Dynamic adverse selection and asset sales. [PDF](Job Market Paper) This paper presents a model of dynamic trading in OTC markets. Investors meet over-the-counter to trade heterogeneous assets under asymmetric information. The cream-skimming effect endogenously emerges due to the heterogeneous sophistication among buyers, where the low-type seller strategically forgoes trading opportunities with gains from trade in order to take advantage of the unsophisticated buyers in the market. In equilibrium, the price and volume depend on not only about initial prior about the asset quality but also about future market condition. The implications and predictions on initial public offerings and real estate market are discussed in the paper.
Secret Scouting (with Xuelin Li) [PDF] (MFA 2020 Best Ph.D. Paper Award) VCs prefer secrecy when searching for targets. As a result, only the investments inviable startups are disclosed, but the failed ones are discarded silently. We extend the standard preemption game to explain the efficiency loss and the individual rationale of doing so. We show that secrecy creates pessimism. Compared to the fully disclosing case, VCs will stop hunting for startups too early in an initially promising industry. This could happen even if no technology failures are observed in realization. However, hiding failures becomes a dominant strategy when the return of the VC industry is right-skewed. VCs use secret scouting to make the competitors believe that the industry is a dead end and reduce the preemption threats.
Working in progress
Disclosure and Crowdfunding Crowdfunding involves the sequential interaction and observational learning among investors. I build on the classical rational herding model with multiple actions to discuss the interaction among investors and how does the issuer affect this interaction by disclosing different precision information. In the model, each investor arrives sequentially, observing private signals and decide whether to contribute to the project how much to contribute. The issuer chooses the precision of signal to maximize the probability of financing. Depending on the precision of signals, there are up to three herding regions. In particular, when the signal precision is very low, there are three herding regions, in the low herding region, all the investors are not willing to contribute, while in the intermediate herding region, all the investors are contributing the minimum amount; for the high herding region, all the investors are contributing the maximum amount regardless of their private signals. The financing probability is not monotone in transparency. When the public belief is very high, the issuer is willing to disclose less informative signals so that all the investors are willing to contribute the maximum amount. When the public belief is in the middle, and the minimum contribution from the investors are not sufficient to initiate the project, then the issuer prefers to high transparency to eliminate the intermediate herding, which could improve the financing probability.