The registration system reform is a significant reform in China’s securities market, marking the direction and determination of the function of the stock market, and the efficiency of resource allocation is low. The market-oriented reform of China’s stock issuance system. For a long time, China’s stock market has implemented an approval system and an approval system, and the government’s excessive supervision has made it difficult to effectively perform the price discovery

On June 13, 2019, the Science and Technology Innovation Board was launched on the Shanghai Stock Exchange and took the lead in piloting the registration system. Then, on August 24, 2020, the GEM also piloted the reform of the registration system. If the registration system was implemented on the Science and Technology Innovation Board or the Growth Enterprise Market, the original intention was to test the feasibility of the registration system in China. Research is required to prove whether the registration system can be fully implemented in China’s stock market and play a role in improving the efficiency of IPO pricing. We need to find out the problems existing in the trial implementation of the registration system in China and find solutions so as to exert the system effect of the registration system.

To study whether the reform of the registration system has improved the efficiency of stock issuance and pricing, the stochastic frontier model was used to systematically study the IPO pricing efficiency. The efficiency of IPO pricing was analysed by comparing the degree of deviation between the actual pricing of IPOs and the unbiased estimates of stock pricing. Regarding the sample data, this paper selects the data of listed companies on the Science and Technology Innovation Board and the data of listed companies after the reform of the GEM registration system, and data of listed companies before the reform of the GEM registration system as a comparison sample are also selected.

The research contribution of this article mainly has the following two points: first, the existing literature only uses the data of the Science and Technology Innovation Board to explore the reform effect of the registration system. In our research, we innovatively introduced the data before and after the reform of the ChiNext to discuss the effect of China’s registration system reform. Therefore, the research perspective of this article is highly comprehensive. Second, in this study, we deny the inertial thinking of IPO underpricing in previous studies. After examining the actual situation of Chinese stocks, we added the cost model to the stochastic frontier model to explore the degree of overvaluation of Chinese IPOs. This expands the research on IPO pricing efficiency under the registration system in China.

The research on the efficiency of IPO pricing was first put forward by scholars such as Logue (1973) and Ibbotson (1975), who found that investors who participated in the subscription of new shares could obtain returns that exceeded the market average. The existence of this excess return on new shares indicates that there is an efficiency loss in the new share issuance market. Subsequently, many scholars carried out research on the issue of IPO pricing efficiency and have formed a rich theoretical system. At present, it is mainly divided into two categories: information asymmetry theory and behavioural finance theory.

Information asymmetry refers to the phenomenon in which the party with more information is at an advantage and the party with less information is at a disadvantage due to differences in the information obtained by all parties. In the process of IPO, the main participants are divided into three parties, namely the issuer, the underwriter and the investor. Information asymmetry between underwriters and issuers can lead to principal-agent problems. Benveniste and Spindt (1989) pointed out that if the underwriter has an information advantage, the underwriter may lower the offering price to avoid the failure of the offering or to obtain more offer information from institutional investors. Liu Jianlei and Li Yuan (2019) explored the impact of marketisation process on IPO underpricing and its theoretical explanation from the perspective of information asymmetry. They found that only principal-agent theory explained the negative relationship between marketisation process and IPO underpricing. Information asymmetry among investors can lead to a “winner’s curse” problem. Zhang Yade and Lu Yuehui (2014) used 859 IPOs in the A-share market as samples to test the “winner’s curse” hypothesis first proposed by Rock and proved that the “winner’s curse” hypothesis can explain the underpricing of China’s A-share market. Information asymmetry between issuers and investors generates signalling theory. Welch (1989) found that companies will issue a discount to reveal that their quality is superior to other companies’ information to issue new shares at high prices in the future. After the 1990s, the theory of behavioural finance emerged. It is believed that micro-individual behaviours and the psychological motivations that produce such behaviours can explain the development of financial markets.

