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- Publisher
- Taylor & Francis
- Copyright
- © 2016 Accounting Society of China
- ISSN
- 2169-7221
- eISSN
- 2169-7213
- DOI
- 10.1080/21697213.2016.1144970
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Abstract
China Journal of aCC ounting StudieS , 2016 Vol . 4, no . 1, 15–33 http://dx.doi.org/10.1080/21697213.2016.1144970 Earnings management, IPO screening and resource allocation efficiency* a b Jun Huang and Ting Li a b institute of a ccounting and f inance, Shanghai university of f inance and economics, China; School of a ccountancy, Shanghai university of f inance and economics, China KEYWORDS ABSTRACT earnings management; iPo Employing IPO firms from 2006 to 2012 as the sample and taking screening; Soes; political the perspective of earnings management, this paper examines the connections; business decision efficiency of the Stock Issue Examination and Verification complexity Committee (SIEVC) in China. We find that the rate of passing IPO screening is negatively correlated with firms’ earnings management, but this relationship is only found for the high-level earnings management. Further analyses show that when IPO firms are state-owned enterprises (SOEs) or receive industry support from government, their IPO applications are less likely to be rejected for earnings management. Meanwhile, political connection and business complexity weaken the negative relationship between the rate of passing the IPO screening and firms’ earnings management. Finally, we find that IPO firms with more earnings management have lower stock returns and worse accounting performance after listing. 1. Introduction An interesting debate exists in China over the registration system of stock issuance and the discussion on ‘non-administration’ of the regulation of initial public offerings (IPO). In 2001 an approval system was adopted whereby the Stock Issue Examination and Verification Committee (SIEVC) replaced previous administrative organisations. The China Securities Regulatory Commission (CSRC) issued the Interim Provision of Stock Issuance Examination and Verification Committee to reform the IPO regulation system and the structure of SIEVC in 2003. That reform was expected to improve the specialisation, authority and validity of SIEVC through introducing committee members in the fields of accounting and law. As the ‘gate keeper’ of the securities market, the decision of SIEVC is related to the development of the capital market and the protection of investors’ interests. However, the literature has provided limited discussions on the decision efficiency of SIEVC. Earnings management by listed companies, especially in the process of IPO, has drawn broad attention from academic researchers. As evidenced by the literature on other countries, IPO companies are strongly motivated by financial considerations in order to obtain listing CONTACT Jun huang huangjun@mail.shufe.edu.cn *Paper accepted by Kangtao Ye © 2016 a ccounting Society of China 16 J. Hu Ang And T. LI qualifications and issue shares at a high price (Aharony, Lin, & Loeb, 1993; d echow & Skinner, 2000; Teoh, Wong & Rao, 1998). Further, firms have to satisfy some IPO requirements in China, such as continuously positive earnings in three recent years. So earnings management by IPO firms is likely to be more prevalent in the Chinese stock market (Lin & Wei, 2000). As the final decision maker of IPO, the SIEVC has the responsibility to select good firms for listing so as to optimise the allocation of social capital (Hu, 2011). However, there are disputes over the decision eci ffi ency of SIEVC. For instance, as shown by some studies, social network and political connections influence the decisions of SIEVC and limit the capability of SIEVC to select good companies (Liu, Tang, & Tian, 2013; Yang, 2013). This paper aims to examine the decision efficiency of SIEVC from the perspective of earnings management and provide evidence to evaluate the present IPO regulation system in China. u sing companies applying for listing from 2006 to 2012 as the sample, we find that the rate of passing IPO screening is negatively correlated with r fi ms’ earnings management. However, the relationship is only found at the high-level earnings management. Further analyses show that when IPO companies are state-owned enterprises (SOEs) or enjoy industry support from government, the negative correlation between the rate of passing IPO screening and firms’ earnings management is weaker. Meanwhile, for politically connected firms or firms with complicated business, their IPO applications are less likely to be rejected for earnings management. Finally, IPO companies with more earnings management experience poorer stock returns and worse accounting performance after listing. Our paper makes several contributions to the literature. First, the researchers of earn- ings management in IPO mainly focus on accounting manipulation. We do not know how earnings management influences the success of firms’ IPO, particularly under the regula- tory background of the Chinese stock market. From the view of earnings management, our analysis provides empirical evidence about the decision efficiency of SIEVC. Further, prior financial and accounting researchers are mainly interested in companies’ post-listing behaviour. There are few analyses on the firms’ IPO process, and the related studies mainly examine the IPO underpricing. The listing process is still a ‘black box’. This paper investigates how earnings management influences the IPO screening, enhancing our understanding of IPO process in China. Third, China has launched a market-oriented reform to stock issuance regulation by adopting the approval system in 2001. under the approval system, the SIEVC reviews the materials of IPO applicants and determines whether they are qualified for listing. However, there are still disputes over the approval system. Through examining the role and consequence of earnings management in firms’ IPO, our study is helpful in evaluating the Chinese IPO regulation system and provides some policy implications for optimal resource allocation in the capital market. The paper proceeds as follows. Section 2 reviews the literature. Section 3 develops the hypotheses based on theoretical deduction. We introduce the sample, data and variables in Section 4. Section 5 presents the empirical results, whereas Section 6 does several robustness tests to verify our conclusions. Finally, Section 7 draws conclusions. 