Emilia Bonaccorsi di Patti Banca d’ Italia Anil K Kashyap University of Chicago Booth School of Business, NBER and FRB Chicago Which Banks Recover from a Banking Crisis? 1

Banking crises have been pervasive in the last 3 decades Massive government intervention during the current crisis – Capital injections – Asset purchases – Liability guarantees But little known about which types of rescues or other regulatory interventions work and which do not…. – Is prompt corrective action the right way to go?  Disagreements about the best way for regulators to proceed during a crisis: PCA versus adjustments to portfolio, other? Motivation: Policy 2

Various theories of financial intermediation make different predictions about best response to a large shock Monitoring based theories Presumes special role of banks in providing credit to opaque borrowers  efficiency costs of bank failure governed by changes in credit to opaque borrowers Liquidity provision theories Presumes banks key service is financing on demand  the removal of credit lines is potentially costly, even if the borrowers are large (credit crisis of 2007) Almost no empirical evidence on whether or how banks respond to shocks Motivation: Theory 3

Analyze bank-level data to determine factors that govern recovery from distress. Employ matched bank and borrower data to study how lending policies change after the shock to compare recovering and non-recovering banks Limitations: The (un-modeled) initial shock to profits taken as given – Can’t say anything about how to avoid the shocks or what they are Hope that reduced form analysis is a useful starting point given the absence of prior evidence…. Our paper 4

1.What are the key differences in the characteristics of the banks that do and do not recover? 2.How much of the recovery depends on macro or regional conditions that are out of the hands of the individual banks? 3.To the extent that bank choices do matter, which ones are most important and why? Specific Questions 5

Outline Background on the Italian banking industry Definition of distress and comparison of recovering and non-recovering banks Bank-level analysis of recovery Bank-borrower matched analysis 6

% Recent Italian macro trends 7

Number of banks Banking Developments 8

Defining distress Sample is all bank operating in Italy excluding: i) cooperative banks, ii) foreign bank branches, iii) banks with assets<51 million euros as of 1995, iv) banks with charter less than four years old A bank is defined to be distressed in year t if: i) ROA (profits before tax/total assets) drops by at least 50% ii) the bank moves from above to below the 25 th percentile of the distribution of ROA 9

Year # of banks Total Assets (% of Sample) Median Assets Median ROA Median NPL ratio Default rate Sample of Distressed Banks 10

Recovery is a combination of improved performance and persistence of improvement A bank is considered to have recovered if any holds: 1) At t = 1 its ROA is greater than 25th percentile and at t=2 ROA is greater or equal to ROA the year before the shock or the ROA percentile is greater or equal to the percentile observed the year before the shock; or 2) At t = 2 its ROA is greater than the 25th percentile and at t=3 ROA is greater or equal to ROA the year before the shock, or the ROA percentile is greater or equal to the percentile observed in the year before the shock; or 3) At t = 3 ROA is greater than the 25th percentile and at t=4 ROA is greater or equal to ROA the year before the shock, or the ROA percentile is greater or equal to the percentile observed in the year before the shock  42 recovering banks Defining Recovery 11

North West North East CenterSouth Total banks operating in all years Number of banks experiencing a crisis Number of banks recovering Total banks operating Number of banks experiencing a crisis Number of crisis banks that recovered 6443 Recovering banks 12

Comparing Recovering and Non-Recovering Banks Net income = Interest margin + Other revenues – Operating costs + Net loan write-downs and provisions + Other write-downs and provisions 13

Comparing Recovering and Non-Recovering Banks 14

Recover=1, 0 otherwise, conditional on survival for at least 1 year after shock 1. Macroeconomic factors: recovery is pre-determined and depends on external conditions: regional dummies for Northwest, Northeast and South (Center is excluded) and regional GDP growth in post crisis years 2. Bank-Specific Factors: Recovery is determined by the size of the initial profit decline (use ROA relative to the average value of ROA for banks in the same region) Banks take actions to improve  R/NR banks tend to differ in the default rate after the shock: add the default rate normalized relative to the rate that prevails at banks in the same region  Decompose default rate into parts related to customer type, region and bank’s idiosyncratic risk Logit regressions for banks’ recovery 15

Default rate decomposition Bank Specific Default Rate - National Default Rate ≡ Bank Specific Default Rate – Predicted Bank Specific Rate + “(idiosyncratic)” (Predicted Bank Specific Rate - Predicted Regional Default Rate)+ “(customer)” (Predicted Regional Default Rate – National Default Rate) “(regional)” 16

