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Prediction of BRIC Stock Price Using ARIMA, SutteARIMA, and Holt-Winters

Ansari Saleh Ahmar1, Pawan Kumar Singh2, Nguyen Van Thanh3,*, Nguyen Viet Tinh3, Vo Minh Hieu3

1 Department of Statistics, Faculty of Mathematics and Natural Sciencces, Universitas Negeri Makassar, Makassar, 90223, Indonesia
2 School of Humanities and Social Sciences, Thapar Institute of Engineering and Technology, Patiala, 147004, India
3 Faculty of Commerce, Van Lang University, Ho Chi Minh City, 70000, Vietnam

* Corresponding Author: Nguyen Van Thanh. Email: email

(This article belongs to the Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)

Computers, Materials & Continua 2022, 70(1), 523-534. https://doi.org/10.32604/cmc.2022.017068

Abstract

The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine the most effective model for predicting data. The stock closing price of BRIC country data was obtained from Yahoo Finance. That data dates from 01 November 2019 to 11 December 2020, then divided into two categories--training data and test data. Training data covers 01 November 2019 to 02 December 2020. Seven days (03 December 2020 to 11 December 2020) of data was tested to determine the accuracy of the models using training data as a reference. To measure the accuracy of the models, we obtained the means absolute percentage error (MAPE) and mean square error (MSE). Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price (BVSP) while MAPE (0.50) and MSE (579272.65) with Holt-Winters (smaller than ARIMA and SutteARIMA), model SutteARIMA was found most appropriate to predict the stock prices of Russia (IMOEX.ME), India (BSESN), and China (SSE) when compared to ARIMA and Holt-Winters. MAPE and MSE with SutteARIMA: Russia (MAPE:0.7; MSE:940.20), India (MAPE:0.90; MSE:207271.16), and China (MAPE: 0.72; MSE: 786.28). Finally, Holt-Winters predicted the daily forecast values for the Brazil stock price (BVSP) (12 December to 14 December 2020 i.e., 115757.6, 116150.9 and 116544.1), while SutteARIMA predicted the daily forecast values of Russia stock prices (IMOEX.ME) (12 December to 14 December 2020 i.e., 3238.06, 3241.54 and 3245.01), India stock price (BSESN) (12 December to 14 December 2020 i.e.,. 45709.38, 45828.71 and 45948.05), and China stock price (SSE) (11 December to 13 December 2020 i.e., 3397.56, 3390.59 and 3383.61) for the three time periods.

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APA Style
Ahmar, A.S., Singh, P.K., Thanh, N.V., Tinh, N.V., Hieu, V.M. (2022). Prediction of BRIC stock price using ARIMA, suttearima, and holt-winters. Computers, Materials & Continua, 70(1), 523-534. https://doi.org/10.32604/cmc.2022.017068
Vancouver Style
Ahmar AS, Singh PK, Thanh NV, Tinh NV, Hieu VM. Prediction of BRIC stock price using ARIMA, suttearima, and holt-winters. Comput Mater Contin. 2022;70(1):523-534 https://doi.org/10.32604/cmc.2022.017068
IEEE Style
A.S. Ahmar, P.K. Singh, N.V. Thanh, N.V. Tinh, and V.M. Hieu "Prediction of BRIC Stock Price Using ARIMA, SutteARIMA, and Holt-Winters," Comput. Mater. Contin., vol. 70, no. 1, pp. 523-534. 2022. https://doi.org/10.32604/cmc.2022.017068



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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