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C. Shannon (1948)
A mathematical theory of communicationBell Syst. Tech. J., 27
J. Kelly (1956)
A new interpretation of information rateIRE Trans. Inf. Theory, 2
(2004)
Prediction markets"Jou171al 0/Economic Pel'spectives, VoL 18 No.2, pp.l07·26
Thomas Mann, R. Dowen (1996)
Are hog and pig reports informativeJournal of Futures Markets, 16
Phil Colling, S. Irwin (1990)
The Reaction of Live Hog Futures Prices to USDA Hogs and Pigs ReportsAmerican Journal of Agricultural Economics, 72
V. Smith (2007)
Experimental Economics : Induced Value Theory
J. Marsh (2003)
Impacts of Declining U.S. Retail Beef Demand on Farm‐Level Beef Prices and ProductionERN: Agricultural Economics (Topic)
Robert Forsythe, F. Nelson, G. Neumann, Jack Wright (1992)
Anatomy of an Experimental Political Stock Market
Emile Servan-Schreiber, J. Wolfers, David Pennock, B. Galebach (2004)
Prediction Markets: Does Money Matter?Electron. Mark., 14
David Pennock, S. Lawrence, C. Giles, F. Nielsen (2001)
The Real Power of Artificial MarketsScience, 291
DW Ramapo (2008)
The use of knowledge about societyJournal of Economic Behavior and Organization, 67
O. Isengildina, S. Irwin, D. Good (2006)
The Value of USDA Situation and Outlook Information in Hog and Cattle MarketsJournal of Agricultural and Resource Economics, 31
(2006)
Five open questions about prediction markets "
D. Kahneman, A. Tversky (1979)
Prospect theory: An analysis of decision under risk Econometrica 47
C. Plott, Kay-Yut Chen (2002)
Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem
(2003)
Accm-acy and forecast standard error of prediction markets", working paper, Department of Accounting, Economics and Finance, Henry B
C. Shannon (1950)
The mathematical theory of communication
Joyce Berg, F. Nelson, Thomas Rietz, Henry Tippie (2003)
Accuracy and Forecast Standard Error of Prediction Markets
J. Frank, P. Garcia, S. Irwin (2008)
To What Surprises Do Hog Futures Markets Respond?Journal of Agricultural and Applied Economics, 40
Information Systems Frontiers 5:1, 79–93, 2003 C ○ 2003 Kluwer Academic Publishers. Manufactured in The Netherlands. Prediction Markets as Decision Support Systems
O. Grunewald, Mark McNulty, A. Biere (1993)
Live Cattle Futures Response to Cattle on Feed ReportsAmerican Journal of Agricultural Economics, 75
(1995)
Ambiguity Aversion and Comparative Ignorance
R. Shiller (1998)
Human Behavior and the Efficiency of the Financial SystemSPGMI: Compustat Fundamentals (Topic)
(1998)
Human Behavior and the E/ficiency 0/ the Finatlciai System, Cowles Foundation for Research in Economics Discussion Paper No. 1172
J.E. Berg, T.A. Rietz
Prediction markets as decision support systems
P. Rhode, Koleman Strumpf (2004)
Historical presidential betting marketsJournal of Economic Perspectives, 18
(2006)
bl!01mation Markets: A New Way oj Makirlg Decisions, American Enterprise Institute-BrookingJoint Center for Regulatory Studies, Washington, DC
(2007)
Intelpretatio17 0/ the USD.4 Cattle on Feed Repol't, Extension Report No. 850
K. Dhuyvetter, T. Schroeder, J. Parcell (1997)
The Effect of USDA Cattle on Feed Reports on Feeder Cattle Futures Prices
E. Fama (1970)
EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*Journal of Finance, 25
R. Hahn, Paul Tetlock (2006)
Information Markets: A New Way of Making DecisionsColumbia: Finance & Economics (Topic)
Purpose – The purpose of this paper is to use prediction markets to forecast an agricultural event: United States Department of Agriculture's number of cattle on feed (COF). Prediction markets are increasingly popular forecast tools due to their flexibility and proven accuracy to forecast a diverse array of events. Design/methodology/approach – During spring 2008, a market was constructed comprised of student traders in which they bought and sold contracts whose value was contingent on the number of COF to be reported on April 18, 2008. During a nine‐week period, students were presented three types of contracts to forecast the number of COF. To estimate forecasts a uniform price sealed bid auction mechanism was used. Findings – The results showed that prediction markets forecasted 11.5 million head on feed, which was about 1.6 percent lower than the actual number of COF (11.684 million). The prediction market also fared slightly worse than analysts' predictions, which on average suggested there would be about 11.795 million head (an over‐estimate of about 1 percent). Originality/value – The contribution of this study was not to provide conclusive evidence on the efficacy of using prediction markets to forecast COF, but rather to present an empirical example that will spark interest among agricultural economists on the promises and pitfalls of a research method that has been relatively underutilized in the agricultural economics literature.
Agricultural Finance Review – Emerald Publishing
Published: Nov 9, 2010
Keywords: Animal feed; Experimental design; Forecasting; United States of America
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