accounting for intangibles and earnings management: the

47
Accounting for Intangibles and Earnings Management: the Role of Useful Life Name: Jeroen Schoemaker Student number: 11298774 Thesis supervisor: Dennis Jullens Date: June 21, 2018 Word count: 12259 MSc Accountancy & Control, specialization Accountancy Faculty of Economics and Business, University of Amsterdam

Upload: others

Post on 16-Apr-2022

2 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Accounting for Intangibles and Earnings Management: the

Accounting for Intangibles and Earnings Management: the Role

of Useful Life

Name: Jeroen Schoemaker

Student number: 11298774

Thesis supervisor: Dennis Jullens

Date: June 21, 2018

Word count: 12259

MSc Accountancy & Control, specialization Accountancy

Faculty of Economics and Business, University of Amsterdam

Page 2: Accounting for Intangibles and Earnings Management: the

2

Statement of Originality

This document is written by student Jeroen Schoemaker 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.

Page 3: Accounting for Intangibles and Earnings Management: the

3

Abstract

In this thesis, I analyse the variation in the expected useful life of intangible assets under

International Financial Reporting Standards (IFRS). The annual review of the expected useful

life (of intangible assets) may signal private information about the underlying economics of the

firm or be used as an opportunistic accrual-based earnings management tool. The thesis

analyses a hand-collected sample of relatively R&D-intensive European firms from the

EUROSTOXX 600 using multiple experimental variables. The results show that, on average,

firms have a stable amortization period for their intangible assets. There is a positive

relationship between the change in the expected useful life relative to the previous year and

avoiding to report negative earnings and beating past year’s earnings. However, the results

show that there is no association between the change in the expected useful life and the forecast

error or beating analysts’ forecasts. The LIFE is too stable to have an any association with the

generic earnings management proxies. The findings are robust to controls for statistical

diagnostics, sample composition, and various alternative explanations.

Page 4: Accounting for Intangibles and Earnings Management: the

4

Contents

1 Introduction ........................................................................................................................ 6

2 Concepts and theories ........................................................................................................ 9

2.1 Agency costs ............................................................................................................... 9

2.2 Earnings management ................................................................................................. 9

3 Literature review .............................................................................................................. 13

3.1 The attributes of intangible assets ............................................................................. 13

3.2 Analysts and the capitalization of intangible assets .................................................. 16

3.3 Intangible assets and earnings management: signaling private information or

misrepresenting R&D success ............................................................................................. 18

3.4 Audit quality and earnings management ................................................................... 19

3.5 Benchmark beating and earnings management ......................................................... 20

3.6 Financial analysts and earnings management ........................................................... 20

4 Hypothesis development .................................................................................................. 21

5 Empirical design .............................................................................................................. 23

5.1 Variables and empirical design – H1 ........................................................................ 23

5.2 Variables and empirical design – H2 ........................................................................ 26

6 Sample and Empirical Analysis ....................................................................................... 30

6.1 Sample and data ........................................................................................................ 30

6.2 Descriptive statistics .................................................................................................. 32

6.3 Main empirical results and robustness tests .............................................................. 35

6.3.1 Main empirical results – Forecast Error............................................................. 35

6.3.2 Main empirical results – MEET&BEAT ........................................................... 36

6.4 Robustness checks ..................................................................................................... 39

7 Conclusion ....................................................................................................................... 41

8 References ........................................................................................................................ 43

Page 5: Accounting for Intangibles and Earnings Management: the

5

9 Appendix .......................................................................................................................... 46

Page 6: Accounting for Intangibles and Earnings Management: the

6

1 Introduction

Accounting rules provide managers with great discretion on the choices that relate to the

classification and presentation of economic information in the financial statements. One such

accounting choice, that may shape reporting outcomes, is the decision to determine the

expected useful life of intangible assets under International Financial Reporting Standards

(IFRS) in Europe. The decision to increase the expected useful life of an intangible asset allows

managers to defer the amortization charge to a future period and provisionally inflate the year-

end earnings.

The purpose of this master’s thesis will be to investigate if firms use the annual review of the

expected useful life of intangible assets to manage earnings under International Financial

Reporting Standards (IFRS) in Europe. Much of the extant literature has illustrated that

discretion drives the capitalization of intangible assets. For instance, most recently Dinh et al.

(2015) investigate if the capitalization of R&D assets is used to meet or beat earnings

benchmarks. In the literature there are two perspectives on the accounting choice to capitalize

intangible assets on the face of the balance sheet. On the one hand, the capitalization may be

used to signal private information to the market or management may use the uncertainty

associated with intangible assets for opportunistic behavior. The existing literature has no

definitive answer to the discussion. This master thesis will do so similarly for the accounting

estimate to review the useful life of intangible assets.

So far, no empirical study that I am aware of has investigated the expected useful life of

intangible assets in relation to earnings management. This thesis will follow the literature and

investigate if the accounting discretion to review the useful life of intangibles assets is

associated with meeting or beating performance thresholds and the variation in analysts’

forecast error as proxies for earnings management. Hence, the empirical question is formulated

as follows:

RQ: Do companies use the expected useful life of capitalized intangible assets to

manage earnings under IFRS?

Providing an answer to this research question is relevant, in particular given the stewardship

role of financial reporting. Earnings management may obstruct the efficient allocation of

resources, as the managers may receive a premium for using accounting methods to artificially

inflate their earnings. In addition, the intended users of financial reports expect the presented

Page 7: Accounting for Intangibles and Earnings Management: the

7

figures to reflect the underlying economics of the entity in order to facilitate optimal economic

decision-making.

Moreover, this thesis may conceptually inform the controversial issue of accounting for

intangible assets. Intangible assets are associated with uncertainty as the probability that future

economic benefits cannot be reliably estimated. This belief reflects the widely-held position on

intangible assets in general (see Lev, 2001). Hence, it is important to understand if the

information that is communicated to the intended users of financial reports is reliable and

informs decision-making. Therefore, the results of this thesis are expected to be of relevance

to users of financial reports and standard-setters. Especially given that IFRS is a principled-

based standard that allows for more discretion and flexibility in making accounting choices.

The results also matter for external auditors that verify the financial statements. Accounting for

intangibles is very subjective and requires professional judgment that may not be verifiable

against external information. The auditors may have to consider the pattern of changes in the

useful life and the economic effects this may have for the users of financial standards.

The analysis is based on the EUROSTOXX 600 firms between 2007 and 2017, the sample

represents the largest publicly listed firms in the European market (all IFRS). In the first set, I

analyse if adjustments in the expected useful life of intangible assets are positively associated

with the forecast error. The results show that the relationship is not statistically significant.

Firms, on average, have a stable amortization period for their intangible assets. If the

amortization period remains stable, then there is little to no uncertainty for financial analysts.

Moreover, LIFE may represent a structural reform of the intangible assets. Such information

is generally impounded in the market, therefore it is not an unexpected surprise. Hence, the

theorization that LIFE contributes to information uncertainty is incorrect.

Secondly, the empirical results show that the LIFE variable has a positive association with

the earnings management proxy. In other words, an increase in the useful life relative to the

previous year is positively associated with avoiding to report negative earnings and beating

past year’s earnings. However, the LIFE variable is not statistically associated to meeting or

beating analysts’ forecasts. The LIFE is too stable to have an any association with a generic

earnings management proxy. All the results are robust to sensitivity checks and controls for

statistical checks.

Page 8: Accounting for Intangibles and Earnings Management: the

8

Finally, the results suggest that in the European Union under IFRS the capitalization of

intangible assets is used to signal positive information to the market. Namely, an increase in

the amount of capitalization reduces the forecast error. Further research is required to

investigate this relationship.

The remainder of this thesis is organized as follows: A conceptual model of earnings

management is presented in Section 2. Section 3 provides a literature review. The literature

view discusses earnings management, financial analysts and intangible accounting. Section 4

presents the hypothesis development. Section 5 explains the variable selection for the empirical

model. In section 6 the sample and all main empirical results are provided. The concluding

remarks are in Section 7.

Page 9: Accounting for Intangibles and Earnings Management: the

9

2 Concepts and theories

The accounting for intangible assets is a controversial issue. For instance, while the IASB

allows the R&D expenditures in the ‘development’ phase to be capitalized as assets, the

standard setter in the US, the FASB, prescribes immediate expenditure of all costs related to

R&D. This relates to two different views on how managers may use discretion. Managers may

use discretion to signal positive information to the market or use it opportunistically. This thesis

is interested in the latter, and therefore will introduce the concepts of earning management and

the tools associated with it – accrual-based earnings management, real earnings management

and classification shifting. A change in the expected useful life may be classified as accrual-

based earnings management. First, this thesis will discuss the concept of agency cost in order

to explain that managers may be incentivized to expropriate wealth from shareholders as there

is separation of control and ownership. Secondly, this thesis will discuss the accounting concept

of earnings management and discuss the model of Cohen and Zarowin (2010), who investigate

the use of accrual-based and real-based earnings management to arrive at a cost-benefit

analysis. Moreover, this thesis will delve into the incentives of earnings management.

