Capitalizing research and development (R&D) expendituresDo the perceived benefits materialize in practical application?
Tami Dinh Thi*
Wolfgang Schultze**
Abstract: In this study, we analyze whether the perceived benefits of capitalizing research and
development (R&D) expenditures in fact materialize under IFRS. We analyze the
consequences of R&D capitalization on information asymmetries, measured by bid-ask-spreads
and forecast errors, and market pricing. We find no evidence that actual R&D capitalization
under IAS 38 reduces information asymmetries. Consistent with that we find that the market
does not price the resulting accrual component. Our results are consistent with the notion that
market participants are wary of actual R&D capitalization due to the discretion and possible
manipulation. To the contrary, we find that market participants seem to apply adjustment
procedures similar to full R&D capitalization to arrive at their value estimates.
Key Terms: Analysts’ forecasts, bid-ask-spreads, research and development, accruals,
JEL Classifications
G13 · M41
Data availability: The data used in this study are available from commercial providers (Thomson Financial
Datastream, Bloomberg) as well as public sources.
Current Date: February 2011
* University of New South Wales, Sydney, Australia, e-mail: t.dinhthi@unsw.edu.au
** University of Augsburg, Augsburg, Germany, e-mail: w.schultze@wiwi.uni-augsburg.de
We gratefully acknowledge helpful comments by Ken Klassen, Baruch Lev, Theresa Libby, Russel
Lundholm, Zoltan Matolcsy, Richard Morris, Mari Paananen, Ulf Schiller, Thorsten Sellhorn, Dushyantkumar
Vyas, Peter Wells, Christine Wiedman, Anne Wyatt, and participants at the IAS meeting of the AAA 2008 in
San Diego, the annual EAA congress 2009 in Istanbul, and seminar participants at the University of
Technology, Sydney, the University of Waterloo, and the University of Augsburg. Tami Dinh Thi gratefully
acknowledges financial support from the German Academic Exchange Service.
1
1. Introduction
This paper studies the possible benefits of capitalizing research and development
(R&D) expenditures under IAS 38. While studies based on adjusted R&D “as if capitalized”
data or software development have provided evidence of capitalization being beneficial in
terms of market value explanatory power and information asymmetries (Lev Sougiannis 1996;
Healy et al. 2002; Aboody and Lev 1998; Mohd 2005), studies based on actual R&D
capitalization have not consistently confirmed these benefits (e.g. Oswald 2008). Software
development being exposed to a different level of uncertainty than other R&D, these results
may or may not apply to R&D capitalization in general (Mohd 2005). Also, IAS 38 prescribes
partial R&D capitalization when certain criteria are met and thus involves managerial
discretion. This discretion can either be used to signal private information or for opportunism
(e.g. Ahmed and Falk 2006). Also, as Aboody and Lev 1998 show, this partial recognition
imposes additional challenges for forecasting earnings and may result in increased forecast
errors. Market participants may therefore be wary of actual R&D capitalization. Hence, we ask
whether the hypothetical benefits of capitalizing R&D demonstrated in prior studies do
materialize in practical application under IAS 38, when managerial discretion is involved and
market participants are sceptical of R&D capitalization. As the treatment of internally
generated intangible assets remains one of the major differences between IFRS and US-GAAP,
our study sheds light on the possible effects of adopting IFRS for US firms.
We analyze this question from the broader perspective of the benefits of accrual
accounting (Dechow 1994; Penman and Yehuda 2009). We concentrate on the accrual
information of R&D capitalization as the source of possible informativeness. The immediate
expensing of R&D is a prime example for poor matching (Dichev and Tang 2008). We
compare the benefits of full R&D capitalization resulting from perfect matching (Dichev and
Tang 2008) with the signaling effects of discretionary, partial capitalization (Ahmed and Falk
2
2006; Oswald 2008). If the capitalization of R&D is informative due to signaling or better
matching, information asymmetries should be reduced (Mohd 2005) and the market should
price this accrual component.
Studies based on actual, observable data (e.g. Oswald 2008) have concentrated on the
discretion involved in the choice to capitalize a certain portion of R&D and the resulting ability
of managers to signal private information to the market (e.g. Ahmed and Falk 2006). In
Australia, where the capitalization of intangibles has been routine, analysts expect firms with
relatively certain intangible investments to signal this fact by capitalizing intangible assets
(Matolcsy and Wyatt 2006). For the UK, as for the US, analysts have been found to prefer
expensing (Goodacre 1991; AIMR 1994; Oswald 2008). In different environments, the
capitalization of R&D may therefore be perceived differently and be used differently in the
decision and valuation process of investors. In countries where investors are sceptical of
capitalization, the hypothetical benefits of capitalization may not materialize when practically
applied.
To analyze this question, we use the German stock market, which, during our sample
period, was in transition to IFRS, forfeiting much of its conservative tradition (Hung and
Subramanyam 2007). After 1998, firms had the option to adopt IFRS, which allowed numerous
firms to capitalize development expenditures according to IAS 38, prohibited under German
GAAP (Handelsgesetzbuch, HGB) until very recently. This setting allows us to hold constant
country-specific factors associated with reporting incentives and accounting properties (e.g.
Ball et al. 2000; Leuz et al. 2003). The German environment is especially interesting in this
context for its considerable importance of technology, R&D, and other intangible resources due
to the country’s lack of physical resources. Overall, the country’s R&D intensity of around
2.5% of GDP is about equivalent to the US (OECD 2008). In line with the US, German
3
analysts are reported to be sceptical of R&D capitalization and prefer immediate expensing
(Haller et al. 2008). Our results therefore are likely to translate to similar environments.
Our analysis proceeds in two stages. First, we analyze adjusted data to confirm the
effects demonstrated in prior studies. We adjust the firms’ accounts to either reflect full
capitalization or full expensing. We receive two samples of identical size, consisting of
identical firms only differing with respect to the treatment of R&D. We assume that prices
reflect the market’s expectations about the profitability of the R&D investments undertaken.
This allows us to make inferences on which form of R&D-accounting more closely reflects the
capital market’s judgment of firm value. Consistent with prior research on the benefits of
accrual accounting and R&D capitalization, we find adjusted full R&D capitalization to show
higher market value explanatory power compared to R&D expensing and the R&D-accrual
component to significantly add to the explanatory power of the regression.
Next, we analyze R&D data as reported under IFRS. Since IAS 38.57 permits the
capitalization of development costs only from the point in time when the recognition criteria
are met, actual R&D capitalization will only involve partial R&D capitalization. The actual
data thus differ from the “as-if“ data in the amount and by the involved discretion. We compare
the actual data with the “as-if” data and find that the explanatory power of actual capitalization
does not exceed expensing but is inferior to full “as-if” capitalization. These results are
consistent with the notion of general benefits to the better matching of capitalization, but not
with the signaling hypothesis of discretionary capitalization. In fact, our results suggest that the
signals of actual capitalization are not credible to market participants and thus just as
informative as expensing.
Next, we analyze the influence of partial R&D capitalization on information
asymmetry. Mohd 2005 finds that the capitalization of software development under SFAS 86
reduces information asymmetries measured by bid-ask-spreads. We find no evidence
4
confirming these findings. We further analyze forecast errors and find that, consistent with the
results in Aboody and Lev 1998, R&D capitalization is significantly associated with higher
forecast errors.
In a battery of additional analyses we confirm these findings. Based on earnings
multiples, we show that market values are highly associated with earnings before R&D and that
R&D expenditures result in an additional component of market value.
Our results are robust to various sensitivity checks. We apply a two-stage least squares
regression to control for endogeneity, identifying determinants influencing the decision to
capitalize or to expense, like e.g. firm size, profitability, risk, and lagged R&D capitalization
(Oswald 2008).
Our contribution to the existing literature is two-fold: Firstly, we contribute to the
ongoing debate of the accounting for R&D. To our knowledge, we are the first to analyze the
practical application of IAS 38. We use a triangular approach including information
asymmetry, market pricing, and analysts’ forecasts. We provide evidence R&D capitalization
under IFRS does not reduce information asymmetry and is associated with higher forecast
errors. Consistently, the market does not price actual R&D capitalization. The results are
consistent with the notion that market participants are wary of earnings management and
sceptical about actual R&D capitalization. Our results likely translate to other environments
with a similar conservative tradition in R&D accounting, like the US.
Secondly, we contribute to the debate of the benefits of accrual accounting (Dechow
1994; Dichev and Tang 2008; Penman and Yehuda 2009). Expensing R&D being an often
cited, typical example for poor matching, we provide direct evidence for the benefits of full
matching. In our setting, the market does not price partial, discretionary R&D accruals but
accruals resulting from full R&D capitalization. We find that market values exhibit
significantly higher association with full R&D capitalization than with mandatory expensing
5
and discretionary R&D accounting. This indicates that market participants apply procedures
similar to full matching to arrive at their value estimates.
In additional analyses we further analyze this result. We find that market values are
strongly associated with R&D expenditures rather than capitalized R&D. Likewise forecasted
future earnings are strongly associated with R&D expenditures rather than actual partial R&D
capitalization. This corroborates our interpretation of the above results that market participants
derive their own value estimates from R&D outlays rather than from capitalized amounts.
The remainder of the paper is organized as follows. Section 2 reviews relevant prior
literature and develops the hypotheses. Section 3 describes our methodology and sample.
Section 4 presents the empirical findings. Section 5 concludes.
2. Prior research and hypotheses development
Empirical studies in the US have found that capitalizing R&D increases market value
explanatory power (e.g. Lev and Sougiannis 1996; Healy et al. 2002; Chambers et al. 2003).1
The results of these studies are one of the main reasons why the IASB decided to prescribe
R&D capitalization when certain criteria are met (IAS 38 BCZ 39 c):
“certain research studies, particularly in the United States, have established a costvalue association for research and development expenditures. The studies establish that
capitalization of research and development expenditure yields value relevant information to
investors.”
However, as R&D capitalization is not permitted under US-GAAP, these studies had to
rely on adjusted “as-if” data (Kothari et al. 2002). The recognition of software development
under SFAS 86 being the exception to immediate expensing, Aboody and Lev (1998) find
1
For a review paper on the value relevance of financial and non-financial information of intangibles see Wyatt
(2008). She stresses the difficulties to capture reliability directly in value relevance studies. In our study, we
consider the reliability aspect only indirectly. Within the different R&D accounting methods that we investigate,
different levels of reliability are reflected by the amount of managerial discretion involved. High discretion in
R&D accounting refers to lower reliability and vice versa.
6
higher market value explanatory power for capitalized software development. Mohd 2005 finds
that capitalizing software development reduces information asymmetries in terms of bid-ask
spreads and share turnover. She stresses that the FASB has likely allowed the capitalization of
software development rather than other R&D because it is the one area, where future payoffs
can reliably be measured. So her results likely do not translate to all R&D.
Besides the US studies, there is a large number of studies in environments where R&D
capitalization is allowed and therefore directly observable (e.g. Ahmed and Falk 2006;
Markarian et al. 2008; Oswald 2008). Such studies have found partially contradictory results
due to different country settings and methodologies. Particularly studies based on accounting
information under rules which allow for discretionary R&D capitalization, as used to be the
case in France, Italy, UK, Australia, and Canada, come up with differing results.2 In some
studies, discretionary R&D capitalization improves accounting informativeness due to the
possibility to signal managers’ private information on the R&D projects to the market
(Abrahams and Sidhu 1998 for Australia; Ahmed and Falk 2006 for Australia, Smith et al.
