Skip to main content
SpringerLink
Log in

Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Over the years, high-dimensional, noisy, and time-varying natures of the stock markets are analyzed to carry out accurate prediction. Particularly, speculators and investors are understandably eager to accurately predict stock price since millions of dollars flow through the stock markets. At this point, soft computing models have empowered them to capture the data patterns and characteristics of stock markets. However, one of the open problems in soft computing models is how to systematically determine architecture of models for given applications. In this study, Harmony Search is utilized to optimize the architecture of Neural Network, Jordan Recurrent Neural Network, Extreme Learning Machine, Recurrent Extreme Learning Machine, Generalized Linear Model, Regression Tree, and Gaussian Process Regression for 1-, 2-, 3-, 5-, 7-, and 10-day-ahead stock price prediction. The experimental results show worthy findings of stock market behavior over different prediction terms and stocks. This study also helps researchers understand which prediction model performed the best and how different conditions affect the prediction accuracy of the models. Proposed hybrid models can be successfully used by speculators and investors to make the investment or to hedge against potential risk in stock markets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Agrawal S, Murarka PD (2013) Stock price forecasting: comparison of short term and long term stock price forecasting using various techniques of artificial neural networks. Int J Adv Res Comput Sci Softw Eng 3(6):154–170

    Google Scholar 

  2. Park K, Shin H (2013) Stock price prediction based on a complex interrelation network of economic factors. Eng Appl Artif Intell 26(5–6):1550–1561

    Article  Google Scholar 

  3. Novak MG, Velušček D (2016) Prediction of stock price movement based on daily high prices. Quant Financ 16(5):793–826

    Article  Google Scholar 

  4. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  5. Huang SH (2015) Supervised feature selection: a tutorial. Artif Intel Res 4(2):22–37

    Google Scholar 

  6. Zhu Q-Y, Qin AK, Suganthan PN, Huang G-B (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763

    Article  MATH  Google Scholar 

  7. Suresh S, Saraswathi S, Sundararajan N (2010) Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell 23(7):1149–1157

    Article  Google Scholar 

  8. Lan Y, Soh YC, Huang G-B (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73:3191–3199

    Article  Google Scholar 

  9. Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M (2011) ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE ACM T Comput Bi 8(2):452–463

    Google Scholar 

  10. Lahoz D, Lacruz B, Mateo PM (2011) A bi-objective micro genetic extreme learning machine. In: 2011 IEEE workshop on hybrid intelligent models and applications, pp 68–75

  11. Huang Y-W, Lai D-H (2012) Hidden node optimization for extreme learning machine. AASRI Proc 3:375–380

    Article  Google Scholar 

  12. Xue B, Ma X, Gu J, Li Y (2013) An improved extreme learning machine based on variable-length particle swarm optimization. In: Proceeding of the IEEE international conference on robotics and biomimetics (ROBIO) Shenzhen, China, pp 1030–1035

  13. Bazi Y, Alajlan N, Melgani F, AlHichri H, Malek S, Yager RR (2014) Differential evolution extreme learning machine for the classification of hyperspectral images. IEEE Geosci Remote Sens Lett 11(6):1066–1070

    Article  Google Scholar 

  14. Hegazy O, Soliman OS, Salam MA (2015) FPA-ELM model for stock market prediction. Int J Adv Res Comput Sci Softw Eng 5(2):1050–1063

    Google Scholar 

  15. Yang Z, Zhang T, Zhang D (2016) A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training. Cogn Neurodyn 10:73–83

    Article  Google Scholar 

  16. Janakiraman VM, Nguyen X, Assanis D (2015) Nonlinear model predictive control of a gasoline HCCI engine using extreme learning machines. In: Review, special issue on neurodynamic systems for optimization and applications, pp 1–15. https://arxiv.org/pdf/1501.03969v1.pdf. Accessed 28 June 2017

  17. Ertugrul ÖF (2016) Forecasting electricity load by a novel recurrent extreme learning machines approach. Int J Electr Power Energy Syst 78:429–435

    Article  Google Scholar 

  18. Ruxanda G, Badea LM (2014) Configuring artificial neural networks for stock market predictions. Technol Econ Dev Econ 20(1):116–132

    Article  Google Scholar 

  19. Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38(8):10389–10397

    Article  Google Scholar 

  20. Hsieh T-J, Hsiao H-F, Yeh W-C (2011) Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm. Appl Soft Comput 11:2510–2525

    Article  Google Scholar 

  21. Wei L-Y, Cheng C-H (2012) A hybrid recurrent neural networks model based on synthesis features to forecast the Taiwan stock market. Int J Innov Comput Inf Control 8(8):5559–5571

