Urban Economic Openness and IPO Underpricing

The underpricing of initial public offerings (IPOs) in emerging markets is significant and previous research applying models used in developed markets has failed to explain this phenomenon and its cross-sectional variation—see Mok and Hui (1998), Chan et al. (2004), Gao (2010) and Tian (2011). In fact, most studies suggest that IPO underpricing is a result of the issuing firm’s valuation and IPO underwriting process. According to the classic information theory, firm-specific features (e.g. size and age) reflect the level of uncertainty of the firm’s valuation and, following a behavioural argument, the pre-IPO market performance represents the investors’ sentiment about this uncertainty. Over the last two decades a new stream of literature has shown that the level of trade openness (or integration in global markets) experienced by a region affects the pricing of assets located in that region. Hence, we expect that the openness (or integration) of the region where a company operates should impact on its pricing. We build on this evidence and combine the two research streams to explain the IPO underpricing puzzle experienced in emerging markets using a macroeconomic approach.

Trade openness can affect IPO performance in several ways. In particular, we examine urban economic openness (UEO), which is defined as the degree of trades a certain region or urban area within a country has with foreign countries. The UEO of a company would then be measured as the relative trade between cities where the issuing firm invests and their foreign counterparties. We argue that UEO could affect the company’s IPO valuation through different channels. Firstly, a city with higher openness should experience a more pronounced growth in productivity and output. As a consequence, the demand for real estate increases. Given the short-term inelastic housing supply, property prices increase and they therefore drive the valuation of companies up. Secondly, a company operating in a city with higher UEO could benefit from the increase in local real estate prices through the Balassa-Samuelson effect. For the first time, Balassa (1964) and Samuelson (1964) explore the connection between trade openness and the market of non-tradable goods. When international trades increase, the tradable sector experiences a greater productivity increase than the non-tradable sector due to the more efficient information spillover, bigger market size and faster technology development. This would lead to a higher output in the tradable sector than in the non-tradable one. However, as labour is mobile across regions and industries, the average wage should equally increase—see Samuelson (1964), Dollar (1992), Sachs and Warner (1995), Edwards (1998) and Miller and Upadhyay (2000) As a result, there will be an increase in the relative prices of non-tradable goods as compared to tradable goods. Considered as the largest non-tradable asset, real estate prices should increase in areas with higher UEO, leading to a rise in the profitability of real estate companies.¹ Thirdly, companies could increase their profitability not only indirectly through the change in their asset values, but also directly through the increase in overall productivity in regions with high UEO ((Demsets 1973, Peltzman 1977, Eilon 1985, Jovanovic 1982, Stierwald 2010).² Finally, an increase in the UEO of a city can also lead to a rise in property prices by fostering an increase of foreign (and domestic) real estate investments in more open regions, due to an easier flow of capital—Saleryd and Vlachos (2002), Beck (2002), Basu and Morey (2005), Law (2008), Baltagi et al. (2009), Lim and Kim (2011) and Kim et al. (2012). Overall, the above mechanisms demonstrate how UEO can decrease the uncertainty about the company’s future earnings, leading to a more accurate firm valuation. As a result, the underpricing is lowered.

In particular, we believe that Chinese real estate companies represent a suitable laboratory to explain the IPO underpricing puzzle in emerging markets using a macroeconomic approach. Firstly, since the IPO underpricing in this sector is high and varies across companies, the significant variation of openness across Chinese regions may be related to a different geographical investment focus of issuing firms. China is a developing country with cities rich in development opportunities and almost all real estate companies include a significant development portfolio (reported as inventory or intangible asset in financial reports along with buildings held as investments). The land is owned by the central government and each company bids for land-use rights, whose value is reported in the IPO prospectus. Secondly, because companies need to bid for land-use rights from local governments, most real estate firms tend to concentrate their businesses in one location where they believe they have informational advantages. This might be caused by future development opportunities in a city, as well as connections with local governments and institutions to obtain land-use rights. In fact, Miao and Zhu (2005) show that Chinese real estate markets are still very much localized (with varying regional policies and regulations). As a consequence, the barriers to enter a new market are so high that most real estate companies prefer to remain focused in one region and compete with other local real estate companies than to diversify geographically. Finally, even if some companies operate across regions (normally two main areas represent a significant proportion of the overall portfolio) the real estate business (investing in physical non-movable assets, i.e. buildings and land) allows us to identify the exact portfolio of locations where the business is operated. Consequently, an index of openness can be calculated for each company based on the locations where the company operates and the level of trade openness each location offers.

