Does Non-Discretionary Conservatism Really Exist in Japan?
－Evidence and Implications from Japanese Listed Companies－

1. Introduction
Conservatism is among the most important characteristics guiding accounting practice.
However, researchers continue to discuss the merits of conservatism in accounting
practices.

Lawrence, Sloan, and Sun [2013] (LSS hereafter) attribute the rise of conservative
accounting, especially the recognition of impairment loss, to the regulatory force ofgenerally accepted accounting principles (GAAP hereafter) (i.e., non-discretionary
conservatism). On the other hand, LSS also found that asset write-downs are
peculiarly prone to managerial discretion around a book-to-market (BTM hereafter)
ratio of one (i.e., discretionary conservatism). It would be impracticable and almost
impossible for outside parties, including auditors and scholars, to observe all theprocedures performed by management. Consequently, it is possible that management
increases their wealth at the expense of shareholders by exercising discretionary
accounting without violating related accounting standards. However, LSS emphasizedthat discretionary conservatism arising from the subjectivity of accounting standards
would be eliminated by non- discretionary conservatism and the empirical result is
consistent with their expectations.
Despite some differences, Japanese accounting standards also incorporate a two-step
approach which requires an impairment test before recognizing an impairment loss on
the financial statement. Considering the high similarity between the two standards,
there is good reason to expect that a similar correlation may exist between
non-discretionary conservatism and the current-period write-downs.
This study presents evidence from Japanese companies that reveals the characteristics
of conservatism in Japanese companies and is summarized as follows.
Empirical evidence in this study shows that the implementation of asset write-downs
fail to reconcile with the demands for conservatism even when strongly warranted bycircumstances. In other words, unlike American companies, non-discretionary
conservatism does not seem to be found in Japanese listed companies, which is the mostprofound difference between this study and that of LSS. Instead, results in this paper
confirm that debt contracting, rather than non-discretionary conservatism, is thedriving force for asset write-downs in Japanese listed companies. The discrepancy
between the USA and Japan is likely due to earnings management and/or the differencein their accounting regimes.
The findings of this study also contribute to the recent debate on the role ofunconditional conservatism in financial reporting. Even though Financial AccountingStandards Board (FASB hereafter) and International Accounting Standards Board

1

(IASB) exhibit strong inclinations to purge conservatism from accounting’s conceptual
framework,1 empirical evidence suggests that conservatism is useful because it reduces,
rather than increases, information asymmetry between inside managers and outside
investors (e.g., LaFond and Watts [2008]). Previous studies also suggest that greater
unconditional conservatism (e.g., immediate expensing of intangible assets such as
internally developed R&D or excessive depreciation) would reduce subsequentconditional conservatism (e.g., lower of cost or market accounting for inventory andimpairment write-downs), because a lower book value of assets results in less assets tobe written down later (e.g., Beaver and Ryan [2005], Basu [2005]). Furthermore, thefindings of this study reveal that when legal enforcement fails to control reporting
incentives and accounting discretion, unconditional conservatism plays a fundamentalrole in the properties of accounting numbers.
In addition to the classical regression method adopted in LSS, this study provides
evidence for non-linearity between beginning-of-period ASSET-BTM and asset
write-downs by employing the quantile regression (QR) (e.g., Koenker and Bassett[1978]) and the Adaptive LASSO regularized Quantile Regression (LASSO: Least
Absolute Shrinkage Selection Operator) (e.g. Wu and Liu [2009], Fan, Fan, and Barut[2014]). The subjects of financial accounting research are always under the influence ofa variety of factors, QR test and QR-LASSO may provide the needed solution to solve
conflicting interpretations and divergent opinions documented in previous studies. I
choose the QR test as an alternative solution to conventional linear regression because
the conditional distribution of beginning-of-period ASSET-BTM and current-period
asset write-downs may fail to fulfill the basic assumption of homoscedasticity.2 The
results of the QR test confirmed the presence of a buffer zone. On the other hand,
QR-LASSO analysis can detect predictors with the strongest influence on asset
write-downs at any quantile. It further confirms that beginning-of-period ASSET-BTM
is not the driving force for asset write-downs in Japanese listed companies. Additional
details concerning QR and QR-LASSO are provided in Appendix 1.
Although the findings of this study agree with statistical results, this study

1

FASB responded to questions on the role of conservatism as follows:“Financial information needs to be neutral – free from bias intended to influence a decision oroutcome. To that end, the common conceptual framework should not include conservatism or prudence
among the desirable qualitative characteristics of accounting information. However, the framework
should note the continuing need to be careful in the face of uncertainty.”
This statement explicitly expresses the concerns of FASB that the existence of conservatism will leadto more information asymmetry.
2 Homoscedasticity is also referred to as homogeneity of variance. It describes a situation in which
dependent variable exhibits similar variance across all values of independent variables. The
assumption of homoscedasticity is central to linear regression models.

2

nonetheless possesses the following limitations. First, all the samples are divided at
equal intervals to test the features of each group. Such an artificial subdivision lowers
the comparability between groups. However, it is almost impossible to completely
remove arbitrariness regardless of the division. Moreover, securing a sufficient numberof observations in each group will be difficult if they are broken down at much smaller
intervals.
Second, this study adopted the same aggregated measure of ASSET-BTM as that
adopted by LSS. The numerator (book value of total assets) is underestimated whenthose assets are recorded on the balance sheet – an effect attributable to the adoption of
unconditional conservatism. In addition, assets subject to other impairment accounting
standards, such as software, are included in the equation. Thus, even if the
beginning-of-period ASSET-BTM is less than one, impairment accounting procedures
for assets other than fixed assets may have been taken according to GAAP. Anotherconcern is that an asset (group) with a beginning-of-period ASSET-BTM higher than
one could be overlooked due to the principle of materiality. As a result, thecurrent-period actual impairment loss caused by fixed assets would be lower than the
expectations. In other words, the adoption of ASSET-BTM might potentially bias theoutcome.
Third, there are still conflicting arguments about the validity about the validity of the“Asymmetric Timeliness coefficient” developed in Basu [1997] (Basu coefficienthereafter). Dietrich, Muller, and Riedl [2007] and Patatoukas and Thomas [2011]
assert that Basu coefficient should be avoided because the problem of endogeneity andscale effect. On the other hand, Ball, Kothari, and Nikolaev [2013a] disputed theprevious findings on the basis of variable definition. Furthermore, Ball, Kothari, andNikolaev [2013b] confirmed that the overall accuracy of Basu coefficient can besubstantially improved by controlling firm characteristics like size, BTM ratio andleverage. Given that disagreement remains about the validity of some measures forconditional conservatism, I applied 𝑇_𝑆𝐶𝑂𝑅𝐸 (e.g.,Khan and Watts [2009]), theasymmetrically timely recognition of gains and losses using accruals and cash flows
from operation (e.g.,Ball and Shivakumar [2006]) to verify the robustness of thefindings in this paper.
The remainder of this study is organized as follows. Section 2 overviews some previous
studies. Section 3 explains the hypotheses. Section 4 describes the research design ofthis study. Section 5 presents the statistics. Section 6 summarizes additional discussionof the test results and concludes the study.

3

2. Previous Research
The approach to understanding conservatism has undergone a dramatic change overthe past three decades due to the efforts to empirically quantitate conservatism. One of
the most pioneering studies to model conservatism can be attributed to that of Basu
[1997]. He defined conservatism as a tendency for bad news to be dealt with in atimelier manner than good news (i.e., accelerate the recognition of loss and defer therecognition of gains) and developed the Basu coefficient for measuring earnings
conservatism. In other words, the Basu coefficient captures the different timelinesswith which bad and good news are reported in contemporaneous earnings. Among theavailable empirical methods for evaluating earnings conservatism, Basu’s [1997]
framework (Basu model hereafter) has become dominant in the literature.
Unconditional conservatism is primarily described as understating the book value ofnet asset relative to its market value on the balance sheet (i.e., recognition of cost is
enforced before real depreciation is observed in asset value) (e.g., Watts [2003a]).
Beaver and Ryan [2000] employed the measurement of BTM ratio to examine andexplain the nature of unconditional conservatism. They defined conservatism as asystematic and persistent bias in the recognition of income and regressed the BTM ratio
on lagged returns to filter out the temporary or transitory effects due to other economic
factors.
Another line of thought (e.g., Beaver and Ryan [2005], Pae, Thornton, and Welker[2005]) has attempted to bridge these two conceptual frameworks and examine how thetwo forms of conservatism interact with each other.
Conservatism

canbe further divided

into conditional

conservatism 3 andunconditional conservatism4 (e.g., Beaver and Ryan [2005]). Previous studies (e.g., Pae,
Thornton, and Welker [2005], Roychowdhury and Watts [2007]) have indicated that thetwo forms of conservatism are negatively connected as the higher the degree of
unconditional conservatism that is followed at the beginning of a fiscal year, the lower
the degree of conditional conservatism will be during the year. Although the Basu
coefficient is expected to positively correlate with the BTM ratio (e.g., Beaver and Ryan
[2005]), some studies have presented empirical evidence that contradicts thisexpectation, thereby questioning the validity of the Basu coefficient (e.g., Givoly and3Conditional conservatism is also referred to as earnings conservatism (e.g., LaFond and Watts[2008], Khan and Watts [2009]); ex post conservatism (e.g., Pope and Walker [2003]); or
news-dependent conservatism (e.g., Chandra, Wasley, and Waymire [2004]) in the accounting
literature.
4 Unconditional conservatism is also referred to as stock conservatism (e.g., LaFond and Watts [2008],
Khan and Watts [2009]); ex ante conservatism (e.g., Pope and Walker [2003]); or news-independent
conservatism (e.g., Chandra, Wasley, and Waymire [2004]) in the accounting literature.

4

Hayn [2007], Dietrich, Muller, and Riedl [2007]). Roychowdhury and Watts [2007] notedthat the annual estimate of the Basu coefficient is affected by firms’ failure to record
asset write-downs because previous asset value increases were not recorded due toconservatism (the “buffer” problem), and higher asymmetric response to bad news vsgood news would eventually generate lower BTM ratio over longer horizons. LSS adds
to this line of thought by offering an alternative explanation from the perspective of
non-discretionary conservatism that indicates the necessity of controlling the BTM
when measuring the Basu coefficient. In particular, they documented a non-monotonic
and exceptionally positive relation between asset write-downs and the BTM ratio inregion with beginning-of-period BTM ratio greater than one led by the enforcement of
GAAP. In this regard, the results of this study are consistent with that of LSS giventhat the degree of conditional conservatism observed in samples with highbeginning-of-period ASSET-BTM is greatly superior to that in those with low
beginning-of-period ASSET-BTM.
LSS also integrates the concept of conservatism to relate to the study on earnings
management. They found that the incentives to follow accounting standards are
positively associated with penalties under enforcement mechanisms. They asserted that
although the subjectivity inherent in the accounting standards gives rise to discretional
activities, GAAP plays an important role in facilitating contracting efficiency bydeterring management from engaging in further discretional activities. As the first
follow-up

study

of

LSS,

Roychowdhury

andMartin[2013]

characterized

non-discretionary conservatism as “normal conservatism”. They indicated thatreporting opportunism arising from contractual factors not only has a great impact on
discretionary conservatism, but also weighs against non- discretionary conservatism as
it can mold the form of “normality”.
LSS employed ASSET-BTM as the proxy of non-discretionary conservatism5 which
5

Under US accounting standards, the ratio of an asset’s carrying value and its fair value (BTM) is
used as a threshold of impairment proceedings. LSS used ASSET-BTM due to the inaccessibility of theunderlying asset’s fair value.
“In order to model non-discretionary conservatism, we need measures of both the book values and thefair values of firms’ assets. The book values are readily available, but the fair values are more difficult
to obtain. We address this issue by focusing on the aggregate book values and fair values of firms’
assets, where fair values are estimated by summing the market value of common equity and the bookvalues of liabilities. We then model aggregate asset write-downs as a function of the ratio of theaggregate book value to the aggregate market value of a firm's assets (BTM).” (LSS, p. 116)
Under Japanese accounting standards, the book value and the recoverable amount (net realizablevalue or value in use) of the underlying asset (group) are indispensable in determining whether anasset (group) should be written down. However, while the book value of the fixed asset can be easily
identified, the information that management employed to estimate the recoverable amount (i.e.,
discount rate, future cash flows) is almost impossible to obtain. This constraint in turn affects the5denotes the required asset write down.

ASSET-BTM ＝

total assets
market capitalization+ total assets− common equity

Roychowdhury and Watts [2007] indicated that conditionally conservative accounting
is positively related to beginning-of-period BTM but not necessarily to the end-of-period
BTM. Thus, a higher beginning-of-period BTM than the previous period may suggestthat the value of certain assets might have declined. Because the end-of-period bookvalue is measured after taking such write-downs, this study followed LSS and used the
beginning-of-period BTM as the proxy of non-discretionary conservatism.
Oler [2014] investigated the impact of certain FASB standards (SFAS 87, 106, 121, 142,
and 123R6) on accounting conservatism (that lower the probability of firms having a
BTM ratio greater than one). He found that SFAS 121, as well as SFAS 123R, caused
decreases in conservatism, which he attributed to the greater flexibility in SFAS 121 on
the timing of an impairment. Ramanna and Watts [2012] focused on theimplementation of SFAS 142, which also solely depends on management estimates of
goodwill’s fair value to determine these write-downs, and indicated that goodwill
write-downs are subject to motives predicted by agency theory such as CEO
compensation and debt-covenant. The results of this study are closer to that of Oler[2014] and Ramanna and Watts [2012] in that certain accounting standards, howeverstrictly enforced, may not correspond to the expectations of regulators to improve theusefulness of accounting information.
FASB has consistently attempted to marginalize conservatism, reflecting the line orreasoning which holds that understatement of net assets and cumulative profits
generated by conservatism presumably interferes in the decision-making process offinancial statements users. Movement, actions such as capitalization of development
cost, apparently abhorrent to the principle of conservatism has become prevalent in therealm of accounting regulations. In other words, the need for neutrality or relevance
prevails over the need for a defense against uncertainty.

accuracy of the computing result. Considering the above limitation, it can be assumed that theaggregate measure ASSET-BTM can also be applied to the investigation of Japanese companies.
6 SFAS 87: Employers’ Accounting for Pensions;
SFAS 106: Employers’ Accounting for Postretirement Benefits Other Than Pension;
SFAS 121: Accounting for the Impairment of Long-Lived Assets and for Long-Lived Assets to Be
Disposed Of;
SFAS 142: Goodwill and Other Intangible Assets; andSFAS 123R: Share Based Payments

6However, chances are that management prioritizes their own interests over those ofother stakeholders, maintaining unconditional conservatism in accounting practices
will offset such managerial opportunism. When book value of assets is kept sufficiently
low from the beginning or is not capitalized at all, managers will find few assets subject
to accounting discretion if the economic environment changes adversely. Therefore, thisstudy focuses on the effect of accounting discretion on the implementation of
impairment accounting standards.
To the best of my knowledge, there still lacks of study on non-discretionary
conservatism in Japan. In this work, I try to fill this void. In addition, when impairment
accounting is performed, more often than not, the losses run into considerable sums.
These significant declines in earnings will be matched by high volatility in the capital
markets. Thus, an understanding of how accounting standards affect impairment
accounting is of great interests not only to researchers, but also of essential importanceto standard setters, shareholders, and lenders. This is the primary motivation for thisstudy.

3. Hypotheses
The first question I address is whether impairment accounting standards allow for
discretional management in Japanese companies. Beaver and Ryan [2005] suggested
that the BTM ratio primarily reflects the extent to which unconditional conservatism
forestalls the application of conditionally conservative accounting. Thus, thebeginning-of-period BTM is expected to be positively associated with asset write-downs,
hereby proxies for conditional conservatism. Although the two-step procedures are
expected to serve to prevent the abusive use of impairment (big bath), as well as reduce
operational burdens, the abstract recognition criteria involving management
subjectivity judgment gives rise to the concern that it will eventually spur the demands
for discretional accounting choices (e.g., Bartov, Lindahl, and Ricks [1998], Elliott and
Hanna [1996], Rees, Gill, and Gore [1996]). A large body of research has indicated thatmanagement has strong incentives to adapt accounting standards in ways thatmaximize their own benefits (e.g., Dechow, Hutton, and Sloan [1999]). LSS stressedthat the closer the beginning-of-period ASSET-BTM approaches one, the more likely itis that discretionary conservatism will be triggered. In accordance with LSS, I also
predicted that a discontinuity will exist between the correlation of beginning-of-period
ASSET-BTM and current-period write-downs. This leads to my first hypothesis.

