Developing Prediction Models on S&P 500 Index using Social Sentiment and News Events

Project is to import ten year’s of historical data on news events, public sentiment metrics and the price movement of S&P 500 related equities for study and analysis through the latest Data Mining and Machine Learning techniques. The goal is to uncover correlation and causality between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly). Specifically, the research would answer the question which features (metrics) generated from initial news and sentiment data have predictive power and which don't. Predictive models based on the studies will be developed, compared, and evaluated.

Intern: 
Denis Zubo
Faculty Supervisor: 
Fred Popowich
Province: 
British Columbia
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