The effects of earnings-management motivations on the capitalization of

intangible assets

Program: MSc Accountancy & Control, variant Accountancy track Student: Wouter Vente Student number: 10668012 Supervisor: Mr. dr. P. Kroos Date: 22 of June, 2015

Statement of Originality

This document is written by student Wouter Vente who declares to take full responsibility for

the contents of this document.

I declare that the text and the work presented in this document is original and that no sources

other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of

completion of the work, not for the contents.

Abstract Purpose – This study aims to examine whether executives with earnings management

motivations may take advantages of the discretion inherent in the accounting for intangibles.

Method – This study is based on a regression analysis, based on listed companies under

IFRS. Empirical material is derived from the Compustat Global database and from the

Boardex database.

Findings – The study finds that volatility in reported income is associated with the

capitalization of intangible assets, but the study didn’t find support that the company's

likelihood of debt covenant violation and bonus-plan incentives are associated with the

capitalization of intangible assets. The findings are robust against controlling for industry-

effects.

Research limitations/implications – This study examines the effect of earnings-management

motivations on the capitalization of intangible assets, with the focus on firms only under

IFRS. Nevertheless, it provides insight into the degree in which accounting choices with

regard to intangibles are made opportunistically by managers.

Practical implications – Addressing this research question exhibits practice relevance as

many rules and legislation that has passed since 2002 have been focused on increasing the

integrity of financial reporting. Therefore, the findings of this paper are relevant to regulators,

academics, and practitioners in the determination of an appropriate accounting treatment of

intangible assets.

Originality/value – Prior research is inconclusive on the role of opportunistic motivations of

executive managers whether or not to capitalize intangible assets, this thesis contributes by

adopting a comprehensive focus.

Acknowledgements – I would like to thank Mr. dr. P. Kroos, my thesis supervisor, for his

valuable support during the development of this thesis.

Keywords – Intangible Assets, Earnings management, Cost capitalization, Income

smoothing, Debt covenant, Bonus-plan incentives

Paper type – Research paper

TABLE OF CONTENTS

1. Introduction 5 1.1 Background and research question 5 1.2 Motivation and contribution 6 1.3 Structure 7

2. Literature review and hypotheses development 8 2.1 Treatment of intangible assets and expense capitalization 8

2.1.1 Accounting standards acquired goodwill and aqcuired identifiable intangible asset 9 2.1.2 Accounting standards internally generated intangible assets 9

2.2 Accrual-based earnings management 10 2.3 Hypotheses development 12

2.3.1 Income smoothing and accounting for intangibles 12 2.3.2 Debt covenant and accounting for intangibles 13 2.3.3 Incentive plans and accounting for intangibles 14

3. Method 16 3.1 Sample 16 3.2 Empirical model 17 3.3 Measurement of control variables 18

4. Results 20 4.1 Descriptive results 20 4.2 Results of hypothesis tests 22

5. Conclusion 25

References 26

Appendix 1 30

Appendix 2 31

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1. INTRODUCTION

1.1 BACKGROUND AND RESEARCH QUESTION

Nowadays there is a lot of debate with regard to the capitalization of intangible assets and the

reliability and relevance of these components in the balance sheet. This debate has recently

been raised under US GAAP and IAS/IFRS. There is evidence that the contribution of

intangible assets in creating value is estimated by noisy or incomplete measures (Hunter et al.,

2012). This means that there could be differences between the market value and the book

value of these intangible assets. Greater accounting discretion associated with accounting for

intangibles assets may be used by executive managers opportunistically. That means that

there is a risk that in certain cases intangibles are capitalized instead of expensed, which leads

to higher short-term profits.

Specifically, prior research suggests that the accounting information on intangibles is

useful for investors (Wyatt, 2005; Aboody & Lev, 1998), recent papers have also suggested

that executives may take advantages of the discretion inherent in the accounting for

intangibles. Financial distressed firms capitalize intangible assets more aggressively than non-

financial distressed firms and the manager’s propensity to capitalize intangible assets has a

strong statistical association with earnings management proxies (Jones, 2011). Russell (2014)

found evidence that the magnitude of intangible assets is associated with executive bonus

plans, share issue, leverage and the probability of firm failure. Companies also tend to use

R&D capitalization for earnings-smoothing purposes (Markarian et al., 2007). Shalev et al.

(2013) found evidence that CEOs whose compensation packages rely more on earnings-based

bonuses are more likely to over-allocate the purchase price to capitalized goodwill.

However, the research on the accounting for intangibles is still inconclusive (Jones,

2011; Markarian et al., 2007; Russell, 2014; Shalev et al., 2013; Wyatt, 2005).

Therefore this study intends to represent a comprehensive study into the degree in

which accounting choices with regard to intangibles are made opportunistically by managers.

My research question is:

’What is the effect of earnings-management motivations on the capitalization of intangible

assets?’’

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According to Markarian et al. (2007) the three most important motivations for earnings

management are: income smoothing, debt covenant and bonus-plan incentives. More

specifically, previous accounting research has provided some evidence that managers engage

in income smoothing, that managers that are close to violate debt covenants make accounting

choices to reduce the likelihood of violating debt covenants, and that managers with bonus

plan incentives based on profit or share price may manipulate earnings to increase their own

bonus compensation (Watts & Zimmerman, 1986).

