Valuation of China’s Stock Market: Mispricing of Earnings Components

Abstract

This paper investigates whether Chinese equity investors price major earnings

components correctly. Total earnings are decomposed into core and non-core earnings

according to a classification framework of Chinese accounting principles. The results show

that, as expected, core earnings are more persistent than non-core earnings. Most importantly,

the market underestimates (overestimates) the value implications of changes in current core

(non-core) earnings for future earnings changes. Therefore, future stock returns can be

predicted based on the information that is contained in the components of current earnings.

Both portfolio tests and regression analysis generate economically significant abnormal

returns that are robust to sensitivity checks.

Keywords: Valuation; Mispricing; Accounting Earnings; Market Efficiency; China.

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Valuation of China’s Stock Market: Mispricing of Earnings Components

1. Introduction

A growing body of evidence indicates that the stock market may not be as efficient as

once believed in reflecting the information that is contained in accounting earnings. One

branch of the relevant literature decomposes total earnings into components and examines

whether the market fully understands the difference between these components in their

implications for future earnings. Sloan (1996) shows that although operating cash flows are

more persistent in predicting future earnings than are accruals, the market fails to recognize

this difference in persistence and appears to attach the same weight to accruals and operating

cash flows in setting stock prices.1 Other studies examine the mispricing of earnings by

decomposing earnings into foreign and domestic income (Thomas, 2000) or by focusing on

special items that have relatively straightforward implications for future earnings (Burgstahler

et al., 2002).2

The study reported herein adds to this body of literature by examining whether China’s

stock markets properly price earnings components. The rapid development of these markets

has directed the attention of researchers to the role played by accounting information in the

valuation of securities. Despite the short history of Chinese listed firms, extant studies find

that accounting reports in the country’s stock markets are surprisingly highly relevant to

Chinese investors in pricing stocks. Underlying these studies is the assumption that these

markets are efficient. Although the efficient market hypothesis provides a parsimonious

framework for understanding capital markets, it may preclude the possibility of researchers

1 Xie (2001) finds that the mispricing of accruals is largely due to the part most subject to managerial

manipulation. The effects of accruals on future stock returns are shown to coexist with

post-earnings-announcement drifts (Collins and Hribar, 2000), to be robust to firms with different

characteristics (Ali et al., 2000), and to be overlooked by professional investment intermediaries

(Bradshaw et al., 2001).

2 Another notable accounting-based anomaly is the post-earnings-announcement drift: the market does

not fully understand the autocorrelation structure of quarterly earnings, and thus underreacts to them

(Bernard and Thomas, 1990). Ball and Bartov (1996) show that, on average, the market underestimates

the implications of earnings changes in the current quarter for future earnings by about 50% across the

four subsequent quarters.

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discovering anything that is not already “known” by the market (Bernard, 1995). Abdel-khalik

et al. (1999) doubt whether one can make sense of accounting numbers in China, after

observing that they are important in setting the prices for A-shares (issued to domestic

investors) but not those for B-shares (issued to foreign investors).3 One potential reason for

their suspicion is the descriptive validity of the market efficiency assumption in China. The

current study thus puts to empirical test whether equity investors properly value the

accounting earnings of listed Chinese firms.

Taking advantage of the standard classification of income statements that are prepared

under Chinese accounting rules, we decompose total annual earnings into their core earnings

and non-core earnings components. The former are those from the principal operations of the

firm, and the latter are an aggregation of all other income statement items, most of which are

non-recurring. This decomposition is consistent with Chen and Yuan’s (2004) study of Chinese

listed firms, and is similar to the characterization of U.S. firms by Collins et al. (1997). Haw et

al. (2005) report that Chinese listed firms often manipulate their bottom-line earnings by

timing the occurrence of non-core earnings. Because such earnings are non-recurring and

more likely to be managed, they should be less persistent than core earnings.

Our empirical findings based on data from 1995 to 2005 reveal that, as expected, core

earnings are indeed more persistent. However, in a regression of contemporaneous stock

returns on earnings components, both types of earnings exhibit similar levels of earnings

response coefficients. This evidence suggests that Chinese investors appear to be unaware of

the difference in persistence between core and non-core earnings. Rather, the market

undervalues both firms with positive changes in core earnings and those with negative changes

in non-core earnings. Furthermore, investors overvalue firms with negative changes in core

3 Listed firms in China issue several types of shares. A-shares are issued to domestic investors and

B-shares to foreign investors. Some firms also issue shares to Hong Kong investors and are listed on

the Hong Kong Stock Exchange (H-shares). By the end of 2005, 109 and 122 firms had issued B- and

H-shares, respectively. Firms that issue both A-shares and B- or H-shares must provide International

Accounting Standards (IAS)-based financial statements to foreign investors, in addition to the Chinese

Generally Accepted Account Principles (GAAP)-based standards. Although earnings determined by

Chinese GAAP may be quite different from those based on IAS (Chen et al., 1999), Ball et al. (2000)

find no discernable difference in conservatism between the two sets of earnings numbers.

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earnings and those with positive changes in non-core earnings. Hedge portfolios that take a

long position in undervalued stocks (i.e., firms with positive changes in core earnings and/or

negative changes in non-core earnings) and a short position in overvalued stocks (i.e., firms

with negative changes in core earnings and/or positive changes in non-core earnings) yield

positive abnormal returns, not only over the entire sample period, but also in most of the

sample years. These results are robust to a variety of sensitivity tests.

This study contributes to the ongoing debate about market efficiency by documenting

evidence of the existence and extent of the mispricing of earnings components in China. As

the extant studies of financial statement data mispricing concentrate on U.S. firms, our study

also serves as an out-of-sample analysis of the accumulated evidence on market inefficiency in

the U.S. Our evidence of the systematic misestimation of the value implications of current

earnings components for future earnings suggests that caution should be exercised in drawing

inferences from extant studies on the degree of value relevance of Chinese financial data. The

applicability of (semi-strong) market efficiency in the Chinese stock market (and in other

emerging markets) with respect to the financial information that is disclosed warrants a

re-examination of the institutional factors that determine the unique information environment

of the Chinese market. Because accounting information plays a crucial role in facilitating the

efficient allocation of scarce investment resources, an enhanced understanding of the

usefulness and limitations of that information in equity valuation represents a first step

towards optimal investment decisions in China and other emerging economies.

The remainder of the paper is organized as follows. Section 2 provides the institutional

background of the Chinese stock market. Section 3 describes the sample, and Section 4

presents the empirical results. Section 5 concludes the paper.

2. Chinese Stock Markets and Core versus Non-Core Earnings

2.1 Chinese Economy and Stock Markets

The impressive growth of the Chinese economy over the past two decades has attracted

considerable attention, not only from the business and investment communities, but also from

financial academics. The following excerpt from a business magazine succinctly summarizes

the increasing role that China plays in the rapidly integrating world economy:

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In the five years since Asia’s financial crisis, China has clocked annual growth rates of

7% to 8%. Exports surged 21% last year to $322 billion, making it the largest exporter

of goods to the U.S. The UN estimates that China took in about $50 billion in foreign

direct investment, more than the rest of Asia combined (Fortune, January 20, 2003, p.

48).

China’s stock market has also experienced rapid growth. As shown in Panel A of Figure 1,

since the listing of the first eight companies on the Shanghai Stock Exchange in December

1990, the number of A-share firms listed on the Shanghai and Shenzhen stock exchanges has

grown to more than 1,500, as of the end of 2007. The total market capitalization of these

firms’ tradable shares had reached RMB 9,070 billion (Panel B of Figure 1) by the end of that

year. Since April 2001, the market capitalization of the country’s stock market has been the

third largest in Asia, lagging behind only Tokyo and Hong Kong. Furthermore, the market

value of companies based in mainland China accounts for about 28% of that of Hong Kong’s

capital market. This dramatic increase in market capitalization is not merely because more

firms are now listed. As Panel C of Figure 1 shows, the Shanghai A-share index has

experienced steady growth over the years. The log-linear model printed in the plot indicates

that the value of the market portfolio grew at a rate of 13.1% per annum between 1990 and

2007.4

Insert Figure 1 here.

Since its accession to the World Trade Organization in 2002, China has become more

closely connected to the global business community. Foreign money managers have become

increasingly willing to invest in China. In October 2002, the Chinese Securities Regulatory

Commission (CSRC), the equivalent of the Securities and Exchange Commission in the U.S.,

issued the first Sino-foreign fund management license. In May 2003, Nomura Securities and

UBS were allowed to invest directly in China’s stock and bond markets after the CSRC issued

them with Qualified Foreign Institutional Investors (QFII) licenses, and they were followed by

Goldman Sachs, Morgan Stanley, CS First Boston, Citigroup, Morgan Chase, HSBC, ING, and

Deutsche Bank. The Chinese stock market has even attracted American newsletter writers

(February 25, 2004, Asian Wall Street Journal, M5).

4 For details on the development of the stock market in China, see Qi et al. (2000), Sun and Tong (2003)

and Chan et al. (2004).

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In valuing accounting earnings, investors in China’s stock market are likely to be less

sophisticated than those in developed economies. Most are small individual investors (Security

Times, January 4, 2003). Less than 1% of the accounts on the Shanghai Stock Exchange have a

portfolio value of RMB 100,000 (or approximately US$12,000) or more, which indicates that

large investors, including institutional investors, do not dominate the market. In studying the

market’s pricing of other receivables in China, Jiang et al. (2010) report that the mean

ownership by all institutional investors (including mutual funds, social security funds, and

pension funds) is only 3.75% at the end of 2004.5 Compared to institutional investors, small

investors are more likely to ignore the value implications of different earnings components and

to be functionally fixated on bottom-line earnings (Hand, 1990). Furthermore, the turnover of

stocks in China is very high: the average annual turnover ratio of those for A-share firms was

more than 400% during our sample period. Although the turnover ratios of stocks in emerging

markets are typically higher than those in developed markets, China ranks first among 31

emerging markets (Füss, 2001).6 Given that Chinese listed firms voluntarily disclose little

information and that financial press coverage of these firms is limited (Haw et al., 2000), it

would be difficult to argue that such a high turnover ratio is based on fundamental news about

the firms. Hence, part of the high trading volume is likely to be driven by noisy traders who

think that the noise they are trading on is information (Black, 1986). In the presence of noisy

trading, the implications of different earnings components for future earnings are unlikely to

be fully reflected in stock prices.

2.2 Core Earnings and Non-Core Earnings

Figure 2 illustrates a standard income statement prepared under Chinese accounting rules.

Income from investments, both short-term and long-term, is reported separately below income

5 Jiang et al. (2010) use other receivables as a proxy for inter-corporate loans, a common form of

diverting resources from listed firms to controlling shareholders in China. They show that a hedge

portfolio longing the top-decile other receivables stocks and shorting the bottom decile earns over 1%

per month over the next 12 months. They interpret such mispricing evidence as a result of low

institutional ownership in China’s stock market.

6 Füss (2001) fails to consider that most shares are not tradable in China. Thus, his turnover ratio

measure of 150% for the country is underestimated.

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from operations. Below investment income is subsidy income, which typically includes

subsidies received from the government for policy-related losses and tax reductions. Other

items that are not directly related to the company’s operating activities are aggregated and

reported as non-operating income or expenses, and these items include gains or losses from

the disposal of fixed assets, assets revaluation, debt restructuring, donations, and fines.

Insert Figure 2 here.

Income items below income from operations (i.e., non-core earnings) are transitory.

Similar to the accounting rules in most other countries, under Chinese GAAP, unrealized gains

on investment assets or subsidiary companies cannot be recognized in income statements.

Furthermore, before the enactment of the Accounting System for Shareholding Companies in

1998, Chinese GAAP did not allow firms to make provisions for short- or long-term

investments.7 Consequently, economic losses arising from the reduced present value of these

investments are not incorporated into income as long as these assets are not sold. Therefore,

the time-series properties of realized investment income resemble those of other non-recurring

non-core earnings items. Moreover, Chinese companies often manage earnings to meet

regulatory benchmarks by timing the occurrence of non-core earnings (Chen and Yuan, 2004;

Haw et al., 2005). As such earnings are non-recurring and subject to manipulation, they should

be less persistent than income from operations (i.e., core earnings). If core earnings are more

persistent than non-core earnings, then the efficient market hypothesis predicts that investors

should price these two components differently.

Non-core earnings are similar to the special items under U.S. GAAP and the exceptional

or extraordinary items under U.K. GAAP. Previous research has shown that investors in these

countries differentiate such items from other more permanent items in pricing equity securities.

Elliott and Hanna (1996) and Chen and Schoderbek (2000) show that U.S. investors place less

weight on special items than on income from continuing operations. Strong and Walker (1993)

report that the valuation implications of earnings before exceptional items for U.K. investors

7 Listed firms were able to make allowances for investments on a voluntary basis when the Accounting

System for Shareholding Companies was enacted in 1998. However, most firms did not make these

allowances until 1999, when the Ministry of Finance required them for receivables, investments, and

inventories.

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are different from those of exceptional items or extraordinary items. However, Burgstahler et

al. (2002) report that U.S. investors do not fully incorporate the transitory nature of special

items into stock prices, although portfolios constructed according to the mispricing of special

items yield only small abnormal returns.

3. Sample and Data

Our sample is taken from the population of A-shares of the listed Chinese firms on the

Shenzhen and Shanghai Stock Exchanges from 1995 to 2005, based on the China Stock

Market and Accounting Research (CSMAR) database. This database provides data on the

financial statements and stock prices of all listed companies. However, the CSMAR does not

provide pre-initial public offering (IPO) financial data. To avoid a bias against market

efficiency due to the deletion of data (Kothari et al., 2005), we obtain our pre-IPO financial

data from the Genius database produced by Shenzhen Genius Information Technology Ltd.

The CSMAR includes 10,584 non-financial firm-years for the sample period. Among these

observations, 15 firms were delisted and thus have missing accounting values for the

subsequent year (year t+1). Required financial statement items in year t-1 are missing for 59

observations in the Genius database, and thus our final sample comprises 10,510 firm-years.

Panel A of Table 1 shows the distribution of the observations in the two stock exchanges

across the sample years. The number of observations increases steadily from 291 in 1995 to

1,336 in 2005, which reflects the rapid development of China’s stock market.

Insert Table 1 here.

We define abnormal stock returns as the size- and book-to-market (BE/ME)-adjusted

returns to control for returns from the rational pricing of the risk factors proxied by size and

BE/ME (Fama and French, 1996) and the mispricing associated with these two variables (Ali

et al., 2003).8 To form benchmark portfolios, stocks are first sorted into quintile groups

8 Haw et al. (2004) find that firm size and the book-to-market ratio of equity (BE/ME) are two

important determinants of cross-sectional variation in average stock returns in China’s capital market,

and that other potential determinants identified in the extant literature, such as market beta,

earnings-to-price ratios, leverage, and idiosyncratic risks, have no incremental explanatory power for

the cross-sectional variation in monthly excess returns. This finding is consistent with those of studies

carried out in the U.S. (Fama and French, 1992), Japan (Chan et al., 1991; Daniel et al., 2001), and

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according to the market capitalization at the beginning of the month.9 Each of the size

quintiles is then sorted into quintiles by BE/ME, computed as the book value of equity per

share divided by the A-share price at the beginning of the month. Thus, there are 25 size- and

BE/ME-matched portfolios for each calendar month.10 Figure 3 shows the mean monthly

excess returns for these benchmark portfolios. Clearly, there are substantial variations in

realized returns across both size and BE/ME, which suggests that it is necessary to control for

these two factors in computing abnormal returns. Abnormal returns (SBMAR) are thus defined

as a stock’s 12-month buy-and-hold return beginning in May after the fiscal year-end minus

the buy-and-hold equal-weighted return of the benchmark size- and BE/ME portfolio. This

definition of abnormal returns is free of look-ahead bias because all Chinese firms are required

to use the calendar year-end as the fiscal year-end and to release their annual reports by the

end of April.

Insert Figure 3 here.

Core earnings (CE) are defined as income from operations, which is consistent with

previous research (Chen and Yuan, 2004; Haw et al., 2005). All other income items are

aggregated into a single category––non-core earnings (NCE).11 As these earnings components

are reported on a pre-tax basis, total earnings (E) are defined as pre-tax total profits. All of the

earnings variables are scaled by average total assets at the end of year t and t-1 to reflect the

effect of firm expansion on earnings performance. To mitigate the effects of potential outliers,

the earnings variables are winsorized at the top and bottom percentiles according to their

yearly distribution.

other countries (Fama and French, 1998). Chan et al. (2004) also employed the size- and

BE/ME-matched portfolio in examining the long-term performance of IPOs in China.

9 Measuring size as the market capitalization of tradable A-shares produces similar results.

10 The number of stocks in each portfolio could be small under the 5×5 benchmark portfolio formation

scheme for the early years of the sample period. The results are similar using a 3×3 formation scheme.

11 We also examine investment income and other non-core earnings separately. These two

subcategories have similar (and statistically indifferent) persistence. Furthermore, the actual persistence

of both subcategories is significantly less than the implied persistence based on the Mishkin (1983) test.

This suggests that investment income is similar to other non-core earnings in terms of persistence and

that the market does not differentiate one from the other.

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Panel B of Table 1 presents the descriptive statistics for the returns and the levels of and

changes in earnings variables. The average annual buy-and-hold raw return (BHRET) is 8.4%,

with a standard deviation of 58.2%, which is consistent with the high return-high volatility

patterns that characterize emerging markets (Harvey, 1995). The mean values of the level of or

change in core earnings are smaller than the median values, which indicates the presence of

income conservatism (i.e., recognizing negative economic news in earnings faster than

positive news). However, this cannot be observed in non-core earnings. Thus, non-core

earnings are likely to be the outcome of earnings management, rather than the application of

accounting conservatism (see Basu [1997] for U.S. evidence).

4. Empirical Evidence

4.1 Actual Persistence versus the Market’s Perception of the Persistence of Earnings

Components

We first provide evidence to determine whether the market fully appreciates the effect of

different earnings components on the expectations of future earnings. We adopt the framework

of the Mishkin (1983) test, as employed by Sloan (1996) and Dechow and Sloan (1997).

Suppose that the earnings composition disclosed at the end of year t is useful in predicting

earnings changes in year t+1 (∆Et+1). In an efficient market, the implications of current

earnings components for the next period’s earnings changes should be fully reflected in stock

prices once the current earnings have been revealed. To evaluate whether the market rationally

prices the information that is contained in current earnings components, the two following

equations are estimated (firm subscript i is omitted for simplicity).

∆Et+1 = α0 + α1∆CEt + α2∆NCEt + εt, and (1)

SBMARt+1

= β0 + β1εt + ωt+1

= β0 + β1(∆Et+1 – α0 – α1*∆CEt – α2

*∆NCEt) + ωt+1, (2)

where εt is the innovation of ∆Et+1 that cannot be predicated by earnings components in year t,

and ωt+1 is white noise. Equation (1) specifies the conditional dependence of ∆Et+1 on earnings

components in year t, whereas equation (2) examines the market’s reaction to ∆Et+1

conditional upon ∆CEt and ∆NCEt. α1* and α2

*, the estimated coefficients on ∆CEt and ∆NCEt,

respectively, in equation (2), represent the implications of ∆CEt and ∆NCEt for future earnings

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changes as perceived by the market. Testing whether stock prices fully impound the

implications of ∆CEt and ∆NCEt for ∆Et+1 is a test for the consistency of the coefficients in

these two equations (Mishkin, 1983). That is, if investors fully incorporate the information in

the current earnings composition into stock prices at the beginning of the SBMARt+1 window,

then α1* = α1 and α2

* = α2. We estimate the two equations simultaneously using the iterative

non-linear least squares (NLLS) method and test the equality of the coefficients across the

equations by the likelihood ratio statistic:

2n Ln (SSRC/SSRU) ~ χ2(q), (3)

where n is the number of observations, Ln (•) is the natural logarithm operator, SSR C and SSRU

are the sum of the squared residuals from the constrained and unconstrained regression system,

respectively, and q is the number of constraints imposed by the null hypothesis of market

efficiency.

