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Predicting the stock opening price of Apple company
- 1 University College London
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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
[1]. Surya B G C 2006 Stock Market and Economic Development: A Causality Test. The Journal of Nepalese Business Studies, 3, 1.
[2]. Song D, Chung B A and Kim N 2021 Forecasting Stock Market Indices Using Padding-Based Fourier Transform Denoising and Time Series Deep Learning Models. in IEEE Access, 9, 83786-83796.
[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.
[7]. Mehar V, et al. 2020 Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 167, 599-606.
[8]. Fang Y Q, Lu Z and Ge J W 2022 Joint RMSE loss LSTM CNN model for stock price prediction. Computer Engineering and Applications.
[9]. Zhang R X and Hao Y T 2023 Research on Stock Price Prediction Based on Deep Learning. Computer Knowledge and Technology, 33, 8-10.
[10]. Yang M and Wang J 2023 A spatial-temporal attention based BiLSTM for stock index prediction. Operations Research and Management Science, 32(8), 174-180.
Cite this article
Li,G. (2024).Predicting the stock opening price of Apple company.Theoretical and Natural Science,39,14-22.
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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