Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Earnings Management via Loan Loss Provisions: A
Comparison of United States and Russian Bank Behavior
Gavin Kretzschmar* and Serzhan Nurgozhin**
This study examines earnings management behavior via loan loss provisions the
largest US and Russian banks. The research question in this paper examines the
difference between the degree of earnings management via loan loss provisions
between US and Russian banks.
Findings in this work are based on 176 bank-year observations covering the period
2007-2010 incorporating Russian commercial banks and 200 bank-year observations
for the same period covering US banks. We find a significantly lower explanatory
2
power (measured by adjusted R ) for the banks in Russian Federation when
regressing loan loss provisions on independent variables, identified in the prior
literature as important in defining loan loss provisions.. This result is clearly indicative
of higher earnings management via loan loss provisions by commercial banks in the
Russian region.
We investigate the variability in earnings (scaled by beginning common equity) of US
and Russian banks. We find that standard deviation of earnings for US banks
significantly increases during global economic crisis years (2008-2009). Interestingly,
the same metric for Russian banks in the same years increases significantly more, as
compared to US banks, suggesting much higher systemic risk for Russian banks.
We conclude that banks in Russian Federation, supposedly an economy with less
effective banking regulation, may be involved in earnings management via loan loss
provisions to a higher degree compared to US banks.
Results in this study suggest that banks in Russian Federation may be a subject to
higher systemic risk compared to more mature market economies. Our results are
important for regulators in emerging markets.
JEL code: M41
Key words: earnings management, commercial banks, United States, Russia.
1. Introduction
This study examines earnings management behavior via loan loss provisions the largest
US and Russian banks. Prior research has focused mainly on financial institutions in
developed and mature markets; in particular, research tends to focus on US banks
earnings management. The research question in this paper examines the difference
between the degree of earnings management via loan loss provisions between US and
Russian banks.
Findings in this work are based on 176 bank-year observations covering the period
2007-2010 incorporating Russian commercial banks and 200 bank-year observations
for the same period covering US banks. We find a significantly lower explanatory power
(measured by adjusted R2) for the banks in Russian Federation when regressing loan
loss provisions on independent variables, identified in the prior literature as important in
defining loan loss provisions. This result is clearly indicative of higher earnings
management via loan loss provisions by commercial banks in the Russian region.
*Dr. Gavin Kretzschmar, Finance and Management Control Department of Accounting and Finance,
Escuela de Alta Dirección y Administración, Spain, Email: gkretzschmar@eada.edu
**Serzhan Nurgozhin, Department of Accounting, KIMEP University, Kazakhstan,
Email:serzhan@kimep.kz
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
In order to examine whether the global financial crisis had the effect on banks’ earnings
we split the observations into annual periods, pre-, crisis and post-crisis. We find that
the ratio of standard deviation to mean earnings in the crisis years is considerably
higher for Russian commercial banks compared to US banks. This result suggests
considerably higher potential systemic risk for Russian banks as earnings for the entire
banking system in Russian Federation are significantly more volatile than they are for
US banks. In addition, the distribution of earnings (scaled by common equity) for
Russian banks is significantly different from normal, suggesting a higher level of
systemic risk for regulators and bank management to cope with. US banks are
characterized by a higher level of skeweness and a higher level of kurtosis for crisis
years.
2. Literature Review and Hypotheses Development
Cornett et al. (2009) use the largest US bank holding companies data for the period
1994-2002, noting in their study that loan loss provisions are a major tool used by
management in manipulating earnings across commercial banks. They mention that
loan loss provisions tend be used to manage earnings with the overall objective of
stabilizing and smoothing earnings over time. This intuitive result is supported by Liu
and Ryan (2006).
US experience of earnings smoothing suggests a positive relationship between changes
in earnings before provisions (EBP) and loan loss provisions (LLP), i.e., when earnings
before provisions increase, banks would tend to increase loan loss provisions, thus
decreasing earnings. In developing this concept Shen and Huang (2013) have recently
proposed the following measure of earnings smoothing in the banking industry:
EM1i,t = ρ (ΔLLPi,t, ΔEBPi,t)
(1)
TAi,t-1 TAi,t
where ρ denotes the correlation coefficient; TAi represents the total assets of bank i;
and EBPi is earnings before provisions for bank i, defined as net income plus LLP. If
earnings smoothing theory is correct, the statistic EM1 should be positive. This measure
is considered to be highly intuitive; therefore, we use statistic EM1 to measure earnings
smoothing across US and Russian commercial banks.
