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Trading by Professional Traders: An Experiment



We examine how professional traders behave in two financial market experiments; we contrast professional traders’ behavior to that of undergraduate students, the typical experimental subject pool. In our first experiment, both sets of participants trade an asset over multiple periods after receiving private information about its value. Second, participants play the Guessing Game. Finally, they play a novel, individual-level version of the Guessing Game and we collect data on their cognitive abilities, risk preferences, and confidence levels. We find three differences between traders and students: Traders do not generate the price bubbles observed in previous studies with student subjects; traders aggregate private information better; and traders show higher levels of strategic sophistication in the Guessing Game. Rather than reflecting differences in cognitive abilities or other individual characteristics, these results point to the impact of traders’ on-the-job learning and traders’ beliefs about their peers’ strategic sophistication.

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  • , 2020. "Trading by Professional Traders: An Experiment," Staff Reports 939, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:88552

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    References listed on IDEAS

    1. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    2. Paolo Crosetto & Antonio Filippin, 2016. "A theoretical and experimental appraisal of four risk elicitation methods," Experimental Economics, Springer;Economic Science Association, vol. 19(3), pages 613-641, September.
    3. Angrisani, Marco & Cipriani, Marco & Guarino, Antonio & Kendall, Ryan & Ortiz de Zarate Pina, Julen, 2020. "Risk Preferences at the Time of COVID-19: An Experiment with Professional Traders and Students," CEPR Discussion Papers 15108, C.E.P.R. Discussion Papers.
    4. Michailova, Julija, 2010. "Overconfidence and bubbles in experimental asset markets," MPRA Paper 26388, University Library of Munich, Germany.
    5. Smith, Vernon L & Suchanek, Gerry L & Williams, Arlington W, 1988. "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, Econometric Society, vol. 56(5), pages 1119-1151, September.
    6. Utz Weitzel & Christoph Huber & Florian Lindner & Jürgen Huber & Julia Rose & Michael Kirchler, 2018. "Bubbles and financial professionals," Working Papers 2018-04, Faculty of Economics and Statistics, University of Innsbruck, revised Oct 2018.
    7. Brice Corgnet & Mark DeSantis & David Porter, 2015. "Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency," Working Papers 15-15, Chapman University, Economic Science Institute.
    8. Stefan Palan, 2013. "A Review Of Bubbles And Crashes In Experimental Asset Markets," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 570-588, July.
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    More about this item


    bubbles; experiments; financial markets; information aggregation; professional traders; strategic sophistication;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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