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Financial Expertise as an Arms Race

Vincent Glode
University of Pennsylvania - The Wharton School

Richard C. Green
Carnegie Mellon University - David A. Tepper School of Business

Richard Lowery
University of Texas-Austin


February 4, 2010


Abstract:     
We propose a model in which firms involved in trading securities overinvest in financial expertise. Intermediaries or traders in the model meet and bargain over a financial asset. As in the bargaining model in Dang (2008), counterparties endogenously decide whether to acquire information, and improve their bargaining positions, even though the information creates adverse selection. We add to this setting the concept of "financial expertise" as resources invested to lower the cost of later acquiring information about the value of the asset being traded. These investments are made before the parties know about their role in the bargaining game, as proposer or responder, buyer or seller. A prisoner's dilemma arises because investments to lower information acquisition costs improve bargaining outcomes given the other party's information costs, even though the information has no social benefit. These investments lead to breakdowns in trade, or liquidity crises, in response to random but infrequent increases in asset volatility.

JEL Classifications: G20, D82, C78

Working Paper Series

Date posted: August 30, 2009 ; Last revised: February 06, 2010

Suggested Citation

Glode, Vincent, Green, Richard C. and Lowery, Richard, Financial Expertise as an Arms Race (February 4, 2010). Available at SSRN: http://ssrn.com/abstract=1463077


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Contact Information

Vincent Glode (Contact Author)
University of Pennsylvania - The Wharton School ( email )
3641 Locust Walk
Philadelphia, PA 19104-6365
United States
Richard C. Green
Carnegie Mellon University - David A. Tepper School of Business ( email )
315B Schenley Park
Pittsburgh, PA 15213-3890
United States
412-268-2302 (Phone)
412-268-7064 (Fax)
Richard Lowery
University of Texas-Austin ( email )
Red McCombs School of Business
Austin, TX 78712
United States
Feedback to SSRN (Beta)


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