2. Literature review Earnings management refers to the choice of accounting policy and real transactions by managers in an attempt to mislead the stakeholders’ understanding of firm performance or to affect contracts based on reported earnings (Healy & Wahlen, 1999). As evidenced by CHInA JOuRnAL OF ACCOunTIng STudIES 17 prior studies, firms have a motivation to manage earnings for the purpose of initial public offerings (Aharony et al., 1993; d echow & Skinner, 2000; Teoh, Wong & Rao, 1998). 2.1. US evidence The literature has widely discussed various activities of firms’ earnings management, such as executive compensation (Healy, 1985), tariff protection (Jones, 1991), CEO turnover (Murphy & Zimmerman, 1993) and debt covenants (d efond & Jiambalvo, 1994) since positive account- ing theory was first proposed by Watts and Zimmerman (1978). In relation to earnings management in the IPO, d echow and Skinner (2000) argue that managers have a financial incentive to increase offer prices and reduce financing cost. Aharony, Lin, and Loeb (1993) find that firms’ accruals are at the highest level in the IPO year and are negatively correlated with net incomes and cash flows in the future. Friedlan (1994) examines the role of accounting information in IPO pricing using the modified d eAngelo (1986) model. He finds that most companies adopt accounting policies of increasing earnings before the IPO. Teoh, Wong, and Rao (1998) employ the data of 1,682 listed companies and document that companies increase earnings in the IPO year through charging less depreci- ation and a lower debt allowance (i.e. a lower provision for doubtful debts). Teoh, Welch, and Wong (1998) examine the economic consequences of earnings man- agement during the IPO. They find that companies with higher earnings management have lower stock returns after IPO, and have difficulty in issuing secondary offering. Moreover, their accounting performance deteriorates after listing due to the reversal of accruals. d uCharme, Malatesta, and Sefcik (2004) suggest that firms are more likely to face law suits if investors discover their earnings management in the IPO. 2.2. Chinese studies d ue to the IPO regulation by the government in China, particularly the control of the offer price, earnings management of Chinese firms has drawn much attention from researchers. Aharony, Lee, and Wong (2000) analyse IPO firms’ earnings management in the Chinese securities market. They find that companies present a continuous increase of earnings three years before listing and a continuous decrease of earnings three years after listing. Lin and Wei (2000) document that A-share companies report high earnings one or two years before the IPO whereas they present a significant decrease of earnings in the IPO year. Chen (2010) employs the data of listed companies from 2001 to 2007 and finds that accounting manip - ulation has a significant and positive effect on the IPO price. Pan, Wu, and Shi (2010) study the impact of social capital on IPO firms’ earnings management from a macro view. They find that IPO companies have lower earnings management when they locate in the provinces with a higher level of social capital. Wang and Lian (2012) examine the regulation change of earnings forecast from mandatory disclosure to voluntary disclosure. Their analyses show that voluntary disclosure of an earnings forecast significantly reduces earnings management of IPO firms. Wang, Li, and Chen (2015) empirically examine the degree of upwards earnings management by companies under cyclical fluctuations of the macro-economy. They find that there exists a pro-cyclical effect of upwards earnings management. Recently there are some research reports about the IPO process in China. Hu (2011) exam- ines the effect of financial and industrial features on the rate of passing IPO screening. He 18 J. Hu Ang And T. LI finds that companies of larger size, with less debts and ranking top in their respective indus - tries are more likely to achieve IPO approval. Li and Liu (2012) argue that IPO firms have a higher probability of successful listing if they employ intermediate organisations with more social capital. Yang (2013) argues that if accounting firms’ partners become members of SIEVC, their clients’ IPO is less likely to be rejected. d u, Li, and d u (2013) are also interested in how the connection with SIEVC influences the rate of passing IPO scrutiny. They find that companies connected with SIEVC enjoy a higher rate of passing and the effect increases with the strength of connection. Chen, Zheng, and Li (2014) show that private firms estab - lishing connection with SIEVC via intermediate organisations have a higher success rate in IPO screening and obtain a larger IPO underpricing, but their performance and growth are lower after listing. Overall, there are numerous studies on earnings management of IPO firms and some researchers have analysed the IPO approval system in China. However, the literature fails to examine the IPO screening from the perspective of earnings management. Meanwhile, how IPO firms’ earnings management affects the resource allocation in the capital market is unclear. This paper aims to analyse the decision efficiency of SIEVC based on earnings management and provide some policy implications for IPO regulation in China. 