Understanding UAE Labour Law: Key Points for Employers and Employees
Ijaim 02-2016-0014
1. International Journal of Accounting & Information Management
Goodwill impairment loss and bond credit rating
Li Sun, Joseph H. Zhang,
Article information:
To cite this document:
Li Sun, Joseph H. Zhang, (2017) "Goodwill impairment loss and bond credit rating", International
Journal of Accounting & Information Management, Vol. 25 Issue: 1, pp.2-20, https://doi.org/10.1108/
IJAIM-02-2016-0014
Permanent link to this document:
https://doi.org/10.1108/IJAIM-02-2016-0014
Downloaded on: 20 April 2018, At: 13:46 (PT)
References: this document contains references to 46 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 548 times since 2017*
Users who downloaded this article also downloaded:
(2017),"Regime change in the accounting for goodwill: Goodwill write-offs and the value relevance
of older goodwill", International Journal of Accounting & Information Management, Vol. 25
Iss 1 pp. 43-69 <a href="https://doi.org/10.1108/IJAIM-02-2016-0018">https://doi.org/10.1108/
IJAIM-02-2016-0018</a>
(2017),"The role of audit quality and culture influence on earnings management in companies
with excessive free cash flow: Evidence from the Asia-Pacific region", International Journal of
Accounting & Information Management, Vol. 25 Iss 1 pp. 21-42 <a href="https://doi.org/10.1108/
IJAIM-05-2016-0059">https://doi.org/10.1108/IJAIM-05-2016-0059</a>
Access to this document was granted through an Emerald subscription provided by emerald-
srm:619260 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald
for Authors service information about how to choose which publication to write for and submission
guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as
well as providing an extensive range of online products and additional customer resources and
services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the
Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for
digital archive preservation.
*Related content and download information correct at time of download.
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
3. corporate asset (Jennings et al., 1996). Goodwill valuation is also a key input when
assessing a firm’s future cash flows (Hayn and Hughes, 2006). Investors extract goodwill
information to form appropriate perceptions concerning a firm’s intangible assets.
Second, Accounting Standards Codification 350-20 (ASC 350-20), Goodwill and Other,
requires the goodwill impairment test if there is a decline in the fair value of a reporting
unit. Thus, goodwill is regarded as the most sensitive asset to a decline in firm value
(Filip et al., 2015). Third, goodwill impairment reflects managerial inability to extract
value from prior acquisitions. Fourth, goodwill impairment is a leading indicator of
future firm performance stemming from the failure to realize the expected benefits of
prior acquisitions (Li et al., 2011). Lastly, the frequency of goodwill impairments has
drastically increased and goodwill impairment losses have become economically
significant events (Darrough et al., 2014).
We first identify a sample including goodwill impairment firms from 2002 to 2014. We
start at 2002 because ASC 350-20 became effective that year. Our empirical results show a
negative relationship between the amount of goodwill impairment losses and bond credit
ratings, suggesting that firms with goodwill impairment losses receive lower credit ratings.
We perform various additional tests, including subsamples in good or bad market times,
changes analysis, first-time impairments vs subsequent impairments and the two-stage least
squares regression analysis (2SLS), to address potential endogeneity issues. Our main
results persist. Overall, the findings support our conjecture that goodwill impairment losses
and bond credit ratings are negatively associated.
Our study makes the following contributions. First, the paper links and contributes to two
streams of literature:
(1) goodwill impairment in accounting literature; and
(2) bond credit ratings in finance literature.
Whether a firm’s goodwill impairment losses affect the firm’s bond credit rating remains an
interesting question that has not been examined previously. To the best of our knowledge,
this is the first study that directly examines the relationship between goodwill impairment
losses and bond ratings at the firm level. Second, this study is incremental to the literature on
the determinants of bond rating (Bhojraj and Sengupta, 2003; Ashbaugh-Skaife et al., 2006).
Although this study does not attempt to construct a prediction model for bond rating, the
findings from this study provide an avenue for future research on bond ratings. The
inclusion of goodwill impairment may help users of financial statements better assess
the credit rating. Third, our study complements the findings and associated interpretations
in Ramanna and Watts (2012) and Li and Sloan (2015). Both studies suggest that managers
exploit the discretion granted by ASC 350-20 to manipulate or even delay goodwill
impairment because goodwill impairment leads to negative consequences such as reduced
stock price and reduced compensation. This study suggests another motivation for
managers to manipulate or delay goodwill impairment losses by providing evidence that
goodwill impairment leads to lower bond ratings. Fourth, the evidence continues to suggest
that credit rating agencies may directly use the information on goodwill impairment losses
when assessing a firm’s creditworthiness. From a practical perspective, the results should be
of interest to policymakers who design and implement guidelines on goodwill impairment
and credit rating.
The rest of this paper is organized as follows. Section 2 presents the literature review and
hypothesis development. Section 3 describes the research design, Section 4 presents the main
results and additional analyses, and, in Section 5, we conclude this study.
3
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
4. 2. Literature review and hypothesis development
2.1 Accounting Standards Codification 350-20
Prior to 2001, goodwill accounting in the USA was governed by Accounting Principles Board
(APB) Opinion No. 16. Under APB 16, any excess of purchase price over the fair value of the
acquired firm’s net assets was recognized as goodwill. Goodwill was viewed as a depreciable
asset. The value of goodwill in a purchase acquisition was then amortized over a period of up
to 40 years. To avoid the impact of goodwill amortization expenses on earnings, many firms
chose the pooling of interest acquisition method in which purchased goodwill was not
recognized and amortized.
In FASB (2001) issued ASC 350-20, Goodwill and Other. ASC 350-20 eliminates the
pooling of interest acquisition method and requires that all business acquisitions be
accounted for by the purchase acquisition method. In addition, ASC 350-20 requires
sufficient disclosure of the allocation of the purchase price among the assets acquired. It
requires annual tests for goodwill and other intangible assets. Specifically, goodwill should
be tested for impairment using a two-step process. In the first step, companies compare the
carrying value of the reporting unit (including goodwill) to the estimated fair value of the
reporting unit. If the carrying value of the reporting unit is less than the estimated fair value
of the reporting unit, no impairment in goodwill exists. If the carrying value of the reporting
unit exceeds the estimated fair value of the reporting unit, companies must perform the
second step: to determine and recognize the magnitude of goodwill impairment loss, which is
recorded against earnings. The impairment loss is measured as the difference between the
implied value and the carrying value of goodwill. In addition, any reversals of goodwill
impairment losses are prohibited. ASC 350-20 also requires a firm to disclose the carrying
value and any changes in the carrying value of goodwill.
2.2 Goodwill impairment
Prior studies on goodwill impairment can be classified into two main categories. The first
category examines the impact of goodwill impairment on the stock market and on various
firm characteristics. Prior studies (Francis et al., 1996; Hirschey and Richardson, 2002;
Henning and Shaw, 2003; Li et al., 2011; Xu et al., 2011) find that goodwill impairment is value
relevant to the market, and, normally, investors view goodwill impairment as negative news.
For instance, Li et al. (2011) find that investors react negatively to goodwill impairment and
conclude that goodwill impairment is a leading indicator of a decline in future firm
performance. Regarding the impact of impairment on firm characteristics, Darrough et al.
(2014) examine the relationship between goodwill impairment losses and CEO compensation
and document that goodwill impairment losses lead to reduced CEO compensation.
The second category investigates the determinants of goodwill impairment. Prior studies
examine and find that the cause of many goodwill impairment losses is that the target firm
is overpaid at the time of acquisition (Beatty and Weber, 2006; Hayn and Hughes, 2006; Gu
and Lev, 2011; Li et al., 2011; Olante, 2013). Specifically, Beatty and Weber (2006) examine a
sample of firms that are likely to have recorded a goodwill impairment loss and show that a
firm’s decision to accelerate or delay recognition of the loss is related to managerial
incentives. They find evidence suggesting that firms are less likely to accelerate recognition
of goodwill impairment if they have debt covenants affected by impairment, are listed on an
exchange with delisting requirements, or have earnings-based bonus plans, and more likely
to accelerate recognition when they have a CEO with a short tenure or a high earnings
multiple. Olante (2013) estimates that approximately 40 per cent of goodwill impairment
losses are caused by overpayment at acquisition. Some studies investigate whether goodwill
impairment is associated with economic factors at the firm level. For example, Chen et al.
IJAIM
25,1
4
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
5. (2008) and Chalmers et al. (2011) find that goodwill impairments better reflect the underlying
economics of goodwill after the adoption of ASC 350-20, supporting the FASB’s claim that
ASC 350-20 “will improve financial reporting because the financial statements of entities that
acquire goodwill and other intangible assets will better reflect the underlying economics of
those assets” (ASC 350-20, page 7). Other studies examine the role of managers’ opportunistic
behavior in determining goodwill impairment. Ramanna and Watts (2012) argue that
managers may avoid goodwill impairment under ASC 350-20 when they have agency-based
private information because the current fair value of goodwill is a function of management’s
future actions such as firm strategy implementation. They find a negative relationship
between CEO tenure and goodwill impairment. Similarly, Li and Sloan (2015) argue that
managers exploit the discretion granted by ASC 350-20 to delay goodwill impairment. Sun
(2016) finds that more-able managers better prevent and reduce goodwill impairment losses,
relative to less-able managers.
2.3 Bond credit ratings
Empirical studies on bond credit ratings can be divided into three categories. The first
category examines whether credit ratings measure what they claim to measure. For example,
Zhou (2001) and Jorion and Zhang (2007) examine the relationship between ratings and
corporate default risks. The second category examines the impact of credit ratings on the
capital market. Pinches and Singleton (1978) examine monthly stock returns surrounding a
rating change and find cumulative abnormal returns in certain months prior to either an
upgrade or a downgrade. Hand et al. (1992) document significantly negative average excess
bond and stock returns for downgrades but weaker positive average excess bond and stock
returns for upgrades, suggesting that both bond and stock price effects are associated with
these two types of announcements. Goh and Ederington (1993) find that the market only
reacts to downgrades associated with deteriorating financial performance of a firm. Dichev
and Piotroski (2001) investigate long-run stock returns following bond rating changes and
find significant abnormal returns following downgrades. Jorion et al. (2005) examine the
market reaction to bond rating changes before and after the SEC’s Fair Disclosure Regulation
(Reg FD), which requires firms to release any information to the entire market. They find that
the market only reacts to downgrades before Reg FD and reacts to both downgrades and
upgrades after Reg FD.
Choy et al. (2006) examine the market’s reaction to the bond rating changes of Australian
companies and find the market only reacts to downgrades, consistent with the evidence
documented for US companies.
The third category investigates the determinants of bond ratings. Some studies
(Horrigan, 1966; West, 1970; Pinches and Mingo, 1973; Gentry et al., 1988; Kim and Gu, 2003)
suggest that bond ratings are largely determined by commonly used financial ratios
including leverage, liquidity, debt coverage, profitability, operating efficiency and size.
Other studies argue that corporate governance is a possible determinant of bond ratings. For
example, Bhojraj and Sengupta (2003) suggest that firms with greater institutional
ownership and higher proportions of independent directors enjoy lower bond yields and
higher ratings on their new bond issues. Similarly, Ashbaugh-Skaife et al. (2006) document a
variety of governance attributes explaining firm credit ratings, suggesting that corporate
governance plays an important role in bond ratings. This study belongs to the third category
by examining the impact of goodwill impairment losses on bond ratings.
2.4 Hypothesis development
Taken together, the goodwill impairment literature suggests that goodwill impairment is a
leading indicator of a decline in future firm performance. When goodwill impairment losses
5
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
6. occur, information users, especially credit investors and credit rating agencies, naturally
raise concerns over a firm’s future performance, especially the ability to make timely future
cash payments. The ability to make timely future cash payments is an important factor used
by credit rating agencies when assessing a firm’s creditworthiness. As anecdotal evidence
suggests that credit rating agencies may collect and process impairment-related information
in assessing a firm’s creditworthiness, we posit a negative relationship between goodwill
impairment losses and bond credit ratings. Our hypothesis is stated as below:
H1. Goodwill impairment losses are negatively related to bond credit ratings.
3. Research design
3.1 Measures of credit rating
A credit rating is an evaluation of creditworthiness that can also be interpreted as the
probability of default. Default probability normally increases as credit rating drops. There
are three major credit rating agencies:
(1) Standard and Poor’s (S&P);
(2) Fitch Ratings; and
(3) Moody’s Investing Service.
Following Liu and Jiraporn (2010) and Attig et al. (2013), we use S&P ratings in this study.
S&P rates bonds from AAA to D. Each letter is known as a “class”. S&P also assigns
modifiers (e.g. Bϩ, BBϪ) for the AA to CCC classes. Following Klock et al. (2005), we
compute bond ratings using a conversion process in which AAA-rated bonds are assigned a
value of 22 and D-rated bonds a value of 1. For example, a firm with a Bϩ rating from S&P
would receive a score of 9. Appendix 1 presents the classifications of S&P credit ratings and
bond rating conversion.
3.2 Model specification
We use the following regression model to examine the effect of goodwill impairment losses
on bond credit ratings:
BR ϭ 0 ϩ 1 ϫ GWILOSS ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ
ϩ 6 ϫ MTB ϩ 7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ (1)
where the dependent variable captures the bond ratings (BR). The independent variable of
interest, GWILOSS, measures the goodwill losses (GDWLIP) scaled by total assets. Because
GDWLIP is reported as a negative number in Compustat, we multiply GDWLIP by Ϫ1. If H1
is valid, we expect a negative and significant coefficient on GWILOSS (i.e. 1 Ͻ 0). Following
Ashbaugh-Skaife et al. (2006), we use ordered logit regression because bond ratings are
ordinal.
In addition to the explanatory variable of interest, we also control for factors associated
with bond credit ratings established in prior literature. Specifically, we control firm size
(SIZE), operational performance (ROA), financial leverage (LEV), liquidity ratio (LIQ),
goodwill (GDW), institutional ownership (IO) and insolvency ratio (ALTMANZ). These
control variables are also used in other relevant studies. For example, Ashbaugh-Skaife et al.
(2006) control for SIZE, IO, and ROA. Ashbaugh-Skaife et al. (2006) and Kisgen (2006) argue
that corporate governance plays an important role in the credit rating of a firm. In addition,
Bhojraj and Sengupta (2003) document that firms with greater institutional ownership and
stronger outside control of the board enjoy lower bond yields and higher ratings on their new
IJAIM
25,1
6
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
7. bond issues. Hence, we include IO in our regression analysis to control for governance.
