Download pdf
Predicting Apple Stock Price Based on ARIMA Model
- 1 Department of Statistics, University of Connecticut, Storrs, CT,06269, US
* Author to whom correspondence should be addressed.
Abstract
ARIMA stands for Auto-Regressive Integrated Moving Average and is a popular statistical model to forecast time-series. It has since been used to predict the behaviour of time series based on historical data, and a wide range of applications in which it is useful like stock market forecasting, economic growth analysis, etc. While the ARIMA model has significant benefits, it is a model with limitations, particularly when the underlying data are polluted by noise or present non-linear patterns. Long-term forecasting with ARIMA is less accurate because it is a linear model which does not always accurately reflect the complexity or immediate changes found in most real-world financial data. Our focus will be on Apple stock price data from 2014 to 2024 (working days for simplicity), which we analyze using ARIMA model, and we compare with other analysis models. In this analysis, we would like to point out two things — why ARIMA models are not suitable for longer-term market predictions and demonstrate how poorly it fits on nonlinear data.
Keywords
Auto-regressive Integrated Moving Average (ARIMA), Apple stock data, long term prediction, R language
[1]. Kubilay, İ.A. (2015) The Founding of Apple and the Reasons Behind Its Success. Procedia-Social and Behavioral Sciences, 195, 2019-2028.
[2]. Li, T. (2024) Apple: A Clever Integration of Psychology and Scientific Marketing. International Journal of Global Economics and Management, 3(2), 280-285.
[3]. Peng, J., Deng, C., & Chen, Y.C. (2022) Application of ARIMA Time Series Model in Stock Data Prediction. In International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 341, 223-232
[4]. Shumway, R. H., Stoffer, D. S., Shumway, R. H., & Stoffer, D. S. (2017). ARIMA models. Time series analysis and its applications: with R examples, 75-163.
[5]. Yıldırım, E., & Cengiz, M. A. (2022). Modeling and Forecasting of USD/TRY Exchange Rate Using ARMA-GARCH Approach. İstatistik Araştırma Dergisi, 12(2), 1-13.
[6]. Guo, K., Jiang, Z., & Zhang, Y. (2023). Prediction of S&P500 Stock Index Using ARIM and Linear Regression. Highlights in Science, Engineering and Technology, 38, 399-407.
[7]. Xu, S., & Liang, X. (2019) Research on Stock Price Prediction Based on ARIMA-GARCH Model. Journal of Henan Institute of Education (Natural Science Edition), 28(04), 20-24.
[8]. Trivez, F.J., & Catalan, B. (2009) Detecting Level Shifts in ARMA-GARCH (1, 1) Models. Journal of Applied Statistics, 36(6), 679-697.
[9]. Bank, S., & Yazar, E.E. (2023) Brand Index: A New Suggestion for Stock Market Indices. International Journal of Economics and Business Research, 26(2), 179-200.
[10]. Yagi, I., Masuda, Y., & Mizuta, T. (2020) Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity. IEEE Transactions on Computational Social Systems, 7(6), 1324-1334.
Cite this article
Cao,E. (2024). Predicting Apple Stock Price Based on ARIMA Model. Advances in Economics, Management and Political Sciences,141,200-205.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of ICFTBA 2024 Workshop: Finance's Role in the Just Transition
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).