Subsequently, behavioural finance has been used by scholars from various countries to explain financial anomalies, among which the famous results include investor sentiment hypothesis, information waterfall theory, prospect theory and mental account hypothesis. Song Shunlin and Wang Yanchao (2016) used a sample of 917 IPO companies from 2006 to 2011 to analyse how investor sentiment affects stock pricing and found that market sentiment significantly affects IPO premium. He Qing and Zhou Yidan (2020) found that the private information of investors in the process of price inquiry can affect investor sentiment, which in turn affects the price of new shares, resulting in different degrees of IPO underpricing. Welch (1992) pointed out that the investment decisions of subsequent investors will be influenced by the investors who have already subscribed, thus producing the “information waterfall” effect. Loughran and Bitter (2002) explained the underpricing phenomenon using prospect theory and mental accounting theory. They point out that underpricing is not caused by investors but by the behavioural bias of issuers’ decision makers.

The judgement of IPO pricing efficiency is essentially to judge whether the determination of its price in the primary issuance market is accurate. At present, the judging perspectives of IPO pricing efficiency are mainly divided into three categories: the degree of IPO underpricing, the information content of stock prices and the degree of deviation between IPO pricing and the intrinsic value of the company. Most of the existing studies use the degree of IPO underpricing as a measure of the pricing efficiency of IPOs, but these studies obviously ignore the research premise of the secondary market being effective. In addition to the perspective of judgement, the method used to price stocks is also important. According to the different ways of calculating the value, it can be roughly divided into the direct valuation method and indirect valuation method.

Indirect valuation refers to comparison with prices already existing in other similar businesses or industries. Under the conditions of the efficient market and no-arbitrage hypothesis, this approach argues that identical or similar firms should be similar in value. Therefore, the target company and the comparable company can be estimated by a multiplier to determine the stock price. Commonly used multipliers are as follows: price-earnings ratio, price-to-sales ratio and price-to-book ratio. Bradshaw (2002) studied 103 US market analyst reports and found that price-earnings ratio and dynamic price-earnings ratio are the most favoured valuation methods. But some emerging tech companies may have negative earnings per share, at which point price-to-earnings ratios no longer apply. The research group of Beijing Securities Regulatory Bureau (2020) believes that different industries and different development stages have different characteristics. When valuing a company, it is necessary to choose a method that is suitable for the characteristics of the industry and its own development. For example, for the new material industry, it is recommended to use the price-to-sales ratio at the start-up stage, the price-to-earnings ratio method is applicable to the development stage and the price-to-earnings ratio and the price-to-book ratio are both feasible for the mature stage.

Direct valuation refers to the estimation of intrinsic value directly based on the company’s own historical conditions and possible future conditions. Direct valuation methods mainly include discounted cash flow model, company value model and econometric model. Khoirunnisa (2007) believes that the purpose of companies choosing to go public is to find financial support for the project to obtain a higher return on investment in the future. Therefore, the EVA method is highly reasonable in IPO valuation. Hendrawan and Tugiman (2019) compare the discounted cash flow model with the price-earnings ratio method. It is proved that the discounted cash flow model is reliable and reasonable, and its valuation results can be used as a reference for investment decisions. But the discount model’s estimates of future cash flows are subject to great uncertainty, and choosing a reasonable discount rate is not an easy task. Based on this consideration, the econometric model is more objective. Luo Qi and Wu Jingdong (2017) used the stochastic frontier method and found that the price of the IPO was significantly higher than the intrinsic value of the company.

The empirical research of this paper mainly consists of two parts: one is to quantitatively estimate the IPO pricing efficiency of the pilot registration system listed companies and the other is to compare the changes of IPO pricing efficiency before and after the registration system reform.

In quantitative estimation of the IPO pricing efficiency of listed companies, we have observed the Chinese stock market and found that China does not deliberately lower the IPO price. The reasons are as follows: first, investors can obtain excess returns by purchasing IPO stocks in most cases. Offline investors may bid up to increase the allocation of shares. Second, issuers and underwriters have little risk of breakouts. This is because issuers are hungry for financing and underwriters may be linked to the total amount of financing. Not only will the underwriters not lower the IPO price but also there is a possibility of raising the IPO price. Therefore, when we study the IPO pricing efficiency, we added the production function into the stochastic frontier model and take the deviation between the actual IPO pricing and the unbiased estimate as an indicator to examine the IPO pricing efficiency.

In comparing the efficiency of IPO pricing before and after the registration system reform, in this study, we selected listed companies before the registration system reformed on the GEM as a comparative sample and compared with the pricing efficiency of GEM companies after the reformation of the registration system. This research can more reasonably express the conclusion of whether the registration system improves the efficiency of IPO pricing.

