Data Mining in Financial Domain

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