ArticlePDF Available

Bank business models

Authors:

Abstract and Figures

We identify three business models using balance sheet characteristics of 222 international banks and a data-driven procedure. We find that institutions engaging mainly in commercial banking activities have lower costs and more stable profits than those more heavily involved in capital market activities, mainly trading. We also find that retail banking has gained ground post-crisis, reversing a pre-crisis trend.
Content may be subject to copyright.
BIS Quarterly Review, December 2014 55
Rungporn Roengpitya
rungporr@bot.or.th
Nikola Tarashev
nikola.tarashev@bis.org
Kostas Tsatsaronis
ktsatsaronis@bis.org
Bank business models1
We identify three business models using balance sheet characteristics of 222 international
banks and a data-driven procedure. We find that institutions engaging mainly in commercial
banking activities have lower costs and more stable profits than those more heavily involved in
capital market activities, mainly trading. We also find that retail banking has gained ground
post-crisis, reversing a pre-crisis trend.
JEL classification: D20, G21, L21, L25.
Banks choose to be different from one another. They engage strategically in
different intermediation activities and select their balance sheet structure to fit their
business objectives. In a competitive pursuit of growth opportunities, banks choose
a business model to leverage the strengths of their organisation.
This article has three objectives. The first is to define and characterise banks’
business models. We identify a small set of key ratios that differentiate banks’
business profiles and use a broader set of variables to provide a more complete
characterisation of these profiles. The second objective is to analyse the
performance of these business models in terms of profitability and operating costs.
The final objective is to track how banks changed their business models before and
after the recent crisis.
We identify three business models: a retail-funded commercial bank, a
wholesale-funded commercial bank and a capital markets-oriented bank. The first
two models differ mainly in terms of banks’ funding mix, while the third category
stands out primarily because of banks’ greater engagement in trading activities. On
average, retail-focused commercial banks exhibit the least volatile earnings, while
wholesale funded commercial banks are the most efficient. On the other hand,
trading banks struggle to consistently outperform the other two business types.
Banks’ profiles evolve over time in response to changes in the economic
environment and to new rules and regulations. We find that transition patterns
changed around the recent financial crisis. While several banks increased their
1 The views expressed in this article are those of the authors and do not necessarily reflect those of
the Bank of Thailand or the Bank for International Settlements. We would like to thank Michela
Scatigna for outstanding work and valuable advice in the construction of the data on banks. We
also acknowledge, without implication, very helpful comments by Claudio Borio, Christian Upper
and Hyun Song Shin. All errors remain our responsibility.
56 BIS Quarterly Review, December 2014
reliance on wholesale funding prior to the crisis, in its wake more banks have
adopted more traditional business profiles geared towards commercial banking.
The rest of this article is organised in four sections. In the first section, we lay
out the methodology we employ to classify banks into distinct business models. In
the second section, we characterise the three business models in terms of banks’
balance sheet composition, while in the third we highlight systematic differences in
the performance of banks in different business model groups. In the last section, we
look into the transitions of banks across the three groups.
Classifying banks: the methodology
The procedure we use to classify banks into distinct business models is primarily
driven by data but incorporates judgmental elements. It shares many technical
aspects with the procedure employed by Ayadi and de Groen (2014), but differs in
terms of the judgmental elements and the data used. In contrast to their analysis,
which focuses exclusively on European banks, we use annual data for 222 individual
banks from 34 countries, covering the period between 2005 and 2013. The unit of
our analysis (ie a data point) is a bank in a given year (bank/year pair). Given that
the available data do not cover the entire period for each bank, we work with 1,299
bank/year observations. By focusing on bank/year pairs our approach allows
institutions to switch between business models at any point in the period of analysis
(an aspect that we explore in the last section). In this section we provide a
description of the classification methodology, leaving the more technical details for
the box.
The inputs to the classification are bank characteristics. These are balance sheet
ratios, which we interpret as reflecting strategic management choices. We use eight
ratios expressed in terms of balance sheet size and evenly split between the asset
and liability sides of the ledger. They relate to the share of loans, traded securities,
deposits and wholesale debt, as well as the interbank activity of the firm.2 We
distinguish this set of variables from other variables that we use in the third section
to characterise the performance of different business models. We view these other
variables, which capture profitability, income composition, leverage and cost
efficiency, as reflecting the interaction between banks’ strategic choices and the
market environment. We thus treat them as variables that relate to outcomes as
opposed to choices.
The core of the methodology is a statistical clustering algorithm. Based on a
pre-specified set of input variables, the algorithm partitions the 1,299 bank/year
observations into distinct groups. We select inputs from the set of choice variables.
The idea is that banks with similar business model strategies have made similar
choices regarding the composition of their assets and liabilities. We make no a
priori decisions as to which choice variables are more important in defining business
models or as to the general profile of these models. In that sense, the methodology
is data-driven. We rely on the repeated use of the clustering algorithm and a
goodness-of-fit metric (the F-index, which is described in the box) to guide the
2 This is another aspect where our approach differs from that of Ayadi and de Groen (2014). They
classify banks using interbank loans, trading assets, interbank liabilities, customer deposits, debt
liabilities and derivative exposures.
BIS Quarterly Review, December 2014 57
selection of the most appropriate partitioning of the observations universe into a
small number of distinct business model groups.
At various stages, our approach incorporates judgmental elements in order to
help narrow down the search for a robust, intuitive and parsimonious classification
of banks into distinct business models. The general strategy is as follows. We run
the clustering algorithm for each subset of at least three choice variables, ignoring
all subsets that include simultaneously pairs of variables that are very highly
correlated with each other, hence providing little independent information. The
clustering algorithm produces a hierarchy of partitions ranging from the very coarse
Using statistical clustering to identify business models
This box more precisely defines the variables used as inputs and discusses the more technical aspects of the
statistical classification (clustering) procedure.
The eight input variables from which we selected the key characteristics of the business models are evenly split
between the asset and liability sides of the balance sheet. All ratios are expressed as a share of total assets net of
derivatives positions. The reason for this is to avoid distortions of the metrics related by differences in the applicable
accounting standards in different jurisdictions. The asset side ratios relate to: (i) total loans; (ii) securities (measured
as the sum of trading assets and liabilities net of derivatives); (iii) the size of the trading book (measured as the sum
of trading securities and fair value through income book); and (iv) interbank lending (measured as the sum of loans
and advances to banks, reverse repos and cash collateral). The liability side ratios relate to: (i) customer deposits;
(ii) wholesale debt (measured as the sum of other deposits, short-term borrowing and long-term funding); (iii) stable
funding (measured as the sum of total customer deposits and long-term funding); and (iv) interbank borrowing
(measured as deposits from banks plus repos and cash collateral).
We employ the statistical classification algorithm proposed by Ward (1963). The algorithm is a hierarchical
classification method that can be applied to a universe of individual observations (in our case, these are the
bank/year pairs). Each observation is described by a set of scores (in our case, the balance sheet ratios). This is an
agglomerative algorithm, which starts from individual observations and successively builds up groups (clusters) by
joining observations that are closest to each other. It proceeds by forming progressively larger groups
(ie partitioning the universe of observations more coarsely), maximising the similarities of any two observations
within each group and maximising the differences across groups. The algorithm measures the distance between two
observations by the sum of squared differences of their scores. One could present the results of the hierarchical
classification in the form of the roots of a tree. The single observations would be automatically the most
homogeneous groups at the bottom of the hierarchy. The algorithm first groups individual observations on the basis
of the closeness of their scores. These small groups are successively merged with each other, forming fewer and
larger groups at higher levels of the hierarchy, with the universe being a single group at the very top.
Which partition (ie step in the hierarchy) represents a good compromise between the homogeneity within each
group and the number of groups? There are no hard rules for determining this. We use the pseudo F-index
proposed by Calinśki and Harabasz (1974) to help us decide. The index balances parsimony (ie a small number of
groups) with the ability to discriminate (ie the groups have sufficiently distinct characteristics from each other). It
increases when observations are more alike within a group (ie their scores are closer together) but more distinct
across groups, and decreases as the number of groups gets larger. The closeness of observations is measured by the
ratio of the average distance between bank/years that belong to different groups to the corresponding average of
observations that belong to the same group. The number of groups is penalised based on the ratio of the total
number of observations to that of groups in the particular partition. The criterion is similar in spirit to the Akaike and
Schwarz information criteria that are often used to select the appropriate number of lags in time series regressions.
The clustering algorithm is run for all combinations of at least three choice variables from the set of eight. If we
had considered all their combinations, there would have been 325 runs. We reduce this number by ignoring subsets
that include two choice variables that are highly correlated because the simultaneous presence of these variables
provides little additional information. We impose a threshold for the correlation coefficient of 60% (in absolute
value), which means that we do not examine sets of input variables that include simultaneously the securities and
trading book variables, or the wholesale debt and stable funding variables.
58 BIS Quarterly Review, December 2014
(ie few groups) to the very fine (ie many small groups). We select the partition in
this hierarchy with the highest F-index. This becomes the candidate partition for this
run (ie this subset of choice variables).
We use judgmental criteria to eliminate candidates that do not represent clear
and easily interpretable groups (ie distinct bank business models). One such
criterion is to eliminate candidates that produce fewer than three or more than five
groups as fewer than three do not allow for a meaningful differentiation of banks
and more than five are difficult to interpret. The other criterion is to focus only on
partitions that are “clear winners” among all other partitions based on the same set
of choice variables. To this effect we require that the top scoring partition has an
associated F-index score at least 15% higher than that of the partition with the
second highest score within the same hierarchy (ie the same set of input variables).
We dropped candidates that failed this test. This elimination procedure leaves us
with five partitions (ie five different sets of groups) based on five different subsets
of the choice variables.
To these five groups we apply a final judgmental criterion that seeks to capture
the stability of outcomes over time. For each of the five combinations of choice
variables we create two partitions of the banks in the universe. We first partition
banks using only data up to 2012, and then using all available data. We then
calculate the share of observations that are classified in the same group in both
partitions over the overlapping period. We select the partition with the highest
overlap ratio, which is 85%. This partition classifies the 1,299 bank/year observations
into three groups, which we refer to as bank business models. We next characterise
these models in terms of the whole set of eight choice variables.
Three distinct business models: the characteristics that matter
The classification process identifies three distinct business models and selects three
ratios as the key differentiating choice variables: the share of loans, the share of
non-deposit debt and the share of interbank liabilities to total assets (net of
derivatives exposures). This partition satisfies our criteria of robustness, parsimony
and stability. The share of gross loans is the only variable relating to the
composition of the banks’ assets. The other two ratios differentiate banks in terms
of their funding structure.
Table 1 characterises the three business model profiles in terms of all eight
choice variables (rows). The cells report the average ratio for all banks that were
classified in the corresponding business model (columns). For comparison, the last
column provides the average value of the corresponding ratio for the universe of
observations.
The first business model group we label commercial “retail-funded”, and it is
characterised by a high share of loans on the balance sheet and high reliance on
stable funding sources including deposits. In fact, customer deposits are about two
thirds of the overall liabilities of the average bank in this group. This is the largest
group in our universe with 737 bank/year observations over the entire period.
The second business model group we label commercial “wholesale-funded”.
The average bank in this group has an asset profile that is remarkably similar to the
profile of the retail funded banks in the first group. The main differences between
the two relate to the funding mix. Wholesale-funded banks have a higher share of
interbank liabilities (13.8% versus 7.8%) and a much higher share of wholesale debt
BIS Quarterly Review, December 2014 59
(36.7% versus 10.8%), with the balance being a lower reliance on customer deposits
(35.6% versus 66.7%). There are half as many observations in the wholesale-funded
group compared to the retail-funded group.
The third group is more capital markets-oriented. Banks in this category hold
half of their assets in the form of tradable securities and are predominately funded
in wholesale markets. In fact, the average bank in this group is most active in the
interbank market, with related assets and liabilities accounting for about one fifth of
the balance sheet. We label this business model “trading bank”. It is the smallest
group in terms of observations (203 bank/years) in our sample.
By comparison, Ayadi and de Groen (2014) classify European banks into four
business models, which they label as investment banks, wholesale banks, diversified
retail and focused retail. Drawing rough parallels with the classification in this paper,
which involves a more global universe of banks, their investment bank model
corresponds to our trading model, the two wholesale models correspond to each
other, and the diversified and focused retail models together correspond to our
retail-funded model. That said, an exact comparison would require comparing
individual banks in the two universes.
We find that the popularity of business models differs with banks’ nationality
(Table 2). Looking only at the last year of our data (2013), the North American banks
in our universe had either a retail-funded or trading profile; none belonged to the
wholesale-funded group. At the same time, one third of the European banks had a
wholesale-funded model. In turn, banks domiciled in emerging market economies
(EMEs) clearly preferred the retail-funded model (90%).
We also look at the distribution of global systemically important banks (G-SIBs)
across business models (Table 2). Our data for 2013 cover 28 firms that were part of
the banking organisations designated as G-SIBs by international policymakers
Business model profiles
Average values of ratios to total assets1 (in per cent) Table 1
Choice variable2 Retail-funded Wholesale-funded Trading All banks
Gross loans 62.2 65.2 25.5 57.5
Trade 22.4 20.7 51.2 26.5
Trading book 5.1 7.1 17.3 7.1
Interbank lending 8.5 8.2 21.8 10.5
Interbank borrowing 7.8 13.8 19.1 11.2
Wholesale debt 10.8 36.7 18.2 19.1
Stable funding 73.8 63.1 48.6 66.9
Deposits 66.7 35.6 38.0 53.6
Memo: number of bank/years 737 359 203 1,299
Trade = trading assets plus liabilities, net of derivatives; trading book = trading securities plus fair value through income book; interbank
lending = loans and advances to banks plus reverse repos and cash collateral; wholesale debt = other deposits plus short-term borrowing
plus long-term funding; stable funding = total customer deposits plus long-term funding; interbank borrowing = deposits from banks plus
repos and cash collateral.
1 Total assets are net of derivatives. 2 Variables in bold are those that were selected as the key drivers in defining the partition.
Sources: Bankscope; authors’ calculations.
60 BIS Quarterly Review, December 2014
(Financial Stability Board (2014)).3 The list – which includes institutions from both
advanced and emerging market economies – was roughly equally split between the
retail-funded and trading models.
Business models and bank performance
Are there systematic differences in the performance of banks with different business
models? The question is pertinent for understanding the impact of banks’ choices
on shareholder value but also on financial stability, which depends on sustainable
performance by financial intermediaries. In this section we examine the
performance of banks in the different business model categories both in a cross
section and over time.
In analysing the performance of different bank models, we use what we label
“outcome” variables. In contrast to the choice variables that we used to define the
business models, we interpret outcome variables as the result of the interaction
between the strategic choices made by the bank in terms of business area focus and
the market environment. Examples of such variables are indicators of profitability,
(for example, banks’ return-on-equity (RoE)), the composition of bank earnings (for
instance, the share of interest income in total income) and indicators of efficiency
(for example, the cost-to-income ratio).
