1. The Effect of Banking Relationships on the Future of Financially Distressed Firms Claire M. Rosenfeld September 21, 2007 Disclaimer: The analysis presented does not necessarily reflect the official opinion of the FDIC.

2. Financial Distress Definition: The inability to make debt payments Why distressed firms are special: Critical need of funding True financial position unknown

3. Banking Relationships Most basic form: repeated provider of credit Repeated lending provides “soft” information Banking relationships with financially distressed firms: firm in dire need of funding heightened information asymmetries

4. Objective Determine the effect that banking relationships have on the future success of financially distressed firms Address Endogeneity

5. Prior Findings Industry-wide distress adversely affects creditor recoveries from defaulted firms (Acharya, Bharath, Srinivasan 2007) Firm falls susceptible to bank over-monitoring (Weinstein & Yafeh 1998) Relationship lender provides liquidity insurance (Elsas & Krahnen 1998) Relationship lenders make capital easier to obtain (Petersen 1999) Relationship lending leads to better loan terms (Petersen & Rajan 1994 and Berger & Udell 1995, Santos and Winton 2006)

6. Prior Findings (cont’d) Relationship DIP lenders lead to quicker bankruptcy resolution (Dahiya et al 2003) –Firms in bankruptcy proceedings Loans have less risk from DIP financing priority –Examine time to resolution

7. Literature Limitations Transaction-oriented Specific data –German: Elsas & Krahnen 1998, Elsas 2005 –Japanese: Weinstein & Yafeh 1998 –Belgian: Degryse & Ongena 2005 –Norwegian: Ongena & Smith 2001 –Small American: Petersen & Rajan 1994, Berger & Udell 1995, Petersen 1999 –Large DIP: Dahiya et al 2003 –Publicly traded U.S.: Houston & James 1996 & 2001, Schenone 2005 & 2006

8. Contribution Long-term perspective Publicly traded U.S. firms Address endogeneity

9. Null Hypothesis Banking relationships have no effect on the future success of financially distressed firms.

10. Methods Probit regressions –Effect of banking relationships on the probability of future success –Control for firm, loan timing, industry, macroeconomy, and information asymmetry

11. Sample Universe COMPUSTAT: Financial statements CRSP: Trading data DealScan: Loan data –First loan: 1982 –2+ loans per firm Intersection of DealScan, COMPUSTAT, CRSP No finance sector No start-ups 30,641 loans to 5685 firms

12. Loan Statistics (Table I)

13. Sample Definition KMV-Merton Model from Bharath & Shumway (2004) –Equity of firm is call option on firm’s underlying value Strike price=Face Value of debt –Generate expected default frequencies –Rank to identify financially distressed firms

14. Sample Definition: Benefits Model-based mechanism for ex-ante measure of financial distress Used by academics and practitioners Based on probability of default –Not bankruptcy or extinction Lacks survivorship bias Quarterly expected default frequencies (edfs)

15. Sample Definition Specifics SAS Code provided in Bharath & Shumway (2004) 1.Face value of debt = Book value; one year timeline 2.Collect risk-free rates and firm’s market equity 3.Estimate equity volatility from historical stock returns 4.Iteratively solve simultaneous equations for firm value and volatility of firm value: 5.Calculate distance to default: 6. Convert to Expected Default Frequency (edf): edf = N(-DD)

16. Ranked EDFs Rank preserved if Normal distribution incorrect Under normal distribution, rank cutoffs:

17. Sample Definition Analyze firms with edfs ranked 7, 8, or 9 –Create sub-samples with various degrees of distress Include only first matched distressed observation for each firm

18. Failure Definition 3 years after distress identification Denoted with indicator Method of failure: –Delisted from exchange Not due to going private or merging –Halting financial reporting Not due to going private or merging –No recovery to edf below distress rank Omit firms that merge or go private

19. Example Minimum Distress Rank March 1999 March 2000 March 2001 March 2002 Fail 79997 99997 Moore-Handley Inc

20. Example Minimum Distress Rank March 1999 March 2000 March 2001 March 2002 Fail 799971 99997 Moore-Handley Inc

21. Example Minimum Distress Rank March 1999 March 2000 March 2001 March 2002 Fail 799971 999970 Moore-Handley Inc

22. Relationship Loan Definition Distressed loan –In six months prior to distress identification –Closest loan to distress identification Relationship loan –Any lead lender on distressed loan was any prior lender –Denoted with indicator –Tracked through bank mergers

23. Observations By Fiscal Year Table III

24. Table III: Totals (cont’d) Fail Sample Min. Rank Failure Min. Rank ObsRelationshipTotalDelistDistress 77166165.98%47.20%6.08%43.17% 88125766.83%41.21%7.80%36.20% 9967869.03%38.94%9.59%33.19%

25. Other Controls Firm –Age –Leverage: Debt/Market Value of Assets –Operating Profit Margin –Fixed Assets/Total Assets –Net Sales/Total Assets –Assets –Operating Cash/Interest Paid Timing: Distress Date – Loan Date Industry Indicators –Manufacturing, Retail, Wholesale, Services Macroeconomy: CFNAI

26. Sample Statistics: Table IV

27. Table V: Probit Regressions Min. Distress Rank: 7 LHS: Firm Success Significance: *=10% **=5% ***=1* Marginal Effects and p-values

28. Table V: Probit Regressions Min. Distress Rank: 8 LHS: Firm Success Significance: *=10% **=5% ***=1* Marginal Effects and p-values

29. Table V: Probit Regressions Min. Distress Rank: 9 LHS: Firm Success Significance: *=10% **=5% ***=1* Marginal Effects and p-values

30. Findings Evidence that lending relationships are positively related to future of financially distressed firms –Sample must include moderately distressed firms

31. Endogeneity Methodology: Bivariate Probit –Simultaneously predict –Future firm success »Given actual relationship »Includes all controls –Nature of lending relationship »Given instruments »Includes all controls

32. Endogeneity: Instruments Banking Market Concentration –Affects lending policies Banks’ reliance upon relationship loans –HHI(Deposits), winsorized at 1% and 99% Competitive: HHI < 1000 Moderately Concentrated: 1000 <= HHI <= 1800 Concentrated: HHI>1800

33. Endogeneity: Instruments Informational Proxy –Analyst Coverage Indicator of analysts providing quarterly earnings estimates over 4 quarters prior to distress identification Also interact with leverage –Control for influence of debt funding driving analyst coverage

34. Endogeneity: Instruments Lagged Relationship Indicator –From most recent loan prior to distressed loan –Captures firm’s recent reliance upon relationship funding –Does not capture continuity of relationship through distress

35. Sample Statistics: Table IV

36. Rho “…[rho] measures (roughly) the correlation between the outcomes after the influence of the included factors is accounted for.”— Greene (2000) p. 854 If [rho] is insignificant, “the model consists of independent probit equations, which can be estimated separately”—Greene (2000) p. 851

37. Predicting Relationships From Table VII: Coefficients and p-values Significance: *=10% **=5% ***=1*

38. Predicting Future Success From Table VII: Coefficients and p-values Significance: *=10% **=5% ***=1*

39. Findings After controlling for endogeneity, still strong evidence of positive effect of lending relationships on future performance of financially distressed firms –Results not robust to severely distressed firms Decreases in information asymmetry increase likelihood of obtaining a relationship loan Prior firm reliance upon relationship funding predicts future firm reliance upon relationship funding

40. Expanded Sample Purpose: Evaluate impact of lending relationships on future of non- financially distressed firms Method: Allow all firm observations Multiple observation per firm –At least three years apart Vary minimum failure rank: 7, 8 or 9

41. Table VI: Probit Regressions Min. Failure Rank: 7 LHS: Firm Success Significance: *=10% **=5% ***=1* Marginal Effects and p-values

42. Table VI: Probit Regressions Min. Failure Rank: 8 LHS: Firm Success Significance: *=10% **=5% ***=1* Marginal Effects and p-values

43. Table VI: Probit Regressions Min. Failure Rank: 9 LHS: Firm Success Significance: *=10% **=5% ***=1* Marginal Effects and p-values

44. Predicting Relationships From Table VIII: Coefficients and p-values Significance: *=10% **=5% ***=1*

45. Predicting Future Success From Table VIII: Coefficients and p-values Significance: *=10% **=5% ***=1*

46. Robustness Definition of Financial Distress –Low Interest Coverage Ratios –Shumway’s Model DealScan Coverage: Years >= 1992 Inclusion of Merging and Going Private Loan Window –[-6 months, +6 months] –[0, +6 months]

47. Summary of Findings Banking relationships have a significantly positive impact on the future of firms –Robust to degree of failure –Not robust to severely distressed firms –Long-term effect –Publicly traded U.S. firms Relationships determined by: –Analyst coverage –Lagged relationship indicator

48. Consistent Stories Banks find that there is a point beyond which costs of relationship exceed benefits Have found benefits to lending relationships which could stem from: –Monitoring –and/or Controlling –and/or Screening

49. Conclusion Thank you for your time and comments