UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
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UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
Understanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07fstra UniversityDina Naples Layish Binghamton UniversityDraft: February 2007J.E.L. Classification Codes: G33, G34, C25, C15, C52.Keywords: Default, Financial Distress, Liquidation, Reorganization, Bankruptcy, Restructuring, Credit Risk, Discrete Regression, Bootstrap Methods, Forecasting, Classifica UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07tion Accuracy1 Corresponding author: Senior Financial Economist, Credit Risk Modelling Group, Risk Analysis Division, Office of the Comptroller of theUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
Currency, 250 E Street sw. Suite 2165, Washington. DC 20024, 202-874-4728. m 1chael.jacobsfflocc,treas,gov■ The views herein are (hose of (he author Understanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07minants 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 UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07an indication for the type of resolution and financial statement data from Compustat at the time of default. Various qualitative dependent variable moUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
dels are estimated and compared: ordered logistic regression (OLR), multiple discriminant analysis (MDA), local regression models (LRMs) and feedforwaUnderstanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07 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 bankrup UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07tcy filing vs. out-of-court settlement, a stepwise analysis of models in the preferred OLR class shows variables capturing 8 of these dimensions (leveUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
rage, tangibility, liquidity, cash flow, proportion of secured debt, abnormal equity returns, proportion of secured debt, number of creditor classes, Understanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07 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) regar UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07ding intrinsic value, asset size, respectively; in line (at variance with) with Lenn and Poulson (1989) (Jensen (1991)) regarding cash flow; inconsistUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
ent (consistent) on profitability (overall firm quality) with Kahl (2002); consistent with Matsunga et al (1991) and Bryan et al (2001) regarding the Understanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07o 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) UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07as compared to naive random benchmarks. While in- and out-of-sample performance along these dimensions exhibits wide variation across models and criteUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
ria, the OLR and LRM models are found to perform comparably, while the FNN model is found to consistently underperform. The statistical significance oUnderstanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07istics, confirming these observations.21introduction and SummaryIn 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 ma UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07ndatory under Chapter 11 of the 1978 bankruptcy code, where management and owners seek court protection against creditors and other claimants. BankrupUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
tcy is usually settled with a court approved rehabilitation scheme in about 1.5 years from filing. However, the following alternative resolutions may Understanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07 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 UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07finance literature, the problem of predicting bankruptcy resolution has not been studied as extensively as that of predicting financial distress. ThisUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
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 Understanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07zing2. 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 discriminan UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07t analysis - MDA and feed-forward neural networks ■ FNN), with various combinations of these variables, identifying a candidate models based upon in-sUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
ample as well as out-of-sample classification accuracy.Classification accuracy is evaluated by choosing cutoff probabilities that are optimal with resUnderstanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07orm 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: in UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07vestors in distressed equity and debt may use these results to build strategies; stakeholders in often prolonged court deliberations in developing a pUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
lan of negotiation; risk managers in building practical credit risk models; as well as guidance for specialists in banking workout departments. We belUnderstanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07d 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 o UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07n debt at default, or adjudication in certain filing districts is associated with a greater likelihood of liquidation versus reorganization; whereas gUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
reater asset size, higher leverage, increased free cash flow, more intangibles to total assets, longer time debt outstanding or a pre-packaged bankrupUnderstanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07not contribute, whereas all the other variables do contribute, significantly to the joint explanation of the liquidation probability.• Reorganization includes acquisition by another entity as well as emergence as a new entity. See Bamiv et al (2003) for a three-group classification.4•The OLR model i UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07s found to be superior to either the MD'......................terms of consistency with hypotheses, fidelity to the data and classification accuracy.•UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
In the preferred OLR model excluding assets, 10 (5) out of 14 variables jointly (individually) significant, pseudo r-squared is 18.6% and overall clasUnderstanding and Predicting the Resolution of Financial DistressMichael Jacobs, Jr.1Office of the Comptroller of the CurrencyAhmet K. Karagozoglu Hof UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb079.3% 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 th UnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07e OLR model, signs of coefficient estimates are not consistent with theory, and in-sample fit is significantly worse than competing models (7.0% r-squUnderstandingAndPredictingTheResolutionOfFinancialDistress_JKL_Feb07
ared and classification accuracy of 50-81%).•In comparing results to the prior literature regarding the determinants of successful resolution outcomesGọi ngay
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