Following Kisgen (2006, 2009), Liu (2011) and Ahmed and Ali (2015), we control for LEV.
Beatty and Weber (2006) indicate that firms’ equity market considerations affect their
preferences for above-the-line versus below-the-line accounting treatment, and firms’ debt
contracting, bonus, turnover and exchange delisting incentives affect their decisions to
accelerate or delay expense recognition. Hence, we add the market-to-book ratio (MTB) as a
control variable in our model. We also control for liquidity ratio (LIQ), operating cash flows
scaled by total liabilities because credit rating agencies (i.e. S&P) consider the liquidity ratio
an important factor in the rating process. Last, we control for insolvency using Altman Z
scores (ALTMANZ). We winsorize the continuous variables at the 1 and 99 per cent level by
fiscal year and include year and industry indicators (based on the Fama–French 12 industry
classifications) in regression tests. Refer to Appendix 2 for variable definition.
3.3 Sample selection and descriptive statistics
We use Compustat North America database to obtain bond ratings data (SPLTICRM, #280)
and financial statement data, which includes goodwill impairment losses (GDWLIP,
#368)[1], total net sales (SALES, #12), income before extraordinary items (IB, #18), net
income (NI, #172), current assets (ACT, #4), current liabilities (LCT, #5), retained earnings
(RE, #36), total assets (AT, #6), long-term liabilities (DLTT, #9), total liabilities (LT, #181),
goodwill (GDWL, #204), cash flows from operating activities (OANCF, #308), book value of
equity (CEQ, #60), stock price at fiscal-year end (PRCC_F, #24) and common stock shares
(CSHO, #25). The institutional ownership data are from the CDA/Spectrum database of 13F
institutional investors. Consistent with prior work (Darrough et al., 2014), we use 2002 as the
starting year because ASC 350-20 became effective in 2002. Some observations are lost
because of missing data. The final sample with non-missing variables consists of 1,013
firm-year observations from 2002 to 2014.
Table I reports the distribution of firm-year observations by fiscal year for the goodwill
impairment sample firms. For example, there are 71 firm-year observations in 2002 and 59
observations in 2014. The year of 2008 has the largest number of observations (i.e. 174). This
is consistent with Darrough et al. (2014), who also find that 2008 has the largest number of
goodwill impairments. The year of 2009 has the second largest number (i.e. 101). This
suggests that businesses experience more goodwill impairments in a financial distress
period. Table II reports the distribution of firm-year observations by industry for the
Table I.
Distribution of
goodwill impairment
sample: distribution of
firm-year observations
by year
Year No. of observations (%) of sample Cumulative (%)
2002 71 7.01 7.01
2003 78 7.70 14.71
2004 68 6.71 21.42
2005 63 6.22 27.64
2006 54 5.33 32.97
2007 69 6.81 39.78
2008 174 17.18 56.96
2009 101 9.97 66.93
2010 57 5.63 72.56
2011 75 7.40 79.96
2012 79 7.80 87.76
2013 65 6.42 94.18
2014 59 5.82 100.00
Total 1,013 100.00
7
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
8. Table II.
Distribution of
goodwill impairment
sample: distribution of
sample observations
by industry
SIC-2Industrydescription
No.of
observations
(%)of
sampleSIC-2Industrydescription
No.of
observations
(%)of
sample
01Agriculturalproduction-crops30.3047Transportationservices10.10
10Metalmining222.1748Communications666.52
13Oilandgasextraction333.2649Electricgasandsanitaryservices535.23
14Miningandquarrying-nonmetallic20.2050Wholesaletrade-durablegoods282.76
15Buildingconstruction222.1751Wholesaletrade-nondurablegoods131.28
16Heavyconstructionexceptbuilding60.5952Buildingmaterialsandhardware10.10
17Construction-specialtradecontractors30.3053Generalmerchandisestores131.28
20Foodandkindredproducts454.4454Foodstores181.78
21Tobaccoproducts50.4955Automotivedealersandservice40.39
22Textilemillproducts40.3956Apparelandaccessorystores151.48
23Apparelandotherfinishedproducts151.4857Homefurnitureandfurnishingsstores80.79
24Lumberandwoodproducts70.6958Eatinganddrinkingplaces121.18
25Furnitureandfixtures100.9959Miscellaneousretail40.39
26Paperandalliedproducts252.4760Depositoryinstitutions393.85
27Printingpublishingandalliedindustries292.8661Non-depositorycreditinstitutions50.49
28Chemicalsandalliedproducts686.7162Securityandcommoditybrokers90.89
29Petroleumrefining90.8963Insurancecarriers212.07
30Rubberandmiscellaneousplastics40.3964Insuranceagentsbrokersandservice60.59
32Stoneclayglassandconcreteproducts70.6965Realestate40.39
33Primarymetalindustries232.2767Holdingandotherinvestmentoffices20.20
34Fabricatedmetalproducts222.1770Hotelsroominghousesandcamps20.20
35Industrialandcommercialmachinery828.0972Personalservices70.69
36Electronicandotherelectrical
equipment
585.7373Businessservices232.27
37Transportationequipment444.3475Autorepairservicesandparking50.49
38Measuringandanalyzinginstruments252.4778Motionpictures10.10
39Miscellaneousmanufacturing80.7979Amusementandrecreationservices70.69
40Railroadtransportation10.1080Healthservices141.38
42Motorfreighttransportation/warehouse70.6982Educationalservices70.69
44Watertransportation50.4987Engineeringandaccountingservice40.39
45Transportationbyair151.4899Non-classifiedestablishments111.09
46Pipelinesexceptnaturalgas10.10Total1,013100.00
IJAIM
25,1
8
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
9. goodwill impairment sample firms. For example, there are 33 firm-year observations in oil
and gas extraction industries, and there are 68 observations in chemical industries. The most
heavily represented industry is commercial machinery (8.09 per cent, SIC 35), followed by
chemical (6.71 per cent, SIC 28) and communications (6.52 per cent, SIC 48).
Table III presents descriptive statistics for the sample firms. Specifically, it reports the
mean, standard deviation, median, the 25th percentile and 75th percentile of the following
variables: BR, GWILOSS, SIZE, ROA, LEV, LIQ, MTB, GDW, IO and ALTMANZ. The
mean (median) value of bond credit rating score is 12.912 (13.000), suggesting that the
average value of bond ratings is BBBϪ, just at the threshold between junk and investment
grade, consistent with prior research (Murfin, 2012). The mean and median values of
goodwill impairment losses (GWILOSS) are 0.039 and 0.007, respectively. The mean and
median values of goodwill (GDW) are 0.130 and 0.090, respectively. The median value of
ROA (income before extraordinary items scaled by total assets) is 0.019.
Table IV provides the correlation matrices for selected variables. For each pair of
variables, the Pearson and Spearman correlation coefficients and related p-values are
provided. Both Pearson and Spearman correlations show a significant and negative
relationship between goodwill losses (GWILOSS) and bond credit ratings (BR), suggesting
that firms with goodwill impairment losses experience lower credit ratings, consistent with
H1. We also find that goodwill impairment losses are negatively correlated with firm size,
profitability (ROA) and institutional holding percentage (IO) and positively associated with
insolvency ratio (ALTMANZ).
