Cooking the books: Recipes and costs of falsified financial statements in China

Electronic copy available at: http://ssrn.com/abstract=1708148

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Cooking the books: Recipes and costs of falsified financial statements in China

Michael Firth1

Department of Finance and Insurance, Lingnan University, Hong Kong

Oliver M. Rui2

School of Accountancy, Chinese University of Hong Kong, Hong Kong

Wenfeng Wu3

Antai School of Management, Shanghai Jiaotong University, Shanghai, China

The authors thank Gordon Richardson and workshop participants at The Chinese University of Hong Kong, City University, and Lingnan University for helpful comments on the paper. The authors also acknowledge financial support from a Hong Kong SAR Competitive Earmarked Research Grant (LU340307).

1Corresponding author. Department of Finance and Insurance, Lingnan University, Hong Kong, China. Phone: (852) 2616 8950. Fax: (852) 2462 1073. E-mail: [email protected] 2 Faculty of Business Administration, The Chinese University of Hong Kong, Shatin, Hong Kong, China. Phone: (852) 2609-7594. Fax: (852) 2603-5114. E-mail: [email protected] 3 Management School, Shanghai Jiaotong University, Shanghai 200052, China. Phone: (86) 21-52301194. Fax: (86) 21-52301087. Email: [email protected]

Electronic copy available at: http://ssrn.com/abstract=1708148

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Abstract

We examine the causes and consequences of falsified financial statements in China. Using

bivariate probit regression analysis, we find that firms with high debt and that plan to make

equity issues are more likely to manipulate their earnings and thus have to restate their financial

reports in subsequent years. We also find that corporate governance structures have an effect on

the occurrence and detection of falsified financial statements. There are significant negative

consequences to financial misrepresentations. Restating firms suffer negative abnormal stock

returns, increases in their cost of capital, wider bid-ask spreads, a greater frequency of modified

audit opinions, and greater CEO turnover. We also find that firms located in highly developed

regions suffer more severe consequences when they manipulate their accounts.

Keywords: Falsified accounts, Financial restatements, Regional development, Corporate

governance, Causes and consequences of restatements

JEL classification: G14, K22, M41

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Cooking the books: Recipes and costs of falsified financial statements in China

1. Introduction

High quality financial information is a necessary condition for an efficient and vibrant stock

market. However, trying to measure quality is a challenging task for researchers. The quality of

financial statements is often examined with reference to “earnings management” or “earnings

quality”.1 However, the measurement of earnings management and earnings quality as done in

accounting studies does not provide direct evidence that managers have manipulated earnings

(Agrawal and Chadha, 2005). In contrast, a financial restatement is often a direct admission by

managers of false accounting and financial misrepresentation. Restatements represent corrections

to previously-issued financial statements and these corrections usually occur because of

accounting manipulations in prior years. Thus, the restatements are prima facie evidence of low

quality financial information disclosures in prior periods.

We examine financial restatements made by listed firms in China. In particular, we

investigate the characteristics of firms that make restatements in order to understand why they

occur. We then examine the consequences of restatements. While there are several research

studies on restatements, they mainly use U.S. data and many of them relate to violations of

accounting principles (e.g., Anderson and Yohn, 2002; Hennes et al., 2008; Palmrose et al., 2004

Plumlee and Yohn, 2009).2 In contrast, we investigate falsified accounts rather than technical

violations of accounting standards or unintentional errors of omission. The deliberate

1 Schipper and Vincent (2003) posit that earnings quality is multi-dimensional. Possible measures of earnings quality include

persistence of earnings, value relevance of earnings, ability of earnings to predict future cash flows, and the conservative recognition of earnings. 2 There are also studies on accounting errors and fraud uncovered in the SEC’s enforcement actions in the U.S. (e.g., Karpoff et

al., 2009).

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falsification of the accounts is a type of financial fraud. Given the different historical, legal, and

institutional backgrounds between China and the U.S. (Allen et al., 2005), we should not

automatically impute or generalize the findings from the latter to the former. Nevertheless, there

are some similarities between the two countries, not least of which is China’s willingness to

adopt or modify the best governance practices from the developed countries, and this allows us

to use the U.S. studies as a point of reference in our research.

In addition to examining financial statement misrepresentation in a different market setting,

we also extend the literature in two other important ways. First, we use a two-stage procedure

that examines the propensity to falsify the accounts and the reasons for its subsequent discovery

and disclosure. This methodological advance is important as it allows us to gain a better

understanding of the forces behind financial restatements.3 Second, we recognize that there are

substantial differences in economic and market development across the different regions of

China and this can have a profound affect on our results. We therefore incorporate these regional

differences into our research design.4

Restatements of the financial accounts are the result of a multi-stage process. The first stage

is the decision to falsify the statements and therefore commit financial fraud. Subsequent stages

include the discovery of the false accounting and the reporting of it in a restatement. However,

most prior studies use a basic probit or logit model of restatements and do not differentiate

between the different stages leading to the revelation of financial fraud (Dechow et al., 2010).

One innovation of our study is that we use a two-stage process where we model the propensity to

3 As we discuss later in section 3.4, there are practical problems in implementing the bivariate probit model and these need to be overcome to achieve meaningful results. 4 As we will review and discuss later, there are several studies on financial restatements in China. These studies do not use a two-stage process to explain restatements and do not control for a region’s economic and market development. Furthermore, these other studies limit their examination of consequences of restatements to stock returns whereas we examine a large number of consequences. For the stock returns analysis, we examine the first announcement of false accounting rather than its subsequent disclosure as a restatement in the annual report.

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commit accounting fraud and, in the second stage, model the reporting of the fraud in a

subsequent period. To identify the characteristics of, and the motivations behind, the propensity

to falsify the financial statements and its subsequent disclosure, we use the bivariate probit

regression with partial observability technique. This approach allows us to overcome the

problem of distinguishing between the motives to manipulate the financial statements and the

subsequent detection and reporting of the falsified accounts.

Recent research has focused on institutional factors to explain earnings quality and

differences in quality across countries (e.g., Bushman et al., 2004; Raonic et al., 2004).

Institutional factors include the prevailing economic conditions, the governance of firms,

institutional and regulatory frameworks, and the legal environment (e.g., investor protection,

legal enforcement). One little understood characteristic of China’s reforms is the very uneven

distribution of economic and legal development across the country. Natural resources and human

capital resources account for some of the regional differences in development but political

connections with the country’s leadership elite are also very important. Great variations in

regional per capita income and education levels are one manifestation of the vast differences in

development across the country. We argue that the differences in regional development have

significant effects on the incidence of, and consequences of, falsified accounts. In particular, we

believe financial statement quality is higher for those firms located in well-developed provinces

and consequently investors rely heavily on financial statements in their decision-making. This

implies that the market will react strongly to the financial restatements of firms located in

well-developed regions but there will be a much more muted response in poorly developed

regions as investors have already discounted the low quality of financial reports. To test our

beliefs, we explicitly account for market development using a set of indexes designed to capture

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differences in economic, political, legal, and institutional factors across regions. The advantage

of conducting inter-regional studies within one country is that we can capture the effect of

institutions on the quality of financial information free of contamination due to country

differences in reporting and disclosure rules, taxation, and bankruptcy laws.

China has now emerged from the embryonic stage of capitalism and become a major

economic power with significant inward investment from other parts of the world. Portfolio

investment from the U.S. and elsewhere is accelerating as international investors search for high

growth markets with substantial domestic demand. One impediment to this growth, however, is

investors’ lack of knowledge about China’s capital markets environment and the quality of

financial information. These concerns are real as high quality financial information is a major

factor in assuring the supply of inward international investment. Our study is therefore important

as it yields key insights into the quality of financial information by making an in-depth study of

the causes and consequences of falsified accounts. We establish reasons that lead firms to

manipulate their financial statements and we examine whether there are safeguards or

governance features that help reduce the accounting fraud from taking place. We also investigate

the influences that lead to the detection and disclosure of the false accounting. We provide a

comprehensive examination of the consequences of restatements and this contrasts with prior

research studies, which tend to focus on one or two consequences at a time (e.g., stock returns or

executive turnover) while ignoring others. Examining market consequences is important as an

absence of them may imply that investors ignore financial information altogether.

We identify 813 restatements in the period 2000 to 2005. Of these cases, 542 relate to the

corrections of non-financial information and we do not consider these any further in our study.

The remaining 271 cases involve corrections to the income statement and the balance sheet. On

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average, about 3.7% of listed firms restate their financial numbers each year. We find evidence

that firms are more likely to manipulate their financial statements if they make equity issues, are

highly levered, are controlled by the central government, and are located in less developed

regions. This proclivity to restate is tempered, however, if a firm has a high percentage of

directors with financial expertise. Given that false accounting has occurred, disclosure of it

through the medium of restatements is more likely when the board is independent and the firm is

non-government controlled and located in a more developed region.

The restatements have important consequences for firms. First, there is a significant

negative stock return when the accounting fraud is announced. Second, the restating firm’s cost

of capital increases and its bid-ask spread widens. Third, firms that are forced to restate their

accounts find it harder to sell more shares after the restatement. Thus, capital markets extract a

cost from firms that engage in false accounting. Fourth, there is increased uncertainty about the

veracity of future financial statements, which is manifested by an increase in the proportion of

modified audit opinions. Fifth, top management turnover increases implying there are costs to

managers as well as to the stockholders. In general, these consequences are much more

pronounced for firms located in better-developed provinces. Investors expect high quality

financial statements in the better-developed provinces and so when firms are forced to restate

their earnings the market punishes them severely.

We organize the remainder of the paper as follows. The next section briefly reviews China’s

economic reforms, regional development, and the rules regarding restatements. Section 3

discusses the research design and the sample. The characteristics of the restating firms are

discussed in section 4 and the consequences of restatements are described in section 5.

Conclusions are presented in the last section.

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2. China’s economic reforms and fraudulent financial statements

China began its transition from a centrally planned socialist system to a market-based

economy in the late 1970s. The transition can be characterized as a gradualist approach with the

reforms slowly unfolding (Chen et al., 2006a). In 1990 and 1991, China opened its two stock

exchanges in Shanghai and Shenzhen, respectively, and there are now more than 1600 listed

firms whose combined market capitalization is the second largest in the world. The central or

local government and wholly state owned companies (SOEs) are often the major stockholders in

many listed firms.