This paper investigates whether these earnings management motivations have impact

on the capitalization of intangible assets like: R&D, other acquired identifiable intangibles as

a group (patents, brand names, mastheads, licenses), and acquired unidentifiable assets

(goodwill). This paper suggests that a manager will capitalize more intangible assets when a

manager has motivations for income increasing earnings management.

1.2 MOTIVATION AND CONTRIBUTION

This paper contributes to the debate on capitalizing and fair value accounting of intangible

assets. The main motivation for this paper is to investigate whether earnings management

motivations have impact on the capitalization of intangible assets like: R&D, other acquired

identifiable intangibles as a group (patents, brand names, mastheads, licenses), and acquired

unidentifiable intangible assets (goodwill). Current measurement and capitalization of

intangible assets is not properly linked to its future earnings (Hunter et al., 2012). The

intangible assets information is not reliable to determine outcomes like earnings and

performance, because it is estimated by noisy and incomplete measures and the valuation of

intangible assets is characterized by ambiguous guidelines and a great amount of discretion

for the respective managers.

A lot of research has mainly been focused on accounting recognition of intangible

assets as well as on the determinants of capitalization (such as earnings-based bonus plans)

(Wyatt, 2005). Given that prior research is inconclusive on the role of opportunistic

motivations of executive managers whether or not to capitalize intangible assets, this thesis

contributes by adopting a comprehensive focus. In this study I will look at multiple earnings

management motivations as potential drivers of managers opportunistic decisions to capitalize

specific expenditures.

Addressing this research question also exhibits practice relevance as many rules and

legislation that has passed since 2002 have been focused on increasing the integrity of

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financial reporting. However, a survey in 2005 of Graham et al. (2005) provide evidence that

about 78% of the CFOs acknowlegde that personal incentives play a role in their financial

reporting decisions. This despite the effect that such earnings management creates a wedge

between the reported earnings and the underlying economic value. Capitalizing intangible

assets is perceived to lead to erosion in the quality of financial reporting, rendering financial

statements more subjective, less informative, and potentially open to manipulation and even

fraudulent misstatement (Watss, 2003, 2006). Expenditure on intangibles is growing overtime

relative to the total investments of a firm (Eliasson, 1990). Therefore, the findings of this

paper are relevant to regulators, academics, and practitioners in the determination of an

appropriate accounting treatment of intangible assets.

1.3 STRUCTURE

The remainder of this paper will be structured as follows. Section two describes the literature

review and hypotheses development. Section 3 outlines the research methodology and

discusses the sample data and empirical model. Section 4 presents the empirical results and

section 5 will concludes and give the limitations of the paper.

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2. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

2.1 TREATMENT OF INTANGIBLE ASSETS AND EXPENSE CAPITALIZATION

Both IFRS (IAS 38, Intangible Assets) and US GAAP (ASC 350, Intangibles - Goodwill and

Other) define intangible assets as identifiable non-monetary assets without physical

substance. An asset is identifiable if it either: (a) is separable (capable of being separated and

sold, transferred, licensed, rented, or exchanged, either individually or together with a related

contract); or (b) arises from contractual or other legal rights, regardless of whether those

rights are transferable or separable from the entity or from other rights and obligations. Both

accounting frameworks require an entity to recognize an intangible asset when it’s probable

that the future economic benefits that are attributable to the asset will flow to the entity and

the cost of the asset can be measured reliably. Otherwise the costs will be recognized as an

expense when incurred. While goodwill is an intangible asset, the term intangible asset is used

in IAS 38 and ASC 350 to refer to an intangible asset other than goodwill. According to IFRS

(IFRS 3, Business Combinations) and US GAAP (ASC 805, Business Combinations)

goodwill can only be capitalized as an intangible asset when it’s acquired in a business

combination. IFRS 3 and ASC 805 provide guidance for the reporting and financial

accounting for acquired goodwill and acquired identifiable intangible assets.

Both accounting frameworks amortize intangible assets with finite useful lives over

their expected lives, while acquired goodwill and other intangible assets are not amortized,

but subject to impairment testing at least annually. The initial measurement of intangible

assets depends on whether the intangibles are acquired in a business combination, or are

generated internally under both frameworks. In general, under IFRS intangible assets are

initially measured at cost and US GAAP have different measurements for the initial

recognition of intangible assets. Intangible assets may be revaluated to fair value if there is an

active market available. IFRS 3 requires that most intangible assets acquired in a business

combination should be recorded at fair value. However, IFRS 3 provides little guidance how

to estimate these fair values. Respective managers have discretion in different estimation

techniques for the fair value measurement of intangible assets. Given that fair value

measurement always requires professional judgment under IFRS. Revaluation of intangible

assets is not permitted under US GAAP.

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2.1.1 ACCOUNTING STANDARDS ACQUIRED GOODWILL AND AQCUIRED IDENTIFIABLE INTANGIBLE ASSETS

According to IFRS 3 and ASC 805 internally generated brands, mastheads, publishing titles,

customer lists and items similar in substance shall not be recognized as intangible assets, with

the exception of acquiring these intangibles in a business combination. IFRS 3 requires that

the acquirer should capitalize the acquired identifiable intangible assets at fair value, meeting

the recognition criteria, separately from goodwill, even when the acquiree didn’t recognize

these assets in his own financial statements and when the acquiree developed the intangibles

internally and charged the related costs to expense. Expenditure for an intangible that doesn’t

meet the criteria of an intangible asset should be form part of the amount attributed to the

goodwill recognized at the acquisition date.