4. Empirical results
4.1 Main tests
Table V reports the results of ordered logit regression testing our hypothesis[2]. We find that
the coefficient on GWILOSS is Ϫ2.891 (with p-value ϭ 0.007), suggesting that goodwill
impairment is negatively related to bond rating at a significant level[3]. For the control
variables, BR is significantly and positively associated with SIZE, ROA, LIQ and
ALTMANZ but negatively associated with LEV and IO. The positive relationship between
BR and ALTMANZ suggests that financially healthy firms receive high credit ratings,
consistent with general expectations. The negative relationship between BR and IO suggests
that large institutional ownership have a negative impact on a firm’s credit rating, consistent
with Ashbaugh-Skaife et al. (2006).
Table III.
Descriptive statistics
Variable Mean SD 25% Median 75%
BR 12.912 3.369 10.000 13.000 15.000
GWILOSS 0.039 0.088 0.001 0.007 0.035
SIZE 9.061 1.559 7.986 8.962 10.096
ROA 0.022 0.120 Ϫ0.011 0.019 0.042
LEV 0.283 0.207 0.148 0.246 0.370
LIQ 0.131 0.136 0.049 0.111 0.189
MTB 3.109 5.807 0.947 1.582 2.684
GDW 0.130 0.161 0.018 0.090 0.221
IO 0.555 0.092 0.328 0.526 0.710
ALTMANZ 2.802 1.521 1.930 3.611 4.839
Notes: This table presents the descriptive statistics of the sample of 1,013 firm-year observations from
2002-2014 inclusive; to remove outliers, the distributions of continuous variables (except the logged value of
firm size) are winsorized by year at the 1 and 99% level; refer to Appendix 2 for variable definitions
9
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
11. 4.2 Financial distress period vs good market period
During the financial crisis 2007-2009, we observe 344 observations of impairment losses. As
a robustness check, we use two alternative sample periods, normal (good) period (2002-2006
and 2010-2014, inclusive) versus financial distress (bad) period (2007-2009, inclusive). This
test examines the extent to which changes in firm level and macroeconomic risk factors affect
the relationship between goodwill impairment losses and bond ratings. Table VI reports the
regression results testing H1 for both periods. For the sample during the financial distress
period, the coefficient on GWILOSS is Ϫ4.065 (with p-value Ͻ 0.001). For the sample in the
good market period, the coefficient on GWILOSS is Ϫ2.796 (with p-value ϭ 0.048). The
negative and significant coefficients again support H1, indicating that goodwill impairment
losses are negatively related to bond ratings during both good and bad market times.
Coefficient comparison indicates that the coefficient on GWILOSS in the financial distress
period are significantly lower than that of in the normal period (F-stat ϭ 14.19 with
p-value Ͻ 0.001). Thus, evidence from Table VI indicates that the relationship between
goodwill impairment and bond rating is stronger for firms in the financial distress (bad)
period than firms in the normal (good) period.
4.3 Changes analysis
Jorion and Zhang (2007) argue that prior studies on bond rating largely ignore the prior value
of the rating (prior rating) and suggest future studies should take into account the prior
rating. The omitted prior rating may cause biased results. For example, a downgrade from
AAϩ to A should have more information content than a downgrade from AAϩ to AA.
Following Jorion and Zhang (2007), we use a changes analysis to provide additional evidence
that the differences in bond ratings can be attributed to the differences in goodwill
impairment losses. Specifically, we conduct a bivariate change analysis by regressing
changes in bond ratings (⌬BR) from year t Ϫ 1 to year t on the corresponding changes in
Table V.
Goodwill impairment
losses and bond
ratings
Variable Coefficient z-value Pr Ͼ |z|
Intercept 3.614 5.20 Ͻ0.0001
GWILOSS Ϫ2.891*** 3.19 0.007
SIZE 1.072*** 3.88 Ͻ0.0001
ROA 0.023** 2.15 0.032
LEV Ϫ3.425*** 4.07 Ͻ0.0001
LIQ 2.560*** 3.93 Ͻ0.0001
MTB 0.004 0.62 0.540
GDW 0.533 0.79 0.434
IO Ϫ0.542** 2.13 0.031
ALTMANZ 0.152*** 3.19 0.009
Industry YES
Year YES
Observations 1,013
Pseudo R2
0.281
Notes: BR ϭ 0 ϩ 1 ϫ GWILOSS ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ ϩ 6 ϫ MTB ϩ
7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ ; this table reports ordered logistic regression results of
estimating the relationship between goodwill impairment losses and bond credit ratings; all continuous
variables (except the logged value of firm size) are winsorized at the 1 and 99% percentiles each year before
entering regressions; Refer to Appendix 2 for variable descriptions; ***, ** and *denote the regression
coefficient is statistically significant at the two-tailed 1, 5 and 10% level, respectively
11
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
12. goodwill impairment losses (⌬GWILOSS) from year t Ϫ 1 to year t. Control variables are also
transformed relative to prior year. The changes model is specified as follows:
⌬BR ϭ 0 ϩ 1 ϫ ⌬GWILOSS ϩ 2 ϫ ⌬SIZE ϩ 3 ϫ ⌬ROA ϩ 4 ϫ ⌬LEV
ϩ 5 ϫ ⌬LIQ ϩ 6 ϫ ⌬MTB ϩ 7 ϫ ⌬GDW ϩ 8 ϫ ⌬IO
ϩ 9 ϫ ⌬ALTMANZ ϩ
(2)
Table VII presents the results of the change analysis of the relationship between goodwill
impairment losses (⌬GWILOSS) and bond ratings (⌬BR). A sample of 720 observations
enters the changes analysis. We find that the changes in GWILOSS are negatively (Ϫ2.582)
and significantly (with p-value ϭ 0.041) related to changes in BR. These results suggest that
an increase in goodwill impairment losses can lead to a decrease in bond ratings, consistent
with the primary results[4].
4.4 First time impairment vs subsequent impairment
Chen et al. (2008) make a distinction between initial goodwill impairment and subsequent
impairments and find that first year impairment has a significant relationship with share
prices in the previous year. Hence, Chen et al. (2008) suggest that the market demonstrates a
stronger reaction to initial goodwill impairment than to subsequent impairment. Motivated
by Chen et al. (2008), we divide the sample (observations ϭ 1,013) into two subsamples:
first-time impairments versus subsequent impairments and perform the same regression
analysis to the two subsamples. We identify 476 first-year impairment observations and 537
subsequent-year impairment observations. Table VIII reports that the coefficient on
Table VI.
