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Experts online: An analysis of trading activity in a public Internet chat room

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  • Mizrach, Bruce
  • Weerts, Susan

Abstract

We analyze the trading activity in an Internet chat room over a 4-year period. The data set contains nearly 9000 trades from 676 traders. We find these traders are more skilled than retail investors analyzed in other studies. 55 percent make profits after transaction costs, and they have statistically significant [alpha] s of 0.17 percent per day after controlling for the Fama-French factors and momentum. Traders hold their winners 25 percent longer than their losers. 42 percent trade both long and short, with equal success rates, and almost double the profit per trade when short. The estimates show a strong influence from other traders, with a buy (sell) order 40.7 percent more likely to be of the same sign if there has been a recent post. Traders improve their skill over time, earning an extra $189 per month for each year of trading experience. They also gain expertise in trading particular stocks. Traders who raise their Herfindahl index by 0.1 raise their profitability by $46 per trade.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Behavior & Organization.

Volume (Year): 70 (2009)
Issue (Month): 1-2 (May)
Pages: 266-281

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Handle: RePEc:eee:jeborg:v:70:y:2009:i:1-2:p:266-281

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Web page: http://www.elsevier.com/locate/jebo

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Keywords: Behavioral finance Day trading Familiarity bias Disposition effect Experts;

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Citations

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Cited by:
  1. Philippe Bernard & Michel Blanchard, 2013. "The performance of amateur traders on a public internet site: a case of a stock-exchange contest," Economics Bulletin, AccessEcon, vol. 33(3), pages 1729-1737.
  2. Jonathan E. Alevy & Michael K. Price, 2014. "Advice in the Marketplace: A Laboratory Study," Experimental Economics Center Working Paper Series 2014-03, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University.
  3. David Goldbaum, 2009. "Follow the Leader: Steady State Analysis of a Dynamic Social Network," Working Paper Series 158, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
  4. Jonathan E. Alevy & Michael K. Price, 2012. "Advice and Fictive Learning: The Pricing of Assets in the Laboratory," Working Papers 2012-07, University of Alaska Anchorage, Department of Economics.
  5. Antonios Siganos, 2010. "Can small investors exploit the momentum effect?," Financial Markets and Portfolio Management, Springer, vol. 24(2), pages 171-192, June.
  6. Chiao-Yi Chang, 2013. "Daily momentum profits with firm characteristics and investors’ optimism in the Taiwan market," Journal of Economics and Finance, Springer, vol. 37(2), pages 253-273, April.

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