In our study, we collect data of Chinese real estate IPOs from 1992 to 2013 and find that, after controlling for firm- and issue-specific characteristics, UEO significantly and negatively affects IPO underpricing. Companies operating in cities with higher UEO experience less underpricing when they go public and this result remains robust once we account for IPO location, ownership structure or other local macroeconomic variables. Our findings can help issuers, underwriters and investors to better understand the dynamics of IPO markets in emerging markets using UEO. They also provide policy implications with regards to the ties between the local economy and the performance of regional firms and implications for state-owned companies going public. Finally, although focusing on Chinese real estate IPOs, our results could also apply to other countries and sectors with similar market structures.

The paper is structured as follows: the next section presents a literature review that is relevant to our hypothesis. Section 3 describes the data and the methodology. Empirical results and robustness tests are discussed in Section 4, while main conclusions are presented in the last section.

Empirical evidence shows that an average positive initial return for IPOs – the gap between closing price on the first-trading day and initial offer price – exists, implying that the offer price, set by the firm and underwriters is lower than the price that the market is willing to pay (at least in the short-run). This phenomenon represents a financial puzzle known as IPO underpricing, which has been explained mainly from two angles so far: the classic information asymmetry theory (i.e. the underpricing is pre-market determined) and the behavioural argument (i.e. the underpricing is driven by market sentiment). Three main parties – issuers, underwriters and investors – are involved in an IPO event. Classic explanations follow the information asymmetry theory (Rock 1986) which assumes that a certain group of investors have better information about the firm’s value than other investors, underwriters or issuers. Because rationing by underwriters exists, the uninformed investors cannot obtain the underpriced shares and eventually become reluctant to participate in the IPO. As a result, the initial offer needs to be underpriced enough to attract enough investors and compensate for the asymmetric information bias. The famous winner’s curse theory (Beatty and Ritter 1986) and information extraction theory (Benveniste and Spindt 1989) were built upon this broader literature. The former shows that, if investors were allocated shares, they would think that those shares were mispriced and unwanted by other informed investors. As a result, uninformed investors would only request IPO shares when offerings were underpriced enough to compensate for their uncertainty. The latter, instead, shows that underwriters underprice shares on purpose to obtain useful information from informed investors in return. Furthermore, the signaling theory assumes that issuers are best informed about their company and underpricing is a strategy to signal to the public that the firm is of “good quality” and can recover the cost of underpricing in seasoned offerings, as opposed to “bad-quality” firms which cannot afford the cost of underpricing (Allen and Faulhaber 1989). Finally, behavioural arguments have started to become more popular. An example is offered by Loughran and Ritter (2002), where higher pre-IPO market return indicates the investors’ sentiment and may lead to after-market demand driven high initial returns.

One one hand empirical studies focusing on developed markets find confirmation to these theories, on the other hand, they are less successful in explaining extremely high underpricing of IPOs in emerging markets. Relying purely on company-specific characteristics may yield weak and distorted results because underdeveloped institutional features would be missed. In China for example, the average underpricing of all IPOs between 1990 and 1993 was 289% according to Mok and Hui (1998) and 178% between 1993 and 1998 according to Chan et al. (2004). Tian (2011) also finds extremely high underpricing of 247% in a sample of 1377 IPOs from 1992 to 2004 and argues that information asymmetry is far from being a major determinant. Similarly, Gao (2010) finds no significant relationship between the initial returns and proxies for information asymmetry.

With regards to real estate IPOs in particular, Chan et al. (2001) show that US REITs IPOs experience abnormally low underpricing compared to IPOs in other industries. One possible explanation rests in the presence of more individual and fewer institutional investors in REITs than in other industries as this may reduce the impact of informed investors. On the other hand, REITs may also behave more similarly to funds than to operating companies, and they generate low uncertainty leading to a low IPO underpricing (a common characteristic of funds in general). Finally, the characteristics of underlying assets of real estate companies might also represent a further explanation. The ownership structure and similarity with funds does not reflect the IPO market of Chinese real estate companies because the property market has received enough attention from institutional investors and the majority of companies are operating companies (including developments in their portfolio). As our study presents an analysis of IPO performance from a “non-tradable goods perspective”, we believe that Chinese real estate IPOs offer an adequate laboratory to test our hypothesis focusing on the role of underlying assets.

