Last Second Comebacks: Examining Influencers of Bankruptcy Success
by Eric Junzhe Zhang
Abstract
The American bankruptcy system allows for companies to file for Chapter 11 bankruptcy to protect their assets from creditors and reorganize their business operations to continue operating after going through bankruptcy court. While the process is meant to help improve the financial health and business operations of companies after they exit the bankruptcy process, supposedly remedied firms will often find themselves filing again for bankruptcy despite the drastic changes they underwent to avoid such a fate. As such, it is difficult to determine what exactly makes a bankruptcy successful, as oftentimes a company with one metric that deems the bankruptcy successful may have another conflicting metric that deems it unsuccessful. This thesis seeks to contribute to prior knowledge on bankruptcy analysis by examining what in-court factors and company metrics drive bankruptcy success, with the change in debt-to-asset ratio and refiling likelihood post emergence being used as measures of bankruptcy success. Probit regression is used to analyze the change in the debt-to-asset ratio from bankruptcy filing to emergence while multivariable regression analysis is used to analyze the likelihood of refiling post-bankruptcy emergence. Explanatory variables which will be examined across these two variables will be the time spent in bankruptcy court, whether there was forum shopping to Delaware or New York, size of assets / EBIT of the firm, hedge fund presence, CEO turnover, whether a case was prepackaged, unionization rate, prime rate at filing and emergence, whether there was a 363 asset sale, whether a firm remained public following emergence, and debtor in possession financing. Results suggest that likelihood of refiling is a better measure of bankruptcy success than relative change in debt-to-asset ratio, which faces issues with the significance of its variables and their explanatory power.
Professor Connel Fullenkamp, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: G33, K22, G34
Measuring the Likelihood of Small Business Loan Default: Community Development Financial Institutions (CDFIs) and the use of Credit-Scoring to Minimize Default Risk1
By Andrea Coravos
Community development financial institutions (CDFIs) provide financial services to underserved markets and populations. Using small business loan portfolio data from a national CDFI, this paper identifies the specific borrower, lender, and loan characteristics and changes in economic conditions that increase the likelihood of default. These results lay the foundation for an in-house credit-scoring model, which could decrease the CDFI’s underwriting costs while maintaining their social mission. Credit-scoring models help CDFIs quantify their risk, which often allows them to extend more credit in the small business community.*
Advisor: Charles Becker | JEL Codes: K22, M1,
