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Currency trading (Forex) is the largest world market in terms of volume. Weanalyze trading and tweeting about the EUR-USD currency pair over a period ofthree years. First, a large number of tweets were manually labeled, and aTwitter stance classification model is constructed. The model then classifiesall the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD).The Twitter stance is compared to the actual currency rates by applying theevent study methodology, well-known in financial economics. It turns out thatthere are large differences in Twitter stance distribution and potentialtrading returns between the four groups of Twitter users: trading robots,spammers, trading companies, and individual traders. Additionally, we observeattempts of reputation manipulation by post festum removal of tweets with poorpredictions, and deleting/reposting of identical tweets to increase thevisibility without tainting one's Twitter timeline.

Forex trading and Twitter: Spam, bots, and reputation manipulation