Shen and Huang’s (2013) focus is on the effect of earnings management on the bank
cost of debt. They give little emphasis to the difference in the nature and extent of
earnings management across various regions, a rich area of research which we seek to
develop using the following methodology.
Our hypothesis is that there should be a discretionary, or behavioral, component, in loan
loss provisions, which, along with other, more objective, factors drives loan loss
provisions. This is consistent with Beaver and Engel (1996), as well as with Cheng et al.
(2011), who apply the following regression model to estimate a discretionary component
in loan loss provisions:
LLPi,t = γ0 (1/GBVi,t) + γ1 COi,t + γ2 ΔLoani,t + γ3 ΔNPAi,t + γ4 ΔNPAi, t+1 + zi,t
(2)
where GBVi,t is net book value of common equity plus total allowance for loan losses
from the balance sheet; COi,t is net loan charge-offs for bank I during period t ; ΔLOANi,t
2
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
is change in total loans (LOANi,t – LOANi, t-1); ΔNPAi,t is the most recent change in
nonperforming assets (NPAi,t – NPAi, t-1); ΔNPAi, t+1 is one-period-ahead change in
nonperforming assets, and residual from regression (2), zi,t. serves as an estimate of
discretionary LLP. All the variables are deflated by GBVi,t to reduce heterogeneity
problem. Regression (2) constitutes an important model, which is applied to data in this
paper.
Net current charge-offs provide information about collectibility of current loans, and thus
provide insight into collectibility of future loans; naturally, non-collectible loans increase
as total loans increase; and, as non-performing assets increase, loan loss provisions
should increase as well; change in non-performing assets one period ahead is used as
a proxy for other information about quality of loans, that is available to management and
is not reflected on other explanatory variables. Consequently, we apply the variables
identified in Model (2) to the data in our paper.
If collectibility of loans and, accordingly, loan loss provisions, are affected by the factors
identified in Model (2) above, then residuals from this model may be viewed as an
estimate of discretionary, or managed, loan loss provisions (Cheng et al., 2011).
Accordingly, higher residuals, or, equivalently, lower adjusted R2, may be a signal of
higher earnings management. Our second measure of earnings management is, then,
adjusted R2 in the Model (2).
EM2 = adjusted R2 from Model (2).
Shen and Huang (2013) hypothesize that information asymmetry is lower in countries
with more extensive and effective banking regulation but higher in those with less
extensive and effective banking regulation. This view is supported by Vives (2006), who
notes that emerging economies are characterized by more acute asymmetric
information problems and a weak institutional structure. Higher information asymmetry
means that banks in emerging economies may be more involved in more earnings
manipulation (in particular, via loan loss provisions). This notion is consistent with
Burgstahler et al. (2006), who hypothesize that firms in countries with weak legal
enforcement are more likely to abuse discretion afforded by accounting standards.
This drives the comparison of our US banking sample to Russian banking sample,
where US is viewed as a country with more effective banking regulation, and Russian
Federation is viewed as an example of emerging market economy, with less extensive
and effective banking regulation. Accordingly, this body of literature underpins our
hypothesis that commercial banks in Russian Federation with less extensive and
effective banking regulation are more likely to be involved in active earnings
management. More formally, we postulate the following hypotheses:
H1: EM1 R > EM1, US
where EM1 R is statistic EM1 (as discussed above) for Russian banks, and EM1US is the
same statistic for US banks.
H2: Adjusted R2 in Model (2) is lower for Russian banks than for US banks
3
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
We expect that earnings smoothing in Russian Federation should be higher than
earnings smoothing in the US (as measured by statistic EM1). Consistent with our
hypothesis, we expect discretionary component in loan loss provisions in Russian
Federation to be higher, than in US; adjusted R2 for Russian banks in regression (2)
should be lower than adjusted R2 for US banks in the same regression.
3. Data and Methodology
Data on US banks is manually collected from Consolidated Reports of Condition and
Income (Call Reports), available on Federal Financial Institutions Examination Council
(FFIEC) site. Our sample includes 50 largest U.S. commercial banks by assets for the
period 2006-20101.
Data on banks in Russian Federation is manually collected from published financial
statements, issued under International Financial Reporting Standards (IFRS). Financial
reports are taken from banks sites. Not all Russian banks in our sample present
detailed financial reports each year during the period studied. Accordingly, our sample
size varies from year to year. In addition, financial reports before 2006 are usually
unavailable for Russian banks; since we use changes in loans and in non-performing
assets, data availability limits our sample period for Russian banks by 2007. Details on
data are given in the Table 3-1.
Our sample includes major banks playing an important role in the economy of U.S. and
Russian Federation, such as JPMorgan Chase and Co., Bank of America, Wells Fargo
in U.S., and SberBank, VTB Bank, Alfa-Bank in Russia, and other major banks.
Following Cornett at el. (2009) as well as Collins et al. (1995) we define non-performing
loans as loans that are past due 90 days or more and still accruing interest and loans in
nonaccrual status. U.S. banks provide information on non-performing loans in a uniform
format in their Call Reports 2. For banks in Russian Federation details on past due loans
are usually given in the notes to financial statements and we have manually extracted
this information from the notes to IFRS financial reports.
Table 3-1
Panel A: Data availability on Earnings scaled by common equity
Year
Number of observations
US
Russian Federation
2006
50
42
2007
50
46
2008
50
63
2009
50
64
2010
50
63
Total No. of observations
250
278
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Panel B: Data availability for Model (2)
Number of observations
US
Russian Federation
2006
50
n/a
2007
50
27
2008
50
42
2009
50
52
2010
50
58
Total No. of observations
250
179
Year
We report descriptive statistics on earnings scaled by common equity both for our US
and Russian banks sample in the table 3-2.
Year
2006
2007
2008
2009
2010
Year
2006
2007
2008
2009
2010
Table 3-2
Panel A: Earnings Scaled by Common Equity, US banks
Mean
Median
Standard
Ratio
of Skeweness
deviation
standard
deviation
to mean
value
0.129
0.130
0.076
0.59
0.599
0.101
0.100
0.082
0.81
1.428
0.048
0.060
0.121
2.51
-0.582
-0.011
0.024
0.203
-18.45
3.066
0.068
0.071
0.091
1.34
1.286
Panel B: Earnings Scaled by Common Equity, Russian banks
Mean
Median
Standard
Ratio
of Skeweness
deviation
standard
deviation
to mean
value
0.147
0.135
0.103
0.70
2.023
0.128
0.133
0.073
0.57
1.334
-0.044
0.070
0.831
-18.88
-6.959
0.007
0.035
0.489
70
-5.131
0.073
0.074
0.252
3.45
-5.583
Kurtosis
3.135
4.504
3.195
12.077
11.834
Kurtosis
8.030
7.583
51.923
37.907
40.250
The ratio of standard deviation to the mean earnings for Russian banks dramatically
increases in 2008, and even more increases in 2009, then decreases in 2010, but this
ratio is still higher in 2010 for Russian banks than for their US counterparts. Higher ratio
of standard deviation to mean value implies higher systemic risk for Russian banks:
distribution of earnings for Russian banks has significantly longer tails than that for U.S.
banks. Moreover, this ratio for Russian banks increases considerably more than the
ratio for U.S. banks. Such variability of earnings for Russian banks may result in higher
earnings management by Russian commercial banks, as they may need more actively
to manage earnings to stabilize their earnings.
