# Presentation: 520607

## On Loan Sales, Loan Contracting, and Lending Relationships

- Steven Drucker
- Columbia Business School
- &
- Manju Puri
- Fuqua School of Business, Duke University
- September 2006

## Motivation

- Loan selling is important for banks and the economy
- Trading Volume: $154.8 billion in 2004 vs. $8.0 billion in 1991
- Diversification; Comply with Risk-Adequacy Regulations
- Allows Origination even when Funding Constrained
- Loan Selling Fundamentally Alters Lending

**Notes:**
Emphasize – tradign volume for SECONDARY LOAN SALES – DIRECT SALES OF COMMERICAL AND INDUSTRIAL LOANS.  Not GOING TO BE TALKING ABOUT MORTGAGES.  THESE ARE STRAIGHT LOAN SALES.  
Diversification – Pavel and Philis 1987, Demsetz 2000, Haubrich and Thompson 1996
Risk-Adequacy – Pennacchi 1988; Pavel and Phillis 1987.  
Capital contratint / internal lending costs – Pennacchi 1988; Carlstrom and Samolyk 1995 – banks facing a binding capital constraint may have difficulty funding loans so use loans sales.

Loan selling fundamentallly alters the lending process
Then go through a Typical Loan--- Banks are “special” collect information, allows for origination.  Then they monitor and manage the risk.  THIS ARRANGEMENT INDUCES THE BANK TO EXERT EFFORT IN INFORMATION PRODUCTION SINCE HOLDS RISK AND ALSO ENCOURAGES ONGOING INTERACTION BETWEEN THE LENDER AND BORROWER – THE SOURCE FO MANY RELATIONSHIP BENEFITS.

Sale separates the screening from the monitoring/funding. This separation raises many issues, two of which are the focus of this study.  

COULD SAY - I AM NOT GOING TO BE TALKING ABOUT SECURITIZATION TODAY – CLOs – WHERE THE LOANS GET POOLED AND TRANCHED INTO NEW SECURITIES. THIS MARKET IS APPROXIMATELY 300 BILLION (IN OUTSTANDING) and another means by which banks can manage credit risk
-- if comes up, why do this vs. securitization?  One reason is simply size.  Most straight loan sales are small in size – 1 to 5 million.  CLO CONSOLIDATOR.  Also, Demarzo (2005, RFS).  CDOs ten to be more expensive than straight sales.
-- Why not just use the CDS market?  Two reasons.  Regultory – motivation to reduce capital charge. CDS market is really only developed for investment grade credits and this market has more risky firms.  (only 8% of CDS is junk).  CDS does have some beneifts – cost reasons and contract flexibilyt – see Duffe and Zhou (JME 2001) for a good compariosn.

## Main Issues

- Loan Sales Separate Origination from Funding
- Agency Problems
- Moral Hazard (Pennacchi 1988; Gorton & Pennacchi 1995)
- Adverse Selection
- How do banks overcome these problems?
- Loan Contracting
- Access to Loans and Lending Relationships
- Harm?
- Reduce interaction that is critical to benefits (Diamond 1991)
- Help?
- Increased Capital From Non-Banks
- Risk-Management  Flexibility in Future Loan Origination

**Notes:**
Moral Hazard – Bank doesn’t have incentive to screen or expost monitor because it doesn’t 
Adverse selction because may collect private information in origination so has incentive to sell lemons

When talk about loan contracting  Say two things.
Loan contracts may be structured to either resolve or get around agency problems.  

Lending relationship – well known to be beneficial – access to capital, lower collateral requirements, help in financial distrress
Many models (diamond 91, boot and thakor) show that these benefits are from continued interaction.  If just a middleman, then this may harm relationships.  But there may actually be benefits to borrowers.   read what is on the slide.

So the goal of this paper is to investigate these two issues – DO WE OBSERVE DIFFERENCES IN LOAN CONTRACTING WHENLOANS ARE SOLD?  HOW DO LOAN SALES AFFECT LENDING RELATIONSHIPS?

## Outline

- Data, Sample Selection, and Descriptive Statistics
- Loan Sales and Loan Contracting
- Secondary Market Sales and Covenants
- Agency Problems or Signaling?
- Loan Sales, Access to Loans, and Lending Relationships
- Current Loans
- Lending Relationships and Future Loans
- Summary

**Notes:**
This is the outline for the talk.  I will begin talking about the novel dataset employed in this study and also give you some interesting facts about the types of borrowers and loans that are sold.  One of the contributions of this paper is that it provides some of the first descriptive evidence on these aspects of the market.

## Data and Sample Selection

- Sample Period: 1999 - 2004
- Data from four sources with some data matched by hand
- Loans & Lending Relationships (LPC *DealScan*)
- Identify Traded Loans (LSTA Mark-to-Market Pricing)
- Borrower Financial Characteristics (Compustat)
- Equity Market Data (CRSP)
- Sample Sizes _[(link)](slide28.xml)_

**Notes:**
Sample Period determined by availablity of data on traded loans
-LPC Dealscan is main loan source – syndicated and some sole-lender loans of medium to large sized borrowers.  Contains Loan Contract terms (INCLUDING COVENANTS), identities of borrower and lender.  We focus on non-financial firms in this study
-LSTA – dataset of seconary market loan quotations gathered by 3rd party providers from relationships with over 30 leading dealers.  We merge this into Dealscan.  Covers 80% of the loan sales market - IMPORTANT – allows us to identify individual loans that are traded. 
-We use crsp and compustat to help with getting firm-level characteristics.  These are matched BY BORROWER NAME – mainly handmatching.  So this is NOT an easy dataset to put together.

BEFORE CLICKING
OUR TESTS WILL USE THIS DATA TO EXAMINE DIFFERENCES IN LOAN COVENANTS ACROSS LOANS THAT ARE SOLD / NOT SOLD.  WHAT WE WILL DO IS look at if loans that are sold have more covenants and also whether they have TIGHTER covenants.  EXPLAIN TIGHTNESS AGAIN.  For this 

CLICK – we will focus on two subsampels in the analysis what I will call net wroth sample and current ratio sample.  Net worth sample includes loans that have a covenant that says the borrower must maintain a minimum net worth or else be in technical default.  Current ratio sample, similar.  
WHY THESE TWO COVENANTS?
-- REASON 1 IS THAT THESE ARE THE TWO COVENANTS THAT OTHER AUTHORS HAVE SHOWN TO BE MOST VIOLATED (BENEISH AND PRESS, SWEENEY).  
-- REASON 2 IS THAT THESE ARE STANDARDIZED, WHICH ALLOW FOR ACCURATE MEASUREMENT.  GO THROUGH THE DEBT ISSUE.

CAN MENTION – ALMOST ALL LOANS HAVE COVENANTS – LSTA REPORTS THAT 95% OF BBB-RATED LOANS AND 80% OF A-RATED LOANS HAVE FINANCIAL COVENANTS.  WITH DEALSCAN THEY DON’T ALWAYS REPORT COVENANTS YIELDING MUCH LOWER PROPORTIONS OF LOANS WITH COVEANTS.  WE DEAL WITH THIS BY REMOVING ALL LOANS WHERE DEALSCAN DOESN’T REPORT COVEANTS.  THIS REMOVES 5% OF THE LOAN SALES SAMPLE AND WE MAY BE THROWING OUT SOME LOANS WHERE LOANS THEY DON’T ACTUALLY HAVE COVENANTS.  BUT IN THE END, WE GET A SAMPLE WITH FINANCIAL COVENANT REPORTING IN LINE WITH REALITY AND ALSO, THESE GUYS WE THROW OUT ARE MORE LIKELY TO BE TO SAFER FIRMS – DIFFERENT FROM THE LOAN SALES SAMPLE.
If someone asks for differences in these sample, click on the field – note that loans in the covenant samples – smaller borrowers, less likely to be rated, closer to financial distress – lower repuation banks.  Provides good samples to study agency problems relative to full sample.

## Sold Loans and Information Asymmetry

- Sold Borrowers are larger and rated _[(link)](slide29.xml)_
- Information Transparency
- Sold Loans are Term Loans instead of Credit Lines
- Monitoring
- Lead Lender on Sold Loan has Higher Market Share
- Reputation

**Notes:**
BEFORE GETTING INTO THE specific tests, there are some interesting differences between sold loans and not sold loans that you may be interested in.
- IN particular, there is some evidence consistent with information asymmetry naturally limiting the amount of loan selling.
- 1) borrowers are larger and rated - Info Transparency may limit the amount of loan selling.
2) Most sold contracts are Term loans
	-Monitoring resasons for this.  Credit lines are more likely to need tob e extensively monitored – borrowers incentive to draw when not doing well. (avery and berger)
3) Reputation provides for more accurage information production – limits agency problems

