Faculty Advisor

Hakim, Hossein

Faculty Advisor

Radzicki, Michael J.

Faculty Advisor

Ruiz, Carolina

Abstract

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.

Publisher

Worcester Polytechnic Institute

Date Accepted

April 2016

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

ECE

Advisor Department

Social Science and Policy Studies

Advisor Department

Computer Science

Share

COinS