The final stream of literature relevant to this work is related to globalisation and the linkages between openness and markets of tradable goods. First, empirical studies find that countries’ foreign trade exposure leads to greater productivity growth - Dollar (1992), Sachs and Warner (1995), Edwards (1998) and Miller and Upadhyay (2000). Following the evidence that productivity is a major determinant of company’s profitability—Demsets (1973), Eilon (1985), Jovanovic (1982), Peltzman (1977) and Stierwald (2010)—, Miller and Upadhyay (2000) use a panel data to assess the determinants of total factor productivity. By using the classic National Bureau of Economic Research (NBER) calculation of trade openness (total import and export as a share of GDP), they find that the effects of trade openness on total productivity are significantly positive. Moreover, for a sample of 29 provinces in China between 1981 and 2005, Jiang (2011) finds that UEO has direct positive effects on Chinese regional productivity growth, and this is consistent with previous country-level studies. In recent years, research has also shown that trade openness has an impact on the degree of financial market development. According to Rajan and Zingales (2003), an increase in both cross-country trades and capital flows leads to an increase in the degree of financial development. Saleryd and Vlachos (2002), Beck (2002) and Kim et al. (2012) find a reverse causal effect between trade openness and financial development (i.e. the increase in trade openness occurs following an increase in financial development). Finally, Law (2008) and Baltagi et al. (2009) find that trade openness and capital account openness jointly and positively affect the financial development in both the banking sector and the stock market in Malaysia. Law (2008), in particular, shows that the effect of trade openness on financial development is greater than that of capital account openness. Moreover, the effect of trade openness is significant in both the banking sector and stock market, while the effect of capital account openness is less so. By using panel data, Kukeli (2012) finds that trade openness is one of the most important determinants of a country’s capital formation.

Within this stream, a group of studies examines the relationship between openness and stock market performance in emerging markets. Li et al. (2004) employ an openness approach to explain the increased firm-specific variation in the 1990s and find that capital market openness positively affects the firm-specific variation while trade openness positively affects the market variation. Basu and Morey (2005) argue that, once a country opens its trades to the world, stock prices start to follow a random walk whilst they were serially correlated beforehand. Following an increase in trade openness, the development in technology will be captured by the equilibrium stock prices leading to a decrease in excess returns and, eventually, an increase in stock market efficiency. Lim and Kim (2011) show that trade openness should boost firms’ future profitability and reduce uncertainty. As a result, investors would react faster to the information on the market. Therefore, the trade openness would finally enhance the informational efficiency of the stock market. However, the authors also find that the de facto measure, i.e. classic NBER measure, has a significantly negative impact on stock return serial correlations but the alternative de jure measure, i.e. country’s international trade freedom index constructed according to a country’s trade policy, does not show any significance. As the de facto measure reflects real output and there is no de jure measure at city level in China, we use the de facto measure in our empirical analysis.

Being the biggest asset of the non-tradable goods market, real estate and its links with trade openness have already been studied. Initially, Bardhan et al. (2004) empirically test the relationship between trade openness and real estate rents using monthly data for 46 countries. For the measurement of openness, standard NBER indices are used. Results show that wage levels, population, and trade openness positively affect rents, and they are consistent with the Balassa-Samuelson effect. This research has been then extended to a regional level study by Wang et al. (2011) who analyze trade openness and real estate prices at the urban level. The study focuses on China, as it represents a good example of an emerging market with an urbanization rate increasing from 30% in 1998 to 46% in 2008. The sample covers 35 cities from 1998 to 2006 and the authors find that real estate prices increase by 2.82% when UEO increases by 10%. To appreciate its importance, between 1998 and 2006, 16% of the increase in Chinese real estate prices is attributed to the increase of UEO alone. Bardhan et al. (2008) examine the effects of country-level trade openness on returns of securitized real estate by using a sample of 946 listed real estate companies from 16 countries. They find that trade openness has a significantly negative impact on the excess returns of real estate stocks, controlling for global and domestic variables. Case et al. (1999) point out that even though country-level factors were driving real estate performance, regional output was a more important determinant.

Our sample includes IPOs of Chinese real estate companies from 1992 to 2013. The Shanghai Stock Exchange was founded in 1990 and the Shenzhen Stock Exchange was founded in 1991. So, our sample traces back to almost the very beginning of stock markets in China in order to capture the changes in IPO performance throughout the economic cycles. The main IPO data is obtained from Thomson One. Due to the incomplete records of Chinese IPOs, especially those in the early years of our sample, most data has been manually collected from various local sources, such as the Shanghai Stock Exchange, Shenzhen Stock Exchange, Hong Kong Stock Exchange, Singapore Stock Exchange, as well as financial websites (e.g. jrj.com and sina.com). Firm-specific data (e.g. age, state ownership, land reserves, income and real estate assets) are collected manually form the issuers’ IPO prospectus, which are obtained from different sources including Thomson One, the Hong Kong Exchange HKExnews (a designated website providing regulatory filings and disclosure of listed companies) and companies’ websites (with the majority of prospectuses being in Chinese). Economic data (e.g. foreign trade volume and GDP) are extracted from the City Annual Statistical Reports, obtained from the China National Knowledge Infrastructure (CNKI) database. City Annual Statistical Reports are the official governmental reports of the local annual economic performance. CNKI is a key national e-publishing project in China that was started in 1996, approved by the Press and Publications Administration of the People’s Republic of China (PRC) and backed by Tsinghua University.

In total, there are more than 150 listed Chinese real estate companies in the full database. However, some of them were not predominantly real estate companies when they went public and some others went public through a “back-door listing”, whereby the company goes public by acquiring an already listed company. Hence, we were left with 70 IPOs meeting our selection criteria of being listed directly as a real estate IPO and having the full set of information for all the variables of interest.³

The dependent variable (INIRE _i ) represents the initial return of company i, a widely recognized measure of IPO underpricing and computed as the percentage difference between the offer price (OP _i ) and the closing price on the first day of trading (CP _{1day , ii} ):

The higher the initial return, the higher the IPO underpricing is. Some studies deduct the market performance between the issuing and the listing dates from the IPO initial returns to control for market effects. Since the average IPO underpricing in China is extremely high, the overall market performance is unlikely to affect the underpricing. Therefore, we keep the original form of measuring IPO underpricing. Instead, we will control for the pre-IPO market performance in different specifications. We also estimate model with the adjusted measure but results do not change and hence we do not report them.

The main variable of interest is urban economic openness (UEO), which is measured by the regional trade openness. As discussed above, we adopt the de facto measure by NBRE, which is computed as the sum of imports and exports as a percentage of GDP: Since most studies focus on a country-level analysis and use the NBER definition, we adjust the formula as required by our single country study to compute the city-level measure by using total annual imports and exports between city j and its foreign counterparties and the city’s annual GDP.

The amount of foreign trade of a city is recorded in the City Annual Statistical Reports by the local government and is expressed in US dollars. The total sum of imports and exports is converted into Chinese Yuan (RMB) by using the average annual US Dollar to Chinese Yuan exchange rate in the same year.

Most Chinese real estate companies focus their businesses in one area—usually one city—while some of them have businesses in several cities (often geographically close). Only very few big real estate companies run businesses widely spreading across China, mainly focusing on big developed cities such as Shanghai or Beijing which usually have similar level of trade openness. Hence, for companies operating in more than one city, we obtain the UEO measure for one company as the weighted average of the individual UEO measures of the cities in which the company invests (UEO _j , i ). The weights are constructed as the ratio between the available land with land-use rights company i has in city j (Land _j , i ) and the overall land with land-use rights company i owns (Land _i ). The information regarding the locations of investments for each company and the information about land-use rights are manually extracted from IPO prospectuses and the financial reports before IPO. The UEO measure for company i is then computed as follows:

In our sample, 50% of companies operate assets locally in one city. The UEO for these companies does not require weighing and is simply the annual city-level foreign trade as a share of the annual city-level GDP. For the remaining companies, the majority is based in two different cities and 21% of companies have more than 50% of their businesses focused in one city. Overall, 90% of companies in our sample have at least 30% of their businesses in one city. As a robustness check, we also split the sample into companies operating in one city and companies operating in more than one city. We run separate estimations and we do not find significantly different results. Therefore, we present and discuss only results obtained with the overall sample.

