Should Banks Be Diversified? Evidence from Individual Bank Loan Portfolios
ABSTRACT We study the effect of loan portfolio focus versus diversification on the return and the risk of 105 Italian banks over the period 1993–99 using data on bank-by-bank exposures to different industries and sectors. We find that diversification is not guaranteed to produce superior performance and/or greater safety for banks. For high-risk banks, diversification reduces bank return while producing riskier loans. For low-risk banks, diversification produces either an inefficient risk-return trade-off or only a marginal improvement. Our results are consistent with a deterioration in the effectiveness of bank monitoring at high risk-levels and upon lending expansion into newer or competitive industries.
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ABSTRACT: Theoretical studies have noted that loan applicants rejected by one bank can apply at another bank, systematically worsening the pool of applicants faced by all banks. This study presents the first empirical evidence of this effect and explores some additional ramifications, including the role of common filterssuch as commercially available credit scoring modelsin mitigating this adverse selection; implications for de novo banks; implications for banks' incentives to comply with fair lending laws; and macroeconomic effects. The evidence supports the simple theory regarding loan loss rates but indicates a positive association between bank structure and income growth. Journal of Economic Literature Classification Numbers: G21, D80, L10. Copyright 1998 Academic Press.Journal of Financial Intermediation 09/1998; 7(4):359-392. · 1.81 Impact Factor
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BIS Working Papers
Should banks be diversified?
Evidence from individual bank
by Viral V Acharya, IftekharHasan and AnthonySaunders
Monetary and Econom ic Departm ent
Septem ber 2002
BIS Working Papers are written by members of the Monetary and Economic Department of the Bank
for International Settlements, and from time to time by other economists, and are published by the
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© Bank for International Settlements 2002. All rights reserved. Brief excerpts may be reproduced
or translated provided the source is cited.
We study empirically the effect of focus (specialization) vs. diversification on the return and the risk of
banks using data from 105 Italian banks over the period 1993–1999. Specifically, we analyze the
tradeoffs between (loan portfolio) focus and diversification using a unique data set that is able to
identify individual bank loan exposures to different industries, to different sectors, and to different
geographical regions. Our results are consistent with a theory that predicts a deterioration in bank
monitoring quality at high levels of risk and a deterioration in bank monitoring quality upon lending
expansion into newer or competitive industries. Our most important findings are that industrial loan
diversification reduces bank return while endogenously producing riskier loans for all banks in our
sample (this effect being most powerful for high risk banks), sectoral loan diversification produces an
inefficient risk–return tradeoff only for high risk banks, and geographical diversification results in an
improvement in the risk–return tradeoff for banks with low levels of risk. A robust result that emerges
from our empirical findings is that diversification of bank assets is not guaranteed to produce superior
performance and/or greater safety for banks.
JEL Classification: G21, G28, G31, G32
Keywords: Focus, Diversification, Monitoring, Bank risk, Bank return
Should financial institutions (FIs) and banks be focused or diversified? What is the effect
of focus and diversification on the quality of the loan portfolio of FIs and banks? Does
diversification, based on traditional portfolio theory wisdom, lead to greater safety for FIs
and banks? In this paper, we undertake an empirical investigation of these questions. The
evidence we present suggests that, in contrast to the recommendations of traditional portfolio
and banking theories, diversification of bank assets is not guaranteed to produce superior
return performance and/or greater safety for banks.
There are several reasons why the focus vs. diversification issue is important in the context
of FIs and banks. First, FIs and banks can enjoy a great deal of flexibility in achieving either
focus or diversification compared to ordinary firms by investing or disinvesting financial
claims (loans) in certain industries and markets. In contrast, a standard corporation has
a somewhat limited choice in expanding its product range and the transaction costs of
adjusting its portfolio of real–sector activities may be high. In addition, FIs face several
(often conflicting) regulations that create incentives either to diversify or focus their asset
portfolios, such as the imposition of capital requirements that are tied to the risk of assets,
branching and asset investment restrictions, etc. Hence, from an economic as well as a policy
standpoint, it is interesting to ask if FIs and banks benefit from diversification of their loan
portfolio to more industries and countries.
