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Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting

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Abstract

One of the major activities of financial firms and private investors is to predict future prices of stocks. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, chaotic and nonlinear dynamic system. As stock markets are highly dynamic and exhibit wide variation, it may be more realistic and practical that assumed the stock index data are a nonlinear mixture data. In this study, a hybrid stock index prediction model by utilizing nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes NLICA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. In order to evaluate the performance of the proposed approach, the closing indexes of the Taiwan stock exchange capitalization weighted stock index, Shanghai stock exchange composite index and Bombay stock exchange index are used as illustrative examples. Experimental results showed that the proposed hybrid stock index prediction method significantly outperforms the other six comparison models. It is an efficient and effective alternative for stock index forecasting.

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References

  1. Back AD, Weigend AS (1997) A first application of independent component analysis to extracting structure from stock returns. Int J Neural Syst 8:473–484

    Article  Google Scholar 

  2. Kiviluoto K, Oja E (1998) Independent component analysis for parallel financial time series. In: Proceedings of the international conference on neural information, Tokyo, Japan

  3. Malaroiu S, Kiviluoto K, Oja E (2000) Time series prediction with independent component analysis. In: Proceedings of international conference on advanced investment technology, Gold Coast, Australia

  4. Górriz JM, Puntonet CG, Salmeron M, Lang EW (2003) Time series prediction using ICA algorithms. Int Sci J Comput 2(2):69–75

    Google Scholar 

  5. Mok PY, Lam KP, Ng HS (2004) An ICA design of intraday stock prediction models with automatic variable selection. In: Proceedings of 2004 IEEE international joint conference on neural networks, Budapest, Hungary

  6. Cao LJ, Chong WK (2002) Feature extraction in support vector machine: a comparison of PCA XPCA and ICA. In: Proceedings of the 9th international conference on neural information processing, Singapore

  7. Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1–2):321–336

    Google Scholar 

  8. Kao LJ, Chiu CC, Lu CJ, Yang JL (2012) Integration of nonlinear independent component analysis and support vector regression for stock price forecasting. Neurocomputing. doi:10.1016/j.neucom.2012.06.037

  9. Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New York

    Book  Google Scholar 

  10. Almeida LB (2004) MISEP—Linear and nonlinear ICA based on mutual information. J Mach Learn Res 4(7–8):1297–1318

    MATH  Google Scholar 

  11. Hyvärinen A, Pajunen P (1999) Nonlinear independent component analysis: existence and uniqueness results. Neural Networks 12(3):429–439

    Article  Google Scholar 

  12. Valpola H (2000) Nonlinear independent component analysis using ensemble learning: theory. In: Proceedings of the 2nd international workshop on independent component analysis and blind signal separation, Espoo, Finland

  13. Valpola H, Giannakopoulos X, Honkela A, Karhunen J (2000) Nonlinear independent component analysis using ensemble learning: experiments and discussion. In: Proceedings of 2nd international workshop on independent component analysis and blind signal separation, Espoo, Finland

  14. Oja E, Kiviluoto K, Malaroiu S (2000) Independent component analysis for financial time series. In: Proceedings of the IEEE 2000 adaptive systems for signal processing, communications and control symposium, Lake Louise, Canada

  15. Lu CJ, Lee TS, Chiu CC (2009) Financial time series forecasting using independent component analysis and support vector regression. Decis Support Syst 47(2):115–125

    Article  Google Scholar 

  16. Lu CJ (2010) Integrating independent component analysis-based denoising scheme with neural network for stock price prediction. Expert Syst Appl 37(10):7056–7064

    Article  Google Scholar 

  17. Haritopoulos M, Yin H, Allinson NM (2002) Image denoising using self-organizing map-based nonlinear independent component analysis. Neural Netw 37(10):1085–1098

    Article  Google Scholar 

  18. Zhang K, Chan L (2007) Nonlinear independent component analysis with minimal nonlinear distortion. In: Proceedings of the 24th international conference on machine learning, Oregon, USA

  19. Lu CJ, Chiu CC, Yang JL (2009) Integrating nonlinear independent component analysis and neural network in stock price prediction. Lecture Notes Comput Sci 5579:614–623

    Article  Google Scholar 

  20. Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques—part II: soft computing methods. Expert Syst Appl 36(3):5932–5941

