Full metadata
Title
Using Logistic Regression to Predict Stock Trends Based on Bag-of-Words Representations of News Article Headlines
Description
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
Date Created
2021-05
Contributors
- Barolli, Adeiron (Author)
- Jimenez Arista, Laura (Thesis director)
- Wilson, Jeffrey (Committee member)
- School of Life Sciences (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
14 pages
Language
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2020-2021
Handle
https://hdl.handle.net/2286/R.I.64069
Level of coding
minimal
Cataloging Standards
System Created
- 2021-05-01 12:23:06
System Modified
- 2021-08-11 04:09:57
- 2 years 3 months ago
Additional Formats