120 banks 110 banks that exist after year 0 NameDescription 25 th percentile Mean 75 th percentile 25 th percentile Mean 75 th percentile RECOV Recovery NORTHWEST Dummy North Western Region NORTHEAST Dummy North Eastern Region SOUTH Dummy Southern Region D9294 Dummy 1992 to GDPGROWTH Average Regional GDP Growth from years 1 to ROADEV Return on Asset Deviation from industry mean in year DEFAULTDEV Average Default rate deviation from industry portfolio years 1 to 3 NA IDRISK Idiosyncratic Risk NA CUSTRISK Customer Mix Risk NA REGRISK Regional Risk NA

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Results 1.Northeastern banks 35 percentage points more likely to recover than Southern banks 2.Increase regional growth from 25 th percentile to 75 th percentile raised probability of recovery 7 percentage points 3.No differences in recovery probabilities in vs other times 4.Size of the initial drop matters somewhat: comparing 25 th to 75 th percentile, gives 4.7 percentage points higher recovery 5.Default rate is really important: comparing 25 th to 75 th percentile, gives 15 percentage points higher recovery 6.Default rate matters because of the idiosyncratic component 19

Merge firms’ balance sheet and income statement data contained in the Company Accounts Data Set (CADS) with data on loans in Italian Central Credit Register (CR): CADS: proprietary data base containing financial data on a sample of around 25,000 Italian firms (around 49 percent of total sales of nonfinancial firms in the national income accounts) CADS contains a z score measuring the probability of default on a loan computed with linear discriminant analysis (9 point scale) CR contains information on loans for all relationships above a threshold of €75,000 in loans or commitments (more than 900,000 borrowing firms) Matching banks and borrowers 20

Identify all loans in CR made to CADS firms Select any firm that borrowed at least once from the sample banks between Shift to event time, find all borrowers at t=0 & track relationships back to t-1 to t+3 Require €10,000 outstanding  64,200 credit relationships ZSCORE: probability of default & groups firms into 9 categories increasing in risk mapped by CADS into: safe (1, 2), solvent (3, 4), vulnerable (5, 6), risky (7,8,9) Data construction Final data set: 46,600 observations involving 97 banks 21

Data continued Limitation: do not see the entire portfolio; conditional on borrowers affiliated at t=0 But exercise is useful for two reasons: 1.Corporate lending is largest component of portfolios (loans to households 15% in 1995) and are mainly mortgages; 2.Loans to CADS firms is on average more than 30% of loan portfolio for banks in sample. 3.  T hese customers are more insulated from credit reductions than others; any effects that we do find understate what might occur for the smaller more typical bank customers Not confident that we can pinpoint timing, so we measure average values over the post-distress window. 22

MeanStd. Dev.MinMax CREDITGROW Average credit growth between years 1 and 3 for firm i at bank j. The average is computed over the years in which the relationship is observed CREDITUP Dummy equal to 1 if credit in year 3 is greater than credit in year 0; if the relationship is not observed or the firm has defaulted in year 3, credit is measured the in the last year the relationship is observed prior to year RECOV Equal to 1 if the bank recovers, and 0 otherwise EBITNEG Equal to 1 if the firm has negative average Earnings Before Interest and Taxes in years 1 to DEFAULTOTHThe firm defaults between 1 and 3 on a loan at a bank other than bank j DEFAULTThe firm defaults between 1 and 3 on a loan at bank i DZSCORE3Dummy equal to 1 if the zscore of the firm in year 0 is equal to 3, 0 otherwise DZSCORE4Dummy equal to 1 if the zscore of the firm in year 0 is equal to 4, 0 otherwise DZSCORE5Dummy equal to 1 if the zscore of the firm in year 0 is equal to 5, 0 otherwise DZSCORE6Dummy equal to 1 if the zscore of the firm in year 0 is equal to 6, 0 otherwise DZSCORE7Dummy equal to 1 if the zscore of the firm in year 0 is equal to 7, 0 otherwise DZSCORE8Dummy equal to 1 if the zscore of the firm in year 0 is equal to 8, 0 otherwise DZSCORE9Dummy equal to 1 if the zscore of the firm in year 0 is equal to 9, 0 otherwise LOGCREDIT_0Log of total credit to firm i from bank j in year BANKSHARECredit of firm i from bank j divided by total credit of firm j RELLENGTHLog of the length of the relationship in years; the number of years is equal to 4 if greater than Number of observations: total 64,200, of which 46,636 are for borrowers with z-scores 23

CREDITGROW = f(Risk Proxy, Risk Proxy*Recover, Borrower Controls, Bank Fixed Effects) Risk Proxies: EBITNEG, Default & Defaultoth, Z’s Borrower controls: Logcredit_0, Bankshare, Rellength Regression specifications 24

