Categorical listing criteria, co-investment system and growth manipulation: empirical evidence based on the implementation of the registration system of STAR market

ABSTRACT

China’s STAR Market, with its registration-based IPO system, blends market-oriented features with growth criteria, making it an ideal research setting to analyze IPO companies’ growth manipulation and its economic impacts. Using data from 2019 to 2021, our study employs non-parametric methods to reveal significant growth manipulation among STAR Market companies during IPOs. We observe that manipulation intensifies across categorical listing criteria I to V, particularly among higher-valued companies. However, the coinvestment system helps curb manipulation in Pre-IPOs. Notably, lower profitability and industry status amplify the effects of listing criteria and valuation on manipulation. Economic tests confirm that Pre-IPO manipulation hastens listings but results in higher IPO underpricing. Our findings shed light on the repercussions of the registration-based IPO system, vital for refining the STAR Market and extending similar reforms to other segments.

KEYWORDS:

• STAR market
• growth manipulation
• categorical listing criteria
• company valuation
• co-investment system

1. Introduction

Strengthening the financial function of the capital market in serving strategic industries is of great significance for promoting high-quality economic growth. However, the traditional approval-based IPO system has gradually exposed drawbacks, such as the ‘three highs’ of IPO (listed companies have high issue prices for new shares, high issue price-to-earnings ratios, and ultra-high raised funds), performance changes after IPO (Lu et al., 2015b, Xiong & Yang, 2017a), and rent-seeking (Du et al., 2013, Huang & Xie, 2016). As a result, China has put the reform of the IPO system back on the agenda and took the lead in piloting the registration-based IPO system reform on the STAR Market in 2019. Compared with the previous approval-based IPO system, which focused on ‘substantive examination’, the registration on the STAR Market emphasises ‘formal examination’ and information disclosure. This is an important system innovation in China’s capital market. The STAR Market specifically targets Technology Entrepreneur Companies that have not entered the mature stage but have high growth potential.

Nevertheless, while the registration-based IPO system of the STAR Market has reduced the emphasis on profitability indexes for Pre-IPOs, its listing rules also clearly stipulate that firms should meet the requirement of high growth potential. This includes quantitative requirements, such as operating income and company valuation in the categorical listing criteria, as well as qualitative requirements¹ of ‘growth’ in the provisions on the examination of listing. This means that the income of Pre-IPOs on the STAR Market should demonstrate the prospect of breakthrough growth in the future.

A successful IPO implies a significant strategic growth opportunity for the company. Prior literature has found that proposed listed companies are prone to conduct earnings management to achieve IPO rapidly, especially those under the approval-based IPO system (Aharony et al., 2010, Chen et al., 2013, Liu & Lu 2015a, Zhang & Wu, 2016, Gao et al., 2017, Lu et al., 2019). However, prevalent earnings management has drawn attention from the IPO department, which can actually reduce the passing rate of IPO screening (Huang & Li, 2016). Furthermore, manipulating business activities may also be a common phenomenon for companies (Armstrong et al., 2015). Therefore, companies may manipulate operating activities other than earnings management to achieve their IPO. In this regard, Friedlan (1994) find that American IPO firms would manipulate revenue growth to send positive signals to the market to obtain higher issuance premiums. Long and Zhang (2021) study patent management behaviour, which is similar to earnings management, during the IPO period and argue that firms will tactically use low-quality and non- innovative patents for patent management activities. Nevertheless, there is limited literature investigating the motives and drivers of growth manipulation of IPO firms under the registration-based IPO system.

Since the registration-based IPO system of the STAR Market emphasises information disclosure and growth requirements, it is reasonable to argue that companies may have tactical motives and opportunistic behaviours to manipulate growth during the IPO period to meet listing requirements. The five major listing criteria for IPOs under the ‘Rules of the Shanghai Stock Exchange for the Examination of the Offering and Listing of Securities by Companies Listed on the STAR Market’ (hereinafter referred to as ‘Listing Rules’) are focus on the company’s future value rather than historical profitability, which may strengthen the motivation of growth manipulation during the IPO period. At the same time, the role of sponsor institutions in the IPO process is vital, particularly the ‘Guidelines of the Shanghai Stock Exchange for the Offering and Underwriting of Stocks on the STAR Market’ (hereinafter referred to as ‘Offering Guidelines’), which regulate the co-investment system for sponsor institutions on the STAR Market. Under the background of the reform of the registration system that strengthens agencies’ responsibility, the co-investment system may also lead to linkages between sponsor institutions and companies, thereby affecting the company’s growth manipulation behaviour during the IPO period.

The broker-dealer direct investment model is directly related to the co-investment system on the STAR Market. Related studies have shown that venture capitalists with brokerage backgrounds have certification effects by reducing information asymmetry (Zhang et al., 2014), thus governing corporate opportunistic behaviour. However, under the U.S. registration-based IPO system, investment banks could create conflicts of interest by obtaining huge benefits from IPO equity exits (Gompers & Lerner, 1999), which is not conducive to the governance of corporate opportunistic behaviour. Then, what kind of governance function does the co-investment system on the STAR Market have in the company’s growth manipulation behaviour? Zhang and Wu (2021) find that the interest alignment effect of the co-investment system makes brokers more cautious in their sponsorship business, which can curb the opportunistic behaviour of firms. An empirical study on STEM companies (science, technology, engineering, and mathematics) clearly points out that accounting metrics, such as revenue growth, impact firm value more than bottom-line earnings and other accounting items (Fedyk et al., 2017). Thus, it is natural to ask how the growth indicators, such as revenue growth during the IPO period, will be examined for companies listed on the STAR Market, which have similar characteristics to STEM companies. Moreover, what is the role of the sponsor institutions under the unique co-investment system?

Undoubtedly, the effect of IPO system reform will eventually affect firms and participants in the capital markets. In this regard, it is of great theoretical and practical significance to study the strategic change of enterprise behaviour under the registration-based IPO system and its linkage mechanism with intermediaries. Based on this, we focus on revenue accounting indicators that are more related to the value of the company in the context of the registration-based IPO system of the STAR Market. We explore the possible growth manipulation behaviour of the company and its driving mechanism, as well as the governance boundaries of intermediaries. Specifically, we intend to answer the following three questions: First, do Pre-IPOs on the STAR Market exhibit growth manipulation behaviour? Second, if so, what are the institutional incentives for growth manipulation? Third, as the main market participants in the company’s IPO business, what kind of governance boundaries do sponsor institutions have under the background of strengthening the responsibility of broker institutions in the registration-based IPO system? Using a dataset of companies listed on the STAR Market from 2019 to 2021 and non-parametric methods, such as breakpoint smoothness tests, we show that there is growth manipulation behaviour during the IPO period. Besides, the degree of growth manipulation gradually increases for the companies under the categorical listing criteria I to V stipulated by the STAR Market. The higher the valuation, the higher the degree of growth manipulation. Additionally, the co-investment system can alleviate the driving effect of categorical listing criteria and company valuation on growth manipulation.

Our academic contributions and innovations are as follows: Firstly, we scientifically identify the growth manipulation behaviour of companies under the registration-based IPO system on the STAR Market during the IPO period. We reveal the institutional inducements of growth manipulation behaviour from the perspective of categorical listing criteria and company valuation. Pre-IPOs have growth manipulation motives to achieve a higher valuation. However, compared with the threshold conditions for listing under the approval-based IPO system and the existence of shell value, regulatory authorities have clearly defined growth criteria for companies listed on the STAR Market that implement the registration-based IPO system. Moreover, the unique categorical listing criteria and company valuation standards provide an excellent scenario for studying the growth manipulation behaviour and its institutional inducements. Using breakpoint smoothness tests and other methods, we expand relevant research on companies’ growth manipulation behaviour during the IPO period.

Secondly, from the perspective of a company’s growth manipulation on the STAR Market, we verify the governance effect of the co-investment system of sponsor institutions, providing a theoretical reference for improving the co-investment system of the STAR Market. With the implementation of the registration-based IPO system and the increased participation of brokers in IPO pricing, the STAR Market introduced the co-investment system in a pioneering way to prevent excessive pricing by brokers. Although Zhang and Wu (2021) test the correlation between the co-investment system and IPO pricing efficiency, we verify the governance effect of the co-investment system from the perspective of growth manipulation on the STAR Market, offering a theoretical reference for improving the registration-based IPO system of the STAR Market and promoting it to other sectors of the capital market.

Thirdly, from the perspective of IPO companies’ growth manipulation, we scientifically evaluate the economic consequences of implementing the registration-based IPO system on the STAR Market, extending relevant research on the registration system. Before the implementation of the registration-based IPO system, prior literature mainly focused on studying the economic consequences of the approval-based IPO system and mostly criticised it (Du et al., 2013, Lu, et al., 2015b, Huang & Xie, 2016, Xiong & Yang, 2017a). Since the implementation of the registration-based IPO system on China’s STAR Market in 2019, few studies have explored the pricing efficiency, information disclosure, other economic consequences of the registration-based IPO system from the perspective of investors (Hu & Wang, 2021, Jiang & Zhang, 2021, Zhang & Wu, 2021), and qualitative research on the evaluation and improvement of the legal system (Tang & Wei, 2016, Song, 2021). This paper scientifically evaluates the economic consequences of the registration-based IPO system on the STAR Market from the perspective of growth manipulation during the IPO period, expanding relevant research on the registration-based IPO system.

2. Institutional background and research hypothesis

2.1. Institutional background

The reform of China’s IPO system is gradual. Before launching the registration-based IPO system, its path has evolved roughly along a line of ‘multi-departmental supervision’ → approval system under ‘quota management’ → approval system under ‘indicator management’ → approval system under ‘channel system’ → approval system of ‘channel system and sponsorship system in parallel’ → approval system for ‘sponsorship system’.² The main logic behind it is the rising marketisation of IPO and the eventual withdrawal of government regulatory authorities. Overall, the gradual development of the IPO system matched the development of the transitional economic system at each stage, helping to improve the efficiency of resource allocation. Previous literature also suggests that gradual reforms of the IPO system can reduce the extent of excess IPO underpricing (Tian et al., 2013). However, the traditional approval-based IPO system also exposed problems, such as earnings management and performance change (Li et al., 2014a, Liu & Lu, 2015a, Lu et al., 2015b, Xiong & Yang, 2017a). As a core institution of the approval-based IPO system, the Issuance Appraisal Commission also plays a negative role under the approval-based IPO system (Du et al., 2013, Fang et al., 2020).

