Skip to main content

Advertisement

Springer Nature Link
Log in

Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm

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

Abstract

Stock index price forecasting is the influential indicator for investors and financial investigators by which decision making capability to achieve maximum benefit with minimum risk can be improved. So, a robust engine with capability to administer useful information is desired to achieve the success. The forecasting effectiveness of stock market is improved in this paper by integrating a modified crow search algorithm (CSA) and extreme learning machine (ELM). The effectiveness of proposed modified CSA entitled as Particle Swarm Optimization (PSO)-based Group oriented CSA (PGCSA) to outperform other existing algorithms is observed by solving 12 benchmark problems. PGCSA algorithm is used to achieve relevant weights and biases of ELM to improve the effectiveness of conventional ELM. The impact of hybrid PGCSA ELM model to predict next day closing price of seven different stock indices is observed by using performance measures, technical indicators and hypothesis test (paired t-test). The seven stock indices are considered by incorporating data during COVID-19 outbreak. This model is tested by comparing with existing techniques proposed in published works. The simulation results provide that PGCSA ELM model can be considered as a suitable tool to predict next day closing price.

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

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. R Schabacker 2005 Technical analysis and stock market profits Harriman House Limited Petersfield

    Google Scholar 

  2. GE Box GM Jenkins GC Reinsel GM Ljung 2015 Time series analysis: forecasting and control John Wiley & Sons

    MATH  Google Scholar 

  3. M O'Hara GS Oldfield 1986 The microeconomics of market making J Financ Quant Anal 21 4 361 376

    Article  Google Scholar 

  4. CM Bilson TJ Brailsford VJ Hooper 2001 Selecting macroeconomic variables as explanatory factors of emerging stock market returns Pac Basin Financ J 9 4 401 426 https://doi.org/10.1016/S0927-538X(01)00020-8

    Article  Google Scholar 

  5. S Singh KS Parmar J Kumar 2021 Soft computing model coupled with statistical models to estimate future of stock market Neural Comput Appl https://doi.org/10.1007/s00521-020-05506-1

    Article  Google Scholar 

  6. O Bustos A Pomares-Quimbaya 2020 Stock market movement forecast: a systematic review Expert Syst Appl 156 113464 https://doi.org/10.1016/j.eswa.2020.113464

    Article  Google Scholar 

  7. AH Moghaddam MH Moghaddam M Esfandyari 2016 Stock market index prediction using artificial neural network J Econ Financ Adm Sci 21 41 89 93 https://doi.org/10.1016/j.jefas.2016.07.002

    Article  Google Scholar 

  8. M Qiu Y Song F Akagi 2016 Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market Chaos Solitons Fract 85 1 7 https://doi.org/10.1016/j.chaos.2016.01.004

    Article  MathSciNet  Google Scholar 

  9. D Vukovic Y Vyklyuk N Matsiuk M Maiti 2020 Neural network forecasting in prediction Sharpe ratio: evidence from EU debt market Physica A 542 123331 https://doi.org/10.1016/j.physa.2019.123331

    Article  Google Scholar 

  10. E Guresen G Kayakutlu TU Daim 2011 Using artificial neural network models in stock market index prediction Expert Syst Appl 38 8 10389 10397 https://doi.org/10.1016/j.eswa.2011.02.068

    Article  Google Scholar 

  11. S Gupta LP Wang 2010 Stock forecasting with feedforward neural networks and gradual data sub-sampling Aust J Intel Inf Process Syst 11 4 14 17

    Google Scholar 

  12. LO Orimoloye MC Sung T Ma JE Johnson 2020 Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices Expert Syst Appl 139 112828 https://doi.org/10.1016/j.eswa.2019.112828

    Article  Google Scholar 

  13. G Dudek 2020 Multilayer perceptron for short-term load forecasting: from global to local approach Neural Comput Appl 32 8 3695 3707 https://doi.org/10.1007/s00521-019-04130-y

    Article  Google Scholar 

  14. JZ Wang JJ Wang ZG Zhang SP Guo 2011 Forecasting stock indices with back propagation neural network Expert Syst Appl 38 11 14346 14355 https://doi.org/10.1016/j.eswa.2011.04.222

    Article  Google Scholar 

  15. D Zhang S Lou 2020 The application research of neural network and BP algorithm in stock price pattern classification and prediction Futur Gener Comput Syst 115 872 879 https://doi.org/10.1016/j.future.2020.10.009

    Article  Google Scholar 

  16. H Gunduz Y Yaslan Z Cataltepe 2017 Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations Knowl-Based Syst 137 138 148 https://doi.org/10.1016/j.knosys.2017.09.023

    Article  Google Scholar 

  17. E Hoseinzade S Haratizadeh 2019 CNNpred: CNN-based stock market prediction using a diverse set of variables Expert Syst Appl 129 273 285 https://doi.org/10.1016/j.eswa.2019.03.029

    Article  Google Scholar 

  18. C Cortes V Vapnik 1995 Support-vector networks Machine Learn 20 3 273 297 https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  19. JD Wu CT Liu 2011 Finger-vein pattern identification using SVM and neural network technique Expert Syst Appl 38 11 14284 14289 https://doi.org/10.1016/j.eswa.2011.05.086

    Article  Google Scholar 

  20. H Tang P Dong Y Shi 2019 A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points Appl Soft Comput 78 685 696 https://doi.org/10.1016/j.asoc.2019.02.039

    Article  Google Scholar 

  21. X Li Y Sun 2020 Stock intelligent investment strategy based on support vector machine parameter optimization algorithm Neural Comput Appl 32 6 1765 1775 https://doi.org/10.1007/s00521-019-04566-2

    Article  Google Scholar 

  22. M Nikou G Mansourfar J Bagherzadeh 2019 Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms Intel Syst Account Financ Manag 26 4 164 174 https://doi.org/10.1002/isaf.1459

    Article  Google Scholar 

  23. GB Huang QY Zhu CK Siew 2006 Extreme learning machine: theory and applications Neurocomputing 70 1–3 489 501 https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  24. Cheng GJ, Cai L, Pan HX (2009) Comparison of extreme learning machine with support vector regression for reservoir permeability prediction. In 2009 International Conference on Computational Intelligence and Security. IEEE. 2:173–176. Doi: https://doi.org/10.1109/CIS.2009.124

  25. GB Huang H Zhou X Ding R Zhang 2011 Extreme learning machine for regression and multiclass classification IEEE Trans Syst Man Cybern Part B Cybern 42 2 513 529 https://doi.org/10.1109/TSMCB.2011.2168604

    Article  Google Scholar 

  26. X Li H Xie R Wang Y Cai J Cao F Wang H Min X Deng 2016 Empirical analysis: stock market prediction via extreme learning machine Neural Comput Appl 27 1 67 78 https://doi.org/10.1007/s00521-014-1550-z

    Article  Google Scholar 

  27. F Sun KA Toh MG Romay K Mao 2014 Extreme learning machines: algorithms and applications Springer International Publishing Berlin

    Google Scholar 

  28. E Hadavandi H Shavandi A Ghanbari 2010 Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting Knowl-Based Syst 23 8 800 808 https://doi.org/10.1016/j.knosys.2010.05.004

    Article  Google Scholar 

  29. R Choudhry K Garg 2008 A hybrid machine learning system for stock market forecasting World Acad Sci Eng Technol 39 3 315 318

    Google Scholar 

  30. Y Perwej A Perwej 2012 Prediction of the Bombay Stock Exchange (BSE) market returns using artificial neural network and genetic algorithm J Intell Learn Syst Appl 4 2 108 119 https://doi.org/10.4236/jilsa.2012.42010

    Article  Google Scholar 

  31. H Chung KS Shin 2020 Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction Neural Comput Appl 32 12 7897 7914 https://doi.org/10.1007/s00521-019-04236-3

    Article  Google Scholar 

  32. XD Zhang A Li R Pan 2016 Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine Appl Soft Comput 49 385 398 https://doi.org/10.1016/j.asoc.2016.08.026

    Article  Google Scholar 

  33. Hegazy O, Soliman OS, Salam MA (2014) A machine learning model for stock market prediction. arXiv preprint ar Xiv:1402. 7351. 4(12):17–23

  34. AK Rout B Biswal PK Dash 2014 A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices Int J Knowl based Intell Eng Syst 18 1 23 41 https://doi.org/10.3233/KES-130283

    Article  Google Scholar 

  35. W Shen X Guo C Wu D Wu 2011 Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm Knowl-Based Syst 24 3 378 385 https://doi.org/10.1016/j.knosys.2010.11.001

    Article  Google Scholar 

  36. SK Chandar 2021 Grey Wolf optimization-Elman neural network model for stock price prediction Soft Comput 25 1 649 658 https://doi.org/10.1007/s00500-020-05174-2

    Article  Google Scholar 

  37. A Kazem E Sharifi FK Hussain M Saberi OK Hussain 2013 Support vector regression with chaos-based firefly algorithm for stock market price forecasting Appl Soft Comput 13 2 947 958 https://doi.org/10.1016/j.asoc.2012.09.024

    Article  Google Scholar 

  38. T Xiong Y Bao Z Hu R Chiong 2015 Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms Inf Sci 305 77 92 https://doi.org/10.1016/j.ins.2015.01.029