Dependent Variable: CREDITGROW EBITNEG-0.113*** ***-- (0.009)--(0.008)-- EBITNEG*RECOV (0.018)--(0.015)-- DEFAULTOTH-0.035** ***- -(0.017)--(0.015)- DEFAULTOTH*RECOV **- -(0.029)--(0.027)- DEFAULT *** ***- -(0.022)--(0.023)- DEFAULT*RECOV (0.027)-- - DZSCORE ** *** --(0.023)--(0.019) DZSCORE3*RECOV * --(0.038)--(0.036) DZSCORE * *** --(0.025)--(0.019) DZSCORE4*RECOV * ** --(0.031)--(0.027) DZSCORE *** --(0.021)--(0.015) DZSCORE5*RECOV ** *** --(0.027)--(0.024) DZSCORE (0.024)--(0.019) DZSCORE6*RECOV ** ** --(0.031)--(0.029) DZSCORE (0.024)--(0.019) DZSCORE7*RECOV * --(0.030)--(0.029) DZSCORE *** *** --(0.023)--(0.024) DZSCORE8*RECOV * * --(0.036)-- DZSCORE *** *** --(0.034)-- DZSCORE9*RECOV *** *** --(0.047)--(0.049) LOGCREDIT_ ***.0.072***-0.083*** ---(0.008)(0.007)(0.008) RELLENGTH ** ** ---(0.013)(0.012) BANKSHARE ***-0.008***-0.008*** ---(0.001) 1/2BANKSHARE ***0.0001***0.0001*** ---(0.000) CONSTANT0.103***0.103*** ***1.041***1.177*** (0.001) (0.014)(0.108)(0.085)(0.108) BANK FIXED EFFECTS INCLUDED (NOT SHOWN) Adj. R squared Number of observations43,64764,20046,63643,64764,20046,636 25

Main findings Baseline credit growth is 8% per year Negative EBIT  11% lower credit growth per year, Recovering & Non-Recovering banks similar Defaulting firms get 15.5% lower credit growth per year, R & NR banks similar Borrowers that default at other banks +6.7% for NR banks, 0 for R banks (Evergreening!) High Z (7, 8 9) borrowers significantly less credit from R banks (Z=9  for NR banks, for R banks) 26

Other results Borrowers that get more initially tend to have slower subsequent credit growth Borrowers with longer relationships more credit Similar qualitative results if we only check whether credit was higher or lower Similar results if we exclude 3 large banks that account for about half the observations 27

EBITNEG-0.110***-- (0.016)-- EBITNEG*RECOV (0.020)-- DEFAULTOTH-0.142***- -(0.030)- DEFAULTOTH*RECOV *- -(0.042)- DEFAULT ***- -(0.029)- DEFAULT*RECOV (0.042)- DZSCORE *** --(0.018) DZSCORE3*RECOV ** --(0.027) DZSCORE *** --(0.014) DZSCORE4*RECOV (0.030) DZSCORE *** --(0.018) DZSCORE5*RECOV (0.039) DZSCORE (0.020) DZSCORE6*RECOV (0.043) DZSCORE (0.018) DZSCORE7*RECOV (0.044) DZSCORE ** --(0.027) DZSCORE8*RECOV (0.044) DZSCORE *** --(0.042) DZSCORE9*RECOV * --(0.058) 28

EBITNEG-0.089***-- (0.006)-- EBITNEG*RECOV (0.029)-- DEFAULTOTH-0.092***- -(0.020)- DEFAULTOTH*RECOV *- -(0.043)- DEFAULT ***- -(0.020)- DEFAULT*RECOV **- -(0.035)- DZSCORE *** --(0.019) DZSCORE3*RECOV * --(0.036) DZSCORE *** --(0.019) DZSCORE4*RECOV ** --(0.027) DZSCORE *** --(0.015) DZSCORE5*RECOV *** --(0.024) DZSCORE (0.019) DZSCORE6*RECOV ** --(0.029) DZSCORE (0.019) DZSCORE7*RECOV * --(0.029) DZSCORE *** --(0.024) DZSCORE8*RECOV * --(0.036) DZSCORE *** --(0.034) DZSCORE9*RECOV *** --(0.049) 29

1. Banks that get into trouble were lending to riskier clients than the average in the overall economy. 2. Recovery depends both on factors that banks can and cannot control: Size of the initial profit drop that occurs at the onset of distress General business climate after the shock also matters, Adjustments made by the bank in the wake of the shock: ability to adjust the loan portfolio and reduce default rate is critical 3.Matched loan/bank data: recovering banks are more aggressive in trimming lending to the riskiest customers Conclusions 30