2.1 Agency costs

Agency costs arise from the separation of ownership and control. Therefore, Jensen and

Meckling (1976) define an agency relationship as “a contract under which one or more persons

(the principal(s)) engage another person (the agent) to perform some service on their behalf

which involves delegating some decision making authority to the agent” (p. 308). The

relationship between the stakeholders and management of a firm is characterized by a

separation of ownership and control. If both parties are utility maximizers, then the agent

(management) may act against the interests of the stakeholders (principals) when it is in the

agent’s interest to do so. Jensen and Meckling (1976) argue, therefore, that agency costs may

be defined as the sum of monitoring cost by the principal, bonding cost by the agent and the

residual loss.

2.2 Earnings management

Healy and Whalen (1999) provide a seminal review of the earnings management literature.

Accounting standards define a language that management uses to communicate with the firm’s

external stakeholders. Financial reporting, therefore, has to provide a cost-effective means for

Page 10: Accounting for Intangibles and Earnings Management: the

10

management to separate the firms that perform well from the poor performers to help facilitate

efficient resource allocation from investors and stewardship information to stakeholders. The

standard-setters have to take into consideration the timeliness and credibility of the

information. However, there is a trade-off: standards that emphasize credibility generally

provide less relevant and timely information. The standards thus must permit some judgment

and subjectivity in financial reporting, which provides opportunities for earnings management.

Therefore, they define earnings management as:

“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.” (p. 368)

An important element in the definition is that the stakeholders are being misled, which implies

that stakeholders may not have access to the same information as management. Management

will thus only have an incentive to manage earnings if they expect that stakeholders cannot

undo all earnings management. As will be discussed in the following section, this is what

predominantly drives the accounting choice between the accrual-based earnings management

and the other types of earnings management.

Moreover, this thesis will discuss two motives for earnings management: capital market

motivations and contracting motivations (Healy and Wahlen, 1999). The first motive is the

capital market where investors and financial analysts use accounting information to value

stocks. There is empirical evidence that management may be incentivized to increase short-

term stock returns, for instance, firms that have a high percentage of ownership by institutions

with trading strategies and portfolio turnover are more likely to cut R&D spending to manage

their earnings upward. Firms with high percentage of institutional ownership, on the other,

generally do not reduce R&D expenditure to improve the reported earnings.

The second motive to engage in earnings management is related to contracting. The contracts

between management and external stakeholders seek to align the interests of both parties. There

are several types of contracts that may incentive management to manage earnings (for an

overview see Healy and Whalen (1999)). Numerous studies have investigated management

compensation contracts, for instance, there is substantial evidence that management is more

likely to defer income when the bonus target is met than firms with comparable performance

that have not met the bonus cap. Lending contracts with restructuring covenants also induce

Page 11: Accounting for Intangibles and Earnings Management: the

11

management to boost the earnings to avoid costly restructuring. However, there is very remote

evidence on the frequency and magnitude and which accruals are used to manage earnings.

In summary, the extant literature evidences that managers behave opportunistically. The

corporate structure that separates control from ownership may create divergence of interests

between the agent and the principal. Empirical evidence suggests that the agent indeed is an

utility-maximizer and may have multiple motives to engage in earnings management to

increase their own welfare at the expense of the principal. Therefore, this thesis will regard

earnings management as opportunistic behavior by management that seeks to maximize their

own remuneration.

2.3 Earnings management tools

Cohen and Zarowin (2010) investigate the use of accrual-based and real transaction-based

earnings management. The key characteristic of accrual-based earnings management is that

they do not have any underlying cash flows and therefore accrual-based earnings management

is dependent on the choices permitted by the accounting standards. The accounting choices are

generally associated with the acceleration of recognizing revenue and a deceleration of

recognizing expenses in an accounting period. However, accrual-based earnings management

is very costly because, for instance, current expenses are deferred to a future period, but this

will also increase the expenses in future periods by the same amount. Moreover, there is a

reputational concern for management. As discussed in the previous section, earnings

management is only effective if the stakeholders cannot uncover it, otherwise it results in

reputational loss for the managers.

The practice of real transaction-based earnings management, on the other, concerns the

manipulation of “real” activities by management. For instance, managers may use price

discounts to temporarily boost sales or reduce R&D-related expenditure to improve margins.

The use of real earnings management activities, however, has an effect on the future

performance of the firm and on the future cash flows. Both methods increase the expectation

of future earnings and have an effect on future earnings. The key difference is that it is relatively

more difficult to detect real transaction-based earnings management, because the activities

cannot be separated from daily business routines and strategies.

Lastly, there is classification shifting which is defined as “the deliberate misclassification of

items within the income statement” (McVay, 2006). Classification shifting has no effect on the

bottom-line earnings, but may portray the information more favorably by reclassifying

Page 12: Accounting for Intangibles and Earnings Management: the

12

persistent items into transitory items on the income statement. Therefore, all of the earnings

management tools share that they increase the expectations about future earnings, but

classification shifting and real-based earnings management are more difficult to detect.

In sum, managers consider the costs and benefits associated with each earnings management

tool. Based on the above, Cohen and Zarowin (2010) identify three types of cost for accrual-

based earnings management: a) the scrutiny provided by the capital markets; b) the potential

penalty of detection; and c) the difficulty of achieving a given earnings target. Otherwise other

forms of earnings management are more likely to be used by management (see Cohen and

Zarowin (2010)). This thesis will focus on accrual-based earnings management under IFRS,

specifically the use of the accounting choice to annually review the expected useful life of

intangible assets under IFRS.

Page 13: Accounting for Intangibles and Earnings Management: the

13

3 Literature review

3.1 The attributes of intangible assets

Lev (2001) explains the four economic characteristics of intangible assets in his seminal book.

The first characteristic of intangible assets is that they are generally nonrival, in other words,

“they may be deployed at the same time in multiple uses, where a given deployment does not

detract from the usefulness of the asset in other deployments” (pp. 18). For this reason,

intangible assets have negligible opportunity cost other than the original investment. An

example given is the development of a software product which is characterized by a high initial

investment in research and development activities, but the marginal costs are negligible

because the production costs of the software is marginal – the software can be downloaded

from the cloud. Therefore, many intangible assets do not share the diminishing returns

characteristic of physical assets to the same extent. The effect of the nonrivalry attribute of

intangible assets is that the scalability is only limited by the size of the market, hence

intangibles are generally characterized by increasing returns to scale. Another driver of the

scalability is the network effect of intangible assets, which arises when consumers and users

appreciate large networks. For instance, the usefulness of a computer operating systems

increases with its ability to connect to more other users.

Secondly, intangibles assets are characterized by hazy property rights (partial excludability and

spillovers). The risk that non-owners will benefit from the investments is substantial. The most

straightforward example is the training of employees whose knowledge will spill-over to

competitors when they switch employers. Even when a patent expires, the invention may be

used freely by competitors in the market or even before the expiration date expires the

competitors may use the re-engineering to subvert the property rights conferred by patents.

This is evidenced by the number of patent infringement lawsuits that retaining the benefits of

patents is very difficult. Cohen et al. (2000) have used a survey to investigate if the use of

patents has strengthened the protection of intellectual capital. Even though the patent protection

has strengthened, the U.S. manufacturing firms surveyed relied more extensively on secrecy

and first-to-market time to gain returns on investments than on the protection of their patents

by legal entities. In fine, the partial excludability gives rise to spillovers that create a tension

between the potential to generate value from investments in intangibles and to secure the

benefits from the investments in the absence of comprehensive legal control.

Page 14: Accounting for Intangibles and Earnings Management: the

14

Thirdly, many intangibles, in particular R&D investments, are inputs for the innovation

processes of enterprises. A key characteristic of innovation processes is that they are highly

risky relative to regular business activities. Many empirical studies attest that the research and

development success is concentrated in only small number of projects. Therefore, the third

characteristic is that intangible assets are inherently risky relative to physical or even financial

assets. In fact, Kothari et al. (1998) conducted a comparative study of earnings volatility (a

measure of risk) associated with R&D and property, plant and equipment assets. The volatility

for R&D is on average three times larger. Essentially, IFRS defines the R&D into two

components: research is the prospect of gaining new knowledge and the development part is

when the acquired knowledge or insight is used for commercial purposes. Essentially, the two

major risks arise from this process, if discovery will take place and subsequently what the

potential is to commercially benefit from any discoveries. Not all intangible assets share these

risks to a similar extent, especially since some are developed to be used internally (see p. 35-

36).

