DP2020-31

Incorporating Piecewise-linear
Variables into an Empirical Modelof Non-current Asset Impairment
Timeliness
Keishi FUJIYAMA

Revised October 25, 2021

Incorporating Piecewise-linear Variables into an Empirical Model of Non-current
Asset Impairment Timeliness
Abstract: While prior research employs linear stock returns as a proxy for economic losses, this study
uses piecewise-linear stock returns to separate positive and negative stock returns. It also examines
the relationships of non-current asset impairments with changes in sales and cash flows fromoperations, which can be viewed as short-term indicators of economic impairments. I find a negative
relationship between non-current asset impairments and negative stock returns in year t–5, consistent
with prior research. I also find such a relationship in year t, contrary to prior research. The results
indicate that the relationships are stronger in years t–1 and t–2 than in years t and t–3. These results
suggest that non-current asset impairment losses reported by Japanese firms are consistent with theJapanese accounting standard, although such losses are not necessarily reported in a timely manner.
In addition, I find evidence suggesting that changes in sales and cash flows from operations in year t
are short-term indicators of non-current asset impairments. Overall, incorporating piecewise-linear
variables improves the empirical model of non-current asset impairment timeliness.

1. Introduction
The purpose of this study is to investigate whether incorporating piecewise-linear stock returns andshort-term indicators improves the empirical model of non-current asset impairment timeliness. Prior
research suggests that a non-current asset impairment is subject to management discretion and reflects
management incentives such as “big bath,” income smoothing and management turnovers in Japan(e.g., Enomoto 2008; Obinata and Okada 2008; Hu and Kurumado 2012; Fujiyama 2014a, 2014c;
Kimura 2015). Consistently, using data on Japanese firms, Fujiyama (2014b) finds that non-current
asset impairments are not correlated with linear returns in the years of their recognition, but that such
relationships exist in the period from one year to four (or five) years before their recognition; thissuggests that Japanese firms can record non-current asset impairments in a less timely manner than
expected by the accounting standards.1 Moreover, the timeliness of non-current asset impairment isPrior research investigates the timeliness of asset impairments using other models and provides mixed evidence
regarding the timeliness of the impairment losses of assets, such as non-current asset and goodwill (Chen et al. 2008;
Choi 2008; Li and Sloan 2017). The timeliness of goodwill impairment losses is affected by narrative disclosure quality(Iatridis et al. 2021), CEO overconfidence (Chung and Hribar 2021), and audit quality (Albersmann and Quick 2020).
André et al. (2016) show that goodwill impairment practices differ in the U.S. and European countries from the11particularly controversial in Japan. In general, the degree of loss recognition timeliness, or conditional
conservatism, is lower in Japan than in common law countries such as the U.S. and the U.K. (Ball et
al. 2000). Non-current asset impairment losses are one of the major components of accounting
conservatism (Beaver and Ryan 2005). Therefore, non-current asset impairment timeliness is of great
concern to practitioners and researchers and it is important to improve the empirical models used inprior research, such as that in Fujiyama (2014), and construct a better model.
While Fujiyama (2014b) employs linear stock returns as a proxy for economic losses toinvestigate non-current asset impairment timeliness in Japan, Banker et al. (2017) recommend
piecewise-linear stock returns and short-term indicators.2 Non-current asset impairment losses reflect
only economic losses or bad news, as the accounting standards prohibit the recording of upward
valuation profits. Thus, separating positive (good news) from negative stock returns (bad news) is
expected to better reflect the asymmetric timeliness nature of profit and loss accounting. Accordingly,
prior research on accounting conservatism captures the asymmetric timeliness of gains and losses byusing piecewise-linear stock returns (e.g. Basu, 1997). However, studies on asset impairments employ
linear stock returns to capture economic losses (e.g., Riedl 2004; Lapointe-Antunes et al. 2009;
Fujiyama 2014b; Albersmann and Quick 2020), which may result in estimation bias. An exception is
Iatridis et al. (2021), who employ piecewise-linear stock returns only for year t but not for the years
before impairment recognition. Moreover, Banker et al. (2017) suggest that different indicators, suchas stock returns and changes in sales and cash flows from operations, predict cash flows over varying
time horizons and have different impacts among asset classes. Stock returns capture changes in cash
flows over a long-term period; changes in sales and operating cash flows capture such changes over ashort-term period. Thus, changes in sales and operating cash flows are expected to indicate non-current

viewpoint of timeliness. Glaum et al. (2018) find that country-level enforcement determines goodwill impairment
timeliness.
2
Note that some argue that a piece-wise linear regression of earnings on stock returns provides an estimation bias in
the presence of the skewness of stock returns (Jarva and Lof 2021; Obinata 2021)2

asset impairments that are incremental to stock returns. As tangible assets have a finite useful life andgoodwill has an indefinite future life, goodwill impairments are more strongly correlated to stock
returns than tangible asset impairments. Note that stock returns are the more important impairment
indicator even for tangible assets than operating cash flows and sales changes (Banker et al. 2017).
Consistently, Banker et al. (2017) show that the results of Riedl (2004) change when the empiricalmodel incorporates piecewise-linear stock returns and piecewise-linear short-term indicators of asset
impairments. Therefore, it is important to investigate whether a new empirical model alters Fujiyama’s(2014b) results.
In this study, I re-examine the timeliness of non-current asset impairments based on
Fujiyama (2014b) and by using piecewise-linear stock returns, as suggested by Banker et al. (2017).
In addition, I examine the relationships of non-current asset impairments with changes in sales andcash flows from operations (CFO), which are suggested by Banker et al. (2017) and can be viewed as
the short-term indicators of economic loss. Using data on Japanese firms during the period 2007–2019,
I find a negative relationship between non-current asset impairments (positive values) in year t andnegative stock returns in year t–5, consistent with Fujiyama (2014b). Second, non-current asset
impairments are negatively correlated with negative stock returns in year t, which is inconsistent with
Fujiyama (2014b), who does not observe any correlation between impairments and linear stock returns
in year t. Third, the relationships between non-current asset impairments and negative stock returns
are the strongest in years t–1 and t–2. These results suggest that consistent with the “probability
criterion” of the impairment recognition that the Japanese standard employs contrary to the “economic
criterion” employed by International Financial Reporting Standards, the relationship between
impairments and negative stock returns is stronger in years t–1 and t–2 than in year t.3 Meanwhile,

For the recognition of non-current asset impairments, the Japanese standard and the U.S. GAAP employ “probability
criterion,” which requires a firm to recognize an impairment loss “only if it is considered probable that the carrying
amount of an asset cannot be fully recovered” (IAS 36, APPENDIX B, B81); IFRS employs “economic criterion,”
which requires a firm to recognize an impairment loss “whenever the recoverable amount of an asset is below its
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the negative relationships from years t–5 to t–3 indicate that Japanese firms do not necessarily
recognize non-current asset impairments in a timely manner. Fourth, I find statistically significant
correlations of non-current asset impairments with negative changes in sales and CFO, suggesting thatthe changes in sales and CFO are short-term indicators of economic losses. Finally, additional analyses
reveal that the use of non-zero thresholds for non-current asset impairment trigger provides a minor
improvement in the explanatory power of the empirical model of non-current asset impairment
timeliness.
This study contributes to the literature in two ways. First, it improves the findings of
Fujiyama (2014b). While the finding of a negative relationship between non-current asset impairments
and stock returns in year t–5 is consistent between Fujiyama (2014b) and this study, this study presents
a statistically significant relationship between impairments and negative stock returns in year t.
Moreover, adj-R2 is higher for the models employed in this study. My findings suggest that piecewiselinear stock returns better reflect the asymmetric timeliness of non-current asset impairments and thatpiecewise-linear changes in sales and CFO capture the existence of non-current asset impairments. In
addition, my findings suggest using statistical tests that the largest economic losses occur one or two
years prior to their accounting recognition.
Second, this study contributes to research on the determinants of non-current asset
impairments. Prior research on the determinants of non-current asset impairments often employs themodel developed by Riedl (2004) (e.g., Enomoto 2008; Hu and Kurumado 2012; Fujiyama 2014c).
Although this study does not replicate Riedl’s (2004) model with piecewise-linear indicators such as

carrying amount” (IAS 36, APPENDIX, B88). Further, see Gordon and Hsu (2018) for the difference in the recognition
criteria for asset impairments. The Japanese standard requires a firm to test an impairment of an asset when the signsof a non-current asset impairment, including operating losses or operating cash flows in two consecutive years or more,are observed and compare the undiscounted future cash flows with the carrying amount of an asset. IAS 36 provides
several examples of asset impairment indications, including a significant decline in an asset’s market value, the ratioof the carrying amount of a firm’s net asset over its market capitalization, and unfavorable internal reporting. It requires
a firm to compare value in use, such as discounted future cash flows, with the carrying amount of an asset when someindicators are observed.

