Abstract
We propose the development of a prediction market to provide a form of collective intelligence for forecasting prices for “toxic assets” to be transferred from Irish banks to the National Asset Management Agency. Such a market allows participants to assume a stake in a security whose value is tied to a future event. We propose that securities are created whose value hinges on the transfer amount paid for loans from the agency to a bank. In essence, bets are accepted on whether the price is higher or lower than a quoted figure. The prices of securities indicate expected transfer costs for toxic assets. Prediction markets offer a proven means of aggregating distributed knowledge pertaining to estimates of uncertain quantities and are robust to strategic manipulation. We propose that a prediction market runs in parallel to a pricing procedure for individual assets conducted by the government agency. We advocate an approach whereby prices are chosen as a convex combination of the agency’s internal estimate and that of the prediction market. We argue that this will substantially reduce the cognitive burden for the government agency and improve the accuracy, speed and scalability of pricing. This approach also offers a means of empowering both property experts and non-experts in a cost-effective and transparent manner.
This work is funded by Enterprise Ireland (grant number PC/2008/0367).
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References
Bebchuk, L.A.: Buying troubled assets. Yale Journal on Regulation 29 (2009) (forthcoming)
Bragues, G.: Prediction markets: The practical and normative possibilities for the social production of knowledge. Episteme 6, 91–106 (2009)
European Commission: Communication from the commission on the treatment of impaired assets in the community banking sector (2009), http://ec.europa.eu/competition/state_aid/legislation/impaired_assets.pdf
Gurdgiev, C., Lucey, B.: What’s wrong with NAMA (April 2009), http://trueeconomics.blogspot.com/2009/04/whats-wrong-with-nama.html (Unedited version of article in Business and Finance) (April 23)
Hanson, R.: Combinatorial information market design. Information Systems Frontiers 5(1), 107–119 (2003)
Hanson, R., Oprea, R.: Manipulators increase information market accuracy, mimeo, George Mason University (2005)
Holland, A.: A prediction market for toxic assets prices. arXiv:0905.4171v1 [cs.CE] (May 2009)
Kagel, J.H., Levin, D.: The winners curse and public information in common values auctions. American Economic Review 76, 894–920 (1986)
Pagourtizi, E., Assimakopoulos, V., Hatzichristos, T., French, N.: Real estate appraisal: A review of valuation methods. Journal of Property Investment and Finance 21(4), 383–401 (2003)
Snowberg, E., Wolfers, J., Zitzewitz, E.: Partisan impacts on the stockmarket: Evidence from prediction markets and close elections, mimeo, University of Pennsylvania (2006)
Surowiecki, J.: The Wisdom of Crowds. Anchor, New York (August 2005)
Whelan, K.: Panel discussion. RTE Primetime, April 30 (2009)
Wolfers, J., Zitzewitz, E.: Prediction markets. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke (2008)
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Holland, A. (2010). A Prediction Market for Toxic Assets. In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_19
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DOI: https://doi.org/10.1007/978-3-642-17080-5_19
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