Fourthly, there is an absence of active and competitive markets in intangible assets. They are

not as easily exchanged as financial or physical assets. The intangibles assets therefore share

the attribute of nontradability which has consequences for the valuation and measurement,

especially as there are no observable market prices. Markets provide its users with information

to optimize resource allocation and the producers of goods and services with liquidity. This

largely a consequence of what David Teece states, “It is inherent in an industry experiencing

rapid technological improvement that a new product, incorporating the most advanced

technology, cannot be contracted for by detailed specification of the final product. It is precisely

the impossibility of specifying final product characteristics in a well-defined way in advance

that renders competitive bidding impossible in the industry” (in Lev 2001, p. 39). This is

essentially a prerequisite of an active market, otherwise volume cannot be created. The

negligible marginal costs of producing the goods or services and the hazy property rights also

complicate the trading of intangible assets.

Page 15: Accounting for Intangibles and Earnings Management: the

15

In sum, as a business enterprise does not have strict control over most intangibles, the

accounting profession has difficulty recognizing intangibles as assets on the balance sheet. The

direct expensing of intangible investments results in a deterioration of the usefulness of

financial information to stakeholders (Lev and Zarowin (1999). The belief that the prospects

of intangible investments are too uncertain results in the immediate expensing of such business

activities. Hence, they are associated with information risk.

Page 16: Accounting for Intangibles and Earnings Management: the

16

3.2 Analysts and the capitalization of intangible assets

The forecasts of analysts reflect information risk and intangible assets may increase

information risk (see Healy et al., 1999; Lang et al., 2003). Intangibles assets are associated

with greater uncertainty relative to physical and financial asset. In particular, firms that are

characterized by high underlying intangibles. Chalmers et al. (2012) provides two examples of

operating environments with high underlying intangible assets: R&D firms or large advertising

(brand value) firms. Such firms may be particularly difficult to unravel for financial analysts.

The accounting choice to capitalize R&D expenditures or intangible assets on the face of the

balance sheet, on the one hand, may potentially convey useful information to the users of the

financial statements. This will reduce uncertainty, by signaling positive information to the

users. The signaling perspective posits that forecast accuracy is positively associated with

disclosure of intangible assets (Chalmers et al, 2012, Anagnostopoulou, 2010; Jones, 2007).

Wyatt (2005) finds evidence that in the Australian setting, management capitalizes intangible

assets when they are more certain – in other words, based on the underlying economics of the

firm. In a collaborative study, Matolscy and Wyatt (2006) confirm the previous study. They

analyse if firms that capitalize a higher proportion of their underlying intangible assets have an

improved analyst information environment relative to other firms. They find that the

capitalization of intangibles is negatively associated with the earnings forecast error of a firm.

Cheong et al. (2010) similarly investigates the effect of IFRS adoption on the association

between reported intangibles and forecast accuracy. He finds a positive association between

capitalization of intangible assets and the analyst information environment. The studies of

Chalmers et al. (2012) and Cheong et al. (2010) address the question if the accounting option

Page 17: Accounting for Intangibles and Earnings Management: the

17

to capitalize intangible assets increases information risk, therefore making it more difficult for

analysts to interpret the current information and use it to make earnings forecasts for future

periods. Their findings suggest that managers use the option to reduce information asymmetry.

Another stream of research, however, finds that the capitalization of intangible assets is used

to misrepresent the financials of company. In the United States, Barron et al. (2002) find that

analysts make larger earnings forecast errors when firms have higher capitalized intangibles

and Barth et al. (2001) find that firms with higher underlying intangible assets have a greater

analyst following. Gu and Wang (2003) find a positive relationship between forecast error and

the cost of processing information (task complexity) and the levels of underlying intangibles

that are capitalized. In the United States, however, the majority of intangible assets are

unrecognized. Analysts are interested in firms that outperform other firms, hence they are more

likely to cover firms with performance expectations that are higher relative to their peers. If

analysts follow well-performing firms, they reduce the risk of losing their clients because they

provide more useful recommendations (McNichols and O’Brien, 1997; Francis and Willis,

2001). Matolscy and Wyatt (2006), therefore, posit that analysts prefer firms with higher

intangibles because they expect that their performance will be higher than firms with lower

intangibles. The choice, however, to capitalize may not reflect the underlying economics of

the firm. Prior research suggests a negative association between intangible assets and analyst

forecast properties in the United States.

Intangible assets may increase information risk because the accounting choice to capitalize

may not reflect the underlying economics of the firm and the benefits that flow to the entity

may be relatively more uncertain. However, Gu and Wang (2003) investigate the effect of

information complexity on analysts’ forecast errors. One measure for information complexity

is investments in intangibles greater than the industry average. Firms that outspend other firms

in intangibles are generally associated to be engaged in pioneering innovations. Innovation is

understood as creating new products or services fundamentally different from the existing ones.

Much of the prior literature finds a positive relation between the complexity of forecasting task

and analysts’ forecast errors. Hence, the capitalization of intangibles assets may also be

perceived to contribute to the information complexity of the analysts’ information

environment.

Page 18: Accounting for Intangibles and Earnings Management: the

18

In sum, analysts have difficulty unraveling intangible-intensive firms. As the review of the

expected useful life is performed ex-post, it is likely that it contributes to the information

complexity of intangible assets. Therefore, this master’s thesis expects that the accounting

choice to review the expected useful life is negatively associated with forecast errors.

3.3 Intangible assets and earnings management: signaling private information or

misrepresenting R&D success

The existing literature has no definitive answer to the discussion. Research and development

capitalization, on the one, may be used to signal private information of management to the

market. The managers essentially may use the discretion to capitalize research and

development assets to signal that future benefits may flow to the entity because of R&D

success.1 Interestingly, the setting in which proponents have found that R&D capitalization is

used for signaling private information in a way that increases the usefulness of the financial

statements, R&D capitalization was allowed on a discretionary basis under domestic GAAP –

specifically Australia, France and the UK. On the other hand, R&D development may be used

for earnings management. Most recently Dinh. et al. (2015) have illustrated that R&D

capitalization is strongly related to benchmark beating. They have analysed 150 of the largest

German publicly listed firms between 1998 and 2012. Hence, there are two different

perspectives on capitalization of R&D assets – signaling private information to the market

(IASB) and discretion used for earnings management (FASB).

However, the IASB has introduced significant restrictions on the capitalization of R&D,

especially IAS 38.57 criteria that are used to test the technical and commercial feasibility. The

purpose of the standard is to only allow the capitalization of ventures that have a very high

probability of succeeding. The recognition criteria, however, still allow for management

discretion. There is a significant degree of difficulty in distinguishing research activities from

development activities; the development phase is initiated when all the criteria set by the IASB

are met.2 In fact, the capitalization of internally generated intangible assets under IAS 38.57 is

1 In the Australian context there is a consistent pattern over time that the capitalization of intangible assets is associated with an increase in useful information for investors. For example, a) Ahmed & Falk (2006) under Austrialian GAAP have found R&D capitalization to be informative; b) Matolscy and Wyatt (2006) under Australian GAAP also find that capitalization of intangible investments provides useful information to investors; c) more recently Chalmers et al. (2012) have investigated the question addressed by Matolscy and Wyatt and arrive at a similar conclusion. The previous findings extend to Oswald & Zarowin (2006) under UK GAAP. 2 Paragraph 57 of IAS 38 is the key paragraph that contains a list of criteria of which all must be met before any internally generated intangible assets’ development outlays may be capitalized:

Page 19: Accounting for Intangibles and Earnings Management: the

19

one of the main issues in financial reporting where material misrepresentations have been

detected by the German Financial Reporting Enforcement Panel (Meyer & Naumann, 2009).

Although the standard-setters have enforced strict criteria on R&D capitalization, the

uncertainty associated with intangible assets has created opportunities for earnings

management. In other words, the previous literature suggests that intangibles assets are

susceptible to misrepresentation. This can be theoretically extended to the useful life of

intangible assets.

3.4 Audit quality and earnings management

Meyer & Naumann examination may suggest that external auditors experience difficulty in

uncovering any attempts to misrepresent the financial statements. Indeed, very recently Mazzi

et al. (2017) have investigated the capitalization of R&D under IFRS and country-level

corruption. They find that substantial positive relation between country-level corruption and

the amount of development costs capitalized. Additionally, they also have taken into

consideration the future profitability of the firms. If the country-level corruption is significantly

higher, the contribution of capitalized R&D costs is significantly lower to future profitability.

So the enforcement regime and auditor’s quality may inhibit the use of management discretion

for earnings management, but in the extant literature there are more determinants for the

accounting choice to capitalize R&D expenditure. In particular, the findings of Dinh et al.

(2015) in the German setting with one of the lowest corruption ranking in the world (based on

Transparency Index), suggests that the subjectivity and judgment in capitalizing internally

generated assets may be used not only to signal private information to investors but also to

manage earnings by management. Much of the literature has confirmed the discretion that

drives the capitalization of internally generated intangible assets (Ciftci, 2010; and Mmohd,

2005). Therefore, this master’s thesis will not expect audit quality to have an effect on earnings

management.