Goodwill impairment
losses and bond
ratings financial
distress period vs
normal period
Financial distress period Normal period
Parameter Coefficient Pr Ͼ |z| Parameter Coefficient Pr Ͼ |z|
Intercept 4.357 Ͻ0.0001 Intercept 4.551 Ͻ0.0001
GWILOSS Ϫ4.065*** Ͻ0.001 GWILOSS Ϫ2.796** 0.048
SIZE 1.070*** Ͻ0.0001 SIZE 1.064*** Ͻ0.0001
ROA 0.020 0.522 ROA 0.015*** Ͻ0.0001
LEV Ϫ4.787*** Ͻ0.0001 LEV Ϫ3.502*** Ͻ0.0001
LIQ 6.807*** Ͻ0.0001 LIQ 7.030*** Ͻ0.0001
MTB 0.000 0.707 MTB Ϫ0.007 0.194
GDW 0.460 0.611 GDW 1.321 0.122
IO Ϫ0.514* 0.083 IO Ϫ0.472* 0.095
ALTMANZ 0.096** 0.015 ALTMANZ 0.091* 0.076
Industry YES Industry YES
Year YES Year YES
Observations 344 Obs. 669
Pseudo R2
0.269 Pseudo R2
0.273
Notes: BR ϭ 0 ϩ 1 ϫ GWILOSS ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ ϩ 6 ϫ MTB ϩ
7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ ; coefficient comparison; test of GWILOSS (Financial distress
period) ϭ GWILOSS (Normal period); f-Stat. ϭ 14.19; p-value ϭ 0.000; this table reports the regression results
of estimating the relationship between goodwill impairment losses and bond credit ratings subsampled by
good market time vs bad market time; all continuous variables (except the logged value of firm size) are
winsorized at the 1 and 99% percentiles each year before entering regressions; refer to Appendix 2 for variable
descriptions; ***, ** and *denote the regression coefficient is statistically significant at the two-tailed 1, 5
and 10% level, respectively
IJAIM
25,1
12
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
13. Table VII.
Goodwill impairment
losses and bond
ratings changes
analysis
Dependent variable ϭ ⌬BR
Parameter Coefficient z-value Pr Ͼ |z|
Intercept 0.235 3.41 0.001
⌬GWILOSS Ϫ2.582** 2.04 0.041
⌬SIZE 0.078*** 2.98 0.003
⌬ROA 0.016** 2.12 0.034
⌬LEV Ϫ1.355 1.37 0.171
⌬LIQ 1.031* 1.68 0.097
⌬MTB 0.004 0.85 0.396
⌬GDW 0.583* 1.81 0.071
⌬IO Ϫ0.332** 2.36 0.019
⌬ALTMANZ 0.546** 2.01 0.045
Industry YES
Year YES
Observation 720
Pseudo R2
0.213
Notes: ⌬BR ϭ 0 ϩ 1 ϫ ⌬GWILOSS ϩ 2 ϫ ⌬SIZE ϩ 3 ϫ ⌬ROA ϩ 4 ϫ ⌬LEV ϩ 5 ϫ ⌬LIQ ϩ
6 ϫ ⌬MTB ϩ 7 ϫ ⌬GDW ϩ 8 ϫ ⌬IO ϩ 9 ϫ ⌬ALTMANZ ϩ ; this table reports the regression results
of estimating the relationship between the change of goodwill impairment losses and the change of bond credit
ratings; all continuous variables (except the logged value of firm size) are winsorized at the 1% and 99%
percentiles each year before entering regressions; refer to Appendix 2 for variable descriptions; ***, **
and *denote the regression coefficient is statistically significant at the two-tailed 1, 5 and 10% level,
respectively
Table VIII.
Goodwill impairment
losses and bond
ratings first time
impairment vs
subsequent
impairment
First time impairment Subsequent impairment
Parameter Estimate z-value Pr Ͼ |z| Parameter Estimate z-value Pr Ͼ |z|
Intercept 4.235 10.30 Ͻ0.0001 Intercept 4.241 9.67 Ͻ0.0001
GWILOSS Ϫ3.627*** 3.85 0.000 GWILOSS Ϫ2.463* 1.91 0.058
SIZE 1.856*** 5.16 Ͻ0.0001 SIZE 1.722*** 5.10 Ͻ0.0001
ROA 0.029** 1.98 0.048 ROA 0.029** 1.98 0.048
LEV Ϫ3.624*** 3.37 0.000 LEV Ϫ3.624*** 3.37 0.000
LIQ 2.527** 2.18 0.030 LIQ 2.527** 2.18 0.030
MTB 0.005 1.06 0.289 MTB 0.005 1.06 0.289
GDW 0.364* 1.81 0.071 GDW 0.364* 1.81 0.071
IO Ϫ1.520** 2.39 0.017 IO Ϫ1.520** 2.39 0.017
ALTMANZ 4.643** 2.01 0.045 ALTMANZ 4.643** 2.01 0.045
Industry YES Industry YES
Year YES Year YES
Observations 476 Observations 537
Pseudo R2
0.532 Adjusted R2
0.530
Notes: BR ϭ 0 ϩ 1 ϫ ⌬GWILOSS ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ ϩ
6 ϫ MTB ϩ 7 ϫ GDW ϩ 8 ϫ IO ϩ 9ALTMANZ ϩ ; coefficient comparison; test of GWILOSS (first
time impairment) ϭ GWILOSS (subsequent impairment); f-Stat. ϭ 7.10 p-value ϭ 0.008; this table reports the
regression results of estimating the relationship between first time goodwill impairment losses and bond
credit ratings; all continuous variables (except the logged value of firm size) are winsorized at the 1 and 99%
percentiles each year before entering regressions; refer to Appendix 2 for variable descriptions; ***, **,
and *denote the regression coefficient is statistically significant at the two-tailed 1, 5 and 10% level,
respectively
13
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
14. GWILOSS is negative (Ϫ3.627) and significant (with p-value ϭ 0.000) for the first-time
impairment sample, and the coefficient on GWILOSS is negative (Ϫ2.463) and significant
(with p-value ϭ 0.058) for the subsequent impairment sample. Coefficient comparison
indicates that the coefficient on GWILOSS for first time impairment observations is
significantly lower than that for subsequent impairment (F-stat ϭ 7.10 with p-value ϭ 0.008),
suggesting that our results are stronger in the first-time impairment sample than in the
subsequent impairment sample.
4.5 Two-stage least squares regression
It is possible that some omitted time variant variables are correlated with both goodwill
impairments and bond credit ratings, thereby, biasing our results. Additionally, it is possible
that some unknown firm characteristics may affect goodwill impairment and bond ratings at
the same time. We explore potential endogeneity issues. Following Jiraporn et al. (2014), we
perform a two-stage least squares regression (2SLS) analysis which controls for possible
reverse causality. The 2SLS analysis requires identifying an instrumental variable, which is
highly correlated to a firm’s goodwill impairment losses but does not influence bond ratings
except through goodwill impairment losses. We use the average goodwill impairment losses
of the firms in the same industry (using two-digit SIC code). This variable is clearly related to
the goodwill impairment losses of a given firm, but it does not relate to the bond rating of a
given firm. In the first stage of 2SLS, we estimate goodwill impairment losses using the
average score of goodwill impairment losses of the firms in the same industry. We include all
of the control variables, as well as the industry and year dummy variables. In the second
stage of 2SLS, we use the instrumented values of goodwill impairment losses (GWILOSS)
from the first stage and include it as an independent variable in the second stage regression.
We still use the same control variables in the second stage regression.
Table IX reports the IV-2SLS results for testing H1. For the relationship between
GWILOSS and BR, the first stage regression results report the average GWILOSS is
positively related (0.803) to individual GWILOSS at the significant level (with p-value Ͻ
0.001). The second stage reports that the coefficient of the instrumented GWILOSS is
negative (Ϫ2.879) and highly significant (with p-value ϭ 0.002), suggesting that firms with
goodwill impairment losses experience lower bond ratings. Overall, the 2SLS lends support
to the main results.
