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The Efficiency Effects of Information Quality in Failed-Bank Auctions

时间:2019-01-14

Finance Seminar2019-04)

Topic: The Efficiency Effects of Information Quality in Failed-Bank Auctions

Speaker: Siyu (Eric) Lu, Carnegie Mellon University

Time: Monday, 14 January, 13:30-15:00

Location: Room 217, Guanghua Building 2

Abstract:

In the aftermath of the most recent financial crisis, the Federal Deposit Insurance Corporation auctioned off over 400 failed banks with around $650 billion in book assets to more than 200 acquirers. The need for rapid resolution of these banks significantly limits bidders’ due diligence time, introducing noise in the signals bidders receive about targets’ value, thus leading to misallocation where acquirers do not have the highest valuation of targets. Estimating a structural auction model using bidding data from the recent crisis, I find a 1% reduction in bidders’ signal noise increases the expected winner’s valuation by up to 1.5% due to reduced misallocation. The expected auction revenue, however, rises by only 0.5%, indicating the revenue-motivated incentive to improve information quality is vastly weaker than the valuation-motivated one. Moreover, better information quality strongly complements other policies, including increasing participation and using of Loss Share Agreements, which protects acquirers against future loss on acquired assets. The benefit from either of these policies for the expected winner’s valuation is larger if noise is eliminated. Exploiting this complementarity promotes more efficient auction outcomes.

Introduction:

Siyu (Eric) Lu is a Ph.D. candidate in Financial Economics at the Tepper School of Business at Carnegie Mellon University. He holds a B.A. in Econ&Fin (Minor in Statistics) from The University of Hong Kong, a. M.Sc. in Business Administration (Finance) from University of British Columbia, and a M.Sc. in Finance from Carnegie Mellon University. His research interests include Banking, Financial Regulation, Empirical Corporate Finance, Structural Models, Machine Learning.

//sites.google.com/andrew.cmu.edu/siyuericlu/

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