Compared with the approval-based IPO system, the registration-based IPO system pays more attention to information disclosure and emphasises the marketisation pricing mechanism. It also takes the implementation of ‘formal examination’ as the core, which can play a decisive role in the market’s resource allocation. In fact, the public has been looking forward to the reform of the registration-based IPO system for a long time. From the perspective of policies and laws, in November 2013, the Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform was proposed in the Third Plenary Session of the 18th CPC Central Committee. It proposed to push forward the reform of the IPO registration system. In 2014, the Government Work Report first included ‘promoting the reform of registration-based IPO system of stock issuance’. In December 2014, the 18th meeting of the Standing Committee of the 12th National People’s Congress passed the Decision to authorize The State Council to adjust and apply relevant provisions of the Securities Law in implementing the stock issuance registration system. The Decision solved the legal problems of the reform of the registration-based IPO system before the adoption of the new Securities Law. However, due to the drastic changes in stock prices in 2015, the reform of the registration-based IPO system between 2016 and 2018 tended to be stagnant.

On 5 November 2018, at the first China International Import Expo opening ceremony, President Xi Jinping announced the establishment of the STAR Market and the pilot registration system, marking the launch of the registration-based IPO system reformation. On 30 January 2019, China Securities Regulatory Commission (CSRC) issued the Implementation Opinions on Setting up the STAR Market and Launching the Pilot Program of the Registration System on the Shanghai Stock Exchange. On 1 March 2019, the Measures for the Administration of the Registration of IPO Stocks on the STAR Market (for Trial Implementation) and the Measures for the Continuous Supervision of Listed Companies on the STAR Market (for Trial Implementation) were issued. The first batch of companies on the STAR Market was listed on 22 July 2019. In December of the same year, the new Securities Law was amended and came into effect on 1 March 2020. Subsequently, the registration system reform pilot was extended to the Growth Enterprise Market (GEM). In June, 2020, the CSRC issued the Measures for the Administration of the Registration of IPO Stocks on GEM (for Trial Implementation), the Measures for the Administration of the Offering of Securities by Companies Listed on GEM (for Trial Implementation), the Measures for the Continuous Regulation of Companies Listed on the ChiNext (for Trial Implementation), and the Measures for the Administration of the Sponsor Business of Securities Issuance and Listing. The first batch of companies on GEM under the registration system was listed in August of the same year.

2.2. STAR market registration system and IPO companies’ growth manipulation

With the development of the financial market and the scale of the economy, the expansion of companies has become more dependent on financing in the capital market, with IPOs being a key factor. Pre-IPOs on the STAR Market are usually in a period of rapid development and face financial constraints. Successful IPOs will provide strategic opportunities for them to obtain more external resource support. On the one hand, listing helps the companies improve their bargaining power with the government, obtain more political or economic resources, and increase the company’s investment opportunities. A successful IPO can reduce information asymmetry and increase portfolio investment opportunities for investors (Zhang et al., 2017b). Additionally, the increased information content of the share price due to enhanced equity liquidity can reduce the firm’s cost of capital (Fang et al., 2009). Listing also helps alleviate the company’s financing constraints, enhances the building of an innovative workforce, and promotes innovation (Zhang et al., 2017b). On the other hand, original equity is typically held by founding shareholders and essential employees, such as core managers or technicians, in varying proportions. The resulting conflicts of interest can mitigate agency conflicts (Jensen & Meckling, 1976). Additionally, IPO markets exhibit greater information asymmetry compared to other markets (Ljungqvist & Wilhelm, 2003), and transitional economies often experience varying levels of deficiencies in their legal systems and enforcement scales (Allen et al., 2005). Both these factors contribute to a reduction in potential legal costs incurred by firms.

Existing literature has proven that companies under the approval-based IPO system tend to manipulate earnings to expedite the listing process or increase the probability of IPO (Aharony et al., 2010, Zhang & Wu, 2016). There are also incentives for such companies to engage in tactical patent management (Long & Zhang 2021). However, the crucial role of firm growth criteria in the IPO under the registration-based IPO system has been overlooked. The trend of revenue changes in different periods is the core index for measuring the company’s growth. The revenue index helps users understand the source of the company’s profitability and value creation in a specific period (Wagenhofer, 2014). Additionally, the revenue growth rate, along with enterprise value, are essential indicators to describe the company’s growth (Zhou et al., 2020). The registration-based IPO system of the STAR Market, which is more market-oriented, features a double-layer examination by both the Stock Exchange and the CSRC. While it lowers the profitability threshold, the listing rules and the corresponding audit rules establish regulatory requirements that companies should have higher growth standards. Table 1 summarises the descriptions of operating income, company valuation criteria, and related qualitative requirements representing growth. It can be seen that the ‘growth’ requirement, portrayed by operating income and company valuation, occupies a crucial position in the listing standard system, while the profitability standard of the proposed listed company is diluted. Thus, future growth becomes crucial.

Table 1. Relevant regulations on the growth of pre-IPOs on the STAR market.

Category	Operating revenue	company valuation		Other requirements
Panel A: Listing Rules Criteria
Standard I	Operating revenue ≥RMB 100 million in the most recent year	≥RMB 1 billion		Standard I: positive net profit in the most recent two years and cumulative net profit of not less than RMB 50 million; or, positive net profit in the most recent year and operating revenue of not less than RMB 100 million
Standard II	Operating revenue ≥RMB 200 million in the most recent year	≥RMB 1.5 billion
Standard III	Operating revenue ≥RMB 300 million in the most recent year	≥RMB 2 billion
Standard IV	Operating revenue ≥RMB 300 million in the most recent year	≥RMB 3 billion
Standard V	main business or products … large market	≥RMB 4 billion
Document basis			Growth related description
Panel B: Other policy documents
Rules Governing the Listing of Stocks on the Shanghai Stock Exchange			Companies with strong growth potential
Q&A for Governing the Listing of Stocks on the Shanghai Stock Exchange (March 2019)			Whether the core technology can support the company’s sustainable growth

The study finds that manipulative activities to achieve revenue growth by recognising accounting revenue always occur (Nelson et al., 2002). Investors focus on the revenue growth of high-growth companies, especially when the corporate value contains less information about accounting earnings. Market participants also tend to evaluate the market value of loss-making or negative cash-flow companies based on revenue levels or revenue growth rates (Callen et al., 2008). For example, studies found that the high revenue of internet high-tech companies significantly increases firm value (Demers & Lev, 2001, Debreceny et al., 2002). After making large investments in fixed assets, high-tech companies can leverage with fewer additional costs, making it easier to assess their value creation capacity from a revenue perspective. Therefore, unlike traditional firms, the core determinant of the value of high-tech companies is revenue rather than earnings (Fedyk et al., 2017). Hence, management may manipulate revenue levels during the IPO period based on the value correlation between accounting items and investors. IPO companies on the STAR Market are usually high-tech companies that are not fully mature but have high growth potential in the future. Therefore, it is reasonable to assume that high-tech and high-growth IPO companies on the STAR Market, with growth potential, will manipulate growth indicators to meet the listing standards under the registration system, which focuses on the company’s future growth and lowers earnings requirements.

2.3. Categorical listing criteria, company valuation and growth manipulation

As mentioned earlier, we have deeply analysed the reason why companies prefer to conduct growth manipulation, and it is because these companies cater to the growth requirements of listed companies on the STAR Market. In this section, we will analyse the driving mechanism of the growth manipulation of different IPO companies from the perspective of categorical listing criteria and company valuation.

Firstly, the Listing Rules of the STAR Market stipulate five category listing standards, and the main contents are shown in Table 1.³ By examining the sorting of the listing standard system from criteria I to criteria V, we can identify the following characteristics: Firstly, its requirements for profitability are gradually reduced. For example, categorical listing criteria I has clear requirements for net profit, while categorical listing criteria II to categorical listing criteria V has no specific requirements on profitability. Secondly, the requirements of the categorical listing criteria on operating income are gradually increasing, from RMB 100 million for the categorical listing criteria I to RMB 300 million of the categorical listing criteria IV. Although categorical listing criteria V does not specify the operating income requirement, it clearly emphasises that the main business or products should have the qualitative requirement of ‘large market space’, largely supported by the company’s future revenue growth potential. Finally, the standard requirements for the estimated market value of the categorical listing criteria have gradually increased, from RMB 1 billion for categorical listing criteria I to RMB 4 billion for categorical listing criteria V.

Based on the above analysis, it can be concluded that the requirements for operating income and estimated market value of IPO companies tend to become stricter from categorical listing criteria I to criteria V. Compared with the realised net profit requirement, operating income and estimated market value are more dependent on the company’s future growth. In addition, the Shanghai Stock Exchange also clearly points out that companies applying for registration on the STAR Market need to have strong innovation ability and high growth. Similar to how IPO companies conduct earnings management under the profit threshold requirements of the approval-based IPO system, the implementation of categorical listing criteria I to V objectively stimulates the motivation of IPO companies to manipulate specific performance indicators to cater to regulatory authorities. Although China’s STAR Market implements a more market-oriented registration system, when the company chooses the categorical listing criteria with a lower profit threshold or higher operating income threshold, Pre-IPOs have a stronger motivation to carry out growth manipulation to better signal high growth to the regulatory authority or the capital market (Callen et al., 2008).

The company’s valuation attaches great importance to future growth indicators, and companies with high valuations must be supported by high growth, especially for high-tech companies (Wang & Zhang, 2011). In the real options valuation model used by Zhang (2000), similar to profitability, book value, and other indicators, growth indicators play an equally important role in company valuation. Therefore, the higher the company’s growth, the greater the company’s value. Fedyk et al. (2017) finds that compared with traditional accounting income indicators, investors pay more attention to the growth of high-tech companies and other indicators. Zhang et al. (2020) argue that growth management behaviours exist in registration IPO companies on technology-based and growth-oriented firms. Based on this, when conducting higher valuations for IPO companies, high-tech companies listed on the STAR Market are more motivated to carry out growth manipulation to support higher valuations. This strategy helps them send a signal of high growth to the IPO examination agency or the capital market.

Based on the above theoretical analysis, we thus state our hypotheses as follows:

H1:

The degree of growth manipulation of Pre-IPOs of the STAR Market from categorical listing criteria I to V gradually improves.

H2:

The larger the company’s valuation, the higher the degree of growth manipulation of Pre-IPOs of the STAR Market.

2.4. Categorical listing criteria, company valuation and growth manipulation: the governance effect of co-investment system

According to Hypothesis 1 and Hypothesis 2, categorical listing criteria and company valuation are the important factors that drive the growth manipulation of Pre-IPOs on the STAR Market. As such, does the co-investment system introduced by the STAR Market have a governance effect? It has been found that the direct investment model of brokers under the approval-based IPO system has a significant governance effect on Pre-IPOs (Zhang et al., 2014, Zhang & Wu, 2021). Although the co-investment system of the STAR Market has some similarities with the direct investment mode of brokers, the special provisions of the co-investment system of the STAR Market also make some differences. The Offering Guidelines of the Shanghai Stock Exchange stipulate that the sponsor institution and its related subsidiaries must subscribe for 2% to 5% of the IPO shares of the issuers or the quotas of investment with different amounts at the issue price. This is equivalent to directly limiting the number or amount range of shares that the sponsor institutions must invest in after the company has obtained the IPO qualification. The Offering Guidelines also stipulate that the shares co-invested by the sponsor institutions have a lock-up period of 24 months. The rules of the co-investment ratio or amount of the sponsor institutions are shown in Table 2.

Table 2. The summary of the rules on the percentage of co-investment required by sponsor institutions of different sizes.

Category	Issuance scale	Co-investment ratio	Co-investment amount
one	≤RMB 1 billion	≤5％	≤RMB 40 million
two	RMB 1 billion < and ≤ RMB 2 billion	≤4％	≤RMB 60 million
three	RMB 2 billion < and ≤ RMB 5 billion	≤3％	≤RMB 100 million
four	RMB 5 billion	≥2％	≤RMB 1 billion
lock-up period	24 months from the date of the initial public offering and listing of the issuer

These requirements suggest that the co-investment system locks the long realisation cycle of the co-investment stock of sponsor institutions. Therefore, there is a strong correlation between future investment income and the performance of IPO companies after listing, realising interest binding, and producing a collaborative governance effect (Y. Zhang & Wu, 2021). Furthermore, if the IPO company is not successfully listed, its sponsor institutions do not have to co-invest in the stock. They only need to bear the sunk cost or opportunity cost of the sponsoring business, making their investment loss relatively controllable. Therefore, the sponsor institutions will be more cautious in underwriting companies for IPO on the STAR Market. The above analysis shows that categorical listing criteria and company valuation will drive the growth manipulation of IPO companies on the STAR Market. However, sponsor institutions have strong professional competence, and the co-investment system gives them dual characteristics of both venture capital and underwriter. As a result, sponsor institutions have the incentive and ability to take measures to avoid sending false signals to the market due to the inflated growth of the company, inhibiting the driving effect of categorical listing criteria and company valuation on growth manipulation. Based on the above theoretical analysis, we propose the following research hypotheses:

H3:

The larger the proportion of sponsor institutions co-invested, the weaker the promoting effect of categorical listing criteria on the growth manipulation of Pre-IPOs on the STAR Market.

H4:

The larger the proportion of sponsor institutions co-invested, the weaker the promoting effect of company valuation on the growth manipulation of Pre-IPOs on the STAR Market.

The core logical framework of this paper is shown in Figure 1:

Figure 1. The logic framework of this article.

3. Sample selection and research design

3.1. Sample selection

This paper focuses on the growth manipulation of companies on the STAR Market during the IPO period. The registration-based IPO system of the STAR Market was launched in 2019, so we select the IPO companies of the STAR Market in 2019–2021 as the research object. Based on the IPO prospectus, we have taken data from the three years before listing and the year of listing, finally obtaining 1,107 valid observations from 369 companies. The data are obtained from the CSMAR database and are manually arranged according to the IPO prospectus. To control for possible outliers, all continuous variables are winsorised at the 1% level at both tails of their distributions.

3.2. Variable definition and model design

3.2.1. Variable definition

(1) Explained variable: growth manipulation. We define the growth manipulation proxy variable based on Fedyk et al. (2017), characterised by $\mathit{Dsales}$, as follows: ① According to Fedyk et al. (2017), we establish the estimation model of normal income level:

(1) $\begin{array}{rl}& \frac{\mathrm{\Delta }\mathrm{A}{\mathrm{R}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}={\mathit{\alpha }}_0+{\mathit{\beta }}_1\frac{1}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}+{\mathit{\beta }}_2\frac{\mathrm{\Delta }\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}+{\mathit{\beta }}_3\frac{\mathrm{\Delta }\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}\times \mathrm{Siz}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}+{\mathit{\beta }}_4\frac{\mathrm{\Delta }\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}\times \mathrm{GrI}\mathrm{\_}{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}\\ & \text{break}+{\mathit{\beta }}_5\frac{\mathrm{\Delta }\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}\times \mathrm{GrI}\_{\mathrm{N}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}+{\mathit{\beta }}_6\frac{\mathrm{\Delta }\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}\times \mathrm{Gpr}{\mathrm{I}}_{\mathrm{i},\mathrm{t}}}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}+{\mathit{\beta }}_7\frac{\mathrm{\Delta }\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}{\times \mathrm{G}\mathrm{prI}}_{\mathrm{i},\mathrm{t}}^2}{{\mathrm{A}}_{\mathrm{i},\mathrm{t}}}+{\text{varepsilona}}_{\mathrm{i},\mathrm{t}}\end{array}$(1)

where ΔAR is the change of accounts receivable; A is the total assets at the end of the period; ΔSales is the change of sales revenue; Size is the natural logarithm of total assets at the end of the period; GrI_A is defined as 0 when the sales revenue growth rate adjusted by the industry median is more than 0; GrI_N is defined as 0 when the sales revenue growth rate adjusted by the industry median is less than 0; Gpr_I is the sales gross margin adjusted by the industry median; the above model controls the firm fixed effect and the year fixed effect.

② To estimate the expected sales revenue growth rate ($\mathit{ExpR}\_\mathit{Sale}{s}_{\mathit{i},\mathit{t}})$ based on the regression coefficient of the model (1), we then calculate the abnormal sales revenue growth rate ($\mathit{Dsales}$) with the actual sales revenue growth rate ($\mathit{ActuR}\_\mathit{Sales}$) minus $\mathit{ExpR}\_\mathit{Sale}{s}_{\mathit{i},\mathit{t}}$. The larger the index value ($\mathit{Dsales}$), the higher the degree of growth manipulation. The calculation formula is:

(2) $\mathrm{Dsale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}=\mathrm{Actu}\_\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}-\mathrm{ExpR}\_\mathrm{Sale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}$(2)

(2) The explanatory variables include categorical listing criteria (IPOT) and the company valuation (MV). Specifically, categorical listing criteria (IPOT) is defined as the categorical variable. The categorical listing criteria I to V are assigned values 1, 2, 3, 4, and 5, respectively, and the data come from the Wind database.

Company valuation (MV) is defined as the natural logarithm of the company’s total market value (in RMB billion) at the end of the period, with the total market value confirmed by the company valuation of the previous equity transfers disclosed in the IPO prospectus.

(3) The moderating variable is the co-investment system of the sponsor institutions, specifically, the proportion of co-investment shares, denoted by Finstu. It is defined as the ratio of the number of shares co-invested by the sponsor institutions in the IPO company to the total number of shares issued for the first time. The data come from the number and proportion of sponsor institutions disclosed in the IPO prospectus through manual collection.

In addition, following Xia and Dong (2014) and Zhang and Chen (2015), we also introduce several other control variables (Control): asset-liability ratio (Lev), current ratio (Cur _ r), cash holdings (Cash), total assets (Assets), asset net profit margin (Roa), earnings management (AEM),⁴ innovation output (Inno), number of employees (Empty), the largest shareholder stake (Shf), board of directors (Bod), leadership structure (Dual),⁵ company age (Fage), industry effect (Ind), and annual effect (Year). Definitions of all research variables are shown in Table 3.

Table 3. Variable definitions.

Variable	Definition and measurement
Panel A: Main_vars
Dsales	Growth manipulation variable: Following Fedyk et al. (2017), we construct a model to estimate the expected sales growth rate. The actual sales growth rate is then subtracted from the expected rate, yielding the abnormal sales revenue growth rate, i.e. a measure of growth manipulation
IPOT	Categorical variable: Categorical listing criteria I to V, assigned values of 1, 2, 3, 4, and 5, respectively
MV	Market value of the company at the end of the period (in RMB billion) is logarithmically transformed
Finstu	Co-investment system variable: Percentage of co-investment equity by sponsor institutions at the time of the company’s IPO
Panel B: Control
Lev	The ratio of total liabilities to total assets at the end of the period
Cur_r	The ratio of current assets to current liabilities at the end of the period
Cash	Operating net cash flow divided by total assets at the end of the period
Assets	Natural logarithm of total assets at the end of the period
Roa	Net profit divided by total assets at the end of the period
AEM	Based on the modified Jones Model (Dechow et al., 1995), and taking into account performance (Kothari et al., 2005), the absolute values are calculated by industry and year, as detailed in the footnote above
Inno	Natural logarithm of the number of patents granted
Empy	Natural logarithm of the total number of employees at the end of the period
Shf	Shareholding ratio of the largest shareholder at the end of the period
Bod	Natural logarithm of the total number of board members at the end of the period
Dual	Dummy variables that equal 1 if the chairman and general manager are the same person, and 0 otherwise
Fage	Natural logarithm of the company’s age of establishment
Ind/Year	Industry (CSRC 2012 two-digit industry code) and annual dummy variables

3.2.2. Model design

To verify Hypothesis 1 and Hypothesis 2, the following model was established:

(3) $\mathrm{Dsale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}=\mathit{\alpha }+{\mathit{\beta }}_1\mathrm{Ipo}{\mathrm{T}}_{\mathrm{i},\mathrm{t}}/\mathrm{M}{\mathrm{V}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_{\mathrm{i}}\sum _{\mathit{t}=2}^{\mathrm{n}}\mathrm{Contro}{\mathrm{l}}_{\mathrm{i},\mathrm{t}}+{\text{varepsilona}}_{\mathrm{i},\mathrm{t}}$(3)

In view of the theoretical assumptions about the driving effect of categorical listing criteria and company valuation on growth manipulation, we expect the coefficient ${\beta }_1$ to be significantly positive.

To verify Hypothesis 3 and Hypothesis 4, we further introduce the moderating variable co-investment system (Finstu) and establish its interaction term with categorical listing criteria (IPOT) and company valuation (MV). The empirical model is designed as follows:

(4) $\mathrm{Dsale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}=\mathit{\alpha }+{\mathit{\beta }}_1\mathrm{Ipo}{\mathrm{T}}_{\mathrm{i},\mathrm{t}}/\mathrm{M}{\mathrm{V}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_2\mathrm{Finst}{\mathrm{u}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_3\mathrm{Ipo}{\mathrm{T}}_{\mathrm{i},\mathrm{t}}/\mathrm{M}{\mathrm{V}}_{\mathrm{i},\mathrm{t}}\times \mathrm{Finst}{\mathrm{u}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_{\mathrm{i}}\sum _{\mathit{t}=4}^{\mathrm{n}}\mathrm{Contro}{\mathrm{l}}_{\mathrm{i},\mathrm{t}}+{\text{varepsilona}}_{\mathrm{i},\mathrm{t}}$(4)

Given the theoretical speculation that a co-investment system would mitigate the promotion of categorical listing criteria and company valuation for growth manipulation, this paper expects the regression coefficients of Finstu$\times$IPOT and Finstu$\times$MV are both negatively significant.

4. Empirical results and analysis

4.1. Descriptive statistics

Table 4 presents the descriptive statistical results of the main variables. The mean and standard deviation (SD) values of the growth manipulation proxy variable (Dsales) are 0.035 and 0.019. Using the sktest test, we found that the p-values of both skewness and kurtosis of Dsales are equal to 0, which is in accordance with the assumption of normal distribution, and the maximum and minimum values are 0.106 and − 0.013, respectively. It shows that there is a large difference between the samples. The mean of the proxy explanatory variable categorical listing criteria (IPOT) is 1.360, with a SD of 0.998. The mean of the company valuation (MV) is 3.567, indicating an average valuation of approximately 3.5 billion for the sample companies, which is close to the median of 3.476. In addition, the mean of the proxy variable Finstu for the co-system is 4.394, and the descriptive statistics for the control variables are similar to the existing studies.

Table 4. Descriptive statistics of main variables.

VARIABLES	N	Mean	SD	Min	Max	Median
Dsales	1107	0.035	0.019	−0.013	0.106	0.032
IPOT	1107	1.360	0.998	1	5	1
MV	1036	3.567	1.117	1.334	6.351	3.476
Finstu	1107	4.394	0.836	2.000	5.000	5.000
Lev	1107	31.810	18.420	3.867	83.840	28.920
Cur_r	1107	4.492	4.595	0.739	26.980	2.827
Cash	1107	7.005	10.670	−27.700	39.750	6.808
Assets	1107	20.820	0.972	19.060	24.110	20.720
Roa	1107	10.730	11.510	−46.670	48.320	10.100
AEM	1107	0.060	0.039	0.001	0.179	0.054
Inno	1107	2.671	1.163	0.693	5.948	2.639
Empy	1107	6.415	0.871	4.673	9.556	6.358
Shf	1107	37.410	18.170	4.680	93.050	35.000
Bod	1107	2.041	0.249	1.099	2.485	2.197
Dual	1107	0.607	0.489	0.000	1.000	1.000
Fage	1107	2.636	0.383	1.609	3.466	2.708

Note: The decrease in the sample size of MV is due to the lack of relevant equity transaction data in some IPO prospectuses, which leads to missing values. The following table is the same as above.

4.2. Identification of growth manipulation of IPO companies in STAR market

The growth manipulation behaviour of IPO companies is primarily the manipulation of operating incomes artificially, which eventually manifests itself as continuous high growth. Therefore, it is important to standardise the operating income to identify the growth manipulation behaviours of IPO companies effectively. Theoretically, a company’s growth targets are influenced by its historical targets, historical growth levels, and the growth targets of competitors in the same industry (Cyert & March, 1963, Greve, 1998). When companies set targets, they usually refer to their historical performance and industry averages (Greve, 2003, O’Brien & David, 2014). In practice, whether it is an IPO prospectus or research reports issued by intermediaries, they all compare the differences between the company’s current income growth and its historical growth or industry growth level. Therefore, we argue that if a company needs to manipulate the revenue growth rate to send a signal of its future growth potential, on the premise of meeting the listing requirements, it will not only consider its relative historical revenue growth level vertically but also look at the revenue growth of industry competitors on a horizontal basis. Moreover, the categorical listing criteria and company valuation in the listing rules are closely related to the revenue growth rate. As such, we standardise the sample company’s current operating income growth rate as an industry median adjustment, expressed by Gpr_I&H, to identify the growth of IPO manipulation.

Figure 2 displays the columnar distribution of the standardised operating income growth rate (Gpr_I&H) within the range of [−20%, 40%]. The vertical line on the abscissa at 0 represents the Gpr_I&H reference line. It is noticeable that the number of samples on the left side of the benchmark reference line is significantly lower than that on the right side. This figure preliminarily concludes that China’s STAR Market IPO companies exhibit evident operating income manipulation behaviour, aiming to achieve or slightly surpass the industry average income growth rate and the company’s historical income level.

Figure 2. Histogram of standardized revenue growth rate (Gpr_I&H) sample distribution.

Next, we calculate the distribution of samples on both sides of different threshold intervals based on the Gpr_I&H reference point and conducted sample difference statistics. In the statistical results in Table 5, it is evident that there are more samples on the right side than on the left. Furthermore, the test results are significant at the 1% level, which indicates a significant disparity on both sides, providing further confirmation of the growth manipulation behaviour exhibited by the sample companies.

Table 5. Statistical tests for observations with different threshold intervals.

Intervals	Gpr_I&H		statistics
	the left side	the sight side
[−3％，3％]	130	231	15.776***
[−2％，2％]	107	153	13.319***
[−1％，1％]	64	81	9.823***

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

To verify the growth manipulation behaviour of China’s STAR Market companies during the IPO period, we employ the RDD breakpoint smoothness test principle following Mccrary (2008) and Bugni and Canay (2021) for the McCrary test. Specifically, the basic principle of this test is that if the operating income growth rate is not manipulated, the sample distribution on both sides of the ‘breakpoint’ should be continuous; otherwise, the sample distribution should be discontinuous. In Figure 3, it can be observed that the confidence interval boundaries of the density function estimates on both sides of the Gpr_I&H ‘breakpoint’ are clear, and there is no overlap, implying that the samples on both sides of the ‘breakpoint’ are significantly different. Therefore, the test results confirm the existence of endogenous grouping in Gpr_I&H and further validate the behaviour of growth manipulation in the sample companies.

Figure 3. McCrary test of standardized operating revenue growth rate (Gpr_I&H).

4.3. The effect of categorical listing criteria, company valuation on growth manipulation

Table 6 presents the empirical results for our Hypotheses 1 and Hypotheses 2. Specifically, column (1) shows the relationship between growth manipulation (Dsales) and categorical listing criteria (IPOT). The regression coefficient of IpoT is 0.002 and statistically significant at the 1% level, suggesting that the degree of growth manipulation of companies listed from categorical listing standard I to V gradually increases, thus supporting the theoretical inference of Hypothesis 1.

Table 6. The effect of categorical listing criteria, and company valuation on company growth manipulation.

	Dependent Variable: Dsales
	(1)	(2)
IPOT	0.002***
	(4.028)
MV		0.001**
		(2.229)
Lev	0.000***	0.000***
	(8.338)	(7.820)
Cur_r	−0.000**	−0.000**
	(−2.310)	(−2.503)
Cash	−0.000***	−0.000***
	(−4.241)	(−5.155)
Assets	−0.005***	−0.005***
	(−5.751)	(−5.214)
Roa	0.001***	0.001***
	(11.609)	(11.573)
AEM	0.132***	0.138***
	(8.933)	(9.111)
Inno	−0.000	−0.000
	(−0.466)	(−1.006)
Empy	0.001	0.001
	(1.051)	(1.604)
Shf	−0.000	−0.000
	(−1.559)	(−1.408)
Bod	−0.006***	−0.006***
	(−2.790)	(−2.792)
Dual	−0.001	−0.001
	(−1.480)	(−1.229)
Fage	−0.007***	−0.007***
	(−5.383)	(−5.541)
Ind	Yes	Yes
Year	Yes	Yes
Constant	0.135***	0.141***
	(9.277)	(8.212)
Observations	1107	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

The results regarding the impact of company valuation (MV) on growth manipulation (Dsales) are presented in Column (2). The coefficient of MV is 0.001 and significant at the 5% level, which supports the theoretical speculation that the higher the valuation level of the company, the higher the degree of growth manipulation of Pre-IPOs on the STAR Market, confirming Hypothesis 2.

4.4. The role of the co-investment system on the effect of categorical listing criteria, company valuation on growth manipulation

To test Hypothesis 3 and Hypothesis 4, we perform regression analysis based on Model (4). Table 7 reports the results. In column (1), Finstu$\times$IPOT is the interaction term between the co-investment system (Finstu) and the categorical listing criteria (IPOT), and its coefficient is negative and significant at the 1% level, suggesting that the co-investment system can considerably inhibit the driving effect of categorical listing criteria on growth manipulation, verifying the governance role of the sponsor’s co-investment system. Therefore, Hypothesis 3 is empirically supported. Similarly, column (2) reports the regression results of the moderating effect of the co-investment system (Finstu) on the relationship between company valuation (MV) and growth manipulation (Dsales). The empirical results show that the coefficient of the interaction term (Finstu$\times$MV) between Finstu and MV is significantly negative at the 1% statistical level. This confirms that the co-investment system considerably alleviates the driving effect of company valuation on growth manipulation, and Hypothesis 4 is empirically supported.

Table 7. Categorical listing criteria/company valuation, co-investment system and company growth manipulation.

