IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpex/9410001.html
   My bibliography  Save this paper

Behaviour of a Small Political Call Market

Author

Listed:
  • Klaus Beckmann

    (Universitaet Passau)

  • Martin Werding

    (Universitaet Passau)

Abstract

We present a preliminary overview of a political stock market experiment we have conducted at the Universitaet Passau. This experiment differs from previous work (e.g. the renowned Iowa Electronic Markets) in that it is built on the call market institution rather than on double auction principles. The predictions (for the Bavarian state election in Germany) derived from our market are less accurate than those typically achieved by double auction markets. We suggest, and discuss, a number of reasons for this, outlining some directions for research on our second, and more substantial, political stock market for the German Bundestag election.

Suggested Citation

  • Klaus Beckmann & Martin Werding, 1994. "Behaviour of a Small Political Call Market," Experimental 9410001, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpex:9410001
    Note: 25 pages, Postscript file
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/exp/papers/9410/9410001.pdf
    Download Restriction: no

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/exp/papers/9410/9410001.ps.gz
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Madhavan, Ananth, 1992. "Trading Mechanisms in Securities Markets," Journal of Finance, American Finance Association, vol. 47(2), pages 607-641, June.
    2. Forsythe, Robert & Forrest Nelson & George R. Neumann & Jack Wright, 1992. "Anatomy of an Experimental Political Stock Market," American Economic Review, American Economic Association, vol. 82(5), pages 1142-1161, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin Spann & Bernd Skiera, 2003. "Internet-Based Virtual Stock Markets for Business Forecasting," Management Science, INFORMS, vol. 49(10), pages 1310-1326, October.
    2. Berg, Joyce E. & Rietz, Thomas A., 2019. "Longshots, overconfidence and efficiency on the Iowa Electronic Market," International Journal of Forecasting, Elsevier, vol. 35(1), pages 271-287.
    3. Xianfeng Jiang & Yongdong Shi, 2006. "The Impact of Insider Trading on the Secondary Market with Order-Driven System," Annals of Economics and Finance, Society for AEF, vol. 7(1), pages 129-143, May.
    4. Ping‐Wen Sun & Yifan Shen & Meifen Qian & Wu Yan, 2021. "Risk of holding stocks with liquidity sensitive to market uncertainty: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(S1), pages 1993-2029, April.
    5. Marko Corn & Nejc Rov{z}man, 2021. "Unihedge -- A decentralized market prediction platform," Papers 2108.11631, arXiv.org, revised Dec 2021.
    6. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," Review of Economic Studies, Oxford University Press, vol. 86(1), pages 313-332.
    7. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    8. Lewis-Beck, Michael S. & Tien, Charles, 1999. "Voters as forecasters: a micromodel of election prediction," International Journal of Forecasting, Elsevier, vol. 15(2), pages 175-184, April.
    9. Mathias Drehmann & Jörg Oechssler & Andreas Roider, 2005. "Herding and Contrarian Behavior in Financial Markets: An Internet Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1403-1426, December.
    10. Gernot Hinterleitner & Philipp Hornung & Ulrike Leopold-Wildburger & Roland Mestel & Stefan Palan, 2012. "A Good Beginning Makes a Good Market: The Effect of Different Market Opening Structures on Market Quality," Working Paper Series, Social and Economic Sciences 2012-01, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
    11. Comerton-Forde, Carole & Rydge, James, 2006. "Call auction algorithm design and market manipulation," Journal of Multinational Financial Management, Elsevier, vol. 16(2), pages 184-198, April.
    12. Meihui Guo & Yi-Ting Guo & Chi-Jeng Wang & Liang-Ching Lin, 2015. "Assessing influential trade effects via high-frequency market reactions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1458-1471, July.
    13. Tsung-Yu Hsieh, 2015. "Information disclosure and price manipulation during the pre-closing session: evidence from an order-driven market," Applied Economics, Taylor & Francis Journals, vol. 47(43), pages 4670-4684, September.
    14. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    15. Comerton-Forde, Carole & Rydge, James, 2006. "The influence of call auction algorithm rules on market efficiency," Journal of Financial Markets, Elsevier, vol. 9(2), pages 199-222, May.
    16. Hwang, Hae-shin & Jindapon, Paan, 2020. "Market making with convex quotes," Finance Research Letters, Elsevier, vol. 37(C).
    17. Bondarenko, Oleg, 2001. "Competing market makers, liquidity provision, and bid-ask spreads," Journal of Financial Markets, Elsevier, vol. 4(3), pages 269-308, June.
    18. 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.
    19. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    20. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, January.

    More about this item

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpex:9410001. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.