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“Good management or good finances? An agent-based study on the
causes of bank failure”
AUTHORS
Stathis Polyzos https://orcid.org/0000-0002-4317-1809
Khadija Abdulrahman
Apostolos Christopoulos
ARTICLE INFO
Stathis Polyzos, Khadija Abdulrahman and Apostolos Christopoulos
(2018). Good management or good finances? An agent-based study on
the causes of bank failure.
Banks and Bank Systems
,
13
(3), 95-105.
doi:10.21511/bbs.13(3).2018.09
DOI http://dx.doi.org/10.21511/bbs.13(3).2018.09
RELEASED ON Tuesday, 11 September 2018
RECEIVED ON Friday, 25 May 2018
ACCEPTED ON Wednesday, 15 August 2018
LICENSE
This work is licensed under a Creative Commons Attribution-
NonCommercial 4.0 International License
JOURNAL "Banks and Bank Systems"
ISSN PRINT 1816-7403
ISSN ONLINE 1991-7074
PUBLISHER LLC “Consulting Publishing Company “Business Perspectives”
FOUNDER LLC “Consulting Publishing Company “Business Perspectives”
NUMBER OF REFERENCES
44
NUMBER OF FIGURES
4
NUMBER OF TABLES
3
© The author(s) 2018. This publication is an open access article.
businessperspectives.org
95
Banks and Bank Systems, Volume 13, Issue 3, 2018
Abstraсt
e recent series of banking crises in the United States and in the Eurozone has result-
ed in numerous bank failures. In this paper, an agent-based model is employed to test
for factors that determine bank viability in times of distress, focusing mainly on the en-
dogenous risk of nancial institutions. e authors test for the eects of both manage-
ment and nancial factors on the institutions’ ability to weather the storm during times
when the banking system experiences distress. e agent-based simulation process is
split into a setup period, when the simulation builds the structural characteristics of
each bank, and a testing period, where these characteristics are tested against the nal
result, which is the bank’s viability. A risk estimation model is built and it is found that
the proposed model is successful in predicting whether a particular bank can endure
a stress testing situation. e empirical results conrm the relevant literature and put
further emphasis on the policy implications regarding banking supervision and regula-
tion, particularly in context of the Eurozone banking union.
Stathis Polyzos (Greece), Khadija Abdulrahman (United Arab Emirates),
Apostolos Christopoulos (Greece)
BUSINESS PERSPECTIVES
LLC “P “Business Perspectives”
Hryhorii Skovoroda lane, 10, Sumy,
40022, Ukraine
www.businessperspectives.org
Good management
or good finances?
An agent-based study on
the causes of bank failure
Received on: 25 of May, 2018
Accepted on: 15 of August, 2018
INTRODUCTION
e world banking system has vivid memories from the nancial tur-
moil of 2008, where several nancial institutions were faced with ex-
tremely strenuous conditions. e 2008 crisis extended beyond the
nancial sector, hurting total output and thus damaging societal pros-
perity. Researchers still attempt to locate the distinguishing charac-
teristics of banks, which allowed some to recover from the crisis and
drove others to default. Most argue that there must exist a set of traits,
ranging from sound management to solid nances, that would permit
a forecast of the ability of a bank to weather the storm during distress.
In this paper, an agent-based model is employed in order to examine
the causes of bank distress. It is proposed that banks fail due to both -
nancial and corporate governance factors and introduce these features
in the authors modelling platform. e authors of the current article
attempt a link between these characteristics of the nancial institu-
tion and its nal state at the end of the simulation and employ this link
to develop a simple forecasting model, verifying its robustness.
e current paper contributes to three aspects of the existing litera-
ture. Firstly, to the best of the authors’ knowledge, it is the rst eort to
utilize an agent-based modelling platform as the medium with which
to carry out simulations in the elds of management and corporate
governance. Secondly, the validity of the results of existing literature
© Stathis Polyzos, Khadija
Abdulrahman, Apostolos
Christopoulos, 2018
Stathis Polyzos, Ph.D., Department
of Business Administration, Business
School, University of the Aegean,
Greece.
Khadija Abdulrahman, Assistant
Professor, College of Business, Zayed
University, United Arab Emirates.
