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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. Motivations and Goals. Motivations Safety and soundness of banking system Protection of FDIC insurance fund

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benchmarking the performance of us banks

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

motivations and goals
Motivations and Goals
  • Motivations
    • Safety and soundness of banking system
    • Protection of FDIC insurance fund
    • Best allocation of examiner resources
  • Goals
    • Prioritization of on-site examinations
    • Early-warning indicators of troubled banks
objectives of the research
Objectives of the Research
  • Benchmark the U.S. banking system over the last decade
  • Assess performance with DEA-based model
  • Isolate best- and worst-practice banks
  • Support bank auditors by predicting trouble
  • Evaluate DEA in large-scale benchmarking role
previous work
Previous Work
  • Measuring bank management quality with DEA
    • Barr, Seiford, Siems, 1993
  • Bank Failure Prediction Model
    • DEA score as input to logit forecasting model
    • Barr and Siems, 1996
  • Technical report versions available at:
    • www.smu.edu/~barr
data envelopment analysis
Data Envelopment Analysis
  • A methodology for integrating and analyzing benchmarking data that:
    • Performs a multi-dimensional “gap analysis”
    • Considers interactions, tradeoffs, substitutions
    • Integrates all performance measures
    • Gives an overall performance rating
    • Suggests credible organizational goals, benchmarking partners, ….
bank performance model
Bank Performance Model

Inputs

(Resources, Xs)

Outputs

(Desired outcomes, Ys)

  • Earning assets
  • Interest income
  • Noninterest income
  • Salary expense
  • Premises & fixed assets
  • Other noninterest expense
  • Interest expense
  • Purchased funds
defining efficiency
Defining Efficiency
  • Efficiency = ratio of weighted sums of the inputs and outputs (>0)
  • Defines best practice in a DEA model
how dea works
How DEA Works
  • Instead of using fixed weights for all units under evaluation,
    • DEA computes a separate set of weights for each bank
    • Weights optimized to make that bank’s score the best possible
    • Constraints: no bank’s efficiency exceeds 1 when using the same weights
formulating a dea model
Formulating a DEA Model
  • There are many DEA models
  • The basic idea in each is to choose a set of weights for DMU k that:
measuring distance
Measuring Distance

Efficient frontier of best practice

f1

z

f

Inefficient bank

introducing expert judgment
Introducing Expert Judgment
  • Classic models may result in unreasonable weight assignments for inputs & outputs
    • e » 0 weights on unflattering dimensions
    • Can overemphasize secondary factors
  • We added weight multipliers to the DEA
    • Based on survey of 12 FRB bank examiners
    • Used response ranges to set UB/LBs on weights
survey derived constraints
Survey-Derived Constraints

Analytic Hierarchy

Survey range

Survey average

process weights

Inputs

Salary Expense

15.8% - 35.9%

23.10%

25.20%

Premises/Fixed Assets

3.1% - 15.7%

9.60%

11.40%

Other Noninterest Expense

15.8% - 35.9%

22.70%

19.80%

Interest Expense

17.2% - 42.8%

25.90%

23.50%

Purchased Funds

12.1% - 34.0%

18.80%

20.20%

Outputs

Earning Assets

40.9% - 69.5%

51.30%

52.50%

Interest Income

25.7% - 46.9%

34.30%

33.80%

Noninterest Income

10.2% - 20.2%

14.40%

13.70%

banking industry test data
Banking Industry Test Data
  • End of year data for:
    • 1991 11,397 banks
    • 1994 10,224 banks
    • 1997 8,628 banks
  • Used constrained CCR-I model
  • Run with large-scale specialized DEA software
1991 profiles by dea e quartile
1991 Profiles by DEA E-Quartile

