Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 30, 24 January 2024
Open
Access | Article
The comprehensive analysis of Google’s stock using ARIMA model
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Abstract
Predicting stock prices has long been a subject of keen interest due to its financial implications and inherent complexity. The examination of existing literature suggests the need for a focused study encompassing a diverse spectrum of stocks within a specific sector. In this research, the author evaluates the efficacy of the AutoRegressive Integrated Moving Average (ARIMA) model in forecasting Google’s stock performance. The data used in this paper comes from the Chinese corn market price of 2018 to October 2023. The selection of the ARIMA model is based on its widespread acceptance and straightforward nature. This paper also explores how the accuracy of predictions is influenced by various historical data points. Simultaneously, the projections indicate that Google’s stock is poised for continued growth in the upcoming weeks. This investigation aims to provide valuable insights into the stock market’s behaviour, particularly within the context of Google, by leveraging the ARIMA model’s capabilities.
Keywords
Stock Market, Arima, ACF, PACF, prediction
References
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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 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-283-1
- ISBN (Online)
- 978-1-83558-284-8
- Published Date
- 24 January 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/30/20241062
- Copyright
- 24 January 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