# Presentation: 323110

## Measuring Inter-Industry Financial Transmission of Shocks

- October 25th 2006
- Daniel Paravisini
- Columbia University GSB

- Federal Deposit Insurance Corporation – CRF 2006 Fall Workshop

**Work in Progress Report**

**Notes:**
I wish to thank the FDIC’s Center for Financial Reasearch for it’s support in this project.
I will present work in progress and I am very looking forward to your comments.

## Motivation

- Financial intermediaries may transmit real shocks across industries
- Loan/equity losses weaken bank balance sheets and induce decline in supply of credit (Holmstrom and Tirole (1997))
- Natural experiment evidence: Peek and Rosengren (1997)
- Chava and Purnanandam (2006), Gan (2006)
- Open questions:
**Cross section**: Through which banks?
**Within banks**: Change with bank characteristics (derivatives, securitization)?
**Time series**: Change with the business cycle, monetary policy?

**Notes:**
This work is part of a research agenda on how frictions in financial intermediation markets affect the allocation of credit and the efficiency of investment

One the channels through which intermediation markets can affect the efficiency of the allocation of capital is by acting as a resonance box and amplifier for real shocks to the economy.

Loan losses in Real Estate, for example, undermine the banks equity and weaken its balance sheet. In the presence of frinctions (financing frictions, for example) this will induce a decline in lending to other sectors.

The best known paper to document this financial transmission channel is Peek and Rosengren. It shows that the Real Estate bust in the early 90s’ caused a decline in lending of Japanese banks in the US. 

The chief advantage of this and other papers based on the Natural experiment approach is to focus on shocks to the balance sheet of the bank that are plausibly uncorrelated with the demand for credit and other fundamentals affecting the credit market outcomes.

The main limitation of the natural experiment approach is that it provides local estimates around the particular shock. Thus, its use islimited in answering quastions that would allow to characterize more generally the nature of the financial transmission channnel.

We would like to know for example:
Cross section: Which banks are more likely to be a conduit for financial transmission? 
Within banks: Does the magnitude of change when bank characteristics change (derivatives, securitization)?
Time series: Does the magnitude of financial transmission change with the business cycle, monetary policy or the overall liquidity of the banking system? 

## This Presentation

- Methodology to measure financial transmission more generally
- Reduced form approach
- Compare firms that differ according to the exposure of **their lenders** to shocks
- Illustrate with application
- Measure the financial transmission of the Telecoms defaults in 2002 (WorldCom, Adelphia)

**Notes:**

The one line intuition of the methodology is: I will compare how the outcomes of two firms react to a shock, when the two firms are identical in all respects except for the exposure of their lenders to the shock. 

## Example: Financial Transmission of Telecoms Defaults in 2002

- Mission Resources Corp

- Swift Energy Company

**JP Morgan Chase** 3.1% of loan portfolio to WorlCom, Adelphia

**Bank One**    	 0.2% of loan portfolio to WorlCom, Adelphia

- Main Lender, Q1 2002

- Texas based, energy sector, similar size

- Differential responses to the shock across otherwise similar firms can be attributed to financial transmission

- WorldCom, Adelphia Default

- Q2 2002

**Notes:**
Differential responses of the outcomes to the telecoms shock across the two otherwise similar firms, can be attributed to financial transmission

The reason is that these two firms share the other fundamental drivers of the demand for credit, the usual confounding factor when identifying financial transmission. 

Basic Description: Mission Resources Corporation is an independent oil and gas company with proved reserves of 41.6 MMBO and 154.1 BCF of natural gas, or 67.3 MMBOE, at year-end 2001. Approximately 62 percent of the company's proved reserves are crude oil. Mission's operating profile combines an aggressive acquisition program, property enhancement through development drilling, and low to moderate risk development exploration to expand reserves, production and cash flow. The company's operations are concentrated in the Permian Basin of West Texas, along the Texas and Louisiana Gulf Coast and in the Gulf of Mexico. The company employs 27 people. 

