Determinants of the long-term performance of initial public offerings (IPOs) in the port industry

Transportation Research Part A 103 (2017) 135–153

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Determinants of the long-term performance of initial public offerings (IPOs) in the port industry Giovanni Satta a,b,⇑, Theo Notteboom c,d,e,f, Francesco Parola a,b, Luca Persico a,b a

University of Genoa, Department of Economics and Business, Italy Italian Center of Excellence on Integrated Logistics (CIELI), Italy c Shanghai Maritime University, China Institute of FTZ Supply Chain, China d Ghent University, Maritime Institute, Faculty of Law, Belgium e University of Antwerp, Faculty of Applied Economics, Belgium f Antwerp Maritime Academy, Belgium b

a r t i c l e

i n f o

Article history: Received 17 October 2016 Received in revised form 4 March 2017 Accepted 31 May 2017

Keywords: Port Initial public offering (IPO) Performance Financial markets Institutional factors

a b s t r a c t Market players active in the port industry deploy a range of financial sources to meet the growing investment requirements in port infrastructure and to fund their (overseas) expansion strategies. Recent empirical evidence shows that equity capital markets are expected to extend their role in this regard. This paper deals with the long-term performance of IPOs in the seaport industry as a strategic financial dimension for determining the success/failure of the issuance. The study goes beyond general finance theories on IPO performance as it places institutional theory side by side with informational asymmetry theories and symmetric information theories to assess the determinants of long term IPOs’ success in the port domain. The paper presents an overarching conceptual framework for addressing IPO performance, thereby focusing on the explanatory power of ‘‘financial markets”, ‘‘institutional factors” and ‘‘industry specific variables”. An ordinary least squares (OLS) regression analysis is performed on a dataset of over 90 port-related IPOs to test the antecedents of the long-term performance of port-related IPOs. The performance of extant IPOs is expected to influence both the capacity of ports and terminal operating companies to gather additional financial resources from equity capital markets in the future, and the related cost of funding. In addition, it may shape the attitude of private investors toward this equity asset class. Ó 2017 Elsevier Ltd. All rights reserved.

1. Background The growth of seaborne trade and scale increases in vessel size of the past few decades have urged port actors to upgrade their infrastructure and superstructure (Nguyen et al., 2015), not only in the port but also in relation to the hinterland (Heaver, 2006; Monios and Wilmsmeier, 2012). The associated multiplication of mega port projects exacerbated the need for additional financial resources (Van Marrewijk, 2007). Large port-related projects typically require huge up-front nondivisible investments (over 100 million USD) and are characterized by a high and inflexible asset specificity (Taneja et al., ⇑ Corresponding author at: University of Genoa, Department of Economics and Business Studies, Italy. E-mail addresses: [email protected] (G. Satta), [email protected] (T. Notteboom), [email protected] (F. Parola), luca. [email protected] (L. Persico). http://dx.doi.org/10.1016/j.tra.2017.05.032 0965-8564/Ó 2017 Elsevier Ltd. All rights reserved.

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2010; Rodrigue et al., 2011; Parola et al., 2013b). As the implementation of port reform favored a massive commitment of private investors in the management and financing of port facilities, several private firms experienced unprecedented international growth patterns, leveraging on aggressive external growth strategies (Olivier, 2005; Satta and Persico, 2015). Furthermore, merger & acquisition (M&A) activity among port-related actors accelerated since the beginning of the century, forcing buyers to gather additional fresh funds to finance the purchase of additional assets (Notteboom and Rodrigue, 2012). The huge amount of resources required for upgrading/upsizing extant infra- and superstructures, developing new mega projects and fuelling private terminal operators’ overseas expansion imply that traditional sources of capital reach their limits. Traditionally, equity capital markets have been a secondary source of funding for port and terminal industries, compared with other financial resources such as retained earnings, governmental support and corporate bank loans in covering capital expenditures and working capital requirements (Frankel, 1992; Notteboom and Winkelmans, 2001; Stopford, 2009; Zhang, 2016). However, recent empirical evidence supports the notion that, as is the case in shipping, equity capital markets are expected to extend their role in the development of the port industry (Pallis and Syriopoulos, 2007; Merikas et al., 2009). In the last decade, several terminal operators and (corporatized) port authorities have gathered huge amounts of financial resources by relying on initial public offerings (IPOs), i.e. the first issuance of shares on a stock exchange. In November 2007, for example, DP World Limited was listed on the Dubai International Financial Exchange (DIFX) in the largest IPO of the Middle East valued at more than USD 4.5 billion. The offering whetted institutional and retail investors’ appetites and was oversubscribed more than 15 times. In March 2011, HPH Trust’s IPO raised gross proceeds of approximately USD 5.45 billion. This financial transaction attracted big names such as the Singaporean state-owned Temasek Holdings, and well-known hedge fund managers such as Paulson & Co, and Capital Research & Management. DBS, Deutsche Bank and Goldman Sachs acted as joint book-runners and issue managers. Despite a growing interest of port actors in equity capital markets as a source of funding, port-related IPOs received only limited attention in academic circles. In a pioneering contribution on the issue, Pallis and Syriopoulos (2007) outline that the public listing of some Greek port authorities, as the final stage of the corporatization path, transformed public undertakings into companies under private corporate law. Rodrigue et al. (2011) introduce the notion of ‘‘financialization” of the port industry, and emphasize that changing patterns in risk perception have supported a bubble in the 2002–2008 period, favouring the listing of some port-related companies. More recently, Baird (2013) questions the sustainability of private equity (PE) funds strategies directed to the acquisition of UK ports, and outlines that PE investments are often realized through IPO, providing a (full/partial) realization to the financial sponsor. No prior contributions focus on the IPO process of stevedores and port-related companies as a key research area. This paper aims at partly filling this literature gap by focusing on the performance of IPOs in the seaport industry, along with a stock valuation perspective, as a strategic financial dimension for determining the success/failure of the issuance. Scholars assess IPO performance using multiple theoretical perspectives, e.g. the firms’ vs. investors’ perspective; the economic vs. financial perspective; the short-term vs. the long-term perspective, etc. (Certo, 2003; Daily et al., 2003). This study goes beyond general finance theories on IPO performance as we place institutional theory side by side with informational asymmetry theories and symmetric information theories (Ritter and Welch, 2002) to assess the determinants of long term IPOs’ success/failure in the port domain. In following an institutional approach, we earmark institutional factors of the home country and host country as potential valuable predictors of IPOs long-term aftermarket performance. We propose and test an overarching conceptual framework for assessing the long-term success of IPOs, thereby focusing on the explanatory power of ‘‘financial market characteristics”, ‘‘institutional factors” and ‘‘industry specific variables”. For this purpose, the empirical analysis provided in this paper covers 90 port-related IPOs initiated worldwide since 2000. An ordinary least squares (OLS) regression analysis is performed in view of testing the antecedents of the long-term performance of these port-related IPOs. We argue that the performance of port-related IPOs presents a valuable and promising topic for both academics and practitioners. The performance of extant port-related IPOs, in fact, is expected to influence both the capacity of ports and terminal operating companies to gather additional financial resources from equity capital markets in the future, and the related cost of funding. In addition, it may shape the attitude of private investors toward this equity asset class. The paper is structured as follows. Section 2 introduces the background of the study and develops research hypotheses. Section 3 provides insights on data and method. Section 4 presents the empirical outcomes as well as the results of the robustness checks. Finally, Section 5 discusses potential implications for academia and practice before concluding.