Apostolos Christopoulos, Lecturer
of Banking and Finance, Faculty of
Economics, University of Athens,
Greece.
is is an Open Access article,
distributed under the terms of the
Creative Commons Attribution-Non-
Commercial 4.0 International license,
which permits re-use, distribution,
and reproduction, provided the
materials aren’t used for commercial
purposes and the original work is
properly cited.
corporate governance, agent-based nance, endogenous
risk, bank management
Keywords
JEL Classification G01, G32, G21, G28, H3
96
Banks and Bank Systems, Volume 13, Issue 3, 2018
on the causes of bank failure is tested. irdly, possible policy implications are examined with respect
to banking supervision, especially in the context of protecting societal prosperity.
e paper is structured as follows: Section 1 presents the relevant literature. In Section 2, the agent-
based model is discussed and its main points are briey presented. Section 3 includes the methodologi-
cal issues of the research work and the variables used. In Section 4, the outcome of the simulations is
presented and the last Section includes the concluding remarks.
1 e SIR Model is a compartmental model in Epidemiology which classies the population into three health states: Susceptible, Infected,
Recovered (thus SIR). In mathematical epidemiology, compartmental models help understand the dynamics of the spread of an epidemic.
1. LITERATURE REVIEW
ere exists a new trend in academic research that
has turned the focus on modelling bank surviv-
ability as opposed to protability, which was the
favored topic before the nancial crisis of 2008.
Existing studies mainly examine risk and risk
management and have linked these to the nan-
cial characteristics of banks. Philippas et al. (2015)
implement the SIR1 epidemiological model in an
eort to predict the nal state of a bank during a
banking crisis. Haq and Heaney (2012) nd a sig-
nicant negative relationship between total bank-
ing risk and the dividend payout ratio, which they
attribute to the eort of banking rms to increase
income for their shareholders. Broll et al. (2015)
also attempt to model the relationship between
risk and return in banking institutions.
Note that some researchers make the case that
greater risk-taking can be in the best interest of
shareholders in the presence of deposit insurance
(Beltratti & Stulz, 2009). Caluzzo and Dong (2015)
suggest that risk in the nancial sector has shied
away from individual risk towards systemic risk,
adding that banking systems are now more sus-
ceptible to systemic contagion (as opposed to con-
tagion in the banking system). Simper et al. (2015)
also show that risk management practices play an
important part in bank performance.
Contrary to existing research on bank perfor-
mance and viability, this paper expands to the
eld of management and additionally includes
corporate governance features. Macey and O’Hara
(2003) provide a thorough review of corporate gov-
ernance in the banking sector and its implications
on the nancial institutions and on the econom-
ic system as a whole. O’Connor and Byrne (2015)
show that “sound” corporate governance is linked
with rm maturity. Barr et al. (1993) also demon-
strate that management quality is closely linked
with bank survivability. Sullivan and Spong (2007)
show that insider wealth limits risk-taking behav-
ior, whereas stock ownership by hired managers
may increase risk. Additionally, wealth concentra-
tion, which is the proportion of one’s wealth at risk
in a given nancial institution, was also showed to
have a positive eect on risk management (lower
total risk), provided that the individual is in a po-
sition to inuence relevant managerial decisions
(Iannotta et al., 2007). Konishi and Yasuda (2004)
examine the Japanese banking sector and reach
similar conclusions, establishing a nonlinear em-
pirical relationship of stable ownership and bank-
ing risk. García-Marco and Robles-Fernández
(2008) corroborate these ndings for the Spanish
market.
Kangis and Kareklis (2001) demonstrate that the
mix between public and private ownership can
have an eect on bank performance. Barry et al.
(2011), and Haque and Shahid (2016) also conrm
the results showing the important role of own-
ership structure, especially for privately owned
banks, where institutional investors tend to imple-
ment riskier strategies when owning higher stakes
in banks. Wu and Li (2015) examine Chinese
rms and comment positively on the eects of
board independence on rm performance, while
Kaur Virk (2017) shows that board independence
is linked with a smaller number of regulatory vio-
lations. Laeven and Levine (2009) and Mullineux
(2006) also stress the importance of regulation.