A major challenge for the economic reforms has been the need to develop the legal and

financial infrastructure necessary for private ownership and stock market investment. To his end,

China has copied, with modifications, the best practices from the U.S. and other developed

nations. For example, accounting standards have been introduced (the first standard appeared in

1993) and strong progress is being made to convert them to international standards. In 1998/9,

auditing firms disaffiliated from their former state owners and became independent. The first

auditing standards were introduced in 1995 and they have started to reflect international norms

and practices. The government has promoted the development of mutual funds and investment

banks to provide financial services and advice to individuals and corporations. China’s regulator

for securities markets and listed firms, the China Securities and Regulatory Commission (CSRC)

is modeled, in part, on the SEC in the U.S. and the Securities and Futures Commission (SFC) in

Hong Kong. The CSRC monitors listed firms and other stock market participants, which include

banks, accountants, and auditors. The CSRC investigates companies and their financial

statements on a regular basis and in special cases when there are allegations of wrong-doing

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(Chen et al., 2005). Just like the SEC in the U.S., the CSRC has limited resources and only

pursues those cases where it believes it has a strong chance of proving guilt.

Despite the gradualist approach to the economic reforms, bottlenecks have appeared that

impede progress. A major constraint is the lack of experienced and qualified personnel, a

problem that pervades the legal, economic, financial, business, and accounting/auditing sectors.

Another problem area is the lack of good ethical behavior by business executives, which is due

to ignorance and weak law enforcement that makes people think they can “get away” with fraud

and other wrong-doing. While companies in the developed world have evolved systems of

checks and balances and developed good corporate governance mechanisms in order to deter

fraudulent financial reporting5, these systems are more rudimentary in China. In some cases,

good governance systems exist on paper but they are not implemented in practice. In some other

cases, the implementation is perfunctory and amounts to little more than paying lip service to the

ideals of good governance. Financial restatements occur because of incompetence or the

deliberate manipulation of financial statements in prior periods.

2.1. Financial statement fraud

The CSRC requires listed firms to restate the financial statements when errors are detected.

In about 67% of cases (542 out of 813 cases), the restatement consists of correcting information

relating to ownership, top management, directors, and other non-financial-fraud matters.

However, we limit ourselves in this study to an investigation of false financial reporting, where

the income statement and balance sheets have been corrected for previous manipulations.6

Companies are required to disclose the restated financial statements as a “Material Events

Special Alert”. The Special Alert is sent to the firm’s shareholders, the stock exchanges, and

5 However, this has not prevented all fraud as recent scandals attest (e.g., Enron, Worldcom, Parmalat, Royal Ahold, and HIH). 6 Our sample is restricted to deliberate distortions or falsifications by managers.

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published in financial newspapers and the company’s website. Immediate disclosure is required.

The restated financial accounts have to be audited. The accounting errors may have been

identified by a number of sources including the CSRC, the Chinese Institute of Certified Public

Accountants (CICPA) via their monitoring and quality assurance reviews, the firm’s auditors, or

the firm itself. The Special Alerts rarely state which source identified the accounting error. The

Alerts, however, do give details of the manipulated accounts, the money amounts7, and dates.

Appendix 1 summarizes the regulations covering restatements and Appendix 2 gives an example

of a Special Alert issued by WeiDa Medical Applied Technology Co. Ltd. WeiDa used a

fraudulent accounting treatment for shutdown losses and so reported a profit in 2002. The profit

enabled WeiDa to come out of its “Special Treatment” designation. However, after the

restatement, the profit became a loss and WeiDa was reclassified by the CSRC and the Shenzhen

Stock Exchange as a Special Treatment firm. The Special Treatment (ST) moniker means the

firm has two years of losses and risks being de-listed if the losses continue for a third year. The

ST designation is a warning to both the firm and to investors and is something firms want to

avoid and something that may drive the managers to falsify the financial statements.

Financial restatements in China have also been studied by Zhou and Ma (2005), Li (2008)

and Wu and Wang (2009). Zhou and Ma (2005) and Li (2008) examine all restatements including

those of a minor nature that do not include falsified accounts. All three studies use a basic

one-stage logit model. Between them, the studies find that firm size, financial leverage,

ownership, losses, and auditor have associations with the likelihood of a restatement. Li (2008)

reports a negative stock return of -2.26% that accompanied the announcement of the restatement.

In contrast, Wu and Wang (2009) reported a non-significant three day abnormal return of -0.48%;

7 Unfortunately, the level of disclosure of the money amounts involved (e.g., the distortion in net income) varies across cases.

The lack of a consistent disclosure of money amounts means we cannot use this information in our cross-sectional tests.

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these returns were measured at the same time the annual earnings were announced and so it is

difficult to disentangle the impact of the restatement from the concurrent earnings announcement

for the latest year. None of these studies used a two-stage model and so they mix up the

propensity to falsify the accounts stage and the detection and disclosure of fraud stage. Moreover,

they limit their analysis of the consequences to stock returns and do not control for regional

differences in economic and legal development. Our study therefore extends prior China-based

research on restatements in terms of research method (two-stage model), control variables (e.g.,

the impact of regional development), and an extensive analysis of economic consequences.

******************** Appendices 1 and 2 here ********************

2.2. Regional development

While China’s reforms have led to rapid national economic growth and a substantial

increase in personal wealth, these gains are not evenly spread throughout the country (Demurger,

2001; Demurger et al., 2002; Tsui, 1996).8 In addition, the implementation of legal and financial

markets reforms have not been consistently applied throughout China.9 For example, the coastal

regions and the major municipalities (e.g., Guangdong, Beijing, and Shanghai) have developed

much faster than the western and inland provinces. In part, the regional disparities reflect the

preferences of China’s top leaders (e.g., the leaders’ favor the region they come from or where

they built their career).

We believe that the reasons for, and the consequences of, financial restatements will depend,

8 There is a strand of research that shows that differences in financial practice and economic performance across nations depends

on the legal and institutional underpinnings of the countries concerned (e.g., LaPorta et al., 2000; Bushman et al., 2004; Raonic et

al., 2004). These studies assume homogeneity within a country and while this is a valid assumption for most nations, it does not

apply in China where regional differences are enormous. 9 Although there are national laws, national accounting standards, and national governance guidelines, the enforcement of them varies significantly across the regions.

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in part, on where the company is located. For example, the skill and experience levels of

financial executives and people’s views of ethics will likely differ across regions depending on

the degree of market development of the region. Most firms have a dominant investor and that

investor or its representative is located in the same province as the firm. The dominant investors

have a strong influence on the quality of the firm’s financial statements. These investors’ views

on ethics, governance, and attitudes towards minority shareholders will vary depending on where

they come from. Likewise, the judicial system, including, importantly, law and regulatory

enforcement, varies a lot across regions and this will have an impact on a firm’s incentives to

manipulate the financial statements. In particular, we expect that false accounting will be more

likely for those firms located in provinces with low legal and economic development (Xia and

Fang, 2005; Sun et al., 2005). Investors recognize that different levels of development may have

an impact on the quality of financial reporting and so they factor this into their decision-making.

Assuming that investors believe the financial reporting quality of a firm located in a poor

developed province is low then they will not be surprised to see a restatement (compared to firms

located in a well-developed province) and there will be fewer adverse consequences. It is

therefore important that we account for market development in our analyses.

To study the effect of regional development we make use of an index of market

intermediaries and legal environment (including investor protection and protection of property

rights). The index (MLEGAL) has a development score for each province and major

municipality and is compiled by China’s National Economic Research Institute (NERI) (Fan and

Wang, 2003). We also use a comprehensive index (MINDEX) of regional development (also

compiled by NERI), which captures the following aspects 10 : (1) the relations between

10 Demurger et al. (2002) also compile indices of regional development in China using data up to 1999. The Fan and Wang index

is more up to date and more appropriate for our needs.

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government and markets, such as the role of markets in allocating resources and enterprise

burden in addition to normal taxes; (2) the development of non-state business, such as the ratio

of industrial output by the private sector to total industrial output; (3) development of product

markets, such as regional trade barriers; (4) development of factor markets such as FDI and

mobility of labor; (5) development of market intermediaries and the legal environment (e.g., the

protection of property rights). The comprehensive index (MINDEX) gives similar rankings to the

index based on legal and institutional factors (MLEGAL).

3. Research design

3.1. Sample

We undertake a thorough examination of corporate and stock exchange announcements to

identify financial restatements in China in the years 2000 to 2005. These restatements are the

result of deliberate manipulation of the financial reports. We exclude firms in the finance and

financial services industries as they are subject to different regulations. The restatements and

some governance data are hand collected, while the other governance data and stock price data

are taken from the China Stock Market and Accounting Research (CSMAR) database.

Table 1, Panel A, shows the number of restating firms in each year. For example, in 2004

there are 18 firms listed on the Shanghai Stock Exchange that restated their financial statements;

these 18 firms represent 2.2% of all listed firms in Shanghai (818 firms) in 2004. On average,

about 3.7% of listed firms restate their financial statements each year. Panel B shows the industry

distribution of restatements. Quite clearly, the Agriculture industry has the highest incidence of

restatements, while timber/furniture and construction have no restatements at all.

Panel C shows the regional distribution of firms that restate. For example, during

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2000-2005, 19 firms located in Guangdong province restate their financial statements and this

represents 2.3% of all firm-years in that province. Guangdong is considered to be a relatively

well-developed province as its market development scores (MINDEX and MLEGAL) are high.

There is a relatively high incidence of restatements for firms located in Hebei, Liaoning, and

Heilongjiang and a low incidence for firms located in Jiangxi. The correlation between the

proportion of restatements in a province and the market development score (MLEGAL) of that

province is -0.234, which is significant at the 0.05 level. Thus, there is a higher incidence of

restatements by firms located in less developed regions; this finding is consistent with our

arguments outlined earlier.

****************** Table 1 here

****************** 3.2. Control firms

We use control firms as a benchmark when evaluating the characteristics of, and

consequences of, financial restatements; Efendi et al. (2007) and Agrawal and Chadha (2005)

also use a control firm approach in their study of restatements and accounting scandals in the U.S.

The use of a control firm is important in our study as changes in government policy can affect

time-series comparisons (thus, before and after comparisons need to be evaluated against a

control firm). The construction of the control firm group is as follows. For each restating firm we

chose a non-restating firm that is in the same two-digit industry code, has been listed for the

same number of years on the same stock exchange, and is nearest in size (sales and total assets).

There are no significant mean and median differences between the sample and control firms as

regards company size (sales, total assets) and age (years since incorporation and years since first

listing).

3.3. Why do firms’ restate their financial statements?

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In an attempt to find out why some firms restate while others do not, we examine the

financial characteristics of restating companies that differentiate them from non-restating firms.

Firms that restate had previously manipulated their earnings and in most cases this means

reported earnings are greater than they should have been. Possible motives for manipulating

earnings upwards are to avoid reporting three years of losses (firms are delisted if they report

three years of consecutive losses), to issue equity capital more cheaply by showing higher

profitability11, and to give confidence to the investors and creditors of highly levered companies.

Furthermore, CEO and top management compensation in Chinese firms is dependent on a firm’s

reported earnings (Firth et al., 2006a) and so managers may be tempted to fraudulently boost

reported net income. We therefore construct variables to capture losses, equity issues, and

leverage. LOSS is coded one (1) if the firm reports two years of consecutive losses in the year

prior to the manipulation and the manipulation turned a loss into a profit in the following year.