Over time, the overheated market sentiment in the early stage of the registration system reform is gradually returning to rationality. Also, the market’s self-correcting function gradually comes into play. In this case, institutional investors and underwriters will also be more cautious about IPO pricing. Based on the above analysis, my research hypothesis 1 is as follows:

H1: The implementation of the registration system has gradually improved the efficiency of IPO pricing in the Chinese stock market.

Because the Science and Technology Innovation Board is a newly opened market, there is no reasonable comparable company as a reference for IPO valuation. However, the GEM has both stock and increment. If the registration system is implemented on the GEM, there will be more companies that can be referred to in pricing and valuation; so, investor sentiment will be more stable. Based on the above analysis, my research hypothesis 2 is as follows:

H2: The pricing efficiency of the GEM IPO under the registration system is higher than that of the Science and Technology Innovation Board.

Underwriters are an important intermediary between issuers and investors and play an important role in the IPO pricing process. It is generally believed that underwriters with a higher reputation can provide higher quality prospectuses to reduce information asymmetry between issuers and investors. In this way, the IPO pricing will be closer to the intrinsic value of the company. Based on the above analysis, my research hypothesis 3 is as follows:

H3: Underwriter reputation has a positive effect on IPO pricing efficiency. The higher the underwriter reputation, the higher the pricing efficiency.

At present, the commonly used methods for efficiency measurement include the nonparametric data envelope model (DEA) and the parametric stochastic frontier model (SFA). They all measure the technical efficiency by constructing the frontier production function and judging the distance between the output and the frontier. DEA is a linear programming model used to evaluate a set of homogeneous decision-making units with multiple inputs and multiple outputs. It is a method of nonparametric frontier efficiency analysis. However, nonparametric models have certain limitations. It mainly uses the linear programming method for computational efficiency, rather than the parametric method, which has the statistical test number as a reference for the sample fit and statistical properties. In addition, the nonparametric method has certain restrictions on the observed values, and sometimes, some sample values must be discarded, which affects the stability of the observed value results. Moreover, DEA does not consider the influence of random factors. Therefore, we choose the parametric method to calculate the efficiency. The parametric method - the stochastic frontier model is introduced below.

The stochastic frontier model (SFA) believes that the “real output” of any firm cannot exceed the “output boundary”, and the degree of deviation between the two is regarded as an efficiency loss. Hunt (1996) found that the measure of IPO pricing efficiency is similar to that of production efficiency; so, the stochastic frontier model was introduced into the study of IPO pricing efficiency. In this study, Hunt regards the price maker as a producer and regards the influencing factors of pricing as an input; then, the issue price is an output. The research is based on a stochastic frontier model in the form of a production function. Because in China’s stock market, there is no situation where the issue price of shares is deliberately lowered. Therefore, we added the cost function into the stochastic frontier model and constructed a stochastic frontier cost model, which is specifically expressed as follows:

In this model, _{i}_{i}_{i}_{i}_{i}_{i}_{i}_{i}_{i}

We define the IPO pricing efficiency as the ratio eff between the actual IPO pricing and the potential effective price, which is specifically expressed as follows:

The stochastic frontier model divides the error between the actual price of IPO pricing and the estimated effective price into random errors and unilateral systematic errors; so, the premise of using the stochastic frontier model is the existence of an inefficiency term. Coelli (1996) used the parameter

The stochastic frontier cost model is used to compare the degree of deviation between the actual IPO pricing and the intrinsic value of the company to analyse the IPO pricing efficiency before and after the registration system reform. The constructed model is as follows:

In this model, the explained variable _{i}_{i}_{i}_{i}_{i}_{i}_{i}_{i}

Variable definition table

p | Corporate IPO price |

naps | Earnings per share after deducting recurring gains and losses in the year prior to listing |

size | Number of new shares issued |

asset | Total assets of the company in the year before listing |

eps | Net assets per share in the year prior to listing |

alr | Asset-liability ratio in the year before listing |

growth | (Operating income for the current period/operating income for the previous period) - 1 |

rd | Intangible assets/total assets |

fee | Total issuance fee/total issuance |

To test whether the reform of China’s registration system has achieved the purpose of improving the efficiency of IPO pricing, we select the companies after the reform of the registration system on the Sci-Tech Innovation Board and the GEM as a sample and select the companies before the registration system reform on the GEM as a comparison sample. There are two reasons for this consideration: first, the listed companies on the Science and Technology Innovation Board and the GEM are both high-tech enterprises; so, the two boards are comparable. Second, the Science and Technology Innovation Board is an incremental market, and the GEM is a market in which stock and increment are synchronised. At the same time, the data of two sections are selected, which is more complete and time sensitive in the selection of samples. The data can make the research more reasonable to reflect whether the registration system has improved the efficiency of Chinese IPO pricing.

After excluding samples with missing important data, 271 listed companies since the opening of the Science and Technology Innovation Board on July 22, 2019 and 127 companies since the GEM pilot registration system on August 24, 2020 were screened. Then, 761 listed companies under the non-registration system were considered since the GEM launched as a comparative sample. All data come from the Cathay Pacific Database (CSMAR). Tables 2–4 shows the descriptive statistics for each sample variable.

Descriptive statistics of sample variables of the Science and Technology Innovation Board

p | 1.22 | 557.80 | 35.75 | 43.02 | 6.05 | 59.48 |

naps | 0.09 | 23.33 | 5.39 | 3.46 | 2.12 | 6.551 |

eps | −10.82 | 14.03 | 0.85 | 1.30 | 1.56 | 47.91 |

alr | 0.00 | 1.05 | 0.32 | 0.20 | 0.41 | −.018 |

rd | 0.00 | 0.48 | 0.04 | 0.05 | 4.56 | 29.21 |

growth | −1.00 | 14.02 | 0.42 | 1.17 | 7.56 | 70.12 |

size | 905.72 | 308366 | 7227.72 | 24350.10 | 8.87 | 87.43 |

fee | 0.05 | 23.71 | 3.06 | 2.52 | 3.09 | 16.23 |

asset | 124905957 | 271557772000 | 3047095757 | 15989822924 | 13.53 | 212.00 |

Descriptive statistics of sample variables of GEM (registration system)

p | 3.37 | 168.00 | 28.46 | 26.66 | 2.52 | 8.25 |

naps | 1.45 | 22.05 | 5.88 | 3.12 | 2.32 | 7.91 |

eps | 0.19 | 6.54 | 1.18 | 0.89 | 2.96 | 12.03 |

alr | 0.08 | 0.71 | 0.36 | 0.16 | 0.23 | −0.84 |

rd | 0 | 0.578 | 0.05 | 0.06 | 4.95 | 35.91 |

growth | −0.18 | 20.05 | 1.28 | 2.35 | 4.95 | 32.67 |

size | 1065.00 | 54215.92 | 3649.99 | 4852.64 | 8.87 | 91.32 |

fee | 0.38 | 11.92 | 2.65 | 1.94 | 2.31 | 7.18 |

asset | 220424812 | 170684965000 | 2263841990 | 14810526566 | 11.40 | 130.57 |

Descriptive statistics of sample variables of GEM (non-registration system)

