Measuring Loss Given Default on Commercial Loans for the JP Morgan Chase Wholesale Bank

Understanding and Predicting the Resolution of Financial Distress

Michael Jacobs, Jr.[1]

Office of the Comptroller of the Currency

Ahmet K. Karagozoglu

HofstraUniversity

Dina Naples Layish

BinghamtonUniversity

Draft: February 2007

J.E.L. Classification Codes: G33, G34, C25, C15, C52.

Keywords: Default, Financial Distress, Liquidation, Reorganization, Bankruptcy, Restructuring, Credit Risk, Discrete Regression, Bootstrap Methods, Forecasting, Classification Accuracy

Abstract

In this study we empirically investigate the determinants of the resolution of financial distress, bankruptcy or out-of-court settlement given default, as well as liquidation (Chapter 7) or reorganization (Chapter 11) given bankruptcy. This is done for a sample of 518 S&P and Moody’s rated defaulted firms in the period 1985-2005 for which there is an indication for the type of resolution and financial statement data from Compustat at the time of default. Various qualitative dependent variable models are estimated and compared: ordered logistic regression (OLR), multiple discriminant analysis (MDA), local regression models (LRMs) and feedforward neural network (FNN). Based upon a combination of prior research and exploratory data analysis, we select several accounting and economic variables at the time of default which are expected to influence these outcomes. Estimation results reveal the OLR specification to achieve best balance between in-sample fit, consistency with financial theory and out-of-sample classification accuracy. In predicting liquidation vs. reorganization and bankruptcy filing vs. out-of-court settlement, a stepwise analysis of models in the preferred OLR class shows variables capturing 8 of these dimensions (leverage, tangibility, liquidity, cash flow, proportion of secured debt, abnormal equity returns, proportion of secured debt, number of creditor classes, macroeconomic state, indicator for NY /Delaware filing district or pre-packaged bankruptcy and an auditor’s score) contribute significantly to overall fit joint explanation of liquidation or bankruptcy filing likelihood, having signs consistent with hypotheses. In comparing results to the prior literature regarding the determinants of successful resolution outcomes, we are consistent with White (1983, 1989), Hotchkiss (1993) and Bris (2006) regarding intrinsic value, asset size, respectively; in line (at variance with) with Lenn and Poulson (1989) (Jensen (1991)) regarding cash flow; inconsistent (consistent) on profitability (overall firm quality) with Kahl (2002); consistent with Matsunga et al (1991) and Bryan et al (2001) regarding the interest coverage ratio.

Model performance is assessed on the dimensions of discriminatory power, predictive and classification accuracy. The former two are measured by implementing standard tests (power curve analysis and chi-squared tests), while classification accuracy is assessed according to alternative categorization criteria (expected cost of misclassification, minimization of total misclassification and deviation from historical averages) as compared to naïve random benchmarks. While in- and out-of-sample performance along these dimensions exhibits wide variation across models and criteria, the OLR and LRM models are found to perform comparably, while the FNN model is found to consistently underperform. The statistical significance of these results is rigorously analyzed and confirmed through a resampling procedure, yielding estimated sampling distributions of the performance statistics, confirming these observations.

1.Introduction and Summary

In situations of default or financial distress, when a private arrangement amongst a firm’s stakeholders cannot be made, firms in the U.S. file for bankruptcy and are placed under court supervision. Filing for corporate bankruptcy is mandatory under Chapter 11 of the 1978 bankruptcy code, where management and owners seek court protection against creditors and other claimants. Bankruptcy is usually settled with a court approved rehabilitation scheme in about 1.5 years from filing. However, the following alternative resolutions may occur: emergence as an independent entity, acquisition by other firms or liquidation of assets and the distribution of proceeds to stakeholders. Since firms filing for bankruptcy or in private workout share similar characteristics (i.e., declining revenues, earnings, asset and equity values), it is more difficult to differentiate between them and classify the final outcome, as compared to predicting financial distress. Consequently, in the prior finance literature, the problem of predicting bankruptcy resolution has not been studied as extensively as that of predicting financial distress. This is one of the first studies to do this in an econometrically rigorous fashion with an application to a current dataset of public defaults. First, we specify variables determining, and postulate relationships to, the likelihood of a defaulted firm in bankruptcy ultimately liquidating versus reorganizing[2]. Explanatory variables are chosen based upon economic theory, prior empirical results, and exploratory data analysis (all subject to availability). Second, we estimate and compare several qualitative dependent variable econometric models (ordered logistic regression - OLR, multiple discriminant analysis - MDA and feed-forward neural networks - FNN), with various combinations of these variables, identifying a candidate models based upon in-sample as well as out-of-sample classification accuracy. Classification accuracy is evaluated by choosing cutoff probabilities that are optimal with respect to various classification criteria – expected cost of misclassification (ECM), unweighted minimization of misclassification (UMM) and deviation form historical averages (DHA). Finally, we conduct a bootstrap experiment in order to assess the out-of-sample predictive capability of the models. This exercise in predicting bankruptcy outcome is not only of academic interest but is of importance to a range of players in this domain of finance: investors in distressed equity and debt may use these results to build strategies; stakeholders in often prolonged court deliberations in developing a plan of negotiation; risk managers in building practical credit risk models; as well as guidance for specialists in banking workout departments. We believe that this modeling exercise can contribute significantly to informed decisions regarding the allocation of scarce resources to an often costly and time consuming process. A brief summary of our methodology, data and results is as follows:

Theory, exploratory data analysis and estimation results reveal that ten variables satisfactorily explain bankruptcy resolution: higher interest coverage ratio, greater percent secured debt, higher spread on debt at default, or adjudication in certain filing districts is associated with a greater likelihood of liquidation versus reorganization; whereas greater asset size, higher leverage, increased free cash flow, more intangibles to total assets, longer time debt outstanding or a pre-packaged bankruptcy decreases this probability.

Stepwise regression procedures show that classes of debt, profit margin, industry indicator or macroeconomic state do not contribute, whereas all the other variables do contribute, significantly to the joint explanation of the liquidation probability.

The OLR model is found to be superior to either the MDA or FNN models in terms of consistency with hypotheses, fidelity to the data and classification accuracy.

In the preferred OLR model excluding assets, 10 (5) out of 14 variables jointly (individually) significant, pseudo r-squared is 18.6% and overall classification accuracy (depending upon classification criteria) ranges from 70-83%.

While the FNN model has superior in-sample fit (pseudo r-squared of 19.3% and classification accuracy of 63-83%), coefficient estimates are not consistent with theory and out-of-sample performance is significantly worse than alternative models, at a much greater computational cost.

While the MDA model exhibits comparable out-of-sample classification performance to the OLR model, signs of coefficient estimates are not consistent with theory, and in-sample fit is significantly worse than competing models (7.0% r-squared and classification accuracy of 50-81%).

In comparing results to the prior literature regarding the determinants of successful resolution outcomes, we are consistent with White (1983, 1989) and Hotchkiss (1993) regarding intrinsic value and asset size, respectively; in line (at variance with) with Lenn and Poulson (1989) (Jensen (1991)) regarding cash flow; inconsistent (consistent) on profitability (overall firm quality) with Kahl (2002); consistent with Matsunga et al (1991) and Bryan et al (2001) regarding the interest coverage ratio.

Out-of-sample analysis of classification accuracy reveals that while the models can generally beat random benchmarks, there is much variation in model performance depending upon classification criteria employed.

As out-of-sample results on a split sample basis are not conclusive, we implement a bootstrap procedure, to measure the statistical significance of classification accuracy statistics relative to random benchmarks.

The resampling experiment leads to sharper conclusions than the split sample exercise: under the ECM criterion, the MDA model outperforms OLR in overall classification accuracy, while OLR is better in classifying liquidation outcomes. Under the UMM or DHA criteria, this is reversed.

Across classification criteria and outcome, it is found that the FNN model consistently under-performs competing models in out-of-sample classification accuracy.

Future directions for this research include exploring different variables (accounting, economic or financial market), further variations on econometric models, extension of the data-set and joint prediction of with other quantities of interest (e.g., loss severity or time-to-resolution)

2.Review of the Literature

This purpose of this paper is to evaluate the outcome and resolution of financial distress. While the path to financial distress will reflect similar trends - decline in profits, decline in cash flows, loss of revenues, etc. - the outcome of the distress can follow several paths. Once a firm is in default there are only two possible paths, either the firm will file for bankruptcy reorganization or the firm will resolve the financial distress out of court. This paper will attempt to classify these two types of firms according to which path is followed. Once this choice is made, there are several possible outcomes of the negotiation (either in or out of court). In either case, we see firms that are acquired or firms that emerge as an independent entity. There is a third possible outcome for firms that file for bankruptcy: liquidation. So, in general, we see five paths that a financially distressed firm can follow (see Figure A1).

Using the S&P LossStatsTM database we are able to obtain detailed information about the financial distress and the resolution of this distress on 519 publicly traded firms. The S&P LossStatsTM database is one of the most extensive loss severity database of public defaults (Keisman et al, 2001). It contains data on 2,102 defaulted instruments from 1986 – 2003 for 560 borrowers, having some publicly traded debt and for which there is information on all classes of debt. All instruments are detailed by type, security, collateral type, position in the capital structure, original and defaulted amount resolution type, instrument price at emergence from as well as the value of the securities received in settlement from bankruptcy.

Most of the firms in the sample file for bankruptcy and successfully emerge as an independent entity (see Table 1). For the firms that are able to resolve their financial distress outside of the court system, most firms (94%) emerge as an independent entity. A smaller percentage of the firms that file for bankruptcy are able to remain independent, only 74% of the firms that file for bankruptcy are able to successfully resolve the financial distress. The remaining firms are either acquired (9.5%) or liquidated (16.5%). We also see that no matter which path is followed, in court or out of court negotiations, most firms (78%) remain independent following the resolution of the financial distress. And the likelihood of remaining an independent firm increases with an out of court restructuring.

