PT Hexindo Adiperkasa TBK Stock Price Prediction for 2026-2027 Using the Autoregressive Integrated Moving Average (ARIMA) Method: JEL Classification: C53, C58, G11, G17

Venike Leony Suteja Putri; Tarisa Everalda Sainyakit  Nanthi; Yunita Ade Irma  Seran; Rizky Rivaldi  Patora; Elisabet  Kareth; Radian Januari  Situmeang

doi:10.55885/jmap.v6i2.1029

Authors

Venike Leony Suteja Putri Department of Accounting, Faculty of Economics and Business, Cenderawasih University, Indonesia
Tarisa Everalda Sainyakit Nanthi Department of Accounting, Faculty of Economics and Business, Cenderawasih University, Indonesia
Yunita Ade Irma Seran Department of Accounting, Faculty of Economics and Business, Cenderawasih University, Indonesia
Rizky Rivaldi Patora Department of Accounting, Faculty of Economics and Business, Cenderawasih University, Indonesia
Elisabet Kareth Department of Accounting, Faculty of Economics and Business, Cenderawasih University, Indonesia
Radian Januari Situmeang Department of Statistics, Faculty of Mathematics and Natural Sciences, Cenderawasih University, Indonesia

DOI:

https://doi.org/10.55885/jmap.v6i2.1029

Keywords:

Stock Price, HEXA, ARIMA, Stock Forecasting, RStudio

Abstract

This study aims to predict the stock price of PT Hexindo Adiperkasa Tbk, listed under the ticker symbol HEXA, using the Autoregressive Integrated Moving Average (ARIMA) method in RStudio. The data used consist of monthly HEXA stock prices. Historical data from January 2021 to April 2025 were used as the model training dataset, while actual data from May 2025 to April 2026 were used as a benchmark for evaluating prediction results. The main forecasting period in this study covers May 2026 to December 2027. The research stages include data pattern identification, the Augmented Dickey-Fuller (ADF) stationarity test, differencing, ACF and PACF analysis, model selection based on AIC and BIC, parameter significance testing, diagnostic checking using the Ljung-Box test and Q-Q Plot, and accuracy measurement using MAPE, MAE, and RMSE. Based on the RStudio output, the best model is ARIMA (0,2,1), with an AIC value of 753.2217 and a BIC value of 757.0457. The accuracy results during the comparison period indicate a MAPE of 5.26%, an MAE of 268.67, and an RMSE of 398.31. The main forecasting results show that the HEXA stock price is predicted to reach 4,400 in May 2026 and 3,947 in December 2027. Although the MAPE value during the comparison period falls into the very good category according to the output table, the findings should still be interpreted cautiously because the Q-Q Plot indicates deviations in the tails of the residual distribution, and the prediction interval becomes wider over a longer forecasting horizon.

References

Abbas, M. A., Saputra, H. D., Octaviantara, A., Effendi, A. I., & Yasrizal. (2024). Prediksi harga saham berdasarkan data histori menggunakan algoritma LSTM, GRU & RNN. InComTech: Jurnal Telekomunikasi dan Komputer, 14(3). https://doi.org/10.22441/incomtech.v14i3.25132

Abuselidze, G. D., & Slobodianyk, A. N. (2021). Value assessment of shares of corporate issuers by applying the methods of fundamental analysis in the stock exchange market. In The Challenge of Sustainability in Agricultural Systems: Volume 2 (pp. 25-39). Cham: Springer International Publishing.

Agatón Lombera, D. I., Cardoso López, D. A., López Cabrera, J. A., & Nuñez Mora, J. A. (2024). Market reactions to US financial indices: A comparison of the GFC versus the COVID-19 pandemic crisis. Economies, 12(7), 165. https://doi.org/10.3390/economies12070165

Agrawal, S., Kumar, N., Rathee, G., Kerrache, C. A., Calafate, C. T., & Bilal, M. (2025). Improving stock market prediction accuracy using sentiment and technical analysis: S. Agrawal et al. Electronic Commerce Research, 25(5), 4103-4126. https://doi.org/10.1007/s10660-024-09874-x

Alawiyah, T., Permadi, I., Afuan, L., Maryanto, E., & Rahayu, S. P. (2024). Implementation of autoregressive integrated moving average (ARIMA) method for PT XL Axiata Tbk stock price prediction with website-based dashboard visualization. Jurnal Teknik Informatika (JUTIF), 5(4), 1205-1211. https://doi.org/10.52436/1.jutif.2024.5.4.2700

Allen, F., Carletti, E., & Valenzuela, P. (2013). Financial intermediation, markets, and alternative financial sectors. Handbook of the Economics of Finance, 2, 759-798. https://doi.org/10.1016/B978-0-44-453594-8.00011-2

Ansari, M., & Alam, M. (2024). A novel hybrid time-series approach for IoT-cloud-enabled environment monitoring: M. Ansari ans M. Alam. The Journal of Supercomputing, 80(7), 9019-9053. https://doi.org/10.1007/s11227-023-05782-3

