the effect of managerial ability on earnings quality in...

Proceedings of the Third Middle East Conference on Global Business, Economics, Finance and Banking

(ME15Dubai October Conference), ISBN - 978-1-63102-286-9

16-18 October, 2015 Paper ID: DF523

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www.globalbizresearch.org

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.


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.


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.


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).


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.


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




16


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




17


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.




18


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.




19


Table 6: Earnings Quality and High Managerial Ability

│DA│=α0+α1Ability_H+α2Post+α3Ability_H×Post+α4DEBT+α5ABS_TA


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


Adj. R-squared 33.52% 20.30%


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.



4.***, ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level,

respectively.




20


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


Adj. R-squared 33.52% 20.30%


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.


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.




21


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


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


Adj. R-squared 32.99% 19.54%


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.







22


Table 9: Earnings Quality and Managerial Ability-Excluding data of year 2005

│DA│=α0+α1Ability_H+α2Post+α3Ability_H×Post+α4DEBT+α5ABS_TA



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


Adj. R-squared 33.00% 19.62%


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.




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