In evaluating the outcomes of financial distress this paper will answer three main questions. The first question will examine the characteristics of firms that are able to resolve their financial distress out of court compared to firms that aren’t and file for bankruptcy. Specifically, we will attempt to determine what is different about the firms that are able to restructure privately. The second question will focus on firms that file for bankruptcy. In this sample of firms, we will examine what determines the outcome. For example, are there any indicators that separate firms that are able to emerge as independent going concerns with firms that are acquired and/or firms that are liquidated. And the third question will examine the five paths following financial distress (see Figure A1). Are there any firm characteristics that can predict which path a financially distressed firm will follow? See Table A2 for a breakdown of the possible paths a financially distressed firm can follow.

Capital structure theory, under very strict assumptions of firm behavior and market conditions, assumes away the costs of bankruptcy. Miller and Modigliani (1958 and 1963) assume firms can costlessly enter bankruptcy. This theory provides an excellent foundation for understanding the decisions of firms that are far enough away from financial distress. It is safe to assume that firms that are not in danger of filing for bankruptcy do indeed have very small, almost zero, costs of bankruptcy. But for firms that are in danger of filing for bankruptcy, the costs, both explicit and implicit, of bankruptcy is substantial. As the probability of bankruptcy increases, bankruptcy costs become significant and we may see a shift in the goals of the firm.

The cost to society of firms that file for bankruptcy can also be substantial. The loss of employment, equity value and confidence in business can impose substantial hardship on those directly involved, as well as on society as a whole. Recent bankruptcies, such as Enron and WorldCom, clearly show the impact bankruptcy can have on society. As a result of this impact, in 2002 the Sarbanes-Oxley Act became a federal law that heightened accountability standards for individuals responsible for documenting and reporting the financial health of a publicly traded firm. We have seen a general decline in the overall trust and confidence placed in the financial reporting of publicly traded firms as a result of these highly publicized bankruptcies. While one would expect managers of all firms to attempt to maximize the value of the firm, firms that are in financial distress may not make the same decisions as a firm that is not in financial distress, further imposing costs on society. One can argue that due to the small probability of filing for bankruptcy (less than 1% of all firms file) the costs of bankruptcy are also very small. But for the subset of the population that does file for bankruptcy, bankruptcy costs are substantial.

Firms in financial distress experience significant loss in value prior to, during and following the resolution of the financial distress, imposing significant costs on all of the claimants of the firm and society in general. Bris, Welch and Zhu (2004) find that bankruptcy costs can be as high as 20% of the firm’s value prior to the bankruptcy filing. The resolution of financial distress can take two general forms: an out-of-court restructuring or a bankruptcy filing through legal channels. Most bankruptcy filings begin as an out of court restructuring with the firm only filing for bankruptcy when the negotiations fail or to facilitate the pre-filing negotiations, more commonly known as a prepackaged bankruptcy. In the United States, once a firm decides to file for bankruptcy it can decide whether to reorganize under the Chapter 11 procedure or to liquidate under the Chapter 7 procedure . Under Chapter 11, the court provides an automatic stay on the firm’s assets, that is the firm is protected against creditors, secured and unsecured, attempting to force repayment. In almost all Chapter 11 cases, the firm’s existing management remains in control of the firm, as debtor in possession, and continues to make operating decision for the firm and deal with the reorganization procedure. Under Chapter 7, the firm is liquidated. A trustee is assigned to the case and is responsible for selling the assets of the firm and repaying creditors according to the priority structure of the firm’s capital structure.

There is considerable debate in the literature about the most efficient bankruptcy procedure. The purpose of any bankruptcy code is to facilitate the redistribution of assets to their best use. Two distinct types of bankruptcy codes exist in the world today, creditor based and debtor based. Creditor based systems, found in Japan and Germany, automatically remove the firm’s management and install a bankruptcy trustee who is responsible for determining the final outcome of the procedure. Debtor based systems, found in the United States and Canada, allow existing management to stay in control of the firm’s operating decisions. Arguments have been made both for and against these two opposing systems. Critics of the current bankruptcy laws in the United States argue that the system is pro-debtor, allowing for the reorganization of inefficient firms while incumbent management remains in control of the firm’s assets, for example Jensen (1991), Baird (1986) and Bradley and Rosenzweig (1992). Whereas, Berkovitch, et al. (1998) argue that it is essential that bankruptcy laws are pro-debtor in order to properly incentivize managers to maximize firm value, even when facing financial distress. While several authors argue for an auction-like system (see Baird (1993) and Easterbrook (1990)) to better redistribute assets, Stromberg (2000) shows that, in Sweden, the auction system does not eliminate the agency problem among claimants in a financially distressed firm. He further reports that the cash auction system currently operating in Sweden, looks more like the US reorganization procedure, with similar advantages and disadvantages. Theoretically an auction system may allow assets to be redistributed to their best use, but practically implementing such a system is extremely difficult.