Evidence of Asset Impairment Reversals from China: Economic Reality or Earnings Management?

Shimin Chen Department of Accounting and Finance

China Europe International Business School cshimin@ceibs.edu

Yuetang Wang Department of Accounting

Nanjing University wangyt@nju.edu.cn

Ziye Zhao School of Accountancy

Shanghai University of Finance and Economics zhao.ziye@mail.shufe.edu.cn

The 2008 Journal of Accounting, Auditing and Finance & KPMG Foundation Conference

September 19-20 2008 New York City

We have received useful comments from Eli Bartov, Ole-Kristian Hope, Bikki Jaggi, John Goodwin, Suresh Radhakrishnan, Nancy Su, Donghui Wu, Jenny Zhou, and the participants of research workshops at the Hong Kong Polytechnic University, Sun Yat-Sen University, and Xiamen University. The project was completed when Shimin Chen was a faculty member at the Hong Kong Polytechnic University. We are grateful to the Hong Kong University Research Grant Council for its financial support (PolyU 5441/06H).

Evidence of Asset Impairment Reversals from China: Economic Reality or

Earnings Management?

(Abstract)

While asset impairment reversals are practiced in many jurisdictions, empirical

evidence is rather limited. China provides us with a unique opportunity to

examine both the determinants and consequences of impairment reversals due to

its recent developments in standard-setting. Based on a sample of reversal firms in

China, we find the following: First, although economic factors and reporting

incentives both explain reversals, reporting incentives dominate. Second, while

total reversals provide investors with useful information, the price-earnings

multiples of this information are significantly weaker as compared to other

income statement components. Finally and more importantly, we find that the

valuation consequence of reversals, if unrealized, disappears completely. Taken

together, we conclude that managerial opportunism may have reduced the

reliability of otherwise value-relevant reversal information. While the intention of

granting discretion in impairment reversal is for management to communicate

private value recovery information, our findings suggest that a seemingly

improved accounting standard does not necessarily lead to its intended

consequence in financial reporting.

Keywords: Asset write-down reversals, earnings management, accounting

discretion

I. INTRODUCTION This study examines the determinants and consequences of asset impairment

reversals by exploring a unique research setting in China. The study is motivated by

the following two factors. First, the recent accounting developments in China provides

us with a rare opportunity to shed light on whether according managerial discretion

improves the quality of accounting information or induces opportunistic behavior.

This is a particularly timely issue considering that regulators across the world strive to

improve accounting standards to ensure the growth of securities markets. Second,

although asset impairment reversals are widely practiced in different jurisdictions, our

understanding based on empirical evidence is rather limited regarding what drives a

company to report impairment reversals and what is the impact of such reversals on

financial reporting quality.

Over the past decade, China has witnessed historic progress in the

internationalization of its accounting standards, especially in the area of asset

impairment. In1998, listed companies were required, for the first time, to recognize

asset impairments for accounts receivable, inventories, and short- and long-term

investments. By 2001, the recognition of asset impairments has extended to fixed

assets, construction in progress, intangible assets, and commission loans. Although the

intention of these reforms is to improve the quality of accounting information, in

particular by correcting the overstated balance sheets,1 the new impairment standards

lead to much more discretion for management than before. The Chinese developments

are not isolated; rather they reflect an international trend in the area of asset

impairment accounting. Since the mid-1990s, both U.S. and international accounting

standards have increasingly emphasized impairment recognition as shown in:

1 Aharony et al. (2000) found that Chinese companies significantly overstated their assets to meet IPO requirements. 1

Statement of Financial Accounting Standards (SFAS) No. 121, Accounting for the

Impairment of Long-Lived Assets in 1995; SFAS No. 144, Accounting for the

Impairment or Disposal of Long-Lived Assets in 2001; and International Accounting

Standards (IAS) 36, Impairment of Assets in 1998 initially, and amended in 2004.

While impairment testing and recognition do not differ too much, the practice of

impairment reversals varies significantly among different jurisdictions. The total

reversals of asset impairments come from two sources: realized reversals from assets

disposed and unrealized reversals from asset still held. Realized reversals effectively

exist in any jurisdictions through asset disposals. While IAS permits unrealized

reversals for all assets except goodwill, U.S. GAAP does not allows any reversals of

asset impairment. As summarized in Figure 1, China swings between the two

approaches: From 2001 to 2006, Chinese GAAP followed IAS in principle, but

starting from 2007, China has moved closer to the U.S. by imposing restriction on

reversals.

(Insert Figure 1)

Without doubt, regulators around the world face this dilemma: Should

management be allowed to communicate private information of impairment recovery

or prohibited from doing so in order to minimize opportunistic behavior? This is a

particularly important concern given that impairment reversals directly increase net

income. Empirical evidence is needed to answer this question. Although there have

been many studies on impairment recognition (e.g., Elliot and Shaw 1988; Zucca and

Compbell 1992; Francis et al. 1996; Rees et al. 1996; Riedl 2004; and Yang et al.

2005), there has been no published research on asset impairment reversals due to data

constraint. While the disclosure of unrealized reversals is required under IAS, it is

difficult, if not impossible, to obtain a large reversal sample from notes to the

2

financial statements. Further, the scattered note disclosures make it more difficult to

enforce consistency among companies. As for realized reversals, disclosure practice

varies even more.2 In comparison, the disclosure of impairment reversals in China is

both uniform and systematic. Out of the concern that impairment reversals may be

used opportunistically for earnings management, the Chinese authority requires listed

companies to disclose total reversals as part of the non-recurring income and

unrealized reversals for each type of impaired assets in a separate asset write-down

disclosure table.3

We manually collected the reversal information from annual reports over three

years from 2003 to 2005. Averagely speaking, about 50 percent of listed firms in each

year reverse asset write-downs made in previous years, which provides us with a

sufficiently large sample for this study. Based on this unique reversal sample, we

2 Under neither IAS nor U.S. GAAP, the disclosure of realized reversals is dealt with in any of the asset impairment standards. Rather, such disclosure is implied as a disclosure for changes in accounting estimates. Consequently, practice varies substantially. For example, Keiso et al. (2007, p. 428) cites three companies including Vishay Technologies, Transwitch, and Cisco Systems to illustrate the variation in the disclosure of realized reversal of inventory write-downs. 3 While the Accounting System for Business Enterprises in 2001 explicitly granted reversal rights, detailed disclosure requirements were not available until 2003 when the Ministry of Finance issued “Questions and Answers No. 2: Implementing the 2001 Accounting System for Business Enterprises and Related Accounting Standards.” According to this document, listed companies must disclose, in a separate table, two types of decreases in asset impairment provision accounts: (1) unrealized reversals due to the recovery of asset impairments, and (2) write-offs due to the disposal of assets. Realized reversals are part of the write-offs. The following year in 2004, the China Securities Regulatory Commission issued “Questions and Answers No. 1: Disclosure Regulations - Non-recurring Items” and required listed companies to disclose the total reversals of asset impairments (both realized and unrealized) as a non-recurring item. The appendix contains an example of reversal disclosure obtained from the 2005 annual report of Beijing New Building Materials Public Limited Company listed in the Shenzhen Stock Exchange. The company recognizes a total reversal of RMB 2,308,406 as a non-recurring item and discloses an unrealized reversal of RMB 1,718,879 involving bad debts, inventories and fixed assets. While realized reversals are part of the “write-offs” column, the figure can be computed as the difference between the total and the unrealized reversals in this example. 3

report the following: First, while both economic factors and reporting incentives

explain impairment reversals, reporting incentives dominate. Second, the total

reversals of asset impairments provide investors with useful information, but the

usefulness of this information is significantly less than other components in the

income statement. Third, and more importantly, when we focus on unrealized

reversals, we find that the value-relevance of reversal information disappears

completely. Our primary conclusions remain valid under various sensitivity tests.

Taken together, our results suggest that managerial opportunistic behaviors may have

reduced the reliability of otherwise value-relevant reversal information. Overall these

results are consistent with the mass media’s negative attitude towards reversal.4

This study contributes to the accounting literature in several ways. First, our

findings add another piece of evidence regarding earnings management through

expense reversal in a more comprehensive manner including both determinants and

consequences. To our knowledge, Moehrle (2002) is the only published study on

expense reversal, which shows that U.S. companies reverse restructuring charges to

meet or exceed analyst forecasts, and to avoid earnings declines or losses. Second,

impairment reversals are an important type of accounting discretion available in

jurisdictions that follow IAS/IFRS. Even for countries such as the U.S. that do no

allow unrealized reversals, realized reversals exist through asset disposals.

Nonetheless, there has been virtually no empirical evidence on impairment reversals

in general and unrealized reversals in particular. Our study is the first to provide such

4 For example, China Securities News contained a typical big bath and reversal story on August 22, 2004. Tian Dai Tian Cai (stock code 000836) reported a huge loss in 2003 after deducting an asset impairment loss of RMB 152,800,000. In the following year, the company showed a small profit of RMB 6,380,000 together with a relatively large amount of impairment reversal at RMB 63,740,000. Without this reversal, the company would have reported a loss in two consecutive years, leading to the receipt of a special treatment (ST) symbol. 4

evidence. Third, this study contributes to the accrual-based earnings management

literature through examining a group of identifiable accrual items, i.e., the reversal of

asset write-downs. Relatively, our study is less subject to the measurement error

associated with the use of the discretionary accrual estimation models (Dechow et al.