    Google Scholar 

  22. Zahedi J, Rounaghi MM (2015) Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Phys A 438:178–187

    Article  MathSciNet  Google Scholar 

  23. Anish CM, Majhi B (2016) Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis. J Korean Stat Soc 45:64–76

    Article  MathSciNet  MATH  Google Scholar 

  24. Dash SK, Bisoi R, Dash PK (2016) A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting. Neural Comput Appl 27:2123–2140

    Article  Google Scholar 

  25. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933

    Article  MATH  Google Scholar 

  26. Yang XS (2009) Harmony search as a metaheuristic algorithm, in music-inspired harmony search algorithm: theory and applications. In: Geem ZW (ed) Studies in computational intelligence. Springer, Berlin, pp 1–14

    Google Scholar 

  27. Göçken M, Özçalıcı M, Boru A, Dosdoğru AT (2016) Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst Appl 44:320–331

    Article  Google Scholar 

  28. Fernández-Blanco P, Bodas-Sagi DJ, Soltero FJ, Hidalgo JI (2008) Technical market indicators optimization using evolutionary algorithms. In: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, pp 1851–1858

  29. Diao R, Shen Q (2012) Feature selection with harmony search. IEEE Trans Syst Man Cybern B Cybern 42(6):1509–1523

    Article  Google Scholar 

  30. Chakraborty P, Roy GG, Das S, Jain D, Abraham A (2009) An improved harmony search algorithm with differential mutation operator. Fundam Inform 95:1–26

    MathSciNet  MATH  Google Scholar 

  31. Geem ZW, Cho Y-H (2011) Optimal design of water distribution networks using parameter-setting-free harmony search for two major parameters. J. Water Resour Plan Manag 137:377–380

    Article  Google Scholar 

  32. Ojha VK, Abraham A, Snášel V (2014) Simultaneous optimization of neural network weights and active nodes using metaheuristics. In: 14th international conference on hybrid intelligent systems, pp 248–253

  33. Hikawa H, Araga Y (2011) Study on gesture recognition system using posture classifier and Jordan recurrent neural network. In: Proceedings of international joint conference on neural networks, San Jose, California, USA, pp 405–412

  34. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  35. Dudek G (2016) Extreme learning machine as a function approximator: Initialization of input weights and biases. In: Proceedings of the 9th international conference on computer recognition systems CORES 2015. Springer, Berlin, pp 59–69

  36. Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100

    Article  Google Scholar 

  37. Yang J, Stenzel J (2006) Short-term load forecasting with increment regression tree. Electr Power Syst Res 76:880–888

    Article  Google Scholar 

  38. Seo S, Wallat M, Graepel T, Obermayer K (2000) Gaussian process regression: active data selection and test point rejection. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks, pp 241–246

  39. Makrıdakıs S, Hıbon M (1995) Evaluating accuracy (or error) measures, insead. http://www.insead.edu/facultyresearch/research/doc.cfm?did=46875. Accessed 23 Aug 2016

  40. Lu C-J (2013) Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting. Neural Comput Appl 23:2417–2427

    Article  Google Scholar 

  41. Dai W, Shao YE, Lu C-J (2013) Incorporating feature selection method into support vector regression for stock index forecasting. Neural Comput Appl 23:1551–1561

    Article  Google Scholar 

  42. Thenmozhi M, Chand GS (2016) Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Comput Appl 27:805–824

    Article  Google Scholar 

  43. Dematos G, Boyd MS, Kermanshahi B, Kohzadi N, Kaastra I (1996) Feedforward versus recurrent neural networks for forecasting monthly Japanese yen exchange rates. Financ Eng Jpn Mark 3:59–75

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Department, Adana Science and Technology University, Yeşiloba Yerleşkesi, Yeşiloba mah., Ögretmenler Bulvarı, 46278 sok., No: 3, 01180, Seyhan, Adana, Turkey

    Mustafa Göçken, Aslı Boru & Ayşe Tuğba Dosdoğru

  2. International Trade and Logistics Department, Kilis 7 Aralık University, Kilis, Turkey

    Mehmet Özçalıcı

Authors
  1. Mustafa Göçken
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehmet Özçalıcı
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aslı Boru
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayşe Tuğba Dosdoğru
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustafa Göçken.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Göçken, M., Özçalıcı, M., Boru, A. et al. Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection. Neural Comput & Applic 31, 577–592 (2019). https://doi.org/10.1007/s00521-017-3089-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3089-2

Keywords

Search

Navigation