Graduation Semester and Year
Spring 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Dr. Bhanu Jain
Second Advisor
Dr. Manfred Huber
Third Advisor
Dr. Athitsos, Vassilis
Abstract
Capturing the volatility of stock prices helps individual traders, stock analysts, and institutions alike increase their returns in the stock market. Financial news headlines have been shown to have a significant effect on stock price mobility. Lately, many financial portals have restricted web scraping of stock prices and other related financial data of companies from their websites. In this study we demonstrate that emotion analysis of financial news headlines alone can be sufficient in predicting stock price movement, even in the absence of any financial data. We propose an approach that eliminates the need for web scraping of financial data. We use API based mechanism to retrieve financial news headlines. In this study we train and subsequently leverage light and computationally fast Distilled LLM Model to gather emotional tone and strength of financial news headlines for companies. We then use this information with several machine learning-based classification algorithms to predict the stock price direction based solely on the emotion analysis of news. We demon- strate that emotion analysis-based attributes of financial news headlines are as accurate in predicting the price direction as running the algorithms with the financial data alone.
Keywords
LLM, NLP, Machine Learning, BERT
Disciplines
Other Computer Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Bhat, Rithesh H., "Stock Price Trend Prediction using Emotion Analysis of Financial Headlines with Distilled LLM Model" (2024). Computer Science and Engineering Theses. 4.
https://mavmatrix.uta.edu/cse_theses/4