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stock returns, sales, and CFO, as implemented by Banker et al. (2017), my findings indicate that noncurrent asset impairments have non-linear relationships with those indicators, suggesting thatpiecewise-linear variables better control for the economic determinants of non-current asset
impairments. Moreover, the findings suggest that research on accounting conservatism and timelinessof earnings can better reflect differences in the time horizons of cash flows among accounting items
by incorporating piecewise changes in sales and cash flows from operations.
2. Timeliness Models for Non-current Asset Impairments in Prior ResearchPrior research investigates the timeliness of non-current asset impairment losses, especially goodwill
impairments, using several empirical models. In this section, I review the empirical models used inprior research. The most frequently used model employs the amount or recognition of impairment
losses as the explained variable and stock returns as an explanatory variable (Lapointe-Antunes et al.
2009; Glaum et al. 2018; Albersmann and Quick 2020; Iatridis et al. 2021). Lapointe-Antunes et al.
(2009) estimate the following OLS regression:
GoodwillImpairmenti,t = α0 + α1Reti,t + α2Reti,t-1 + α3Reti,t-2 + εi,t(1),where GoodwillImpairmenti,t is the reported goodwill and Reti,t is firm i’s annual stock return. 4 Glaumet al. (2018) employ a recognition dummy as the explained variable, include only linear stock returns
in years t and t–1, and compare the magnitude of these two coefficients; Albersmann and Quick (2020)
also employ a recognition dummy as the explained variable and examine its relationships with linear
stock returns in years t, t–1, and t–2; Iatridis et al. (2021) investigate only the relationship between thereported goodwill impairments and stock returns in year t but incorporate piecewise-linear stock
returns. In line with this stream of research, Fujiyama (2014b) incorporates linear stock returns fromyear t to t–6. Similarly, Chen et al. (2008) use earnings before extraordinary items but after impairment

As the purpose of this section is to explain the overview of the empirical models used in prior research, I do notdiscuss the details of the definitions of the variables employed by prior research.
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charges as the explained variable and incorporate an interaction term between linear stock returns andimpairment recognition as an explanatory variable. Another timeliness model employs stock returns
as the explained variable and impairment losses as an explanatory variable, controlling for the reported
earnings (Choi 2008; Chen et al. 2008).
The following two studies identify the signs of economic goodwill impairment. André et al.
(2016) define impairment indicators as [market value of equity – book value of equity] < bookgoodwill, market-to-book ratio less than one, or EBITDA less than zero. Further, they compare therecognition of goodwill impairments by the E.U. and U.S. firms when observing each indicator. Li
and Sloan (2017) consider the following two indicators. One is the book-to-market ratio of more than
one in year t–1. The other takes the value of one if [book goodwill/total assets] is more than 10% andthe return on assets (ROA) is less than zero, the negative value of one if [book goodwill/total assets]is less than 5% and ROA is more than 5%, and zero otherwise in year t–1. The negative value of one
indicates that firms are unlikely to experience economic impairments in book goodwill. Then, theyregress the goodwill impairment dummy in year t on these two indicators in year t–1. They interpret
goodwill impairments as less timely if these indicators in year t–1 predict goodwill impairments inyear t.
Chung and Hribar (2021) employ the Cox proportional hazards model and investigate therelationship between non-impairment duration and CEO overconfidence.
Overall, prior research investigates the timeliness of non-current asset impairments,
especially goodwill impairments, by (1) using stock returns as an explanatory variable, (2) using stock
returns as the explained variable, (3) identifying impairment indicators, and (4) employing a hazard
model. As my focus is on non-current assets and not only on goodwill, the use of (3) impairment
indicators and (4) a hazard model has some limitations. Specifically, a frequently used indicator of
goodwill impairment, that is the book-to-market ratio, is not suitable for non-current assets such as
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fixed assets (e.g., property, plant, and equipment). For example, if a firm without book goodwill
experiences a more than one BTM ratio, there lies a possibility that its inventories are economically
impaired; they are not subject to accounting for non-current asset impairments in Japan. A hazard
model may not be suitable for non-current asset impairments. As the U.S. Generally Accepted
Accounting Principles (GAAP) and International Financial Reporting Standards require a nonamortization method for goodwill, book goodwill is expensed only as goodwill impairment. This
allows a study to identify the timing of expensing book goodwill. However, a proportion of non-current
assets are subject to periodical depreciation and amortization, which complicates the identification of
the duration between the economic existence and recognition timing of non-current asset impairments.
Moreover, as observed in (2), the use of stock returns as the explained variable makes it difficult tocontrol for the economic losses that occur in other fiscal years. Therefore, the use of stock returns as
an explanatory variable constructs a better model for investigating the timeliness of non-current asset
impairments. This study aims to improve the model that uses stock returns as an explanatory variable
by introducing piecewise stock returns considering the years before impairment recognition in a settingof non-current asset impairments.
3．Empirical Model
Following Lapointe-Antunes et al. (2009), Fujiyama (2014b) estimates the following OLS regression:
Impi,t = a0 + a1Ri,t + a2Ri,t-1 + a3Ri,t-2 + a4Ri,t-3 + a5Ri,t-4 + a6Ri,t-5 + a7Ri,t-6+ ΣYear + εi,t(2),where Impi,t is the positive value of the reported non-current asset impairment losses in year t, deflated
by market capitalization at the end of year t–1, and Ri,k is firm i’s buy-and-hold annual stock return; kdenotes the observed year (i.e., year t, year t–1…year t–6). Fujiyama (2014b) assumes a linear
relationship between non-current asset impairments and stock returns.
However, Banker et al. (2017) point to differing relationships of non-current asset
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impairments with positive and negative stock returns. Thus, they propose incorporating a dummy
variable, Deci,k, that takes the value of one if firm i’s stock return is negative and zero otherwise, andits interaction with Ri,k. Accordingly, I estimate the following OLS regression model incorporating
those variables:
Impi,t = b0 + b1Ri,t + b2Ri,t-1 + b3Ri,t-2 + b4Ri,t-3 + b5Ri,t-4 + b6Ri,t-5 + b7Ri,t-6+ b8Ri,t*Deci,t + b9Ri,t-1*Deci,t-1 + b10Ri,t-2*Deci,t-2 + b11Ri,t-3*Deci,t-3+ b12Ri,t-4*Deci,t-4 + b13Ri,t-5*Deci,t-5 + b14Ri,t-6*Deci,t-6 +b15Deci,t+ b16Deci,t-1 + b17Deci,t-2 + b18Deci,t-3 + b19Deci,t-4 + b20Deci,t-5+ b21Deci,t-6 + ΣYear + ΣIndustry +εi,t(3).

Note that in this study, I compute Ri,k by adjusting dividends and stock splits and Impi,t by adjusting
stock splits.5 Equation (3) also controls for industry fixed effects in addition to year fixed effects.
In addition, following Ball and Shivakumar (2006) and Banker et al. (2017), I estimate thefollowing OLS regression model, incorporating short-term indicators:
Impi,t = c0 + c1Ri,t + c2Ri,t-1 + c3Ri,t-2 + c4Ri,t-3 + c5Ri,t-4 + c6Ri,t-5 + c7Ri,t-6+ c8Ri,t*Deci,t + c9Ri,t-1*Deci,t-1 + c10Ri,t-2*Deci,t-2 + c11Ri,t-3*Deci,t-3+ c12Ri,t-4 *Deci,t-4 + c13Ri,t-5*Deci,t-5 + c14Ri,t-6*Deci,t-6 + c15ΔSalesi,t+ c16ΔSalesi,t*DecSalesi,t + c17DecSalesi,t + c18ΔCFOi,t+ c19ΔCFOi,t*DecCFOi,t + c20DecCFOi,t + c21Deci,t + c22Deci,t-1+ c23Deci,t-2 + c24Deci,t-3 + c25Deci,t-4 + c26Deci,t-5 + c27Deci,t-6+ΣYear +ΣIndustry + εi,t(4),where ΔSalesi,t is the adjusted per-share change in sales in year t and ΔCFOi,t is the adjusted per-sharechange in CFO in year t. Both ΔSalesi,t and ΔCFOi,t are deflated by a dividend and share-split adjusted

