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Assessing Machine Learning Linear Models to Predict Egyptian Stock Market Prices

by Ismail M. Hagag
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 43
Year of Publication: 2023
Authors: Ismail M. Hagag
10.5120/ijca2023922543

Ismail M. Hagag . Assessing Machine Learning Linear Models to Predict Egyptian Stock Market Prices. International Journal of Computer Applications. 184, 43 ( Jan 2023), 33-43. DOI=10.5120/ijca2023922543

@article{ 10.5120/ijca2023922543,
author = { Ismail M. Hagag },
title = { Assessing Machine Learning Linear Models to Predict Egyptian Stock Market Prices },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2023 },
volume = { 184 },
number = { 43 },
month = { Jan },
year = { 2023 },
issn = { 0975-8887 },
pages = { 33-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number43/32599-2023922543/ },
doi = { 10.5120/ijca2023922543 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:54.757581+05:30
%A Ismail M. Hagag
%T Assessing Machine Learning Linear Models to Predict Egyptian Stock Market Prices
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 43
%P 33-43
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Despite significantly related difficulties, the search for models to predict the prices of financial markets remains a highly researched subject. Financial time series are challenging to forecast because of the non-linear, chaotic, and dynamic nature of the prices of financial assets. Given their capacity to recognize complex patterns in various applications, machine learning models are among the most researched of the most recent techniques. The objective of this research is to identify the most effective method (among the selected methods) for forecasting stock markets by evaluating the accuracy of these models using the EGX100 market indicator. By applying nine different algorithms on the EGX100 daily prices we, namely Decision Tree (DT), Support Vector Machine (SVM), extreme Gradient Boost (XGBoost), AdaBoost, Multiple layer perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and K Nearest Neighbour (KNN). we have found that Linear regression is by far the best machine-learning algorithm for this type of problem. This result is reached through the usage of four different metrics after changing the problem into a classification problem.

