Skip to main content
SpringerLink
Log in

A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms

  • Application Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

Today the accurate prediction of stock price movement is one of the most effective tools for investors. This prediction becomes more challenging when the stock market is naturally chaotic and uncertain. These stock attributes prevent most forecasting models from valuable stock data. In this sense, In this research, our purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used. It can be seen that suport vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the performance of two other meta heuristic algorithm including the neural network and Cuckoo search algoritm (CS). Based on the result, we can say that all the new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural network (basic research) and SVM-CS are 71.2, 71.4 respectively. In this study, we examined the years 2013–2021, and if longer periods are used, it may now be possible to achieve more optimal results. The results show that the performance of SVM-PSO is superior to the performance of the SVM-CS and most importantly the feed-forward static neural network algorithm of the literature as the standard one. In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model while, we use historical data, unexpected events have not been determined. However, for comparison, the hit rate is considered the percentage of correct predictions for 16 days.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and material

All data and materials as well as software application or custom code support published claims and comply with field standards.

References

  1. Jasemi, M., Kimiagari, A.M., Memariani, A.: A conceptual model for portfolio management sensitive to mass psychology of market. Int. J. Ind. Eng. Theory Appl. Pract. 18(1), 1–15 (2011)

    Google Scholar 

  2. Jasemi, M., Kimiagari, A.M., Memariani, A.: A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick. Expert Syst. Appl. 38(4), 3884–3890 (2011)

    Article  Google Scholar 

  3. Barak, S., Heidary, J., Dahooie, T.T.: Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Syst. Appl. 42(23), 9221–9235 (2015)

    Article  Google Scholar 

  4. Ahmadi, E., Abooie, M.H., Jasemi, M., Zare Mehrjardi, Y.: A nonlinear autoregressive model with exogenous variables neural network for stock market timing: the candlestick technical analysis. Int. J. Eng. 29(12), 1717–1725 (2016)

    Google Scholar 

  5. Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M., Mahmoodi, A., Amini Jam, P.: New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the support vector machine and heuristic algorithms of imperialist competition and genetic. Expert Syst. Appl. 94, 21–31 (2018). https://doi.org/10.1016/j.eswa.10.023

    Article  Google Scholar 

  6. Sahin, U., Ozbayoglu, A.M.: TN-RSI: trend-normalized RSI indicator for stock trading systems with evolutionary computation. Procedia Comput. Sci. 36, 240–245 (2014)

    Article  Google Scholar 

  7. Majhi, B., Rout, M., Baghel, V.: On the development and performance evaluation of a multi-objective GA-based RBF adaptive model for the prediction of stock indices. J. King Saud Univ. Comput. Inf. Sci. 26(3), 319–331 (2014)

    Google Scholar 

  8. Sankar, C.P., Vidyaraj, R., Kumar, K.S.: Trust based stock recommendation system—a social network analysis approach. Procedia Comput. Sci. 46, 299–305 (2015)

    Article  Google Scholar 

  9. Anbalagan, T., Maheswari, S.U.: Classification and prediction of stock market index based on fuzzy metagraph. Procedia Comput. Sci. 47, 214–221 (2015)

    Article  Google Scholar 

  10. Lan, Q., Zhang, D., Xiong, L.: Reversal pattern discovery in financial time series based on fuzzy candlestick lines. Syst. Eng. Procedia 2, 182–190 (2011)

    Article  Google Scholar 

  11. Lee, K.H., Jo, G.S.: Expert system for predicting stock market timing using a candlestick chart. Expert Syst. Appl. 16(4), 357–364 (1999)

    Article  Google Scholar 

  12. Xie, H., Zhao, X., Wang, S.: A comprehensive look at the predictive information in Japanese candlestick. Procedia Comput. Sci. 9, 1219–1227 (2012)

    Article  Google Scholar 

  13. Barak, S., Arjmand, A., Ortobelli, S.: Fusion of multiple diverse predictors in stock market. J. Inf. Fusion 36, 90–102 (2017)

    Article  Google Scholar 

  14. Dahal, K., Almejalli, K., Hossain, M.A., Chen, W.: GA-based learning for rule identification in fuzzy neural networks. Appl. Soft Comput. 35, 605–617 (2015)

    Article  Google Scholar 

  15. De Campos, L.M.L., de Oliveira, R.C.L., Roisenberg, M.: Optimization of neural networks through grammatical evolution and a genetic algorithm. Expert Syst. Appl. 56, 368–384 (2016)

