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Sequencing strategies in large, competitive, ascending price automobile auctions: An experimental examination

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  • Grether, David M.
  • Plott, Charles R.

Abstract

This paper reports on a large-scale field experiment testing strategies available to a seller participating in simultaneous competitive sequential ascending price automobile auctions. Every other week, the seller offered approximately 100 vehicles for sale in an auction environment in which several competing sellers offered on the order of 3000 vehicles. The experiment tested various sequences in which the seller could offer the vehicles, such as high values first or low values first. Surprisingly, and contrary to intuition drawn from the theory of single item and single seller auctions, the worst performing sequence from those tested is for the seller to order vehicles from highest to lowest values. The best sequence is to group the vehicles by type and offer the low valued vehicles first and then move to offer the higher valued vehicles.
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  • Grether, David M. & Plott, Charles R., "undated". "Sequencing strategies in large, competitive, ascending price automobile auctions: An experimental examination," Working Papers 1253, California Institute of Technology, Division of the Humanities and Social Sciences.
    • Handle: RePEc:clt:sswopa:1253
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    References listed on IDEAS

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    Cited by:

    1. David Grether & David Porter & Matthew Shum, 2011. "Intimidation or Impatience? Jump Bidding in On-line Ascending Automobile Auctions," Working Papers 11-07, Chapman University, Economic Science Institute.
    2. Shiang-Min Meng & Li-Jen Liu & Mikhail Kuritsyn & Vladislav Pechnikov, 2019. "Price Determinants on Used Car Auction in Taiwan," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 9(1), pages 48-58, January.
    3. Anthony M. Kwasnica & Katerina Sherstyuk, 2013. "Multiunit Auctions," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 461-490, July.
    4. Cem Ozturk, O. & Karabatı, Selçuk, 2017. "A decision support framework for evaluating revenue performance in sequential purchase contexts," European Journal of Operational Research, Elsevier, vol. 263(3), pages 922-934.
    5. Verwer, S. & Zhang, Y., 2011. "Revenue Prediction in Budget-constrained Sequential Auctions with Complementarities," ERIM Report Series Research in Management ERS-2011-020-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Eric Overby & Karthik Kannan, 2015. "How Reduced Search Costs and the Distribution of Bidder Participation Affect Auction Prices," Management Science, INFORMS, vol. 61(6), pages 1398-1420, June.
    7. Ma, Gang & Zheng, Junjun & Wei, Ju & Wang, Shilei & Han, Yefan, 2021. "Robust optimization strategies for seller based on uncertainty sets in context of sequential auction," Applied Mathematics and Computation, Elsevier, vol. 390(C).
    8. David Grether & David Porter & Matthew Shum, 2015. "Cyber-Shilling in Automobile Auctions: Evidence from a Field Experiment," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 85-103, August.

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