2001 for Australia and Canada, and Oswald 2008 for the UK). On the other hand, discretionary
R&D capitalization has also been shown to be a tool for managing earnings, resulting in lower
value relevance (Mande et al. 2000 for Japan; Cazavan-Jeny and Jeanjean 2006 for France;
Markarian et al. 2008 for Italy).3 In a comparative approach, Zhao (2002) analyzes different
countries and finds that the relative value relevance of R&D is a function of both the reporting
environments and the R&D accounting standards.
2
The discretionary R&D capitalization in these countries refers to their national GAAP before IFRS was
introduced there.
3
In a French-GAAP environment Cazavan-Jeny and Jeanjean (2006) find that firms that capitalize are smaller,
highly leveraged, less profitable and have less growth opportunities concluding that the capitalization choice
might be a self selection issue. In contrast to other studies, their results show a negative association between
capitalization and stock return, i.e. the market considers capitalization as bad news not expecting future benefits.
However, the authors stress that this might be a special case because France has a low legal enforcement and as
such managers have a more opportunistic approach to the use of R&D capitalization.
7
Ahmed and Falk (2006) analyze an Australian sample and create “as-if” expensers
which they compare to actual, discretionary capitalizers and expensers. They find that
discretionary R&D accounting (expensing or capitalizing) dominates mandatory (“as-if”) R&D
expensing. Among the two discretionary treatments, they find that capitalization dominates
expensing. These results are in line with similar findings by Matolcsy and Wyatt (2006) for
other intangibles.
Oswald (2008) compares actual data with “as-if” data for the UK. He creates both “asif” expensing data for actual capitalizers and “as-if” capitalization data for actual expensers. He
estimates capitalization rates which he applies to actual expensers to reproduce discretionary,
partial R&D capitalization. He finds little difference in the relevance of adjusted and reported
numbers for both capitalizers and expensers. His conclusion is that changing the accounting
does not improve the explanatory power of earnings, indicating that managers consistently
choose the “correct” method of accounting for R&D, that is, capitalizing or expensing. These
results are in contrast to the above-mentioned studies based on “as-if“ data in the US-setting.
However, he does not consider the case of non-discretionary full R&D capitalization as was
used in the US studies.
Overall, prior research does not provide an answer to the apparent contradiction why
full capitalization was consistently shown to be beneficial compared to expensing in “as-if”
studies, while based on observable data, partial, discretionary capitalization did not consistently
dominate expensing.
To address this issue, we compare (1) actual expensing and capitalizing, (2) “as-if” full
expensing, and (3) “as-if” full capitalization. The latter has only been examined in conjunction
with actual R&D expensing but not with actual R&D capitalization yet. Further, by analyzing
(2) and (3) we are able to isolate the effects of managerial discretion.
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Studies based on “as-if” data and studies based on actual data build on two different
theoretical arguments. Studies using observable R&D capitalization focus on managerial
discretion and the resulting signaling as main drivers of the informativeness of capitalization.
Instead, the underlying theory for the “as-if” studies in the US is the basic premise of accrual
accounting and improved matching (Dechow 1994; Dichev and Tang 2008; Penman and
Yehuda 2009). R&D expenditures represent investments resulting in future economic benefits;
the capitalization of such investments results in a better matching of costs and revenues (Lev
and Sougiannis 1996; Lev and Zarowin 1999).
Accrual accounting has been found to improve earnings’ ability to measure firm
performance and mitigate the timing and matching problems of cash flows (Dechow 1994).
While expensing R&D is fundamentally equal to cash accounting, capitalizing R&D reflects
accrual accounting. We therefore build our analysis on the accrual framework established by
Dechow (1994). Dichev and Tang (2008) further develop the discussion on accruals vs. cash
flows in the context of the matching of revenues and expenses. They find that for US firms,
accounting matching has worsened over time, negatively affecting the properties of earnings.
They list several reasons for this fact, including expensing R&D, which results in poor
matching. Yet, at the same time, accruals suffer from unavoidable estimation errors (Dechow
and Dichev, 2002). Especially accruals of low reliability lead to lower earnings persistence
(Richardson et al., 2005). Since investors do not fully capture the lower earnings persistence of
less reliable accruals, securities may be significantly mispriced (Sloan 1996; Xie 2001).
Therefore, in order to analyze the benefits of R&D capitalization in real world
application, we concentrate on the accrual information of R&D capitalization as the source of
possible informativeness. If the capitalization of R&D is informative to the market as a signal
or for matching purposes, this should result in lower information asymmetry and the market
should price this accrual component.
9
R&D is a typical example of the trade-off between relevance and reliability. While
studies have demonstrated the positive benefits accruing to R&D activities (e.g. Hand 2003),
these benefits are also known to be subject to high uncertainty. Investments in R&D result in
higher earnings variability than investments in property, plant, equipment (Amir et al. 2007),
and generate less reliable future economic benefits (Kothari et al. 2002). Their capitalization
may therefore not be beneficial in terms of information asymmetries and market values.
Tutticci et al. 2007 analyze the perceived reliability of capitalized R&D and find for Australia
that the market positively values R&D costs when expensed as incurred, while seems to put
less weight on capitalized amounts. Chan et al. (2007) find for Australia that firms with higher
R&D intensity perform better, regardless of the accounting method used. In addition, the
discretion involved in capitalization may be used opportunistically by managers (e. g.
Markarian et al. 2008). In practical application within Germany, IAS 38.57 is considered an
accounting choice due to the large amount of discretion involved (e.g. Baetge et al. 2008). It is
also one of the main areas of investigation by the German financial reporting enforcement
panel (Meyer and Naumann 2009). Investors may therefore be highly sceptical about the
information provided in R&D capitalization.
We conduct our analysis in two steps. First, we analyze the adjusted “as-if” data. Our
“as-if” data differ from the actual data in that all R&D is unanimously subject to a specific
accounting rule, expensing or full capitalization, without any discrection involved. In line with
prior research, we expect full capitalization to exhibit superior market value explanatory power
compared to expensing. We argue that this is due to the better matching of revenues and costs
and use an accruals approach (Dechow 1994) for testing our hypotheses. We expect that the
accrual component resulting from R&D capitalization is priced by the market and significantly
adds to the explanatory power of the regression. We test the following hypotheses (stated in the
alternative form):
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H1a:
Financial information based on adjusted full R&D capitalization shows higher
association with market values than financial information based on adjusted full R&D
expensing.
H1b: In a setting of full R&D capitalization, the R&D accrual component adds
explanatory power to the regression.
In a second step, we compare the explanatory power of the “as-if” data to actual data.
We do not have an a priori expectation about the direction of the relationship between adjusted
and actual data due to different possible theoretical arguments:
a) Full capitalization as compared to partial capitalization and expensing of R&D leads to
a better matching of costs and revenues (Dechow and Dichev 2002; Dichev and Tang
2008). Better matching is beneficial for forecasting long-run economic profitability (Su
2005). From an accrual and matching perspective, one would therefore expect full
capitalization to dominate partial capitalization and expensing.
b) From a signaling perspective, discretionary partial capitalization may be used to signal
managers’ private information on the prospects of the R&D projects to the market
(Ahmed and Falk 2006; Oswald 2008). Therefore discretionary R&D accounting may
dominate the adjusted “as-if” data.
c) Yet, discretion also gives managers the opportunity to conduct earnings management
(Mande et al. 2000; Cazavan-Jeny and Jeanjean 2006; Markarian et al. 2008). Due to
estimation error, the resulting accruals may be of low reliability (Richardson et al.
2005) and market participants may not trust discretionary capitalization. The adjusted
data for “as-if” full expensing and “as-if” full capitalization do not involve discretion
and may therefore dominate the discretionary data due to a higher degree of reliability,
especially in a conservative environment.
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Actual capitalization under IAS 38 is affected by accounting choice, allowing managers to
signal private information. As a consequence, actual discretionary R&D accounting may be
more informative than full “as-if” capitalization. If the signaling aspect of capitalization
dominates the matching benefits of full capitalization, we expect the following:
H2a.
Financial information based on R&D figures as reported shows higher association with
market values than financial information based on adjusted full R&D capitalization.
However, the signal of R&D capitalization may not be credible to investors as it is
difficult to verify. For the signal to be credible, there need to be costs involved that prevent
firms from sending false signals. Evidence has shown that R&D capitalization has been used
for earnings management. Hence it is possible that investors do not trust R&D capitalization.
Based on arguments a) and c) we thus expect full “as-if” capitalization to display higher market
value explanatory power than actual capitalization. We test the following hypothesis (stated in
the alternative form):
H2b. Financial information based on adjusted full R&D capitalization shows higher
association with market values than financial information based on R&D figures as
reported.
Based on this argument, we can expect to find the accrual component of earnings resulting
from actual capitalization to not be priced by the market and not significantly add to the
explanatory power of the regression:
H2c.
Based on R&D figures as reported, the R&D accrual component does not add
explanatory power to the regression.
Based on actual data only, we next investigate the impact of R&D capitalization on information
asymmetry. Mohd (2005) provides evidence that the implementation of SFAS 86 and hence,
12
the capitalization of software development costs significantly reduces information asymmetry.
Since the recognition criteria under IAS 38 are very similar to SFAS 86, we expect results in
accordance with Mohd (2005). However, as these results were based on software development.
Software development being exposed to a different level of uncertainty than other R&D, it is
well possible that the results of Mohd (2005) do not hold for R&D capitalization under IAS 38.
Hence, we test the following hypothesis with no ex ante expectation regarding the direction:
H3a:
Based on R&D figures as reported, R&D capitalization is associated with lower
information asymmetry compared to expensing.
We further analyze the impact of R&D capitalization on forecast accuracy. R&D capitalization
being informative, it should facilitate the forecasting process. However, Aboody and Lev
(1998) provide evidence that capitalizing software development expenditures under SFAS 86
increases analysts’ forecast errors. They refer this to the increased complexity of forecasting
future earnings when including capitalization ratios, possible write-offs of previously
recognized R&D and amortization charges. We therefore hypothesize:
H3b. Based on R&D figures as reported, R&D capitalization is associated with higher
forecast errors compared to expensing.
3. Research methodology and sample
3.1. Research methodology
In the following, we lay out our triangular approach including market pricing,
information asymmetry, and analysts’ forecasts. The accounting relations that govern accrual
accounting are built on the accrual and matching framework (Dechow 1994; Dichev and Tang
2008). We disaggregate earnings into its earnings components. The additional market value
13
explanatory power of actual (“as-if”) R&D capitalization is observable in the actual (“as-if”)
R&D accrual component that is generated. In line with Penman and Yehuda (2009), we first
lay out the accounting structure (section 3.1.1). This is followed by a description of the
regressions analyzing, market pricing and information asymmetry (section 3.1.2), which will
then be taken to the data (section 4).
3.1.1. Accounting relations that produce R&D accruals
In line with the accrual literature, we disaggregate earnings into a cash flow and several
accrual components. Accruals are defined as the difference of earnings and cash flows (e. g.
Dechow et al. 1995; Leuz et al. 2003):
E  OCF  TACC
(1)
Earnings (E) are decomposed into operating cash flows (OCF) and total accruals
(TACC). E and OCF are used in the analyses as reported, i.e. as reported in the income
statement (E) and as reported in the cash flow statement (OCF). TACC are hand-collected data
from the cash flow statement as well. Such data are fairly unique in accrual research, since
studies regularly estimate those from changes in the balance sheet.4 Hribar and Collins (2002)
show that severe errors are introduced by the balance sheet approach. Our approach allows us
to capture the complete set of differences between earnings and cash flows that have been
neglected in prior studies.
TACC consist of long term accruals (LTACC: depreciation, amortization, accruals for
pensions, foreign currency translations), working capital accruals (WCACC: change in net
working capital), and other accruals (OTHACC: reclassifications, accounting changes etc.).
Most studies use the following equation: total accruals = (Δcurrent assets – Δcash) – (Δcurrent liabilities –
Δshort-term debt – Δtaxes paid) – depreciation and amortization. Alternatively, accruals are also estimated as
Δaccounts receivable + Δinventory + Δaccounts payable + depreciation (e. g. Barth et al. 2005). Only few studies
like Barth et al. (2001b) also consider a variable that captures remaining accruals: Δaccounts receivable +
Δinventory + Δaccounts payable + depreciation + other.
4
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E  OCF  TACC  WCACC  OTHACC
(2)
In order to separate the effect of R&D capitalization (RDCAP) and R&D amortization
(RDAMORT), OCF and LTACC need to be adjusted. OCF is reduced by RDCAP (OCF’ =
OCF – RDCAP). LTACC are reduced by R&D amortization (LTACC’ = LTACC –
RDAMORT). This allows us to separate R&D capitalization and R&D amortization and to
analyze their net effect reflected in R&D accruals (RDACC = RDAMORT – RDCAP):
E  OCF'  LTACC' WCACC  OTHACC  RDACC
(3)
3.1.2. Panel regressions and adjustments
We use equation (3) in a regression on market value, in order to investigate the
differences of R&D accounting on market value explanatory power from an accrual
perspective. We extend Dechow (1994) by making explicit use of the Ohlson (1995) model.
Similar to Barth et al. (1999), we use a generalized version of the extended framework in
Ohlson (1999). We further decompose total accruals into their major accrual components
including R&D accruals.
We run the following regression analyzing the pricing of earnings components using
firm fixed effects.5 All regressions include control variables for industry, GAAP, leverage, loss
firm-years, general growth (measured by market to book ratio), and growth in R&D activities.
The variables are not included in the presented tables due to reading convenience. We will
discuss the use of control variables in detail in section 4.4 and Table 7.
Pricing model (PM):
MVit   0   1 BVit   2 OCF'it   3WCACCit   4 OTHACCit   5 LTACC'it
  6 RDACCit   i  
(4)
where the dependent variable, MVit, is market value (number of shares times share price
5
We include the variable αi, which captures firm fixed effects and leads to firm specific intercepts, with αi = 0 +
1Zi (holding constant the unobserved firm characteristics Z).
15
at the end of fiscal year) for firm i in year t.6 The independent variables (with subscripts
omitted) include: book value (BV), operating cash flow (OCF), working capital accruals
(WCACC), other accruals (OTHACC), long term accruals (LTACC), and R&D accruals
(RDACC). All variables are scaled by lagged total assets.7
Our research methodology, with the aim of comparing the effect of different accounting
for R&D on information asymmetry, market pricing, and analysts’ forecasts, requires a number
of adjustments to the original financial information. We run the pricing model for all three
samples based on (1) actual R&D capitalization, (2) adjusted full R&D expensing, and (3)
adjusted full R&D capitalization. Hence, for case (2) RDACC will equal 0. Depending on the
method, accounting for R&D affects the following variables differently: BV, E, OCF, TACC,
LTACC, RDACC.
Table 1 illustrates our adjustments from R&D accounting as reported (actual data with
no adjustments) to “as-if” full R&D expensing and “as-if” full R&D capitalization:
[Insert Table 1 about here]
In a first step, we convert all companies under IFRS with capitalized development costs
in order to obtain an adjusted sample of fully expensed R&D (column 3). By taking off the
actual R&D capitalization, BV decreases by the R&D asset (– RDA), E decreases by R&D
capitalization (– RDCAP) and increases by R&D amortization (+ RDAMORT), i.e. the net
effect on E is + RDACC, OCF decreases by RDCAP, TACC and LTACC both decrease by
RDAMORT. For the “as-if” full R&D expensing sample, RDACC equals 0 (– RDACC).
The second adjusted sample (column 4) is modeled to allow for full capitalization of all
R&D expenditures. This sample includes the adjustments that have been made for the sample
of “as-if” full R&D expensing and further contains the adjustments necessary to receive a
setting of “as-if” full R&D capitalization. In this case, the amount of R&D capitalized
6
7
We obtain equivalent results when using share price at the fiscal year end + 3 months.
Our findings do not change when using alternative deflators as outlined in section 4.4.
16
(RDCAPADJ) equals the annual amount of R&D expenditures (RDINV). For the amortization
adjustments we presume different amortization rates of 15-25% according to a firm’s industry.
Mead (2007) provides a literature review of the amortization rates for both all R&D capital and
industry-level R&D capital used in prior studies. He finds that studies of business R&D use
amortization rates ranging from 12% to 29%. Consistent with prior literature we presume the
lowest amortization rate of 15% for companies that belong to the chemicals industry (SIC code
28). For firms manufacturing scientific instruments, electronic equipment and similar, we
presume the highest amortization rate of 25% (SIC code 35-38). The rest of the companies are
assumed to amortize with a rate of 20% (e.g. Lev et al., 2005).
We further assume a basis of the adjusted R&D assets capitalized (RDAADJ) in prior
periods as the mean of all R&D expenditures (RDINV) over the sample period multiplied by
1/amortization rate for the first year. This allows us to assume that, on average, investment and
amortization of R&D occur steadily. In line with the sample based on actual data, the dynamic
effect is reflected in adjusted R&D accruals (RDACCADJ), defined as the net effect of adjusted
R&D amortization (RDAMORTADJ) minus adjusted R&D capitalization (RDCAPADJ), i.e.:
RDACCADJ = RDAMORTADJ – RDCAPADJ. Beginning from the second year, adjusted R&D
assets (RDAADJ) are calculated as the adjusted R&D asset in the previous year + RDCAPADJ
– RDAMORTADJ. The adjusted amount of R&D amortization equals the adjusted R&D asset
in the previous year multiplied by the industry-specific amortization rate as outlined above.
Consistent with Ahmed and Falk (2006), we argue that although the adjusted figures do not
represent actual reporting, they can still be reflected by the market. All the data that we use for
our R&D adjustments are publicly available and investors can easily convert the data as we do
(for “as-if” R&D data, see also Lev and Sougiannis 1996; Kothari et al. 2002; Oswald 2008).
BV for the case of “as-if” full R&D capitalization increases by RDAADJ while E
increases by RDCAPADJ and decreases by RDAMORTADJ (i.e. – RDACCADJ) compared to
17
the case of “as-if” full R&D expensing. OCF increases by RDCAPADJ. Both TACC and
LTACC increase by the amount of adjusted R&D amortization (RDAMORTADJ). The effects
of the adjustments on the data are presented in the following section 3.2.
In order to confirm Hypothesis H1a, we expect for the pricing model (PM), the financial
information based on “as-if” full R&D expensing (PMas-if
exp
) to contain lower market value
explanatory power compared to the financial information based on “as-if” full R&D
capitalization (PMas-if
cap
), i.e. the coefficients of determination of PMas-if
cap
> PMas-if
exp
. In
order to confirm Hypothesis H1b, we expect a significant regression coefficient for the adjusted
R&D accruals in the case of “as-if” full R&D capitalization (PMas-if cap). The coefficient of the
adjusted R&D accruals is expected to be negative in order to positively add explanatory power
to market value since investments are negatively defined. In an additional analysis, we
investigate the effect on the association of financial information with market value for the
adjusted R&D capitalization and R&D amortization separately and for the adjusted R&D asset.
The estimated regression coefficients for adjusted R&D asset and adjusted R&D accruals are
expected to be positive; the regression coefficient for adjusted R&D amortization is expected to
be negative. Significant results indicate that all components of financial information add
explanatory power to market value.
In a next step, we integrate the data based on actual R&D figures. We run an additional
regression based on the pricing model using financial information as reported (PMactual). Ex
ante we do not have an expectation about the direction, i.e. whether regarding pricing, “as-if”
financial information (PMas-if exp, PMas-if cap) dominates actual data (PMactual) or vice versa. If
financial information based on actual R&D capitalization shows higher association with market
value compared to financial information based on adjusted full R&D capitalization without
managerial discretion (“as-if” data), we find evidence for the signaling hypothesis in Germany
(H2a); i.e. PMactual > PMas-if cap. If however, financial information shows a higher association
18
with market value if all R&D expenditures are capitalized leaving out any discretion, we find
empirical support for H2b; i.e. the matching benefits of R&D capitalization would outweigh
the signaling benefits (PMactual < PMas-if
cap
). In this case, we also expect the estimated
regression coefficient of actual R&D accruals in PMactual not to be significant (H2c).
While the pricing of earnings components reflects one possibility to capture the
usefulness of accounting information from R&D capitalization, we further investigate the
impact on the forecasting process. We run the following regressions for our next set of
hypotheses related to information asymmetry (H3a) and forecast errors (H3b). First, we analyze
whether actual R&D capitalization under IFRS also reduces information asymmetry as shown
in prior research for SFAS 86 (Mohd, 2005) or not. We apply a model with bid-ask spreads as a
proxy for information asymmetry.
Information asymmetry (IA):
SPREADit   0  1d _ capit   2 SIZEit   3 LEVit   4VOLit   5TURNOVERit
  6 BETAit  
(5)
where the dependent variable, SPREADit, is the annual average of the logarithm of the
daily relative bid-ask spread, defined as the absolute value of the bid-ask spread divided by the
average of bid and ask as in (Mohd, 2005). The independent variables (with subscripts omitted)
include: a dummy variable that equals 1 if the firm capitalizes R&D in the year considered
(d_cap)8, the logarithm of market capitalization as of fiscal year-end (SIZE), a proxy for
financial risk measured by leverage (LEV), the logarithm of share volatility (VOL), the annual
average of the logarithm of daily turnover, defined as trading volume in shares divided by
8
Prior literature argues that the decision to capitalize R&D depends on specific determinants causing problems of
endogeneity (e.g. Oswald 2008). We control for endogeneity by running a two-stage-least-square regression as
will be outlined in the following.
19
number of shares outstanding (TURNOVER), and a proxy for systematic risk (BETA).9 We
further include year and industry dummies. If the coefficient of d_cap shows a negative sign,
R&D capitalization under IFRS reduces information asymmetry, consistent with H3a.
