Using of R Software for GGRM Daily Stock Price Forecasting Through ARIMA Model

Authors

  • Edwin Setiawan Nugraha Study Program of Actuarial Science, School of Business, President University, 17550, Indonesia
  • Celine Alvina Study Program of Actuarial Science, School of Business, President University, 17550, Indonesia
  • Samsul Arifin Statistics Department, Faculty of Humanities, Bina Nusantara University. Jakarta. Indonesia.
  • Suwarno Primary Teacher Education Department, Faculty of Humanities, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Agus Eka Sopian Hidayat Study Program of Actuarial Science, School of Business, President University, 17550, Indonesia
  • Fauziah Nur Fahira Sudding Study Program of Actuarial Science, School of Business, President University, 17550, Indonesia

Keywords:

Stocks, forecasting, arima, time series, R

Abstract

Stocks are one of the attractive investment instruments for companies and individuals to raise their finances. However, there are volatility of stocks made risk  for the investor. Statistical tools offer a approach to predict the stocks price to minimize the risk. ARIMA(p,d,q) technical analysis will be used in this study to predict the stock price of PT. Gudang Garam Tbk. for 8 days from March 1, 2022, to March 8, 2022. This forecasting uses PT. Gudang Garam Tbk. historical stock price data from December 2021 to February 2022 was obtained from the Yahoo Finance website. Based on the test results of 24 ARIMA models, the researcher got model 2 ARIMA (4,1,3) as the best model with the equation . This model is the best because it has the second smallest AIC value, which is 874.42, and the smallest MAPE value, which is 3.14%. This study shows that the stock price is predicted to fall for the next five days from March 1 to March 5, 2022 and will rise again from March 6 to March 8, 2022.

Downloads

Download data is not yet available.

References

B. E. Indonesia, “Saham,” 2018. https://www.idx.co.id/produk/saham/

Revina, “Alasan Investasi Saham Penting untuk Dilakukan Sejak Dini,” Ajaib, 2020. https://ajaib.co.id/alasan-investasi-saham-penting-untuk-dilakukan-sejak-dini/

Z. Afdika, “14 Saham Blue Chip Terbaik di Indonesia pada 2021 Yang Layak dibeli,” Qoala, 2021. https://www.qoala.app/id/blog/keuangan/investasi/saham-blue-chip-terbaik/

Web Team, “PT Surya Madistrindo,” 2002. https://www.gudanggaramtbk.com/suryamadistrindo/ (accessed Jun. 15, 2020).

E. Asror, “Lis Lengkap Saham Emiten Rokok Berikut Profil Mininya,” 2021. https://bigalpha.id/news/lis-lengkap-saham-emiten-rokok-berikut-profil-mininya

G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” vol. 50, pp. 159–175, 2003.

D. Maulani and D. Riani, “Autoregressive Integrated Moving Average (ARIMA) pada Industri Manufaktur di Bursa Efek Indonesia (STUDI KASUS PT. GUDANG GARAM Tbk.),” Pros. LPPM UIKA BOGOR; 2020, 2020, [Online]. Available: http://pkm.uika-bogor.ac.id/index.php/prosiding/article/view/624

H. Winata and Y. D. Hapsari, “PENGGUNAAN METODE TRESHOLD GARCH DALAM MEMPREDIKSI HARGA SAHAM PT. GUDANG GARAM, Tbk.,” Optim. J. Ekon. dan Pembang., vol. 7, no. 1, p. 59, 2017, doi: 10.12928/optimum.v7i1.7893.

Tableau, “Time Series Analysis: Definition, Types, Techniques, and When It’s Used.” https://www.tableau.com/learn/articles/time-series-analysis

S. Arifin, I. B. Muktyas, and J. M. Mandei, “Graph coloring program for variation of exam scheduling modeling at Binus University based on Welsh and Powell algorithm,” in International Conference Advanced in Applied Mathematics (ICAAM 2021), 2022, pp. 1–5. doi: 10.1088/1742-6596/2279/1/012005.

M. Žáček, “Introduction to Time Series,” 2017, pp. 32–52. doi: 10.4018/978-1-5225-0565-5.ch002.

H. Song, “Review of Time Series Analysis and Its Applications With R Examples (3rd Edition), by Robert H. Shumway & David S. Stoffer,” Struct. Equ. Model. A Multidiscip. J., vol. 24, no. 5, pp. 800–802, Sep. 2017, doi: 10.1080/10705511.2017.1299578.

H. Syafwan, M. Syafwan, E. Syafwan, and A. F. Hadi, “Forecasting Unemployment in North Sumatra Using Double Exponential Smoothing Method Forecasting Unemployment in North Sumatra Using Double Exponential Smoothing Method,” 2021, doi: 10.1088/1742-6596/1783/1/012008.

H. Wang and K. Li, “Resistance of IID Noise in Differentially Private Schemes for Trajectory Publishing,” Comput. J., vol. 63, no. 4, pp. 549–566, Apr. 2020, doi: 10.1093/comjnl/bxz097.

M. Lefebvre, “The homing problem for autoregressive processes,” IMA J. Math. Control Inf., vol. 39, no. 1, pp. 322–344, Mar. 2022, doi: 10.1093/imamci/dnab047.

