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The Value Relevance of Intangible
Assets in Korean Firms
Hoejun Min
29th May, 2012
The Value Relevance of Intangible Assets
in Korean Firms
Master thesis
Department Accountancy
Faculty of Economics and Business Studies
Tilburg University
Supervised by Yachang Zeng
ANR 944738
Hoejun Min
Completed on 29th May, 2012
Date of Graduation: 12th June, 2012
1
Summary
Despite the increasing importance and role of intangible assets in today’s economic
environment, accounting practice for intangible assets is one of the most
controversial issues in that they are not fully reflected in financial statements. As an
attempt to explore the relevance of reported intangible assets, this study examines
the market value relevance of different intangible asset classes (goodwill, intellectual
properties, capitalized development expenditure, capitalized software expenditure
and other intangible assets) reported by listed Korean firms during the period of
2001~2010, using the popular Ohlson (1995) framework. Result indicates that some
intangible assets have positive associations with the market value of firms, while
some others have negative associations. When this analysis is extended to the
difference between industries, some intangible assets are found to be more value
relevant for information technology firms and some to be less value relevant.
1. Introduction
As the structure of modern economy rapidly changes with the unprecedented technological
progress in most industries, the main source of corporate value is also changing from classical
tangible assets such as land or capital to intangible assets based on human knowledge and ideas.
Today intangible assets are generally recognized as the primary resource which strengthen the
firm’s competitive position and ensure its future viability (Cañibano et al., 2000), and even
further, as the most important factors to economic growth and societal wealth (Blair and
Wallman, 2001).
On the contrary, current accounting standards mainly focus on tangible assets, not sufficiently
reflecting the value of intangible assets. The current situation that intangible assets are not
adequately demonstrated or quantified in financial statements despite their significant
contribution to value creation is referred as the Value Paradox (Blaug and Lekhi, 2009).
Considering the inherent uncertainties regarding the future economic benefit and economic life
of intangible assets, the difficulties in the valuation (Barth et al., 2001) and proper financial
2
reporting of intangible assets are in a sense unavoidable. However, in many industries where
firms make significant value-enhancing investments on intangibles, conventional accounting
standards may lead to distorted values of earnings and assets, making the financial statements
inadequate to their users (Amir and Lev, 1996).
With the recognition of these issues, this study examines the value relevance of reported
intangible assets during the period of 2001~2010 under Korean setting. The primary
environmental factor that justifies an interest in this Korean setting is that the accounting
practice for reporting intangible assets was relatively more generous. For example, Korean
accounting standard allows capitalization of qualified development expenditure 1 and therefore
lots of firms report related item on financial statements, which is not allowed in many
accounting settings including US. Korean GAAP also allows capitalization of internally
generated software regardless of industry, but this is only limitedly allowed in US. This
accounting environment, combined with the data availability of detailed intangible asset class
accounts, offers a good opportunity to explore intangible assets. Most prior studies that
examined specific intangible assets in other settings (e.g., Aboody and Lev 1998; Kallapur and
Kwan 2004; Oliveira et al. 2010) relied on hand-collected data, since global database such as
Datastream and Compustat provide only aggregated intangible assets. But this is not the case in
Korea, where data of every individual intangible asset class as reported by firms is available via
a local database.
More specifically, this study tests the hypothesis that different intangible asset classes such as
goodwill, intellectual properties, capitalized development expenditure, capitalized software
expenditure and other intangible assets would be relevant to firm’s market value. In addition,
whether the value relevance of intangible assets is different among industries is also tested. In
line with the argument of Blair and Wallman (2001) that the explanatory power of accounting
information of reported intangible assets for firm value is significantly decreasing in high-
1 Conditions for capitalization are as follows: the technical feasibility of completing the production of the
intangible asset, firm’s intention to complete and use or sell, firm’s ability to use or sell, highly probable
future economic benefits, the availability of adequate technical or financial resource for completion, a
reliable measurement of expenditure.
3
technology firms, the difference in value relevance of reported intangible assets for
IT(information technology) firms and non-IT firms is examined. Both tests are performed by
multiple regression models based on the Ohlson (1995) framework, which is widely used by
prior studies.
Results from regression models which incorporate individual intangible asset classes
separately and 5,242 sample firm-years indicate that all intangible asset classes have significant
associations with the market value of the firm. While intellectual properties, capitalized
development expenditure and software expenditure showed positive associations, goodwill and
other intangible assets showed negative associations. Some of these findings are in consistent
with prior studies but others contradict them. Negative association of goodwill may have been
resulted from investor’s concern for an excess acquisition cost, or the mandatory amortization of
goodwill under old standard which may not be adequate for many firms according to the finding
of Jennings et al. (1996). For information technology (IT) firms, all intangible asset classes
except goodwill showed differences in the level of value relevance when compared to non-IT
firms, with relatively lower value relevance of capitalized development expenditure and
software expenditure and relatively higher value relevance of intellectual properties and other
intangible assets. These results may imply investors’ different perceptions for each intangible
asset reported by IT-firms based on the relative objectiveness of its recognition. In other words,
investors may have critical attitudes for the future benefits of capitalized development and
software expenditures, because recognizing these assets are open to managerial judgement to a
large extent.
