Date of Award
Spring 2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chairperson
Md Amiruzzaman, Ph.D.
Committee Member
Richard Burns, Ph.D.
Committee Member
Ashik Ahmed Bhuiyan, Ph.D.
Abstract
This thesis presents a predicting stock index by analyzing the macroeconomic Factors using Machine Learning to generate market sentiment. The study explores popular US stock index funds like S&P 500 to critical economic indicators like GDP, Unemployment rate, Consumer Price Index, Money Supply, Retail Sales, etc. The economic data is collected from open-sourced datasets from the Federal Reserve, NASDAQ, and news websites. Data is cleaned and transformed to build a narrative to estimate quarterly fund returns. Tree-based algorithms like XGBoost and Random Forest are used for the prediction. Market sentiment is generated using traditional natural language processing methods and attempts to leverage large language models to summarize market sentiment. Overall, the index forecast performance is evaluated by different market cycles and geopolitical events using XGBoost Algorithm.
Recommended Citation
Anem, Sai Sravya, "Predicting Financial Data with Macroeconomic Factors using Machine Learning" (2024). West Chester University Master’s Theses. 340.
https://digitalcommons.wcupa.edu/all_theses/340