Predictive Analytics On Public Data - The Case Of Stock Markets

This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor’s 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.