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Emotional sentiment about a firm's stock that spreads rapidly through social media is more likely to be incorporated quickly into stock prices (e.g., on the same trading day it was expressed), while sentiment that spreads slowly takes longer to be incorporated into stock prices and thus is more likely to predict stock prices on future days. We analyzed the cumulative sentiment (positive and negative) in 2.5 million Twitter postings about individual S&P 500 firms and compared this to the stock returns of those firms. Our results show that the sentiment in tweets about a specific firm from users with less than 171 followers (the median in our sample) had a significant impact on the stock's returns on the next trading day, the next 10 days, and the next 20 days. Interestingly, sentiment in tweets from users with fewer than 171 followers that were not<\/jats:italic> retweeted had the greatest impact on future stock returns. A trading strategy based on these findings produced meaningful economic gains on the order of an 11\u201315% annual return.<\/jats:p>","DOI":"10.1111\/deci.12229","type":"journal-article","created":{"date-parts":[[2016,6,25]],"date-time":"2016-06-25T15:20:55Z","timestamp":1466868055000},"page":"454-488","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":168,"title":["Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns"],"prefix":"10.1111","volume":"48","author":[{"given":"Hong Kee","family":"Sul","sequence":"first","affiliation":[{"name":"Wharton Research Data Service The Wharton School, University of Pennsylvania Philadelphia PA 19104"}]},{"given":"Alan R.","family":"Dennis","sequence":"additional","affiliation":[{"name":"Operations and Decision Technologies Department Kelley School of Business Indiana University, Bloomington IN 47405"}]},{"given":"Lingyao (Ivy)","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Supply Chain and Information Systems, College of Business Iowa State University, Ames IA 50011"}]}],"member":"311","published-online":{"date-parts":[[2016,6,23]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/25148873"},{"key":"e_1_2_7_3_1","unstructured":"About Twitter Inc. 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