Cornell, B., Landsman, W. R., & Stubben, S. R. (2017). Accounting information, investor sentiment, and market pricing. Journal of Law, Finance, and Accounting, 2(2), 325-345. https://doi.org/10.1561/108.00000017

Dewanti, R. T., Zukhronah, E., & Sulandari, W. (2024). Peramalan harga saham PT Indofood Sukses Makmur Tbk menggunakan model hibrida Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA). Jurnal Gaussian, 13(2), 270-279. https://doi.org/10.14710/j.gauss.13.2.270-279

Fadhilah, D. N., Parmikanti, K., & Ruchjana, B. N. (2024). Peramalan return saham subsektor perbankan menggunakan model ARIMA-GARCH. Jurnal Fourier, 13(1), 1-19. https://doi.org/10.14421/fourier.2024.131.1-19

Fauziah, F., Moeljadi, M., & Ratnawati, K. (2015). Dynamic relationship between exchange rates and stock prices in Asia, 2009-2013. Journal of Economics Finance and Accounting, 2(1).

Fitri, R., Suparti, S., & Kartikasari, P. (2023). Peramalan indeks Jakarta Islamic Index (JII) dengan pendekatan regresi parametrik linier sederhana dan regresi nonparametrik kernel dilengkapi GUI R-Shiny. Jurnal Gaussian, 12(2), 221-230. https://doi.org/10.14710/j.gauss.12.2.221-230

Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001

Grennan, J., & Michaely, R. (2021). Fintechs and the market for financial analysis. Journal of financial and quantitative analysis, 56(6), 1877-1907. https://doi.org/10.1017/S0022109020000721

Hamdani, A. M., Iriawan, N., & Irhamah. (2025). Akurasi kinerja metode hybrid geometric Brownian motion-Kalman filter dalam peramalan harga saham Indonesia. Jurnal Gaussian, 14(2), 524-534. https://doi.org/10.14710/j.gauss.14.2.524-534

Hicham, B., Abdellatif, H., & Karim, B. (2024). The Future of Islamic Real Estate Investment Trusts in Stimulating Financial Markets. Financial Markets, Institutions and Risks, 8(3), 206-226. https://doi.org/10.61093/fmir.8(3).206-226.2024

Hirshleifer, D., Lim, S. S., & Teoh, S. H. (2011). Limited investor attention and stock market misreactions to accounting information. The Review of Asset Pricing Studies, 1(1), 35-73. https://doi.org/10.1093/rapstu/rar002

Ishlah, A. W., Sudarno, S., & Kartikasari, P. (2023). Implementasi GridSearchCV pada support vector regression (SVR) untuk peramalan harga saham. Jurnal Gaussian, 12(2), 276-286. https://doi.org/10.14710/j.gauss.12.2.276-286

Kumar, G., Jain, S., & Singh, U. P. (2021). Stock Market Forecasting Using Computational Intelligence: A Survey: G. Kumar et al. Archives of computational methods in engineering, 28(3), 1069-1101. https://doi.org/10.1007/s11831-020-09413-5

Lee, W. S., Huang, A. Y., Chang, Y. Y., & Cheng, C. M. (2011). Analysis of decision making factors for equity investment by DEMATEL and Analytic Network Process. Expert Systems with Applications, 38(7), 8375-8383. https://doi.org/10.1016/j.eswa.2011.01.027

Maddodi, S., & Kunte, S. R. (2024). Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions. Journal of Capital Markets Studies, 8(2), 173-194.

Markus, T., Sofia, A., & Mahrani, D. (2025). Classification analysis using minimum spanning tree and predictions using ARIMA on the most influential stocks on the LQ45 index. Jurnal Statistika dan Aplikasinya, 9(1), 153-164. https://doi.org/10.21009/JSA.09113

Meyer, B., Mihaylov, E., Barrero, J. M., Davis, S. J., Altig, D., & Bloom, N. (2022). Pandemic-era uncertainty. Journal of Risk and Financial Management, 15(8), 338. https://doi.org/10.3390/jrfm15080338

Naseem, S., Mohsin, M., Hui, W., Liyan, G., & Penglai, K. (2021). The investor psychology and stock market behavior during the initial era of COVID-19: a study of China, Japan, and the United States. Frontiers in psychology, 12, 626934. https://doi.org/10.3389/fpsyg.2021.626934

Nensi, A. I. E., Al Maida, M., Notodiputro, K. A., Angraini, Y., & Mualifah, L. N. A. (2025). Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 10(2), 588-604. https://doi.org/10.18860/cauchy.v10i2.33379

Nisani, D., Qadan, M., & Shelef, A. (2022). Risk and Uncertainty at the Outbreak of the COVID-19 Pandemic. Sustainability, 14(14), 8527. https://doi.org/10.3390/su14148527

Nugraha, G. M. A. C., & Wirama, D. G. (2023). Nilai tukar, inflasi dan return saham dengan jenis industri sebagai variabel pemoderasi. E-Jurnal Akuntansi, 33(4), 919-934. https://doi.org/10.24843/EJA.2023.v33.i04.p04

