the effect of managerial ability on earnings quality in...
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Proceedings of the Third Middle East Conference on Global Business, Economics, Finance and Banking
(ME15Dubai October Conference), ISBN - 978-1-63102-286-9
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The Effect of Managerial Ability on Earnings Quality in the
Pre and Post IFRS Adoption Periods
Weitzu Chen,
Department of Accountancy,
National Taipei University, Taiwan.
E-mail: [email protected]
Chia-Wei Tai,
Department of Accountancy,
National Taipei University, Taiwan.
E-mail: [email protected]
________________________________________________________________________
Abstract
This study examines the effect of managerial ability on providing financial information.
Generally, compared with ones with inferior ability in making judgments and estimates,
managers with superior ability provide informative reports with earnings of higher quality to
investors due to their better knowledge of firms’ conditions and prospects. Managers with
superior ability have no need to manipulate earnings through discretionary accruals. In
addition, due to many accounting treatments in International Financial Reporting Standards
(IFRS) based on fair value measurements, the purpose of fair value accounting is to increase
the information transparency for investors to make a better decision. Based on
aforementioned assumptions of managerial ability and fair value accounting, this study
investigates the effect of managerial ability on the preparation of financial information in the
pre- and post-IFRS adoption periods. For this study, we extract data from Global Compustat
to examine that the effect of managerial ability in the UK listed firms on firm's earnings
quality in the pre- and post- IFRS adoption periods. We focus on the UK listed firms because
the UK has the largest capital market in the European Union (EU). Based on our evidence,
we find that earnings quality is negatively associated with managerial ability in the pre-IFRS
adoption period. In addition, managers of superior ability are associated with better earnings
quality in the post-IFRS adaption period. Compared empirical findings between the pre- and
post-IFRS adoption periods, our results imply that managerial ability in making judgments
and estimates affects earnings quality primarily due to fair value accounting in the IFRS.
Overall, fair value accounting in the post-IFRS adoption period can mitigate managerial
ability in manipulating earnings.
___________________________________________________________________________
Keywords: Managerial ability; fair value; International Financial Reporting Standards
(IFRS).
JEL Classification: M41, N24
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1. Introduction
There are many sources of firm value. Among others, managerial skill is the most
important factor in the human capital of a firm. Previous research (Demerjian, Lev, and
McVay 2012) has shown that the characteristics of managers including ability, talent,
reputation, and style can affect economic outcomes. Therefore, managerial abilities are
important for research and practical applications in the fields of economics, finance,
accounting, and management. More capable managers have stronger professional knowledge
related to the firm, enabling them to make better judgments and decisions. In addition, more
capable managers show a more flexible understanding and application of accounting
standards. Therefore, the financial reporting of managers to investors varies according to the
intentions and choices of managers.
Although it is obviously difficult to capture how the intentions and choices of managers
are used to manipulate the quality of financial reporting, when compared to less capable
managers, more capable managers show better understanding of the industry and the overall
economic environment, as well as the ability to apply accounting standards more flexibly, and
therefore have a significant influence on the quality of information.
Better earnings quality can be used to appropriately reflect the firm’s current operating
performance through financial reports. Furthermore, it can enable better decision-making on
the distribution of earnings from the perspective of investors (Dechow et al. 2010). More
capable managers are able to better allocate resources, and create a positive impact on the
quality of financial reporting. Related research on the quality of financial reporting including
earnings forecasts also provides strong evidence for the impact of the ability of managers on
financial reporting. Trueman (1986) points out ability of managers to anticipate future
changes in the firm’s economic environment and to adjust the firm’s production plan
accordingly is valuable for investors because managers with stock incentive compensation
have incentives to voluntarily release earnings forecasts as signals to markets. Therefore,
compared to firms that do not release earnings forecasts, firms that release earnings forecasts
have higher firm value. That is, earnings forecasts include useful signals about the ability of
managers, increasing the value of earnings information in forecasts.
The ability of managers inevitably affects the accuracy and relevance of financial
reporting. Therefore, more capable managers have a better understanding of a firm’s overall
economic environment and future development. In addition, managers play a key role in the
distribution of firm resources, and therefore also influence the development of firms as well.
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In addition, managerial ability has a significant influence on the accuracy and relevance of
financial reporting (Baik, Farber, and Lee 2011). However, it is not easy to observe the ability
of managers. Therefore, this study adopts the data envelopment analysis (DEA) method
developed by Demerjian, Lev, and McVay (2012) to estimate managerial ability. This study
uses DEA to estimate firm efficiency, and then uses a regression model to estimate the
portion of company efficiency that is attributable to the efficiency of managers. This measure
is used as a proxy for the ability of managers.
In addition to influence of managerial ability on financial reporting, many studies related
to accounting and management research investigate whether managers use accounting
methods and discretionary power to influence earnings reports, so called earnings
management. At the same time, the preparation of financial reports is regulated by accounting
standards. Currently, there is a global trend toward the adoption of the International Financial
Reporting Standards (IFRS). Different countries in the world have gradually moved from
their local standards to fully adopted or incorporated IFRS. The European Union (EU) fully
adopted IFRS in 2005. Listed companies in Taiwan started full adoption of IFRS in 2013.1
The main aims of IFRS are to enhance earnings quality and to achieve a high level of
comparability in financial statements (Regulation (EC) No 1606/2002 of the European
Parliament and of the Council). Higher quality financial statements enable the users of these
statements to make better decisions.
Following the global adoption of IFRS, there has been widespread discussion on whether
this move has delivered the expected increase in financial reporting quality. However the
consensus has not yet been reached. Barth, Landsman, and Lang (2008) and Chen, Tang,
Jiang, and Lin. (2010) find that the adoption of IFRS can improve the quality of financial
reporting. However, Jeanjean and Stolowy (2008) and Ahmed, Neel, and Wang (2013) find
deterioration in the quality of earnings reporting following the adoption of IFRS.2
The contribution of this study is to investigate the influence of managerial ability on
earnings quality following the adoption of IFRS. This study covers the period from before- to
1 Before Taiwan formally adopted IFRS in 2013, national financial reporting standards had already
begun to make reference to IFRS in 2001. 2 Jeanjean and Stolowy (2008) use data from Australia, France, and the United Kingdom to test
whether there was an improvement in earnings quality after the adoption of IFRS. Ahmed, Neel, and
Wang (2010) look at 20 countries that fully adopted IFRS in 2005 as the experimental group: Greece,
Italy, the Philippines, Portugal, South Africa, Spain, Australia, Austria, Belgium, Denmark, Finland,
France, Germany, Hong Kong, Ireland, the Netherlands, Norway, Sweden, Switzerland, and the United
Kingdom. The experimental group was paired with a reference group of 15 countries that did not fully
adopt IFRS in 2005: Argentina, Brazil, Chile, India, Israel, South Korea, Malaysia, Mexico, Pakistan,
Taiwan, Thailand, Canada, Japan, New Zealand, and the United States. The authors use these two
groups to compare and test earnings quality.
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after-adoption of IFRS. Therefore, we can explore the role of managerial ability in the
weakening of reliability of information due to bias in fair value measurements between
before- and after-adoption periods of IFRS. This study provides strong evidence with regard
to the inconsistent findings in the existing literature on financial reporting quality for the
before- and after-adoption periods of IFRS, and shows the importance of management by
examining the effect of managerial ability on the relationship between IFRS and earnings
quality.
This study is divided into five sections. The next section reviews the relevant literature and
outlines the research hypotheses. The third section details the research design, including
sample selection, data sources, and the empirical model. The fourth section contains the
empirical analysis. The fifth section presents the research findings and recommendations.
2. Literature Review
2.1 Managerial Ability and Earnings Quality
Earnings quality is a very important factor in the effective allocation of resources. The
reason for this is that earnings information is a critical item in the decision-making models of
investors. Therefore, firms with poor earnings quality may have a higher cost of equity
(Francis et al. 2004), and be more likely to receive attention from securities supervisory
bodies.
Why do managers carry out earnings management by using discretionary accruals to alter
reported earnings figures? Subramanyam (1996) and Kasanen, Kinnunen, and Niskanen
(1996) consider why managers manipulate discretionary accruals, identifying two important
issues. First, how do users of financial statements think that discretionary accruals should be
interpreted? This is important because the discretionary accruals will increase or decrease the
information value of earnings reporting. Second, legislators also hope that the design and
formulation of guidelines can reduce the ability of managers to opportunistically manipulate
financial reports (Bernard and Skinner, 1996).
In research on managerial ability and earnings management, Libby and Luft (1993) find
that since more competent managers have a better understanding of a firm’s economic
situation, they can produce higher quality financial reporting information. Research by
Demerjian et al. (2013) find that a correlation between higher managerial ability and lower
frequencies of subsequent earnings restatements, higher earning and accrual persistence,
lower errors and bad debt provisions, and better ability to estimate accruals. In addition, with
regard to earnings management motivation, some scholars find that more capable managers
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are less likely to release earnings forecasts for opportunistic reasons (Francis et al. 2008;
Malmendier and Tate 2009). Because earnings forecasts are also a means to influence the
market, we can find that more capable managers have less motivation for opportunistic
behavior. This is related to reputation costs.
Research by Jeanjean and Stolowy (2008) has pointed out that incentives for managers and
national institutional factors play a key role in financial reporting quality. Therefore, if
managers have a better understanding of the industry and environment together with
discretion over accounting standards, the quality of financial reports will be affected by
managerial manipulation of accounts. Although we cannot be certain of the process by which
managers choose to interfere in financial reports by carrying out earnings management, this
may come from opportunistic interference in reported earnings, or it may come from use of
discretionary power to increase the information value of financial results (Watts and
Zimmerman 1986; Healy and Palepu 1993; Bernard and Skinner 1996).
2.2 International Financial Reporting Standards and Earnings Quality
The International Accounting Standards Committee (IASC) was established in 1973, and
published International Accounting Standards in 1975. Subsequently, these standards have
undergone major changes. In 2001, the International Accounting Standards Board (IASB)
reached a peak in its operations. Beginning in 2000, the International Organization of
Securities Commissions (IOSCO) recommended that its member countries allow dealers in
cross-border securities to use IFRS to prepare financial statements when applying for the
issue and listing of securities.
IFRS aims to provide consistent, high-quality global accounting standards. Limiting
accounting options and gradually adopting fair value ensure that the actual economic situation
and performance of the firm are better reflected. The limitation of accounting options reduces
opportunities for discretionary earnings for company managers (Ashbaugh and Pincus 2001),
enhances financial reporting quality, and provides investors with better quality information
for making investment decisions.
However, there has been no consensus on whether the IFRS has actually produced
financial reports with higher quality. Barth, Landsman, and Lang (2008) find that in twenty-
one countries that adopted IFRS, when compared to matched samples from non-US
companies, accounting numbers from US companies show less earnings management, more
timely loss recognition, and higher value relevance. Horton, Serafeim and Serafeim (2013)
also reach a similar conclusion, finding that the imposition of IFRS can improve the quality of
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information in the investment market. By upgrading the quality and comparability of
information, firms can improve their information environment. In addition, other studies
using different methods support this finding. For example, Ozkan, Singer, and You (2012)
look at compensation committees, examining whether accounting information is useful for
evaluation. Their research finds that after the EU mandated IFRS in 2005, compensation
committees significantly increased the weighting of accounting information in executive
compensation. This finding clearly shows that from the perspective of compensation
committees, IFRS does indeed improve the quality of financial reports, thereby increasing its
use in executive compensation.
Conversely, other studies find that IFRS does not produce an improvement in financial
reporting quality. Jeanjean and Stolowy (2008) look at three countries3 implementing IFRS
for the first time and find that earnings management in these three countries did not decline
with the implementation of IFRS and in fact increased in France. Their study points out that
sharing rules is not a sufficient condition to create a common business language and instead
argue that it is managerial incentives and institutional factors rather than accounting standards
alone to affect financial reporting quality.
In summary, past studies generally agree that managers influence earnings quality, but they
have no conclusion on what form this influence takes. In addition, managerial ability
including understanding of the industry, economic environment, and accounting standards
may influence earnings management behavior. At the same time, with the adoption of IFRS,
managers have less opportunity for manipulating earnings under limited accounting choices.
On this basis, this study argues that the use of IFRS reduces the influence of managerial
ability on earnings management. Therefore, we propose the following hypothesis:
Hypothesis 1: Firm's earnings quality increases with higher managerial ability in the
post-IFRS adoption period.
However, as Baik, Farber, and Lee (2011) find, managerial ability has an important effect
on the relevance and accuracy of financial reports. This study expects that if managers have a
positive ability in understanding firm’s operations, decision-making, and financial situation,
this will influence financial reporting information. Managers with this positive ability are
more affected than those with negative ability by the adoption of IFRS, which uses fair value
accounting. Therefore, our second hypothesis is as follows:
3 Australia, France, and the United Kingdom.
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Hypothesis 2: Firms with higher managerial ability have greater earnings quality than
those with lower managerial ability in the post-IFRS adoption period.
3. Methodology
3.1 Empirical Model
First, this study uses the method proposed by Demerjian, Lev, and McVay (2012) to
estimate managerial ability, using data envelopment analysis (DEA) to solve the following
optimization problem:
OtherIntvGoodwillvDRvOpsLeasevPPEvASGvCogsv
Salesv
7654321 &&max
(1)
In this study, the only output is Sales. Inputs include the following: property, plant and
equipment (PPE), net operating leases (OpsLease), net R&D expenditure (R&D), cost of
goods sold (Cogs), goodwill (Goodwill), other intangible assets (OtherInt), and selling,
general and administrative expense (SG&A). Total firm efficiency is estimated using a single
output and seven inputs. Therefore, we can obtain the firm’s efficiency value from Equation
(1).
The firm efficiency value includes efficiency motivations specific to both firms and
managers. Therefore, the regression model in this study includes four firm specific factors.
These four firm specific factors represent firm-specific efficiency motivation, while the
residuals are efficiency motivation for managers, meaning managerial ability. The regression
model is as follows:
εdicatorIndustryIntorYearIndica
FCIβFCFIβeMarketSharβts)(TotalAsseβαencyFirmEffici
4321 ln (2)
Where Total Assets is the total assets of the company. Larger firms have higher market share,
and have greater negotiating capacity than smaller firms. This is one of the firm specific
efficiency motivations. Market Share refers to firm market share. Firms with higher market
share have operational advantages over firms with smaller market share. FCFI is the free cash
flow indicator. When firms have higher free cash flow, this indicates that they are able to
more effectively carry out positive net present value investments. FCI is the foreign currency
indicator, capturing the diversification of the company. This indicator is based on whether
firms have foreign currency exchange item. When companies are more diversified, managers
face greater resource allocation challenges, and need to bring together a broader range of
knowledge. The model also controls for the year and the industry.