This study would contribute to the literature in that it examines the value relevance of
multiple intangible asset classes. As will be outlined later, majority of prior studies focus on one
specific item or aggregated total intangible assets. Further, this differentiation of multiple
intangible asset classes’ value relevance is extended to different industries, raising a question
about the differences in market value associations among industries. While extensive
explorations for the reasons of different value relevance are left to future studies, these findings
suggest that investors’ evaluations for intangible assets do not coincide indeed but on the
4
contrary differ substantially. From the viewpoint of the relevance in accounting information,
this may leave a room for a consideration of alternative accounting policies either in the
standards or in the practical applications of standards. Specifically, if investors’ critical attitudes
for capitalized development and software expenditures in IT firms are resulted from problematic
accounting practice in the industry, regulatory actions are needed to improve the relevance of
accounting information.
This paper is structured as follows. In the following section some brief backgrounds to the
definition and classification of intangible assets, along with accounting treatments for intangible
assets, are presented. This is followed by a summary of the previous literature that led to the
research questions. The research methods, including variable definitions are presented with the
research questions, and data collection and the findings follow. Finally, conclusion and
limitations of the study are outlined.
2. Accounting for intangibles & prior studies
1) Accounting standards for intangible assets
Despite the absence of a universal definition which is resulted from different perspectives of
various stakeholders (Ali et al., 2010), intangible assets in accounting context are often defined
as ‘assets which lack a physical substance, but result from legal or contractual rights and are
likely to produce future benefits’ (Cañibano et al., 2000). According to International
Accounting Standards (IAS) 38 which defines intangible asset as ‘an identifiable non-monetary
asset without physical substance’, an asset is reported as intangible asset only when it meets the
conditions of identifiability, control over a resource and the existence of future economic
benefits. If it fails to meet this definition, then related expenditure should be expensed unless
part of a business combination, when it should be treated as part of goodwill.
As intangible assets differ in their fundamental characteristics, they are often classified into
sub-categories. The classification of intangible assets is also a matter in need of further
5
agreement. However, several national and international standard setting bodies and institutions
have made significant efforts in order to develop a classification of intangibles (Cañibano et al.,
2000). For example, Financial Accounting Standards Board (FASB) classifies intangible assets
according to related economic activities such as marketing, customer relationship, contraction
(FAS 141), while EU (2003) takes a broader perspective by using the class of ‘separately
identifiable intangible assets’ in which many kinds of intangible assets other than intellectual
property and goodwill are included. Table 1 shows more details about the classifications of
FASB and EU.
It is noted that goodwill is often excluded in many classifications (including that of FASB) of
Table 1. Classification of intangible assets
Classification & specific items (examples) under classes
FASB
(statement
141)
1. Marketing-related intangible assets
Trademarks, Service marks, Trade dress, Newspaper mastheads, Internet domain
names, Non-competition agreements
2. Customer-related intangible assets
Customer lists, Order or production backlog, Customer contracts and related
customer relationships, Non-contractual customer relationships
3. Artistic-related intangible assets
Plays, Books, Musical works, Pictures, Video and audiovisual material
4. Contract-based intangible assets
Licensing, Advertising, Lease agreements, Construction permits, Franchise
agreements, Operating and broadcasting rights, Use rights, Servicing contracts,
Employment contracts
5. Technology-based intangible assets
Patented technology, Computer software and mask works, Unpatented technology,
Database, Trade secrets
EU (2003)
1. Intellectual property : Intangible assets with legal or contractual rights including
Patents, Trademarks, Designs, Licenses, Copyrights, Film rights, Mastheads
2. Separately identifiable intangible assets
Information systems, Networks, Administrative structures and process, Market and
technical knowledge, Human capital (if embodied in a codified form), Brands,
Intangibles embodied in capital equipment, Trade secrets, Internally generated
software, Drawings
3. Goodwill (non-separable intangible assets)
Prior intangible investment embodied in organizations, Management expertise,
Geographic position, Monopoly market niche
6
intangible assets. In this case, goodwill is reported separately on financial statements and
distinguished from ‘identifiable intangible assets’, in that goodwill is not identifiable. This is
indeed the same for current Korean standard which took effect as of 2011 2 with the
introduction of the International Financial Reporting Standards (IFRS). However, this study
includes goodwill as a class of intangible assets because goodwill was indeed reported as such
in Korea until 2010, and this inclusion has no practical effect on the value relevance of other
intangible assets than goodwill.
Before the introduction of IFRS, Korean accounting standards 3 for intangible assets were
consistent during the period of focus in this study (2001~2010). In the course of the
International Monetary Fund (IMF) aid program which was introduced in Korea as a result of
East Asian financial crisis of 1997, Korean accounting standards were significantly reformed in
a direction closer to Western standards (Han and Manry, 2004). This new set of standards,
including the introduction of PP&E impairment, immediate recognition of foreign currency
translation gain/loss in the income statement, tightened capitalization conditions for R&D
related expenditure 4, among others, took effect in 1999. In Korean standards, intangible asset
was defined as ‘identifiable non-monetary asset without physical substance but has future
economic benefit, which firms possess with the purpose to utilize in the course of
manufacturing goods, providing services or lease to others or manage.’ 5 A Classification
method based on characteristics and benefits for business was generally recommended, while
examples of sub-classes such as industrial properties, license & franchise, copy right, software,
capitalized development expenditure, premium in renting a building, mining/fishing rights were
suggested. 6
2 Major changes with the introduction of K-IFRS (Korea IFRS) in accounting for intangible assets are 1)
the definition of intangible asset, 2) the impairment & amortization of intangible assets. These will be
addressed in corresponding contexts. 3 For the convenience of understanding, ‘Korean accounting standards (or Korean standards)’ henceforth
means the standards during the period of 1999~2010, unless otherwise stated like ‘K-IFRS’ which is the
official name of the new standard. 4 It was no longer allowed to capitalize research expenditure under the new standard. 5 The definition in K-IFRS is just ‘identifiable non-monetary asset without physical substance’, in
accordance with IAS 38. 6 In K-IFRS, mining right was excluded from the Statement 1038 (intangible assets) of Korea Accounting
7
The most significant difference between Korean standards and International standards was
probably the treatment regarding impairment and/or amortization of intangible assets. While the
finiteness of an intangible asset’s useful life is assessed and amortization takes place only for
intangible assets with finite useful life when IAS 38 is applied, Korean standard required that
any intangible asset had a finite useful life (typically not longer than 20 years 7) and the
amortization took place once an intangible asset was ready to use. An evaluation for an asset’s
recoverable amount was required at least annually if its useful life was longer than 20 years.
