Abstract: We model investors’ allocation of order flow across over-the-counter dealers jointly with dealers’ acquisition of expertise, used to take advantage of investors across transactions. Whereas investors have incentives to flock toward the dealer expected to have the lowest level of expertise, a dealer that expects to attract many investors has incentives to acquire additional expertise. In contrast with standard models, we allow dealers’ expertise to exhibit limited spillovers
across transactions. As a result, investors prefer dealers that intermediate large volumes of
transactions and, in equilibrium, order flow may concentrate around the dealer making the
largest investments in expertise.
Dan Bernhardt, University of Illinois-Urbana-Champaign
Tingjun Liu, University of Hong Kong
Xiaorong Ma, University of Macau
Abstract: Implementation of optimal or near-optimal mechanisms with heterogeneous bidders is informationally demanding for auctioneers. Such mechanisms invariably employ discriminatory winning and payment rules---creating legal and moral hazard concerns. We show how sellers can exploit information feedback from capital markets, linking auction outcomes to post-auction market prices to obtain high revenues even when arbitrarily heterogeneous bidders pay with different securities. Steeper securities always generate greater revenues, and we identify conditions where near-optimal revenues obtain. Crucially, the market collects information and responds to details ex post when pricing the winner, so the selling mechanism can be nondiscriminatory and detail-free ex ante.
John Chi-Fong Kuong, Chinese University of Hong Kong
Abstract: This paper endogenizes the value and persistence of trading relationships in non-anonymous over-the-counter (OTC) markets. In the model, a liquidity-driven client initially chooses to trade with a number of dealers. She would like to commit to trading with these dealers again in the future in exchange for better pricing or services today. When non-anonymity allows dealers to infer the client’s trading motives (i.e., whether driven by liquidity needs or private information) from past trading, the client can credibly commit to these valuable relationships by trading information-sensitive assets, because deviating to trade with alternative dealers would incur an adverse selection discount. This mechanism yields several unconventional results: liquidity-driven, uninformed clients could benefit from trading 1) risky assets that are subject to adverse selection, 2) with a costly technology, 3) in an opaque environment. These results help rationalize why investors trade risky, complex, and illiquid assets. The paper also derives novel testable predictions about how the number of relationships depends on client and asset characteristics.
Discussant: Chaojun Wang, University of Pennsylvania
