Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network

  • Jiasheng CaoEmail author
  • Jinghan Wang


In order to establish an accurate effective stock forecasting model, the principal component analysis (PCA) was first used to analyze the main financial index data of some listed companies and the comprehensive score of evaluation index was obtained in this study. Then, the financial indicator data and the transaction indicator data were simultaneously used as the input variables of the stock price prediction research, three back propagation (BP) neural network algorithms were used for experiment, and its prediction situation was compared. Results show that the BP neural network based on Bayesian regularization algorithm has the highest prediction accuracy and can avoid over-fitting phenomenon in the training process of the model; the error between the predicted value and the actual value is small. Finally, this study constructed a stock price prediction study based on PCA and BP neural network algorithm as well as an investment stock selection strategy based on traditional stock selection analysis method. As a result, the proposed model is proved to be effective.


Stock price forecasting Investment guidance Principal component analysis Bayesian regularization algorithm BP neural network 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Anish CM, Majhi B (2016) Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis. J Korean Stat Soc 45(1):64–76MathSciNetzbMATHGoogle Scholar
  2. Ausín MC, Gómez-Villegas MA, González-Pérez B, Rodríguez-Bernal MT, Salazar I, Sanz L (2011) Bayesian analysis of multiple hypothesis testing with applications to microarray experiments. Commun Stat 40(13):2276–2291MathSciNetzbMATHGoogle Scholar
  3. Bao C, Pu Y, Yi Z (2018) Fractional-order deep backpropagation neural network. Comput Intell Neurosci 2018:1–10Google Scholar
  4. Blaschke B, Neubauer A, Scherzer O (2018) On convergence rates for the iteratively regularized Gauss–Newton method. IMA J Numer Anal 17(3):421–436MathSciNetzbMATHGoogle Scholar
  5. Burger M, Lorz A, Wolfram MT (2016) Balanced growth path solutions of a Boltzmann mean field game model for knowledge growth. Kinet Relat Models 10(1):117–140MathSciNetzbMATHGoogle Scholar
  6. Chae YT, Horesh R, Hwang Y et al (2016) Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build 111:184–194Google Scholar
  7. Chapman BP, Weiss A, Duberstein PR (2016) Statistical learning theory for high dimensional prediction: application to criterion-keyed scale development. Psychol Methods 21(4):603–620Google Scholar
  8. Chen MY (2014) A high-order fuzzy time series forecasting model for internet stock trading. Future Gener Comput Syst 37(7):461–467Google Scholar
  9. Chen MY, Chen BT (2014) Online fuzzy time series analysis based on entropy discretization and a fast Fourier transform. Appl Soft Comput 14(1):156–166Google Scholar
  10. Chen MY, Chen BT (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294(2):227–241MathSciNetGoogle Scholar
  11. Chen T, Yue N, Jabbour S et al (2016) SU-G-BRA-03: PCA based imaging angle optimization for 2D cine MRI based radiotherapy guidance. Med Phys 43(6):3635Google Scholar
  12. Díaz RF, Almenara JM, Santerne A, Moutou C, Lethuillier A, Deleuil M (2018) Pastis: Bayesian extrasolar planet validation—I. General framework, models, and performance. Monthly Not R Astron Soc 441(2):983–1004Google Scholar
  13. Duan J, Soussen C, Brie D et al (2016) Generalized lasso with under-determined regularization matrices. Sig Process 127:239–246Google Scholar
  14. Duong BP, Kim JM (2018) Non-mutually exclusive deep neural network classifier for combined modes of bearing fault diagnosis. Sensors 18(4):1129Google Scholar
  15. Fan Q, Wu W, Zurada JM (2016) Convergence of batch gradient learning with smoothing regularization and adaptive momentum for neural networks. Springerplus 5(1):295Google Scholar
  16. Gagné J, Mamajek EE, Malo L, Riedel A, Rodriguez D, Lafrenière D et al (2018) BANYAN. XI. The Banyan Σ multivariate bayesian algorithm to identify members of young associations within 150 pc. Astrophys J 856(1):L21Google Scholar