3. Hypothesis development As the ‘gate keeper’ of the securities market, the decisions of SIEVC are directly related to the development of the capital market and the protection of investors’ interests. The SIEVC was set up to determine the stock issuance of IPO applicants after the adoption of the approval system in 2001. To promote the publicity, transparency and fairness of the IPO approval system, the CSRC issued the Interim Provision of Stock Issuance Examination Committee and reformed the structure of SIEVC in 2003, such as reducing the members of SIEVC from 80 to 25 (35 members for growth enterprises board), including experts from accounting and law firms as well as representatives from regulation bodies. This reform aims to improve the profession, authority and validity of SIEVC. Meanwhile, the provision requires that the SIEVC members should pay attention to whether accounting information reflects the financial status and business achievements of IPO companies and whether there are serious risks for IPO applicants. IPO qualification is still a rare resource in China. g oing public can help firms obtain financ - ing opportunities, expand financing channels, improve the transparency of information disclosure as well as enhance corporate images (Huang, Cheng, Li & Wei, 2014). The literature shows that IPO firms have the strong motivation of a financial package in order to obtain listing quotas and increase offering prices (Aharony, Lin & Loeb, 1993; d echow & Skinner, 2000; Teoh, Wong & Rao, 1998). Further, since Chinese IPO companies have to satisfy some regulation conditions, such as continuously positive earnings in three recent years, earnings management is likely to be more prevalent in the IPO process. As the final decision maker on IPO, the SIEVC is responsible for selecting enterprises with growth potential for listing, so as to optimise the resource allocation in the capital market. Some studies show that the regulatory body pays attention to firms’ earnings management. For example, companies applying for SEOs are less likely to get approval if they manage earnings to meet the regulatory requirements (Chen & Yuan, 2004). So it is expected that the rate of passing IPO screening is negatively correlated with firms’ earnings management. CHInA JOuRnAL OF ACCOunTIng STudIES 19 However, the accruals-based accounting and the elasticity of accounting policy choice pro- vide some opportunities for IPO companies to manage earnings (Pan, Wu & Shi, 2010). We speculate that IPO firms’ earnings management may not definitely lower the rate of passing IPO screening, particularly for low-level earnings management. Therefore, we have the first hypothesis as follows. H1: The rate of passing IPO screening is negatively correlated with firms’ earnings management; however, this relationship is only found at the high level of earnings management. We further analyse how IPO firms’ earnings management affects the resource allocation efficiency in the capital market. The prior studies show that IPO companies’ earnings man- agement is negatively correlated with their post-IPO performance (Lu & Zhu, 2001; Teoh, Welch & Wong, 1998). The higher the firms’ earnings management in the IPO, the worse their performance after listing. We speculate that the explanation is that earnings management before listing misleads investors to overestimate the stock value in the IPO. After listing, more firm information would be disclosed and the company’s true value would be identified by investors. As a result, the company’s stock price will gradually return to its intrinsic value, leading to poor post-IPO performance ( Teoh, Welch & Wong, 1998). In addition, the reversal of accruals after listing may also cause the decrease of operating performance for IPO firms (Aharony, Lee & Wong, 2000). Therefore, from a long-term perspective, IPO firms with more earnings management have lower stock returns and worse accounting performance after listing. We then propose the second hypothesis. H2: IPO firms with more earnings management have lower stock returns and worse accounting performance after listing. 4. Data and statistics 4.1. Sample and data Our analysis employs companies applying for IPO from 2006 to 2012 as the sample. Financial companies are excluded in consideration of die ff rences in financial reports and listing require - ments. We also drop companies with missing data from the analysis. Finally, we have 1,342 sample firms, in which 1,055 companies pass the IPO screening of SIEVC and 287 companies do not. All data are from the CSMAR and WInd databases. 4.2. Variable definition 4.2.1. Earnings management The literature shows that the modified Jones model is good at estimating firms’ earnings management (Bartov, gul, & Tsui, 2000). We employ the modified Jones model to calculate the discretionary accruals of IPO firms. The modified Jones model is stated as follows: TAC = E − CFO (1) j,t j,t j,t TAC ΔSALES PPE j,t i j,t j,t = + + + (2) 0 1 2 j,t TA TA TA TA j,t−1 j,t−1 j,t−1 j,t−1 20 J. Hu Ang And T. LI ΔSALES −ΔAR PPE j,t j,t j,t NDTAC = + + j,t 0 1 2 (3) TA TA TA j,t−1 j,t−1 j,t−1 TAC j,t DTAC = − NDTAC (4) j,t j,t TA j,t−1 where TAC refers to total accruals in year t; E denotes net profits in year t; CFO represents j,t j,t j,t operating cash flows in year t; ΔSALES is the change of revenues in year t; PPE refers to j,t j,t the original value of fixed assets in year t; NDTAC equals discretionary accruals in year t; j,t ΔAR is the change of accounting receivables in year t; TA denotes total assets in year t–1. j,t j,t-1 We first use equation (1) to calculate total accruals (TAC ) of IPO firms. Then we run the j,t regression of equation (2) by industry after eliminating new IPO firms and firms in the indus - tries with fewer than 15 observations. Equation (3) employs the estimated coefficients to calculate non-discretionary accruals (NDTAC ). We deduct non-discretionary accruals from j,t total accruals to get discretionary accruals (DTAC ). Finally, our measure of earnings man- j,t agement is the absolute value of discretionary accruals. 4.2.2. IPO approval The variable PASS denotes whether companies pass the IPO screening of SIEVC. It takes the value of one if firms get IPO approval, and zero otherwise. 4.2.3. Post-IPO performance We measure the market performance of IPO companies by using buy-and-hold return (Return) in three years subsequent to listing. Meanwhile, we construct the variable △ROE to measure firms’ operating performance after IPO, which takes the value of one if the change of ROE between one year after IPO and IPO year exceeds the mean value of sample firms, and zero otherwise. 4.2.4. Other variables Following the literature we control other factors that might influence the success of IPO. The ownership variable (SOE) equals to one if IPO firms are SOEs and zero otherwise. The audit variable (BIG4) takes the value of one if IPO companies are audited by one of ‘Big Four’ accounting firms and zero otherwise. The firm size (SIZE) is defined as the natural logarithm of total assets. The performance variable (ROE) equals to net profits divided by net assets. The debt variable (LEV) refers to the ratio of total debts to total assets. Finally, the growth variable (GROWTH) is defined as the annual growth of firms’ sales. d efinitions of the above variables are given in Table 1. 4.3. Descriptive statistics Table 2 presents the summary statistics of variables. There are 1,342 companies applying for IPO during the sample period. Among them 1,055 firms pass the IPO screening by SIEVC, accounting for 78.6% of sample firms. The statistic of DTAC shows that the average value of discretionary accruals is 0.088. Among firms that have passed the IPO screening of SIEVC, their average buy-and-hold return in three years after listing is 18.7%. In total, 16.4% of IPO CHInA JOuRnAL OF ACCOunTIng STudIES 21 Table 1. Variable definitions. Variables d efinition Dependent variables PASS t he variable denotes whether companies pass the iPo screening by Sie VC. it equals 1 if firms receive the iPo approval, and 0 otherwise. Return Post-iPo performance, equal to buy-and-hold return in three years subsequent to listing. △ROE Post-iPo performance, a dummy variable that equals to 1 if the change of ROE between one year after iPo and iPo year exceeds the mean value of sample firms, and 0 otherwise. Main independent variable DTAC earnings management variable, equal to the absolute value of discretionary accruals which is obtained through calculation according to Models (1)~(4). Control variables SOE t he nature of the firm’s ownership, a dummy variable to 1 if the firm is state-owned, and 0 otherwise. BIG4 t he variable equals to 1 if iPo companies are audited by one of ‘Big f our’ accounting firms, and 0 otherwise. SIZE f irm size, the nature log of the firm’s total assets. ROE Profitability, equal to net profits divided by net assets. LEV l everage, refer to the ratio of total debts to total assets. GROWTH t he variable is defined as the annual growth of firms’ sales. Table 2. Summary statistics of variables. Obs. Mean Median Std. d ev. Min. Max. PASS 1342 0.786 1.000 0.410 0.000 1.000 Return 709 0.187 -0.120 1.005 –0.771 5.716 △ROE 1055 0.536 1.000 0.499 0.000 1.000 DTAC 1342 0.088 0.061 0.088 0.001 0.562 SOE 1342 0.164 0.000 0.370 0.000 1.000 BIG4 1342 0.113 0.000 0.317 0.000 1.000 SIZE 1342 20.135 19.922 1.076 17.901 24.679 ROE 1342 0.281 0.260 0.116 0.046 0.814 LEV 1342 0.401 0.392 0.099 0.047 1.000 GROWTH 1342 0.314 0.264 0.312 –0.232 1.721 firms are SOEs. The percentage of firms employing ‘Big Four’ auditors is 11.3%. The mean value of ROE is 0.281, the average debt ratio is 40.1%, and the average sales growth is 0.314. Table 3 reports the Pearson correlation of variables. The correlation coefficient of PASS and DTAC is –0.061 and significant at the 5% level, indicating that IPO companies with higher earnings management are less likely to pass the SIEVC’s screening. Further, SOEs have a higher passing rate than private firms. IPO firms audited by the ‘Big Four’ have the lower passing rate. Moreover, firms with larger size, better performance and lower leverage are more likely to get IPO approval. Finally, the statistics show that the correlation coefficients among variables are smaller than 0.5, suggesting that multi-collinearity is not a serious problem in our analysis. 5. Empirical results 5.1. Univariate analysis Table 4 compares some characteristics of companies passing and non-passing the IPO screen- ing of SIEVC. It is shown that the average number of discretionary accruals of passing firms 22 J. Hu Ang And T. LI Table 3. Pearson correlation of variables. PASS DTAC SOE BIG4 SIZE ROE LEV DTAC –0.061** (0.025) SOE 0.074*** –0.024 (0.007) (0.37) BIG4 –0.249*** –0.043 0.121*** (0.000) (0.118) (0.000) SIZE 0.082*** –0.222*** 0.346*** 0.213*** (0.003) (0.000) (0.000) (0.000) ROE 0.059** 0.212*** –0.185*** –0.081*** –0.151*** (0.030) (0.000) (0.000) (0.003) (0.000) LEV –0.422*** –0.098*** 0.049* 0.150*** 0.310*** –0.035 (0.000) (0.000) (0.073) (0.000) (0.000) (0.198) GROWTH –0.045 0.144*** –0.069** 0.005 –0.048* 0.385*** 0.069** (0.101) (0.000) (0.011) (0.856) (0.077) (0.000) (0.012) note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. Table 4. univariate analysis between passing and non-passing firms. Variables Passing(n=1055) non-passing (n=287) difference P-Value DTAC 0.085 0.098 –0.013** 0.025 SOE 0.178 0.111 0.067*** 0.003 BIG4 0.072 0.265 –0.193*** 0.000 SIZE 20.181 19.965 0.216*** 0.003 ROE 0.285 0.268 0.017** 0.015 LEV 0.379 0.481 –0.102*** 0.000 GROWTH 0.307 0.341 –0.034 0.510 note: ***, **, * indicate significance at 1%, 5%, 10% respectively. are 0.085, and the corresponding number for non-passing firms is 0.098. The difference is significant at the 5% level. This means that earnings management of passing firms is lower than non-passing firms. The comparison of other variables shows that SOEs account for a larger percentage of firms getting IPO approval. Most passing firms are not audited by the ‘Big Four’. Finally, passing samples are characterised as firms with larger size, better perfor - mance and lower leverage. 5.2. Multiple regression To investigate the impact of earnings management on the IPO screening, we construct the following model: Prob(PASS = 1)= + DTAC + SOE + BIG4 + SIZE 0 1 2 3 4 (5) + ROE + LEV + GROWTH + 5 6 7 The dependent variable PASS takes the value of one if companies pass the IPO screening and zero otherwise. The major independent variable DTAC denotes earnings management of IPO firms. The others are control variables, including firm ownership (SOE ), auditor (BIG4), size (SIZE), performance (ROE), leverage (LEV) and growth (GROWTH). Table 5 presents the regression results. In column (1), the coefficient on DTAC is –0.137 and is significant at the 1% level, indicating that firms’ earnings management lowers the rate CHInA JOuRnAL OF ACCOunTIng STudIES 23 Table 5. t he regression of earnings management and iPo screening. d ependent variable: PASS Full sample Coefficient Odds ratio Low level Middle level High level (1) (2) (3) (4) (5) INTERCEPT –8.468*** –3.114 –12.611*** -4.188*** (0.000) (0.135) (0.000) (0.010) DTAC –0.137*** 0.872 1.288* –0.577 -0.139*** (0.001) (0.061) (0.167) (0.007) SOE 0.405*** 1.499 0.594** 0.147 0.476** (0.003) (0.022) (0.570) (0.045) BIG4 –1.100*** 0.333 –0.983*** –0.985*** -1.366*** (0.000) (0.000) (0.000) (0.000) SIZE 0.480*** 2.609 0.377*** 0.823*** 0.346*** (0.000) (0.000) (0.000) (0.000) ROE 0.020*** 1.616 0.033*** 0.026*** 0.009 (0.000) (0.002) (0.006) (0.158) LEV –0.678*** 0.508 –0.796*** –0.912*** -0.487*** (0.000) (0.000) (0.000) (0.000) GROWTH –0.076 0.927 –0.060 –0.189 -0.047 (0.581) (0.818) (0.468) (0.832) obs. 1342 448 447 447 Pseudo R-sq(%) 26.37 33.48 32.88 20.01 note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. of passing IPO screening. In column (2), the odds ratio of DTAC is 0.872. This result suggests that the probability of being awarded the IPO approval would decrease by 12.8% when dis- cretionary accruals of companies increase by one unit. The ee ff ct is economically significant. The results of other variables show that SOEs enjoy a higher passing rate. Firms audited by the ‘Big Four’ are less likely to get IPO approval. Finally, companies with larger size, better performance and lower leverage are more likely to pass the IPO screening. Furthermore, we divide the sample firms into groups of low, middle and high levels of earnings management and run the sub-sample regressions. The results are reported from column (3) to column (5) in Table 5. We find that the coefficient on DTAC is –0.139 and significant at the 1% level for companies with high-level earnings management. However, in column (4) the coefficient on DTAC is no longer significant for firms with middle-level earnings management. Moreover, DTAC generates a significant and positive coefficient for firms with low-level earnings management. These results suggest that the negative correlation between firms’ earnings management and the IPO passing rate is only found for the high-level earnings management. 5.3. Further analyses To have a more comprehensive understanding of the decision of SIEVC, we next analyse how firm ownership, industry policy, political connection and business complexity influence the relationship between earnings management and the IPO screening. 5.3.1. Firm ownership The Chinese stock market is regulated by government, and the initial purpose of setting up the securities exchange is to solve the financing problems of SOEs (Zhou, 1999). As a result, SOEs account for a high proportion of listed companies. Meanwhile, SOEs are naturally connected 24 J. Hu Ang And T. LI Table 6. t he interaction regression of firm ownership, industry policy and political connection. d ependent variable: PASS (1) (2) (3) INTERCEPT –6.608*** –6.449*** –6.215*** (0.000) (0.000) (0.000) DTAC –0.130*** –0.152*** –0.173*** (0.004) (0.000) (0.000) SOE 0.139*** 0.146*** 0.139*** (0.008) (0.003) (0.007) SOE×DTAC 0.100** (0.047) IP 0.063 (0.136) IP×DTAC 0.080* (0.065) CONN 0.172 (0.144) CONN×DTAC 0.091** (0.021) BIG4 –1.096*** –1.110*** –1.072*** (0.000) (0.000) (0.000) SIZE 0.489*** 0.481*** 0.469*** (0.000) (0.000) (0.000) ROE 0.019*** 0.019*** 0.020*** (0.000) (0.000) (0.000) LEV –0.678*** –0.667*** –0.694*** (0.000) (0.000) (0.000) GROWTH –0.057 –0.094 –0.055 (0.692) (0.491) (0.704) obs. 1342 1342 1342 a dj. R-sq(%) 26.91 26.75 27.26 f t est DTAC+SOE×DTAC DTAC+IP×DTAC DTAC+CONN×DTAC Prob>F=0.006 Prob>F=0.001 Prob>F=0.000 note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. with government due to their ownership structure, compared with private firms, so they obtain more support from government during the IPO. For example, under the approval system the regulation body still considers local government interests, local government support and own- ership background of IPO firms even though local governments no longer participate in the IPO approval (Xia, 2006). We expect that the background of state-owned enterprises will weaken the negative relationship between firms’ earnings management and the IPO passing rate. To examine the above argument, we introduce an interaction item SOE×DTAC in the regression. The results are shown in column (1) of Table 6. The coefficient of SOE ×DTAC is 0.100 and significant at the 5% level. Further, the sum of the main effect and the interaction (DTAC + SOE × DTAC) is negative and statistically different from zero. These results indicate that the negative effect of earnings management on the IPO screening mitigates for SOEs. 5.3.2. Industry policy The government plays a critical role in the Chinese stock market. Although the IPO regulation moved from an administrative system to an approval system in 2001, the government can still intervene in the IPO process through the IPO screening (Xia, 2006). As a kind of macroeconomic policy, industry policy achieves the government goal through controlling access to the capital market. Further, industry policy can serve as a means of government intervention and influence resource allocation in the capital market since the government might encourage investment in CHInA JOuRnAL OF ACCOunTIng STudIES 25 some industries from the public interest. So the approval decisions by SIEVC might be affected by national industrial policies. Meanwhile, the researchers show that companies in the industries supported by government have more opportunities of external financing and face less financing restrictions (Chen, Li, & Xin, 2010). We expect that the SIEVC’s screening will relax if IPO firms belong to industries supported by government industry policies, which offsets the negative correlation between earnings management and the IPO passing rate. We include the interaction term of industry policy and earnings management in the regression. Following the literature (Chen, Li & Xin, 2010; Song & Wang, 2013), we construct the variable IP, which takes the value of one if companies are in the industries supported by government and zero otherwise. The government supporting industries are identified from official documents of ‘Eleventh Five-Year Planning’ and ‘Twelfth Five-Year Planning’ by the CPC Central Committee. Column (2) of Table 6 reports the regression results. The coefficient of IP×DTAC is significantly positive. Further, the coefficient of DTAC plus that of IP×DTAC passes the F test. These results suggest that earnings management has a less negative effect on the IPO screening for firms in industries supported by government. 