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Tracking Traders' Understanding of the Market Using e-Communication Data

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  • Serguei Saavedra
  • Jordi Duch
  • Brian Uzzi

Abstract

Tracking the volume of keywords in Internet searches, message boards, or Tweets has provided an alternative for following or predicting associations between popular interest or disease incidences. Here, we extend that research by examining the role of e-communications among day traders and their collective understanding of the market. Our study introduces a general method that focuses on bundles of words that behave differently from daily communication routines, and uses original data covering the content of instant messages among all day traders at a trading firm over a 40-month period. Analyses show that two word bundles convey traders' understanding of same day market events and potential next day market events. We find that when market volatility is high, traders' communications are dominated by same day events, and when volatility is low, communications are dominated by next day events. We show that the stronger the traders' attention to either same day or next day events, the higher their collective trading performance. We conclude that e-communication among traders is a product of mass collaboration over diverse viewpoints that embodies unique information about their weak or strong understanding of the market.

Suggested Citation

  • Serguei Saavedra & Jordi Duch & Brian Uzzi, 2011. "Tracking Traders' Understanding of the Market Using e-Communication Data," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-7, October.
    • Handle: RePEc:plo:pone00:0026705
    DOI: 10.1371/journal.pone.0026705
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    Cited by:

    1. Alberto Acerbi & Vasileios Lampos & Philip Garnett & R Alexander Bentley, 2013. "The Expression of Emotions in 20th Century Books," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-6, March.
    2. Niccolò Casnici & Pierpaolo Dondio & Roberto Casarin & Flaminio Squazzoni, 2015. "Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    3. Herm, Steffen & Callsen-Bracker, Hans-Markus & Kreis, Henning, 2014. "When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community," Sport Management Review, Elsevier, vol. 17(4), pages 484-492.
    4. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    5. Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    6. Serguei Saavedra & Luis J. Gilarranz & Rudolf P. Rohr & Michael Schnabel & Brian Uzzi & Jordi Bascompte, 2014. "Stock fluctuations are correlated and amplified across networks of interlocking directorates," Papers 1410.6646, arXiv.org.
    7. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.

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