2.1.2 ACCOUNTING STANDARDS INTERNALLY GENERATED INTANGIBLE ASSETS

Both US GAAP (ASC 350) and IFRS (IAS 38) require that internally developed intangibles

shouldn’t be capitalized, with the exception of development costs. Costs for research in the

research phase of research and development (R&D) are expensed under both accounting

frameworks. Almost all development costs are expensed under US GAAP, with the exception

of costs related to developed computer software. IFRS allows more discretion in IAS 38 for

the capitalization of development costs, but only after technical and commercial feasibility of

the asset for sale or use has been established. This means that development phase expenditure

could be capitalized when the following criteria are met: (1) Technical feasibility; (2)

Intention to complete; (3) Ability to use or sell; (4) Probable future economic benefits; (5)

Available resources; (6) Ability to measure. This is a subjective approach and managers have

in practice the discretion to decide whether these criteria are met. So IAS 38 requires

professional judgment in the accounting of development costs.

Advertising and promotional costs are expensed under US GAAP, with the exception

of direct response advertising, which may be capitalized when the following criteria in ASC

340-20 are met: (1) the primary purpose of advertising is to promote sales to certain

customers and whose responses to the advertising can be documented; (2) and it’s probable

that there will be future benefits from direct-response advertising.

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Advertising and promotional costs are also expensed under IFRS, with the exception of a

prepayment, which may be capitalized when payment for the goods or services is made in

advance of the entity having access to the goods or receiving the services.

2.2 ACCRUAL-BASED EARNINGS MANAGEMENT

Earnings management occurs when managers use judgment in financial reporting and in

structuring transactions to alter financial reports to either mislead some stakeholders about the

underlying economic performance of the company or to influence contractual outcomes that

depend on reported accounting numbers (Healy & Wahlen, 1990). Managers can manipulate

earnings with real earnings management and accrual-based earnings management. Real

earnings management has direct impact on cash flows and accrual-based earnings

management has not direct impact on cash flows (Roychowdhury, 2006). The capitalization

of intangible assets could be used as an accrual to manage earnings.

However, discretion that is inherent in accrual accounting can also be used

opportunistically to serve managers personal gain. Accrual-based earnings management may

have real economic consequences as e.g. investors base their decisions on reported income

numbers that deviate from the underlying economic reality, but accrual-based earnings

management does not in itself affect operations and cash flows of the firm (unlike real

earnings management). So if management uses their discretion to opportunistically

manipulate accruals, earnings will become a less reliable measure of firm performance and

cash flows could be preferable (Dechow, 1994). However, Graham et al. (2005) found

evidence that earnings are the key financial metric considered by outsiders and not cash

flows.

The accounting literature provides multiple motivations why managers use their

accounting discretion to achieve several desirable earnings goals such as: capital market

motivations, contracting motivations and regulatory motivations (Healy & Wahlen, 1990). In

terms of capital market motivations, growing firms that fail to meet their benchmark suffer

large negative stock price reactions, when this information comes available to the capital

market (Skinner & Sloan, 2002). Graham et al. (2005) provide evidence that more than 80%

of the CFOs acknowlegde that meeting benchmarks builds credibility with the capital market

and helps maintain or increase the firm’s stock price. In terms of contracting motivations,

Healy (1985) found evidence that managers use their accounting discretion to maximize the

value of their bonus compensation contracts and about 89% of the CFOs acknowlegde that

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bonus compensation leads to earnings management (Dichev et al., 2013). Watts &

Zimmerman (1986) found evidence that earnings might be managed to reduce the probability

of violating a debt covenant contract: about 73% of the CFOs believe that this is a important

motivation (Dichev et al., 2013). In terms of regulatory motivations, managers could also use

their accounting discretion to divert the attention of accounting scructiny when they are under

stringent capital requirements from e.g. government regulatory (Watts & Zimmerman, 1990).

Current understanding on why people commit accrual-based earnings management is

grounded in the triangle developed by Cressey (1953). The fraud triangle consists of three

dimensions: opportunity, incentive/pressure and attitude/rationalism. The triangle presumes

that earnings management risk results from the influence of the triangle’s dimensions.

(Morales et al., 2014).

In terms of opportunity, earnings management is more likely in companies where there

is a weak internal control system, poor security over company property, little fear of exposure

and likelihood of detection, or unclear policies with regard to acceptable behavior. Research

has shown that some employees are totally honest; some are totally dishonest, but that many

are swayed by opportunity (CIMA, 2008, p. 13). Stronger corporate governance mitigates

earnings management (Klein, 2002). Kanagaretnam et al. (2010) found evidence that auditor

reputation moderates earnings management, because of the fear of exposure and likelihood of

detection.

In terms of incentive/pressure, some people with good objective principles can fall

into bad company and develop tastes for the fast life, which tempts them to earnings

management. Others are tempted only when faced with ruin anyway (CIMA, 2008, p. 13).

Dichev et al. (2013) provide evidence that more than 90% of the CFOs feel outside and inside

pressure to meet earnings benchmarks and that about 80% of the CFOs believe that senior

managers misrepresent earnings to avoid adverse career consequences.