4.6 Broad rating downgrade vs one notch rating downgrade
Prior research (Kisgen, 2006) argues that firms are more concerned about rating changes
from one broad rating category to another than they are about rating changes within a broad
rating category. Broad rating refers to rating levels without a distinction of minus, middle
and plus specification (Kisgen, 2006). For example, a broad rating of BB refers to firms with
ratings of BBϩ, BB or BBϪ. The effect of rating downgrades on firms’ ability to access credit
market should not be the same across the ratings. For instance, the impact of a downgrade
from Aϩ to AϪ may not be the same as the impact of a downgrade from AϪ to BBBϩ.
Brown et al. (2015) suggest that firms in the rating categories near the
investment-speculative board line use more aggressive income-increasing real earnings
management. We implement an additional test considering broad bond rating downgrades
(94 firm-year observations) and one-notch rating downgrades (201 firm-year observations)
from year t to year t ϩ 1. Table X reports that a positive (0.247; 0.209) and significant
(p-value Ͻ 0.001; p-value ϭ 0.012) coefficient on GWILOSS for broad rating downgrades and
one-notch rating downgrades, respectively. The evidence suggests that our results are
stronger when a firm experiences a broad bond rating change, consistent with prior research.
IJAIM
25,1
14
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
15. Table IX.
Goodwill impairment
losses and bond
ratings two-stage least
squares regression
First stage Second stage
Parameter Estimate Pr Ͼ |t| Parameter Estimate Pr Ͼ |t|
Intercept 0.064** 0.023 Intercept 3.610*** Ͻ0.0001
GWILOSS (mean) 0.803*** Ͻ0.0001 GWILOSS (instrumented) Ϫ2.879*** 0.002
SIZE Ϫ0.010*** Ͻ0.0001 SIZE 1.071*** Ͻ0.0001
ROA 0.003*** Ͻ0.0001 ROA 0.020*** Ͻ0.0001
LEV 0.001 0.972 LEV Ϫ3.416*** Ͻ0.0001
LIQ Ϫ0.020 0.312 LIQ 2.557*** Ͻ0.0001
MTB 0.001 0.422 MTB 0.006 0.158
GDW 0.025 0.294 GDW 0.529 0.442
IO Ϫ0.072 0.243 GOV Ϫ5.731*** 0.001
ALTMANZ Ϫ0.814*** Ͻ0.0001 ALTMANZ 7.999** 0.012
Industry YES Industry YES
Year YES Year YES
Adjusted R2
0.233 Adjusted R2
0.583
Observations 1,013 Observations 1,013
Notes: Stage 1: BR ϭ 0 ϩ 1 ϫ BR_mean ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ ϩ 6 ϫ
MTB ϩ 7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ ; Stage 2: BR ϭ 0 ϩ 1 ϫ
GWILOSS_instrumented ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ ϩ 6 ϫ MTB ϩ
7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ ; this table presents the regression results of estimating the
effect of goodwill impairment losses on bond ratings; to address endogeneity concerns, we use IV-2SLS
regressions. Refer to Appendix 2 for variable descriptions; the dependent variable in the equation (1) is
GWILOSS; for the 2nd stage regression, we use predicted raw GWILOSS as the explanatory
variable; ***, ** and *denote the regression coefficient is statistically significant at the two-tailed 1, 5 and
10% level, respectively
Table X.
Goodwill impairment
losses and bond
ratings broad rating
downgrades vs one-
notch rating
downgrades
Broad rating downgrades One-notch rating downgrades
Parameter Coefficient Pr Ͼ |z| Parameter Coefficient Pr Ͼ |z|
Intercept 0.057 Ͻ0.0001 Intercept 0.036 Ͻ0.0001
GWILOSS 0.247*** Ͻ0.0001 GWILOSS 0.209** 0.012
SIZE Ϫ0.114*** Ͻ0.0001 SIZE Ϫ0.094*** Ͻ0.0001
ROA Ϫ0.402*** 0.003 ROA Ϫ0.393*** Ͻ0.0001
LEV 0.262*** Ͻ0.0001 LEV 0.246*** Ͻ0.0001
LIQ Ϫ0.298*** Ͻ0.0001 LIQ Ϫ0.475*** Ͻ0.0001
MTB 0.015 0.125 MTB 0.006 0.234
GDW 1.660* 0.089 GDW** 2.142** 0.037
IO 0.064*** Ͻ0.0001 IO 0.058*** 0.003
ALTMANZ 0.081** 0.040 ALTMANZ 0.060*** 0.001
Industry YES Industry YES
Year YES Year YES
Obs. 1,013 Obs. 1,013
Pseudo R2
0.183 Pseudo R2
0.205
Notes: DOWNGRADE ϭ 0 ϩ 1 ϫ GWILOSS ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ ϩ
6 ϫ MTB ϩ 7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ ; this table reports the regression results of
estimating the relationship between goodwill impairment losses and bond credit ratings subsampled by one
broad rating downgrades vs one notch rating downgrades; DOWNGRADE is a dummy of 1 if a firm
experiences a broad bond rating downgrade or a one-notch downgrade from prior year, 0 otherwise; all
continuous variables (except the logged value of firm size) are winsorized at the 1 and 99% percentiles each
year before entering regressions; refer to Appendix 2 for variable descriptions; ***, ** and *denote the
regression coefficient is statistically significant at the two-tailed 1, 5 and 10% level, respectively
15
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
16. 5. Conclusion
In this study, we examine the impact of goodwill impairment losses on bond credit ratings.
We posit and find a significant and negative relationship between goodwill impairment
losses and S&P bond ratings, suggesting that firms with goodwill impairment losses receive
lower credit ratings. Our results are robust to alternative model specifications and various
additional tests. Overall, we provide evidence that bond rating agencies use information
about goodwill impairment losses when assessing firms’ creditworthiness.
Albeit the supporting results, our study has some limitations. It is difficult to measure a
firm’s creditworthiness because it is multi-dimensional. The sample size is relatively small in
this study. Hence, the empirical evidence should therefore be interpreted with caution. Next,
it is possible that the information asymmetry caused by goodwill impairment may lead to the
bond rating downgrade. Ramanna and Watts (2012) find that goodwill impairment losses are
highly correlated with unverifiable assets, which reflect the level of information asymmetry
between the firm and outsiders. Third, Darrough et al. (2014) suggest that firm events such
as restructuring charges, long-term assets write-downs, special items and goodwill
impairments usually happen simultaneously. It is perhaps the combined effect of the above
firm events that cause bond rating downgrades. Thus, as a possible counter argument, we
argue that goodwill impairment alone may not necessarily lead to bond rating
downgrades[5]. Last, Liu (2011) suggests that different treatment of business acquisitions
lead to different accounting outcomes in an international context. Hence, goodwill
impairments, which originally stem from goodwill as a result of business acquisitions, may
have a different impact on bond ratings in different countries.
Whether our results still hold in an international setting remains unknown. This is a good
avenue for future research.
Notes
1. Less than 1 per cent of Compustat firms report goodwill impairment losses.
2. Petersen (2009) argues that the residuals of a given firm may be correlated across years (firm effect)
and the residuals of a given year may be correlated across different firms (time effect) in studies
using panel data sets. To better control for the firm and time effects, he suggests the use of clustered
standard errors. Following Petersen (2009) and Kosmidis and Stavropoulos (2014), we also apply
clustered standard errors by firm in regression analysis and obtain similar results. In addition,
motivated by Kosmidis and Stavropoulos (2014), we use different value relevant variables in the
model. Specifically, we include operation risk and growth opportunities (Alissa et al., 2013) in our
model. We find that bond ratings are negatively related to operation risk (measured as earnings
volatility) and positively related to growth opportunities.