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For earnings quality, this study uses discretionary accruals (e.g. DeAngelo 1986; Jones
1991; Dechow et al. 1995) as the substitute variable for earnings quality, and applies the
model proposed by Kothari, Leone, and Wasley (2005) for estimation:
ijtijt
ijt
ijt
ijt
ijtijt
ijtijt
ijtROA
A
PPE
A
RECREV
AA
TA
4
1
3
1
2
1
10 )()()1
( ( 3)
Where TAijt is the total accruals for company i in industry j at period t, A ijt-1 is the total
assets for company i in industry j at the end of period t, ΔREVijt is the net change in operating
revenue for company i in industry j at the end of period t, ΔRECijt is the change in accounts
receivable for company i in industry j at the end of period t, PPEijt is the gross property, plant
and equipment for company i in industry j at the end of period t, ROAijt is the return on assets
for company i in industry j at the end of period t, εijt is the error term for company i in industry
j at the end of period t. The regression in Equation (3) obtains the parameter estimates through
ordinary least squares, obtaining the non-discretionary accruals NDAijt, and subtracts NDAijt
from TAijt to obtain the earnings quality variable DAijt. This is the discretionary accrual. As
firms have different earnings management intentions, there is a possibility that earnings will
be manipulated both upwards and downwards. This study seeks to capture whether or not a
firm has engaged in earnings management behavior, and therefore sets DAijt as an absolute
value. This study sets the regression model below to test Hypothesis 1:
CFOSIZELOSSOUTS
TAABSDEBTPostAbilityPostAbilityDA
9876
543210 _ (4)
Where │DA│ is the absolute value discretionary accruals, Ability is managerial ability,
extracting residuals from equation 2. However, Demerjian et al. (2013), in order to avoid the
effects of extreme values, used the deciles value of managerial ability as a substitute variable
to examine the relationship between managerial ability and earnings restatements.4 Post is a
dummy variable, set to 1 when IFRS is adopted, otherwise to 0. DEBT is the interest-bearing
debt ratio. Beneish and Press (1993) and Watts and Zimmerman (1990) point out debt
covenants as a potential motivation for earnings management. Defond and Jiambalvo (1994)
also point out the relationship between debt covenants and the preferred accounting methods
of managers. At the same time, in order to avoid violating debt covenants, managers will
inflate the firm’s earnings. ABS_TA is the absolute value of total accruals. Francis, Maydew,
and Sparks (1999) believe that since it is difficult for external users of financial statements to
distinguish between discretionary accruals and non-discretionary accruals, when the firm has
4 We use deciles value of managerial ability in the additional testing section.
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the greater potential internal accruals, there is greater uncertainty in its profits. In addition,
there is no perfect way to distinguish discretionary and non-discretionary accruals within the
total accruals. This study adopts the approach of Beckrer et al. (1998), considering the
absolute value of the total number of accruals on discretionary accruals. OUTS is a dummy
variable, which equals one if outstanding change in shareholding exceeds 10%, and is
otherwise zero. Teoh, Welch, and Wong (1998) find that when firms want a cash injection,
they use a relatively good earnings situation to obtain better cost of capital, and therefore have
a motivation to adjust discretionary accruals. LOSS is a dummy variable, which equals one if
a firm has negative earnings in the current period and zero if it does not. Studies by Antle et
al. (2006) and Frankel et al. (2002) find that when firms have negative earnings, they will
manipulate discretionary accruals. SIZE is the size of the firm, using total assets to obtain a
natural logarithm. Becker et al. (1998) believe that firm size represents a large number of
missing variables. Therefore, including firm size can increase the testing ability of the model.
CFO is cash flow from operating activities. Dechow et al. (1995) and Becker et al. (1998)
find a negative correlation between operational cash flow and discretionary accruals. This
study sets the regression model below to test Hypothesis 2:
CFOSIZELOSSOUTSTAABS
DEBTPostHAbilityPostHAbilityDA
98765
43210
_
__ (5)
Where Ability_H is the ability dummy variable, which equals one if a firm has negative
earnings in the current period and zero if it does not. Ability_H*Post is the product term of the
ability dummy variable and use of the IFRS dummy variable. The remaining variables are as
described in Equation (4).
3.2 Sample Selection and Data Sources
The study period was between 1998 and 2013, including the period from before- to after-
adoption of IFRS in the United Kingdom. Since the sample group covers the period between
before- and after-adoption of IFRS, we can effectively test the relationship between
managerial ability and earnings quality before and after the adoption of IFRS. In this study,
financial variables and variables related to earnings quality come from Compustat Global data
for listed companies in the United Kingdom, which we use to calculate firm efficiency,
managerial efficiency, and other data required in this study.