## Outline

- Data, Sample Selection, and Descriptive Statistics
- Loan Sales and Loan Contracting
- Secondary Market Sales and Covenants
- Agency Problems or Signaling?
- Loan Sales, Access to Loans, and Lending Relationships
- Current Loans
- Lending Relationships and Future Loans
- Summary

**Notes:**
This is the outline for the talk.  I will begin talking about the novel dataset employed in this study and also give you some interesting facts about the types of borrowers and loans that are sold.  One of the contributions of this paper is that it provides some of the first descriptive evidence on these aspects of the market.

## Secondary Market Sales and Covenants

- Selling Loans Can Induce Agency Problems
- Financial Covenants Reduce Reliance on Seller’s Information
- Monitoring Mechanism
- Buyer Exercises Control When Firm Performs Poorly

**Notes:**
Development of market goes in the face of traditional banking theory that says bank will need to do origination, monitoring and funding
Else – no incentive to screen and monitor (P, 1988; G+P, 1995).  Also incentive to sell lemons.

Linked to observable financial data – provided that hard information type devices can be used to monitor, this provides a way to monitor
Control – Critical.  Even if due dilligence is poor or if buy lemon, control given to buyer.

## Secondary Market Sales and Covenants

- Univariate Relationship Between Covenants and Loan Sales
- Multivariate Logit Model (Table 4) _[(link)](slide32.xml)_
- Dependent Variable: Indicator for Loan is Sold
- Key Independent Variables
- # of Financial Covenants (+)
- Net Worth Slack (-)
- Control Variables
- Lender Market Share; Borrower and Loan Variables

**Notes:**
TALK ABOUT WHAT SLACK MEANS!!!  GIVE EXAMPLE.
Lender Market Share: Reputation proxy.  Could be way to mitigate agency problems.  We include this to see the relative importance of reputation vs. contracting provisions.  
EMPHASIZE CONTROL FOR RISK
Borrower Characteristics: 
	Information: Log of Assets (+); Rating Dummy (+)
	Risk: NI-to-Assets (-); DTD (-); Debt-to-Assets (+)
	Other: Industry
Loan Characteristics: Log of Loan size (+); Log of Loan Tenor (+); Loan is syndicated (+); Year of the loan; Type of Loan (TL v. CL); 				Purpose

## Secondary Market Sales and Covenants

- Changes in Predicted Probabilities (link)
- One Std. Dev Change Around the Mean (t-stats in parentheses)

- Key Results:
- More Covenants  Increases Probability of Loan Sale
- Tighter Covenants  Increases Probability of Loan Sale

**Notes:**
IF ANYONE WANTS TO SEE THE FULL SAMPLE RESULTS< PLEASE LET ME KNOW
-- explain that columns.  NOTE THAT WE DON’T USE THE CURRENT RATIO SAMPLE HERE BECAUSE THERE ARE ONLY 17 LOANS SOLD.
If Click on Link – just show that the variables are significant.  Can point out the effect on reputation.
One-Std Deviation is: Num of Fin Covs: 2.1 to 3.5 covenants; Net Worth Slack: 6 percent to 20 percent of assets
Note that the results are robust to restricting to just syndicated loans.

## Are Loans Structured to Sell to Others?

- Sold Loans are Nearly Always Syndicated (Primary Market)
- Loans are Sold Close to the Loan Origination Date (link)

**Notes:**
The story that I have set out for you is one where the selling bank already has in mind that it plans to sell the loan when it does the loan structuring. I am going to suggest that covenants are put in up front to enable selling ex-post. An alternative story that you may have in mind is basically the reverse – the bank does not know which loans it wants to sell, a loan begins to perform poorly, and the only ones it can sell at reasonable prices are those with tight covenants. The ex-ante structuring is going to be key to interpreting the results so let me give you some statistics on why we think this is the right way to think about the problem.  
Sold loans are syndicated up front.  To the extent that there are some similar agency problems in the primary and secondary market, then there may be similar contracting terms.  We will look at this a bit deeeper in a few sldies.
Also, many of the loans in the sample are institutional term loans which include institutional participants.  Anectodal evidence suggests that these are the primary buyers of loans in this market – so this also points to banks structuring loans upfront to sell later
 Sold loans are sold very close to origination.  This seems to suggest that explaining any relationship between sales and contracting should be thought of as “ex-ante” structuring.