1995). Finally, our findings shed light on the consequences of accounting standards in

general and in emerging markets in particular. Regulators around the world face the

dilemma of whether to grant more discretion so that managers can make the most

appropriate accounting choice to reflect underlying economics. The development of

asset impairment accounting in China provides an interesting experiment, the results

of which suggest that a seemingly improved accounting standard may not necessarily

accomplish its intention of improving financial reporting without the supporting

infrastructure for constraining opportunistic earnings management.

The remainder of the paper proceeds as follows. The next section provides a

review of relevant literature and develops our research hypotheses. In the following

section, we describe our reversal sample in detail. Sections four and five contain our

primary tests of the determinants and consequences of impairment reversals.

Following the primary tests, we present a battery of sensitivity analysis. Finally, we

conclude the paper with a summary of findings and implications for research and

practice.

II. LITERATURE AND HYPOTHESIS

Although there has been no research on impairment reversals, prior studies on

asset impairment are relevant for the development of our hypotheses. Managerial

discretion is a focal point in this literature. Management cannot communicate its

private information about asset value to outside parties without necessary discretion;

however, such discretion also provides opportunities for earnings manipulation. A

5

long standing emphasis in the literature is to examine whether asset write-downs are

driven by underlying economics or reporting incentives for earnings management. A

number of studies attempt to distinguish between the two perspectives solely based on

market responses. If asset write-downs reflect the confidence of management in the

future performance of the firm, investors should respond positively (Frantz 1999).

Strong and Meyer (1987) provide supporting evidence in that asset write-downs have

a positive effect on stock prices. Similarly, Francis et al. (1996) find that the market

responds positively to the announcement of restructuring charges. On the other hand,

managers may write down assets even if they have unfavorable information about the

future of the firm. Taking a big bath in the current period makes it more likely for

earnings and managerial compensation to increase in the future (Frantz 1999). Under

this circumstance, asset write-down information is noisy, which explains why the

market may not show any responses as reported by Zucca and Campbell (1992) or

respond negatively as documented by Elliot and Shaw (1988).

Other studies go beyond the valuation consequence of asset impairment. Rees et

al. (1996) find that firms are more likely to write down assets when earnings are

already low and that abnormal accruals are significantly negative in the write-down

year, both of which suggest earnings manipulation. However, their follow-up tests

show that these abnormal accruals are positively associated with stock returns and are

not reversed during the years after the write-down, which contradicts the earnings

management story. Francis et al. (1996) also report mixed evidence on the causes and

valuation consequences of discretionary asset write-offs. While they find that both

earnings management incentives and economic factors cause asset write-offs,

reporting incentives play little role in determining inventory and PP&E write-offs but

an important role in explaining more discretionary items such as restructuring charges.

6

Nonetheless, their tests of shareholder wealth effects are not entirely consistent with

the determinants of write-offs. Although the market responds negatively to write-off

announcements, they find a positive market reaction to restructuring charges

suggesting that restructuring write-downs are likely a signal of information about

expected future performance. Riedl (2004) compares the characteristics of asset

impairments reported before and after the issuance of Statement of Financial

Accounting Standards (SFAS) No. 121. The study reports a weaker association

between economic factors, but a stronger association between big-bath behavior, and

write-downs after SFAS No. 121, suggesting a decrease in the quality of write-down

information under SFAS No. 121.

A few recent studies examine asset impairments in China. Chen et al. (2004)

document a positive valuation effect of the voluntary asset write-downs introduced in

1998, which is consistent with the write-downs signaling improvement in future

performance. Yang et al. (2005) compare the value-relevance of historical cost

accounting versus the lower of cost or market accounting and report mixed evidence.

Li (2001) examines incentives leading to over- or under-estimating asset write-downs.

When write-downs were voluntary in 1998, companies tended to underestimate.

However, when they became compulsory in 1999, companies underestimated

(overestimated) asset impairments to report higher (lower) earnings.5 Similarly, Cai

and Zhang (2004) report earnings management as a primary reason for determining

the retroactive amount of asset impairments in 1999.

Following the logic of the asset impairment literature, this current study examines

whether the reversals of asset impairment are driven by underlying economics of asset

5 When asset write-down was initially introduced as a voluntary accounting choice in 1998, companies had to write off cumulative impairments to the income statement. But in 1999 and 2001 when asset write-down became mandatory, companies were allowed to adjust the beginning stockholders’ equity for cumulative impairments. 7

value recovery or reporting incentives for earnings management. Similar to Francis et

al. (1996), we distinguish between these two views by investigating both determinants

and valuation consequences. As compared with asset impairment, impairment reversal

provides a better setting for developing a set of unambiguous hypotheses under each

view. It is difficult to predict investor responses to asset write-downs under either the

efficient or the opportunistic views. While one may expect a positive market response

to write-downs being viewed as a signal of information about future performance,

investor reactions should be negative if write-downs are purely driven by asset

impairments. Both are consistent with underlying economics determining write-down

decisions. Furthermore, the possibility of a big path may cause investors to either

ignore or negatively evaluate write-down information. Since impairment reversals

increase net income, investor reaction should be unambiguously positive if underlying

economics are the only driver. The accounting literature, however, contains little

empirical evidence on impairment reversals. Moehrle (2002) finds that companies

reverse restructuring charges to meet or exceed analyst forecasts, and to avoid

earnings declines or losses, which is consistent with the use of restructuring reversals

for earnings management. Bartov (1993) examines whether managers manipulate

earnings through the timing of disposal of long-lived assets and investments. While

there is a similarity in underlying motive between the timing of asset sales in Bartov

and the reporting of realized reversals in this study, Bartov does not specifically deals

with impairment reversals. A few studies (e.g., Easton et al. 1993; Aboody et al. 1999)

examine upward asset revaluations allowed in countries such as Australia and the

U.K., and conclude that these revaluations are likely reflecting increases in future cash

flows as evaluated by investors. The conclusion is not surprising given that an upward

revaluation is adjusted to equity without impact on current earnings. Because asset

8

impairment reversals directly increase net income, it is more interesting to distinguish

between underlying economics for reality and reporting incentives for earnings

management.

As a priori, neither the economic reality nor the earnings management hypothesis

can be ruled out. The original intention of the Chinese authority to comply with the

IAS/IFRS standards on asset impairment accounting was to improve the quality of

financial reporting. At least, two considerations support the economic reality

hypothesis. First, with asset impairment standards, listed Chinese firms are required to

correct the overstatement of assets on their balance sheets. Aharony et al. (2000)

report that companies in China significantly overstate their assets to meet IPO

requirements. With properly implemented asset impairment accounting, the financial

statements should be more reflective of economic reality. As part of the asset

impairment accounting, reversal discretion will further allow management to

incorporate value recovery into the financial statements. Second, the reversal statistics

in Table 1 seem to reveal patterns that are consistent with the underlying economics of

different industries.

(Insert Table 1)

The industry grouping is based on the 13 industry classification by the China

Securities Regulatory Commission (CSRC). As there are many manufacturing firms,

we further divide the manufacturing into seven groups, resulting in a total of 19

industries sorted in a descending order by the mean reversal to total assets. As shown

in the second column, the mean reversal of the real estate development industry is

much higher than other industries, consistent with the recent property market boom in

China. As reported in the third and fourth columns, industries with higher frequencies

of reversals are those that employ more tangible assets such as electronics and civil

9

engineering or those that experience frequent asset realizations such as wholesale and

retail trade. On the other hand, intangible asset-intensive industries such as media and

labor-intensive industries such as furniture manufacturing apparently have less

frequent reversals. The fifth and sixth columns show the distribution of unrealized

reversals by industry, which accounts for about half of the total reversals. The last four

columns further reveal that metal manufacturing has the highest frequency of

short-term asset impairment reversals, which is likely due to the larger amount of

inventory in that industry. In comparison, the power, gas and water industry has the

highest frequency of long-term asset impairment reversals, which is probably because

of more reliance on fixed assets in this industry.

Support for the earnings management hypothesis can also be easily identified. As

reviewed earlier, even in mature markets where asset impairment accounting has been

practiced for many years, managers write down assets opportunistically to increase

compensation, take a big-bath or smooth earnings (Francis et al., 1996; Riedl, 2004).

Similar contracting-based incentives also exist in China, and to a certain extent, one

may argue that managerial opportunism may be even more likely (Li 2001; Cai and Li

2004; Yang et al. 2005). Some institutional features in China increase the incentives

for, but lower the cost of, earnings management. IPO, refinancing, and delisting are

primarily regulated based on earnings performance. While such a system may help to

channel resources to more profitable firms, it creates a strong incentive for earnings

management (Aharony et al. 2000; Chen et al. 2001; Chen and Yuan 2004).

