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1 Benchmarking the Performance of US Banks R. Barr, SMU T. Siems, Federal Reserve Bank of Dallas S. Zimmel, SMU Financial Industry Studies, Dec. 1998: www.dallasfed.org

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2 Motivations and Goals n Motivations u Safety and soundness of banking system u Protection of FDIC insurance fund u Best allocation of examiner resources n Goals u Prioritization of on-site examinations u Early-warning indicators of troubled banks

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3 Objectives of the Research n Benchmark the U.S. banking system over the last decade n Assess performance with DEA-based model n Isolate best- and worst-practice banks n Support bank auditors by predicting trouble n Evaluate DEA in large-scale benchmarking role

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4 Previous Work n Measuring bank management quality with DEA u Barr, Seiford, Siems, 1993 n Bank Failure Prediction Model u DEA score as input to logit forecasting model u Barr and Siems, 1996 n Technical report versions available at: u www.smu.edu/~barr

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5 Data Envelopment Analysis n A methodology for integrating and analyzing benchmarking data that: u Performs a multi-dimensional gap analysis u Considers interactions, tradeoffs, substitutions u Integrates all performance measures u Gives an overall performance rating u Suggests credible organizational goals, benchmarking partners, ….

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6 Bank Performance Model Inputs (Resources, Xs) Outputs (Desired outcomes, Ys) Salary expense Premises & fixed assets Other noninterest expense Interest expense Purchased funds Earning assets Interest income Noninterest income

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7 Defining Efficiency n Efficiency = ratio of weighted sums of the inputs and outputs (>0) n Defines best practice in a DEA model

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8 How DEA Works n Instead of using fixed weights for all units under evaluation, u DEA computes a separate set of weights for each bank u Weights optimized to make that banks score the best possible u Constraints: no banks efficiency exceeds 1 when using the same weights

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9 Formulating a DEA Model n There are many DEA models n The basic idea in each is to choose a set of weights for DMU k that:

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10 Measuring Distance f f1f1f1f1 z Efficient frontier of best practice Inefficient bank

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11 Introducing Expert Judgment n Classic models may result in unreasonable weight assignments for inputs & outputs 0 weights on unflattering dimensions 0 weights on unflattering dimensions u Can overemphasize secondary factors n We added weight multipliers to the DEA u Based on survey of 12 FRB bank examiners u Used response ranges to set UB/LBs on weights

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12 Survey-Derived Constraints Analytic Hierarchy Survey rangeSurvey averageprocess weights Inputs Salary Expense15.8% - 35.9%23.10%25.20% Premises/Fixed Assets 3.1% - 15.7%9.60%11.40% Other Noninterest Expense15.8% - 35.9%22.70%19.80% Interest Expense17.2% - 42.8%25.90%23.50% Purchased Funds12.1% - 34.0%18.80%20.20% Outputs Earning Assets40.9% - 69.5%51.30%52.50% Interest Income25.7% - 46.9%34.30%33.80% Noninterest Income10.2% - 20.2%14.40%13.70%

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13 Banking Industry Test Data n End of year data for: u 199111,397 banks u 199410,224 banks u 19978,628 banks n Used constrained CCR-I model n Run with large-scale specialized DEA software

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14 1991 Profiles by DEA E-Quartile (Values expressed as a percent of total bank assets) 1991 data DEA Efficiency Quartile most to 1234 least efficient most efficientleast efficientdifference INPUTS Salary Expense / Total Assets1.43%1.54%1.65%1.83% -0.40%* Premises and Fixed Assets / Total Assets1.00%1.48%1.76%2.22% -1.22%* Other Noninterest Expense / Total Assets1.53%1.62%1.84%2.41% -0.87%* Interest Expense / Total Assets4.71%4.70%4.66%4.62% 0.08%* Purchased Funds / Total Assets6.29%8.17%11.12%16.07% -9.78%* OUTPUTS Earning Assets / Total Assets92.68%91.67%90.59%88.24% 4.44%* Interest Income / Total Assets8.68%8.71%8.67%8.55% 0.13%* Noninterest Income / Total Assets0.95%0.79%0.89%1.00%-0.05% N2,8502,8482,8492,850 average efficiency score0.73400.59820.53870.4611 0.2728* lower boundary0.63340.56650.50920.0000 upper boundary1.00000.63340.56640.5091* Significant at 0.01

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15 1997 Profiles by DEA E-Quartile 1997 data DEA Efficiency Quartile most to 1234 least efficient most efficientleast efficientdifference INPUTS Salary Expense / Total Assets1.67%1.60%1.64%1.75%-0.08% Premises and Fixed Assets / Total Assets0.98%1.55%1.94%2.44% -1.45%* Other Noninterest Expense / Total Assets1.85%1.31%1.50%1.92%-0.07% Interest Expense / Total Assets3.29%3.30%3.27%3.15% 0.14%* Purchased Funds / Total Assets10.46%12.33%13.63%15.32% -4.85%* OUTPUTS Earning Assets / Total Assets92.99%92.60%91.83%90.65% 2.33%* Interest Income / Total Assets7.45%7.41%7.37%7.33%0.13%~ Noninterest Income / Total Assets1.80%0.77%0.84%0.90% * N2,157 average efficiency score0.66850.43130.37170.3067 0.3617* lower boundary0.47220.39820.34510.0000 upper boundary1.00000.47210.39810.3450

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16 Analysis of Results n 1991 significant differences, Q1-Q4: u All inputs, and most outputs u DEA scores n Changed by 1997: u Inputs: Salary, other non-interest (not sig.) u Outputs: non-interest income now signif. n Noninterest income a new focus for banks u Fee income u Off-balance sheet activities

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17 Other Bank Performance Metrics

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18 Relationship with Other Metrics n Efficient banks: u Greater return on assets u Higher equity capital u Fewer risky assets n 1991 vs. 1997 u Not comparable scores u But underlying trends of variables importance help explain banking industry changes

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19 FRB Bank Examination Criteria n Capital adequacy n Asset quality n Management quality n Earnings n Liquidity

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20 Bank Examiner Ratings n Confidential scores from on-site visits n On each CAMEL factor and overall n Values from 1 to 5 1 = sound in every respect 2 = sound, modest weaknesses 3 = weaknesses that give cause for concern 4 = serious weaknesses 5 = critical weaknesses, failure probable

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21 CAMEL Ratings & DEA Scores n Compared CAMEL ratings and DEA efficiency scores n Included banks examined recently: 1991: 7,487 banks 1994: 7,679 banks 1997: 4,494 banks n CAMEL rating groups u Strong: 1 or 2 rating u Weak: 3-5 rating n DEA-score groups u Quintile, by efficiency n If no relationship, each group should contain 20% of each of the other metrics groups

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22 Efficiency vs. CAMEL Ratings

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23 Strong vs. Weak CAMELs

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24 In Summary n DEA useful in benchmarking in service industry n Can provide information for examiners, but not perfect predictor n Large-scale efficiency analyses can give insight into industry dynamics and structure changes

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