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

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.