Goodwill was also amortized over its useful life not exceeding 20 years under Korean standard,
while IFRS 3 prohibits the amortization of goodwill but requires the impairment test in accord
with IAS 36. 8 As a result, it is highly probable that intangible assets reported by Korean firms
had quite different book value if international standards were applied. If this is the case, it
naturally follows that there also could be differences in value relevance of reported intangible
assets.
As previously noted, there are more generous aspects in Korean accounting practice
regarding the recognition of intangible assets when compared to major accounting environments.
Unlike US, Japan and Germany where development expenditure should always be expensed 9,
Korean standards allow capitalization of qualified expenditure and therefore are on the same
side with IFRS in this issue, probably one of the most controversial issues in accounting for
intangible assets. Even though a presence of ‘highly probable future economic benefits’ 10 was
required by Korean standards as one of the conditions for capitalizing development expenditure
during the period of 1999~2010, it should be noted that this might not necessarily resulted in a
more conservative recognition of development expenditure asset by Korean firms, and there was
Standards which is in accordance with IAS 38. However, it is still allowed to report mining right as an
intangible asset. 7 Except when a monopolistic or exclusive right was given by related laws or contracts. 8 These discrepancies were all eliminated with the introduction of K-IFRS. 9 In UK and Australia, capitalizing of only successful development costs was allowed until 2005 when
the IFRS was introduced. 10 The corresponding implicit probability was 80%, while the requirement of IAS 38 (‘probable future
economic benefits’) implicitly assumes the probability of 50%. However, this standard also changed in
accordance with IAS 38 in K-IFRS: that is, ‘highly’ was removed in this phrase.
8
still significant flexibility in the capitalization of development costs (Han & Manry, 2004). 11
Korean standards also allow capitalization of internally generated software in all industries and
therefore quite a number firms report this item 12, but in US only firms operating in software
industry are allowed to recognize software expenditure. Further, capitalization is not allowed at
all in Japan and Germany. 13 In addition, an upward revaluation of intangible assets is a
possible option in Korea when objective evidence of increased value is available. But this is
strictly prohibited in US, Japan, and Germany. Considering these features, it can be concluded
that in certain aspects Korean accounting practice was less conservative and therefore reporting
intangible assets was more generously allowed, when compared to some major accounting
environments.
The standard setting bodies in most leading capital markets are endeavoring to internationally
harmonize accounting standards nowadays, and as a part of this process the subject of intangible
assets and the proper accounting treatment for intangible assets is being heavily debated
(Dahmash et al., 2009). It is likely that this debate will continue in the near future, because
accounting for intangible assets is very controversial issue that standard setters have come to
confront (Jennings et al., 1996). This suggests that, for the foreseeable future, accountants and
financial economists are likely to be concerned with finding better ways to recognize and value
intangible assets (Chauvin and Hirschey, 1994; Dahmash et al., 2009).
2) Review of prior studies
With the recognition that accounting information without important intangibles-related items
may not provides correct information to market participants, various studies examined the value
relevance of intangible assets as a way of evaluating the properness of reported accounting
information. Majority of these studies can be classified according to the specific intangible
assets on which those studies focused. More specifically, many studies examined R&D assets,
11 In the total sample firm-year of 5,242 (as is shown in later), capitalized development expenditure was
reported by 77.1% (4,039 firm-years). 12 Capitalized software expenditure was reported by 26.7% (1,402 firm-years) of total sample. 13 It is of note that software was reported as a tangible asset in UK and Australia until 2005.
9
goodwill and brand.
Unlike Korean or international standards, the US GAAP does not allow capitalizing the
expenses of R&D, a deliberate economic investment activity that drives corporate growth,
innovations/inventions and technological advances (Zhao, 2002). This practice motivated
several researchers to investigate the value relevance of R&D expenditure by assuming the
capitalization of those expenditures. Lev and Sougiannis (1996) examined the value relevance
of capitalized R&D and concluded that R&D capitalization is value relevant to U.S. investors
by yielding statistically reliable and economically relevant information. Healy et al. (2002) also
found that capitalizing R&D expenditure could explain economic returns better than fully
expensing in pharmaceutical industry. Zhao (2002) showed that while the reporting of R&D
costs provides additional information to accounting earnings and book value in US, the
allocation of R&D costs between capitalization and expense further increases the value
relevance in France and the UK, where capitalization is allowed.
The software capitalization, which is the major exception of immediate expensing in US,
were also found to be significantly associated with capital market variables such as stock price
or return (Aboody and Lev, 1998). Ballester et al. (2003) also argued that market participants
behave as if R&D expenditures have significant future economic benefits to the firm covered by
the Compustat database, while there are significant differences in industries between the time-
series and the cross-sectional estimates of the parameters and the economic value of the R&D
asset. In Korean context, Han and Manry (2004) showed that R&D expenditures were positively
associated with stock price and the association is stronger for the portion of R&D expenditure
that was capitalized, rather than expensed.
