Strategic Jump Bidding in English Auctions

Avery, Christopher

doi:10.1111/1467-937x.00041

Cited by 175 publications

(130 citation statements)

References 22 publications

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“…and β f,1 , for every parametrization of the simulated data, and by overall higher coefficients for the parametrization presented in Table CII 4 See Chowdhry and Nanda (1993), Bulow, Huang and Klemperer (1999), and Povel and Singh (2010), who model takeover contests as button auctions, and Fishman (1988Fishman ( , 1989, Avery (1998), and Daniel and Hirshleifer (1998) for models, in which jump bids occur in equilibrium.…”

Section: B Our Model Of Takeover Auctions Versus Linear Regression Omentioning

confidence: 99%

Strategic and Financial Bidders in Takeover Auctions

Gorbenko

Malenko

2010

SSRN Journal

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Using data on auctions of companies, we estimate valuations (maximum willingness to pay) of strategic and financial bidders from their bids. We find that a typical target is valued higher by strategic bidders. However, 22.4% of targets in our sample are valued higher by financial bidders. These are mature poorly-performing companies. We also find that (i) valuations of different strategic bidders are more dispersed, (ii) valuations of financial bidders are correlated with aggregate economic conditions. Our results suggest that different targets appeal to different types of bidders, rather than that strategic bidders always value targets more because of synergies.Keywords: mergers and acquisitions, strategic bidders, financial bidders, takeover auctions JEL Classification Numbers: D44, G32, G34 * Gorbenko is with London Business School. Malenko is with MIT Sloan School of Management. We are grateful to two anonymous referees, the anonymous Associate Editor, Jules van Binsbergen, Jonathan Cohn, Peter DeMarzo, Darrell Duffie, Campbell Harvey (the Editor), Han Hong, Dirk Jenter, Ron Kaniel, Arthur Korteweg, Ilan Kremer, John Lazarev, Nadya Malenko, Ian Martin, Gregor Matvos (WashU discussant), Michael Ostrovsky, Francisco Perez-Gonzalez, Matthew Rhodes-Kropf (WFA discussant), Ilya Strebulaev, Jessica Yang, Jeffrey Zwiebel, seminar participants at Boston University, Higher School of Economics, London Business School, London School of Economics, Norwegian School of Economics, University of Connecticut, University of Rochester, University of Utah, and participants at the 2010 Western Finance Association Meeting in Victoria and the 8th Annual Corporate Finance Conference at Washington University in St. Louis for helpful comments. 1Electronic copy available at: http://ssrn.com/abstract=1559481The market for corporate control is one of the largest corporate markets. In 2007 alone, the value of M&A transactions worldwide was a staggering $4.8 trillion. While some takeovers proceed as negotiations of a target with a single acquirer, many takeovers face competition among several bidders.1 The set of bidders is comprised of two groups: strategic and financial.Strategic bidders are usually companies in a related type of business, such as competitors, suppliers, or customers. They tend to look for targets that offer long-term operational synergies and integrate them into their own business. In contrast, financial bidders, typically private equity firms, look for undervalued targets with a potential to generate high cash flow, often after a reorganization. After the acquisition, a financial bidder treats the target as a part of its financial portfolio and sells it once exit opportunities become sufficiently appealing.Despite their recognized importance, 2 the differences between strategic and financial bidders remain largely unexplored. A common view is that strategic bidders are systematically willing to pay more than financial bidders. For example, as Mark E. Thompson and Michael J. O'Brien, practitioners in the privat...

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Section: B Our Model Of Takeover Auctions Versus Linear Regression Omentioning

confidence: 99%

Strategic and Financial Bidders in Takeover Auctions

Gorbenko

Malenko

2010

SSRN Journal

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“…In order to capture this phenomenon for the data we used, we consider di erent time horizons, denoted by S, before the end of the auction: 3 days, 6 hours, 10 minutes and 1 minute. For each separate S, w e partition the time interval 0 S ] i n to ten subintervals a 1 Figure 2 suggests that the distribution of the timing of these bids is identical for the times S equal to 3 days, 6 hours and 10 minutes. For S equals 1 minute, it is still the same for all but the rst interval a 1 , that is, within 6 seconds, before the end of the auction.…”

Section: Estimation Of Parametersmentioning

confidence: 99%

“…However, for the nal subinterval for S=1 minute, submitted bids are accepted bids with probability p. W e assume that the distribution of submitted bids over a particular interval S is the same for all intervals. For S = 1 minute, the observed fraction of bids arriving in interval a 1 is P(accepted in a 1 jaccepted) = P(accepted in a 1 ) P(accepted) = P(accepted in a 1 jsubmitted in a 1 )P(submitted in a 1 ) 10 P i=1 P(accepted in a i jsubmitted in a i )P(submitted in a i ) : (18) P(accepted in a i jsubmitted in a i ) = p for i = 1 and equals 1, otherwise. .…”

Section: Estimation Of Parametersmentioning

confidence: 99%

Optimal bidding in online auctions

Bertsimas¹,

Hawkins²,

Perakis³

2009

J Revenue Pricing Manag

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On-line auctions are arguably one of the most important and distinctly new applications of the Internet. The predominant p l a yer in on-line auctions, eBay, has over 42 million users, and it was the host of over $9.3 billion worth of goods sold in the year 2001. Using methods from approximate dynamic programming and integer programming, we design algorithms for optimally bidding for a single item in an on-line auction, and in simultaneous or overlapping multiple on-line auctions. We report computational evidence using data from eBay's web site from 1772 completed auctions for personal digital assistants and from 4208 completed auctions for stamp collections that shows that (a) the optimal dynamic policy outperforms simple but widely used static heuristic rules for a single auction, and (b) a new approach f o r t h e m ultiple auctions problem that uses the value functions of single auctions found by dynamic programming in an integer programming framework produces high quality solutions fast and reliably.

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“…3) Signaling and threat: As pointing out by Avery (1998), under affiliated values paradigm, jumps may signal to and coordinate with opponents about an agent's valuation. In addition, jumps may signal that an agent is a strong candidate and will bid aggressively to win the object.…”

Section: Literature Surveymentioning

confidence: 99%

The Role of the Bidding Process in Price Determination: Jump Bidding in Sequential English Auctions

Raviv

2004

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract. I study the sequence of bidding in an open-outcry English auction to examine how the strategic bidding process affects price determination. I do this by studying the anomalous nature of "jump bidding" in data I have collected from a series of public auctions of used cars in New Jersey. Jump bidding occur when a new offer is submitted that is above the old offer plus the minimum bid increment permitted. I find that jump biddings are an empirical regularity in all items sold. The jumps are a function of the presale estimate of the item's price but are not affected by the selling order. I suggest a way to use the Jump Biddings to determine whether an open-outcry auction is best interpreted with models that assume private-or common-item valuations, and conclude that these auctions are consistent with the common values interpretation. Terms of use: Documents in EconStor mayJEL Classification: D44

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Strategic Jump Bidding in English Auctions

Cited by 175 publications

References 22 publications

Strategic and Financial Bidders in Takeover Auctions

Strategic and Financial Bidders in Takeover Auctions

Optimal bidding in online auctions

The Role of the Bidding Process in Price Determination: Jump Bidding in Sequential English Auctions

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