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
For the largest US commercial banks both mean and median earnings, scaled by
common equity, consistently decrease since 2006 till 2009, becoming negative in 2009,
and start restoring in 2010. The ratio of standard deviation to the mean value is
relatively small in the 2006-2007, increases in year 2008, dramatically increases in year
2009, and considerably decreases in 2010. For the largest Russian banks, we observe
consistent decreases in both mean and median earnings for the period 2006 through
2009. Again, earnings restored somewhat in 2010, but are still below 2206-2007 level.
Degeorge et al. (1999) argue that earnings in the prior comparable period may serve as
an important psychological threshold that drives earnings management. Firms have
motivation to report profit, which is not lower than earnings reported in the previous
period. Thus, commercial banks in the period 2006 through 2009 may have been under
pressure to manage their earnings to meet certain benchmark: performance in the
previous year. In addition, in years 2008-2010 the average value of variable Earnings is
close to zero, with the value of 2009 being slightly negative for US banks, and negative
value for Russian banks in 2008. Degeorge at al. (1999) also conclude that reporting
positive profits (i.e., profits above zero) is predominant threshold; thus, in the period
2008 throughout 2010 commercial banks both in the US and Russia may have
motivation to manage earnings to report profits above zero. To summarize, throughout
the whole period 2006-2011 commercial banks both in US and Russia may have been
involved in earnings management because of two possible motivations: to report
positive (above zero) profits, and to report earnings, which are not lower than for the
previous year.
We performed t-test to check whether the declines in earnings are statistically
significant. Results are reported in the Table 3-3 (panels A and B).
Table 3-3: T-test: differences in mean values of earnings, year-by-year
Panel A: US banks, the period 2006 through 2010.
t-statistic
Period
2007 relative to -1.766*
2006
2008 relative to -2.547**
2007
2009 relative to -1.765*
2008
2010 relative to 2.495**
2009
Panel B: Russian banks, the period 2006 through 2010.
t-statistic: Russian banks
Period
2007 relative to 2006
-1.009
2008 relative to 2007
-1.634
2009 relative to 2008
0.422
2010 relative to 2009
0.965
*significant at 5 per cent level (one-sided significance)
** significant at 1 per cent level (one-sided significance)
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
We can see from the Table 3-3 that differences in earnings between consecutive years
(and, therefore, declines in earnings) are significant at five per cent level for US banks,
but not significant for Russian banks, even though earnings’ declines for Russian banks
are approximately equal to decreases in earnings for US banks. This observation may
be interpreted as an indicator of possible earnings management by Russian banks.
Banks may manipulate earnings in order to report profits not lower than in the prior
period: as a result, earnings statistically are not different from prior-year results.
Other data characteristics from the table 3-2 also highlight interesting changes in
behavior of earnings of commercial banks. Thus, for US banks kurtosis is close to three
since 2006 through 2008 (which is an indicator of normal distribution), but considerably
increases to about 12 in the period 2009-2010. For Russian banks kurtosis is
significantly higher than that for US banks each year during the period under analysis.
In addition, for Russian banks kurtosis considerably increases in 2008, decreases in
2009 and stays approximately at 2009 level in 2010.
High kurtosis values in 2009-2010 may arise as result higher concentration of banks
around important psychological thresholds, such as positive profits or profits not lower
than reported in the previous year. Again, considerably higher kurtosis for Russian
banks may arise as a result of relatively larger earnings management and/or as a
consequence of larger variability in earnings of Russian banks.
Review of skeweness also highlights interesting changes in data distribution. For US
banks skeweness is close to zero in 2006. This observation, combined with kurtosis
close to 3 for the same year, indicates distribution close to normal. Skeweness
becomes positive in 2007, even though mean and median Earnings in 2007 decrease
relative to 2006, and this decline is significant at five per cent level. This observation
may mean that commercial banks in US try to report profits not lower than in the prior
period even under pressure of depressed earnings. Then, in the period 2008 through
2010, skeweness becomes considerably negative. This fact, combined with the mean
value being close to zero, may be an indication of banks trying to report profits close to
zero. Thus, US banks may try to meet an important psychological benchmark of
reporting profits above zero and may have been involved in earnings management in
this period.
Summary descriptive statistics for variables, which might be important in determining
loan loss provisions are presented in the Table 3-5. We calculate the ratio of the mean
value for each variable in our Russian banks sample to the mean value for the same
variable in our US banks sample. We find significant differences in the behavior of these
variables between US and Russian banks, as shown in the Table 3-4.