Profitability and efficiency have varied markedly across models as well as over
time (Graph 1). The outbreak of the recent crisis marked a steep drop in advanced
economy banks’ RoE across all business models (Graph 1, left-hand panel). But while
RoE stabilised for retail banks after 2009, it remained volatile for trading and
wholesale-funded banks. In fact, trading banks as a group show the highest
volatility of RoE across the three groups, swinging repeatedly between the top and
bottom of the relative ranking. The story is qualitatively similar in terms of return-
3 The list of G-SIBs refers to consolidated entities. In our data we have at times more than one firm
that belongs to a consolidated group. The reason for this is that in order to use bank/year
observations with relatively pure business profiles in some cases we avoided using conglomerate
firms. When possible for the largest institutions we opted instead to use individual subsidiaries
(banks and securities firms) and not the holding company.
Distribution of business models in 2013 Table 2
Retail-funded Wholesale-funded Trading Total
North America 16 6 22
Europe 36 22 9 67
Advanced Asia-Pacific1 11 3 3 17
Emerging market economies 45 2 3 50
G-SIBs 14 2 12 28
Non-G-SIBs 94 25 9 128
1 Australia and Japan.
Source: Authors’ calculations.
BIS Quarterly Review, December 2014 61
on-assets (RoA, not reported here), an alternative metric of profitability that is
insensitive to leverage (see also Table 3).
All three business models show relatively stable costs in relation to income
(Graph 1, centre panel). A spike in the cost-to-income ratio around 2008 is readily
explained by the drop in earnings in the midst of the crisis. Compared to the other
two business models, trading banks had a persistently high cost base throughout
the period of analysis, despite their more mixed record in terms of profitability.
Interestingly, high costs relative to income have persisted post-crisis despite the
decline in these banks’ profitability. A possible explanation can be found in staff
remuneration rates, although this would be difficult to decipher from our data.
Post-crisis markets appear rather sceptical about the prospects of all three
business models, judging from the price-to-book ratio of banks in advanced
economies (Graph 1, right-hand panel). This ratio relates the banks’ stock market
capitalisation to the equity they report in their financial accounts. A value higher
than unity suggests that the equity market has a more positive view on the
franchise value of the bank than what is recorded on the basis of accounting rules.
A value below unity suggests the opposite. The ratio declined dramatically around
the crisis for banks in all three business models. In fact, it has been persistently
below unity since 2009 for most advanced economy banks, reflecting market
scepticism about their prospects.
Banks domiciled in EMEs (dashed lines in Graph 1) remained largely unscathed
by the 2007–09 crisis. These lenders are almost exclusively classified in the retail-
funded model. But even compared to their advanced economy peers with a similar
business model, they achieved a more stable performance. And while a more
favourable macroeconomic environment has certainly contributed to their higher
profitability in recent years, the overall stability of their performance is underpinned
by greater cost efficiency, ie a lower cost-to-income ratio. In line with these results,
Efficiency and earnings stability go hand in hand
In per cent Graph 1
Return-on-equity Cost-to-income ratio Price-to-book ratio1
Number of banks in brackets.
1 The data refer to 50 advanced economy and 20 EME banks.
Sources: Bankscope; authors’ estimates.
0
5
1
0
1
5
2
0
06 07 08 09 10 11 12 13
Retail-funded
Advanced economies (65): Trading
4
0
5
0
6
0
7
0
8
0
06 07 08 09 10 11 12 13
Wholesale-funded
0.
5
1.
0
1.
5
2.
0
2.
5
06 07 08 09 10 11 12 13
Retail-funded
Emerging market economies (30):
62 BIS Quarterly Review, December 2014
market valuations are quite generous for EME banks with price-to-book ratios
persistently higher than unity, although they are on a declining trend.
Table 3 compares the three business models in terms of a number of other
outcome variables across the entire sample period. Besides RoA and RoE, which
confirm the ranking from Graph 1, we also calculate risk-adjusted versions of these
profitability statistics, which subtract from the earnings variable (the numerator of
the ratio) the cost of capital that is necessary to cover for the risk inherent to the
activity of the bank. The approach follows closely the rationale of standard industry
approaches to calculate the risk-adjusted return on capital (or RAROC).4 More
specifically, we subtract from the bank’s gross earnings the associated operational
expenses and losses (including credit losses and provisions) as well as the cost of
capital set aside to cover possible future losses. This last component is the product
of the quantity of capital held by the bank (proxied by the regulatory capital
requirement linked to risk-weighted assets) multiplied by the cost of equity capital
(estimated by a standard capital asset pricing model).5
Regardless of the profitability metric, the retail-funded model is the top
performer. This is true in almost every year in our sample (not reported
here).6 Trading banks come in second place, with the exception of the risk-adjusted
RoE, which penalises the volatility of their earnings base. Trading banks differ very
significantly from their commercial bank peers in terms of the source of revenue.
They collect about 44% of their total profit through fees, a share that is almost
double that of the average other bank.
