Micrtsoft_Stock_Price: An Efficient Framework For Microsoft Stock Price Prediction Using Computational Intelligence

Document Type : Original Article

Authors

1 Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

2 Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

3 Department of Business Information Systems, Faculty of Buisness, El Alamien International University, El Alamein, Egypt

Abstract

Econometrics uses statistical methods to analyze relationships using data. While its name suggests a focus on economics, it's widely used in various social sciences and beyond.One of the challenges in predicting stock prices is data availability since obtaining data can often be quite challenging.Predicting stock prices is difficult because it involves analyzing data with various methods, but it's not always accurate due to many factors involved. These methods help understand trends but aren't foolproof for making investment decisions.In this paper, we have proposed an efficient framework for the prediction of Microsoft stock price using nine different machine learning algorithms (AdaBoost, kNN, Linear Regression, Gradient Boosting, Tree, Neural Network, SVM, Constant, Random Forest) on six different datasets.The best algorithm in the four datasets was adaboost, with the smallest percentage of errors, 0.004, and the best algorithm in the two datasets was linear regression.The best result algorithm in all datasets is AdaBoost.

Keywords

References

[1]      Dougherty, C. (2011). Introduction to econometrics. Oxford university press, USA.
[2]      Hayashi, F. (2011). Econometrics. Princeton University Press.
[3]      Durlauf, S. N., Johnson, P. A., & Temple, J. R. (2005). Growth econometrics. Handbook of economic growth, 1, 555-677.
[4]      Andrews, D. W. (1994). Empirical process methods in econometrics. Handbook of econometrics, 4, 2247-2294.
[5]      Khuat, T. T., & Le, M. H. (2017). An application of artificial neural networks and fuzzy logic on the stock price prediction problem. JOIV: International Journal on Informatics Visualization, 1(2), 40-49.‏
[6]      Lee, J. W. (2001, June). Stock price prediction using reinforcement learning. In ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570) (Vol. 1, pp. 690-695). IEEE.‏
[7]      Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
[8]      El Naqa, I., & Murphy, M. J. (2015). What is machine learning? (pp. 3-11). Springer International Publishing.
[9]      Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., ... & Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002.
[10]    Leung, C. K. S., MacKinnon, R. K., & Wang, Y. (2014, July). A machine learning approach for stock price prediction. In Proceedings of the 18th International Database Engineering & Applications Symposium (pp. 274-277).‏
[11]    Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.‏
[12]    Tsai, C. F., & Wang, S. P. (2009, March). Stock price forecasting by hybrid machine learning techniques. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1, No. 755, p. 60).
[13]    Bansal, M., Goyal, A., & Choudhary, A. (2022). Stock market prediction with high Accuracy using machine learning techniques. Procedia Computer Science, 215, 247-265.
[14]    Usmani, M., Adil, S. H., Raza, K., & Ali, S. S. A. (2016, August). Stock market prediction using machine learning techniques. In 2016 3rd international conference on computer and information sciences (ICCOINS) (pp. 322-327). IEEE.
[15]    Kohli, P. P. S., Zargar, S., Arora, S., & Gupta, P. (2019). Stock prediction using machine learning algorithms. In Applications of Artificial Intelligence Techniques in Engineering: SIGMA 2018, Volume 1 (pp. 405-414). Springer Singapore.
[16]    Wang, H. (2020, July). Stock price prediction based on machine learning approaches. In Proceedings of the 3rd International Conference on Data Science and Information Technology (pp. 1-5).‏
[17]    Jakkula, V. (2006). Tutorial on support vector machine (svm). School of EECS, Washington State University, 37(2.5), 3.
[18]    Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(4), 140-147.‏
[19]    Ying, C., Qi-Guang, M., Jia-Chen, L., & Lin, G. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6), 745-758.
[20]    Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25, 197-227.
[21]    Freund, Y., & Mason, L. (1999, June). The alternating decision tree learning algorithm. In icml (Vol. 99, pp. 124-133).‏
[22]    Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.
[23]    Zhang, S., Cheng, D., Deng, Z., Zong, M., & Deng, X. (2018). A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters, 109, 44-54.
[24]    Islam, M., Chen, G., & Jin, S. (2019). An overview of neural Network. American Journal of Neural Networks and Applications, 5(1), 7-11.
[25]    Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. 

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