Nukala, V. B., & Prasada Rao, S. S. (2021). Role of debt-to-equity ratio in project investment valuation, assessing risk and return in capital markets. Future Business Journal, 7(1), 13. https://doi.org/10.1186/s43093-021-00058-9

Paidi, I., Tundo, T., Rasiban, R., & Suropati, U. (2024). Implementasi regresi linear dan single exponential smoothing dalam prediksi harga saham ANTM. TEKNOKOM, 7(2), 291-297. https://doi.org/10.31943/teknokom.v7i2.222

Parveen, S., Satti, Z. W., Subhan, Q. A., Riaz, N., Baber, S. F., & Bashir, T. (2023). Examining investors' sentiments, behavioral biases and investment decisions during COVID-19 in the emerging stock market: a case of Pakistan stock market. Journal of Economic and Administrative Sciences, 39(3), 549-570. https://doi.org/10.1108/JEAS-08-2020-0153

Piero, R. A., & Natsir, K. (2023). A study on internal and external factors influencing manufacturing company share price. Jurnal Manajemen, 27(3), 594-611. https://doi.org/10.24912/jm.v27i3.1357

Ro'ifah, M. (2024). Peramalan harga saham PT Bank Rakyat Indonesia (Persero) Tbk menggunakan metode Auto Regressive Integrated Moving Average (ARIMA). Kadikma, 15(1), 24-33. https://doi.org/10.19184/kdma.v15i1.49098

Saranj, A., & Zolfaghari, M. (2025). Trading behavior-stock market volatility nexus among institutional and individual investors. Financial Innovation, 11(1), 98. https://doi.org/10.1186/s40854-024-00717-0

Seasholes, M. S., & Zhu, N. (2010). Individual investors and local bias. The Journal of Finance, 65(5), 1987-2010. https://doi.org/10.1111/j.1540-6261.2010.01600.x

Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26. https://doi.org/10.3390/ijfs7020026

Simanjuntak, M. M., Tarno, T., & Kartikasari, P. (2023). Pemodelan dan prediksi harga saham perusahaan fast moving customer goods menggunakan vector autoregressive with exogenous variables (VARX). Jurnal Gaussian, 12(2), 166-177. https://doi.org/10.14710/j.gauss.12.2.166-177

Singh, J., & Khushi, M. (2021). Feature learning for stock price prediction shows a significant role of analyst rating. Applied System Innovation, 4(1), 17. https://doi.org/10.3390/asi4010017

Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12007.x

Sukri, N. N., & Abundanti, N. (2023). Pengaruh net foreign fund, harga emas, dan suku bunga terhadap IHSG di BEI pada masa pandemi Covid-19. E-Jurnal Ekonomi dan Bisnis Universitas Udayana, 12(2), 214-221. https://doi.org/10.24843/EEB.2023.v12.i02.p06

Tarape, M. A., & Yusuf, S. D. (2025). Analisis dinamis hubungan suku bunga dan harga saham di Indonesia: Pendekatan model VAR 2010-2024. Jurnal Akuntansi dan Keuangan Kontemporer (JAKK), 8(1), 107-114. https://doi.org/10.30596/jakk.v8i1.25412

Teixeira, D. M., & Barbosa, R. S. (2024). Stock price prediction in the financial market using machine learning models. Computation, 13(1), 3. https://doi.org/10.3390/computation13010003

Tirta, I. M., Riski, A., & Sholikhah, N. (2024). Prediksi harga saham PT Bank Rakyat Indonesia Tbk menggunakan AutoML H2O. Jurnal Ilmiah Komputasi, 23(3), 397-404. https://doi.org/10.32409/jikstik.23.3.3624

Tuarob, S., Wettayakorn, P., Phetchai, P., Traivijitkhun, S., Lim, S., Noraset, T., & Thaipisutikul, T. (2021). DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction. Financial innovation, 7(1), 56. https://doi.org/10.1186/s40854-021-00269-7

Véron, N., & Wolff, G. B. (2016). Capital Markets Union: a vision for the long term. Journal of Financial Regulation, 2(1), 130-153. https://doi.org/10.1093/jfr/fjw006

Woodford, M. (2010). Financial intermediation and macroeconomic analysis. Journal of Economic Perspectives, 24(4), 21-44. https://doi.org/10.1257/jep.24.4.21

Zam, E. D. A. A., Nabilla, W. I., Pohan, A. S., & Lubis, R. S. (2025). Penerapan model ARIMA pada nilai penutupan Indeks Harga Saham Gabungan untuk data yang hilang. JISTech (Journal of Islamic Science and Technology), 10(2), 198-207. https://doi.org/10.30829/jistech.v10i2.28361

Zili, A. H. A., Hendri, D., & Kharis, S. A. A. (2022). Peramalan harga saham dengan model hybrid ARIMA-GARCH dan metode walk forward. Jurnal Statistika dan Aplikasinya, 6(2), 341-354. https://doi.org/10.21009/JSA.06218