Forecasting Stock Prices Using Social Media Analysis

Abstract

Stock market prices are becoming more and more volatile, largely due to improvements in technology and increased trading volume. Speculation affects business owners, investors, and policymakers alike. While these seemingly unpredictable trends continue, investors and consumers take to social media to share thoughts and opinions. We use information shared over StockTwits, a social media platform for investors, to better understand and predict individual stock prices. We designed and implemented three machine learning models to forecast stock prices using the dataset collected from StockTwits. We also evaluated our models with conclusions drawn from previous researchers in this field. Our first model found no correlation between general StockTwits postings and stock price. However, our second and third models considered a novel approach and successfully filtered through the twits to find important posts. These important twits could predict stock price movements with greater accuracy (average around 65%) based on sentiment analysis and smart user identification. We consider a user "smart" based on number of likes, follower count and more importantly how often the user is right about a stock.