4 RAROC is a commonly used approach for measuring investment performance and comparing the
profitability of different business lines. See, for instance, Zaik et al (1996).
5 The cost of equity here is measured in terms of the systematic relationship between the rate of
return on the stock of the bank in excess of the risk-free rate and the excess return on the
corresponding broad market price index. The parameter was estimated using monthly data.
6 The top performance of retail-funded banks is consistent with the findings in Altunbas et al (2011),
who document that banks with a greater share of deposits in their funding mix fared significantly
better in the crisis than their peers.
Characteristics of business models
Average values of ratios in per cent (unless otherwise indicated) Table 3
Retail-funded Wholesale-funded Trading All banks
Return-on-assets (RoA) 1.16 0.45 0.98 0.94
Risk-adjusted RoA 0.68 0.09 0.57 0.48
Return-on-equity (RoE) 12.49 5.81 8.08 9.95
Risk-adjusted RoE 8.76 2.57 -9.55 4.29
Share of fee income 22.11 23.28 44.30 25.84
Capital adequacy 14.56 12.23 17.29 14.27
Cost of equity1 12 3 11 9
Total assets (in USD bn) 361.5 321.6 787.8 417.1
Memo: number of bank/years 737 359 203 1299
1 Reflects the systematic relationship between the rate of return on bank stocks in excess of the risk-free rate and the excess return on the
corresponding broad market price index.
Source: Authors’ calculations.
BIS Quarterly Review, December 2014 63
Wholesale-funded banks have the thinnest capital buffers among the three
business models, while they also have the lowest cost of equity. Somewhat
surprisingly, trading banks do not seem to be too different from retail-funded banks
in terms of these yardsticks. However, they do stand out in terms of total asset size.
The average trading bank is more than twice as large as the average commercial
bank, even those that are primarily funded in the wholesale markets.
Shifting popularity of bank business models
The crisis-driven reshaping of the banking sector has affected its concentration and
business model mix. A number of institutions failed or were absorbed by others,
thus increasing the concentration in the sector despite tighter regulatory constraints
on banks with a large systemic footprint. And many of the surviving banks adjusted
their strategies in line with the business models’ relative performance.
Table 4 presents a summary of banks’ shifts across different business models
before and after the crisis. Each cell reports the number of banks that started the
period in the model identified by the row heading and finished it in the model
named in the column heading. The large numbers along the diagonal indicate that
there is considerable persistence in the classification of banks, as the majority of
institutions remain in the same business model group over time.
In recent years, most of the transitions have been between the retail- and
wholesale-funded models of commercial banks. The group of trading-oriented
banks is fairly constant throughout the period. The direction of change in bank
business models, however, is very different post-crisis from that prevailing prior to
2007. During the boom period, market forces favoured wholesale funding, as
Business models: traditional banking regains popularity
Number of banks1 Table 4
Business model in 2007
Retail-funded Wholesale-funded Trading Total
Business Retail-funded 53 10 0 63
model Wholesale-funded 3 25 2 30
in 2005 Trading 2 0 13 15
Total 58 35 15 108
Business model in 2013
Retail-funded Wholesale-funded Trading Total
Business Retail-funded 57 1 0 58
model Wholesale-funded 16 16 3 35
in 2007 Trading 3 1 11 15
Total 76 18 14 108
1 A non-italicised entry indicates the number of banks that started a period with the business model indicated in the row heading and
finished the period with the business model indicated in the column heading. Based on a sample of 108 banks from advanced and
emerging market economies.
Sources: Bankscope; BIS calculations.
64 BIS Quarterly Review, December 2014
bankers tapped debt and interbank market sources of finance. About one in six
retail banks in our 2005 universe increased their capital market funding share to the
point that they could be reclassified as wholesale-funded by 2007 (first row of
Table 4).
The opposite trend characterises the post-crisis period. About two fifths of the
banks that entered the crisis in 2007 as wholesale-funded or trading banks
(ie 19 out of 50 institutions) ended up with a retail-funded business model in 2013.
Meanwhile, only one bank switched from retail-funded to another business model
post-crisis, confirming the relative appeal of stable income and funding sources.
While we observe transformations of banks in ways that result in their
reclassification under a different business model, we cannot pinpoint the underlying
economic drivers. We can, however, look at performance statistics to examine
whether bank shifts correlate with a turnaround of the firm. We find that a change
in bank business model actually hurts profitability, but improves efficiency relative
to the firm’s peer group.
To do this, we select all the banks in our sample that switched models and for
which we have data for at least two years before and two years after the switch. We
focus on two performance ratios: RoE and cost-to-income. We benchmark the
performance of the bank against a comparator group that comprises all banks that
belonged to the same business model as the switching bank prior to the switch and
remained in that model. We determine that the switching bank outperformed its old
peers if the difference between its average post-switch and average pre-switch RoE
is greater than the difference between the corresponding averages in the
comparator group. On the basis of this criterion, we find that only a third of the
banks that switched their business model outperformed their old peers in terms of
profitability. The remaining two thirds underperformed. However, applying the same
criterion to the cost-to-income ratio reveals that, among the banks that switched
business model, two thirds registered post-switch efficiency gains relative to their
peers.
