1 / 49

The Effect of Banking Relationships on the Future of Financially Distressed Firms

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. Financial Distress. Definition: The inability to make debt payments

gamma
Download Presentation

The Effect of Banking Relationships on the Future of Financially Distressed Firms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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) • Face value of debt = Book value; one year timeline • Collect risk-free rates and firm’s market equity • Estimate equity volatility from historical stock returns • 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 Moore-Handley Inc

  20. Example Moore-Handley Inc

  21. Example 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)

  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 RegressionsMin. Distress Rank: 7 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*

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

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

  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 RegressionsMin. Failure Rank: 7 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*

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

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

  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

More Related