, C, Xiang, S, Nie, F, Song, Y (2009) Nonlinear dimensionality reduction with relative distance comparison. Neurocomputing 72: pp. 1719-1731 CrossRef
  36. Zhu, X, Huang, Z, Yang, Y, Shen, HT, Xu, C, Luo, J (2013) Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognit 46: pp. 215-229 CrossRef
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

This paper presents a kernel-based principal component analysis (kernel PCA) to extract critical features for improving the performance of a stock trading model. The feature extraction method is one of the techniques to solve dimensionality reduction problems (DRP). The kernel PCA is a feature extraction approach which has been applied to data transformation from known variables to capture critical information. The kernel PCA is a kernel-based data mapping tool that has characteristics of both principal component analysis and non-linear mapping. The feature selection method is another DRP technique that selects only a small set of features from known variables, but these features still indicate possible collinearity problems that fail to reflect clear information. However, most feature extraction methods use a variable mapping application to eliminate noisy and collinear variables. In this research, we use the kernel-PCA method in a stock trading model to transform stock technical indices (TI) which allows features of smaller dimension to be formed. The kernel-PCA method has been applied to various stocks and sliding window testing methods using both half-year and 1-year testing strategies. The experimental results show that the proposed method generates more profits than other DRP methods on the America stock market. This stock trading model is very practical for real-world application, and it can be implemented in a real-time environment.