2. Conceptual framework and literature review 2.1. Long-term aftermarket performance in port IPOs: an institutional perspective IPO performance constitutes a well-researched topic in general finance literature. Scholars have proposed multiple theoretical perspectives for evaluating the success or failure of an issuance (Fama, 1998; Ljungqvist and Wilhelm, 2003; Moshirian et al., 2010). Four strategic dimensions can be distinguished. The first dimension relates to the subject who assesses the performance. The analysis can be undertaken following the issuer’s perspective or, alternatively, the view of the investors in the primary or in the secondary market. The second dimension focuses on the objective, i.e. the profile of the performance under

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investigation. Economic, financial and operational performance can be examined, as well as the impact on company’s governance and managerial settings. The third dimension captures the timeframe of the analysis, i.e. a short, mid, or long run perspective. The last dimension concerns the analytical method for calculating the performance. Different performance proxies can be measured such as raw vs. abnormal return and cumulative vs. buy-and-hold abnormal results. These four dimensions are inevitably interrelated and mutually dependent, as they are intrinsically associated with the aim and the purpose of each analysis. Although general finance literature on IPO performance is abundant and satisfactory, sectorial research is rather scarce (Merikas et al., 2009). In particular, contributions dedicated to the maritime and logistics industry are still scarce (Cullinane and Gong, 2002; Merikas et al., 2010). The few studies available predominantly focus on shipping-related IPOs while port-related companies are neglected. As empirical evidence suggests that financial markets will extend their role in fuelling the port industry, by providing additional and alternative financial resources, IPOs of port-related companies is expected to become a prominent research area for both scholars and managers. This paper assesses the performance of port-related IPOs by assuming an investors’ perspective. This implies we analyze and interpret the success/failure of IPOs through the lens of the investors. Moreover, we scrutinize in which circumstances this asset class could be attractive for private (institutional and retailer) investors according to a long-run perspective. This time horizon, in fact, is coherent with port business’ innate characteristics (Rodrigue et al., 2011) and discourages purely speculative approaches to the sector. While earlier studies in mainstream finance literature provide broad empirical evidence that IPOs are generally underpriced and underperform comparable stocks in the long term (Aggarwal and Rivoli, 1990; Schultz, 2003; Grammenos and Arkoulis, 1999), the determinants of these phenomena are still subject to a vibrant debate. General finance theories on IPO’s performance are typically grouped in two categories, i.e., information asymmetry theories, and symmetric information theories (Ritter and Welch, 2002). The former, such as the principal-agent theory and the signalling theory (Baron, 1982; Allen and Faulharber, 1989; Ljungqvist and Wilhelm, 2003), suggest that IPOs’ underpricing and aftermarket underperformance originate from asymmetric information between informed and uninformed market participants (Aktasß et al., 2003). The latter, which predominantly groups the ‘divergence of opinion’ hypothesis, the timing hypothesis, and the insurance (or legal liability) theory, propose alternative determinants of IPO’s success/failure. In this perspective, the heterogeneity of investors’ expectations (Miller, 1977), the existence of a window of opportunity before starting an IPO (Loughran and Ritter, 1995) and or the role of the market power of investment banks (Ritter, 1984; Tinic, 1988) are argued to impact on IPOs’ performance both in the short and in the long run. Some more recent contributions present a convincing institutional view on IPOs’ success or failure (Bell et al., 2012). Institutional theories pose that strategic decisions are triggered by the interactions between institutions and organizations (Peng et al., 2009). In institutional economics, a distinction is made between the institutional environment, i.e. sets of formal and informal customs and rules, and institutional arrangements, i.e. particular organisational forms and governance systems (Martin, 2000). The interactions between institutional environment and arrangements shape and also change institutions over time, either through incremental adaptation or abruptly through revolutions (Boschma and Frenken, 2006). Factors related to the institutional environment and arrangements in both the home country and the host country may affect an IPO’s performance (Moore et al., 2010). These theories are not mutually exclusive, and some determinants can be more relevant for IPOs issued in specific countries or sectors (Moshirian et al., 2010). Given the above discussion, a number of variables are suggested as (groups of) determinants of IPO performance, such as firm characteristics, transaction features, and macro-economic variables (e.g., Ibbotson et al., 1994; Grammenos and Papapostolou, 2012, etc.). Macro-economic variables, in particular, include characteristics of the financial markets, institutional factors and industry-specific variables (Grammenos and Arkoulis, 2002). Extant literature predominantly looked at firm characteristics and transaction features, thereby overlooking the predictive role of macroeconomic variables. This is surprising given that macro-economic variables are expected to hold a significant explanatory role, when assessing IPOs’ performance in the long run or when focusing on high-regulated and local-embedded industries, such as transport and ports (Parola et al., 2013b; Satta et al., 2014). Therefore, we propose an all-embracing theoretical model, which seeks to examine the explanatory power of ‘‘financial market characteristics”, ‘‘institutional factors” and ‘‘industry specific variables” as antecedents of the long-term aftermarket performance of port-related IPOs. 2.2. Hypotheses development on the determinants of IPOs’ performance 2.2.1. Characteristics of the financial markets Extant literature suggests that several variables related to financial markets may affect both IPOs’ initial return and longterm aftermarket performance (Grammenos and Papapostolou, 2012). In particular, the analysis in Merikas et al. (2009) on global shipping IPOs shows that the reputation of the stock market is positively associated with the short-term IPO performance, as issuances listed on highly reputed markets suffer lower level of underpricing. In addition, in line with the signalling theory, the listing on a first tier stock exchange can reduce risks perceived by institutional and retail investors, and thus lead to higher aftermarket performance. First tier stock exchanges, in fact, have much more stringent selection criteria for listing. Cetorelli and Peristiani (2010) find that firms listing their stocks on first tier markets enjoy significant valuation gains over the five-year period following the IPO. Therefore, we expect that:

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H.1.1. Port-related IPOs issued on first tier stock exchanges experience higher aftermarket performance in the long run. Prior contributions argue that long-run IPO performance is a function of the condition and quality of markets at the time when firms decide to go public as they significantly influence investor sentiment and bounded rationality (Loughran and Ritter, 1995; Helwege and Liang, 2004). In particular, during ‘‘hot” periods, characterized by high IPO volumes and demand, IPO euphoria occurs and high levels of initial return are experienced by new issuances (Ibbotson and Jaffe, 1975; Ritter, 1984). Kooli and Suret (2004) find that investors buying IPOs immediately after listing and holding them for five years tend to make significant capital loss, due to the initial price run-up followed by subsequent underperformance. Scholars refer to this phenomenon as ‘‘hot issues” (Ljungqvist et al. 2006), and, in line with the timing hypothesis, firms have been found to ‘‘time” their IPOs when valuations are exaggerated (Baker and Wurgler, 2006). Nonetheless, favorable market conditions, which typically coincidence with bullish cycles, are expected to reduce risk perception in the port industry (Rodrigue et al., 2011) and to support stock prices, determining higher long-term performance. In line with the above, two alternative hypotheses are formulated: H1.2a. Port-related IPOs issued during bullish cycles experience lower aftermarket performance in the long run. H1.2b. Port-related IPOs issued during bullish cycles experience higher aftermarket performance in the long run. 2.2.2. Institutional factors The legal institutions and corporate governance settings in the host country (i.e. the institutional arrangements) and the associated set of legal rules and regulations (as part of the institutional environment) are expected to impact on IPO performance. Consequently, the evaluation of its success should take into account not only firm-specific variables, but also the institutional environment and arrangements of the host country where the issuance takes place (Bell et al., 2012). Prior studies demonstrate that corporate governance rules and norms are embedded within country-based legal institutions (Aggarwal et al., 2007). In this regard, the use of external financing for firms will be greater if investors can rely on formal and informal institutions that protect the rights of minority investors and on strong and respected law settings, (Demirgüç-Kunt and Maksimovic, 1998). In the port and transport industry, legal and political institutions are particularly relevant for long-term investors. These institutions set the legislative background for infrastructure development and are expected to mitigate uncertainty for market actors and financial investors by defining rules in a business context (Scott and Davis, 2007; Notteboom and Rodrigue, 2012). Therefore, we select host country voice and accountability, as well as political stability as relevant institutional factors for determining the long-term performance of port-related IPOs. Voice and accountability are institutional reflections of the extent to which citizens are allowed to be involved in selecting their government, as well as the liberty of association, expression, and communication (free media) (Kaufmann et al., 2009). This dimension reflects whether people can hold governments accountable for their political actions, and includes characteristics of the political process as well as the compliance of the actions of representative institutions (e.g. parliament) with the formal rules in force (Daude and Stein, 2007). A high degree of voice and accountability contributes to a safe institutional background, and positively influences the transparency and stability of the business environment, also reducing information asymmetry in capital markets (Demirgüç-Kunt and Maksimovic, 1999). Hence, in countries where voice and accountability are guaranteed to citizens and firms, capital markets are more stable and transparent, as well as less subject to the opportunist behavior of investors. This condition should enable firms to achieve a better long-term aftermarket performance. Therefore, we hypothesize that: H.2.1. Port-related IPOs issued in host capital markets with a high level of voice and accountability experience higher aftermarket performance in the long run. Political stability measures perceptions of the likelihood of political unexpected events or disruptive shocks. In this regard, institutional theory has recognized the influence of this environmental factor on the commercial and financial success (or failure) of firms investing in a specific country (Busse and Hefeker, 2007; Holmes et al., 2013). Timing is of the essence here. For example, Julio and Yook (2012) demonstrate that political uncertainty linked to elections leads firms to reduce investment expenditures until the electoral uncertainty is resolved. In particular, port activities are strongly affected by the political stability of the nation in which they are embedded, as the degree of political stability may reduce or increase seaborne trade and therefore the commercial attractiveness of ports (Medda and Caschili, 2015; Fraser et al., 2016). In turn, capital markets are influenced by the political stability of the host country as violence, terrorist attacks, and broadly speaking the durability and integrity of the government regime can generate extensive fluctuations in stock prices (Fligstein, 1996). Thus, firms located, listed or operating in countries characterized by unstable political environments are expected to generate much lower long-term aftermarket performance. Therefore, we propose that: H.2.2. Port-related IPOs issued in host capital markets with a high level of political stability experience higher aftermarket performance in the long run.

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2.2.3. Industry specific variables Industry specific variables may play a significant role in shaping IPO performance (Merikas et al., 2009; Moshirian et al., 2010). In the maritime logistics domain, laid up tonnage, industrial production, inflation and oil prices have been suggested, among others, as potential antecedents of the long-term performance of IPOs in global shipping (Grammenos and Arkoulis, 1999, 2002; Merikas et al., 2009, 2010; Grammenos and Papapostolou, 2012). As concern the port sector, port reform and governance settings characterizing the port system of the country each issuer comes from, are expected to influence the attractiveness of the IPO, and the risk perception of investors. This affects IPO performance in the long run. While port reform and governance settings can be regarded as institutional factors, we included these factors in the category of the industryspecific variables as they are highly port/industry-specific. Typically, companies coming from the public sector are highly scrutinized before listing (Megginson and Netter, 2001) and international evidence suggests that companies undergoing a privatization process tend to experience lower underpricing and higher long-run returns (Jones et al., 1999; Keloharju et al., 2008). A public listing which constitutes the final step of a corporatization path of a port authority or port company is expected to experience a less volatile operating performance given the prior state-owned governance and ownership (Pallis and Syriopoulos, 2007). Nonetheless, it is reasonable to expect that major pre-IPO shareholders, typically central or local governmental bodies, focus more on the success of the port restructuring process rather than on the IPO’s financial return. In addition, (corporatized) port authorities, due to their ‘‘hybrid nature” heritage (Notteboom et al., 2015), are argued to purpose ‘‘stakeholders-oriented” strategies, being less devoted to the shareholder value maximization paradigm. Given the above, we hypothesize that: H.3.1. Port-related IPOs issued by (corporatized) port authorities and port companies experience lower aftermarket performance in the long run. The economic growth and the increase of international maritime trade volumes in the 1990s exercised strong pressures on extant port infrastructures and caused a dramatic need for additional investments in major transport and logistics infrastructure projects (Guasch, 2004; Zhang, 2005). To overcome budgetary constraints and compensate for their lack of expertise in operations (Flinders, 2005; Hammami et al., 2006), governments were forced to permit a high involvement of the private sector in port management and finance. The consecutive massive port liberalization and privatization programs undertaken by several advanced and emerging economies attracted the interest of private investors searching for new business opportunities and high-growth potentials (Tongzon and Heng, 2005). A number of private investment holdings exploited time-window opportunities and secured future potential returns from the port sector by entering the market (Olivier, 2005; Rodrigue et al, 2011; Satta and Persico, 2015). Conversely, latecomers typically find less investment opportunities and pay higher prices for acquiring stakes in extant infrastructures or obtaining concessions in new or existing port facilities (Rodrigue et al., 2011; Parola et al., 2013b). Consequently, port-related companies operating in highly liberalized markets with lower monopolistic rents may face lower profitability and financial returns (Turkisch, 2011). Therefore, we propose: H.3.2. Port-related IPOs issued by companies, headquartered in countries, which started port liberalization and privatization processes a long time ago, experience a lower aftermarket performance in the long run. Notably, in the last decade, ports have experienced a dramatic increase in competition, due to technological and organizational innovations (Fleming and Baird, 1999; Notteboom and Winkelmans, 2001). In this context, several investment opportunities emerged for multinational enterprises willing to expand their terminal business overseas (Peters, 2001; Bichou and Bell, 2007; Parola et al., 2013a; Satta and Persico, 2015). A number of powerful international players, including ocean carriers, terminal operators and financial investors were able to build up a network of terminal facilities worldwide in diverse subsectors such as container handling, dry bulk handling and tank storage (Olivier et al., 2007; Parola and Musso, 2007; Notteboom and Rodrigue, 2012). Pioneering countries, which were among the first to reform port governance settings and to open the market to foreign investors, were able to capture the commitment of highly skilled terminal operators that invested significant amounts of financial resources in the host port systems, thus fostering market attractiveness and business opportunities (Farrell, 2012). Relatedly, those countries, which as prime movers opened their stevedoring market to foreign investors, were able to exploit the experience and the business network of incoming players in order to develop their competitive position within the international supply chains (Peters, 2001; Notteboom and Winkelmans, 2001). Finally, the degree of openness towards foreign investors can also be considered as a predictor of successful cross-cultural arrangements in the port domain (Parola et al., 2013b), and port systems open to foreign players are perceived as less risky markets (Rodrigue et al., 2011). Therefore, port-related issuers holding their homeport base in these countries are expected to attract a number of new investors catching sound opportunities for capital allocation in ports. IPOs issued by terminal operators and port companies located in internationally open markets are also argued to be attractive for both retail and institutional investors, such as investment banks, pension funds and Private Equity (PE) funds. In particular, terminal assets’ features perfectly match pension funds’ investment interests, as they are typically long-term investors seeking for sustainable returns over a 20/30-year timeframe (Siemiatycki, 2015).

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H.3.3. Port-related IPOs issued by companies headquartered in countries characterized by internationally open port systems experience a higher aftermarket performance in the long run.

Fig. 1 shows the overall conceptual framework including the research hypotheses and expected signs. The internal validity of the proposed research design is supported by the above discussion on the proposed causal relations between the independent variables and the dependent variable (i.e. the IPO long-term performance). In the next section we elaborate further on the construct validity aspect, i.e. the measurement of the proposed constructs, as well as the data collection process. 3. Data collection and measurement methodology 3.1. Empirical context and sampling frame For the aim of the study, we develop an ad-hoc dataset on port-related IPOs, relying on S&P Capital I-Q database, DataStream, IPO prospectuses and companies’ corporate websites as primary sources. The sampling process includes a threestage selection procedure. In the first stage, all common stock IPOs issued by firms operating in the port industry in the 2000–2015 timeframe were gathered from S&P Capital I-Q database. For this purpose, four Capital I-Q codes were included in the analysis (both as issuer’s core or non-core business), i.e. ‘‘Marine Ports and Services”, ‘‘Marine cargo services”, ‘‘Dock and Pier Operations” and also ‘‘Floating Dry Docks”, considering that ship repair facilities are an indispensable item within the spectrum of services offered by seaports. Basically, they refer to Standard International Classification (SIC) codes 4491, 4499 and 4225. The first list of IPOs extracted is made up of 143 statistical units. In the second stage, we excluded all the companies whose annual sales from port-related operations for the last three years prior to going public were lower than 20% of the overall revenues. The selected threshold, in line with similar studies (Cullinane and Gong, 2002; Parola et al., 2015), allows to include in the sample those companies which started to diversify their portfolio significantly by investing in the port business, and to exclude those firms which hold only minority interests in this industry. This exercise resulted in 116 IPOs matching the above criteria. In stage three, we collected corporate and business information about each issuer for the first three years after listing, as well as data on initial and long term total returns of the equity securities. Data on initial raw and market-adjusted returns, as well as aftermarket performance were calculated based on information reported by S&P Capital I-Q and DataStream. The data gathered also included share prices and prices of the general stock exchange index where each IPO has been listed. IPOs with incomplete data were eliminated from the list, leading to a final sample of 93 port-related common stock IPOs worldwide.1 Table 1 provides details on the number, size, proportion of equity offered, listing timeframe and age of the company prior to going public. Data are also provided on the listing stock exchange, the issuer’s home country, the type of offering and IPO features. The average size of port-related IPOs is USD 277.45 million calculated as gross proceeds, and it took on average two months (59 days) to accomplish the listing procedures. The average age of companies prior to going public is around 26 years and the proportion of equity offered is 28.72% of the outstanding shares, on average. Industrials (71 issues; 76.34%) and energy (15 issues, 16.13%) are the most represented primary industries as core business of the issuer. Sample portrelated companies prefer to go public by listing on the stock exchanges (SE) of Hong Kong (13), Malaysia (9), Singapore (7), Indonesia (6) and Brazil (5). The headquarters of the issuer are mainly located in Asia and the Pacific (63 issues; 67.74%) as concerns. In particular, most issuers come from Asian economic growth regions such as China (16 issues; 17.20%), Malaysia (9; 9.68%), Singapore (7; 7.53%), Hong Kong (6; 6.45%) and Indonesia (6; 6.45%). The listing timeframe is shorter for the IPOs of companies headquartered in Asia (55.73 days) and longer for those of USbased companies (103 days). European companies are more aged (around 55 years) compared with companies from Asia (19.56), Africa/Middle East (17.00) and United States and Canada (3.50). Companies from Africa/Middle East and Latin America and Caribbean show high average first-day capitalization figures, whereas companies headquartered in China and Brazil are inclined to offer to investors a large percentage of shares respect to the outstanding (39.78% and 38.10%, respectively). Tables 2 reports the main descriptive statistics related to the type of offering, the type of offeror and IPO features. When it comes to the type of offering, only 39 out of 93 IPOs (41.94%) are ‘‘global and domestic offerings”, having simultaneous taken place in their home country and abroad, whereas 52 IPOs (55.91%) are ‘‘domestic only offerings”. On average, global offerings allow to gather higher gross proceeds compared to domestic only offerings (USD 528.07 vs. 96.11 million), require a shorter average listing timeframe (around 42 vs. 66 days), permit firms to reach a higher first-day capitalization (USD 2158 million vs. USD 324 million) and are characterized by higher average shares offered (31.59% vs. 26.67%). Looking at major IPO features, 7 deals (7.53%) are ‘‘carve out”, and the transaction allowed the parent company, already listed on a stock exchange, to sell a minority stake of its subsidiary/business division (33.72% on average), retaining the rest (Pagano et al., 1998). In five cases (5.38%) the IPO is sponsor-backed, and the transaction involves a PE investor that 1 Due to difficulties in tracking stock prices in the sample period, 13 IPOs related to companies headquartered in Vietnam were removed from the sample. The large amount of port-related IPOs from Vietnam originates from the recent decision (2014) of the Ministry of Transport of Vietnam to develop an action plan for reducing public investments in ports.

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Fig. 1. Research hypotheses and predicted signs. Source: own elaboration by authors.