Although widely applied in the literature (e.g., Sloan 1996; Dechow and Sloan 1997;

Thomas 2000; Elgers et al. 2001; Xie 2001), the Mishkin test is asymptotically equivalent to

regressing one-year-ahead stock returns on accounting variables (Kraft et al. 2007). However,

this conclusion is based on large samples [the average number of firms per year ranges from

about 2,000 to more than 3,000 in Kraft et al. (2007)]. Given the modest sample size in China,

we use the Mishkin test. More importantly, by comparing the difference between α and α*, the

Mishkin-type test allows us to infer the magnitude of the deviation of the market’s use of

information from rational pricing [see Ball and Bartov (1996) for the application of this

method in the context of post-earnings-announcement drift]. Such an inference is not available

from the regression approach.12

The results of the two aforementioned equations estimated by the pooled sample are

reported in Panel A of Table 2. In the first column, we show the actual persistence of earnings

components estimated by equation (1). The changes in current earnings components, ∆CEt and

∆NCEt, are both significantly negatively related to changes in future earnings, ∆Et+1, which

indicates that both components are mean-reverting. More importantly, the coefficient on ∆CEt

is significantly less negative than that on ∆NCEt at the 1% level, which indicates that core

12

In Section 4.4, we also use the regression approach, as advocated by Kraft et al. (2007), to control

for the variables that are correlated with both non-core earnings and future stock returns.

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earnings are more persistent than non-core earnings.13 However, the estimates from equation

(2), which are reported in the second column, reveal that the market does not differentiate

between the two components. There is no significant difference between the implied

coefficient estimates, α1* and α2

*. Moreover, the market’s perception of the time-series

behavior of core and non-core earnings is different from the actual persistence of these

components. The estimated α1* and α2

* are -0.385 and -0.265, respectively, compared to the

actual persistence estimates of α1 = -0.128 and α2 = -0.437. Column (3) shows that for ∆CEt,

when we constrain α1* to be equal to α1, the likelihood ratio statistic is 21.99 (p<0.01 for d.f. =

1). We thus reject the null for the consistency between α1* and α1. The likelihood ratio statistic

for the consistency of the coefficients on ∆NCEt between equations (1) and (2) is 3.73, again

allowing us to reject the null for the consistency of the coefficients between the two equations

(p<0.10 for d.f. = 1).14 Therefore, the Mishkin (1983) test rejects the null hypothesis that

investors fully employ the information contained in current changes in core or non-core

earnings in predicting future earnings changes. The evidence suggests that the market

over-estimates the mean-reverting of ∆CEt at the magnitude of 201% [= -0.385/-0.128 - 1]

while the persistence of ∆NCEt is under-estimated by the market by -39% [= -0.265/-0.437 -

1).

Insert Table 2 here.

As the significance level in testing market efficiency may be overstated when

observations from different periods are pooled (Kraft et al., 2003), we also estimate the

coefficients in equations (1) and (2) by year and examine the consistency of these coefficients

by the Fama and MacBeth (1973) procedure. That is, we base the statistical tests for both the

significance of and equality between αi and αi* on the time-series variation of the annual

coefficients. The results are quite similar to those obtained from the NLLS procedure, as

shown in Panel B of Table 2.

13

We also estimate the firm-specific regressions of equation (1) using 1,047 firms with at least six

years’ data. The median coefficients on ∆CEt and ∆NCEt are -0.033 and -0.330, respectively, and the

difference between these coefficients is significant at the 1% level in both the t test and the Wilcoxon

rank sum test.

14 When we impose the constraints of both α1

* = α1 and α2

* = α2 in estimating equations (1) and (2)

simultaneously by the NLLS, the likelihood ratio statistic becomes 23.19 (p < 0.01 for d.f. = 2).

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To summarize, the results in Table 2 suggest that core earnings are more persistent than

non-core earnings. However, the weight assigned by investors to the changes in core earnings

(non-core earnings) is significantly less (greater) than the actual persistence of this component.

We test for whether this implies that excess returns can be made by taking advantage of the

market’s mispricing of earnings components in the next section.

4.2 Predicting Future Stock Returns by Current Earnings Composition

If the market acts as though it assigns a smaller (larger) valuation coefficient to ΔCE

(ΔNCE) relative to its actual predictability for earnings changes in the following year, then the

stock prices of firms with a positive change in ΔCE (ΔNCE) are likely to be undervalued

(overvalued), and the stock prices of those with a negative change in ΔCE (ΔNCE) are likely

to be overvalued (undervalued). When realized earnings in subsequent periods differ from

those expected by investors, the market corrects the mispricing, but with a delay, which

suggests that part of the future returns can be predicted by using the information that is

contained in the components of current earnings. We perform the portfolio test to examine the

predictability of future returns by current earnings components. Specifically, a trading strategy

that is long in undervalued stocks and simultaneously short in overvalued stocks can yield

positive abnormal returns in the subsequent year. To ensure that the trading rule is free of

hindsight bias, the hedge portfolios are formed at the beginning of May after the fiscal

year-end, when information about earnings in year t is publicly available for all of the firms in

the market. These portfolios are held for one year until new ones are created. This portfolio

test provides direct evidence of the economic significance of the extent of current earnings

component mispricing.

Three hedge portfolios are formed according to firms’ earnings composition. The first,

∆CE Portfolios, are designed to take advantage of the market’s under-reaction to core earnings.

To rank stocks by changes in core earnings (∆CE) while controlling for changes in total

earnings (∆E), we first sort stocks by ∆E in year t and place them into decile groups for each

year (Thomas, 2000). The ∆CE Portfolios are then created by grouping firms into quintiles

based on their annual ∆CE rankings in year t within each ∆E group, and then taking a long

(short) position in stocks in the quintile with the largest positive (most negative) ∆CE. To

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exploit the market’s over-reaction to non-core earnings, the second hedge portfolios, ∆NCE

Portfolios, are created by assigning firms into quintiles based on their annual rankings of

changes in non-core earnings (∆NCE) in year t within each ∆E group. The long (short)

position comprises the stocks of firms in the lowest (highest) quintile of changes in ∆NCE. As

both core and non-core earnings are mispriced, and their mispricing occurs in opposite

directions, the final strategy, labeled ∆CE&∆NCE Portfolios, utilizes information on both the

∆CE and ∆NCE variables simultaneously. Stocks are first grouped into quintiles by ∆CE and

∆NCE in year t independently. We then take a long position in stocks that are in both the top

∆CEt quintile and bottom ∆NCEt quintile (and thus are most likely under-valued) and short

those in both the top ∆NCEt quintile and bottom ∆CEt quintile (and thus most likely to be

over-valued).

Abnormal returns to the portfolios are computed as mean buy-and-hold size- and

BE/ME-adjusted returns, value-weighted by the beginning market value of tradable A-shares.15

Although firm size and BE/ME effects are controlled for in measuring abnormal returns,

certain unknown, and thus unidentified, risk factors could still lead to positive hedge portfolio

returns. One way to deal with this problem is to examine whether the abnormal returns from

these hedge portfolios are consistently positive over time (e.g., Bernard et al., 1997). Given the

limited knowledge about risk factors in China, it is important to examine the time-series of

abnormal returns from the portfolios. We therefore present the year-by-year abnormal returns

to them in Table 3.

Insert Table 3 here.

In Column (1) of Table 3, the mean abnormal returns to the long position in the ∆CE

Portfolios are positive in 10 out of the 11 years. Judging by the signs of these abnormal returns,

this result is significant at the 1% level in the binomial test. The temporal mean of these

returns is 3.678%, which is significantly different from zero at the 1% level in the t-test. The

abnormal returns to the short position are negative (-3.945%) in nine of the years and also

statistically significant in the t- or binomial test. The hedge portfolios earn a positive abnormal

return in each year examined, with an average of 7.624% over the sample period. As for the

15

Results based on equally weighted abnormal returns are similar.

- 14 -

∆NCE Portfolios presented in Column (2), both the long and short positions yield abnormal

returns, with expected signs in at least eight out of the 11 years and temporal means of 4.101%

and -3.922% for the long and short positions, respectively, both of which are statistically

significant. These hedge portfolios earn positive abnormal returns of 8.022% per annum.

Finally, a trading rule based on both markets’ under-reaction to ∆CE and over-reaction to

∆NCE leads to increased abnormal returns to the trading strategy. The ∆CE&∆NCE Portfolios

in Column (3) yield mean hedge abnormal returns of 9.731%, and these returns are positive in

10 out of the 11 years.16

We also compute the hedge portfolios’ raw returns (i.e., without adjusting the firm size

and BE/ME effects), value-weighted by the market value of tradable A-shares at the end of

April following year t. The means of the value-weighted raw returns over the sample period

are 7.80%, 7.62%, and 9.13% for the ∆CE, ∆NCE, and ∆CE&∆NCE hedge portfolios,

respectively. In addition to the foregoing trading strategies, we also form hedge portfolios

based on the changes in core or non-core earnings independently, i.e., without controlling for

changes in total earnings (∆E). The results are similar to those previously reported: the annual

means of the hedge abnormal returns to the ∆CEt and ∆NCEt Portfolios are 10.05% and 5.18%,

respectively. For all of these alternative methods, the hedge portfolio returns are positive in at

least eight of the years, and significant at the 10% level in the binomial test.

To summarize, the hedge portfolios that are formed by the current earnings components

earn positive abnormal returns. Trading rules taking advantage of changes in core earnings or

non-core earnings generate annual abnormal returns of approximately 8%. This is

economically significant, given that the average one-year buy-and-hold returns were about

8.4%, and the two-round trading cost in the Shanghai and Shenzhen stock exchanges about

1.5%, during the sample period. Moreover, this result does not appear to be a random outcome

or due to some unspecified risk factors, as the abnormal returns on the hedge portfolios are

positive in most years.

16

Abnormal returns appear to be unusual in 1995. Year t+1 for 1995 corresponds to the May 1996 to

April 1997 period. This period is unusual because the cumulative equal- and value-weighted market

returns are 96.88% and 138.04%, respectively. The results are actually stronger when that year’s

observations are deleted (due to reduced standard errors).