In terms of attitude/rationalism, many people don’t violate the law because they are

afraid of being shamed of rejected by people they care about it if they are caught. However,

some people are able to rationalize earnings management actions when the actions are:

necessary, harmless or/and justified (CIMA, 2008, p. 13). Executive managers who have

higher levels of professional commitment are less likely to engage in earnings management

and to act opportunistically (Greenfield et al., 2008). An individual’s ethical orientation

(relativism vs. idealism) has impact on the likelihood to engage in earnings management.

Individuals who tend to be more idealistic should not decide to manage earnings, because

these actions could harm others and idealistic individuals want to avoid this (Elias, 2002). On

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the other hand, individuals who tend to be more relativistic consider the circumstances first,

instead of the consequences of their actions might have. Graham et al. (2005) suggest that

external reputation are important drivers of financial reporting practices.

2.3 HYPOTHESES DEVELOPMENT

The focus in this study will be on the effects of three earnings-management motivations on

the capitalization of intangible assets, which are income smoothing, debt covenants and

bonus-plan incentives.

2.3.1 INCOME SMOOTHING AND ACCOUNTING FOR INTANGIBLES

Income smoothing is the process of manipulating the time profile of earnings or earnings

reports to make the reported income stream less variable (Fudenberg & Tirole, 1995). More

specifically, when the income in a year is low, managers take actions to report a higher

income and when the income in a year is high managers take actions to decrease the reported

income. A high volatile annual income leads to a heavier use of accruals to mitigate the

matching and timing risks of these cash flows (Gu et al., 2005). This may lead to that firms

smooth earnings by shifting earnings from peak years to less successful periods (Copeland,

1968).

The accounting literature provides several motivations why managers use their

accounting discretion for income smoothing. Graham et al. (2005) found evidence that more

than 96% of their respondents preferred a smooth earnings path. An important motivation for

this is to reduce risk perceived by investors and to satisfy external and internal users of

financial statements, with steady and less volatile annual income (Brayshaw & Eldin, 1989).

The variability in annual income is an important measure of the total risk of the firm and has a

direct effect on the behavior of investors and an adverse effect on the value of firm’s shares

(Barth et al., 1995). Investors are more likely to pay or invest in a firm when the annual

incomes are smooth (Ronen & Sadan, 1981). Income smoothing could also be used as a tool

to pay every year a steady stream of dividend to the owners of shares (Kasanen et al., 1996).

Other important motivations of income smoothing are related to contracts and

accounting numbers. By smoothing income the manager may be able to reduce debtholders

assessment of the probability of bankruptcy and this could lead to a decrease in the cost of

capital for the firm (Trueman & Titman, 1988). According to Fields et al. (2001) managers

use income smoothing to manage earnings in order to maximize their bonus compensation.

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Managers boost reported income in bad times for job security, and managers tend to decrease

reported income in good times, because they are less concerned about their job tenure and

they have a incentive to save reported income for future bad times (Fudenberg & Tirole,

1995). This is in contradiction with Healy (1985), which suggest that managers tend to

decrease earnings when the earnings are far below the minimum requirement for a bonus.

However, several studies contradicted the validility of Healy’s hypothesis (Gaver et al., 1995;

Holthausen et al., 1995), suggesting that managers don’t manipulate earnings downwards

when earnings are below the minimum requirement to receive any bonus.

Other explanations of income smoothing are that income smoothing averts attention

from high earnings and can be used by managers to decrease or eliminate political attention

(Watts & Zimmerman, 1986). Managers could also use income smoothing to get tax

incentives (Kinney & Wempe, 2004). However, deferred income may have an undesired

effect on taxes in the future (Copeland, 1968).

Income smoothing is more likely when managers have the flexibility to smooth

income and when the smoothing costs are sufficiently small (Trueman & Titman, 1988).

Nelson et al. (2003) found evidence that cost capitalization is an important method of

earnings management. Therefore, the expectations are that companies will capitalize more

intangible assets when their reported income is lower than the past years, while they expense

more intangibles when the reported income is higher than the past years. The following

hypothesis will be tested:

H1. There is a relationship between volatility in reported income and the capitalization

of intangible assets.

2.3.2 DEBT COVENANT AND ACCOUNTING FOR INTANGIBLES

Debt covenants are contracts intended to restrict manager behavior in order to protect the

wealth of the debtholders (DeFond & Jiambalvo, 1994). The primary concern of debtholders

is to ensure that they get their money back, with some interest (Guay, 2008). Therefore,

covenants restrict manager’s ability to invest, pay out dividends, and take on additional debt,

if minimum accounting numbers are not maintained and thereby limit actions that potentially

hurt debtholders (Begley, 1990; Nikolaev, 2010).

Debt covenants are in most cases based on accounting numbers and violation of these

covenants is costly. Managers that are close to violate debt covenants make accounting

choices to reduce the likelihood of violating debt covenants (Watts & Zimmerman, 1986).

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Even when violating of debt covenants cannot be avoided by manipulation of accounting

numbers, managers are still likely to make income-increasing accounting choices that can

lead to a better bargain position in the event of renegotiation (DeFond & Jiambalvo, 1993).

However, litigation risk may offset managers opportunistically behavior to manage earnings

upwards in order to loosen covenants (DeAngelo et al., 1994). Almost all studies support the

debt-covenant hypothesis (Healy & Palepu, 1990; DeAngelo et al., 1994; DeFond &

Jiambalvo, 1994; Sweeney, 1994; Beatty & Weber, 2003).

