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Information Flow Between Prediction Markets, Polls and Media: Evidence from the 2008 Presidential Primaries

Author

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  • Urmee Khan

    (Department of Economics, University of California Riverside)

  • Robert Lieli
Abstract
Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster’s surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.
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Suggested Citation

  • Urmee Khan & Robert Lieli, 2017. "Information Flow Between Prediction Markets, Polls and Media: Evidence from the 2008 Presidential Primaries," Working Papers 201711, University of California at Riverside, Department of Economics.
  • Handle: RePEc:ucr:wpaper:201711
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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.
    2. Berg, Joyce & Forsythe, Robert & Nelson, Forrest & Rietz, Thomas, 2008. "Results from a Dozen Years of Election Futures Markets Research," Handbook of Experimental Economics Results, in: Charles R. Plott & Vernon L. Smith (ed.), Handbook of Experimental Economics Results, edition 1, volume 1, chapter 80, pages 742-751, Elsevier.
    3. Andrew Leigh & Justin Wolfers, 2006. "Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets," The Economic Record, The Economic Society of Australia, vol. 82(258), pages 325-340, September.
    4. Jonathan B. Hill, 2007. "Efficient tests of long-run causation in trivariate VAR processes with a rolling window study of the money-income relationship," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 747-765.
    5. Kou, S. G. & Sobel, Michael E., 2004. "Forecasting the Vote: A Theoretical Comparison of Election Markets and Public Opinion Polls," Political Analysis, Cambridge University Press, vol. 12(3), pages 277-295, July.
    6. Christian Franz Horn & Bjoern Sven Ivens & Michael Ohneberg & Alexander Brem, 2014. "Ideas Markets: Prediction Markets – A literature review 2014," Journal of Prediction Markets, University of Buckingham Press, vol. 8(2), pages 89-126.
    7. Taamouti, Abderrahim & Bouezmarni, Taoufik & El Ghouch, Anouar, 2014. "Nonparametric estimation and inference for conditional density based Granger causality measures," Journal of Econometrics, Elsevier, vol. 180(2), pages 251-264.
    8. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    9. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    10. Joyce E. Berg & Thomas A. Rietz, 2003. "Prediction Markets as Decision Support Systems," Information Systems Frontiers, Springer, vol. 5(1), pages 79-93, January.
    11. Jean-Marie Dufour & Eric Renault, 1998. "Short Run and Long Run Causality in Time Series: Theory," Econometrica, Econometric Society, vol. 66(5), pages 1099-1126, September.
    12. Steger, Wayne P., 2008. "Forecasting the presidential primary vote: Viability, ideology and momentum," International Journal of Forecasting, Elsevier, vol. 24(2), pages 193-208.
    13. Lewis-Beck, Michael S. & Skalaban, Andrew, 1989. "Citizen Forecasting: Can Voters See into the Future?," British Journal of Political Science, Cambridge University Press, vol. 19(1), pages 146-153, January.
    14. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    15. Murr, Andreas E., 2015. "The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 916-929.
    16. Gelman, Andrew & King, Gary, 1993. "Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable?," British Journal of Political Science, Cambridge University Press, vol. 23(4), pages 409-451, October.
    17. 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.
    18. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    19. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    20. Geweke, John & Meese, Richard & Dent, Warren, 1983. "Comparing alternative tests of causality in temporal systems : Analytic results and experimental evidence," Journal of Econometrics, Elsevier, vol. 21(2), pages 161-194, February.
    21. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    22. Georgios Tziralis & Ilias Tatsiopoulos, 2007. "Prediction Markets: An Extended Literature Review," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 75-91, February.
    23. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
    24. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    25. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
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