Williams and Nguyen (2005) implement the
technical ineciency eects model of Battese
and Coelli (1995) using bank governance varia-
bles, similar to ours. is methodology was em-
ployed in the current article in order to implement
97
Banks and Bank Systems, Volume 13, Issue 3, 2018
a risk-governance index in the authors model,
which describes bank features that tend to show
“sound” management strategies. Additionally,
Gupta et al. (2013) employ an additive index to
quantify forty two bank governance factors. ey
nd that corporate governance “failed” during
the nancial crisis, since the factors that existing
literature considered as positive did little to help
large corporations. A similar index is constructed
by Koerniadi et al. (2014), who nd that good gov-
ernance practices are associated with lower levels
of risk. Agoraki et al. (2010) link board size and
composition to bank eciency, suggesting that a
small board size may signify better risk manage-
ment. Similar results are demonstrated in Conyon
and Peck (1998), who nd that a smaller board size
results in better corporate performance.
ElKelish (2017) performs a multi-country analy-
sis of corporate governance risks, linking them to
agency costs. Similarly, Aebi et al. (2012) propose
a series of measures of corporate governance that
are better suited to the banking sector. ey use
empirical data from banks in Europe and in the
US and nd that independent risk management is
crucial to the bank’s performance during a nan-
cial crisis. On the other hand, standard govern-
ance indicators seem to contribute little, if at all,
to the amelioration of these results. However, they
note the negative eects of risk governance on per-
formance during “normal” times, using common
performance indicators for the banking sector.
Reddy and Locke (2014) reach similar conclusions
from data regarding rms in New Zealand.
2. GENERAL MODEL
DESCRIPTION
e agent-based nancial model employed was
developed by Samitas and Polyzos (2015) and ex-
tended by Polyzos and Samitas (2015). e mod-
el was designed to simulate the behavior of eco-
nomic agents and is loosely based on the work of
Tsomocos (2003a, 2003b). However, the Tsomocos
model was extended to include agent-based char-
acteristics, which are a new trend oen seen in
simulation research (see for example Bookstaber
et al., 2018, and Riccetti et al., 2015). e specif-
ic agent-based model has also been used to sim-
ulate the post-Brexit economic system (Samitas et
al., 2018) and has also been applied to the Greek
banking system (Samitas & Polyzos, 2015).
e model incorporates three main types of eco-
nomic agents, namely Banks, Households and
Firms. ese agents operate under a given super-
visory framework, which is set forth by a market
regulator. In this setup, there is a constant, but
not unconditional, ow of funds between these
agents, which can take place in various ways,
ranging from the exchange of nancial goods be-
tween banks and their customers to the payment
of wages from rms to households. Firms operate
and improve their productive capacity using -
nancing from the banking system, which draws
liquidity from the funds of depositors. e mod-
el also employs the idea that agents can go bank-
rupt. Bankruptcy occurs when agents are unable
to meet their nancial obligations. e insolven-
cy conditions are stricter for banks than they are
for other agents and, naturally, the consequences
are dierent as well. e model supports various
methods of handling banks in distress, including
the bail-in solution, which was implemented to re-
solve the 2013 Cyprus nancial crisis.
3. METHODS
A thorough description of the latest version of the
model, including a formal model denition, can
be found in Samitas et al. (2018). In the current
paper, this work is extended, in order to mod-
el the risk of nancial institutions according to
both their nancial and their corporate govern-
ance characteristics. Each of the governance fea-
tures inuences the bank’s behaviour in a dier-
ent manner; this is something that the agent-based
nature of the authors model allows to implement.
e nancial features are calculated at a snapshot
of the nancial institution aer some time peri-
ods have elapsed. It must be noted that the pro-
posed methodology does not examine bank per-
formance, eciency or protability. At the current
stage, these are not handled by the extension of
the model, since the goal was to examine the caus-
es of failure, rather than the causes of success.
Extending the Samitas et al. (2018) model, specif-
ic characteristics have been introduced for each
bank. ese variables are monitored in order to
98
Banks and Bank Systems, Volume 13, Issue 3, 2018
link them with the end state of each nancial in-
stitution and to try to deduce an underlying rela-
tionship. In terms of governance features, the rst
monitored variable in the simulation is the pres-
ence of a Credit Risk Ocer (CRO) in the execu-
tive board. Aebi et al. (2012) suggest that when the
CRO has an active say in the executive board, this
generally results in better risk management. In the
current implementation, the bank is more capable
of discerning the probability of rms to default
on their loans. Additionally, banks with a CRO in
the board of directors have the capacity to oer -
nancing at customized interest rates, according to
the credit status of the borrower2.
Another variable implemented is the board size.
Aebi et al. (2012) and Beltratti and Stulz (2009)
show that a smaller board size can work in the
benet of exibility allowing the bank to respond
faster to changing market conditions. Both stud-
ies propose the use of further measures regarding
the Board of Directors, such as the attendance of
members to board meetings, but these were not
included in the authors simulations. However, if
the board size is too small, it is possible that the
lack of polyphony will hinder eective risk man-
agement. In the proposed model, a large board
size has a negative eect on the ability of the bank
to oer the appropriate interest rate for each rm
and to set its base deposit rate, which eects both
its cost of capital and its earnings3.
e board independence, which is the percent-
age of board members without further relation
to the bank, is also an implemented variable.
Additionally, a variable measuring the director
experience has been included, which is calculated
as the number of directors in the board with -
nancial background. Aebi et al. (2012) have imple-
mented this variable as the percentage of directors
with experience as an executive ocer in a bank or
insurance company. Both these variables tend to
improve risk management as they increase.
In terms of ownership, three variables have been
included, namely the percentage of total equity
2 See step 1.12 of the basic model, where the active rms seek nancing from banks from their proposed investment projects.
3 is is handled at step 1.11 of the basic model.
4 CEO: Chief Executive Ocer.
5 Note that this ratio will dier greatly from the expected values of a real-world bank, since the authors are only simulating part of a nancial
institution’s balance sheet.
owned by the CEO4, the percentage owned by the
public sector and the percentage owned by insti-
tutional investors. It has been shown (Barry et al.,
2011) that institutional investors tend to enforce
riskier strategies when their ownership percent
permits them to exert managerial control. On the
other hand, Barry et al. also show that public sec-
tor ownership is associated with lower risk, while
other research (Iannotta et al., 2007) suggests low-
er loan quality and higher insolvency. Ownership
concentration is associated with better risk man-
agement (Iannotta et al., 2007), while a high CEO
ownership seems to reduce overall risk (Sullivan &
Spong, 2007).
e monitored nancial variables include the
bank’s ratio of assets to liabilities5 and the ratio of
loans to deposits as shown below:
, 10
, 10
, 10
, 10
,
bt
bt
at
aA
b
lt
lL
Amt
Assets to Liabilities Amt
=
=
=
∈
=
∈
=∑
∑
(1)
, , 10
, , 10
, 10
, 10
l
bt bt
bt bt
b
at
aA
lt
lL
Loans to Deposits
Amt
Amt where is of type Deposit
=
=
=
∈
=
∈
=
= .
∑
∑ (2)
In terms of the bank’s position in the marketplace,
the ratio of the average interest rate of deposits
and the ratio of the average interest rate of loans
over the market average were computed.
( )
, 10
, 10
, 10 , 10
, 10
.
bt
bt
b
at at
aA
at
aA
Average Interest Rate Loans
ir Amt
Amt
Market Average
=
=
= =
∈
=
∈
=
×
=
∑
∑ (3)
( )
, 10
, 10
, 10 , 10
, 10
,
bt
bt
b
at bat
lL
ba t
lL
Average Interest Rate Deposits
ir Amt
Amt
Market Average
=
=
= =
∈
=
∈
=
×
=
∑
∑
(4)
where l is of type Deposit.
99
Banks and Bank Systems, Volume 13, Issue 3, 2018
Also, the model uses the average spread (denoted
by the average interest rate of loans minus that of
deposits) and the prot margin, which is the aver-
age interest rate of loans less the WACC6. e latter
is the weighted average of the interest rates of the
bank’s liabilities.
, 10
, 10
, 10
, 10
, 10 , 10
, 10
, 10 , 10
, 10
Spread
,
bt
bt
bt
bt
at at
aA
b
at
aA
at bat
lL
ba t
lL
ir Amt
Average Amt
ir Amt
Amt
=
=
=
=
= =
∈
=
∈
= =
∈
=
∈
×
= −
×
−
∑
∑
∑
∑
(5)
where l is of type Deposit.
, 10
, 10
, 10
, 10
, 10 , 10
, 10
, 10 , 10
, 10
.
bt
bt
bt
bt
at at
aA
b
at
aA
at bat
lL
ba t
lL
ir Amt
Profit Margin Amt
ir Amt
Amt
=
=
=
=
= =
∈
=
∈
= =
∈
=
∈
×
= −
×
−
∑
∑
∑
∑
(6)
Note that equations 5 and 6 dier in the fact the
latter takes into account all liabilities of the bank
(i.e. includes interbank loans), while the former
only considers deposits.
With respect to the particulars of the banking sec-
tor, the authors monitor the amount of cash over
the weighted assets7, the percentage of non-per-
forming loans on total loans and the interbank ex-
posure of the bank, which is the percentage of in-
terbank loans over on loans. Increased interbank
exposure has been shown to deteriorate a bank’s
expected viability due to increased contagion
risks (Drehmann & Tarashev, 2013).
, 10
, 10
,
bt
b
bt
CB
CashtoWeighted Assets wa
=
=
=
(7)
, 10
, 10
', 10
'
, 10
,
bt
bt
b
at
aA
at
aA
NPLs
Amt wherea has missed payments
Amt
=
=
=
∈
=
∈
′
=
=∑
∑ (8)
, 10
, 10
', 10 , 10
'
, 10
.
bt
bt
b
at bt
aA
at
aA
Interbank Exposure
Amt suchthat a L b B
Amt
=
=
′
= =
∈
=
∈
′′
=
∈∈
=∑
∑ (9)
6 Weighted Average Cost of Capital.
7 is could be considered an approximation to the Tier-1 capital.
Aer the implementation of these variables in the
proposed agent-based model, a virtual economy is
designed, consisting of 1,000 households, 10 banks
and 40 rms. Basel III was enforced as a regulato-
ry framework for the banking system and a bail-in
was the solution of choice for the Regulator to save
a bank in distress. e time span for each simula-
tion was 30 periods and 10,000 simulations were
executed.
e governance features were assigned to each
bank at the start of the simulation. eir values
are random and the probability distribution has
been manipulated to follow the ndings of Aebi
et al. (2012), who recorded these variables over a
large sample of international banks. Each bank is
logged in the system with these variables at the
start of each simulation. e nancial variables
were recorded at period 10, when the banks had
enough time to interact with rms and house-
holds, in order to build their asset and liability list.
e nal state of the bank was then recorded, giv-
en four alternatives, as follows:
• Bankrupt: In this state, the bank has gone
bankrupt. Note that in this case, the Regulator
was unable to rescue the bank, using the de-
posits the bank carries.
• Needs nancing: In this state, the bank is still
working but is unable to meet the require-
ments of the regulatory framework and will
need a cash injection.
• Balanced: is is the initial state of the bank.
is state will be assigned to banks in all cas-
es where they cannot be included in any other
state.
• Prosperous: is is the ideal state of the bank.
In this case, the bank’s total assets including
its available cash exceed its liabilities. is
state is an indication that the bank is well
equipped to deal with nancial distress.
e nal state of the bank is the dependent var-
iable on the regression analysis proposed by the
authors. It was examined which of the above var-
100
Banks and Bank Systems, Volume 13, Issue 3, 2018
iables are signicant in the prediction of the nal
state and a forecasting model was built to predict
the outcome of the simulations. is methodology
is similar to Aebi et al. (2012), the dierence being
that the data is generated from the simulations of
the model. Following this process, the model was
executed again to verify its predictive eciency.
e results are presented in the following section.
4. EMPIRICAL RESULTS
Table 1 shows a summary of the monitored varia-
bles for each of the four nal states. e sample is
100,000 banks (10,000 simulations with 10 banks
each) with random governance features, as de-
scribed earlier. is table shows the distribution
patterns for each of the variables over the entire
sample of 100,000 observations, according to the
nal states. e table is indicative of the rm link
between the bank’s nal state and both its govern-
ance and nancial features.
Firstly, it is clear that CRO presence improves the
bank’s nal state, since the worse-o states show
lower average CRO presence in the board of di-
rectors (Figure 1). e board size does not seem
important in determining the nal state, but it
seems that an increased number of independent
members is benecial (Figure 2).
In terms of the ownership structure, it is evident
that a larger value in CEO ownership as well as in
institutional ownership will tend to improve the
Table 1. Summaries of monitored variables for each nal state
Bankrupt, % Needs financing, % Balanced, % Prosperous, %
No CRO in board 66.0 61.0 53.0 53.0
CRO in board 34.0 39.0 4 7. 0 4 7. 0
Board size (independent/dependent members) 12 (8/4) 13 ( 8 /5) 13 (9/ 4) 13 ( 9/4)
CEO ownership 20.5 23.7 25.2 25.2
Public ownership 28.6 28 .1 37. 2 30.5
Institutional ownership 20.9 23.2 22.6 24.3
Assets to liabilities 1,2 21 1, 0 98 73 691
Loans to deposits 3,702 2,165 156 1, 49 4
Deposit rate to market average 101.8 97.4 93.7 93.9
Loan rate to market average 102. 3 9 7. 2 94.2 95.3
Spread 6.41 5.95 5.88 5.91
Profit margin 5.28 5.42 5.58 5.42
Non-performing loans 9.88 15 .38 1. 59 9.16
Interbank exposure 28.7 54.4 1.4 39.3
Cash to weighted assets 25.6 24.6 36.1 31.8
Note: is table includes the summaries of monitored variable of the simulation set, for each of the nal states of banks. e
summary for the CRO variables is the percentage of the banks where the particular feature was true, except for the board size,
which shows the average number of members. e summaries for the nancial variables, as well as of ownership variables (CEO
ownership, public ownership and institutional ownership) represent the average values recorded at the snapshot period (period
10), linked with the end state of the bank aer the end of the simulation.
Figure 1. CRO presence for each of the four nal states
0%
20%
40%
60%
80%
100%
Bankrupt Needs financing Balanced Prosperous
No CRO in board CRO in board
101
Banks and Bank Systems, Volume 13, Issue 3, 2018
bank’s future. On the other hand, greater public
ownership seems to lead the bank to the balanced
state more oen, which is an expected result, since
publicly owned banks tend to exhibit lower risk
and lower protability. e latter variable (public
ownership) does not seem to exhibit a linear rela-
tionship with the dependent variable (nal state).
Moving on to nancial information, it is impor-
tant to note the existence of “extreme” values for
all states except the balanced state. It must also
be noted that the amount of loans that bankrupt
banks carry in their asset list is substantially high-
er than the other states. However, the existence of
extreme values in the prosperous state leads us to
deduce that banks cannot prosper if risks are not
assumed. Nevertheless, it must be made clear to
investors and depositors that these risks may re-
sult in bank failure. Risks must also be assumed
by the nancing department, where interestingly
Figure 2. Dependent and independent board members for each of the four states
0
2
4
6
8
10
12
14
Bankrupt Needs financing Balanced Prosperous
Independent members Dependent members
Figure 3. Average ownership percentages for each of the nal states
0%
5%
10%
15%
20%
25%
30%
35%
40%
Bankrupt Needs financing Balanced Prosperous
CEO ownership Public ownership Institutional ownership
Figure 4. Interest rates over the respecve market average
88%
90%
92%
94%
96%
98%
100%
102%
104%
Bankrupt Needs financing Balanced Prosperous
Deposit rate to market average Loan rate to market average
102
Banks and Bank Systems, Volume 13, Issue 3, 2018
enough data for the NPLs8 and the interbank ex-
posure at the snapshot period (period 10, as men-
tioned earlier) are similar for banks which ended
up in the bankrupt and prosperous states, albeit
interbank exposure is somewhat higher for the
prosperous state.
With respect to the market position, it must be
noted that the simulations appear to suggest an
interest rate strategy for banks. e ndings show
that oering lower interest rates, vis-à-vis the mar-
ket average, both for deposits and for loans, will
improve the bank’s future, the particulars of the
prisoner’s dilemma notwithstanding. A lower in-
terest rate spread is also advisable, as is the use of
a lower prot margin, even though the results are
not clear on the latter.