We further analyze the effect of R&D capitalization on analysts’ forecasts. Hence, in
order to test H3b, we run regression (6) similar to Aboody and Lev (1998) with forecast errors
as the dependent variable:
Forecast errors (FE)
FEit  0  1d _ capt   2 RISKit  3 IFRSit   4 LEVit  5MBit  6 PROFit
  7 SCH it   8 SIZEit  
(6)
where the dependent variable, FEit, is measured as the logarithm of absolute consensus
analyst forecast error, computed as the difference between actual earnings per share and
forecasted earnings per share by a financial analyst. d_cap is an indicator variable that equals 1
if a firm capitalizes R&D and 0 otherwise10; RISK is the logarithm of a firm-year’s 1year beta
based on HDAX; IFRS is the logarithm of the number of years a firm utilizes IFRS; LEV is a
firm-year’s logarithm of (total assets – book value of equity)/book value of equity); MB is the
logarithm of the market-to-book ratio; PROF is the logarithm of return on assets; SCH is
change in sales divided by lagged total assets; SIZE is the logarithm of a firm-year’s market
capitalization at the end of fiscal year.
In the presence of decreasing information asymmetry one would expect analysts’
forecast errors also to decrease. The discretion involved in IAS 38 however, may impede an
accurate forecasting process leading to higher forecast errors. This is in line with prior findings
by Aboody and Lev (1998) and consistent with our hypothesis H3b.
9
In our information asymmetry model we try to be consistent with Mohd (2005) for a major part regarding the use
of independent variables or control variables, respectively. Our control variables differ from hers due to our
specific German setting where e.g. leverage plays a more important role than in the US while listing is less
important because all our companies are listed on the German stock exchange.
10
As in regression (5) d_cap will be instrumented based on a number of determinants of R&D capitalization as
will be explained in the following.
20
3.2. Descriptive statistics
Our sample consists of the 152 largest German public firms listed in the H-DAX for the
years 2001 through 2008 comprising companies of the former DAX 100, nowadays represented
by the Prime Standard. We obtained market data from Thomson Datastream database and
Bloomberg as well as other information from the annual reports. Forecast information was
retrieved from I/B/E/S. Our initial sample contains companies reporting under German GAAP,
where the capitalization of R&D is prohibited, as well as companies reporting according to
IFRS with partial capitalization of development costs (IAS 38), and also companies according
to US-GAAP with partial capitalization of software development costs (SFAS 86).
We exclude 203 observations of firms that engage in financial and/or insurance services
(SIC Code 60-67) from the initial sample of 1,216 firm-year observations (152 companies from
2001-2008), due to their unique nature of accounting. We further only analyze companies with
R&D activities, i.e. we exclude all observations that do not show any R&D expenditures or
R&D investments, respectively, during the sample period (170 observations). We also exclude
6 observations of US-GAAP adopters who capitalize R&D according to SFAS 86 in order to
restrict our analysis to IAS 38. IAS 38.126 requires companies to disclose the aggregate
amount of R&D expenditures11 recognized as an expense during the period. We exclude 39
observations that did not provide any information on the amount of R&D expensed while only
providing the amount of R&D capitalized. Finally, we exclude 200 observations with missing
data over the sample period resulting in a final sample of 598 observations.
The final sample consist of 228 observations of actual capitalizers and 370 actual
expenser. The number of observations in the two adjusted samples (“as-if” full R&D expensers
and “as-if” full R&D capitalizers) equals the sum of actual capitalizers and actual expensers (N
= 598). Financial information is based on the same observations in all three samples. This
11
R&D expenditures represent R&D investments and are defined as: R&D capitalization recognized as an asset in
the balance sheet + R&D expenses as reported in the income statement (excluding R&D amortization).
21
allows us to directly compare differences in market value explanatory power between the
groups. Empirical research regularly measures market value explanatory power by the
coefficient of determination of a model (Barth et al., 2001a). For different samples it is
econometrically not correct to use R² and other information criteria statistics like the Akaike
(AIC) and Schwarz information criteria (SIC) as model selection criteria.12
3.2.1. Actual R&D accounting
The first three columns in Table 2 show market values and financial information on the
observations based on R&D accounting as reported. Consistent with literature, the values for
market value (MV), book value (BV), earnings (E), and operating cash flow (OCF) are positive.
All variables are scaled by lagged total assets. 370 observations of the remaining 598
observations in the sample that show R&D activities, immediately expense all R&D
expenditures (RDINV). 228 observations capitalize some R&D expenditures according to IAS
38. On average, these observations capitalize around 13% of their annual R&D expenditures
(not scaled). The earnings of actual R&D capitalizers are less volatile than of actual R&D
expensers (St. Dev. 0.082 vs. 0.134). This finding is consistent with prior literature that
managers smooth earnings by capitalizing R&D (Markarian et al. 2008). Actual capitalizers are
also smaller than actual expensers measured by MV (mean 1.067 vs. 1.220) and show both
significantly higher values of leverage and lower market-to-book ratios (not tabulated).
The negative sign of R&D accruals (RDACC) indicates that on average, companies in
our sample capitalize more than they amortize R&D during the year. R&D asset (RDA)
represents the net effect of cumulative R&D capitalization minus cumulative R&D
amortization that is shown on the balance sheet.
Besides R², the Akaike (AIC) and Schwarz information criteria (SIC) are further model selection criteria which
consider the idea of penalization when adding regressors. According to R² both information criteria are defined
based on residual sum of squares (RSS) with ln AIC = (2k/n) + ln(RSS/n) and ln SIC = k/n * ln n + ln (RSS/n),
with n observations and k regressors. SIC imposes even greater penalty when adding regressors compared to AIC,
see Gujarati (2003), pp. 536.
12
22
[Insert Table 2 about here]
3.2.2. Adjusted data for “as-if” R&D expensers and “as-if” R&D capitalizers
The last two columns of figures in Table 2 show descriptive statistics for the two
samples of “as-if” full R&D expensing and “as-if” full R&D capitalization. MV, WCACC,
OTHACC, and RDINV show the same value for all three samples of actual
capitalizers/expensers, “as-if” full expensers, and “as-if” full capitalizers. For all other
variables, adjustments have been made as described in section 3.1. in order to reflect the
different accounting for R&D and the corresponding effects on balance sheet, income
statement, and cash flow statement. E are higher in the case of adjusted full R&D capitalization
(0.049) compared to actual data (0.045) since all R&D expenditures are capitalized. The effect
can be directly observed in the R&D figures: for R&D capitalization RDCAP (0.049 > 0.003),
for R&D accruals RDACC (–0.005 > –0.001)13, and for R&D assets RDA (0.228 > 0.006). To
the contrary, in the case of “as-if” full R&D expensing, E are smaller since all R&D
expenditures are immediately expensed, reducing income (0.044).
The effect of R&D accounting on the balance sheet is observable in BV. In the case of
“as-if” full R&D expensing, BV is smaller than in the case of actual R&D accounting by the
amount of the actual R&D asset, since no asset has been recorded in prior years (0.434 <
0.439). In the case of “as-if” full R&D capitalization, BV based on “as-if” full R&D expensing
increases by the adjusted R&D asset (0.657).
OCF shows the lowest value in the case of full expensing (0.085) because the expensing
effect negatively affects both E and OCF by the same amount. TACC increases in the case of
R&D capitalization since the amortization effect reflected in RDAMORT represents an
additional accrual component (0.086 > 0.043 > 0.041). In particular, RDAMORT is a specific
LTACC like other depreciation or amortization on property, plant, equipment, or other
13
Since R&D accruals are negatively defined, more negative amounts of R&D accruals represent higher R&D
capitalization.
23
intangible assets. Thus, in the case of RDCAP (both as reported and adjusted for full
capitalization), the same increasing effect that can be observed in TACC is true for LTACC
(0.106 > 0.064 > 0.062) also.
4. Empirical results
We use panel data referring to 152 firms observed in 8 different time periods (20012008). We use panel OLS regression with entity (firm) fixed effects to eliminate omitted
variable bias generated by variables such as reporting behavior or managerial discretion, which
may be correlated with various accruals. We use the Hausman specification test to justify the
fixed effects model and reject the random effects model. The results of the Hausman test (not
tabulated) show significant p-values for all samples and models. The results indicate that the
random effects model is not appropriate and suggest applying the fixed effects model including
firm fixed effects.14 The results for the pricing model based on both “as-if” data and
observable, actual data are discussed first, followed by the results regarding information
asymmetry and analysts’ forecasts.
4.1. Tests on “as-if” data only
The first two columns in Panel A of Table 3 show the results for the pricing model
(PM) based on disaggregated earnings for “as-if” expensers (PMas-if exp) and “as-if” capitalizers
(PMas-if cap). The two models are statistically significant at a p-level better than 0.01 and their R²
within values are high (60.5 and 63.1), indicating strong explanatory power of our models.15
The variance inflation factors (VIF) around 1.00 show no sign of collinearity among the
independent variables. Except for longterm accruals (LTACC), all the regression coefficients
Random effects result in better p-values as they are a more efficient estimator. They occur when some omitted
variables are constant over time but vary between firms while others are fixed between firms but vary over time.
The Hausman test compares a more efficient model (random effects model) against a less efficient but consistent
model (fixed effects model) to validate that the more efficient model gives consistent results. The underlying null
hypothesis implies that the estimators of both the fixed and the random effects model do not differ substantially.
Therefore it is only safe to use the random fixed effects model if the null hypothesis is not rejected. For a detailed
discussion on the Hausman specification test, see Baltagi (2005), pp. 66-74.
15
R² within is used for fixed effects models to examine their explanatory power, see Baltagi (2005), pp. 12.
14
24
are statistically significant (p < 0.01).
[Insert Table 3 about here]
Consistent with Hypothesis 1a, market value explanatory power of book value and
earnings components for “as-if” R&D capitalizers is greater than for “as-if” R&D expensers
(PMas-if
cap
: R² within = 63.1% > PMas-if
exp
: R² within = 60.5%). The corresponding model
selection criteria AIC (1,073.85 vs. 1,112.40) and SIC (1,135.37 vs. 1,169.51) show the
opposite direction and confirm these findings. In comparing two models, the model with the
lower value of AIC or SIC, respectively, is preferred. The Vuong’s (1989) Z-Statistic of 2.75
suggests that the R² within for PMas-if cap is significantly higher than the R² within for PMas-if exp
(p<0.01).16 The results indicate that financial information reflecting “as-if” full R&D
capitalization shows a higher association with market value compared to “as-if” full R&D
expensing.
Assuming full capitalization of all R&D expenditures therefore leads to greater market
value explanatory power than expensing such costs immediately. This finding is in line with
the expected benefits of accrual accounting (Dechow 1994; Penman and Yehuda 2009). The
full capitalization of R&D results in a better matching of costs and revenues (Dichev and Tang
2008). Earnings and earnings components that are adjusted by the accrual process of R&D
capitalization better explain market value than financial information that is based on the cash
information of R&D, only. The findings allow us to confirm Hypothesis (1a).17
Further, our empirical results also allow us to confirm Hypothesis (1b). The second
column in Panel A of Table 3 shows that adjusted R&D accruals add explanatory power to
market value. The estimated regression coefficient for “as-if” RDACC is statistically significant
16
For a detailed discussion of the Vuong test, see Dechow (1994), Appendix 2, pp. 37-40.
