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Experts Online: An Analysis of Trading Activity in a Public Internet Chat Room

Author

Listed:
  • Bruce Mizrach

    (Rutgers University)

  • Susan Weerts

    (Rutgers University)

Abstract

We analyze the trading activity in an Internet chat room with approximately 1,300 participants. Traders make posts in real time about their activities. We find these traders are more skilled than retail investors analyzed in other studies. 55% make profits after transaction costs, and they earn $153 per trade. Traders hold their winners 25% longer than their losers. They have statistically significant alphas of 0.41% per day after controlling for the Fama-French factors and momentum. 38% of profits persist in the next year. 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. 42% trade both long and short, with equal success rates, and almost double the profit per trade when short.

Suggested Citation

  • Bruce Mizrach & Susan Weerts, 2004. "Experts Online: An Analysis of Trading Activity in a Public Internet Chat Room," Departmental Working Papers 200412, Rutgers University, Department of Economics.
    • Handle: RePEc:rut:rutres:200412
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    Cited by:

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    2. Liu, Peipei & Xia, Xinping & Li, Antai, 2018. "Tweeting the financial market: Media effect in the era of Big Data," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 267-290.
    3. 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.
    4. David Goldbaum, 2008. "Follow the Leader: Simulations on a Dynamic Social Network," Working Paper Series 155, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    5. 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.
    6. 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.
    7. Alexandros Karakostas & Giles Morgan & Daniel John Zizzo, 2023. "Socially interdependent risk taking," Theory and Decision, Springer, vol. 95(3), pages 365-378, October.
    8. 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.
    9. Kaleem Ullah Malik & Muhammad Shaukat Malik & Muhammad Irfan & Hussain Mehdi, 2022. "The Role of Heuristic Factors in Investment Performance: Exploring the Market Anomalies in a Volatile Environment," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 41(73), pages 63-84, February.
    10. 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.
    11. Rawley Heimer & David Simon, 2015. "Facebook Finance: How Social Interaction Propagates Active Investing," Working Papers (Old Series) 1522, Federal Reserve Bank of Cleveland.
    12. 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.
    13. Abdin, Syed Zain ul & Farooq, Omer & Sultana, Naheed & Farooq, Mariam, 2017. "The impact of heuristics on investment decision and performance: Exploring multiple mediation mechanisms," Research in International Business and Finance, Elsevier, vol. 42(C), pages 674-688.
    14. 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.
    15. Yingying Xu & Zhixin Liu & Jichang Zhao & Chiwei Su, 2017. "Weibo sentiments and stock return: A time-frequency view," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.
    16. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.

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    More about this item

    Keywords

    behavioral finance; familiarity bias; day trading; experts; disposition effect;
    All these keywords.

    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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