(a) The technical feasibility of completing the intangible asset so that it will be available for use or sale. (b) Its intention to complete the intangible asset and use or sell it. (c) Its ability to use or sell the intangible asset. (d) How the intangible asset will generate probably future economic benefits. Among other things, the

entity can demonstrate the existence of a market for the output of the intangible asset or the intangible asset itself or, if it is to be used internally, the usefulness of the intangible asset.

(e) The availability of adequate technical, financial and other resources to complete the development and to use or sell the intangible asset.

(f) Its ability to measure reliably the expenditure attributable to the intangible asset during its developments

Page 20: Accounting for Intangibles and Earnings Management: the

20

3.5 Benchmark beating and earnings management

Accounting earnings are considered to be the most important information provided by the

financial statements. Habib (2007) explains that the importance of earnings figures suggests

that investors evaluate firm performance based on a) reporting positive earnings; b) reporting

earnings greater than last year; c) reporting earnings greater than the consensus analyst forecast.

The prospect theory has been used to explain why investors rely on such simple heuristics to

evaluate firm performance. The theory states that “decision-makers derive value from gains

and losses with respect to a reference point, rather than from absolute levels of wealth” (Habib,

2007). Moreover, the experience of loss is asymmetrical to that of gains, i.e. losses are

experienced as more displeasing. Academic research is consistent with the proposition that a

small gain may result in a premium (see Habib (2007) for an overview). Hence there are

managerial incentives to beat benchmarks as discussed in section 2.2. Although the discussion

in this section is very limited, I refer to section 2 where the theoretical constructs are designed

and I refer to section 5.1 where the empirical design for earnings management proxies are

constructed based on the research of Dinh et al. (2015) and the heuristics of Habib (2007).

3.6 Financial analysts and earnings management

Dinh et al. (2015) have summarized the literature about analysts’ ability to uncover earnings

management. Analysts are generally aware of earnings management and the prior literature

shows that they react negatively to the capitalization of R&D assets in a significant number of

markets, but they are not able to discover if earnings management occurred. Burghstahler and

Eames (2003) also find evidence that analysts react to signal that may be related to earnings

management. It remains an open question to what extent analysts anticipate earnings

management and what types of earnings management they effectively consider. If analysts

adjust their targets upward or downward based on the expected useful life has not been

investigated by the current literature. However, the review of the useful life is performed ex-

post. Therefore, it is highly unlikely that analysts are able to predict the economic

consequences. Hence, the scrutiny of capital markets may even be less applicable to the

expected useful life of intangible assets. In particular, the nature of intangibles allows estimates

to be less reliable. Intangibles have a distinct and unique nature for which there commonly is

only internally-generated information available. More concretely, the intangible-related

activities are associated with uncertainty and therefore reliable and external information

regarding intangible assets may be more difficult to obtain.

Page 21: Accounting for Intangibles and Earnings Management: the

21

4 Hypothesis development

The longevity of internally generated intangible assets has an effect on the amortization charge

in the income statement. The discretion may be used by management to defer incurred costs to

a further period by decreasing the amortization charge. Paragraph 90 provides a list of factors

that should be considered when determining the useful life of an asset.3 As Picker et al. argue

the principles in IAS 38.90 provide management with more discretion but simultaneously give

the possibility to provide more relevant information – i.e., signaling or misrepresentation.

Moreover, the useful life should be reviewed at least at the end of each annual reporting period,

which provides management with the opportunity to partially defer the amortization charge to

a future period. Hence, the useful life of capitalized intangible assets is expected to have a

direct effect on the earnings of any given firm, which may potentially be used in order to beat

analyst benchmarks. The extant literature has documented that management has incentives to

maximize their own remuneration at the expense of the stakeholders. In particular, information

related to R&D and intangible assets in general is associated with internally-generated

information which inhibits market scrutiny. Therefore, the costs applicable to accrual-based

earnings management may be substantially lower for the adjustments of the expected useful

life and more difficult for an external auditor to uncover. So, the adjustments in useful life of

intangible assets may either be a signal of economic benefits that may flow to the entity or be

used to manage earnings, however, this thesis expects that there is a greater likelihood that an

increase of the useful life is used to manage earnings as determining the useful life a priori is

not likely done reliably, hence:

H1: An increase in the expected useful life of intangible assets is positively associated

with meeting and/or beating earnings benchmarks

3 The list of factors that should be considered are: a) The expected use of the asset by the entity and whether the asset could be managed efficiently by another

management team. b) Typical product life cycles for the asset, and public information on estimates of useful lives of similar assets

that are used in a similar way c) Technical, technological, commercial or other types of obsolescence d) The stability of the industry, and changes in market demand e) Expected actions by competitors f) The level of maintenance expenditure required and the entity’s ability and intent to reach such a level g) The period of control over the asset and legal or similar limits on the use of the asset h) Whether the useful life of the asset depends on the useful lives of other assets of the entity

Page 22: Accounting for Intangibles and Earnings Management: the

22

In the literature review, I have summarized the analysts’ perspective of earnings management

and capitalization of intangible assets in general. As they are not able to uncover earnings

management, it is theoretically plausible they also cannot predict what will occur when the

review of the useful life will be performed in a future period and what its effect may be on the

firm’s earnings. There is information risk for analysts as the disclosed information is not

sufficient to accurately translate into future events. Moreover, research has shown that analysts

have difficultly interpreting intangible-intensive firms (Barron et al., 2002; Barth et al., 2001;

Gu and Wang, 2003). Hence, the adjustments in the expected useful life of R&D and/or

intangible assets may reduce the information content of analyst forecasts, hence:

H2: An increase in the expected useful life of intangible assets is associated with a

greater analyst forecast error

Page 23: Accounting for Intangibles and Earnings Management: the

23

5 Empirical design

5.1 Variables and empirical design – H1

In the first hypothesis, this master’s thesis intends to empirically test if the annual review of the

useful life of intangible assets is used to meet or beat earnings benchmarks. Recall:

H1: An increase in the expected useful life of intangible assets is positively associated

with meeting and/or beating earnings benchmarks

The research design will be based on Dinh et al. (2015) and Chalmers et al. (2012). This thesis,

however, will introduce several experimental variables based on the design of Dinh et al.

(2015). Equation 1 represents the earnings management proxies and equation 2 represents

meeting or beating analysts’ earnings forecasts.

(1) 𝑀𝐸𝐸𝑇&𝐵𝐸𝐴𝑇𝑖,𝑡 = 𝛽0 + 𝛽1𝛥𝐿𝐼𝐹𝐸𝑖,𝑡 + 𝛽2𝑆𝑖𝑧𝑒𝑖,𝑡 + 𝛽3𝑃𝑟𝑜𝑓𝑖𝑡𝑖,𝑡 + 𝛽4𝐿𝐸𝑉𝑖,𝑡 +

𝛽5𝐼𝐴

𝑀𝑉𝐴𝐷𝑖,𝑡+ 𝑒𝑖,𝑡

(2) 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑖,𝑡 = 𝛽0 + 𝛽1𝛥𝐿𝐼𝐹𝐸𝑖,𝑡 + 𝛽2𝑆𝑖𝑧𝑒𝑖,𝑡 + 𝛽3𝑃𝑟𝑜𝑓𝑖𝑡𝑖,𝑡 + 𝛽4𝐿𝐸𝑉𝑖,𝑡 +

𝛽5𝐼𝐴

𝑀𝑉𝐴𝐷𝑖,𝑡+ 𝑒𝑖,𝑡

The control variables for H1 relate to the significance of intangible assets and the uncertainty

of earnings. The adjustment of the useful life of intangible assets is a form of accrual-based

earnings management. The accounting option to increase or decrease the expected useful life

of the intangible assets is dependent on the economic significance of intangible assets for the

firm, economic characteristics that are firm-specific, and the economic performance of the firm.

If intangible assets are insignificant, its effect on earnings will be negligible. Moreover, well-

performing firms will beat or meet earnings benchmarks more often relative to poor performing

firms. Therefore, several control variables will be used in the regression analysis for H1.

(1) LIFE

The expected useful life of intangible assets is not disclosed in the notes of the annual report.

