Belmont University Research Symposium (BURS)
Technology Stock Predictive Analysis Using Statistical Modeling & Machine Learning
Publication Date
Spring 4-18-2022
College
Sciences and Mathematics, College of
Department
Math and Computer Science, Department of
BURS Faculty Advisor
Dr. Daniel C. Biles
Presentation Type
Oral Presentation
Abstract
Within the S&P 500, technology stocks (or tech stocks) are often the leading indicator for the index, the stock market, and the economy. Utilizing statistical modeling and various predictive analytic and machine learning methods such as: simple linear regression, polynomial regression, k-nearest neighbors (KNN), support vector machines (SVM), and random forests to, as accurately as possible, predict the percent change in price of the some of the top tech stocks of 2021 over a 6-month period.
Recommended Citation
Rose, Ethan M., "Technology Stock Predictive Analysis Using Statistical Modeling & Machine Learning" (2022). Belmont University Research Symposium (BURS). 58.
https://repository.belmont.edu/burs/58
