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Manipulation in political stock markets - preconditions and evidence

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  • Jan Hansen
  • Carsten Schmidt
  • Martin Strobel

Abstract

Political stock markets (PSM) are sometimes seen as substitutes for opinion polls. On the bases of a behavioural model, specific preconditions were drawn out under which manipulation in PSM can weaken this argument. Evidence for manipulation is reported from the data of two separate PSM during the Berlin 1999 state elections.

Suggested Citation

  • Jan Hansen & Carsten Schmidt & Martin Strobel, 2004. "Manipulation in political stock markets - preconditions and evidence," Applied Economics Letters, Taylor & Francis Journals, vol. 11(7), pages 459-463.
    • Handle: RePEc:taf:apeclt:v:11:y:2004:i:7:p:459-463
    DOI: 10.1080/1350485042000191700
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    1. Jacobsen, Ben & Potters, Jan & Schram, Arthur & van Winden, Frans & Wit, Jorgen, 2000. "(In)accuracy of a European political stock market: The influence of common value structures," European Economic Review, Elsevier, vol. 44(2), pages 205-230, February.
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    3. Berlemann, Michael & Schmidt, Carsten, 2001. "Predictive accuracy of political stock markets: Empirical evidence from a European perspective," SFB 373 Discussion Papers 2001,57, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    7. repec:bla:scandj:v:101:y:1999:i:2:p:205-22 is not listed on IDEAS
    8. Peter Bohm & Joakim Sonnegard, 1999. "Political Stock Markets and Unreliable Polls," Scandinavian Journal of Economics, Wiley Blackwell, vol. 101(2), pages 205-222, June.
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    10. Colin Camerer, 1998. "Can asset markets be manipulated? A field experiment with racetrack betting," Natural Field Experiments 00222, The Field Experiments Website.
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    Citations

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    Cited by:

    1. Paul Rhode & Koleman Strumpf, 2006. "Manipulating political stock markets: A field experiment and a century of observational data," Natural Field Experiments 00325, The Field Experiments Website.
    2. 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.
    3. 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.
    4. RYan Oprea & David Porter & Chris Hibbert & Robin Hanson & Dorina Tila, 2008. "Can Manipulators Mislead Prediction Market Observers?," Working Papers 08-01, Chapman University, Economic Science Institute.
    5. Imlak Shaikh, 2019. "The U.S. Presidential Election 2012/2016 and Investors’ Sentiment: The Case of CBOE Market Volatility Index," SAGE Open, , vol. 9(3), pages 21582440198, July.
    6. Lionel Page & Robert T. Clemen, 2013. "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?-super-," Economic Journal, Royal Economic Society, vol. 123(568), pages 491-513, May.
    7. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    8. Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    9. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    10. Shaikh, Imlak, 2017. "The 2016 U.S. presidential election and the Stock, FX and VIX markets," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 546-563.
    11. Dorina Tila & David Porter, 2008. "Group Prediction in Information Markets With and Without Trading Information and Price Manipulation Incentives," Working Papers 08-06, Chapman University, Economic Science Institute.
    12. Robin Hanson, 2006. "Designing real terrorism futures," Public Choice, Springer, vol. 128(1), pages 257-274, July.
    13. Frank M. A. Klingert & Matthias Meyer, 2018. "Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-7.
    14. Junjie Wang & Shuigeng Zhou & Jihong Guan, 2011. "Detecting Collusive Cliques in Futures Markets Based on Trading Behaviors from Real Data," Papers 1110.1522, arXiv.org.
    15. 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.
    16. Jens Grossklags & Carsten Schmidt, 2002. "Artificial Software Agents on Thin Double Auction Markets - A Human Trader Experiment," Papers on Strategic Interaction 2002-45, Max Planck Institute of Economics, Strategic Interaction Group.

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    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • G1 - Financial Economics - - General Financial Markets

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