Untabulated results show that also for aggregated versions of earnings and total accruals, our inferences hold.
These results are also consistent with prior literature on the benefits of disaggregating earnings (e. g. Barth et al.,
2005). For each sample, the coefficients of determination and the model selection criteria show higher market
value explanatory power for the model with earnings disaggregated into major accruals including R&D accruals.
17
25
(–4.289; p<0.01) with the expected negative sign. Recall from section 3.2.3 that investments
are negatively defined. Additional analysis (not tabulated) shows that the adjusted R&D asset is
also significant (p<0.01) with the expected positive sign. We also separately analyse “as-if”
R&D capitalization and R&D amortization and find a significant and positive regression
coefficient for R&D capitalization, only (not tabulated). Thus, the significantly negative net
effect of R&D capitalization and R&D amortization on market value, reflected in R&D
accruals, is dominated by the capitalization of R&D expenditures.
4.2. Tests on “as-if” data and observable, actual data
When investigating actual R&D capitalization, we follow a triangular approach and
strengthen our analyses by taking an information asymmetry approach with bid-ask spreads and
forecast errors in addition to our tests on market pricing.
4.2.1. Pricing model
The last column of Table 3 Panel A shows the regression results for the pricing model
(PM) using observable, actual R&D data (PMactual). Since the observations comprise
discretionary capitalizers and discretionary expensers, the number of observations is 598, also.
We can make comparisons across the samples regarding market value explanatory power
because they are all based on the same firm-years. The results suggest that “as-if” full
expensing and actual R&D capitalization show similar market value explanatory power
(Vuong’s Z-Statistic –0.69, p>0.10). This result differs from Ahmed and Falk (2006) who find
lower market value explanatory power for the group of “as-if” expensers compared to
discretionary capitalizers and expensers supporting the signaling hypothesis. Beyond the
samples in Ahmed and Falk (actual expensers, actual capitalizers, “as-if” expensers), our study
also investigates a group of “as-if” mandatory capitalizers. In the previous section, we
compared these “as-if” full capitalizers with “as-if” full expensers, similar to the US-studies
from prior literature. In this section, we compare the group of “as-if” capitalizers with a sample
26
based on actual R&D accounting. This allows us to analyze the accounting choice
symmetrically, similar to Oswald (2008) but without admitting any discretion concerning the
R&D capitalization. We provide evidence that “as-if” full capitalization dominates
discretionary actual R&D capitalization. The R² within for PMactual (60.4%) is significantly
smaller than for PMas-if
cap
(Vuong’s Z-Statistic 2.73, p<0.01) and the corresponding model
selection criteria AIC (1,115.07) and SIC (1,176.58) for PMactual are higher than for PMas-if cap.
Our findings suggest that actual R&D capitalization does not dominate “as-if” R&D
accounting and the signaling hypothesis (2a) is not supported. Further, the analysis of the
estimated regression coefficient of the observable R&D accruals RDACC shows no significant
results (1.213, p>0.10). This provides evidence that opposed to “as-if” R&D accruals the
market does not price actual R&D accruals as reported.18 The findings allow us to confirm H2b
and H2c. The evidence is consistent with the explanation that the market disclaims the
managerial discretion involved in IAS 38 and does not trust actual capitalization.
In order to confirm these assumptions, we run additional analyses in order to investigate
the different groups of real expensers and real capitalizers. We are interested in the differences
in perception of market participants towards capitalizers and expensers when pricing their
R&D. We include a dummy variable d_cap in our model that indicates whether a company
belongs to the group of capitalizers or not.19 The independent variables are interacted with
d_cap in order to investigate the different effects of financial information for capitalizers and
expensers on market value. We run the following regression based on the pricing model using
“as-if” full capitalization data (7):
PMas-if cap including d_cap:
MVit   0   1 BVit   2OCF'it   3WCACCit   4 OTHACCit   5 LTACC'it
18
Actual R&D accruals are also not priced by the market if we run the regression for the group of real capitalizers
only (N=228, not tabulated). By excluding the group of real expensers, the R² within increases up to 89.28% and
the regression coefficient of RDACC is also insignificant (-2.723; p>0.10).
19
d_cap equals one if firm i capitalizes R&D in year t, otherwise zero.
27
  6 RDACCit  7 d _ capit   8 BVit * d _ capit   9 OCF'it * d _ capit
  10WCACCit * d _ capit   11OTHACCit * d _ capit   12 LTACC'it * d _ capit
  13 RDACCit * d _ capit   i  
(7)
The left column of Table 3 Panel B shows the results for regression (7). Interacting
d_cap with the variables of PMas-if cap allows us to investigate the differences in market value
explanatory power of financial information for capitalizers versus expensers. Particularly, we
are able to analyze whether the market prices the “as-if” R&D accruals differently for
capitalizers and expensers. For expensers, the estimated regression coefficient β6 of “as-if”
R&D accruals without the interaction is significantly negative (–3.663, p<0.01), whereas for
the capitalizers, the estimated regression coefficient β13 of the interaction of d_cap with the
adjusted R&D accruals is not significant (–0.300, p>0.10). The latter result shows that there is
no incremental information of R&D accruals for the group of capitalizers. This suggests that
the market prices “as-if” R&D accruals independent of the actual R&D accounting method
since β13 is not significant. In other words, regarding the pricing of R&D expenditures, the
market seems not to distinguish between real capitalizers and real expensers. In section 4.3 we
analyze this result thoroughly based on additional tests.
The decision to capitalize and market values are likely influenced by identical
determinants, possibly biasing our results. We therefore need to control for this endogenity.
Oswald (2008) shows that the decision to capitalize R&D, reflected in our dummy variable
d_cap, is dependent on several firm characteristics like e.g. firm size, earnings sign, or
leverage. We apply a two-stage-least-squares (2SLS) approach and estimate d_cap in the first
stage. Based on prior literature, we assume that the capitalization decision d_cap depends on
RDINT (R&D intensity = R&D expenditures/total sales), PROF (profitability = return on
assets), MB (growth = market-to-book ratio), LEV (leverage = (total assets – book value of
equity)/book value of equity), LOSS (equal to one if earnings for firm i in year t is negative,
28
zero otherwise), BETA (systematic risk measured by 1 year beta based on HDAX), Y_IFRS
(number of years a company applies IFRS), LAG_CAP (prior year’s capitalization ratio), SIZE
(firm size measured by market capitalization), EVAR (earnings variance of a firm over 20012008), NEG_GROWTH (average change in R&D activities <–0.07), and POS_GROWTH
(average change in R&D activities >0.07).20 PROF, MB, LEV, and LOSS are adjusted to before
R&D capitalization-figures. BETA, Y_IFRS, SIZE, EVAR are based on firm i’s percentile
ranking within each firm’s industry. In order to test the endogeneity between d_cap and the
determinants, we run the Durbin-Wu-Hausman test based on the following assumption:
d _ capit  0  1 RDINTit   2 PROFit   3 MBit   4 LEVit   5 LOSSit  6 BETAit
 7Y _ IFRSit   8 LAG _ CAPit   9 SIZEit   10 EVARit   11 NEG _ GROWTH it
  12 POS _ GROWTH it  
(8)
Consequently, we rerun regression (7) using the 2SLS approach taking into account the
above determinants. In the first stage, d_cap is estimated based on the determinants (being
instrumented). The resulting estimate of d_cap is then used in the second stage to rerun
regression (7). We expect to confirm our prior OLS results when controlling for endogeneity
(Larcker and Rusticus 2010).
The prior results are confirmed when we control for endogeneity as shown in the right
column of Table 3 Panel B. Since we do not have the information on the determinants for all
598 observations, the sample size is reduced to 550. The estimated regression coefficient β6 of
“as-if” R&D accruals without the interaction for the group of expensers is still significant and
negative (–9.049, p<0.01), whereas the estimated regression coefficient β13 of the interaction of
the estimated d_cap with the adjusted R&D accruals is not (1.124, p>0.10). When we control
To our knowledge, the influence of the change in R&D expenditures (NEG_GROWTH and POS_GROWTH) on
the capitalization decision has not yet been investigated. We argue that a firm’s cycle stage concerning R&D
activities significantly influences the R&D capitalization and amortization of a firm. A detailed analysis on the
variables is provided in section 4.4.
20
29
for endogeneity, the results still suggest that the market prices “as-if” R&D accruals not
distinguishing between capitalizers and expensers
Table 3 Panel C presents the results of the Durbin-Wu-Hausman test. Consistent with
regression (8), we investigate twelve different determinants to be endogenous. The evidence
suggests that endogeneity exists regarding PROF (0.647; p<0.01), MB (–0.009, p<0.10), BETA
(0.001; p<0.05), LAG_CAP (0.415; p<0.01), SIZE (–0.002; p<0.05), EVAR (0.000; p<0.05),
and POS_GROWTH (–0.054; p<0.05). We do not find significant results for RDINT, LEV,
LOSS, Y_IFRS, and NEG_GROWTH. The model is highly significant and shows high
explanatory power (F value 69.09; Adj. R² 78.80%). The test on endogeneity suggests that OLS
is not consistent and that endogeneity exists (F value for test d_cap residuals: 11.08).
4.2.2. Information asymmetry model
Table 4 presents the findings for the information asymmetry model as in regression (5).
Consistent with our findings in the prior section, the regression coefficient of the instrumented
binary variable d_cap is not significant (0.010, p>0.10). The evidence does not support the
hypothesis that R&D capitalization is associated with lower information asymmetry proxied by
bid-ask spreads. The findings remain valid when replacing d_cap by the capitalized amount of
R&D as reported (deflated by lagged total assets). Further untabulated analyses show that on
average the bid-ask spread is higher for capitalizing firm-years (0.329) compared to expensing
firm-years (0.239).21 For our control variables in the information asymmetry model, VOL,
SIZE, and TURNOVER show significant coefficients consistent with prior literature (e.g. Mohd,
2005). Larger firms tend to face lower information asymmetry as well as firms with higher
share turnover and lower share volatility. These findings suggest that R&D capitalization
according to IAS 38 does associated with lower information asymmetries. Hence, our empirical
The results include controls for endogeneity using a two-stage least square regression since d_cap or the
decision to capitalize or not may be influenced by several determinants. We use instruments consistent with prior
research (e.g. Mohd, 2005 and Oswald, 2006) as will be outlined in detail in section 4.2.2.
21
30
findings do not support hypothesis H3a.
[Insert Table 4 about here]
4.2.3. Forecast errors
Table 5 presents the findings for regression (6) with forecast errors as the dependent
variable. In line with prior research in the US-setting and the capitalization of software
development costs (e.g. Aboody and Lev, 1998), R&D capitalization under IAS 38 increases
analysts’ forecast errors. This is consistent with our hypothesis H3b. The positive regression
coefficient of d_cap (0.549) is significant at the 0.05 level. Note that consistent with prior
analyses, d_cap is instrumented according to regression (8). We also find similar results when
d_cap is replaced by the actual amount of R&D capitalized deflated by lagged total assets as in
the regression for information asymmetry. We acknowledge that the number of observations
decreases down to 361 due to missing data. However the distribution of capitalizers (152 firmyears) and expensers (209 firm-years) remains constant. We also rerun regression (6) including
the logarithm of the number of analysts following a firm as an explanatory variable consistent
with prior research. The results remain unchanged. Due to further missing data concerning
analysts’ following we tabulate the findings excluding this independent variable. Overall, the
findings of the impact of R&D capitalization on forecast errors are consistent with our prior
results on the market pricing and information asymmetry: the market does not price actual
R&D capitalization and information asymmetry does not decrease when R&D expenditures are
capitalized under IAS 38. To the contrary, capitalization impedes an accurate forecasting
process and forecast errors increase.
