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Positioning

EconomicTheory

EconometricMethodology

Empirical BankingResults

EmpiricalBanking

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Positioning

EconomicTheory

EconometricMethodology

Empirical BankingResults

EmpiricalBanking

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Rules versus DiscretionHeteroscedastic Model

Cerqueiro, G., H. Degryse and S. Ongena, 2011, Rules versus discretion in loan rate

setting, Journal of Financial Intermediation, 20, 503-529.

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Who Makes the Credit Decisions?

Person or Machine Behind the Desk?

Are credit decisions identical?

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“Rules” versus “Discretion”

• “Rules” – a Computer that uses:– A standardized pricing model– Only objective criteria as inputs

Predictable loan rates

• “Discretion” – a Loan Officer who may:– Add subjective judgements as inputs– Combine different inputs in any subjective way– Make pricing mistakes

Hard-to-predict loan rates

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This Paper

• Uses a heteroscedastic regression model to assess the determinants of the importance of “Rules” and “Discretion” on contracted loan rates

– Loan rates should reflect some latent combination of objective (“rules”) and subjective (“discretion”) criteria

– Heteroscedastic model analyzes how the predictive power of a linear loan-pricing model changes with given firm, market and loan characteristics

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Heterogeneity in Loan Pricing Models

• Sample split regressions (by loan size)– From Degryse & Ongena (JF 2005)

– Specification: Loan Rate = Controls + Residual

Loan Size ($) # Obs. R2

Small (< 5,000) 5,850 0.01

Large (> 50,000) 1,850 0.67

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Econometric Model

• Heteroscedastic regression model:

Mean equation: yi = β'Xi + ui

Variance equation: σi = exp(γ‘Zi)

• Extreme cases:

– “Rules”: R2 of mean equation → 1– “Discretion”: R2 of mean equation → 0

• Model estimated by MLE (normality assumption)

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Conclusions

• Heteroscedastic model identifies determinants of unexplained dispersion of loan rates (“discretion”)

• “Discretion” increases with...– Borrower opaqueness (Switching costs)

– Public information about the firm

• And decreases in...– Loan size (Information search costs)

– Prime Rate

• “Discretion” has decreased over the last 15 years for small loans to opaque firms

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Duration Analysis

Ongena S. and D.C. Smith, 2001, The duration of bank relationships, Journal of

Financial Economics 61, 449-475.

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Motivation

• Theory suggests that the “special” role of a bank arises through the firm-bank relationship

• Learn more about the value of firm-bank relationships

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Overview

• Collect annual data on “bank connections” of Oslo Stock Exchange firms 1979-1995

• Estimate likelihood firm will end a bank relationship – conditional on duration of relationship.– conditional on set of firm characteristics

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Overview

• Control for censoring in the data

• Censoring means that some important information required to make a calculation is not available to us. i.e. censored

– Cannot observe bank relationship before 1979 or after 1995

– Cannot observe bank relationship before listing or after delisting

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Summary of Results

• No strong evidence of duration dependence– Short relationships are as likely to end as long

relationships– Some evidence of non-linear relationship

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Applications

• Whenever data is in the form of a duration:– Span between firm entry and exit– Span in a particular status: e.g.

• Job Tenure• Time before stock price reached a minimum

(max) threshold• Time to sales take off

Event Study Methodology

Ongena S., D. Michalsen, 2003, Firms and their distressed banks: lessons from the Norwegian

banking сrisis, Journal of Financial Economics, 67.1, 81-112.

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Do negative shocks to bank lending cause declines in growth in real sector?

• YES. Bernanke (AER 1983), Bernanke and Blinder (AER 1988), Kahyap, Stein and Wilcox (AER 1993), Slovin, Sushka and Polonchek (JF 1993), ...

• NO. Black (JFE 1975), King and Plosser (AER 1984), ...

• IT DEPENDS (on the financial system) Allen and Gale (2000), Rajan and Zingales (JACF 1998, 2000), and Greenspan (1999).

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This Study

• We take as our laboratory the near-collapse of Norwegian banking system between 1988-91:– Take first announcements of bank distress.– Couple announcements with information linking publicly-

listed firms to their banks.– Conduct an event study of impact of bank distress

announcements on stock price of firms associated with banks.