The main focus of these arguments is that, capitalization enables management to provide
more useful information about the percentage of R&D outlays capitalized versus expensed and
about the period of amortization. The users of financial statements thus know better the
performance of projects and management’s expectation of their probable future benefits (Ang et
al., 2008). However, some studies found different evidences. Using firm-specific model
10
parameters, Callen and Morel (2005) found weak empirical support for the value relevance of
capitalized R&D expenditure at the firm level. In a French context, Cazavan-Jeny and Jeanjean
(2005) found that capitalized R&D is negatively associated with stock prices and returns. They
interpreted this result as implying that investors are concerned with and react negatively to
capitalization of R&D, and suggested that French managements’ opportunistic approach to the
use of R&D capitalization could explain the non-relevance of R&D capitalization in their
setting.
Another important intangible asset that is often focused in itself is goodwill. With ever
increasing business scope and area of firms, recent decades have seen lots of M&A in most
industries in all regions over the world. As a consequence more and more firms are reporting
goodwill in their financial statements, and sometimes goodwill has a very significant portion in
total assets. Among many researchers who paid attention to the value relevance of goodwill,
Chauvin and Hirschey (1994) reported a consistently positive market-value influence of
accounting goodwill numbers and McCarthy and Schneider (1995) found that the market
perceived goodwill as an asset and incorporated the information in the valuation of a firm.
While these studies examined goodwill as it was actually reported, Jennings et al. (1996)
assumed a hypothetic reporting without the amortization of goodwill, to find that its value
relevance was higher than the value relevance of the actual reporting with the amortization. In
UK setting, Jifri and Citron (2009) found that both recognized and note-reported goodwill are
significantly associated with stock price.
Even though brand is not universally recognized under most current accounting standards, it
is indeed a quite valuable asset that significantly contributes to the performance of the firm.
Brand values of global firms such as Coca cola or IBM are even comparable to their market
capitalization. 14 Barth et al. (1998) examined the value relevance of brand assets using the
brand value published by Financial World as the value of brand assets, to find that the brand
14 The brand value of Coca cola is $71.9Billion(2011, Interbrand) and the market capitalization as of 31th
DEC, 2011 was $158.4B, and IBM’s brand value is $69.9B while the market capitalization was $215.9B.
11
value was significantly associated with the stock price and stock return. Under UK setting,
which is one of the few exceptions of brand recognition as reporting brand assets including self-
generated ones is allowed, Kallapur and Kwan (2004) found that reported brand assets in 33
firms are positively associated with stock prices.
While these studies focused on one specific intangible asset, some other studies looked into
the total intangible assets as a whole or multiple intangible asset classes. Ely and Waymire
(1999) investigated the relation between intangible assets, earnings and stock prices under a
reporting regime which permitted considerable flexibility for managers to capitalize intangible
assets (that is, the pre-SEC era in the US). They found the evidence that the coefficient relating
earnings to stock price decreased with the level of capitalized intangibles, and interpreted this
result as being consistent with the perception by investors that managers might be overstating
earnings through capitalizing what indeed should be expensed. The study by Klock and Megna
(2000) is also unique in the sense that they examined industry-specific intangibles. They
investigated the measurement and valuation of intangible capital in the US wireless
telecommunications industry and found that the spectrum license explained 60% of the variation
in Tobin’s q, while advertising expenditure, customer base and brand royalty also contributed to
variation in Tobin’s q.
Studies focusing on multiple intangible items appear to be more prevalent in countries where
capitalization of intangible-related expenditure is more generously allowed, and Australia is a
representative example of those countries. In Australian context, Ritter and Wells (2006) found
evidences that voluntarily recognized & disclosed identifiable intangible assets, along with
goodwill, have positive associations with stock prices. Goodwin and Ahmed (2006) reported
that the value relevance of earnings increased for intangible capitalizing firms compared with
non-capitalizers. Dahmash et al. (2009) found that the information presented by the average
Australian company with respect to goodwill and identifiable intangible assets is value relevant
but biased, in that goodwill tended to be reported conservatively and identifiable intangible
assets aggressively.
12
Some studies in other countries also reported similar findings. Oliveira et al. (2010) found
that reported goodwill and other intangible assets are highly significantly associated with stock
price in Portugal, and Ali et al. (2010) reported the value relevance of intangible assets in
Malaysia’s top 50 firms. In Korean context, reported intangible assets were found to be not less
value relevant than other assets in the studies of Lee and Kim (2003) and Chung and Cho (2004)
for the period of 1991~2002 and 1991~2001, respectively.
However, it is noteworthy that few studies explicitly compared the value relevance of specific
reported intangible assets. Except the study of wireless telecommunication industry by Klock
and Megna (2000) which might not be generalized, only Oliveira et al. (2010), Lee and Kim
(2003) and Chung and Cho (2004) looked into specific intangible items other than goodwill
(Intellectual property, capitalized R&D expenditure and other intangible assets for Oliveira et
al., capitalized R&D expenditure and other intangible assets for Lee and Kim, Chung and Cho).
Likewise, explicit differences in value relevance between industries have not attracted much
attention, with few exceptions such as Ballester et al. (2003) (which focused on economic value
of capitalized R&D asset in different industries) and Dahmash et al. (2009) (which examined
the value relevance of goodwill and identifiable intangibles in 8 different industries in the
robustness test for the aggregated sample). As a consequence, an approach focusing on both
multiple specific intangible items and different industries is hard to find. By using this approach,
this study aims to examine the different value relevance of different reported intangible asset
classes, not only for the overall firms but for firms in different industries.
