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Trading by Professional Traders: An Experiment

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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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  • Marco Cipriani & Roberta De Filippis & Antonio Guarino & Ryan Kendall, 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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    1. Michael S. Haigh & John A. List, 2005. "Do Professional Traders Exhibit Myopic Loss Aversion? An Experimental Analysis," Journal of Finance, American Finance Association, vol. 60(1), pages 523-534, February.
    2. Marco Cipriani & Antonio Guarino, 2005. "Herd Behavior in a Laboratory Financial Market," American Economic Review, American Economic Association, vol. 95(5), pages 1427-1443, December.
    3. Marco Cipriani & Antonio Guarino, 2009. "Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 206-233, March.
    4. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 4.
    5. Michailova, Julija, 2010. "Overconfidence and bubbles in experimental asset markets," MPRA Paper 26388, University Library of Munich, Germany.
    6. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 2.
    7. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    8. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance," Journal of Finance, American Finance Association, vol. 73(3), pages 1113-1137, June.
    9. Sunder, S., 1992. "Experimental Asset Markets: A Survey," GSIA Working Papers 1992-19, Carnegie Mellon University, Tepper School of Business.
    10. Jonathan E. Alevy & Michael S. Haigh & John A. List, 2007. "Information Cascades: Evidence from a Field Experiment with Financial Market Professionals," Journal of Finance, American Finance Association, vol. 62(1), pages 151-180, February.
    11. John A. List & Michael S. Haigh, 2010. "Investment Under Uncertainty: Testing the Options Model with Professional Traders," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 974-984, November.
    12. 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.
    13. Utz Weitzel & Christoph Huber & Jürgen Huber & Michael Kirchler & Florian Lindner & Julia Rose & Lauren Cohen, 2020. "Bubbles and Financial Professionals [Margin, short sell, and lotteries in experimental asset markets]," The Review of Financial Studies, Society for Financial Studies, vol. 33(6), pages 2659-2696.
    14. Porter, David P & Smith, Vernon L, 1995. "Futures Contracting and Dividend Uncertainty in Experimental Asset Markets," The Journal of Business, University of Chicago Press, vol. 68(4), pages 509-541, October.
    15. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 6.
    16. 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.
    17. 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.
    18. J. M. Keynes, 1937. "The General Theory of Employment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 51(2), pages 209-223.
    19. 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.
    20. Cipriani, Marco & Angrisani, 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.
    21. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 7.
    22. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 1.
    23. Brice Corgnet & Mark Desantis & David Porter, 2018. "What Makes a Good Trader? : On the Role of Reflection and Intuition on Trader Performance," Post-Print hal-02312062, HAL.
    24. 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.
    25. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 3.
    26. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 8.
    27. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 5.
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    Cited by:

    1. Rocco Caferra & Andrea Morone & Piergiuseppe Morone & Paolo Storelli, 2022. "Professional traders’ individual and social preferences under risk: Does group's wealth matter?," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 93(4), pages 1063-1082, December.

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    More about this item

    Keywords

    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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