Here, the manipulation is made to ensure the company makes a profit and so avoid three years of

losses, which would lead to de-listing. SEO is coded one (1) if a firm makes an equity issue in

the year after the manipulation.12 LEV is the firm’s leverage (debt/total assets) at the date of the

financial fraud.

Manipulation is more difficult to do if the firm has good governance mechanisms in place

and so we examine the internal and external governance features of the company. These

governance features include some of those used in prior research (Agrawal and Chadha, 2005;

Chen et al., 2006b; Firth et al., 2007; Park and Shin, 2004; Xie et al., 2003). BOARD is the

11 Firms have to achieve specific earnings thresholds to issue seasoned equity offerings. For example, return on equity (ROE) has

to average 10% or more in the three years prior to an application to make a rights issue and to average 6% or more for a private or

public placement. 12 In sensitivity tests, we alternatively code SEO as one if a firm just satisfies the profitability criterion for making a SEO. The results from using this alternative measure of SEO are qualitatively the same as the ones reported in this paper.

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number of directors on the board. Some critics argue that large boards do not function well and

become less effective in constraining the wanton behavior of a CEO or executive chairman (who

might be behind the manipulation) (Jensen, 1993). On the other hand, large boards make it more

difficult for the CEO or chairperson to obtain unanimous consent for fraudulent, questionable, or

controversial actions. We examine the proportion of outside non-executive directors (OUT%) on

the board. These directors often represent the controlling shareholder or other major investor, or

are independent directors.13 The non-executive directors’ impact on earnings manipulation may

be different from that in the West (Dahya and McConnell, 2005) although the exact form of the

difference is an empirical matter. DUAL is coded one (1) if the CEO and Chairman is the same

person. If the CEO and chairperson is the same person they have a lot of power and they have

more ability to manipulate earnings if they want to do so (Brickley et al., 1997; Efendi et al.,

2007).

CFO is coded one (1) if the chief financial officer is a member of the board. If the CFO is

on the board this gives them more power and influence and non-financial directors will be more

willing to accept the advice of the CFO (either for or against manipulation). FINBACK% is the

number of directors who have a financial background (as evidenced by professional

qualifications in accounting, economics, or finance). These directors are more likely to

understand the (potential) financial fraud and they either can acquiesce (in which case the

non-financial directors will follow their advice) or object (which would lead to less

manipulation). Thus, FINBACK% could have a positive or a negative relation with financial

13 Since 2003 listed firms have been required to have boards where at least one-third of the directors are independent. Before this

date there were few independent directors. Even after 2003 there are questions as to how independent the independent directors

are, and whether they fully understand their duties and responsibilities.

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restatements.14 While directors with financial expertise should reduce inadvertent errors, this

does not mean that deliberate manipulation will be reduced. Indeed, having financial expertise

enables directors and CFOs to undertake and perpetuate complex accounting fraud.

In the international literature, many studies argue that there are differences in audit quality

across audit firms and the Big4 auditors provide the best quality (Chung et al., 2009). In China,

the international Big4 audit firms typically audit domestic listed firms through their local

affiliates and so their quality is not identical to that of the Big4 in the U.S. and other developed

countries. In 2003, the CSRC identified and named 15 auditors that it believed had the highest

quality. We code Big15 one (1) if the auditor is one of the CSRC-designated auditors. The Big15

auditors may inhibit earnings manipulation and have a negative relation with accounting fraud.

We include three ownership variables in our analysis. They are: the proportion of shares

owned by the largest stockholder (TOP); a dummy variable (CENTRAL) set equal to one (1) if

the largest stockholder is the central government; and a dummy variable (PRIVATE) set equal to

one (1) if the largest stockholder is a private investor or a foreign firm. Chinese firms are

characterised as having a dominant stockholder who owns substantially more shares than the

second largest owner does (Chen et al., 2009a). The major stockholder is effectively the

controlling stockholder as the other institutional investors are not very active or vocal. In

determining ownership, we take care to trace the ultimate owner. A dominant owner (e.g., when

TOP is high) has a lot of influence over the firm. The dominant owner can be a force for good or

for bad (e.g., committing financial fraud).

The bureaucrats who administer the central government’s shareholdings are usually career

civil servants who have little commercial acumen. Hence they exercise little oversight over a

14 In a different context, Guner et al. (2008) show that increasing financial expertise on U.S. firms’ boards does not always

benefit stockholders.

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firm’s managers. Furthermore, these bureaucrats are held responsible for achieving the

government’s economic and social goals and they may use the listed firm to meet these

objectives (e.g., by expropriating wealth away from the listed firm). Financial reports may be

falsified to hide these activities and to show better performance to the government. Based on the

above reasoning, we expect that firms where the central government is the largest shareholder

will have more financial restatements.

We use the t-test and Mann-Whitney Z-test to test for differences in means between the

restatement group and the control group. In addition, we use a bivariate probit model with partial

observability to identify the characteristics that help us differentiate between the restating firms

and non-restating firms. The bivariate probit model is described in the next section.

3.4. Bivariate probit model

One inherent problem with the basic regression approach is that it is possible that the

non-restating firms have manipulated their financial statements but they have not restated them.

This can arise if the CSRC, the firm’s directors, and the auditors have not identified the

manipulation (or if the firm has identified the error, it may not want to disclose it). This issue

represents an identification problem and it reduces the ability of the model to explain the

restatement (Wang, 2004; Chen et al., 2006b) and makes it difficult to interpret the coefficients.

To illustrate, an independent variable (e.g., a governance variable such as Big15) could have a

negative effect on earnings manipulation and a positive effect in discovering it (and making a

restatement). The simple probit model does not catch this subtlety and the coefficient on the

variable will be difficult to interpret. One approach to resolve the inherent problem of treating

non-observed financial reporting failure as no-financial reporting failure is to make use of a

bivariate probit model with partial observability; see Poirier (1980) and Haque and Haque (2008)

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for details of the technique. Here, we model detected financial reporting failure as a function of

the joint realizations of two latent variables (financial reporting failure and restatement)15:

Financial Reporting Failure: Fj = 1 if firm j has a financial reporting failure. Otherwise, Fj = 0.

Detected: Dj = 1 if firm j’s financial reporting failure is detected (i.e., earnings are restated).

Otherwise Dj = 0.

From this, we have the following reduced form equations:

jjj uxF += 11 β

jjj vxD += 22 β

where x1j is the vector of variables that helps explain a firm’s propensity to manipulate earnings

and x2j is the vector of variables that helps explain why financial reporting failure is detected. uj

and vj are the disturbance terms.

The interaction of Fj and Dj is denoted Zj. Thus:

jjj DFZ ∗=

Then Zj = 1 indicates a restatement. The empirical model for Zj is

),(

)1|1()1(

)1&1()1(

2211 ββ jj

jjj

jjj

xx

FDPFP

DFPZP

Φ=

====

====

),(1

)1|0()1()0(

)00()0(

2211 ββ jj

jjjj

jjj

xx

FDPFPFP

orDFPZP

Φ−=

===+==

====

The log-likelihood function for the model is

)]},(1ln[)1()],(ln[{),( 2211221121 ββββββ jjjjjj xxzxxzL Φ−−+Φ=∑

Full identification of the model parameters requires that x1j and x2j do not contain exactly the 15 The following section draws heavily on Poirier (1980) and Wang (2004).

20

same variables (Poirier, 1980; Greene, 1995).

Note that a simple probit model, which is used in most prior restatement studies, is as follows:

)()1Pr()1Pr( 1 β

εβ

jjj

jjjj

xFZ

xFZ

Φ====

+==

If Dj is not always one, the coefficients in the simple probit model will differ from those in the

bivariate probit model. Wang (2004) uses a similar bivariate probit with partial observability

approach in her study of securities class action litigation in the U.S. Abowd and Farber (1982)

and Chidambaran and Prabhala (2003) are others who have used this method in their studies on

labor markets and stock option repricing.

While the bivariate probit model is conceptually the best approach to use, there are practical

difficulties in implementing it. One issue is that there is no developed theory on what variables

and what functional forms explain accounting fraud and its subsequent disclosure. In the absence

of a formal theory, we use empiricism to develop an appropriate model. The peculiar nature of

identification in partially observed bivariate probit models (Poirier, 1980) results in the models

having poor convergence properties (see Farber, 1983; Heywood and Mohanty, 1993, 1994). The

poor convergence is exacerbated when there are a large number of independent variables (Haque

and Haque, 2008; Comola, 2009) and when the independent variables are correlated (Heywood

and Mohanty, 1994). We face similar problems in our tests. To improve convergence properties,

we explore a number of parsimonious models. The model (P(Fj=1)) that provides the best fit is:

0 1 2 3 4 5 6

7 8 9

RESTATE OUT% CFO FINBACK CENTRAL BIG15 MLEGAL LEV LOSS SEO

= β +β +β +β +β +β +β+β +β +β

The conditional detection model (P(Dj=1/Fj=1)) includes OUT%, CFO, FINBACK, CENTRAL,

BIG15, MLEGAL, and SEO. Regulators (CSRC, stock exchanges) may investigate firms making

SEOs and their examinations may culminate in the firm having to restate their financial

21

statements. Several governance variables are included in the detection model to see if they are

associated with the detection of fraud and the restatements of the accounts.

3.5. Consequences of restatements

Financial restatements are likely to have negative consequences for firms and their top

executives (Karpoff et al., 2008a, 2008b). To measure economic consequences we examine

changes in stock returns, cost of capital, capital raising exercises, bid-ask spreads, and the

incidence of modified audit reports. In addition, we examine whether restating firms are more

likely to change their CEO. We calculate these measures with respect to the control group of

firms.

To evaluate the impact on stockholder wealth we perform an event study in which we

estimate the cumulative abnormal stock returns of the firms around the restatement. We identify

the event day as the first day that the public is informed about the restatement. The abnormal

return for security i on event date t is

)|( ,,, ttititi IRERAR −=

where ARi,t, Ri,t, and )|( , tti IRE are the abnormal, actual, and expected returns for time period t,

respectively. It is the information on which the expected return depends. We use the market

adjusted returns model, the matched-firm model, and the market model to calculate the expected

returns. The three methods yield similar conclusions and so we just report the market adjusted

returns model results. We accumulate ARi,t to obtain cumulative abnormal returns (CARs), using

various event windows ranging from 10 days before to 10 days after the event day.

Easley and O’Hara (2004) show that information risks cannot be diversified away.

Information risk refers to the likelihood that firm-specific information pertinent to the investor

pricing decision is of poor quality. The restatements capture information risk in this study. Using

22

U.S. data, Murphy et al. (2009) show that reputation losses from financial fraud increase the cost

of capital. To examine whether financial restatements increase the cost of equity, we need to

estimate the expected rate of return on equity. There is, however, a continuing debate on how to

estimate the expected rate of return. The literature shows that reverse-engineering valuation

models are appropriate to obtain estimates of the expected rate of return on equity investment.

These reverse-engineering valuation models include the residual income valuation model, the

abnormal growth in earnings model, and the dividend capitalization model (e.g., Claus and

Thomas, 2001; Hribar and Jenkins, 2004, Daske, 2006; Attig et al., 2008; Pastor et al., 2008;

Chen et al., 2009b). However, all these models require estimating the future growth rate of a firm

and for this purpose most studies use growth rate estimates provided by financial analysts.

The models used in prior studies are of the form:

)(

)1](*)[( 1

jj

jjtjjtjtjt gr

gbpsrROEbpsp

−

+−+= −

Where jtp is the stock price for firm j at the end of year t, jtbps and 1−jtbps are the book value

of equity for firm j at the end of years t and t-1, respectively. jtROE is the return on equity for

firm j at the end of year t. jr is cost of equity and jg is the future growth of residual income. The

empirical testing of these types of models makes use of analysts’ earnings growth forecasts.

However, this approach has been criticized as the forecasts may be biased and therefore do not

accurately reflect the market’s expectations (Easton, 2006; Easton and Sommers, 2007; Easton

2009; Hou et al., 2010).

O’Hanlon and Steele (2000)16 transform the above model into the following regression

model to estimate the cost of equity and future growth rate: 16 See Easton (2006) for a discussion of this model. He states that the O’Hanlon and Steele (2000) model is the most suitable model for estimating the cost of capital. The model does not use analysts’ forecasts and so is free from the bias inherent in the forecasts.

23

jtjt

jtjt

jt

jt

bpsbpsp

bpseps

εδδ +−

+=−− 1

101

Where jteps is the earnings per share at the end of year t. This regression may be estimated for

any group/portfolio of stocks to obtain estimates of the expected rate of return, r, and the

expected growth rate, g’ for the portfolio. Thus, there is no need for analysts’ forecasts of growth.

0δ is the estimated cost of equity for the portfolio and )1/()( 11 δδ +−= rg . Because there was a

lack of analyst growth forecasts in China at the time of our study, and because of severe conflicts

of interest that bias analysts’ forecasts, we adopt the O’Hanlon and Steele model to calculate the

cost of capital. We compare the difference in 0δ between the restatement group and the

non-restatement group. To date, there have been few applications of the O’Hanlon and Steele

model and our paper is the first to use it on emerging markets data.

In order to test whether the difference in the cost of capital before and after the restatement

is significant, we employ the following regression with the before and after samples of the

restatement and control samples:

( ) jtjt

jtjtDAR

DARjt

jt

bpsbpsp

AfterRestategAftergRestateg

AfterRestaterAfterrRestaterbpseps

εδ

δ

+−

⋅⋅⋅+⋅+⋅++

⋅⋅+⋅+⋅+=

−

−

11

01

where “Restate” is a dummy variable, which is equal to one if it is a restatement firm, otherwise

equal to zero; “After” is a dummy variable, which is coded one if the sample is after the

restatement, else coded zero. The estimated coefficient of rD is used to test whether there is a

difference in the change of cost of capital between the restatement and control samples. The

coefficient of rR is the difference in the cost of capital between the restatement and control

samples before the restatement. The coefficient of rA is the difference in cost of capital before

and after the restatement year for the control sample.

24

As another measure of cost of capital, we use the realized market-adjusted rates of return. In

particular, we use the realized market-adjusted return in the month prior to the restatement

(where we exclude the month of the restatement) and the one-month after the restatement. The

change in the market-adjusted returns indicates the change in cost of capital. As a comparison,

we calculate the change in cost of capital for the control group. We also examine changes in

Tobin’s Q as it has an inverse relation with the cost of capital (Daske et al., 2008).

Glosten and Milgrom (1985) argue that when information asymmetries exist among

investors, the less-informed investors are concerned they will systematically lose money when

they trade with well-informed investors. To protect themselves against the potential losses from

trading with more-informed investors, the less-informed investors will decrease the price at

which they are willing to buy and increase the price at which they are willing to sell. This will

result in higher bid-ask spreads and lower liquidity. Financial restatements can increase the

adverse selection problem by increasing information asymmetries between the less-informed and

better-informed investors. We therefore test whether restatements are associated with wider

bid-ask spreads.

Another costly consequence of a restatement occurs if the likelihood of receiving a

modified (i.e., qualified) audit opinion (MAO) increases. The auditor may believe audit risk

increases after a restatement. We predicate this on the belief that a restatement signals that

management is less competent and more dishonest than previously thought. Issuing a modified

audit opinion is a rational response by an audit firm when they perceive the client has become

more risky. We therefore examine whether MAOs increase after a financial restatement.

The consequences we outline above relate to potential losses to investors. However, we also

examine the consequences for top management. In particular, we investigate whether CEO

25

turnover increases in the year after restatement. Given that restatements are the result of

manipulations of the accounts, we expect that the top manager is more likely to be replaced. In

the U.S., Hennes et al. (2008), find that outside director and top management turnover increases

after restatements; further, the dismissed executives suffer reductions in pay and benefits if and

when they find new jobs.17 Fich and Shivdasani (2007) find that outside directors lose reputation

if their firms engage in financial fraud.

4. Characteristics of restating firms

Table 2 compares the financial and governance characteristics of restating and non-restating

firms. Restating firms have a small percentage of directors with an accounting and financial

background (the mean is 26.5%) when compared to the control firm (the mean is 31.1%). The

difference is significant at the 0.01 level. Thus, a relative lack of financial expertise in the

boardroom appears to be a precursor to financial restatements and thus accounting manipulation.

The other boardroom variables are not important in differentiating between restating and

non-restating firms. The Big15 auditors are less likely to have clients that restate their accounts.18

Restating firms are more likely to have an agency of the central government as the controlling

shareholder. Consistent with our hypotheses on the motives for accounting manipulation we find

that restating firms are more likely to have made an SEO, have two years of losses (followed by

a profit in the year of the financial manipulation), and have higher leverage. However, only SEO

is statistically significant. Firms located in the less developed provinces are more likely to restate

their financial reports.

****************** Table 2 here

17 However, earlier evidence (e.g., Agrawal et al., 1999) found no evidence of increased CEO turnover. 18 Similar results obtain if we use the Big10 or the international Big4 (Big4’s local affiliates) in place of the Big15.

26

****************** The univariate tests detailed in Table 2 do not distinguish between the different stages of

false accounting and the subsequent restatements. To examine the joint impacts of the different

variables, and to model both the fraud and the disclosure of fraud, we turn to bivariate probit

regression with partial observability. Table 3 shows the results.19 The P(Fj=1) column represents

the fraud model while the P(Dj=1/Fj=1) column represents the detection and reporting of the

false accounting. Companies making SEOs and highly levered firms are associated with the

propensity to commit fraud and this is consistent with the motives for false manipulation that we

discussed earlier. These firms have incentives to report higher earnings and this may lead them to

issue false financial statements. LOSS has a positive sign as expected although it is not

significant at conventional levels.

Firms that have many directors with a financial background are less likely to be associated

with financial statement fraud. Earlier, in section 3, we argued that finance-competent directors

could have a positive relation with fraud (e.g., they have the ability to perpetrate complex

financial fraud) or a negative relation (e.g., they have high ethical standards and understand the

harmful effects of financial fraud). Our results show that financially-savvy directors help to

reduce accounting fraud. Listed firms that are controlled by the central government are more

likely to have fraudulent financial statements. This reflects the lack of oversight exercised by the

government bureaucrats and-or their efforts to falsify the accounts to show better performance.

Firms located in highly developed provinces have fewer financial frauds.20 This reflects more

rigorous law enforcement and perhaps a better appreciation of good ethics. In contrast, a weak

19 As mentioned previously, there is no theory to guide us in the selection of variables and so we examine a number of corporate governance mechanisms and various functional forms of the model. Correlations among the variables render some models very unstable. The reported results provide the best fit. 20 The reported results use MLEGAL for the development index. However, similar conclusions are drawn when we use

MINDEX instead of MLEGAL.

27

market environment may fail to produce credible disciplinary mechanisms to ensure that firms

and their managers act honestly.

In the detection model, we find that OUT%, CENTRAL, and MLEGAL are significant.

Firms with more independent boards are more likely to detect and disclose false accounting

reports while state controlled firms are less likely to report fraud. False accounting is more likely

to be reported by firms located in the more developed regions.

****************** Table 3 here

******************

5. Consequences of restatements

5.1. Stock returns

Table 4 shows the market-adjusted returns for the restating firms. We have sufficient returns

data for 267 observations; in four cases, the shares are suspended from trading. There are

significant negative abnormal returns in the periods [-10, 1], [-5, -1], [0, 5], [-5, 5], [0, 10], and

[-10, 10] where day 0 is the announcement date. Disclosure of a restatement results in a

statistically significant fall in stock price.21 The stock returns analysis indicates that investors

regard financial statements as being price-sensitive information as the announcement of the

accounting fraud causes a decline in prices. Panel B shows that the returns are more negative for

companies located in highly developed provinces (MINDEX and MLEGAL above the median)

although the differences are not statistically significant. The results are also shown in Figure 1.

********************* Table 4 and Figure 1 here *********************

5.2. Cost of capital 21 Morck et al. (2000) demonstrate that stock returns in China are not very sensitive to firm-specific news; instead, an individual firm’s returns are strongly linked to market-wide movements. The fact that we find a significant negative abnormal stock return is therefore unusual and shows that investors treat accounting restatements seriously.

28

Table 5 shows the results for the cost of capital tests. In Panel A, we show the cost of capital

before and after the restatement date for the restating firms and for the control firms (a control

sample firm uses the same restatement date as the matched restatement firm). The cost of capital

( 0δ ) is 3.94% before restatement and 5.89% after restatement.22 The corresponding costs of

capital for the control group are 3.96% and 4.08%. Our estimates of cost of capital are plausible.

While the estimated cost of capital is lower than many estimates for U.S. firms, there are good

reasons for this. First, the interest rate on bank deposits has been fixed at 1.71% or 1.98%

throughout the period of our study and so our estimates of cost of equity capital are

approximately one and a half to three times the interest rate. Second, people in China have very

few investment opportunities unlike their counterparts in the West. Bank deposits and stock

investment are the only two investments ordinary people can make (in very recent years real

property has become an investment opportunity but only for the wealthy). The Chinese people

have very high saving rates but very few investment alternatives in which to invest. This leads to

a lower cost of capital than in Western countries. The Chinese government set the interest rate

low (which leads to a lower cost of capital) to achieve high growth and high employment. The

low cost of capital has led to a high growth rate in GDP and poor profitability by Western

standards (as the hurdle rate is low); this corroborates the findings in Chen et al. (2006a). China’s

ability to impose low interest rates and cost of capital is facilitated by the non-convertibility of its

currency, the renminbi (RMB).

We find that the cost of capital increases by 1.96% for the restating firms but it increases by

just 0.11% for the control sample. The rate of increase (from 3.94% to 5.89%) is 50% and this is

much higher than the percentage increase reported in the U.S. (Hribar and Jenkins, 2004). To test 22 Our estimation procedure uses a firm’s stock price and this price may be influenced by an announced rights issue (SEO). In light of this, we repeat all of our cost of capital analyses on the sample of restatement and control firms that do not have rights issues. The results are very close to those reported in Table 5. We thank the reviewer for alerting us to this question.

29

if the increase in cost of capital is significant we run a regression model that distinguishes

between the cost of capital before and after restatements and the results are shown in Panel B.

The coefficient rD is positive and statistically significant (rD = 0.018). Thus, one important

consequence for a restating firm is that there is a significant increase in its cost of capital. Panels

C and D show that the increase in the costs of capital for restating firms are more prominent for

those companies located in highly developed provinces (MINDEX and MLEGAL above the

median). This result is consistent with investors believing that firms located in highly developed

provinces have high quality financial reports and so the occurrence of a restatement causes a

major reassessment of management credibility and overall earnings quality. In contrast, a

restatement by a firm in a poor developed province is less of a shock for investors.

We also use realized market-adjusted returns to represent a firm’s cost of capital. The results

show a significant decline in cost of capital from before to after the restatement (Table 5, Panel

E). In contrast, there is no change in cost of capital for the control sample. Table 5, Panel F,

shows that the increases in cost of capital are mainly for those firms located in more highly

developed provinces. The results for the stock return estimates of cost of capital (Panels E and F)

are similar to the estimates of cost of capital using the O’Hanlon and Steele (2000) method

(Panels A and C). Thus, our conclusions are robust to the two different measures of cost of

capital.

Daske et al. (2008) argue that an increase in the cost of capital, ceteris paribus, should lead

to a decrease in Tobin’s Q. We therefore examine the changes in Tobin’s Q from before the

restatement to after. Tobin’s Q is calculated as (total assets – book value of equity + market value

of equity)/total assets. We show the results in Table 5, Panel G. There is a significant decline in

Tobin’s Q for the restatement firms, which implies an increase in the cost of capital. Panel H of

30

Table 5 show that the declines in control sample-adjusted Tobin’s Q are more apparent in highly

developed regions.

Another consequence of the disclosure of false accounting is that firms may find it more

difficult to sell new shares.23 This is partly the result of the increases in cost of capital discussed

above. As a direct test of the ability to sell new shares, we examine the proportion of firms that

make SEOs in the three years prior to restatement and the proportion that make SEOs in the three

years after a restatement. The results are shown in panel I of Table 5. Restating firms make more

SEOs before the restatement than do the control firms although the difference is not statistically

significant (p=0.364). After the restatement, the proportion of firms making SEOs falls

dramatically for the restating firms. The reduction is more severe for the restating firms than for

the control firms.24 The evidence indicates that restating firms find it much more difficult to

issue new shares in the aftermath of the restatement.

****************** Table 5 here

****************** 5.3. Bid-ask spread

We compute the average bid-ask spread for the month prior to the restatement date and

compare it with the bid-ask spread in the one month after restatement.25 A comparison is then

made with the control group. We show the results in Table 6. Panel A shows the bid-ask spread

increases for the restatement firms but declines for the control group firms. The difference in the

changes (0.0122%) is significant at the 0.01 level. Our result contrasts with the U.S. experience

23 We thank the reviewer for suggesting this analysis. 24 The proportion of control firms that make SEOs also falls. The reduction in SEOs over time reflects capital controls imposed by the government (via the CSRC) in the later part of our sample period. This illustrates the importance of using control firms as benchmarks. 25 We follow Cai (2004) and others and calculate the spread as (Ask – Bid)/(Ask + Bid)/2. Note that prices are quoted in fen and the minimum price movement is one fen (approximately $0.001U.S.). Thus, bid-ask spreads are small in comparison to those in the U.S.

31

where Palmrose et al. (2004) found no significant evidence of restatements affecting bid-ask

spreads. In Panel B we show that the increase in bid-ask spreads is stronger for firms that are

located in provinces with high market development (i.e., those with MLEGAL scores above the

median). The results show that firms that need to restate their financial statements are viewed as

becoming more risky and so the bid-ask spreads widen.

****************** Table 6 here

****************** 5.4. Modified (Qualified) audit reports (MAOs)

When a firm admits, via a financial restatement, that its prior accounts are erroneous this

implies the errors are a deliberate act by management and that the governance structures and

monitoring mechanisms are unable to prevent such an act from occurring. This is likely to

increase the audit risk as perceived by the external auditor. A rational response of the auditor to

the increased audit risk is to increase the threshold for giving a clean opinion. Therefore we

expect to see an increase in the proportion of modified audit opinions (MAOs) being given to

restating firms after the restatement. To test this hypothesis, we compare the change in MAOs

from before a restatement to after and compare this change to the control group. The results are

shown in Table 7. In Panel A we see that there is an increase in MAOs for restating firms

whereas the control group has a decrease. The difference is statistically significant with a p-value

of 0.002. In Panel B we find that the relative increase in MAOs for restating firms is mainly

confined to firms located in provinces with a high development score.

****************** Table 7 here

****************** 5.5. CEO turnover

So far, we have considered the consequences of false financial reporting for investors by

32

examining stock returns, cost of capital, bid-ask spreads, and modified audit reports. However,

there could also be consequences for top management. We therefore examine CEO turnover

before and after the restatement (one year before and one year after26) and compare this to the

control group.27 Table 8 shows the results. The turnover increases after a restatement (to 30.8%);

thus, about 31% of CEOs lose their jobs within one year of the restatement. In comparison, the

turnover rates decline for the control group. The difference in changes in turnover rates for

restating and non-restating firms (10.5%) is statistically significant. The evidence suggests that

accounting restatements have costly consequences for CEOs. The CEOs are more likely to be

dismissed after a restatement as they carry the responsibility for the false accounting. Our results

are consistent with those reported in the U.S. (Hennes et al., 2008). Panel B shows that the

increases in CEO turnover after a restatement do not depend on the level of market development

where the firm is located. Unfortunately, we are unable to trace where the CEO goes after

leaving the restating firm and are therefore unable to ascertain whether they obtain a worse (or

similar, or better) position.28 Thus, we cannot carry out the type of analyses undertaken by

Karpoff et al. (2008a) in the U.S. Furthermore, we do not have data on the other officers and

directors of the firm and so we cannot examine the consequences of restatements on them.

****************** Table 8 here

******************

6. Conclusion 26 A review of the public announcements that disclose the CEO replacement indicates that falsified financial statements are a major reason for top executive dismissal. 27 In a robustness test, the control group is refined to match restatement firms and non-restatement firms based on return on assets. We do this because prior research (Firth et al., 2006b) shows that a firm’s profitability is an important factor in the executive turnover decision. The results from this additional test mirror those reported in Table 8 and so our findings are robust to alternative specifications of the control group. 28 Some CEOs may return to government jobs, parent SOEs, or move to foreign firms. Data on the top management jobs at these

organizations are not publicly available.

33

The quality of a firm’s published financial statements is a major concern to investors,

regulators, and other parties. However, the measurement of quality is contentious and a broad

consensus on its definition remains elusive. Nevertheless, if a firm restates its financial

statements it represents an admission that its prior accounts are false. Financial restatements are

extensively studied in the U.S. but there is relatively little literature on restatements in other

countries. To help remedy this deficiency our study examines restatements of financial reports in

China.

Falsified financial statements are common in China. We find that firms are more likely to

manipulate their financial information when the firm issues new equity and when it is controlled

by the central government. The manipulation allows a firm’s financial statements to look better

than they should although it leads to a restatement in a subsequent year. The relations between

restatements and governance variables are complex. For example, firms that have many directors

with a financial background are less likely to have restatements. The percentage of independent

directors and the presence of a CFO on the board have no relation with fraudulent financial

statements. We find no evidence that a major audit firm inhibits financial fraud in listed firms.

Restatements are more likely for firms located in less developed provinces. Detection and

reporting of false accounting is more likely when the central government is not the major

stockholder and when firms are located in more developed regions. There is some weak evidence

that an independent board is more likely to insist on restatements.

Financial restatements in China have economic consequences. Stock prices fall, cost of

capital increases, access to capital markets declines, and bid-ask spreads widen. Restatements

increase the risk perception of the firm as manifested in widening bid-ask spreads and an

increase in modified audit opinions. Top management is not immune to the consequences of

34

restatements as we show that the CEO is more likely to be replaced in the year after the

restatement.

Our results also show that the causes and consequences of restatements depend on where

the firm is located. Although there are national laws, standards, and governance guidelines, the

application and enforcement of them varies a lot. In particular, the application and enforcement

are greater in more developed provinces. Thus, investors expect better financial reporting quality

in highly developed provinces and so restatements are a major shock that lead to negative

consequences for firms (e.g., an increase in the cost of capital and widening of the bid ask

spread). In contrast, investors believe that firms located in poorly developed provinces have

lower quality financial statements and so restatements are less of a shock. While many previous

studies have shown that the institutional and legal environment of a country have an impact on

firm value and accounting quality, our study shows that there can also be differences inside a

country. Thus, in large transition and emerging market economies, the progress of change can be

very different across the regions of a country and this will have an impact on the prevalence and

consequences of falsified financial reporting.

35

References: Abowd, J. M. and H. S. Farber, 1982. Job queues and union status of workers. Industrial and Labor Relations Review 35, 354-367. Agrawal, A. and S. Chadha, 2005. Corporate governance and accounting scandals. Journal of Law and Economics 48, 371-406. Agrawal, A., J.F. Jaffe and J.M. Karpoff, 1999. Management turnover and governance changes following the revelation of fraud. Journal of Law and Economics 42, 209-341. Allen, F., M. Qian and J. Qian, 2005. Law, finance, and economic growth in China. Journal of Financial Economics 77, 57-116. Anderson, K. L. and T.L.Yohn, 2002. The Effect of 10K Restatements on Firm Value, Information Asymmetries, and Investors' Reliance on Earnings (September 2002). Available at SSRN: http://ssrn.com/abstract=332380 or doi:10.2139/ssrn.332380. Attig, N., O. Guedhami and D. Mishra, 2008. Multiple shareholders, control contests, and implied cost of equity. Journal of Corporate Finance 14, 721-737. Brickley, J.A., J. Coles and G. Jarrell, 1997. Leadership structure: Separating the CEO and chairman of the board. Journal of Corporate Finance 3, 189-220. Bushman, R., J. Piotroski and A. Smith, 2004. What determines corporate transparency? Journal of Accounting Research 42, 207-252. Cai, J., 2004. Bid-ask spreads for trading Chinese stocks listed on domestic and international exchanges. Working paper, Shanghai Stock Exchange. Chen, G..M., M. Firth, D.N. Gao and O.M. Rui, 2005. Is China’s securities regulatory agency a toothless tiger? Evidence from enforcement actions. Journal of Accounting and Public Policy 24, 451-488. Chen, G.M., M. Firth and O.M. Rui, 2006a. Have China’s enterprise reforms led to improved efficiency and profitability? Emerging Markets Review 7, 82-109. Chen, G.M., M. Firth, D.N. Gao and O.M. Rui, 2006b. Do ownership structure and governance mechanisms have an influence on deterring corporate financial fraud in a transitional economy? Regulatory enforcement actions in China. Journal of Corporate Finance 12, 424-448. Chen, G.M., M. Firth and L. Xu, 2009a. Does the type of ownership control matter? Evidence from China’s listed companies. Journal of Banking and Finance 33, 171-181. Chen, K.C.W., Z. Chen, and K.C.J. Wei, 2009b. Legal protection of investors, corporate governance, and the cost of equity capital. Journal of Corporate Finance 15, 273-289.