3. Research questions & methods
1) Hypotheses and research models
Based on prior empirical studies, the value relevance of intangible assets reported by Korean
firms is to be tested. The first research question examines whether intangible assets help explain
the market values, that is, are value relevant, once the effects of book value and earnings are
13
controlled for.
H1 : Reported intangible assets are value relevant in explaining market equity value.
Despite the widely-observed positive value relevance, for specific reported intangible assets
there are also different findings such as Callen and Morel (2005) and Cazavan-Jeny and
Jeanjean (2005) as already outlined. If investors expect different future benefits for different
reported intangible assets, their resultant value relevance would also be different. Moreover, if a
reported intangible asset is positively associated with the market value while another asset is
negatively associated, aggregating them together may result in a loss of explanatory power. The
findings of Ely and Waymire (1999) that the relations of intangibles to stock price differ with
the characteristics of the intangible assets (e.g., rights-baseness or subjection to periodic
amortization or downward revaluations), which was contributed to investors’ different
perceptions of the reliability of intangibles’ carrying values, also support the conjecture that
different reported intangible assets may show different associations with the market value.
Consequently, total intangible asset is divided into goodwill, intellectual properties,
capitalized development expenditure and software expenditure, and other intangible assets in
this study. These items are the most frequently observed ones in the financial statements of
Korean firms. Therefore, above classification (4 individual asset classes and aggregated other
intangible assets) can be considered as a compromising choice between a detailed classification
to include as many different intangible asset classes as possible and the need to secure minimum
sizes of samples for specific items. In addition, a dummy variable is added to distinguish loss-
reporting firms, to reflect the finding of Collins et al. (1999) that loss-reporting firms show
different price-earnings relation.
First item of focus is goodwill, which has been found to be value relevant in the majority of
prior studies such as Chauvin and Hirschey (1994), McCarthy and Schneider (1995) and Jifri
and Citron (2009), among others. Consistently, it is expected that goodwill is also value relevant
in this study and therefore the first subset of the first hypothesis is:
H1a : Goodwill is value relevant in explaining market equity value.
14
Intellectual properties, as policy tools for encouraging innovation, were also found to be
generally value relevant in the literature review of Greenhalgh and Rogers (2007) who focused
on patents, trademarks and copyrights. 15 Consistently, intellectual properties are expected to be
value relevant and this yields the second subset:
H1b : Intellectual properties are value relevant in explaining market equity value.
For capitalized development expenditure, most prior studies are also reporting value
relevance (e.g., Zhao 2002; Healy et al. 2002; Ballester et al. 2003; Han and Manry 2004) with
few exceptions such as Cazavan-Jeny and Jeanjean (2005). Consequently, capitalized
development expenditure is also expected to be value relevant in Korean context and therefore
the third subset is:
H1c : Capitalized development expenditure is value relevant in explaining market equity value.
As previously noted, capitalized software expenditure has not been yet popularly covered in
literature. However, in consistent with Aboody and Lev (1998), capitalized software
expenditure is expected to be value relevant in Korean context:
H1d : Capitalized software expenditure is value relevant in explaining market equity value.
The fifth and last subset includes other intangible assets. Even though Oliveira et al. (2010)
found a positive association between stock price and other intangible assets, it should be noted
that this item aggregates all other intangible assets and therefore is a ‘residual’. This means that
it should not be treated as the same variable as that of other studies. However, in the context of
the first hypothesis, it is expected that other intangible assets are also value relevant:
H1e : Other intangible assets are value relevant in explaining market equity value.
Like most of prior studies that examined value relevance of accounting numbers, the test of
value relevance in this study is based on the Ohlson (1995) model, in which a firm’s market
value is a function of the book value of equity and earnings. The main idea is that if accounting
15 Some studies (e.g., Bosworth and Rogers; 2001) took alternative approach by taking the frequencies of
application activities for patent or trademarks as independent variables, and also found significantly
positive associations between these variables and market value of firms.
15
data are good summary measures of the events incorporated in security prices, they are value-
relevant because they provide a value of the firm which is close to its market value (Oliveira et
al., 2010). Consequently, the model to test above hypotheses is as follows.
Model 1 : Pt + dt = a0 + a1 (BVE – IA)t + a2NIt + a3NIt LOSS + a4 GWt + a5 IPt + a6DVt
+ a7SWt + a8OIt + et
where,
Pt = Stock price as of 31st March of the year t+1.
dt = dividend per share of year t.
(BVE – IA)t = book value of equity minus the amount of intangible assets (including
goodwill), per share at the end of year t.
NIt = net income per share of year t.
LOSS = 1 for loss-reporting firms (that is, NIt < 0) and 0 for others.
GWt = goodwill per share at the end of year t.
IPt = intellectual properties per share at the end of year t.
DVt = capitalized development expenditure per share at the end of year t.
SWt = capitalized software expenditure per share at the end of year t.
OIt = all other intangible assets per share at the end of year t.
et = residuals.
All variables are per-share values, consistent with many of previous studies such as Oliveira
et al. (2010), Ritter and Wells (2006) and Zhao (2002).
With this model, whether each independent variable coefficient differs significantly from zero
is to be tested.
The second hypothesis is about the difference of value relevance between industries.