Accrual-based earnings management can be easily rationalized in the name of the

company.

The cost of earnings management through asset impairment in China is relatively

low. The detailed guidelines on asset impairment accounting lag behind practice; for

10

example, the estimation of present value based on an asset group, comparable to the

cash generating unit concept in IFRS, was not introduced until 2006. The lack of

implementation guidance often results in management having access to accounting

discretion not intended by accounting standards, making it more difficult for auditors

to evaluate such discretion. Further, when the asset impairment standards were issued

in 1998 and 2001, listed firms were allowed to adjust cumulative asset impairments to

the beginning equity, which makes it less costly for the firm to create hidden reserves

in one year and reverse write-downs in another. Finally, neither write-downs nor

reversals have any tax implications in China.6 Enhancing current earnings through

impairment reversals will not bring about any additional tax costs.

To summarize, the quality of reversal information is dependent on motivation.

Reversals motivated by economic reality should be of high quality and provide useful

information to investors, while reversals motivated by earnings management should

be of low quality, therefore ignored by investors. We test our expectation through the

following hypotheses:

H1: Consistent with the economic reality view, the reversals of asset

impairment are associated with variables measuring underlying

economics of asset value recovery, and the reversal information is priced

by the stock market.

H2: Consistent with the reporting incentive view, the reversals of asset

impairment are associated with variables measuring reporting incentives

6 According to the Interim Enterprise Income Tax Ordinance of PRC issued on 13 December 1993 and the Income Tax Law of PRC for Foreign-Invested Enterprises and Foreign Enterprises issued on 9 April 1991 by the State Council, part of the bad debt expenses is tax deductible upon approval from the tax authorities. Domestic enterprises may deduct no more than 0.5% of the ending receivables balance, while enterprises with foreign investments may deduct up to 3%. Other asset write-downs and reversals will not affect the taxable earnings. 11

for earnings management, and the reversal information is ignored by the

stock market.

III. SAMPLE

Listed firms are required to disclose reversals systematically since 2003. Our

sample is comprised of A-share listed companies in non-financial industries from

2003 to 2005. We manually collect reversal information from the annual reports. Over

the three year period, there are 2,025 reversal observations. Since 209 observations

disclose only unrealized reversals, we treat the unrealized as total reversals for these

observations. Because of this, the amount of total reversals in our sample may have

been underestimated. After excluding 137 observations with missing variables, we

obtain 1,888 clean reversal observations, of which 897 explicitly disclose unrealized

reversals. Our sampling process is shown in Panel A of Table 2.

(Insert Table 2)

Panel B of Table 2 presents the magnitude of total reversals. Although asset

impairment reversals in China appear to be smaller in magnitude than restructuring

reversals in the U.S. (Moehrle 2002), they are much more frequent with nearly 50

percent of listed companies reporting reversals every year. Panel C of Table 2

indicates that bad debt provisions and inventory write-downs are the two most

frequently reversed items, while the reversals of long-term asset impairments are less

frequent. In terms of median values, write-down reversals in intangible assets and

constructions in progress have a larger impact on current earnings. Furthermore, we

find substantial differences in the mean and median statistics of reversal magnitude,

suggesting outliers in the sample. We winsorize each continuous variable by year at

the top and bottom 1% and present outlier-adjusted descriptive statistics in Table 3.

(Insert Table 3)

12

Even with the winsorization, the maximum amount of reversals over total assets

(RVTA) still reaches 5 percent, and the standard deviation is twice as large as the

mean value. Apparently, some companies reported large amounts of asset write-down

reversals during the sample period. We obtained market returns and stock prices from

the Taiwan Economic Journal (TEJ) database. Other research variables are mainly

from the China Stock Market Accounting Research (CSMAR) database produced by

GTA Information Technology.7 Besides the scaled reversal variable, RVTA, the

variables measuring underlying economics or reporting incentives will be described in

the next section, and the variables used in our valuation models will be discussed in

Section V.

Finally, we should note that, while there are 1,888 reversal observations in Panel

A of Table 2, the total sample of this study contains 2,817 firm/year observations

including both reversal and non-reversal observations of companies that report at least

one reversal item during the sample period. Assuming consistency in reversal

disclosure, this time-series sample will help us distinguish between the economic

reality and the earnings management hypothesis because the non-reversal year

observations will serve as the benchmark for examining the determinants and the

consequences of reversals. Based on the same principle, we employ a sample of 1,749

firm/year observations when examining unrealized reversals, although there are 897

unrealized reversal items disclosed over the three years period.

IV. DETERMINANTS OF IMPAIRMENT REVERSALS

Although impairment reversals should be driven by underlying economics of

asset value recovery, reporting incentives may affect managerial decisions. In the

7 We did not use the CSMAR database for market variables due to data availability. For our sample period between 2003 and 2005, we need market returns from April 2003 to April 2006. When we collected data, the CSMAR database had not been updated beyond 2005. 13

absence of monitoring, a company may report a larger amount of reversals if there are

incentives for reporting higher earnings. Extant research shows that companies, even

in mature markets, manage earnings to avoid losses, stop earnings decline, and meet

analysts’ forecasts (Burgstahler and Dichev 1997; Degeorge et al. 1999; Brown 2001;

Richardson et al. 2004; Burgstahler and Eames 2006). We turn to the literature on

asset write-downs and expense reversals (e.g., Francis et al. 1996; Moehrle 2002;

Chen et al. 2004; Riedl 2004) and build the following model to examine whether

economic factors or reporting incentives or both drive impairment reversals in China:

εββββββββββββ

ββββββ

+++++++++++Δ+Δ+Δ+Δ++++=

SIZELEVFCDECLOSSBATHMOPMGTAVSTSTMIMTA

OCFSALESINDMTAINDGROWINDROARVTA

1716151413

1211109876

543210

(1)

RVTA is impairment reversals divided by beginning total assets. β1 to β7 are the

coefficients of seven economic factors measuring the likelihood of asset impairment

recovery. INDROA, INDGROW, and INDMTA represent median changes from t-1 to

t in industry returns on assets, sales growth, and market to asset ratios. They intend to

capture industry-specific changes in the underlying economics. A larger measure

suggests a promising prospect in this industry and a greater likelihood that a firm in

this industry will recover from asset impairments. ΔSALES, ΔOCF, ΔMTA, and ΔMI

measure firm-specific changes in asset value. While ΔSALES and ΔMTA are

percentage changes in sales and market to asset ratio from t-1 to t, ΔCFO and ΔMI are

changes in operating cash flows and main operating income divided by beginning

total assets.8 A larger value in these variables suggests a likelihood of recovery from

8 Riedl (2004) uses an earnings change before write-downs. Similarly, we could use a change in net income before reversals. However, Chinese listed firms started to reverse asset write-downs in 2002, but the disclosure of reversals was not available until 2003. Without reversal information for 2002, a net income change variable will be understated in 2003. Given that reversals do not affect income from main 14

asset impairments in this firm. According to our economic reality hypothesis (H1), we

expect β1 to β7 to be significantly positive.

β8 to β15 are the coefficients of reporting incentive variables. ST is a dummy

variable with a value of 1 for companies whose stock codes carry a “special

treatment” symbol in t-1due to two consecutive years of loss. AVST is another dummy

variable with a value of 1 for companies reporting a loss in t-1 but in t-2. These two

dummy variables are intended to capture incentives to meet regulatory requirements

in profitability to avoid de-listing. Starting from 1 January 2002, the stock code of a

listed firm with two consecutive loss years is marked with an “ST” symbol. In

addition to the requirement of submitting an audited semi-annual report to the

regulator, an ST company faces some trading restrictions, such as a daily price

fluctuation of no more than 5 percent, while the normal price fluctuation allowed for a

listed company is 10 percent. If the ST company continues to lose in the third year, it

will be signified by a “*ST” and suspended from trading. A further loss in the

following quarter will lead to delisting. An ST or AVST company will have a strong

incentive to use reversals to avoid a continued loss in the current year.

MGT is an indicator variable equal to 1 for firms having a new chairman or CEO

from an external source in period t-1. 9 Previous studies show that incoming

executives, especially those from external sources, tend to write down assets to

increase the likelihood of performance improvement in the future (Strong and Meyer

1987; Francis et al. 1996). New management may also use reversals to show an

increase in earnings in period t to demonstrate its ability to improve firm performance.

operations, we use the change in main operating income to measure the degree of earnings improvement without reversals. 9 Since we are primarily interested in a change of top management due to performance reasons, we exclude management changes resulted from the change of control rights. 15

MOP is a dummy variable with a value of 1 if a company receives a modified audit

opinion with an explanation related to asset write-downs. A positive coefficient is

expected for this variable because companies with problems in asset write-downs are

more likely to use reversals opportunistically. BATH is another dummy variable equal

to 1 if a company’s asset write-downs in t-1 exceed the 75th percentile of its industry

average. Since big-bath write-downs are more likely reversed, we expect a positive

coefficient. LOSS, DEC, and FC measure incentives for avoiding loss, reducing

earnings decline, and meeting analysts’ forecasts. Moehrle (2002) shows the

importance of these incentives for the reversal of restructuring charges in the U.S.,

and these three variables are defined in the same way as in Moehrle (2002). LOSS

equals the absolute value of loss amount if a company reports a loss before reversal,

and 0 otherwise; DEC equals the absolute value of earnings decline if a company

reports a negative earnings change from t-1, and 0 otherwise; and FC is equals to 1 if

earnings before reversal are below the median earnings forecast by financial analysts,

and 0 otherwise. Finally, we control for debt ratio (LEV), firm size (SIZE), and

industry differences. For simplicity, Table 4 presents Tobit regression results with the

18 industry dummies omitted.

