We use 633 days of tweets about the Euro/dollar exchange rate to determine their information content and the profitability of trading based on Twitter Sentiment. We develop a detailed lexicon used by FX traders to translate verbal tweets into positive, negative and neutral opinions. The methodologically novel aspect of our approach is the use of a model with heterogeneous private information to interpret the data from FX tweets. After estimating model parameters, we compute the Sharpe ratio from a trading strategy based on Twitter Sentiment. The Sharpe ratio outperforms that based on the well-known carry trade and is precisely estimated.
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