(partially/fully) disinvests a certain amount of its stake (35.42%). In 20 cases (21.51%), the IPO prospectus clearly states that a proportion of the shares on offer is reserved for specific categories (e.g. reserved shares for the employees). As a large portion of the shares is offered to foreign investors, several port-related IPOs are undertaken according to regulation S (29 IPOs, 31.18%) and Rule 144A2 (22 IPOs, 23.66%). The 93 IPOs together raised more than USD 25.8 billion of equity resources. In 28 cases (30.11%) both company and shareholders offered shares, whereas in 58 cases (62.37%) the shares were offered only by the company and in only 7 cases (7.53%) the IPO is undertaken exclusively to permit pre-IPO shareholders to exploit favorable market conditions for pursuing exit strategies from port investments. 3.2. Measurement of the long-term IPO performance In order to measure the dependent variable (i.e. the long-term IPO performance) we first calculate initial raw returns and abnormal returns of port-related IPOs. Notably, the initial return refers to the difference between the post-IPO equilibrium price – typically the first trading price, the first closing price or a closing price observed a few days after the IPO date (Loughran and Ritter, 2002) – and the offering price (Ljungqvist and Wilhelm, 2003). In particular, the initial (i.e. firstday) raw return of stock ‘‘i” (Ri;1 ) is calculated as reported in Eq. (1), coherently with Cullinane and Gong (2002):

Ri;1 ¼

Pi;1  Pi;0 Pi;0

ð1Þ

where Pi;0 is the offering price of stock ‘‘i”, and P i;1 is the closing price of the stock ‘‘i” on the first trading day on the stock exchange. A significantly positive (negative) initial day return signals underpricing (overpricing) for the IPO. We also check for stock-market-wide movements on the day each IPO takes place, by measuring the initial market-adjusted (i.e. abnormal) return of stock ‘‘i” (ARi;1 ), as defined by Eq. (2):

ARi;I ¼ Ri;I  Rmkt;I

ð2Þ

where the initial abnormal return of stock ‘‘i” (ARi;I ) is the first-day raw return (Ri;I ) of stock ‘‘i”, minus the return of the local stock market index of the issue, on the same day (Rmkt;I ). To assess IPOs’ long-term aftermarket performance, we use both cumulative abnormal return (CARs) and buy-and-hold abnormal returns (BHARs) for robustness purposes. 2 Regulation S provides an exclusion from the registration requirements stipulated in Section 5 of the Security Act of 1933 when offerings are made outside the United States, by US or foreign issuers. Rule 144A modifies the two-year holding period requested for privately placed securities and allows qualified institutional buyers to trade positions among themselves, thus making the institutional resale market for unregistered securities more liquid and efficient.

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Table 1 Descriptive statistics of the IPOs in the sample based on geographical features and industry classification. Source: own elaboration by authors. Average gross proceeds (USD million)

Average listing timeframe (days)

Average age prior going public (years)

Average first-day capitalization

Average shares offered (% of outstanding shares)

100.00%

277.45

58.88

26.20

1137.85

28.72%

Geographic area (headquarter of the issuer) Africa/Middle East 6 Asia/Pacific 63 Europe 14 Latin America and Caribbean 8 United States and Canada 2

6.45% 67.74% 15.05% 8.60% 2.15%

782.03 253.01 204.30 278.66 40.61

65.50 55.73 61.86 62.50 103.00

17.00 19.56 55.00 40.75 3.50

4855.95 870.70 669.62 1412.54 54.24

23.29% 27.26% 31.08% 31.78% 62.46%

Home country (headquarter of the issuer) China 16 Malaysia 9 Singapore 7 Hong Kong 6 Indonesia 6 Brazil 4 India 4 Bangladesh 2 Germany 2 Greece 2 Others 35

17.20% 9.68% 7.53% 6.45% 6.45% 4.30% 4.30% 2.15% 2.15% 2.15% 37.63%

418.37 195.65 628.45 208.33 72.89 429.44 173.84 3.05 764.84 119.43 163.74

36.75 60.22 24.86 30.33 36.17 63.00 150.00 107.50 18.50 202.50 61.67

13.44 19.56 15.14 15.67 17.67 19.25 11.00 8.00 123.50 41.50 37.02

1064.07 1119.63 1506.44 960.36 349.54 2360.58 1396.53 28.19 2859.00 429.95 754.10

39.78% 15.58% 29.09% 26.46% 28.57% 38.10% 18.36% 10.92% 34.24% 26.74% 27.36%

13

13.98%

407.99

21.62

17.77

1431.87

35.74%

9 7 6 5 4 4 4 3 2 36

9.68% 7.53% 6.45% 5.38% 4.30% 4.30% 4.30% 3.23% 2.15% 38.71%

195.65 628.61 72.89 405.55 173.84 393.84 326.99 162.21 123.35 257.98

60.22 27.86 36.17 60.40 150.00 62.00 49.00 48.00 231.00 60.38

19.56 9.29 17.67 49.40 11.00 9.50 9.00 30.67 5.00 39.64

1119.63 1506.56 349.54 2055.45 1396.53 457.43 816.86 380.69 339.42 1219.68

15.58% 29.18% 28.57% 37.90% 18.36% 34.89% 41.79% 22.56% 56.90% 26.34%

71 15 2 2 1 1 1

76.34% 16.13% 2.15% 2.15% 1.08% 1.08% 1.08%

301.76 126.91 144.90 441.55 1126.52 1.58 173.49

56.11 87.20 24.50 24.00 22.00 12.00 53.00

30.03 18.40 7.00 3.50 6.00 2.00 0.00

1141.50 584.04 1002.59 2841.55 4506.08 5.27 336.22

30.25% 23.91% 14.72% 14.54% 25.00% 30.00% 51.60%

IPOs

Overall sample

Stock exchange The stock exchange of Hong Kong Ltd. Bursa Malaysia Singapore exchange Jakarta stock exchange Bolsa de Valores de Sao Paulo Mumbai stock exchange Shanghai stock exchange Shenzhen stock exchange Warsaw stock exchange New York stock exchange Others Primary industry (of the issuer) Industrials Energy Consumer discretionary Financials Healthcare Information technology Materials

