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

TL;DR

This study examines the relationship between Twitter activity and Forex trading for EUR-USD, revealing differences among user groups and uncovering reputation manipulation tactics affecting trading signals.

Contribution

It introduces a Twitter stance classification model for Forex trading signals and analyzes user behavior, including manipulation tactics, over three years.

Findings

Twitter stance varies significantly among user groups

Reputation manipulation tactics are prevalent among traders

Twitter signals can influence or reflect currency market movements

Abstract

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.