## Are Loans Structured to Sell to Others?

- Banks are _not_ dumping poorly performing loans
- Distribution of Initial Sale Prices
- Performance of Sold Borrowers: Origination to Sale

**Notes:**
Finally the alternative view would seem to be strongest for loans that are performing poorly.
We see here that most sold loans are not performing poorly – they are sold close to par value.  And further, the borrowers have not fallen in credit quality.
And if you extend this analysis out 1 to 3 years post-sale, you do not see attrition as far as increased downgrades or big changes in distance-to-default.

All of this is consistent with the idea that banks are structuring loans upfront to be sold in the secondary market.

## Agency or Signaling?

- Syndication: Primary Market Sales
- Additional Mechanisms for Mitigating Agency Problems
- Hold Larger Piece of Loan (e.g. Dennis & Mullineaux 2000)
- Syndicate Structure and Composition (Lee & Mullineaux 2004; Sufi 2005)
- _Expect Smaller Agency Problems_
- Empirical Prediction
- # of Covenants / Tighter Covenants have _LESS_ influence on probability of loan syndication (compared to loan sale)
- Empirical Model: Probability of Syndication (Table 5) link)
- (

**Notes:**
The prior results indicate that sold loans have additional, tighter covenants and that banks are structuring loans up front to sell in the after-market. The next series of tests are intended to explore two hypothesis for the role of covenants – 1 is that covenatns are included in sold loans to resolve agency problems between buyers and sellers.  The second is that covenants are used as a signalling mechanism -  if borrowers signal they are “good” by agreeing to restrictions, this information revelation can facilitate sale.  So how do we separate these>?

We would expect agency problems would be less severe in loan syndication.  For the reasons on the slide.

If covenants are in indeed mitigating agency problems, then would expect them to have less of an effect in facilitating syndication.

COULD ADD - However, syndicated lending is sometimes thought to be similar to public debt because of the many participants on the contract.  Public debt does not have many covenants or tight covenants because of high coordination costs of renegotiating covenants.  So this may limit the use of covenants.

Financial covenants – moving +/- 1⁄2 sd increases 5 to 16% for syndication – was about 12% for loans sales
NW Slack -- moving +/- 1⁄2 sd reduces prob by 2% for syndication – was about 11% for loans sales
CR Slack -- moving +/- 1⁄2 sd reduces prob by 4%to 8% for syndication – was about 11% for loans sales

Depending on Sample – get different means.  
Full sample: Num cov goes from 2.1 to 3.5
NW SAMPLE: Num covs goes from 2.8 to 4, slack from 6% to 20%
CR SAMPLE: Num covs goes from 3.2 to 4.4; slack from .18 to 1.28

## Primary Market Sales and Covenants

- Changes in Predicted Probabilities (link)
- One Std. Dev Change Around the Mean (t-stats in parentheses)

- Compared to Secondary Market Sales:
- More Covenants – Similar Effect
- Tighter Covenants – Weaker Effect

_**Evidence is consistent with agency view**_

**Notes:**
Financial covenants – moving +/- 1⁄2 sd increases 5 to 16% for syndication – was about 12% for loans sales
NW Slack -- moving +/- 1⁄2 sd reduces prob by 2% for syndication – was about 11% for loans sales
CR Slack -- moving +/- 1⁄2 sd reduces prob by 4%to 8% for syndication – was about 11% for loans sales
THIS IS ALL NORMALIZED SO WE CAN DO THIS COMPARISON BETWEEN SYNDICATION AND SALES
Depending on Sample – get different means.  
Full sample: Num cov goes from 2.1 to 3.5
NW SAMPLE: Num covs goes from 2.8 to 4, slack from 6% to 20%
CR SAMPLE: Num covs goes from 3.2 to 4.4; slack from .18 to 1.28

RECALL – for loan sales, num fin covs increased by about 12%, net worth slack was 11%.

So these results show that covenants are more associated with primary market loan selling but the effects are less pronounced than in the secondary market.  This is supportive of covenants being used to facilitate loan selling when agency problems are hgiher – the secondary market.