However, the implied useful life of intangible assets may be calculated using the following

formula:

[(Adjusted gross amount – (indefinite intangible assets – adjusted goodwill – intangible assets

that are not amortized)) / Total amortization charge t]

Page 24: Accounting for Intangibles and Earnings Management: the

24

The implied useful life variable requires the gross amount so that the implied useful life is

calculated using an identical base. The gross amount of the previous year will be used because

any year-end additions are amortized the subsequent year. However, the gross amount t-1 of

the capitalized intangible assets will have to be adjusted for any acquisitions, impairment

charges and disposals that occur in the year t. Appendix A shows the effect of the disposals,

impairment charges and additions that occur in year t on the calculation of the implied useful

life. The date of acquisition is not reported in the footnotes of the annual report, therefore the

precise implied useful life cannot be reliably deduced from the annual report. For example, if

the acquisition of an intangible assets occurs at the end of the third quarter, the assets will only

be amortized over the fourth quarter of the fiscal-year (see Appendix A). Therefore, this thesis

will consistently proxy for any such effects by taking the average gross amount throughout the

period. This is calculated as follows:

[(Gross intangible asset t-1 + Gross intangible assetst)/2]

Any intangible assets that have an indefinite useful life, such as goodwill, will be subtracted

from the adjusted gross amount. This thesis is interested in the year-on-year variation. The

independent variable, therefore, will be LIFE which is calculated as follows:

[Implied useful lifet - Implied useful lifet-1]

(2) MEET&BEAT

The dependent variable for equation 1 will be a dummy variable MEET&BEAT. The variable

will be based on the heuristics of Habib (2007), who explains that the importance of earnings

figures suggests that investors evaluate firm performance based on a) reporting positive

earnings; b) reporting earnings greater than last year; c) reporting earnings greater than the

consensus analyst forecast. Hence, in the extant literature the independent variable used is a

MEET&BEAT dummy variable that will equal 1 when any of the firm-year observations meets

or beats one of the earnings benchmarks, and 0 if otherwise.

1. EPS ≥ 0 (reporting earnings greater than zero)

2. EPS ≥ 0 (management reports earnings greater than last year)

3. EPS ≥ EPS Forecast (earnings greater than the consensus forecast)

Page 25: Accounting for Intangibles and Earnings Management: the

25

However, in my sample this variable returns 1 for nearly every single firm-year observation.

Moreover, the MEET&BEAT variable is empirically hazy as it cannot explain if the increase

in the useful life’s effect on the amortization charge was required to meet or beat the earnings

benchmark (see theoretical model). Therefore, following Dinh et al. (2015) this thesis has

constructed experimental variables.

First, the variable EFFECTEARN will be constructed. EFFECTEARN represents the effect

LIFE variable on the earnings through the amortization charge in year t. The variable may be

calculated in two ways. The first method, however, is used in this thesis:

[Adjusted gross intangible assets/(Useful lifet - LIFEt) – (Adjusted gross intangible

assets/Useful lifet)]

OR

[Amortization charget – (Amortization charget * (1 - %LIFEt)]

PASTBEAT represents 1 for all firm-year observations that meet or beat past year’s earnings

and do not meet or beat past year’s earnings after deducting EFFECTEARN from the

ΔEARNINGS, and 0 if otherwise.

BeatZERO is a similar variable that represents 1 for all firm-year observations with earnings

greater than zero and report earnings below zero after deducting EFFECTEARN from the

earnings, and 0 if otherwise.

The two variables will be used to construct the MEET&BEAT variable. MEET&BEAT returns

1 for all firm-year observations that meet or beat one of the below earnings benchmarks, and 0

if otherwise.

1. [(Earnings ≥ 0 – EFFECTEARN)] < 0 (reporting earnings greater than last year)

2. [(Earnings ≥ 0) – EFFECTEARN)] < 0 (management reports earnings greater than

last year)

(3) FORECAST

FORECAST is a dummy variable that will equal 1 when any of the firm-year observations

meets or beats analysts’ consensus earnings forecast, and 0 if otherwise.

Page 26: Accounting for Intangibles and Earnings Management: the

26

1. EPS ≥ EPS Forecast (earnings greater than the consensus forecast)

(4) SIZE

The variable SIZE will be calculated as the natural logarithm of the market value of the

company. Dinh et al. (2015) propose that IAS 38 requires firms to have internal management

accounting systems to monitor and determine any benefits from R&D projects. As larger firms

have more sophisticated control systems, they expect that they may capitalize more relative to

small firms. In a similar fashion, this argument may apply to review of the useful life as well.

(5) PROFIT

The variable PROFIT will measure the returns on assets. Firms with lower performance have

an incentive to increase their earnings, because any small increment in the reported earnings

may result in a significant premium (see Habib, 2007; Healy and Wahlen, 1999).

(6) LEV

The variable LEV will be the leverage calculated as the total liabilities scaled by the market

value of equity. This is a measure of the financial health of a firm and may incentivize managers

to manage earnings in order to comply with debt covenants (see Dinh et al. 2015).

5.2 Variables and empirical design – H2

The second hypothesis tests if the annual review of the useful life results in a greater forecast

error. Recall:

H2: An increase in the expected useful life of intangible assets is associated with a

greater analyst forecast error

As the review is performed ex-post, the analysts may experience difficultly to translate the

information into future earnings. Therefore, it may increase the information risk. Therefore,

the research design follows Matolscy and Wyatt (2006) and the operational uncertainty

measures are based on the research of Alford and Berger (1999) and Demers (2002). Equation

3 represents the regression model that will be used to test the hypothesis. For the definition of

the independent variable (1) LIFE see section 5.1.

Page 27: Accounting for Intangibles and Earnings Management: the

27

(3) 𝐹𝐶𝐸𝑅𝑅𝑂𝑅𝑖,𝑡 = 𝛽0 + 𝛽1𝛥𝐿𝐼𝐹𝐸𝑖,𝑡 + 𝛽2𝑆𝑖𝑧𝑒𝑖,𝑡 + 𝛽3𝑃𝑟𝑜𝑓𝑖𝑡𝑖,𝑡 + 𝛽4𝐿𝐸𝑉𝑖,𝑡 +

𝛽5𝐼𝐴

𝑀𝑉𝐴𝐷𝑖,𝑡+ 𝛽6𝑀𝑉𝐴𝐷𝑖,𝑡+ 𝛽7

𝑀𝑉𝐴𝐷

𝑀𝑉 𝑖,𝑡+ 𝛽8𝑅&𝐷/𝑇𝐴𝑖,𝑡 + 𝛽9𝐹𝑜𝑙𝑙𝑜𝑤𝑖,𝑡 +

𝛽10𝑂𝑃/𝐷𝐸𝐵𝑇𝑖,𝑡 + 𝛽11𝛥𝑃𝑅𝐼𝐶𝐸 + 𝛽12𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑆𝐷𝑖,𝑡 + 𝛽13𝐿𝑂𝑆𝑆𝑖,𝑡 +

𝛽14σRET𝑖,𝑡 + 𝑒𝑖,𝑡

The control variables for H2 relate to the significance of intangible assets as previously

discussed and the uncertainty of earnings. Prior research has demonstrated that uncertainty and

volatility of firm performance influences earnings forecast dispersion and forecast errors

(Matolscy and Wyatt, 2006; Lang and Lundholm, 1996; Ang and Ciccone, 2000; Ciccone,

2001).

(7) FCERROR:

The dependent variable will be the forecast error. The forecast error is measured as the absolute

value of the earnings forecast minus the mean forecast of earnings for firm i for the fiscal year

t.

(8) IA/MVAD

The variable that proxies for the accounting option to capitalize the underlying intangible

investments is IA/MVAD, which is the capitalized intangible assets divided by a proxy for the

underlying intangible investments (Matolscy and Wyatt, 2006). Chalmers et al. (2012) also use

the MVAD variable to capture the market value of the intangible assets. The market value

added (MVAD) is calculated by taking the market value of equity and subtracting it with the

book value of equity adjusted for the total amount of intangible assets. IA captures the total

amount of intangible assets recognized on the balance sheet. The variable may not be applicable

to the European market, however, in the Australian market the higher capitalization of

intangibles relative to the underlying intangibles resulted in lower forecast errors (Matolscy

and Wyatt, 2006; Chalmers et al. 2012). In addition, Cheong et al. 2010 find that under IFRS

that there is a positive association between capitalized intangibles and forecast accuracy.

Moreover, Barron et al. (2002) provide evidence that firms with more underlying intangible

assets have greater forecast errors and dispersion.

Page 28: Accounting for Intangibles and Earnings Management: the

28

(9) MVAD/MV

This variable will be used to control for the level of underlying intangible assets and its

expected positive association to the earnings forecast error (Matolscy and Wyatt, 2006).

(10) R&D/TA

This variable will capture the volatility (riskiness) of the intangible assets. Research and

development may improve current intangible assets or initiate technological obsolescence of

others. Chung and Charoenwong (1991) show that intangible-intensive firms have greater

upside earnings potential as well as higher downside risk relative to other firms. Therefore, one

may expect greater earnings volatility for intangible-intensive companies. Gu and Wang (2003)

find that technologies (R&D) that are more innovative are positively associated with forecast

errors. Technologies that are more innovative tend to be riskier, hence are more difficult for

analysts to process. Although their measures are more comprehensive, this measure will be

sufficient to test this hypothesis.