	Dependent Variable: Dsales
	(1)	(2)
IPOT	0.006***
	(3.367)
IPOT$\times$ Finstu	−0.001**
	(−2.037)
MV		0.006***
		(3.392)
MV$\times$ Finstu		−0.001***
		(−2.764)
Finstu	−0.000	0.003
	(−0.240)	(1.440)
Lev	0.000***	0.000***
	(8.418)	(8.151)
Cur_r	−0.000*	−0.000**
	(−1.824)	(−2.135)
Cash	−0.000	−0.000**
	(−1.223)	(−2.310)
Assets	−0.007***	−0.008***
	(−8.560)	(−8.267)
Roa	0.001***	0.000***
	(9.319)	(8.621)
AEM	0.090***	0.095***
	(6.807)	(6.902)
Inno	0.000	−0.000
	(0.043)	(−0.617)
Empy	0.002***	0.002***
	(2.795)	(2.974)
Shf	−0.000***	−0.000***
	(−3.170)	(−3.161)
Bod	−0.003*	−0.004*
	(−1.906)	(−1.885)
Dual	−0.001	−0.000
	(−0.712)	(−0.391)
Fage	−0.004***	−0.004***
	(−3.425)	(−3.375)
Ind	Yes	Yes
Year	Yes	Yes
Constant	0.164***	0.166***
	(9.916)	(8.592)
Observations	1107	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

4.5. Robustness test

4.5.1. Alternative explanations excluding growth manipulation

Thus far, we present consistent evidence that categorical listing criteria and company valuation will positively drive the growth manipulation behaviour of Pre-IPOs, while the co-investment system of sponsors will alleviate this effect. However, one might be concerned that the accuracy of growth manipulation identification may influence our results. Therefore, we provide the following two alternative explanations for the company’s performance changes during the listing period and verify them.

4.5.1.1. “Average income growth level” or “growth manipulation”?

To ensure that the standardised revenue growth rate is indicative of ‘growth manipulation’ by the company to enhance the likelihood of expediting the IPO process, rather than merely representing the average threshold of the revenue growth rate of companies listed on the STAR Market, we conduct the RDD breakpoint smoothness test following Mccrary (2008) and Bugni and Canay (2021). This test assesses whether the sample distribution on both sides of the ‘breakpoint’ under the impact of exogenous events is continuous, indicating potential traces of ‘artificial manipulation’.

The results of the RD Manipulation test in Table 8 show that the p-value of the non-random test for the standardised revenue growth rate proxy variable Gpr_I&H on both sides of the ‘breakpoint’ is 0.008, which is significant at the 1% level. The discontinuous characteristics of the sample distribution on both sides of the ‘breakpoint’ are confirmed. The above results validate that the revenue growth rate is more likely to reflect the company’s growth manipulation rather than the average revenue growth level.

Table 8. ‘Breakpoint’ density function number continuity test for the growth rate of operating income: rddensity command.

Variables Cutoff c=0	Gpr_I&H
	Left side	Right side
Sample	395	712
Valid samples	242	453
p-value	0.008***

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

4.5.1.2. “Optimal income growth level” or “growth manipulation”?

Similarly, to ensure that the standardised revenue growth rate is not the optimal economic choice produced by the company under the constraints of existing resources, we follow Yeh et al. (2010), setting the model as:

(5) $\mathrm{IPO}{\mathrm{V}}_{\mathrm{i},\mathrm{t}}=\mathit{\alpha }+{\mathit{\beta }}_1{\mathrm{D}}_{\mathrm{i},\mathrm{t}}(\mathrm{Gpr}\_\mathrm{I}\text{amp}{\mathrm{H}}_{\mathrm{i},\mathrm{t}}<\mathit{T})+{\mathit{\beta }}_2{\mathrm{D}}_{\mathrm{i},\mathrm{t}}(\mathrm{Gpr}\_\mathrm{I}\text{amp}{\mathrm{H}}_{\mathrm{i},\mathrm{t}}>\mathit{T})+{\mathit{\beta }}_{\mathrm{i}}\sum _{\mathit{t}=3}^{\mathrm{n}}\mathrm{Contro}{\mathrm{l}}_{\mathrm{i},\mathrm{t}}+{\mathit{\epsilon }}_{\mathrm{i},\mathrm{t}}$(5)

where IPOV is the company’s market capitalisation at the time of stock issuance. The segment variable D is defined as the five intervals of Gpr_I&H. The remaining variables are defined above, as detailed in Table 3.

Column (1) of Table 9 reports the regression results for five segmentation intervals with 0.5% of the Gpr_I&H value as the threshold value. The segmentation intervals are Gpr_I&H$\le$-0.5%, −0.5%$<\mathit{Gpr}\_\mathit{I}\le$0%, 0%$<\mathit{Gpr}\_\mathit{I}\le$0.5%, 0.5%$<\mathit{Gpr}\_\mathit{I}\le$1%, and Gpr_I&H$>$1%. We find that the ‘positive’ interval closest to the critical value of 0, that is, 0%$<\mathit{Gpr}\_\mathit{I}\le$0.5%, the regression coefficient of standardised revenue growth (Gpr_I&H) on company market capitalisation (IPOV), is − 20.835 and is significant at the 10% level. Similarly, we divide five segmented intervals with a threshold value of 1% for Gpr_I&H and perform the regression analysis again. The results in column (2) of Table 9 show that in the ‘positive’ interval closest to the critical value of 0 (0%)$<\mathit{Gpr}\_\mathit{I}\le$1%), the coefficient of regression of Gpr_I&H on IPOV is − 15.032 and is significant at the 5% level. These results suggest that the standardised revenue growth rate (Gpr_I&H) exceeds the threshold value due to growth manipulation rather than the optimal revenue growth rate, thus further validating the existence of company growth manipulation.

Table 9. Regression results for different segmentation intervals of Gpr_I&H.

	Dependent Variable: IPOV
	(1)		(2)
D(Gpr_I&H$\le$ −0.5%)	-	D(Gpr_I&H$\le$ −1%)	-
D(−0.5%)$<$ Gpr_I&H$\le$ 0%)	1.617	D(−1%)$<$ Gpr_I&H$\le$ 0%)	−8.192
	(0.160)		(−0.970)
D(0%)$<$ Gpr_I&H$\le$ 0.5%)	−20.835*	D(0%)$<$ Gpr_I&H$\le$ 1%)	−15.032**
	(−1.910)		(−1.974)
D(0.5%)$<$ Gpr_I&H$\le$ 1%)	−5.536	D(1%)$<$ Gpr_I&H$\le$ 2%)	−1.637
	(−0.554)		(−0.235)
D(Gpr_I&H $>$ 1%)	5.013	D(Gpr_I&H $>$ 2%)	28.989***
	(1.209)		(5.760)
Lev	−0.813***	Lev	−0.873***
	(−5.485)		(−5.998)
Cur_r	−1.243**	Cur_Ratio	−1.100*
	(−2.068)		(−1.861)
Cash	0.010	Cash	0.090
	(0.042)		(0.381)
Lnassets	56.733***	Lnassets	55.391***
	(17.381)		(17.219)
Roa	−0.612**	Roa	−0.678***
	(−2.560)		(−2.928)
Empy	332.270***	Empy	319.226***
	(5.686)		(5.549)
Shf	−1.600	Shf	−1.373
	(−0.936)		(−0.816)
Bod	4.446	Bod	6.531*
	(1.272)		(1.892)
Dual	0.667***	Dual	0.694***
	(6.059)		(6.395)
Fage	5.065	Fage	10.120
	(0.629)		(1.273)
Ind	Yes	Ind	Yes
Year	Yes	Year	Yes
Constant	−1073.721***	Constant	−1084.581***
	(−18.391)		(−18.872)
Observations	1107	Observations	1107

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

4.5.2. Retest of growth manipulation behaviour identification

Although the existence of growth manipulation has been tested above, the differences among categorical listing criteria may also interfere with the identification of growth manipulation. Thus, we further use the different connotations of categorical listing criteria to conduct a robustness test for the standardised treatment of revenue growth rate. Specifically, we take categorical listing criteria as the benchmark and calculate the average annual revenue growth rate of the listed year, the first year before listing, and the second year before listing according to the industry, respectively. Moreover, we take it as the standardised ‘threshold’ of the revenue growth rate and as the growth manipulation proxy variable, expressed by Gpr_CLS. We then test the robustness of the growth manipulation behaviour as follows.

First, following a similar approach as above, we present a histogram of the Gpr_CLS sample distribution and test the difference statistic on both sides of the sample distribution for different threshold intervals. The histogram is shown in Figure 4. In line with our expectations, the number of samples on the left side of the Gpr_CLS benchmark reference point 0 is less than that on the right side, which verifies the growth manipulation of IPO companies on China’s STAR Market.

Figure 4. Histogram of the sample distribution of standardized revenue growth rate (Gpr_CLS).

Table 10 reports the statistical test results of Gpr_CLS on both sides of samples with different threshold intervals. There are more samples on the right than on the left in all intervals, and the statistical test is significant at the 1% level, showing that there is a significant difference in the distribution of samples on both sides of the Gpr_CLS reference point. This further validates statistically the veracity of our arguments for growth manipulation.

Table 10. Statistical tests of observed values in different threshold intervals.

interval	Gpr_CLS		statistic
	Left side	Right side
[−3％，3％]	71	84	10.710***
[−2％，2％]	48	64	9.033***
[−1％，1％]	30	32	6.771***

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Second, we conduct the McCrary test and use the Stata command DCdensity to draw graphs for observation. Figure 5 shows that the confidence intervals of the density function estimates on both sides of the Gpr_CLS ‘breakpoint’ are well-defined and do not overlap. It indicates that the distribution of samples on both sides of the ‘breakpoint’ is significantly different, and there is a discontinuity of ‘artificial manipulation’ characteristics, thus further verifying the conclusions above.

Figure 5. McCrary test of standardized operating income growth rate (Gpr_CLS).

Finally, we perform the RD Manipulation test of local polynomial density estimation, and the results are shown in Table 11. The p-value of the non-random test of Gpr_GLS for the samples on both sides of the ‘breakpoint’ is 0.036, which is significant at the 5% level, confirming the growth manipulation behaviour during the IPO of the company.

Table 11. Density function continuity test of ‘breakpoint’ of operating income growth rate: based on rddensity command.

