Vol. 2024 (2024)
Articles

Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices.

Rusul Mansoor Al-Amri
College of Nursing, University of Al-Ameed, Karbala, PO No: 198, Iraq .
Ahmed Adnan Hadi
Computer Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babil, Iraq .
Mayameen S. Kadhim
Department of Medical Instruments Techniques Engineering, Technical College of Engineering, Al-Bayan University, Baghdad, Iraq.
Ayad Hameed Mousa
College of computer science and Information technology University of Kerbala, Iraq.
Ali Z.K Matloob
Computer Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babil, Iraq .
Hasanain Flayyih Hasan
College of Computer Science and Information Technology, University of Wasit, Wasit, Iraq .

Published 2024-09-25

Keywords

  • Gradient Boosting machine (GBM),
  • Natural Language Processing (NLP),
  • Stock Market,
  • Sentiment Analysis

How to Cite

Al-Amri , R. M., Hadi , A. A., Kadhim , M. S., Mousa, A. H., Matloob, A. Z., & Hasan , H. F. (2024). Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices. Babylonian Journal of Artificial Intelligence, 2024, 102–111. https://doi.org/10.58496/BJAI/2024/012

Abstract

This paper objectives to improve stock market prediction accuracy by training data on sentiment analysis of tweets, overcoming volatility and complexity. Utilizing the Use of natural language processing (NLP) algorithms, the tweet's sentiments were classified into (negative - neutral - and positive). The stock value price was predicted by implementing Machine learning algorithms (KNN, SVM, GBM, LR, DT, RF, EL). Among the techniques of ML, (GBM) achieved the greatest results in terms of accuracy (96%). Its results were compared with the results of a deep learning algorithm that uses the same data where GBM   got better results, and other algorithms showed results (KNN = 55%, SVM=90%, LR=82%, DT=90%, RF=90%, EL = 88%). The results obtained were superior to previous studies.

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