According to Chan et al. (1990), stock price changes after announcements of R&D spending
were different between high-technology firms and low-technology firms : high-technology firms
that announced increases in R&D spending experienced positive abnormal returns, where low-
technology firms experienced negative abnormal returns. Meanwhile, Lev and Zarowin (1999)
showed that the informativeness of accounting earnings decreased during the period of
16
1976~1995 and that this decrease was abnormally steep for R&D-intense firms, arguing that
immediate expensing of R&D expenditure that would contribute to future earnings resulted in
the decreased informativeness.
These findings, despite the difference between their contexts, motivate a question about the
value relevance of reported intangible assets between industries. If investors’ expectations of
future economic benefit from intangible assets are different between industries, it is also
possible that there are differences in the value relevance of intangible assets. In this study, in a
similar context with prior studies, the difference between information technology (IT) firms and
other firms is examined. If the argument of Blair and Wallman (2001) about the decreasing
explanatory power for firm value of reported intangible assets in high-technology firms is
extended to Korean setting, it is expected that value relevance of intangible assets would be
lower in Korean high-technology firms, which IT-firms are proxies for, when compared to non-
IT firms. This leads to the second hypothesis:
H2 : The value relevance of reported intangible assets in IT firms is different from that of non-
IT firms.
To test this hypothesis, model 1 is revised with an additional dummy variable of ITF
multiplied by each intangible asset variable.
Model 2 : Pt + dt = b0 + b1 (BVE – IA)t + b2 NIt + b3NIt LOSS + b4GWt + b5IPt + b6DVt
+ b7SWt + b8OIt + b9GWt ITF + b10IPt ITF + b11 DVt ITF + b12 SWt ITF
+ b13 OIt ITF + nt
where,
ITF = 1 for firms classified as IT firms (by Korean industry classification code 45000) and 0
for other firms.16
nt = residuals.
With this model, whether the value relevance of each item is different between IT firms and
non-IT firms is to be tested.
16 The sub-categories of IT industry include software & service, hardware & equipment, semiconductor equipment industries.
17
Table 2 : Sample selection
2001~2010
Starting number of firm-years 26,420
Less :
Financial firms or firms with other than 31st December year end - 5,559
Firms without stock prices - 6,430
Firms with audit opinions other than ‘unqualified’ - 163
Firms with negative book values of equity - 114
Firms with intangible assets less than 1% of total assets - 8,912
Final number of firm-years = 5,242
2) Data Retrieval
As previously noted, this study depends on the Korean Investor Service database (KIS-Value),
the most popular local database in Korea because global database such as Compustat or
Datastream are not providing detailed intangible asset accounts reported by firms. The data
includes firms listed on Korean stock market during the period of 2001~2010. The final sample
of 5,242 observations is derived from a potential sample of 26,420 observations following a
filtering process described in Table 2.
5,559 observations were excluded at first because they were for firms in financial industry or
who do not have a 31st December fiscal year end (as is usual in Korea). Financial firms are
excluded following Ahmed et al. (2000), because they usually have a minimal level of operating
assets and are subject to additional regulatory requirements that potentially affect the relation
between their accounting numbers and stock market values. In addition, firms with fiscal year
ends other than 31st December were excluded to ensure that all sample firms are at the same
stage in the financial reporting process for any given valuation date, just like the study of
Dahmash et al. (2009).
Also excluded are : 6,430 observations without stock prices; 163 observations with the
auditor opinions other than ‘unqualified’; 114 observations with negative book value of equity;
8,912 observations with less than 1% of intangible assets (compared to total assets). Of final
sample of 5,242 firm-years, which is used to test both the 1st and 2nd hypothesis, 2,169 firm-
years are for IT firms and 3,073 are for non-IT firms, while 3,341 firm-years include net profit
18
and 1,901 net loss.
4. Findings
1) Descriptive statistics and correlation analysis
Table 3 shows the descriptive statistics of sample data. While the sample indeed covers a
wide range of listed firms in Korea, it is noteworthy that the data is highly skewed and the
minimum values are zero for all 5 individual intangible asset classes. Furthermore, many of the
quartile values are also zero. This is mainly because many firms do not have multiple intangible
assets, even after filtering our firms with intangible assets less than 1% of total assets. As these
skewness might indicate a potential heteroskedasticity problem in the regression analysis, an
inspection of the residual errors would be necessary.
Table 3. Descriptive Statistics (in KRW)
Variable Mean Std. Dev. Min. 1Q Med. 3Q Max.
P + d 11,565 38,873 19 1,540 3,450 8,608 1,050,997
BVE – IA 7,147 20,463 - 2,791 1,046 2,395 5,418 526,327
NI 500 3,607 -57,554 -216 101 497 97,946
GW 144 1,067 0 0 0 0 25,259
IP 33 215 0 0 1 7 6,439
DV 262 536 0 3 89 302 8,612
SW 27 250 0 0 0 1 7,245
OI 208 1,271 0 0 3 48 33,281
Frequency Percent
Total 5,242 100.00
IT 0 3,073 58.62
1 2,169 41.38
LOSS 0 3,341 63.74
1 1,901 36.26
# EUR 1 = 1,473.23 KRW (Korean Won) (2012.5.29)
19
Table 4. Correlations (Pearson) matrix
P + d BVE – IA NI GW IP DV SW OI
P + d 1
BVE – IA 0.756** 1
NI 0.770** 0.772** 1
GW 0.276** 0.279** 0.312** 1
IP 0.221** 0.267** 0.158** 0.104** 1
DV 0.205** 0.195** 0.166** 0.010 0.101** 1
SW 0.365** 0.254** 0.271** 0.553** 0.056** 0.018 1
OI 0.204** 0.447** 0.225** 0.329** 0.033* 0.004 0.293** 1
# significant at the 0.05(*), 0.01(**) level (2-tailed)
Table 4 shows the Pearson correlation coefficients between variables. The coefficients show
that all the independent variables are correlated positively with stock price added by dividend
(henceforth ‘market value’). Many of independent variables are also correlated between
themselves, while most of the values range between 0.15 and 0.3.