p | 2.82 | 110.00 | 22.83 | 15.01 | 1.79 | 10.78 |

naps | 1.06 | 16.25 | 4.2937 | 2.15 | 1.59 | 3.76 |

eps | 0.16 | 7.50 | 0.94 | 0.63 | 3.21 | 19.30 |

alr | .040 | 0.854 | 0.38 | 0.15 | 0.11 | −0.50 |

rd | 0.00 | 37.42 | 4.68 | 4.51 | 2.34 | 10.19 |

growth | −0.75 | 42.55 | 0.93 | 2.50 | 8.66 | 112.14 |

size | 867 | 30000 | 2590.66 | 1851.21 | 6.54 | 74.34 |

fee | 0.23 | 11.78 | 2.09 | 1.11 | 2.28 | 10.78 |

asset | 68515927 | 49662885758 | 672246398 | 1945871596 | 20.36 | 487.63 |

It can be seen from the descriptive statistics table that the average IPO pricing of the Sci-Tech Innovation Board is the highest at 35.75 yuan, followed by the GEM after the reform of the registration system, which is 28.46 yuan, and the GEM IPO before the reform of the registration system, with the lowest average price of 22.83 yuan. This shows that the registration system has opened the price-earnings ratio limit, making IPOs tend to set high prices. The same rules are followed in terms of issuance costs per share. The Science and Technology Innovation Board is the highest, followed by the GEM after the reform of the registration system. The GEM before the reform of the registration system has the lowest issuance cost per share. This shows that the reform of the registration system has increased the cost of due diligence for underwriters and increased the cost of issuance. From the perspective of company size and total assets, the asset size of companies listed on the Science and Technology Innovation Board is higher than that of the Growth Enterprise Market. This may be due to the fact that the Growth Enterprise Market encourages and supports SMEs that do not meet the scale requirements for listing on the main board to conduct equity financing. In addition, the assets of GEM listed companies after the reform of the registration system have declined, indicating that the registration system has lower requirements for enterprise scale. From the perspective of asset-liability ratio, the average asset-liability ratio of STAR Market companies is lower than that of GEM companies, and among GEM companies, the asset-liability ratio after the registration system has declined. This shows that the implementation of the registration system makes enterprises more inclined to raise funds directly through issuance and listing.

Using Frontier 4.1, a piece of software suitable for stochastic frontier analysis, the samples of the Science and Technology Innovation Board and the GEM are analysed. The estimated results are shown in Table 5.

Estimation results of stochastic frontier cost model of IPO pricing efficiency

Cons | −2.6331*** | −3.4030 | 3.8259*** |

lnnaps | 0.1218* | 0.0127 | −0.3247*** |

Lnfee | 1.0642*** | 1.1705*** | 0.7226*** |

Lneps | 0.1083*** | 0.3371*** | 0.6740*** |

lngrowth | 0.1026*** | 0.0121 | −0.0224** |

lnsize | 0.3808*** | 0.4284** | 0.3478*** |

lnrd | −0.0221 | −0.0387** | 0.0109 |

lnasset | 0.0720 | 0.0877 | −0.1811*** |

lnalr | −0.04871 | −0.1348** | 0.1497*** |

sigma2 | 0.3836*** | 0.0855*** | 0.2992* |

gamma | 0.7208*** | 0.0065 | 0.7910*** |

LR test | 4.6054 | −23.6198 | 16.9099 |

mean efficiency | 1.2387 | 1.0089 | 1.3760 |

In Table 5, the gamma value of the Science and Technology Innovation Board is 0.7208, which is significantly not zero. There is a systematic one-sided error. After the registration system reform, the gamma value of the ChiNext board is 0.0065, which tends to zero significantly. Therefore, there is no systematic one-sided error, and the MLE estimation degenerates into an OLS estimation. Before the registration system reform, the GEM gamma value was 0.7910, which was significantly different from zero. There is a systematic one-sided error, suitable for stochastic frontier models.

Using the stochastic frontier cost model, the average pricing efficiency of the Sci-Tech Innovation Board of 1.2387 was estimated, and the degree of overestimation is 23.87%. The average pricing efficiency value of GEM after the reform of the registration system is 1.0089; so, the pricing efficiency is very high. Before the reform of the registration system, the average pricing efficiency of the GEM was 1.3760, and the degree of pricing overestimation was 37.60%. It can be seen that the registration system reform has improved the efficiency of IPO pricing. Compared with the Science and Technology Innovation Board, the GEM with a certain stock base has higher pricing efficiency. This conclusion proves my research hypotheses 1 and 2 and also shows that it is feasible to implement the registration system in the A-share and other incremental markets in the future. Regarding the phenomenon that the pricing efficiency of the Science and Technology Innovation Board is lower than that of the ChiNext, we think the main reasons are as follows: first, the Science and Technology Innovation Board is a newly opened market. When IPO pricing, the lack of reasonable comparable companies as a reference limits the pricing power of lead underwriters to a certain extent. This makes the IPO pricing efficiency temporarily lower than that of the ChiNext under the registration system. Second, compared with the ChiNext, the Science and Technology Innovation Board gathers industries such as biomedicine, software information and computer communications. These industries are strongly supported by the state and contain high policy dividends. Finally, since the outbreak of the new crown epidemic in 2020, the industries on the Science and Technology Innovation Board have been less affected or even profitable, which has made the estimates of related companies hit new highs. Therefore, it naturally led to an increase in IPO pricing.