5.3.3. Political connection Politically connected firms normally have more social links and resources. It is easy for them to establish connection with the SIEVC, which helps obtain private information during the IPO screening – information such as preferences of SIEVC members and key issues reviewed by SIEVC members. under the Chinese culture of ‘ guanxi’, a good relationship with SIEVC can serve as the lubricant to pass the IPO screening. For example, Yang (2013) finds that if the partners of accounting firms are chosen as SIEVC members, it is less likely for their clients to be rejected during IPO screening. Therefore, the political connection of IPO firms will reduce the negative effect of earnings management on the IPO passing rate. We add the interaction term CONN×DTAC in the model. The variable CONN denotes whether an IPO firm is politically connected. Following Fan, Wong, and Zhang (2007), we define the value of CONN as one if the ultimate owners, the chairmen or the CEOs once worked in the government or were selected as the representatives of nPC and PPCC, and zero otherwise. The regression results are presented in column (3) of Table 6. The coefficient of CONN×DTAC is signica fi ntly positive, and the sum of coefficients of DTAC and CONN×DTAC is significantly negative, indicating that political connection weakens the negative correlation between earnings management and the rate of passing IPO screening. 5.3.4. Business complexity Finally, we examine the impact of business complexity on the SIEVC’s approval decisions. The literature suggests that earnings management is negatively correlated with corporate transparency (Xia & Lu, 2005). The complicated business reduces the corporate transparency and provides more opportunities for accounting manipulation. The low transparency will further intensify the information asymmetry between IPO companies and the SIEVC, increase the screening difficulty of SIEVC, and lead to the lower decision efficiency of SIEVC. Moreover, according to practice, the SIEVC members have limited time to review the materials of IPO applicants. This further decreases the decision efficiency of SIEVC. t he SieVC members usually received the materials of iPo firms five days prior to the approval meeting, before the CSrC reformed the preliminary disclosure regulation of iPo firms in f ebruary, 2012. a fter that, the time changed to 30 days in advance. 26 J. Hu Ang And T. LI Table 7. t he interaction regression of business complexity. d ependent variable: PASS (1) (2) (3) INTERCEPT –5.875*** –6.924*** –6.492*** (0.000) (0.000) (0.000) DTAC –0.281*** –0.295*** –2.251** (0.000) (0.000) (0.019) SEGMENT 0.105* (0.081) SEGMENT×DTAC 0.106** (0.041) AGE –0.011 (0.233) AGE×DTAC 0.017* (0.059) SIZE×DTAC 0.108** (0.028) SOE 0.378*** 0.416*** 0.424*** (0.010) (0.004) (0.003) BIG4 –1.108*** –1.040*** –1.066*** (0.000) (0.000) (0.000) SIZE 0.450*** 0.520*** 0.487*** (0.000) (0.000) (0.000) ROE 0.022*** 0.020*** 0.019*** (0.000) (0.000) (0.000) LEV –6.951*** –7.191*** –6.977*** (0.000) (0.000) (0.000) GROWTH –0.126 –0.108 –0.075 (0.383) (0.452) (0.600) obs. 1342 1342 1342 a dj. R-sq(%) 27.43 27.91 26.73 f t est DTAC+ SEGMENT×DTAC DTAC+ AGE×DTAC DTAC+ SIZE×DTAC Prob>F=0.000 Prob>F=0.001 Prob>F=0.019 note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. Following Chu and Xie (2008), we use the number of subsidiaries (SEGMENT), firm age (AGE) and firm size (SIZE) to measure business complexity. The information of subsidiaries is manually collected from companies’ prospectuses. For firms with more subsidiaries, longer history and larger size, more resources are under firm control and business is more compli- cated. We include the interaction term of business complexity and earnings management in the regression. In column (1) of Table 7, the coefficient of SEGMENT×DTAC is significantly positive. The sum of the main effect and the interaction (DTAC + SEGMENT × DTAC) passes the F Test. These results indicate that business complexity mitigates the negative effect of earnings management on the IPO screening. This may be due to the fact that when firms’ operations are complicated, information asymmetry between companies and SIEVC increases the diffi - culties of IPO screening. Further in columns (2) and (3), the regressions employing firm age and size as measures of business complexity achieve similar results. 5.4. Earnings management and resource allocation Finally, we examine how IPO firms’ earnings management influences resource allocation in the capital market. Specifically, we analyse the post-IPO stock returns and accounting performance. CHInA JOuRnAL OF ACCOunTIng STudIES 27 Table 8. t he regression of earnings management and post-iPo performance. Full sample Low and middle level Return △ROE Return △ ROE d ependent variables: (1) (2) (3) (4) INTERCEPT 0.287* 0.641*** 0.269 1.306*** (0.093) (0.000) (0.233) (0.000) DTAC –1.329*** –0.493*** –1.452*** –1.783*** (0.003) (0.008) (0.010) (0.003) SOE –0.137 0.082* –0.016 0.117*** (0.176) (0.059) (0.910) (0.003) BIG4 0.298** 0.037** 0.371* 0.098 (0.032) (0.047) (0.058) (0.110) SIZE –0.195*** 0.013 –0.307** 0.097*** (0.004) (0.645) (0.029) (0.004) ROE –0.004 –0.003 (0.279) (0.585) LEV –0.004 –0.000 –0.004 0.001 (0.158) (0.830) (0.218) (0.587) GROWTH 0.397*** –0.238*** 0.258 –0.066* (0.000) (0.000) (0.110) (0.095) obs. 709 1055 437 703 a dj. R-sq(%) 4.80 3.90 2.74 5.32 note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. 