Supervisory effectiveness, Sanctions and Bank risk-taking
Extended Abstract
1. Background and aim of the paper
This paper addresses the issue of banking supervisory effectiveness investigating the existence of a
relationship between the banking sanctions imposed by the banking regulators and the consequent
impact on the banks sanctioned, in terms of risk taking.
The topic is relevant in today's market crisis, in which several authors show substantial
shortcomings in the effectiveness of regulation and sanctioning activity carried out by the
Authority (among others, Blanchard, 2008; Caprio, Demirgucx-Kunt, and Kane, 2008).
It should be noted that the prudential supervision ensures compliance with the principles of sound
and prudent management as well as fairness and transparency of the banks; in this context,
regulatory sanctions promote the objectives pursued by the rules of prudential supervision and
regulatory information. In the same light, the Pillar II (Basel 2) emphasizes the role of on-site
inspections highlighting how they are able to: identify any weaknesses or inefficiencies in banking
managerial processes, administration and corporate control, verify the effectiveness and the quality
of internal controls, ensure that communications provided by the intermediaries for the purpose of
supervision is accurate and reliable (Basel Committee on Banking supervision, 2006). Therefore,
the on-site inspections and sanctions are essential tools used by supervisory authorities to ensure
the stability of the financial system (Quintyn and Taylor, 2002).
Specifically, the research focuses on a sample of Italian banks in the period 2005-2011 and
captures the effect of the rules on penalties on banks in terms of risk taking.
Previous studies that investigate the impact of sanctions on the behavior of banks are limited and
use measures of the effectiveness of prudential regulation approximate and indirect.
For example, the International Monetary Fund (2009) has estimated, on the basis of expert opinion,
the degree of compliance with the Basel Committee's Core Principles of Banking Supervision of
the banking regulation in order to assess their effectiveness in terms of supervision of banks; the
results of this analysis could therefore be affected by the discretion of the panelists.
Further empirical studies estimate, however, the quality of the supervisory regulations using some
proxy. For example, La Porta et al. (2006) consider the degree of independence and authority of
supervisors in the exercise of its control over intermediaries; Noy (2004), however, considers the
degree of corruption and political freedom of the government authorities. These studies, however,
focus attention on the text of legislation, rather than on the reality of the markets. Other authors
(Barth, Caprio, and Levine, 2008) evaluate the effectiveness of the supervisory regulations through
surveys and questionnaires to the same authority, leading to questionable results and probably
affected by the opinion of those interviewed.
Based on our knowledge, there are no empirical studies that compare the impact of the types of
sanctions and risk exposure of sanctioned banks.
Although this paper is inspired by the study of Staikouras and Delis (2011), it differs from the
same in many aspects related to the nature of the relationship between the supervisory activities
and the level of bank risk taking. In detail, the level of bank risk-taking is measured by a complete
dataset. The risk measure used in Staikouras and Delis (2011) refers to the Z-score, which measure
the overall risk of the bank, or the indicator NPLs / Loans that measure the credit risk only. On the
contrary, this paper uses detailed risk indicators that can capture both the complex and exposure of
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the bank and the various possible impacts of the sanctions imposed. We use, therefore, data on
credit risk, market and operational risks on the basis of quantification of regulatory capital, and
others ratio suitable to capture the individual type of the bank's risk. Another element of originality
of the work concerns the depth of the dataset penalties; we clustered sanction on the type of
infringement found by the on-site inspections (deficiencies in the organization, internal controls
and management of credit; deficiencies in lending management and control of credit; impaired
loans and provisions for losses not reported to the Supervisory Authority; lack of transparency;
infringement of the rules on risk concentration) together with the person sanctioned (Director,
Auditor, CEO) and the amount of the fine (in terms of amount). Combining both datasets, we
intend to examine whether, in the year n, the bank showed a reduction in the level of risk taking as
a result of a sanction received during an on-site inspection made in year n-1.
