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Trader Performance in a Market Experiment with Human and Computerized Traders

Abstract

We use computerized agents, which trade according to an active information processing strategy, to compare their returns to those of human subjects trading in the same market. All subjects are assigned to one of five information levels, each of which is populated by two human and two computerized traders. We find that the computerized traders earn higher returns than their human counterparts of the same information level for all but one of five information levels.

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References

  1. Bloomfield, Robert and Maureen O’Hara (1999), Market Transparency: Who Wins and Who Loses?, The Review of Financial Studies 12 (1), 5–35.

    Article  Google Scholar 

  2. Diamond, Douglas W. and Robert E. Verrecchia (1981), Information Aggregation in a Noisy Rational Expectations Economy, Journal of Financial Economics 9, 221–235.

    Article  Google Scholar 

  3. Dufwenberg, Martin, Tobias Lindqvist, and Evan Moore (2005), Bubbles and Experience: An Experiment, The American Economic Review 95 (5), 1731–1737.

    Article  Google Scholar 

  4. Fama, Eugene F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance 25 (2), 383–417.

    Article  Google Scholar 

  5. Fischbacher, Urs (2007), Z-Tree: Zurich Toolbox for Ready-Made Economic Experiments, Experimental Economics 10 (2), 171–178.

    Article  Google Scholar 

  6. Glosten, Lawrence R. and Paul R. Milgrom (1985), Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders, Journal of Financial Economics 14, 71–100.

    Article  Google Scholar 

  7. Greiner, Ben (2004), An Online Recruitment System for Economic Experiments, in Kurt Kremer and Volker Macho (eds.), Forschung und wissenschafliches Rechnen 2003. GWDG Bericht 63, Goettingen: Gesellschaft für Wiss. Datenverarbeitung, 79–93.

    Google Scholar 

  8. Grossman, Sanford J. (1976), On the Efficiency of Competitive Stock Markets where Traders Have Diverse Information, Journal of Finance 31, 573–585.

    Article  Google Scholar 

  9. Grossman, Sanford J. and Joseph E. Stiglitz (1980), On the Impossibility of Informationally Efficient Markets, The American Economic Review 70, 393–408.

    Google Scholar 

  10. Haruvy, Ernan and Charles N. Noussair (2006), The Effect of Short Selling on Bubbles and Crashes in Experimental Spot Asset Markets, The Journal of Finance 61 (3), 1119–1157.

    Article  Google Scholar 

  11. Hauser, Florian and Bob Kaempff (2011), Evolution of Trading Strategies in a Market with Heterogeneously Informed Agents, Journal of Evolutionary Economics, Online First, DOI 10.1007/s00191-011-0232p6.

    Google Scholar 

  12. Hauser, Florian and Bob Kaempff (2010), Trading on Marginal Information, Progress in Artificial Economics — Computational and Agent-Based Models, Lecture Notes in Economics and Mathematical Systems. Springer 645, 15–24.

    Article  Google Scholar 

  13. Hayek, Friedrich A. (1945), The Use of Knowledge in Society, The American Economic Review 35 (4), 519–530.

    Google Scholar 

  14. Hirshleifer, David (2001), Investor Psychology and Asset Pricing, Journal of Finance 56, 1533–1597.

    Article  Google Scholar 

  15. Hommes, Cars (2006), Heterogeneous Agent Models in Economics and Finance, Handbook of Computational Economics 2, 1109–1186.

    Article  Google Scholar 

  16. Huber, Jürgen (2007), “J”-Shaped Returns to Timing Advantage in Access to Information — Experimental Evidence and a Tentative Explanation, Journal of Economic Dynamics and Control 31, 2536–2572.

    Article  Google Scholar 

  17. Huber, Jürgen and Michael Kirchler (2012), The Impact of Instructions and Procedure on Reducing Confusion and Bubbles in Experimental Asset Market, Experimental Economics 15, 89–105.

    Article  Google Scholar 

  18. Huber, Jürgen, Martin Angerer, and Michael Kirchler (2010), Experimental Asset Markets with Endogenous Choice of Costly Information, Experimental Economics 14, 223–240.

    Article  Google Scholar 

  19. Huber, Jürgen, Michael Kirchler, and Matthias Sutter (2008), Is More Information Always Better? Experimental Financial Markets with Cumulative Information, Journal of Economic Behavior & Organization 65, 86–104.

    Article  Google Scholar 

  20. Huber, Jürgen, Michael Kirchler, and Matthias Sutter (2006), Vom Nutzen zusätzlicher Information auf Märkten mit unterschiedlich informierten Händlern — Eine experimentelle Studie, Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung 58, 188–211.

