Abstract
The stock market is a complex and dynamic industry that has garnered the attention of experts who seek to understand its various trends. Accurately predicting stock prices is crucial for investors to minimize their risk of losing money. However, due to the volatile and non-linear nature of financial stock markets, this is a challenging task. This study aimed to address the challenge of accurately predicting stock prices by utilizing an ensemble regression approach, which combines multiple sources of data. The proposed approach was evaluated using Tesla company data over a four-year period and demonstrated its efficiency in predicting stock closing prices. The results showed that the ensemble regression approach was able to accurately predict stock prices under different scenarios, handle fluctuations, anticipate sudden changes, and predict both simple and radical changes in stock prices. This study highlights the potential of machine learning techniques and increased computational capabilities in improving prediction methods for the complex and dynamic stock market industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumbure, M.M., Lohrmann, C., Luukka, P., Porras, J.: Machine learning techniques and data for stock market forecasting: a literature review. Expert Syst. Appl. 197, 116659 (2022). https://doi.org/10.1016/j.eswa.2022.116659
Jung, H., Raj, M., Riyanto, Y.E.: Finance and trade: a cross-country empirical analysis on the impact of financial development and asset tangibility on international trade. World Dev. 34(10), 1728–1741 (2006)
Vijh, M., Chandola, D., Tikkiwal, V.A., Kumar, A.: Stock closing price prediction using machine learning techniques. Proc. Comput. Sci. 167, 599–606 (2020). https://doi.org/10.1016/j.procs.2020.03.326
Albahli, S., Irtaza, A., Nazir, T., Mehmood, A., Alkhalifah, A., Albattah, W.: A machine learning method for prediction of stock market using real-time twitter data. Electronics 11(20), 3414 (2022). https://doi.org/10.3390/electronics11203414
Khan, W., Ghazanfar, M.A., Azam, M.A., et al.: Stock market prediction using machine learning classifiers and social media, news. J. Ambient Intell. Hum. Comput. 13, 3433–3456 (2022). https://doi.org/10.1007/s12652-020-01839-w
Ravikumar, S., Saraf, P.: Prediction of stock prices using machine learning (regression, classification) algorithms. In: 2020 International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1–5 (2020). https://doi.org/10.1109/INCET49848.2020.9154061
Alakwah, H.: Tesla stock price prediction. Mendeley Data, V1 (2023). https://doi.org/10.17632/c7r6ky4xgc.1
Kiran, A., Vasumathi, D.: Data Mining: min–max normalization based data perturbation technique for privacy preservation. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics. Advances in Intelligent Systems and Computing, Vol. 1090. Springer, Singapore. 2020. https://doi.org/10.1007/978-981-15-1480-7_66
Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Woźniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fus. 37, 132–156 (2017). https://doi.org/10.1016/j.inffus.2017.02.004
Souhaila, C., Mohamed, M.: Ensemble methods comparison to predict the power produced by photovoltaic panels. Proc. Comput. Sci. 191, 385–390 (2021). https://doi.org/10.1016/j.procs.2021.07.049
Brnabic, A., Hess, L.M.: Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Med. Informat. Decis. Making 21(1),(2021). https://doi.org/10.1186/s12911-021-01403-2
Doreswamy, H.K.S., Yogesh, K.M., Gad, I.: Forecasting air pollution particulate matter (PM2.5) using machine learning regression models. Proc. Comput. Sci. 171, 2057–2066 (2020). https://doi.org/10.1016/j.procs.2020.04.221
Abdelghafar, S., Goda, E., Darwish, A., Hassanien, A.E.: Satellite lithium-ion battery remaining useful life estimation by coyote optimization algorithm. In: 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, pp. 124–129 (2019). https://doi.org/10.1109/ICICIS46948.2019.9014752
Abdelghafar, S., Darwish, A., Ali, A.: Short-term forecasting of GDP growth for the petroleum exporting countries based on ARIMA model. In: Hassanien, A.E., et al. (eds.) The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), 5–7 March 2023, AICV 2023, Lecture Notes on Data Engineering and Communications Technologies, vol. 164, pp. 399–406. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27762-7_37
Colin Cameron, A., Windmeijer, F.A.G.: An R-squared measure of goodness of fit for some common nonlinear regression models. J. Econometrics 77(2), 329–342 (1997). https://doi.org/10.1016/S0304-4076(96)01818-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Elshamy, A., Afifi, A., Mabrok, A., Al Akwah, H., Ezzat, D., Abdelghafar, S. (2023). Data Fusion for Improved Stock Closing Price Prediction: Ensemble Regression Approach. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-43247-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43246-0
Online ISBN: 978-3-031-43247-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)