The study of the Italian case appears worthy of interest in the literature on banks, at least for the
following three reasons: 1) during the financial crisis it has not received financial assistance of the
State, unlike other European countries, so the banking risk indicators are not mitigated by
contributions from third parties, other than those of supervisors; 2) the dataset of accounting data
used in the analysis is extremely deep and allows the construction of risk indicators that have not
been investigated by the previous studies; 3) the dataset of sanctions is unique and consistent
throughout the period of the analysis.
The results of the analysis may be of interest to regulators, supervisors and professionals,
interested in improving the effectiveness of the penalty rules in defense of the sound and prudent
management of banks, also with a view to reducing the cost of regulation to the financial
intermediaries.
In this preliminary version, the rest of the paper is organized as follows. The par. 2 describes the
sample and the analysis methodology. The par. 3 illustrates the expected results and the some
implications.
2. Sample and Empirical model
The sample consists of data extracted from three different sources.
The first is represented by individual bank balance sheet data for the years 2005-2011, collected by
ABI Banking Data, which are 4,675 observations as a whole. The average coverage of the sample,
in terms of total assets than in the Italian banking system, is over 80%.
The second type of data is represented by the sanctions, which are derived from the census of 84
bulletins (issued monthly) by the Bank of Italy in the years 2005-2011. During the seven years of
analysis are detected 3,588 persons sanctioned in banks which are subject to a penalty (they are
distinguished in: Board of directors, Directors, Chief Executive Officers). In this regard, it should be
remembered that the sanctions are imposed, usually (though not exclusively) by inspections, a
supervisory banking tool that provides to collect information and data on the results of
intermediaries (Bank of Italy, 2013).
The third type of data is that of the regional GDP, taken from ISTAT.
The empirical analysis is based on multivariate regression model (time-series and cross-section).
The following equation illustrates the relationship between banking supervision and bank risks:
Yit = β0 + β1SanctionCRit-1 + β2SanctionMRit-1 + β3SanctionORit-1 + β4Boardit-1 + β5Auditit-1 +
β6CEOit-1 + β7SAmmountit-1 + β8 GDPit-1 + β9TAt + β10ROAt + εit + ui
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where i identifies the individual bank belonging to the sample (i = 1, 2, 3,..., 4,675); Yit is the nthbank’s measure risk; t expresses the time variable (t = 2005, 2007 and 2011); β1, β2,… β10 are the
parameters to be estimated. Also in the model are indicated the constant (β0) and the error terms (εit;
ut).
The three variables, Sanction CR, Sanction MR and SanctionOR, are dummies that take the value 1
when it was imposed a sanction related to Credit Risk, Operational Risk and Market Risk or 0
otherwise. Variables Board, Audit and CEO are also dummies taking value 1 when the sanction was
imposed to the Board, the Audit (Collegio Sindacale) and the CEO and 0 otherwise. The variable
SAmmount is equal to the natural logarithm of the total amount of the penalty. In the model are also
included some control variables related to the bank's characteristics (Total assets, TA and Return on
Asset, Roa), in view of the fact that the imposition of sanctions by the Supervisory Authority will be
assessed taking into account the size of the company, in accordance with the principle of
proportionality (Bank of Italy, 1999 and 2012), and finally, the model takes into account the
economic scenario (GDP).
3. Expected Result and Implications
The results of the empirical analysis could confirm the existence of an inverse relationship
between the supervisory sanctions imposed on banks and the consequences of risk-taking
intermediaries themselves in the year following the penalty. Thus, the results could confirm the
effectiveness of the penalty rules of banking supervision.
The results of the analysis are relevant from the point of view of regulators, supervisors and
practitioners. With regard to the regulators and supervisors, the analysis could detect any possible
improvements of the system of penalties in terms of, for example, the nature and frequency of
inspection work on intermediaries that may reduce the cost of regulation at the level of the overall
system. With reference to the practitioners, we believe that the analysis could be of interest for the
banks where highlights the extent and nature of the sanctions and, in this way, finds that failings or
deficiencies at all levels of the organization that generate the enforcement actions by the
supervisory authority.
Ultimately, the paper aims to contribute to improving the effectiveness of supervisory regulations
on the banking system, capable of detecting and punishing behaviors related to banking
"excessive" risk-taking at the expense of the stability of the system as a whole.
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