Variables Cutoff c=0	Gpr_CLS
	Left side	Right side
Sample	553	554
Valid samples	302	352
p-value	0.036**

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

4.5.3. Control variables lagged by one period

While the main hypothesis of our paper is the relationship between categorical listing criteria, the co-investment system, and growth manipulation, they could still be affected by other economic-level variables, such as regulatory requirements or business strategies that change during the listing period. To address this issue, we lag the independent and control variables and re-run the regression analysis based on models (1) and (2) above. In column (1) and column (3) of Table 12, the regression results of growth manipulation (Dsales) with categorical listing criteria (IPOT_t-1) and company valuation (MV_t-1) are $\mathit{\beta }$₁ = 0.002 (p₁ < 0.01) and $\mathit{\beta }$₂ = 0.001 (p₂ < 0.05), respectively. This shows that growth manipulation is significantly positively correlated with categorical listing criteria and company valuation at the level of 1% and 5%, respectively. In other words, the conclusion about Hypothesis 1 and Hypothesis 2 remains unchanged.

Table 12. Categorical listing criteria, company valuation, co-investment system, and growth manipulation: control variables lagged by one period.

	Dependent Variable: Dsales
	(1)	(2)	(3)	(4)
IPOTt-1	0.002***	0.006**
	(3.170)	(2.418)
IPOTt-1$\times$ Finstu t-1		−0.001**
		(−1.997)
MV t-1			0.001**	0.006***
			(2.124)	(2.620)
MVt-1$\times$ Finstu t-1				−0.001**
				(−2.063)
Finstu t-1		0.002		0.001
		(1.380)		(0.483)
Lev t-1	0.000***	0.000***	0.000***	0.000***
	(4.041)	(2.784)	(3.660)	(3.655)
Cur_Ratio t-1	−0.000	−0.000	−0.000	0.000
	(−0.239)	(−0.800)	(−0.204)	(0.079)
Cash t-1	−0.000	−0.000	−0.000**	−0.000
	(−1.295)	(−0.605)	(−2.115)	(−1.581)
Assets t-1	−0.008***	−0.008***	−0.008***	−0.010***
	(−6.961)	(−6.590)	(−6.559)	(−7.681)
Roa t-1	0.000**	0.000	0.000	0.000
	(2.430)	(1.237)	(1.253)	(0.550)
AEM t-1	−0.032	−0.022	−0.024	−0.042**
	(−1.642)	(−1.193)	(−1.244)	(−2.108)
Inno t-1	0.001**	0.001**	0.001	0.001
	(2.529)	(2.390)	(1.532)	(1.281)
Empy t-1	0.001	0.002**	0.002	0.002**
	(1.030)	(2.335)	(1.493)	(2.104)
Shf t-1	0.000	−0.000	0.000	−0.000
	(0.710)	(−0.028)	(0.580)	(−0.859)
Bod t-1	−0.007***	−0.005***	−0.008***	−0.007***
	(−3.852)	(−2.649)	(−4.211)	(−3.736)
Dual t-1	−0.003**	−0.002*	−0.003**	−0.002*
	(−2.239)	(−1.678)	(−2.560)	(−1.692)
Fage t-1	−0.006***	−0.005***	−0.007***	−0.005***
	(−3.887)	(−2.984)	(−4.298)	(−3.107)
Ind	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Constant	0.210***	0.177***	0.207***	0.233***
	(10.537)	(7.919)	(9.990)	(9.004)
Observations	738	738	667	667

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are in parentheses.

Columns (2) and (4) of Table 12 report the regression results of the moderating effects of categorical listing criteria (IPOT_t-1) and company valuation (MV_t-1) on the effects of the growth manipulation (Dsales) in the presence of the co-investment system (Finstu_t-1), respectively. The coefficient of the interaction term (Finstu_t-1$\times$IPOT_t-1) between Finstu_t-1 and IPOT_t-1 is $\mathit{\beta }$ = −0.001 (p < 0.05), which is significantly negative at the 5% level and robustly supports Research Hypothesis 3. Similarly, the regression results of the interaction term (Finstu_t-1$\times$ MV_t-1) between Finstu_t-1 and MV_t-1 is $\mathit{\beta }$ = −0.001 (p < 0.05), which still robustly supports Hypothesis 4.

4.5.4. Alternative measures of the dependent variable

To ensure that our results are not driven by the downward growth manipulation (negative values), we redefine the dependent variable, denoted by Dsales_0. Specifically, if the value of Dsales is less than 0, it is assigned a value of 0. Table 13 reports the regression results with Dsales_0. In line with our baseline results, the coefficients remain significant across all four columns, confirming that our main results are robust to alternative proxies for categorical listing criteria as the dependent variable.

Table 13. Categorical listing criteria/firm valuation, co-investment system and growth manipulation: dependent variable substitution (1).

	Dependent Variable: Dsales_0
	(1)	(2)	(3)	(4)
IPOT	0.002***	0.006***
	(4.322)	(3.336)
IPOT$\times$ Finstu		−0.001*
		(−1.953)
MV			0.001**	0.006***
			(2.326)	(3.331)
MV$\times$ Finstu				−0.001***
				(−2.714)
Finstu		−0.000		0.002
		(−0.413)		(1.313)
Lev	0.000***	0.000***	0.000***	0.000***
	(8.621)	(8.546)	(8.096)	(8.262)
Cur_Ratio	−0.000**	−0.000*	−0.000**	−0.000**
	(−2.345)	(−1.908)	(−2.565)	(−2.229)
Cash	−0.000***	−0.000	−0.000***	−0.000**
	(−4.320)	(−1.260)	(−5.265)	(−2.357)
Assets	−0.005***	−0.007***	−0.005***	−0.008***
	(−5.876)	(−8.819)	(−5.313)	(−8.479)
Roa	0.001***	0.001***	0.001***	0.000***
	(11.948)	(9.495)	(11.845)	(8.767)
AEM	0.130***	0.090***	0.137***	0.095***
	(9.113)	(6.996)	(9.330)	(7.115)
Inno	−0.000	0.000	−0.000	−0.000
	(−0.342)	(0.137)	(−0.906)	(−0.535)
Empy	0.001	0.002***	0.001*	0.002***
	(1.107)	(2.864)	(1.667)	(3.058)
Shf	−0.000	−0.000***	−0.000	−0.000***
	(−1.581)	(−3.133)	(−1.405)	(−3.142)
Bod	−0.006***	−0.003**	−0.006***	−0.004*
	(−2.888)	(−1.983)	(−2.900)	(−1.955)
Dual	−0.001	−0.001	−0.001	−0.000
	(−1.555)	(−0.869)	(−1.300)	(−0.528)
Fage	−0.007***	−0.004***	−0.007***	−0.004***
	(−5.435)	(−3.436)	(−5.612)	(−3.394)
Ind	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Constant	0.134***	0.165***	0.139***	0.167***
	(9.464)	(10.255)	(8.375)	(8.900)
Observations	1107	1107	1036	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

We also use a dummy variable to describe the probability of the company’s growth manipulation, labelled as Dsales_sum. Specifically, if Dsales is bigger than 0, Dsales_sum is assigned a value of 1; otherwise, 0 is assigned. As shown in Table 14, the regression results are consistent with our theoretical expectations from Hypothesis 1 to Hypothesis 4.

Table 14. Categorical listing criteria/company valuation, co-investment system and growth manipulation: dependent variable substitution (2).

	Dependent Variable: Dsales_dum
	(1)	(2)	(3)	(4)
IPOT	0.145*	0.463*
	(1.712)	(1.872)
IPOT$\times$Finstu		−0.115*
		(−1.832)
MV			0.253**	0.465
			(2.233)	(1.569)
MV$\times$Finstu				−0.136**
				(−2.044)
Finstu		0.350**		0.520
		(2.298)		(1.640)
Lev	−0.001	0.002	0.001	0.002
	(−0.189)	(0.308)	(0.173)	(0.265)
Cur_Ratio	−0.025	−0.030	−0.013	−0.019
	(−1.321)	(−1.557)	(−0.510)	(−1.117)
Cash	−0.006	−0.011	−0.012	−0.015
	(−0.615)	(−0.976)	(−1.110)	(−1.460)
Assets	−0.252**	−0.134	−0.161	−0.348**
	(−2.153)	(−0.991)	(−0.968)	(−2.546)
Roa	0.014	0.007	0.013	0.011
	(1.360)	(0.828)	(1.120)	(1.289)
AEM	3.804	5.493**	0.422	5.281**
	(1.606)	(2.266)	(0.158)	(2.260)
Inno	−0.110*	−0.039	−0.019	−0.247***
	(−1.829)	(−0.466)	(−0.254)	(−3.971)
Empy	0.232*	0.113	0.032	0.406***
	(1.852)	(0.789)	(0.201)	(3.078)
Shf	−0.005	−0.008	−0.002	−0.004
	(−1.225)	(−1.341)	(−0.474)	(−0.870)
Bod	−0.311	0.167	0.144	−0.068
	(−1.006)	(0.429)	(0.416)	(−0.218)
Dual	0.018	0.245	0.090	0.089
	(0.128)	(1.376)	(0.537)	(0.508)
Fage	0.052	0.089	−0.058	−0.144
	(0.283)	(0.393)	(−0.249)	(−0.672)
Ind	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Constant	−230.367*	93.707	−124.764	−167.119
	(−1.675)	(0.516)	(−0.715)	(−0.951)
Observations	1107	1107	1036	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

4.5.5. Alternative measures of the co-investment system

To ensure that our main findings are not sensitive to a particular measure of the co-system, we take the natural logarithm of the number of shares invested as its measurement variable, characterised by FinstuNO, and re-examine Hypotheses 3 and Hypotheses 4. The regression results reported in Table 15 show that the coefficients of growth manipulation (Dsales) with interaction terms (FinstuNO$\times$IPOT & FinstuNO$\times$MV) are both negative and significant at the 5% and 10% levels, respectively. Therefore, the empirical results of Hypotheses 3 and Hypotheses 4 remain robust.

Table 15. Categorical listing criteria/company valuation, co-investment system and growth manipulation: moderating variable substitution.

	Dependent Variable: Dsales
	(1)	(2)
IPOT	0.017**
	(2.309)
IPOT$\times$Finstu	−0.001**
	(−1.976)
MV		0.015*
		(1.815)
MV$\times$Finstu		−0.001*
		(−1.689)
Finstu	0.001	0.006**
	(0.557)	(2.276)
Lev	0.000***	0.000***
	(8.683)	(7.488)
Cur_Ratio	−0.000**	−0.000***
	(−2.492)	(−2.856)
Cash	−0.000***	−0.000
	(−4.004)	(−1.482)
Assets	−0.004***	−0.003***
	(−5.269)	(−2.786)
Roa	0.001***	0.001***
	(12.126)	(11.537)
AEM	0.123***	0.148***
	(8.696)	(9.499)
Inno	−0.000	−0.001
	(−0.189)	(−1.442)
Empy	0.001	0.002**
	(1.254)	(1.994)
Shf	−0.000	−0.000**
	(−1.356)	(−2.192)
Bod	−0.005***	−0.004*
	(−2.693)	(−1.684)
Dual	−0.002*	−0.001
	(−1.787)	(−1.086)
Fage	−0.006***	−0.007***
	(−5.311)	(−4.920)
Ind	Yes	Yes
Year	Yes	Yes
Constant	0.113***	0.003
	(5.502)	(0.083)
Observations	1107	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are in parentheses.