## Agency or Signaling?

- Lower Reputation Lender  Increased Agency Problems
- Gorton & Haubrich (1987); Pichler & Wilhelm (2001)
- Empirical Prediction
- Low Reputation Lender  # of Covenants / Tighter Covenants have _LARGER_ influence on probability of loan selling
- Modify Loan Sales Model (Table 6) (link)
- Interact Reputation Indicators with Covenant Variables

**Notes:**
Reputation is considered a mechanism for avoiding agency problems – limits shirking on screening and montioring and also prevents selling of lemons.
In loan selling and syndication, we have this formalized in models by G+J amd P+W
G_H, Pichler – fear of reputaiton loss can limit shirking on inofmraiton production and monitoring responsibilities

TOP 10 – higher reputation.

## Lender Reputation

- Changes in Predicted Probabilities of Selling Loans (link)
- One Std. Dev Change Around the Mean (t-stats in parentheses)

- Key Result:
- Tighter Covenants  Increases Probability of Loan Sale when lenders have _LOW REPUTATION_

_**Evidence is consistent with agency view**_

**Notes:**
First note that we have some evidence that lower reputation lenders use more covenants when selling loans.  IN the main sample, they arent statistically different but in the second sample they are (just above the 10% level) 

Main result is there for lender NOT in top 10

## Agency or Signaling?

- Does Restrictive Covenant Package _Signal_ Borrower Quality?
- Empirical Prediction
- High Risk  Covenants have LARGER influence on probability of loan selling
- Modify Loan Sales Model (Table 7) (link)
- Interact Distance-to-Default Indicators with Covenant Variables

- KEY RESULTS: Largest Effect on Loan Sale Probability
- # of Financial Covenants: LOW RISK BORROWERS
- Net Worth Slack: MID RISK BORROWERS

_**Evidence is NOT consistent with signaling view**_

**Notes:**
THE PRIOR TWO RESULTS ARE CONSISTENT WITH THE MORE RESTRICTIVE COVENANT PACKAGES BEING USED IN LOAN SALES WHEN THERE ARE LIKELY TO BE MORE INFORMATION PROBLEMS / AGENCY PROBLEMS WITH THE SELLER.

ANOTHER POSSIBILITY IS THAT COVENANTS ARE USED TO SIGNAL BORROWER QUALITY AND AS A RESULT, LOAN SELLERS BUY THESE CONTRACTS BECAUSE THEY ARE GETTING BETTER BORROWERS.  
SINCE IT IS COSTLY FOR BORROWERS TO TAKE ON A MORE RESTRICTIVE COVENANT PACKAGE, THIS IS A VALID SIGNAL.
-- THE EMPIRICAL PREDICTION: WHEN SIGNALLING IS MOST IMPORTANT, WE WOULD SEE THE RELATIONSHIP BETWEEN COVENANTS AND SALES THE STRONGEST.  THIS WOULD BE THE CASE FOR THE OBSERVABLY HIGH RISK BORROWERS.

IMPORTANT – FOR THE EMPIRICAL PREDICTION
-- this is not a statement that covenants will be tighter for the riskiest firms – it is a statement that the relationship between covenants and loan selling will be strongest for the high risk borrowers.  In other words, we need to be looking at an INTERACTION TERM.

## Outline

- Data, Sample Selection, and Descriptive Statistics
- Loan Sales and Loan Contracting
- Secondary Market Sales and Covenants
- Agency Problems or Signaling?
- Loan Sales, Access to Loans, and Lending Relationships
- Current Loans
- Lending Relationships and Future Loans
- Summary

**Notes:**
Summarize prior section – 
Sold loans have more covenants and tighter covenants.  Additional analysis suggests that covenants are used to reduce agency problems in loan sales.
This raises some interesting questions for the effects on borrowers, particularly related to how loan sales and tight covenants have affected borrowers.  We focus on loan access in this paper.