In addition, observing the impact of various variables on the IPO pricing, it is found that the issuance fee per share (fee) has a significant positive correlation with the pricing of new shares. The higher the issue fee per share, the higher the price of the new issue. Regarding the issue fee per share, we use it to express the reputation of the underwriter. Generally, the higher the reputation of the underwriter, the higher the success rate of the IPO and the higher the fees charged. That is, there is consistency between issuance fees and the reputation of the underwriters. Therefore, the higher the reputation of the underwriters, the more inclined they are to set high prices and the lower the efficiency of IPO pricing. This is inconsistent with hypothesis 3 of my research. The possible reasons are as follows: first, the underwriter’s sponsorship fee is linked to the total amount of funds raised. Reputable underwriters are more likely to use their industry position and reputation to intentionally set IPO prices higher to capture more underwriting fees. Second, the underwriting fees are too high, which can offset or even cover the risk of loss caused by co-investment. Regulators have set up a sponsor co-investment mechanism for underwriters to restrain the moral hazard of underwriters. However, the proportion of co-investment is basically at the level of 2%-3%, while the cost of sponsor underwriting is about 5%. Therefore, the sponsorship fee can far cover the risk of loss caused by co-investment.

The impact of net assets per share (eps) on the pricing of new shares is significant and positively correlated, that is, the higher the intrinsic value per share, the higher the stock price, which is in line with common sense. For the size of IPOs, there is a significant positive impact in all three sectors. It shows that the larger the issuance scale, the higher the IPO pricing and the lower the pricing efficiency. In terms of total enterprise assets, the impact on the Sci-tech Innovation Board under the registration system and the GEM is not significant, but it has a significant impact on the GEM under the non-registration system. This reflects the reduced focus on company size in IPO pricing under the registration-based system. The asset-liability ratio (alr) has an insignificant impact on the Sci-tech Innovation Board, while its impact on the GEM has changed from positive to negative. This shows that the registration system makes enterprises tend to direct financing and pay less attention to the asset-liability ratio when pricing. The impact of enterprise science and technology innovation attributes (rd) on the science and technology innovation board is not significant. It reflects that on the Sci-Tech Innovation Board, investors are mostly speculating on concepts but not paying much attention to the actual technological innovation attributes of companies. In terms of the company’s earnings per share (naps) and the company’s growth ability (growth), the impact on the Sci-tech Innovation Board is significantly positive, but it has no significant impact on the ChiNext after the reform of the registration system.

The implementation of the registration system is the inevitable result of the improvement of the degree of marketisation of the capital market. Therefore, market-oriented reform is the general direction and the only way to reform various systems of China’s capital market. From the perspective of market supply and demand, the registration system is obviously superior to the approval system, especially in solving problems such as the distortion of supply and demand in the stock market and the difficulty of clearing the market. In addition, under the registration system, the listing threshold of enterprises is lowered and the market is more inclusive, which will provide more innovative and entrepreneurial enterprises with more opportunities to enter the capital market, thereby reducing the financing cost of enterprises and improving the quality of economic development. Therefore, we must find out the problem in time during the pilot process and find out the reason behind it. It is particularly important for better exerting the system effect of the registration system. This research quantitatively analyses the IPO pricing efficiency under the registration system and draws the following two main conclusions.

First, the study found that the average IPO pricing efficiency of the Science and Technology Innovation Board was 1.2387, with an overestimation of 23.87%. The average IPO pricing efficiency of the GEM before the registration-based reform was 1.3760, and the degree of overvaluation was 37.60%. After the registration system reform, the average IPO pricing efficiency of GEM was 1.0089, and the pricing efficiency was at a relatively high level. It can be seen that the registration system reform has improved the efficiency of IPO pricing. Compared with the Science and Technology Innovation Board, the GEM with a certain stock base has higher pricing efficiency. It shows that it is feasible to implement the registration system in A shares and other incremental markets.

Second, the reputation of underwriters has a significant negative impact on the efficiency of IPO pricing. It shows that the current regulatory policy does not have enough moral constraints on underwriters, especially for underwriters with high reputation. Because reputable underwriters charge higher underwriting fees, these fees are sufficient to cover the risk of loss caused by sponsor co-investment. Therefore, they are more likely to use the overheated investor sentiment to raise the issue price and maximise their own interests. For underwriters with a low reputation, due to the constraints of the co-investment sponsorship mechanism and the low cost of underwriting sponsorship, higher pricing will increase the risk of breakouts; so, they will be more cautious when pricing IPOs.

