• ISSN: 2301-3567
    • Frequency: Quarterly (2013-2014); Monthly (Since 2015)
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Chloe Wu
    • Abstracting/ Indexing: Engineering & Technology Library,  Electronic Journals Library, Ulrich's Periodicals Directory, MESLibrary, Google Scholar, Crossref, and ProQuest.
    • E-mail: joebm@ejournal.net
JOEBM 2016 Vol.4(3): 183-187 ISSN: 2301-3567
DOI: 10.7763/JOEBM.2016.V4.388

Stock Market Prediction Based on Term Frequency-Inverse Document Frequency

Mu’tasem Jarrah and Naomie Salim
Abstract— This paper presents a new method to predicting the change of stock prices by utilizing text mining news of the stock market. Term Frequency Inverse Document Frequency (TF- IDF) is one of the most useful and widely used concepts in information retrieval. The method can handle without difficulty unstructured news of Saudi stock market (Tadawul) through reading and analysis of the news and build a relationship between the contents of the news, and keywords (core phrases). This technique must identified by analysts and financial specialists that affect the direction of the share price up or down. The aim of this paper is to explore the possibility of using text mining to automate the identification of financial news articles. The empirical results show that the proposed techniques can predict the up and down on a stock price after the news announce or released. The proposed method presented in the study is straightforward, simple and valuable for the short-term investors.

Index Terms— Stock market prediction, term frequency inverse document frequency (TF-IDF), text mining

M. Jarrah is with the King Abdulaziz University, Information Technology Department, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia, and also with Universiti Teknologi Malaysia, Faculty of Computing, Johor Bahru, Malaysia (e-mail: mmjarrah@kau.edu.sa).
N. Salim is with the Universiti Teknologi Malaysia, Faculty of Computing, Johor Bahru, Malaysia (e-mail: naomie@utm.my).

[PDF]

Cite: Mu’tasem Jarrah and Naomie Salim, " Stock Market Prediction Based on Term Frequency-Inverse Document Frequency," Journal of Economics, Business and Management vol. 4, no. 3, pp. 183-187, 2016.

Copyright © 2008-2015. Journal of Economics, Business and Management. All rights reserved.
E-mail: joebm@ejournal.net