As the relative level of debt rises, a firm is more likely to have tighter accounting

constraints, increasing the possibility of failure to comply with covenants (Press & Weintrop,

1990). However, firms with debt covenants based on accounting numbers use income- and

asset-increasing accounting procedures to reduce the probability of failure to comply with

covenants (Begley, 1990). Therefore, the expectations are that companies will capitalize more

intangibles assets when the likelihood of violating a debt covenant is higher. The following

hypothesis will be tested:

H2. There is a positive relationship between a company's likelihood of debt covenant

violation and the capitalization of intangible assets.

2.3.3 INCENTIVE PLANS AND ACCOUNTING FOR INTANGIBLES

Top-executive contracts generally contain incentive provision designed to motivate

executives to maximize firm value and minimize agency problems (Dechow & Sloan, 1991).

These bonus plans are related to payment of cash, stock and options and usually linked to

accounting-earnings performance and stock-price performance (Ronen & Sadan, 1981). Most

bonus plans are contingent on some accounting measures (such as earnings or accounting

returns) sometimes supplemented with nonfinancial and/or subjective measures. Despite that

bonus plans are intended to align the interests of managers with that of shareholders, bonus

plans may also be a manifestation of agency problems given that managers may manipulate

earnings in order to increase their own bonus compensation (Watts & Zimmerman, 1986).

This is in the literature referred to as opportunistic earnings management. In general, many

studies documented a positive relationship between managers’ incentive plans and accrual-

based earnings management. Specifically, managers with a short horizon are more likely to

engage in earnings management in order to boost their short-term earnings-based

compensation, because they are expecting to leave their position in the near future (Dechow

& Sloan, 1991). In the survey of Graham et al. (2005), it is documented that about 90% of the

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financial executives acknowledged that personal incentives play an important role in their

financial reporting decisions.

Healy (1985) finds that when earnings already yield the maximum bonus or are far

below the minimum requirement for a bonus, the manager has incentives to reduce earnings.

Taking an ‘’earnings bath’’ by selecting negative discretionary accruals could increase future

profit. However, several studies contradicted the hypothesis (Gaver et al., 1995; Holthausen et

al., 1995), suggesting that when earnings are low, managers prefere income-increasing

behavior.

As has already been described above, earnings management is positively associated

with bonuses (Gao & Shrieves, 2002). Therefore, the expectations in this study are that

managers with bonus plan incentives capitalized more intangibles to increase their own bonus

compensation.

H3. There is a positive relationship between bonus-plan incentives and the

capitalization of intangible assets

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3. METHOD

3.1 SAMPLE

The sample will be based on listed companies under IFRS given the discretion in the

capitalization of intangible assets compared to US GAAP. I start with an initial sample of

Compustat Global firms over the period 2004 to 2013. This yields an initial sample of

259.095 firm-year observations. I eliminate firms that are not generally in accordance with or

fully compliant with IFRS. This results in a sample of 65.534 firm-year observations. To test

my first hypothesis, data is required on the ROA of the prior 5 years, as well as an leverage.

Missing data leads to the exclusion of 46.819 firm-year observations. Furthermore I exclude

firms without complete data related to intangibles. This leads to a elimination of 8.592 firm-

year observations. Missing data on the control variables leads to the exclusion of 8.580 firm-

year observations. My final sample for hypothesis one and two is composed of 1.543 firm-

year observations over the period 2009 to 2013. Tabel 1 summarizes the sample selection of

the final sample for testing hypothesis one and two.

Hypothesis three will be based on a random sample from the final sample of

hypothesis 1 and 2 and consists of 336 firm-year observations with additional hand-collected

information about executive bonuses from the BoardEx database. All variables in the samples

are winsorized at the 1st and 99th percentile, to mitigate the effect of outliers.

Table 1 Sample selection

1 I require that firms have data on the return on assets (ROA) of the prior 5 years, to calculate the standard deviation of earnings volatility. I keep only variables related to the period of 2009 to 2013.

Initial sample with data available in Compustat (2004-2013) 259.095 less firms that are not generally in accordance with or fully compliant with IFRS

(193.561)

Remaining sample 65.534 less firms with missing data on the ROA of the previous consecutive 5 years1 and missing data on Leverage

(46.819)

Remaining sample 18.715 less observations with missing Intangibles (8.592) less observations with missing control variables (8.580) Final sample (2009-2013) 1.543

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3.2 EMPIRICAL MODEL

This study predicts that earnings management motivations affect the capitalization of

intangible assets. I calculate a number of variables for each firm to be used in the statistical

tests. My dependent variable in all models is the intangible assets capitalization variable:

Capitalization, which is measured as the sum of reported intangibles for firm i at financial-

year end t, scaled by total assets at financial-year end t.2

To test hypothesis one (i.e., the relationship between volatility in reported income and

the capitalization of intangible assets), I use the following model:

Capitalizationi = ß0 + ß1Earn_voli + Controlsi + εi

Earn_vol for firm i is defined as the standard deviation of its ROA calculated over the last 5

previous years. ROA3 is measured as earnings before interest and taxes (EBIT) at financial-

year end t, scaled by total assets at financial-year end t. On the basis of my first hypothesis, I

expect ß1 ≠ 0.

