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.

Developing Prediction Models on London Stock Exchange (LSE) Equitiesand Indicies using Microsoft Azure Machine Learning and Data Mining

I am to import ten year’s worth of amassed historical data on news events, price movement of equities and public sentiment metrics to Microsoft Azure platform for study and analysis through the latest Data Mining techniques with an Economics point of view to uncover the hidden correlation and casualty between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly).