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An efficient equilibrium optimizer with support vector regression for stock market prediction

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Abstract

A hybridized method that relies on using the support vector regression (SVR) method with equilibrium optimizer (EO) is proposed to foresee the closing prices of Egyptian Exchange (EGX). Three indices are modeled and employed: EGX 30, EGX 30 capped, and EGX 50 EWI. The efficiency of using the technical indicators and statistical measures in the forecasting process is evaluated. The proposed EO-SVR-based forecasting model is adopted and evaluated using mean absolute percentage error, average, standard deviation, best fit, worst fit, and CPU time. Also, it is compared with recently developed metaheuristic optimization algorithms published in the literature such as whale optimization algorithm, salp swarm algorithm, Harris Hawks optimization, gray wolf optimizer, Henry gas solubility optimization, Barnacles mating optimizer, Manta ray foraging optimization, and slime mold algorithm. The proposed EO-SVR model got better results than other the counterparts, and EO-SVR is considered the optimal model according to its superior outcomes. Moreover, there is no need to use technical indicators and statistical measures as their effect is not noticeable.

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  1. www.egidegypt.com.

  2. www.egx.com.eg.

  3. www.iti.gov.eg.

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Acknowledgements

The authors would like to thank Information Technology Institute (ITI)\(^{3}\) for supporting this research.

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, 61519, Egypt

    Essam H. Houssein, Mahmoud Dirar & Waleed M. Mohamed

  2. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  3. School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia

    Laith Abualigah

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  1. Essam H. Houssein
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Essam H. Houssein was involved in supervision, methodology, conceptualization, software, data curation, formal analysis, writing, reviewing, and editing. Mahmoud Dirar took part in methodology, software, resources, data curation, writing the original draft, writing, reviewing, and editing. Laith Abualigah participated in conceptualization, formal analysis, writing reviewing, and editing. Waleed M. Mohamed had contributed to conceptualization, formal analysis, writing, reviewing, and editing. All authors read and approved the final paper.

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Houssein, E.H., Dirar, M., Abualigah, L. et al. An efficient equilibrium optimizer with support vector regression for stock market prediction. Neural Comput & Applic 34, 3165–3200 (2022). https://doi.org/10.1007/s00521-021-06580-9

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