Rizzo, Antonino EmanueleCosta, Davide Gomes2022-06-222022-06-222022-01-102021-12-17http://hdl.handle.net/10362/140471The recent surge of emerging technologies, combined with the growth of social media securities-related microblogging, instigated academics to explore new proxies for sentiment. This research dissects the association between 1-month lagged Twitter sentiment and stock returns for the S&P500 constituents from 2008 to 2021 through the sentiment analysis of approximately 34.7million tweets. Evidence shows a consistent variation pattern of returns across the scope of the anomaly. Furthermore, abnormal returns associated with high Twitter sentiment are pervasive and significant, particularly for value-weight returns. In contrast, there is insufficient evidence on the pervasiveness of abnormal returns for low Twitter sentiment. Key words: Asset Pricing, Big Data, Sentiment Analysis, Stock Return, Investor Sentiment.engAsset pricingBig dataSentiment analysisStock returnInvestor sentimentmaster thesis202973158