Swift Energy Company is an independent oil and natural gas company engaged in the development, exploration, acquisition, and operation of oil and gas properties, with a focus in the United States on onshore and inland water areas of the Texas and Louisiana Gulf Coast and in New Zealand on onshore areas of the Taranaki Basin. 

## Main Potential Concerns

- Data requirements
- Bank loan portfolio composition
- Link firms to their lenders
- 			Dealscan
- Sample: large banks, public firms
- Requires differences in bank exposures
- Hedging by banks and firms
- Fraction of lending may overestimate exposure (credit derivatives, loans sales)
- Multiple sources of capital
- 			Potentially find no effect

**Notes:**
Main concerns (I will mention a few and I’m sure that Tony and Matt will have quite a few of their own)

## Results from Telecoms Application

- Banks exposed to defaults reduce supply of credit
- Firms experience a 3 percentage point decline in leverage if their lenders had high exposure to WorldCom/Adelphia before defaults
- Heterogeneity across banks
- Smaller/none for banks with larger use of credit derivatives

## Roadmap

- Data and variable definition
- Dealscan, Call Reports, Compustat
- Proxy for industry composition of loan portfolios
- Descriptive statistics
- Application: transmission of Telecoms defaults
- Classify banks by exposure to Adelphia/Worldcom
- Classify firms by exposure of their lenders
- Firm level specification
- Results
- Conclusions and next steps

## Data: Portfolio Proxy Construction

- Dealscan initial sample (1990-2005):
- 45,459 loans to U.S. firms (96% syndicated)
- 2,706 different lenders
- Missing repayment, renegotiated lines of credit
- Term loans: repaid linearly between origination and maturity
- Credit lines: outstanding until min{maturity, 3 years}
- Lender shares missing/incomplete (72% of facilities)
- Logit on observable characteristics to impute lender shares (lender, year of origination, borrower industry, loan type, lead, deal amount, facility amount, maturity, secured, number of participants)
- 75% of facilities with imputed shares

**Notes:**
I begin with a sample of...

I wish to:
Look at the implied amount outstanding for each of these facilities
The aggregate these amounts up to the bank level to construct portfolio prixies

Several papers suggest that the structure of these loans is in fact predictible. So I impute lender shares using observable loan characteristics, like...

## Descriptive Statistics: Portfolio Proxy

- Calculate amount outstanding for every firm/bank/quarter
- Implied by imputed lender shares and repayment schedule by facility
- Total outstanding by bank/quarter:
- Average 52.3% of C&I loans from Call Reports using the 1995 to 2004 sample
- Substantial time series and cross sectional variation in industry composition of portfolios

## Time Series of Total Bank Portfolio Allocation, top 6 industries (2-digit SIC)

**Notes:**
Lots of variation in the time series

## Portfolio Composition of two Banks in 2002, top industries (2-digit SIC)

- Bank of America

- Non-depository institutions

- Citibank

**Notes:**

I choose a couple of the largest banks in the US in 2002 and show you how the composition of their loan portfolios differs.

Non-depository institutions: these are the financial branch of corporations like GM, GE, or AT&T.

## Portfolio Composition of two Banks in 2002, top industries (2-digit SIC)

- Electric, Gas and Sanitary Services

- Bank of America

- Citibank

**Notes:**

There is substantial variation in exposures.

I will be able to construct ex ante measures of potential exposures to industry shocks.

The question remains if I will able to see anything with these measures if banks are hedging these exposures through the use of derivatives (loan sales).

## Roadmap

- Data and variable definition
- Dealscan, Call Reports, Compustat
- Proxy for industry composition of loan portfolios
- Descriptive statistics
- Application: transmission of Telecoms defaults
- Classify banks by exposure to Adelphia/Worldcom
- Classify firms according to exposure of their lenders
- Firm level specification
- Results
- Conclusions and next steps

## Banks Classified by Fraction of Lending to Adelphia/WorldCom in 2002-Q1

- Debt with 36 banks (avg fraction of loans = 1.7%, median = 0.05%)
- Define a bank as ‘exposed’ if fraction of lending in top 10th-percentile in Q1

**Table II: Bank Descriptive Statistics, by exposure (2002)**

**Notes:**
The Exposed and non-exposed bans are very different.

It would be incorrect to compare lending and other outcomes of banks in each category, since these banks are, at least ex ante, very different from each other.

