Degree Name
MS (Master of Science)
Program
Computer and Information Sciences
Date of Award
5-2023
Committee Chair or Co-Chairs
Brian Bennett
Committee Members
Ahmad Al Doulat, Ghaith Hussum Husari
Abstract
The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not contain sentiment analysis-related features. The results indicated that sentiment containing datasets were typically better predictors, with improved model accuracy. However, the results did not reflect the improvements shown by similar research and will require further research to determine the nature of the relationship between sentiment and higher model performance.
Document Type
Thesis - unrestricted
Recommended Citation
Grisham, Ian L., "Predicting High-Cap Tech Stock Polarity: A Combined Approach using Support Vector Machines and Bidirectional Encoders from Transformers" (2023). Electronic Theses and Dissertations. Paper 4207. https://dc.etsu.edu/etd/4207
Copyright
Copyright 2023 by Ian Grisham All Rights Reserved
Included in
Artificial Intelligence and Robotics Commons, Computational Linguistics Commons, Data Science Commons, Finance Commons, Probability Commons