(Insert Table 4)

For the sample of total or unrealized reversals, we first present the results

separately for economic factors or reporting incentives, and then combine them in a

single regression. We observe four interesting findings from Table 4. First, similar to

the previous studies of asset impairments (Francis et al. 1996; Li 2001; Chen et al.

2004; Riedl 2004), both economic factors and reporting incentives explain the choice

as well as the magnitude of reversals, suggesting the co-existence of economic and

reporting incentives for reversal decisions.

16

Second, the relative explanatory power of the economic variables is substantially

lower than that of the reporting incentive variables. The economic factor regressions

have much lower R-squares for either the total or the unrealized reversal samples.

While ΔOCF is highly significant for the total reversal, the level of significance drops

to 9% for the unrealized reversal sample. ΔSALES is marginally significant at the

12% level in the unrealized reversal model, but insignificant in the total reversal

regression. In contrast, the reporting incentive models show much stronger results:

The R-squares of both samples are substantially higher, and five out of the eight

reporting incentive variables are significant as expected in each regression. An

analysis of changes in R-squares from the economic factor model to the combined

model further confirms the relative importance of reporting incentives. Adding the

eight reporting incentive variables to the seven economic factors in the total reversal

sample increases R-square by more than 9%, but adding the seven economic factors to

the eight reporting incentive variables, we only observe an increase of less than 1%.

The results using the unrealized reversal sample demonstrate a very similar pattern in

R-square changes.

Third, the results of the reporting incentive regressions are highly consistent with

the institutional features in China. Because of the performance-based regulatory

approach, listed companies have strong incentives to use impairment reversals to

avoid “*ST” or “ST”. Further, a significantly positive BATH variable is consistent

with the anecdotal evidence of the so called “big bath and large reversal”

phenomenon.10

Finally, similar to the findings of Moehrler (2002) using U.S. data, the three

common reporting incentive variables, LOSS, DEC, and FC, also appear to be

10 See footnote 4 for such a story about the accounting practice. 17

important in explaining impairment reversals, in particular unrealized reversals, in

China. For the unrealized reversal sample, both LOSS and FC are significantly

positive suggesting that companies report a larger amount of unrealized reversals to

avoid a loss in the current year and to meet analyst forecasts. However, these results

do not stay the same for the total reversal sample where LOSS and FC are

insignificant, but FC becomes significant.

V. VALUATION CONSEQUENCES OF IMPAIRMENT REVERSALS

Overall, our tests of the determinants of impairment reversals appear to be more

consistent with the earnings management (H2) rather than the economic reality (H1)

hypothesis. This section further analyzes valuation consequences to complete our

hypothesis testing. If reversals reflect a recovery of asset impairments in future cash

flows, the reversal information should be positively related to stock valuation;

whereas, if investors consider the reversals to be earnings management, one should

observe no such an association. The valuation consequences of impairment reversals

can be assessed using either a short-window event study or a long-window association

study. Given that Chinese companies announce reversals through annual reports, the

event study approach may be more affected by confounding information both in and

outside an annual report. We turn to the value relevance literature in the Chinese stock

market (Eccher and Healy 2000; Chen et al. 2001; Chen and Wang 2004; and Yang et

al. 2005) and apply the association study methodology as our primary way of

analyzing the valuation consequences of impairment reversals. We also report the

event study results as part of the sensitivity tests.

According to the suggestion of Kothari and Zimmerman (1995), we use a return

model to test the association of reversals with the change in stock value over a year,

but a price model to test the association of reversals with the ending stock value. We

18

conduct two types of analysis: (1) a test on the absolute association of impairment

reversals with stock valuation, and (2) a test on the relative association of impairment

reversals with stock valuation in comparison with other earnings components. We

decompose earnings into three components and construct the valuation models:

εβββββ +++++= SIZERPSPBFRPSPFRPSP_ORET 0 4321 _ (2)

εββββββ ++++++= SIZEBVPSRPSBFRPSFRPS_OP 20 5431 _ (3)

RET is an annual industry-median adjusted return from May to April. FRPSP_O,

FRPSP_B, and RPSP represent three earnings components. FRPSP_O is recurring

income per share divided by beginning stock price, where we define recurring income

as operating income minus other income minus the amount of reversals. FRPSP_B is

below-the-line income per share divided by beginning stock price, where the

below-the-line income is defined as non-operating income plus other income minus

the amount of reversals. 11 RPSP is impairment reversals per share divided by

beginning stock price. SIZE is the natural logarithm of total assets. The price model

includes ending stock price P, net assets per share BVPS, and the three earnings

components per share, namely FRPS_O, FRPS_B, and RPS. In both models, we also

include two year dummies. To test the absolute value relevance of reversal items, we

examine β3 in each model. A significantly positive β3 suggests the usefulness of the

reversal information as evaluated by investors. To examine relative value relevance,

we compare the magnitudes of β1, β2 and β3 based on joint F tests. Table 5 presents the

return model in Panel A and the price model in Panel B. All p-values have been 11 According to Chinese GAAP, the reversals of bad debt and inventory write-downs are part of above-the-line recurring income, while other reversals are recognized as below-the-line items. When examining total reversals, we cannot identify different reversal items, and therefore we exclude the amount of total reversals from both the recurring income and the below-the-line items, which results in an understatement in both types of income. However, for the sample of unrealized reversals, we are able to clearly identify specific items of reversal, and thus the partition of net income into the three components is without any measurement error. 19

adjusted according to the White-test.

(Insert Table 5)

We first estimate regular OLS regressions for the sample of total reversals. The

coefficient of recurring income (β1) is significantly positive in both the return and

price models, but the coefficient of below-the-line items (β2) is significant only in the

return model. Further, β1 is significantly larger than β2 in both models, consistent with

the expectation that recurring income is of higher value relevance than below-the-line

items. The coefficient of reversals (β3) is significantly positive in the return model, but

insignificant in the price model. However, the magnitude of this coefficient seems out

of proportion relative to β1 and β2. A further check reveals that the standard deviation

of this variable is substantially larger than the standard deviation of either the

recurring income variable or the below-the-line items.12 It appears that the results are

affected by outliers even though the data have been winsorized. We employ the rank

regression for a further analysis.13 As shown in the third column of Table 5, the rank

regression improves R-squares in both models, and the standard deviations of

different variables become comparable. The coefficients of all three earnings variables,

β1, β2 and β3 are significantly positive in both models. The F tests of coefficient

differences show that both (β1 – β2) and (β2 – β3) are significantly greater than zero at

the 1% level, suggesting decreasing value relevance in the order of recurring income,

below-the-line items, and reversals. In fact, the coefficient magnitude of reversal is

substantially lower than below-the-line items in both models. Consequently, our tests

based on total reversals suggest that, while the reversal information appears to be

12 The standard deviations of recurring income, below-the-line items, and reversals are 0.07, 0.16, and 1.22, respectively, in the return model, and 0.15, 0.28, and 2.31 in the price model. 13 All variables are ranked in an ascending order, and then the ranks are divided by sample size to replace the law variables in the regressions. 20

incorporated in stock valuation, the association of this information with stock value is

substantially weaker than other earnings components. The economic usefulness of the

reversal information is questionable.

We further analyze unrealized reversals, the results of which are presented to the

right side of the total reversal sample in Panels A and B. Although the test results are

quite similar, evidence is stronger in support of the earnings management hypothesis.

The descending order of coefficient magnitude remains highly significant from β1 to

β2 and to β3, but the reversal variable β3 becomes insignificant in the rank regressions.