I use the cumulative adjustment coefficients B and A from NEED FinancialQuest 2.0 for Ri,k and Impi,t, respectively.
Cumulative adjustment coefficients A and B adjust share splits, share consolidation, and so on, excluding and including
dividends, respectively.
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stock price. DecSalesi,t and DecCFOi,t take the value of one if ΔSalesi,t and ΔCFOi,t are negative,
respectively. I also estimate models incorporating ΔSalesi,k and ΔCFOi,k from year t to t–6 to confirm
that they are short-term indicators of economic impairment.
As stock returns capture long-term changes in cash flows, they can be assumed to overlap ashort-term change in future cash flows, which is captured by changes in operating cash flows and sales.
Nevertheless, the incorporation of these short-term indicators is important and interesting in two
respects. First, short-term indicators are likely to capture the management and auditor decision
processes. Operating cash flow forecasts are a major basis for the impairment tests for non-current
assets, and sales forecasts are a major input in operating cash flow forecasts. Referring to Chase (2013),
Banker et al. (2017) argue that since a sales forecast is based on the projection of a recent sales trend,a current sales change is an important determinant of a cash flow forecast by accountants. Thus, theincorporation of short-term indicators partly controls for the impairment testing processes of managers
and auditors, which are expected to affect the timeliness of non-current asset impairments. Second,
research on non-current asset impairment determinants generally does not control for lagged stock
returns. The use of piecewise short-term indicators can improve the empirical models of non-current
asset impairment determinants to the extent that lagged stock returns can capture a part of the economic
losses captured by short-term indicators.
4. Data
Financial and price data are obtained from NEEDS-FinancialQUEST 2.0. I obtain the data on firms,
whose fiscal year ends in March, for the period 2007–2019. I exclude the firms whose accounting
period is not 12 months, whose accounting standards are not Japanese GAAP, which belong tofinancial industries, and whose necessary data are not available.6 As a result of this sample selection,

This study does not exclude observations with multiple impairment losses recorded in year t and other years (fromyear t–1 to year t–6), assuming that impairment losses in years t–1 to t–6 are not correlated to those in year t.
69the final sample size is 25,860 firm-year observations. Each continuous variable is winsorized at theupper and bottom 1st percentile values by year. Impi,t is winsorized at the upper 1st percentile value
by year.
5. Empirical Results
5.1. Descriptive statistics and correlation matrix
Table 1 reports the descriptive statistics. The mean value of Impi,t is 0.0068 and its median value is0.0000, indicating that most firms do not record impairment losses. The second row of Table 1 presents
the descriptive statistics of Impi,t for only the impairment firms. The mean and median values are
0.0147 and 0.0037, respectively, while its 75th percentile value is 0.0121. Thus, a small portion of
impairment firms record large impairment losses.
Table 2 reports the correlation matrix. Stock returns are not highly correlated each year.
ΔSlaest is not highly correlated with ΔCFOt. However, ΔCFOt is relatively highly correlated with
ΔCFOt-1, (coefficient = –0.3627; untabulated). ΔCFOt is not highly correlated with ΔCFOt-2(coefficient = –0.0231; untabulated).
5.2. Timeliness of non-current asset impairment losses
Table 3 presents the results of the timeliness test of non-current asset impairments. Panel A of Table 3
reports the regression results. Column (1) reports the result of Fujiyama (2014b). Column (2) presents
the result of the replication of Fujiyama (2014b). These results are similar, suggesting that the sample
used in this study is similar to that of Fujiyama (2014b).
Columns (3) to (5) present the results obtained after using dividend- and share-split-adjusted
returns but not employing the piecewise regression method. These results are similar to those of
Fujiyama (2014), except for the result of Ri,t in Columns (3) and (4). In Column (5), the coefficient of
Ri,t is negative but not statistically significant (coefficient = –0.0022; t = –1.29). The difference
between Columns (4) and (5) is the standard error adjustment. As Fujiyama (2014c) shows that non10

current asset impairment losses are correlated with the changes in gross domestic product in Japan,
macroeconomic conditions, such as years, are factors affecting firms’ non-current asset impairments.
Moreover, stock returns are also affected by macroeconomic conditions. Thus, it may be better toadjust standard errors by clustering by year instead of using White’s (1980) method in addition to year
fixed effects.
The results in Columns (6) and (7) are my main interest and are obtained by estimating thepiecewise-linear regression models. Ri,k * Deci,k is statistically significant from year t–4 to year t,
indicating the asymmetric relationships of non-current asset impairments with positive and negative
stock returns. In addition, the use of the piecewise-linear regression model improves the explanatory
power of the estimation model; that is, the adjusted R2 in Column (5) is 3.42% and that in Columns(6) and (7) is 7.23% and 8.52%, improving 3.81% and 5.1% of the explanatory power, respectively.
The coefficients of Ri,k are negative and statistically significant for years t to t–6, suggesting that
impairment firms experience a smaller increase in their stock prices even when their stock prices
increase before their impairment recognition.
Panel B of Table 3 presents the coefficients of stock returns for observations with negative
stock returns (i.e., Ri,k + Ri,k * Deci,k) and comparisons of these coefficients. The coefficients forobservations with negative stock returns are statistically significant at least at the 5% level from year
t–5 to year t in Columns (6) and (7) and at the 10% level in year t–6 in Column (6). This suggests that
non-current asset impairments gradually occur since approximately five years before their recognition.
Contrary to Fujiyama (2014b), I find a statistically significant association between non-current asset
impairments and stock returns for observations with negative stock returns in year t (coefficient = –0.0179 and –0.0186; t = –4.49 and –4.67 in Columns 6 and 7, respectively). These results suggest that
economic impairments occur gradually for several years before their recognition.
For comparisons of the coefficients for observations with negative stock returns, the11difference between those for years t and t–1 is 0.0116 and statistically significant at the 1% level (t =3.13 and 3.26 in Columns 6 and 7, respectively), indicating that the coefficients for year t–1 are smaller
than those for year t. While the difference between those for years t–1 and t–2 is insignificant, thedifferences between those for years t–2 and t–3 are –0.0091 and –0.0092 and statistically significant
at the 10% level in Columns (6) and (7), respectively. In addition, the difference between years t–1
and t–3 is statistically significant at the 1% level (untabulated). These results suggest that the economic
impairments of non-current assets occur to a greater extent in years t–1 and t–2, consistent with theaccounting standard that employs the “probability criterion” instead of the “economic criterion.” Anexample of the signs of non-current asset impairments under Japanese GAAP is to record negative
CFO or operating losses for a cash-generating unit for two consecutive years.
Overall, the use of piecewise-linear regression is likely to improve the tests of non-current
asset impairment timeliness. In addition, the results suggest that the reported impairment losses aretimelier than Fujiyama (2014b) finds; however, such losses are not necessarily recorded in a timely
manner as they gradually occurred since five years before their recognition.
5.3. Test of the short-term indicators of non-current asset impairments
Table 4 reports the results incorporating changes in sales and CFO as the short-term indicators of noncurrent asset impairments. In Panel A, only results for the observations with negative stock returns
and/or changes in sales and/or CFO are presented. Only changes in sales and CFO in year t areincorporated in Column (1) while those for years t to t–6 are incorporated in Columns (2) to (6). In
Column (1), the coefficients of ΔSalest * DecΔSalest and ΔCFOt * DecΔCFOt are negative andstatistically significant at the 1% level (coefficient = –0.0081 and –0.0218; t = –4.28 and –5.74 for
ΔSalest * DecΔSalest and ΔCFOt * DecΔCFOt, respectively). The negative coefficients of ΔSalest *DecΔSalest and ΔCFOt * DecΔCFOt are consistently presented in Columns (2) to (6). These
coefficients for year t–1 are negative but insignificant in Columns (2) to (6). The coefficients of ΔSalesk
12* DecΔSalesk and ΔCFOk * DecΔCFOk before year t–1 are insignificant, while the coefficient of
ΔCFOt-6 * DecΔCFOt-6 is negative and statistically significant at the 10% level in Column (5) but notin Columns (4) and (6). Thus, positive and negative changes in sales and CFO, which are expected to
be short-term indicators of non-current asset impairments, have asymmetry relationships with reported
non-current asset impairment losses only in year t.
Panel B of Table 4 presents the coefficients of stock returns and changes in sales and cash
flows from operations for observations with negative returns or changes in sales or CFO. In Columns(1), (2), (3), and (6), the combined coefficients of ΔSalest and ΔSalest * DecΔSalest are consistently
negative and statistically significant at the 1% level. In Columns (1), (4), (5), and (6), the combinedcoefficients of ΔCFOt and ΔCFOt * DecΔCFOt are consistently negative and statistically significant
at the 1% level. The combined coefficients of both ΔSalesk and ΔSalesk * DecΔSalesk and ΔCFOk and
ΔCFOk * DecΔCFOk are insignificant for years t–6 to t–1. The combined coefficients of ΔCFOt-4 and
ΔCFOt-4 * DecΔCFOt-4 are positive and statistically significant. However, the sign is opposite to theprediction and is not interpretable.
The results for the negative stock returns are consistent with those in Table 3. The differencein the combined coefficients of Rk and Rk * Deck between years t and t–1 is consistently and statisticallysignificant at the 5% level, even when controlling for changes in sales and CFO. The difference
between years t–2 and t–3 is statistically significant at the 10% level in Column (1) but not in Columns(3), (5), and (6). Note that the difference between years t–1 and t–3 is consistently statisticallysignificant at the 1% level in all columns (untabulated). Assuming that changes in operating cash flows
and sales partly control for the non-current asset impairment testing processes of managers andauditors because they are major inputs of impairment tests, the effectiveness of the timeliness modelholds even after undertaking such management and auditor decision processes.
Interestingly, the combined coefficients of Rk and Rk * Deck in Column (1) in Panel B of13

