Managing Operations Under Bankruptcy Risk

This dissertation focuses on the relationship between a firm's operational decisions and its bankruptcy risk. It consists of three self-contained chapters. All three chapters are joint work with Professor Vishal Gaur. Chapter 1 studies the implications of asset based lending for operational investment, probability of bankruptcy, and capital structure for a borrower firm. We set up a single-period game with two players, a business owner and a bank. The business owner decides how to allocate her capital between the equity of a new business and the external capital market in order to maximize her expected profit. We model the new business as a single-period inventory (newsvendor) model. The bank does not know the newsvendor's demand distribution, and sets an asset based credit limit to maximize its expected profits. We show that the equilibrium order quantity is a function of market parameters, and deviates from the classical newsvendor solution. In this solution, asset based lending leads to an upper limit on the potential loss faced by the bank, and thus, helps manage bankruptcy risk. In particular, the collateral value of inventory is a function of the bank's belief regarding the firm's demand distribution because the amount of inventory that will have to be liquidated in case of a default is random and depends on the realized demand. We also show that the probability of bankruptcy and the capital structure at equilibrium are functions of information asymmetry, bankruptcy costs, and the newsvendor model parameters. Chapter 2 focuses on a cash constrained firm that has to balance growth and bankruptcy risk when making its operational and borrowing decisions. We study the operational implications of this tradeoff by setting up a finite horizon cash-constrained inventory model with non-stationary demand, which is a function of the firm's past sales. We analyze four different growth scenarios: unconstrained growth, self-financing growth, growth under reorganization bankruptcy, and growth under liquidation bankruptcy. These scenarios capture different aspects of the impact of financing constraints and the bankruptcy process on a firm's operational decisions. Our analysis shows that a self-financing growth strategy, which avoids risky borrowing, is overly conservative and that growth requires making risky operational investment decisions. That is, a cash constrained firm should take some risk and over-invest (i.e., order more than the classical single period newsvendor quantity) to fuel growth. However, the firm should be cautious because over-investment amplifies bankruptcy risk. Hence, we show that the firm needs to achieve the right balance between growth and bankruptcy risk to maximize its long term profits. Chapter 3 investigates whether inventory productivity explains financial distress for retailers. Inventory is a key management item, which usually is the largest current asset in a retailer's books. Since a vast majority of a retailer's operational decisions are related to inventory, we hypothesize that retailers with high inventory productivity have lower probability of bankruptcy. Using a data set of retail bankruptcies, we test this hypothesis by adding inventory turnover as an explanatory variable to three commonly used bankruptcy prediction models. Our analysis shows that inventory turnover significantly improves the model fit in all three models and that retailers with high inventory turnover have lower probability of bankruptcy. These results have important implications for bankruptcy prediction and turnaround management