A simple linear regression on the results shows
that the important variables are the presence of
the CRO in the board, the ownership variables
and the interest rate strategy variables. ese were
included in the nal prediction model.
It is not surprising that the public ownership var-
iable does not exhibit high correlation, since, as
was shown earlier, its relationship with the nal
state is not a linear one and consequently a linear
regression of these variables will fail to describe
the dependent variable’s values. Admittedly, the
use of a linear regression is simplistic and is one of
the shortcomings of the current work. However, as
one will see below, the linear regression is success-
ful in describing the model and the resulting fore-
casting system can predict the bank’s nal state
with a fair amount of certainty.
8 Non-Performing Loans.
Table 2 shows the coecients for the variables in
the proposed prediction model, which are signi-
cant at the 95% condence level. is regression
model has a satisfactory R–2 value and was imple-
mented in the model in an eort to predict the
nal state of the nancial institution. Once the
prediction model was implemented, the simula-
tions were executed 1,000 more times to verify ro-
bustness and the outcome (displayed in Table 3)
was encouraging. On the snapshot period, the -
nancial variables were calculated and used in con-
junction with the governance variables in order
to compute a prediction for the bank’s nal state.
e authors let the simulation complete and com-
pared the predicted state to the actual nal state.
Table 3. Robustness check of the predicon
model over 1,000 simulaons
Percentage,
%
Successful prediction 64.25
Unsuccessful prediction 35.75
Better state than predicted 57. 9 8
Worse state than predicted 42.02
In most cases, the prediction model was successful
in forecasting the bank’s nal state, since in only
35% of the simulations the prediction was false. In
these latter cases, only 42% would be damaging to
the investors, since the nal state of the bank was
worse than the predicted one. Consequently, even
though one can argue that a prediction of a worse
state than the nal one can also prove damaging,
only a mere 15% of predictions could make an in-
vestor or depositor worse o if they followed it.
Table 2. Linear regression model for the predicon of the nal state of the bank
BStandard error
(Constant) –1.82 0.018
CRO in board 0.65 0.006
Loans to deposits –0.02 0.000
Public ownership –0.28 0 .012
Institutional ownership 0.32 0 . 013
CEO ownership 0.19 0 .013
Deposit rate to average – 0.15 0.014
Loan rate to average –0.36 0.029
Note: e model’s R2 value is 0.62, which means that an important proportion of the variance in the dependent variable (Final
State) can be predicted from the given set of independent variables. e specic value (0.62) shows that the model is a good t
for the given data set.
103
Banks and Bank Systems, Volume 13, Issue 3, 2018
CONCLUSION
Concluding this paper, the authors have shown that both governance and nancial variables need to be
taken into account when discussing bank viability and when predicting whether the bank has enough
potential to handle a nancial crisis. e ndings agree with the relevant literature, which places em-
phasis on the presence of a CRO in the board of directors, on board independence and on the ownership
structure of the nancial institutions, when discussing bank performance and hence viability.
Additionally, the introduction of a low interest rate strategy is proposed, which needs further verica-
tion though, since it appears to be a case of prisoner’s dilemma. If all banks follow this strategy, then it
will simply be ineective. Consequently, a bank will need to be careful when using this strategy as a tool
for better results.
e ndings have also led to a simple, linear prediction model for the bank’s end state, but it must be
noted that the eectiveness is limited to the economic system of the agent-based model in its current
version. e model seems to fail to predict a worse-o nal state in only 15% of cases.
e empirical results have some important policy implications. Banking supervision pays little impor-
tance to the corporate governance features of the nancial institutions. Additionally, authorities seem to
focus more on capital requirements, which have been shown to hinder banking activity, with negative ef-
fects on the real economy and society. e results of the simulations suggest that regulators should take
into account management characteristics of each bank as well. Policy makers can use this information
to improve their stress testing systems in order to yield better results. e lack of statistical signicance
for commonly quoted gures, such as the NPLs and the interbank exposure, implies that banking au-
thorities need to evolve their models and include more characteristics which might not have been taken
previously into account. In today’s corporate environment, where the role of banks is not limited to -
nancial services but extends to many aspects of the modern society, bank failure can have severe adverse
eects in community prosperity.
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