Table 4 are slightly different from those in Column (7) in Panel B of Table 3. Specifically, those in
Tables 3 and 4 are –0.0186 and –0.0169 for year t, –0.0302 and –0.0241 for year t–1, –0.0219 and –0.0180 for year t–2, –0.0127 and –0.0100 for year t–3, –0.0077 and –0.0064 for year t–4, and –0.0054
and –0.0042 for year t–5, respectively. The difference is 0.0017 for year t, 0.0061 for year t–1, 0.0039
for year t–2, 0.0027 for year t–3, 0.0013 for year t–4, and 0.0012 for year t–5, respectively. The
difference in the combined coefficients in Tables 3 and 4 is the largest in year t–1 and decreases
afterwards. This result is consistent with the view that lagged stock returns capture long-term changes
in cash flows, which are partly captured by short-term indicators. It is useful to recognize the tradeoff between the control for impairment testing processes and overlap between short-term indicatorsand lagged stock returns when future research on non-current asset impairment timeliness employs
the full model, that is, equation (3).
Overall, the results suggest that changes in sales and CFO the are short-term indicators of
non-current asset impairments. To the extent that changes in sales and CFO are short-term indicatorsof non-current asset impairments, a model incorporating sales and CFO variables only for year t canbetter reflect short-term changes in firms’ economic conditions.
6. Additional Analysis
As Banker et al. (2017) suggest that an impairment trigger is different from the value of zero for all
the three indicators, that is stock return, sales change and operating cash flow change, I analyze thetimeliness of non-current asset impairments by using different impairment trigger thresholds.
Although Banker et al. (2017) employ a censored regression model, I employ an OLS regression model
because the purpose of this study is to improve Fujiyama’s (2014b) impairment timeliness model.
Moreover, while they use grid search, I use certain thresholds of Deci,k, DecSalesi,t, and DecCFOi,t:0.1, 0.05, –0.05, –0.1, –0.15, –0.2, –0.25, and –0.3 because an optimal value will change as a sample
changes.
14

Table 5 presents the results for the different thresholds for stock returns. R2 is the highest for
the threshold of –0.1, improving 0.12% of R2 compared with the zero threshold (Panel A). Panel B
reports the results of the coefficients of stock returns for observations with negative stock returns (i.e.,Ri,k + Ri,k * Deci,k) and draws comparisons between the coefficients. For all the thresholds, thecoefficients for observations below the thresholds are the largest in year t–1, which is consistent withthe main result. Although there are some differences for Dec<–0.25 and Dec<–0.3, the results are
qualitatively similar to those of Dec<0 in the case of other thresholds.
Panels A and B of Table 6 present the results of the different thresholds for changes in sales
and CFO, respectively. In Panel A, the coefficients of sales changes for all the thresholds are similar
to those of the main analysis except for those of DecSalesi,t <–0.25 and –0.3. In Panel B, thecoefficients of CFO changes for all the thresholds are similar to those of the main analysis. Moreover,R2 for changes in sales and CFO are the highest for the thresholds of –0.1 and –0.05, respectively.
However, minor improvements of the explanatory power are observed (0.05% and 0.01% for changes
in sales and CFO, respectively).
Overall, although a non-zero threshold would improve the model of non-current asset
impairment timeliness, the improvement appears to be minor for the both long- and short-term
indicators. Therefore, if one sets a non-zero threshold, it is better for one to justify the threshold bycomparing other thresholds.
7. Conclusions
This study re-examines the timeliness of non-current asset impairment losses, which Fujiyama (2014b)
investigates using a piecewise-linear regression model proposed by Banker et al. (2017). Similar to
Fujiyama (2014b), I find a negative relationship between non-current asset impairments (positive
value) and negative stock returns in year t–5. However, contrary to Fujiyama (2014b), I find a negative
relationship between non-current asset impairments and negative stock returns in year t. The negative15

relationship is stronger in years t–1 and t–2 than in years t and t–3. These results suggest that while
the reported non-current asset impairments are not necessarily timely, the recognition of impairment
losses is consistent with the “probability criterion” employed by the accounting standard for noncurrent asset impairment in Japan. In addition, I find a relationship between non-current asset
impairments and changes in sales and cash flows from operations (CFO) in year t, suggesting that thechanges in sales and CFO can be the short-term indicators of non-current asset impairments. Therefore,
future research on non-current asset impairments and possibly accounting conservatism can better
observe the timeliness of losses and control for the economic factors of losses by employing thepiecewise-linear regression model recommended by Banker et al. (2017). Moreover, it can control for
economic factors by piecewise-linear variables of changes in sales and CFO.
However, this study has at least two limitations. First, the model becomes complicated whena study focuses on two factors. As the model uses a piecewise interaction term, one needs toincorporate a four-variable interaction term when adding two factors. In this case, the model that uses
stock returns as the explained variable may be the better one, introducing a three-variable interaction
term, that is, an interaction term among an impairment variable, factor 1, and factor 2. Second, thepiecewise interaction term may not be suitable for non-linear regression models, as questioned by Ai
and Norton (2003) in the context of probit and logistic regression models. For example, Riedl (2004)
and Glaum et al. (2018) employ the tobit and logistic regression models, respectively, while Ramannaand Watts (2012) employ the OLS regression model. Thus, one needs to pay attention to model
specification when using a piecewise-linear stock return.

16

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sonshitsu keijo no ishikettei ni oyobosu keieisha kotai no eikyo). Sangyo Keiri 74 (2): 157-168(in Japanese).
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enforcement and monitoring by institutional investors. The Accounting Review 93 (6): 149-180.
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18

Table 1: Descriptive statistics (n =25,860)
Mean
SD
25P
Median75P
Impt0.00680.02280.00000.00000.0030Impt (only Impt > 0)0.01470.03190.00100.00370.0121Rt
0.06790.3857-0.1663
0.02180.2252
Rt-1
0.1076
0.4096-0.1398
0.05330.2715Rt-2
0.10930.4164-0.1406
0.05240.2733Rt-3
0.1491
0.4843-0.1373
0.06400.3197Rt-4
0.14550.4939-0.1450
0.06140.3181
Rt-5
0.11970.5103-0.17190.03380.2843Rt-6
0.09740.5180-0.19170.01120.2599ΔSalest-0.02310.6088-0.07620.02330.1230
ΔSalest-1-0.0285
1.0326-0.07930.02510.1301ΔSalest-2-0.01641.9183-0.0837
0.02600.1357
ΔSalest-3-0.0067
2.1041-0.0857
0.02960.1471ΔSalest-4-0.01972.2240-0.09410.02950.1521ΔSalest-5
0.01173.4337-0.1153
0.02360.1476
ΔSalest-6
0.06033.0436-0.1187
0.02160.1444
ΔCFOt
0.00680.1863-0.0460
0.00220.0546
ΔCFOt-1
0.00650.2118-0.0485
0.00280.0575
ΔCFOt-2
0.00580.2236-0.0508
0.00290.0593
ΔCFOt-3
0.00800.2453-0.05410.00270.0625
ΔCFOt-4
0.00980.2645-0.0565
0.00350.0672
ΔCFOt-50.01030.2747-0.05930.00300.0689ΔCFOt-6
0.01190.2628-0.0609
0.00200.0685
Note: Impt is non-current asset impairment losses per share in year t, deflated by the adjusted stock price at the end of fiscal year t–1.
Rk is the adjusted stock return in year k. ΔSalesk is a change in sales in year k, deflated by the adjusted stock price at the end of fiscal
year k–1. ΔCFOk is a change in cash flows from operations in year k, deflated by the adjusted stock price at the end of fiscal year k–
1. The number of firm-year observations with impairment losses is 11,868 (Row 2).