In the Table 3-4 we report the ratios of mean values for loan loss provisions (LLP),
charge-offs, change in loans (Delta Loans), change in non-performing assets of the
current period (Delta NPA), as well as change in non-performing assets for the next
period (Delta NPAt+1) from our Russian banks sample to mean values for the same
variables from our US banks sample. All these variables are, in turn, scaled by gross
book value to make them more comparable.
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table 3-4. Ratios of mean values of variables identified in Model (2) for Russian banks
to mean values of the same variables for US banks
Year LLP
Charge Offs Delta Loans Delta NPAt Delta NPAt+1
2007 1.30
0.23
1.7
4.67
3.48
2008 0.90
0.37
9.40
1.54
5.49
2009 1.32
0.25
1.17
0.37
35.5
2010 0.81
0.34
0.26
0.33
0.65
We can see that loan loss provisions for Russian banks are reasonably variable,
ranging from 1.3 times of US banks loss provisions in 2007 to 0.81 times in 2010. At the
same time loan loss provisions as a percentage of gross book value for Russian banks
are consistently roughly equal to loan loss provisions (again, scaled by gross book
value) for US banks. The trend in loss provisions seems to contradict to the trend in
explanatory variables, which, according to prior literature, should affect loan loss
provisions. Charge-offs scaled by gross book value for Russian banks are consistently
three-four times lower than charge-offs for US banks. This observation may indicate that
Russian banks try not to recognize and write off their bad loans; a finding of potential
high interest for banks regulators in Russian Federation. This conclusion is corroborated
by observations for other variables. In particular, Delta Loans for Russian banks is
considerably higher than Delta Loans for US banks each year prior to 2010, and these
significant differences in change in loans seem to be not reflected in loan loss
provisions by Russian banks. Changes in non-performing assets of the current and the
next period are also considerably different for Russian banks from such changes for US
banks, and these differences also seem not to fully reflect in loss provisions of Russian
banks. All these observations are indicative of comparatively weaker banking regulation
in Russian Federation compared to US. It seems that Russian banks tend to charge off
less bad loans, and tend to reflect non-performing assets in loan loss provisions to a
lower degree, compared to US banks, and bank regulators seem not to be able to
correct banks behavior in Russian Federation.
Table 3-5 presents descriptive statistics for variables described in Model (2) for US
banks (Panel A) and Russian banks (panel B).
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Proceedings of 28th International Business Research Conference
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Table 3-5. Descriptive statistic for variables identified in Model (2)
Panel A: US Banks
LLP
1/GB Charge
DeltaLoan DeltaNPAt DeltaNPAt+1
to
V
Offs
GBV
Year 2006
Mean
Median
Standar
d
Deviatio
n
Year 2007
Mean
Median
Standar
d
Deviatio
n
Year 2008
Mean
Median
Standard
Deviation
Year 2009
Mean
Median
Standard
Deviation
Year 2010
Mean
Median
Standard
Deviation
0.023
0.010
0.034
0.000
0.000
0.000
0.02
0.008
0.030
0.956
0.535
2.095
0.006
0.004
0.012
0.042
0.031
0.063
0.043
0.027
0.048
0.000
0.000
0.000
0.030
0.015
0.038
1.136
0.496
2.602
0.025
0.025
0.028
0.089
0.082
0.071
0.11
5
0.10
8
0.08
8
0.000
0.066
0.518
0.077
0.135
0.000
0.059
0.414
0.065
0.079
0.000
0.054
0.689
0.060
0.179
0.15
2
0.14
3
0.09
5
0.000
0.113
-0.103
0.096
-0.002
0.000
0.105
-0.151
0.069
-0.016
0.000
0.078
1.208
0.104
0.134
0.09
1
0.06
9
0.08
3
0.000
0.105
0.226
-0.018
-0.023
0.000
0.072
-0.013
-0.016
-0.026
0.000
0.112
1.075
0.079
0.048
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Proceedings of 28th International Business Research Conference
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Year 2007
Mean
Median