The state ownership of the issuing company has been a popular approach to explain the extreme IPO short-run performance in China. The political system is different from developed countries and a strong government intervention in financial markets exists. Therefore, researchers argue that state ownership represents a negative signal on the firm’s independent governance, indicating low transparency due to a higher possibility of manipulation and corruption within the firm. Therefore, state-owned companies are expected to experience more underpricing than privately owned ones. Following this argument, we summarize our data by state ownership. The first two rows in Panel B of Table 1 report the statistics of IPOs of state-owned and non-state-owned companies. Consistent with our arguments above, the average IPO initial return, standard deviation and interval of non-state-owned companies are much smaller than the ones of state-owned companies. Wong et al. (2013) study the influence of IPO location on IPO underpricing of Chinese developers, arguing that the IPO location (Mainland China vs Hong Kong) represents a signal for developers’ quality and levels of transparency. Since our sample includes developers as well as investors, we have access to firms listed in three different stock exchanges: Mainland China (35 companies), Hong Kong (33 companies), and Singapore (2 companies only). The last two rows in Panel B of Table 1 report the statistics of initial returns by IPO location. Our results are consistent with the findings by Wong et al. (2013) as the initial returns of the IPOs listed in Mainland China are much larger than those listed outside.⁴

However, since the state-ownership and IPO location variables are highly correlated (0.72) as they may proxy for the same factor, we decide to use them alternatively in our estimations and we explain the listing preference of different ownership structures as follows: stated-owned companies are more likely to choose to be listed in Mainland China because they can use their political connection more efficiently than in other countries/markets, while private companies try to avoid the political manipulation in Mainland China and choose more competitive and transparent markets like Hong Kong and Singapore. As a robustness check, we also estimate our model including both variables and main results for our variable of interest (UEO) do not change significantly and hence we do not report them.

Beatty and Ritter (1986) argue that the underwriters’ subsequent market shares decrease if they fail to price the IPO shares at an adequate level. A substantial amount of empirical studies support the negative relationship between the underwriters’ reputation and the level of IPO underpricing. However, the effect of the underwriters’ reputation remains inconclusive as the reversed relationship has also been documented not only in the US market but also in other developed markets. If we consider Chinese IPOs where the market is far more exposed to the political system, the role of the underwriters’ reputation seems to be less significant. While the majority of IPO studies in China have ignored the underwriters influence, Su and Bangassa (2011) follow the classic theories and systematically examine the effects of the underwriters’ reputation on IPO underpricing in China and find little influence. Despite the lack of information on Chinese investment banks, unlike Su and Bangassa (2011) and many other studies on underwriters’ reputation, our sample includes IPOs from more than one market, with IPOs listed in Mainland China often using Chinese investment banks and those listed in Hong Kong and Singapore often using international investment banks, which means it is difficult to rank the underwriters. The nature of our sample makes the measurement of the reputation very much correlated with state ownership and the dummy on the stock exchange where companies are listed. Hence, we do not include this variable in our study.⁵

As the extreme IPO underpricing in emerging markets is not fully explained by existing empirical studies using arguments valid in developed markets, we suggest an approach to improve the explanation of this phenomenon. This paper represents the first attempt to study the effect of the macroeconomy associated with the degree of urban economic openness on IPO underpricing. We analyse how the short-run IPO performance of companies with localised businesses is affected by the UEO of the regions where firms are invested. Higher UEO can lead to higher demand for real estate, or through the Balassa Samuelson effect, to higher wages in the non-tradable goods sector and hence to higher real estate prices. Moreover, an improvement in UEO can increase productivity, which is found to be a major determinant of a firm’s profitability. All these channels may decrease the uncertainty regarding future profits and hence the IPO valuation of the company. As a result, issuers have less incentive to underprice IPO shares. Chinese real estate companies provide a suitable experimental laboratory to study the extremely large underpricing in developing markets, which cannot solely be accounted for by firm-specific characteristics. In fact, these firms show strong geographic investment patterns focused locally – usually at city level – and we also observe a highly heterogeneous degree of trade across Chinese cities.

Overall, we find that real estate companies in cities with higher UEO experience a smaller IPO underpricing. Signaling the importance of globalisation and economic integration. State-owned companies going public are found to experience higher underpricing than privately owned companies. Moreover, unlike previous studies, we show that 60- rather than 30-day market return is a better proxy for market sentiment and we attribute this to the immaturity of the Chinese IPO market. Finally, results are robust to the estimation of models with alternative specifications, including 2SLS models with disposable household income per capita and distance to ports as instruments.

To conclude, our results have implications for investors, owners and underwriters who may differentiate between IPOs of locally operating companies in more or less open regions. Our findings also provide policy implications focused on the ties between the local economy and its exposure to foreign trade as well as the performance of local firms.