2) Regression results
Table 5 shows the regression results for both models, which are derived by using SPSS
(version 19). 17
For model 1, which is designed to examine different intangible items’ value relevance, all
coefficients for independent variables are significant but there are quite differences among their
signs. Book value of equity subtracted by total intangible assets, along with net profit, shows
significantly positive associations with market value, and the association between profit and
market value significantly decreases for firms with net loss. These results are all in line with
prior studies such as Collins et al. (1999), Zhao (2002), Ballester et al. (2003), Han and Manry
(2004), Jifri and Citron (2009), Oliveira et al. (2010), among others. For separately examined 5
intangible asset classes, intellectual property, capitalized development and software expenditure
are positively associated to market value, while goodwill and other intangible assets are
17 The results of checking residual errors are presented in Appendix. Results suggest that there are not
serious heteroskedasticity problems.
20
Table 5. Regression results
Model 0 1) Model 1 Model 2
(coefficient) (t-statistics) (coefficient) (t-statistics) (coefficient) (t-statistics)
BVE – IA 0.291 11.564*** 0.387 12.934*** 0.278 9.833***
NI 8.263 49.936*** 7.623 43.741*** 7.025 44.084***
NI× LOSS -8.746 -29.801*** -8.105 -27.916*** -7.382 -28.326***
GW -3.558 -10.801*** -0.663 -1.532
GW× ITF -0.096 -0.143
IP 6.798 5.503*** 2.014 1.527
IP× ITF 17.803 6.100***
DV 2.632 4.494*** 4.453 8.709***
DV× ITF -2.465 -2.417**
SW 30.740 22.896*** 116.249 44.469***
SW× ITF -115.996 -37.529***
OI -2.353 -8.721*** -1.411 -5.444***
OI× ITF 5.631 6.696***
Adj. R2 0.706 0.738 0.794
Observations 5,242 5,242 5,242
F-statistics 4,194.73*** 1,843.544*** 1,556.255***
Note : * p < 0.1, ** p < 0.05, *** p < 0.01
1) For the comparison of adjusted-R2 : Intangible asset is not separated here. That is, model 0 is
Pt + dt = c0 + c1 BVEt + c2 NIt + c3NIt LOSS
negatively associated. It is also noted that there are significant differences in the magnitudes of
positive coefficients, which range from 2.632 (development expenditure) to 30.740 (software
expenditure). This model has an adjusted R2 of 0.738, explaining 73.8% of the total variance in
the market value. Compared to the adjusted R2 of 0.706 when intangible asset is not separated,
segregating individual intangible assets improves R2 by 0.032 or 3.2%p. 18
The negative value relevance of goodwill (coefficient = -3.558, p < 0.01) is not consistent
with many prior studies which found positive associations. However, in Korean context, results
18 Alternative analyses in which each intangible asset is dropped yield different values of R2 in consistent
with Table 5; Dropping each intangible asset decreases R2 from 0.738 to 0.730 (without GW), 0.737
(without IP and DV), 0.712 (without SW), and 0.733 (without OI), respectively. It is verified that
corresponding decrease(s) in R2 is biggest for SW which shows the largest absolute value of t-statistics in
Table 5, and smallest for IP and DV which show the smallest absolute values of t-statistics in Table 5.
21
are conflicting in that Lee and Kim (2003) found a positive association but Chung and Cho
(2004) found a negative (but insignificant) association of goodwill. The result of this study
might be contributed to the different time frame of sample data (1991~2001 or 1991~2002 for
prior studies vs. 2001~2010 for this study), with the conclusion that the stock market did not
acknowledge the future benefits of goodwill, but on the contrary, reacted negatively to reported
goodwill. One probable way to explain the negative association of goodwill is to assume that
investors considered the reported goodwill as a result of an excess payment over the fair value
of the acquired firm in an acquisition. Alternatively, the mandatory amortization of goodwill
during the sample period might have a substantial impact on the association with a systematic
decrease in the amount of reported goodwill, which no longer happens under the new standard
since 2011. This conjecture is supported by Jennings et al. (1996) who found evidence which
suggests that goodwill may not be declining in value for many firms. Jifri and Citron (2009)
also argued that goodwill subject to impairment reviews would be valued more realistically, and
therefore be associated more closely with market prices.
Other intangible assets also shows a negative value relevance (coefficient = -2.353, p < 0.01),
which is not consistent with a positive association in Oliveira et al. (2010) or positive but
insignificant associations in Lee and Kim (2003) and Chung and Cho (2004). However, as
previously noted, it may not have much meaning in direct comparison with other studies where
the components of ‘other intangible assets’ are all different.
Value relevance of intellectual properties, capitalized development and software expenditure
are all significantly positive, which can be interpreted as an acknowledgement for the future
benefits of these individual assets by market participants. Value relevance of capitalized
development expenditure (coefficient = 2.632, p < 0.01) is consistent with many prior studies
that reported positive associations between stock price and development expenditure under
various settings. Similar to the finding of Aboody and Lev (1998), capitalized software
expenditure also showed a positive association (coefficient = 30.740, p < 0.01) with the market
value. Value relevance of intellectual properties (coefficient = 7.698, p < 0.01) is consistent with
22
Greenhalgh and Rogers (2007), but different from Oliveira et al. (2010) who found an
insignificantly negative coefficient and contributed it to the undervaluation of intellectual
properties on financial statements.