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Cumulative Abnormal Returns

1,0,1, ˆˆˆ)1,1( jjjCAR

jt

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20kjktjkmtjjjt εδγrβαr

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Event Study Results(Using a World Market Index, in %)

CAR window Bank CAR Firm CAR

(-1, +1) -10.6** -1.7**

(-3, +3) -11.2* 1.5

(0, +10) -9.6 0.9

(-10, -1) 0.1 -0.3

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Conclusions

• Present evidence from Norwegian banking crisis suggesting publicly-listed firms are not hurt when their bank becomes distressed.

• Evidence contrasts with studies from East Asian countries, in particular Japan.

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Possible Explanations

• We miss informationally relevant dates or firms are squeezed and switch before distress dates

• Negative and significant bank CARs

• Ultimate collapse was not credible, investors knew Government would step in.

• No bank failure since 1923• Period of deregulation: bank industry against intervention• Norion Bank: under administration, liquidated in 1989, some depositors loose

• Press: uncertainty about bail-out, exact structure

• CRISIS LENGTH

• Publicly-listed firms versus other firms• Other studies also focus on publicly listed firms

• LN SALES, AGE

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Conclusion Event Study

• Can be very useful if appropriate (and well motivated)

• Can be linked with other empirical models

• But be aware of:– pitfalls– unpopularity in some quarters …

Choice of Bank Nationality and Reach

Nested Multinomial Logit

Berger A.N., Q. Dai, S. Ongena and D.C. Smith, 2003, Тo what extent will the banking industry be globalized? A study of bank nationality and reaсh in 20 European

nations, Journal of Banking and Finance, 27, 383-415.

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Will the banking industry be globalized?

• Anecdotal Evidence mixed:

– Yes. Barriers to competition and entry have fallen dramatically.

– No. Few financial institutions are taking advantage of the barrier reductions.

• Better question might be:

– Not when or if banking industry will become globalized,

– But the extent to which it will be globalized.

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Answering the question: Our study

• Examines the provision of cash management banking services to foreign affiliates of large multinational corporations.

– “Cash management” refers to virtually all core banking services (lending, deposit-taking, etc.), but on short-term basis.

– “Foreign affiliates” refers to the operations of multinational corporations outside the nation in which they are headquartered.

– Sample includes over 2,000 affiliates operating in 20 European nations.

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Definitions• Bank Nationality

– A host nation bank is headquartered in the nation in which the affiliate operates.

– A home nation bank is headquartered in the nation of the affiliate’s corporate headquarters.

– A third nation bank is headquartered outside the host and home nations.

• Bank Reach– A global bank provides services to sample firms in at least 9 of

the 20 host nations and has at least $100 billion in assets in 1995.

– A local bank provides services to sample firms only in the nation of the bank’s headquarters.

– A regional bank is neither global nor regional.

Multinomial Logit

(Unordered dependent variable case)

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Using logit modelsfor more than two outcomes.

• We could construct logit models to compare dichotomous outcomes (e.g. “Host” = 1 or 0)

• Problem: we lose a lot of information, example: if we do a logit on “Host” = 1 or 0, we may not get a statistically significant effect of size if large firms:

• are less likely to choose “Third” (“Host” = 0)• and more likely to choose “Home” (“Host” = 0)

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Definition of a multinomial logit:

• In a multinomial logit model, we have a set of covariates that predicts ln(p2/p1), ln(p3/p1), …

all at the same time.

where p1 , p2 , p3 , … refer to all possible outcome categories

and where p1 refers to the comparison category.

• Also called the simultaneous fitting approach

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Bank Nationality

The Tree of probabilities:

Pr(host) 65.5%

(1,387 firms)

Pr(home)17.7%

(374 firms)

Pr(third)16.8%

(357 firms)

Pr(global| host)

20.5%(285 firms)

Pr(regional| host)

61.1%(847 firms)

Pr(local| host)

18.4%(255 firms)

Pr(global| home)

62.3%(233 firms)

Pr(regional| home)

37.7%(141 firms)

Pr(global| third)

63.3%(226 firms)

Pr(regional| third)

36.7%(131 firms)

Bank Reach

 Total sample of foreign affiliates across 20 European nations(2,118 firms)

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Summary and ConclusionsExamine two dimensions of globalization:

bank nationality and bank reach.

• Show that firms, by far, prefer host nation banks to home or third nation banks – 2/3 host nation banks, 1/3 split between home and third nation banks

• Choice of reach depends on choice of nationality.• Very low levels of financial development (i.e. in former

socialist nations) imply lower usage of host nation banks and stronger preference for global banks

Are there limits to the Globalization of the Banking Industry?