(10) OP/DEBT

OP/DEBT variable is calculated as the operating cash flows divided by the total debt of the

firm.

(11) LOSS

A dummy variable LOSS will be created that equals one if the firm experienced earnings loss

in a given year and zero if otherwise.

(12) Size

The variable LG(MV) will be used because larger firms tend to have more stable earnings (i.e.

less risky) (see also Dinh et al., 2015).

(13) LEV

The variable LEV comprises the leverage calculated as the total liabilities divided by the book

value of equity. This variable is used to control for the financial risk of the firm.

(14) σRET

The σRET variable covers the variance of stock returns, although Matolscy and Wyatt (2006)

use a rolling measure over the years in the preceding and current fiscal period in which the firm

Page 29: Accounting for Intangibles and Earnings Management: the

29

appears in the sample, in this master’s thesis the rolling calculation is based on monthly stock

returns. Given that in the sample many fiscal-years are missing, the rolling calculation on

monthly stock returns provides more accurate estimation of the volatility of stock price.

(15) PRICE

The PRICE variable will control for earnings management incentives that are associated with

previous stock price changes. It will be calculated as stock price in time t subtracted by stock

price time t-1 and subsequently divided by t-1 price (see Abarbanell and Lehavy, 2000 as

referenced in Matolscy and Wyatt, 2006).

(16) Follow

This represents the total number of analysts that issue earnings forecast for the given firm in

year t. More analyst following is associated with an improved analyst information environment,

given that more effort is expended to understand the financials of the firm.

Page 30: Accounting for Intangibles and Earnings Management: the

30

6 Sample and Empirical Analysis

6.1 Sample and data

This thesis uses the Wharton Research Data Services (WRDS) database for the control

variables, such as company-year level accounting data for European firms that are available

through the Compustat Fundaments Annual Database. Moreover, the analyst forecast

information is available through the WRDS database, specifically the I/B/E/S database. The

Datastream database will also be used for company-year level accounting data. IFRS was

officially adopted in the European Union in the year 2005. Hence, I will use a period range of

13 years since the year of adoption. The sample comprises the listed firms of the EURO

STOXX 600 with analyst forecast data available between 2005 and 2017 on I/B/E/S database

files. The firms of the EURO STOXX 600 are the largest European firms that have to report

under IFRS. After removing all the missing observations, a random sample of 100 firms out of

the 600 listed firms were selected with a total of 830 firm-year observations out of the total

sample of 3607 firm-year observations.

For this sample, the intangible assets with an indefinite useful life and other capitalized

intangible assets that are not amortized were manually collected. After determining the LIFE

variable, 656 usable firm-year observations remain for empirical analysis. The LIFE variable

cannot be calculated using single years, hence for any firm-years without preceding firm-years

an error was returned. The outlier analysis and diagnostics of the data set revealed additional

Page 31: Accounting for Intangibles and Earnings Management: the

31

erroneous firm-years that were theoretically not feasible (e.g. a LIFE of 835). For the forecast

regression, an additional 100 outliers have been removed and for the MEET&BEAT regression

31 outliers have been removed. For the latter, the majority concerned LIFE variable with a

value greater than 25. Therefore, the final sample for the MEET&BEAT regression has 625

firm-years and 556 firm-years for the forecast regression. In the literature, the intangible

regression sample sizes are usually around 600-800 units of observation in the European

setting, given the limited availability of company-year information for many variables.

Furthermore, the diagnostics on the regressions are summarized in table 2. The DWT stands

for the DurbinWatson test which controls for autocorrelation in both regression models. An

additional variable was created that assigns a random value to all firm-years so that the

statistical language R objectively calculates the DW-statistic. The DW-statistic is near value of

2 for both regressions, this is an excellent result. The mean VIFs are not substantially greater

than 1, therefore no problems should arise from multicollinearity. The tolerance of all

individual variables has also been performed on all the other regressions, however, only the

MEET&BEAT regression is presented to eliminate any concerns about how the experimental

variables are determined. The tolerance may not be below 0.2 and the individual VIF may not

be greater than 10. This is not the case for any of the variables. The MEET&BEAT, therefore,

has a strong validity to represent the phenomenon of earnings management that intends to

reflect. The model used for MEET&BEAT proxies for the choice to increase the useful life to

meet or beat earnings benchmarks. It is important to note that the model cannot imply causality.

Page 32: Accounting for Intangibles and Earnings Management: the

32

6.2 Descriptive statistics

The experimental variable LIFE has a mean of 0.27 (Forecast) and 0.30 (MEET&BEAT) and

a median of 0.42. The outcomes of the descriptive statistics, thus, demonstrate that in my

sample the average firm has a stable amortization period for their intangible assets. The minor

adjustments in the implied amortization period are expected as annually new intangible assets

are acquired and old ones are disposed (see Appendix A for an illustration). This outcome is in

line with the expectations because any adjustments to the amortization period requires a change

in accounting estimates. Hence, this generally only occurs when there is a structural change in

the underlying economics of the firm. On the one hand, the descriptive statistics show that the

LIFE proxy for the amortization period of the intangible assets produces a reliable estimation.

On the other hand, any large adjustments in the value of the LIFE proxy are very infrequent.

This has two consequences: a) any large increase of LIFE proxy may represent a structural

reform of the intangible asset rather than an earnings management decision; b) as any large

Page 33: Accounting for Intangibles and Earnings Management: the

33

increase of LIFE proxy is very infrequent, it is expected that there will be no effect on the

forecast error. If the amortization period remains stable, then there is little to no uncertainty for

financial analysts. Moreover, any structural reform that may be reflected in the LIFE proxy

will be available to the market. Hence, the information may not be unexpected as theorized.

For the former (consequence a) additional robustness tests will be designed in the regression

model for MEET&BEAT. Moreover, the infrequent use of LIFE proxy for earnings

management purposes is also reflected in the MEET&BEAT variable. The average value for

the latter is 0.03.

There are several other interesting observations, one being the sample represents the 600 largest

publicly listed firms in Europe. There are no outliers for the size variable and therefore the Size

variable may not have a significant effect on the analysis given that there may be thresholds.

In particular, the conjecture that large firms have more sophisticated control systems to monitor

and determine benefits (Dinh et al., 2015). Moreover, the sample may reflect a bias as large

firms have relatively more stable earnings. The former may increase the LIFE proxy as

additional internally-generated information may be used to justify a change in accounting

policy. There are several firm-years with a negative MVAD. As Matolscy and Wyatt (2006)

argue that the negative MVAD are most likely the result of write-offs of intangible assets that

are preceded by falling market prices. The FORECAST variable occurs relatively frequently

with an average value of 0.26. Hence, it is unlikely that LIFE proxy will be significantly

related to this variable as it is employed infrequently. Finally, the average R&D/TA is relatively

high with 0.05 (this represents the largest 25% in the sample). This thesis expected that this

would be associated with a relatively larger LIFE proxy. However, the descriptive statistics

suggests that this is not the case.

To summarize: adjustments in the LIFE proxy occur infrequently in the sample, this is in line

with the expectations because any large increase generally requires a change in accounting

policy. Therefore, the LIFE proxy may represent a structural reform of the intangible asset

rather than an earnings management decision. Additional robustness tests will be used in the

regression model to take this into consideration. Secondly, as any large increase of LIFE

proxy is very infrequent, it is expected that there will be no effect on the forecast error. If the

amortization period remains stable, then there is little to no uncertainty for financial analysts.

Moreover, any structural reform that may be reflected in the LIFE proxy will be available to

the market. Hence, the information may not be unexpected as theorized. The relatively large

Page 34: Accounting for Intangibles and Earnings Management: the

34

R&D expenditures seem to not have any effect on the consistency of the amortization period

that is used for the intangible assets. Lastly, given that the sample only contains large publicly

listed firms, there may be a bias in the sample.

Page 35: Accounting for Intangibles and Earnings Management: the

35

6.3 Main empirical results and robustness tests

6.3.1 Main empirical results – Forecast Error

The main empirical results that investigate the association of LIFE in relation to the forecast

error (H2) are summarized below in table 4. The LIFE is not statistically significantly related

to the forecast error. This is not a surprise, in the descriptive statistics I have identified that the

adjustments in the LIFE proxy occur infrequently in the sample, this is in line with the

expectations because any large increase generally requires a change in accounting policy.

Therefore, I expected that there will be no effect on the forecast error. If the amortization period

remains stable, then there is little to no uncertainty for financial analysts. Moreover, any

structural reform that may be reflected in the LIFE proxy will likely be available to the

market. Thus, this thesis may conclude that the annual review of the useful life of intangible

assets is not related to the forecast error. I theorized that the useful life could reflect uncertainty

and information complexity for analysts, however, the analysis suggests that managers tend to

infrequently revise any assumption that have been used to determine the expected life. The

empirical results are conclusive, hence no additional tests are employed.