## Loan Sales and Access to Loans

- Restrictive Covenants / Loan Sales Impose Costs on Borrowers
- Managerial Flexibility
- Additional Lenders  Renegotiation Costs
- Why do Borrowers Agree to Restrictive Covenants / Loan Sales?
- EXPLORE: Access to Private Debt Capital
- Loan Buyers are Usually NonBanks  Additional Capital

**Notes:**
Why do we focus on loan access????
POINT 1 – AN IMPORTANT BY PRODUCT OF TIGHTER COVENANTS AND LOAN SELING IS THAT IT DOES IMPOSE A COST ON BORROWERS.  COVENANTS RESTRICT FLEXIBILITY AND LOAN SELLIN MAY MAKE IT MORE COSTLY TO RENEGOTIATE LOANS IN THE FUTURE DUE TO ADDITIONAL LENDERS WHO MAY NOT HAVE A LONG-TERM VIEW OF THE COMPANY.
CLICK---
POINT 2 – THIS RAISES AN IMPORTANT QUESTION ABOUT WHY A BORROWER WOULD AGREE TO RESTRICTIVE COVENANTS AND HAVING LOANS SOLD
-- INTEREST RATES (Bradley and Roberts – Guner)
POINT 3--
However, an important aspect of loan selling is that by separating origination from funding, non-banks can enter into this market.  This could potentially increase the amount of capital available to borrowers.
SO WE EXPLORE THIS POSSIBILITY

## Current Loans

- Key Results
- Sold Borrowers are Growing and Debt-dependent
- Sold Year: Increase in Private Debt Issuance

**Notes:**
This table gives some information on borrower characteristics.  We track each borrower in the sample over the five year period from 1999 to 2004 and calculate their yearly financial charetistics.  For sold borrowers, we group them into buckets based on when their loans are sold to see if there any noticable time series trends.  We also compare these to the borrowers that are not sold.

CLICK – first we notice that borrowers are generally growing up to and past the year of the sold loan based on their sales figures and also have comparaable market-to-book values.  Importantly, from thinking about the benefits of debt access, sold borrowers are highly leveraged and debt dependent.
CLICK – If we dig deeper into the debt figures, we see that interest-bearing debt levels are increasing and spike in the year of the sold loan and this increase is due to a large increase in private debt issuance.  In general, the sold loan is much larger than non-sold loans.

Putting this together with the earlier results – This all suggests a potential story.
1) large, debt dependent Borrower needs funding – 2) lenders may not be able to fund the whole loan so they look to sell. – 3) The buyer of the loan needs assurances on the loan due to information problems
Covenants are included to do this and the borrower agrees to restrictions in order to get additional financing.

## Loan Sales and Lending Relationships

- Loan Sales Separate Origination from Monitoring / Funding
- Harm Access to Future Credit / Relationships?
- Reduced Ongoing Interaction (Diamond 1991; Boot & Thakor 1994)
- Help Access to Future Credit / Relationships?
- New Source of Capital
- Reduced Exposure to Individual Borrowers  Flexibility

**Notes:**
Beyond Current loans, laon sales may also have an affect on future loans.
Lets go back to the picture – Screening/origination is separate from monitoring.
As theory suggests, this oculd harm relaitonships that rely on ongoing intereactions
But could help relationships also. Allows for a new source of capital to come in.  Also allows bankst o manage risk, increasing flexiblity.

## Loan Sales and Lending Relationships

- Univariate Results (Table 9)

- Sold Borrowers are
- More Likely to Receive Future Loans
- More Likely to Continue Lending Relationships

**Notes:**
To assess the impact of loan selling on relationships, we assess two things.
What percentage of borrowers receive a future loan?  
Conditional on recieivn another loan, how many stay with the bank.

What you see is that borrowers that are sold have more future access to loans.  Future, they are more likely to continue their lending relationships.  So at least the univariate figures don’t suggest that lending relationships are being harmed.

## Loan Sales and Lending Relationships

- Explore: Risk Management  Lending Flexibility
- Do Loan Sales Increase Relationship Durability for High Risk Firms?  High Reputation Lenders?
- Univariate Results (Table 9):
- Conditional on Receiving Another Loan, % that Keep Same Lead Bank

**Notes:**
To dig a bit deeper, a working hypothesis is that the reason why we see this is because loan selling allows for lenders to access additional sources of capital, allowing banks to better share risks, leading to flexibility in relationship lending.  
-- on this point, we know from before that it is exactly among high risk firms that we see the most loan selling and this is where relationships AND RISK MANAGEMENT are likely to be the most important. 
-- IF loan selling does improve flexiblity due to risk-management, then we should see durabiltiy improve the MOST among risky firms.  Also, you may think that higher repuation banks would have more at stake and more incentive to create flexiblity – are there differnces based on the reputation of the bank?