    Article  Google Scholar 

  21. Jeng, Lesley A., Andrew Metrick, and Richard Zeckhauser (2003), Estimating the Returns to Insider Trading, a Performance-Evaluation Perspective, Review of Economics and Statistics 85, 453–471.

    Article  Google Scholar 

  22. Kirchler, Michael (2010), Partial Knowledge is a Dangerous Thing — On the Value of Asymmetric Fundamental Information in Asset Markets, Journal of Economic Psychology 21, 643–658.

    Article  Google Scholar 

  23. Kirchler, Michael, Jürgen Huber, and Thomas Stoeckl (2012), Thar She Bursts — Reducing Confusion Reduces Bubbles, The American Economic Review 102 (2), 865–883.

    Article  Google Scholar 

  24. Lakonishok, Josef and Inmoo Lee (2001), Are Insiders’ Trades Informative?, Review of Financial Studies 14, 79–111.

    Article  Google Scholar 

  25. Lin, Ji-Chai and John S. Howe (1990), Insider Trading in the OTC Market, Journal of Finance 45, 173–1284.

    Article  Google Scholar 

  26. Lux, Thomas (1998), The Socio-Economic Dynamics of Speculative Markets, Interacting Agents, Chaos, and the Fat Tails of Return Distributions, Journal of Economic Behavior and Organization 33, 143–165.

    Article  Google Scholar 

  27. Lux, Thomas and Michele Marchesi (2000), Volatility Clustering in Financial Markets, a Microsimulation of Interacting Agents, International Journal of Theoretical and Applied Finance 3, 675–702.

    Article  Google Scholar 

  28. Mannaro, Katiuscia, Michele Marchesi, and Alessio Setzu (2008), Using an Artificial Financial Market for Assessing the Impact of Tobin-Like Transaction Taxes, Journal of Economic Behavior and Organization 67, 445–462.

    Article  Google Scholar 

  29. Noussair, Charles N. and Owen Powell (2010), Peaks and Valleys: Price Discovery in Experimental Asset Markets with Non-Monotonic Fundamentals, Journal of Economic Studies 37 (2), 152–180.

    Article  Google Scholar 

  30. Pfeifer, Christian, Klaus Schredelseker, and Gilg U.H. Seeber (2009), On the Negative Value of Information in Informationally Inefficient Markets: Calculations for Large Number of Traders, European Journal of Operational Research 195, 117–126.

    Article  Google Scholar 

  31. Porter, David P. and Vernon L. Smith (1995), Futures Contracting and Dividend Uncertainty in Experimental Asset Markets, The Journal of Business 68 (4), 509–541.

    Article  Google Scholar 

  32. Schredelseker, Klaus (1984), Anlagestrategie und Informationsnutzen am Aktienmarkt, Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung 36, 44–59.

    Google Scholar 

  33. Schredelseker, Klaus (2001), Is the Usefulness Approach Useful? Some Reflections on the Utility of Public Information, in Stuart McLeay and Angelo Riccaboni (eds.), Contemporary Issues in Accounting Regulation, Kluwer Academic Publishers, 135–153.

    Chapter  Google Scholar 

  34. Shiller, Robert J. (2003), From Efficient Markets Theory to Behavioral Finance, Journal of Economic Perspectives 17, 83–104.

    Article  Google Scholar 

  35. Smith, Vernon L., Gerry L. Suchanek, and Arlington W. Williams (1988), Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets, Econometrica 56 (5), 1119–1151.

    Article  Google Scholar 

  36. Sutter, Matthias, Jürgen Huber, and Michael Kirchler (2012), Bubbles and Information: An Experiment, Management Science 58, 384–393.

    Article  Google Scholar 

  37. Sunder, Shyam (1992), Market for Information: Experimental Evidence, Econometrica 60, 667–695.

    Article  Google Scholar 

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Authors

Corresponding author

Correspondence to Martin Angerer.

Additional information

The authors thank the editors Wolfgang Ballwieser and Alfred Wagenhofer and two anonymous referees for helpful suggestions. Financial support by the University of Innsbruck (Nachwuchsfoerderung Michael Kirchler) and the University of Liechtenstein (Forschungsförderungsfond Projekt fdl-2-11) are gratefully acknowledged.

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Angerer, M., Huber, J. & Kirchler, M. Trader Performance in a Market Experiment with Human and Computerized Traders. Schmalenbach Bus Rev 66, 224–244 (2014). https://doi.org/10.1007/BF03396906

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JEL Classification

  • C91
  • D82
  • D84
  • G00

Keywords

  • Asset Markets
  • Asymmetric Information
  • Experiment
  • Fundamental Information
  • Value of Information