    Article  Google Scholar 

  39. Worasucheep C (2015) Forecasting currency exchange rates with an Artificial Bee Colony-optimized neural network. In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE. 3319-3326. doi:https://doi.org/10.1109/CEC.2015.7257305

  40. G Dhiman V Kumar 2017 Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications Adv Eng Softw 114 48 70 https://doi.org/10.1016/j.advengsoft.2017.05.014

    Article  Google Scholar 

  41. DE Goldberg JH Holland 1988 Genetic algorithms and machine learning Mach Learn 3 2 95 99

    Article  Google Scholar 

  42. R Storn K Price 1997 Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces J Global Optim 11 4 341 359 https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  43. E Rashedi H Nezamabadi-Pour S Saryazdi 2009 GSA: a gravitational search algorithm Inf Sci 179 13 2232 2248 https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  44. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE. 39–43. doi: https://doi.org/10.1109/MHS.1995.494215.

  45. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department. 200:1–10

  46. S Saremi S Mirjalili A Lewis 2017 Grasshopper optimisation algorithm: theory and application Adv Eng Softw 105 30 47 https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  47. MY Cheng D Prayogo 2014 Symbiotic organisms search: a new metaheuristic optimization algorithm Comput Struct 139 98 112 https://doi.org/10.1016/j.compstruc.2014.03.007

    Article  Google Scholar 

  48. A Askarzadeh 2016 A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm Comput Struct 169 1 12 https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  49. Sayed GI, Darwish A, Hassanien AE (2017) Chaotic crow search algorithm for engineering and constrained problems. In 2017 12th International Conference on Computer Engineering and Systems (ICCES). IEEE. 676–681. doi:https://doi.org/10.1109/ICCES.2017.8275390.

  50. GI Sayed AE Hassanien AT Azar 2019 Feature selection via a novel chaotic crow search algorithm Neural Comput Appl 31 1 171 188 https://doi.org/10.1007/s00521-017-2988-6

    Article  Google Scholar 

  51. F Mohammadi H Abdi 2018 A modified crow search algorithm (MCSA) for solving economic load dispatch problem Appl Soft Comput 71 51 65 https://doi.org/10.1016/j.asoc.2018.06.040

    Article  Google Scholar 

  52. AE Hassanien RM Rizk-Allah M Elhoseny 2018 A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems J Ambient Intell Humaniz Comput https://doi.org/10.1007/s12652-018-0924-y

    Article  Google Scholar 

  53. Pasandideh SHR., Khalilpourazari S (2018) Sine cosine crow search algorithm: a powerful hybrid meta heuristic for global optimization. arXiv preprint ar Xiv:1801. 08485.

  54. R Hafezi J Shahrabi E Hadavandi 2015 A bat-neural network multi-agent system (BNNMAS) for stock price prediction: case study of DAX stock price Appl Soft Comput 29 196 210 https://doi.org/10.1016/j.asoc.2014.12.028

    Article  Google Scholar 

  55. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation. IEEE. 4661-4667. doi:https://doi.org/10.1109/CEC.2007.4425083

  56. S Kim H Kim 2016 A new metric of absolute percentage error for intermittent demand forecasts Int J Forecast 32 3 669 679 https://doi.org/10.1016/j.ijforecast.2015.12.003

    Article  Google Scholar 

  57. GN Gregoriou JP Gueyie 2003 Risk-adjusted performance of funds of hedge funds using a modified Sharpe ratio J Wealth Manag 6 3 77 83 https://doi.org/10.3905/jwm.2003.442378

    Article  Google Scholar 

  58. RV Rao VJ Savsani DP Vakharia 2011 Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems Comput Aided Des 43 3 303 315 https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  59. S Mirjalili AH Gandomi SZ Mirjalili S Saremi H Faris SM Mirjalili 2017 Salp swarm algorithm: a bio-inspired optimizer for engineering design problems Adv Eng Softw 114 163 191 https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology, Raipur, Chhattisgarh, India

    Sudeepa Das, Tirath Prasad Sahu & Rekh Ram Janghel

  2. Department of Electrical Engineering, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Binod Kumar Sahu

Authors
  1. Sudeepa Das
    View author publications

    Search author on:PubMed Google Scholar

  2. Tirath Prasad Sahu
    View author publications

    Search author on:PubMed Google Scholar

  3. Rekh Ram Janghel
    View author publications

    Search author on:PubMed Google Scholar

  4. Binod Kumar Sahu
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Sudeepa Das.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table

Table 17 Comparative analysis of GDAXI predicted value
Full size table

17.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, S., Sahu, T.P., Janghel, R.R. et al. Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm. Neural Comput & Applic 34, 555–591 (2022). https://doi.org/10.1007/s00521-021-06403-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06403-x

Keywords