STRONG EVIDENCE ON BAK RISK – suggestive evidence on lender reputation
HIGH RISK BORROWERS SEE THEIR RELATIONSHIP DURABILITY IMPROVE THE MOST
HIGH REP LENDERS SEE THEIR RELATIOSHIPS IMPROVE POST-LAON SELLING

This type of result is consistent with banks – particular high reputaiton banks who may be particular concerned with a borrower defaulting – and high risk firms – those that are most lkely to default – seeing relatinoship durabilyt increase! 

This is preliminary and needs more testing, but my belief is that loan selling is allowing for flexibilty through risk management.

## Loan Sales and Lending Relationships

- Nested Logit Model (Table 10) _[(link)](slide38.xml)_
- Independent Variables: “Borrow Again”
- Borrower Characteristics; Year Fixed Effects
- Independent Variables: “Borrow Again” and “Keep Lender”
- Loan is Sold; Lender Market Share; Prior Lending Relationship

**Notes:**
Of course this could be driven by sample differences so we need a multivaraite model.  We use a nested logit model.  Two stage process.
Borrow again v. not borrow again
	Log of Assets (+); Rating Dummy (+); NI-to-Assets (+); DTD (+); Debt-to-Assets (+)
	Other: Industry; Year
Afecting both stages:
--- Loan is SOLD (+); Lender market share (+) prior lending relationship (+)
MORE REPUTABLE will KEEP BORROWERS.  PRIOR LENDERS WILL HAVE INCREASED ACCESS TO THE FUTURE (DURABLE)

## Outline

- Data, Sample Selection, and Descriptive Statistics
- Loan Sales and Loan Contracting
- Secondary Market Sales and Covenants
- Agency Problems or Signaling?
- Loan Sales, Access to Loans, and Lending Relationships
- Current Loans
- Lending Relationships and Future Loans
- Summary

**Notes:**
This is the outline for the talk.  I will begin talking about the novel dataset employed in this study and also give you some interesting facts about the types of borrowers and loans that are sold.  One of the contributions of this paper is that it provides some of the first descriptive evidence on these aspects of the market.

## Summary

- Loan Selling Separates Origination and Funding
- Agency Problems Loan Contracting
- Access to Loans and Lending Relationships
- Sold Loans Have Additional, Tighter Covenants
- Consistent With Covenants Used When Agency Problems Are Larger
- Signaling Not Supported
- Sold Borrowers: Debt-Dependent, Receive Additional Private Debt
- Benefit to Offset Costs of Restrictive Covenants / Loan Sales
- Sold Borrowers get Future Loans, Keep Lending Relationships
- Additional Capital and Flexibility for Lenders

**Notes:**
LOAN SELLING IS AN ECONOMICALLY IMPORTANT MARKET THAT FUNDAMENTALLY ALTERS LENDING
-- COULD GIVE RISE TO AGENCY PROBLEMS AND WE EXPLORE IF LOAN CONTRACTS ARE DESIGNED WITH THESE PROBLEMS IN MIND
-- COULD ALSO HAVE MAJOR EFFECTS ON LENDING RELATIONSHIPS

WE FIND  -- JUST READ OFF THE SLIDES.

## Tables

## Table 1: Loan Sample Summary Statistics (link)

## Table 2: Univariate Analysis- Sold Loans vs. Not Sold Loans)

## Table 2 (continued):Univariate Analysis - Sold Loans vs. Not Sold Loans (link)

## Table 3: Sold Loans – Timing, Pricing, and Performance)

**(link)**

## Table 4: Probability of Selling Loans  (link)_x000b_

## Table 5: Probability of Loan Syndication (link)

## Table 6: Probability of Selling Loans - Lender Reputation (link)

## Table 7: Probability of Selling Loans – Borrower Risk (link)

## Table 8: Loan Sales and Debt Capital

## Table 9: Univariate Analysis - Keeping the Same Lender

## Table 10: Nested Logit Models- Keeping the Same Lender

## Table 10 (continued): Nested Logit Models – Borrowing Again (link)

## Are Restrictive Covenants Used to Mitigate Agency Problems?

- Primary Market Sales  Lower Agency Problems
- Lower Reputation Lender  More Agency Problems
_**Evidence is consistent with agency view**_

- Compared to Secondary Market Sales (Table 5): link)
- More Covenants – Similar Effect
- Tighter Covenants – Weaker Effect

- Key Result (Table 6) (link)
- Tighter Covenants  Increases Probability of Loan Sale when lenders have _LOW REPUTATION_

