Yu, Fangyuan2021-09-242021-09-242021-06https://hdl.handle.net/11299/224642University of Minnesota Ph.D. dissertation. June 2021. Major: Business Administration. Advisors: Andrew Winton, Raj Singh. 1 computer file (PDF); vii, 95 pages.In real world markets with asymmtric information, making decisions take time. In the real estatemarket, it takes time for the seller to find a potential buyer in the market and potential buyer can learn through the listing time; in the Venture Capital industry, VCs also spend a lot of time learning about the potential startup before make their financing decision. In these environments, timing decision features dynamic learning, and it can reveal information about the underlying house quality or the profitability of the startup. In my dissertation, I explicitly take the dynamic learning into consideration, and investigate how does the dynamic learning over time affect the agents’ behavior. In the first essay ”Dynamic Adverse Selection and Asset Sales”, I present a dynamic adverse selection model in the decentralized market with bilateral trading. Investors meet in decentralized market to trade heterogeneous assets under asymmetric information. The cream-skimming effect 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 investors in the market. When the market is pessimistic, time to sale increases in asset quality, heterogeneous sophistication improves market liquidity; when the market is optimistic, time to sale decreases in asset quality, cream-skimming incentive endogenously occurs, which reduces the trading efficiency. The implications and predictions on initial public offerings, real estate market are discussed in the paper. In the second essay ”Secret Scouting”, coathored with Xuelin Li, we consider the dynamic learning model in Venture Capital industry when there is asymmetric information about the profiability of the startups among VCs. We find that VCs prefer secrecy when searching for targets. As a result, only the investments in viable 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.enAsymmetric InformationDynamic LearningVenture CapitalThesis or Dissertation