We begin processing of sample data from the firm efficiency value. We follow the
approach in Demerjian et al. (2012), using DEA to calculate firm efficiency of each firm. The
original sample contained a total of 23,464 annual observations. After excluding 13,489
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observations that did not cover calendar years, 60 observations where input and output items
were negative, and 37 missing values, we are left with a total effective sample for firm
efficiency of 9,878 observations, as shown in Table 1. After using DEA to calculate the
annual efficiency value of each firm, we use Equation (2) to calculate managerial efficiency
value, and matched with the information needed to calculate managerial ability. After
deducting 117 observations with missing data, there were 9,761 valid samples to measure
managerial efficiency.
For calculation of discretionary accruals, we use Equation (3). After excluding non-
calendar year data, missing values, and initial data required for the model, we end up with
5,626 observations, and matched these observations with the information needed to calculate
the managerial ability efficiency value. Finally, we have 5,578 valid observations that can be
used for hypothesis testing.
4. Results and Discussion
4.1 Data Description
Table 2 provides a description of variables that are used in this paper. We put the sample
into higher and lower ability and use t-test to compare two sample mean. All of variables are
significant at the 5 percent level in a two-tailed test excepted absolute value of discretionary
accruals and firm size.
(Insert Table 2 about here)
4.2 Univariate Correlation
To avoid problems of multicollinearity, we check each variable’s variance inflation factor
(Variance Inflation Factor, VIF) before the multiple regression analysis. Table 3 shows that
the VIF values for each variable are less than 10, initially ruled out multicollinearity.
(Insert Table 3 about here)
Table 4 reports the Pearson and Spearman correlations of the variables. In the Pearson
correlations, absolute value of discretionary accruals is positively and significantly related
with ABS_TA, OUTS, and LOSS and negatively and significantly associated with SIZE and
CFO. The results of correlations in Spearman are same with Pearson substantially.
(Insert Table 4 about here)
4.3 Empirical Findings
In hypotheses 1, we examine the effect of on managerial ability on earnings quality in the
post-IFRS adoption period. Table 5 reports, whether or not including firm fixed effects, the
coefficient on the interaction variable of ability with the Post, Ability×Post, is significantly
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negative (including the firm fixed effect=-0.0262, t=-2.12; excluding the firm fixed effect=-
0.0423, t=-3.77). In addition, we find that absolute value of discretionary accruals is
significantly positive with Ability in the pre-IFRS adoption period (including the firm fixed
effect=0.0198,t=1.70; excluding the firm fixed effect=0.0441, t=4.34)and significantly
negative with Post (including the firm fixed effect=-0.0100, t=-3.62; excluding the firm fixed
effect=-0.0046, t=-2.04). Compared above empirical findings between the pre- and post-IFRS
adoption periods, the result implies the effect of managerial ability on earnings quality is
affected by implementation of IFRS.
We also observe in Table 5 that a number of control variables are as expected and
significantly related to absolute value of discretionary accruals both in models of including
and excluding firm fixed effect. Specifically, the coefficient on ABS_TA (0.6836, t=7.13;
0.6429, t=9.11), OUTS (0.0137, t=3.80; 0.0206, t=6.03) and LOSS (0.0103, t=2.77; 0.0273,
t=8.73) are positive. In model of excluding firm fixed effect, DEBT (-0.0104, t=-1.86) and
SIZE (-0.0032, t=-6.36) are significantly negative.
(Insert Table 5 about here)
In Table 6 we investigate the impact of higher managerial ability on earnings quality in the
post-IFRS adoption periods and examine our hypothesis 2 by investigating that firms with
higher managerial ability have greater earnings quality than those with lower managerial
ability. In the first column of estimates, which control for firm fixed effects, the coefficients
on Ability_H×Post (=-0.0110, t=-1.92) and Post (=-0.0065, t=-2.14) are significantly negative
and on Ability_H (=0.0103, t=2.01) are significantly positive. Those results imply impact of
positive manager ability on earnings quality was improved by implementation of IFRS. In the
second column of estimation, we find similar results when firm fixed effects are excluded.
The coefficients on Ability_H×Post (=-0.0192, t=-3.80) are significantly negative and on
Ability_H (=0.0103, t=2.01) are significantly positive. Similarly, we fine the same results in
control variables, including ABS_TA (=0.6842, t=7.12; 0.6437, t=9.09), OUTS (=0.0137,
t=3.78; 0.0206, t=6.00) and LOSS (=0.0103, t=2.79; 0.0273, t=8.73), DEBT (=-0.0107, t=-
1.90) and SIZE (=-0.0031, t=-6.28).
(Insert Table 6 about here)
4.4 Additional Test
Following research of Demerjian et al. (2013), we create decile ranks of our earnings
quality and managerial ability variables by year and industry. Table 7 shows that regardless of
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whether the firm fixed effects are included or not, the result is consistent with the main
empirical results.
(Insert Table 7 about here)
Furthermore, we exclude the data in year 2005 here. Ozkan, Singer, and You (2012)
examine how the mandatory adoption of IFRS in Continental Europe affects the contractual
usefulness of accounting information in executive compensation. Ozkan et al. (2012) think
that this is the first year firms were required to report according to IFRS, and companies may
have needed some time to adjust their compensation contracts after adopting IFRS. After
excluding observations in year 2005, we have 5,200 firm-year observations. Table 8 and
Table 9 have the same results as the main model substantially.