Moreover, the main empirical results return multiple statistically significant variables as

demonstrated in the previous literature (see Lang and Lundholm, 1996; Demers, 2002). Unlike

the empirical results of Matolscy and Wyatt (2006) for the full sample regression, my

regression additionally reports a statistically significant association on OP/DEBT, IA/MVAD,

ΔPRICE and EarningsSD in relation to the forecast error. There is a statistically significant

negative association between MVAD/MV and IA/MVAD in the main empirical results. The

MVAD/MV variable is used to control for the level of underlying intangible assets and the

IA/MVAD variable captures the amount of intangibles that are capitalized (Matolscy and

Wyatt, 2006). The results suggest that in the European Union under IFRS the capitalization of

intangible assets is used to signal positive information to the market as an increase in the

amount of capitalization reduces the forecast error. This is in line with the findings of Matolscy

and Wyatt (2006) in the Australian market. Wyatt (2005) finds evidence that in the Australian

setting, management capitalizes intangible assets when they are more certain – in other words,

based on the underlying economics of the firm. In a collaborative study, Matolscy and Wyatt

(2006) confirm the previous study. They analyse if firms that capitalize a higher proportion of

their underlying intangible assets have an improved analyst information environment relative

to other firms. They find that the capitalization of intangibles is negatively associated with the

Page 36: Accounting for Intangibles and Earnings Management: the

36

earnings forecast error of a firm. Cheong et al. (2010) similarly investigates the effect of IFRS

adoption on the association between reported intangibles and forecast accuracy. He finds a

positive association between capitalization of intangible assets and the analyst information

environment in Asia. In the United States, Barron et al. (2002) find that analysts make larger

earnings forecast errors when firms have higher capitalized intangibles and Barth et al. (2001)

find that firms with higher underlying intangible assets have a greater analyst following.

Although this thesis does not intend to answer this question, the results are in stark contrast

with the US evidence. Further research should investigate this question.

6.3.2 Main empirical results – MEET&BEAT

The main empirical results for the MEET&BEAT regression are reported in Panel A of table

5. The LIFE variable is statistically significant (<0.001) and has a positive association with

the earnings management proxy. In other words, an increase in the useful life relative to the

Page 37: Accounting for Intangibles and Earnings Management: the

37

previous year is positively associated with avoiding to report negative earnings and beating

past year’s earnings. It is important to note that the logit regression, however, is based on

probability. This outcome holds true for all subsets, however, for the increasers subset the

LIFE variable is significant at 0.1 level respectively, two-tailed. The other variables are not

statistically significant. The increase of LIFE is infrequently used for earnings management

(low R2 0.095 and coefficient 0.209), hence the sample size may not be sufficient to find any

significant relationships between the control variables and the dependent variable. Profit is as

expected negatively associated with MEET&BEAT, because firms do not have to engage in

earnings management if their current performance meets or beats analyst forecasts. The

IA/MVAD also has a positive association, if more intangible assets are capitalized the

amortization is more likely to have a greater effect on the earnings. There are, however,

implications to be derived from the former and latter observation. If both variables were

statistically significant additional concerns may arise. If IA/MVAD or Profit variables were

statistically significant, the empirical proxy MEET&BEAT may likely reflect those

relationship (i.e., likely to be not significant). When there are low profits, the LIFE is far more

likely to return benchmark beating because its effect on earnings would have to be significantly

smaller. Similarly, if IA/MVAD is greater, then the LIFE is far more likely to return

benchmark beating because its effect on earnings would be relatively much greater. Hence, the

regression model has ruled out other explanations why the LIFE may result in avoiding to

report negative earnings and beat past year’s earnings. Additional tests will be discussed in the

robustness checks section 6.4.

Panel B reports the main empirical results for the FORECAST model. The LIFE variable is

not statistically significant and has a positive association with meeting or beating analysts’

forecasts. The dependent variable in this regression is generic, which confirms that the LIFE

is too infrequent to have a statistically significant effect in this test. As expected Profit and Size

are statistically significantly related to meeting and or beating earnings benchmarks as

demonstrated in the extant literature (see Dinh et al., 2015). The empirical results suggest that

in the sample firms who report losses are more likely to beat analysts’ earnings forecasts. The

results for our main variable LIFE are conclusive, hence no additional tests are necessary.

Page 38: Accounting for Intangibles and Earnings Management: the

38

Page 39: Accounting for Intangibles and Earnings Management: the

39

6.4 Robustness checks

The findings are robust to a number of sensitivity checks. In the previous discussion, this thesis

has pointed to several additional events that MEET&BEAT regression may reflect. First, the

dependent variable may reflect poor firm performance, therefore an increase in LIFE is more

likely to result in avoiding to report negative earnings or beating past year’s earnings. Second,

the dependent variable may also reflect aggressive intangible asset capitalization which

increases the likelihood that LIFE is far more likely to return benchmark beating because its

effect on earnings would be relatively much greater. For both issues the regression model has

used control variables.

The third alternative explanation may be that the dependent variable reflects a structural reform

of the underlying economics of the firm. The adjustment of the expected useful life of

intangible assets occurs infrequently, therefore the major upward observations may confound

the relationship with the earnings management proxy. Therefore, the regressions are repeated

for a sample where all outliers greater than 9 LIFE are removed. The results are reported in

Panel B of table 5. The main empirical results for this regression return a stronger positive

association between LIFE and MEET&BEAT and are statistically significant (<0.001). In

Page 40: Accounting for Intangibles and Earnings Management: the

40

other words, by removing the LIFE observations that may reflect structural reforms of the

intangible assets, the coefficient LIFE of 0.209 increases to 0.431. The firm-year observations

that have major adjustments in the expected useful life, therefore, are not associated with the

earnings management proxy. Thus, the additional regression controls for the confounding

effect that the LIFE may reflect a structural reform of the underlying economics.

Theoretically, the empirical results add up. The risk of detection determines how likely it is

that management will execute earnings management policies. As discussed in the conceptual

framework, earnings management is only effective if the stakeholders cannot uncover it,

otherwise it results in reputational loss for the managers. Any major structural reform will have

to reflect the underlying economics, otherwise the risk of detection will be accordingly high

that any of the potential benefits will be far outweighed by the potential costs. The minor

upward adjustments of the LIFE are highly unlikely to reflect improved underlying

economics, especially since IAS 38 paragraph 57 is a conservative accounting standard, most

commonly used for downward revisions. Neither are they likely to reflect earnings

management as their impact on the earnings is insufficient. Therefore, the majority of minor

upward adjustments are attributable to year-on-year changes in the composition of intangible

assets. Hence, the adjustments that are inside the range of the minor and major increases in the

LIFE are theoretically more likely to be associated with earnings management because the

risk of detection is significantly lower and the impact is sufficiently large. The empirical model

presented in the thesis attests this.

Additionally, an additional regression is performed on only the firm-year observations where

LIFE is increased. The purpose of this regression is twofold: a) the firm-year observations

that decrease LIFE will not do so to meet or beat earnings benchmarks, therefore they may

inflate the empirical results; b) to tests if the increase in the coefficient of LIFE variable in

regression that was performed on the LIFE<9 sample is the result of removing positive LIFE

observations that have a value greater than 9. The main empirical results for this regression are

also reported in Panel B of table 5. For both increaser samples the LIFE is positively

associated with MEET&BEAT and are statistically significant. Moreover, the coefficient of

the LIFE increases from 0.124 to 0.329. Lastly, the sample size reflects mandatory adoption

period of IFRS (2006-2017). Therefore, there is also controlled for the effect of voluntary

adoption of IFRS. In sum, the main empirical results remain statistically significant and are

robust to the additional tests.

Page 41: Accounting for Intangibles and Earnings Management: the

41

7 Conclusion

Previous studies have shown that discretion drives the capitalization of intangible assets. In

this study, I sought to answer the question if the expected useful life of intangible assets is used

for earnings management. Managers have the discretion to annually review the expected useful

life of the assets on the face of the balance sheet.

The results demonstrate that firms on average have a stable amortization period for their

intangible assets. A simple explanation for the stable expected useful life is that any large

increase or decrease generally requires a change in accounting policy. Large adjustments in the

LIFE proxy are very infrequent, therefore it is unlikely that the expected useful life of

intangible assets will be positively associated to the forecast error. If the amortization period

remains stable, then there is little to no uncertainty for financial analysts. Moreover, LIFE

may represent a structural reform of the intangible assets. Such information is generally

impounded in the market, therefore it is not an unexpected surprise. The main empirical results

confirm this, the association of LIFE in relation to the analysts’ forecast error is not

statistically significant in the model. Hence, the theorization that LIFE contributes to

information uncertainty is incorrect.

