IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v27y2011i4p1259-1270.html
   My bibliography  Save this article

Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting

Author

Listed:
  • Dass, Mayukh
  • Jank, Wolfgang
  • Shmueli, Galit

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.

Suggested Citation

  • Dass, Mayukh & Jank, Wolfgang & Shmueli, Galit, 2011. "Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1259-1270, October.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1259-1270
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207011000264
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Alvin E. Roth & Axel Ockenfels, 2002. "Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet," American Economic Review, American Economic Association, vol. 92(4), pages 1093-1103, September.
    3. David Lucking‐Reiley & Doug Bryan & Naghi Prasad & Daniel Reeves, 2007. "Pennies From Ebay: The Determinants Of Price In Online Auctions," Journal of Industrial Economics, Wiley Blackwell, vol. 55(2), pages 223-233, June.
    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. Michael H. Rothkopf & Aleksandar Pekev{c} & Ronald M. Harstad, 1998. "Computationally Manageable Combinational Auctions," Management Science, INFORMS, vol. 44(8), pages 1131-1147, August.
    6. Olivier Chanel & Louis-André Gérard-Varet & Victor Ginsburgh, 1996. "The relevance of hedonic price indices," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 20(1), pages 1-24, March.
    7. Anthony M. Kwasnica & Katerina Sherstyuk, 2007. "Collusion and Equilibrium Selection in Auctions," Economic Journal, Royal Economic Society, vol. 117(516), pages 120-145, January.
    8. 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.
    9. Milgrom, Paul, 1998. "Game theory and the spectrum auctions," European Economic Review, Elsevier, vol. 42(3-5), pages 771-778, May.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dass, Mayukh & Reddy, Srinivas K. & Iacobucci, Dawn, 2014. "A Network Bidder Behavior Model in Online Auctions: A Case of Fine Art Auctions," Journal of Retailing, Elsevier, vol. 90(4), pages 445-462.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dass, Mayukh & Reddy, Srinivas K. & Iacobucci, Dawn, 2014. "A Network Bidder Behavior Model in Online Auctions: A Case of Fine Art Auctions," Journal of Retailing, Elsevier, vol. 90(4), pages 445-462.
    2. Bocart, Fabian Y. R. P. & Hafner, Christian M., 2012. "Volatility of price indices for heterogeneous goods," SFB 649 Discussion Papers 2012-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Richard Agnello & Xiaowen Xu, 2006. "Art Prices and Race: Paintings by African American Artists and Their White Contemporaries," Working Papers 06-06, University of Delaware, Department of Economics.
    4. Belma Öztürkkal & Aslı Togan-Eğrican, 2020. "Art investment: hedging or safe haven through financial crises," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(3), pages 481-529, September.
    5. Ernan Haruvy & Peter Popkowski Leszczyc & Octavian Carare & James Cox & Eric Greenleaf & Wolfgang Jank & Sandy Jap & Young-Hoon Park & Michael Rothkopf, 2008. "Competition between auctions," Marketing Letters, Springer, vol. 19(3), pages 431-448, December.
    6. Bocart, Fabian Y. R. P. & Hafner, Christian M., 2012. "Volatility of price indices for heterogeneous goods," SFB 649 Discussion Papers 2012-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Marinelli, Nicoletta & Palomba, Giulio, 2011. "A model for pricing Italian Contemporary Art paintings at auction," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 212-224, May.
    8. Marie BLUM & Régis BLAZY, 2021. "The three stages of an auction: how do the bid dynamics influence auction prices? Evidence from live art auctions," Working Papers of LaRGE Research Center 2021-10, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    9. Seçkin Aylin & Atukeren Erdal, 2012. "A Heckit Model of Sales Dynamics in Turkish Art Auctions: 2005-2008," Review of Middle East Economics and Finance, De Gruyter, vol. 7(3), pages 1-32, May.
    10. Kräussl, Roman & Elsland, Niels van, 2008. "Constructing the true art market index: A novel 2-step hedonic approach and its application to the German art market," CFS Working Paper Series 2008/11, Center for Financial Studies (CFS).
    11. Federico Etro & Laura Pagani, 2013. "The market for paintings in the Venetian Republic from Renaissance to Rococò," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(4), pages 391-415, November.
    12. Patrick Georges & Aylin Seçkin, 2013. "Black notes and white noise: a hedonic approach to auction prices of classical music manuscripts," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(1), pages 33-60, February.
    13. Ventura Charlin & Arturo Cifuentes, 2013. "A new financial metric for the art market," Papers 1309.6929, arXiv.org, revised Jul 2015.
    14. Luc Renneboog & Christophe Spaenjers, 2013. "Buying Beauty: On Prices and Returns in the Art Market," Management Science, INFORMS, vol. 59(1), pages 36-53, February.
    15. Agnello, Richard J., 2016. "Do U.S. paintings follow the CAPM? Findings disaggregated by subject, artist, and value of the work," Research in Economics, Elsevier, vol. 70(3), pages 403-411.
    16. Daiva Jurevičienė & Božena Kostecka, 2014. "Peculiarities of selection of investment artworks," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2014(5), pages 71-88.
    17. Kraeussl, Roman & Logher, Robin, 2010. "Emerging art markets," Emerging Markets Review, Elsevier, vol. 11(4), pages 301-318, December.
    18. Horky, Florian & Rachel, Carolina & Fidrmuc, Jarko, 2022. "Price determinants of non-fungible tokens in the digital art market," Finance Research Letters, Elsevier, vol. 48(C).
    19. Arthur Korteweg & Roman Kräussl & Patrick Verwijmeren, 2016. "Does it Pay to Invest in Art? A Selection-Corrected Returns Perspective," The Review of Financial Studies, Society for Financial Studies, vol. 29(4), pages 1007-1038.
    20. Chanont Banternghansa & Kathryn Graddy, 2011. "The impact of the Droit de Suite in the UK: an empirical analysis," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 35(2), pages 81-100, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1259-1270. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.