Hello! I'm Jian Sun (孙健)
I'm an Assistant Professor of Finance at the Lee Kong Chian School of Business, Singapore Management University.
I received my Ph.D. in Finance from MIT Sloan School of Management in May 2022.
Learning from Manipulable Signals, R&R at American Economic Review
with Mehmet Ekmekci, Leandro Gorno, Lucas Maestri and Dong Wei
Abstract: We study a dynamic stopping game between a principal and an agent. The agent is privately informed about his type. The principal learns about the agent's type from a noisy performance measure, which can be manipulated by the agent via a costly and hidden action. We fully characterize the unique Markov equilibrium of this game. We find that terminations/market crashes are often preceded by a spike in (expected) performance. Our model also predicts that, due to endogenous signal manipulation, too much transparency can inhibit learning. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.
From Market Making to Matchmaking: Does Bank Regulation Harm Market Liquidity? Online Appendix R&R at Review of Financial Studies
with Gideon Saar, Ron Yang and Haoxiang Zhu
Abstract: Post-crisis bank regulations raised market-making costs for bank-affiliated dealers. We show that this can, somewhat surprisingly, improve overall investor welfare and reduce average transaction costs despite the increased cost of immediacy. Bank dealers in OTC markets optimize between two parallel trading mechanisms: market making and matchmaking. Bank regulations that increase market-making costs change the market structure by intensifying competitive pressure from non-bank dealers and incentivizing bank dealers to shift their business activities toward matchmaking. Thus, post-crisis bank regulations have the (unintended) benefit of replacing costly bank balance sheets with a more efficient form of financial intermediation.
Abstract: I study the optimal algorithmic disclosure in a lending market where lenders use a predictive algorithm to mitigate adverse selection. The predictive algorithm is unobservable to borrowers and uses a manipulable borrower feature as input. A regulator maximizes market efficiency by disclosing information about the statistical properties of variables embedded in the predictive algorithm to borrowers. Under the optimal disclosure policy, the posterior belief consists of two disjoint regions in which the borrower feature is more relevant and less relevant in predicting borrower quality, respectively. The optimal disclosure policy differentiates posterior lending market equilibria by the equilibrium data manipulation levels. Equilibria with more data manipulation hurt market efficiency, but also discourage lenders’ use of the borrower feature. Equilibria with less data manipulation benefit from that and generate more efficient market outcomes. Unconditionally, the borrower feature is used less intensively under optimal disclosure. This information design problem can be reduced to a one-dimensional maximization problem by imposing a mild distributional assumption on manipulation cost. As an extension, I also discuss the joint design of algorithmic disclosure and costly verification.
Abstract: We study SPACs (Special Purpose Acquisition Companies) in a finite-horizon continuous-time delegated investment model. Due to the misalignment in incentives, the sponsor has an increasing incentive to propose unprofitable deals to the investor as the SPAC approaches its deadline. As a response, the investor redeems shares more aggressively over time. The investor's current redemption reduces the sponsor's expected payoff from proposing unprofitable deals, but future redemption reduces his expected payoff from waiting. We discuss the welfare implications of SPAC designs related to investors' redemption: 1) prohibiting the investor from redeeming shares in late periods can be a Pareto improvement; 2) coupling the investor's deal rejection with redemption benefits the sponsor; and 3) the participation of investors with behavioral biases can be a Pareto improvement.
Abstract: We study a continuous-time model of long-run employment relationship with fixed wage and at-will firing; that is, termination of the relationship is non-contractible. Depending on his type, the worker either always works hard, or can freely choose his effort level. The firm does not know the worker’s type and the monitoring is imperfect. We show that, in the unique Markov equilibrium, as the worker’s reputation worsens, his job becomes less secure and the strategic worker works harder. We further demonstrate that the relationship between average productivity and job insecurity is U-shaped, which is consistent with typical findings in the organizational psychology literature.