Essays on Corporate Bankruptcy and Debtor-in-possession Financing

This dissertation consists of two essays in financial economics. The first essay, included in Chapter 2, concerns the effect of debtor-in-possession (DIP) financing and DIP financing lenders on the outcome of Chapter 11 bankruptcy. When firms file for protection under Chapter 11 bankruptcy, their access to outside financing will be limited. The Bankruptcy Reform Act of 1978 has resolved this issue under section 364 of the US Bankruptcy Code by defining laws for the DIP financing, which is the unique type of financing available to firms filing for Chapter 11 bankruptcy. DIP financing is usually senior to all other securities issued by a firm and violates the absolute priority rule by standing ahead of a company’s existing debts for payment. Among the characteristics of DIP financing, limited attention has been given to the type of lender of the DIP financing. There is not much empirical evidence on whether financing a DIP loan from different types of lenders can lead to different bankruptcy outcomes. In this essay, I investigate the role of DIP financing, especially the DIP lender in the bankruptcy process. I provide evidence for the role of DIP lender, bank versus non-bank, in bankruptcy outcome, while controlling for potential endogeneity of the lender’s type. In order to control for the endogeneity of the DIP lender type, I use an instrumental variable (IV) approach. My results show that even after controlling for the endogeneity of the lender type, the source of the DIP loan still matters for the outcome of the bankruptcy process. More specifically, receiving the DIP loan from banks increases the likelihood of emerging from bankruptcy as a going concern for the bankrupt firm. The second essay, included in Chapter 2, concerns predicting bankruptcy outcome using a machine learning approach and using the bankruptcy outcome predictions to predict firms’ CDS spreads. First, I develop a machine learning model using Extreme Gradient Boosting to predict the outcome of the bankruptcy. I compare the performance of this model with that of a traditional logistics regression model and show that, while both perform well, the machine learning model outperforms the traditional model, mainly because it is able to identify non-linear patterns in the data. I, then, use the predicted probabilities of emerging from bankruptcy, combined with the predicted probabilities of bankruptcy, produced by a second machine learning model, to predict CDS spreads. I show that the predicted probability of bankruptcy and probability of emerging from bankruptcy can be used to predict firms’ CDS spreads and can improve the prediction power of benchmark models. This study contributes to the bankruptcy and bankruptcy outcome prediction literature by providing empirical evidence of the association between a firm’s characteristics and its bankruptcy outcome. I also show that using machine learning techniques to predict the bankruptcy outcome can help predict CDS spreads more accurately.