References
  1. Boivin, J., Kiley, M. T., & Mishkin, F. S. (2010). How has the monetary transmission mechanism evolved over time?. In Handbook of monetary economics (Vol. 3, pp. 369-422). Elsevier.
  2. Ahmed, A. A., Abd El-Baky, M. M. H., Zaki, M. F., & Abd El-Aal, F. S. (2011). Effect of foliar application of active yeast extract and zinc on growth, yield and quality of potato plant (Solanum tuberosum L.). Journal of Applied Sciences Research, 7(12), 2479-2488.
  3. Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251.
  4. Malkiel, B. G., & Fama, E. F. J. T. (1970). Efficient capital markets: A review of theory and empirical work” nn, 25 (2), 383-417. DOI: https://doi. org/10.1111/j, 1540-6261.
  5. Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.
  6. Kumar, D., Meghwani, S. S., & Thakur, M. (2016). Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets. Journal of Computational Science, 17, 1-13.
  7. Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with applications, 36(3), 5932-5941.
  8. Malkiel, B. G. (2003). Passive investment strategies and efficient markets. European Financial Management, 9(1), 1-10.
  9. Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.
  10. Benjamin, H. S. (1942). The dow theory of stock prices. Social Research, 204-224.
  11. (Chen et al., 2017; Zhong and Enke, 2017)
  12. (Chen, Cheng, and Tsai, 2014, pp. 329-330)
  13. (Cavalcante, Brasileiro, Souza, Nobrega, and Oliveira, 2016, p. 194),
  14. (Kumar and Thenmozhi, 2014; Wang, Wang, Zhang, and Guo, 2012, p. 285; p. 758)
  15. (Chen et al., 2017, pp. 340–341)
  16. Chiang, Enke, Wu, and Wang (2016)
  17. Hsu, Lessmann, Sung, Ma, and Johnson (2016, p. 215).
  18. L. K. Morrison, et al., "Utility of a rapid Bnatriuretic peptide assay in differentiating congestive heart failure from lung disease in patients presenting with dyspnea," Journal of the American College of Cardiology, vol. 39, pp. 202-209, 2002.
  19. Forecasting EGX30 index time series using vector autoregressive models VARS
  20. Ezzat, H. M. (2021). Principal component regression for egyptian stock market prediction. The International Journal of Informatics, Media and Communication Technology, 3(1), 23-39.
  21. Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251.
  22. Elwasify, A. I. (2015). A combined model between Artificial Neural Networks and ARIMA Models. Int J Recent Res Commer Econ Manag, 2(2), 134-140.
  23. Houssein, E. H., Dirar, M., Hussain, K., & Mohamed, W. M. (2021). Assess deep learning models for Egyptian exchange prediction using nonlinear artificial neural networks. Neural Computing and Applications, 33(11), 5965-5987.
  24. Xiao, Y., Xiao, J., Lu, F., & Wang, S. (2014). Ensemble ANNs-PSO-GA approach for day-ahead stock e-exchange prices forecasting. International Journal of Computational Intelligence Systems, 7(2), 272-290.
  25. Kamble, R. A. (2017, June). Short and long term stock trend prediction using decision tree. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1371-1375). IEEE.
  26. Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). Efficient stock-market prediction using ensemble support vector machine. Open Computer Science, 10(1), 153-163.
  27. Er, X., & Sun, Y. (2021, July). Visualization Analysis of Stock Data and Intelligent Time Series Stock Price Prediction Based on Extreme Gradient Boosting. In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 272-279). IEEE.
  28. Wu, Y., & Gao, J. (2018). AdaBoost-based long short-term memory ensemble learning approach for financial time series forecasting. Current Science, 115(1), 159-165.
  29. Devadoss, A. V., & Ligori, T. A. A. (2013). Forecasting of stock prices using multi layer perceptron. International journal of computing algorithm, 2(1), 440-449.
  30. Khaidem, L., Saha, S., & Dey, S. R. (2016). Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003.
  31. Dey, S., Kumar, Y., Saha, S., & Basak, S. (2016). Forecasting to Classification: Predicting the direction of stock market price using Xtreme Gradient Boosting. PESIT South Campus.
  32. Ali, S. S., Mubeen, M., Lal, I., & Hussain, A. (2018). Prediction of stock performance by using logistic regression model: evidence from Pakistan Stock Exchange (PSX). Asian Journal of Empirical Research, 8(7), 247-258.
  33. Zhang, N., Lin, A., & Shang, P. (2017). Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A: Statistical Mechanics and its Applications, 477, 161-173.
  34. Ezzat, H. M. (2021). The effect of COVID-19 on the Egyptian exchange using principal component analysis. Journal of Humanities and Applied Social Sciences.
  35. B. Ratner, Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data: CRC Press, 2017
  36. A. Natekin and A. Knoll, "Gradient boosting machines, a tutorial," Frontiers in neurorobotics, vol. 7, p. 21, 2013.
  37. H. Hu, et al., "A comparative study of classification methods for microarray data analysis," in Proceedings of the 5th Australasian Data Mining Conference (AusDM 2006): Data Mining and Analytics 2006, 2006, pp. 33-37.
  38. S. Sangeetha and S. Saradhambekai, "Python Libraries and Packages for Data Mining-A Survey," 2019.
  39. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  40. T. Chengsheng, et al., "AdaBoost typical Algorithm and its application research," in MATEC Web of Conferences, 2017, p. 00222.
  41. Abirami, S., & Chitra, P. (2020). Energy-efficient edge based real-time healthcare support system. In Advances in computers (Vol. 117, No. 1, pp. 339-368). Elsevier.
  42. O. Maimon and L. Rokach, "Data mining and knowledge discovery handbook," 2005
  43. M. Maalouf, "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, vol. 3, pp. 281-299, 2011.
  44. D. Cheng, et al., "kNN algorithm with datadriven k value," in International Conference on Advanced Data Mining and Applications, 2014, pp. 499-512.
  45. M. Sokolova, et al., "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation," in AI 2006: Advances in Artificial Intelligence, ed: Springer, 2006, pp. 1015-1021.
  46. T. T. Nguyen and G. Armitage, "A survey of techniques for internet traffic classification using machine learning," Communications Surveys & Tutorials, IEEE, vol. 10, pp. 56-76, 2008.
  47. Kuhn, M., & Johnson, K. (2019). Feature engineering and selection: A practical approach for predictive models. CRC Press.
  48. Bengfort, B., & Bilbro, R. (2019). Yellowbrick: Visualizing the scikit-learn model selection process. Journal of Open Source Software, 4(35), 1075.
Index Terms

Computer Science
Information Sciences

Keywords

Stock Market EGX100 ARIMA Machine Learning KNN MSE

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