Soft Computing

, Volume 21, Issue 18, pp 5387–5398 | Cite as

Stock market trend prediction using AHP and weighted kernel LS-SVM

  • Ivana Marković
  • Miloš Stojanović
  • Jelena Stanković
  • Milena Stanković
Methodologies and Application


Nowadays, stock market trend prediction represents a challenging subject both in terms of the choice of the prediction model and in terms of constructing the set of features that model will use for prediction. To address both of these aspects, we propose a feature ranking and feature selection approach in combination with weighted kernel least squares support vector machines (LS-SVMs). We introduce the analytic hierarchy process (AHP) into the stock market and propose evaluation criteria which provide the prediction model with relevant knowledge of the underlying processes of the studied stock market. The feature weights obtained by the AHP method are used for feature ranking and selection, and used with the LS-SVMs through a weighted kernel. The test results indicate that the proposed model outperforms the benchmark models. In addition, the set of feature weights obtained by the proposed approach can also independently be incorporated into other kernel-based learners.


Analytic hierarchy process Stock market trend prediction Least squares support vector machines Weighted kernel 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.


  1. Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79MathSciNetCrossRefMATHGoogle Scholar
  2. Atsalakis SG, Valavanis PK (2009) Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst Appl 36(7):10696–10700. doi: 10.1016/j.eswa.2009.02.043 CrossRefGoogle Scholar
  3. Atsalakis SG, Valavanis PK (2009b) Surveying stock market forecasting techniques—Part II: Soft computing methods. Expert Syst Appl 36(3):5932–5941. doi: 10.1016/j.eswa.2008.07.006 CrossRefGoogle Scholar
  4. Barak S, Modarres M (2015) Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Syst Appl 42(3):1325–1339. doi: 10.1016/j.eswa.2014.09.026 CrossRefGoogle Scholar
  5. De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K, De Moor B, Vandewalle J, Suykens JAK (2011) LS-SVMlab toolbox user’s guide version 1.8.
  6. Breiman L (2001) Random forests. Mach Learn 45(1):5–32Google Scholar
  7. Coyle G (2004) Practical strategy: structured tools and techniques. Pearson, New YorkGoogle Scholar
  8. Crone SF, Kourentzes N (2010) Feature selection for time series prediction—a combined filter and wrapper approach for neural networks. Neurocomputing 73(10–12):1923–1936. doi: 10.1016/j.neucom.2010.01.017 CrossRefGoogle Scholar
  9. Chai J, Du J, Lai KK, Lee YP (2015) A hybrid least square support vector machine model with parameters optimization for stock forecasting. Math Probl Eng (article ID 231394, 7 pages). doi: 10.1155/2015/231394
  10. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:27Google Scholar
  11. Dai W, Wu J-Y, Lu C-J (2012) Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes. Expert Syst Appl 39(4):4444–4452. doi: 10.1016/j.eswa.2011.09.145 CrossRefGoogle Scholar
  12. Dešmar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetGoogle Scholar
  13. Fama E (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25:383–417CrossRefGoogle Scholar
  14. Fung GPC, Yu JX, Lam W (2002) News sensitive stock trend prediction. In: Advances in knowledge discovery and data mining. Springer, Berlin, pp 481–493. doi: 10.1007/3-540-47887-6_48
  15. Genuer R, Poggi J-M, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recognit Lett 31(14):2225–2236CrossRefGoogle Scholar
  16. Giveki D, Salimi H, Bahmanyar G, Khademian Y (2012) Automatic detection of diabetes diagnosis using feature weighted support vector machines based on mutual information and modified Cuckoo search. arXiv:1201.2173
  17. Gómez-Verdejo V, Verleysen M, Fleury J (2009) Information-theoretic feature selection for functional data classification. Neurocomputing 72(16–18):3580–3589. doi: 10.1016/j.neucom.2008.12.035 CrossRefGoogle Scholar
  18. Guo B, Gunn SR, Damper RI (2008) Customizing kernel functions for SVM-based hyperspectral image classification. IEEE Trans Image Process 17(4):622–629. doi: 10.1109/TIP.2008.918955 MathSciNetCrossRefGoogle Scholar
  19. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATHGoogle Scholar
  20. Hawawini G, Keim DB (1995) On the predictability of common stock returns: world-wide evidence. In: Handbooks in operations research and management science, vol \(9\). North-Holland, Amsterdam, pp 497–544Google Scholar
  21. He Y, Fataliyev K, Wang L (2013) ICONIP 2013, Part II, LNCS 8227. In: Lee M et al (eds) Feature selection for stock market analysis. Springer, Berlin, pp 737–744Google Scholar
  22. Huang W, Nakamori Y, Wang S-Y (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32(10):2513–2522. doi: 10.1016/j.cor.2004.03.016 CrossRefMATHGoogle Scholar
  23. Kara Y, Boyacioglu MA, Baykan ÖK (2011) Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Syst Appl 38(5):5311–5319. doi: 10.1016/j.eswa.2010.10.027 CrossRefGoogle Scholar
  24. Kaufman PJ (2003) A short course in technical trading. Wiley, New YorkGoogle Scholar
