Forecasting Stock Prices of Three Industries using ARIMA and ARFIMA Model
DOI:
https://doi.org/10.21834/e-bpj.v10iSI41.7728Keywords:
ARFIMA, ARIMA, stock prices, time seriesAbstract
Stock prices reflect a company's market value. This study aims to model the stock prices of three industries using the autoregressive integrated moving average (ARIMA) and the autoregressive fractionally integrated moving average (ARFIMA) models, and to forecast stock prices using the best model. Data for three companies were obtained from Yahoo Finance to represent the telecommunications, financial, and construction sectors. The forecast results show that all daily stock values were within 99% of the expected ranges for both models. According to the evaluation results, the ARFIMA model outperformed the ARIMA model.
References
Alghamdi, T., Elgazzar, K., Bayoumi, M., Sharaf, T., & Shah, S. (2019). Forecasting traffic congestion using ARIMA Modeling. 15th International Wireless Communications & Mobile Computing Conference (IWCMC),1227-1232. https://doi:10.1109/IWCMC.2019.8766698 DOI: https://doi.org/10.1109/IWCMC.2019.8766698
Al Wadi, S., Sadam, J., Aladwan, M., & Bouqaleh, B. (2022). Forecasting earnings of firms listed in ASE using the ARIMA model. Italian Journal of Pure and Applied Mathematics.
Bao, G., Xie, Z., Bao, J., Guo, Y., Xie, W., & Zhang. (2020). Model for stock price trend prediction based on LSTM and GA. Computer Science, 47, 467–473.
Chen, K., Zhou, Y., & Dai, F. A. (2015). LSTM-based method for stock returns prediction: A case study of the China stock market. In the 2015 IEEE International Conference on Big Data. IEEE. DOI: https://doi.org/10.1109/BigData.2015.7364089
Choudhary, R., & Gianey, H. K. (2017). Comprehensive review on supervised machine learning algorithms. In the 2017 International Conference on Machine Learning and Data Science (MLDS) (pp. 37–43). IEEE. DOI: https://doi.org/10.1109/MLDS.2017.11
Fu, Y., & Li, F. (2012). Application of ARMA model in sports stock price prediction in China. Statistics and Decision, 21(3), 101–103.
Jessica, L., Keogh, E., Lonardi, S., & Chu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. ACM.
Khashei, M., & Bijari, M. (2010). An artificial neural network (p,d,q) model for time series forecasting. Expert Systems with Applications, 37(1), 479–489. https://doi.org/10.1016/j.eswa.2009.05.044 DOI: https://doi.org/10.1016/j.eswa.2009.05.044
Lawrence, R. (1997). Using neural networks to forecast stock market prices. https://people.ok.ubc.ca/rlawrenc/research/Papers/nn.pdf
Nur, A., Azan, A., Faizatul, N., Mohd, A., Mototo, Z., Jin, P., & Mah, W. (2021). The comparison between ARIMA and ARFIMA model to forecast Kijang Emas (Gold) prices in Malaysia using MAE, RMSE, and MAPE highlights. Journal of Computing Research and Innovation, 6(3), 22–33. DOI: https://doi.org/10.24191/jcrinn.v6i3.225
Safitri, D., Mustafid, Ispriyanti, D., & Sugito, (2019). Gold price modelling in Indonesia using ARFIMA method. Journal of Physics: Conference Series,1217.https://doi.org/10.1088/1742-6596/1217/1/012087 DOI: https://doi.org/10.1088/1742-6596/1217/1/012087
Shah, D., Campbell, W., & Zulkernine, F. A. (2018). Comparative study of LSTM and DNN for stock market forecasting. In 2018 IEEE International Conference on Big Data. IEEE. DOI: https://doi.org/10.1109/BigData.2018.8622462
Yen, G., & Lee, C. F. (2008). Efficient market hypothesis (EMH): Past, present, and future. Review of Pacific Basin Financial Markets and Policies, 11(2), 305–329. https://doi.org/10.1142/S0219091508001354 DOI: https://doi.org/10.1142/S0219091508001362
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Siti Aida Sheikh Hussin, Muhammad Fakhri Syaabani, Harizatul Nadia Abdul Rahman, Zalina Zahid
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