It is not surprising that the results based on unrealized reversals are more consistent

with earnings management. In comparison to realized reversals, unrealized reversals

are accrual entries that are based on managerial estimates without the constraint of

market transactions. With more discretion, opportunism is more likely in unrealized

reversals, which causes reliability to be lower.14

Taken together, the results of this and previous sections suggest that managerial

opportunism in reversal decisions driven by reporting incentives significantly reduces

the quality of reversal information as viewed by investors. The evidence is stronger

for unrealized than for total reversals. To supplement the valuation tests, we further

analyze the persistence of earnings information. If reversal information is not

reflected in stock valuation due to its low quality, we should also observe lower

persistence of reversal items as compared with other earnings components. For such a

test, we follow Richardson et al. (2005) to construct the following earnings

persistence model:

εββββ ++++= RVTANITA_BONITAFNITA 30 21 _ (4)

14 The lower value-relevance of unrealized reversals than total reversals is also consistent with the results of reversal determinant in Table 4, where the economic factors have a weaker explanatory power for unrealized than total reversals. 21

FNITA is net income in t+1 divided by beginning total assets. Since 2006 annual

reports are not available, we estimate the persistence model only for 2003 and 2004

observations. NITA_O, NITA_B, and RVTA are recurring income before reversals,

below-the-line items before reversals, and reversal items in t. All three variables are

scaled by beginning total assets. Panel C of Table 5 presents the persistence model

results. Although we estimate both regular OLS and rank regressions, we focus on

interpreting the rank regression results.15 As shown in the third column, while all

significant, the magnitude of β1, β2 and β3 is in a clear descending order, comparable to

the valuation results in Panels A and B. As compared with the other two earnings

components, impairment reversals are the least persistent. The results based on

unrealized reversals are quite similar.

To sum up, the valuation and persistence tests in this section, together with the

results of the economic factor and reporting incentive model in the previous section,

present a fairly consistent story regarding the determinants and consequences of

impairment reversals in China. While we find some weak evidence of economic

considerations, the dominance of reporting incentives in managerial reversal decisions

apparently affects the reliability of reversal information in such a way that the

usefulness of reversal information to investors is significantly reduced. Further, we

find that, consistent with the earnings management hypothesis, reversals are

significantly less persistent than either recurring income or below-the-line items.

VI. SENSITIVITY ANALYSES

This section analyzes the robustness of our main results. First, we change the

sample by excluding consecutive reversals. Second, we use the event study approach.

Third, we investigate the difference among various items of unrealized reversals.

15 The OLS regression results are stronger in support of the earnings management hypothesis. 22

Fourth, we examine the effect of industry. Finally, we examine valuation

consequences using a fixed effect model. Overall, all these robustness tests confirm

our main findings.

Reduced Sample

Within our sample, there are companies that recognize impairment reversals for

the three years consecutively. Two potential problems exist with these observations.

First, they do not have their own non-reversal years as time-series benchmarks, and

second, they may be different in some unknown aspects from other observations in

the sample. Consequently, we re-run our tests based on a reduced sample after

excluding these observations.

Overall, our conclusions remain the same. To keep our presentation short, the

results are not tabulated. For the determinant model with total reversals as the

dependent variable, △OCF (operating cash flow change) and △MTA (market to

asset change) are significantly positive among the economic variables. As for the

reporting incentive variables, ST (ST companies) and AVST (incentive to avoid ST)

are positive and significant at the two tailed conventional level of 5%. MOP (asset

impairment related audit modification), BATH (big-bath), and DEC (incentive to

avoid earnings decline) are significantly positive only based on one-tailed tests. When

using unrealized reversals as the dependent variable, △SALES (sales change) is the

only economic variable that is significantly positive. However, for reporting

incentives, ST, MOP, LOSS (incentive to avoid loss), and FC (incentive to meet

analyst forecast) are all significantly positive as expected. BATH is also significant

but only based on a one-tailed t-test.

The results of valuation consequence based on this reduced sample are also very

similar to those reported in Table 5. The total reversal variable is significantly positive

23

in both models (OLS or rank regressions), but the variable becomes insignificant for

unrealized reversals. Further, the persistence model also produces similar results.

While the reversal variable is significant for total reversals, there is no evidence of

persistence for unrealized reversals.

Short-Window Tests

Within a short window, if a reversal announcement communicates information on

asset value recovery not expected by the market, investors should respond positively;

on the other hand, if the reversal decision is driven by earnings management, we

should not observe such a wealth effect. Using the event study approach, we construct

the following models:

SIZERPSOPUNEPSCAR W) W,( 43210 βββββ ++++=− (5)

SIZEFRPSOPUNEPSCAR W) W,( 43210 βββββ ++++=− (6)

Both models test the short-term wealth effect of reversal decisions, where CAR is the

cumulative amount of daily abnormal returns (AR). We use two methods to calculate

AR: (1) the daily return of a sample company minus the market return of the Shanghai

or Shenzhen Stock Exchange Index, and (2) the abnormal return of a company based

on the market model estimated over a 120-day window from day -150 to day -31. In

both models, UNEPS is unexpected earnings per share measured as a change in

earnings before reversals. OP is a dummy variable for audit opinions, which equals to

1 if a company receives a modified opinion. SIZE is the natural logarithm of ending

total assets. The two models differ in how the reversal variable is measured. In Model

(5), RPS is the amount of reversals per share, while in Model (6), URPS is the change

of reversals per share. Since we measure changes in reversal, Model (6) is estimated

based on the two year data of 2004 and 2005.

(Insert Table 6)

24

As shown in Table 6, the results are fairly consistent no matter whether they are

based on a one-day or a two-day window and how CAR is calculated. In both panels,

β1 is significantly positive, β2 significantly negative, and β3 insignificant. These results

suggest that unexpected earnings are positively related to cumulative abnormal returns;

a modified audit opinion has a negative wealth effect; and reversals have no valuation

consequence.

Different Types of Impairment Reversals

Managerial discretion may vary among different types of impairment reversals.

For example, it is hard to find market prices for many firm specific long-term assets,

and value recovery is estimated with more subjectivity than short-term assets such as

short-term investments and inventory for which market indicators are more readily

available. To examine potential differences in information quality, we divide

unrealized impairment reversals into three categories of descending reliability:16 (1)

short-term investments and inventory, (2) long-term investments and bad debts, and (3)

long-term assets. We then evaluate the valuation consequences of these different

reversal items using the following return and price models:

εβββββββ

+++++++=

SIZERPSPRPSPRPSPBFRPSPFRPSP_ORET 0

65

4321

321_

(7)

εββββββββ

++++++++=

SIZEBVPSRPSRPSRPSBFRPSFRPS_OP 20

54

5431 321_ (8)

where RPSP1, RPSP2, and RPSP3 are the three types of reversals per share divided

by beginning stock price, and RPS1, RPS2, and RPS3 are the three reversals per share.

The results from both the regular OLS and rank regressions are presented in Table 7.

(Insert Table 7)

16 As explained earlier, detailed reversal information is available only for unrealized impairment reversals disclosed in an asset write-down table. 25

For simplicity, we focus on discussing the rank regression results.17 Both the

recurring income and the below-the-line item variables are significantly positive in the

return and price models with an expected difference in magnitude between the two

variables. In contrast, none of the reversal variables (β3, β4, or β5) is significant, which

suggests that the market does not differentiate among the three types of reversals

despite of the potential differences in reliability.

Industry Effect

Considering that some industries report more reversals than others, we examine

whether our primary results are affected by industry differences. We first rank

industries according to the average of reversals, and then create a dummy variable

INDUS taking the value of 1 for companies in one of the top three reversal industries.

As presented in Table 8, the interaction of INDUS with the reversal variable allows us

to examine industry effect.

(Insert Table 8)

We only present the results based on total reversals because unrealized reversals

produce very similar evidence. The coefficient of INDUS*RPSP in the regular OLS

and rank return models is respectively -3.59 (p-value = 0.28) and 0.03 (p-value =

0.54). The return model does not produce any evidence of industry effect. In the price

model, while the coefficient of INDUS*RPS is significantly positive, the size of the

coefficient of 14.86 is suspiciously large. Nonetheless, the coefficient of this

interaction term, INDUS*RPS, becomes insignificant in the rank regression.

Fixed Effect Model

Finally, we estimate a fixed effect model using a balanced sample over the three

17 The regular OLS results are somewhat inconsistent in terms of significant variables and the sign and/or the magnitude of variable coefficients. As discussed earlier, outliers are still a serious concern for the reversal variable. 26

year period. For total reversals, the fixed effect model includes 2673 observations,

while for unrealized reversals, the model includes 1674 observations. The results are

basically similar to those presented in Table 5. For total reversals, both the return and

price models using the rank regression produce a significantly positive coefficient

for the reversal variable (RPSP = 0.07, p-value = 0.01 in the return model, and RPS

= 0.02, p-value = 0.03 in the price model). However, for unrealized reversals, RPSP

is insignificantly negative and RPS is positive, but marginally significant with a

p-value of 0.1. Consistent with Table 5, absolute value relevance exists only for total

reversals, but not for unrealized reversals. As for relative value relevance, the results

are similar but slightly weaker than those reported in Table 5. For total reversals, we

find β2 (the below-the-line item variable) significantly larger than β3 (the reversal

variable) only in the return model. For unrealized reversals, the value relevance of

the three earnings components ranks consistently in the order of β1 > β2 > β3 in both

models. Overall, the fixed effect model analysis supports our main findings.