For the first hypothesis, the empirical results show that the LIFE variable has a positive

association with the earnings management proxy. In other words, an increase in the useful life

relative to the previous year is positively associated with avoiding to report negative earnings

and beating past year’s earnings. However, the LIFE variable is not statistically associated to

meeting or beating analysts’ forecasts. The LIFE is too stable to have an any association with

a generic earnings management proxy.

The findings are robust to a number of sensitivity checks. The experimental LIFE and

MEET&BEAT variables return appropriate diagnostics in multiple regression models. The

empirical model is additionally tested for multiple alternative explanations. Control variables

are used to test if the dependent variable reflects poor firm performance or if it reflects

aggressive intangible asset capitalization. Additional regression models are used to control that

the LIFE reflects a structural reform of the intangible assets rather than an earnings

management decision. The main empirical results are robust to the additional tests.

Page 42: Accounting for Intangibles and Earnings Management: the

42

Finally, the results suggest that in the European Union under IFRS the capitalization of

intangible assets is used to signal positive information to the market as an increase in the

amount of capitalization reduces the forecast error. Further research is required to investigate

this relationship.

The study contributes to the discussion of intangible accounting. Previous research shows that

beating earnings benchmarks is a driver for the capitalization of intangible assets, in particular

R&D expenditure, under IAS 38 (Dinh et al, 2015). The results show that beating earnings

benchmarks is also an important driver of the review of the expected useful life of intangible

assets. The uncertainty associated with intangible assets seems to allow for more discretion.

To the best of my knowledge, this thesis has demonstrated a new accrual-based earnings

management tool. Earnings management may obstruct the efficient allocation of resources. The

intended users of financial reports expect the presented figures to reflect the underlying

economics of the entity in order to facilitate optimal economic decision-making. Therefore, the

results of this thesis are of relevance to users of financial reports and standard-setters.

There are several limitations to my study. Earnings management is very subjective and

therefore difficult to investigate. In the literature, however, benchmark beating and analyst

information environment are used extensively as proxies for earnings management. The

experimental variables employed in this thesis are statistically sound. Secondly, the sample is

relatively small because several variables had to be hand-collected. The sample of the

EUROSTOX 600, however, represents a significant portion of the broader European economy.

Simultaneously they only represent large publicly listed firms, therefore the results may be

biased.

Page 43: Accounting for Intangibles and Earnings Management: the

43

8 References

Abarbanell, J., and R. Lehavy. 2000. Biased forecasts or biased earnings? The role of

earnings management in explaining apparent optimism and inefficiency in analysts’

earnings forecasts. Working Paper (University of North Carolina, Chapel Hill, NC).

Ahmed, K. and H. Falk. 2006. The value relevance of management’s research and

development reporting choice: Evidence from Australia. Journal of Accounting and

Public Policy, 25(3), 231–264.

Anagnostopoulou, S. C.. 2010. Does the capitalization of development costs improve analyst

forecast accuracy? Evidence from the UK. Journal of International Financial

management and Accounting 21: 62–83.

Ang, J., and S. Ciccone. 2000. International differences in analyst forecast properties.

Working Paper (Florida State University, Tallahassee, FL).

Alford, A. W., and P. G. Berger, 1999, A simultaneous equations analysis of forecast

accuracy, analyst following, and trading volume. Journal of Accounting, Auditing and

Finance 14:219–240.

Barron, O. E., D. Byard, C. Kile, and E. J. Riedl. 2002. High-technology intangibles and

analysts’ forecasts. Journal of Accounting Research 40: 289–312.

Barth, M. E., R. Kasznik, and M. F. McNichols, 2001, Analyst coverage and intangible

assets. Journal of Accounting Research 39: 1–34.

Burgstahler, D. and Eames, M.J. 2003. Earnings management to avoid losses and earnings

decreases: Are analysts fooled? Contemporary Accounting Research 20.2: 253-294.

Chalmers, K., et al. 2012. Intangible assets, IFRS and analysts’ earnings forecasts.

Accounting & Finance 52.3: 691-721.

Cheong, C. S., S. Kim, and R. Zurbruegg. 2010. The impact of IFRS on financial analysts’

forecast accuracy in the Asia-Pacific region. Pacific Accounting Review 2.2: 124–

146.

Chung, K. H., and C. Charoenwong. 1991. Investment options, Assets in Place, and the

Risk of Stocks. Financial Management 20: 21–33.

Ciccone, S.. 2001. Analyst forecast properties, financial distress, and business risk. Working

Paper (University of New Hampshire, Durham, NH).

Ciftci, M. 2010. Accounting choice and earnings quality: The case of software development.

European Accounting Review 19.3: 429–459.

Cohen, Wesley, Richard Nelson, and John Walsh. 2000. Protecting Their Intellectual Assets:

Appropriability Conditions and Why U.S. Manufacturing Firms Patent (or Not).

Working Paper 7552. Cambridge, Mass.: National Bureau of Economic Research.

Page 44: Accounting for Intangibles and Earnings Management: the

44

Cohen, D.A. and P. Zarowin. 2010. Accrual-based and real earnings management activities

around seasoned equity offerings. Journal of accounting and Economics 50.1: 2-19.

Demers, E. 2002. Discussion of high-technology intangibles and analysts’ forecasts. Journal

of Accounting Research 40: 313–319.

Dinh, T., et al. 2015. Capitalizing research & development: Signaling or earnings

management? European Accounting Review 25.2: 373-401.

Francis, J., and R. H. Willis. 2001. An alternative test for self-selection in analysts’ forecasts.

Working Paper (Duke University, Durham, NC).

Gu, F., and W. Wang, 2003. Intangible assets and analysts’ earnings forecast. Working Paper

(Boston University, Boston, MA).

Habib, A. 2007. Benchmark Beating as Evidence of Earnings Management: Review of the

Literature. https://ssrn.com/abstract=873939 or http://dx.doi.org/10.2139/ssrn.873939

Healy, P. M., & Wahlen, J. M. 1999. A review of the earnings management literature and

its implications for standard setting. Accounting horizons 13.4: 365-383.

Jensen, M. C., & Meckling, W. H. 1976. Theory of the firm: Managerial behavior, agency

costs and ownership structure. Journal of financial economics 3.4: 305-360.

Jones, D. A. 2007. Voluntary disclosure in R&D-intensive industries. Contemporary

Accounting Research 24: 489–522.

Kothari, S. P., Ted Laguesse, and Andrew Leone. 1998. Capitalization versus Expensing:

Evidence on the Uncertainty of Future Earnings from Current Investments in PP&E

versus R&D. Working Paper. University of Rochester.

Lang, M. H., and R. J. Lundholm. 1996. Corporate disclosure policy and analyst behavior.

The Accounting Review 7.1: 467–492.

Lang, M. H., K. V. Lins, and D. P. Miller. 2003. ADRs, analysts and accuracy: does cross

listing in the United States improve a firm’s information environment and increase

market value? Journal of Accounting Research 41: 317–345.

Lev, B. 2001. Intangibles: Management, Measurement, and Reporting (Washington, DC:

Brookings Institution Press). Google Ebook version.

Lev, B, and Paul Zarowin. 1999. The Boundaries of Financial Reporting and How to Extend

Them. Journal of Accounting Research 37: 353-85.

Matolcsy, Z and A. Wyatt. 2006. Capitalized intangibles and financial analysts. Accounting

& Finance 46.3: 457-479.

Mazzi, F., et al. 2017. Country level corruption and accounting choice: research &

development capitalisation under IFRS. Available at

http://dx.doi.org/10.2139/ssrn.2979300

Page 45: Accounting for Intangibles and Earnings Management: the

45

Meyer, H., & Naumann, K. P. 2009) Enforcement der Rechnungslegung – aktuelle

Entwicklungen [Accounting enforcement – Current developments]. Die

Wirtschaftsprüfung 62(16): 807–811.

McNichols, M., and P. C. O’Brien. 1997. Self-selection and analysts coverage. Journal of

Accounting Research 35 (Suppl.): 167–199.

McVay, S. E. 2006. Earnings management using classification shifting: An examination of

core earnings and special items. The Accounting Review 81.3: 501-531.

Mohd, E. 2005. Accounting for software development costs and information asymmetry.

The Accounting Review 80.4: 1211–1231.

Oswald, D. R. 2008. The determinants and value relevance of the choice of accounting for

research and development expenditures in the United Kingdom. Journal of Business

Finance and Accounting 35(1–2): 1–24.

Oswald, D. R. and P. Zarowin. 2007. Capitalization of R&D and the informativeness of stock

prices. European Accounting Review 16(4): 703–726.

Picker, R., Cark, K, Dunn, J., Kolitz, D, Livne, G., Loftus. J., van der Tas, L. 2016. Applying

IFRS Standards. Wiley & Sons.

Page 46: Accounting for Intangibles and Earnings Management: the

46

9 Appendix

Page 47: Accounting for Intangibles and Earnings Management: the

47

10 Appendix A