In response to the phenomenon that the issuance and pricing efficiency of the Science and Technology Innovation Board is lower than that of the ChiNext, we think that with the passage of time, the continuous increase in the sample size of the Science and Technology Innovation Board will increase the number of companies that can be referred to for IPO pricing. In addition, the overheated investor sentiment at the time of the opening of the Science and Technology Innovation Board is gradually returning to rationality. Therefore, we believe that the issue price of the Sci-tech Innovation Board will gradually fall, its IPO pricing efficiency will be further improved and the gap with the ChiNext will be narrowed. The breakout of companies such as Jianlong Micro Nano and Jiuri New Materials also proves the denial of investors’ high issue price, and it is also a manifestation of the role of the market correction function. And this situation will in turn prompt the underwriters and institutional investors in the primary market to return to rational behaviour and be more prudent and strengthen self-discipline when choosing sponsorship projects and IPO valuation. However, we must note that the self-correction function of the invisible hand will be accompanied by losses of investors and will also dampen the enthusiasm of investors. Therefore, for the problems exposed in the registration system pilot process, regulatory agencies should regulate them through system construction and strict supervision. Based on the above questions, we make the following suggestions.

First, further improve the information disclosure system and improve the quality of information disclosure. Make information disclosure more concise and easier to understand. At present, the premium of IPO pricing on the Science and Technology Innovation Board is still very high. One of the reasons is that there is a strong information asymmetry among investors in the market, and the actual value of stocks cannot be estimated rationally. Improving the information disclosure system will help investors to estimate the intrinsic value of listed companies based on their basic conditions, which in turn will force the efficiency of IPO pricing to increase. At present, China has implemented a registration system on the Science and Technology Innovation Board and the GEM, which has strengthened the requirements for information disclosure of companies going public, and has formed preliminary regulations on information disclosure for new stock offerings. However, there are still problems of insufficient and inaccurate corporate information disclosure. To this end, regulators should increase penalties for false information disclosure and improve the quality of corporate information disclosure, improve the transparency of corporate listing information and standardise the implementation of the registration system. Regulators can also try a “safe harbour” system of predictive disclosure to encourage companies to strengthen information disclosure.

Second, strengthen the moral restraint of underwriters and promote their price guidance function. Judging from the research results, the reputation mechanism of underwriters not only failed to play its due role but instead played a certain counterproductive role. To prevent the moral hazard of underwriters, regulators have set up a follow-up sponsorship mechanism. However, the effect of the sponsorship system is not good. Regulators should consider setting up mechanisms to prevent underwriters, especially leading underwriters, from passing on the risk of co-investment losses to issuers.

Third, improve the professional service level of underwriters and promote their price guidance function to standardise the writing of underwriters’ investment value reports. It is also necessary to do a good job in the organisational work in the process of issuing new shares and give full play to the information transmission function as an intermediary. In addition, underwriters must make independent value judgements on companies in different industries. Most of the companies on the Science and Technology Innovation Board and ChiNext are new-type companies. The core technologies and business models of these companies may be different from those of conventional companies. Underwriters must fully understand the company and then conduct reasonable valuations to guide market price discovery, thereby promoting the optimal allocation of resources.

Finally, it is recommended to establish targeted policies for emerging industries. At present, the practical obstacles faced by Internet-based new economy enterprises mainly include listing profit requirements, equity structure and registration restrictions. Although the threshold for listing on the ChiNext after the registration system reform has been greatly reduced, it is required to “make profits in the last two consecutive years, and the accumulated net profit in the last two years is not <10 million yuan; or make a profit in the last year, and the operating income in the last year is not <50 million yuan”. But the capital market pays more attention to the future rather than the past performance. This optimistic expectation comes from China’s huge market and high-growth charm. It is hoped that regulators can introduce multiple sets of financial indicator combinations in line with international practice, allow high-quality emerging companies to be listed for financing, and keep new economy companies in the Chinese market.