5. Further analysis

It is evident that a company’s current profitability and its position in the industry influence investors’ assessments of its future value. Therefore, we further introduce profit margin and industry position to examine their impact on the relationship between categorical listing criteria or company valuation and growth manipulation.

5.1. Profit rate, categorical listing criteria/company valuation and growth manipulation

Profit is a crucial factor in measuring the value of traditional enterprises. While it may be challenging to convey investment value signals to regulators or capital markets through current profits, Pre-IPOs with poor profit margins have a stronger incentive to signal ‘growth potential’ through growth manipulation, potentially amplifying the impact of categorical listing criteria and company valuation.

Therefore, by constructing the interaction between the dummy variable for profit rate (PrftR) with IPOT and MV, we test whether the lower profit margin of Pre-IPOs will aggravate the driving effect of categorical listing criteria and company valuation on growth manipulation. The profit rate is defined as the ratio of net profit to sales revenue. We then establish the dummy variable for profit rate (PrftR), defined as 1 if the sample profit rate is lower than the industry average, and 0 otherwise. The coefficients are shown in Table 16 and are both significant at the 1% level. The regression results confirm the theoretical hypothesis mentioned above, further substantiating the theoretical logic of our main research conclusions.

Table 16. Profit rate, categorical listing criteria/company valuation and growth manipulation.

	Dependent Variable: Dsales
	(1)	(2)
IPOT	−0.000
	(−0.543)
IPOT$\times$PrftR	0.003***
	(2.732)
MV		0.001
		(1.107)
MV$\times$PrftR		0.002***
		(2.701)
PrftR	0.002	−0.002
	(1.096)	(−0.714)
Lev	0.000***	0.000***
	(7.255)	(7.469)
Cur_r	−0.000**	−0.000*
	(−2.092)	(−1.731)
Cash	−0.000***	−0.000***
	(−4.332)	(−5.423)
Assets	−0.004***	−0.005***
	(−5.476)	(−5.558)
Roa	0.001***	0.001***
	(10.791)	(12.542)
AEM	0.139***	0.140***
	(8.794)	(9.338)
Inno	−0.000	−0.001
	(−0.847)	(−1.602)
Empy	0.000	0.001
	(0.068)	(1.102)
Shf	−0.000	−0.000
	(−0.670)	(−1.258)
Bod	−0.006***	−0.006***
	(−3.081)	(−2.677)
Dual	−0.002*	−0.001
	(−1.656)	(−1.389)
Fage	−0.006***	−0.007***
	(−5.106)	(−5.240)
Ind	Yes	Yes
Year	Yes	Yes
Constant	0.136***	0.148***
	(9.396)	(8.673)
Observations	1107	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

5.2. Industry status, categorical listing criteria/company valuation and growth manipulation

The industry status of the IPO company in its field holds crucial reference value for capital market investors and is one of the core factors used to evaluate the company’s future value. As the sales scale is a key factor reflecting its industry status, when the industry position characterised by sales scale is at a low level, the company has a stronger incentive to manipulate the revenue growth rate to signal its future growth potential. Consequently, this amplifies the promotional effect of categorical listing criteria and company valuation on growth manipulation.

Therefore, by constructing the interaction between the industry status with IPOT and MV, we test whether the lower position of Pre-IPOs will aggravate the driving effect of categorical listing criteria and company valuation on growth manipulation. Specifically, industry status is defined by whether the sales revenue is higher than the average sales revenue of all companies within the same industry, represented by Position. If the sample company’s income is less than the industry average, the value is 1; otherwise, 0 is assigned. The above regression results are reported in Table 17. It can be observed that the coefficients of IPOT$\times$Position and MV$\times$Position are 0.003 and 0.002, respectively, both significant at the 1% and 5% statistical levels. This shows that our theoretical inference is supported by empirical results, further reinforcing the theoretical logic of the main research conclusions.

Table 17. Industry status, categorical listing criteria/company valuation and growth manipulation.

	Dependent Variable: Dsales
	(1)	(2)
IPOT	0.001
	(0.840)
IPOT$\times$Position	0.003***
	(3.166)
MV		0.0001
		(0.028)
MV$\times$Position		0.002**
		(2.203)
Position	−0.016***	−0.019***
	(−7.940)	(−4.672)
Lev	0.000***	0.000***
	(8.027)	(7.702)
Cur_r	−0.000	−0.000*
	(−1.612)	(−1.808)
Cash	−0.000***	−0.000***
	(−4.762)	(−5.542)
Assets	−0.007***	−0.008***
	(−8.622)	(−7.606)
Roa	0.001***	0.001***
	(11.654)	(10.840)
AEM	0.114***	0.129***
	(7.972)	(8.677)
Inno	−0.000	−0.000
	(−0.094)	(−1.010)
Empy	0.000	0.001
	(0.379)	(1.214)
Shf	−0.000**	−0.000*
	(−1.983)	(−1.801)
Bod	−0.005***	−0.005**
	(−2.747)	(−2.503)
Dual	−0.001	−0.001
	(−1.147)	(−0.959)
Fage	−0.007***	−0.008***
	(−5.496)	(−5.852)
Ind	Yes	Yes
Year	Yes	Yes
Constant	0.206***	0.212***
	(12.611)	(11.148)
Observations	1107	1036

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

6. Economic consequences test

6.1. Growth manipulation and listing process

An important goal of the company’s growth manipulation is to expedite the listing process, reduce listing costs, and achieve higher marginal revenue from listing. Therefore, we further explore the relationship between the listing process and growth manipulation. To mitigate potential endogenous issues stemming from sample selection bias, missing variables, and other factors, we introduce control variables such as categorical listing criteria (IPOT), company valuation (MV), and the co-investment system (Finstu)⁶ based on the original control variables. Additionally, we employ the propensity score matching (PSM) technique. The PSM procedure unfolds as follows. Firstly, we divide the samples into two groups using the median of growth manipulation (Dsales) as the boundary, designating samples above the median as the treatment group and the rest as the control group. Secondly, employing the control variables, we conduct nearest-neighbour one-to-one matching for the treatment group samples, resulting in a total of 434 successfully paired observations.⁷ Then, we established the regression model as follows:

(6) $\mathrm{LnIPO}\_{\mathrm{D}}_{\mathrm{i},\mathrm{t}}=\mathit{\alpha }+{\mathit{\beta }}_1\mathrm{Dsale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_2\mathrm{Ipo}{\mathrm{T}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_3\mathrm{M}{\mathrm{V}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_4\mathrm{Finst}{\mathrm{u}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_{\mathrm{i}}\sum _{\mathit{t}=5}^{\mathrm{n}}\mathrm{Contro}{\mathrm{l}}_{\mathrm{i},\mathrm{t}}+{\mathit{\epsilon }}_{\mathrm{i},\mathrm{t}}$(6)

where LnIPO_D is the dependent variable, which is defined as the natural logarithm of the cumulative time (days) spent from the IPO initial filing date to the listing date. The date is taken from the Wind database.

As reported in column (1) of Table 18, the coefficient of Dsales is − 1.297, which is significant at the 5% level. The PSM test results in column (2) also show that the regression coefficient is significantly negative at the 10% level, confirming that growth manipulation significantly accelerates the process of company listing.

Table 18. Economic consequences of growth manipulation: listing process.

	LnIPO_D
	(1) OLS	(2) PSM
Dsales	−1.297**	−1.660*
	(−2.026)	(−1.687)
IPOT	0.059***	0.072***
	(4.801)	(3.654)
MV	−0.105***	−0.123***
	(−7.630)	(−5.460)
Finstu	−0.031**	−0.016
	(−1.997)	(−0.629)
Lev	0.002**	0.002*
	(1.987)	(1.929)
Cur_r	−0.005*	−0.002
	(−1.798)	(−0.349)
Cash	0.001	−0.001
	(1.129)	(−0.662)
Assets	−0.058***	−0.014
	(−2.695)	(−0.375)
Roa	0.002	0.006**
	(1.087)	(2.544)
AEM	0.124	0.433
	(0.386)	(0.819)
Inno	−0.022**	−0.027*
	(−2.573)	(−1.898)
Empy	0.042**	0.025
	(2.300)	(0.863)
Shf	−0.000	0.000
	(−0.046)	(0.497)
Bod	−0.067	−0.078
	(−1.589)	(−1.095)
Dual	−0.074***	−0.071**
	(−3.739)	(−2.314)
Fage	0.014	0.001
	(0.518)	(0.017)
Ind	Yes	Yes
Year	Yes	Yes
Constant	7.082***	6.272***
	(16.925)	(8.974)
Observations	1036	434

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. t-statistics are in parentheses.

6.2. Growth manipulation and the IPO underprice rate

We have confirmed that, in order to accelerate the listing process, the Pre-IPOs on the STAR Market have a strong incentive to manipulate growth. However, will this motivation further aggravate information asymmetry and lead to higher IPO underpricing? In this regard, we establish the following model to verify the relationship between growth manipulation and the IPO underprice rate

(7) $\mathrm{Underp}{\mathrm{r}}_{\mathrm{i},\mathrm{t}}=\mathit{\alpha }+{\beta }_1\mathrm{Dsale}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_2\mathrm{Ipo}{\mathrm{T}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_3\mathrm{M}{\mathrm{V}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_4\mathrm{Finst}{\mathrm{u}}_{\mathrm{i},\mathrm{t}}+{\mathit{\beta }}_{\mathrm{i}}\sum _{\mathit{t}=5}^{\mathrm{n}}\mathrm{Control}\_\mathrm{var}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}$(7)

where the IPO underprice rate is expressed as Underpr. Following Xue and Wang’s (2022) methodology, the formula is set as follows:

(8) $\mathrm{Underpr}=\frac{P_n-{\mathrm{P}}_0}{{\mathrm{P}}_0}$(8)

where P_n is company’s closing price on the first day of listing or the 10th day after listing, and P₀ is the issue price. A larger Underpr value indicates a higher IPO underprice level.