3. Table II suggests that goodwill impairments are clustered in some industries. We conduct a
sensitivity test by including only the industries with less than 20 firm-year observations and the
main results still hold.
4. We repeat the same test in the following modified model and obtain similar inference for the
relationship between change of bond ratings (⌬BR) and the level pf impairment losses (GWILOSS),
the model equation is specified as below:
⌬BR ϭ 0 ϩ 1 ϫ GWILOSS ϩ 2 ϫ SIZE ϩ 3 ϫ ROA ϩ 4 ϫ LEV ϩ 5 ϫ LIQ
ϩ 6 ϫ MTB ϩ 7 ϫ GDW ϩ 8 ϫ IO ϩ 9 ϫ ALTMANZ ϩ
5. As a robustness check, we control for the above firm events in our model tests and still obtain
similar results.
IJAIM
25,1
16
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
17. References
Ahmed, K. and Ali, M.J. (2015), “Has the harmonization of accounting practices improved? Evidence
from South Asia”, International Journal of Accounting & Information Management, Vol. 23
No. 4, pp. 327-348.
Alissa, W., Bonsall, S., Koharki, K. and Penn, M. (2013), “Firms’ use of accounting discretion to influence
their credit rating”, Journal of Accounting and Economics, Vol. 55 Nos 2/3, pp. 129-147.
Ashbaugh-Skaife, H., Collins, D. and LaFond, R. (2006), “The effects of corporate governance on firms’
credit ratings”, Journal of Accounting and Economics, Vol. 42 Nos 1/2, pp. 203-243.
Attig, N., El Ghoul, S., Guedhami, O. and Suh, J. (2013), “Corporate social responsibility and credit
ratings”, Journal of Business Ethics, Vol. 117 No. 4, pp. 679-694.
Beatty, A. and Weber, J. (2006), “Accounting discretion in fair value estimates: an examination of SFAS
142 goodwill impairments”, Journal of Accounting Research, Vol. 44 No. 2, pp. 257-288.
Bhojraj, S. and Sengupta, P. (2003), “Effect of corporate governance on bond ratings and yields: the role
of institutional investors and the outside directors”, Journal of Business, Vol. 76 No. 3,
pp. 455-475.
Brown, K., Chen, V.Y.S. and Kim, M. (2015), “Earnings management through real activities choices of
firms near the investment-speculative grade borderline”, Journal of Accounting and Public Policy,
Vol. 34 No. 1, pp. 74-94.
Chalmers, K.G., Godfrey, J.M. and Webster, J.C. (2011), “Does a goodwill impairment regime better
reflect the underlying economic attributes of goodwill?”, Accounting & Finance, Vol. 51 No. 3,
pp. 634-650.
Chen, C., Kohlbeck, M. and Warfield, T. (2008), “Goodwill valuation effects of the initial adoption of
SFAS 142”, Advances in Accounting, Vol. 24 No. 1, pp. 72-81.
Choy, E., Gray, S. and Ragunathan, V. (2006), “Effect of credit rating changes on Australian stock
return”, Accounting and Finance, Vol. 46 No. 5, pp. 755-769.
Darrough, M., Guler, L. and Wang, P. (2014), “Goodwill impairment losses and CEO compensation”,
Journal of Accounting, Auditing and Finance, Vol. 29 No. 4, pp. 435-463.
Dichev, I.D. and Piotroski, J.D. (2001), “The long-run stock returns following bond ratings changes”,
Journal of Finance, Vol. 56 No. 1, pp. 173-204.
Filip, A., Jeanjean, T. and Paugam, L. (2015), “Using real activities to avoid goodwill impairment losses:
evidence and effect on future performance”, Journal of Business, Finance and Accounting, Vol. 42
Nos 3/4, pp. 515-554.
Financial Accounting Standards Board (FASB) (2001), Goodwill and Other, Accounting Standard
Codifications No. 350-20, Norwalk, CT.
Francis, J., Hanna, J.D. and Vincent, L. (1996), “Causes and effects of discretionary assets writeoffs”,
Journal of Accounting Research, Vol. 34, pp. 117-134.
Gentry, J.A., Whitford, D.T. and Newbold, P. (1988), “Predicting industrial bond ratings with a probit
model and funds flow components”, Financial Review, Vol. 23 No. 3, pp. 269-286.
Goh, J.C. and Ederington, L.H. (1993), “Is a bond rating downgrade bad news, good news, or no news for
stockholders?”, Journal of Finance, Vol. 48 No. 5, pp. 2001-2008.
Gu, F. and Lev, B. (2011), “Overpriced shares, ill-advised acquisitions, and goodwill impairment”, The
Accounting Review, Vol. 86 No. 6, pp. 1995-2022.
Hand, J., Holthausen, R.W. and Leftwich, R.W. (1992), “The effect of bond rating agency announcements
on bond and stock prices”, Journal of Finance, Vol. 47 No. 2, pp. 733-752.
Hayn, C. and Hughes, P.J. (2006), “Leading indicators of goodwill impairment”, Journal of Accounting,
Auditing and Finance, Vol. 21 No. 3, pp. 223-265.
Henning, S.L. and Shaw, W.H. (2003), “Is the selection of the amortization period of goodwill a strategic
choice?”, Review of Quantitative Finance and Accounting, Vol. 20 No. 4, pp. 315-333.
17
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
18. Hirschey, M. and Richardson, V.J. (2002), “Information content of accounting goodwill numbers”,
Journal of Accounting and Public Policy, Vol. 21 No. 3, pp. 173-191.
Horrigan, J.O. (1966), “The determination of long-term credit standing with financial ratios”, Empirical
Research in Accounting: Selective Studies, Vol. 62, pp. 44-62.
Jennings, R., Robinson, J., Thompson, R.B. and Duvall, L. (1996), “The relation between accounting
goodwill numbers and equity values”, Journal of Business, Finance and Accounting Vol. 23 No. 4,
pp. 513-533.
Jiraporn, P., Jiraporn, N., Boeprasert, A. and Chang, K. (2014), “Does corporate social responsibility
improve credit ratings? Evidence from geographic identification”, Financial Management,
Vol. 43 No. 3, pp. 505-531.
Jorion, P. and Zhang, G. (2007), “Information effects of bond rating changes: the role of the rating prior
to the announcement”, Journal of Fixed Income, Vol. 16 No. 4, pp. 45-59.
Jorion, P., Liu, Z. and Shi, C. (2005), “Information effects of regulation FD: evidence from rating
agencies”, Journal of Financial Economics, Vol. 76 No. 2, pp. 309-330.
Kim, H. and Gu, Z. (2003), “Financial determinants of corporate bond ratings: an examination of hotel
and casino firms”, Journal of Hospitality and Tourism Research, Vol. 28 No. 1, pp. 95-108.
Kisgen, D.J. (2006), “Credit ratings and capital structure”, Journal of Finance, Vol. 61 No. 3,
pp. 1035-1072.
Kisgen, D.J. (2009), “Do firms target credit ratings or leverage levels?”, Journal of Financial and
Quantitative Analysis, Vol. 44 No. 6, pp. 1323-1344.
Klock, M., Mansi, S. and Maxwell, W. (2005), “Does corporate governance matter to bondholder?”,
Journal of Financial and Quantitative Analysis, Vol. 40 No. 4, pp. 693-719.
Kosmidis, K. and Stavropoulos, A. (2014), “Corporate failure diagnosis in SMEs: a longitudinal analysis
based on alternative prediction models”, International Journal of Accounting & Information
Management, Vol. 22 No. 1, pp. 49-67.
Li, K. and Sloan, R.G. (2015), “Has goodwill accounting gone bad?”, Working Paper, University of
California, Berkeley.
Li, Z., Shroff, P.K., Venkataraman, R. and Zhang, I.X. (2011), “Causes and consequences of goodwill
impairment losses”, Review of Accounting Studies, Vol. 16 No. 4, pp. 745-778.
Liu, C. (2011), “IFRS and US-GAAP comparability before release No. 33-8879: some evidence from
US-listed Chinese companies”, International Journal of Accounting & Information Management,
Vol. 19 No. 1, pp. 24-33.
Liu, Y. and Jiraporn, P. (2010), “The effect of CEO power on bond ratings and yields”, Journal of
Empirical Finance, Vol. 17 No. 4, pp. 744-762.
Murfin, J. (2012), “The supply-side determinants of loan contract strictness”, Journal of Finance, Vol. 67
No. 5, pp. 1565-1601.
Olante, M.E. (2013), “Overpaid acquisitions and goodwill impairment losses: evidence from the US”,
Advances in Accounting, Vol. 29 No. 2, pp. 243-254.
Petersen, M.A. (2009), “Estimating standard errors in finance panel data sets: comparing approaches”,
Review of Financial Studies, Vol. 22 No. 1, pp. 435-480.
Pinches, G.E. and Mingo, K.A. (1973), “A multivariate analysis of industrial bond ratings”, Journal of
Finance, Vol. 28 No. 1, pp. 1-18.
Pinches, G.E. and Singleton, J.C. (1978), “The adjustment of stock prices to bond rating changes”,
Journal of Finance, Vol. 33 No. 1, pp. 29-44.
Ramanna, K. and Watts, R. (2012), “Evidence on the use of unverifiable estimates in required goodwill
impairment”, Review of Accounting Studies, Vol. 17 No. 4, pp. 749-780.
Sun, L. (2016), “Managerial ability and goodwill impairment”, Advances in Accounting.
IJAIM
25,1
18
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
19. West, R.R. (1970), “An alternative approach to predicting corporate bond ratings”, Journal of
Accounting Research, Vol. 8 No. 1, pp. 118-125.
Xu, W., Anandarajana, A. and Curatolab, A. (2011), “The value relevance of goodwill impairment”,
Research in Accounting Regulation, Vol. 23 No. 2, pp. 145-148.
Zhou, C. (2001), “The term structure of credit spreads with jump risk”, Journal of Banking & Finance,
Vol. 25 No. 11, pp. 2015-2040.
Appendix 1
Table AI.
Standard and poor’s
classification of credit
ratings
Variable S&P rating Conversion no.
Highest grade AAA 22
High grade AAϩ 21
AA 20
AAϪ 19
Upper medium grade Aϩ 18
A 17
AϪ 16
Medium grade BBBϩ 15
BBB 14
BBBϪ 13
Lower medium grade BBϩ 12
BB 11
BBϪ 10
Speculative grade Bϩ 9
B 8
BϪ 7
Poor standing grade CCCϩ 6
CCC 5
CCCϪ 4
Highly speculative grade CC 3
Lowest quality grade C 2
In default D 1
19
Goodwill
impairment
loss
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
20. Appendix 2
Corresponding author
Li Sun can be contacted at: li-sun@utulsa.edu
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
Table AII.
Variable definitions
Variable Definition
BR ϭ S&P Long-Term Domestic Issuer Credit Rating (#280); refer to Appendix 1
for numeric coding
GWILOSS ϭ Goodwill impairment losses [GDWLIP (#368) ϫ (Ϫ1)] scaled by total
assets at prior year
SIZE ϭ The natural log of total assets (AT, #6)
ROA ϭ Income before extraordinary items (IB, #18) scaled by total assets (AT, #6)
LEV ϭ Long-term liabilities (DLTT, #9) divided by total assets (AT, #6)
LIQ ϭ Operating cash flows (OANCF, #308) scaled by total liabilities (LT, #181)
MTB ϭ Market-to-book ratio, calculated as [Outstanding common shares (CHSO,
#25) ϫ Stock price at fiscal-year end (PRCC_F, #24)] divided by total book
value (CEQ, #60)
GDW ϭ Total goodwill (GDWL, #204) scaled by total assets (AT, #6)
IO ϭ The percentage of the company’s common stock held by institutions at the
fiscal yearend
ALTMANZ ϭ Altman Z-score for insolvency ratio, calculated as 3.3 ϫ [net income (NI,
#172)/total assets (AT, #6)] ϩ 1.0 ϫ [sales (SALE, #12)/total assets (AT,
#6)] ϩ 0.6 ϫ [market value of equity (CHSO, #25 ϫ PRCC_F, #24)]/total
liabilities (LT, #181) ϩ 1.2 ϫ (working capital (ACT, #4–LCT, #5)/total
assets (AT, #6) ϩ 1.4 ϫ [retained earnings (RE, #36)/total assets (AT, #6)]
DOWNGRADE ϭ A dummy of 1 if a firm experiences a broad bond rating downgrade or a
one-notch downgrade from prior year, 0 otherwise
IJAIM
25,1
20
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)
![International Journal of Accounting & Information Management
Goodwill impairment loss and bond credit rating
Li Sun, Joseph H. Zhang,
Article information:
To cite this document:
Li Sun, Joseph H. Zhang, (2017) "Goodwill impairment loss and bond credit rating", International
Journal of Accounting & Information Management, Vol. 25 Issue: 1, pp.2-20, https://doi.org/10.1108/
IJAIM-02-2016-0014
Permanent link to this document:
https://doi.org/10.1108/IJAIM-02-2016-0014
Downloaded on: 20 April 2018, At: 13:46 (PT)
References: this document contains references to 46 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 548 times since 2017*
Users who downloaded this article also downloaded:
(2017),"Regime change in the accounting for goodwill: Goodwill write-offs and the value relevance
of older goodwill", International Journal of Accounting & Information Management, Vol. 25
Iss 1 pp. 43-69 <a href="https://doi.org/10.1108/IJAIM-02-2016-0018">https://doi.org/10.1108/
IJAIM-02-2016-0018</a>
(2017),"The role of audit quality and culture influence on earnings management in companies
with excessive free cash flow: Evidence from the Asia-Pacific region", International Journal of
Accounting & Information Management, Vol. 25 Iss 1 pp. 21-42 <a href="https://doi.org/10.1108/
IJAIM-05-2016-0059">https://doi.org/10.1108/IJAIM-05-2016-0059</a>
Access to this document was granted through an Emerald subscription provided by emerald-
srm:619260 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald
for Authors service information about how to choose which publication to write for and submission
guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as
well as providing an extensive range of online products and additional customer resources and
services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the
Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for
digital archive preservation.
*Related content and download information correct at time of download.
DownloadedbyUniversidadeEstadualdeGoiasAt13:4620April2018(PT)](https://image.slidesharecdn.com/ijaim-02-2016-0014-180421134339/85/Ijaim-02-2016-0014-1-320.jpg)