7H1: The relation between beginning-of-period ASSET-BTM and current-period asset
write-downs is positive and nonlinear.
Figure 1 demonstrates the correlation between current-period write-downs on fixed
assets (𝑊𝐷 plotted on the vertical axis) and beginning-of-period ASSET-BTM (𝐴𝐵𝑇𝑀
plotted on the horizontal axis), using past performance figures of Japanese listedcompanies from fiscal year 2005 to 2014. Figure 1 shows that, as 𝐴𝐵𝑇𝑀 approaches one,the gentle curve formed from the two indicators undergoes a dramatic rise and 𝑊𝐷
peaks just after 𝐴𝐵𝑇𝑀 exceeds one. Thus far, the relation between 𝑊𝐷 and 𝐴𝐵𝑇𝑀
conforms to that reported by LSS. In other words, when 𝐴𝐵𝑇𝑀 is sufficiently low, there
is little need to activate conditional conservatism. However, as 𝐴𝐵𝑇𝑀 moves toward
one, the deterrent effect of unconditional conservatism begins to recede. In accordancewith LSS, I also predicted that a discontinuity will exist between the correlation of
beginning-of-period ASSET-BTM and current-period write-downs.

2
0

1

WD

3

4

Figure 1:

-2

0

2

4

ABTM

Note:
Figure 1 demonstrates the correlation between current-period asset write-downs (𝑊𝐷) and beginning-of-period
ASSET-BTM (𝐴𝐵𝑇𝑀). Data was standardized for further tests.

However, distribution of asset write-downs seems to deviate from LSS’s prediction, as
the total impairment loss seems more likely to decline when the beginning-of-period
ASSET-BTM moves beyond one.
One of the explanations for the deviation comes from the institutional distinctions
between the two accounting regimes. Particularly, U.S. accounting rules prohibit
expensing goodwill unless it is deemed impaired. In addition, impairment losses

8recognized under ASC 360-10 cannot be allocated to goodwill and other non-amortizing
intangible assets, even if those assets are included in the asset groups being tested forrecoverability. 7 In contrast, Japanese listed companies must amortize goodwill
regularly over a period not exceeding 20 years. Because amortized goodwill is recorded
as selling, general, and administrative expenses attributable to operating income,extensive amortization of goodwill could squeeze the reported financial performance foryears. Proponents of goodwill amortization stress that purchased goodwill should bereflected on income statements before any deterioration in real earning capacity isobserved. They argue that goodwill acquired by M&As is non-durable and graduallyreplaced internally-developed goodwill. It is a reasonable assumption that internallydeveloped synergies need effort to achieve and time to accumulate. Thus, Japanesecompanies are reluctant to record additional asset impairment losses when they believe
an asset (group) is overly depreciated.
The other source of the differences between Japan and the U.S., which were identified
in the implementation of asset impairment accounting standards, is reporting
incentives. Shaped by capital market forces and institutional factors, financial
reporting incentives strongly influence financial reporting because the application of
accounting standards involves discretion and judgement (e.g., Watts and Zimmerman[1986], Ball, Robin, and Wu [2003]). Riedl [2004] also suggested that earnings
management related to impairment loss tends to rise because of the intrinsic nature of
its accounting setting. Therefore, opportunistic behavior in financial reporting bymanagement will not be completely held back as a result of market imperfections,
namely information asymmetry and agency conflicts. Consequently, I state my second
hypothesis in its null form and expect it would be rejected based on the previous
discussion.
H2: The positive relation between the beginning-of-period ASSET-BTM andcurrent-period

asset

write-downs

isstrongerforcompanies

whose

beginning-of-period ASSET-BTM is greater than one.

4 Research Design
4.1 Grouping
First,

allsampleswillbedivided

into

7

eight

groupsaccording

to

their

LSS also identified non-discretionary items in the implementation process for goodwill under ASC
350. Applying the same implementation process to goodwill may have altered the size of the bufferzone but have little influence on non-discretionary items.

9beginning-of-period ASSET-BTM at intervals of 0.2.8 All samples would initially beseparated using beginning-of-period ASSET-BTM of one as a dividing line. Then, they
will further be divided into smaller groups based on the same interval of 0.2. The eightgroups are arranged in an ascending order of beginning-of-period ASSET-BTM. I
expected companies belonging to Group 1 would show the least need for asset
write-downs, whereas companies belonging to Group 8 would show the opposite
tendency. Groups 5 and 6 are the cohorts of most interest as they reflect samples whose
beginning-of-period ASSET-BTMs are just less or greater than one.

4.2 Test for non-linearity
4.2.1 the Adaptive LASSO regularized Quantile Regression (QR-LASSO)
In an attempt to refine the Basu model, LSS assumes that management’s commitment
to accounting conservatism primarily derives from regulatory enforcement compared toother contractual imperatives (i.e., debt contracting) (Watts [2003a]). However, asizable literature in conservatism empirically supports the assertion that thecontracting theory links debt structure and conservatism (Aier, Chen, and Pevzner[2014], Nikolaev [2010], Khan and Watts [2009], Beatty, Weber, and Yu [2008], Zhang[2008], Ball, Robin, and Sadka [2008]), since debt-holders’ fixed financial claims on
earnings render them the first-order demander for accounting conservatism (e.g., Guay
[2006], Roychowdhury and Martin [2013]). On the other hand, LSS also contrasts withagency theory, which characterizes a large body of research in earnings management,that management has ex post incentives to choose aggressive accounting overconservatism.
The issue then arises as to what factors prevent/facilitate departures fromconservatism. I apply the Adaptive-LASSO penalized quantile regression (QR-LASSO)(e.g., Wu and Liu [2009]) to address the problem above.
High dimensional data are commonly encountered in accounting studies. However, alarge number of predictor variables make it difﬁcult to interpret the model and might
decrease its predictive ability. A smaller set of predictors with the strongest effects not
only increases prediction accuracy but also boosts a better understanding of how each
8

The entire sample is divided into eight groups based on their rank of beginning-of-period
ASSET-BTM. However this approach is only effective when the companies belonging to the samegroup have a similar degree of conservatism throughout the entire observation period. In general, thebeginning-of-period ASSET-BTM is affected by the accounting procedures executed at the end of theprevious fiscal year. For example, if a significant impairment procedure has been performed at the endof the previous fiscal year in a company belonging to a high-ranking group, there is a strong possibility
that such a company’s beginning-of-period ASSET-BTM would drop sharply. Therefore, test results
obtained from the companies belonging to low-ranking groups might have been distorted.

10

predictor influences the response variable. QR-LASSO is an integrative approach tovariable selection which can screen out irrelevant noise for variables affecting asset
write-downs by pushing the less significant coefficients to zero and in the meantime
evaluate effects of predictor variables on asset write-downs at any quantile. In
QR-LASSO, the regularization term is set to be a constant value beneath the weighted
𝐿1 norm in the OLS solution.
Other than ASSET-BTM (𝐴𝐵𝑇𝑀), I examined the following variables, which have been
deemed influential in shaping accounting choices for asset impairment and/or
conservatism. The variables are firm size (𝑆𝐼𝑍𝐸 ), debt contracting ( 𝐿𝐸𝑉𝑀𝑉 ), andproportion of goodwill to total assets (𝐺𝑊 ).
Firm size (𝑆𝐼𝑍𝐸) is commonly referred to as the market value of a company’s equity.
Given the four drivers of conservatism advanced in Watts [2003a],9 larger companies
are more motivated to reduce regulatory cost and litigation risk arising from
non-compliance with accounting regulations and corporation law. However, larger firms
also enjoy comparative advantages of highly diversified business models and deeplyinterdependent management structure over smaller firms. Such flexible businessenvironment allows for management to exercise discretion, making it more difficult foroutside parties to retrieve information on true economic performance.
Debt covenants are viewed as the one of the primary inputs for conservatism (Watts[2003a]). Earlier studies document robust empirical relations between debt covenants
and timely loss recognition. However, debt-contracting incentives might evoke
aggressive accounting policies (e.g., LSS, Nikolaev [2010]). Therefore, the question of
how tradeoffs affect implementation of asset impairment by listed companies in Japan
becomes empirical in nature.
𝐿𝐸𝑉𝑀𝑉𝑡−1 represents debt contracting in this study, measured as the ratio of total
liabilities10 to market value of common equity (e.g., Beaver and Ryan [2000]). Goodwill
primarily consists of future economic benefits and synergies in existing operations. As
stated in Section 3, I added the variable for goodwill (𝐺𝑊) because it indicates one of thedifferences existing in the two accounting systems.
Viewed in general terms, companies with a high BTM ratio are more likely going
9

Watts [2003a] hypothesized four resources of conservatism, which are contracting, shareholder
litigation, taxation, and regulation.
10 Instead of market value of total liabilities, I used book value of total liabilities to compute market
value leverage (𝐿𝐸𝑉𝑀𝑉). Long-term liability such as corporate bonds is required to be recognized atamortized cost under Japanese accounting standards. Other liabilities are also required either to bemarked to market (i.e., bank loans) or to be recorded extremely close to their market values (i.e.,
reserve for retirement allowance). Furthermore, book value of liability is customarily used as proxyfor market value in studies of corporate finance (i.e., the calculation of corporation value). In summary,book value of liability is an appropriate substitute for its market value.

11through financial distress and, therefore, are in even greater need of financial support.
(e.g., Jung, Kim, and Stulz [1996], Smith and Watts [1992]). It is possible that a rise in
the amount of equity and/or debt will alter the management’s accounting choices. I
employed two variables indicating two dominant sources of external financing, 𝐷𝐸𝐵𝑇𝑡−1
and 𝐸𝑄𝑈𝐼𝑇𝑌𝑡−1, to denote proceeds from debt issuance and sale of common stock in year
t－1 (deflated by market capitalization), respectively.
Instead of controlling for industry, I added the proportion of property, plant andequipment assets to total assets in year t － 1 ( 𝑃𝑃𝐸𝑡−1 ). As accounting standards
generally apply to all firms, there is no a priori reason to suppose that conservatism will
be higher for a certain industry. On the contrary, failing to control for the size of fixed
assets might cause the effects of other variables to be misrepresented.
Finally, as with LSS, I apply two dummy variables to control for the non-discretionary
element in conditional conservatism. 𝑊𝐸𝐴𝐾𝑡−1 takes a value of 1 if 𝐿𝑅𝑂𝐴𝑡 is below 5%
and 0 otherwise. 𝐿𝑅𝑂𝐴t is a lag indicator for 𝑅𝑂𝐴 and is computed as the averagevalue of 𝑅𝑂𝐴𝑡−1 and 𝑅𝑂𝐴𝑡−2. 𝑊𝐸𝐴𝐾 reveals a company's ability to generate earnings
from its investments. 𝐵𝑇𝐿𝐷𝑡−2 indicates the accounting slack accumulated over time. It
takes a value of 1 if 𝐴𝐵𝑇𝑀𝑡−2 is higher than 1 and 0 otherwise. When both variables
take the value of 1, companies with higher than 1 ASSET-BTM in the previous two
accounting periods undoubtedly require a higher level of conservative accounting.
4.2.2 Quantile regression (QR)
To provide evidence of a discontinuity in the relationship between beginning-of-period
ASSET-BTM and asset write-downs, I adopt the robust technique of Quantile
regression. LSS used the traditional OLS to verify their prediction regarding therelation

betweenASSET-BTM.11

current-period

asset

write-downs

andbeginning-of-period

The empirical distribution of asset write-downs closely approximates acontinuous inverted U-shape. Although Ordinary Least Squares (OLS) is a classical andwidely used approach in regression analysis, its effectiveness largely depends on strong
assumptions about the distribution of residuals. Solutions produced by OLS are
highlighted in Figure 2 as the black solid line. Applying OLS in this instance would
generate a coefficient estimate that is not fully indicative of the effects on the lower tailof the distribution. On the other hand, quantile regression models heterogeneous effectsof variables on a response and allows for heteroscedasticity. I predict that predictors
will respond incrementally stronger to the current-period asset write-downs as
11Results of the test model applied in LSS are consistent with the findings in this study. Detailed
analyses and test results are provided in Appendix 5.

12quantiles grows higher. In other words, if asset write-downs increase rapidly after the
beginning-of-period ASSET-BTM reaches one, coefficients on 𝐴𝐵𝑇𝑀t−1 should display adramatic change between high quantiles for 𝑊𝐷.

0.0

0.5

1.0

WD

1.5

2.0

2.5

Figure 2

-1

0

1

2

ABTM

Note:
Figure A demonstrates the effects of beginning-of-period ABTM on asset write-downs estimated by OLS analysis.
𝑊𝐷：asset write-downs measured at the end of fiscal year t /market capitalization measured at the end of fiscal year
t－1. 𝐴𝐵𝑇𝑀: total assets / market capitalization + total assets - common equity, both measured at the end of fiscal
year t－1. Data was standardized for further tests.

The QR test uncovers trends among variables across all quantiles on the basis of Eq. 1.
𝑄𝑊𝐷 (𝜏|𝑋𝑖 ) = ∑𝑖 𝛽𝑖,𝜏 𝑋𝑖 + 𝜀𝑖,𝜏𝑡𝑊𝐷𝑡

𝜏 ∈ (0,100)𝑖 ∈ [1,6]

1= current-period asset write-downs

𝑋𝑖 := total assets deflated by the sum of market capitalization and total assets minus common
equity, both measured at the end of fiscal year t－1
𝑆𝐼𝑍𝐸𝑡−1= natural logarithm of market value at the end of fiscal year t－1
𝐿𝐸𝑉𝑀𝑉𝑡−1 = total liabilities deflated by market value of common equity at the end of fiscal year t－1.
𝐺𝑊𝑡−1
= book value of goodwill deflated by total assets, both measured at the end of fiscal year t－1.
𝑊𝐸𝐴𝐾𝑡−1 = a dummy variable that takes a value of 1 if 𝐿𝑅𝑂𝐴𝑡 is below 5% and 0 otherwise. 𝐿𝑅𝑂𝐴t is alag indicator for 𝑅𝑂𝐴, computed as the average value of 𝑅𝑂𝐴𝑡−1 and 𝑅𝑂𝐴𝑡−2 .
𝑃𝑃𝐸𝑡−1= proportion of property, plant and equipment assets to total assets, measured at the end ofyear t－1.
𝐷𝐸𝐵𝑇𝑡−1 = proceeds from the issuance of bonds in year t－1 deflated by market capitalization ofcommon equity at the end of year t－1.
𝐸𝑄𝑈𝐼𝑇𝑌𝑡−1 = proceeds from the issuance of common stock in year t－1 deflated by market capitalizationof common equity at the end of year t－1.
𝐵𝑇𝐿𝐷t−2= 1 if 𝐴𝐵𝑇𝑀𝑡−2 is higher than 1 and 0 otherwise.
𝐴𝐵𝑇𝑀𝑡−113Here, τ denotes the quantile, and 𝑦 represents current-period asset write-downs(𝑊𝐷𝑡). 𝛽𝑖,τ represents the slope coefficient of a specific variable, selected by Step 2, on
the dependent variable (𝑊𝐷𝑡) for a specific quantile τ. For instance, for τ = 75, 𝛽1,75
denotes the effect of 𝐴𝐵𝑇𝑀𝑡−1 at the 75th percentile of 𝑊𝐷𝑡.
4.3 Test for non-discretionary conservatism
LSS declared that as beginning-of-period ASSET-BTM becomes greater than one,non-discretionary conservatism plays an increasingly influential role. Given the
different distributions (Figure 1) shown by samples with respect to Japanese listedcompanies, I predict that non-discretionary conservatism does not function as
effectively in Japanese listed companies as it does in American listed companies. To
learn more regarding non-discretionary conservatism, I conducted a close investigation
into the three groups with individual ASSET-BTM higher than one. In addition, I trace
and compare the levels of all groups’ conditional conservatism based on the models
applied by Basu [1997], Pae, Thornton, and Welker [2005], Khan and Watts [2009], andBall and Shivakumar [2006].

5 Sample and Descriptive Statistics
5.1 Sample Selection
The initial sample in this study includes all Japanese listed firms with necessary data
on NIKKE NEEDS Financial Quest covering an analysis period from fiscal year 2005 to
2014. I collect stock return data from NPM Daily Return Database (Financial Data
Solutions). To reduce analytical complexity, financial institutions, companies with afiscal year ending other than March; companies who have been delisted; and those who
had changed their year-end in the middle of a fiscal year were excluded from theobservations. I eliminated a total of 853 firm/year samples which do not have sufficient
data to compute the measure of ASSET-BTM (i.e., total assets and market
capitalization). A further 22 firm/year samples with negative common equity and one
sample with negative asset write-downs were also excluded from the analyses. The final
sample includes 17,152 firm/years fulfilling the aforementioned requirements.
Table 1 presents the sample selection process. Table 2 presents the number of samples
in each group.
TABLE 1
Process of sample selection

14

initial sample
27670
1 analytical complexity

Δ9643

2 required accounting data

Δ853

3 negative common equity and asset write-downs

Δ22

total

17152

Note:
The required accounting data includes total assets, common equity and market capitalization. In order to reduceanalytical complexity, firms with fiscal year ending other than March, companies who have been delisted; and those
who had changed their year-end in the middle of a fiscal year were excluded. Samples which do not have sufficient
data to compute the measure of ASSET-BTM and those with negative common equity and asset write-downs werealso excluded.