Finally, the very nature of an intermediary’s business activities makes the question of
focus versus diversification an interesting economic issue to explore. FIs and banks act
as “delegated monitors” in the sense of Diamond (1984). The very act of performing this
delegated monitoring function renders them “special” on the lending side in that they have
(at least some form of) information monopoly over the firms they lend to, as noted by
Fama (1980, 1985), and James (1987), and as modelled by Rajan (1992) and Sharpe (1990).
The downside risk of borrowing firms translates into the riskiness of the loans held by FIs
and banks. The quality of banks’ and FIs’ delegated monitoring thus directly affects the
1We acknowledge the Interbank Deposit Protection Fund of Italy (FITD) and the Italian Bankers’ Asso-
ciation for providing us with the data set employed in this paper, to Cristiano Zazzara and Marco Pellegini
for their help in acquisition, translation, and understanding of this publicly available data set, and the Bank
for International Settlements (BIS) for provision of data on stock market indices for Italy. We thank Linda
Allen, Mike Fishman, Robert Hauswald, Philip Lowe, Mitch Petersen, Paola Sapienza, Henri Servaes, and
the seminar participants at London Business School, Rutgers, INSEAD, Cambridge, Indian Institute of Man-
agement (IIM) – Ahmedabad, IIM – Bangalore, London School of Economics, Oxford, BIS, ICICI Research
Centre (India) and Federal Reserve Bank of Chicago Conference on Bank Structure and Competition, for
very useful comments. This paper is part of a project carried out when Viral Acharya visited the BIS during
July 2001. Iftekhar Hasan acknowledges the support of Bank of Finland. The views expressed are exclusively
those of the authors.
endogenous quality of their loans and in turn their default risk. However, due to equityholder–
creditor conflicts, incentives to monitor are affected by the extent of debt in the FI’s capital
structure and the downside risk of the firms to whom the FI lends.2
For the sake of illustration, consider the extreme case where the FI’s debt level is ex-
tremely high so that all benefits from monitoring accrue only to creditors (e.g., uninsured
depositors and providers of borrowed funds). In this case, bankowners (equityholders or
managers assumed to be fully aligned with equityholders) have little “incentive to monitor.”
In general, the FI’s underinvestment in monitoring will be more severe the greater its debt
or leverage or in banking terminology the lower its capital ratio(s). All else being equal,
this implies that the FI’s underinvestment in monitoring will be more severe the greater its
downside risk of failure. Under such an incentive structure, can FIs and banks monitor their
loans effectively as they expand into different industries and segments of the loan markets?
How does the decision to be focused or diversified affect their monitoring incentives and the
endogenous quality, i.e., the risk and the return, of their loans?
To this end, we examine data on the asset and loan portfolio composition of individual
Italian banks during the period 1993–1999. The choice of Italian banks is driven by the
availability of detailed data on the industrial, sectoral, and geographical composition of
their balance-sheets. By contrast, in the United States, publicly available data on bank loan
portfolios is restricted to call reports which do not contain such “fine” asset decompositions.3
In particular, U.S. regulators do not provide a breakdown of individual (or aggregate) bank
lending to specific industries or industrial sectors. Instead, the general level of disaggregation
is highly “macro” in nature, e.g., household sector loans, commercial and industrial loans,
etc. We obtain results that are sufficiently strong and robust to warrant a closer look at
the wisdom of simply advocating banks to diversify as much as possible, and suggest a more
careful assessment needs to be made of the costs and benefits of diversification in banking
2For example, a survey of bank defaults by the Office of the Comptroller of the Currency in 1988 examining
defaulted banks in the preceding decade established that the asset quality of these banks played an important
role along with internal controls in determining their financial health (OCC, 1988).
