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Manipulating political stock markets: A field experiment and a century of observational data

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  • Paul Rhode
  • Koleman Strumpf

Abstract

Political stock markets have a long history in the United States. Organized prediction markets for Presidential elections have operated on Wall Street (1880-1944), the Iowa Electronic Market (1988-present), and TradeSports (2001-present). Proponents claim such markets efficiently aggregate information and provide forecasts superior to polls. An important counterclaim is that such markets may be subject to manipulation by interested parties. We analyze this argument by studying alleged and actual speculative attacks- large trades, uninformed by fundamentals, intended to change prices- in these three markets. We first examine the historical Wall Street markets where political operatives from the contending parties actively and openly bet on city, state and national races; the record is rife with accusations that parties tried to boost their candidates through investments and wash bets. Next we report the results of a field experiment involving a series of planned, random investments-- accounting for two percent of total market volume-- in the Iowa Electronic Market in 2000. Finally, we investigate the speculative attacks on TradeSports market in 2004 when a single trader made a series of large investments in an apparent attempt to make one candidate appear stronger. In the cases studied here, the speculative attack initially moved prices, but these changes were quickly undone and prices returned close to their previous levels. We find little evidence that political stock markets can be systematically manipulated beyond short time periods.

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  • 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.
  • Handle: RePEc:feb:natura:00325
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    Cited by:

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    2. 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.
    3. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2011. "How Prediction Markets can Save Event Studies," CAMA Working Papers 2011-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. James Schmitz & David Rothschild, 2019. "Understanding market functionality and trading success," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-28, August.
    5. 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.
    6. Pankaj Pandey & Einar Snekkenes, 2016. "Using Financial Instruments to Transfer the Information Security Risks," Future Internet, MDPI, vol. 8(2), pages 1-62, May.
    7. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    8. Riekhof, Hans-Christian & Riekhof, Marie-Catherine & Brinkhoff, Stefan, 2012. "Predictive Markets: Ein vielversprechender Weg zur Verbesserung der Prognosequalität im Unternehmen?," PFH Forschungspapiere/Research Papers 2012/07, PFH Private University of Applied Sciences, Göttingen.
    9. Chezum, Brian & Stowe, C. Jill, 2010. "Some Evidence of Information Aggregation in Auction Prices," 2011 Annual Meeting, February 5-8, 2011, Corpus Christi, Texas 98528, Southern Agricultural Economics Association.
    10. Masami Imai & Cameron A. Shelton, 2010. "Elections and Political Risk: New Evidence from Political Prediction Markets in Taiwan," Wesleyan Economics Working Papers 2010-001, Wesleyan University, Department of Economics.

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