(Insert Table 8 and 9 about here)
5. Conclusions and Recommendations
Managers have been an important factor in the company’s operating activities. Managers
show their abilities in understanding for the prospect forecast, industry and economic
environment, thereby providing the results of the company’s operating activities through
financial reports to investors. On the other hand, the preparations of the financial reports are
governed and regulated by accounting standards. In particular, in the impact of
implementation of IFRS, which the use of fair value to measure and have limited selection of
accounting methods, the quality of financial reports will be affected. Because the use of fair
value will increase errors of measure and limited accounting choices will mitigate
opportunities to manipulate earnings, the relationship between the managerial ability and
earnings quality, according to findings of this study, are affected by the implementation of
IFRS.
In this study, we gathered data from UK listed firms to investigate relationship between
managerial ability and earnings quality. The results of this study show, when IFRS in place,
discretionary accruals decrease with increasing managerial ability. It implies managers can
exert their ability to minipulate earnings and be effectively reduced their opportunities to
manipulate earnings in the post-IFRS adoption period, and thus enhance the quality of the
overall financial reports. In addition, Compared better (positive) to poor (negative)
managerial ability, we fine firms with higher managerial ability have greater earnings quality
than those with lower managerial ability in the post-IFRS adoption periods. Because
managers with higher ability better able to deal with issues related to IFRS and provide less
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discretionary financial information. It implies the impact of higher (positive) managerial
ability on quality of earnings was significantly improved after the implementation of IFRS.
Overall, the managerial ability plays a key role in the financial reporting process and is
affected by IFRS, which can effectively improve the overall quality of information. The
adoption of discretionary accruals, there are possibilities of opportunism and enhancing the
value of information. No matter what the managers’ intentions, the implementation of IFRS
can effectively mitigate the exertion of managers to use discretionary accruals. The possible
reasons for this are IFRS can reduce the managers' exertion and capability to speculatively
use the discretionary accruals or effectively improve the quality of financial reporting by its
norm (e.g., fair value measurement), thereby reducing managers use discretionary accruals
necessary adjustments. The related reason is yet to be discussion.
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Table 1: Number of observations (1998-2013)
Year n of obs. Year n of obs.
1998 569 2006 745
1999 571 2007 718
2000 562 2008 646
2001 576 2009 602
2002 592 2010 590
2003 645 2011 566
2004 707 2012 532
2005 737 2013 520
Total 9,878
Table 2: Descriptive Statistics
Variable Ability Mean Std. Dev. Min Max t-value
Year:2000-2013,n of obs.:5,578(ability value>=0:3450;<0:2128)
DA Low -0.0048 0.1177 -0.5367 0.5345
-3.69 High 0.0070 0.1124 -0.4698 0.5345
│DA│ Low 0.0787 0.0876 0.0000 0.5367
-0.05 High 0.0788 0.0804 0.0000 0.5345
Ability Low -0.1522 0.0939 -0.4950 -0.0003
-97.03 High 0.2600 0.2190 0.0001 0.8019
Post Low 0.6270 0.4837 0.0000 1.0000
--- High 0.7754 0.4174 0.0000 1.0000
DEBT Low 0.4851 0.2973 0.0128 4.3416
-6.26 High 0.5365 0.2997 0.0128 4.3416
ABS_TA Low 0.0112 0.0440 0.0000 0.5924 2.32
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High 0.0086 0.0349 0.0000 0.5924
OUTS Low 0.2058 0.4043 0.0000 1.0000
6.22 High 0.1400 0.3471 0.0000 1.0000
LOSS Low 0.3951 0.4889 0.0000 1.0000
7.68 High 0.2942 0.4558 0.0000 1.0000
SIZE Low 4.5930 2.3267 -0.7809 11.1905
-0.68 High 4.6364 2.2872 -0.7809 11.3280
CFO Low 0.0108 0.2299 -1.4028 0.3612
-5.47 High 0.0420 0.1619 -1.4028 0.4147
1.Variable definitions: DA=Discretionary accruals; ABS_DA=Absolute value of discretionary accruals;
Ability=Managerial ability; Post=Dummy variable that takes the value of 1 after year 2005 and 0 otherwise;
DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets;
OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise;
LOSS= Dummy variable that takes the value of 1 if firm’s bottom line is loss and 0 otherwise; SIZE=The
natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets.
2.t-statistics significant at the 5 percent level in a two-tailed test are in boldface.