5.4.1. Stock returns We construct the following model to investigate the effect of earnings management on IPO firms’ stock returns after listing: Return = + DTAC + SOE + BIG4 + SIZE + ROE + LEV + GROWTH + (6) 0 1 2 3 4 5 6 7 where Return is the buy-and-hold return in three years after IPO; DTAC measures the level of earnings management by IPO firms; other control variables are defined as before. The regression results are shown in column (1) of Table 8. The coefficient on DTAC is sig- nificantly negative, revealing that the more earnings management by IPO companies, the lower stock returns after listing. The results suggest that firms obtaining listing qualification through earnings management have poor post-IPO stock performance. Earnings manage- ment misleads investors into judging the IPO firms’ value and decreases resource allocation efficiency in the capital market. 5.4.2. Accounting performance We also analyse the relationship between earnings management and post-IPO accounting performance. We choose return on equity (ROE) as the performance measure and employ the following model: Prob(ΔROE)= + DTAC + SOE + BIG4 + SIZE + LEV + GROWTH + (7) 0 1 2 3 4 5 6 where ΔROE is a dummy variable, taking the value of one if the change of ROE between one year after IPO and the IPO year exceeds the mean value of sample firms, and zero otherwise; DTAC denotes IPO firms’ earnings management; other variables are defined as before. Column (2) of Table 8 reports the regression results. The coefficient on DTAC is –0.493 and is significant at the 1% level, indicating that companies with more earnings management present worse accounting performance after IPO. The results suggest that when low quality 28 J. Hu Ang And T. LI firms get the IPO approval through earnings management, social capitals are mistakenly allocated to unqualified companies, resulting in lower resource allocation efficiency in the capital market. 5.4.3. The level of earnings management Our previous analysis suggests the passing rate is not correlated with low and middle level earnings management of IPO firms. So it is interesting to examine whether low and middle level earnings management by IPO companies distorts resource allocation in the capital market. We exclude firms with high-level earnings management and re-run the regressions of post-IPO performance. The results are presented in column (3) and (4) of Table 8. It is shown that DTAC is negatively correlated with post-IPO stock returns and accounting per- formance. As for the economic significance, the absolute values of coefficients on DTAC in columns (3) and (4) are larger than those in the full sample, implying that low and middle level earnings management has a more negative effect on resource allocation efficiency in the capital market. 6. Robustness tests 6.1. Endogeneity problem Our analysis indicates that the rate of passing IPO screening is negatively correlated with firms’ earnings management. In China, companies applying for an IPO have to satisfy specific conditions. For instance, the Provision of Stock Issuance Administration requires that IPO companies generate sustainable profit, such as continuously positive earnings in recent three years. On the one hand, low-quality companies have more incentives to manage earnings in an attempt to pass the IPO screening. On the other hand, the IPO passing rate is lower for companies of low quality than those of high quality. Our analysis might then have an endogeneity problem. We do the following tests to control the endogeneity issue. The literature argues that companies with small profits (ROA between 0 and 0.01) have strong incentives for earnings management (Burgstahler & dichev, 1997; Jacob & Jorgensen, 2007). The IPO profit requirement by CSRC further increases the motivation of firms to under - take earnings management. Meanwhile, the profitability can reflect firm quality to some extent. Hence, the above results can be driven by companies with small profits. To control the endogeneity, we construct a variable DUMROA, taking the value of one if firms’ ROAs belong to the window [0, 0.01] in at least one of three years before IPO, and zero otherwise. We add this variable in the regression of model (5). The statistics show that 1.9% of compa- nies passing the IPO screening have small and positive profits, whereas the corresponding figure for non-passing companies is 11.5%. Column (1) in Table 9 shows that the coefficient on DTAC is significantly negative even after controlling the characteristics of firms’ small profits. The results confirm that earnings management is negatively correlated with the IPO passing rate, providing further evidence to our conclusions. Moreover, the coefficient on DUMROA is significant and negative, indicating that companies with poorer quality are less likely to pass the IPO screening. Further, we divide sample firms into groups of high and low quality and run subsample regression to test the effect of endogeneity on our analysis. Specifically, if the average ROA CHInA JOuRnAL OF ACCOunTIng STudIES 29 Table 9. t he regression of resolving the endogeneity problem. d ependent variable: PASS Full sample Company with high quality Company with low quality (1) (2) (3) INTERCEPT –13.896*** –6.647* –16.068*** (0.000) (0.052) (0.000) DTAC –0.241*** –0.278*** –0.271** (0.002) (0.004) (0.039) DUMROA –1.250*** (0.007) SOE 0.578* 0.673 0.598* (0.037) (0.220) (0.093) BIG4 –2.268*** –2.828*** –1.940*** (0.000) (0.000) (0.000) SIZE 1.010*** 0.479** 1.346*** (0.000) (0.010) (0.000) ROE 0.037*** 0.052*** 0.082*** (0.000) (0.000) (0.000) LEV –1.455*** –0.718** –2.646*** (0.000) (0.011) (0.000) GROWTH –0.087 –0.594* 0.194 (0.745) (0.079) (0.596) obs. 1278 539 739 Pseudo R-sq(%) 30.40 17.60 52.13 note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. in three years before IPO is lower than the mean value of sample firms, the company is regarded as low quality, and vice versa. If our analysis has the endogeneity problem that companies with poorer quality are more likely to manage earnings and their IPO passing rates are lower, we can only find the negative correlation between earnings management and the IPO passing rate in the group of low quality firms. As shown in columns (2) and (3) of Table 9, the coefficients of earnings management are both negative for the regression of companies of high and low quality. This means that our findings are not affected by the endogeneity problem. 