Columns (1) and (3) in Table 19 report the regression results of the full sample. We find that the coefficient of Dsales is positive and significant when the dependent variable is Underpr. Columns (2) and (4) present the estimated results of PSM. Similarly, the results also show that coefficients are all positive and significant at the 5% level. This indicates that companies send signals of growth potential through growth manipulation, which alleviates the information asymmetry between companies and investors, resulting in a higher IPO underprice rate for them.

Table 19. Economic consequences of growth manipulation: the IPO underprice rate.

	Underpr1		Underpr10
	(1) OLS	(2) PSM	(3) OLS	(4) PSM
Dsales	8.235***	10.761**	8.743***	12.144**
	(2.920)	(2.328)	(2.846)	(2.388)
IPOT	0.082	0.032	0.067	0.047
	(1.499)	(0.346)	(1.125)	(0.463)
MV	0.266***	0.279***	0.275***	0.402***
	(4.378)	(2.637)	(4.162)	(3.448)
Finstu	0.734***	0.566***	0.601***	0.509***
	(10.479)	(4.728)	(7.877)	(3.868)
Lev	0.001	0.006	−0.001	0.003
	(0.282)	(1.011)	(−0.184)	(0.514)
Cur_r	−0.011	0.023	−0.013	0.024
	(−0.794)	(1.064)	(−0.868)	(1.003)
Cash	0.007	−0.007	0.010*	0.004
	(1.220)	(−0.705)	(1.674)	(0.398)
Assets	0.138	−0.118	0.046	−0.152
	(1.456)	(−0.692)	(0.446)	(−0.814)
Roa	−0.001	0.008	−0.001	0.005
	(−0.203)	(0.737)	(−0.180)	(0.362)
AEM	−4.628***	−8.262***	−5.650***	−7.020***
	(−3.278)	(−3.389)	(−3.674)	(−2.618)
Inno	0.067*	0.074	0.110***	0.098
	(1.728)	(1.143)	(2.602)	(1.377)
Empy	−0.220***	−0.141	−0.158*	−0.177
	(−2.743)	(−1.069)	(−1.810)	(−1.216)
Shf	−0.003	−0.002	0.000	−0.002
	(−1.025)	(−0.500)	(0.110)	(−0.505)
Bod	0.194	0.305	0.236	0.147
	(1.033)	(0.923)	(1.151)	(0.404)
Dual	−0.243***	−0.132	−0.152	−0.036
	(−2.773)	(−0.921)	(−1.589)	(−0.228)
Fage	−0.020	0.001	−0.344***	−0.226
	(−0.167)	(0.004)	(−2.617)	(−1.098)
Ind	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Constant	−4.328**	0.725	−1.884	2.128
	(−2.325)	(0.222)	(−0.929)	(0.592)
Observations	1036	434	1036	434

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. T-statistics are in parentheses.

7. Research findings and policy implications

The implementation of the registration-based IPO system in the STAR Market is a crucial reform initiative in China’s capital market. Extending the experience of the STAR Market’s registration-based IPO system to other segments of the capital market effectively is a major direction for the future reform of China’s capital market. This research examines the economic effects of China’s STAR Market registration-based IPO system from the perspective of growth manipulation, considering the unique situation where the STAR Market has implemented both a registration system with marketisation characteristics and a growth criterion with audit characteristics.

We use a dataset of companies on the STAR Market between 2019 and 2021 to empirically examine the growth manipulation behaviour of Pre-IPOs and its driving mechanism and governance boundaries. Our findings indicate the existence of growth manipulation during the Pre-IPOs of STAR Market companies. Additionally, companies listed under categorical listing criteria I to V exhibit an increasing degree of growth manipulation. Moreover, the degree of growth manipulation is significantly higher for companies with a higher valuation, while the co-investment system can mitigate the driving effect of categorical listing criteria and company valuation on it. Non-parametric methods support these findings, such as the breakpoint smoothness test and a series of robustness tests, including the exclusion of alternative explanations. Additional research demonstrates that a lower profit margin or sector status would contribute to the driving effect of categorical listing criteria and company valuation on the growth manipulation of Pre-IPOs. The economic consequences test suggests that growth manipulation accelerates the company listing process and expands the IPO price suppression rate.

The findings of this paper carry significant policy implications. Firstly, the top-level institutional design of the STAR Market’s registration-based IPO system should undergo continuous improvement to enhance the effective allocation of innovation resources. Our research demonstrates that when regulators impose growth incentive conditions on Pre-IPOs, it inadvertently encourages growth manipulation, expediting the listing process. However, this also distorts the role of the STAR Market in allocating innovation resources and hinders effective support for high-tech businesses. To enhance the efficiency of the STAR Market, governments should investigate the scientific setup of listing conditions further. Secondly, the governance effect of the co-investment system should be fully leveraged, and incentives for the reputation mechanism of intermediaries should be strengthened to enhance the quality of information disclosure by listed companies. While our research indicates that the growth manipulation motivation of Pre-IPOs is stimulated by the growth incentive conditions of the STAR Market, the co-investment system significantly inhibits growth manipulation, showcasing its governance impact. Consequently, incentive measures for market intermediaries, such as brokers, and the reputation mechanism of intermediaries should be continuously improved. Thirdly, categorised listing criteria and company valuations drive the growth manipulation motive of companies. Therefore, adopting more scientific listing criteria and achieving a fair balance between quantitative and qualitative indicators is essential to effectively identify truly high-growth science and technology-based enterprises.

Acknowledgments

We appreciate helpful comments from Guliang Tang (editor) and anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Social Science Fund of China (22BGL040).

Notes

¹ Regarding quantitative requirements, categorical listing criteria I stipulates that the proposed listed company’s business revenue in the last year should exceed RMB 100 million, and the company’s valuation should not be below RMB 1 billion. In terms of qualitative requirements, the ‘Rules Governing the Listing of Stocks on the Shanghai Stock Exchange’ mentions ‘companies with strong growth potential’, while the ‘Questions and Answers by the Shanghai Stock Exchange on the Examination of the Issuance and Listing of Shares on the STAR Market (March 2019)’ focuses on ‘whether the core technology can support the sustainable growth of the company’ and other related descriptions. Further details can be found below.

² In this paper, our focus is solely on the registration system; for a comprehensive overview of other IPO systems, please refer to relevant literature, such as Tang and Wei (2016).

³ For more details, please refer to the Review Rules for the Issuance and Listing of Stocks on the STAR Market of the Shanghai Stock Exchange.

⁴ Building on previous literature (Liu et al., 2014b, Chen & Chen, 2018), earning management is measured using a performance-adjusted model, as described in Kothari et al. (2005). The specific steps are as follows: First, the following regression model is established:

$\frac{\mathit{T}{\mathit{A}}_{\mathit{i},\mathit{t}}}{\mathit{Asset}{s}_{\mathit{i},\mathit{t}-1}}={\alpha }_0+{\alpha }_1\frac{1}{\mathit{Asset}{\mathit{s}}_{\mathit{i},\mathit{t}-1}}+{\alpha }_2\frac{\Delta \mathit{Sale}{\mathit{s}}_{\mathit{i},\mathit{t}}}{\mathit{Asset}{s}_{\mathit{i},\mathit{t}-1}}+{\alpha }_3\frac{\mathit{PP}{\mathit{E}}_{\mathit{i},\mathit{t}}}{\mathit{Asset}{s}_{\mathit{i},\mathit{t}-1}}+{\alpha }_4\mathit{Ro}{a}_{\mathit{i},\mathit{t}-1}+{\epsilon }_{\mathit{i},\mathit{t}}$ (i)

where TA is total accruals, calculated as operating profit minus net cash flow from operating activities; Assets denotes total assets; Δ Sales is the difference between current sales revenue and previous sales revenue; PPE refers to fixed assets; ε represents the residual value.

Then, by substituting the estimated coefficient values ${\hat{\alpha }}_0$, ${\hat{\alpha }}_1$, ${\hat{\alpha }}_2$, and ${\hat{\alpha }}_3$ from the above regression equation (i) into the following formula, the variable EM is calculated, resulting in the absolute value of EM expressed as AIM:

$\mathit{E}{M}_{\mathit{i},\mathit{t}}=\frac{\mathit{T}{\mathit{A}}_{\mathit{i},\mathit{t}}}{\mathit{Asset}{s}_{\mathit{i},\mathit{t}-1}}-{\hat{\alpha }}_0-{\hat{\alpha }}_1\frac{1}{\mathit{Asset}{\mathit{s}}_{\mathit{i},\mathit{t}-1}}-{\hat{\alpha }}_2\frac{\Delta \mathit{Sale}{\mathit{s}}_{\mathit{i},\mathit{t}}-\Delta \mathit{Re}{c}_{\mathit{i},\mathit{t}}}{\mathit{Asset}{s}_{\mathit{i},\mathit{t}-1}}-{\hat{\alpha }}_3\frac{\mathit{PP}{\mathit{E}}_{\mathit{i},\mathit{t}}}{\mathit{Asset}{s}_{\mathit{i},\mathit{t}-1}}-{\hat{\alpha }}_4\mathit{Ro}{a}_{\mathit{i},\mathit{t}-1}$ (ii)

Where: ΔRec is the difference between the current receivables and the previous receivables; the meaning of the other variables is consistent with Equation (i).

⁵ The company mainly carries out patent management behaviour through utility model patents and design patents. Therefore, this paper measures the innovation output variable (Inno) as the natural logarithm of the total number of invention patents, utility model patents, and design patents. The data are sourced from the CSMR, iFinD database, and the necessary website of the National Patent Office. Variables such as the number of employees (Empy), the proportion of the largest shareholder (Shf), the number of directors (Bod), the combination of two posts (Dual), and other relevant variables are manually arranged based on the content provided in the IPO prospectus.

⁶ Thanks for the constructive comments from anonymous reviewers. Literary responsibility.

⁷ The PSM results showed significant differences in the t-values of each variable between the treatment group and the control group. Due to limited space, details are not reported. Additionally, the same PSM matching method is also employed to verify the relationship between growth manipulation and the IPO underprice rate. The following is no longer repeated.