Using Logistic Regression to Predict Stock Trends Based on Bag-of-Words Representations of News Article Headlines
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%.
- Barolli, Adeiron (Author)
- Jimenez Arista, Laura (Thesis director)
- Wilson, Jeffrey (Committee member)
- School of Life Sciences (Contributor)
- Barrett, The Honors College (Contributor)
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)