19

Table 2: Pearson correlation matrix
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(1)Impt
1.0000(2)Rt-0.0111 1.0000(3)Rt-1-0.1225 -0.0381 1.0000(4)Rt-2
1.0000-0.1011 -0.1182 0.0022(5)Rt-3
1.0000-0.0500 -0.1120 -0.0008 0.0199(6)Rt-4-0.0236 -0.0757 -0.1540 0.0011 -0.0255 1.0000(7)Rt-50.0143 -0.0884 -0.0922 -0.1749 -0.0502 -0.0029 1.0000(8)Rt-6
0.02470.0675 -0.0993 -0.1100 -0.1945 -0.0269 -0.0026 1.0000(9)ΔSalest -0.1206 0.0581
0.16580.09630.0384 -0.0421 -0.0711 -0.1285 1.0000(10)ΔCFOt
0.00190.0976 -0.0564 -0.0196 0.0122
0.0124 -0.0053 0.0191 0.0527
Note: Impt is non-current asset impairment losses per share in year t, deflated by the adjusted stock price at the end of fiscal year t–1.
Rk is the adjusted stock return in year k. ΔSalesk is a change in sales in year k, deflated by the adjusted stock price at the end of fiscal
year k–1. ΔCFOk is a change in cash flows from operations in year k, deflated by the adjusted stock price at the end of fiscal year k–
1. Bold letters indicate statistical significance at the 5% level.

20

Table 3: Test of timeliness of non-current asset impairment losses
Panel A: OLS regression results(1) Fujiyama(2014b)(2) Sample period andresearch design of
Fujiyama (2014b)
Coef.
t-value
0.0127***13.76-0.0012-0.89-0.0094***-9.74-0.0076***-9.26-0.0026***-4.01-0.0023***-2.99-0.0014*-1.71
0.0005
0.60(3) Sample period sameas Fujiyama (2014b)(4) Full sample period(5) Full sample period(6) Full sample period(7) Full sample period

Coef.
0.0115***-0.0033***-0.0097***-0.0075***-0.0036***-0.0036***-0.0023*-0.0003

Coef.
0.0098***-0.0022***-0.0067***-0.0054***-0.0033***-0.0024***-0.0018***-0.0005

Coef.
t-value0.0098 ***6.07-0.0022-1.29-0.0067 *** -3.93-0.0054 *** -3.69-0.0033 *** -3.12-0.0024 *** -3.13-0.0018 *** -3.43-0.0005
-0.98

Coef.
0.0042***0.0006-0.0042***-0.0044***-0.0038***-0.0032***-0.0030***-0.0013*t-value
5.28
1.29
-4.88
-9.19
-3.36
-4.29
-4.31
-1.87

Coef.
0.0051***0.0006-0.0042***-0.0045***-0.0038***-0.0032***-0.0031***-0.0014*t-value
3.95
1.30
-4.73
-8.81
-3.31
-4.27
-4.58
-1.97

Rt * Dect
Rt-1 * Dect-1
Rt-2 * Dect-2
Rt-3 * Dect-3
Rt-4 * Dect-4
Rt-5 * Dect-5
Rt-6 * Dect-6-0.0185***-0.0253***-0.0168***-0.0084**-0.0044*-0.0024-0.0016

-4.87
-6.78
-3.51
-3.00
-1.90
-1.48
-1.03

-0.0191***-0.0260***-0.0174***-0.0089***-0.0045*-0.0023-0.0010

-5.07
-6.66
-3.80
-3.61
-1.92
-1.37
-0.56

Dect
Dect-1Dect-2Dect-3Dect-4Dect-5Dect-6-0.0002-0.0011*-0.00070.00010.0002-0.0002-0.0001

-0.25
-1.79
-1.64
0.24
0.59
-0.42
-0.24

-0.0002-0.0012-0.0008
0.00000.0002-0.0002-0.0001

-0.30
-1.78
-1.58
0.11
0.50
-0.39
-0.18

Dep = Impt
Constant
Rt
Rt-1
Rt-2
Rt-3
Rt-4
Rt-5
Rt-6

Coef.
t-value0.0134*** 12.74
0.00080.44-0.0098*** -8.47-0.0075*** -7.56-0.0003*** -4.28-0.0025*** -3.12-0.0019**-2.10
0.00050.52

t-value
16.56-2.93-12.46-11.23
-7.33
-6.10
-3.54
-0.51

t-value
19.66-3.52-16.35-13.86
-9.73
-6.31
-4.44
-0.99

Year
Industry

Yes
No

Yes
No

Yes
No

Yes
No

Yes
No

Yes
No

Yes
Yes

SE adjustment

RobustRobustRobustRobustyear-clusteredyear-clusteredyear-clustered# of observations
R2

14,0940.0195

13,9980.0242

13,9980.036525,8600.0342

25,8600.0342

25,8600.072325,8600.085221

Panel B: Coefficients for observations with negative stock returns and their comparisons[1] Rt + Rt * Dect[2] Rt-1 + Rt-1 * Dect-1[3] Rt-2 + Rt-2 * Dect-2[4] Rt-3 + Rt-3 * Dect-3[5] Rt-4 + Rt-4 * Dect-4[6] Rt-5 + Rt-5 * Dect-5[7] Rt-6 + Rt-6 * Dect-6a(6) Full sample period
Coef./Diff.
t-value-0.0179***-4.49-0.0295***-7.69-0.0213***-4.36-0.0122***-4.74-0.0076***-3.49-0.0054**-3.13-0.0028*-2.16(7) Full sample period
Coef./Diff.
t-value-0.0186 ***-4.67-0.0302 ***-7.53-0.0219 ***-4.64-0.0127 ***-5.58-0.0077 ***-3.47-0.0054 **-2.97-0.0024-1.58[1] - [2]0.0116***3.13
0.0116***
3.26[2] - [3]-0.0082-1.66-0.0083-1.76[3] - [4]-0.0091*-1.90-0.0092 *-2.05[4] - [5]-0.0046-1.16-0.0050-1.30[5] - [6]-0.0022-0.87-0.0023-0.88[6] - [7]-0.0026-1.13-0.0030-1.20
Note: Panel A presents the OLS regression results of the test of the timeliness of non-current asset impairments. Panel B reports the coefficients of stock returns for observations with negative stock returns
and their comparisons between years k and k–1. Impt is non-current asset impairment losses per share in year t, deflated by the adjusted stock price at the end of fiscal year t–1. Rk is the adjusted stock returnin year k. Deck takes the value of one if Rk is negative. Column (1) is the result of Fujiyama (2014b) using market capitalization to compute stock returns. Column (2) employs the same regression model as
Fujiyama (2014b) and estimates it with my sample. Columns (3) to (6) use dividend- and share-split-adjusted stock prices to compute stock returns. In Columns (1) to (4), standard errors are adjusted basedon White (1980) as in Fujiyama (2014b). In Columns (5) and (6), standard errors are clustered by year. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

22

Table 4: Short-term indicators of non-current asset impairment losses
Panel A: OLS regression results(1) Changes in sales and
CFO in year t
Coef.
t-value-0.0162***-5.02-0.0203***-7.31-0.0141***-3.33-0.0067***-3.01-0.0036*-1.84-0.0014-1.00-0.0001-0.03(2) Only changes in
sales
Coef.
t-value(3) Stock returns andchanges in sales
Coef.
t-value-0.0162***-4.83-0.0208***-7.25-0.0139***-3.19-0.0069***-3.14-0.0040*-2.06-0.0015-1.03
0.00060.34

Δsalest * DecΔSalest
Δsalest-1 * DecΔSalest-1
Δsalest-2 * DecΔSalest-2
Δsalest-3 * DecΔSalest-3
Δsalest-4 * DecΔSalest-4
Δsalest-5 * DecΔSalest-5
Δsalest-6 * DecΔSalest-6-0.0081***-4.28-0.0153***0.00190.00070.00040.0002-0.0004
0.0004-0.0116***0.00160.00040.0002-0.0002-0.0005
0.0003

ΔCFOt * DecΔCFOt
ΔCFOt-1 * DecΔCFOt-1
ΔCFOt-2 * DecΔCFOt-2
ΔCFOt-3 * DecΔCFOt-3
ΔCFOt-4 * DecΔCFOt-4
ΔCFOt-5 * DecΔCFOt-5
ΔCFOt-6 * DecΔCFOt-6-0.0218***-5.74

Dep = Impt
Rt * Dect
Rt-1 * Dect-1
Rt-2 * Dect-2
Rt-3 * Dect-3
Rt-4 * Dect-4
Rt-5 * Dect-5
Rt-6 * Dect-6

-8.02
1.21
0.68
0.62
0.41
-0.98
0.79

(4) Only changes in
CFO
Coef.
t-value(5) Stock returns andchanges in CFO
Coef.
t-value-0.0179***-4.99-0.0230***-6.61-0.0150***-3.16-0.0073***-3.04-0.0036-1.66-0.0017-1.09-0.0007
-0.45