3In fact, the production of greater information by Italian banks occurred following a major crisis in the
banking system in the early 1990s.
4While there are natural differences between the banking sectors of any two countries, there are several
dimensions along which the Italian banking system is similar to that in the U.S.: (1) Unlike other banking
systems in Continental Europe, Italy has a large number of banks (about 850 at the beginning of our
sample) giving rise to a less concentrated banking system like that of the U.S. (2) The branching restrictions
on banks in Italy were removed in 1990 as they were in the U.S. in the mid 1980s. (3) There has been a
wave of consolidation in the banking system in 1990s mirrorring that in the U.S. (4) The banking system
comprises of a few very large banks and a large number of medium-to-small sized banks as in the U.S. In
addition, the risk levels of Italian banks in our sample exhibit economically significant variability, from being
very safe to being very risky, which lends an element of robustness and generality to our results. Finally,
Some of these issues have been examined at a theoretical level in a recent paper by
Winton (1999). Specifically, Winton presents a theoretical framework to investigate the
merit to FIs and banks of the proverbial wisdom of not putting all your eggs in one basket.
The essence of Winton’s model lies in understanding that the quality of bank loan portfolios
is endogenous: it is determined, in part, by the levels of monitoring induced by a change in
the bank’s focus or diversification.5Winton’s model provides a number of testable empirical
hypotheses which we use to frame the empirical tests below. These hypotheses are central
to the focus versus diversification debate in banking and we discuss them below:
(inverted U–shaped). To be precise, diversification across loan sectors helps a bank’s return
most when loans have moderate exposure to sector downturns (downside risk)6; when loans
have low downside risk, diversification has little benefit; when loans have sufficiently high
downside risk, diversification may actually reduce returns.
The relationship between bank return and diversification is non–linear in bank risk
INSERT FIGURE 1 HERE.
From traditional portfolio theory, we know that diversification increases the central ten-
dency of the distribution of a loan portfolio. However, as Winton (1999) notes, when debt is
risky and high enough compared to this central tendency, diversification can in fact increase
the probability of default. For the sake of illustration, Figure 1 plots the cumulative prob-
ability function for two normal distributions with different standard deviations and with a
common mean of zero. If the level of debt is to the left of zero (under a suitable scale),
e.g., at x = −1, then a decrease in standard deviation, by reducing the likelihood of events
in the left tail of the distribution (the “default” states), reduces the probability of default.
although Italy differs from the U.S. in that many of its banks are state-owned, our results are found to
hold (see Section 4.3) for both the privately-owned and the state-owned samples of banks. These stylized
facts and the use of Italian banking data to address other important economic issues such as the benefit of
relationship banking (Degatriache et al., 2000) and the effect of bank mergers on loan contracts (Sapienza,
2002a) lead us to believe that our results would generalize to banking sectors of other countries, includng
5Winton motivates the issue by comparing the following two advices: “It’s the part of a wise man to keep
himself today for tomorrow and not venture all his eggs in one basket” by Miguel de Cervantes (Don Quixote
de la Mancha, 1605), and, Behold the fool saith “Put not thine eggs in one basket” - which is but a manner
of saying, “Scatter your money and attention”; but the wise man saith “Put all your eggs in one basket and
watch that basket” by Mark Twain (Pudd’nhead Wilson, 1894).
6By portfolio “downside risk,” we mean the likelihood that the portfolio return will be lower than a given
threshold (e.g., level of deposits in the bank’s capital structure), an event that constitutes a “default.” An
alternative measure of downside risk, and one that is employed in the paper due to its greater measurability,
is the losses on the loans that constitute the portfolio. We have verified the robustness of our results with
several other measures of bank risk, both expected and unexpected, as we discuss later.
However, if the level of debt is to the right of zero, e.g., at x = 1, then a decrease in stan-
dard deviation, by reducing the likelihood of events in the right tail of the distribution (the
“no-default” states), in fact increases the probability of default. The left skewed nature of
a typical loan portfolio’s return distribution implies that the level of debt, in fact, may not
need to be too high for this effect to arise.