TABLE 2
Number of samples in each group
𝑁

𝑁%

Group1

𝐴𝐵𝑇𝑀𝑡−1 <0.2

170

1%

Group20.2≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.4

371

2%

Group30.4≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.6

980

6%

Group40.6≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.8

2280

13%

Group50.8≤ 𝐴𝐵𝑇𝑀𝑡−1 <1

4325

25%

Group6

1≤ 𝐴𝐵𝑇𝑀𝑡−1 <1.2

4749

28%

Group7

1.2≤ 𝐴𝐵𝑇𝑀𝑡−1 <1.4

2328

14%

Group81.4≤ 𝐴𝐵𝑇𝑀𝑡−1

1949

11%

Note:
The samples were separated into eight groups using an interval of 0.2. Then, the eight groups were arranged in an
ascending order of beginning-of-period ASSET-BTM. I expected companies belonging to Group 1 would show theleast need for asset write-downs, whereas companies belonging to Group 8 would show the opposite
tendency. 𝐴𝐵𝑇𝑀𝑡−1 : total assets / the sum of market capitalization and total assets minus common equity, bothmeasured at the end of fiscal year t－1. 𝑁 denotes the number of observations in each ASSET-BTM group. 𝑁%
denotes the percentage of each group’s firm/years in all observations. 𝑁% refers to the percentage of eachgroup’s firm/years in all observations.

5.2 Descriptive Statistics
Table 3 Panel A reports descriptive statistics for variables of particular importance inthis study. 𝑊𝐷t denotes asset write-downs scaled by market capitalization measured at
the end of fiscal year t－1. Observing a higher mean value (0.0081) than the medianvalue (0) and 3rd quartile (0.0008) for 𝑊𝐷𝑡 indicates the presence of “big bath” – that is,a minor portion of the samples take up the majority of asset write-downs recognized ateach year end. 𝐴𝐵𝑇𝑀𝑡−1 denotes ASSET-BTM measured at the end of fiscal year t－1,

15

computed as the book value of total assets deflated by the sum of market capitalizationand total assets minus common equity. 𝐵𝑇𝑀𝐷t−1 is a dummy variable which takes avalue of 1 if 𝐴𝐵𝑇𝑀𝑡−1 is higher than 1 and 0 otherwise. The mean value of 𝐴𝐵𝑇𝑀𝑡−1
and 𝐵𝑇𝑀𝐷𝑡−1 are 1.0305 and 0.5262, respectively, both of which show thatapproximately more than half of the samples have lower market values than their bookvalues. This differs hugely from the analysis performed in the study of LSS, whoreported that only the upper 25% of the observations were expected to write down theirassets.
Panel B of Table 3 compares some important statistical results across groups. First,
recall that groups are classified by ASSET-BTM measured at the end of year t－1 in an
ascending order. As expected, the mean 𝑊𝐷 of Groups 6, 7 and 8 are 0.0102, 0.0117,
and 0.0218, respectively, all of which noticeably surpass those of groups with abeginning-of-period ASSET-BTM lower than one. This is consistent with the findings of
LSS. Firm size (𝑆𝐼𝑍𝐸) is calculated as the natural logarithm of market capitalization at
the end of fiscal year t－1. From Panel B, a trend toward a rapid decline in 𝑆𝐼𝑍𝐸 isevident from Group 3 to Group 8, which is consistent with the trend observed inprevious research, indicating that larger companies have a preference for moreconservative accounting (e.g., Watts and Zimmerman [1986]).
A growing number of studies have shown that debt covenants are a most important
reporting incentive to predict loss recognition timeliness. 𝐿𝐸𝑉𝑀𝑉 peaks in Group 6(3.7706), but decreases sharply with Group 8 (1.6959) being the lowest among groups
with higher than one beginning-of-period ASSET-BTM.
The pecking order theory predicts that management prioritizes debt issuance overequity when external financing is required. In other words, the issue of debt implies
underestimation of stock price and the issue of equity otherwise. (e.g., Jung, Kim, andStulz [1996], Smith and Watts [1992]). Consistent with the pecking order theory, firms
from Group 8 issue considerably more new stock than the other groups as leverage
ratios decline rapidly through Groups 6 － 8.
Panel C in Table 3 considers the operating performance of all samples. 𝑅𝑡−1 is the
buy-and-hold return on common stock for the 12 months ending three months after theend of fiscal year t－1. Return on assets (𝑅𝑂𝐴) is used extensively to investigate acompany’s earning capacity. 𝑅𝑂𝐴𝑡 is computed as income before extraordinary items at
the end of fiscal year t scaled by the book value of total assets. 𝐿𝑅𝑂𝐴t is a lag indicatorfor 𝑅𝑂𝐴, computed as the average value of 𝑅𝑂𝐴𝑡−1 and 𝑅𝑂𝐴𝑡−2 . 𝑊𝐸𝐴𝐾𝑡−1 is a dummy
variable, that takes a value of 1 if 𝐿𝑅𝑂𝐴𝑡 is below 5% and 0 otherwise. 𝑃𝑃𝐸𝑡−1 denotes

16

proportion of property, plant and equipment assets to total assets, measured at the endof year t－1.
TABLE 3

Descriptive Statistics

Panel A:
mean

std.

1st quartile

median

3rd quartile

𝑊𝐷𝑡

0.0081

0.06690.00000.00000.0008

𝐴𝐵𝑇𝑀𝑡−1

1.03050.36310.82811.01601.1995

𝐵𝑇𝑀𝐷𝑡−1

0.52620.49930

1

1

𝑆𝐼𝑍𝐸𝑡−1

10.1709

1.81298.87069.9396

11.2959𝑊𝐷𝑡

𝑆𝐼𝑍𝐸𝑡−1

Panel B:

𝐿𝐸𝑉𝑀𝑉𝑡−1

𝐷𝐸𝐵𝑇𝑡−1

𝐸𝑄𝑈𝐼𝑇𝑌𝑡−1

𝐵𝑇𝑀𝐷𝑡−2Group10.0002

9.52720.38020.00350.01160.3176

Group20.00059.70000.23180.00330.0171

0.0943

Group30.000910.2374

0.37830.00660.01430.0918Group40.001510.3327

0.79270.01050.00920.1443

Group50.004010.3913

1.95920.02190.01200.2860Group60.0102

10.15913.77060.02810.01300.7412

Group70.01179.97312.46850.01400.00750.9055

Group80.02189.86991.69590.00800.02170.9523

𝑅𝑡−1

𝐷𝑅𝑡Group10.80410.46470.04860.44710.03400.1342

Group20.19220.54990.04940.37200.01360.1857

Group30.13180.54180.04730.38780.01390.2349Group40.06950.53900.03720.64080.01040.2929Group50.0316

0.56180.02500.82380.00680.3320Group6

0.04670.56390.0186

0.89220.00570.3031

Group7－0.0425

0.54380.02090.89220.00400.3015

Group8－0.0695

0.55260.01520.91120.00390.2913

Panel C
𝐿𝑅𝑂𝐴𝑡

𝑊𝐸𝐴𝐾𝑡

𝐺𝑊𝑡−1

𝑃𝑃𝐸𝑡−1Panel D:𝑊𝐷𝑡
mean

𝐴𝐵𝑇𝑀𝑡−1
3rd quartile

mean

17

3rd quartile2005

0.001000.96301.10082006

0.002000.80000.97212007

0.000500.88381.06492008

0.00630.00020.98861.16112009

0.02140.00611.2082

1.35962010

0.01120.00491.1323

1.27822011

0.01170.0033

1.12781.2875

2012

0.00890.00291.1762

1.32802013

0.01140.00351.00101.1723

2014

0.00610.00281.00471.1820Notes:
𝑊𝐷t ：asset write-downs measured at the end of fiscal year t /market capitalization measured at the end of fiscal
year t－1. 𝐴𝐵𝑇𝑀𝑡−1 : total assets / market capitalization + total assets －common equity, both measured at the endof fiscal year t－1. 𝐵𝑇𝑀𝐷t−1 : dummy variable which take a value of 1 if 𝐴𝐵𝑇𝑀𝑡−1 is higher than 1, and 0 otherwise.
𝑅𝑡−1 : the buy-and-hold return on common stock for the twelve months ending three months after the end of fiscal
year t－1. 𝐷𝑅𝑡 : dummy variable, taking a value of 1 if 𝑅𝑡−1 is negative, and 0 otherwise. 𝑅𝑂𝐴𝑡 ：income before
extraordinary items / book value of total assets, both measured at the end of fiscal year t. 𝐿𝑅𝑂𝐴t : lag indicator for
𝑅𝑂𝐴, computing as an average value of 𝑅𝑂𝐴 for the previous two accounting periods. 𝑊𝐸𝐴𝐾𝑡−1 : dummy variable,
taking a value of 1 if 𝐿𝑅𝑂𝐴t is less than 5%, and 0 otherwise. 𝑆𝐼𝑍𝐸𝑡−1 : the natural logarithm of market
capitalization at the end of fiscal year t－1. 𝐺𝑊𝑡−1 : book value of goodwill deflated by total assets, both measured at
the end of fiscal year t－1. 𝐿𝐸𝑉𝑀𝑉𝑡−1 : book value of total liabilities deflated by market value of common equity atthe end of fiscal year t－1. 𝑃𝑃𝐸𝑡−1 : proportion of property, plant and equipment assets to total assets, measured at
the end of year t－1. 𝐷𝐸𝐵𝑇𝑡−1 : proceeds from debt issuance deflated by market capitalization, both measured at
the end of year t－1. 𝐸𝑄𝑈𝐼𝑇𝑌𝑡−1 : proceeds from sale of common stock deflated by market capitalization, bothmeasured at the end of year t－1.

𝑅𝑡−1 decreases as ASSET-BTM grows and bottoms at －0.0695 in Group 8. 𝐿𝑅𝑂𝐴
negatively interacts with ASSET-BTM, with Group 8 the lowest (0.0152). The result for
𝑊𝐸𝐴𝐾 is briefly accordant with the rank of beginning-of-period ASSET-BTM. In
particular, the financial performance of Groups 5 to Group 8 is relatively weaker thanthat of the other groups with lower beginning-of-period ASSET-BTM. 𝑊𝐸𝐴𝐾 for Group
8 runs up to 0.9112, suggesting that more than 90% of the observations belonging toGroup 8 suffered depressed financial performance. It is noteworthy that 𝑊𝐸𝐴𝐾 for US
listed companies, as documented by LSS, ranges from 0.603 (0.3≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.5) to
0.847 ( 𝐴𝐵𝑇𝑀𝑡−1 > 1.2). In other words, Japanese listed companies in high ASSET-BTM
groups exhibit lower operational effectiveness than their counterparts in America,
whereas those in low ASSET-BTM groups enjoy financial performance surpassing their
American counterparts.
Panel D compares 𝑊𝐷 and 𝐴𝐵𝑇𝑀 on a yearly basis. An economic deterioration, suchas the global financial crisis in 2008, which put stock markets around the world on a
downward trajectory, can significantly affect the implementations of asset impairment.

18

The mean value of 𝑊𝐷 over time shows that asset impairment losses soared to 0.0214
in 2009, which underscores the unprecedented challenges posed by the financial crisis.
This is coincident with 𝐴𝐵𝑇𝑀, which records its peak value (1.2082) in the sameaccounting period. Although the economy has steadily emerged from the financial crisis(the mean value of 𝐴𝐵𝑇𝑀 declined from 1.2082 to 1.0305), Japanese listed companies
are still struggling as 𝐴𝐵𝑇𝑀 has remained over 1 since 2009.
Panel A of Table 4 contains the results of the actual asset write-downs in terms offrequency and volume. 𝑁 denotes the number of observations in each ASSET-BTM
group. Among all the observations of 17,152 firm/years, 5,085 sampling firms wrote
down their assets. 𝐺𝑁 represents the number of companies who have written down
their assets. 𝑊𝐷𝑁% represents the percentage of such companies in all samples (17,152
firm/years). Group 6, together with Groups 7 and 8, occupy a dominant portion of 53%
with respect to 𝑊𝐷𝑁%. 𝑊𝐷𝑁𝐺% equals the percentage of companies who have written
down their assets in each group. Groups with high beginning-of-period ASSET-BTM(Group 6 through Group 8) do not exhibit a tremendous difference between each other.
On the other hand, 𝑆𝑈𝑀𝑊𝐷𝐺 represents the sum of actual asset write-downs in eachgroup. 𝑆𝑈𝑀𝑊𝐷% compares the amount of actual asset write-downs between the eightgroups. In this respect, the three groups with a beginning-of-period ASSET-BTM higher
than one (Group 6 through Group 8) constitute approximately 84% of the total actual
asset write-downs. In brief, groups with higher ASSET-BTM outrank the other groups
not only by the frequency but amount as well. From this, it is clear that when
ASSET-BTM exceeds one, the application of impairment standards explodes.
If the “non-discretionary conservatism takes over theory” holds, both the frequency
and amount of asset write-downs are expected to increase monotonically from Group 6
to Group 8. However, Groups 7 and 8 only take up 14% and 11% of 𝑊𝐷𝑁%, respectively,
levels that are inferior to Group 6 (28%) even after Groups 7 and 8 are combined. In
addition, the indicator 𝑆𝑈𝑀𝑊𝐷% of Group 6 (35%) is also higher than that of Group 7(19%) and Group 8 (30%). According to a previous analysis on changes of ASSET-BTM
over time, more than 40% of the entire observations have ASSET-BTM greater than one
for three years in succession, indicating that a considerable proportion of Japaneselisted companies postpone the application of impairment standards when circumstances
warrant.
TABLE 4
Panel A:
Results of actual asset write-downs in terms of frequency and volume

19

𝑁

𝐺𝑁

𝑊𝐷𝑁%

𝑊𝐷𝑁𝐺%

𝑆𝑈𝑀𝑊𝐷𝐺𝑆𝑈𝑀𝑊𝐷%

Group1

170

18

0.10%

11%

0.02820%Group2

371

66

0.38%

18%

0.17170%Group3

980

197

1.15%

20%

0.8333

1%

Group4

2280

555

3.24%

24%

3.4093

2%

Group5

4325

1260

7.35%

29%

17.4105

12%

Group6

4749

1573

9.17%

33%

48.2238

35%

Group7

2328

742

4.33%

32%

27.1528

19%

Group8

1949

674

3.93%

35%

42.4499

30%

Total

17152

5085

29.65%

100%

139.6795

100%

Panel B:
Further details of groups with beginning-of-period ASSET-BTM greater than one
𝑁

𝑁1

𝑊𝐷1%

𝑁2

𝑊𝐷2%

Group6

4749

3176

67%

2336

49%

Group7

2328

1586

68%

1441

62%

Group8

1949

1275

65%

1217

62%

Note:
𝐺𝑁 represents the number of companies who have written down their assets. 𝑊𝐷𝑁% represents the percentage ofcompanies who have written down their assets in all samples. 𝑊𝐷𝑁𝐺% equals the percentage of companies who
have written down their assets in each group. 𝑆𝑈𝑀𝑊𝐷𝐺 denotes the sum of actual asset write-downs in eachgroup. 𝑆𝑈𝑀𝑊𝐷% denotes the percentage of each group’s actual asset write-downs in the total actual asset
write-downs. 𝑁1 depicts the number of companies who did not record asset write-downs at the end of fiscal year t.
𝑊𝐷1% represents the percentage of companies who delay the application of impairment standards in each group.
𝑁2 depicts the number of companies who shelved the impairment procedures two fiscal years in a row. 𝑊𝐷2%
represents the percentage of companies who shelve the impairment procedures for two fiscal years.

Panel B of Table 4 reports on a deeper examination of groups with beginning-of-period
ASSET-BTM greater than one. 𝑁 denotes the number of observations in each
ASSET-BTM group. 𝑁1 depicts the number of companies who did not record asset
write-downs at the end of fiscal year t. 𝑊𝐷1%, the third row in Table 4, represents thepercentage per group of such companies. Approximately 68% and 65% of the companies
in Groups 7 and 8, respectively, potentially delayed the implementation of impairment
even when their beginning-of-period ASSET-BTMs strongly imply a decline in the valueof their assets. 𝑁2 depicts the number of companies who shelved the impairment
procedures for two fiscal years in a row. 𝑊𝐷2%, the last row in Table 4, then represents
the percentage of such companies in each group. Although the number of suchcompanies decreases by 18% in Group 6 (from 67% to 49%), the percentage of companies
leaving their depreciating assets untouched in Group 7 (62%) and Group 8 (62%)

20remains surprisingly high despite the presence of such red flags for two consecutive
fiscal years. This is counter to that seen in the study of LSS, but consistent with myexpectation that non-discretionary conservatism is not as prevalent in Japanese listedcompanies as it is in American listed companies.

5.3 Tests of nonlinear relation
5.3.1 the Adaptive LASSO regularized Quantile Regression (QR-LASSO)
Results of the QR-LASSO at the 70th, 75th, 80th, 85th, 90th, and 95th quantiles,
respectively, are shown in Figure 3.12 The upper plots show how the model at a specificquantile evolves through the selection process. Each colored line represents the value
taken by a different variable. The vertical axis reveals the fit statistics of the variables
and assesses the relative importance of the effects selected at any step of the selection
process. The horizontal axis provides information as to when effects of the selectedvariables enter the model. The lower plot in the panel shows the stopping criterion used
to choose the model and how it changes as variables enter or leave the model. The
vertical gray line connecting the upper plot and the lower plot indicates the maximum
number of steps, which when reached, denotes the termination of the selection process.
The effects chosen by then are viewed as the optimal model to explain the response
variable for that quantile.
I applied the Adjusted R-square statistic (𝐴𝑑𝑗. 𝑅 2), Akaike’s information criterion (AIC),
Corrected Akaike’s information criterion (AICC) and Schwarz Bayesian information
criterion (SBC) in all the tests to evaluate the quality of the models produced by the
QR-LASSO. Eventually, all models are selected by SBC, which favors a smaller model
than AIC and AICC. Moreover, because the information criterion is usually used in thecontext of comparing models, not as an absolute criterion by itself, the magnitude of theinformation criterion (AIC/SBC) for a specific model is less of interest.
As previously noted, conservative accounting pertaining to asset write-downs
disclosure may involve a set of managerial incentives rather than a single constraint. 9
predictors are considered in the model: beginning-of-period ASSET-BTM (𝐴𝐵𝑇𝑀), firm
size (𝑆𝐼𝑍𝐸), financial leverage (𝐿𝐸𝑉𝑀𝑉), proportions of goodwill to total assets (𝐺𝑊), theproportion of property, plant and equipment assets to total assets (𝑃𝑃𝐸), operating
efficiency (𝑊𝐸𝐴𝐾) and accumulated accounting slack (𝐵𝑇𝐿𝐷).

12

I also performed the tests for samples below the 70th quantile, but no variables are validated as
effective at lower than the 70th quantiles.

21

Figure 3

22

Notes:The upper plots show how the model at a specific quantile evolves through the selection process. I performed QR-LASSO at the 70th, 75th, 80th, 85th, 90th, and 95th quantile, respectively. The vertical axis
reveals the fit statistics of the variables and assesses the relative importance of the effects selected at any step of the selection process. The horizontal axis provides information as to when effects of the selectedvariables enter the model. The lower plot in the panel shows the stopping criterion used to choose the model and how it changes as variables enter or leave the model. The vertical gray line connecting the upper
plot and the lower plot indicates the maximum number of steps, which when reached, denotes the termination of the selection process. I used the Adaptive LASSO as the shrinkage method in the selection
process. The horizontal axis represents maximum permissible values of the weighted 𝐿1 norm. Each colored line represents the value taken by a different variable.
𝑊𝐷 ：asset write-downs measured at the end of fiscal year t /market capitalization measured at the end of fiscal year t－1. 𝐴𝐵𝑇𝑀1: total assets / the sum of market capitalization and total assets minus
common equity, both measured at the end of fiscal year t－1. 𝑆𝐼𝑍𝐸1: the natural logarithm of market value at the end of fiscal year t－1. 𝐺𝑊1: book value of goodwill deflated by total assets, both measured at
the end of fiscal year t－1. 𝐿𝐸𝑉𝑀𝑉1: book value of total liabilities deflated by market value of common equity at the end of fiscal year t－1. 𝑊𝐸𝐴𝐾: a dummy variable, that takes a value of 1 if 𝐿𝑅𝑂𝐴𝑡 is below
5% and 0 otherwise. 𝐿𝑅𝑂𝐴t : lag indicator for 𝑅𝑂𝐴, computed as the average value of 𝑅𝑂𝐴𝑡−1 and 𝑅𝑂𝐴𝑡−2 . 𝑃𝑃𝐸1 : proportion of property, plant and equipment assets to total assets, measured at the end ofyear t－1. 𝐵𝑇𝑀D2 : a dummy variable which takes a value of 1 if 𝐴𝐵𝑇𝑀𝑡−2 is higher than 1 and 0 otherwise. 𝐷𝐸𝐵𝑇1 : proceeds from the issuance of bonds in year t－1 deflated by market capitalization ofcommon equity at the end of year t－1. .𝐸𝑄𝑈𝐼𝑇𝑌1 : proceeds from the issuance of common stock in year t－1 deflated by market capitalization of common equity at the end of year t－1.
SBC = n log(SSE/n) + 𝑝 𝑙𝑜𝑔(𝑛), where n denotes the number of observations and p denotes the number of parameters including the intercept. SSE is the error sum of squares.

23

First of all, the financial leverage (𝐿𝐸𝑉𝑀𝑉) is the first variable to become active in theeffect selection process at all quantiles tested. It can be intuitively observed from allplots that leverage (𝐿𝐸𝑉𝑀𝑉), rather than the beginning-of-period ASSET-BTM (𝐴𝐵𝑇𝑀),
is the dominant impetuses for asset write-downs.
As the amount of shrinkage decreases from left to right on the horizontal axis, themodel complexity increases. More predictor variables are retained to explain theextreme asset write-downs. However, the selected effects enter/leave the model in
different sequences at different quantiles. Particularly, as indicated in Table 5 Panel B,
the beginning-of-period ASSET-BTM exceeds the value of 1 around the 80th quantile
from where 𝐴𝐵𝑇𝑀 begins to show statistically significant effects.
With SBC being the stopping criteria, the lower the SBC value, the more the model fits
the given data. Hence, the effect selection process stops when dropping or adding any
effect increases the SBC statistic. Cumulatively, the variable that denote impact from
intangible assets (𝐺𝑊), debt issuance (𝐷𝐸𝐵𝑇), and sale of common stock (𝐸𝑄𝑈𝐼𝑇𝑌) show
almost no contribution to asset write-downs.
5.3.2 Test of nonlinearity by Quantile Regression
In this section, I employ the Quantile Regression (QR hereafter) (Koenker and Bassett[1978]) to identify nonlinearity in the relationship between beginning-of-period
ASSET-BTM and asset write-downs. QR estimates conditional quantiles of variables fora probability distribution. Application of QR also paints a broader picture of how asset
write-downs interact with the selected variables along lower or upper boundaries.
In Table 5, Panel A reports slope estimates produced by OLS with beginning-of-period
ASSET-BTM (𝐴𝐵𝑇𝑀) being the only predictor variable as suggested in LSS. Panel B
reports the QR solutions (𝛽∗𝜏 ) when beginning-of-period ASSET-BTM (𝐴𝐵𝑇𝑀𝑡−1 ) is the
only predictor for write-downs. Panel C reports QR solutions estimated by models
selected by the QR-LASSO. For brevity, only slope coefficients (𝛽𝑖,𝜏 ) estimated at the70th, 75th, 80th, 85th, 90th, and 95th quantiles are reported. 𝑃𝑙𝑜𝑡 indicates the plot
number corresponding to that quantile. Quantile standard errors are bootstrapped,using 100 replications. 𝑊𝐷𝑚𝑒𝑎𝑛 denotes the mean value for the current period asset
write-downs, while 𝑊𝐷𝜏 denotes the value of the current period asset write-downs atthe τth quantile. Similarly, 𝐴𝐵𝑇𝑀𝑚𝑒𝑎𝑛 (𝐴𝐵𝑇𝑀𝑚𝑒𝑑𝑖𝑎𝑛 ) and 𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑎𝑛 ( 𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑑𝑖𝑎𝑛 )denote the mean value (the median value) of the beginning-of-period ASSET-BTM andthe number of subgroup specified in the research design (Step 1), respectively.

24

TABLE 5

Panel A: Slope estimates produced by OLS
OLS

Plot

𝛽𝐴𝐵𝑇𝑀

intercept

Fit a/1

0.0173***－0.001***

𝑊𝐷𝑚𝑒𝑎𝑛𝐴𝐵𝑇𝑀𝑚𝑒𝑎𝑛

𝐴𝐵𝑇𝑀𝑚𝑒𝑑𝑖𝑎𝑛𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑎𝑛

𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑑𝑖𝑎𝑛

0.00811.03051.01606

6

Panel B: Slope estimates produced by QR∗
𝑄𝑊𝐷 (𝜏|𝐴𝐵𝑇𝑀𝑡−1 ) = 𝛽𝐴𝐵𝑇𝑀,𝜏
𝐴𝐵𝑇𝑀𝑡−1 + 𝜀𝜏

Plot

𝜏

1∗

𝜏 ∈ (0,100)∗
𝛽𝐴𝐵𝑇𝑀,𝜏
.

intercept

𝑊𝐷𝜏

𝐴𝐵𝑇𝑀𝑚𝑒𝑎𝑛

𝐴𝐵𝑇𝑀𝑚𝑒𝑑𝑖𝑎𝑛𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑎𝑛

𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑑𝑖𝑎𝑛

70th

－

0.0019***－0.1179***

0

0.80

0.80

4.52

5

75th

Fit b

0.0097***－0.1019***0.0008

0.97

0.95

5.32

5

80th

Fit c

0.0216***－0.0759***

0.0023

1.03

1.03

5.57

6

85th

Fit d

0.0433***－0.0269***

0.0051

1.07

1.03

5.73

6

90th

Fit e

0.0813***0.0647***0.0107

1.11

1.06

5.93

6

95th

Fit f

0.1870***0.3329***0.0281

1.19

1.12

6.25

6

Panel C: Slope estimates for models selected by QR-LASSO𝑄𝑊𝐷 (𝜏|𝑋𝑖 ) = ∑𝑖 𝛽𝑖,𝜏 𝑋𝑖 + 𝜀𝑖,𝜏1

τ ∈ (0,100)

𝜏

Plot

𝛽𝐿𝐸𝑉𝑀𝑉,𝜏

70th

Fit 2

0.02***

75th

Fit 3

0.05***

0.0048***

80th

Fit 4

0.11***

0.0082***0.004***

85th

Fit 5

0.19***

0.0142***0.008***

0.0064***

90th

Fit 6

0.35 ***

0.0255***0.016***

0.0107***－0.009***

95th

Fit 7

0.77 ***

0.0441***0.047***

0.0274***－0.016***𝛽𝑃𝑃𝐸,𝜏

𝛽𝐴𝐵𝑇𝑀,𝜏

𝛽𝐵𝑇𝐿𝐷,𝜏

𝛽𝑆𝐼𝑍𝐸,𝜏

𝛽𝑊𝐸𝐴𝐾,𝜏

𝛽𝐸𝑄𝑈𝐼𝑇𝑌,𝜏

𝛽𝐺𝑊,𝜏

intercept－0.11***－0.09***－0.06***

Notes:

25－0.003***0.103***－0.013***0.078*

0.006**

0.383***

Panel B reports numerical slope estimates for each variable involved. I performed QR test at the 70th, 75th, 80th, 85th, 90th, and 95th quantile, respectively. 𝑋 denotes a set of independent variables∗
characterized as predictors of conservatism. 𝛽𝐴𝐵𝑇𝑀,𝜏
: coefficients on 𝐴𝐵𝑇𝑀t−1 at the τth quantile when 𝐴𝐵𝑇𝑀t−1 is the only predictor in the model. 𝛽𝐴𝐵𝑇𝑀,𝜏 : coefficient on 𝐴𝐵𝑇𝑀t−1 at the τthquantile. 𝐴𝐵𝑇𝑀𝑡−1 : the total assets / market capitalization + total assets － common equity, both measured at the end of fiscal year t－1. 𝛽𝑆𝐼𝑍𝐸,𝜏 : coefficients on 𝑆𝐼𝑍𝐸𝑡−1 at the τth quantile. 𝑆𝐼𝑍𝐸𝑡−1 :the natural logarithm of market value at the end of fiscal year t－1. 𝛽𝐺𝑊,𝜏 : coefficients on 𝐺𝑊t−1 at the τth quantile. 𝐺𝑊𝑡−1 : book value of goodwill deflated by total assets, both measured at the end of
fiscal year t－1. 𝛽𝐿𝐸𝑉𝑀𝑉,𝜏 : coefficients on 𝐿𝐸𝑉𝑀𝑉t−1 at the τth quantile. 𝐿𝐸𝑉𝑀𝑉𝑡−1: book value of total liabilities deflated by market value of common equity at the end of fiscal year t－1. 𝛽𝑃𝑃𝐸,𝜏 :coefficients on 𝑃𝑃𝐸t−1 at the τth quantile. 𝑃𝑃𝐸𝑡−1 : the proportion of property, plant and equipment assets to total assets, measured at the end of year t－1. 𝛽𝑊𝐸𝐴𝐾,𝜏 : coefficients on 𝑊𝐸𝐴𝐾t at the τthquantile. 𝑊𝐸𝐴𝐾t : a dummy variable, that takes a value of 1 if 𝐿𝑅𝑂𝐴𝑡 is below 5% and 0 otherwise. 𝐿𝑅𝑂𝐴t : lag indicator for 𝑅𝑂𝐴, computed as the average value of 𝑅𝑂𝐴𝑡−1 and 𝑅𝑂𝐴𝑡−2. 𝛽𝐸𝑄𝑈𝐼𝑇𝑌,𝜏 :coefficients on 𝐸𝑄𝑈𝐼𝑇𝑌t−1 at the τth quantile. 𝐸𝑄𝑈𝐼𝑇𝑌𝑡−1 : sale of common stock deflated by market capitalization, both measured at the end of year t－1. 𝛽𝐵𝑇𝐿𝐷,𝜏 : coefficients on 𝐵𝑇𝐿𝐷t−2 at the τthquantile. 𝐵𝑇𝑀Dt−2 : a dummy variable which takes a value of 1 if 𝐴𝐵𝑇𝑀𝑡−2 is higher than 1 and 0 otherwise. 𝑊𝐷𝑚𝑒𝑎𝑛 : the mean value for the current period asset write-downs. 𝑊𝐷𝜏 : the value of thecurrent period asset write-downs at the τth quantile. 𝐴𝐵𝑇𝑀𝑚𝑒𝑎𝑛 (𝐴𝐵𝑇𝑀𝑚𝑒𝑑𝑖𝑎𝑛 ): the mean value (the median value) of the beginning-of-period ASSET-BTM. 𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑎𝑛 (𝐺𝑟𝑜𝑢𝑝𝑚𝑒𝑑𝑖𝑎𝑛 ): the mean value(the median value) of the number of subgroup specified in the research design (Step 1). ***, **,* indicate significance at the two-tailed 1%, 5%, 10% confidence level, respectively.

26

In Panel C, it is noteworthy that the slope coefficient for leverage (𝐿𝐸𝑉𝑀𝑉) strengthens
incrementally as quantiles increase and outweighs that of beginning-of-period∗
ASSET-BTM (𝐴𝐵𝑇𝑀) across all quantiles. Comparison of Panel B (𝛽𝐴𝐵𝑇𝑀,𝜏
) and Panel C( 𝛽𝐴𝐵𝑇𝑀,𝜏 ) reveals that the influence of beginning-of-period ASSET-BTM markedly
decreases along the quantiles tested after specific variables are controlled. For instance,∗
slope coefficients estimated at the 95th quantile plunge from 0.1870*** (𝛽𝐴𝐵𝑇𝑀,95) to0.047*** (𝛽𝐴𝐵𝑇𝑀,95 ). However, effects from the beginning-of-period ASSET-BTM (𝐴𝐵𝑇𝑀)
do intensify for extreme asset write-downs, as coefficient on 𝐴𝐵𝑇𝑀 at the 95th quantile(𝛽𝐴𝐵𝑇𝑀,95 ) is almost three-fold stronger than that for the 90th quantile (𝛽𝐴𝐵𝑇𝑀,90 =
0.016***). This is consistent with the prediction in this study that slope coefficients on
𝐴𝐵𝑇𝑀 estimated at different quantiles do not increase proportionately with gradual
growth in their corresponding quantiles. On the other hand, the beginning-of-period
ASSET-BTM is not the leading driver for asset impairment even though it does
stimulate great asset write-downs. For instance, beginning-of-period ASSET-BTM(𝐴𝐵𝑇𝑀) accounts for 1% and 4% of total asset write-downs at the 90th and 95th quantiles,
respectively. However, market value leverage (𝐿𝐸𝑉𝑀𝑉) accounts for about 35% and 77%of total asset write-downs at the same quantiles.
To summarize, results of QR test suggest that effects from the beginning-of-period
ASSET-BTM (𝐴𝐵𝑇𝑀) and accumulated accounting slack (𝐵𝑇𝐿𝐷) hold positive on asset
write-downs, which agree with the results in LSS that GAAP motivates management
against arbitrary accounting choices. However, the weak performance (𝑊𝐸𝐴𝐾), anothervariable which is supposed to control for the non-discretionary component inconservatism, shows a countervailing effect on asset write-downs. Furthermore, though
beginning-of-period ASSET-BTM accounts for a portion of extreme asset write-downs, it
is not the predominant driver of accounting conservatism. This may lead us to conclude
that high quality reporting is unlikely to be secured by accounting standards alone,
however strictly enforced.
Nevertheless, factors that affect actual asset write-downs are not limited to those
proposed in this study. Furthermore, I did not take interaction terms into consideration.
Future analyses should explore more variables and employ a better-fitting analyticalmodel.
5.3.3 Effects of the financial crisis
This study investigates the magnitude of discretionary asset write-downs for a random
sample of Japanese listed companies. However, it is undeniable that a volatile global
stock market and the prospect of recession would affect the management’s accounting

27

decisions. This section explores the effects from the financial crisis and the subsequenteconomic malaise.
Figure 4 demonstrate scatter plots of 𝐴𝐵𝑇𝑀 and 𝑊𝐷 for each sample accounting
period (2005~2014). As can be inferred from Figure 4, management did not act promptly
according to the accounting standards for asset impairment when it came into effect at
the end fiscal year of 2005. However, the shape of the scatter plots for fiscal years 2008－2014 is in line with that displayed in Figure 1. The historical economic downturn, in asense, improved compliance with established standards. Nonetheless, whether theaccounting practice has been performed as required is open to discussion.

28

Figure 4

0

2

2011

2010

2009

3
4

2

2006

2005

4

ABTM

1
ABTM

4

0

ABTM

4

-2

0

2

ABTM

4

0

0
2

0

0
0

-2

1

1

1
0
-2

2
1

2

3
WD

3
2

WD

3
2

WD

0

4

4

ABTM

4

ABTM

3

0
-2

-2

4

4

3

2

ABTM

2

WD

0

2

-2

1

4

WD

2

2

0

4

-2

0

0

0

0

1

1

1

1

2

WD

WD

3

4

2007

4

4
3
2

WD

3
2

WD

3
2

WD

2008

2012

4

2013

4

2014

-2
-2

0

2

4

0

2
ABTM

4

-2

0

2

4

ABTM

ABTM

Notes:
Figure 4 demonstrates the correlation between current-period write-downs (𝑊𝐷) and beginning-of-period ASSET-BTM (ABTM). 𝑊𝐷 ：asset write-downs measured at the end of
fiscal year t /market capitalization measured at the end of fiscal year t－1. 𝐴𝐵𝑇𝑀 : total assets / market capitalization + total assets - common equity, both measured at the end of
fiscal year t－1.

29

5.4 Test for non-discretionary conservatism5.4.1 Measuring conditional conservatism based on Basu model
In this section, I use four alternative measures to more precisely determine the level ofconditional conservatism in each group. If non-discretionary conservatism entails
appropriate exertions of asset write-downs after ASSET-BTM exceeds one, as depictedin the study of LSS, all measures of Group 8 should be significantly higher than that ofGroup 6.
Basu [1997] is one of the seminal studies in the literature on accounting conservatism.
The following model is the regression equation employed in the study of Basu [1997].
𝐸𝑖,𝑡 = 𝛼0 + 𝛼1 𝐷𝑅𝑖,𝑡 + 𝛽0 𝑅𝑖,𝑡 + 𝛽1 𝑅𝑖,𝑡 ∗ 𝐷𝑅𝑖,𝑡2Basu [1997] developed a measure of “asymmetric timeliness coefficient” by inversely
regressing earnings on returns. 𝐸𝑡 denotes the net income in fiscal year t deflated bymarket capitalization measured at the end of fiscal year t － 1. 𝑅𝑡 denotes the
buy-and-hold return on common stock for the twelve months ending three months after
the end of fiscal year t. 𝐷𝑅𝑡 is a dummy variable that equals 1 if 𝑅𝑡 is negative and is 0
otherwise. In this regression equation, which is also known as the Basu model, the slope
coefficient (𝛽1 ) represents the difference in sensitivity of earnings for bad news versus
good news.
Measure 𝐶 was proposed in Pae, Thornton, and Welker [2005], resorting to the Basumodel. As with Pae, Thornton, and Welker [2005], I further divided the samples of eachpartition into good news and bad news sets. If the return at the end of fiscal year t was
negative, it would be subsumed to the bad news set; and to the good news set otherwise.
Then, all the necessary data will be substituted into Eq. 3 and Eq. 4 for each
ASSET-BTM partition to calculate measure 𝐶 － a metric that gauges the degree towhich bad news is reported in earnings in a timelier manner than good news.
𝐸𝑗,𝑡 = 𝛼𝑗 + 𝛽𝑗 𝑅𝑗,𝑡 + 𝜀𝑗,𝑡3

𝑐𝑗 = 𝛽𝐵𝐷
− 𝛽𝐺𝐷
𝑗
𝑗

4

The dependent variable 𝐸𝑡 represents net income for fiscal year t deflated by themarket capitalization at the end of fiscal year t－1. 𝑅𝑡 , the explanatory variable,
represents the buy-and-hold return on common stock for the twelve months ending
three months after the end of fiscal year t, and 𝜀𝑡 is the residual. The coefficient 𝛽𝑗 on30

𝑅𝑡 denotes the measure of asymmetric timeliness (Basu coefficient), which is developed
from the study of Basu [1997]. In the given samples, I estimate coefficient 𝛽𝑗 for eachsubset. That is, 𝛽𝑗𝐵𝐷 denotes the timeliness with which bad news is reflected on firm j’s
income statement, and 𝛽𝑗𝐺𝐷 denotes the timeliness with which good news is reflected on
firm j’s income statement. Accordingly, the measure 𝑐𝑗 represents the firm-specificdegree of conditional conservatism. A higher measure 𝐶 implies a stronger tendency
toward conditional conservatism. The computing results for measure 𝐶 aresummarized in Table 7.
TABLE 7
Computing results of measure 𝐶interval

𝛽𝑗𝐵𝐷

𝛽𝑗𝐺𝐷

𝑐𝑗 = 𝛽𝐵𝐷− 𝛽𝐺𝐷
𝑗𝑗Group1

𝐴𝐵𝑇𝑀𝑡−1 <0.2

0.0261－0.00005

0.0262

Group20.2≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.4

0.0291－0.0027

0.0318Group30.4≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.6

0.04980.00740.0424

Group40.6≤ 𝐴𝐵𝑇𝑀𝑡−1 <0.8

0.04360.01490.0287

Group50.8≤ 𝐴𝐵𝑇𝑀𝑡−1 <10.07360.0125

0.0611

Group6

1≤ 𝐴𝐵𝑇𝑀𝑡−1 <1.2

0.2484－0.06120.3096

Group7

1.2≤ 𝐴𝐵𝑇𝑀𝑡−1 <1.4

0.8146－0.04380.8584

Group81.4≤ 𝐴𝐵𝑇𝑀𝑡−1

0.3865－0.4072

0.79370.2090－0.0600

0.2690mean
t-statistic(2.4423**)

𝐴𝐵𝑇𝑀𝑡−1 <10.0559－0.0002

0.0561

𝐴𝐵𝑇𝑀𝑡−1 ≥10.4125－0.2046

0.6171Note:
The samples were separated into eight groups using an interval of 0.2. Then, the eight groups were arranged in an
ascending order of beginning-of-period ASSET-BTM. I expected companies belonging to Group 1 would show theleast need for asset write-downs, whereas companies belonging to Group 8 would show the opposite tendency.
𝐴𝐵𝑇𝑀𝑡−1 : total assets / the sum of market capitalization and total assets minus common equity, both measured at
the end of fiscal year t－1. .𝐸𝑡 represents net income for fiscal year t deflated by market capitalization at the end of
fiscal year t－1. 𝛽𝑗𝐵𝐷 : the degree with which bad news is reported in earnings for group j. 𝛽𝑗𝐺𝐷 : the degree withwhich good news is reported in earnings for group j. 𝑐𝑗 : the degree by which bad news is reported in earnings in atimelier manner than good news. ***, **,* indicate significance at the two-tailed 1%, 5%, 10% confidence level,
respectively

In general, the difference between 𝛽𝐵𝐷and 𝛽𝐺𝐷
is 0.2690, with a significant
𝑗𝑗t-statistic of 2.4423 (5% two-tailed). The measure 𝐶 for Group 6 is 0.3096, which isextreme, much higher than that of Group 4 (0.0287) and Group 5 (0.0611). This is in linewith hypothesis 1 and LSS, that there exists a substantial leap in asset write-downs

31around region of having a beginning-of-period ASSET-BTM of one. Similarly, measure
𝐶 for Group 6 is 0.3096, which comprises only about 36% of that found for group 7(0.8584). However, measure 𝐶 for Group 8 drops back to 0.7937. Group 8 contains all
samples with beginning-of-period ASSET-BTMs greater than 1.4, which is considered tobe the group that confronting the most imminent need for asset impairment. The
remarkable decline in measure 𝐶 for Group 8 strongly suggests that non-discretionary
conservatism was overridden by the demand for managerial discretion. In other words,
the results of this test invalidate hypothesis 2 and broadly support the prediction in thisstudy that non-discretionary conservatism does not acquire the same competence inJapanese listed companies as it does in American listed companies.
To check the robustness of the results of hypothesis 2, I perform an additional analysis
by applying 𝐶_𝑆𝐶𝑂𝑅𝐸 (e.g., Khan and Watts [2009]).
Khan and Watts [2009] incorporated three firm-specific characteristics into the Basumodel to estimate an annual across-sectional Basu coefficient. These are firm size
(𝑆𝐼𝑍𝐸), marker-to-book ratio (𝑀𝑇𝐵), and market value leverage (𝐿𝐸𝑉𝑀𝑉). 𝐺_𝑆𝐶𝑂𝑅𝐸 in
Eq. 5 denotes the timeliness of good news being reflected on income statements, and
𝐶_𝑆𝐶𝑂𝑅𝐸 in Eq. 6 denotes the incremental timeliness of bad news being reflected on
income statements. However, Eq. 5 and Eq. 6 are not regression models. Instead, Khanand Watts [2009] substituted them into the Basu model to estimate parameters 𝜇𝑖 and𝛾𝑖 (i=1~4). Then, 𝜇𝑖 and 𝛾𝑖 (i=1~4) were in turn substituted into Eq. 5 and Eq. 6 as
empirical estimators to compute annual 𝐺_𝑆𝐶𝑂𝑅𝐸 and 𝐶_𝑆𝐶𝑂𝑅𝐸 for each firm/year
sample. 𝑇_𝑆𝐶𝑂𝑅𝐸 is thus the sum of 𝐺_𝑆𝐶𝑂𝑅𝐸 and 𝐶_𝑆𝐶𝑂𝑅𝐸, which measures thedegree of conditional conservatism.
Unlike Khan and Watts [2009]13, I employed pooling data to verify the robustness offindings. The model applied in this study is outlined below, where 𝑆𝐼𝑍𝐸𝑡 represents thenatural log of market capitalization; 𝑀𝑇𝐵𝑡 represents the ratio of market capitalization
to the book value of common equity at the end of the year t. 𝐿𝐸𝑉𝑀𝑉𝑡 represents
leverage which is calculated as book value of total liabilities deflated by the market
capitalization. In this study, 𝐸𝑡 , the dependent variable in Eq. 7, denotes the net
income in fiscal year t deflated by market capitalization measured at the end of fiscal
year t－1. 𝑅𝑡 is the buy-and-hold return on common stock for the twelve months ending
three months after the end of fiscal year t. As with Basu [1997], 𝐷𝑅𝑡 is a dummy
variable that equals 1 if 𝑅𝑡 is negative and is 0 otherwise.

13

Khan and Watts [2009] estimated the regressions annually to allow the coefficients to varyannually, and report the mean coefficients over an analysis period of 43 fiscal years.

32

𝐺_𝑆𝐶𝑂𝑅𝐸＝𝛽3 = 𝜇̂1 + 𝜇̂𝑆𝐼𝑍𝐸̂𝑀𝑇𝐵̂𝐿𝐸𝑉𝑀𝑉2𝑖,𝑡 + 𝜇3
𝑖,𝑡 + 𝜇4𝑖,𝑡5𝐶_𝑆𝐶𝑂𝑅𝐸 = 𝛽4 = 𝛾̂1 + ̂𝑆𝐼𝑍𝐸
𝛾2𝛾3𝛾4𝑖,𝑡 + ̂𝑀𝑇𝐵𝑖,𝑡 + ̂𝐿𝐸𝑉𝑀𝑉
𝑖,𝑡

6

𝐸𝑖,𝑡

=

𝛽1 + 𝛽2 𝐷𝑅𝑖,𝑡 + 𝑅𝑖,𝑡 (𝜇1 + 𝜇2 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝜇3 𝑀𝑇𝐵𝑖,𝑡 + 𝜇4 𝐿𝐸𝑉𝑀𝑉𝑖,𝑡 ) + 𝐷𝑅𝑖,𝑡 𝑅𝑖,𝑡 ( 𝛾1 +𝛾2 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛾3 𝑀𝑇𝐵𝑖,𝑡 + 𝛾4 𝐿𝐸𝑉𝑀𝑉𝑖,𝑡 ) + ( 𝛿1 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛿2 𝑀𝑇𝐵𝑖,𝑡 + 𝛿3 𝐿𝐸𝑉𝑀𝑉𝑖,𝑡 +7

𝛿4 𝐷𝑅𝑖,𝑡 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛿5 𝐷𝑅𝑖,𝑡𝑀𝑇𝐵𝑖,𝑡 + 𝛿6 𝐷𝑅𝑖,𝑡 𝐿𝐸𝑉𝑀𝑉𝑖,𝑡 ) + 𝜀𝑖

Table 8 reports the regression results for the estimation of Eq. 7. As with prior studies(e.g., Basu [1997], Khan and Watts [2009]), coefficients on bad news companies are all
statistically significant. The coefficient on 𝐷𝑅𝑡 * 𝑅𝑡 (0.8266***) indicates that earnings
are generally conservatively processed. Khan and Watts [2009] argued that companies
holding greater market values are usually either willing or obliged to provide moreinternal information to reduce information asymmetries. As a result, larger companies
are considered to have lower demands for conservatism. Therefore, the expected sign for(𝐷𝑅𝑡 * 𝑅𝑡 * 𝑆𝐼𝑍𝐸𝑡 ) is negative and the result (－0.0828***) of this study is consistent withthat of Khan and Watts [2009]. They also predicted a positive coefficient for 𝐷𝑅𝑡 *𝑅𝑡 * 𝑀𝑇𝐵𝑡 ( 𝐷𝑅𝑡 * 𝑅𝑡 * 𝐿𝐸𝑉𝑀𝑉𝑡 ), implying that companies with more growth options(higher leverage) are more in favor of conservative accounting choices. The results ofthis study (0.0042 and 0.0772***) are identical with their predictions.
Table 11 reports the results for the sum of 𝐶_𝑆𝐶𝑂𝑅𝐸 and 𝐺_𝑆𝐶𝑂𝑅𝐸 ( 𝑇_𝑆𝐶𝑂𝑅𝐸
hereafter). Though they do not completely correlate with the rank of ASSET-BTM,
Groups 6, 7, and 8 prominently lead in 𝑇_𝑆𝐶𝑂𝑅𝐸, which is consistent with hypothesis 1.
However, when comparing these three groups one by one, their 𝑇_𝑆𝐶𝑂𝑅𝐸 ranking takes
up exact the opposite order of that of ASSET-BTM. Group 6 has the highest 𝑇_𝑆𝐶𝑂𝑅𝐸
of 0.2528. This clearly justifies the prediction that non-discretionary conservatism isless directly evident in Japanese listed companies.
TABLE 8Regression results of Eq. 7
Exp. signpooled data

Khan and Watts[2009]

𝛽1 (intercept)－0.0106

0.083***

𝛽2 (𝐷𝑅)

0.0341－0.024***0.03680.031－0.00450.005**－0.0002－0.006**

𝜇1 (𝑅)
𝜇2 (𝑅 ∗ 𝑆𝐼𝑍𝐸)
𝜇3 (𝑅 ∗ 𝑀𝑇𝐵)

+
+
－

33

𝜇4 (𝑅 ∗ 𝐿𝐸𝑉𝑀𝑉)－－0.0098***

0.005

𝛾1 (𝐷𝑅 ∗ 𝑅)+－

0.8266***

0.237***－0.0828***－0.033***++0.0042－0.0070.0772***

0.033

𝑁

17152

115516

𝐴𝑑𝑗. 𝑅 2

0.03940.24

𝛾2 (𝐷𝑅 ∗ 𝑅 ∗ 𝑆𝐼𝑍𝐸)
𝛾3 (𝐷𝑅 ∗ 𝑅 ∗ 𝑀𝑇𝐵)
𝛾4 (𝐷𝑅 ∗ 𝑅 ∗ 𝐿𝐸𝑉𝑀𝑉)Note:
𝑁 denotes the number of observations in each ASSET-BTM group. 𝐸𝑡 denotes the net income in fiscal year t,
deflated by market capitalization at the end of fiscal year t－1. 𝑅𝑡 is the buy-and-hold return on common stock forthe twelve months ending three months after the end of fiscal year t. 𝐷𝑅𝑡 is a dummy variable that equals one if
𝑅 is negative and is zero otherwise. 𝑆𝐼𝑍𝐸𝑡 represents the natural log of market capitalization. 𝑀𝑇𝐵𝑡 represents
market capitalization to book value of common equity at the end of fiscal year t. 𝐿𝐸𝑉𝑀𝑉𝑖,𝑡 represents leverage,
which is calculated as book value of total liabilities deflated by market capitalization at the end of fiscal year t. ***,**,* indicate significance at the two-tailed 1%, 5%, 10% confidence level, respectively.

