The document proposes using disparate data sources like social media, news articles, search trends and secondary financial indicators to predict stock market movement. It finds support vector machines perform best at prediction when using all input data types. Multiple prediction targets are evaluated to better track market changes, with accuracy ranging from 82-89%. Future work could integrate additional data sources like Twitter sentiment and improve linguistic and fuzzy modeling methods.