Information Systems and e-Business Management

, Volume 8, Issue 4, pp 395–413 | Cite as

Fraud detection in play-money prediction markets

  • Michael BlumeEmail author
  • Stefan Luckner
  • Christof Weinhardt
Original Article


Prediction markets are increasingly used to aggregate information on particular future events of interest such as elections, sports events, and Oscar winners. However, prediction markets are sensitive to manipulation and price distortions. In this paper, we show evidence for fraud in a play-money sports prediction market. In contrast to the often considered outcome manipulation in the context of political stock markets we were only looking for violations of our general terms and conditions, i.e., for traders creating multiple user accounts and trading against themselves in order to transfer cash from one account to another. We found evidence of suchlike coalitions and received complaints about it by annoyed traders. We discuss possible countermeasures in order to avoid or at least detect this kind of fraud in play-money prediction markets and present a tool which can be used to detect coalition building while operating a market.


Prediction markets Fraud detection Manipulation Coalition building 



This work is based on research funded by the German Federal Ministry for Education and Research under grant number 01HQ0522 and by the German Research Foundation (DFG) within the scope of the Graduate School Information Management and Market Engineering (IME). The authors are responsible for the content of this publication.


  1. Allen F, Gale D (1992) Stock-price manipulation. Rev Financ Stud 5(3):503–529CrossRefGoogle Scholar
  2. Berg JE, Forsythe R, Nelson F, Rietz TA (2001) Results from a Dozen years of election futures markets research. In: Plott CR, Smith VL (eds) Handbook of experimental economic results. Elsevier Press, New YorkGoogle Scholar
  3. Bohm PJG, Sonnegård J (1999) Political stock markets and unreliable polls. Scand J Econ 101(2):205–222CrossRefGoogle Scholar
  4. Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 17(3):235–255CrossRefGoogle Scholar
  5. Camerer CF (1998) Can asset markets be manipulated? A field experiment with racetrack betting. J Polit Econ 106(3):457–481CrossRefGoogle Scholar
  6. Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25(2):383–417CrossRefGoogle Scholar
  7. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. (W.H.) Freeman, New YorkGoogle Scholar
  8. Gini C (1936) On the measure of concentration with special reference to income and wealth. Abstracts of papers presented at the Cowels Commission Research Conference on Economics and Statistics. Colorado Springs, Colorado College, pp 73–80Google Scholar
  9. Hansen J, Schmidt C, Strobel M (2004) Manipulation in political stock markets—preconditions and evidence. Appl Econ Lett 11(7):459–463CrossRefGoogle Scholar
  10. Hanson R, Oprea R (2004) Manipulators increase information market accuracy. George Mason University Working PaperGoogle Scholar
  11. Hanson R, Oprea R, Porter D (2006) Information aggregation and manipulation in an experimental market. J Econ Behav Organ 60(4):449–459CrossRefGoogle Scholar
  12. Inokuchi A, Washio T, Motoda H (2003) Complete mining of frequent patterns from graphs: mining graph data. Mach Learn 50(3):321–354CrossRefGoogle Scholar
  13. Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53(6):1315–1335CrossRefGoogle Scholar
  14. Kyle AS (1989) Informed speculation with imperfect competition. Rev Econ Stud 56(3):317–355CrossRefGoogle Scholar
  15. Luckner S, Weinhardt C (2007) How to pay trader in information markets? Results from a field experiment. J Prediction Mark 1(2):147–156Google Scholar
  16. Oprea R, Porter D, Hibert C, Hanson R, Tila D (2006) Can manipulators mislead prediction market observers? George Mason University Working PaperGoogle Scholar
  17. Ottaviani M, Sørensen PN (2007) Outcome manipulation in corporate prediction markets. J Eur Econ Assoc 5(2–3):554–563CrossRefGoogle Scholar
  18. Rolli D, Neumann D, Weinhardt C (2004) A minimal market model in ephemeral markets. In: Proceedings of the form EMC, Toledo, SpainGoogle Scholar
  19. Slamka C, Luckner S, Seemann T, Schroeder J (2008) An empirical investigation of the forecast accuracy of play-money prediction markets and professional betting markets. In: Proceeding of the 16th European Conference on Information Systems (ECIS), Galway, IrelandGoogle Scholar
  20. Vila J-L (1989) Simple games of market manipulation. Econ Lett 29(1):21–26CrossRefGoogle Scholar
  21. Wolfers J, Zitzewitz E (2006) Five open questions about prediction markets. In: Tetlock P, Hahn RW (eds) Information markets: a new way of making decisions. AEI-Brookings Joint Center for Regulatory Studies, pp 13–39Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Michael Blume
    • 1
    Email author
  • Stefan Luckner
    • 1
  • Christof Weinhardt
    • 1
  1. 1.Institute of Information Systems and Management (IISM)University of KarlsruheKarlsruheGermany

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