4.3 Additional analyses
In the following, we further analyze the above result that the market prices adjusted “asif” capitalization, but not actual capitalization. This result is in line with Tutticci et al. (2007)
finding that the market prices expensed R&D, putting higher weight on it than on capitalized
31
R&D. Our results suggest that the market uses techniques consistent with full R&D
capitalization independent of the actual accounting. This is consistent with the explanation that
market participants consider R&D expenditures as investments and not as expenses and
therefore base their valuation on adjusted earnings numbers. This suggests that market
participants base their value estimates and forecasts on R&D expenditures rather than on
expensed or capitalized amounts.
In the following, we consider a simple valuation model based on earnings multiples. We
expect that market values are associated with current earnings before R&D and R&D
expenditures separately. We analyze the difference between valuing expensers and capitalizers
by the following regression model:
MVit  0  1E _ BEFOREit  2 RDINVit  3d _ capit  4 E _ BEFOREit * d _ capit
  5 RDINVit * d _ capit  
(9)
where the dependent variable, is market value of equity (number of shares * share price
at the end of fiscal year). The independent variables include: earnings before R&D
expenditures (E_BEFORE)22 and R&D expenditures (RDINV) for the current period. For the
regression with market value as the dependent variable, d_cap is a dummy variable that is
being instrumented within the two-stage least square approach through the same variables as in
section 4.2 to control for endogeneity effects. D_cap equals 1 if firm i capitalizes R&D in
period t, zero otherwise. The results are presented in Panel A of Table 6. The findings indicate
that market values are based on earnings before R&D and an additional value component
derived from R&D expenditures. E_BEFORE shows a significant and positive regression
coefficient (4.780) just like RDINV (5.634). For capitalizers, the coefficient on earnings
E_BEFORE = earnings as reported + R&D expenditures + R&D accruals, i.e. if R&D expenditures had been
expensed, the amount is added back to earnings and if R&D expenditures had been capitalized and amortized, the
effects in earnings are also eliminated. E_BEFORE represents earnings before any R&D accounting, which allows
the separate analysis of R&D expenditures (RDINV).
22
32
including the interaction is significantly higher (4.780 + 3.856 = 8.636), but the additional
value component of R&D expenditures is reduced and partly offset (5.633 – 5.136 = 0.497).
This evidence suggests that the market does evaluate expensers and capitalizers differently, but
does not derive its value estimates based on capitalized amounts but based on R&D outlays.
The market applying a specific multiple to R&D expenditures, rather than capitalized R&D,
explains why market values are consistent with full R&D capitalization, rather than capitalized
R&D. The regression results were repeated for various definitions of earnings23 and with
different controls for firm characteristics but remained valid.
[Insert Table 6 about here]
We further investigate whether analysts use R&D expenditures, rather than actual R&D
capitalization, to derive their forecasts for future earnings. We run two regressions of earninsg
forecasts on current earnings and R&D expenditures or R&D capitalization. We adjust current
earnings and earnings forecasts to exclude R&D expenditures (E_BEFORE). Regression (10)
uses the actual amount of R&D capitalized as an explanatory variable for forecasted future
earnings, while regression (11) uses R&D expenditures:
Forecasting model (FM):
E _ BEFOREforecast,it 1  0  1 E _ BEFOREit   2 RDCAPit   i  
(10)
E _ BEFOREforecast,it 1  0  1 E _ BEFOREit   2 RDINVit   i  
(11)
where the dependent variable, E_BEFOREforecast is the analysts’ consensus forecast
about future earnings before R&D expenditures one year ahead. The independent variables
include current earnings before R&D expenditures (E_BEFORE) and the amount of R&D
expenditures capitalized according to IAS 38 (RDCAP). We replace RDCAP by RDINV in
regression (11). Consistent with our primary results we expect that analysts do not use the
23
like earnings adjusted for full R&D capitalization and earnings as reported.
33
actual amount of capitalized R&D but the whole amount of R&D expenditures for their
forecasts. Table 6 Panel B provides the empirical findings.
The results are consistent with our prior findings about market pricing. For regression
(10) the coefficient β2 of RDCAP is not significant indicating that analysts’ forecasted earnings
are not associated with the amount of R&D capitalization as reported.24 To the contrary, the
regression coefficient of RDINV is highly statistically significant at the 0.01 level and positive
(0.420). This finding is visualized in the following scatter plots of forecasted earnings (the
dependent variable in regression (10) and (11)) and a) actual R&D capitalization (regression
(10)) or b) R&D expenditures (regression (11)), respectively.
Figure 1 Scatter plots
forecasted earnings / lagged total assets (1 year ahead)
-.5
0
.5
1
a) Forecasted earnings (1 year ahead) and capitalized R&D as reported (both variables scaled by lagged
total assets)
0
.02
.04
actual R&D capitalization / lagged total assets
24
.06
This result is consistent with Aboody and Lev (1998) who find higher value relevance for the capitalization of
software development costs under US-GAAP but at the same time evidence for higher analysts’ forecast errors.
34
forecasted earnings / lagged total assets (1 year ahead)
-.5
0
.5
1
b) Forecasted earnings (1 year ahead) and R&D expenditures (both variables scaled by lagged total
assets)
0
.02
.04
R&D expenditures / lagged total assets
.06
Scatter plot a) shows a very diffuse distribution indicating no association between
earnings forecasted by analysts and actual R&D capitalization. To the contrary, scatter plot b)
clearly depicts a much closer relationship between forecasted earnings and R&D expenditures.
In summary, our findings suggest that market participants seem to be wary of actual
R&D capitalization and do not use actual R&D capitalization for making forecasts. However,
they seem to apply adjustment procedures similar to full matching to arrive at their value
estimates. The discretion involved in IAS 38 leading to partially capitalized R&D expenditures
may induce noise into the information of accounting figures. In particular, analysts have to
make forecasts about the capitalization ratio, the expected useful life, and possible
impairments. The adjusted figures for “as-if” full R&D capitalizing with all R&D expenditures
being capitalized take away the possible discretion. “As-if” full capitalization does not involve
any discretion similar to full expensing. In addition, recall that our findings show that
discretionary partial R&D capitalization is just as informative for market value explanatory
power as a simple R&D accounting rule like full R&D expensing (coefficient of determination
of pricing models PMactual = PMas-if exp).
35
4.4. Sensitivity analyses
We conduct several sensitivity analyses to confirm our findings. We use alternative
deflators for lagged total assets like total assets at the end of fiscal year, average total assets,
lagged market value, and lagged total sales (e. g. Ahmed and Falk 2006) without a substantial
effect on our result. The same is true when we use market value plus 3 months after the balance
sheet date instead of market value at the end of fiscal year as the dependent variable in our
regressions. We run all regressions including control variables for industry, GAAP, leverage,
loss firm-years, general growth (measured by market to book ratio), and growth in R&D
activities. The control variables were used in all regressions but left from the tables for reasons
of reading convenience. Table 7 shows the estimated regression coefficients of the control
variables for PMas-if
cap
and PMactual that are exemplary for all other regressions: MB, LEV,
POS_GROWTH, and LOSS are all statistically significant (p<0.01). MB, POS_GROWTH, and
LOSS show a positive sign and LEV shows a negative sign, consistent with prior literature.
GAAP and industry are not significant (not tabulated).
[Insert Table 7 about here]
For the control variable growth in R&D activities, we examine the average change in
R&D expenditures for each firm during the sample period. This allows us to classify three
different groups of R&D status: firms in steady-state, firms with negative growth, and firms
with positive growth regarding R&D expenditures. We argue that the changes in R&D
expenditures are indicators for a firm’s R&D capitalization and amortization behavior. Firms in
steady-state are expected to capitalize and amortize almost the same amount of R&D per year;
i.e. for such companies R&D accruals should have less strong effect on market value. Firms
with decreasing R&D activities will most likely even annually amortize more than they
capitalize. To the contrary, firms with high positive growth of R&D expenditures are expected
to capitalize significantly more than they amortize during the sample period. Therefore in such
36
cases, R&D accruals are expected to have the strongest effect on market value.
The results presented in the paper are based on the interval [–0.07; 0.07], i.e. firms in
steady state have an average change in R&D expenditures during the sample period of [–0.07;
0.07], firms with positive growth show an average increase of R&D expenditures >0.07 and
firms with negative growth show an average decrease of R&D expenditures <–0.07. Table 7
Panel A provides descriptive statistics on the analysis.
[Insert Table 8 about here]
After deducting 28 firms that belong to the financial industry and 20 firms with no
R&D activities (i.e. R&D expenditures = 0) during the sample period, 104 companies of 152
remain that can be classified into the three groups. 47 of those are classified as being in steadystate concerning their R&D program since the average change in R&D expenditures during the
sample period hardly changed for them. 21 firms are classified as firms with negative growth of
R&D expenditures and 36 firms with positive growth.
Table 8 Panel B provides the results of t-tests to show significant differences regarding
R&D accruals between the groups of different R&D status. Firms with positive growth show
the lowest value of adjusted R&D accruals (–0.002). This indicates that such firms capitalize
more R&D than they amortize during the year opposed to the rest of firms. For firms in steadystate and for firms with negative growth, the amortization effect equals the capitalization effect
and R&D accruals are zero, on average (0.000). The difference of R&D accruals between firms
with increasing R&D activities and firms with decreasing R&D activities is significant
(p<0.01). The same is true for the difference of R&D accruals between firms with increasing
R&D activities and firms in steady state (p<0.01). Therefore, we include positive growth in
R&D expenditures as a control variable in all regressions. As outlined above, the estimated
regression coefficient for POS_GROWTH is significantly positive in all regressions and
therefore adds explanatory power to market value. The findings remain valid when using
37
alternative intervals for the change in R&D activities like [–0.05;0.05] and [–0.10;0.10].
5. Concluding remarks
This paper analyzes the possible benefits of R&D capitalization under IFRS. To our
knowledge, we are the first to analyze the application of IAS 38. This question is controversial
because prior studies have found conflicting results based on adjusted “as-if” capitalized data
as opposed to actual, observable data. In particular, the actual capitalization - as opposed to
adjusted data - involves discretion which may be beneficial when used to signal private
information, but also detrimental when used for earnings management. In this paper, we
analyze this trade-off by comparing the matching benefits of full R&D capitalization to the
signaling benefits of discretionary, partial R&D capitalization. Our triangular approach
including information asymmetry, market pricing, and analysts’ forecasts shows that the
hypothetical benefits from R&D capitalization do not materialize in practical application.
