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Future trends of AI stocks prediction using ARIMA model
- 1 School of Physical Science, University of California, Irvine, CA 92617, United States
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Abstract
This study uses an autoregressive integrated moving average (ARIMA) model to forecast the stock movements of artificial intelligence-related companies using IBM's historical stock price data from 2019 to 2024. Due to the high volatility and unique externalities of AI stocks, traditional financial models may not provide accurate forecasts. In this study, the ARIMA (1,1,0) model is chosen based on analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF). The prediction results indicate that IBM's stock price will trend upward in the short term. The model predicts that IBM's stock price will remain on an upward trend through late 2024 and is likely to exceed $200 with an optimistic market status. However, this prediction is more uncertain because the ARIMA model does not take into account external factors such as economic policies or earnings reports, which may affect the stock price. In conclusion, this study uses ARIMA to forecast AI stock trends, providing an analytical tool for investors and financial analysts, as well as recognizing the importance to incorporate external factors to improve forecast accuracy in high volatility stock market.
Keywords
ARIMA Model, AI stocks, forecast.
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Cite this article
Jiang,W. (2024). Future trends of AI stocks prediction using ARIMA model. Theoretical and Natural Science,42,112-119.
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
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