    Article  Google Scholar 

  16. Kuo, S.C., Lin, C.J., Liao, J.R.: 3D reconstruction and face recognition using kernel-based ICA and neural networks. Expert Syst. Appl. 38(5), 5406–5415 (2011)

    Article  Google Scholar 

  17. Liu, Q., Chen, W., Hu, H., Zhu, Q., Xie, Z.: An optimal NARX neural network identification model for a magnetorheological damper with force-distortion behavior. Front. Mater 7, 10 (2020)

    Article  Google Scholar 

  18. Nunno, F., de Marinis, G., Gargano, R., Granata, F.: Tide prediction in the venice lagoon using nonlinear autoregressive exogenous (NARX) neural network. Water 13, 1173 (2021)

    Article  Google Scholar 

  19. Xia, Y., Zhao, J., Ding, Q., Jiang, A.: Incipient chiller fault diagnosis using an optimized least squares support vector machine with gravitational search algorithm. Front. Energy Res. 9, 755649 (2021). https://doi.org/10.3389/fenrg.2021.755649

    Article  Google Scholar 

  20. Indra, G., Jemi gold, P., Pavithra, P., Akila, K.: Applicability of Svm & Narx for prediction alayis of flood in humid and semi-humid regions. Ann. Roman. Soc. Cell Biol. 25(6), 6282–6293 (2021)

    Google Scholar 

  21. Fernandez-Lozano, C., Canto, C., Gestal, M., Andrade-Garda, J.M., Rabuñal, J.R., Dorado, J., Pazos, A.: Hybrid model based on genetic algorithms and SVM applied to variable selection within fruit juice classification. Sci. World J. 2013, 1–13 (2013)

    Article  Google Scholar 

  22. Huang, W., Nakamori, Y., Wang, S.Y.: Forecasting stock market movement direction with support vector machine. Comput. Oper. Res. 32(10), 2513–2522 (2005)

    Article  Google Scholar 

  23. Tay, F.E., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29(4), 309–317 (2001)

    Article  Google Scholar 

  24. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  Google Scholar 

  25. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    Google Scholar 

  26. Wei, J., Jian-qi, Z., Xiang, Z.: Face recognition method based on support vector machine and particle swarm optimization. Expert Syst. Appl. 38(4), 4390–4439 (2011). https://doi.org/10.1016/j.eswa.2010.09.108

    Article  Google Scholar 

  27. Basari, A.S.H., Hussin, B., Ananta, I.G.P., Zeniarja, J.: Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Eng. 53, 453–462 (2013). https://doi.org/10.1016/j.proeng.2013.02.059

    Article  Google Scholar 

  28. Haq, A.: Muti-objective Production Planning Problem for a Locks Industry: A Case Study nad Mathematical Model, Conference: RTMM-2019, Vol. 1, pp. 22–38 (2019)

  29. Wang, W., Xu, Z., Weizhen, L.J.: Three improved neural network models for air quality forecasting. Eng. Comput. 20(2), 192–210 (2003)

    Article  Google Scholar 

  30. Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 17(1), 113–126 (2004)

    Article  Google Scholar 

  31. Pai, P.F., Hong, W.C.: An improved neural network model in forecasting arrivals. Ann. Tour. Res. 32(4), 1138–1141 (2005)

    Article  Google Scholar 

  32. Pai, P.F., Hong, W.C.: Software reliability forecasting by support vector machines with simulated annealing algorithms. J. Syst. Softw. 79(6), 747–755 (2006)

    Article  Google Scholar 

  33. Hong, W.C., Dong, Y., Zheng, F., Lai, C.Y.: Forecasting urban traffic flow by SVR with continuous ACO. Appl. Math. Model. 35(3), 1282–1291 (2011)

    Article  Google Scholar 

  34. Hong, W.C., Dong, Y., Chen, L.Y., Wei, S.Y.: SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Appl. Soft Comput. 11(2), 1881–1890 (2011)

    Article  Google Scholar 

  35. Bartz-Beielstein, T.: SPOT: an R package for automatic and interactive tuning of optimization algorithms by sequential parameter optimization (2010) arXiv:1006.4645

  36. Ardjani, F., Sadouni, K.: Optimization of SVM multiclass by particle swarm (PSO-SVM). Int. J. Mod. Educ. Comput. Sci. 2, 32–38 (2010)