Conclusions
We identified bank business models that have had different experiences over the
past decade. Given the consistently stable performance of retail-funded banks
engaging in traditional activities, it comes as little surprise that their model has
recently gained in popularity. More surprising is the stability of the group of trading
banks, which exhibited sub-par return-on-equity over most of the sample, both in
absolute and risk-adjusted terms. While further analysis is needed to uncover the
clear benefits to these banks’ shareholders, high cost-to-income ratios suggest
outsize benefits to their managers.
BIS Quarterly Review, December 2014 65
References
Altunbas, Y, S Manganelli and D Marques-Ibanez (2011): “Bank risk during the
financial crisis: do business models matter?”, ECB Working Paper Series, no 1394,
November.
Ayadi, R and W de Groen (2014): Banking business models monitor 2014 – Europe,
Centre for European Policy Studies and International Observatory on Financial
Services Cooperatives.
Calinśki, T and J Harabasz (1974): “A dendrite method for cluster analysis”,
Communications in Statistics, no 3, pp 1–27.
Financial Stability Board (2014): “2014 update of list of global systemically important
banks (G-SIBs)”, www.financialstabilityboard.org/2014/11/2014-update-of-list-of-
global-systemically-important-banks.
Ward, J H Jr (1963): “Hierarchical grouping to optimise an objective function”
Journal of the American Statistical Association, no 58, pp 236–44.
Zaik, E, J Walter, G Kelling and C James (1996): “RAROC at the Bank of America: from
theory to practice”, Journal of Applied Corporate Finance, vol 9, no 2, pp 83–93.
... 5 In this paper, we run an in-depth analysis of the negative policy rates impact on Italian banks, which we classify into distinct bank business models based on the balance sheet characteristics. More specifically, we identify three business models according to much of the literature on this topic (e.g., Ayadi et al. 2011;Ayadi and De Groen 2014;Grossmann and Scholz 2018;Roengpitya et al. 2014): (i) retail-funded banks, characterized by high shares of loans and deposits; (ii) capital market-oriented banks, which include banks with substantial trading assets and interbank funding; and (iii) wholesale-funded banks, whose asset profile is similar to the one of the first group, while showing a funding structure dependent less on deposits and more on long-term funding. We collect data on balance sheets for 125 Italian banks over the period from 2011 to 2017, and compare the actions of retail and market-oriented banks with those of wholesale banks (our "control group") in a 'difference-in-differences' panel regression framework. ...
... As noted by Farné and Vouldis (2017), banks are characterized by varying degrees within a range of activities relative to the composition of their profit earning assets, on the one hand, and their funding sources, on the other. From this viewpoint, balance sheet structures can help identify major business models reflecting risk characteristics (e.g., Altunbas et al. 2011, Ayadi andDe Groen 2014), profitability and business activities (e.g., Roengpitya et al. 2014). By and large, the literature on this topic is mainly oriented to process data on balance sheet structure to identify business models, using both data-driven approaches (e.g., cluster analysis or principal component analysis) and expert judgments. ...
... By and large, the literature on this topic is mainly oriented to process data on balance sheet structure to identify business models, using both data-driven approaches (e.g., cluster analysis or principal component analysis) and expert judgments. The empirical evidence from both approaches mainly lead us to identify three major business models (Ayadi et al. 2011;Roengpitya et al. 2014; Grossmann and Scholz 2018): ...
Article
Full-text available
Using data from Italian banks over the period 2011–2017, we study how negative interest rate policy and prudential regulation impact on bank business models. We report four key findings. First, banks shifted into retail- and market-oriented business models. Second, high- and low-deposit banks reduced loans and increased security/liquid assets; only market-oriented banks expanded lending. Third, interest rate income compression induced by negative rates has been substantial for the Italian banking system as a whole, although retail banks seem to have suffered less. Fourth, non-interest incomes played a compensatory effect. The portfolio reshuffling, as we observed for wholesale and retail banks (less lending and more securities/liquid assets), is related to the goal of reducing risk exposures and, in turn, the connected capital absorption required by prudential regulation.
... During the recent years a number of studies aim to identify banks' business models using as input set a narrow set of predefined variables (Ayadi et al., 2015;Flori et al., 2019;Hryckiewicz & Kozlowski, 2017;Lucas et al., 2017;Mergaerts & Vennet, 2016;Roengpitya et al., 2014), usually dictated by data availability. Other studies use a classification provided by the data provider (e.g. ...
... The empirical strategies followed to classify banks into strategic groups usually focus on balance sheet choice variables (Amel & Rhoades, 1988;DeSarbo & Grewal, 2008;Mehra, 1996). Roengpitya et al., (2014) (RTT henceforth) classify an international sample of banks based on eight balance sheet ratios (loans, securities, trading book, interbank lending, customer deposits, wholesale debt, stable funding and interbank borrowing) which are interpreted as "reflecting strategic management choices" that leverage on the strengths of each organisation. Halaj & Zochowski (2009), include additionally income and cost components, however this expansion of the type of variables is justified as a proxy for the unavailability of granular balance sheet breakdowns. ...
