Skip to main content
SpringerLink
Log in

Stock price forecast based on combined model of ARI-MA-LS-SVM

  • S.I. : ATCI 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Stock forecasting is a very complex non-stationary, nonlinear time series forecasting, and is often affected by many factors, making it difficult to predict it with a simple model. Support vector machine (SVM) is one of the common data mining methods in the field of machine learning. It has outstanding advantages compared with other methods and it is widely used in various fields. However, there are still many problems in the practical application of the method, and the model itself has many fields that need to be improved. The purpose of this paper is to accurately predict the trend of stock prices, providing a reference model for the trend of stock market and the tracking method of stock price prediction, and provide value reference for research on the forecasting model of stock market and investor’s investment decision. Research using a combined model to predict stock market trends whether will have a significant improvement compared to using a single model to forecast that. The method of this paper is to analyze the shortcomings of current stock market forecasting methods and standard support vector machines firstly, at the same time, based on this, a cumulative auto-regressive moving average is proposed, which combines the least squares support vector machine synthesis model (ARI-MA-LS-SVM) to make basic predictions for the stock market. Secondly, process the data first for the predictive indicators by using cumulative auto-regressive moving average. Then, use the least squares support vector machine of simple indicator system to predict stock price fluctuations. Therefore, it can be concluded that the combined model based on ARI-MA-LS-SVM is more suitable for stock price forecasting than the single forecasting model, and the actual performance is better. At the same time, a large number of simulation experiments show that the algorithm of multiple model’s fusion can achieve the expected effect, which indicate that the model has universal applicability, market applicability and stability feasibility. This model can bring some guidance and reference value for many investors and market regulators.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chou JS, Nguyen TK (2018) Forward forecast of stock price using sliding-window metaheuristic-optimized machine-learning regression. IEEE Trans Ind Informatics PP(99):1

    Google Scholar 

  2. Chen S, Sun YL, Liu Y (2018) Forecast of stock price fluctuation based on the perspective of volume information in stock and exchange market. China Financ Rev Int 8(3):297–314

    Article  Google Scholar 

  3. Yang Y (2018) Gold price forecast based on esmd multi-frequency combination model. IOP Conf Ser Mater Sci Eng 466(1):012031

    Google Scholar 

  4. Kunze F, Spiwoks M, Bizer K et al (2018) The usefulness of oil price forecasts—evidence from survey predictions. Manag Decis Econ 39(12):427–446

    Article  Google Scholar 

  5. Agustini WF, Affianti IR, Putri ER (2018) Stock price prediction using geometric Brownian motion. J Phys Conf Ser 974(1):012047

    Article  Google Scholar 

  6. Dinh TA, Kwon YK (2018) An empirical study on importance of modeling parameters and trading volume-based features in daily stock trading using neural networks. IEEE Informatics 5(3):36

    Article  Google Scholar 

  7. Mark C, Metzner C, Lautscham L et al (2018) Bayesian model selection for complex dynamic systems. Nat Commun 9(1):1803

    Article  Google Scholar 

  8. Sun R, Deng Y (2019) A new method to identify incomplete frame of discernment in evidence theory. IEEE Access 7:15547–15555

    Article  Google Scholar 

  9. Zheng H, Zhang Y, Liu J et al (2018) A novel model based on wavelet LS-SVM integrated improved PSO algorithm for forecasting of dissolved gas contents in power transformers. Electr Power Syst Res 155:196–205

    Article  Google Scholar 

  10. Sun A, Zhao T, Chen J et al (2018) Comparative study: common ANN and LS-SVM exchange rate performance prediction. Chin J Electron 27(3):561–564

    Article  Google Scholar 

  11. Zhu Xing, Ma Shu-qi, Qiang Xu (2018) A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction. J Mt Sci 15(1):156–166

    Article  Google Scholar 

  12. Prayogo D, Susanto YT (2018) The optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv Civ Eng 4:1–9

    Article  Google Scholar 

  13. Zhang W, Qin Y, Kumar M et al (2018) Application of improved least squares support vector machine in the forecast of daily water consumption. Wirel Pers Commun 6:1–14

    Article  Google Scholar 

  14. Yang ZC (2018) Predictive modeling of hourly water-level fluctuations based on the DCT least-squares extended model. Water Resour Manag 32(3):1117–1131

    Article  Google Scholar 

  15. Wang D, Gao Y (2018) Recursive maximum likelihood identification method for a multivariable controlled autoregressive moving average system. IMA J Math Control Inf 33(4):1015–1031

    Article  MathSciNet  MATH  Google Scholar 

  16. Mehdizadeh S, Sales AK (2018) A comparative study of autoregressive, autoregressive moving average, gene expression programming and Bayesian networks for estimating monthly streamflow. Water Resour Manag 32(15):1–22

    Google Scholar 

  17. Zhang Y, Song W, Karimi M et al (2018) Fractional autoregressive integrated moving average and finite-element modal: the forecast of tire vibration trend. IEEE Access 6(99):1

    Google Scholar 

  18. Zhou X, Liang X, Du X, Zhao J (2018) Structure based user identification across social networks. IEEE Trans Knowl Data Eng 30(6):1178–1191

    Article  Google Scholar 

  19. Li Q, Cao G, Wei X (2018) Relationship research between meteorological disasters and stock markets based on a multifractal detrending moving average algorithm. Int J Mod Phys B 32(1):1750267

    Article  MathSciNet  Google Scholar 

  20. Petukhova T, Ojkic D, Mcewen B et al (2018) Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza a virus frequency in swine in Ontario, Canada. PLoS ONE 13(6):e0198313

    Article  Google Scholar 

  21. Wang D, Liang Z (2018) A fuzzy set-valued autoregressive moving average model and its applications. Symmetry 10(8):324

    Article  MATH  Google Scholar 

  22. Rui R, Wu DD, Liu T (2018) Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Syst J PP(99):1–11

    Google Scholar 

  23. Wang J, Zhang J, Wang W, Yang C (2015) A perturbation analysis of nonconvex block-sparse compressed sensing. Commun Nonlinear Sci Numer Simul 29(1–3):416–426

    Article  MathSciNet  Google Scholar 

  24. Liu B, Li T, Tsai SB (2017) Low carbon strategy analysis of competing supply chains with different power structures. Sustainability 2017(9):835

    Google Scholar 

  25. Tsai SB, Chien MF, Xue Y, Li L et al (2015) Using the fuzzy DEMATEL to determine environmental performance: a case of printed circuit board industry in Taiwan. PLoS ONE 10(6):e0129153

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, China

    Chenglin Xiao, Weili Xia & Jijiao Jiang

Authors
  1. Chenglin Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weili Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jijiao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chenglin Xiao.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, C., Xia, W. & Jiang, J. Stock price forecast based on combined model of ARI-MA-LS-SVM. Neural Comput & Applic 32, 5379–5388 (2020). https://doi.org/10.1007/s00521-019-04698-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04698-5

Keywords

Search

Navigation