Title: Application of bagging and particle swarm optimisation techniques to predict technology sector stock prices in the era of the COVID-19 pandemic using the support vector regression method

Authors: Heni Sulastri; Sheila Maulida Intani; Rianto Rianto

Addresses: Department of Informatics, Faculty of Engineering, Siliwangi University, Tasikmalaya, West Java, Indonesia ' Department of Informatics, Faculty of Engineering, Siliwangi University, Tasikmalaya, West Java, Indonesia ' Department of Informatics, Faculty of Engineering, Siliwangi University, Tasikmalaya, West Java, Indonesia

Abstract: The increase in positive cases of COVID-19 not only affects the health and lifestyle, but also the economy and the stock market. Tech and digital sector stocks can be predicted to be one of the most profitable. Therefore, the prediction of the stock price is required to be able to forecast the prospects of investment in the future. In this study, the prediction of the stock prices of Multipolar Technologies Ltd. (MLPT) was carried out using the support vector regression (SVR) method with bootstrap aggregation (bagging) technique and particle swarm optimisation (PSO) as SVR optimisation. From the results of the prediction process, it is shown that the application of bagging and PSO techniques in predicting stock prices in the technological sector can reduce the root mean squared error (RMSE) value on the SVR, the RMSE value from 22.142 to 21.833. Although it does not have a big impact, it is better to apply a combination of bagging and PSO techniques to SVR than one of them (SVR/SVR - PSO/SVR-Bagging).

Keywords: bootstrap aggregation; bagging; COVID-19; particle swarm optimisation; PSO; prediction; share prices; support vector regression; SVR; root mean squared error; RMSE.

DOI: 10.1504/IJCSE.2023.131507

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.255 - 267

Received: 13 Aug 2021
Accepted: 10 Apr 2022
Published online: 15 Jun 2023 *

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