-8.57
1.29
0.44
0.30
-0.47
-1.18
0.49
-0.0447***-0.0070-0.0006
0.00030.0032**0.0000-0.0013

-9.04
-1.80
-0.24
0.20
2.79
0.02
-0.70

-0.0274***-0.0030-0.0005-0.0006
0.0022*-0.0020-0.0039*

-7.37
-0.82
-0.23
-0.33
2.11
-1.27
-2.01

(6) Stock returns andchanges in sales and CFO
Coef.
t-value-0.0159***-4.75-0.0196***-7.19-0.0133**-2.88-0.0064**-2.85-0.0037*-1.84-0.0015-0.99
0.00030.17-0.0094***0.00160.00040.0003-0.0003-0.00030.0006

-7.28
1.15
0.35
0.37
-0.77
-0.79
0.92

-0.0202***0.0001-0.0007-0.00020.0035***-0.0018-0.0027

-5.56
0.02
-0.24
-0.11
3.09
-1.00
-0.90

Year
Industry

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Other variables

Yes

Yes

Yes

Yes

Yes

Yes

SE adjustment

year-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clustered# of observations
adjR2

25,8600.099725,8600.067425,8600.097925,8600.053825,8600.094725,8600.102023

Panel B: Coefficients for observations with negative stock returns and their comparisonsΔsalest + Δsalest * DecΔSalest
Δsalest-1 + Δsalest-1 * DecΔSalest-1
Δsalest-2 + Δsalest-2 * DecΔSalest-2
Δsalest-3 + Δsalest-3 * DecΔSalest-3
Δsalest-4 + Δsalest-4 * DecΔSalest-4
Δsalest-5 + Δsalest-5 * DecΔSalest-5
Δsalest-6 + Δsalest-6 * DecΔSalest-6(1) Changes in sales
and CFO in year t
Coef.
t-value-0.0033**-2.44

ΔCFOt + ΔCFOt * DecΔCFOt
ΔCFOt-1 + ΔCFOt-1 * DecΔCFOt-1
ΔCFOt-2 + ΔCFOt-2 * DecΔCFOt-2
ΔCFOt-3 + ΔCFOt-3 * DecΔCFOt-3
ΔCFOt-4 + ΔCFOt-4 * DecΔCFOt-4
ΔCFOt-5 + ΔCFOt-5 * DecΔCFOt-5
ΔCFOt-6 + ΔCFOt-6 * DecΔCFOt-6-0.0121***-5.31[1] Rt + Rt * Dect[2] Rt-1 + Rt-1 * Dect-1[3] Rt-2 + Rt-2 * Dect-2[4] Rt-3 + Rt-3 * Dect-3[5] Rt-4 + Rt-4 * Dect-4[6] Rt-5 + Rt-5 * Dect-5[7] Rt-6 + Rt-6 * Dect-6-0.0169***-0.0241***-0.0180***-0.0100***-0.0064***-0.0042**-0.0016

-4.91
-8.58
-4.18
-4.95
-3.37
-2.88
-1.15

(2) Only changes
in salesCoef.
t-value-0.0072*** -4.87-0.0015-1.50-0.0004-0.63
0.0000-0.08
0.00010.31-0.0004-1.11
0.0001
0.37(3) Stock returns andchanges in sales
Coef.
t-value-0.0049***-4.72-0.0005-0.54
0.00010.21
0.00040.98
0.00020.89-0.0004-1.05
0.00010.18-0.0165***-0.0244***-0.0177***-0.0102***-0.0068***-0.0042**-0.0008

-4.65
-8.19
-3.98
-4.93
-3.66
-2.73
-0.60

(4) Only changes
in CFO
Coef.
t-value(5) Stock returns andchanges in CFO
Coef.
t-value-0.0246***-0.0048*-0.0014-0.0009
0.0012*-0.0001-0.0016-0.0163***-0.0027-0.0007-0.0006
0.0016**-0.0002-0.0024*

-5.63
-1.27
-0.37
-0.50
2.27
-0.16
-1.79

-0.0121***-0.0014-0.0009-0.0005
0.0022**-0.0002-0.0017

-4.54
-0.97
-0.38
-0.35
2.56
-0.14
-0.87

-0.0180***-0.0268***-0.0191***-0.0108***-0.0066***-0.0048**-0.0022

-4.71
-7.62
-3.91
-4.83
-3.16
-2.78
-1.62

-0.0164***-0.0228***-0.0169***-0.0096***-0.0064***-0.0043**-0.0012

-4.60
-8.18
-3.56
-4.50
-3.26
-2.58
-0.87

-7.23
-1.93
-0.73
-0.72
2.03
-0.09
-1.23

(6) Stock returns andchanges in sales and CFO
Coef.
t-value-0.0042***-4.30-0.0002-0.23
0.00020.25
0.00041.05
0.00010.62-0.0003-0.68
0.00040.85[1] - [2]0.0072**2.84
0.0079***3.09
0.0088**2.82
0.0064**
2.57[2] - [3]-0.0061-1.42-0.0067-1.57-0.0077-1.51-0.0059-1.25[3] - [4]-0.0080*-1.86-0.0075-1.69-0.0083-1.75-0.0073-1.54[4] - [5]-0.0036-1.10-0.0034-1.02-0.0042-1.12-0.0032-0.91[5] - [6]-0.0023-0.98-0.0026-1.14-0.0019-0.75-0.0021-0.89[6] - [7]-0.0026-1.21-0.0034-1.61-0.0025-1.13-0.0031-1.45
Note: Panel A presents the OLS regression results of the test of the timeliness of non-current asset impairments incorporating changes in sales and cash flows from operations. Panel B reports the coefficientsof stock returns and changes in sales and CFO for observations with negative stock returns or changes in sales or CFO and their comparisons between years k and k–1. Impt is non-current asset impairment
losses per share in year t, deflated by the adjusted stock price at the end of fiscal year t–1. Rk is the adjusted stock return in year k. Deck takes the value of one if Rk is negative. ΔSalesk is a change in sales inyear k, deflated by the adjusted stock price at the end of fiscal year k–1. DecΔSalesk takes the value of one if ΔSalesk is negative. ΔCFOk is a change in cash flows from operations in year k, deflated by the
adjusted stock price at the end of fiscal year k–1. DecΔCFOk takes the value of one if ΔCFOk is negative. Standard errors are clustered by year. ***, **, and * indicate statistical significance at the 1%, 5%, and
10% levels, respectively.

24

Table 5: Impairment trigger thresholds for stock returns
Panel A: Regression results
Dep = Impt
Constant
Rt
Rt-1
Rt-2
Rt-3
Rt-4
Rt-5
Rt-6

Dec < 0.1
Coef. t-value
0.0034** 2.74
0.0014** 2.90
-0.0036*** -4.01-0.0041*** -6.89-0.0038*** -3.35-0.0030*** -4.06-0.0029*** -5.27-0.0013* -1.85

Dec < 0.05
Dec < 0Dec < -0.05
Dec < -0.1
Dec < -0.15
Dec < -0.2
Dec < -0.25
Dec < -0.3
Coef.
t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
0.0041 *** 3.06 0.0051*** 3.95 0.0057 *** 4.88 0.0061*** 5.29 0.0068*** 5.80 0.0071*** 5.97 0.0073*** 6.19 0.0076*** 6.09
0.0010 * 2.06 0.0006
1.30 0.0001
0.30 -0.0003-0.69 -0.0005-1.16 -0.0008* -1.87 -0.0012** -2.53 -0.0014** -2.93-0.0039 *** -4.30 -0.0042*** -4.73 -0.0045*** -5.03 -0.0046*** -5.46 -0.0049*** -5.79 -0.0049*** -6.11 -0.0053*** -6.21 -0.0053*** -6.83-0.0044 *** -7.64 -0.0045*** -8.81 -0.0045*** -9.13 -0.0045*** -8.92 -0.0047*** -8.18 -0.0047*** -8.47 -0.0046*** -8.34 -0.0050*** -7.37-0.0039 *** -3.34 -0.0038*** -3.31 -0.0038*** -3.35 -0.0038*** -3.52 -0.0040*** -3.48 -0.0040*** -3.51 -0.0040*** -3.53 -0.0039*** -3.6
-0.0032 *** -4.09 -0.0032*** -4.27 -0.0033*** -4.37 -0.0034*** -4.70 -0.0034*** -4.75 -0.0034*** -4.68 -0.0034*** -4.78 -0.0033*** -4.74-0.0028 *** -4.48 -0.0031*** -4.58 -0.0032*** -4.87 -0.0031*** -4.33 -0.0032*** -4.67 -0.0032*** -4.49 -0.0032*** -4.2 -0.0031*** -4.42-0.0013-1.71 -0.0014* -1.97 -0.0014* -1.97 -0.0014* -1.79 -0.0014* -1.81 -0.0014-1.77 -0.0014*-1.8 -0.0015** -2.21

