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

Overview of attention for article published in PLOS ONE, October 2011
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Title
Tracking Traders' Understanding of the Market Using e-Communication Data
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0026705
Pubmed ID
Authors

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.

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Mendeley readers

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Geographical breakdown

Country Count As %
Switzerland 3 4%
Spain 2 3%
Germany 1 1%
Sweden 1 1%
Ireland 1 1%
China 1 1%
Luxembourg 1 1%
Unknown 68 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 21%
Student > Master 14 18%
Researcher 7 9%
Student > Bachelor 7 9%
Other 6 8%
Other 18 23%
Unknown 10 13%
Readers by discipline Count As %
Business, Management and Accounting 10 13%
Economics, Econometrics and Finance 10 13%
Computer Science 7 9%
Physics and Astronomy 7 9%
Social Sciences 7 9%
Other 23 29%
Unknown 14 18%