To test hypothesis two (i.e., the relationship between a company's likelihood of debt

covenant violation and the capitalization of intangible assets), I use the following equation:

Capitalizationi = ß0 + ß1Leveragei + Controlsi + εi

Leverage (debt/equity ratio) is measured as the sum of total long-term debt at financial-year

end t scaled by total assets at financial-year end t. Due the cost of accessing actual debt

covenants, the most frequently used proxy to test the debt-covenants hypothesis is the

debt/equity ratio (Watts & Zimmerman, 1986; DeFond & Jiambalvo, 1991). Duke & Hunt

(1990) and Press & Weintrop (1990) used this proxy to test the existence and tightness of

debt-covenants. On the basis of my second hypothesis, I expect ß1 > 0

To test hypothesis three (i.e., the relationship between bonus-plan incentives and the

capitalization of intangible assets), I use the following model:

Capitalizationi = ß0 + ß1Bonusi + Controlsi + εi

2 Total assets as a deflator is also used in other earnings-management studies, for instance the study of Jones (2011) where accruals are deflated by total assets (Markarian et al., 2007). 3 I use EBIT (operating income) instead of net income, as numerator for the measurement of ROA, because net income is biased by different tax rates between the different countries.

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Bonus is measured as the sum of total cash bonuses scaled by total salary and cash bonuses at

financial-year end t. On the basis of my third hypothesis, I expect ß1 > 0

3.3 MEASUREMENT OF CONTROL VARIABLES

My first control variable is Expenditures, which is measured as the total amount of

expenditures in intangibles for firm i at financial-year end, scaled by total assets at financial-

year end t. It is reasonable to expect that there is a relationship between the amount of total

expenditures undertaken by the firm in intangibles and the amount of capitalized intangibles

(Markarian., 2007). Firms that spend more on intangibles will experience on average, a higher

success rate in the capitalization of these expenditures (Aboody & Lev, 1998).

My second control variable is Size, which is measured as the natural logarithmic of

total assets. Generally larger firms are more politically sensitive through firm visibility and

media attention, this induce them to more conservative accounting (Watts & Zimmerman,

1986).

My third control variable is related to the Profitability of a firm. I introduce ROA to

control for the effects of a firm’s profitability, which is measured as earnings before interest

and taxes (EBIT) at financial-year end, scaled by total assets at financial-year end t. ROA is

an accrual-based measure of performance (profitability) and mitigates timing and matching

problems inherent in cash flows, nevertheless current profitability and the extent of

capitalization of intangibles could be affected by earnings-management motivations

(Markarian et al., 2007). More specifically, firms can be motivated to capitalize more

intangibles when their current profit is negative or low when managers want to improve their

accounting performance. However, firms with a high profitability could also expense more

intangibles, to mitigate the risk of political costs (Watts & Zimmerman, 1986). Current profit

could also be seen as a proxy for future profitability (McNichols & Wilson, 1988). Expected

future profitability is associated with capitalization choices of intangibles to recover these

costs in the future (Matolcsy & Wyatt, 2006).

Growth is the fourth control variable in this study, which is measured as the annual

percentage sales growth in total sales at financial-year end t relative to financial-year end t-1.

Growth opportunities increase monitoring difficulty (Smith & Watts, 1992), which creates

more opportunity for managers to use their discretion to opportunistically capitalize

intangibles. Many researchers use change in sales as the indicator of growth (Weinzimmer et

al., 1998).

19

To control for Industry effects, I include industry dummies based on two-digit GIC

codes. Ten different industries are used in the final sample. Therefore, I created nine industry

dummies to control for the industry effects in the OLS regression4. The two-digit GIC codes

are displayed in Appendix 2

4 The nine industry dummies represent the impact of these nine industries relative to one industry that is excluded in the OLS regression, which is Energy (two-digit GIC code 10).

20

4. RESULTS

4.1 DESCRIPTIVE RESULTS

Tabel 2 shows the industry distribution of my sample firms. The most common industry in the

samples is Industrials. Industrials represent 32% of the total firm year observations for my full

sample. Overall, the sample seems relatively evenly distributed across industries.

Table 2 Observations per industry

Table 3 reports the descriptive statistics for the variables used in the OLS regression for

testing the hypotheses. The mean of Capitalization is 1.3%, with about 3% being capitalized

at the 90th percentile. So, for the firms in the highest decile of capitalization, the intangible

assets represent at least 3% of their total assets. The firms are profitable with an average ROA

of 7.9% and have an average an earnings volatility of 3.5%. On average, the sample firms

finance about 16% of their assets through long-term debt. The firms spend an equivalent

about 20% of the average total assets in intangibles, with about 50% at the 90th percentile.

This indicates that firms make a lot of expenditures on intangibles. The mean annual

percentage sales growth is about 8%, with about 19% at the 90th percentile.

Industry N pct cumpct Energy 67 4.34 4.34 Materials 168 10.89 15.23 Industrials 496 32.15 47.38 Consumer Discretionary 319 20.67 68.05 Consumer Staples 140 9.07 77.12 Health Care 93 6.03 83.15 Financials 8 0.52 83.67 Information Technology 158 10.24 93.91 Telecommunication Services 38 2.46 96.37 Utilities 56 3.63 100.00 Total 1543 100.00

21

Table 3 Descriptive statistics

count mean sd median 10% 25% 75% 90% Capitalization 1543 0.013 0.030 0.005 0.001 0.002 0.013 0.030 Earn_vol 1543 0.035 0.037 0.025 0.008 0.015 0.044 0.074 Leverage 1543 0.156 0.164 0.137 0.000 0.030 0.226 0.315 Bonus 336 0.274 0.260 0.266 0.000 0.049 0.428 0.545 Expenditures 1543 0.204 0.189 0.151 0.012 0.044 0.310 0.493 Size 1543 7.408 2.316 7.478 4.310 5.773 8.965 10.496 Profitability 1543 0.079 0.088 0.074 0.012 0.044 0.115 0.163 Growth 1543 0.088 1.338 0.036 -0.103 -0.029 0.104 0.194