Rt * Dect
Rt-1 * Dect-1
Rt-2 * Dect-2
Rt-3 * Dect-3
Rt-4 * Dect-4
Rt-5 * Dect-5
Rt-6 * Dect-6-0.0175***-0.0219***-0.0146***-0.0084***-0.0041*-0.0023*-0.0011

-5.47
-5.79
-3.98
-3.42
-1.85
-1.86
-0.68

-0.0182 ***-0.0236 ***-0.0160 ***-0.0089 ***-0.0048 **-0.0016-0.0007

-5.28
-6.01
-3.90
-3.58
-2.21
-1.04
-0.40

-0.0191***-0.0260***-0.0174***-0.0089***-0.0045*-0.0023-0.0010

-5.07
-6.66
-3.80
-3.61
-1.92
-1.37
-0.56

-0.0202*** -4.84-0.0289*** -7.38-0.0184*** -3.61-0.0092*** -3.73-0.0045 -1.78-0.0029 -1.51-0.0010 -0.51-0.0218***-0.0320***-0.0191***-0.0089***-0.0046-0.0025-0.0003

-4.82
-7.79
-3.36
-3.19
-1.69
-1.06
-0.11

-0.0226***-0.0358***-0.0209***-0.0102***-0.0054*-0.0041*-0.0006

-4.58
-8.68
-3.06
-3.43
-1.85
-1.80
-0.24

-0.0236***-0.0382***-0.0196**-0.0098***-0.0031-0.00490.0005

-4.44
-7.39
-2.82
-5.09
-0.89
-1.64
0.17

-0.0236***-0.0472***-0.0161**-0.0083***-0.0030-0.00430.0010

-4.29
-9.5
-2.3
-5.01
-0.81
-1.07
0.28

-0.0228*** -4.37-0.0503*** -12.87-0.0174** -2.36-0.0058-1.59-0.0009-0.18-0.0035-0.75-0.0027-0.80

Dect
Dect-1Dect-2Dect-3Dect-4Dect-5Dect-60.0012 **0.0008 **0.00060.00010.0005 *0.00000.0000

2.81
2.28
1.61
0.30
2.05
0.01
-0.12

0.00060.0000-0.0002-0.0001
0.00000.00050.0003

1.16
0.06
-0.52
-0.20
0.01
1.07
0.94

-0.0002-0.0012-0.0008
0.00000.0002-0.0002-0.0001

-0.30
-1.78
-1.58
0.11
0.50
-0.39
-0.18

-0.0011 -1.12-0.0027** -2.88-0.0012 -1.68-0.0001 -0.23
0.0002 0.37
-0.0005 -0.98-0.0001 -0.15-0.0022*-0.0042***-0.0016-0.0001
0.0001-0.0001
0.0004

-2.07
-3.36
-1.53
-0.10
0.12
-0.11
0.74

-0.0027*-0.0061***-0.0028-0.0009-0.0004-0.0009
0.0003

-1.92
-4.30
-1.49
-1.31
-0.47
-1.41
0.37

-0.0036**-0.0073***-0.0023-0.0008
0.0008-0.00130.0009

-2.30
-3.28
-1
-0.9
0.81
-1.11
0.82

-0.0041**-0.0124***-0.0006-0.0001
0.0010-0.00080.0012

-2.45
-6.49
-0.21
-0.1
0.76
-0.49
0.89

-0.0041**-0.0141***-0.00200.00110.0021-0.0001-0.0010Year
Industry

-2.42
-8.45
-0.7
0.48
0.95
-0.07
-0.8

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

SE adjustment

year-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clustered# of observations
R2

25,8600.083425,8600.084525,8600.0852

25,8600.086025,8600.086525,8600.086325,8600.086425,8600.086

25,8600.084425

Panel B: Coefficients for bad news observations
Dec < 0.1
Dec < 0.05
Dec < 0Dec < -0.05
Dec < -0.10
Dec < -0.15
Dec < -0.2
Dec < -0.25
Dec < -0.3
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value Coef. t-value Coef. t-value Coef. t-value
Coef. t-value
Coef. t-value[1] Rt + Rt * Dect-0.016082*** -4.85 -0.01717*** -4.71 -0.0186*** -4.67 -0.0201*** -4.56 -0.0221*** -4.65 -0.0231*** -4.51 -0.0244*** -4.45 -0.0247*** -4.38 -0.0242*** -4.52[2] Rt-1 + Rt-1 * Dect-1 -0.025421*** -6.58 -0.027415*** -6.81 -0.0302*** -7.53 -0.0334*** -8.17 -0.0367*** -8.47 -0.0407*** -9.31 -0.0431*** -8.03 -0.0525*** -10.04 -0.0556*** -13.62[3] Rt-2 + Rt-2 * Dect-2 -0.018717*** -4.72 -0.020406*** -4.71 -0.0219*** -4.64 -0.0229*** -4.44 -0.0237*** -4.14 -0.0256*** -3.71 -0.0243*** -3.5 -0.0207** -2.99 -0.0223*** -3.07[4] Rt-3 + Rt-3 * Dect-3 -0.012218*** -5.48 -0.012761*** -5.70 -0.0127*** -5.58 -0.0130*** -5.62 -0.0127*** -4.97 -0.0141*** -5.05 -0.0138*** -7.89 -0.0123*** -7.39 -0.0097** -2.75[5] Rt-4 + Rt-4 * Dect-4 -0.00715*** -3.35 -0.008024*** -3.88 -0.0077*** -3.47 -0.0078*** -3.16 -0.0080** -3.03 -0.0088** -3.09 -0.0065* -1.89 -0.0063 -1.76 -0.0043 -0.83[6] Rt-5 + Rt-5 * Dect-5 -0.005252*** -4.04 -0.004466** -2.83 -0.0054** -2.97 -0.0061** -2.98 -0.0056** -2.29 -0.0073** -3.14 -0.0081** -2.75 -0.0074* -1.86 -0.0066 -1.39[7] Rt-6 + Rt-6 * Dect-6 -0.002396 -1.74 -0.001968 -1.29 -0.0024 -1.58 -0.0024 -1.30 -0.0016 -0.71 -0.0020 -0.81 -0.0008 -0.26 -0.0004 -0.10 -0.0043 -1.23[1] - [2]0.009339** 2.97 0.010245** 2.91 0.0116*** 3.26 0.0134*** 3.52 0.0145*** 3.88 0.0175*** 4.50 0.0187*** 4.85 0.0278*** 6.70 0.0314*** 5.97
[2] - [3]-0.006703* -1.99 -0.007009 -1.73 -0.0083 -1.76 -0.0105* -2.07 -0.0130* -2.34 -0.0151* -2.16 -0.0188** -2.37 -0.0318*** -3.97 -0.0333*** -4.77[3] - [4]-0.006499 -1.64 -0.007645* -1.83 -0.0092* -2.05 -0.0099* -2.13 -0.0109* -2.00 -0.0115 -1.77 -0.0106 -1.47 -0.0084 -1.04 -0.0126 -1.36[4] - [5]-0.005069 -1.38 -0.004737 -1.31 -0.005 -1.30 -0.0052 -1.30 -0.0047 -1.13 -0.0053 -1.29 -0.0073* -1.94 -0.0060* -1.80 -0.0055 -1.28[5] - [6]-0.001898 -0.92 -0.003558 -1.53 -0.0023 -0.88 -0.0017 -0.52 -0.0024 -0.62 -0.0015 -0.36 0.0016 0.32 0.0011 0.17 0.0024 0.27
[6] - [7]-0.002856 -1.49 -0.002498 -1.09 -0.003 -1.20 -0.0037 -1.29 -0.0040 -1.08 -0.0053 -1.45 -0.0073 -1.49 -0.0071 -1.29 -0.0024 -0.45
Note: Impt is non-current asset impairment losses per share in year t, deflated by the adjusted stock price at the end of fiscal year t–1. Rk is the adjusted stock return in year k. Deck takes the value of one if Rk
is below a threshold. ΔSalesk is a change in sales in year k, deflated by the adjusted stock price at the end of fiscal year k–1. DecΔSalesk takes the value of one if ΔSalesk is negative. ΔCFOk is a change incash flows from operations in year k, deflated by the adjusted stock price at the end of fiscal year k–1. DecΔCFOk takes the value of one if ΔCFOk is negative. Standard errors are clustered by year. ***, **, and* indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

