Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk Using ARIMA Method

Celia Christy Merlinda Tantono, Edwin Setiawan Nugraha

Abstract


This study focuses on forecasting the stock price of PT. Bank Negara Indonesia Tbk (BBNI) using the Autoregressive Integrated Moving Average (ARIMA) method. The data used are weekly stock prices from March 3, 2024, to February 23, 2025, with a total of 51 observations. The model aims to forecast stock prices for the next four weeks, from March 2 to March 23, 2025. The Box-Jenkins method is used to identify and evaluate the best-fitting ARIMA model. After conducting stationarity tests, ACF and PACF plots, and performing residual analysis, the ARIMA (1,2,1) is chosen to be the best model. This model gives the lowest error, with a Mean Absolute Percentage Error (MAPE) of 8.58% indicating very accurate predictions. The results of this study show that the ARIMA model is reliable for short-term stock price forecasting which offers valuable insight to help investors. make better decisions by providing a simple but effective forecasting tool.

Keywords


Forecasting; Stock Price; ARIMA

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References


D. Muhammad, I. Ahmed, K. Naveed, and M. Bendechache, “An explainable deep learning approach for stock market trend prediction,” Heliyon, vol. 10, no. 21, Article e40095, 2024, doi: 10.1016/j.heliyon.2024.e40095.

A. G. a. A. Kannan, "Stock price prediction using ARIMA model," International Research Journal of Engineering and Technology (IRJET), vol. 8, no. 8, pp. 226-234, 2021.

U. M. Sirisha, M. C. Belavagi, and G. Attigeri, “Profit prediction using ARIMA, SARIMA, and LSTM models in time series forecasting: A comparison,“IEEE Access, vol. 10, pp. 124715-124727, 2022.

E. Chodakowska, J. Nazarko, L. Nazarko, H. S. Rabayah, R. M. Abendeh, and R. Alawneh, “ARIMA models in solar radiation forecasting in different geographic locations,”Energies, vol. 16, no. 13, p. 5029, 2023, doi: 10.3390/en16135029.

PT. Bank Negara Indonesia (Persero) Tbk, “Sejarah BNI,” BNI, [Online]. Available: https://www.bni.co.id/id-id/perseroan/tentang-bni/sejarah. [Accessed: Apr. 28, 2025].

P. Pardede, M. Sipahutar, and P. Naibaho, “Forecasting stock prices of PT. Bank Negara Indonesia (Persero) Tbk., by method (BOX-JENKINS),” Primanomics: Jurnal Ekonomi & Bisnis, vol. 19, no. 1, pp. 191-205, 2021.

A. I. S. Wardhani and M. R. Yudhanegara, “Forecasting weekly stock price of PT. Aneka Tambang Tbk (ANTM) using ARIMA Box-Jenkins method,” Journal of Actuarial, Finance and Risk Management (JAFRM), vol. 3, no. 2, pp. 20-31, 2025.

J. D. Cryer and K. S. Chan, “Model for Stationary Time Series,” in Time Series Analysis: With Applications in R, 2nd ed., NY, USA, Springer, 2008, pp. 55–77.

G. Mani and R. Volety, "A comparative analysis of LSTM and ARIMA for enhanced real-time air pollutant levels forecasting using sensor fusion with ground station data," Cogent Engineering, vol. 8, no. 1, pp. 1–27, 2021, doi: 10.1080/23311916.2021.1936886.

E. González-Estrada, J. A. Villaseñor, and R. Acosta-Pech, Shapiro-Wilk test for multivariate skew- normality, Computational Statistics, vol. 37, Jan. 2022, doi: https://doi.org/10.1007/s00180-021-01188-y.

J. Dare, A. O. Patrick, and D. O. Oyewola, “Comparison of Stationarity on Ljung Box Test Statistics for Forecasting,” Earthline Journal of Mathematical Sciences, vol. 8, no. 2, pp. 325–336, Feb. 2022, doi: https://doi.org/10.34198/ejms.8222.325336.

C. Ergenc and R. Aktas, "Sector-specific financial forecasting with machine learning algorithm and SHAP interaction values," *Financial Internet Quarterly*, vol. 21, pp. 42-66, 2025, doi: 10.2478/fiqf-2025-0004.

C. D. Lewis, Industrial and Business Forecasting Methods: A Radical Guide to Exponential Smoothing and Curve Fitting, London, Butterworth Scientific, 1982.

J. Saputra, “Forecasting Stock Price of PT. GoTo Gojek Tokopedia Tbk (GOTO.JK) using ARIMA Box- Jenkins Method”, Indones. j. appl. math. stat, vol. 1, no. 2, pp. 79–88, Dec. 2024.


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