Table 3: Variance Inflation Factor
Variable VIF 1/VIF
Ability×POST 5.75 0.17
Ability 5.73 0.17
CFO 1.70 0.59
SIZE 1.46 0.68
LOSS 1.46 0.69
ABS_TA 1.44 0.70
DEBT 1.15 0.87
OUTS 1.13 0.89
POST 1.04 0.96
Mean VIF 2.32
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Table 4: Univariate Correlation
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(1) │DA│ 1 -0.0001 -0.0144 -0.0647 *** 0.3750 *** 0.1630 *** 0.2590 *** -0.2710 *** -0.2420 ***
(2)Ability -0.0241 1 0.0638 *** 0.1220 *** -0.0388 ** -0.1190 *** -0.1510 *** 0.0245 0.0575 ***
(3)Post 0.0056 0.1080 *** 1 -0.0471 *** -0.0532 *** 0.0390 ** 0.0043 -0.0205 -0.1230 ***
(4)DEBT -0.0048 -0.0120 0.0085 1 -0.2080 *** -0.1350 *** -0.1290 *** 0.2900 *** 0.1830 ***
(5)ABS_TA 0.1930 *** -0.0204 0.0182 0.3740 *** 1 0.1820 *** 0.4210 *** -0.8910 *** -0.2880 ***
(6)OUTS 0.1880 *** -0.1020 *** 0.0390 ** -0.0098 0.0294 * 1 0.2730 *** -0.1730 *** -0.3000 ***
(7)LOSS 0.2490 *** -0.1280 *** 0.0043 0.0165 0.0919 *** 0.2730 *** 1 -0.3870 *** -0.5610 ***
(8)SIZE -0.266 *** 0.0065 -0.0078 -0.0477 *** -0.2110 *** -0.1690 *** -0.3780 *** 1 0.4060 ***
(9)CFO -0.1110 *** 0.0575 *** -0.0437 ** -0.8530 *** -0.4750 *** -0.1010 *** -0.2250 *** 0.2420 *** 1
1.Variable definitions: DA=Discretionary accruals; ABS_DA=Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy variable that takes the value
of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable
that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm’s bottom line is loss and 0 otherwise;
SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets.
2.The lower left: Pearson; The upper left: Spearman.
3. ***, ** and* indicate significance level at less than the 1%, 5% and 10% level, respectively.
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Table 5: Earnings Quality and Managerial Ability
│DA│=α0+α1Ability+α2Post+α3Ability×Post+α4DEBT+α5ABS_TA
+α6OUTS+α7LOSS+α8SIZE+α9CFO+ε
Variables Predicted sign Coefficient t-value Coefficient t-value
Intercept 0.0618 5.11 0.0821 21.84
Ability ? 0.0198 1.70 * 0.0441 4.34 ***
Post ? -0.0100 -3.62 *** -0.0046 -2.04 **
Ability×Post - -0.0262 -2.12 ** -0.0423 -3.77 ***
DEBT - -0.0036 -0.32 -0.0104 -1.86 *
ABS_TA + 0.6836 7.13 *** 0.6429 9.11 ***
OUTS + 0.0137 3.80 *** 0.0206 6.03 ***
LOSS + 0.0103 2.77 *** 0.0273 8.73 ***
SIZE ? 0.0027 1.07 -0.0032 -6.36 ***
CFO - 0.0143 0.98 0.0059 0.60
Total Observations 5,578 5,578
Adj. R-squared 33.52% 20.22%
Firm Fixed Effects Included Excluded
1.Variable definitions:│DA│=Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy
variable that takes the value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities;
ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the
value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value
of 1 if firm’s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from
operating activities, deflected by total assets.
2.t-values are the results of White's (1980) robust estimate.
3.all data are winsorized at the 1% and 99% levels.
4.***, ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively.
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Table 6: Earnings Quality and High Managerial Ability
│DA│=α0+α1Ability_H+α2Post+α3Ability_H×Post+α4DEBT+α5ABS_TA
+α6OUTS+α7LOSS+α8SIZE+α9CFO+ε
Variables Predicted sign Coefficient t-value Coefficient t-value
Intercept 0.0586 4.79 0.0748 19.29
Ability_H ? 0.0103 2.01 ** 0.0210 4.77 ***
Post ? -0.0065 -2.14 ** 0.0019 0.71
Ability_H×Post - -0.0110 -1.92 * -0.0192 -3.80 ***
DEBT - -0.0037 -0.33 -0.0107 -1.90 *
ABS_TA + 0.6842 7.12 *** 0.6437 9.09 ***
OUTS + 0.0137 3.78 *** 0.0206 6,00 ***
LOSS + 0.0103 2.79 *** 0.0273 8.73 ***
SIZE ? 0.0027 1.06 -0.0031 -6.28 ***
CFO - 0.0143 0.97 0.0058 0.59
Total Observations 5,578 5,578
Adj. R-squared 33.52% 20.30%
Firm Fixed Effects Included Excluded
1.Variable definitions: │DA│=Absolute value of discretionary accruals; Ability_H= Dummy variable that
takes the value of 1 if managerial ability is positive and 0 otherwise; Post=Dummy variable that takes the
value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute
value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if
outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if
firm’s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from
operating activities, deflected by total assets.
2.t-values are the results of White's (1980) robust estimate.
3.all data are winsorized at the 1% and 99% levels.
4.***, ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level,
respectively.
