Enhancing Financial Market Predictions: Integrating Sentiment Analysis and Historical Data within ChatGPT
Persistent URL
Author(s)
Turner, Jack
Date Issued
March 22, 2024
Abstract
This thesis explores the enhancement of financial market analysis through the integration of sentiment analysis and historical stock data predictions within the ChatGPT framework. Addressing the complexity and dynamic nature of financial markets, the research aims to develop a more accurate and holistic approach to stock market forecasting by combining quantitative historical data and qualitative sentiment insights derived from financial news. Utilizing Python libraries such as pandas, NumPy, yfinance, and natural language processing techniques, the project constructs a predictive model that analyzes historical stock trends alongside market sentiment. The innovation of this research lies in its integration within ChatGPT, facilitating an interactive tool that provides personalized financial insights. The experiments demonstrate the model’s potential in offering improved predictive accuracy and valuable market insights, suggesting a step towards the continued integration of AI in financial analysis. The research contributes to the field by providing a new approach to financial market analysis, merging quantitative historical data with qualitative sentiment insights within ChatGPT, thus offering a comprehensive framework for understanding market dynamics.
Major
Computer Science
First Reader(s)
Jumadinova, Janyl A.
Other Reader(s)
Kapfhammer, Gregory
Department
Computer and Information Science
Type of Publication
Senior Project Paper
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Name
SeniorThesisTurner.pdf
Size
441.34 KB
Format
Adobe PDF
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