For this project, we explored the use of text mining, clustering, and machine learning models to develop a system that combines technical and sentiment analysis to determine the movement of a stock. The final result of our project is a system comprised of a novel sentiment analysis used as input for the larger recurrent neural networks, each trained on a cluster of stocks from the S&P 100. Experimental results show that our system can predict upward movements in stock price over a 65-minute period with up to 77% accuracy for a specific cluster compared to 52% of randomly guessing for the same cluster.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Amos, Sean J.
• Al-Mansouri, Essam R.

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-042816-130221

Advisor

• Ruiz, Carolina
• Hakim, Hossein
• Radzicki, Michael J.

Year

• 2016

Date created

• 2016-04-28

Resource type

• Major Qualifying Project

Major

• Computer Science

Rights statement

• In Copyright