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Table 7: Earnings Quality and Managerial Ability-The deciles rank
│DA│_Decile =α0+α1Ability_Decile+α2Post+α3Ability__Decile ×Post+α4DEBT
+α5ABS_TA+α6OUTS+α7LOSS+α8SIZE+α9CFO+ε
Variable Predicted sign Coefficient t-value Coefficient t-value
Intercept 5.0539 12.16 5.1553 25.14
Ability_Decile ? 0.1012 2.97 *** 0.1406 5.05 ***
Post ? 0.0554 0.26 0.4528 2.45 **
Ability_ Decile×Post - -0.0919 -2.46 ** -0.1152 -3.64 ***
DEBT - -0.0079 -0.03 -0.1480 -1.06
ABS_TA + 11.4822 6.76 *** 10.3061 9.02 ***
OUTS + 0.3688 3.28 *** 0.5838 5.70 ***
LOSS + 0.3962 3.38 *** 0.9230 9.74 ***
SIZE ? -0.0288 -0.37 -0.1653 -8.95 ***
CFO - 0.6772 1.91 * 0.0527 0.22
Total Observations 5,578 5,578
Adj. R-squared 33.52% 20.30%
Firm Fixed Effects Included Excluded
1.Variable definitions: │DA│=Absolute value of discretionary accruals; DA_Decile= The decile rank (by
industry and year) of absolute value of discretionary accruals; Ability_Decile=The decile rank (by industry
and year) of the managerial ability; Post=Dummy variable that takes the value of 1 after year 2005 and 0
otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected
by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and
0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm’s bottom line is loss and 0 otherwise;
SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets.
2.t-values are the results of White's (1980) robust estimate.
3.all of data winsorized at the 1% and 99% levels except │DA│and Ability.
4.***, ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level,
respectively.
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Table 8: Earnings Quality and Managerial Ability-Excluding data of year 2005
│DA│ =α0+α1Ability +α2Post+α3Ability ×Post+α4DEBT+α5ABS_TA+α6OUTS
+α7LOSS+α8SIZE+α9CFO+ε
Variable Predicted sign Coefficient t-value Coefficient t-value
Intercept 0.0649 5.07 0.0817 21.17
Ability ? 0.0192 1.60 0.0438 4.30 ***
Post ? -0.0100 -3.37 *** -0.004 -1.87 *
Ability×Post - -0.0281 -2.19 ** -0.0432 -3.82 ***
DEBT - -0.0024 -0.21 -0.0083 -1.44
ABS_TA + 0.6148 6.32 *** 0.6001 8.50 ***
OUTS + 0.0124 3.27 *** 0.0191 5.41 ***
LOSS + 0.0113 2.96 *** 0.0279 8.67 ***
SIZE ? 0.0021 0.78 -0.0032 -6.22 ***
CFO - 0.0097 0.62 0.0014 0.14
Total Observations 5,200 5,200
Adj. R-squared 32.99% 19.54%
Firm Fixed Effects Included Excluded
1.Variable definitions: │DA│=Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy
variable that takes the value of 1 after year 2006 and 0 otherwise; DEBT=Rate of interest bearing liabilities;
ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value
of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if
firm’s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets ;CFO=Cash flows from operating
activities, deflected by total assets.
2.t-values are the results of White's (1980) robust estimate.
3.all data are winsorized at the 1% and 99% levels.
4.***, ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively.
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Table 9: Earnings Quality and Managerial Ability-Excluding data of year 2005
│DA│=α0+α1Ability_H+α2Post+α3Ability_H×Post+α4DEBT+α5ABS_TA
+α6OUTS+α7LOSS+α8SIZE+α9CFO+ε
Variable Predicted sign Coefficient t-value Coefficient t-value
Intercept 0.0613 4.74 0.0743 18.66
Ability_H ? 0.0108 2.07 ** 0.0209 4.74 ***
Post ? -0.0057 -1.72 * 0.0026 0.95
Ability_H×Post - -0.0136 -2.30 ** -0.0202 -3.94 ***
DEBT - -0.0026 -0.22 -0.0085 -1.47
ABS_TA + 0.6159 6.32 *** 0.6017 8.49 ***
OUTS + 0.0123 3.24 *** 0.0190 5.37 ***
LOSS + 0.0114 2.98 *** 0.0279 8.66 ***
SIZE ? 0.0021 0.81 -0.0031 -6.14 ***
CFO - 0.0096 0.62 0.0013 0.13
Total Observations 5,200 5,200
Adj. R-squared 33.00% 19.62%
Firm Fixed Effects Included Excluded
1.Variable definitions: │DA│=Absolute value of discretionary accruals; Ability_H= Dummy variable that takes
the value of 1 if managerial ability is positive and 0 otherwise; Post=Dummy variable that takes the value of 1
after year 2006 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total
accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change
over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm’s bottom line is loss and 0
otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total
assets.
2.t-values are the results of White's (1980) robust estimate.
3.all data are winsorized at the 1% and 99% levels.
4.***, ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively.