IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1506.02020.html
   My bibliography  Save this paper

Portfolio Allocation for Sellers in Online Advertising

Author

Listed:
  • Ragavendran Gopalakrishnan
  • Eric Bax
  • Krishna Prasad Chitrapura
  • Sachin Garg

Abstract

In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would maximize revenue. The variance of revenue can be decomposed into two components -- variance due to `uncertainty' because the true response rate is unknown, and variance due to `randomness' because realized response statistics fluctuate around the true response rate. Over a sequence of many ad calls, the variance due to randomness nearly vanishes due to the law of large numbers. However, the variance due to uncertainty doesn't diminish. We introduce a technique for ad selection that augments existing estimation and explore-exploit methods. The technique uses methods from portfolio optimization to produce a distribution over ads rather than selecting the single ad that maximizes estimated expected revenue. Over a sequence of similar ad calls, ads are selected according to the distribution. This approach decreases the effects of uncertainty and increases revenue.

Suggested Citation

  • Ragavendran Gopalakrishnan & Eric Bax & Krishna Prasad Chitrapura & Sachin Garg, 2015. "Portfolio Allocation for Sellers in Online Advertising," Papers 1506.02020, arXiv.org.
  • Handle: RePEc:arx:papers:1506.02020
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1506.02020
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yakov Ben-Haim, 2005. "Value-at-risk with info-gap uncertainty," Journal of Risk Finance, Emerald Group Publishing, vol. 6(5), pages 388-403, November.
    2. Krishna, Vijay, 2009. "Auction Theory," Elsevier Monographs, Elsevier, edition 2, number 9780123745071.
    3. Edward Clarke, 1971. "Multipart pricing of public goods," Public Choice, Springer, vol. 11(1), pages 17-33, September.
    4. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    5. Riley, John G & Samuelson, William F, 1981. "Optimal Auctions," American Economic Review, American Economic Association, vol. 71(3), pages 381-392, June.
    6. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    7. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    8. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    9. D. Goldfarb & G. Iyengar, 2003. "Robust Portfolio Selection Problems," Mathematics of Operations Research, INFORMS, vol. 28(1), pages 1-38, February.
    10. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 25(2), pages 65-86.
    11. Xin Chen & Melvyn Sim & Peng Sun, 2007. "A Robust Optimization Perspective on Stochastic Programming," Operations Research, INFORMS, vol. 55(6), pages 1058-1071, December.
    12. Maskin, Eric S & Riley, John G, 1984. "Optimal Auctions with Risk Averse Buyers," Econometrica, Econometric Society, vol. 52(6), pages 1473-1518, November.
    13. Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 279-292, September.
    14. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    15. Vasicek, Oldrich A, 1973. "A Note on Using Cross-Sectional Information in Bayesian Estimation of Security Betas," Journal of Finance, American Finance Association, vol. 28(5), pages 1233-1239, December.
    16. Bax, Eric & Kuratti, Anand & Mcafee, Preston & Romero, Julian, 2012. "Comparing predicted prices in auctions for online advertising," International Journal of Industrial Organization, Elsevier, vol. 30(1), pages 80-88.
    17. Hal R. Varian, 2009. "Online Ad Auctions," American Economic Review, American Economic Association, vol. 99(2), pages 430-434, May.
    18. Groves, Theodore, 1973. "Incentives in Teams," Econometrica, Econometric Society, vol. 41(4), pages 617-631, July.
    Full references (including those not matched with items on IDEAS)

    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. James Li & Eric Bax & Nilanjan Roy & Andrea Leistra, 2015. "VCG Payments for Portfolio Allocations in Online Advertising," Papers 1506.02013, arXiv.org.
    2. Michael Ostrovsky & Michael Schwarz, 2023. "Reserve Prices in Internet Advertising Auctions: A Field Experiment," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3352-3376.
    3. Committee, Nobel Prize, 2020. "Improvements to auction theory and inventions of new auction formats," Nobel Prize in Economics documents 2020-2, Nobel Prize Committee.
    4. Eric Maskin, 2004. "The Unity of Auction Theory: Paul Milgrom's Masterclass," Economics Working Papers 0044, Institute for Advanced Study, School of Social Science.
    5. Jehiel, Philippe & Meyer-ter-Vehn, Moritz & Moldovanu, Benny, 2007. "Mixed bundling auctions," Journal of Economic Theory, Elsevier, vol. 134(1), pages 494-512, May.
    6. Kaplan, Todd R. & Zamir, Shmuel, 2015. "Advances in Auctions," Handbook of Game Theory with Economic Applications,, Elsevier.
    7. Lu, Jingfeng & Ye, Lixin, 2013. "Efficient and optimal mechanisms with private information acquisition costs," Journal of Economic Theory, Elsevier, vol. 148(1), pages 393-408.
    8. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    9. Gustavo Vulcano & Garrett van Ryzin & Costis Maglaras, 2002. "Optimal Dynamic Auctions for Revenue Management," Management Science, INFORMS, vol. 48(11), pages 1388-1407, November.
    10. Bergemann, Dirk & Pavan, Alessandro, 2015. "Introduction to Symposium on Dynamic Contracts and Mechanism Design," Journal of Economic Theory, Elsevier, vol. 159(PB), pages 679-701.
    11. Lorentziadis, Panos L., 2016. "Optimal bidding in auctions from a game theory perspective," European Journal of Operational Research, Elsevier, vol. 248(2), pages 347-371.
    12. Peter M. DeMarzo & Ilan Kremer & Andrzej Skrzypacz, 2005. "Bidding with Securities: Auctions and Security Design," American Economic Review, American Economic Association, vol. 95(4), pages 936-959, September.
    13. Corchón, Luis C., 2008. "The theory of implementation : what did we learn?," UC3M Working papers. Economics we081207, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Axel Ockenfels & David Reiley & Abdolkarim Sadrieh, 2006. "Online Auctions," NBER Working Papers 12785, National Bureau of Economic Research, Inc.
    15. Yonghong Long, 2009. "Bidders¡¯ Risk Preferences in Discriminative Auctions," Annals of Economics and Finance, Society for AEF, vol. 10(1), pages 215-223, May.
    16. Bierbrauer, Felix & Netzer, Nick, 2016. "Mechanism design and intentions," Journal of Economic Theory, Elsevier, vol. 163(C), pages 557-603.
    17. Ivanova-Stenzel, Radosveta & Salmon, Timothy C., 2008. "Revenue equivalence revisited," Games and Economic Behavior, Elsevier, vol. 64(1), pages 171-192, September.
    18. Ødegaard, Fredrik & Anderson, Chris K., 2014. "All-pay auctions with pre- and post-bidding options," European Journal of Operational Research, Elsevier, vol. 239(2), pages 579-592.
    19. Jeong, Seungwon (Eugene) & Lee, Joosung, 2024. "The groupwise-pivotal referral auction: Core-selecting referral strategy-proof mechanism," Games and Economic Behavior, Elsevier, vol. 143(C), pages 191-203.
    20. Figueroa, Nicolás & Skreta, Vasiliki, 2012. "Asymmetric partnerships," Economics Letters, Elsevier, vol. 115(2), pages 268-271.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:1506.02020. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.