An additional impact bolstering this hypothesis (H.1) arises from the interaction of the
perverse effect of diversification on bank risk and the bank’s monitoring incentives. The con-
flict of interest between bankowners and bank creditors (similar to the equityholder vs. cred-
itor conflicts first described in Jensen and Meckling, 1976, and Myers, 1977) implies that an
increase in the probability of default reduces the incentives of bankowners to monitor their
loans. If the loan portfolio has high downside risk, then an improvement in loan monitoring
and, in turn, in loan quality produces greater benefits to the creditors than to the bankown-
ers. Since the cost of monitoring is borne by the bankowners (the residual claimants), it
follows that if the loan portfolio has high downside risk, then an increase in diversification
leads to weaker incentives for bankowners to monitor loans. This, in turn, leads to lower
bank returns giving rise to hypothesis H.1.
sectors, and thus, diversification can result in a poorer quality of loans, i.e., an increase in
the downside risk of the bank’s loan portfolio.
A bank’s monitoring effectiveness may be lower in newly entered and competitive
There are three reasons why this might arise. First, banks may lack the monitoring
expertise in lending to a new sector when learning costs are present. Second, when the
loan sector to which banks migrate is already being supplied with credit by other banks,
the new bank entrants may be subject to adverse selection and a “winner’s curse” effect.
This suggests that diversification could lower returns on bank loans and increase the risk
of failure to a greater degree when the sectors into which the bank expands are subject to
greater competition. Third, diversification can cause a bank to grow in size, subjecting it to
agency–based scale inefficiencies discussed in the corporate finance literature.7
Broadly speaking, these hypotheses reflect the view that a bank’s credit risk depends on
its monitoring incentives (and effectiveness) as well as on its degree of portfolio diversification.
Thus, diversification per se is no guarantee of a reduced risk of failure. By the same token,
regulatory requirements to diversify are no assurance of greater banking system safety or
7We discuss the research that relates the effects of competition on bank loan quality as well as the recent
corporate finance literature on agency–based scale inefficiencies in Section 2.
8For example, in the U.S., regulations restrict a bank’s lending to any one counterparty to a maximum
of 15% of that bank’s capital.
Overall, our results provide strong support for these two hypotheses. We measure focus
using the Herfindahl index for a bank’s (i) non-financial and housing loan portfolio (I–HHI),
(ii) overall asset sector portfolio (A–HHI), and (iii) geographical portfolio (G–HHI).9Thus, a
decrease in HHI implies an increase in diversification and a reduction in focus. We reject the
hypothesis that increased diversification (reduced focus) improves risk–adjusted bank returns
on average, measured either as return on assets, return on equity, stock return (wherever the
bank is publicly traded), and market–adjusted or beta–adjusted stock return. Further, we
find that this relationship between focus and bank return is non–linear in the risk of the bank
and may in fact be U–shaped as implied by hypothesis H.1 above. Specifically, increased
industrial diversification appears to decrease return for all levels of bank risk, the decrease
being the least for moderate risk levels and the greatest for high risk levels. Increased asset
sectoral and geographic diversification, on the other hand, increases return at moderate levels
of risk, but reduces return at very high levels of risk. While we proxy for bank risk using a
bank’s doubtful and non–performing loans to assets ratio, our results are qualitatively robust
to other measures of bank risk as explained later in the paper.
