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Price manipulation in an experimental asset market

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  • Veiga, H.
  • Vorsatz, M.

    (Microeconomics & Public Economics)

Abstract

We analyze in the laboratory whether an uninformed trader is able to manipulate the price of a financial asset by comparing the results of two experimental treatments. In the benchmark treatment, 12 subjects trade a common value asset that takes either a high or a low value. Only three subjects know the actual value of the asset while the market is open for trading. The manipulation treatment is identical to the benchmark treatment apart from the fact that we introduce a computer program as an additional uninformed trader. This robot buys a fixed number of shares in the beginning of a trading period and sells them again afterwards. Our main result shows that the last contract price is significantly higher in the manipulation treatment if the asset takes a low value and that private information is very well disseminated by both markets if the value of the asset is high. Finally, even though this simple manipulation program loses money on average, it is profitable in some instances.
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Suggested Citation

  • Veiga, H. & Vorsatz, M., 2006. "Price manipulation in an experimental asset market," Research Memorandum 024, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    • Handle: RePEc:unm:umamet:2006024
    DOI: 10.26481/umamet.2006024
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    References listed on IDEAS

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    Cited by:

    1. Marc Vorsatz & Helena Veiga, 2008. "The Effect of Short–Selling on the Aggregation of Information in an Experimental Asset Market," Working Papers 2008-26, FEDEA.
    2. Corgnet, Brice & DeSantis, Mark & Siemroth, Christoph, 2023. "Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach," Economics Discussion Papers 36273, University of Essex, Department of Economics.
    3. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2022. "Manipulation and (Mis)trust in Prediction Markets," Management Science, INFORMS, vol. 68(9), pages 6716-6732, September.
    4. Deck, Cary & Lin, Shengle & Porter, David, 2013. "Affecting policy by manipulating prediction markets: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 48-62.
    5. Powell, O.R., 2010. "Essays on experimental bubble markets," Other publications TiSEM b16ad7ae-3741-4f08-8de7-3, Tilburg University, School of Economics and Management.
    6. Corgnet, Brice & DeSantis, Mark & Porter, David, 2020. "The distribution of information and the price efficiency of markets," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    7. Johan Almenberg & Ken Kittlitz & Thomas Pfeiffer, 2009. "An Experiment on Prediction Markets in Science," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
    8. Corgnet, Brice & DeSantis, Mark & Porter, David, 2020. "The distribution of information and the price efficiency of markets," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    9. Friederike Mengel & Ronald Peeters, 2022. "Do markets encourage risk-seeking behaviour?," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1474-1480, October.
    10. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    11. Boris Maciejovsky & David V. Budescu, 2020. "Too Much Trust in Group Decisions: Uncovering Hidden Profiles by Groups and Markets," Organization Science, INFORMS, vol. 31(6), pages 1497-1514, November.
    12. Veiga, Helena & Vorsatz, Marc, 2008. "Aggregation and dissemination of information in experimental asset markets in the presence of a manipulator," DES - Working Papers. Statistics and Econometrics. WS ws084110, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Te Bao & Elizaveta Nekrasova & Tibor Neugebauer & Yohanes E. Riyanto, 2022. "Algorithmic trading in experimental markets with human traders: A literature survey," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 23, pages 302-322, Edward Elgar Publishing.
    14. Peeters, R.J.A.P. & Wolk, K.L., 2014. "Eliciting and aggregating individual expectations: An experimental study," Research Memorandum 029, Maastricht University, Graduate School of Business and Economics (GSBE).

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