Standard
Deviation
Year 2008
Mean
Median
Standard
Deviation
Year 2009
Mean
Median
Standard
Deviation
Year 2010
Mean
Median
Standard
Deviation
LLP
to
GBV
1/GBV
Panel B: Russian Banks
Charge Delta DeltaNPAt
Offs
Loan
DeltaNPAt+1
0.056
0.046
0.047
0.000
0.000
0.000
0.007
0.002
0.017
1.932
1.822
0.914
0.028
0.008
0.093
0.146
0.070
0.488
0.104
0.095
0.069
0.000
0.000
0.000
0.024
0.000
0.097
4.868
0.77
25.90
1
0.119
0.045
0.386
0.741
0.216
2.767
0.201
0.156
0.257
0.000
0.000
0.000
0.028
0.003
0.071
0.12
0.049
1.093
0.260
0.108
0.767
-0.071
-0.001
0.68
0.074
0.059
0.110
0.000
0.000
0.000
0.036
0.008
0.076
0.857
0.736
0.824
0.006
-0.004
0.185
0.015
-0.003
0.574
As we can see from Panel B of the table 3-5, for Russian banks loss provisions (scaled
by gross book value) are qualitatively the same as for US banks, but both kurtosis and
skeweness are considerably higher. This, again, may be a signal of higher earnings
management via loss provisions by Russian banks.
4. Empirical results
We measure statistic EM1 for US and Russian banks for each year in the period 20062010, and for the period 2006-2010 as a whole. Results of this test are reported in the
Table 4-1, Panels A and B, for US and Russian banks, correspondingly.
Year
2006
2007
2008
2009
2010
2007-2010 pooled together
Table 4-1 Statistic EM1
Panel A EM1, US banks
No. of observations
Value of ρ
50
50
50
50
200
0.341**
0.407**
0.418**
0.406**
0.474**
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Proceedings of 28th International Business Research Conference
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Panel B EM1, Russian banks
Year
No. of observations
2006
29
2007
40
2008
46
2009
62
2010
64
2006-2010 pooled together 241
** significant at 1 per cent level (one-sided significance)
Value of ρ
0.581**
0.780**
0.668**
0.591**
0.544**
0.686**
As expected, both for US and Russian banks statistic EM1 (which is Spearman’s
correlation coefficient between ΔLLPi and ΔEBPi,t) is positive and statistically significant
for each year in the period analyzed and for the period as a whole, but statistic EM1, for
Russian banks is consistently higher than EM1, for US banks. Our results are consistent
with those by Shen and Huang (2013), who report EM1, of 0.4213 and 0.4529 for US
and Russian banks, accordingly. This interesting result indicates the presence of
earnings management by banks in Russian Federation; it also indicates higher earnings
smoothing for Russian banks compared to US banks.
Summary statistics on an estimate of discretionary loan loss provisions (DLLP), i.e.,
residual from (Models (2) and (3) are given in the Table 4-2. The results by Cornett et
al. (2009) are reproduced in the third row of the Table 4-2. Since Cornet et al. (2009)
scale DLLP by total assets, in the Table 4-2 we also recompute DLLP in our sample as
percentage of total assets, for comparative purposes. Since initially we compute DLLP
as a percentage of gross book value, to compare our findings with the results by Cornett
et al. (2009), we recompute our error term from Model (2) as follows:
DLLPi,t =( zi,t x GBVi,t) / Total Assetsi,t
(5)
Table 4-2 Summary statistics on DLLP for large US banks between 2006 and 2010, in
percentage terms
Mean Median Std
Minimum Maximum Total Period
Type
of
Dev
covered companies
studied
0.011 -0.055 0.588 -3.296
3.250
250
2006US stand2010
alone
banks
0.003 -0.003 0.050 -0.109
0.582
179
2007Russian
2010
banks
0.005 0.000
0.151 -0.461
0.733
536
1994US bank
2002
holding
companies
While both the average and median levels of DLLP are qualitatively close to the results
by Cornett et al. (2009), standard deviation, minimum and maximum values in our US
banks sample significantly exceed those in the study by Cornett at al (2009). According
to Cornett et al. (2009), this range of DLLP shows that large US banks indeed manage
earnings, even though mean and median DLLP are close to zero. DLLP for Russian
banks are also quite volatile, suggesting earnings management by Russian banks as
well.
