IJMLC 2017 Vol.7(2): 24-29 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.2.614

Using Machine Learning Classifiers to Predict Stock Exchange Index

Mustansar Ali Ghazanfar, Saad Ali Alahmari, Yasmeen Fahad Aldhafiri, Anam Mustaqeem, Muazzam Maqsood, and Muhammad Awais Azam

Abstract—Predicting stock exchange index is an attractive research topic in the field of machine learning. Numerous studies have been conducted using various techniques to predict stock market volume. This paper presents first detailed study on data of Karachi Stock Exchange (KSE) and Saudi Stock Exchange (SSE) to predict the stock market volume of ten different companies. In this study, we have applied and compared salient machine learning algorithms to predict stock exchange volume. The performance of these algorithms have been compared using accuracy metrics on the dataset, collected over the period of six months, by crawling the KSE and SSE website.

Index Terms—Stock exchange prediction, machine learning, SVM, neural networks, Bayesian network, Ada-boost.

Mustansar Ali Ghazanfar is with University of Engineering and Technology Taxila, Pakistan (e-mail:mustansar.ali@uettaxila.edu.pk).
Saad Ali Alahmari is with Department of Computer Science, Shaqra University, Saudi Arabia Yasmeen Fahad Aldhafiri is with Department of Business Administration, Jubail University College, Saudi Arabia.
Anam Mustaqeem, Muazzam Maqsood, and Muhammad Awais Azam are with University of Engineering and Technology Taxila, Pakistan.

[PDF]

Cite: Mustansar Ali Ghazanfar, Saad Ali Alahmari, Yasmeen Fahad Aldhafiri, Anam Mustaqeem, Muazzam Maqsood, and Muhammad Awais Azam, "Using Machine Learning Classifiers to Predict Stock Exchange Index," International Journal of Machine Learning and Computing vol. 7, no. 2, pp. 24-29, 2017.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net