J. Li, J. Cai, R. Li, Q. Li, and L. Zheng, “Wavelet transforms based ARIMA-XGBoost hybrid method for layer actions response time prediction of cloud GIS services,” J. Cloud Comput., vol. 12, no. 1, pp. 1–17, 2023.

S. Bocquet, “Ocean wave autocorrelation function,” Appl. Math. Comput., vol. 426, p. 127114, 2022, doi: https://doi.org/10.1016/j.amc.2022.127114.

Y. Zhang, X. Wang, S. Cui, J. Zhang, and J. Su, “Study on growth prediction of Haematococcus pluvialis based on ARIMA model,” in 2022 7th International Conference on Multimedia and Image Processing, 2022, pp. 177–181.

W. Wang and Y. Lu, “Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model,” IOP Conf. Ser. Mater. Sci. Eng., vol. 324, p. 12049, 2018, doi: 10.1088/1757-899x/324/1/012049.

S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, and W. Xu, “Applications of support vector machine (SVM) learning in cancer genomics,” Cancer genomics and proteomics, vol. 15, no. 1, pp. 41–51, 2018.

D. Fan, H. Sun, J. Yao, K. Zhang, X. Yan, and Z. Sun, “Well production forecasting based on ARIMA-LSTM model considering manual operations,” Energy, vol. 220, p. 119708, 2021.

B. H. Farizan, A. G. Putrada, and R. R. Pahlevi, “Analysis of Support Vector Regression Performance in Prediction of Lettuce Growth for Aeroponic IoT Systems,” in 2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), 2021, pp. 1–6. doi: 10.1109/ICADEIS52521.2021.9702093.

A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean Absolute Percentage Error for regression models,” Neurocomputing, vol. 192, pp. 38–48, 2016, doi: https://doi.org/10.1016/j.neucom.2015.12.114.

N. M. Vural, F. Ilhan, S. F. Yilmaz, S. Ergüt, and S. S. Kozat, “Achieving Online Regression Performance of LSTMs With Simple RNNs,” IEEE Trans. Neural Networks Learn. Syst., pp. 1–12, 2021, doi: 10.1109/TNNLS.2021.3086029.

A. M. Elshewey, M. Y. Shams, Z. Tarek, M. Megahed, E.-S. M. El-Kenawy, and M. A. El-Dosuky, “Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model,” Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 765–781, 2023, doi: 10.32604/csse.2023.034324.

S. Arifin and I. B. Muktyas, “Generate a system of linear equation through unimodular matrix using Python and Latex,” in AIP Conference Proceedings, American Institute of Physics Inc., Apr. 2021. doi: 10.1063/5.0041651.

A. Ebert, P. Wu, K. Mengersen, and F. Ruggeri, “Computationally Efficient Simulation of Queues: The R Package queuecomputer,” J. Stat. Softw., vol. 95, no. 5 SE-Articles, pp. 1–29, Oct. 2020, doi: 10.18637/jss.v095.i05.

W. N. Venables, D. M. Smith, and R. C. Team, “An introduction to R, Notes on R: A Programming Environment for Data Analysis and Graphics Version 3.6. 3.” R Foundation for Statistical Computing Vienna, Austria, 2020.

I. G. A. A. Yudistira, “Pengembangan Simulasi Kejadian Diskret Berbasis Paket Simmer pada R,” Eng. Math. Comput. Sci. J., vol. 3, no. 2, pp. 79–85, 2021, doi: 10.21512/emacsjournal.v3i2.7386.

K. M. Ramachandran and C. P. Tsokos, Mathematical statistics with applications in R. Academic Press, 2020.

S. Tarigan, N. P. Murnaka, and S. Arifin, “Development of teaching material in mathematics ‘Sapta Maino Education’ on topics of plane geometry,” in AIP Conference Proceedings, American Institute of Physics Inc., Apr. 2021, p. 020003. doi: 10.1063/5.0041650.

A. A. Abdillah, Azwardi, S. Permana, I. Susanto, F. Zainuri, and S. Arifin, “Performance Evaluation Of Linear Discriminant Analysis And Support Vector Machines To Classify Cesarean Section,” Eastern-European J. Enterp. Technol., vol. 5, no. 2–113, pp. 37–43, 2021, doi: 10.15587/1729-4061.2021.242798.

Babu, D. R. ., & Sathyanarayana, B. . (2023). Design and Implementation of Technical Analysis Based LSTM Model for Stock Price Prediction. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 01–07. https://doi.org/10.17762/ijritcc.v11i4s.6301

Dhingra, M., Dhabliya, D., Dubey, M. K., Gupta, A., & Reddy, D. H. (2022). A Review on Comparison of Machine Learning Algorithms for Text Classification. 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), 1818–1823. IEEE.

Ms. Mohini Dadhe, Ms. Sneha Miskin. (2015). Optimized Wireless Stethoscope Using Butterworth Filter. International Journal of New Practices in Management and Engineering, 4(03), 01 - 05. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/37

Downloads

Published

16.07.2023

How to Cite

Nugraha, E. S. ., Alvina, C. ., Arifin, S. ., Suwarno, Hidayat, A. E. S. ., & Sudding, F. N. F. . (2023). Using of R Software for GGRM Daily Stock Price Forecasting Through ARIMA Model . International Journal of Intelligent Systems and Applications in Engineering, 11(3), 470–479. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3201

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)