  17. Gao X, Dai K, Wang Z et al (2016) Establishing quantitative structure tribo-ability relationship model using bayesian regularization neural network. Friction 4(2):105–115Google Scholar
  18. Gharani P, Suffoletto B, Chung T et al (2017) An artificial neural network for movement pattern analysis to estimate blood alcohol content level. Sensors 17(12):2897Google Scholar
  19. Hsu YS, Lin SJ (2016) An emerging hybrid mechanism for information disclosure forecasting. Int J Mach Learn Cybernet 7(6):943–952Google Scholar
  20. Hu J, Zhang J, Zhang C et al (2016) A new deep neural network based on a stack of single-hidden-layer feedforward neural networks with randomly fixed hidden neurons. Neurocomputing 171(C):63–72Google Scholar
  21. Lahmiri S (2016) Intraday stock price forecasting based on variational mode decomposition. J Comput Sci 12:23–27Google Scholar
  22. Leite YL, Costa LP, Rocha RG, Batalhafilho H et al (2016) Neotropical forest expansion during the last glacial period challenges refuge hypothesis. Proc Natl Acad Sci USA 113(4):1008–1010Google Scholar
  23. Li D, Harris JM (2018) Full waveform inversion with nonlocal similarity and gradient domain adaptive sparsity-promoting regularization. Geophys J Int 215(3):1841–1864Google Scholar
  24. Li X, Zhang Y, Luo J et al (2016) Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons. Int J Appl Earth Obs Geoinf 44:104–112Google Scholar
  25. Li J, Hu G, Zhou Y et al (2017) Study on temperature and synthetic compensation of piezo-resistive differential pressure sensors by coupled simulated annealing and simplex optimized kernel extreme learning machine. Sensors 17(4):894Google Scholar
  26. Mahersia H, Boulehmi H, Hamrouni K (2016) Development of intelligent systems based on bayesian regularization network and neuro-fuzzy models for mass detection in mammograms. Comput Methods Programs Biomed 126:46–62Google Scholar
  27. Montano V, Jombart T (2017) An eigenvalue test for spatial principal component analysis. BMC Bioinform 18(1):562–564Google Scholar
  28. Nahil A, Lyhyaoui A (2018) Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of casablanca stock exchange. Proc Comput Sci 127:161–169Google Scholar
  29. Peiyong L, Feng G, Chengfang W et al (2018) Research of the curve radius of shape formed in profile cold forming with bp neural networks approach based on experiment. J Ship Prod Des 32(1):50–58Google Scholar
  30. Ray D, Behera MD, Jacob J (2016) Predicting the distribution of rubber trees (Hevea brasiliensis) through ecological niche modelling with climate, soil, topography and socioeconomic factors. Ecol Res 31(1):75–91Google Scholar
  31. Sergeant CJ, Starkey EN, Bartz KK et al (2016) A practitioner’s guide for exploring water quality patterns using principal components analysis and procrustes. Environ Monit Assess 188(4):1–15Google Scholar
  32. Shi S, Li G, Chen H et al (2017) Refrigerant charge fault diagnosis in the VRF system using bayesian artificial neural network combined with Relieff filter. Appl Therm Eng 112:698–706Google Scholar
  33. Stádník B, Raudeliūnienė J, Davidavičienė V (2016) Fourier analysis for stock price forecasting: assumption and evidence. J Bus Econ Manag 17(3):365–380Google Scholar
  34. Su JH, Piao YC, Luo Z, Yan BP (2018) Modeling habitat suitability of migratory birds from remote sensing images using convolutional neural networks. Animals 8(5):66Google Scholar
  35. Verfaillie A, Svetlichnyy D, Imrichova H et al (2016) Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic. Genome Res 26(7):882–895Google Scholar
  36. Wade GA, Folsom CP, Petit P et al (2018) A search for weak or complex magnetic fields in the B3V star ι herculis. Mon Not R Astron Soc 444(3):1993–2004Google Scholar
  37. Xu Z, Huang X, Lin L, Wang Q, Liu J, Yu K et al (2018) Bp neural networks and random forest models to detect damage by Dendrolimus punctatus Walker. J For Res 1:1–15Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.Illinois Institute of TechnologyChicagoUSA

Personalised recommendations