We test hypothesis H.2 by examining endogenous loan quality (risk) and treating risk as
a dependent variable that is affected by the extent of focus (diversification). Our empirical
results suggest that increased focus in terms of industrial sector or asset sectoral exposure
(high values for I–HHI and A–HHI) improves loan quality (reduces risk), whereas geographi-
cal focus (G–HHI) affects loan quality adversely. Further, we find evidence that when banks
enter as lenders into “newer” industries or industries where they had less exposure before
(as measured by a decrease in industrial focus, i.e., a time-series reduction in I–HHI), there
is a contemporaneous deterioration in a bank’s loan quality (increase in its risk).10This
deterioration is greater, the greater the competition for loans that the entering bank faces
for lending to the “new” industry. The results underscore the importance of “watching the
basket” of loans and the advantages to banks from specialization. We also conduct several
robustness checks by: (i) employing alternative measures of bank risk, (ii) conducting a
simultaneous equations estimation of the return and risk effects resulting from focus (di-
versification), (iii) treating focus measures as endogenous variables, and (iv) separating the
sample into state–owned and private banks.
From the combined results on bank loan return and risk, we conclude that increased
industrial loan diversification results in an inefficient risk–return tradeoff for the (Italian)
9The Herfindahl index is the sum of the squared weights corresponding to a bank’s exposure to different
industries, sectors, or geographical areas. A higher value of the index corresponds to greater focus or lower
10We use the qualifier “newer” for industries in the sense that previous exposures of the bank to these
industries had been lower or non–existent, rather than being newer in the sense of technological changes
produced by the industries.
banks in our sample, and sectoral diversification results in an inefficient risk–return tradeoff
for banks with relatively high levels of risk. Geographical diversification on the other hand
does result in an improvement in the risk–return tradeoff for banks with low or moderate
levels of risk.
These results have important and direct implications for the optimal size and scope of
a “bank”. While traditional banking theory based on a delegated monitoring argument
recommends that it is optimal for a bank to be fully diversified across sectors or “projects”
(see, for example, Boyd and Prescott, 1986), our results suggest that there are diseconomies of
scope that arise through weakened monitoring incentives and a poorer quality loan portfolio
when a risky bank expands into additional industries and sectors. This complements the
agency theory based analysis of the boundaries of a bank’s activities as proposed in Cerasi
and Daltung (2000), Stein (2002) and Berger et al.(2001).11It also suggests that the optimal
industrial organization of a banking sector might be one with several focused banks, an
outcome that may also be attractive from an aggregate risk or a systemic risk standpoint as
noted by Acharya (2001) and Shaffer (1994).
From a normative standpoint, our results sound a cautionary note to the adoption of
regulatory mechanisms that encourage bank–level portfolio and/or activity diversification,
or attempt to measure credit portfolio risk through traditional diversification measures.
Our results also help explain the empirically documented phenomenon of DeLong (2001),
who finds that bank mergers which are activity and geography focusing produce superior
economic performance to those that diversify. Finally, our paper is the first to employ a
measure of industrial and sectoral focus (or diversification) for bank loan portfolios. It is
also the first to point out a potentially important and undocumented economic, and perhaps
in turn a micro–level, difference between bank diversification achieved through industrial or
asset sectoral exposures and bank diversification achieved through geographic expansions.
In Section 2 of the paper, we provide a brief overview of the related corporate finance and
banking literature. Section 3 describes our data. Section 4 formalizes the hypotheses, H.1
and H.2, and presents our empirical results. Section 5 provides a discussion and concludes.
11We believe that the agency theories based on conflicts across firm segments proposed in corporate
finance to explain the poor performance of conglomerates cannot completely explain the perverse effect of
diversification on bank returns and risk. A bank’s lending to different industries is much more centralized
than is the operation of a typical conglomerate’s operating segments. Stein (2002) and Berger et al.(2001),
however, tie incomplete contracting to the inability of large banks to process “soft” information about their
borrowers. This potentially leads to diseconomies of scale for FIs and banks.
