# Presentation: 753572

## 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

## Loan Statistics(Table I)

## 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)

**Notes:**
First equation in #4 is the Black-Scholes-Merton option valuation equation
Mu is an estimate of the expected annual return of the firm’s assets
V denotes firm value
F is face value of debt
T is time (one-year horizon)
Book value of debt is 1⁄2 of current liabilities + all long-term debt (data45 and data51)
Moody’s KMV uses its large historical database to estimate the empirical changes in distances to default and it calculates default probabilities based on that distribution.

## 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

| Minimum Distress Rank | March 1999 | March 2000 | March2001 | March 2002 | 
Fail |
| --- | --- | --- | --- | --- | --- |
| 7 | 9 | 9 | 9 | 7 |  |
| 9
 | 9 | 9 | 9 | 7 |  |

- Moore-Handley Inc

## Example

| Minimum Distress Rank | March 1999 | March 2000 | March2001 | March 2002 | 
Fail |
| --- | --- | --- | --- | --- | --- |
| 7 | 9 | 9 | 9 | 7 | 1 |
| 9
 | 9 | 9 | 9 | 7 |  |

- Moore-Handley Inc

## Example

| Minimum Distress Rank | March 1999 | March 2000 | March2001 | March 2002 | 
Fail |
| --- | --- | --- | --- | --- | --- |
| 7 | 9 | 9 | 9 | 7 | 1 |
| 9
 | 9 | 9 | 9 | 7 | 0 |

- 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

## Table III: Totals (cont’d)

|  |  |  |  | Fail |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Sample Min. Rank | Failure Min. Rank | Obs | Relationship | Total | Delist | Distress |
| 7 | 7 | 1661 | 65.98% | 47.20% | 6.08% | 43.17% |
| 8 | 8 | 1257 | 66.83% | 41.21% | 7.80% | 36.20% |
| 9 | 9 | 678 | 69.03% | 38.94% | 9.59% | 33.19% |

## 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

## Sample Statistics: Table IV

## Table V: Probit RegressionsMin. Distress Rank: 7

- Table V: Probit RegressionsMin. Distress Rank: 7

- LHS: Firm Success

- Significance:
- *=10% **=5% ***=1*

- Marginal Effects and p-values

## Table V: Probit RegressionsMin. Distress Rank: 8

- Table V: Probit RegressionsMin. Distress Rank: 8

- LHS: Firm Success

- Significance:
- *=10% **=5% ***=1*

- Marginal Effects and p-values

## Table V: Probit RegressionsMin. Distress Rank: 9

- Table V: Probit RegressionsMin. Distress Rank: 9

- LHS: Firm Success

- Significance:
- *=10% **=5% ***=1*

- Marginal Effects and p-values

## 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

## Sample Statistics: Table IV

## 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 RegressionsMin. Failure Rank: 7

- Table VI: Probit RegressionsMin. Failure Rank: 7

- LHS: Firm Success

- Significance:
- *=10% **=5% ***=1*

- Marginal Effects and p-values

## Table VI: Probit RegressionsMin. Failure Rank: 8

- Table VI: Probit RegressionsMin. Failure Rank: 8

- LHS: Firm Success

- Significance:
- *=10% **=5% ***=1*

- Marginal Effects and p-values

## Table VI: Probit RegressionsMin. Failure Rank: 9

- Table VI: Probit RegressionsMin. Failure Rank: 9

- LHS: Firm Success

- Significance:
- *=10% **=5% ***=1*

- Marginal Effects and p-values

## 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