In model 2 which is for examining the differences in value relevance of intangible asset
classes between IT firms and non-IT firms, the differences were significant for all individual
asset classes except goodwill (coefficient = -0.096, t-statistics = -0.143). Specifically, market
value associations of capitalized development and software expenditure are significantly smaller
for IT firms compared to non-IT firms (coefficient = -2.465, p < 0.05 for DV; coefficient =
-115.996, p < 0.01 for SW) . This result suggests that investors’ positive expectations for future
benefits of capitalized development and software expenditure significantly decreased for IT
firms. Even though a decrease in explanatory power for firm value of accounting information
has already been noted (e.g., Blair and Wallman 2001), an explanation of this negative effect of
capitalized development and software expenditure in IT firms may need further arguments like
that of Cazavan-Jeny and Jeanjean (2005). They suggested investors’ concerns about earnings
management through arbitrary capitalization as a reason for negative value relevance of
capitalized R&D expenditure under French setting with relatively weaker legal enforcement.
Alternatively, investors might not expect future benefits of capitalized development and
software expenditures for IT firms because of significant uncertainties stemming from the rapid
technological changes in the industry.
On the contrary, market value associations of intellectual properties (coefficient = 17.803, p <
0.01) and other intangible assets (coefficient = 5.631, p < 0.01) were significantly larger for IT
firms. Relatively high value relevance of intellectual properties implies investors’ recognition
that intellectual properties in IT industry have more value than those in non-IT industries.
Compared to capitalized development and software expenditure of which recognition require
management’s decisions regarding conditions of capitalization, many intellectual properties
such as patent or trademarks are reported at their acquisition cost and are legal rights granted by
authorized institutions. Like the suggestion of Ely and Waymire (1999), this difference may
23
lead to investors’ different perceptions on the credibility of these individual intangible assets’
carrying values.
Finally, adjusted R2 of this model is 0.794, which is quite larger than that of first model
(0.738). This enhanced R2 suggests that examining different value relevance of intangible assets
between IT firms and non-IT firms indeed helps to improve the explanatory power of the
regression model by 0.056 or 5.6%p.
5. Summary, conclusion, limitations and future research
This study examined the value relevance of reported intangible assets in a sample of Korean
listed firms. The results indicate that all intangible asset classes have significant associations
with the market value of the firm. Intellectual properties, capitalized development expenditure
and software expenditure have positive associations, while goodwill and other intangible assets
have negative associations. The negative association of goodwill may have been resulted from
investor’s concern of an excess acquisition cost, or the mandatory amortization of goodwill
under old standard which may not be adequate for many firms according to Jennings et al.
(1996). If the latter is the case, the introduction of new standard as of 2011 will offer an
opportunity to future research for a comparison of value relevance when sufficient sample data
is available, like the enhanced value relevance of goodwill in Portugal after introduction of
IFRS (Oliveira et al., 2010).
For information technology (IT) firms, all intangible asset classes except goodwill showed
differences in the level of value relevance when compared to non-IT firms. But the directions of
these differences are also different among individual classes; value relevance of capitalized
development expenditure and software expenditure are lower for IT firms than non-IT firms,
while value relevance of intellectual properties and other intangible assets are higher for IT
firms than non-IT firms. While investors’ concern about earnings management through arbitrary
capitalization and/or pessimistic expectations for future benefits in IT industry may have
24
resulted relatively smaller associations of capitalized expenditures, investors appear to
recognize that intellectual properties have more value in IT industry than in non-IT industries,
probably because a more objective reporting process of intellectual properties. This may lead to
a consideration of more rigorous regulations for the accounting practice in certain industries
after a verification of actual situations. However, in-depth explorations of the reasons why the
relative effect differs among intangible asset classes and why investors treated IT firms and non-
IT firms differently in the stock market evaluation of reported intangible assets are open to
future research.
Possible limitations of present study should be noted. First problem is, even though a wide
range of Korean listed firms is covered in the sample data, filtering out firms with intangible
assets less than 1% of total assets might have caused a deviation from typical Korean firms. If
this is the case, generalization of the findings in this study may be challenged. Secondly,
because of data unavailability, this study could not explore the possible effect of new
accounting standards on the value relevance of intangible assets. However, results of this study
may offer a reference point when sufficient sample data is available and future studies attempt
to examine the effect of new standard by comparing the before- and after-new standard periods,
not only for goodwill but for all individual intangible asset class. Third point is that even the
division of the whole sample into IT firms and non-IT firms yielded statistically significant
results, this classification may not give a intuitively clear idea as ‘high-technology industries vs.
low-technology industries.’ If a clearer industry classification or a better proxy for high-
technology industries could be adapted, interpretation of the results would be more convincing
and direct. Fourth point is the limitedness in the interpretation of the negative value relevance of
other intangible assets (OI) that are resulted from an aggregation of many items. Extracting
more specific items from this, if possible, may improve the explanatory power and/or help to
give an understandable interpretation of the result. Finally, as a so-called ‘association study’,
this study is also exposed to the indication that prices contain not only information about
fundamental values but also noise caused by trades and therefore the underlying logic could be
problematic (Ronen, 2001).
25
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Appendix
1. Inspection of homoskedasticity : residual error plots
1) model 1
2) model 2