#### Estimation results of stochastic frontier cost model of IPO pricing efficiency

Cons | −2.6331 |
−3.4030 |
3.8259 |

lnnaps | 0.1218 |
0.0127 |
−0.3247 |

Lnfee | 1.0642 |
1.1705 |
0.7226 |

Lneps | 0.1083 |
0.3371 |
0.6740 |

lngrowth | 0.1026 |
0.0121 |
−0.0224 |

lnsize | 0.3808 |
0.4284 |
0.3478 |

lnrd | −0.0221 |
−0.0387 |
0.0109 |

lnasset | 0.0720 |
0.0877 |
−0.1811 |

lnalr | −0.04871 |
−0.1348 |
0.1497 |

sigma2 | 0.3836 |
0.0855 |
0.2992 |

gamma | 0.7208 |
0.0065 |
0.7910 |

LR test | 4.6054 | −23.6198 | 16.9099 |

mean efficiency | 1.2387 | 1.0089 | 1.3760 |

#### Variable definition table

p | Corporate IPO price |

naps | Earnings per share after deducting recurring gains and losses in the year prior to listing |

size | Number of new shares issued |

asset | Total assets of the company in the year before listing |

eps | Net assets per share in the year prior to listing |

alr | Asset-liability ratio in the year before listing |

growth | (Operating income for the current period/operating income for the previous period) - 1 |

rd | Intangible assets/total assets |

fee | Total issuance fee/total issuance |

#### Descriptive statistics of sample variables of GEM (registration system)

p | 3.37 | 168.00 | 28.46 | 26.66 | 2.52 | 8.25 |

naps | 1.45 | 22.05 | 5.88 | 3.12 | 2.32 | 7.91 |

eps | 0.19 | 6.54 | 1.18 | 0.89 | 2.96 | 12.03 |

alr | 0.08 | 0.71 | 0.36 | 0.16 | 0.23 | −0.84 |

rd | 0 | 0.578 | 0.05 | 0.06 | 4.95 | 35.91 |

growth | −0.18 | 20.05 | 1.28 | 2.35 | 4.95 | 32.67 |

size | 1065.00 | 54215.92 | 3649.99 | 4852.64 | 8.87 | 91.32 |

fee | 0.38 | 11.92 | 2.65 | 1.94 | 2.31 | 7.18 |

asset | 220424812 | 170684965000 | 2263841990 | 14810526566 | 11.40 | 130.57 |

#### Descriptive statistics of sample variables of GEM (non-registration system)

p | 2.82 | 110.00 | 22.83 | 15.01 | 1.79 | 10.78 |

naps | 1.06 | 16.25 | 4.2937 | 2.15 | 1.59 | 3.76 |

eps | 0.16 | 7.50 | 0.94 | 0.63 | 3.21 | 19.30 |

alr | .040 | 0.854 | 0.38 | 0.15 | 0.11 | −0.50 |

rd | 0.00 | 37.42 | 4.68 | 4.51 | 2.34 | 10.19 |

growth | −0.75 | 42.55 | 0.93 | 2.50 | 8.66 | 112.14 |

size | 867 | 30000 | 2590.66 | 1851.21 | 6.54 | 74.34 |

fee | 0.23 | 11.78 | 2.09 | 1.11 | 2.28 | 10.78 |

asset | 68515927 | 49662885758 | 672246398 | 1945871596 | 20.36 | 487.63 |

#### Descriptive statistics of sample variables of the Science and Technology Innovation Board

p | 1.22 | 557.80 | 35.75 | 43.02 | 6.05 | 59.48 |

naps | 0.09 | 23.33 | 5.39 | 3.46 | 2.12 | 6.551 |

eps | −10.82 | 14.03 | 0.85 | 1.30 | 1.56 | 47.91 |

alr | 0.00 | 1.05 | 0.32 | 0.20 | 0.41 | −.018 |

rd | 0.00 | 0.48 | 0.04 | 0.05 | 4.56 | 29.21 |

growth | −1.00 | 14.02 | 0.42 | 1.17 | 7.56 | 70.12 |

size | 905.72 | 308366 | 7227.72 | 24350.10 | 8.87 | 87.43 |

fee | 0.05 | 23.71 | 3.06 | 2.52 | 3.09 | 16.23 |

asset | 124905957 | 271557772000 | 3047095757 | 15989822924 | 13.53 | 212.00 |

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