Ayman E. Khedr

Work place: Faculty of Computers and Information Technology, Future University in Egypt, Information Systems Department, Cairo, Egypt

E-mail: Ayman.khedr@fue.edu.eg


Research Interests: Bioinformatics, Computer systems and computational processes, Data Mining, Data Structures and Algorithms


Ayman E. Khedr is an Associate Professor, now he is the head of Information Systems Department, Faculty of Computers and Information Technology, Future University in Egypt.

Also, he is the supervisor of the Quality Assurance Department and a member of continuing education board. He has worked at the Faculty of Computers and Information, Helwan University in Egypt, and he has been the general manager of Helwan E-Learning Center. His research is concentrated around the themes (scientific) data and model management, data mining, Bioinformatics and cloud computing.

Author Articles
Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

By Ayman E. Khedr S.E.Salama Nagwa Yaseen

DOI: https://doi.org/10.5815/ijisa.2017.07.03, Pub. Date: 8 Jul. 2017

Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

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