Twitter Sentiment and Stock Returns

This study aims to investigate if the sentiment expressed on Twitter has an effect on individual stock returns. We use a uniquely large data-set consisting of129 million tweets related to 31 companies over a 15-month period. The sentiment is derived on a daily basis with Loughran and McDonald’s established finance-focused method, as well as with Vader, a sentiment analysis tool developed specially for social media. Using a panel data regression, we empirically test Twitter sentiment effectual forecasting power on individual stock returns. Our main findings are: 1) the sentiment derived from Twitter can help predict individual stock returns up to two days ahead 2) the individual stock returns are more sensitive to ’cashtag’-tweets than general-company tweets 3) Twitter data can be used to explain the stock return volatility 4) Loughran and McDonald Lexicon-based method outperforms Vader. The results indicate that Twitter data is a suitable data source to understand and forecast stock market movements