1. Measuring Inter-Industry Financial Transmission of Shocks October 25 th 2006 Daniel Paravisini Columbia University GSB Federal Deposit Insurance Corporation – CRF 2006 Fall Workshop Work in Progress Report

2. 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?

3. 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)

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

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

11. Portfolio Composition of two Banks in 2002, top industries (2-digit SIC) Bank of America Non-depository institutions Citibank

12. Portfolio Composition of two Banks in 2002, top industries (2-digit SIC) Electric, Gas and Sanitary Services Bank of AmericaCitibank

13. 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

14. 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 10 th -percentile in Q1 Table II: Bank Descriptive Statistics, by exposure (2002)

15. 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

16. 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)

17. 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

18. Baseline Specification Goal: compare variation of outcomes across firms classified by exposure of lenders Y it = α i + α Industry×t + α State×t + β(DumExposed i ).Post t + ε it  Y it : outcome of firm i at quarter t (e.g. leverage)  DumExposed i : 1 if lenders are exposed  Post t : 1 if in Q2 (sample Q1 and Q2 of 2002)  α i : Deviations from firm mean (FE)  α Industry×t, α State×t : Relative to firms in same industry/state

19. Effect on Leverage Table IV: Financial Transmission of Telecom Defaults

20. Specification w/ Bank Heterogeneity Goal 2: account for differential effect across banks Y it = α i + α Industry×t + α State×t + β(DumExposed i ).Post t + + β H (DumExposed i )(DumHedge i ).Post t + ε it  DumHedge i : 1 if lender has high derivative exposure/assets  DumLarge i : 1 if lender is large (assets)  DumLiquid i : 1 if lender is has high liquid assets/assets  Specification includes all direct effects/interactions

21. Effect on Leverage (bank heterogeneity) Table VI: Financial Transmission of Telecom Defaults

22. Effect on Investment Table V: Financial Transmission of Telecom Defaults

23. 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

24. 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