Article
Full-text available
We propose a clustering method for large dimensional data to classify the 365 largest euro area financial institutions according to their business model. The proposed clustering approach is applied to granular supervisory data on banks’ activities and combines also dimensionality reduction and outlier detection. We identify four business models, namely wholesale funded, securities holding, traditional commercial and complex commercial banks while identifying as outliers the banks that follow idiosyncratic business models. Evidence is provided that the sets of banks following the distinct business models differ with respect to performance and risk indicators.
... The results present 4 clusters of banks as investment banks, wholesale banks, retaildiversified banks, and retail-oriented banks. By the same method, globalscale studies have determined banking business models: banks financed by micro-deposits, banks funded by significant deposits, and banks connected to the capital market are classified as investment banks (Roengpitya, Tarashev, & Tsatsaronis, 2014). ...
Article
Full-text available
The value creation mechanism in a business model is based on a thoughtful configuration of mostly intangible microfoundations. The business model concept is rooted in business logic and strategic management literature. Banking literature lacks the business model concept, which is described based on subjective ideas and qualitative approaches. This article aims to explain the business model concept in banking with a cognitive view. For this purpose, we have qualitatively studied 50 cases of business models of innovative and leading banks and their value proposition structure. Through content analysis, four types of banking business models emerged. The findings proposed a banking business model presented at three levels, i) generic banking business model, ii) types of banking business models, and iii) instances of implementations at the realization level. The constructing microfoundations of each type and their composition are discussed in detail. Actual cases (instances) implemented in the industry are also presented and discussed as evidence of the realization of type models.
Thesis
La gravité de la crise apparue à l’été 2007 a conduit les superviseurs à modifier le cadre réglementaire bancaire international en intégrant le risque de liquidité. Cette thèse analyse comment les normes de liquidité ont été conçues, principalement le LCR, et dans quelle mesure elles ont influencé l’activité des banques. En se fondant sur les données de quatre banques de détail de proximité, ces travaux présentent les incidences du LCR pour un modèle bancaire spécifique en décomposant l’analyse par produit et par marché puis en évaluant les pistes d’optimisation de cette norme. Nos travaux montrent que l’activité des banques est intimement liée au LCR. Plus spécifiquement, la présentation des liens subtils et consubstantiels entre les produits bancaires (crédit, collecte et refinancement) et le LCR permet d’éclairer sur les pratiques, marchés, structures et opérations favorisés par cette norme. L’instauration d’une règle s’apparente indubitablement à un coût pour les banques. Nous montrons que le coût du LCR provient de la détention de titres HQLA et de l’allongement de l’échéancier d’endettement. Ce coût a une incidence substantielle sur le PNB des banques et varie selon la structure de l’établissement et le type de clientèle. Afin de réduire ce coût, nos résultats montrent que les banques pourraient reconsidérer les choix de portefeuille titres. En utilisant une méthodologie intégrant les contraintes du LCR, nous concluons que les banques font face à quatre portefeuilles optimaux dont le choix dépend essentiellement des écarts de taux constatés entre les titres. Le LCR est donc un ratio de flux sensible au taux d’intérêt dont les stratégies peuvent varier en conséquence.
Article
Purpose This paper aims to verify the presence of a management model that confirms or not the one size fits all hypothesis expressed in terms of risk-return. This study will test the existence of stickiness phenomena and discuss the relevance of business model analysis integration with the risk assessment process. Design/methodology/approach The sample consists of 60 credit institutions operating in Europe for 20 years of observations. This study proposes a classification of banks’ business models (BMs) based on an agglomerative hierarchical clustering algorithm analyzing their performance according to risk and return dimensions. To confirm BM stickiness, the authors verify the tendency and frequency with which a bank migrates to other BMs after exogenous events. Findings The results show that it is impossible to define a single model that responds to the one size fits all logic, and there is a tendency to adapt the BM to exogenous factors. In this context, there is a propensity for smaller- and medium-sized institutions to change their BM more frequently than larger institutions. Practical implications Quantitative metrics seem to be only able to represent partially the intrinsic dynamics of BMs, and to include these metrics, it is necessary to resort to a holistic view of the BM. Originality/value This paper provides evidence that BMs’ stickiness indicated in the literature seems to weaken in conjunction with extraordinary events that can undermine institutions’ margins.
Article
Full-text available
Understanding the correlation between different customers’ loss of creditworthiness is crucial to credit risk analysis. This paper describes a novel method, based on a weighted network model, in which a set of firms, customers of the same bank, represent the nodes while their links and weights derive from the total transaction amounts. We explore the contagion mechanism deriving from the transmission of the difficulties of one customer to other clients of the same bank so highlighting areas where contagion risk is higher. We use a real proprietary data set provided by a bank to illustrate the proposed approach.
Article
Does the ongoing, prolonged low interest rate environment affect how monetary policy surprises impact bank valuation? This paper answers this question by analyzing cross-country behavior of bank equity prices. Our results show that monetary easing surprises, which usually elicit a positive response from bank equity prices, tend to instead induce a negative response during periods of prolonged low interest rates, particularly for banks which rely on domestic deposits. This result implies that equity markets interpret a further interest rate cut in a prolonged low interest rate environment as negative information about future bank profitability.
Article
Diversification decisions in banks can be atypical owing to the nature of banking business. Banks diversify across business segments such as retail, wholesale or treasury, and spread their sources of liabilities, asset exposures and income types across customer groups, industry sectors and geographies. While the more granular decisions of diversification have regulatory and policy norms to follow, less guidance is available for making segment diversification. Using data of business segments from annual reports of five private sector banks in India for the period 2008–2009 to 2015–2016, the study attempts to find out the decision factors behind their diversification. It is found that though being highly diversified universal banks now, these banks were initially oriented toward certain segments from where they have shifted their portfolios over time. Bank diversification decision involves pursuing segment growth and executed by making capital expenditure and risk capital allocation to the segments. Banks make higher capital expenditure on segments that contribute more to generation of deposits and liabilities. On the other hand, risk capital allocation is determined by segment’s share of assets as well as their risk adjusted returns. Segment growth may be targeted with the intent of achieving higher profitability and capital efficiency, but the outcome appears to be uncertain. Business segment diversification of private banks, therefore, seem to be intended toward enhancing resources generation and targeting higher profitability while economizing on risk capital.
Chapter
Given banks’ crucial role of financial intermediation between depositors, one of the main goals of the revision of the supervisory system that followed the 2008 financial crisis was increasing bank transparency. From this angle, ‘high-quality’ supervision should not only increase the overall sustainability of banks’ activities and bank ability to manage risks, but also contribute to improve the quality of bank accounting data (BCBS, 2015).
Chapter
Recent research supports the idea that banks characterised by business models drawing more on traditional retail funding, namely customer deposits, and income diversification is likely to engage more in income smoothing than other business models (Di Fabio, Journal of Applied Accounting Research 20:311–330, 2019). Indeed, being income smoothing an accounting behaviour consistent with entities’ aim to show lower riskiness, findings of this research are overall consistent with literature suggesting that traditional funding and income diversification strategies implemented by retail banks are an integral part of a strategy characterised by risk aversion (Köhler, Journal of Financial Stability 16:195–212, 2015). Additionally, evidence provided in the fourth chapter corroborates prior literature supporting the view that supervisory strictness is associated with income smoothing behaviours (Gebhardt & Novotny-Farkas, Journal of Business Finance and Accounting, 38:289–333, 2011; García-Osma et al., Journal of Banking and Finance, 102:156–176, 2019; Di Fabio et al., Journal of International Accounting, Auditing and Taxation, 43: 100385, 2020). This chapter aims to understand whether supervisory features have specifically an impact on accounting behaviour of banks characterised by certain business models.
Article
Full-text available
A method for identifying clusters of points in a multidimensional Euclidean space is described and its application to taxonomy considered. It reconciles, in a sense, two different approaches to the investigation of the spatial relationships between the points, viz., the agglomerative and the divisive methods. A graph, the shortest dendrite of Florek etal. (1951a), is constructed on a nearest neighbour basis and then divided into clusters by applying the criterion of minimum within cluster sum of squares. This procedure ensures an effective reduction of the number of possible splits. The method may be applied to a dichotomous division, but is perfectly suitable also for a global division into any number of clusters. An informal indicator of the "best number" of clusters is suggested. It is a"variance ratio criterion" giving some insight into the structure of the points. The method is illustrated by three examples, one of which is original. The results obtained by the dendrite method are compared with those obtained by using the agglomerative method or Ward (1963) and the divisive method of Edwards and Cavalli-Sforza (1965).
Article
Full-text available
We exploit the 2007-2009 financial crisis to analyze how risk relates to bank business models. Institutions with higher risk exposure had less capital, larger size, greater reliance on short-term market funding, and aggressive credit growth. Business models related to significantly reduced bank risk were characterized by a strong deposit base and greater income diversification. The effect of business models is non-linear: it has a different impact on riskier banks. Finally, it is difficult to establish in real time whether greater stock market capitalization involves real value creation or the accumulation of latent risk.
Article
A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.
In 1993, Bank of America's Risk and Capital Analysis Group was charged with the task of developing and instituting a single corporate-wide system to allocate capital to all the bank's activities. Since 1994, that system has been providing quarterly reports of risk-adjusted returns on capital (RAROC) for each of the bank's 37 major business units. By 1995, B of A had also developed the capability to calculate RAROC down to the level of individual products, transactions, and customer relationships.
Banking business models monitor 2014 -Europe, Centre for European Policy Studies and International Observatory on Financial Services Cooperatives
  • Ayadi
  • De Groen
Ayadi, R and W de Groen (2014): Banking business models monitor 2014 -Europe, Centre for European Policy Studies and International Observatory on Financial Services Cooperatives.
2014 update of list of global systemically important banks (G-SIBs)
  • Financial Stability Board
Financial Stability Board (2014): "2014 update of list of global systemically important banks (G-SIBs)", www.financialstabilityboard.org/2014/11/2014-update-of-list-ofglobal-systemically-important-banks.