Number

%

93

Academics and practitioners have proposed and discussed a number of measures for calculating IPO performance (Moshirian et al., 2010), arguing that the method affects the magnitude of the findings and their statistical significance (Barber and Lyon, 1997; Fama, 1998; Loughran and Ritter, 2002). In this vein, CARs, BHARs and ‘‘Fama-French threefactor model” are well-established methodologies for calculating IPO performance. For each of them, scholars have debated advantages versus drawbacks (Fama, 1998). CARs neglect compounding effects, and are subject to ‘‘measurement”, ‘‘new listing” and ‘‘skewness biases” (Barber and Lyon, 1997), whereas BHARs, which include compounding effects and calculate returns in line with investor experience (Moshirian et al., 2010), tend to over-estimate the long-term performance of IPOs and suffer from new listing bias, survivorship bias and rebalancing bias (Brav et al., 2000). We measure CARs and BHARs using 36- and 24-month time windows, as IPO performance may depend on the pre-determined time points observed. Ideally, longer timeframes are also worth considering (48 or 60 months or even 10 years). However, many IPOs in our sample are of recent date. Extending the timeframe would thus significantly reduce the relevant sample size as 24.73% of the IPOs in our sample were issued less than 4 years ago, and 34.41% less than 5 years ago. The sample size is a major concern given the application of a multivariate data analysis technique. To isolate the underpricing/overpricing effect, CARs and BHARs have been calculating by treating the return index on the first day of listing as a purchase price, consistent with Merikas et al. (2009). Relatedly, CARs are given by Eq. (3) and BHARs are given by Eq. (4), in line with Merikas et al. (2010):

CARi;t ¼

t X Ri;j  Rmkt;j j¼1

ð3Þ

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G. Satta et al. / Transportation Research Part A 103 (2017) 135–153 Table 2 Descriptive statistics on the issuing features of the IPOs in the sample. Source: own elaboration by authors.

Number

%

Average gross proceeds (USD million)

Average listing timeframe (days)

Average age prior going public (years)

Average first-day capitalization

Average shares offered (% of outstanding shares)

Overall sample

93

100.00%

277.45

58.88

26.20

1137.85

28.72%

Type of offering Global and domestic offering Domestic only offering International only offering

39 52 2

41.94% 55.91% 2.15%

528.07 96.11 105.07

41.79 65.71 214.50

28.95 24.88 7.00

2158.58 324.60 372.82

31.59% 26.67% 26.31%

Type of offeror Company and shareholders Company Shareholders

28 58 7

30.11% 62.37% 7.53%

278.70 214.37 795.12

61.00 59.95 41.57

39.11 16.97 51.14

1158.34 707.20 3810.24

28.98% 28.46% 29.86%

IPO features Carve out Regulation S Rule 144 Sponsor-backed Reserved shares Best effort basis

7 29 22 5 20 2

7.53% 31.18% 23.66% 5.38% 21.51% 2.15%

814.25 670.29 787.60 157.02 96.60 26.10

71.14 40.17 40.32 95.80 67.50 94.00

29.43 27.21 32.18 63.40 20.10 6.00

1848.72 2775.22 3250.03 304.28 452.49 92.72

33.72% 34.18% 37.24% 35.42% 21.20% 33.47%

IPOs

BHARi;t ¼

t Y

ð1 þ Ri;j Þ 

j¼1

t Y ð1 þ Rmkt;j Þ

ð4Þ

j¼1

where Ri;j is the monthly return of the issue ‘‘i” in the day ‘‘j” and Rmkt;j is the monthly return of the benchmark index (i.e. local stock market index of the issue) on the same day, with t ranging from 1 to 24 for 24-month CARs and 24-month BHARs and with t ranging from 1 to 36 for 36-month CARs and 36-month BHARs. For month 1, CARs do not consider the initial day return, and in addition the total number of trading days depend on the IPO’ first day of listing. Finally, the equally weighed t-month CAR (CARt ) and BHAR (BHARt ) of a cross-section of ‘‘n” IPOs are given in Eqs. (5) and (6) respectively:

CARt ¼

n 1X CARi;t n i¼1

BHARt ¼

n 1X BHARi;t n i¼1

ð5Þ

ð6Þ

Finally, the value-weighed t-month CAR (CARt ) and BHAR (BHARt ) of a cross-section of ‘‘n” IPOs are given in Eqs. (7) and (8) respectively:

CARt ¼

n X

xi CARi;t

ð7Þ

i¼1

BHARt ¼

n X

xi BHARi;t

ð8Þ

i¼1

where xi is the weight of issue ‘‘i”, paving on the market value of equity at the beginning of the event month. 3.3. Model description Ordinary least squares (OLS) regression analysis is implemented to analyze the determinants of port-related IPOs’ longterm aftermarket performance, considering 36-month BHARs and CARs as well as 24-month BHARs and CARs. Within line with the literature review (Section 2), seven potential predictors of long-term aftermarket performance are selected and their significance levels are tested. In addition, a number of control variables are introduced in the model, including initial abnormal return, firm characteristics and transaction features. In particular, we add the initial abnormal return as, according to the well-established ‘‘fads hypothesis” (Shiller, 1990; DeBondt and Thaler, 1985), the greater the initial return in the first day of listing, the wider the subsequent correction of overpricing by investors (Kooli and Suret, 2004).