Market participants seem to be wary of earnings management and sceptical about actual R&D
capitalization. We establish that, in the conservative German environment, where market
participants are wary of earnings management and skeptical of R&D capitalization, the accrual
and matching aspects of R&D capitalization dominate signaling effects of discretionary R&D
accounting. Our results may translate to other environments with a similar conservative history
for R&D accounting, like the US.
We contribute to recent research postulating the benefits of matching (Dichev and Tang
2008; Penman and Yehuda 2009). While poor matching such as R&D expensing results in
lower explanatory power for market values, full R&D capitalization seems to best reflect the
market pricing and forecasting process. Discretionary, partial R&D capitalization according to
IAS 38 induces noise and therefore does not reduce information asymmetry but even increases
analysts’ forecast errors. Likewise the market does not price the resulting R&D accrual
component.
38
Also, we apply an innovative accrual approach to R&D accounting, based on a
comprehensive earnings decomposition, taking the full array of accruals into consideration.
Consistent with Hribar and Collins (2002), who establish that severe errors are introduced by
the balance sheet approach, we use hand-collected accrual information from the cash flow
statement. This allows us to capture the complete set of differences between earnings and cash
flows that have been neglected in prior studies.
We acknowledge a number of limitations in our study: the specific and detailed data
necessary for this study regarding R&D and accrual figures constrain our sample in terms of
firm numbers and periods investigated. Also, we can only proxy a firm’s life cycle stage
regarding R&D projects via the change in R&D expenditures as used in the sensitivity
analyses. Yet, it does not allow us to infer that specific R&D projects show a higher association
with market value than others.
Our study opens several avenues for future research. While Ahmed and Falk (2006)
show that managerial discretion in R&D accounting enhances value relevance, our findings
suggest that the opposite is true. Yet, in the German setting it is not mandatory expensing that
dominates but rather “mandatory capitalization”. Due to the inherent uncertainty of future
economic benefits related to research costs such an accounting method is less likely. However,
our findings show that a complex accounting rule of partial discretionary R&D capitalization as
in the case of IAS 38 is not desirable for qualitative financial information. Further, Ahmed and
Falk (2006) fear that the adoption of IFRS in Australia will be detrimental for the value
relevance of financial information regarding R&D accounting since managers do not have the
discretion as in the Australian GAAP environment. Our findings suggest that the discretion in
IAS 38 might be the reason for a decreasing association between financial information and
market value compared to “mandatory capitalization”. They also confirm that actual R&D
capitalization is not related to analysts’ forecasted earnings while R&D expenditures are. An
39
interesting research question would be to analyze how the Australian market reacted on the
introduction of AASB 138, the Australian equivalent to IAS 38, in 2005 and to contrast it with
our German findings. Since historically, Germany and Australia represent the two opposite
ends of the spectrum on how to account for R&D, it will be curious to see whether IAS 38 is
applied in the same way and also received by the market as such.
40
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Table 1 Adjustments to “as-if” full expensers and “as-if” full capitalizers
actual data
“as-if” full expenser
BV
---
– RDA
E
---
+ RDACC
OCF
---
– RDCAP
TACC
---
– RDAMORT
WCACC
---
LTACC
---
OTHACC
---
RDACC
---
--– RDAMORT
“as-if” full capitalizer
– RDA
+ RDAADJ
+ RDACC
– RDACCADJ
– RDCAP
+ RDCAPADJ
– RDAMORT
+ RDAMORTADJ
--– RDAMORT
+ RDAMORTADJ
--– RDACC
– RDACC
+ RDACCADJ
Where: BV is book value; E is earnings; OCF is operating cash flow; TACC is total accruals; WCACC is
working capital accruals; LTACC is long term accruals; OTHACC is other accruals; RDACC is R&D accruals.
The content of the cells describes the adjustments to the financial information that is necessary to reflect three
different settings of R&D accounting: actual R&D capitalization; adjusted full R&D expensing; and adjusted
full R&D capitalization. RDA is R&D asset as reported (cumulative R&D capitalization – cumulative R&D
amortization); RDACC is R&D accruals (RDAMORT – RDCAP); RDCAP is the annual R&D capitalization;
RDAMORT is the annual R&D amortization incl. write-offs; RDAADJ is adjusted R&D asset, i.e. for the first
year: mean of RDCAP over sample period * 5years; beginning from the second year: RDAADJ is RDAADJ prior
year + RDCAPADJ – RDAMORTADJ; RDACCADJ is adjusted R&D accruals (RDAMORTADJ – RDCAPADJ);
RDCAPADJ is RDINV (full capitalization of R&D); RDAMORTADJ is amortization rate*RDAADJ
(amortization rate 15-25% according to industry).
44
Table 2 Descriptive statistics
Actual expensersa
Actual capitalizersa
Adjusted full expensersb
Adjusted full capitalizersc
N = 370
N = 228
Actual expensers
+ actual capitalizersa
N = 598
N = 598
N = 598
Mean
Median
St. Dev.
MV
1.220
0.697
1.373
1.067
0.546
1.823
1.162
0.633
1.561
1.162
0.633
1.561
1.162
0.633
1.561
Mean
Median
St. Dev.
BV
0.459
0.401
0.429
0.407
0.299
0.418
0.439
0.358
0.425
0.434
0.355
0.426
0.657
0.491
0.575
Mean
Median
St. Dev.
E
0.043
0.044
0.134
0.050
0.040
0.082
0.045
0.042
0.117
0.044
0.041
0.117
0.049
0.044
0.117
Mean
Median
St. Dev.
OCF
0.095
0.088
0.117
0.077
0.080
0.108
0.088
0.086
0.114
0.085
0.083
0.114
0.135
0.121
0.116
Mean
Median
St. Dev.
TACC
0.052
0.047
0.119
0.028
0.037
0.103
0.043
0.044
0.114
0.041
0.043
0.114
0.086
0.080
0.131
Mean
Median
St. Dev.
WCACC
–0.008
–0.006
0.054
–0.025
–0.006
0.101
–0.015
–0.006
0.076
–0.015
–0.006
0.076
–0.015
–0.006
0.076
Mean
Median
St. Dev.
OTHACC
–0.008
–0.002
0.082
–0.004
0.000
0.038
–0.006
–0.001
0.068
–0.006
–0.001
0.068
–0.006
–0.001
0.068
Mean
Median
St. Dev.
LTACC
0.068
0.053
0.076
0.057
0.053
0.048
0.064
0.053
0.067
0.062
0.052
0.067
0.106
0.086
0.092
Mean
Median
St. Dev.
RDINV
0.055
0.031
0.074
0.041
0.029
0.044
0.049
0.030
0.065
0.049
0.030
0.065
0.049
0.030
0.065
Mean
Median
St. Dev.
RDCAP
na
0.007
0.004
0.011
0.003
0.000
0.008
na
0.049
0.030
0.065
Item
45
Table 2 (continued)
Actual expensersa
Actual capitalizersa
Actual expensers
+ actual capitalizersa
Adjusted full expensersb
Adjusted full capitalizersc
Mean
Median
St. Dev.
RDAMORT
na
0.004
0.001
0.007
0.001
0.000
0.005
na
0.045
0.026
0.063
Mean
Median
St. Dev.
RDACC
na
–0.003
–0.001
0.010
–0.001
0.000
0.006
na
–0.005
–0.001
0.032
Mean
Median
St. Dev.
RDA
na
0.017
0.006
0.000
0.017
na
0.228
0.133
0.308
0.008
0.024
na is not available
a
Market value and financial information based on R&D accounting as reported,
where: MV is market value measured by market capitalization; BV is book value excl. E at fiscal year end; E is earnings (net operating income after all operating and
non-operating income and expense, reserves, income taxes, and extraordinary items); OCF is operating cash flow; TACC is total accruals; WCACC is working capital;
LTACC is long term accruals; OTHACC is other accruals = TACC – WCACC – LTACC; RDINV is total R&D investments or expenditures, respectively, as reported
(incl. R&D capitalization and R&D expensing); R&D capitalization RDCAP, R&D amortization/write-off RDAMORT, R&D accruals RDACC, R&D asset RDA
(cumulative RDCAP – cumulative RDAMORT) not available in the case of R&D expensing.
b
Market value and adjusted data for financial information based on “as-if” full R&D expensing,
where the following adjustments have been made: BV – RDA; E + RDACC; OCF – RDCAP; TACC – RDAMORT; LTACC – RDAMORT; MV, WCACC, OTHACC,
RDINV as defined above; RDCAP, RDAMORT, RDACC, RDA not available in the case of full R&D expensing.
c
Market value and adjusted data for financial information based on “as-if” full R&D capitalization,
where the following adjustments have been made: BV – RDA + RDAADJ; E + RDACC – RDACCADJ; OCF – RDCAP + RDCAPADJ; TACC – RDAMORT +
RDAMORTADJ; LTACC – RDAMORT + RDAMORTADJ; RDCAPADJ is RDINV (full capitalization of R&D); RDAMORTADJ is amortization rate*RDAADJ
(amortization rate 15-25% according to industry); RDAADJ is for the first year: mean of RDCAP over sample period * 1/industry-specific amortization rate; beginning
from the second year: RDAADJ is RDAADJ prior year + RDCAPADJ – RDAMORTADJ; MV, WCACC, OTHACC, RDINV as defined above.
46
Table 3 R&D capitalization and the pricing of earnings components
Panel A: Panel regression estimates (and their significance level) for the pricing model (PM) based on
as-if exp
as-if cap
“as-if” R&D expensing (PM
), “as-if” full R&D capitalization (PM
), and actual data
actual
(PM
)
MVit = β0 + β1BVit + β2OCF’it + β3WCACCit + β4OTHACCit + β5LTACC’it + β6RDACCit + αi + ε
PMas-if exp
PMas-if cap
PMactual
N = 598
–2.092
(0.003)
N = 598
–2.317
(0.001)
N = 598
–2.094
(0.003)
BV
0.828
(0.000)
0.850
(0.000)
0.825
(0.000)
OCF’
4.316
(0.000)
5.273
(0.000)
4.307
(0.000)
WCACC
–5.665
(0.000)
–6.580
(0.000)
–5.659
(0.010)
OTHACC
–1.170
(0.010)
–1.510
(0.001)
–1.168
(0.001)
LTACC’
0.751
(0.433)
–0.287
(0.761)
0.765
(0.425)
–4.370
(0.000)
1.213
(0.830)
63.1
60.4
Constant
RDACC
R² within (%)
Vuong's Z-statistic
AIC
SIC
F value
Model's significance
Highest VIF
60.5
as-if exp vs. as-if cap
as-if cap vs. actual
as-if exp vs. actual
2.76
(0.006)
1,112.40
1,169.51
62.07
0.000
1.43
2.74
(0.006)
1,073.77
1,135.28
63.83
0.000
1.49
–0.69
(0.489 )
1,115.07
1,176.58
57.07
0.000
1.43
Where MV is market value of equity; for the sample of "as-if" full R&D expensing: book value = BV – RDA;
operating cash flow = OCF – RDCAP; WCACC is working capital accruals; OTHACC is other accruals; long
term accruals = LTACC – RDAMORT; for the sample based on "as-if" full R&D capitalization: book value = BV
– RDA + RDAADJ; adjusted R&D accruals RDACC = RDAMORTADJ – RDCAPADJ (RDAMORTADJ is
adjusted R&D amortization, RDCAPADJ is adjusted R&D capitalization that equals R&D expenditures
RDINV); for the sample based on actual R&D capitalization: BV is book value as reported; RDACC is R&D
accruals as reported, i.e. RDAMORT – RDCAP (RDAMORT is actual R&D amortization, RDCAP is actual R&D
capitalization); all variables are scaled by lagged total assets.