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Chidambaran, N. K. and N. R. Prabhala, 2003. Executive stock option repricing, internal governance mechanisms, and management turnover. Journal of Financial Economics 69, 153-189. Chung, R., M. Firth and J.B. Kim, 2009. Big 4 conservatism around the world. Working paper, Concordia University. Claus, J. and J. Thomas, 2001. Equity risk premium as low as three percent? Evidence from analysts’ earnings forecasts for domestic and international stocks. Journal of Finance 56, 1629-1666. Collins, D., A. Masli, A.L. Reitenga, and J.M. Sanchez, 2009. Earnings restatements, the Sarbanes-Oxley Act and the disciplining of chief financial officers. Journal of Accounting, Auditing and Finance 24, 1-34. Comola, M., 2009. The network structure of informal arrangements: Evidence from rural Tanzania. Available at SSRN: http:llssrn.com.abstract=94603 Dahya, J. and J. McConnell, 2005. Outside directors and corporate board decisions. Journal of Corporate Finance 11, 37-60. Daske, H., 2006. Economic benefits of adopting IFRS or U.S.-GAAP-Have the expected costs of equity capital really increased? Journal of Business Finance and Accounting 33, 329-373. Daske, H., L. Hail, C. Leuz and R. Verdi, 2008. Mandatory IFRS reporting around the world: Early evidence on the economic consequences. Working paper, The University of Chicago. Dechow, P.M., W. Ge, C.R. Larson and R.G. Sloan, 2010. Predicting material accounting misstatements. Working paper, University of California, Berkeley. Demurger, S., 2001. Infrastructure development and economic growth: An explanation for regional disparities in China? Journal of Comparative Economics 29, 95-117. Demurger, S., J.D. Sachs, W.T. Woo, S.M. Bao and G. Chang, 2002. The Relative Contributions of Location and Preferential Policies in China’s Regional Development: Being in the Right Place and Having the Right Incentives. China Economic Review, 13, 444–465. Easley, D. and M. O’Hara, 2004. Information and the cost of capital. Journal of Finance 54, 1553-1582. Easton, P., 2006. Use of forecasts of earnings to estimate and compare cost of capital across regimes. Journal of Business Finance and Accounting 33, 374-394. Easton, P., and G. Sommers, 2007. Effects of analysts’ optimism on estimates of the expected rate of return implied by earnings forecasts. Journal of Accounting Research 45, 983-1015.

37

Easton, P., 2009. Estimating the cost of capital implied by market prices and accounting data. Foundations and Trends in Accounting 2, 241-364. Efendi, J., A. Srivastava, and E.P. Swanson, 2007. Why do corporate managers misstate financial statements? The role of option compensation and other factors. Journal of Financial Economics 85, 667-708. Fan, G., and X. L. Wang, 2003. The report on the relative process of marketization of each region in China. The Economic Science Press (in Chinese). Farber, H.S., 1983. The determination of the union status of workers, Econometrica 51, 7-37. Fich, E.M. and A. Shivdasani, 2007. Financial fraud, director reputation, and shareholder wealth. Journal of Financial Economics 86, 306-336. Firth, M., P. Fung and O.M. Rui, 2006a. Corporate governance and CEO compensation in China. Journal of Corporate Finance 12, 693-714. Firth, M., P. Fung and O.M. Rui, 2006b. Firm performance, governance structure, and top management turnover in a transitional Economy. Journal of Management Studies 43, 1289-1330. Firth, M., P. Fung and O.M. Rui, 2007. Ownership, two-tier board structure, and the informativeness of earnings – Evidence from China. Journal of Accounting and Public Policy 26, 463-496. Glosten, L. and P. Milgrom, 1985. Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics 14, 71-100. Greene, W. H., 1995. LIMDEP: Users Manual, Version 7. Econometric Software Inc., Bellport, New York. Guner, A.B., U. Malmendier and G. Tate, 2008. Financial expertise of directors. Journal of Financial Economics 88, 323-354. Haque R. and M. Haque, 2008. Bivariate probit models of labour market status, in Haque R. and M. Haque (eds). Gender, Ethnicity and Employment-Non English Speaking Background Migrant Women in Australia. Springer-Verlag: Heidelberg. 171-120. Heywood J.S. and M.S. Mohanty, 1993. Testing for state and local job queues. Journal of Labor Research 14, 455-67. Heywood J.S. and M.S. Mohanty, 1994. The role of employer and workplace size in the US federal sector job queue. Oxford Bulletin of Economics and Statistics 56, 171-88. Hennes, K., A. Leone and B. Miller, 2008. The importance of distinguishing errors from

38

irregularities in restatement research: The case of restatements and CEO/CFO turnover. Accounting Review 83, 1487-1519. Hou, K., M.A. van Dijk and Y. Zhang, 2010. The implied cost of capital: A new approach. Working paper, Ohio State University. Hribar, P and N. T. Jenkins, 2004. The effect of accounting restatements on earnings revisions and the estimated cost of capital. Review of Accounting Studies 9, 337-356. Jensen, M. C., 1993. The modern industrial revolution, exit and the failure of internal control systems. Journal of Finance 48, 831-880. Karpoff, J., D.S. Lee and G.S. Martin, 2008a. The consequences to managers for financial misrepresentation. Journal of Financial Economics 88, 193-215. Karpoff, J., D.S. Lee and G.S. Martin, 2008b. The cost to firms of cooking the books. Journal of Financial and Quantitative Analysis 43, 581-612. Karpoff, J., D.S. Lee and G.S. Martin, 2009. The legal penalties for financial misrepresentation. Working paper, University of Washington. La Porta, R., F. Lopez-de-Silanes, A. Shleifer and R. Vishny, 2000. Investor protection and corporate governance. Journal of Financial Economics 58, 3-27. Li, C.Q., 2008. The Study of the Quality of Accounting Disclosure by Listed Companies: A Perspective from Restatements of Annual Reports. Shanghai Stock Exchange. Available at: http://www.sse.com.cn/sseportal/webapp/datapresent/SSEDisquisitionAndPublicationAct Morck, R., B. Yeung and W. Yu, 2000. The information content of stock markets: Why do emerging stock markets have synchronous stock price movements? Journal of Financial Economics 58, 215-260. Murphy, D.L., R.E. Shrieves and S.L. Tibbs, 2009. Understanding the penalties associated with corporate misconduct: An empirical examination of earnings and risk. Journal of Financial and Quantitative Analysis 44, 55-83. O’Hanlon, J. and A. Steele, 2000. Estimating the equity risk premium using accounting fundamentals. Journal of Business Finance and Accounting 27, 1051-1084. Palmrose, Z., V. Richardson, and S. Scholz, 2004. Determinants of market reactions to earnings restatements. Journal of Accounting and Economics 37, 59-90. Park, Y., Shin, H., 2004. Board composition and earnings management in Canada. Journal of Corporate Finance 10, 431-457. Pastor, L., M. Sinha and B. Swaminathan, 2008. Estimating the intertemporal risk-return tradeoff

39

using the implied cost of capital. Journal of Finance 6, 2859-2897. Plumlee, M. and T.L. Yohn, 2009. Analysis of the underlying causes of restatements. Working paper, University of Utah. Poirier, D. J., 1980. Partial observability in bivariate probit models. Journal of Econometrics 12, 209-217. Raonic, I., S. McLeay and I. Asimakopoulos, 2004. The timeliness of income recognition by European companies: An analysis of institutional and market complexity. Journal of Business Finance & Accounting 31, 115-148. Schipper, K. and L. Vincent, 2003. Earnings quality. Accounting Horizon 17, 97-110. Sun, Z., F.W. Liu and Z.Q. Li, 2005. Market development, government influence and corporate debt maturity structure. Economic Research Journal 5, 52-63 (in Chinese). Tsui, K.Y., 1996. Economic reform and interprovincial inequalities in China. Journal of Development Economics 50, 353-368. Wang, T. Y., 2004. Economic determinants of corporate fraud propensity and detection. Working paper, University of Maryland. Wu, M. and X. Wang, 2009. The quality of financial reporting in China. Working paper, The University of Hong Kong. Xia, L.J. and Y.Q. Fang, 2005. Government control, institutional environment and firm value: Evidence from the Chinese Securities Market. Economic Research Journal 5, 41-50 (in Chinese). Xie, B., W. Davidson III, and P. DaDalt, 2003. Earnings management and corporate governance: the role of the board and the audit committee. Journal of Corporate Finance 9, 295-316. Zhou, C.S. and G. Ma, 2005. The Ownership Structure and Financial Restatements in Chinese Listed Companies. Financial Research, 82-92.

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Appendix 1

In order to regulate how listed companies disclose the correction of financial information, to enhance their credibility and promptness and to protect the legal rights of investors, the China Securities Regulatory Commission issued “Preparation Conventions of Information Disclosure by Companies Offering Securities to the Public No. 19 – Correction of financial information and related disclosures”. These conventions apply to all publicly listed companies. Preparation Conventions of Information Disclosure by Companies Offering Securities to the Public No. 19 – Correction of financial information and related disclosures

1. In order to regulate how listed companies disclose the correction of financial information, to enhance their credibility and promptness and to protect the legal rights of investors, the China Securities Regulatory Commission has issued the Preparation Conventions of Information Disclosure by Companies Offering Securities to the Public No. 19 in accordance with the “Company Law of the People’s Republic of China” and the “Securities Law of the People’s Republic of China”.

2. The Convention is applicable under the following circumstances:

- A company is ordered by the government authorities to correct errors included in any previous periodic reports

- As determined by the Board of Directors, a company volunteers to disclose errors included in any previous periodic reports, or - Other events that are deemed warranted by the CSRC.

3. Companies that fit the above criteria should release to the public a “Material

Events Special Alert” to disclose the corrected financial information. 4. The modified financial statements should follow the related formats set by the China Securities Regulatory Commission. 5. After a previous set of annual financial statements has been corrected, the modified annual financial statements should be audited by a qualified CPA firm. 6. A special alert should contain the following:

- A detailed explanation of the modifications from the board of directors or company management

- the effects of the modifications on the financial position and business operations of the company

- modified financial statements which have been audited, notes to financial statements associated with the correction, and the name of the CPA firm issuing the audit report.

7. Disclosures on modified financial statements are required under three circumstances:

- If the financial information of previous periods (including annual, semi-annual and quarterly) has been amended, a company should disclose, (i) amongst other sets of annual financial statements under modification due to correction of the events, the

41

modified annual financial statements of the latest full accounting year; (ii) amongst other sets of interim financial statements under modification due to correction of the events, the modified interim financial statements of the latest interim period;

- If the correction is only made on interim financial statements published in the current year, a company should disclose all interim financial statements of the current year (quarterly and semi-annual) which have been modified due to correction of the events; or

- If the correction is made on interim financial statements of the prior year although the annual financial statements of the prior year have yet to been published, a company should disclose all of the interim financial statements which have been modified due to correction of the events.

8. All amended items on the modified financial statement should be highlighted in bold. 9. If a company modifies the annual financial information released three years or before, which has no effects on the financial statements over the past three years, it is not required to reveal the amended financial information thereafter. 10. The China Securities Regulatory Commission reserves the rights to interpret the terms and conditions of the convention.

42

Appendix 2 Stock Symbol: ST Weida Stock Code:000603 Series Code:2003-014

WeiDa Medical Applied Technology CO. LTD Board of Directors

Restatement of 2002 Annual Report

Our company and all the members of the board of directors guarantee the verity, correctness and completeness of this announcement. And we take joint responsibility for any possible false record, misleading statement and significant omission in this announcement. Our company released the annual report of year 2002 and its abstract on April 24th, 2002. After an investigation by the Shenzhen Stock Exchange (SZE), it is found that our company’s accounting policy for 1,122,300 yuan of shutdown losses is not appropriate. According to the requirement of the SZE, we restate the following data: Original: the main profit this year in the income statement and operation data abstract: net profit after deduction of non-recurring gains and losses amounts to 66,779.52 yuan Restatement: net profit after deduction of non-recurring gains and losses amounts to -1,055,520.48 yuan Due to the adjustment of the net profit after deduction of non-recurring gains and losses, the related changes are as follows: Earnings per share after deduction of non-recurring gains and losses -0.00094 yuan Net asset yield rate after deduction of non-recurring gains and losses (diluted) -0.91% Net asset yield rate after deduction of non-recurring gains and losses (weighted average) -1.26% Earnings per share after deduction of non-recurring gains and losses (diluted) -0.00094 yuan Earnings per share after deduction of non-recurring gains and losses (weighted average) –0.00094 yuan After deduction of non-recurring gains and losses, the net profit of our company is a loss. According to the related regulation, the stock of our company will remain under the special treatment. Announcement here by

WeiDa Medical Applied Technology CO. LTD Board of Directors

June 11th, 2003

43

Table 1 Number of restatement samples during 2000-2005 We collect 201 restatement announcements made by China listed companies during the period 2000-2005 (firms in the finance industry are omitted). Panel A: The number of restatements by year and by stock exchange

Shanghai Shenzhen Total Year

Number Percentage Number Percentage Number Percentage

2000 23 4.1% 0 0.0% 23 2.2%

2001 30 4.7% 18 3.6% 48 4.2%

2002 47 6.7% 38 7.7% 85 7.1%

2003 24 3.1% 21 4.3% 45 3.6%

2004 18 2.2% 24 4.6% 42 3.1%

2005 8 1.0% 20 3.8% 28 2.1%

Total 150 3.5% 121 4.0% 271 3.7% Panel B: The number of restatements by industry Industry name Industry code Number of

restatements Percentage of restatements

within an industry

Agriculture A 16 8.5% Mining B 2 1.9% Food, beverage C0 15 4.6% Textile/Apparel C1 8 2.5% Timber, furniture C2 0 0.0% Paper making, printing C3 5 3.7% Petroleum, chemistry, plastics C4 37 4.6% Electronics C5 14 6.3% Metal, non-metal C6 28 4.3% Machinery, equipment, instrument C7 38 3.4% Medicine, biological product C8 17 3.7% Other manufacturing industries C9 4 4.1% Power, gas and water D 9 2.9% Construction E 0 0.0% Transportation F 8 2.7% IT G 20 4.4% Retail H 10 1.9% Real estate J 8 2.3% Social service K 10 4.5%

44

Communication L 2 3.0% Conglomerate M 20 4.3% Total 271 3.7% We use the CSRC (Chinese Securities Regulation Commission) industry classification standard. As most of firms belong to the Manufacturing industry whose code begins with ‘C’, we use the first two codes to classify these samples. Our sample does not include the financial industry whose code begins with ‘I’.

Panel C: Number of restatements and development index by province Province MINDEX

score MLEGAL

score Number of restatement

cases Ratio of

restatements to listed firms

Guangdong 9.02 9.42 19 2.3% Zhejiang 8.27 6.57 10 2.4% Fujian 8.09 6.58 6 2.6% Jiangsu 7.58 5.80 15 3.5% Shanghai 7.44 8.89 26 3.2% Tianjin 6.70 6.60 5 3.6% Shandong 6.63 4.96 14 3.5% Beijing 6.37 10.83 13 3.0% Liaoning 6.04 5.47 20 6.6% Hainan 5.99 5.87 7 5.8% Henan 5.91 4.70 3 1.8% Chongching 5.84 3.52 5 3.2% Anfei 5.78 4.56 11 5.2% Sichuan 5.60 4.12 19 5.2% Guangxi 5.35 4.40 5 4.2% Hebei 5.26 4.96 13 7.5% Hubei 5.08 4.52 13 3.8% Jiangxi 4.99 4.06 2 1.6% Hunan 4.96 3.42 12 5.3% Jilin 4.93 5.02 11 5.6% Yunnan 4.92 4.11 3 2.6% Heilongjiang 4.62 5.36 12 6.5% Neimengru 4.45 4.63 4 4.1% Shanxi 4.42 5.39 3 2.5% Shaanxi 4.21 3.00 5 3.6% Gansu 4.04 3.06 5 4.9% Guizhou 3.93 3.35 2 2.6% Ningxia 3.48 3.06 2 3.2% Xingjiang 3.45 3.87 3 2.2% Qinghai 3.15 4.40 0 0.0%

45

Xizang 1.69 4.41 3 6.4% MINDEX is a comprehensive index to capture the regional market development from the following aspects: (1) the relations between government and markets, such as the role of markets in allocating resources and enterprises’ burden in addition to normal taxes; (2) the development of non-state business, such as ratio of industrial output by the private sector to total industrial output; (3) development of product markets, such as regional trade barriers; (4) development of factor markets such as FDI and mobility of labor; (5) development of market intermediaries and the legal environment (such as the protection of property rights). MLEGAL is a sub-index of MINDEX, which represents the legal environment. The MINDEX and MLEGAL scores shown above are the average scores during period 1999-2002.

46

Table 2. Univariate comparisons of restatement and non-restatement firms

Mean Median Variables Restate Control Difference

(P-value) Restate Control Difference

(P-value) BOARD 2.251 2.228 0.024 (0.283) 2.197 2.197 0.000 (0.309) OUT% 0.778 0.762 0.016 (0.257) 0.778 0.778 0.000 (0.235) DUAL 0.116 0.147 -0.030 (0.337) 0.000 0.000 0.000 (0.337) CFO 0.216 0.220 -0.004 (0.915) 0.000 0.000 0.000 (0.732) FINBACK% 0.265 0.311 -0.046 (0.009)*** 0.267 0.293 -0.026 (0.361) BIG15 0.159 0.237 -0.078 (0.036)** 0.000 0.000 0.000 (0.036)** TOP 0.419 0.440 -0.021 (0.207) 0.399 0.407 -0.008 (0.71) PRIVATE 0.246 0.211 0.034 (0.377) 0.000 0.000 0.000 (0.377) CENTRAL 0.164 0.103 0.060 (0.056)* 0.000 0.000 0.000 (0.056)* GROWTH 0.265 1.300 -1.035 (0.231) 0.132 0.133 -0.001 (0.990) LOSS 0.052 0.030 0.022 (0.242) 0.000 0.000 0.000 (0.242) SEO 0.164 0.089 0.075 (0.061)* 0.000 0.000 0.000 (0.061)* LEV 0.506 0.465 0.041 (0.16) 0.473 0.448 0.026 (0.194) MINDEX 6.305 6.825 -0.520 (0.001)*** 6.030 6.685 -0.655 (0.028)** MLEGAL 5.925 6.448 -0.524 (0.011)** 5.180 5.900 -0.720 (0.095)* Variables Definition BOARD the number of board directors OUT% the proportion of directors who are not members of the management team DUAL a dummy variable taking the value one if the chairman and CEO positions are

held by the same person CFO a dummy variable coded one if the CFO or general accountant is on the board FINBACK% the percentage of directors who have an accounting or financial background. If a

director has a professional certificate of “Accountancy” or “Economy”, we take her/him as having an accounting/financial background

BIG15 a dummy variable coded one (1) if the auditor belongs to the 15 auditors that are designated by the CSRC as a good reputation auditor

TOP the proportion of shares owned by the largest stockholder PRIVATE a dummy variable that equals one if the ultimate controlling stockholder is a

private or foreign investor, else it equals zero CENTRAL a dummy variable that equals one if the ultimate controller is the central

government, else it equals zero GROWTH the sales growth in the two years prior to the date of the restatement LOSS a dummy variable taking the value one if the firm had recorded a loss in each of

the two years prior to the accounting manipulation and made a profit in the year of the manipulation

SEO a dummy variable taking the value one if the firm makes a SEO in the year after the accounting manipulation

LEV debt to total assets

47

MINDEX The marketization index of the province where the firm is located MLEGAL the legal environment index of the province where the firm is located

48

Table 3. The bivariate probit regression results of the characteristics of restating firms P(Fj=1) P(Dj=1|Fj=1) estimate p-value estimate p-value OUT% 0.120 0.798 5.559 0.096 CFO 0.165 0.358 -0.223 0.587 FINBACK -0.680 0.062 -1.990 0.213 CENTRAL 0.846 0.001 -2.849 0.004 BIG15 -0.098 0.574 -1.502 0.105 MLEGAL -0.135 0.000 1.369 0.001 LEV 0.710 0.026

LOSS 0.106 0.780

SEO 0.341 0.009 Intercept 0.446 0.336 -6.271 0.048 Model summary Chi-square 69.45 Prob > chi2 (0.001) Test of rho =0 Chi-square 19.97 Prob >

chi2 (0.001)

Variables Definition OUT% the proportion of directors who are not members of the management team CFO a dummy variable coded one if the CFO or general accountant is on the board FINBACK% the percentage of directors who have an accounting or financial background. If a

director has a professional certificate of “Accountancy” or “Economy”, we take her/him as having an accounting/financial background

CENTRAL a dummy variable that equals one if the ultimate controller is the central government, else it equals zero

BIG15 a dummy variable coded one (1) if the auditor belongs to the 15 auditors that are designated by the CSRC as a good reputation auditor

LOSS a dummy variable taking value the one if the firm had recorded a loss in each of the two years prior to the accounting manipulation and made a profit in the year of the manipulation

SEO a dummy variable taking the value one if the firm makes a SEO in the year after the accounting manipulation

LEV debt to total assets MLEGAL the legal environment index of the province where the firm is located

49

Table 4. Market-adjusted abnormal return around restatement announcement Panel A: market-adjusted CAR, around the restatement date (day = 0) Days N CAAR Z T (-10,-1) 267 -0.86% -1.817* -1.558* (-5,-1) 267 -0.88% -2.625*** -2.570*** (-1,0) 266 -0.03% -0.134 0.123 (0,+1) 267 -0.06% -0.303 -0.404 (0,+5) 267 -0.87% -2.350*** -2.618*** (-5,+5) 267 -1.74% -3.506*** -3.513*** (0,+10) 267 -0.79% -1.608* -1.939** (-10,+10) 267 -1.66% -2.419*** -2.356*** Panel B: CAR (-10,+10) classified by MINDEX and MLEGAL

MINDEX MLEGAL < median > median Difference T-value <

median >

median Difference T-value

-0.54% -1.85% -1.31% 0.47 -1.26% -2.21% -0.95% 1.09 FIGURE 1. Plots of CAR

-2.5

-2

-1.5

-1

-0.5

0

0.5

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1 0 1 2 3 4 5 6 7 8 9

10

All Low_MLEGAL High_MLEGAL

50

Table 5. Comparison of the cost of capital before and after the restatement This table shows whether the cost of capital of restatement firms increases after the restatement. We estimate the cost of capital using the method of O’Hanlon and Steele (2000):

jtjt

jtjt

jt

jt

bpsbpsp

bpseps

εδδ +−

+=−− 1

101

where epsjt and bpsjt are the earnings per share and book value per share of firm j in year t, respectively. pjt is the closing price of firm j in year t. The estimated intercept term 0δ = cost of capital (r), and the estimated parameter 1δ = (r – g)/(1 + g), where g is the expected growth rate. We conduct the above regressions with the restatement sample and control sample before and after the restatement, respectively. The results are shown in Panel A. Panel A: Cost of capital before and after the restatement

Restatement sample Control sample

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

3.94% (0.185)

5.89% (0.115)

1.96% 3.96% (0.161)

4.08% (0.117)

0.11%

The number in parenthesis below the cost of capital is the adjusted R-square of the regression model. In order to test whether the difference in cost of capital before and after the restatement is significant, we employ the following regression with the before and after samples of the restatement and control samples:

( ) jtjt

jtjtDAR

DARjt

jt

bpsbpsp

AfterRestategAftergRestateg

AfterRestaterAfterrRestaterbpseps

εδ

δ

+−

⋅⋅⋅+⋅+⋅++

⋅⋅+⋅+⋅+=

−

−

11

01

where “Restate” is a dummy variable, which is equal to one if it is a restatement firm, otherwise equal to zero; “After” is a dummy variable, which is coded one if the sample is after the restatement, else coded zero. The estimated coefficient of rD is used to test whether there is a difference in the change of cost of capital between the restatement and control samples. The coefficient of rR is the difference in cost of capital between the restatement and control samples before the restatement. The coefficient of rA is the difference in cost of capital before and after the restatement for the control sample. The results are shown in Panel B. Panel B: Difference in cost of capital before and after restatement

0δ Rr Ar Dr Adj-R2 0.040***

(0.000) 0.000

(0.976) 0.001

(0.879) 0.018*

(0.047) 0.185

The numbers in the parentheses below the coefficients are the p-values of the t-test.

51

Panel C: Cost of capital before and after the restatement grouped by market development index

MINDEX (MLEGAL) < median MINDEX (MLEGAL) > median

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

MINDEX 3.48% (0.267)

4.05% (0.264)

0.58% 4.87% (0.219)

6.62% (0.099)

1.75%

MLEGAL 4.05% (0.189)

4.10% (0.232)

0.05% 4.54% (0.269)

6.71% (0.103)

2.17%

The number in parenthesis is the adjusted R-square of the model. In order to test whether there is any difference in terms of cost of capital for firms under different market development conditions, we employ the following regression:

( ) jtjt

jtjtMindexMindex

jt

jt

bpsbpsp

MindexAftergMindexAfterrbpseps

εδδ +−

⋅⋅⋅++⋅⋅+=−− 1

101

where “After” is a dummy variable, which coded one if it is after the restatement, otherwise zero. Mindex is MINDEX, or MLEGAL, which are defined in Table 1. The estimated coefficient of rMindex indicates the effect of Mindex on the change in cost of capital after the restatement. GMindex captures the effect of MINDEX and MLEGAL on the change in expected growth rate after the restatement. The results are reported in Panel D. Panel D: The effect of market development index (MINDEX, MLEGAL) on the difference in the cost of capital from before to after the restatement 0δ Mindexr

coefficient p-value Coefficient p-value Adj-R2

MINDEX 0.038 (0.000) 0.003 (0.014) 0.225 MLEGAL 0.037 (0.000) 0.003 (0.006) 0.228 Panel E: Market-adjusted stock returns from one month before to one month after the restatement (%)

Restatement sample Control sample

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

0.018 -1.341 -1.360 (0.031)**

0.714 0.339 -0.375 (0.600)

Panel F: The effect of market development index (MINDEX, MLEGAL) on the difference in the market-adjusted one-month stock return before and after the restatement

MINDEX (MLEGAL) < median MINDEX (MLEGAL) > median

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

52

MINDEX -0.475 -1.514 -1.038 (0.266)

0.483 -1.180 -1.663 (0.051)*

MLEGAL -0.646 -1.475 -0.829 (0.33)

0.683 -1.208 -1.891 (0.041)**

Panel G: The Tobin’s Q in the year before and after the restatement (%)

Restatement sample Control sample

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

1.588 1.422 0.166 (0.001)***

1.483 1.418 0.064 (0.139)

Panel H: The effect of market development index (MINDEX, MLEGAL) on the difference in the control sample-adjusted Tobin’s Q in the year before and after the restatement

MINDEX (MLEGAL) < median MINDEX (MLEGAL) > median

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

Difference between two

groups’ difference

MINDEX 0.054 0.009 -0.045 (0.417)

0.068 0.005 -0.063 (0.353)

-0.008 (0.85)

MLEGAL 0.027 0.020 -0.006 (0.914)

0.095 -0.006 -0.101 (0.108)

-0.085 (0.039)**

Panel I: The proportion of firms making SEOs before and after restatement

Three years prior to the restatement

Three years after the restatement

Difference

Restatement firms 0.328 0.097 -0.231 Control firms 0.286 0.133 -0.153 Difference 0.042

(0.364) -0.035 (0.276)

-0.078* (0.091)

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Table 6. Comparison of the bid-ask spread before and after accounting restatements To test whether accounting restatements affect the liquidity of the stock, we compare the bid-ask spread before and after the restatement for restatement firms and control firms, respectively. We calculate the average daily relative effective spread for one-month prior to and one-month post the restatement announcement for each firm. In Panel A, we provide the results of the bid-ask spread. We then divide the restatement sample into two groups based on the MINDEX or MLEGAL score. One group is firms with MINDEX (MLEGAL) scores below the median, while the other group is firms with MINDEX (MLEGAL) scores above the median. We report the bid-ask spread one-month before and after the restatement for these two groups. The results are shown in Panel B. Panel A: The differences in bid-ask spreads before and after the restatement

Before the restatement After the restatement Difference

Restatement firms 0.0717% 0.0788% 0.0070%

Control firms 0.0889% 0.0837% -0.0052%

Difference -0.0172% -0.0049% 0.0122%***

** and * indicate the 5% and 10% significance levels of the t-statistics. Panel B: The comparison of the bid-ask spread before and after the restatement for the restatement firms grouped by MINDEX (or MLEGAL)

Below the median Above the median

Before the restatement

After the restatement

Difference Before the restatement

After the restatement

Difference

The difference between the two groups’

differenceMINDEX 0.0812% 0.0814% 0.0002% 0.0636% 0.0708% 0.0072% 0.0071%

MLEGAL 0.0805% 0.0759% -0.0045% 0.0645% 0.0762% 0.0117%** 0.0162%*

** and * indicates the 5% and 10% significance levels of the t-statistics.

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Table 7. Comparison of modified (qualified) audit opinions before and after the restatement As one of the consequences of restatements is an increase in the probability of a modified audit opinion, we compare the percentage of modified audit opinions one-year before and one-year after the restatement announcement for restatement firms and control firms, respectively. Panel A:

Year -1 Year 0 Difference -1,0 Restatement firms 4.69% 9.59% 4.91% Control firms 5.51% 3.31% -2.21% Difference 0.83% -6.29% -7.11%

p-value = 0.002 Panel B:

Year -1 Year 0 Difference -1,0 Above the median 5.04% 13.45% 8.40% Below the median 1.75% 0.88% -0.87% Difference -3.29% -12.57% -9.28%

MINDEX

p-value =0.001 Above the median 2.50% 9.17% 6.67% Below the median 4.43% 5.31% 0.89% Difference 1.93% -3.86% -5.78%

MLEGAL

p-value =0.039

55

Table 8. Comparison of the CEO turnover before and after the restatement As one of the consequences of restatements is an increase in the probability of CEO turnover, we compare the percentage of CEO turnover one-year before and one-year after the restatement announcement for restatement firms and control firms, respectively.

Panel A: The difference in CEO turnover before and after the restatement

Before the restatement After the restatement Difference

Restatement firms 28.5% 30.8% 2.3% Control firms 26.8% 18.7% -8.2% Difference 1.7% 12.1% 10.5%** ** and * indicates the 5% and 10% significance levels of the t-statistics. Panel B: Comparison of CEO turnover for the restatement firms grouped by MINDEX and MLEGAL

MINDEX/MLEGAL Below the median

MINDEX/MLEGAL Above the median

MINDEX or

MLEGAL Before the restatement

CEO turnover

After the restatement

CEO turnover

Difference Before the restatement

CEO turnover

After the restatement

CEO turnover

Difference

The difference between

two groups’

differencesMINDEX 33.04 36.61 3.57 23.28 27.59 4.31 0.74 MLEGAL 33.62 33.62 0.00 22.32 30.36 8.04 8.04