Photo by my wife Yang Yi in 2023
Xuyuanda Qi
Email: xq622@nyu.edu
Cells: +86-157 2138 3500 (China)
I am an Assistant Professor of Finance at NYU Shanghai. My research interests lie primarily in theoretical corporate finance, venture capital contracts, financial innovation and mechanism design.
Working Papers
with Chen Chen
Abstract: We study an information design problem in which a school advisor strategically discloses information to promote her student in a job market with n potential employers. The advisor can send different signals to different employers (i.e., private persuasion) or broadcast the same signal to all employers (i.e., public persuasion). After receiving the signals, the employers can communicate with each other to reduce uncertainty about the candidate in their self-interest. We demonstrate that as long as the candidate can accept at most one offer and has a known preference among the employers, public persuasion is optimal, regardless of how employers communicate. The optimal public persuasion can be derived from a first-best relaxation problem that only imposes the employers' participation constraints. We then focus on a specific case in which the candidate's characteristics can be summarized as a one-dimensional variable, and all of the receivers' utility functions are linear in this variable. We derive the optimal mechanism in a closed form for the two-receiver case. In the general case, a convex optimization problem with n decision variables and constraints can be efficiently solved to obtain an optimal mechanism. We provide structural properties and a better understanding of the optimal mechanism from a dual viewpoint.
Optimal Staging of Early Startups
Abstract: When early startups stage the financing of their capital investments, they are at risk of being severely diluted by venture capitalists later on. It is puzzling that early startups do so in a competitive financial market. This paper shows that staging is beneficial to an early startup with a small upside return and a high capital intensity of early development. In this case, absent staging, the entrepreneur gets a small number of shares, which provides him with weak incentives to increase the startup's value. If the financing is staged, reevaluation of the startup during the follow-on round incorporates all the nonverifiable information about interim performance. Staging provides additional incentives since the entrepreneur gets more shares when interim performance is better. Between round financing and tranched financing, the two most prevalent forms of staging, round financing generates stronger incentives for the entrepreneur, but a lower payoff to the venture capitalist, than tranched financing. As a result, with a smaller upside return and a higher capital intensity of early development, the venture capitalist is less likely to participate in round financing, and tranched financing is more likely to be used to ensure his participation. All the above results are robust in a mechanism design framework.
This figure shows how the choice of financing form depends on two empirically important parameters. The X-axis is the capital intensity of early development, and the Y-axis is the upside return of the startup. Staging is not beneficial in two cases. 1. When the upside return is huge, staging is generally detrimental, and no staging is chosen. 2. When the upside return is minimal and the capital intensity of early development is high, the startup cannot attract any financing, and staging is trivially not helpful. Otherwise, staging is feasible and beneficial. Between two prevalent forms of staging, tranched financing is more likely to be chosen with small upside return and high capital intensity of early development; round financing is more likely to be chosen otherwise.
Optimal AI Adoption in Investment Monitoring
with Yang Yi
Abstract: This paper develops a theoretical framework to analyze the strategic adoption of AI innovations in investment monitoring by financial institutions. In addition to the conventional cost tradeoff, financiers can potentially face a nuanced tradeoff between leveraging informational advantages and enforcing ''hard'' budget constraints when considering AI adoption. As AI-driven information accuracy improves, financiers gain better insights into borrower quality during refinancing. However, this may introduce a soft-budget-constraint problem, compelling financiers to refinance initially speculative borrowers, thus exacerbating adverse selection in initial lending. Consequently, financiers may strategically opt to refrain from AI adoption to mitigate such risks. Our model predicts a hump-shaped relationship between a financier's AI adoption and the quality of its lending pool. Moreover, we establish that reductions in AI adoption costs stimulate adoption only in environments with high average project quality. These findings offer novel insights into comprehending the strategic dynamics of AI adoption in investment monitoring.
When do incomplete contracts matter?
with Aristotelis Boukouras, Kostas Koufopoulos and Giulio Trigilia
Abstract: This paper derives conditions under which the introduction of a third-party agent solves the renegotiation-proofness problem of Moore and Repullo (1988)-type mechanisms, without introducing the potential for other agents to collude with the third-party. The key novelties of our mechanism are: (i) the introduction of a third-party agent only off-equilibrium and with some probability; (ii) the fact that both its existence and its identity are unknown to the other agents. We show that under these conditions, which are satisfied in many empirical applications, a hidden third-party agent can restore the implementation of the efficient allocation. If this agent does not observe the state of the world, we provide a sufficient condition for implementation to succeed.
Leverage Dynamics with Reputation
Presented in the FTG 2019 Summer Program
This work analyzes the classic trade-off theory of capital structure in a dynamic model where the firm does not have any commitment power. The equilibrium analyzed in this paper depends on the firm's whole history (reputation) instead of the firm's current income and debt level. This paper proves that under this non-Markov structure, the firm may repurchase its outstanding debt, which breaks the Leverage Ratchet effect discussed in Admati, DeMarzo, Hellwig, and Pfleiderer (2018). This paper also proves that under mild conditions, the equity value in a non-Markov equilibrium can be higher than the equity value in the Markov equilibrium, the one depicted in DeMarzo and He (2021). Interestingly, with some conditions, the firm can achieve the first best equity value as if the firm has commitment power. In this case, reputation is a perfect substitute for commitment power.
This figure plots the revised Leverage Target policy that maximizes the equity holders' value as if the firm has commitment power. When the firm's income is not too low, the firm maintains its leverage target. In other words, the firm issues more debt when its income rises and repurchases the outstanding debt when its income drops. This breaks the Leverage Ratchet Effect discussed in Admati, DeMarzo, Hellwig and Pfleiderer (2018). When the firm's income is too low, the firm does not issue or repurchase any debt. In this case, if the firm's income goes up later, the firm starts to maintain the leverage target again; and if the firm's income drops further, the firm defaults as predicted in Leland (1998).
Pre-Ph.D. Publication