5.4.2 Measuring conditional conservatism based on Accruals
Considerable controversy remains as to whether the Basu model yields reliable
estimations for conditional conservatism in applying the reverse earnings-on-returns
regression. To reduce the noise, I employed the accrual-based regression model
proposed in Ball and Shivakumar [2006].
Accruals are required to best reflect the economic effects of changes in expected future
cash flows before actual cash flows occur (the gain and loss recognition role of accruals).
Examples of loss accruals are accounts receivable write-downs (i.e., decreases in
expected future cash collections), inventory write-downs (i.e., decreases in expected
future cash flows from the investment in inventory such as physical loss, damage,obsolescence, and declines in market value), decreases in values of trading securities
and derivatives, provisions for litigation settlements, and asset impairment losses.
Examples of gain accruals are increases in values of trading securities and derivatives.
In line with Basu [1997], Ball and Shivakumar [2006] argue that conservatism also
induces asymmetry in the timeliness of gain and loss accrual recognition in which
operating cash flows indicate the bad news and the good news. According to theirframework, a decline in operating cash flows, more often than not, indicates a reductionin the asset’s value (bad news). Hence, loss accruals should be captured in a timelier
manner as conservatism requires management to reflect such value deterioration at thetime the information arises. Empirical evidence agrees with their prediction that
accruals are also asymmetric in the recognition of losses against gains. One of thelimitations of the work of Ball and Shivakumar [2006] is that a rapidly growing

34

company may have negative operating cash flows as it expands, whereas a contracting
company may exhibit positive cash flows when spending falls at a faster rate thanearnings. This dispels the explicit assumption that negative (positive) change in future
expected cash flows is positively associated with decreases (increases) in current-period
operating cash flows based on which they established the theory.
Ball and Shivakumar [2006] suggest three alternative models (i.e., COF model,
Dechow and Dichev [2002] model [DD model],and Jones model) and three different
measures (i.e., the level of cash flow, changes in the cash flow, and industry-adjusted
cash flow) for asymmetrically timely recognition of gains and losses in different
combinations to assess conditional conservatism.
In this study, I employed the Jones Model and the Modified Jones Model to examine
the degree of conditional conservatism. Besides its higher explanatory power exhibitedin the study by Ball and Shivakumar [2006], only the Jones model controls variations inperiodical performance (working capital) that mitigate impact from accrual reversals.
Second, it also explains the magnitude of investment in long-lived assets that filter out
the effects of depreciation accruals. I don’t apply the industry-adjusted measure in thisstudy because all samples had already been divided into smaller groups and further
disaggregation would possibly bias the test results for certain groups. Nonetheless, I
also found support for H2 in other combinations including the modified Jones model andthe COF modified Jones model. In addition to the dependent variable employed by Balland Shivakumar [2006] (ACCt ), I also applied other accrual measures to each model (i.e.,
non-operating accruals proposed by Givoly and Hayn [2000], total accruals proposed by
Kothari, Leone, and Wasley [2005] 14 , net income less cash flows from operations(𝑁𝐼－𝐶𝑂𝐹), and asset impairment losses. The results of those analyses are consistent
with the prediction in this study (the results are not documented for brevity). Moreover,consistent with the findings in Ball and Shivakumar [2006], incorporating conditional
conservatism into the Jones model appreciably increased the average 𝑅 − 𝑠𝑞 value
from the non-linear accruals model relative to that based on the traditional linear model.
Tables 9 and 10 report the regression results for Eqs. 8 and 9.

14

Kothari, Leone, and Wasley [2005] employed the following method to measure total accruals in yeart.
𝑇𝐴𝐶𝐶𝑡 ＝∆𝐶𝐴𝑡 － ∆𝐶𝑎𝑠ℎ𝑡 － (∆𝐶𝐿𝑡 － ∆𝑆𝑇𝐷𝑡 ) － 𝐷𝑒𝑝𝑡∆𝐶𝐴𝑡
: change in current assets∆𝐶𝑎𝑠ℎ𝑡 : change in cash and cash equivalents∆𝐶𝐿𝑡: change in current liabilities∆𝑆𝑇𝐷𝑡 : change in current liabilities transformed from non-current liabilities
𝐷𝑒𝑝𝑡: depreciation expenses

35

ACCt = 𝛼0 + 𝛼1 ∆𝐶𝐹𝑡 + α2 ∆𝑅𝐸𝑉t + 𝛼3 𝐺𝑃𝑃𝐸 + + 𝛼4 𝐷∆𝐶𝐹𝑡
8+ 𝛼5 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑡
ACCt = 𝛼0 + 𝛼1 ∆𝐶𝐹𝑡 + α2 (∆𝑅𝐸𝑉t − ∆𝐴𝑅) + 𝛼3 𝐺𝑃𝑃𝐸 + + 𝛼4 𝐷∆𝐶𝐹𝑡

9+ 𝛼5 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑡

ACCt denotes total accruals in year t. It is computed as earnings before extraordinary
items minus cash flow from operation. ∆𝐶𝐹𝑡 denotes changes in cash flows fromoperations. 𝐷∆𝐶𝐹𝑡 is a dummy variable, taking the value of 1 if ∆𝐶𝐹𝑡 is negative and 0
otherwise. ∆𝑅𝐸𝑉𝑡 denotes changes in net sales in year t. 𝐺𝑃𝑃𝐸t denotes gross property,
plant, and equipment. ∆𝐴𝑅 denotes changes in accounts receivable and 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
includes all the interaction terms. The variables employed in Eqs. 8 and 9 are alldeflated by average total assets in year t. As in Jones model, changes in sales control for
non-discretionary accruals of current assets and liabilities, while property, plant andequipment control for the non-discretionary component of depreciation expenses. Again,
as with Basu [1997], 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t measures the extent to which firms are conservative.
Under conservative reporting, 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t is expected to be positive.
Table 11 reports the comparison of the computing results for the degree of conditional
conservatism by each methodology. Columns ticked with boxes are the group with thehighest

value

for

that

measure.

Taken

as

a

whole,

groupswithhigherbeginning-of-period ASSET-BTM possess a higher degree of conditional conservatism.
However, none of those measures exhibit a monotonic rise in pace with the rise in
ASSET-BTM, which confirms the prediction of this study regarding the features of
non-discretionary conservatism peculiar to Japanese listed companies. These lines ofevidence indicate that Japanese impairment accounting practices fit better to the curve
shown in Panel B Figure 5.
It goes without saying that impairment accounting only constitutes one part ofconditional conservatism. Since each measure captures conditional conservatism in its
entirety, it is quite possible that impairment proceedings may have been performed
properly in high-rank groups even if their values turn out to be lower. However, once
impairment

accounting

proceduresareconducted,asubstantial

amountofextraordinary loss would be recorded at the end of that fiscal year, accompanied by a
rapid decrease in the underlying assets’ (group’s) book value. It seems evident that
impairment accounting produces a great influence on accounting conservatism. In
conclusion, the application of those measures, as in this study, is fully justified.

36

TABLE 9
Results of the Jones Model
8

ACCt = 𝛼0 + 𝛼1 ∆𝐶𝐹𝑡 + α2 ∆𝑅𝐸𝑉t + 𝛼3 𝐺𝑃𝑃𝐸 + + 𝛼4 𝐷∆𝐶𝐹𝑡 + 𝛼5 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑡Group1

Group2

Group3

Group4

Group5

Group6

Group7

Group8𝐷∆𝐶𝐹𝑡

0.0386－0.0050.0023－0.0018－0.00190.0021－0.00120.0124**∆𝐶𝐹𝑡－0.3544***－0.5106***－0.49***－0.3464***－0.2971***－0.6122***－0.505***－0.2935***

𝐷∆𝐶𝐹𝑡 ∗ ∆𝐶𝐹𝑡

0.2181

0.05440.0924－0.2416***－0.4639***0.2372***－0.0251－0.1673**∆𝑅𝐸𝑉𝑡－0.00150.0737***0.0424**0.019***

0.0375***0.0247***0.088***

0.1363***

𝐺𝑃𝑃𝐸𝑡

0.0168－0.0733－0.0789－0.0571***－0.0681***－0.1191***－0.0596**－0.0432

intercept

0.00770.00830.0051－0.0094－0.00690.0109***－0.0106－0.0201**

within0.65460.871

0.56040.576

0.53710.52750.5381

0.5425

between

0.054

0.36490.21970.29290.56620.27980.29620.1404overall

0.20920.59320.3246

0.4121

0.4925

0.391

0.403

0.3414F

8.467

74.71842.9994121.1591

227.3128

247.0901

109.8803

107.988

R − sq within∗

0.53090.81420.44110.56010.47370.50410.50410.4664chi2(15)

37.04

36.67

105.08

49.33

97.02

214.5746.94

321.47

Prob > chi20.00120.00140.00000.00000.00000.00000.00000.0000chibar2(01)

8.90

53.07

112.35158.83317.83344.12

81.79

47.07

Prob > chibar2

0.00140.00000.00000.00000.00000.00000.00000.0000Prob > F

0.00000.00000.00000.00000.00000.00000.00000.0000𝑅 − 𝑠𝑞ℎ𝑎𝑢𝑠𝑚𝑎𝑛𝑏𝑟𝑒𝑢𝑠𝑐ℎ& 𝑝𝑎𝑔𝑎𝑛

Note:
𝐴𝐶𝐶𝑡 denotes total accruals in year t. It is computed as earnings before extraordinary items minus cash flow from operation. ∆𝐶𝐹𝑡 denotes changes in cash flows from operations.
𝐷∆𝐶𝐹𝑡 is a dummy variable, taking the value of 1 if ∆𝐶𝐹𝑡 is negative and 0 otherwise. ∆REVt denotes changes in net sales in year t. 𝐺𝑃𝑃𝐸𝑡 denotes gross property, plant, and equipment.
Variables are all deflated by average total assets in year t. ℎ𝑎𝑢𝑠𝑚𝑎𝑛 indicates results for the Hausman test which differentiates between fixed effects model and random effects model inpanel data. 𝑏𝑟𝑒𝑢𝑠𝑐ℎ& 𝑝𝑎𝑔𝑎𝑛 indicates results for the Breusch–Pagan test which checks for the linear form of heteroscedasticity. Coefficients on interaction terms are omitted for brevity.

37

TABLE 10
Results of the Modified Jones Model
9

ACCt = 𝛼0 + 𝛼1 ∆𝐶𝐹𝑡 + α2 (∆𝑅𝐸𝑉t − ∆𝐴𝑅) + 𝛼3 𝐺𝑃𝑃𝐸 + + 𝛼4 𝐷∆𝐶𝐹𝑡 + 𝛼5 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑡Group1

Group2

Group3

Group4

Group5

Group6

Group7

Group8𝐷∆𝐶𝐹𝑡

0.1571***－0.0439**－0.003－0.003－0.0141***－0.0063－0.0166*

0.0221**∆CFt

0.8644***－0.4736***－0.496***－0.3222***－0.2932***－0.6216***－0.6481***－0.1337*

𝐷∆𝐶𝐹𝑡 ∗ ∆𝐶𝐹𝑡

－1.0754**－0.157－0.1452－0.2979***－0.6655***0.2106***0.1693－0.2749**∆𝑅𝐸𝑉𝑡 − ∆𝐴𝑅－0.0346

0.0306－0.0493*0.0201***0.0261***0.0267***0.0578***0.0224

𝐺𝑃𝑃𝐸𝑡－0.3301－0.4324***－0.0988*－0.0428*－0.0444***－0.1004***－0.03160.2219***intercept－0.0579

0.084***

0.0081－0.0195**－0.0131**0.0045－0.0199*－0.1005***

within0.47260.77460.44510.43820.47270.44710.4406

0.2982

between0.10110.13140.13990.21

0.52110.21920.19480.0186overall

0.14190.31040.2687

0.29650.43830.32750.25370.0801F

4.002438.0233

27.0567

69.5762175.6174

178.9539

74.2986

38.6954chi2(15)

72.43

69.70

117.40

26.26

80.39

116.97

91.11

89.39

Prob > chi2

0.00000.00000.00000.03540.00000.00000.00000.0000chibar2(01)

0.00

22.73

69.87

141.93256.98288.73

41.40

32.53

Prob > chibar2

1.00000.00000.00000.00000.00000.00000.00000.0000Prob > F

0.12160.00000.00000.00000.00000.00000.00000.0000𝑅 − 𝑠𝑞ℎ𝑎𝑢𝑠𝑚𝑎𝑛𝑏𝑟𝑒𝑢𝑠𝑐ℎ& 𝑝𝑎𝑔𝑎𝑛

Note:
𝐴𝐶𝐶𝑡 denotes total accruals in year t. It is computed as earnings before extraordinary items minus cash flow from operation. ∆𝐶𝐹𝑡 denotes changes in cash flows from operations.
𝐷∆𝐶𝐹𝑡 is a dummy variable, taking the value of 1 if ∆𝐶𝐹𝑡 is negative and 0 otherwise. ∆REVt denotes changes in net sales in year t. 𝐺𝑃𝑃𝐸𝑡 denotes gross property, plant, and equipment.
Variables are all deflated by average total assets in year t. ℎ𝑎𝑢𝑠𝑚𝑎𝑛 indicates results for the Hausman test which differentiates between fixed effects model and random effects model inpanel data. 𝑏𝑟𝑒𝑢𝑠𝑐ℎ& 𝑝𝑎𝑔𝑎𝑛 indicates results for the Breusch–Pagan test which checks for the linear form of heteroscedasticity. Coefficients on interaction terms are omitted for brevity.

38

TABLE 11
Comparison of measure for conditional conservatism
𝑐𝑗

𝑇_𝑆𝐶𝑂𝑅𝐸

𝐷∆𝐶𝐹 ∗ ∆𝐶𝐹
Jones model

Modified
Jones modelGroup 10.02620.00980.2181－0.748***15

Group 20.0318－0.0601

0.0544－0.157

Group 30.0424－0.0824

0.0924－0.1452

Group 40.0287－0.0382－0.2416***－0.2979***

Group 5

0.06110.0860－0.4639***－0.6655***

Group 60.30960.25280.2372***0.2106***

Group 70.85840.2069－0.02510.1693

Group 80.79370.1828－0.1673**－0.2749**

Note:
The samples were separated into eight groups using an interval of 0.2. Then, the eight groups were arranged in an
ascending order of beginning-of-period ASSET-BTM. I expected companies belonging to Group 1 would show theleast need for asset write-downs, whereas companies belonging to Group 8 would show the opposite tendency. 𝑐𝑗 :measure of conditional conservatism proposed in Pae, Thornton, and Welker [2005]. 𝑇_𝑆𝐶𝑂𝑅𝐸: the sum of 𝐶_𝑆𝐶𝑂𝑅𝐸
and 𝐺_𝑆𝐶𝑂𝑅𝐸 (measure of conditional conservatism proposed inKhan and Watts [2009]). 𝐷∆𝐶𝐹t ∗ ∆𝐶𝐹t: measureof conditional conservatism proposed in Ball and Shivakumar [2006].

Figure 5
Panel A: U.S.

Panel B: JAPANNotes:𝐴𝐵𝑇𝑀 denotes ASSET-BTM, which is computed as follows: total assets / the sum of market capitalizationand total assets minus common equity, both measured at the end of fiscal year t－1. 𝑊𝐷 is asset write-downs,
measured at the end of fiscal year t deflated by market capitalization measured at the end of fiscal
year t－1.