2 Related Literature
The issue of focus versus diversification of a firm’s business activities has been at the heart
of a large body of recent corporate finance literature. The broad evidence seems to sug-
gest that diversification destroys value (at least for some firms) leading to what is popularly
known as the “diversification discount.”12Several theories have been proposed to explain
this phenomenon such as managerial risk-aversion (Amihud and Lev, 1981), agency prob-
lems between managers and shareholders (Denis, Denis and Sarin, 1997, and Cornett et al.,
2001), the inefficiency of internal capital markets (Scharfstein and Stein, 2000), and power-
struggles between different segments of a firm (Rajan, Servaes and Zingales, 2000). Some
of these studies have also attempted to link their theories to the cross-sectional variation in
diversification discounts and premia.13
This latter issue, however, has not been addressed thoroughly in the context of financial
institutions and banks. This is primarily because it has been difficult to obtain bank-level
(cross-sectional) portfolio data and construct measures of industrial and geographical diver-
sification that are as “fine” or “micro” as those employed in this paper. Using somewhat
coarser measures, Hughes, Lang, Mester and Moon (1996), Saunders and Wilson (2001), and
Berger and DeYoung (2001) examine geographical diversification. Caprio and Wilson (1997)
examine cross–country evidence for a relationship between on–balance sheet concentration
and bank insolvency. Klein and Saidenberg (1998) present portfolio simulations demon-
strating that multi–bank bank holding companies hold less capital and do more lending, on
average, than their pro forma “pure–play” benchmark banks. Berger, Demsetz and Strahan
(1999) find that consolidation in financial services industry has been consistent with greater
diversification of risks on average but with little or no cost efficiency improvements. De-
Long (2001) examines the merger of financial firms in the U.S. and finds that bank mergers
that are focusing in terms of geography and activity produce superior economic performance
relative to those that are diversifying.
12The diversification discount is measured as the average of the difference between the value of a merged
or a diversified firm and the sum of the values of stand-alone firms corresponding to the acquired firms or
the merged business segments. Lang and Stulz (1994) show that diversified firms in the U.S. have poorer
firm performance (Tobin’s q) compared to pure–play firms. Comment and Jarrell (1995) and Berger and
Ofek (1995) document that diversification discount in the U.S. is in the range of 12.7% to 15.2%. Lins and
Servaes (1999) provide evidence for Germany, Japan, and the U.K.
The issue of there being a discount on average is, however, disputed. Campa and Kedia (2000) and
Villalonga (2001) econometrically model the endogenous choice of firms (to be focused or diversified) and
document that the average discount is much lower than previously estimated. Graham, Lemmon and Wolf
(2002) document that diversification often involves acquisition of discounted industry segments. Maksi-
movic and Phillips (2002) provide evidence that the discount is consistent with profit maximization by a
13For example, see Rajan, Servaes and Zingales (2000).
Finally, in addition to Winton (1999), several papers have discussed the adverse effect of
competition on bank loan quality. These include Gehrig (1998), Dell’Arricia, Friedman, and
Marquez (1999), Boot and Thakor (2000), and Hauswald and Marquez (2002) for theory,
and Shaffer (1998) for empirical results.
3.1 Data sources
Data for the industrial, asset, and geographic decompositions of the portfolios of Italian banks
in our study are taken from the regulatory reports submitted by these banks to the Bank
of Italy, the Italian Bankers’ Association (ABI), and the Interbank Deposit Protection Fund
of Italy (FITD). The latter is the Italian equivalent of the U.S. Federal Deposit Insurance
Corporation (FDIC). Our sample starts with a base of 105 primarily commercial banks
that reported their asset portfolio and other data during the entire 1993–1999 period. A
complete list of the banks and the ones that are traded publicly during our sample period
is shown in Appendix A. These 105 banks constitute over 80 percent of the total banking
assets of Italy.14In terms of size, 8 of these banks are “very large” (as defined by the Bank
of Italy), 7 are “large,” 15 are “medium,” and the remaining 75 are “small.” In terms of
geographical scope of banking activities, 8 of these banks are “national,” 18 are “regional,”
14 are “intra–regional,” 10 are “local,” and the remaining 55 are “provincial.” Finally, 34 of
these banks are publicly traded and 62 of them were state–owned at the beginning of 1993.15
Further description of the Italian banking sector can be found in Degatriache et al. (2000)
and Sapienza (2002a) as well as in Footnote 4.16
For each bank, data is available to calculate the following portfolio decompositions:
1. A disaggregated industrial sector decomposition based on each bank’s top five indus-
trial sector exposures with a sixth exposure comprising of the sum of the remaining
exposures, where the exposures could be to any of the 23 industries among: (1) Agri-
cultural, Forestry, and Fishing products, (2) Energy products, (3) Iron and non–iron
14A few of the banks in our sample undertook acquisitions of other banks. The data set, however, does
not provide any details as to which were these acquiring banks and which banks they acquired.
