THE APPLICATION OF STANDARD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (SGARCH) MODEL IN FORECASTING THE STOCK PRICE OF BARITO PACIFIC

  • Edwin Setiawan Nugraha Study Program of Actuarial Science, School of Business, President University, Indonesia https://orcid.org/0000-0002-3043-0031
  • Celine Alvina Study Program of Actuarial Science, School of Business, President University, Indonesia
DOI: https://doi.org/10.30598/barekengvol18iss2pp0849-0862
Keywords: ARIMA, ARCH, GARCH, SGARCH, Time Series, Stock, BRPT

Abstract

Stock potentially yields higher returns than other investments, but is riskier due to volatile prices. To minimize the risk of loss, investors can forecast the stock price to help in deciding whether to buy, sell, or hold the stock.  Several methods are available for forecasting the stock price such as ARIMA, ARCH, and SGARCH. ARIMA model works best for series with a constant variance of error. However, almost all stock price series have a non-constant variance of error, known as heteroscedasticity, as such ARIMA isn’t suited for modeling the stock price. In contrast, the SGARCH model can handle series with heteroscedasticity. This makes it better suited for modeling stock prices as they have similar characteristics. PT Barito Pacific (BRPT) is a publicly traded firm that works mainly in petrochemical and geothermal energy. BRPT’s net profit increased in 2023 by 243% and the demand for geothermal energy is expected to increase due to the government's renewable energy transition project. Therefore, this study forecasts the BRPT’s stock price using the SGARCH model with R Studio. The stock price used ranges from October 1st, 2018 to August 16th, 2023 gotten from the Yahoo Finance Website. Based on the least AIC, this study found that ARMA(6,2)-SGARCH(1,1) is the best model for forecasting the stock price. This model gives a very accurate prediction of the stock price from April 1st, 2023 – April 19th, 2023 with a mean absolute error of 78.11, root mean square error of 89.51, and mean absolute percentage error of 9.81%.

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References

E. S. Nugraha, C. Alvina, S. Arifin, Suwarno, A. E. S. Hidayat, and F. N. F. Sudding, “Using of R Software for GGRM Daily Stock Price Forecasting Through ARIMA Model,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 3, pp. 470–479, 2023.

K. J, I. Sengupta, and S. Chaudhury, “Stock Market Prediction Using Time Series Analysis,” SSRN Electronic Journal, 2018, doi: 10.2139/ssrn.3168423.

C. Brooks, Introductory Econometrics for Finance. New York: Cambridge University Press, 2019.

E. Lewinson, Python for Finance Cookbook. Birmingham: Packt Publishing Ltd, 2020.

“About Us,” Barito Pacific.

Winarni, “Kinerja Emiten: Semester I/2023, Laba (BRPT) Melonjak 243%,” Data Indonesia.

BRIN (Badan Riset dan Inovasi Nasional), “Indonesia Targetkan Pencapain Net-Zero Emission Pada 2060,” BRIN (Badan Riset dan Inovasi Nasional).

J. V. C. Medellu and E. S. Nugraha, “Tea Production Forecasting in Indonesia’s Large Plantation By Using Arima Models,” The 6th International Conference on Family Business and Entrepreneurship, vol. 6, pp. 29–39, 2022.

E. Virginia, J. Ginting, and F. A. M. Elfak, “Application of GARCH Model to Forecast Data and Volatility of Share Price of Energy (Study on Adaro Energy Tbk, LQ45),” International Journal of Energy Economics and Policy, vol. 8, no. 3, pp. 131–140, 2018.

J. Gao, “Research on Stock Price Forecast Based on ARIMA-GARCH Model,” in Proceedings of the 4th Management Science Informatization and Economisc Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China, EAI, 2023. doi: 10.4108/eai.9-12-2022.2327556.

N. M. H. Masoud, “The Impact of Stock Market Performance upon Economic Growth,” International Journal of Economics and Financial, vol. 3, no. 4, pp. 788–798, 2013.

Fakhri Rana Sausan, L. Korawijayanti, and Arum Febriyanti Ciptaningtias, “The Effect of Return on Asset (ROA), Debt to Equity Ratio (DER), Earning per Share (EPS), Total Asset Turnover (TATO) and Exchange Rate on Stock Return of Property and Real Estate Companies at Indonesia Stock Exchange Period 2012-2017,” Ilomata International Journal of Tax and Accounting, vol. 1, no. 2, pp. 103–114, Mar. 2020, doi: 10.52728/ijtc.v1i2.66.

K. Boudt, “GARCH Model in R for Production and Simulation,” Campus DataCamp.

C. Chatfield and H. Xing, The Analysis of Time Series: An Introduction with R. CRC Press, 2019.

J. D. Cryer and K.-S. Chan, Time Series Analysis With Applications in R. Lowa City: Springer, 2008.

R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. Melbourne: OTexts, 2018.

C. D. Lewis, Industrial and business forecasting methods: A Radical guide to exponential smoothing and curve fitting. London: Butterworth Scientific, 1982.

Published
2024-05-25
How to Cite
[1]
E. Nugraha and C. Alvina, “THE APPLICATION OF STANDARD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (SGARCH) MODEL IN FORECASTING THE STOCK PRICE OF BARITO PACIFIC”, BAREKENG: J. Math. & App., vol. 18, no. 2, pp. 0849-0862, May 2024.
Issue
Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2024 Edwin Setiawan Nugraha, Celine Alvina

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