Are dealers' search efforts endogeneous in decentralized markets? How do dealers' search efforts affect market efficiency? We propose a model with dealers choosing idiosyncratic search intensities, and estimate the model using transaction data on U.S. corporate bonds. We find that:  with dealers ranked by their private valuations for a bond, the middle-type dealer chooses the highest search intensity, and she reallocates bond positions from lower-type dealers to higher-type dealers;  the estimated model predicts that the search costs and bond misallocation in current OTC markets generate 13.7% welfare loss relative to a counterfactual frictionless market.
This paper analyzes OTC market participants' endogeneous search intensity in competitive equilibrium and social optimal cases. We develop a random search-and-match model where agents (market participants) are allowed to choose and adjust their search intensities based on two idiosyncratic states: asset position and liquidity need. We find that:  in competitive equilibria with different market parameters, agents can switch between the core and periphery on the trading network.  it is the social optimal case that there is no intermediation, in the sense that no agent searches at positive speeds on both the buy and sell sides of the market. In competitive equilibrium, there always exist some agents over-searching and some other agents under-searching. We also discuss related policy implications.
Which corporate bond's yield is more exposed to search frictions? Is the exposure correlated with dealers' intermediation? We propose a measure of bond's misallocation among dealers and show its correlation with bond's liquidity risk which is attributed to search frictions. This measure is defined as the cross-sectional covariance of dealers' private valuations for a bond and their corresponding inventory positions. Using a transaction-level dataset on U.S. corporate bonds, we verify: a higher misallocation is associated with a higher magnitude of liquidity risk. A search-match model with dealers' endogeneous search efforts offers an explanation on this correlation.
Work in progress
Financial Intermediation and Risk Sharing among Heterogeneous Investors
This paper investigates how financial intermediary prices Arrow-Debru securities in an endowment economy with both centralized stock market and decentralized Arrow-Debru securities market. In this economy, investors are heterogeneous in both risk aversion and endowments of a risky stock which can be traded in the centralized stock market. To faciliate risk sharing, investors can also trade Arrow-Debru securities directly with financial intermediary, but not with each other. This paper gives explicit solutions to financial intermediary's optimal quoting strategy for Arrow-Debru securities and her maximized utility under different constraints. This paper mainly establishes:  financial intermediary's main sources of profit are monopoly power to quote bid and ask for Arrow-Debru securities due to entry barrier to the intermediary sector and also the characteristic of being risk neutral;  investors' total gain from risk sharing through Arrow-Debru securities market is larger when there is larger share of risky stock initially endowed to the more risk averse investor and/or the gap of stock dividend between good and bad states is larger. Finally, this paper characterizes the feedback effect of Arrow-Debru securities' prices in decentralized market on the stock price in centralized market.
Adverse Selection in Venture Capital Market
(with Mengbo Zhang)
This paper investigates the relationship between adverse selection and funding resource allocation in venture capital market. By using micro-level data of all rounds of venture capital funding in the U.S. between 1980 and 2017, we find that: the start-up firms that get first-round funding at an older age are more likely to raise a larger amount of money, but have worse ex-post performances by different measures. This result implies that the adverse selection problem is significant for a start-up firm's first-round funding in the U.S. venture capital market. To explain the empirical facts, we build a competitive search model in which VC investors can enter a series of submarkets to screen the unobserved qualities of start-up firms. The model predicts that start-up firms of high quality prefer the matching speed with VC investors to the amount of funds raised, which makes high-quality firms more likely complete first-round funding at their earlier ages than low-quality firms.