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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
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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 Why distressed firms are special: Critical need of funding True financial position unknown
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
Objective • Determine the effect that banking relationships have on the future success of financially distressed firms • Address Endogeneity
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)
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
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
Contribution • Long-term perspective • Publicly traded U.S. firms • Address endogeneity
Null Hypothesis Banking relationships have no effect on the future success of financially distressed firms.
Methods • Probit regressions • Effect of banking relationships on the probability of future success • Control for firm, loan timing, industry, macroeconomy, and information asymmetry
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
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
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)
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)
Ranked EDFs • Rank preserved if Normal distribution incorrect • Under normal distribution, rank cutoffs:
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
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
Example Moore-Handley Inc
Example Moore-Handley Inc
Example Moore-Handley Inc
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
Observations By Fiscal Year Table III
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
Table V: Probit RegressionsMin. Distress Rank: 7 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*
Table V: Probit RegressionsMin. Distress Rank: 8 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*
Table V: Probit RegressionsMin. Distress Rank: 9 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*
Findings • Evidence that lending relationships are positively related to future of financially distressed firms • Sample must include moderately distressed firms
Endogeneity • Methodology: Bivariate Probit • Simultaneously predict • Future firm success • Given actual relationship • Includes all controls • Nature of lending relationship • Given instruments • Includes all controls
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
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
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
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
Predicting Relationships From Table VII: Coefficients and p-values Significance: *=10% **=5% ***=1*
Predicting Future Success From Table VII: Coefficients and p-values Significance: *=10% **=5% ***=1*
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
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
Table VI: Probit RegressionsMin. Failure Rank: 7 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*
Table VI: Probit RegressionsMin. Failure Rank: 8 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*
Table VI: Probit RegressionsMin. Failure Rank: 9 Marginal Effects and p-values LHS: Firm Success Significance: *=10% **=5% ***=1*
Predicting Relationships From Table VIII: Coefficients and p-values Significance: *=10% **=5% ***=1*
Predicting Future Success From Table VIII: Coefficients and p-values Significance: *=10% **=5% ***=1*
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]
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
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
Conclusion Thank you for your time and comments