15We are very grateful to Paola Sapienza for supplying us the state–ownership dummy for our sample
based on her work on Italian banks in Sapienza (2002b).
16Industry perspectives on the developments of the Italian banking system can also be found in BNP
Paribas (2001) and Goldman Sachs (2001). Three clear trends are apparent over our period of study: an
increase in domestic branching (following the liberalization of branching in 1990), an increase in merger and
acquisition activity (although Italy remains one of the least concentrated banking systems in Europe), and
a decline in the importance of state–owned banks.
Material and Ore, (4) Ores and products based on non-metallic minerals, (5) Chemicals,
(6) Metal products, apart from machinery and means of conveyance, (7) Agricultural
and Industrial machinery, (8) Office, EDP Machinery, and others, (9) Electric mate-
rial, (10) Transport, (11) Food products, Beverages, and Tobacco-based products, (12)
Textile, Leather, Shoes, and Clothing products, (13) Paper, Publishing, and Print-
ing products, (14) Rubber and Plastic products, (15) Other Industrial products, (16)
Construction, (17) Services trade and similar, (18) Hotel and Public firms products,
(19) Internal Transport services, (20) Sea and Air Transport, (21) Transport related
services, (22) Communication services, and (23) Other Sales related services. Note
that in aggregate these exposures (collectively defined in the data as Non–financial
and Household exposures) constitute the dominant part of each bank’s portfolio.
2. A broad asset sector decomposition based on exposures to (1) Sovereigns, (2) Other
governmental authorities, (3) Non–financial corporations, (4) Financial institutions,
(5) Households, and (6) Other counterparties.
3. A geographical decomposition of all credits (other than those to Financial Institutions)
based on exposures to (1) Italy, (2) Other countries of the European Union (EU), and
(3) Other countries (rest of the world).
Note that the size of bank lending to a particular sector, industry, or geographical region in
our data set is net of loans that are already classified as either doubtful or non–performing.
The Financial Statement variables and capital structure variables are obtained from the
Bank of Italy and Bankscope data bases. Stock market data items for the 34 banks that
are publicly traded were taken from the Datastream and Milan Stock exchange information
bases on Italian Banks. A few banks had to be discarded from the sample due to missing
values of relevant variables, e.g., doubtful and non–performing loans.
3.2Construction of Herfindahl indices
We measure focus (diversification) by employing a Hirschman Herfindahl Index (HHI) mea-
sure. HHI is the sum of the squares of exposures as a fraction of total exposure under a
given classification. In our case, we construct three different kinds of HHI’s, which consist
of Industrial and Household sector HHI, more simply referred to as Industrial sector HHI
(I–HHI), Broad Asset sector HHI (A–HHI), and Geographic HHI (G–HHI).
I–HHI is based on the 5 top industries where loans were made for each bank. The 6th
exposure considers the rest of the industrial loan portfolio. For the 6th exposure, we em-
ployed two conventions: first, where the 6th exposure is treated as a separate “hypothetical”
industry, and second, where the 6th exposure is treated as being equally divided among the
remaining 18 industries. Our results were not sensitive to this choice. Hence, we report
results with I–HHI computed using the 6th exposure as a hypothetical industry. Thus, if the
proportional exposures to six industries are X1,X2,X3,X4,X5, and X6, respectively, then
when all loans are made to a single industry.
i=1(Xi/Q)2, where Q =
i=1Xi. Note that the HHI has a maximum of 1
A–HHI is the sum of the squared exposures (measured as a fraction) in the form of
sovereign loans, other governmental loans, non-financial sector loans, financial sector loans,
household sector loans, and other loans.
