Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 7th International Conference on Economic Management and Green Development
Series Vol. 44 , 10 November 2023
Open
Access | Article
Research on the Augmented Dickey-Fuller Test for Predicting Stock Prices and Returns
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Abstract
With the continuous accumulation of theoretical knowledge and progressive applied research, analyzing financial time series data gradually becomes everlasting research in modern days. The simpler Dickey-Fuller originally is a test commonly used in econo-metrics and finance to test the stationarity of financial time series data. Thereafter, simpler Dickey-Fuller is eventually extended to the augmented Dickey-Fuller test to examine the stationarity of financial time series data such as stock prices, returns, and so on. This paper mainly focuses on the utilization of the augment Dickey-Fuller test and tests the stationarity of stock prices and returns for Nike, and Amazon. Both the stock price which is non-stationary, and the return, which is stationary, illustrate that these two companies are market-efficient. Additionally, the paper provides plots of stock prices and returns for these two companies by executing Python code. The results from the augment Dickey-Fuller test not only verify the characteristics of these plots but also indicate that the augment Dickey-Fuller test is useful to predict stock price and return.
Keywords
ADF test, unit root test, time series data, stock price, stationarity
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 7th International Conference on Economic Management and Green Development
- ISBN (Print)
- 978-1-83558-109-4
- ISBN (Online)
- 978-1-83558-110-0
- Published Date
- 10 November 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/44/20232198
- Copyright
- 10 November 2023
- 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