  25. Kraskov A, Stogbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E 69(6):066138MathSciNetCrossRefGoogle Scholar
  26. Lahmiri S (2011) A comparison of PNN and SVM for stock market trend prediction using economic and technical information. Int J Comput Appl 29:24–30Google Scholar
  27. Lee M-C (2009) Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst Appl 36(8):10896–10904. doi: 10.1016/j.eswa.2009.02.038 CrossRefGoogle Scholar
  28. Levy H (2006) Stochastic dominance investment decision making under uncertainty, 2nd edn. Springer, New YorkGoogle Scholar
  29. Liu D-R, Shih Y-Y (2005) Integrating AHP and data mining for product recommendation based on customer lifetime value. Inf Manag 42(3):387–400. doi: 10.1016/ CrossRefGoogle Scholar
  30. Liu D, Tian Z, Luo B, Xia J (2013) Feature ranking in intrusion detection by hybrid algorithm with support vector machine and analytic hierarchy process. Int J Digit Content Technol Appl (JDCTA) 7(7):1005–1013. doi: 10.4156/jdcta.vol7.issue7.119 CrossRefGoogle Scholar
  31. Lo AW (2007) The new palgrave: a dictionary of economics. In: Blume L, Durlauf S (eds) Efficient markets hypothesis. Palgrave Macmillan, BasingstokeGoogle Scholar
  32. Marković I, Stojanović M, Božić M, Stanković J (2015) ICT innovations. In: 2014 proceedings of the advances in intelligent systems and computing. In: Bogdanova AM, Gjorgjevik D (eds) Stock market trend prediction based on the LS-SVM model update algorithm. Springer, New York, pp 105—114Google Scholar
  33. Mittermayer MA (2004) Forecasting intraday stock price trends with text mining techniques. Proc Hawai Int Conf Syst Sci. doi: 10.1109/HICSS.2004.1265201
  34. McNelis PD (2005) Neural networks in finance: gaining predictive edge in the market. Elsevier, New YorkGoogle Scholar
  35. Ni L-P, Ni ZW, Gao YZ (2011) Stock trend prediction based on fractal feature selection and support vector machine. Expert Syst Appl 38(5):5569–5576. doi: 10.1016/j.eswa.2010.10.079 CrossRefGoogle Scholar
  36. Omak EC, Polat K, Gunes S, Arslan A (2007) A new medical decision making system: least square support vector machine (LSSVM) with fuzzy weighting pre-processing. Expert Syst Appl 32(2):409–414. doi: 10.1016/j.eswa.2005.12.001 CrossRefGoogle Scholar
  37. Pauwels S, Inghelbrecht K, Heyman P, Marius D (2011) Technical trading rules in emerging stock markets. World Acad Sci Eng Technol 5:11–20Google Scholar
  38. Rabin M (2000) Risk aversion and expected-utility theory: a calibration theorem. Econometrica 68:1281–1292CrossRefGoogle Scholar
  39. Saaty TL (1999) Monográfico: Problemas complejos de decisión. II. Basic theory of the analytic hierarchy process: how to make a decision. Rev R Acad Cienc Exact Fis Nat (Esp) 93:395–423Google Scholar
  40. Stojanović BM, Božić MM, Stanković MM, Stajić ZP (2014) A methodology for training set instance selection using mutual information in time series prediction. Neurocomputing 141(2):236–245. doi: 10.1016/j.neucom.2014.03.006 CrossRefGoogle Scholar
  41. Suykens JAK, Van Gestel T, Brabanter J De, Moor B De, Vandewalle J (2002) Least squares support vector machines. World Scientific, SingaporeCrossRefMATHGoogle Scholar
  42. Wang Y, Choi I-C (2013) Market index and stock price direction prediction using machine learning techniques: an empirical study on the KOSPI and HIS. arXiv:1309.7119
  43. Wang D, Zhang H (2013) Group AHP and \(K\)-means cluster for a new segmentation of brand customer. Int J Adv Comput Technol (IJACT) 5:213–221Google Scholar
  44. Xing H, Ha M, Hu B, Tian D (2009) Linear feature-weighted support vector machine. Fuzzy Inf Eng 1(3):289–305. doi: 10.1007/s12543-009-0022-0
  45. Yao J, Zhao S, Fan L (2006) Enhanced support vector machine model for intrusion detection. Rough Sets Knowl Technol LNCS 4062:538–543. doi: 10.1007/11795131_78
  46. Yoo P D, Kim MH, Jan T (2005) Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: Computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce, pp 835–841. doi: 10.1109/CIMCA.2005.1631572
  47. Yu L, Wang S, Lai KK (2005) WINE 2005, LNCS 3828 In: Deng X, Ye Y (eds) Mining stock market tendency using GA-based support vector machines. Springer, Berlin, pp 336–345Google Scholar
  48. Yu L, Chen H, Wang S, Lai KK (2009) Evolving least squares support vector machines for stock market trend mining. IEEE Trans Evolut Comput 13(1):87–102. doi: 10.1109/TEVC.2008.928176
  49. Yuling L, Guo H, Hu J (2013) An SVM-based approach for stock market trend prediction. Neural Netw (IJCNN) (IEEE Press, New York) 1– 7. doi: 10.1109/IJCNN.2013.6706743
  50. Zhai Y, Hsu A, Halgamuge SK (2007) ISNN 2007, Part III, LNCS 4493. In: Liu D et al (eds) Combining news and technical indicators in daily stock price trends prediction. Springer, Berlin, pp 1087–1096. doi: 10.1007/3-540-47887-6_48

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ivana Marković
    • 1
  • Miloš Stojanović
    • 2
  • Jelena Stanković
    • 1
  • Milena Stanković
    • 3
  1. 1.Faculty of EconomicsUniversity of NišNišSerbia
  2. 2.College of Applied Technical SciencesNišSerbia
  3. 3.Faculty of Electronic EngineeringUniversity of NišNišSerbia

Personalised recommendations