G–HHI is the sum of the squared exposures (measured as a fraction) to Domestic (Italian)
loans, European Union loans, and Rest of the World loans.
3.3Balance-sheet and Stock market variables
We employ the following (annual) variables obtained from the balance–sheet and stock mar-
ket data for the banks in our sample over the period 1993–1999.
1. ROA: return on assets measured as Net Income / Assets.
2. ROE: return on equity measured as Net Income / Equity.
3. SR: stock return measured as the return over the current year, i.e., as the return from
the end of previous year to the last day of the current year.
4. BSR: market or beta–adjusted stock return measured as the residual from a one–factor
market model which employs MIB General, a weighted arithmetic average of all stocks
listed on the Milan Stock Exchange (Borsa Valori di Milano) as the market and where
the beta is computed for each year using the daily return series over the previous year.
• DOUBT, the doubtful and non–performing assets ratio measured as Doubtful and
Non–performing Loans / Assets. (Note that this can be interpreted as capturing the
level of expected losses).
In addition, we also seek to establish the robustness of our results with the following
measures of unexpected losses:
• STDDOUBT: the standard deviation of DOUBT for each bank during the sample
• STDRET: the annualized stock return volatility for each publicly traded bank based
on daily stock return data.
1. SIZE: asset size of the bank (in million dollars calculated using the spot exchange rate
between USD and Italian Lira at the point of measurement).
2. EQRATIO: capital ratio of the bank measured as Equity (Book–Value) / Assets, the
equivalent of the bank’s Tier 1 capital ratio. This is essentially equivalent to one minus
(book–value) debt to assets ratio for the bank.
3. BRRATIO: branch ratio measured as Number of Bank Branches / Assets. Note that
this is simply the inverse of a measure of average branch size.
4. EMPRATIO: employee ratio measured as Number of Employees / Assets.
INSERT TABLES 1 AND 2 HERE
Table 1 presents the univariate statistics (mean, median, standard deviation, minimum,
and maximum) for these variables and for Herfindahl indices for all the banks over the sample
period of 1993–1999. Note that the mean (median) bank’s size is about 12 billion (3 billion)
USD, the mean (median) capital ratio is 8.732% (8.113%), and the mean (median) ratio of
doubtful and non–performing loans to assets is 5.234 (3.199). The average industrial and
asset sectoral focus measures (I–HHI and A–HHI) are low suggesting a significant degree
of diversification in these areas. However, the average geographical focus (G–HHI) is quite
high capturing the fact that most banks in our sample lent to domestic Italian firms.17
Table 2 completes the descriptive statistics by presenting the correlation matrix among
these variables. As Table 2 illustrates, the three measures of focus, I–HHI, A–HHI, and G–
HHI, are not highly correlated. The correlation between I–HHI and A–HHI is 0.26, between
I–HHI and G–HHI is -0.31, and between A–HHI and G–HHI is -0.02. This suggests the
possibility that the effects of these different diversification measures on bank risk–return
performance may be different. Further, there is significant variation in all the variables we
employ and the correlations suggest a relationship between return measures (ROA, ROE,
and SR) and the balance-sheet control variables (SIZE, BRRATIO, EMPRATIO).
17The 1990s were a particularly difficult period for many Italian banks and industries (see BNP Paribas,
2001, Goldman Sachs, 2001, and Sapienza, 2002a, b). Goldman Sachs (2001) and Sapienza (2002a, b) also
provide corroborating evidence on the level of geographical focus of Italian banks during this period.