VII. CONCLUSIONS

This study examines the determinants and consequences of accounting discretion

by taking advantage of a unique research opportunity in China. Allowing listed

companies to reverse asset impairments, together with a detailed disclosure

requirement, provides us with a rich experimental setting to test whether management

uses reversal discretion to reveal underlying economics or manage earnings. Based on

a sample of companies reporting reversals from 2003 to 2005, we provide the

following findings: First, while both economic factors and reporting incentives

explain reversals, reporting incentives clearly dominate. Second, although reversal

information is reflected in stock valuation, the association of this formation with stock

return or price is significantly weaker as compared with other earnings components.

27

In fact, the magnitude of association is so small that the valuation consequences of

impairment reversals almost do not exist economically. Finally and more importantly,

any statistically significant valuation consequences for total reversals disappear

completely when we examine a sub-sample of unrealized reversals where managerial

discretion is higher. Taken together, we conclude that managerial opportunistic

behavior may have reduced the reliability of otherwise value-relevant reversal

information to such an extent that the usefulness of reversal information is lost.

This study contributes to the literature in several ways. First, our findings add

another piece of evidence regarding earnings management on expense reversal.

Empirical evidence on expense reversal is extremely rare. Moehrle (2002) finds that

U.S. companies reverse restructuring charges to meet or exceed analyst forecasts, and

avoid earnings declines or losses. Our study provides more comprehensive evidence

including both determinants and consequences. Second, impairment reversals are an

important type of accounting discretion in jurisdictions that follow IAS/IFRS. Even

for countries such as the U.S. that do no allow unrealized reversals, realized reversals

exist through asset disposals. Our study is the first to provide such evidence on this

important discretion. Third, this study contributes to the accrual-based earnings

management literature through examining a group of identifiable accrual items, i.e.,

the reversal of asset write-downs. Relatively, our study is less subject to the

measurement error associated with the use of the discretionary accrual estimation

models (Dechow et al. 1995).

In addition to enriching the academic literature, our findings have implications for

accounting standard setting in general and in emerging markets in particular. Recent

years have witnessed great strides towards improving accounting standards to meet

the demands of capital markets. International Financial Reporting Standards (IFRS)

28

have gained increasing acceptance. However, regulators around the world are

struggling with granting discretion to allow managers to communicate their private

information about underlying economics or limiting flexibility to restrain them from

making opportunistic accounting choices. The accounting reforms in China fully

reflect this dilemma. To follow IFRS, listed companies are given discretion for asset

impairment reversal, but our study shows that managerial opportunism may have

altered the original intention of the asset impairment standards in China. To meet the

performance-based regulatory requirements, listed companies may have abused their

discretion in reversal decisions, making reversal information a noisy measure of

impairment recovery. Our results suggest that a seemingly improved accounting

standard may not necessarily lead to improved financial reporting without the

necessary supporting infrastructure for constraining managerial opportunistic behavior.

As mentioned earlier, the recent regulatory change in China is consistent with our

findings. Starting from 2007, companies will face some restrictions in impairment

reversal. Taking a step back from IFRS, reversals are no longer allowed for fixed

assets, constructions in progress, and intangible assets. Although the pendulum swings

towards restricting managerial discretion in this incidence, it remains an open

question as to whether the quality of accounting information will improve.

29

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32

Appendix - Asset Impairment Reversal Disclosure Example

Beijing New Building Materials Public Limited Company (Stock code: 000786) 2005 Annual Report

Net profit: RMB120,208,765.65 Net profit after deducting non-recurring items: RMB119,284,842.45 Items and amounts of non-recurring profit and loss (Page 5):

Items Amount (RMB) Loss from stock and mutual fund investments Goodwill amortization from equity investments Gain from long-term equity investment transfers Provisions for long- and short-term investment write-downs Reversals of write-down provisions Net non-operating income and expenditure Subsidized financial income Gain/loss from current asset stocktaking Entitled enterprise income tax benefits Total Less: adjustment of minority interests Less: adjustment of income tax Net non-recurring items

-41,151.32 -6,824,014.45

721,284.58 -575,579.06

2,308,406.59

-66,313.60 5,715,494.71

773,057.05 7,025,575.83 9,036,760.33 6,172,055.07 1,940,782.06

923,923.20

Write-Down Provision Breakdown (Consolidated, page 44)

Decreases in current year

Items 31 Dec 2004 Increases in

current year

Reversals due to

recovery of asset

impairment

Write-offs due to other

reasons

Total

31 Dec 2005

Provision for bad debts:

11210440.71 7160455.79 856707.51 4668909.11 5525616.62 12845279.88

Accounts receivable

7849321.96 4283257.48 35029.10 3106848.46 3141877.56 8990701.88

Other receivables

3361118.75 2877198.31 821678.41 1562060.65 2383739.06 3854578.00

Provision for short-term investment loss:

210000.00 453722.44 663722.44

Stock 0.00 Bond 0.00 Other 210000.00 453722.44 0.00

Provision for inventory write-down:

4562375.68 1688847.58 591151.33 3822027.57 4413178.90 1838044.36

Finished goods 4562375.68 1489751.30 591151.33 3822027.57 4413178.90 1638948.08 Raw materials 199096.28 199096.28

Provision for long-term investment loss:

537697.08 537697.08

Equity 537697.08 537697.08 Debt

Provision for fixed asset write-down:

2851404.97 4280501.38 271020.65 271020.65 6860885.70

Buildings 0.00 Machinery 2851404.97 3324858.73 6176263.70 Transportation equipment

881539.77 271020.65 271020.65 610519.12

Other equipment

74012.88 74102.88

33

Provision for intangible asset write-down:

Patents Trademarks Non-patented technology

Provision for construction-in-progress write-down

Provision for commissioned loan write-down

Total 18834221.36 13583527.19 1718879.49 8490936.68 10209816.17 22207932.38

34

35

Figure 1 International Comparison of Impairment Reversal Regulations

Chinese GAAP (2003 to 2006)

● Reversals Realized reversals allowed for eight types of assets

Unrealized reversals allowed for eight types of assets

■ Reversal disclosure

Total reversals disclosed as non-recurring items, and unrealized reversals disclosed in a separate asset write-down table

IAS ● Reversals

Realized reversals allowed Unrealized reversals allowed

■ Reversal disclosure

Realized reversal disclosure required in the notes to the financial statements Unrealized reversal disclosure possible in the notes

U.S. GAAP ● Reversals

Realized reversals allowed

■ Reversal disclosure Realized reversal disclosure possible in the notes to the financial statements

Restricting unrealized reversals

Chinese GAAP (after 2007)

● Reversals Realized reversals allowed for eight types of assets

Unrealized reversals allowed only for accounts receivable, inventory, short- and long-term non-equity investments, and commission loans

■ Reversal disclosure Total reversals disclosed as non-recurring items, and unrealized reversals disclosed in a separate asset write-down table

Table 1

Industry Distribution of Firms Reporting Impairment Reversals

Industry/Sector

Industry Mean Percentage of Reversals over

Assets (%)

Number of Reversal

firms

Percentage (%)a

Number of Firms

Disclosing Unrealized Reversals

Percentage (%)

Number of Firms Reversing

Short-Term Asset Write-Downs

Percentage (%)

Number of Firms Reversing

Long-Term Asset Write-Downs

Percentage (%)

Real estate development and operation 1.08 81 50.63 35 21.88 32 20.00 7 4.38 Furniture,

papermaking, and other manufacture 0.44 41 37.61 19 17.43 19 17.43 2 1.83

Wholesale and retail trade 0.37 125 53.65 53 22.75 47 20.17 9 3.86 Textiles, apparel, furs 0.31 93 55.03 42 24.85 37 21.89 10 5.92 Pharmaceuticals and bio-products 0.30 134 49.45 70 25.83 58 21.40 22 8.12 Composite 0.28 163 52.41 83 26.69 75 24.12 16 5.14 Foodstuffs, beverage processing 0.28 93 53.45 46 26.44 42 24.14 11 6.32 Computer and communications 0.27 115 47.33 53 21.81 52 21.40 11 4.53 Machinery manufacturing 0.25 337 54.98 148 24.14 128 20.88 50 8.16 Agriculture, forestry, animal husbandry, fishery 0.15 40 42.55 20 21.28 18 19.15 3 3.19 Oil, chemicals, and plastics 0.15 234 53.42 114 26.03 108 24.66 16 3.65

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Social services 0.14 50 45.05 27 24.32 26 23.42 3 2.70 Electronics processing 0.13 76 60.80 35 28.00 30 24.00 9 7.20 Civil engineering 0.13 42 60.87 18 26.09 17 24.64 2 2.90 Coal, oil and mineral mining 0.12 29 42.03 11 15.94 11 15.94 1 1.45 Metal and non-metal products 0.10 190 52.92 97 27.02 89 24.79 20 5.57 Power, gas, and water 0.07 99 58.58 44 26.04 33 19.53 15 8.88 Transportation and warehousing 0.06 73 44.51 31 18.90 27 16.46 5 3.05 Media 0.05 10 38.46 5 19.23 5 19.23 0 0.00 Total 0.25 2025 51.83 951 24.34 854 21.86 212 5.43

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a: Percentage of reversal firms over total number of firms in that industry/sector.

Table 2

Asset Impairment Reversals: Sample and Magnitude

Panel A: Sample Selection Sample period: 2003 to 2005 Sample scope: non-financial A-share listed firms in the CSMAR database with complete financial data

Observations reversing write-downs made in previous years Observations excluded due to missing data

Reversal observations analyzed in the study Including: Observations disclosing unrealized reversals

Including observations without disclosing total reversals

3907 2025 137

1888

897 209

Panel B: Magnitude of Impairment Reversals

% of Total Assets

% of Write-Down

Assets % of Net Incomea Year Sample

Mean Median Mean Media Mean Median

2003 564 0.84 0.11 0.83 0.11 58.63 3.55

2004 634 0.31 0.08 0.34 0.10 62.48 2.85

2005 690 0.35 0.09 0.37 0.10 49.17 3.06

2003~2005 1888 0.48 0.09 0.50 0.10 56.47 3.13 Panel C: Magnitude of Individual Unrealized Reversal Items

% of Total Assets

% of Write-Down

Assets % of Net Income a Items Sample

Mean Median Mean Media Mean Median

Bad debt provision 349 0.89 0.06 9.39 0.63 50.07 1.93

Short-term investments 243 0.07 0.01 16.16 3.14 5.46 0.51

Inventory 358 0.18 0.04 1.20 0.31 28.65 1.53

Long-term investments 95 0.19 0.03 4.27 0.44 23.13 1.54

Fixed assets 170 0.21 0.04 0.59 0.10 42.30 1.32

Intangible assets 18 0.53 0.11 12.02 2.68 36.57 6.88

Constructions-in-progress 24 0.17 0.06 7.73 1.80 27.19 2.76

Total 897 0.51 0.06 0.52 0.07 17.55 1.17

a: Percentage of the absolute values of reversals over net income before reversals

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39

Table 3 Descriptive Statistics

Variable Sample SD Mean Median Minimum Maximum RVTA 2817 0.005 0.002 0.000 0.000 0.050 INDROA 2817 0.004 -0.003 -0.002 -0.013 0.021 INDGROW 2817 0.082 0.170 0.157 -0.065 0.437 INDBTA 2817 0.069 -0.240 -0.243 -0.356 0.057 ΔSALES 2817 0.595 0.239 0.165 -0.877 6.430 ΔOCF 2817 0.112 0.007 0.009 -0.487 0.452 ΔMTA 2817 0.250 -0.209 -0.245 -0.772 0.765 ΔMI 2817 0.049 0.016 0.011 -0.147 0.301 ST 2817 0.190 0.038 0.000 0.000 1.000 AVST 2817 0.273 0.081 0.000 0.000 1.000 MGT 2817 0.372 0.166 0.000 0.000 1.000 MOP 2817 0.101 0.010 0.000 0.000 1.000 BATH 2817 0.435 0.254 0.000 0.000 1.000 LOSS 2817 0.054 0.017 0.000 0.000 0.361 DEC 2817 0.009 0.004 0.000 0.000 0.074 FC 2817 0.375 0.169 0.000 0.000 1.000 LEV 2817 0.258 0.522 0.512 0.073 2.654 SIZE 2817 0.957 21.263 21.194 18.754 24.308 RETURN 2817 0.315 0.053 -0.004 -0.644 1.817 PRICE 2817 3.592 5.990 5.110 1.250 22.260 FRPSP_O 2817 0.081 0.007 0.016 -0.576 0.196 FRPSP_B 2817 0.036 -0.008 -0.004 -0.244 0.144 FRPS_O 2817 0.437 0.137 0.109 -1.587 1.685 FRPS_B 2817 0.191 -0.053 -0.029 -0.900 0.523 BVPS 2817 1.689 3.053 2.863 -2.364 8.683 RPSP 2817 0.005 0.002 0.000 0.000 0.039 RPS 2817 0.022 0.010 0.001 0.000 0.145 RVTA: reversal amount divided by beginning total assets; INDROA: median annual change in industry returns on assets; NDGROW: median annual change in industry sales growth; INDMTA: median annual change in industry market to asset ratio; ΔSALES: percentage change in sales; ΔOCF: annual change in operating cash flows divided by beginning total assets; ΔMTA: percentage change in market to asset ratio; ΔMI: annual change in main operating income divided by beginning total assets; ST: coded 1 if a firm is specially treated due to reporting losses for two consecutive years, and 0 otherwise; AVST: coded 1 if a firm recognizes a loss for the first time in the previous year, and 0 otherwise; MGT: coded 1 if there is a new board chairman or CEO in the previous year coming from an external source, and 0 otherwise; BATH: coded 1 if write-downs in the previous year exceed the 75th percentile of the industry average, and 0 otherwise; LOSS: the absolute value of loss before reversals or 0 otherwise; DEC: the absolute value of an earnings decline before reversals or 0 otherwise; MOP: coded 1 if a firm receives a modified audit opinion with an explicit explanation that “the auditor is unable to form an opinion on the fairness of asset write-downs due to insufficient audit evidence”, and 0 otherwise; FC: coded 1 if earnings before reversals are below the median forecast earnings, and 0 otherwise; LEV: debt to asset ratio; SIZE: the natural logarithm of ending total assets; RETURN: annual stock return from May to April adjusted for the industry median; PRICE: closing stock price on the last trading day of April; FRPSP_O: recurring income per share before reversals divided by the beginning stock price; FRPSP_B: below-the-line earnings per share before a reversal divided by the beginning stock price; FRPS_O: recurring income per share before reversals; FRPS_B: below-the-line earnings per share before reversals; BVPS: net assets per share; RPSP: reversal amount per share divided by the beginning stock price; RPS: reversal amount per share.

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Table 4 Determinants of Impairment Reversals – Tobit Regression

Total Reversals Unrealized Reversals Variable Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept 0.0147 0.00 0.0049 0.15 0.0056 0.11 0.0094 0.03 0.0057 0.18 0.0057 0.20 INDROA 0.0235 0.70 0.0314 0.59 -0.0925 0.22 -0.0717 0.33 INDGROW -0.0032 0.37 -0.0031 0.37 -0.0010 0.82 -0.0021 0.63 INDBTA -0.0019 0.45 -0.0013 0.58 0.0026 0.39 0.0029 0.34 ΔSALES 0.0003 0.25 0.0003 0.30 0.0005 0.12 0.0004 0.18 ΔOCF 0.0052 0.00 0.0034 0.01 0.0026 0.09 0.0018 0.24 ΔMTA 0.0007 0.24 0.0004 0.44 -0.0012 0.08 -0.0015 0.03 ΔMI -0.0032 0.36 -0.0043 0.22 -0.0002 0.97 0.0002 0.96 ST 0.0092 0.00 0.0090 0.00 0.0058 0.00 0.0056 0.00 AVST 0.0013 0.02 0.0010 0.05 0.0000 0.97 0.0000 0.96 MGT 0.0001 0.80 0.0001 0.81 0.0004 0.35 0.0003 0.45 MOP 0.0022 0.08 0.0022 0.08 0.0023 0.13 0.0022 0.14 BATH 0.0012 0.00 0.0013 0.00 0.0009 0.02 0.0009 0.02 LOSS 0.0026 0.36 0.0027 0.36 0.0076 0.03 0.0089 0.02 DEC 0.0268 0.06 0.0275 0.06 0.0071 0.69 0.0031 0.87 FC 0.0004 0.34 0.0004 0.32 0.0010 0.04 0.0010 0.05 LEV 0.0023 0.00 0.0002 0.79 0.0002 0.77 0.0007 0.16 -0.0005 0.36 -0.0006 0.30 SIZE -0.0007 0.00 -0.0002 0.14 -0.0002 0.12 -0.0004 0.02 -0.0002 0.20 -0.0002 0.21 Sample 2817 2817 2817 1749 1749 1749 Adj R2 0.0161 0.1099 0.1129 0.0003 0.0461 0.0536 Log likelihood 6108.820 6213.157 6218.514 2786.495 2814.914 2820.932

The model includes 18 industry dummies and 2 year dummies not listed for simplicity. All variables are as defined in Table 3.

Table 5

Value Relevance and Earnings Persistence of Impairment Reversals

Panel A: Return Model Total Reversals Unrealized Reversals

Regular OLS Regression

Rank Regression

Regular OLS Regression

Rank Regression Variable

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-valueIntercept -0.54 0.00 0.12 0.00 -0.50 0.00 0.14 0.00 FRPSP_O 1.11 0.00 0.48 0.00 1.15 0.00 0.49 0.00 FRPSP_B 0.63 0.00 0.18 0.00 0.47 0.04 0.19 0.00 RPSP 4.39 0.01 0.05 0.00 4.87 0.06 0.01 0.52 SIZE 0.03 0.00 0.03 0.11 0.02 0.00 0.03 0.22 YEAR04 0.02 0.09 0.00 0.55 0.01 0.38 0.00 0.82 YEAR05 0.09 0.00 0.01 0.49 0.08 0.00 0.00 0.80 N 2817 2817 1749 1749 Adj R2 0.1106 0.1837 0.1127 0.1882 F value 59.36*** 106.61*** 37.99*** 68.54***

The F-test values of FRPSP_O-FRPSP_B are 197.79*** and 135.34***, respectively, and those of FRPSP_B-RPSP are 24.75*** and 25.67***, respectively. Panel B: Price Model

Total Reversals Unrealized Reversals Regular OLS Regression

Rank Regression

Regular OLS Regression

Rank Regression Variable

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-valueIntercept 18.28 0.00 0.32 0.00 17.47 0.00 0.30 0.00 FRPS_O 3.38 0.00 0.49 0.00 3.86 0.00 0.51 0.00 FRPS_B -0.07 0.84 0.11 0.00 0.56 0.23 0.13 0.00 BVPS 0.60 0.00 0.29 0.00 0.56 0.00 0.28 0.00 RPS 3.42 0.12 0.03 0.01 4.77 0.25 0.02 0.31 SIZE -0.60 0.00 -0.17 0.00 -0.56 0.00 -0.15 0.00 YEAR04 -3.17 0.00 -0.31 0.00 -3.13 0.00 -0.31 0.00 YEAR05 -2.54 0.00 -0.27 0.00 -2.56 0.00 -0.27 0.00 N 2817 2817 1749 1749 Adj R2 0.4613 0.5858 0.4880 0.5978 F value 345.50*** 569.84*** 238.98*** 372.17***

The F-test values of FRPS_O-FRPS_B are 612.57*** and 384.05***, respectively, and those of FRPS_B-RPS are 19.19*** and 23.90***, respectively. Panel C: Persistence Model

Total Reversals Unrealized Reversals Regular OLS Regression

Rank Regression

Regular OLS Regression

Rank Regression Variable

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-valueIntercept 0.00 0.09 0.07 0.00 0.00 0.48 0.06 0.06 NITA_O 0.63 0.00 0.74 0.00 0.66 0.00 0.75 0.00 NITA_B 0.18 0.02 0.10 0.00 0.23 0.03 0.15 0.00 RVTA 0.58 0.14 0.07 0.00 0.67 0.23 0.04 0.08 YEAR04 -0.01 0.00 -0.06 0.00 -0.01 0.00 -0.05 0.00 N 1848 1848 1148 1148 Adj R2 0.3235 0.4813 0.3163 0.4694 F value 221.77*** 429.46*** 133.68*** 254.65***

The F-test values of NITA_O-NITA_B are 1097.12*** and 604.08***, respectively, and those of NITA_B-RVTA are 1.18 and 9.34***, respectively.

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Table 6

Short-Term Wealth Effect of Impairment Reversals Panel A: Economic Consequences of Reversals – Level Specification

CAR Estimated Based on Stock Index returns CAR Estimated Based on Market Model

(-1,1) (-2,2) (-1,1) (-2,2) Variable

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept -0.09 0.00 -0.13 0.00 -0.08 0.00 -0.11 0.00

UNEPS 0.01 0.03 0.01 0.02 0.01 0.10 0.01 0.12

OP -0.02 0.00 -0.03 0.00 -0.02 0.00 -0.02 0.00

RPS -0.05 0.37 0.00 0.95 -0.06 0.30 -0.01 0.78

SIZE 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00

YEAR04 -0.01 0.02 -0.01 0.00 -0.01 0.01 -0.01 0.00

YEAR05 0.01 0.00 0.02 0.00 0.01 0.01 0.00 0.16

N 2817 2817 2817 2817

Adj R2 0.0357 0.0458 0.0221 0.0287

F value 18.40*** 23.51*** 11.58*** 14.88***

UNEPS: annual earnings change per share before reversals; OP: audit opinion, coded 1 if a firm is given a modified opinion, and 0 otherwise. The other variables are as defined in Table 3. Panel B: Economic Consequences of Reversals – Change Specification

CAR Estimated Based on Stock Index returns CAR Estimated Based on Market Model

(-1,1) (-2,2) (-1,1) (-2,2) Variable Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept -0.12 0.00 -0.17 0.00 -0.12 0.00 -0.18 0.00

UNEPS 0.01 0.02 0.01 0.01 0.01 0.06 0.01 0.05

OP -0.02 0.00 -0.03 0.00 -0.02 0.00 -0.03 0.00

URPS -0.05 0.46 -0.02 0.80 -0.06 0.38 -0.03 0.74

SIZE 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00

YEAR05 0.02 0.00 0.03 0.00 0.01 0.00 0.02 0.00

N 1913 1913 1913 1913

Adj R2 0.0433 0.0561 0.0297 0.0378

F value 18.31*** 23.74*** 12.70*** 16.01***

UNEPS: annual earnings change per share before reversals; OP: audit opinion, coded 1 if a firm is given a modified opinion, and 0 otherwise; URPS: annual change in reversals per share. The other variables are as defined in Table 3.

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Table 7 Comparison of Value Relevance of Unrealized Reversals by Items

Panel A: Return Model

Regular OLS Regression Rank Regression Variable Coefficient p-value Coefficient p-value Intercept -0.71 0.00 0.13 0.00 FRPSP_O 0.78 0.01 0.49 0.00 FRPSP_B -0.13 0.61 0.19 0.00 RPSP1 -3.24 0.25 0.01 0.63 RPSP2 1.49 0.09 -0.02 0.58 RPSP3 2.15 0.18 0.04 0.36 SIZE 0.03 0.00 0.03 0.22 YEAR04 0.01 0.55 0.00 0.80 YEAR05 0.07 0.00 0.00 0.85 N 1749 1749 Adj R2 0.0761 0.1877 F value 19.00*** 51.50***

RPSP1: short-term investment and inventory write-down reversals per share divided by the beginning stock price; RPSP2: bad debt and long-term investment write-down reversals per share divided by the beginning stock price; RPSP3: long-term asset write-down reversals per share divided by the beginning stock price. The other variables are as defined in Table 3. The F-test value of RPSP1-RPSP2 is 0.52, and that of RPSP2-RPSP3 is 1.12. Panel B: Price Model

Regular OLS Regression Rank Regression Variable Coefficient p-value Coefficient p-value Intercept 16.46 0.00 0.29 0.00 FRPS_O 3.24 0.00 0.51 0.00 FRPS_B -0.30 0.71 0.13 0.00 BVPS 0.53 0.00 0.28 0.00 RPSP1 10.58 0.03 0.00 0.84 RPSP2 0.10 0.90 0.01 0.51 RPSP3 4.39 0.62 0.02 0.36 SIZE -0.50 0.00 -0.15 0.00 YEAR04 -3.07 0.00 -0.31 0.00 YEAR05 -2.50 0.00 -0.27 0.00 N 1749 1749 Adj R2 0.4049 0.5974 F value 133.16*** 289.22***

RPS1: short-term investment and inventory write-down reversals per share; RPS2: bad debt and long-term investment write-down reversals per share; RPSP3: long-term asset write-down reversals per share. The other variables are as defined in Table 3. The F-test value of RPS1-RPS2 is 0.37, and that of RPS2-RPS3 is 0.08.

43

Table 8

Industry Effect on Value Relevance of Reversals Panel A: Return Model

Regular OLS Regression Rank Regression Variable

Coefficient p-value Coefficient p-value Intercept -0.54 0.00 0.12 0.00 FRPSP_O 1.10 0.00 0.48 0.00 FRPSP_B 0.61 0.00 0.18 0.00 RPSP 5.14 0.01 0.05 0.01 SIZE 0.03 0.00 0.03 0.10 YEAR04 0.02 0.08 0.00 0.64 YEAR05 0.09 0.00 0.01 0.56 INDUS 0.00 0.88 0.01 0.70 INDUS*RPSP -3.59 0.28 0.03 0.54 N 2817 2817 Adj R2 0.1106 0.1841 F value 44.77*** 80.41***

INDUS: dummy variable, coded 1 if a firm is among the top three industries reporting the largest mean reversals, and 0 otherwise. The other variables are as defined in Table 3. Panel B: Price Model

Regular OLS Regression Rank Regression Variable Coefficient p-value Coefficient p-value Intercept 18.27 0.00 0.31 0.00 FRPS_O 3.42 0.00 0.50 0.00 FRPS_B 0.02 0.96 0.12 0.00 BVPS 0.60 0.00 0.29 0.00 RPS 0.10 0.93 0.03 0.03 SIZE -0.60 0.00 -0.17 0.00 YEAR04 -3.18 0.00 -0.31 0.00 YEAR05 -2.54 0.00 -0.27 0.00 INDUS 0.02 0.91 0.02 0.28 INDUS*RPS 14.86 0.02 0.00 0.90 N 2817 2817 Adj R2 0.4627 0.5863 F value 270.47*** 444.45***

INDUS: dummy variable, coded 1 if a firm is among the top three industries reporting the largest mean reversals, and 0 otherwise. The other variables are as defined in Table 3.

44