47
Panel B: Panel regression and 2SLS regression estimates (and their significance level) for PMb,
“as-if” full R&D capitalization data
PMb
PMb
"as-if" full capitalizers
"as-if" full capitalizers
dependent variable
MV
MV
(d_cap as reported)
(d_cap instrumented)
N = 598
–1.237
(0.045)
N = 550
0.239
(0.201)
BV
0.409
(0.000)
1.033
(0.000)
OCF’
2.532
(0.000)
5.762
(0.000)
WCACC
–4.360
(0.000)
–6.814
(0.000)
OTHACC
–1.326
(0.001)
–1.620
(0.013)
LTACC’
–0.608
(0.572)
–5.944
(0.000)
RDACC
–3.663
(0.003)
–9.049
(0.000)
d_cap
–1.110
(0.000)
–1.023
(0.013)
BV*d_cap
1.138
(0.000)
0.833
(0.003)
OCF’*d_cap
4.324
(0.000)
1.208
(0.437)
WCACC*d_cap
–2.023
(0.063)
–0.265
(0.881)
OTHACC*d_cap
3.343
(0.023)
–5.577
(0.014)
LTACC’*d_cap
1.718
(0.201)
5.661
(0.003)
RDACC*d_cap
–0.300
(0.884)
1.124
(0.697)
71.20
59.19
0.000
48.06
38.52
0.000
Constant
R² within/ adj. R²
F value
Model's significance
Where: MV is market value of equity; for the sample based on "as-if" full R&D capitalization: book value = BV –
RDA + RDAADJ; adjusted R&D accruals RDACC = RDAMORTADJ – RDCAPADJ (RDAMORTADJ is adjusted
R&D amortization, RDCAPADJ adjusted R&D capitalization that equals R&D expenditures RDINV); d_cap is a
dummy variable equal to 1 if firm i capitalizes R&D in period t, zero otherwise. d_cap is as reported in the left
column and is being instrumented in the right column; all variables are scaled by lagged total assets.
48
Panel C: Regression estimates (and their significance level) of the Durbin-Wu-Hausman (DWH) test
for determinants of d_cap
N = 550
Constant
0.243
(0.000)
RDINT
–0.001
(0.764)
ROAa
0.647
(0.001)
MBa
–0.009
(0.093)
LEVa
0.002
(0.682)
LOSSa
–0.012
(0.735)
BETAb
0.001
(0.048)
Y_IFRSb
0.017
(0.141)
LAG_CAP
0.415
(0.000)
SIZEb
–0.002
(0.014)
EVARb
0.000
(0.048)
NEG_GROWTH
–0.028
(0.367)
POS_GROWTH
–0.054
(0.023)
Adj. R² (%)
F value
Model's significance
Test on endogeneity:
F value (test d_cap residuals)
Adj. R² (%)
78.80
69.09
0.000
11.08
(0.000)
b
adjusted before R&D captialization
firm i's percentile ranking within a firm's industry
With RDINT (R&D intensity = R&D expenditures/total sales); PROF (profitability = return on assets); MB
(growth = market-to-book ratio); LEV (leverage = (total assets – book value of equity)/book value of equity);
LOSS (1 if earnings for firm i in year t is negative, 0 otherwise); BETA (systematic risk measured by 1year beta
based on HDAX); Y_IFRS (number of years a company applies IFRS); LAG_CAP (prior year’s capitalization
ratio); SIZE (firm size measured by market capitalization); EVAR (earnings variance of a firm over 2001-2008);
NEG_GROWTH and POS_GROWTH (average change in R&D activities <–0.07 and >0.07).
Note that Table 4 Panel B only presents regression estimates for the variables that act as determinants of d_cap
(bold if significant at least at the 0.10 level). The regression also includes the variables of the original model
(M3b incl. d_cap) in order to run the DWH test. The results are not tabulated due to reading convenience.
a
49
Table 4 Impact of R&D capitalization on information asymmetry
SPREADit = β0 + β1d_capt + β2RISK it + β3SHARE_VOL it + β4SIZE it + β5LEVit +β6TURNOVERit + ε
Regression estimates (and their significance level)
N = 496
–0.233
(0.244)
Constant
d_cap
0.010
(0.714)
RISK
–0.017
(0.285)
VOL
0.166
(0.000)
SIZE
–0.085
(0.000)
LEV
0.006
(0.624)
TURNOVER
–0.073
(0.000)
Wald Chi²
Prob > Chi²
Adjusted R²
1,632.74
0.000
76.72
The dependent variable SPREAD is measured as the annual average of the logarithm of the daily relative bid-ask
spread, defined as the absolute value of the bid-ask spread divided by the average of the bid and ask. d_cap is an
indicator variable that equals 1 if a firm capitalizes R&D and 0 otherwise and has been instrumented as in Panel C
of Table 3 (using the value of actual R&D capitalized deflated by lagged total assets results in similar findings);
RISK is the logarithm of a firm-year’s 1year beta based on HDAX; VOL is the logarithm of a firm-year’s share
volatility; SIZE is a firm-year’s market capitalization at the end of fiscal year; LEV is a firm-year’s logarithm of
(total assets – book value of equity)/book value of equity); TURNOVER is the annual average of the logarithm of
daily stock price of a firm-year.
50
Table 5 R&D capitalization and forecast errors
FEit = β0 + β1d_capt + β2RISK it + β3IFRSit + β4LEVit + β5MBit +β6PROFit +β7SCHit +β8SIZEit + ε
Regression estimates (and their significance level)
N = 361
Constant
d_cap
0.549
(0.022)
RISK
0.392
(0.003)
IFRS
0.006
(0.841)
LEV
0.102
(0.415)
MB
–0.770
(0.000)
PROF
0.748
(0.000)
SCH
0.137
(0.077)
SIZE
0.063
(0.431)
Wald Chi²
Prob > Chi²
Adjusted R²
127.41
0.000
25.74
The dependent variable FE is measured as the logarithm of absolute consensus analyst forecast error, computed as
the difference between actual earnings per share and forecasted earnings per share by a financial analyst. d_cap is
an indicator variable that equals 1 if a firm capitalizes R&D and 0 otherwise and has been instrumented as in
Panel C of Table 3 (using the value of actual R&D capitalized deflated by lagged total assets results in similar
findings); RISK is the logarithm of a firm-year’s 1year beta based on HDAX; IFRS is the logarithm of the number
of years a firm utilizes IFRS; LEV is a firm-year’s logarithm of (total assets – book value of equity)/book value of
equity); MB is the logarithm of the market-to-book ratio; PROF is the logarithm of return on assets; SCH is
change in sales divided by lagged total assets; SIZE is the logarithm of a firm-year’s market capitalization at the
end of fiscal year.
51
Table 6 Additional analyses
Panel A: Market assessment of R&D expenditures (RDINV) including d_cap (1/0) controlling for
endogeneity effects.
MVit = β0 + β1E_BEFOREit + β2RDINVit + β3d_capit + β4E_BEFOREit*d_capit + β5RDINVit*d_capit
+ε
2SLS regression estimates (and their significance level)
Constant
E_BEFOREt
RDINVt
Adj. R² (%)
F value
Model's significance
N = 550
0.443
(0.000)
4.780
(0.000)
5.634
(0.000)
d_capt
E_BEFOREt*d_capt
RDINVt*d_capt
–0.227
(0.427)
3.856
(0.006)
–5.136
(0.041)
32.36
53.25
0.000
Where: MV is market value of equity; E_BEFORE is earnings before R&D expenditures, i.e. E + RDINV +
RDACC; RDINV is R&D expenditures; d_cap is a dummy variable that equals 1 if firm i capitalizes R&D in
period t, zero otherwise with d_cap being instrumented within the two-stage least square approach in the
regression with MV as the dependent variable (instrumental variables as in Table 3 Panel B to control for
endogeneity effects); all variables are scaled by lagged total assets.
Panel B: R&D capitalization and analysts’ forecasts
E_BEFOREforecast,it+1 = β0 + β1E_BEFOREit + β2RDCAPit + αi + ε
E_BEFOREforecast,it+1 = β0 + β1E_BEFOREit + β2RDINVit + αi + ε
Panel regression estimates (and their significance level) regarding the assessment of partially capitalized R&D
expenditures and R&D expenditures overall by analysts for forecasting earnings.
Constant
E_BEFORE
RDCAP
N = 465
0.065
(0.308)
0.312
(0.000)
N = 465
0.038
(0.547)
0.361
(0.000)
–0.967
(0.227)
RDINV
R² within (%)
F value
Model's significance
0.420
(0.000)
20.27
10.08
0.000
17.62
8.48
0.000
Where: E_BEFOREforecast is consensus of analysts’ earnings forecasts before R&D expenditures, i.e.
forecasted earnings + RDINV + RDACC; E_BEFORE is earnings before R&D expenditures; RDCAP is
capitalized R&D as reported; RDINV is R&D expenditures; all variables are scaled by lagged total assets.
52
Table 7 Panel regression estimates for the control variables in M3b and M3c.
PMas-if cap
N = 598
0.360
(0.000)
PMactual
N = 598
0.374
(0.000)
LEVa
–0.159
(0.000)
–0.171
(0.000)
POS_GROWTH
4.313
(0.000)
4.403
(0.000)
LOSSa
0.305
(0.009)
0.246
(0.040)
MBa
a
adjusted before R&D capitalization
Where: MB (growth = market-to-book ratio); LEV (leverage = (total assets – book value of equity)/book value of
equity); POS_GROWTH equals 1 if average change of R&D expenditures of a firm during the sample period
>0.07; LOSS (1 if earnings for firm i in year t is negative, 0 otherwise).
Table 8 Additional analyses differentiating R&D growth versus R&D steady-state firms
Panel A: Clustering results for groups of firms with differences in changes in R&D activities.
No. of
average change in R&D expenditures
firms
during the sample period (2001-2008)
mean
median
min
max
initial sample
152
financial institutions
28
no R&D activitiesa
20
firms in steady-state: average change
47
in R&D expenditures [–0.07; 0.07]
firms with negative growth: average
change in R&D expenditures <–0.07
21
firms with positive growth: average
change in R&D expenditures >0.07
36
a
0.11
0.04
–0.39
3.54
R&D expenditures = 0 during the sample period
Panel B: Average actual R&D accruals, per group (scaled by lagged total assets).
mean actual R&D accruals
test of change R&D differences
between groups
(p-values)
(1) steady-statea
(2) positive growthb
(3) negative
growthc
0.000
–0.002
0.000
(1) vs. (2)
(0.000)
(2) vs. (3)
(0.003)
(1) vs. (3)
(0.484)
a
firms with change in R&D expenditures during the sample period [–0.07;0.07]
firms with change in R&D expenditures during the sample period >0.07
c
firms with change in R&D expenditures during the sample period <–0.07
b
53