Table 4 presents the Pearson correlation coefficients for the variables used in the OLS

regression. Capitalization is positively related to Earn_vol, indicating that companies will

capitalize more intangible assets when their earnings volatility is higher. Capitalization is not

significantly related to Leverage, indicating that there is no relation between a company's

likelihood of debt covenant violation and the capitalization of intangible assets. Capitalization

is not related with Bonus, suggesting that managers with bonus plan incentives doesn’t use

the capitalization of intangible assets as earnings management tool for increasing their own

bonus compensation. Capitalization is positively related to Expenditures, which supports that

there is a relationship between the amount of total expenditures undertaken by the firm in

intangibles and the amount of capitalized intangibles. There is also a negative relation

between Capitalization and Size, indicating that smaller firms capitalize more intangible

assets, probably due less conservative accounting compared to bigger firms. Profitability is

not significantly related to Capitalization, indicating that there is no relation that more

profitable (unprofitable) firms capitalize less (more) intangible assets in the current year.

Capitalization is also not significantly related with Growth, suggesting that growing firms are

not more likely to capitalize more intangibles.

Table 4 Pearson correlations

1. 2. 3. 4. 5. 6. 7. 8. 1. Capitalization 1.00 2. Earn_vol 0.12 1.00 3. Leverage -0.04 -0.15 1.00 4. Bonus -0.09 -0.30 0.16 1.00 5. Expenditures 0.10 -0.18 0.16 0.02 1.00 6. Size -0.17 -0.28 0.28 0.41 0.05 1.00 7. Profitability -0.03 -0.19 -0.08 0.28 -0.00 0.15 1.00 8. Growth 0.01 -0.02 -0.01 0.04 -0.02 0.03 0.02 1.00 Correlations in bold p<0.05

22

There are no visual indicators that multicollinearity might cause problems in the data (i.e.,

correlations never exceed 0.7 and/or -0.7). To further address this, I performed a

multicollinearity test for the independent variables. According to the Variance inflation factor

(VIF), it can be concluded that there is no concern of multicollinearity between the

independent variables, because all independent variables have a VIF below 5.

4.2 RESULTS OF HYPOTHESIS TESTS

Table 5 displays the results of the OLS regression for testing my hypotheses, without

controlling for industry effects. For hypothesis 1, the independent variable, Earn_vol, is

positive and significant related to Capitalization, indicating that companies would capitalize

more (less) intangible assets when they face greater (less) earnings volatility. Therefore, the

first hypothesis is supported. Inconsistent with my predictions, the independent variable for

hypothesis 2, Leverage, is insignificantly related to Capitalization, suggesting that there is no

relationship between a company's likelihood of debt covenant violation and the capitalization

of intangible assets. A possible explanation for this is that debt covenants could restrict

manager’s ability in accounting choices with regard to intangibles. Higher leverage could also

lead to higher governance and better monitoring that may restrict manager’s discretion to

manipulate earnings (Iturriaga & Hoffmann, 2005). Therefore, the second hypothesis is

rejected. The independent variable, Bonus, is insignificantly related to intangible assets,

indicating that managers with bonus plan incentives doesn’t use the capitalization of

intangible assets as earnings management tool for increasing their own bonus compensation.

Therefore, the third hypothesis is rejected.

Regarding to the control variables, I find for my full sample that Expenditures are

positive and significant related to Capitalization, suggesting that more expenditures in

intangibles leads to more capitalized intangibles. The second control variable, Size, is

negative and significant related to Capitalization, indicating that smaller firms capitalize more

intangible assets, probably due less conservative accounting compared to bigger firms. The

third control variable, Profitability, is not significant, indicating that there is no relation that

more or less profitable firms capitalize more or less intangible assets in the current year. The

last control variable, Growth, is also not significant, suggesting that growing firms are not

more or less likely to capitalize intangibles. The adjusted R2 for the test of hypothesis 1 is

about 4,7%, the adjusted R2 for hypothesis 2 is about 3,7% and the adjusted R2 for hypothesis

23

3 is about 12%, which implies that the models explain between 4% and 12% of the variance

in Capitalization. All three models are significant as indicated by F value (p<0.01).

Table 5 Regression results OLS

(Hypothesis 1) (Hypothesis 2) (Hypothesis 3) Dependent variable Capitalization Capitalization Capitalization Independent variables

Earn_vol 0.085** (0.036) Leverage -0.001 Bonus

(0.005)

0.002

(0.006) Expenditures 0.020*** 0.017*** -0.013 (0.004) (0.004) (0.009) Size -0.002*** -0.002*** -0.005*** (0.000) (0.000) (0.001) Profitability 0.006 0.000 -0.020 (0.022) (0.024) (0.028) Growth 0.000 0.000 -0.000 (0.001) (0.001) (0.000) Industry Effects No No No Constant 0.020*** 0.027*** 0.055*** (0.005) (0.006) (0.007) Observations 1,543 1,543 336 R-squared 0.051 0.041 0.136 adj. R-squared 0.0478 0.0379 0.1230 F-statistic 13.4479 13.5182 9.5099 p(F) 0.0000 0.0000 0.0000

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In the preceding regression model I tested the effect of earnings-management motivations on

the capitalization of intangible assets, without controlling for industry effects. Table 6 reports

the results of testing the hypotheses when I control for industry effects. The results are very

similar to the preceding regression model. Earn_vol, is still positive and significant related to

Capitalization, while Leverage and Bonus are still not related to Capitalization. So, my prior

inferences with regard to the independent variables of interest are robust against controlling

for industry-effects. Also, the relations between the control variables and the capitalization of

intangible assets are similar. The most important difference compared to the preceding

regression model is that the models explain more of the variations in Capitalization, namely

24

about 8% and 16% of the variance in Capitalization. All three models are again significant as

indicated by F value (p<0.01).

