Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Business and Policy Studies
Series Vol. 14 , 13 September 2023
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Access | Article
MOUTAI Stock Price Prediction Based on ARIMA Model
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Abstract
Autoregressive Integrated Moving Average (ARIMA) models are effectively utilized in time series analysis and forecasts. With the focus on MOUTAI, one of the brands in liquor industry that has absorbed great public interest and wide acceptance, this paper studies the pattern of the closing price of MOUTAI stock, fits the data using ARIMA model, and eventually forecasts over the future horizon. Based on daily closing price of the specific stock, a few transformations of the data are carried out, making the fitting process of ARIMA model more accurate. The best-fit ARIMA model is studied and the forecast re-sults are analyzed. According to the increasing trend throughout the interval studied and the unforeseen peak at the beginning of 2021, the causation is discussed: the consumer trend. That is, people are inclined to consume more liquor products during mood swings, which is likely to happen during lock downs and the deteriorated epidemic worldwide. This study provides more insights to the potential investors and buyers of MOUTAI stock and products, and furthermore the liquor industry, giving an overview of the patterns shown in the stock’s closing price and the causation of the forecast results.
Keywords
MOUTAI, ARIMA, 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 2nd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-915371-71-3
- ISBN (Online)
- 978-1-915371-72-0
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/14/20230779
- Copyright
- 13 September 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