
Prediction of the Technology Stock Market in American: based on Apple, Microsoft and Netflix
- 1 King's College London
* Author to whom correspondence should be addressed.
Abstract
Stock market has been and will always be the central of the financial market. It is also a leading indicator of the economy react before the business cycle. Thus, predicted the trend of the stock market should be considered by market participants, policymakers and academicians. Simultaneously, technology stock market is gradually dominating the market. Under such a background, this paper will forecast the trend of the technology stock market based on a few technology companies, which are highly weighted in the technology industry, via the Autoregressive Integrated Moving Average (ARIMA) model. This study has successfully predicted the price of the technology stock market, which the predicted outcome is a close match to the actual price. The trend of the technology market analyzed by the ARIMA model with first order difference parameters, is going upward. The result represents American business cycle is undergoing a transition from contraction phase to recovery phase to some extent.
Keywords
Prediction, Technology stock market, Time series, ARIMA model.
[1]. Joseph, A.S. (1939) Business cycles: A theoretical, historical, and statistical analysis of the capitalist process. McGraw-Hill, New York.
[2]. Bouri, E., Demirer, R., Gupta, R. and Sun, X. (2020) The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global busi- ness cycles. Journal of Forecasting, 39, 957–965.
[3]. Si, D.K. Liu, X.H. and Kong, X.L. (2019) The comovement and causality between stock market cycle and business cycle in China: Evidence from a wavelet analysis, Economic Modelling, 83, 17-30.
[4]. Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A. and Salwana, E.S.S. (2020) Deep Learning for Stock Market Prediction. Entropy. 22(8), 840.
[5]. Ding, G. and Qin, L. (2020) Study on the prediction of stock price based on the associated network model of LSTM. Int. J. Mach. Learn. & Cyber. 11, 1307–1317.
[6]. Rouf, N., Malik, M.B., Arif, T., Sharma, S., Singh, S., Aich, S., Kim, H.C. (2021) Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions. Electronics, 10, 2717.
[7]. Bustos, O. and Pomares-Quimbaya, A. (2020) Stock market movement forecast: A Systematic review, Expert Systems with Applications, 156, 113464.
[8]. Dhyani, B., Kumar, M., Verma, P. and Jain, A. (2020) Stock market forecasting technique using arima model. International Journal of Recent Technology and Engineering. 8(6), 2694.
[9]. Pai, P.F. and Lin, C.S. (2005) A hybrid ARIMA and support vector machines model available in stock price forecasting. Omega,33(6), 497-505.
[10]. Koski, H. and, Pantzar, M. (2019) Data Markets in Making: The Role of Technology Giants, ETLA Working Papers, The Research Institute of the Finnish Economy (ETLA), Helsinki.
Cite this article
Liu,C. (2024). Prediction of the Technology Stock Market in American: based on Apple, Microsoft and Netflix. Theoretical and Natural Science,51,26-33.
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 CONF-MPCS 2024 Workshop: Quantum Machine Learning: Bridging Quantum Physics and Computational Simulations
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