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Proceedings of 28th International Business Research Conference
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Since our H2 focuses on adjusted R2, we report in the Table 4-3 adjusted R2s (with
corresponding F-statistics in brackets) from Regression (2) for US and Russian banks,
year-by year, as well as loadings for statistically significant explanatory variables (with
corresponding t- statistics in brackets.
Table 4-3 Adjusted R2s and significant loadings from Regression (2) for US and
Russian banks, year by year, and for the period as a whole
2
Adjusted R / Variable
US banks
Russian banks
2006
Adjusted R2
0.909 (98.784**)
n/a
Charge-Offs
1.058 (19.244**)
n/a
2007
Adjusted R2
Charge-Offs
Delta NPAt
0.964 (261.644**)
1.212 (34.626**)
0.240 (4.245**)
0.093 (1.535)
0.454 (0.858)
-0.048 (-0.507)
2008
Adjusted R2
Charge-Offs
Delta NPAt
0.952 (195.428**)
1.531 (28.591**)
0.185 (3.342**)
-0.067 (0.496)
-0.034 (-0.294)
0.128 (1.397)
2009
Adjusted R2
Charge-Offs
Delta NPAt
Delta NPAt+1
0.819 (45.292**)
1.107 (14.765**)
0.073 (1.290)
-0.02 (-0.416)
0.516 (12.073**)
1.442 (3.856**)
0.917 (7.635**)
0.992 (7.243**)
2010
Adjusted R2
Charge-Offs
0.887 (78.134**)
0.715 (15.615**)
-0.028 (0.687)
0.330 (1.556)
Years 2007-2010 pooled together
Adjusted R2
0.811 (171.367**)
Constant
0.016 (3.188**)
Delta NPAt
0.946 (27.260**)
Delta NPAt
0.213 (6.295**)
0.064 (3.424**)
0.098 (7.337**)
0.110 (3.894**
0.110 (3.896**)
* significant at 5 per cent; ** significant at 1 per cent
As expected, adjusted R2 for Russian banks is significantly lower than adjusted R2 for
US banks each year during the period 2007-2010 and for the period as a whole.
Interestingly, Model (2) is significant for Russian banks only in year 2009 and for the
period as a whole. The scale of the difference in adjusted R2 suggests that this
difference is not simply due to chance, but it is a result of significantly higher earnings
management via loss provisions by Russian banks, and bank regulators in Russian
Federation allow for this relatively high loan loss provision (and, consequently, earnings)
manipulation. In addition, loadings in regression (2) are considerably different between
US and Russian banks. In particular, in the regression covering the whole period 20072010, loadings for Charge-Offs and for Delta NPAt for Russian banks are,
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
correspondingly 3.52 times and 1.94 times lower than the same loadings for US banks.
This important result suggests that bank regulators in Russian Federation are less able
to force banks to write-off bad loans as well as to recognize non-performing assets.
5. Conclusions
This paper studies differences in earnings management behavior via loan loss
provisions across US and Russian Federation, the former being an example of the
economy with extensive and effective banking regulation, and the latter being example
of an economy with less effective banking regulation. We conclude that banks in
Russian Federation, supposedly an economy with less effective banking regulation,
may be involved in earnings management via loan loss provisions to a higher degree
compared to US banks.
Results in this study suggest that banks in Russian Federation may be a subject to
higher systemic risk compared to more mature market economies. Our results are
important for regulators in emerging markets.
We do not consider directly the effect of corporate governance mechanisms, and other
incentives to manage earnings. Studying earnings management behavior by
commercial banks across various countries, taking into account other incentives to
manage earnings, is an area for future research.
End Notes
1
The banks included in the sample are the largest commercial banks in U.S. by assets
as of 31 December, 2012, according to Federal Reserve data, published in Federal
Reserve Statistical Release.
2
Schedule RC-N “Past Due and Nonaccrual Loans Leases and Other Assets” of the
Call Report provides information on past due and still accruing loans and on
nonaccruing loans
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