    Article  Google Scholar 

  37. Boutte, D., Santhanam, B.: A hybrid ICA-SVM approach to continuous phase modulation recognition. IEEE Signal Process. Lett. 16(5), 402–405 (2009)

    Article  Google Scholar 

  38. Devi, K.N., Bhaskaran, V.M., Kumar, G.P.: Cuckoo optimized SVM for stock market prediction. In: 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, pp. 1–5. https://doi.org/10.1109/ICIIECS.2015.7192906

  39. Ehteram, M., Singh, V.P., Ferdowsi, A., Mousavi, S.F., Farzin, S., Karami, H., et al.: An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration. PLoS ONE 14(5), 0217499 (2019). https://doi.org/10.1371/journal

    Article  Google Scholar 

  40. Zhang, H., Wang, J., Wang, M., Chen, X.: Integration of Cuckoo search and fizzy support vector machine for intelligent diagnosis of production process quality. J. Ind. Manag. Optim. 18(1), 195–217 (2022). https://doi.org/10.3934/jimo.2020150

    Article  Google Scholar 

  41. Huang, C.J., Yang, D.X., Chuang, Y.T.: Application of wrapper approach and composite classifier to the stock trend prediction. Expert Syst. Appl. 34(4), 2870–2878 (2008)

    Article  Google Scholar 

  42. Jamous, R.A.: Modifications of particle swarm optimization techniques and its application on stock market: a survey. Int. J. Adv. Comput. Sci. Appl. 6(3), 99–108 (2015)

    Google Scholar 

  43. Wang, G.L.G.: “The performance of PSO-SVM in inflation forecasting. Int. Conf. Intell. Comput. Technol. Autom. 1(1), 259–262 (2017)

    Google Scholar 

  44. Pandith, T.S.N.: Development of PSO Based Hybrid LSSVM Model for Time Series Prediction, no. November, pp. 16–20

  45. Chen, Q.C.Q., Wu, Y.W.Y., Zhang, X.Z.X., Chen, X.C.X.: Forecasting system based on Wavelet Transform and PSO-SVM. In: 2nd International Conference on Anti-counterfeiting, Security Identifier (2008)

  46. Bashath, S., Ismail, A.R.: Comparison of swarm intelligence algorithms for high dimensional optimization problems. IJEECS 11(1), 300–307 (2018)

    Article  Google Scholar 

  47. Hegazy, O., Soliman, O., Abdul Salam, M.: A machine learning model for stock market prediction. Int. J. Comput. Sci. Telecommun. 4(12), 45–67 (2013)

    Google Scholar 

  48. Mehdizadeh, S., Behmanesh, J., Khalili, K.: Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Comput. Electron. Agric. 139, 103–114 (2017). https://doi.org/10.1016/j.compag.2017.05.002

    Article  Google Scholar 

  49. Jiang, M., Luo, J., Jiang, D., Xiong, J., Song, H., Shen, J.: A Cuckoo search-support vector machine model for predicting dynamic measurement errors of sensors. IEEE Access 4, 5030–5037 (2016). https://doi.org/10.1109/ACCESS.2016.2605041

    Article  Google Scholar 

  50. Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques—part II: soft computing methods. Expert Syst. Appl. 36(3), 5932–5941 (2009)

    Article  Google Scholar 

  51. Lee, M.C.: Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst. Appl. 36(8), 10896–10904 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

The authors sincerely thank the Stock Exchange, Technical Analysis Groups, and Financial Markets for supporting this research.

Funding

No funds, grants, or other support was received.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Islamic Azad University, Masjed Soleyman Branch, Masjedsoleyman, Khuzestan, Iran

    Armin Mahmoodi, Leila Hashemi, Milad Jasemi, Soroush Mehraban, Jeremy Laliberté & Richard C. Millar

Authors
  1. Armin Mahmoodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leila Hashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Milad Jasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soroush Mehraban
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeremy Laliberté
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Richard C. Millar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by AM, LH, MJ, SM, JL and RCM. The first draft of the manuscript was written by AM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Armin Mahmoodi.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmoodi, A., Hashemi, L., Jasemi, M. et al. A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms. OPSEARCH 60, 59–86 (2023). https://doi.org/10.1007/s12597-022-00608-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-022-00608-x

Keywords

Search

Navigation