**Notes:**
Additional Mechanisms for Mitigating Agency Problems 
Hold Larger Piece of Loan (e.g. Dennis & Mullineaux 2000)
Syndicate Structure and Composition (Lee & Mullineaux 2004; Sufi 2005)
Expect Smaller Agency Problems

Lower Reputation Lender  Increased Agency Problems
Gorton & Haubrich (1987); Pichler & Wilhelm (2001)

Empirical Prediction
Low Reputation Lender  # of Covenants / Tighter Covenants have LARGER influence on probability of loan selling 

Modify Loan Sales Model (Table 6)
Replace Lender Market Share with “Lender in Top-10” Indicator 
Interact Reputation Variables with Covenant Variables

## Do Restrictive Covenants Signal Borrower Quality?

- Empirical Prediction
- High Risk  Covenants have LARGER influence on probability of loan selling
_**Evidence is NOT consistent with signaling view**_

- Results (Table 7): Largest Effect on Loan Sale Probability (link)
- # of Financial Covenants: LOW RISK
- Net Worth Slack: MID RISK – (insignificant for HIGH RISK)

**Notes:**
THE PRIOR TWO RESULTS ARE CONSISTENT WITH THE MORE RESTRICTIVE COVENANT PACKAGES BEING USED IN LOAN SALES WHEN THERE ARE LIKELY TO BE MORE INFORMATION PROBLEMS / AGENCY PROBLEMS WITH THE SELLER.

ANOTHER POSSIBILITY IS THAT COVENANTS ARE USED TO SIGNAL BORROWER QUALITY AND AS A RESULT, LOAN SELLERS BUY THESE CONTRACTS BECAUSE THEY ARE GETTING BETTER BORROWERS.  
SINCE IT IS COSTLY FOR BORROWERS TO TAKE ON A MORE RESTRICTIVE COVENANT PACKAGE, THIS IS A VALID SIGNAL.
-- THE EMPIRICAL PREDICTION: WHEN SIGNALLING IS MOST IMPORTANT, WE WOULD SEE THE RELATIONSHIP BETWEEN COVENANTS AND SALES THE STRONGEST.  THIS WOULD BE THE CASE FOR THE OBSERVABLY HIGH RISK BORROWERS.

IMPORTANT – FOR THE EMPIRICAL PREDICTION
-- this is not a statement that covenants will be tighter for the riskiest firms – it is a statement that the relationship between covenants and loan selling will be strongest for the high risk borrowers.  In other words, we need to be looking at an INTERACTION TERM.

## Signaling?

- Changes in Predicted Probabilities of Selling Loans _[(link)](slide35.xml)_
- One Std. Dev Change Around the Mean (t-stats in parentheses)

- Largest Effect on Loan Sale Probability
- # of Financial Covenants: LOW RISK BORROWERS
- Net Worth Slack: MID RISK BORROWERS

**Notes:**
High distance to default means that you are safer.

-- MORE COVENANTS – MODEL 1 – high DTD is sign different han low DTD.  Also sign diff than mid.
                                    MODEL 2 – High DTD is marginally sign relative to low DTD

-- SLACK: -- MID DTD is sign diff than LOW DTD (t=3.96) and also high (2.43 – but don’t mention that).

## Loan Sales and Lending Relationships

- Changes in Conditional Probability of “Keep” vs. “Don’t Keep” _[(link)](slide38.xml)_

- Loan Selling Improves Relationship Durability for
- Risky Borrowers
- High Reputation Lenders

**Notes:**
If comes up  --- UNOBSERVABILITY ISSUES IN LOAN SELLING – BRING UP THE DISTRESSED VS. NON-DISTRESSED ISSUES.
Risky Borrowers
Difference between Low DTD and High DTD statistically significant at 7% level.
Difference between Mid DTD and High DTD not statist sign (t=1.4)

Reputation
Difference between the two are not sig diff (z=1.21) .  

Comment that this is consistent with allowing banks loan selling creating flexibilty in lending relationships particularly when they will need flexibilty – when risk is high – and when it matters themost – when they have reputational capital at stake.

Prior lending relatiosnhi – moving from 0 to 1 has about a 20% increase in keeping in the future
Reputation – lender in top 10, for example, going from 0 to 1 has about a 35% increase in keeping in the future
BUT MORE ANALYSIS IS NEEDED