15

Result of Breusch and Pagan test for Group 1 suggests that the ordinary least squares analysis ispreferred. Hence, I replaced the test result for Group 1 by fixed effect model (－1.0754**) with that
produced by OLS (－0.748***).

39

6 Discussion of the test results and Conclusions
6.1 Effects of the convergence of accounting standards
Academia was generally divided regarding how the mandatory adoption of IFRS
affects the quality of earnings (e.g., Ahmed, Neel, and Wang [2013], Barth, Landsman,Lang, and Williams [2012]). Among them is Skinner [2008], which focused on the
adoption of deferred tax accounting in 1998. He found that deferred tax assets are used
as a tool of regulatory forbearance to give the major Japanese banks the appearance offinancial health when in fact many were insolvent. Skinner [2008] attributed increases
in deferred tax assets to the unique business environment in Japan and managers’
overly optimistic estimations of future earnings. His findings also indicate that theapplication of GAAP-mandated accounting principles might submit to both political andregulatory incentives and firmly established conventionality (e.g., Garrod, Kosi, andValentincic [2008], Iatridis [2012],and Salter, Kang, Gotti, and Doupnik [2013]).
In addition to my findings presented in Section 2, I further extended the analysis
period back to fiscal year 1994. Because asset write-down data are separately available
only from the year end of 2005, I substituted the extraordinary loss for impairment loss
for the accounting period between 1994 and 2004.
Figure 6:
2005-2014

0.0

0.0

0.2

0.2

0.4

0.4

WD

WD

0.6

0.6

0.8

0.8

1.0

1.0

1994-2014

0.5

Note:

1.0

1.5

2.0

0.5

ABTM

1.0

1.5

2.0

ABTM

Figure 6 demonstrates the correlation between current-period write-downs ( 𝑊𝐷 ) and beginning-of-period
ASSET-BTM (ABTM). 𝑊𝐷 ：asset write-downs measured at the end of fiscal year t /market capitalization
measured at the end of fiscal year t－1. 𝐴𝐵𝑇𝑀 : total assets / market capitalization + total assets - common equity,
both measured at the end of fiscal year t－1.

Both charts demonstrate the correlation between 𝑊𝐷t (plotted on the vertical axis)
and 𝐴𝐵𝑇𝑀t−1 (plotted on the horizontal axis), using past performance figures ofJapanese listed companies from fiscal year 2005 (1994) to 2014, respectively. While thedata points are more clustered in the 1994-2014 analysis period, the tendencies
observed in the areas of an 𝐴𝐵𝑇𝑀𝑡−1 ratio higher than 1 are identical. This finding
confirms the management’s reluctance to record extraordinary losses in Japanese listed40

companies. If changing the accounting rules is not sufficient to alter customaryfinancial reporting practice, then the adoption of commonly agreed-upon accounting
principles around the world might, on all accounts, fail to bring into being astandardized financial reporting system with the same level of reliability.
6.2 Sources of variations in accounting conservatism between Japan and the U.S.
I predict that the differences in accounting standards and/or contractual incentives
lead to the discrepancy existing in the practice of asset impairment accounting. I foundsome support for the opportunistic reporting hypothesis. For instance, firm size (𝑆𝐼𝑍𝐸)
and weak performance ( 𝑊𝐸𝐴𝐾 ), which are both supposed to control for the
non-discretionary component in conservatism, show countervailing effects in domains
with extremely high ASSET-BTM. This result reasonably justifies the opportunistic
reporting hypothesis that the effect generated by non-discretionary conservatism may
have been nullified; otherwise, actual asset write-downs might have been higher thanreported in Japanese listed companies. However, the impact of these two factors is,though statistically significant, limited. Furthermore, significantly negative predictors
for middle level asset write-downs (i.e., the 70th－85th quantiles) are indistinct. One of
the alternative explanations for this is the unquantifiable differences that exist inaccounting practice.
Under GAAP in the United States, impairment tests for assets held for use differ fromthose held for sale.16 Japan accounting standards, in contrast, delineate no specifictreatments for fixed assets held for sale in. Even so, changes in the stated reason forholding fixed asset can be considered indications of impairment; therefore, Japanese
accountants are subjecting more assets to impairment tests. Conceivably, the more
assets assigned to an asset group, the more easily can estimation of the entire value ofthe group be adjusted. In other words, increases in target assets may abet managerial
discretion under Japanese accounting standards.
Moreover, under Japanese impairment standards, when more than one business unit
is acquired in a deal wherein goodwill is recognized, the book value of that goodwill isallocated pro-rata across business units. Also, the impairment test is performed on alarger unit including both the asset group and its related goodwill. If the amountrecoverable from a business unit is below its carrying amount, impairment losses areallocated first to reduce book value of goodwill assigned to it. Any excess over book value
16

Assets held for sale must be sold within one year from the date they are classified; hence, they are
exempt from impairment tests. Furthermore, they are also reassessed at their book value or net fairvalue, whichever is lower. Hence, even such assets are subject to impairment tests, and the impact ofsuch an experiment on LSS is negligible.

41

of goodwill is distributed pro-rata to the other assets based on their book value. In otherwords, goodwill is removed from the balance sheet ahead of physical assets.
Consequently, higher proportions of goodwill potentially enable management to delay
decisions to declare assets impaired.
Additionally, management can also signify its confidence in an asset’s (group’s)
earnings potential by not impairing it, even if it shows signs of depreciation undercurrent assumptions for goodwill amortization. The drawback is that the management
can abuse the hypothesis of slowly accumulating internally-developed goodwill to
inflate estimates of future cash flow. In other words, amortization of goodwill invites
manipulation in recognizing impairments.
Japanese accounting standards tally impairment losses as the carrying amount of anasset (group) minus its recoverable value (the higher of net realizable value or value inuse).17 Since calculation of value in use involves management discretion (i.e., amount of
future cash flow, discount rate, and useful life of the primary asset in an asset group),management can minimize impairment losses by adjusting its estimation. In otherwords, impairment losses recognized by Japanese listed companies can be much lower
than those recognized in the United States, other conditions being equal.18
Taken as a whole, current accounting practices in Japan may have induced more
discretion (although the cause for this change is not limited to these factors), which
eventually induced a trend counter to the original prediction of LSS. However, some ofthe effects are empirically difficult to quantize using financial data.
6.3 Future researchTodetectnon-discretionary

conservatism

in

impairment

accountingasacharacteristic of Japanese listed companies, I applied QR test, coupled with the
QR-LASSO, to evaluate the impact of multiple factors on accounting choices for asset
impairment. It provides more detailed insights which cannot be achieved by otherordinary methods prevailing in most previous research. However, the results of thisstudy are only partly consistent with LSS’s findings and substantiate my prediction
17

Recoverable value = net realizable value or value in use , whichever is higher.
Net realizable value= Market Value － Sale-Related Expenses
Value in use = ∑𝑛future cash flows𝑛(discount rate)nwhere n represents the useful life of the primary asset in an asset group.
Unlike USA accounting rules, the cash flow estimation period under Japanese GAAP is the primary
asset’s remaining useful life or 20 years, whichever is shorter. Japanese impairment standards do not
exclude infinite-lived or non-depreciable assets (e.g., land) from candidates for the primary asset of anasset group. Hence, in general, the longer the period of estimated cash flow, the greater future cash
in-flows will be. In regards to audit practice, the impact of this difference in estimation period is hardto determine.
18

42

that non-discretionary conservatism is insufficient in eliminating managerial discretion.
The tests also show that asset write-downs are more sensitive to debt covenants than tobeginning-of-period ASSET-BTM, which complements the argument presented inRoychowdhury and Martin [2013].
LSS argued that the unbiased application of accounting principles embodies
non-discretionary conservatism. In addition, non-discretionary conservatism, whenembarked upon, will invalidate discretionary conservatism and eventually affiliate
efficient contracts stemming from information asymmetries. However, evidence foundin this study runs counter to that found by LSS. The most prominent property I found inJapanese listed companies is that management might have studiously avoided certain
asset write-downs even when the beginning-of-period ASSET-BTM suggests a dire needfor impairment. As pointed out in prior studies, impairment accounting comprises
certain amount of estimates and judgements. Other assets (i.e., goodwill andinventories) that are under the influence of such an accounting process are probably
also subordinated to managerial discretion. Moreover, management is now empowered
to capitalize development costs on the balance sheets, which will contribute to a growing
precariousness of earnings in counties that adopt international financial reporting
standards. In other words, the argument contained herein breeds new wariness ofconditional conservatism and underlines the need to embrace unconditional
conservatism to mitigate further volatility in reporting earnings under uncertain
economic environments.
I found evidence for the presence of a buffer zone, which indicates managerial
opportunism in the timing of asset write-downs. It is also possible that management
postpones timely loss recognition out of proper judgements based on its exclusive
understanding of the company, which Roychowdhury and Martin [2013] refers to as“normal conservatism”. However, neither LSS nor the method employed in this study
manages to distinguish the normal component from the buffer zone. On the other hand,
despite arduous attempts to accurately measure the degree of conservatism, especiallyconditional conservatism, no widely accepted metric exists that can properly reflect thedegree of conservatism. Until such metric comes into being the development of further
research on conservatism will suffer. I will work toward these causes while pursuingfundamental determinants and traits of conservatism in Japanese companies in myfuture research.

43

Appendix 1: Introduction to Quantile Regression (QR)
This appendix relates to the technical discussions on Quantile Regression (QR) tests in
Section 5. Introduced by Koenker and Bassett [1978] as a location model, QR is aconvenient statistical method for estimating conditional quantile functions. A
significant feature of QR is that it can provide more accurate information about howeach predictor drives the response variable at any quantile. In other words, OLS derives
parameter estimates from the conditional mean of the response variable, whereas QR
employs the conditional median or any other quantile of the response variable. A typical
QR function can be expressed as follows:⒈

Q 𝑌| 𝑋 (τ) = 𝑋𝛽𝜏 + 𝜀𝜏 19

where 𝑋 denotes a design matrix of p predictors and 𝜀𝜏 denotes the error term for theτth quantile.
For 𝑛 independent observations, the τth quantile splits the observations into areas τand 1−τ. QR estimates (β𝜏 ) are determined by solving this optimization problem:

1𝛽𝜏 = min𝛽𝜏 {∑𝑛𝑖:𝑦𝑖 ≥ 𝑥𝑖𝛽 𝜏|𝑦𝑖 − 𝑥𝑖 𝛽𝜏 | + ∑𝑛𝑖:𝑦𝑖 < 𝑥𝑖𝛽( 1 − 𝜏)|𝑦𝑖 − 𝑥𝑖 𝛽𝜏 | }𝑛= min𝛽𝜏 ∑𝑛𝑖=1 𝜌𝜏 (𝑦𝑖 − 𝑥𝑖𝑇 𝛽𝜏 ) 20⒉

where 𝜌𝜏 (𝑢) = 𝑢 ∙ {𝜏 − 𝐼(𝑢 < 0)} is the tilted absolute function shown in Figure A,

Suppose Y is a random variable following a cumulative distribution function 𝐹 (𝑦) =𝑃 (𝑌 ≤ 𝑦). The τth quantile of Y is defined as follows:
Q Y (𝜏) = 𝐹 −1 (𝜏) = inf {𝑦 ∈ 𝑅 |𝐹 (𝑦) ≥ 𝜏} , where τ ϵ (0,1)
⒊
19

20

The conditional expectation loss function for QR is shown as follows:𝑦̂∞

L = E [ρ𝜏 (Y − 𝑦̂)] = (𝜏 − 1 ) ∫−∞(𝑦 − 𝑦̂)𝑑𝐹(𝑦) + 𝜏 ∫𝑦̂ (𝑦 − 𝑦̂)𝑑𝐹(𝑦)⒋A specific quantile can be found for variable Y by setting the derivative of the expected
loss function to 0.
∂L
∂𝑦̂

𝑦̂

∞

= (1 − 𝜏 ) ∫−∞ 𝑑𝐹(𝑦) − 𝜏 ∫𝑦̂ 𝑑𝐹(𝑦) = 0⒌

𝐹 (𝑦̂) = 𝜏44and 𝐼 (𝑢 < 0) is the usual indicator function.21 Thus far, quantile regression also canbe viewed as the extension of LAD, 22 which minimizes a sum of asymmetricallyweightedabsolute

residuals

bygivingasymmetricpenalties(1 − τ)|𝜀𝑖 |forover-predicted observations and τ|𝜀𝑖 | for under-predicted observations.
Figure A: Quantile Regression ρ Function

Note: 𝜌𝜏 (𝑢) = 𝑢 ∙ {𝜏 − 𝐼(𝑢 < 0)} is the tilted absolute function. τ denotes the τth quantile.

𝐼 (𝑢 < 0) = 1 𝑖𝑓 𝑢 < 0⒍
𝐼 (𝑢 < 0) = 0 𝑖𝑓 𝑢 ≥ 0
22 For 𝑛 independent observations, each observation includes a response and a vector of 𝑝 predictors.
A linear regression function is defined as follows:21

𝐼 (𝑢 < 0) = {𝑝

𝑦𝑖 = 𝛽0 + ∑𝑗=1 𝛽𝑗 𝑥𝑖𝑗 + 𝜀𝑖 , 𝑖 = 1,2, … , 𝑛.

( 𝐸[ε𝑖 ] = 0 , 𝐸[𝜀𝑖2 ] = 𝜎 2 )⒎𝜀𝑖 denotes the error term for observation 𝑖 , measuring the vertical distance between the 𝑖 thobservation (𝑦𝑖 , 𝑥𝑖 ) and the corresponding point on the regression line. OLS provides solutions thatminimize the residual sum of squared errors (RSS).
𝑝

RSS (β) = ∑𝑛𝑖=1(𝑦 − 𝛽0 − ∑𝑗=1 𝛽𝑗 𝑥𝑖𝑗 )2 = ∑𝑛𝑖=1 𝜀𝑖 2⒏

For the OLS estimators to approximate the unknown parameters, values of ε𝑖 must be both
independent (exogeneity assumption) and identically distributed (homoscedasticity assumption).
Hence, OLS may fail to offer optimal estimators (although still valid) when the homoscedasticity
assumption is violated.
The least absolute deviations (LAD) is an alternative to OLS which minimizes the sum of absolute
errors (SAE). Unlike RSS, SAE represents the sum of the absolute values of the vertical distance
between points in the data set and the corresponding points on the regression line.
𝑝
SAE(β) = ∑𝑛𝑖=1|𝑦 − 𝛽0 − ∑𝑗=1 𝛽𝑗 𝑥𝑖𝑗 | = ∑𝑛𝑖=1|𝜀𝑖 |⒐The symmetry of the piecewise linear absolute loss function implies that minimizing the sum ofsquared residuals is equivalent to minimizing the median of the absolute residuals. Note that themedian is also the 50th quantile. Therefore, we might also define other quantiles as solutions to an
optimization problem by imposing an asymmetric penalty on the absolute residuals as quantiles differ
from median.

45

Appendix 2: Introduction to Least Absolute Shrinkage Selection Operator (LASSO)
This appendix relates to the technical discussion on Least Absolute Shrinkage
Selection Operator (LASSO) and the adaptive LASSO in Section 5. Developed by
Tibshirani [1996] and Belloni and Chernozhukov [2011], LASSO is a shrinkage methodthat imposes an 𝐿1 norm penalty on parameters of an objective function. Shrinkage
methods are a kind of continuous subset selection by adding constraints on the value ofcoefficients. It minimizes the sum of squared errors, subject to the constraint that thesum of absolute values of coefficients (𝐿1 norm of the parameter vector) is less than aconstant. This constraint removes less important parameters from the model by
reducing their coefficients to 0, thereby generating a more sophisticated function. The
rationale of shrinkage methods is to trade some unbiasedness for lower variance toimprove overall prediction accuracy and to retain variables with impacts large enoughto appear in the fitted model. LASSO is highly efficacious in selecting independent
variables of greater importance and estimating regression parameters simultaneously.
LASSO has been applied to archival research in economics and medicine but notfinancial accounting.
Consider a linear model for 𝑛 independent observations, each of which includes a
response (𝑦𝑖 ) and a vector of 𝑝 predictors (𝑥 = (x1 , 𝑥2 , … , 𝑥𝑝 )𝑇 ).
𝑝𝑦𝑖 = 𝛽0 + ∑𝑗=1 𝛽𝑗 𝑥𝑖𝑗 + 𝜀𝑖 , 𝑖 = 1,2, … , 𝑛.

