Twitter and stock returns
Year: 2014
Type: dissertation
Abstract: In this thesis, we investigate whether the sentiment of tweets mentioning stock\ntickers can be used to predict stock performance. In particular we test for leading\nand lagged relationships between the percentage of positive and/or negative\ntweets and the returns of the S&P 500 index. We obtain a longitudinal data set of\nall tweets mentioning stock tickers over a four-month period amounting to\n2,599,277 tweets distributed over 84 trading days. We use daily measures for\npositive and negative sentiment to generate our explanatory variables. Our results\nindicate that an increase in the percentage of positive tweets predicts increased\nstock performance the following day whereas an increase in the percentage of\nemotional tweets predicts a reduction in stock returns after two and three days. An\nincrease in the percentage of negative tweets may predict a reduction in stock\nreturns.
Language: Spanish
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2502687559