## Roadmap

- Data and variable definition
- Dealscan, Call Reports, Compustat
- Proxy for industry composition of loan portfolios
- Descriptive statistics
- Application: transmission of Telecoms defaults
- Classify banks by exposure to Adelphia/Worldcom
- Classify firms according to exposure of their lenders
- Firm level specification
- Results
- Conclusions and next steps

## Firms Classified by Exposure of Lender 

- Firms Classified by Exposure of Lender

- Match Dealscan Borrowers with Compustat
- Classify firms by exposure of lenders (weighted by debt amount)

**Table III: Firm Descriptive Statistics, by exposure (2002)**

**Notes:**
Borrowers classified this way are not statistically different from each other.

A priori, the procedure of classifying firms according to the exposure of their lenders does not lead to enormous differences in the two groups.

This is something that has to be addresses more systematically later, but it leads credibility to the identifying assumption that we are not comparing two very dissimilar groups of firms.  

## Roadmap

- Data and variable definition
- Dealscan, Call Reports, Compustat
- Proxy for industry composition of loan portfolios
- Descriptive statistics
- Application: transmission of Telecoms defaults
- Classify banks by exposure to Adelphia/Worldcom
- Classify firms by exposure of their lenders
- Firm level specification
- Results
- Conclusions and next steps

## Baseline Specification

- Goal: compare variation of outcomes across firms classified by exposure of lenders
- Yit = αi + αIndustry×t + αState×t + β(DumExposedi).Postt + εit
- Yit : outcome of firm i at quarter t (e.g. leverage)
- DumExposedi: 1 if lenders are exposed
- Postt : 1 if in Q2 (sample Q1 and Q2 of 2002)
- αi : Deviations from firm mean (FE)
- αIndustry×t , αState×t : Relative to firms in same industry/state

## Effect on Leverage

**Table IV: Financial Transmission of Telecom Defaults **

## Specification w/ Bank Heterogeneity

- Goal 2: account for differential effect across banks
- Yit = αi + αIndustry×t + αState×t + β(DumExposedi).Postt +
- + βH(DumExposedi)(DumHedgei).Postt + εit
- DumHedgei: 1 if lender has high derivative exposure/assets
- DumLargei : 1 if lender is large (assets)
- DumLiquidi : 1 if lender is has high liquid assets/assets
- Specification includes all direct effects/interactions

**Notes:**
RCSD8764 current credit exposure across all off balance sheet derivatives contracts covered by the risk based capital standard
RCSDA534 (notional amount of credit derivates on which the reporting bank is the guarantor)

## Effect on Leverage (bank heterogeneity)

**Table VI: Financial Transmission of Telecom Defaults **

## Effect on Investment

**Table V: Financial Transmission of Telecom Defaults **

## Summary of Results: Telecoms Application

- Effect on supply of credit by exposed bank
- Borrowers of exposed banks experience a 3 percentage point decline in leverage
- Effect on total supply of capital?
- No overall effect on investment, stock returns
- Look deeper into firm heterogeneity
- Evidence of bank heterogeneity
- Smaller effect for banks with larger use of credit derivatives
- No evidence across size or liquid assets

## Conclusion and Next Steps

- Methodology useful to identify financial transmission
- Generalization: aggregate industry defaults/rating migrations (S&P)
- Potential questions
- Which banks are more likely to be a conduit for financial transmission? Does the magnitude change, within banks, when bank characteristics change?
- Which firms can substitute sources of finance?
- Does the magnitude of financial transmission change with the business cycle, monetary policy?
- Financial transmission versus ‘real’ transmission