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

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Listed:
  • Igor Mozetiv{c}
  • Peter Gabrovv{s}ek
  • Petra Kralj Novak

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.

Suggested Citation

  • Igor Mozetiv{c} & Peter Gabrovv{s}ek & Petra Kralj Novak, 2018. "Forex trading and Twitter: Spam, bots, and reputation manipulation," Papers 1804.02233, arXiv.org, revised Apr 2018.
    • Handle: RePEc:arx:papers:1804.02233
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    References listed on IDEAS

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    1. Igor Mozetič & Luis Torgo & Vitor Cerqueira & Jasmina Smailović, 2018. "How to evaluate sentiment classifiers for Twitter time-ordered data?," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.
    2. A. Craig MacKinlay, 1997. "Event Studies in Economics and Finance," Journal of Economic Literature, American Economic Association, vol. 35(1), pages 13-39, March.
    3. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
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