6.2. Post-IPO performance Our analysis shows that companies passing the IPO screening through more earnings man- agement have lower stock returns and worse accounting performance after listing. However, there is an alternative explanation for this finding that the poor post-IPO performance may be due to the stock selling of controlling shareholders. To exclude this explanation, we drop sample firms whose shares are sold out by controlling shareholders after the lock-up period and re-run the regression. There are 291 companies with shares selling by controlling share- holders in three months after the expiration of the IPO lock-up period, accounting for 27.6% of sample firms. The regression results are reported in Table 10. The coefficient on DTAC is significant and negative whether we employ stock returns or ROE change as performance measures and whether we run the regressions of full sample or firms with low and middle level earnings management. These results testify that companies passing the IPO screening through higher earnings management have poorer stock and accounting performance after listing. 30 J. Hu Ang And T. LI Table 10. t he regression of excluding firms with shares selling by controlling shareholders. Full sample Low and middle level Return △ROE Return △ROE d ependent variables: (1) (2) (3) (4) INTERCEPT 0.190 0.692*** 0.324 1.358*** (0.317) (0.000) (0.195) (0.000) DTAC –1.473*** –0.575*** –1.724*** –2.484*** (0.004) (0.009) (0.006) (0.000) SOE –0.115 0.099** 0.028 0.086* (0.291) (0.045) (0.849) (0.059) BIG4 0.321** 0.095 0.373* 0.148** (0.030) (0.162) (0.071) (0.034) SIZE –0.196*** 0.005 –0.318** 0.097*** (0.003) (0.856) (0.019) (0.564) ROE –0.004 –0.005 (0.350) (0.331) LEV –0.002 –0.001 –0.006 –0.001 (0.375) (0.271) (0.109) (0.504) GROWTH 0.446*** –0.259*** 0.441** –0.091** (0.001) (0.000) (0.034) (0.049) obs. 518 764 329 530 a dj. R-sq(%) 5.75 5.97 4.88 8.30 note: P values are reported in parentheses. ***, **, * indicate significance at 1%, 5%, 10% respectively. 6.3. Other sensitivity analyses We also perform the following tests to verify our conclusions. First, we employ the performance-match accrual model by Kothari, Leone, and Wasley (2005) to control the impact of performance change on firms’ earnings management. Specifically, we add the variable ROA in the estimation regression of accruals to control for the non-linear relationship between accruals and performance, and re-run our regressions. The results are similar. Second, considering that the purpose of earnings management by IPO companies is to increase the probability of going public or the issuance price through improving financial performance, we exclude firms with negative discretionary accruals estimated by the Jones Model. The regressions show that our results do not change. Third, d uan (2012) argues that it is easier to pass the IPO screening for firms applying for an IPO the second time. We drop companies if not applying for IPO for the first time and redo the above analyses. Our results remain the same. Finally, we calculate firms’ stock returns by subtracting the market returns and re-run the regression of post-IPO performance. The analysis shows that our conclusions do not change. To save space we do not tabulate the results. All tables are available upon request. 7. Conclusions As the ‘gate keeper’ of the capital market, the operation of SIEVC has drawn much attention and discussion. This paper aims to examine the decision efficiency of SIEVC from the view - point of earnings management by using the data of IPO companies from 2006 to 2012. We find that the rate of passing IPO screening is negatively correlated with firms’ earnings man- agement. However, this relationship is only found for the high-level earnings management. CHInA JOuRnAL OF ACCOunTIng STudIES 31 Further analyses show that the IPO application is less likely to be rejected for earnings man- agement when firms are SOEs or get industry support from government. Meanwhile, political connection and business complexity weaken the negative effect of earnings management on the IPO screening. Finally, we find that companies passing the IPO screening through higher earnings management have lower stock returns and worse accounting performance after listing. The SIEVC has been the final decision marker of stock issuance since China adopted the IPO approval system in 2001. Its decision is associated with the development of the securities market and the protection of investors’ interests. However, our analysis shows that the neg- ative correlation between earnings management and the IPO passing rate is only found for the high-level earnings management. Meanwhile, the relationship is also affected by factors such as firm ownership, industry policy, political connection and business complexity, which lowers resource allocation efficiency in the capital market. The reform of IPO regulation in the future should try to improve the operation mechanism and decision ec ffi iency of SIEVC in order to select good companies for listing and optimise resource allocation in the capital market. Acknowledgments We think the joint editor Liansheng Wu, the associate editor Kangtao Ye, the discussant Cong Wang in the 2014 Research Conference of the China Journal of Accounting Studies for their constructive comments and suggestions. However, we are completely responsible for the content of this paper. We also acknowledge research support from the national natural Science Foundation (71272008 and 71372038), the MOE Project for Key Research Institutes of Humanities and Social Science in universities (14JJd630010), the Shanghai Philosophy and Social Science Foundation (2013BgL008), the Innovation Program of the Shanghai Municipal Education Commission (14ZS078) and the Program for Innovative Research Team of Shanghai university of Finance and Economics. 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Journal
China Journal of Accounting Studies – Taylor & Francis
Published: Jan 2, 2016
Keywords: Earnings management; IPO screening; SOEs; political connections; business complexity