- 15 -

4.3 Concentration of Abnormal Returns around Future Earnings Announcements

To further investigate whether these results stem from as yet unidentified risk factors, we

examine whether the abnormal returns are concentrated around the subsequent earnings

announcement dates (Bernard et al., 1997; La Porta et al., 1997). Such a concentration would

occur if a large amount of unexpected earnings information became available to market

participants on those dates. Consequently, the market would recognize that realized earnings

are different from expectations and therefore correct the mispricing. However, this test would

yield mixed results if information that is correlated with earnings news were released before

earnings announcements (Soffer and Lys, 1999).

Table 4 reports the abnormal returns during the subsequent earnings announcement

periods for each portfolio formation approach. The earnings announcement period is defined

as a [-1, +1] interval, where day 0 is the earnings announcements date in the subsequent year.

Chinese listed firms have been required to disclose their semi-annual earnings reports since

1994, and the mandatory disclosure of quarterly reports began in the first quarter of 2002.

Therefore, the earnings announcement periods in this study include two three-day windows

centering on the semi-annual and annual earnings announcement dates during the 1995-2001

period and four three-day windows centering on the interim and annual earnings

announcement dates during the 2002-2005 period. In line with our definition of abnormal

returns for the total period (a 12-month interval), the announcement period abnormal returns

are computed as the compounded daily returns over the given interval minus the returns of a

comparable size- and BE/ME portfolio.

Insert Table 4 here.

For all three portfolios, the abnormal returns to the long positions during the

announcement period are not significantly different from zero. Thus, they appear to be evenly

distributed in the holding period. However, for stocks in the short position a disproportionate

amount of price correction occurs during the subsequent earnings announcement period. For

the ∆CE Portfolios, the announcement period abnormal return is -0.67%, which accounts for

17.0% of the abnormal returns of the total period (-3.945%), although the number of trading

days during the announcement period is about 4% of the total trading days for the 12-month

interval (untabulated). Likewise, 22.2% and 41.4% of the abnormal returns to the short

- 16 -

position in the ∆NCE and ∆CE&∆NCE Portfolios, respectively, occur during the

announcement period. Thus, it is difficult to explain the concentration of abnormal returns for

the short position stocks from the risk premium perspective. If earnings announcement days

contain a disproportionately large fraction of the risk premium that is associated with the

annual uncertainty of the stocks, then the realized returns during the announcement days

should be higher than those on other days (Ball and Kothari, 1991). However, our results

indicate that returns to the short position stocks on announcement days are negative, which

suggests the presence of negative earnings surprises for these stocks, rather than any

compensation for risks.17

The presence (absence) of the clustering of abnormal returns in the short (long) positions

is different from Sloan’s (1996) evidence on the mispricing of accruals for U.S. firms. Sloan

shows that the concentration of these returns during subsequent earnings announcements is

more pronounced for stocks in the long position than for those in the short position when the

portfolios are constructed by the magnitude of accruals. He attributes this finding to the

tendency of U.S. firms to voluntarily disclose bad news before earnings announcements to

reduce expected litigation costs. Thus, bad news earnings announcements are more likely to be

preempted. However, for Chinese listed firms, the absence of litigation risks for managers and

board members is likely to encourage the early disclosure of good news (Ball et al., 2000).

Thus, the information that is contained in good news earnings announcements is more likely to

be preempted than that contained in bad news announcements.

4.4 Regression Analysis

Lo and MacKinlay (1990) point out that the portfolio approach may bias the test statistics

and spuriously exaggerate the relationship between hedge portfolio returns and the variables

17

Following La Porta et al. (1997), we also use a regression approach to examine whether the daily

abnormal returns for each portfolio during the earnings announcement period are statistically higher

than those during other trading days. This approach also controls for delayed announcements and loss

warnings, which are important pre-disclosure information events that could affect the market’s reaction

during earnings announcements. The results of this approach indicate that the magnitude of the

announcement period returns is significantly higher than that of the non-announcement period returns

for the short position in the portfolios.

- 17 -

used to form those portfolios. Furthermore, the use of buy-and-hold returns in the portfolio

approach can be affected by unusual observations: for example, large price changes in an early

month during the holding period could severely affect the annual buy-and-hold returns (Fama,

1998). To check for the sensitivity of our portfolio results, we perform regression analysis

using cumulative returns instead of buy-and-hold returns. The regression approach also

facilitates the ability to control for factors that could affect the stock returns and our

experimental variables simultaneously, and to assess whether the effects of core and non-core

earnings on future stock returns are incremental to each other. Our regression is specified as

RETt+1= α + β1∆CEt + β2∆NCEt + γ1BE/MEt + γ2Sizet

+ γ3MKTRETEQt+1 + γ4MKTRETVL

t+1 + γ5ROt + γ6ROt-1 + γ7Delistt + γ8MAOt

+ ε,

(4)

where RETt+1 denotes raw returns cumulated over the 12 months beginning in May after the

fiscal year-end of year t. BE/MEt and Sizet are measured at the beginning of the return window

for RETt+1. MKTRETEQt+1 and MKTRETVL

t+1, which are defined as equal-weighted and

value-weighted cumulative market returns over the same window, respectively, control for the

effects of market-wide economic fluctuation. The regression includes both weights to avoid an

arbitrary choice of market returns. Prior research (Chen and Yuan, 2004; Haw et al., 2005)

suggests that Chinese firms often manipulate earnings via income-increasing non-core

earnings to qualify for stock rights offerings. Evidence on U.S. firms also indicates that equity

offerings are followed by the under-performance of stock returns (e.g., Loughran and Ritter,

1995; Spiess and Affleck-Graves, 1995; Teoh et al., 1998). To control for this effect, we

include ROt and ROt-1, which are indicator variables for observations that have made rights

offerings in years t and t-1, respectively. Haw et al. (1998) also report that Chinese firms use

non-core earnings to avoid reporting a loss for three consecutive years, which would result in

the firms’ stocks being delisted according to China’s Company Law. As firms close to being

delisted are riskier, which affects stock returns, we include a dummy variable for Delistt (1 for

observations that reported losses in both years t and t-1, and zero otherwise). Chen et al. (2001)

find that auditors are more likely to issue modified audit opinions to Chinese firms that

manage their earnings via non-core earnings. As the market could underreact to modified audit

opinions (Taffler et al., 2004; Kausar et al., 2009), we include a dummy variable for modified

- 18 -

opinions (MAOt) as a control variable.

Panel A of Table 5 shows the descriptive statistics for the variables used in the regression

analysis. Both ∆CEt and ∆NCEt are winsorized at the 1st and 99th yearly percentiles. The

BE/ME and Size variables are replaced with their annual quintile ranking (from 0 to 4, scaled

by 4) to mitigate concerns about the influence of extreme values and to accommodate a

monotonic non-linear relationship between future returns and these variables.

Insert Table 5 here.

We report the pooled regression results in Panel B.18 We also summarize the annual

regression results by reporting the mean of the annual coefficients, the Fama and MacBeth

(1973) t-statistics derived from the variation of the annual coefficients, and the number of

times the regression coefficients have positive signs.19 The changes in core earnings (∆CEt)

are positively related to the one-year ahead stock returns, whereas the changes in non-core

earnings (∆NCEt) are negatively associated with them. The pooled OLS t-statistics and

Fama-MacBeth t-statistics indicate that both ∆CEt and ∆NCEt are significant at the 10% or

lower level. Furthermore, the coefficients on ∆CEt are positive for 10 out of the 11 years

examined (p < 0.01 in the binomial test), and those on ∆NCEt have negative signs in eight of

these years (p < 0.10).

For the control variables, the coefficients on BE/MEt (Sizet) are, in general, significantly

positive (negative). For the two market return variables, the equal-weighted variable absorbs

most of the market-wide factors in the pooled regression.20 ROt and ROt-1 are not significant,

18

To check for the sensitivity of the results to the cross-correlation of the residuals in the pooled

regression, we also use a bootstrap procedure to estimate the regression and obtain similar results.

Specifically, the regression is first estimated by the ordinary least squares (OLS) method. The residuals

are then randomly sampled with replacements and added to the predicted values to create a pseudo-data

set. To retain the cross-sectional structure of the data, the random sampling of the residuals is carried

out within each annual data set. The regression coefficients are then re-estimated with the pseudo-data.

These steps are iterated 100 times, and the bootstrap t-statistics are computed as the mean of the

estimated coefficients from the 100 iterations divided by their standard deviations.

19 Because no observation takes a value of one in the Delistt variable in 1995, the annual regression

result for Delistt is based on 10 rather than 11 annual regressions.

20 Using only one of the two market index variables does not change the main results. We exclude the

- 19 -

although their signs are negative and consistent with the post-offering under-performance

literature. The coefficients on Delistt are positive and significant, thus reflecting the inherently

risky nature of such stocks. Finally, the negative coefficients on MAOt suggest that investors

may under-react to the bad news contained in modified audit reports. Overall, these results are

consistent with the mispricing hypothesis, which suggests that Chinese investors undervalue

core earnings and overvalue non-core earnings.

In addition to the results reported in Table 5, we re-estimate equation (4) with an

unexpected earnings change in year t+1 as an additional explanatory variable. We measure

unexpected earnings change as the residuals from the annual regressions of future earnings on

the current earnings components (i.e., equation [1]). Because the unexpected earnings change

in year t+1 represents an earnings surprise that is orthogonal, by design, to the information that

is contained in the current earnings components, the coefficients on ∆CEt or ∆NCEt should be

insignificant if the market fully uses these components to predict future earnings (Bernard and

Thomas, 1990). The results for the regression with the unexpected earnings change variable

are similar to those reported in Table 5 (not tabulated for brevity).

Motivated by evidence on the mispricing of accounting accruals (e.g., Sloan, 1996;

Hirshleifer et al., 2004), we also estimate equation (4) with the level of accruals as an

additional control variable.21 Consistent with this line of research, we find that this level is

negatively related to subsequent stock returns. However, the inclusion of the accruals variable

fails to change the main results.

