Publications:
Optimal Sequential Selling Mechanism and Deal Protections in Mergers and Acquisitions, with Yi Chen
Journal of Finance, Volume 78, Issue 4, Pages 2139-2188, August 2023
(A revised version of my solo-authored PhD dissertation)
Summary: we develop a dynamic mechanism design model to show how deal protections can arise as part of an optimal sale process. While widely used in M&A and bankruptcy sales, such protections remain controversial: proponents claim they incentivize initial bids, while critics argue they deter competition. We reconcile this debate by showing that optimal mechanisms use deal protections to encourage initial entry, but rely on high initial bids—rather than deal protections—to limit later entry. Our mechanism not only closely mirrors real-world practices such as stalking-horse auctions and public M&A sales, but also suggests improvements by aligning them more closely with the theory. Beyond M&A, the paper contributes to auction theory by analyzing a realistic but underexplored setting in which entry fees are infeasible. This paper was cited in an influential literature review on M&A: “Corporate Takeovers: Theory and Evidence” (Eckbo, Malenko, and Thorburn, 2025).
The Strategic Decentralization of Recruiting, with Yi Chen and Thomas Jungbauer
Journal of Economic Theory, Volume 209, April 2023, 105639
Summary: we are motivated by the empirical puzzle that larger, more powerful firms are less likely to decentralize hiring—contrary to the view that decentralization reflects divisional managers’ informational advantages. We develop a theory in which decentralization acts as a strategic commitment to offer higher wages and attract top talent by inducing internal wage competition. While this strategy erodes market power, firms with less market power face smaller losses and are therefore more willing to decentralize. This mechanism explains the observed pattern and clarifies how hiring structure interacts with firm size, market power, minimum wages, and labor market outcomes.
Peer Learning, Enforcement, and Reputation, with Yi Chen, Kai Du, and Phillip Stocken
Accepted, The RAND Journal of Economics
Summary: we consider a two-period learning model featuring a regulator, which tries to build a reputation for strict enforcement, and two self-interested firms, which test the regulator's enforcement propensity through their misconduct. Counterintuitively, greater enforcement transparency—where firms observe each other’s punishment—can increase misconduct. Likewise, a regulator with a longer horizon, such as one in a less polarized political environment, can also encourage misconduct. These findings challenge the conventional belief that transparency and long-term focus always strengthen regulatory enforcement. Besides offering guidance on regulatory designs, we also contribute to strategic experimentation theory by introducing a novel setting in which the object of learning—the regulator—is itself strategic.
Working Papers:
Crafting an AI Compass: The Influence of Global AI Standards on Firms, with Mehmet Canayaz, 2024
Revise & Resubmit, Review of Financial Studies
The 2024 Next Generation of Antitrust, Data Privacy & Data Protection Scholars Conference, Asian Bureau of Finance and Economic Research 11th Annual Conference, the 4th Annual Empirical Research Conference on Standardization, 2024 AI and Platform Evolution Conference, the Columbia & RFS AI in Finance Conference, the 2025 AFFECT workshop, SFS Cavalcade 2025
Summary: we conduct the first empirical analysis of how global AI standardization—both technical and ethical—shapes corporate behavior, motivated by a theory of firm investment in anticipation of standards publications. Using hand-collected data, we show that standards promoting interoperability, big data, and machine learning frameworks boost firm-level AI, R&D, and capital investment, while ethical and privacy-focused standards dampen it. These standards also gradually enhance firm value. As AI regulation gains momentum globally, our findings provide timely evidence on how different types of standards influence innovation and firm strategy.
Creditor Coalitions in Bankruptcy, with Jingzhi Huang and Stefan Lewellen, 2025
2nd Round Revise & Resubmit, Journal of Financial Economics
Featured by Harvard Law School Bankruptcy Roundtable, Oxford Business Law Blog.
The 2024 AFFECT workshop, the 2024 CMU-Pitt-PSU Finance Conference, A Special Conference by the FTG: Bridging Theory and Empirical Research in Finance (2024), the 19th Early Career Women in Finance Conference, EFA 2024, Harvard-Wharton Insolvency and Restructuring Conference (2024), MFA 2025, SAIF conference 2025
Summary: we provide the first systematic empirical analysis of creditor coalitions in U.S. Chapter 11 bankruptcies, motivated by a theory of coalition formation. Although creditor coalitions have become key players in corporate restructurings over the past decade, they remain largely unexplored in academic research. Using novel hand-collected data, we find that coalition formation is driven by debt size, creditor dispersion and type, market liquidity, and creditor familiarity. Bond prices increase following coalition announcements, suggesting that markets associate coalitions with improved recovery. Exploiting a landmark 2017 court ruling, we show that weakened creditor protections are associated with increased coalition formation and class recovery, but also more litigious and lengthier bankruptcy proceedings.
SEC Comment Letters on Mutual Fund Disclosures, with Chengfeng Du, Kai Du, and Shuyang Wang
2nd Round Under Review, The Accounting Review
Summary: we provide the first evidence on the impact of the Securities and Exchange Commission (SEC)’s comment letters on mutual funds. We find that after a mutual fund receives a comment letter, fund disclosures adjust to include more detailed discussions and a more negative tone; meanwhile, investors withdraw from funds, and fund flow becomes less sensitive to disclosed performance. Overall, our findings suggest that regulatory scrutiny improves mutual fund disclosures and heightens investors’ perception of the usefulness of such disclosures.
Venturing Brown to Subsidize Green Subsidies, with Matthew Gustafson
Summary: we tackle a timely policy challenge: public subsidies for green innovation are costly, making it essential to mobilize private investment. We show that joint ventures among brown (emitting) firms—typically viewed as climate obstacles—can reduce the need for public subsidies. By internalizing private benefits from abatement (e.g., lower carbon taxes and financing costs via the greenium), these joint ventures overcome free-rider problems and achieve first-best investment with less public support. This research is particularly timely, as global joint ventures in carbon abatement are beginning to emerge.
Misreporting as Strategic Experimentation: Theory and Evidence, with Yi Chen, Kai Du, and Shuyang Wang
Summary: we propose a two-period model wherein two firms select the level of misreporting in the presence of a regulator whose tolerance for misreporting is unknown to the firms. The model implies that misreporting increases with the mean of enforcement cost and regulatory spillover, decreases with the regulator’s decision horizon, the observability of peer’s misreporting, and the cost correlation between firms, and is convex in regulatory uncertainty. A firm’s responsiveness to its peer’s past misreporting increases with peer observability and cost correlation. Empirical tests generally support the model’s predictions.
Initiation of Merger and Acquisition Negotiation with Two-Sided Private Information, with Yi Chen
Summary: we build a dynamic model of merger negotiation with two-sided private information and endogenous initiation to investigate (1) drivers of the timing of M&A initiation and initiator identity, and (2) why bid premia are different between target- and bidder-initiated deals. The key insight is that initiation timing reveals each party’s private signal—about the target’s stand-alone value and the bidder’s valuation. The model’s predictions align with empirical evidence highlighting the role of private information in deal initiation.
Work in Progress:
Bankruptcy with Volatile Liabilities, with Yi Chen and Ye Li
Machine Learning, Regularization, and Reporting Bias, with Yi Chen, Kai Du, Thomas Jungbauer, and David Wu