题　目：Disentangling the Information Structure in the Bankruptcy Liquidation Market

嘉　宾：Yuan Shi(师远) Ph.D. Candidate University of Michigan

主持人：凃俊 教授 上海交通大学安泰经济与管理学院

时　间：2024年1月19日（周五）09:30-11:00

地　点：上海交通大学 徐汇校区安泰楼A509室

内容简介:

Understanding the information structure in bankruptcy liquidations is important for designing the optimal bankruptcy and financing market. In this paper, I construct a novel comprehensive bid-level bankruptcy liquidation auction data and structurally estimate the buyers' information structure using a state-of-the-art auction model. I show that there is a heterogeneity in the relevant importance of different information sources across different asset types, with tangible asset depending on the appraisal price, intangible assets depending on private synergies, and financial assets depending on bidder common knowledge not captured by appraisers. I text-analyze the appraisal reports using a natural language processing model and show how heterogeneous information production and bankruptcy liquidation frictions (quality management, relocation cost, and misallocation) contribute to the heterogeneity of information structure. A counterfactual simulation shows that the cost of misallocation is high (16.89\%) for intangible assets compared to tangible assets (5.6\%). Overall, my results suggest that tangible assets should be liquidated promptly to avoid maintenance failure, while intangible assets require a longer liquidation period to avoid misallocation costs, and financial assets should be financed by investors with expertise.

演讲人简介：

Yuan Shi is a PhD candidate in finance from the University of Michigan, Ross School of Business. She works on empirical corporate finance, with a special focus on bankruptcy, big data, entrepreneurship, and the private market. Yuan applies a wide range of empirical methodologies, including machine learning, structural estimation, and reduced-form methods. Her job market paper studies the information structure of the corporate bankruptcy liquidation market. This paper has been presented at major conferences, including AEA and FMA, and has won the semi-finalist for best paper award at FMA 2023.

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