⒎β = (β0 , β1 , … , β𝑝 )𝑇 represents a vector of unknown regression coefficients, and 𝜀𝑖
indicates the error term for the 𝑖th observation. LASSO estimators are determined bysolving the following optimization problem:𝑝𝑝min𝛽 {∑𝑛𝑖=1(𝑦 − 𝛽0 − ∑𝑗=1 𝛽𝑗 𝑥𝑖𝑗 )2 + 𝛾 ∑𝑗=1|𝛽𝑗 |}⒑𝑝𝛾 ∑𝑗=1|𝛽𝑗 | is the nonnegative penalty term, in which 𝛾 ≥ 0 is a tuning parameter that
controls the amount of shrinkage: the larger the value of 𝛾, the greater the shrinkage.
When 𝛾 =0, the estimator is equal to the OLS solution. As 𝛾 increases, more shrinkage
is imposed on the regression coefficients and the coefficients are shrunk from OLS
solution toward 0. Since the intercept 𝛽0 was left out of a penalty term, predictor
variables must be standardized in order to strike the intercept. However, the classicalL1 norm penalty has been criticized for not being able to simultaneously achieve theoracle property, namely unbiasedness, sparsity and consistency (e.g., Fan and Li [2001]),
as it equally penalizes coefficients. For example, large parameters can be overly

46

penalized which induces unnecessary bias into the estimation, while small parameters
be under-penalized at the cost of sparsity. Furthermore, the LASSO solutions tend to
remove highly correlated variables altogether or select them all. When related variables
are all included in the model, they enter the model with different signs.
The Adaptive LASSO is proposed by Zou [2006] as an extension of the LASSO toattenuate the aforementioned limitations. The Adaptive LASSO overcomes the selection
bias in the standard LASSO by assigning a consistent weight to each variable. In otherwords, such weights can adjust the amount of penalty imposed on each parameter on
the basis of their relative importance. The superiority of the Adaptive LASSO has been
confirmed in various fields of study. The estimators regularized by the Adaptive LASSO
are determined by solving the following optimization problem:

2

𝑝

𝑝

min𝛽 {∑𝑛𝑖=1(𝑦 − 𝛽0 − ∑𝑗=1 𝛽𝑗 𝑥𝑖𝑗 ) + 𝛾 ∑𝑗=1 𝑤𝑗 |𝛽𝑗 |}where weights are set to be 𝑤𝑗 = |𝛽̂𝑗 |−𝜆⒒(λ > 0).

On the other hand, neither LASSO or the Adaptive LASSO is robust to
high-dimensional data set with error distribution (e.g., Li and Zhu [2008]).

The

Adaptive LASSO regularized quantile regression can be then viewed as a solution to
alleviate the drawbacks of LASSO regularized conditional mean regressions. It
performs effect selection in the framework of quantile regression. The rationale behind
it is to penalize the coefficients at different quantiles by using adaptive weights (e.g.,
Wu and Liu [2009], Fan, Fan, and Barut [2014]). For a specific tuning parameter 𝛾, the
QR-LASSO finds the solution to the following optimization problem at the 𝜏th quantile:𝑝min𝛽 ∑𝑛𝑖=1 𝜌𝜏 (𝑦𝑖 − 𝑥𝑖𝑇 𝛽𝜏 ) + 𝛾 ∑𝑗=1 𝑤
̃𝑗 |𝛽𝜏 |⒓𝜏−𝜆where weights are set to be 𝑤
̃𝑗 = |𝛽̂𝜏 | (λ > 0).

47

Appendix 3: Differences in the process of impairment recognition
Table A presents differences that are considered as important between US GAAP andJapan GAAP.
TABLE A
Differences considered as important
USA GAAP

JAPAN GAAP

Under USA GAAP, the impairment tests forJapan impairment standards do not have such

assets to be held and used differ from those held

a separation for assets tested for impairment.

for sale.
The impairment provisions of ASC 360-10

Japanese impairment standards also apply togenerally apply to long-lived assets other than

goodwill. Furthermore, when more than one

goodwill or other intangible assets that are notbusiness units is acquired in the deal where

being amortized. However, asset groups may

goodwill is recognized, the book value of that

include assets and liabilities outside the scope of

goodwill shall be allocated to those business

ASC 360-10 (for example, goodwill — if certain

units on a pro-rata basis, and the impairment

conditions are met — and other non-amortizing

test should be performed to a larger unit

intangible assets). In this case, the impairment

including both the asset group and its related

loss will reduce the carrying amount of the

goodwill. Finally, the amount of impairment

long-lived assets of a group covered by ASC

loss increased by adding goodwill to an asset

360-10 on a pro-rata basis using the relative(group) should be allocated to goodwill first, andcarrying amounts of those assets. Thus, in no

the excess amount over the book value of thecircumstance

goodwill will be allocated to the other assets on

will

goodwill,

indefinite-lived

intangibles or other assets excluded from thea pro-rata basis.

scope of ASC 360-10 (or liabilities if part of anasset group) be affected by an impairment loss
recognized under ASC 360-10, even if thoseassets or liabilities are included in the asset
group being tested for recoverability.
According

to ASC360-10,only

long-lived

Japanese impairment standards for fixed assets

tangible asset being depreciated (or identifiable

do not exclude non-depreciable assets as asset

intangible asset being amortized) can be thecandidates for the primary asset. Therefore,

primary asset of an asset group, which means

while the cash flow estimation period is based

property such as lands, which do not have a valid

upon the primary asset’s remaining useful life

period, cannot be a primary asset.

under USA GAAP, the cash flow estimation
period under Japan GAAP is determined by48

either remaining useful life of the primary asset
or 20 years, whichever is shorter.
When an asset (group) is deemed unrecoverable,

Japan requires management to calculate it as

ASC360-10 requires management to calculate

the excess of the carrying amount of an asset

impairment loss as the excess of the carrying(group) over its recoverable value (either net

amount of an asset (group) over its fair value.

realizable value or value in use, whichever ishigher23).

23

Recoverable value = net realizable value or value in use , whichever is higher.
Net realizable value= Market Value － Sale-Related Expenses
Value in use = Σ𝑛 (future cash flows𝑛 /discount raten)
where n represents the useful life of the primary asset in an asset group49

Appendix 4: Similarities in the process of impairment recognition
1. Both USA and Japan standards require management to group assets at the lowest
level where there are identifiable cash flows that are largely independent of the cash
flows of other assets (groups). Moreover, they both acknowledge that grouping assets
involves a significant amount of judgement.
2. To assist management in determining when assets should be reviewed for
impairment, both USA and Japan standards provide examples of events or changes incircumstances that indicate that impairment might exist. However, the list is not meant
to be all-inclusive and management should be alert to potential impairment indicators
unique to its business circumstances.
3. A fixed asset (group) is tested for recoverability only when indicators of impairment
are present.
4. After an impairment loss is recognized, the adjusted carrying amount of the asset
shall be its new accounting basis. Thus, future depreciation or amortization would bebased on the asset’s new cost basis.
5. Impairment loss is recognized if the undiscounted cash flows used in the test forrecoverability are less than fixed assets’ (group’s) carrying amount
6. Companies may use their own assumptions in estimating future cash flows. Suchestimations of future cash flows are used in both impairment tests and themeasurement of impairment loss. ASC820 employ the same approaches to estimate fairvalue for long-lived assets as Japanese standards use to estimate use value for fixed
assets (estimate future cash flows are adjusted to present value by a certain discountedrate).
7. Subsequent reversal of a previously recognized impairment loss is prohibited.

50References
AHMED, A. S.; M. NEEL and D. WANG. 'Does Mandatory Adoption of IFRS Improve Accounting
Quality? Preliminary Evidence.' Contemporary Accounting Research

30 (2013):

1344-1372.
AIER, J. K.; L. CHEN and M. PEVZNER. 'Debtholders' Demand for Conservatism: Evidence from
Changes in Directors' Fiduciary Duties.' Journal of Accounting Research

52 (2014):

993-1027.
BALL, R.; S. P. KOTHARI and V. V. NIKOLAEV. 'Econometrics of the Basu Asymmetric Timeliness
Coefficient and Accounting Conservatism.' Journal of Accounting Research

51 (2013a):

1071-1097.
BALL, R.; S. P. KOTHARI and V. V. NIKOLAEV. 'On Estimating Conditional Conservatism.' The
Accounting Review 88 (2013b): 755-787.
BALL, R.; A. ROBIN and G. SADKA. 'Is financial reporting shaped by equity markets or by debt
markets? An international study of timeliness and conservatism.' Review of Accounting
Studies13 (2008): 168-205.

BALL, R.; A. ROBIN and J. S. WU. 'Incentives versus standards: properties of accounting income in four
East Asian countries.' Journal of Accounting & Economics

36 (2003): 235-270.

BALL, R. ,and L. SHIVAKUMAR. 'The role of accruals in asymmetrically timely gain and loss
recognition.' Journal of Accounting Research

44 (2006): 207-242.

BARTH, M. E.; W. R. LANDSMAN; M. LANG and C. WILLIAMS. 'Are IFRS-based and US
GAAP-based accounting amounts comparable? .' Journal of Accounting and Economics 54(2012): 68-93
BARTOV, E.; F. W. LINDAHL and W. E. RICKS. 'Stock Price Behavior Around Announcements ofWrite-Offs.' Review of Accounting Studies

3 (1998): 327-346.

BASU, S. 'The conservatism principle and the asymmetric timeliness of earnings.' Journal of
Accounting & Economics

24 (1997): 3-37.

BASU, S. 'Discussion of "conditional and unconditional conservatism: Concepts and modeling".' Review
of Accounting Studies

10 (2005): 311-321.

BEATTY, A.; J. WEBER and J. J. YU. 'Conservatism and debt.' Journal of Accounting & Economics
45 (2008): 154-174.
BEAVER, W. H. ,and S. G. RYAN. 'Biases and Lags in Book Value and Their Effects on the Ability of
the Book-to-Market Ratio to Predict Book Return on Equity.' Journal of Accounting
Research

38 (2000): 127-148.

BEAVER, W. H. ,and S. G. RYAN. 'Conditional and unconditional conservatism: Concepts andmodeling.' Review of Accounting Studies

10 (2005): 269-309.

BELLONI, A. ,and V. CHERNOZHUKOV. 'L1-Penalized Quantile Regression in High-Dimensional

50

Sparse Models.' Annals of Statistics

39 (2011): 82–130.

CHANDRA, U.; C. WASLEY and G. WAYMIRE. 'Income Conservatism in the U.S. Technology
Sector.' Unpublished paper, University of Kentucky, University of Rochester andEmory

University,

respectively.

(2004):

Availableat

http://www.researchgate.net/profile/Uday_Chandra/publication/228171156_Income_
Conservatism_in_the_U.S._Technology_Sector/links/228171100b228171157d228152
543ae228171150b228171182df228000000.pdf.
DECHOW, P. M.; A. P. HUTTON and R. G. SLOAN. 'An empirical assessment of the residual income
valuation model.' Journal of Accounting & Economics

26 (1999): 1-34.

DIETRICH, J. R.; K. A. MULLER, III and E. J. RIEDL. 'Asymmetric timeliness tests of accounting
conservatism.' Review of Accounting Studies

12 (2007): 95-124.

ELLIOTT, J. A. ,and J. D. HANNA. 'Repeated accounting write-offs and the information content of
earnings.' Journal of Accounting Research

34 (1996): 135-155.

FAN, J.; Y. FAN and E. BARUT. 'Adaptive Robust Variable Selection.' The Annals of Statistics 42(2014): 324–351.
FAN, J. ,and R. LI. 'Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties 'Journal of the American Statistical Association

96 (2001).

GARROD, N.; U. KOSI and A. VALENTINCIC. 'Asset write-offs in the absence of agency problems.'
Journal of Business Finance & Accounting

35 (2008): 307-330.

GIVOLY, D. ,and C. HAYN. 'The changing time-series properties of earnings, cash flows and accruals:
Has financial reporting become more conservative?' Journal of Accounting & Economics
29 (2000): 287-320.
GIVOLY, D. ,and C. K. HAYN. 'Measuring reporting conservatism.' Accounting Review

82 (2007):

65-106.
GUAY, W. 'Discussion of The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition 'Journal of Accounting Research

44 (2006): 243-255.

IATRIDIS, G. E. 'Audit quality in common-law and code-law emerging markets: Evidence on earnings
conservatism, agency costs and cost of equity.' Emerging Markets Review

13 (2012):

101-117.
JUNG, K.; Y. KIM and R. STULZ. 'Timing, investment opportunities, managerial discretion, and the
security issue decision.' Journal of Financial Economics 42 (1996): 159-185.
KHAN, M. ,and R. L. WATTS. 'Estimation and empirical properties of a firm-year measure ofaccounting conservatism.' Journal of Accounting & Economics

48 (2009): 132-150.

KNIGHT, K. ,and W. J. FU. 'Asymptotics for Lasso-type estimators.' Annals of Statistics(2000): 1356-1378.
KOENKER, R. ,and G. BASSETT. 'Regression Quantiles.' Econometrica51

46 (1978): 33-50.

28

KOTHARI, S.; A. LEONE and C. E. WASLEY. 'Performance Matched Discretionary Accrual Measures.'
Journal of Accounting Economics

39 (2005): 163–197.

LAFOND, R. ,and R. L. WATTS. 'The information role of conservatism.' Accounting Review

83(2008): 447-478.
LAWRENCE, A.; R. SLOAN and Y. SUN. 'Non-discretionary conservatism: Evidence and implications.'
Journal of Accounting & Economics

56 (2013): 112-133.

LI, Y. ,and J. ZHU. 'L1-Norm Quantile Regression.' Journal of Computational and Graphical
Statistics 17 (2008): 163-185
NIKOLAEV, V. V. 'Debt Covenants and Accounting Conservatism Journal of Accounting Research
Volume 48, Issue 1.' Journal of Accounting Research

48 (2010): 51-89.

OLER, M. 'Accounting standards effectiveness on equity overstatement – Conservatism when it matters.'
Research in Accounting Regulation

26 (2014).

PAE, J. H.; D. B. THORNTON and M. WELKER. 'The link between earnings conservatism and theprice-to-book ratio.' Contemporary Accounting Research

22 (2005): 693-717.

PATATOUKAS, P. N. ,and J. K. THOMAS. 'More Evidence of Bias in the Differential TimelinessMeasure of Conditional Conservatism.' The Accounting Review

86 (2011): 1765-1793

POPE, P. F. ,and M. WALKER. 'Ex-ante and Ex-post Accounting Conservatism, Asset Recognition andAsymmetric Earnings Timeliness.' Unpublished paper, Lancaster University andUniversityofManchester,

respectively(2003):

Availableat

http://scholar.googleusercontent.com/scholar?q=cache:rq2xrnnh0VMJ:scholar.google.com/&hl=ja&as_sdt=0,5.
RAMANNA, K. ,and R. L. WATTS. 'Evidence on the use of unverifiable estimates in required goodwill
impairment.' Review of Accounting Studies

17 (2012): 749-780.

REES, L.; S. GILL and R. GORE. 'An Investigation of Asset Write-Downs and Concurrent Abnormal
Accruals.' Journal of Accounting Research

34 (1996): 157-169.

RIEDL, E. J. 'An examination of long-lived asset impairments.' Accounting Review

79 (2004):

823-852.
ROYCHOWDHURY, S. ,and X. MARTIN. 'Understanding discretion in conservatism: An alternative
viewpoint.' Journal of Accounting & Economics

56 (2013): 134-146.

ROYCHOWDHURY, S. ,and R. L. WATTS. 'Asymmetric timeliness of earnings, market-to-book andconservatism in financial reporting.' Journal of Accounting & Economics

44 (2007):

2-31.
SALTER, S. B.; T. KANG; G. GOTTI and T. S. DOUPNIK. 'The Role of Social Values, Accounting
ValuesandInstitutions

International Review

inDetermining

Accounting

Conservatism.'

Management

53 (2013): 607-632.

SKINNER, D. J. 'The Rise of Deferred Tax Assets in Japan: The Role of Deferred Tax Accounting in the52

Japanese Banking Crisis.' Journal of Accounting and Economics 46 (2008): 218-239.
SMITH, C. W. ,and R. L. WATTS. 'The investment opportunity set and corporate financing dividend andcompensation policies.' Journal of Financial Economics 29 (1992): 263-292.
TIBSHIRANI, R. 'Regression shrinkage and selection via the Lasso.' Journal of the Royal StatisticalSociety Series B-Methodological58 (1996): 267-288.

WATTS, R. L. 'Conservatism in Accounting Part I: Explanations and Implications.' Accounting
Horizons

17 (2003a): 207-221.

WATTS, R. L. ,and J. L. ZIMMERMAN. Positive Accounting Theory. Prentice-Hall Inc,
1986.
WU, Y. ,and Y. LIU. 'Variable Selection in Quantile Regression.' Statistica Sinica 19 (2009):
801-817.
ZHANG, J. 'The contracting benefits of accounting conservatism to lenders and borrowers.' Journal of
Accounting & Economics

45 (2008): 27-54.

ZOU, H. 'The Adaptive Lasso and Its Oracle Properties ' Journal of the American Statistical
Association

101 (2006): 1418-1429

53