To summarize, the evidence presented in Table 5 is consistent with the portfolio test,

which indicates that part of the stock returns in year t+1 is predictable using the information

contained in the current earnings components. Moreover, regression analysis suggests that the

mispricing of core earnings is incremental to that of non-core earnings, a finding that is not

market index variables for the annual regressions because the observations in each annual regression

take the same value in the market index.

21 We compute accruals as the difference between operating income and operating cash flows reported

in the cash flow statements. Chinese firms began to disclose cash flow statements when the Accounting

Standard for Business Enterprises No. 2: Cash Flow Statements are enacted in 1998. Therefore, we

estimate the regression with accruals as an addition variable only for the post-1998 sample.

- 20 -

obvious in the portfolio test.

4.5 Effect of Delayed Responses on the Value Relevance of Earnings Components

Our final analysis evaluates the extent of the bias that prior studies on China’s stock

market may have suffered in estimating the value relevance of current earnings for explaining

contemporaneous returns. Because core earnings are more persistent than non-core earnings,

innovations in the former should be mapped into stock price changes at a higher rate than

those in the latter (Miller and Rock, 1985; Kormendi and Lipe, 1987). Therefore, the earnings

response coefficients (ERCs)––i.e., coefficients in a regression of abnormal stock returns on

earnings––on these components should also reflect the difference in persistence if investors

fully understand that difference. To estimate the ERCs, we regress annual abnormal returns on

the earnings components (firm subscript i is omitted):

SBMARt = α + β1CEt/Pt-1 + β2∆CEt/Pt-1 + γ1NCEt/Pt-1 + γ2∆NCEt/Pt-1 + ε, (5)

where SBMARt is the 12-month size- and BE/ME-adjusted buy-and-hold stock returns

beginning in May of year t and ending in April of year t+1. Both the level of and changes in

the earnings components are included in the regression as explanatory variables, following

previous research on ERCs (e.g., Ali and Zarowin, 1992), to allow for the different time-series

properties of these earnings components. These earnings variables are per-share amounts and

are scaled by the A-share price at the end of April for year t. The estimated ERCs for CE and

NCE are (β1 + β2) and (γ1 + γ2), respectively. Higher ERCs mean that earnings are mapped into

stock prices at a higher rate, and thus are more relevant to investors in equity valuation. Given

that core-earnings are more persistent, one would expect (β1 + β2) to be greater than (γ1 + γ2),

if investors do take the persistence difference into account when pricing stocks.

Panel A1 of Table 6 shows that in the pooled regression, all of the explanatory variables

have positive coefficients and are significant at the 1% level, expect for ∆NCEt/Pt-1. This

evidence indicates that earnings components are informative in explaining the cross-sectional

variation in annual abnormal returns, which is consistent with previous research (Ball et al.,

2000). Although the ERCs on core earnings are significantly higher than those on non-core

earnings in the pooled regression, these results become insignificant when statistical

inferences are based on the time-series variation in these coefficients in the annual regressions.

- 21 -

Moreover, the ERCs for core earnings are higher than those for non-core earnings in only six

of the 11 years. Thus, there is no strong evidence suggesting that Chinese investors value core

earnings more than non-core earnings when they price stocks, despite that the former is more

persistent and contains more information about future earnings innovations than the latter.22

Insert Table 6 here.

We then re-examine the value relevance of the earnings components by taking into

account the resolution of their mispricing in the subsequent period (year t+1), during which

investors recognize that the realized earnings are different from those expected based on the

prior year’s earnings. Extending the return measurement windows forward allows for better

measurement of the value relevance of accounting data when stock prices lag accounting

earnings (Aboody et al., 2002). This window is extended from a 12-month to a 24-month

interval:

SBMAR[t, t+1] = α + β1CEt/Pt-1 + β2∆CEt/Pt-1 + γ1NCEt/Pt-1 + γ2∆NCEt/Pt-1 + ε, (6)

where SBMAR[t, t+1] is the 24-month size- and BE/ME-adjusted buy-and-hold returns beginning

in May of year t.

Panel A2 of Table 6 shows that the difference in the ERC estimates between core and

non-core earnings increases significantly with two-year returns relative to one-year returns: for

the pooled regression, this difference increases by 107% from 0.832 with one-year returns to

22

We conduct several sensitivity tests, including: (a) the exclusion of observations with negative total

earnings to avoid the biased estimation of ERCs due to losses (Hyan, 1995); (b) the use of a constant

sample covering firms with complete data for the years between 1995 and 2005, which is less subject to

the influence of firm characteristic changes on the ERCs; (c) different definitions of returns, such as

market-adjusted returns or raw returns; and (d) an alternative return window between firms’ earnings

announcement months in years t-1 and t. We find no reliable evidence of higher ERCs on core earnings

in these tests.

We also estimate the price model specified as Pricet = α + β1BVEt + γ1CEt + γ2NCEt + ε, where Pricet is

the per-share stock price at the end of April following fiscal year t, BVE is the book value of equity at

the end of year t, and CE and NCE are the levels of core and non-core earnings in year t, respectively

(all of the accounting variables are expressed as per share amounts). Kothari and Zimmerman (1995)

show that the earnings coefficients are less likely be biased in this price model. We find that the

coefficients on CEt and NCEt are similar and not different from each other statistically in explaining the

stock price level.

- 22 -

1.719 with two-year returns. The increase is even more striking when the estimates are based

on annual regressions (from 0.102 to 1.468). With two-year returns, we can reject the null

hypothesis that the ERC estimates of core earnings are equal to those of non-core earnings in

both the pooled and annual regressions. This evidence suggests that stock prices reflect the

implications of earnings components in year t for future earnings, but with a delay.

Because the only difference between the 12-month and 24-month window regressions is

the dependent variables (abnormal stock returns), we use a seemingly unrelated regression

(SUR) procedure to compare the coefficients across the two regression equations in Panel B.

Consistent with the market’s correction for mispricing, the sum of the coefficients on core

earnings when the return window is [t, t+1] is significantly higher than that when the window

covers only year t; the opposite holds for non-core earnings. The SUR F-values are significant

in both cases at the 1% level.

To summarize, the results presented in Table 6 not only provide further evidence of the

mispricing of earnings components, but also indicate that it is potentially misleading to draw

inferences about the value relevance of the accounting numbers of Chinese listed firms by

relying on contemporaneous returns-earnings regressions but ignoring the mispricing of

earnings components.

5. Conclusions

This study investigates whether investors in China’s stock market correctly value the

information that is contained in current earnings. In our sample of almost all firms listed on

the country’s stock exchanges during the 1995-2005 period, we find that, as expected, core

earnings are more persistent than non-core earnings. However, the expectations of investors,

as reflected in the stock prices, indicate that the market fails to differentiate between the

time-series properties of these earnings components. Investors place greater weight on

non-core earnings than the actual persistence of this component would indicate. Consequently,

abnormal returns in the subsequent year become predictable when the information that is

contained in the current earnings components becomes publicly available. The results from

both the portfolio tests and regression analysis support the mispricing hypothesis and are

robust to a number of sensitivity checks.

- 23 -

These results have implications for studies that use Chinese data. As the market misprices

earnings components, a reassessment of the extant findings on the extent of the value

relevance of Chinese accounting data is warranted. The evidence presented here shows that the

estimates of the earnings-returns relations for core and non-core earnings depend on the length

of the windows used. Taking into account the underreaction to core earnings, the value

relevance of these earnings in China is understated in the traditional approach that relates

current earnings to contemporaneous stock returns. At the same time, considering the

overreaction to non-core earnings, the existing evidence on the value relevance of non-core

earnings is likely to be overstated. Assessment of the overall effects of mispricing on the value

relevance of accounting earnings in this emerging market requires a research design that

differs from the traditional approach.

- 24 -

References

Abdel-khalik, Rashad A., Kie Ann Wong, A., and Annie Wu. “The Information Environment

of China’s A and B Shares: Can We Make Sense of the Numbers?” The International

Journal of Accounting 34 (1999): 467-489.

Aboody, David, John Hughes, and Jing Liu. “Measuring Value Relevance in a (Possibly)

Inefficient Market.” Journal of Accounting Research 40 (2002): 965-986.

Ali, Ashiq, and Paul Zarowin. “The Role of Earnings Levels in Annual Earnings-Returns

Studies.” Journal of Accounting Research 30 (1992): 286-296.

Ali, Ashiq, Lee-Seok Hwang, and Mark Trombley. “Accruals and Future Stock Returns: Tests

of the Naive Investor Hypothesis.” Journal of Accounting, Auditing, and Finance (2000):

161-181.

Ali, Ashiq, Lee-Seok Hwang, and Mark Trombley. “Arbitrage Costs, Investor Sophistication

and the Book-to-Market Anomaly.” Journal of Financial Economics 63 (2003): 355-373.

Ball, Ray, and Eli Bartov. “How Naïve Is the Stock Market’s Use of Earnings Information?”

Journal of Accounting and Economics 13 (1996): 305-340.

Ball, Ray, and S.P. Kothari. “Security Returns around Earnings Announcements.” The

Accounting Review 66 (1991): 718-738.

Ball, Ray, Ashok Robin, and Joanna Shuang Wu. “Accounting Standards, the Institutional

Environment and Issuer Incentives: Effect on Timely Loss Recognition in China.”

Asia-Pacific Journal of Accounting and Economics 7 (2000): 71-96.

Basu, Sudipta. “The Conservatism Principle and the Asymmetric Timeliness of Earnings.”

Journal of Accounting and Economics 24 (1997): 3-37.

Bernard, Victor, and Jacob Thomas. “Evidence that Stock Prices Do Not Fully Reflect the

Implications of Current Earnings for Future Earnings.” Journal of Accounting and

Economics 13 (1990): 305-340.

Bernard, Victor, Jacob Thomas, and James Whalen. “Accounting-Based Stock Price

Anomalies: Separating Market Inefficiencies from Risk.” Contemporary Accounting

Research 14 (1997): 89-136.

Bernard, Victor. “The Feltham-Ohlson Framework: Implications for Empiricists.”

Contemporary Accounting Research 11 (1995): 733-747.

Black, Fischer. “Noise.” Journal of Finance 41 (1986): 529-543.

Bradshaw, M., M. Richardson, and R. Sloan. “Do Analysts and Auditors Use Information in

Accruals?” Journal of Accounting Research 39 (2001): 45-74.

Burgstahler, David, James Jimbalvo, and Terry Shevlin. “Do Stock Prices Fully Reflect the

Implications of Special Items for Future Earnings?” Journal of Accounting Research 40

(2002): 585-612.

Chan, Kalok, Junbo Wang, and John K. C. Wei. “Underpricing and Long-Term Performance

of IPOs in China.” Journal of Corporate Finance 10 (2004): 409-430.

Chan, Louis K. C., Yasushi Hamao, and Josef Lakonishok. “Fundamentals and Stock Returns

in Japan.” Journal of Finance 46 (1991): 1739-1789.

Chen, Charles J.P., Ferdinand Gul, and Xijia Su. “A Comparison of Reported Earnings under

Chinese GAAP vs. IAS”, Accounting Horizons 13 (1999): 91-111.

Chen, Charles J.P., Shimin Chen, and Xijia Su. “Profitability Regulation, Earnings

- 25 -

Management, and Modified Audit Opinions: Evidence from China.” Auditing: A Journal

of Practice and Theory 20 (2001): 9-30.

Chen, Kevin, and Hongqi Yuan. “Earnings Management and Capital Resources Allocations:

Evidence from China’s Accounting-Based Regulation on Rights Issues.” The Accounting

Review 79 (2004): 645-665.

Chen, Kevin, and Michael Schoderbek. “The 1993 Tax Rate Increase and Deferred Tax

Adjustments: A Test of Functional Fixation.” Journal of Accounting Research 38 (2000):

23-44.

Collins, Daniel, Edward Maydew, and Ira Weiss. “Changes in the Value-Relevance of

Earnings and Book Values over the Past Forty Years.” Journal of Accounting and

Economics 24 (1997): 39-67.

Daniel, Kent, Sheridan Titman, and John K. C. Wei. “Explaining the Cross-Section of Stock

Returns in Japan: Factors or Characteristics?” Journal of Finance 56 (2001): 743-766.

Dechow, Patricia, and Richard Sloan. “Returns to Contrarian Investment Strategies: Tests of

Naïve Expectations Hypotheses.” Journal of Financial Economics 43 (1997): 3-27.

Elgers, P.T., M.H. Lo, and R.J. Pfeiffer. “Delayed Security Price Adjustments to Financial

Analysts’ Forecasts of Annual Earnings.” The Accounting Review 76 (2001): 613-632.

Elliott, John A., and Douglas J. Hanna. “Repeated Accounting Write-Offs and the Information

Content of Earnings.” Journal of Accounting Research 34 (1996): 135-155.

Fama, Eugene, and James MacBeth. “Risk, Return, and Equilibrium: Empirical Tests.”

Journal of Political Economy 71 (1973): 607-636.

Fama, Eugene, and Kenneth French. “Multifactor Explanations of Asset Pricing.” Journal of

Finance 51 (1996): 55-84.

Fama, Eugene, and Kenneth French. “The Cross-Section of Expected Stock Returns.” Journal

of Finance 47 (1992): 427-465.

Fama, Eugene, and Kenneth French. “Value versus Growth: The International Evidence.”

Journal of Finance 53 (1998): 1975-1999.

Fama, Eugene. “Market Efficiency, Long-Term Returns, and Behavioral Finance.” Journal of

Financial Economics 49 (1998): 283-306.

Füss, Roland. “The Financial Characteristics between ‘Emerging’ and ‘Developed’ Equity

Markets.” Working paper, Albert Ludwigs University of Freiburg, 2001.

Hand, John. “A Test of Extended Functional Fixation Hypothesis”, The Accounting Review 65

(1990): 740-763.

Harvey, Campbell. “Predictable Risk and Returns in Emerging Markets.” Review of Financial

Studies 8 (1995): 773-816.

Haw, In-Mu, Daqing Qi, and Woody Wu. “Timeliness of Annual Report Releases and

Market Reaction to Earnings Announcements in an Emerging Capital Market: The Case

of China.” Journal of International Financial Management and Accounting 11 (2000):

108-131.

Haw, In-Mu, Daqing Qi, Donghui Wu and Woody Wu. “Market Consequences of Earnings

Management in Response to Security Regulations in China.” Contemporary Accounting

Research 22 (2005): 95-140.

Haw, In-Mu, Daqing Qi, Woody Wu, and Weiguo Zhang. “Earnings Management of Listed

Firms in Response to Security Regulations in China’s Emerging Capital Market.”

- 26 -

Unpublished manuscript, The Chinese University of Hong Kong, 1998.

Haw, In-Mu, Lee-Seok Hwang, Donghui Wu, and Woody W.Y. Wu. “The Average Stock

Returns in China’s Stock Market.” Working paper, the Chinese University of Hong Kong,

2004.

Hirshleifer, David A., Kewei Hou, Siew Hong Teoh, and Yinglei Zhang. “Do Investors

Overvalue Firms with Bloated Balance Sheets?” Journal of Accounting and Economics

20 (2004): 297-331.

Hyan, Carla. “The Information Content of Losses.” Journal of Accounting and Economics 20

(1995), 125-153.

Jiang, Guohua, Charles M.C. Lee, and Heng Yue. “Tunneling through Intercorporate Loans:

The China Experience.” Journal of Financial Economics (2010): forthcoming.

Kausar, Asad, Richard Taffler, and Christine Tan. “The Going-Concern Market Anomaly.”

Journal of Accounting Research 47 (2009): 213-239.

Kormendi, Roger, and Robert Lipe. “Earnings Innovations, Earnings Persistence, and Stock

Returns.” Journal of Business 60 (1987): 323-345.

Kothari, S.P., and Jerold L. Zimmerman. “Price and Return Models.” Journal of Accounting

and Economics 20 (1995): 155-192.

Kothari, S.P., Jowell S. Sabino, and Tzachi Zach. “Implications of Survival and Data

Trimming for Tests of Market Eficiency.” Journal of Accounting and Economics 39

(2005): 129-161.

Kraft, Arthur, Andrew Leone, and Charles Wasley. “Do Investors Naively Overweight

Accruals? An Examination of the Time-Series and Cross-Sectional Behavior of ‘Sloan’s

Accrual Anomaly’.” Working paper, University of Rochester, 2003.

Kraft, Arthur, Andrew Leone, and Charles Wasley. “Regression-Based Tests of the Market

Pricing of Accounting Numbers: The Mishkin Test and Ordinary Least Squares.” Journal

of Accounting Research 45 (2007): 1081-1114.

La Porta, Rafeal, Josef Lakonishok, Andrei Shleifer, and Robert Vishny. “Good News for

Value Stocks: Further Evidence on Market Efficiency.” Journal of Finance 52 (1997):

859-874.

Lo, Andrew, and Craig MacKinlay. “Data-Snooping Biases in Tests of Financial Asset Pricing

Models.” Review of Financial Studies 3 (1990): 431-467.

Loughran, Tim, and Jay Ritter. “The New Issues Puzzle.” Journal of Finance 50 (1995):

23-51.

Miller, Merton, and Kevin Rock. “Dividend Policy under Asymmetric Information.” Journal

of Finance 40 (1985): 1031-1051.

Mishkin, Frederic. A Rational Expectations Approach to Macroeconometrics: Testing Policy

Effectiveness and Efficient Market Models. University of Chicago Press, Chicago, 1983.

Qi, David D., Woody W.Y. Wu, and Hua Zhang. “Shareholding Structure and Corporate

Performance of Partially Privatized Firms: Evidence from Listed Chinese Firms.”

Pacific-Basin Finance Journal 8 (2000): 587-610.

Sloan, Richard. “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about

Future Earnings.” The Accounting Review 71 (1996): 289-315.

Soffer, Leonard, and Thomas Lys. “Post-earnings Announcement Drift and the Dissemination

of Predictable Information.” Contemporary Accounting Research 16 (1999): 305-331.

- 27 -

Spiess, Katherine, and John Affleck-Graves. “Underperformance in Long-Run Stock Returns

Following Seasoned Equity Offerings.” Journal of Financial Economics 38 (1995):

243-267.

Strong, Norman, and Martin Walker. “The Explanatory Power of Earnings for Stock Returns.”

The Accounting Review 68 (1993): 385-399.

Sun, Qian, and Wilson H.S. Tong. “China Share Issue Privatization: The Extent of Its

Success.” Journal of Financial Economics 70 (2003): 183-222.

Taffler, Richard J., Jeffrey Lu and Asad Kausar. “In State of Denial? Market Underreaction to

Going-Concern Audit Report Disclosures.” Journal of Accounting and Economics 38

(2004): 263-296.

Teoh, Siew Hong, Ivo Welch, and T.J. Wong. “Earnings Management and the Long-Run

Underperformance of Seasoned Equity Offerings.” Journal of Financial Economics 50

(1998): 63-100.

Thomas, Wayne. “A Test of the Market’s Mispricing of Domestic and Foreign Earnings.”

Journal of Accounting and Economics 28 (2000): 243-267.

Xie, Hong. “The Mispricing of Abnormal Accruals.” The Accounting Review 76 (2001):

357-373.

- 28 -

FIGURE 1

Development of the Stock Market in China

Panel A: The number of stocks listed on the Shanghai and Shenzhen exchanges

0

200

400

600

800

1,000

1,200

1,400

1,600

1990

/12

1991

/12

1992

/12

1993

/12

1994

/12

1995

/12

1996

/12

1997

/12

1998

/12

1999

/12

2000

/12

2001

/12

2002

/12

2003

/12

2004

/12

2005

/12

2006

/12

2007

/12

N

Panel B: Total market capitalization of stocks listed on the Shanghai and Shenzhen exchanges

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

8,000,000

9,000,000

10,000,000

1990

/12

1991

/12

1992

/12

1993

/12

1994

/12

1995

/12

1996

/12

1997

/12

1998

/12

1999

/12

2000

/12

2001

/12

2002

/12

2003

/12

2004

/12

2005

/12

2006

/12

2007

/12

Mill

ions

RM

B Y

uan

- 29 -

Panel C: The A-Share Index in the Shanghai Stock Exchange

Ln(Index) = 5.746+0.131x

R2 = 70.9%

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1990

/12

1991

/12

1992

/12

1993

/12

1994

/12

1995

/12

1996

/12

1997

/12

1998

/12

1999

/12

2000

/12

2001

/12

2002

/12

2003

/12

2004

/12

2005

/12

2006

/12

2007

/12

The statistics are for stocks that have issued A-shares to domestic investors. Stocks that have issued only B- or

H-shares are not included. In Panel B, market capitalization is computed as the per share price for tradable

A-shares × the number of shares outstanding at the end of the year. In Panel C, the equation in the plot is a

log-linear model, where the natural logarithm of the index is regressed on time (t = 1, 2, 3, … , 18).

- 30 -

FIGURE 2

An Income Statement Prepared under Chinese Accounting Rules

Shanghai Petrochemical Co. Ltd. Consolidated Profit and Loss Account

For the year ended 31 December 2000

Items 2000 1999

Revenue from principal operations 20,467,583 14,386,482

Less: Cost of sales 17,150,495 11,458,011

Business taxes and surcharges 548,713 349,895

Profits from principal operations 2,768,375 2,578,576

Add: Profit from other operations 84,194 69,477

The reversal of provisions for inventories 28,725 0

Less: Provisions for inventories 3,571 18,131

Operating expenses 314,870 275,003

Administrative expenses 1,125,449 1,056,494

Financial expenses 272,186 368,287

Income from operations 1,165,218 930,138

Add: Income from investments -17,748 8,989

Subsidy income 5,465 5,667

Non-operating income 26,077 24,398

Less: Non-operating expenses 98,367 92,951

Total Profit 1,080,645 876,241

Less: Income tax 153,415 122,495

Minority income 23,298 15,932

Net Profit 903,932 737,814

The amounts are in thousands of RMB. Profit from other operations includes rental income,

income from consignments, and income from selling raw materials. Financial expenses include net

interest expenses, foreign exchange gains/losses, and bank charges. Income from operations

includes income from both short- and long-term investments in stocks, bonds, and the equity of

other companies. Subsidy income includes subsidies received from the government for

policy-related losses, as well as tax reductions. Non-operating income (expenses) refers to items

that are not directly related to the company’s operating activities, including gains/losses from the

disposal of fixed assets, asset revaluation, debt restructuring, donations, and fines.

- 31 -

FIGURE 3

Mean Monthly Excess Returns for Benchmark Portfolios Formed by Firm Size and the

Book-to-Market Ratio of Equity

1

2

3

4

5

1

2

3

4

5

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

Avera

ge M

onth

ly E

xce

ss R

etu

rns

BE/ME Quintile

Size Quintile

The period is from May 1994 to December 2007 (164 months). At the beginning of each month, stocks are

sorted by Size, which is measured as the market capitalization of outstanding shares, into five groups. In each

group, the stocks are further allocated to five subgroups according to their BE/ME, the book value of equity per

share divided by the per share price of A-shares. For months between May of year t+1 and April of year t+2, BE

is measured by the book value of equity at the end of year t. Each stock receives the same weight in the

portfolios. The average number of stocks per portfolio ranges from 10 in the first month to 56 in last month of

the sample period. The excess returns are defined as monthly raw returns minus the interest rate for the

three-month deposits set by the People’s Bank of China.

- 32 -

TABLE 1

Sample Distribution and Descriptive Statistics

The sample comprises non-financial firm-years that have no missing values in the necessary data in year t-1, t,

and t+1 during the 1995-2005 period. BHRETt is the 12-month buy-and-hold raw returns beginning in May of

year t and ending in April of the following year. SBMARt is the 12-month buy-and-hold stock returns

beginning in May of year t minus the equal-weighted return of a comparable size and BE/ME portfolio return

over the same period. E is total earnings. CE is core earnings, measured as income from operations. NCE is

non-core earnings, measured as the difference between total earnings and core earnings. ∆ denotes changes in

the earnings variables from year t-1 to year t. All of the earnings variables are measured on a pre-tax basis and

deflated by average total assets. These earnings variables are winsorized at the top and bottom yearly

percentiles.

Panel A: The sample distribution

Exchanges 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total

Shanghai 173 284 371 424 469 554 631 698 758 811 812 5,985

Shenzhen 118 219 344 397 449 496 490 488 484 516 524 4,525

Total 291 503 715 821 918 1,050 1,121 1,186 1,242 1,327 1,336 10,510

Panel B: Descriptive statistics for stock returns and earnings variables

Variables Mean Min. Median Max. Std. Dev.

BHRETt 0.084 -0.861 -0.059 11.125 0.582

SBMARt -0.001 -0.697 -0.057 1.755 0.315

Et 0.041 -0.561 0.046 0.328 0.085

CEt 0.033 -0.377 0.036 0.328 0.074

NCEt 0.007 -0.267 0.004 0.118 0.030

∆Et -0.015 -0.445 -0.009 0.315 0.072

∆CEt -0.013 -0.345 -0.009 0.287 0.058

∆NCEt -0.002 -0.305 -0.001 0.184 0.035

- 33 -

TABLE 2

Actual and Perceived Persistence of Earnings Components

The actual persistence of earnings components is estimated by:

∆Et+1 = α0 + α1∆CEt + α2∆NCEt + εt,

where ∆Et+1 is the change in total earnings from year t to t+1, and ∆CEt and ∆NCEt are changes in core and

non-core earnings, respectively, from year t-1 to t. The persistence of earnings components perceived by the

market is estimated by:

SBMARt+1 = β0 + β1(∆Et+1 – α0 – α1*∆CEt – α2

*∆NCEt) + ωt+1,

where SBMARt+1 is the 12-month buy-and-hold stock returns beginning in May of year t+1 minus the

equal-weighted returns of a comparable size and the BE/ME portfolio returns over the same period.

In Panel A, these two equations are estimated simultaneously by the iterative non-linear least squares method

for the pooled sample. The numbers in parentheses are asymptotic t-statistics for column (1) and the χ2

statistics in column (3). The degree of freedom for the χ2 statistics is 1.

The results in Panel B are obtained from the annual regressions. The coefficients are the mean of the

coefficients obtained from the 11 annual regressions. The numbers in parentheses are the t-statistics computed

as the mean annual coefficients divided by the standard errors of these coefficients.

(1)

Actual persistence

of earnings components

(2)

Persistence perceived

by the market

(3)

Tests for equality of

actual and perceived persistence

Panel A: Pooled sample results (N = 11,510)

α1 -0.128***

α1* -0.385

*** H0: α1

* = α1 21.99

***

(-9.740) (-7.246)

α2 -0.437***

α2* -0.265

*** H0: α2

* = α2 3.73

*

(-20.053) (-3.056)

α1 – α2 0.309***

α1*– α2

* -0.121

(11.292) (-1.100)

Panel B: Fama and MacBeth results (N = 11 years)

α1 -0.106***

α1* -0.356

*** H0: α1

* = α1 4.479

***

(-4.586) (-5.647)

α2 -0.394***

α2* -0.216

* H0: α2

* = α2 -2.266

**

(-8.076) (-2.006)

α1 – α2 0.288***

α1*– α2

* -0.140

(5.524) (-1.037)***

*, **, and *** indicate significance (two-tailed for asymptotic t-statistics) at the 10%, 5%, and 1% levels,

respectively.

- 34 -

TABLE 3

Abnormal Returns (%) to Hedge Portfolios in Year t+1 To construct the ∆CE Portfolios, we first rank firms by changes in total earnings as of the beginning of May after the fiscal year-end and place them into one of five groups.

Then, within each group, firms are assigned to one of five portfolios based on their changes in core earnings. The long (short) position comprises stocks of firms in the

highest (lowest) quintile of changes in core earnings. The ∆NCE Portfolios are created by first ranking firms according to changes in total earnings as of the beginning of

May after the fiscal year-end and placing them into one of five groups. Then, within each group, firms are assigned to one of five portfolios based on their changes in

non-core earnings. The long (short) position comprises stocks of firms in the lowest (highest) quintile of changes in non-core earnings. The ∆CE&∆NCE Portfolios are

created by first grouping stocks into quintiles by ∆CE or ∆NCE in year t independently, and then taking a long position in stocks that are in both the top ∆CEt quintile and

bottom ∆NCEt quintile and shorting those in both the top ∆NCEt quintile and bottom ∆CEt quintile. The abnormal returns are the buy-and-hold stock returns over a

12-month period beginning in May of year t+1, minus the mean returns of a comparable size and BE/ME portfolio returns over the same period. The t-statistics presented in

parentheses are those for the mean abnormal returns of the 11 years. The number of abnormal returns on the portfolios with the expected signs is reported in the last line,

where the significance levels are obtained from the binomial test.

Year (1) ∆CE Portfolios (2) ∆NCE Portfolios (3) ∆CE&∆NCE Portfolios

Long Short Hedge Long Short Hedge Long Short Hedge

1995 13.093 -8.441 21.533 23.340 -4.982 28.321 14.118 -12.097 26.215

1996 11.754 -6.234 17.988 6.888 -4.835 11.722 -0.052 -3.124 3.071

1997 2.174 0.336 1.837 0.968 0.169 0.799 0.000 2.180 -2.180

1998 2.999 -2.315 5.314 8.228 -7.054 15.282 13.934 -8.737 22.672

1999 4.454 -6.935 11.389 5.123 -6.707 11.830 4.481 -0.108 4.589

2000 1.415 -0.753 2.168 -1.485 -1.484 -0.001 -3.993 -6.094 2.101

2001 1.844 0.182 1.661 2.168 1.941 0.228 3.874 -0.013 3.887

2002 2.372 -3.082 5.454 2.563 -4.696 7.259 7.148 -2.049 9.197

2003 2.383 -2.173 4.556 0.948 -4.107 5.055 7.733 -3.865 11.598

2004 -2.819 -4.514 1.696 -0.246 -5.824 5.578 2.871 -8.846 11.717

2005 0.763 -9.508 10.271 -3.389 -5.562 2.172 12.006 -2.169 14.175

Mean 3.678 -3.945 7.624 4.101 -3.922 8.022 5.647 -4.084 9.731

t-statistics (2.600**

) (-3.817***

) (3.667***

) (1.872*) (-4.501

***) (3.136

**) (3.135

**) -(3.083

**) (3.674

***)

# as expected 10***

9**

11***

8* 9

** 10

*** 9

** 10

*** 10

***

*, **, and *** indicate two-tailed significance at the 10%, 5%, and 1% levels, respectively.

- 35 -

TABLE 4

Abnormal Returns (%) to Hedge Portfolios for Earnings Announcement Periods in Year t+1

See the legend to Table 3 for the methods used to form the portfolios. The abnormal returns are the buy-and-hold stock returns during the earnings announcement periods in

year t+1, minus the returns of a comparable size and the BE/ME portfolio returns over the same period. The earnings announcement periods consist of two three-day

windows centering on the semi-annual and annual earnings announcement dates during the 1995-2001 period and four three-day windows centering on the interim and

annual earnings announcement dates during the 2002-2005 period. The mean is the mean abnormal returns for the 11 years, with the t-statistics presented in parentheses.

The number of abnormal returns on the portfolios with the expected signs is reported in the last line, where the significance levels are obtained from the binomial test.

Year (1) ∆CE Portfolios (2) ∆NCE Portfolios (3) ∆CE&∆NCE Portfolios

Long Short Hedge Long Short Hedge Long Short Hedge

1995 -0.536 -2.172 1.636 0.370 -2.633 3.004 3.484 -5.551 9.035

1996 -2.902 -1.466 -1.437 -1.717 -0.826 -0.891 -2.162 -2.112 -0.050

1997 0.258 -0.686 0.944 0.134 -0.184 0.318 0.460 -0.730 1.191

1998 -1.011 -1.624 0.613 -0.897 -2.594 1.697 -1.138 -2.728 1.590

1999 -0.173 -0.664 0.491 -0.679 -0.569 -0.110 -0.667 0.256 -0.923

2000 0.987 0.543 0.444 0.601 0.466 0.135 0.009 -0.882 0.891

2001 -0.602 -0.338 -0.264 -0.303 -0.049 -0.254 0.007 -1.728 1.736

2002 -0.736 -1.768 1.032 -0.027 -1.623 1.596 -1.938 -1.338 -0.601

2003 1.025 0.087 0.938 1.062 -0.953 2.014 2.217 -0.670 2.887

2004 2.068 -0.769 2.837 2.471 0.057 2.413 2.865 -0.981 3.846

2005 1.943 1.482 0.460 2.052 -0.647 2.699 1.421 -2.121 3.542

Mean 0.029 -0.670 0.699 0.279 -0.869 1.147 0.414 -1.689 2.104

t-statistics (0.067) (-2.047*) (2.178

*) (0.745) (-2.800

**) (2.827

**) (0.727) -(3.665

***) (2.505

**)

# as expected 5 8* 9

** 6 9

** 8

* 7 10

*** 8

*

*, **, and *** indicate two-tailed significance at the 10%, 5%, and 1% levels, respectively.

- 36 -

TABLE 5

Regression Analysis for the Mispricing of Earnings Components

The following regression is estimated to examine whether earnings components in year t are correlated with

stock returns in year t+1:

RETt+1 = α + β1∆CEt + β2∆NCEt + γ1BE/MEt + γ2Sizet + γ3MKTRETEQt+1 + γ4MKTRETVL

t+1 + γ5ROt + γ6ROt-1

+ γ7Delistt + γ8MAOt + ε,

where RETt+1 is the 12-month cumulative raw returns beginning in May after the fiscal year-end of year t, CE is

core earnings, NCE is non-core earnings, and ∆ denotes changes in the earnings variables from year t-1 to year

t. All of the earnings variables are deflated by the average total assets and are winsorized at the 1st and 99th

yearly percentiles. BE/MEt is the book-to-market ratio of equity on April 30 of year t+1, Sizet is the market

value of outstanding shares on April 30 of year t+1 (in millions RMB), MKTRETEQt+1 and MKTRETVL

t+1 are

equal- and value-weighted, respectively, cumulative market returns over the same window as RETt+1, ROt is a

dummy variable indicating that the observation has issued rights offerings in year t, ROt-1 is a dummy variable

indicating that the observation has made rights offerings in year t-1, Delistt is a dummy variable indicating that

the observation has reported losses in both years t and t-1, and MAOt is a dummy variable indicating the

observation received a modified audit opinion in year t. In estimating the regression, Sizet and BE/MEt are

replaced with their annual quintile ranking (from 0 to 4, scaled by 4). For the regression results reported in

Panel B, the pooled regression is based on a sample pooling observations from 1995 to 2005. The annual

regression estimates are obtained from 11 annual regressions, except for the Delist variable, which is based on

10 annual regressions. The mean coefficient is the mean of the coefficients from the annual regressions, the

t-statistics are the mean of the annual coefficients divided by their standard error (Fama and MacBeth, 1973),

and # Positive refers to the number of times the regression coefficients had positive signs (the significance is

obtained from the binomial test).

Panel A: Descriptive statistics for the variables used in the regression analysis (N = 10,510)

Variables Mean Min. Median Max. Std. Dev.

RETt+1 0.216 -1.296 0.044 2.921 0.660

∆CEt -0.013 -0.322 -0.009 0.262 0.057

∆NCEt -0.002 -0.305 -0.001 0.158 0.034

BE/MEt 0.334 -8.001 0.280 8.928 0.344

Sizet 959.221 25.020 672.212 20,500.000 1,115.847

MKTRETEQ

t+1 0.222 -0.442 -0.044 1.480 0.577

MKTRETVL

t+1 0.187 -0.354 -0.034 1.286 0.518

ROt 0.080 0.000 0.000 1.000 0.271

ROt-1 0.085 0.000 0.000 1.000 0.279

Delistt 0.037 0.000 0.000 1.000 0.190

MAOt 0.130 0.000 0.000 1.000 0.336

(Continued…)

- 37 -

TABLE 5 (Continued)

Panel B: Regression results

Variables Pooled regression (N = 10,510)

Annual regressions (N = 11 years)

Coefficient t-statistics Mean Coeff. t-statistics # Positive

Intercept -0.019 -2.488**

0.226 1.414 7

∆CEt 0.262 4.340***

0.411 2.168* 10

***

∆NCEt -0.298 -2.998***

-0.353 -2.380**

3*

BE/MEt 0.132 13.532***

0.154 1.866* 8

*

Sizet -0.080 -8.135***

-0.095 -1.488 6

MKTRETEQ

t+1 0.925 18.961***

– – –

MKTRETVL

t+1 0.057 1.050 – – –

ROt -0.005 -0.423 0.014 0.620 7

ROt-1 -0.017 -1.369 -0.007 -0.424 4

Delistt 0.047 2.466**

0.079 2.962**

9***

MAOt -0.029 -2.749***

-0.043 -1.736 3*

Adj. R2 73.41% 12. 35%

*, **, and *** indicate two-tailed significance at the 10%, 5%, and 1% levels, respectively.

- 38 -

TABLE 6

Effect of Delayed Responses on the Value Relevance of Earnings Components

In Panel A, the value relevance of the earnings component is estimated by:

SBMARτ = α + β1CEt/Pt-1 + β2∆CEt/Pt-1 + γ1NCEt/Pt-1 + γ2∆NCEt/Pt-1 + ε,

where SBMARτ is the buy-and-hold stock returns beginning in May of year t, minus the equal-weighted

returns of a comparable size and the BE/ME portfolio returns over the same period. When τ = t, the return

window is a 12-month period ending on April 30 following year t’s fiscal year-end. When τ = [t, t+1], it is a

24-month period ending on April 30 following year t+1’s fiscal year-end. CE is core earnings, NCE is

non-core earnings, and ∆ denotes changes from year t-1 to t. The earnings variables are per-share amounts and

deflated by Pt-1, the share price at the end of April for year t. All of the independent variables are winsorized

at the 1st and 99th yearly percentile. The pooled regression is estimated by pooling observations from 1995 to

2005. The annual regression estimates are obtained from 11 annual regressions. The mean coefficient is the

mean of coefficients from the annual regressions, the t-statistics are the mean of the annual coefficients

divided by their standard error, and # Positive refers to the number of times the regression coefficients had

positive signs (the significance is obtained from the binomial test).

In Panel B, the reported statistics are obtained from the pooled regression. The ERC estimates for CE and

NCE are β1+β2 and γ1+γ2, respectively. The SUR F-values are obtained from the seemingly unrelated

regression for testing the equality of coefficients across different equations.

Panel A: Assessing value-relevance of earnings components

A1. Contemporaneous association between stock returns and earnings components (τ = t)

Variables Pooled Regression

Annual Regressions

Coefficient t-statistics Mean Coeff. t-statistics # Positive

Intercept -0.016 -5.104***

-0.043 -2.301**

0***

CEt/Pt-1 0.976 15.650***

1.151 3.534***

9**

∆CEt/Pt-1 0.868 12.356***

2.613 3.726***

11***

NCEt/Pt-1 0.820 5.145***

2.425 2.894**

10***

∆NCEt/Pt-1 0.192 1.399 1.237 2.097* 9

**

(β1+β2) – (γ1+γ2) 0.832 5.570***

0.102 0.124 6

Adj. R2 8.86% 15.36%

A2. Extending return windows one year forward (τ = [t, t+1])

Intercept -0.030 -6.285***

-0.072 -2.694**

0***

CEt/Pt-1 1.338 13.809***

1.694 3.442***

9**

∆CEt/Pt-1 0.931 8.530***

3.191 3.777***

11***

NCEt/Pt-1 0.789 3.186***

2.779 2.561**

9**

∆NCEt/Pt-1 -0.239 -1.121 0.638 0.719 6

(β1+β2) – (γ1+γ2) 1.719 7.405***

1.468 2.183* 8

*

Adj. R2 5.26% 10.91%

Panel B: Comparison of ERCs between core and non-core earnings with different return windows

Window length Core earnings Non-core earnings

τ = t 1.844 1.012

τ = [t, t+1] 2.269 0.551

SUR F-value 26.83***

9.70***

*,

**, and

*** indicate two-tailed significance at the 10%, 5%, and 1% levels, respectively.