    Article  Google Scholar 

  21. Bahrammirzaee A (2010) A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Comput Appl 19(8):1165–1195

    Article  Google Scholar 

  22. Vapnik VN (2000) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  23. Hong WC, Dong Y, Zheng F, Wei SY (2011) Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Appl Math Comput 217(15):6733–6747

    Article  MathSciNet  MATH  Google Scholar 

  24. Hong WC (2008) Rainfall forecasting by technological machine learning models. Appl Math Comput 200(1):41–57

    Article  MathSciNet  MATH  Google Scholar 

  25. Lu CJ, Wang YW (2010) Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting. Int J Prod Econ 128(2):603–613

    Article  Google Scholar 

  26. Hua Z, Zhang B (2006) A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Appl Math Comput 181(2):1035–1048

    Article  MathSciNet  MATH  Google Scholar 

  27. Huang SC, Wu TK (2008) Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting. Expert Syst 25(2):133–149

    Article  Google Scholar 

  28. Lu CJ, Wu JY (2011) An efficient CMAC neural network for stock index forecasting. Expert Syst Appl 38(12):15194–15201

    Article  Google Scholar 

  29. Yeh CY, Huang CW, Lee SJ (2011) A multiple-kernel support vector regression approach for stock market price forecasting. Expert Syst Appl 38(3):2177–2186

    Article  Google Scholar 

  30. Matías JM, Reboredo JC (2012) Forecasting performance of nonlinear models for intraday stock returns. J Forecast 31(2):172–188

    Article  MathSciNet  Google Scholar 

  31. Cao LJ, Tay FEH (2001) Financial forecasting using support vector machines. Neural Comput Appl 10:184–192

    Article  MATH  Google Scholar 

  32. Pang S, Song L, Kasabov N (2011) Correlation-aided support vector regression for Forex time series prediction. Neural Comput Appl 20(8):1193–1203

    Article  Google Scholar 

  33. Lu CJ, Wu JY, Fan CR, Chiu CC (2009) Forecasting stock price using nonlinear independent component analysis and support vector regression. In: Proceedings of the IEEE international conference on industrial engineering and engineering management, Hong Kong, China

  34. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17:113–126

    Article  MATH  Google Scholar 

  35. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmm Publishers, San Francisco

    Google Scholar 

  36. Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. Part I: background and development. Natural Comput 6:467–484

    Article  MathSciNet  MATH  Google Scholar 

  37. Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1(2–3):235–306

    Article  MathSciNet  MATH  Google Scholar 

  38. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization-an overview. Swarm Intel 1(1):33–57

    Article  Google Scholar 

  39. Tang X, Zhuang L, Jiang C (2009) Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization. Expert Syst Appl 36(9):11853–11857

    Article  Google Scholar 

  40. Hong WC (2009) Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conv Manage 50(1):105–117

    Article  Google Scholar 

  41. Wu Q (2010) A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization. Expert Syst Appl 37(3):2388–2394

    Article  Google Scholar 

  42. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999

    Article  Google Scholar 

  43. Hsu CW, Chang CC, Lin CJ (2011) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/

  44. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. Lecture Notes Comput Sci 1447:591–600

    Article  Google Scholar 

  45. Escalante HJ, Montes M, Sucar LE (2009) Particle swarm model selection. J Mach Learn Res 10:405–440

    Google Scholar 

  46. Balachandher KG, Fauzias MN, Lai MM (2002) An examination of the random walk model and technical trading rules in the Malaysian stock market. Quart J Bus Econ 41:81–104

    Google Scholar 

  47. Leigh W, Hightower R, Modani N (2005) Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike. Expert Syst Appl 28(1):1–8

    Article  Google Scholar 

  48. Schneeweiss H, Mathes H (1995) Factor analysis and principal components. J Multivar Anal 55:105–124

    Article  MathSciNet  MATH  Google Scholar 

  49. Yan W, Powell JG, Shi J, Xu W (2007) Chinese stock market cyclical regimes: 1991–2006. Econ Lett 97(3):235–239

    Article  Google Scholar 

  50. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html

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Acknowledgments

This work is partially supported by the National Science Council of the Republic of China, Grant No. NSC 98-2221-E-231-005.

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  1. Department of Industrial Management, Chien Hsin University of Science and Technology, Taiwan, Republic of China

    Chi-Jie Lu

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  1. Chi-Jie Lu
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Lu, CJ. Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting. Neural Comput & Applic 23, 2417–2427 (2013). https://doi.org/10.1007/s00521-012-1198-5

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