Understanding and Predicting the Resolution of Financial Distress

1. Understanding and Predicting the Resolution of Financial Distress Michael Jacobs, Jr. Senior Financial Economist Credit Risk Modeling, Risk Analysis Division Office of the Comptroller of the Currency Ahmet Karagozoglu Associate Professor of Finance Zarb School of Business Hofstra University Dina Naples Layish Assistant Professor of Finance School of Management Binghamton University The views expressed herein are those of the authors and do not necessarily represent a position taken by of the Office of the Comptroller of the Currency or the U.S. Department of the Treasury.

2. Introduction & Motivation Firms in financial distress are faced with two distinct decision points in the resolution Each decision will affect the value & the going concern probability of the firm Which resolution process to pursue? Work out (out-of-court settlement)– private resolution File for bankruptcy – public resolution Which resolution outcome occurs? Reorganization – successful Liquidation – unsuccessful

3. Introduction & Motivation We see financial distressed firms with very similar characteristics The purpose of this paper is to model the different paths of resolving the financial distress in a unified framework We model both the process and outcome in order to gain better understanding of variables that help differentiate firms

4. Figure 1, Process for and Outcome of the Resolution of Financial Distress

5. Literature Review Discriminating among all firms to predict the possibility of default Altman (1968), Ohlson (1970) Evaluating the resolution process Gilson, John & Lang (1990), Franks & Torous (1994) Evaluating the outcome for those firms that have filed for bankruptcy Hotchkiss (1993), Casey, McGee & Stickney (1986), Barniv, Agarwal & Leach (2002)

6. Literature Review Very few papers have combined the two facets of financial distress: process chosen for and outcome of resolving it Kahl (2001) Examines 95 firms over the 1979-1983 time period throughout the financial distress period Only 1/3 of the firms remain independent He finds survival is positively related to firm performance, but not at all related to size, leverage and complexity of capital structure

7. Our Contribution Our paper extends the analysis of Kahl We model both the process for and outcome of financial distress in a unified framework Larger sample – 518 firms Long history – 1985-2004 A comparison of econometric models as well as validation methodologies Statistically rigorous analysis of classification accuracy

8. Models Two sets of models & classification accuracy test Ordered Logistic Regressions (OLR) for Resolution Process (3 different models-Table 6) Private workout Public bankruptcy Resolution Outcome (4 different models-Table 7) Successful – reorganization Unsuccessful - liquidation

9. Data 518 firms that experienced financial distress between 1985 and 2004 Data complied from SEC filings, Lexis/Nexis, Bloomberg, Compustat and CRSP Default is defined as the earliest date for which we can define a missed interest payment, violation of a covenant or public announcement of distress Following default we separated firms by resolution process and resolution outcome

10. Independent Variables Leverage Size Capital Structure Complexity Variation in type and security of debt Vintage of Debt – time since debt issue Financial Health Profitability, liquidity, free cash flow, credit quality Intangible assets

11. Results, Resolution Process (Table 6) Logit Regression Dependent variable : 1 = public bankruptcy 0 = private workout

12. Results, Resolution Process (Table 7) Logit Regression Dependent variable: 1 = Liquidation 0 = reorganization

13. Alternative Econometric ModelsTables 8 & 9, Figure 2 3 Qualitative dependent variable models are estimated and compared Ordered Logistic Regression (OLR) Local Regression Models (LRM) Feed Forward Neural Network (FNN) Model performance is assessed based on discriminatory power predictive accuracy classification accuracy Our results reveal the OLR specification achieves the best balance between in-sample fit and out-of-sample classification accuracy

14. Conclusions Resolution process Larger firms, higher liquidity, and more secured debt in their capital structure are more likely to follow a public resolution process (file for bankruptcy) Filing in a pro-debtor district also increases the likelihood of filing for bankruptcy Firms with higher Z-scores and total leverage, and technology firms are less likely to follow a public resolution process

15. Conclusions Resolution outcome Firms with greater liquidity, more secured debt in their capital structure and lower abnormal returns are more likely to be liquidated rather than reorganized Firms with more leverage, more intangible assets, and filing a prepackaged bankruptcy are more likely to be reorganized Different variables help identify resolution process and resolution outcome Robustness of our OLR specifications verified for in-sample fit and out-of-sample classification accuracy