↓ Skip to main content

PLOS

A Combination of Dopamine Genes Predicts Success by Professional Wall Street Traders

Overview of attention for article published in PLOS ONE, January 2012
Altmetric Badge

Mentioned by

news
5 news outlets
blogs
3 blogs
twitter
45 X users
facebook
3 Facebook pages
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Readers on

mendeley
104 Mendeley
citeulike
1 CiteULike
Title
A Combination of Dopamine Genes Predicts Success by Professional Wall Street Traders
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0030844
Pubmed ID
Authors

Steve Sapra, Laura E. Beavin, Paul J. Zak

Abstract

What determines success on Wall Street? This study examined if genes affecting dopamine levels of professional traders were associated with their career tenure. Sixty professional Wall Street traders were genotyped and compared to a control group who did not trade stocks. We found that distinct alleles of the dopamine receptor 4 promoter (DRD4P) and catecholamine-O-methyltransferase (COMT) that affect synaptic dopamine were predominant in traders. These alleles are associated with moderate, rather than very high or very low, levels of synaptic dopamine. The activity of these alleles correlated positively with years spent trading stocks on Wall Street. Differences in personality and trading behavior were also correlated with allelic variants. This evidence suggests there may be a genetic basis for the traits that make one a successful trader.

X Demographics

X Demographics

The data shown below were collected from the profiles of 45 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Switzerland 2 2%
France 2 2%
United Kingdom 2 2%
Canada 1 <1%
Denmark 1 <1%
Russia 1 <1%
United States 1 <1%
Unknown 92 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 19%
Student > Master 17 16%
Researcher 15 14%
Student > Bachelor 11 11%
Professor 10 10%
Other 17 16%
Unknown 14 13%
Readers by discipline Count As %
Psychology 32 31%
Economics, Econometrics and Finance 12 12%
Agricultural and Biological Sciences 9 9%
Neuroscience 7 7%
Computer Science 5 5%
Other 20 19%
Unknown 19 18%