Table 6

Regression results OLS (Hypothesis1) (Hypothesis 2) (Hypothesis 3) Dependent variable Capitalization Capitalization Capitalization Independent variables

Earn_vol 0.062* (0.037) Leverage 0.001 Bonus

(0.005) 0.002

(0.006) Expenditures 0.014*** 0.011** -0.012 (0.005) (0.004) (0.009) Size -0.002*** -0.002*** -0.005*** (0.000) (0.000) (0.001) Profitability -0.003 -0.007 -0.013 (0.023) (0.024) (0.029) Growth 0.000 0.000 -0.000 (0.002) (0.002) (0.000) Industry Effects Yes Yes Yes Constant 0.035*** 0.040*** 0.065*** (0.007) (0.008) (0.011) Observations 1,543 1,543 336 R-squared 0.101 0.096 0.198 adj. R-squared 0.0928 0.0879 0.1633 F-statistic 14.5327 14.1483 6.0915 p(F) 0.0000 0.0000 0.0000

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

25

5. CONCLUSION

This paper examines whether earnings management motivations are associated with the

capitalization of intangible assets. Based on prior literature related to cost capitalization and

earnings management, it is expected that executives with earnings management motivations

may take advantages of the discretion inherent in the accounting for intangibles. Using a

sample of 1.543 observations, I found evidence that there is a relationship between volatility

in reported income and the capitalization of intangible assets, indicating that companies

would capitalize more (less) intangible assets when they face greater (less) earnings volatility.

However, I didn’t found evidence that there is a relationship between a company’s likelihood

of debt covenant violation and the capitalization of intangible assets, probably due the fact

that debt covenants could restrict manager’s ability in accounting choices with regard to

intangibles. Using a sample of 336 observations, I didn’t found evidence that there is a

positive relationship between bonus-plan incentives and the capitalization of intangible assets.

All the findings are robust against controlling for industry-effects.

This study sheds a light on the effect of earnings management motivations on the

capitalization of intangible assets and contributes to the debate that has recently been raised

under US GAAP and IAS/IFRS with regard to the capitalization of intangible assets and the

reliability and relevance of these components in the balance sheet. It appears that managers

use the capitalization of intangible assets as tool to smooth income. However, income

smoothing is also an effective way for managers to signal information to the market.

Therefore, it remains a relevance/reliability trade-off accounting issue how intangibles should

be accounted.

The results of this study are subjected to the following limitations. First, all the data

are obtained for companies under IFRS. This means that the generalization of findings is

limited for firms that are not generally in accordance with or fully compliant with IFRS.

Further research is needed to investigate if these findings could be replicated to other

frameworks for financial reporting. Second, my sample for testing hypothesis 3 is relatively

small, which is mainly due the need to hand-collect the information. Nevertheless, I provide a

clean setting where intangibles play an important role for the firms under IFRS.

26

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APPENDIX 1

Variable definitions

Capitalization = The sum of reported intangibles for firm i at financial-year end t,

scaled by total assets at financial-year end t

Earn_vol = Earnings volatility is measured as the standard deviation of its

ROA calculated over the last 5 previous years. ROA is measured

as earnings before interest and taxes (EBIT) at financial-year

end t, scaled by total assets at financial-year end t

Leverage = The sum of total long-term debt at financial-year end t scaled by

total assets at financial-year end t

Bonus = The sum of total cash bonuses scaled by total salary and cash

bonuses at financial-year end t

Expenditures = The total amount of expenditures in intangibles for firm i at

financial-year end, scaled by total assets at financial-year end t

Size = The natural logarithmic of total assets

Profitability = ROA (earnings before interest and taxes (EBIT) at financial-

year end, scaled by total assets at financial-year end)

Growth = The annual percentage sales growth in total sales at financial-

year end t relative to financial-year end t-1

Industry effects = 9 industry dummies based on two-digit GIC codes

31

APPENDIX 2

GIC CODES:

10 Energy

15 Materials

20 Industrials (Capital Goods, Commercial & Professional Services,

Transportation)

25 Consumer Discretionary (Automobiles & Components, Consumer

Durables & Apparel, Consumer Services, Media, Retailing)

30 Consumer Staples (Food & Staples Retailing, Food, Beverage &

Tobacco, Household & Personal Products)

35 Health Care (Health Care Equipment & Services, Pharmaceuticals, Biotechnology & Life Sciences)

40 Financials (Banks, Diversified Financials, Insurance, Real Estate)

45 Information Technology (Software & Services, Technology Hardware & Equipment, Semiconductors & Semiconductor Equipment)

50 Telecommunication Services

55 Utilities