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Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting

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  • Dass, Mayukh
  • Jank, Wolfgang
  • Shmueli, Galit
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    Abstract

    This paper presents a novel intelligent bidding system, called SOABER (Simultaneous Online Auction BiddER), which monitors simultaneous online auctions of high-value fine art items. It supports decision-making by maximizing bidders' surpluses and their chances of winning an auction. One key element of the system is a dynamic forecasting model, which incorporates information about the speed of an auction's price movement, as well as the level of competition both within and across auctions. Other elements include a wallet estimator, which gauges the bidders' willingness to pay, and a bid strategizer, which embeds the forecasting model into a fully automated decision system. We illustrate the performance of our intelligent bidding system on an authentic dataset of online art auctions for Indian contemporary art. We compare our system with several simpler ad-hoc approaches, and find it to be more effective in terms of both the extracted surplus and the resulting winning percentage.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 4 (October)
    Pages: 1259-1270

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    Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1259-1270

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Bidder surplus optimization Dynamic price forecasting Functional data analysis Intelligent bidding system Simultaneous online art auctions;

    References

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    1. Chanel, O. & Gerard, L.A. & Ginsburgh, V., 1992. "The Relevence of Hedonic Price Indices the Case of Paintings," G.R.E.Q.A.M. 92a19, Universite Aix-Marseille III.
    2. Wang, Shanshan & Jank, Wolfgang & Shmueli, Galit, 2008. "Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 144-160, April.
    3. Alvin E. Roth & Axel Ockenfels, . "Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet," Papers on Strategic Interaction 2002-32, Max Planck Institute of Economics, Strategic Interaction Group.
    4. Jianping Mei & Michael Moses, 2002. "Art as an Investment and the Underperformance of Masterpieces," American Economic Review, American Economic Association, vol. 92(5), pages 1656-1668, December.
    5. Anthony M. Kwasnica & Katerina Sherstyuk, 2007. "Collusion and Equilibrium Selection in Auctions," Economic Journal, Royal Economic Society, vol. 117(516), pages 120-145, 01.
    6. Zhang, Shu & Jank, Wolfgang & Shmueli, Galit, 2010. "Real-time forecasting of online auctions via functional K-nearest neighbors," International Journal of Forecasting, Elsevier, vol. 26(4), pages 666-683, October.
    7. Milgrom, Paul, 1998. "Game theory and the spectrum auctions," European Economic Review, Elsevier, vol. 42(3-5), pages 771-778, May.
    8. Srabana Gupta, 2002. "Competition and collusion in a government procurement auction market," Atlantic Economic Journal, International Atlantic Economic Society, vol. 30(1), pages 13-25, March.
    9. Michael H. Rothkopf & Aleksandar Peke\v{c} & Ronald M. Harstad, 1998. "Computationally Manageable Combinational Auctions," Management Science, INFORMS, vol. 44(8), pages 1131-1147, August.
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