Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 39, 21 June 2024


Open Access | Article

Predicting the stock opening price of Apple company

Ge Li * 1
1 University College London

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 39, 14-22
Published 21 June 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Ge Li. Predicting the stock opening price of Apple company. TNS (2024) Vol. 39: 14-22. DOI: 10.54254/2753-8818/39/20240583.

Abstract

As the stock market plays a crucial role in the world economy, researchers have used multiple mathematical and statistical models such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks model to forecast the fluctuation in stock price despite their unpredictability as the stock market, being a stochastic process, would be easily affected by an abundance of factors such as governmental policies, industrial news, and natural calamities. Therefore, based on the previous studies, this paper attempts to forecast the stock opening price of Apple Inc., one of the world-leading companies in the technology industry, utilizing the Autoregressive Integrated Moving Average (ARIMA) model. In order to minimize the impact on the stock market brought by the COVID-19 pandemic, this paper will analyze separately the opening price of Apple stock before and after the epidemic outbreak and will compare the difference the pandemic made in the stock market, as well as the forecasting models.

Keywords

Apple, ARIMA, Stock Price, Forecasting, Time Series

References

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3. Sun L and Yuan H 2023 Research on the Tencent Company Stock Price Based on ARIMA Model. Advances in Economics, Management and Political Sciences.

4. Ahmar A 2016 Predicting Movement of Stock of Apple Inc Using Sutte Indicator. Proceedings The 3rd AISTSSE Trends in Science and Science Education.

5. Tiao G C 2001 Time Series: ARIMA Methods. International Encyclopedia of the Social & Behavioral Sciences. Pergamon, 15704-15709.

6. Khan S and Alghulaiakh H 2020 ARIMA Model for Accurate Time Series Stocks Forecasting. International Journal of Advanced Computer Science and Applications, 11.

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
ISBN (Print)
978-1-83558-463-7
ISBN (Online)
978-1-83558-464-4
Published Date
21 June 2024
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/39/20240583
Copyright
21 June 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated