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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.

Suggested Citation

  • Mizrach, Bruce & Weerts, Susan, 2009. "Experts online: An analysis of trading activity in a public Internet chat room," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 266-281, May.
  • Handle: RePEc:eee:jeborg:v:70:y:2009:i:1-2:p:266-281
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    References listed on IDEAS

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    Cited by:

    1. Antonios Siganos, 2010. "Can small investors exploit the momentum effect?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 24(2), pages 171-192, June.
    2. 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.
    3. Jonathan E. Alevy & Michael K. Price, 2017. "Advice in the marketplace: a laboratory study," Experimental Economics, Springer;Economic Science Association, vol. 20(1), pages 156-180, March.
    4. 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.
    5. Chiao-Yi Chang, 2013. "Daily momentum profits with firm characteristics and investors’ optimism in the Taiwan market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(2), pages 253-273, April.
    6. Oehler, Andreas & Horn, Matthias & Wendt, Stefan, 2016. "Benefits from social trading? Empirical evidence for certificates on wikifolios," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 202-210.
    7. Heimer, Rawley & Simon, David, 2015. "Facebook Finance: How Social Interaction Propagates Active Investing," Working Paper 1522, Federal Reserve Bank of Cleveland.
    8. 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.
    9. repec:eee:riibaf:v:42:y:2017:i:c:p:674-688 is not listed on IDEAS
    10. Zhang, Yongjie & Song, Weixin & Shen, Dehua & Zhang, Wei, 2016. "Market reaction to internet news: Information diffusion and price pressure," Economic Modelling, Elsevier, vol. 56(C), pages 43-49.

    More about this item

    Keywords

    Behavioral finance Day trading Familiarity bias Disposition effect Experts;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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