Forex trading and Twitter: Spam, bots, and reputation manipulation
This addresses the problem of misinformation and manipulation in financial markets for traders and regulators, though it is incremental in applying existing methods to Forex and Twitter data.
The study analyzed EUR-USD trading and Twitter data over three years, classifying tweets into buy/hold/sell stances and comparing them to actual currency rates using event study methodology, finding large differences in stance distribution and potential returns among user groups like bots and spammers, and observing reputation manipulation tactics such as deleting poor predictions.
Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all 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 the event study methodology, well-known in financial economics. It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users: trading robots, spammers, trading companies, and individual traders. Additionally, we observe attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline.