Abstract
Stock prediction based on NLP sentiment analysis is one of the most researched topics due to the revenues they generate for investors. Researchers have used various tools to achieve this, especially fundamental and technical analysis based on historical data helped to achieve this target. Due to the technological advancement and abundance of data, the introduction of machine learning tools accelerated that approach. However, as the public mood affects the stock market, the need for another analysis emerged. Natural language processing sentiment analysis on data from various sources was able to capture public events and moods. NLP is one of the most effective tools since covering the public moods, and capturing the sentiment is the main driver for stock markets. In this research, NLP sentiment analysis shall be applied to news to predict United States technology stock companies and indices during COVID-19 using a natural language toolkit. The contribution of this is the research is creating a model for predicting the technology companies listed in the United States market during the crisis. The model is achieving over 61% accuracy and could be highly improved by adding other resources of news.
Department
Management Department
Degree Name
MS in Finance
Graduation Date
Spring 6-1-2021
Submission Date
1-31-2021
First Advisor
Dr. Medhat Hassanein
Committee Member 1
Islam Azzam
Committee Member 2
Wael Abdallah
Committee Member 3
Mina Ayad
Extent
48 p.
Document Type
Master's Thesis
Institutional Review Board (IRB) Approval
Approval has been obtained for this item
Recommended Citation
APA Citation
Ibrahim, M.
(2021).Stock Prediction using Natural Language Processing Sentiment Analysis on News Headlines During COVID-19 [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/1580
MLA Citation
Ibrahim, Mina. Stock Prediction using Natural Language Processing Sentiment Analysis on News Headlines During COVID-19. 2021. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/1580