26

Table 6: Impairment trigger thresholds for changes in sales and operating cash flows
Panel A: Results for sales changes
DecΔSalest < 0.1

DecΔSalest < 0.05

DecΔSalest < 0

DecΔSalest < -0.05

DecΔSalest < -0.1

DecΔSalest < -0.15

DecΔSalest < -0.2

DecΔSalest < -0.25

DecΔSalest < -0.3

Dep = Impt
Rt
Rt * Dect
Dect

Coef.
-0.0007-0.0167-0.0003t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef.
-1.17 -0.0006-1.44 -0.0006-1.50 -0.0007-1.53 -0.0007-1.58 -0.0007-1.58 -0.0007-1.57 -0.0007-1.56 -0.0006-10.96 -0.0166*** -5.14 -0.0162*** -5.02 -0.0161*** -5.14 -0.0161*** -5.35 -0.0162*** -5.39 -0.0163*** -5.50 -0.0166*** -5.43 -0.0166-0.69 -0.0003-0.52 -0.0003-0.52 -0.0003-0.49 -0.0003-0.47 -0.0003-0.44 -0.0002-0.42 -0.0002-0.41 -0.0002

t-value
-1.54
-5.44
-0.39

ΔSalestΔSalest * DecΔSalest
DecΔSalest

0.0049** 7.42 0.0049*** 3.94 0.0048*** 4.18 0.0048*** 4.54 0.0046*** 4.36 0.0042*** 4.05 0.0039*** 4.16 0.0033*** 4.12 0.0031*** 3.64
-0.0084** -11.14 -0.0084*** -4.40 -0.0081*** -4.28 -0.0078*** -4.18 -0.0073*** -3.95 -0.0068*** -3.74 -0.0063*** -3.59 -0.0057*** -3.54 -0.0054** -3.31
0.0011*** 2.8
0.0013** 2.98 0.0016*** 3.96 0.0023*** 4.21 0.0027*** 4.40 0.0028*** 4.26 0.0031*** 3.81 0.0026*
2.07 0.0029*** 2.17

ΔCFOt
ΔCFOt * DecΔCFOt
DecΔCFOt

0.0098 *** 7.37 0.0098*** 5.18 0.0097*** 5.24 0.0095*** 5.39 0.0095*** 5.44 0.0096*** 5.57 0.0097*** 5.63 0.0101*** 5.86 0.0101*** 5.97
-0.0221*** -10.4 -0.0221*** -5.63 -0.0218*** -5.74 -0.0213*** -5.92 -0.0213*** -6.05 -0.0215*** -6.19 -0.0216*** -6.34 -0.0225*** -6.63 -0.0226*** -6.82
0.00010.44 0.0001*** 0.54 0.0001
0.44 0.0001
0.56 0.0001
0.63 0.0002
0.77 0.0002
0.85 0.0002
0.85 0.0002** 0.81

Other variables
Year
Industry

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

SE adjustment

year-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clustered# of observations
R2

25,8600.099325,8600.099425,8600.099725,8600.1002

25,8600.100325,8600.100025,8600.099925,8600.098925,8600.0989Rt + Rt * Dect-0.0173*** -5.10 -0.0172*** -5.03 -0.0169*** -4.91 -0.0167*** -5.02 -0.0168*** -5.22 -0.0169*** -5.28 -0.0170*** -5.37 -0.0172*** -5.30 -0.0173*** -5.32
ΔSalest + ΔSalest * DecΔSalest -0.0035** -2.66 -0.0034** -2.57 -0.0033** -2.44 -0.0030** -2.24 -0.0028* -2.09 -0.0027* -2.00 -0.0024* -1.85 -0.0024-1.76 -0.0023-1.63
ΔCFOt + ΔCFOt * DecΔCFOt -0.0123*** -5.23 -0.0123*** -5.23 -0.0121*** -5.31 -0.0118*** -5.38 -0.0117*** -5.49 -0.0118*** -5.58 -0.0119*** -5.75 -0.0124*** -6.02 -0.0125*** -6.20

27

Panel B: Results for CFO changes
DecΔCFOt < 0.1

DecΔCFOt < 0.05

t-value
Coef.
-1.33 -0.0006-5-0.0162-0.53 -0.0003

DecΔCFOt < 0

DecΔCFOt < -0.05

DecΔCFOt < -0.1

DecΔCFOt < -0.15

DecΔCFOt < -0.2

DecΔCFOt < -0.25

DecΔCFOt < -0.3

Dep = Impt
Rt
Rt * Dect
Dect

Coef.
-0.0006-0.0163-0.0003t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef. t-value
Coef.
-1.47 -0.0006-1.50 -0.0006-1.49 -0.0006-1.45 -0.0006-1.42 -0.0006-1.38 -0.0005-0.97 -0.0005-5.01 -0.0162*** -5.02 -0.0162*** -5.08 -0.0164*** -5.02 -0.0165*** -5.01 -0.0165*** -5.01 -0.0166*** -10.86 -0.0166-0.55 -0.0003-0.52 -0.0003-0.50 -0.0003-0.45 -0.0003-0.45 -0.0003-0.45 -0.0003-0.60 -0.0002

t-value
-1.20
-5.01
-0.41

ΔSalestΔSalest * DecΔSalest
DecΔSalest

0.0050*** 4.44 0.0049*** 4.19 0.0048*** 4.18 0.0048*** 4.17 0.0050*** 4.38 0.0052*** 4.59 0.0053*** 4.73 0.0055*** 9.49 0.0056*** 5.00
-0.0084*** -4.47 -0.0082*** -4.31 -0.0081*** -4.28 -0.0082*** -4.31 -0.0084*** -4.45 -0.0086*** -4.56 -0.0088*** -4.64 -0.0090*** -13.10 -0.0091*** -4.76
0.0016*** 3.99 0.0016*** 3.98 0.0016*** 3.96 0.0016*** 3.90 0.0017*** 4.04 0.0018*** 4.17 0.0018*** 4.22 0.0019*** 5.53 0.0019*** 4.23

ΔCFOt
ΔCFOt * DecΔCFOt
DecΔCFOt

0.0099 *** 4.92 0.0091*** 5.08 0.0097*** 5.24 0.0093*** 5.39 0.0075*** 6.70 0.0061*** 6.54 0.0054*** 6.19 0.0043*** 4.30 0.0040*** 3.98
-0.0199*** -5.38 -0.0208*** -5.50 -0.0218*** -5.74 -0.0202*** -6.17 -0.0190*** -5.58 -0.0182*** -5.09 -0.0168*** -4.48 -0.0171*** -5.58 -0.0152*** -3.16
0.00081.34 0.0000
0.07 0.0001
0.44 0.0005
1.02 -0.0003-0.51 -0.0010-1.12 -0.0007-0.56 -0.0020-1.43 -0.0010-0.38

Other variables
Year
Industry

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

SE adjustment

year-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clusteredyear-clustered# of observations
R2

25,8600.099325,8600.099625,8600.099725,8600.099825,8600.0992

25,8600.098825,8600.098625,8600.0981

25,8600.0981

Rt + Rt * Dect-0.0168*** -4.87 -0.0169*** -4.89 -0.0169*** -4.91 -0.0169*** -4.98 -0.0170*** -4.91 -0.0171*** -4.90 -0.0171*** -4.89 -0.0171*** -4.87 -0.0171*** -4.86
ΔSalest + ΔSalest * DecΔSalest -0.0034** -2.47 -0.0033** -2.46 -0.0033** -2.44 -0.0033** -2.45 -0.0034** -2.45 -0.0034** -2.46 -0.0034** -2.47 -0.0035** -2.48 -0.0035** -2.48
ΔCFOt + ΔCFOt * DecΔCFOt -0.0100*** -5.06 -0.0116*** -5.33 -0.0121*** -5.31 -0.0109*** -4.74 -0.0116*** -4.20 -0.0121*** -4.18 -0.0114*** -3.70 -0.0128*** -3.15 -0.0112*** -2.48
Note: Impt is non-current asset impairment losses per share in year t, deflated by the adjusted stock price at the end of fiscal year t–1. Rk is the adjusted stock return in year k. Deck takes the value of one if Rk
is negative. ΔSalesk is a change in sales in year k, deflated by the adjusted stock price at the end of fiscal year k–1. DecΔSalesk takes the value of one if ΔSalesk is below a threshold for Panel A and negativefor Panel B. ΔCFOk is a change in cash flows from operations in year k, deflated by the adjusted stock price at the end of fiscal year k–1. DecΔCFOk takes the value of one if ΔCFOk is negative for Panel A
and below a threshold for Panel B. Standard errors are clustered by year. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

28