1991 data

DEA Efficiency Quartile

most to

1

2

3

4

least efficient

most efficient

least efficient

difference

INPUTS

-0.40%

*

Salary Expense / Total Assets

1.43%

1.54%

1.65%

1.83%

-1.22%

*

Premises and Fixed Assets / Total Assets

1.00%

1.48%

1.76%

2.22%

-0.87%

*

Other Noninterest Expense / Total Assets

1.53%

1.62%

1.84%

2.41%

0.08%

*

Interest Expense / Total Assets

4.71%

4.70%

4.66%

4.62%

-9.78%

*

Purchased Funds / Total Assets

6.29%

8.17%

11.12%

16.07%

OUTPUTS

4.44%

*

Earning Assets / Total Assets

92.68%

91.67%

90.59%

88.24%

0.13%

*

Interest Income / Total Assets

8.68%

8.71%

8.67%

8.55%

Noninterest Income / Total Assets

0.95%

0.79%

0.89%

1.00%

-0.05%

N

2,850

2,848

2,849

2,850

0.2728

*

average efficiency score

0.7340

0.5982

0.5387

0.4611

lower boundary

0.6334

0.5665

0.5092

0.0000

upper boundary

1.0000

0.6334

0.5664

0.5091

* Significant

at 0.01

(Values expressed as a percent of total bank assets)

1997 profiles by dea e quartile
1997 Profiles by DEA E-Quartile

1997 data

DEA Efficiency Quartile

most to

1

2

3

4

least efficient

most efficient

least efficient

difference

INPUTS

Salary Expense / Total Assets

1.67%

1.60%

1.64%

1.75%

-0.08%

-1.45%

*

Premises and Fixed Assets / Total Assets

0.98%

1.55%

1.94%

2.44%

Other Noninterest Expense / Total Assets

1.85%

1.31%

1.50%

1.92%

-0.07%

0.14%

*

Interest Expense / Total Assets

3.29%

3.30%

3.27%

3.15%

-4.85%

*

Purchased Funds / Total Assets

10.46%

12.33%

13.63%

15.32%

OUTPUTS

2.33%

*

Earning Assets / Total Assets

92.99%

92.60%

91.83%

90.65%

Interest Income / Total Assets

7.45%

7.41%

7.37%

7.33%

0.13%

~

0.90%

*

Noninterest Income / Total Assets

1.80%

0.77%

0.84%

0.90%

N

2,157

2,157

2,157

2,157

0.3617

*

average efficiency score

0.6685

0.4313

0.3717

0.3067

lower boundary

0.4722

0.3982

0.3451

0.0000

upper boundary

1.0000

0.4721

0.3981

0.3450

analysis of results
1991 significant differences, Q1-Q4:

All inputs, and most outputs

DEA scores

Changed by 1997:

Inputs: Salary, other non-interest (not sig.)

Outputs: non-interest income now signif.

Noninterest income a new focus for banks

Fee income

Off-balance sheet activities

Analysis of Results
relationship with other metrics
Efficient banks:

Greater return on assets

Higher equity capital

Fewer risky assets

1991 vs. 1997

Not comparable scores

But underlying trends of variables’ importance help explain banking industry changes

Relationship with Other Metrics
frb bank examination criteria
FRB Bank Examination Criteria
  • Capital adequacy
  • Asset quality
  • Management quality
  • Earnings
  • Liquidity
bank examiner ratings
Confidential scores from on-site visits

On each CAMEL factor and overall

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

Bank Examiner Ratings
camel ratings dea scores
Compared CAMEL ratings and DEA efficiency scores

Included banks examined recently:

1991: 7,487 banks

1994: 7,679 banks

1997: 4,494 banks

CAMEL rating groups

Strong: 1 or 2 rating

Weak: 3-5 rating

DEA-score groups

Quintile, by efficiency

If no relationship, each group should contain 20% of each of the other metric’s groups

CAMEL Ratings & DEA Scores
in summary
In Summary
  • DEA useful in benchmarking in service industry
  • Can provide information for examiners, but not perfect predictor
  • Large-scale efficiency analyses can give insight into industry dynamics and structure changes