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G. Satta et al. / Transportation Research Part A 103 (2017) 135–153

Moreover, based on extant financial literature (Ritter, 1984; Auret and Britten, 2008; Chen et al., 2010), we consider a set of firm characteristics which are demonstrated to affect IPOs’ long-term aftermarket performance, i.e. firm size, share of port-related revenues, firm profitability, financial leverage, tangible book value and free cash flows. As concern transaction characteristics, we introduce the listing timeframe, offering type and total proceeds in line with prior contributions (Loughran and Ritter, 2002; Merikas et al., 2010). Moreover, as several scholars argue that underwriters hold an important role both in moderating IPO underpricing and preserve the firm from the risk of long-term aftermarket underperformance (Allen and Faulharber, 1989; Carter et al., 1998; Aggarwal et al., 1993), a couple of variables related to the number of underwriters involved in the listing process and their reputation are included. Table 3 lists all the variables included in the regression models, providing their description, measurement and operationalization. Therefore, the regression model is specified as follow:

yi ¼ ai þ b1 FM STEX i þ b2 FM MACOi þ b3 IF VOAC i þ b4 IF POST i þ b5 IS PORT i þ b6 IS LIBEi þ b7 IS INOP i þ b8 C INREi þ b9 C SIZEi þ b10 C COREi þ b11 C PROF i þ b12 C LEVEi þ b13 C BOOK i þ b14 C CASHi þ b15 C LITIi þ b16 C TYPEi þ b17 C PROC i þ b18 C UNDEi þ b18 C REPU i þ ei

ð9Þ

With yi referring to the applicable measures for the long-term performance of IPO i as discussed in the previous section.

4. Empirical results 4.1. General results on port-related IPOs’ performance The empirical outcomes provide useful insights on the performance of port-related IPOs. Table 4 reports IPOs initial market adjusted returns as well as BHARs and CARs. The mean (equally-weighted) of the IPOs’ first-day abnormal return is 15.31%, in line with findings registered by Merikas et al. (2009) for global shipping IPOs (17.69%), but lower than those reported by Cullinane and Gong (2002) for transportation IPOs in China and Hong Kong (70.64%). Results are significantly different if the value-weighted mean is considered. In this case, IPOs do not suffer significant underpricing with a value-weighted average of 1.26%. The variability of the initial market-adjusted returns is high given a standard deviation (54.74%) and minimum and maximum values of 87.17% and +272.21% respectively. The outcomes, therefore, suggest that port-related IPOs tend to experience heterogeneous performance in their first trading day, but underpricing does not constitute a serious concern. As port terminals usually produce stable cash flows, given their guaranteed location-bound ‘‘quasi-monopolistic” status, they are perceived by market actors as less risky investments compared with other transport businesses. Low initial returns are sufficient for compensating investors’ ex-ante uncertainty. The port-related IPOs in the sample experience a poor long-term aftermarket performance and underperform compared to their respective benchmark indices of the stock exchanges where they have been listed. Both BHARs and CARs are negative along with a 24- and a 36-month perspectives. In particular, the average equally-weighted 24-month BHAR amounts to 5.13%, whereas the average equally-weighted 36-month BHAR performs worse with a value of 12.18%. Long-term aftermarket performances are better if CARs are considered (average 24-month CAR = 1.27%; average 36-month CAR = 5.28%). Conversely, port-related long-term IPOs performance decays if value-weighted means are considered. The results are not unexpected as several prior contributions demonstrate that IPOs tend to underperform the market as well as comparable peers (Grammenos and Arkoulis, 1999; Schultz, 2003). In addition, the findings are impacted by the financial market conditions during a large part of the sample timeframe. The 2008 financial crisis heavily impacted on financial markets, also putting the port industry under pressure. The bursting of the financial bubble led to a sharp fall in demand for this type of assets and inevitably has driven investors to rediscover risk (Rodrigue et al., 2011). For example, global terminal operators were forced to implement large-scale operational cost-cutting programs and rescaled, postponed and cancelled planned terminal investments in order to limit any financial damages. The considerable devaluation of terminal assets, which until then had experienced favorable market conditions, undermined stock prices of several port-related firms, thus seriously lowering the long-term performance of a number of port IPOs. The relative recovery in the past few years has not been sufficient for reaching pre-crisis market values of terminal assets.

4.2. The determinants of long-term aftermarket performance Before performing the OLS regression analysis, we present the correlations matrix and main descriptive statistics in Table 5. Although some correlations among variables emerge, additional diagnostic tests show that multi-collinearity does not constitute a threat to OLS results. Tolerance and variance inflation factors (VIF) are largely within the accepted range, being >0.2 and <5 respectively (Hair et al., 1995). To assess the effect of macro-economic variables on port-related IPOs’ long-term aftermarket performance, eight OLS regression models are considered (Table 6). In particular, models 1 and 3 include BHARs as dependent variable, whereas models 2 and 4 use CARs as dependent variable. Models 1 and 2 focus on a 36-month perspective, while models 3 and 4

145

G. Satta et al. / Transportation Research Part A 103 (2017) 135–153 Table 3 Variable description and measurement. Source: own elaboration by authors. Code Dependent variables ‘‘t-months”-CARs

‘‘t-months”BHARs

Variable

Description and measurement

Hypothesis

Predicted/ expected sign

Long-term aftermarket performance (CARs)

It measures the long term aftermarket performance of the IPO issued by calculating the cumulative abnormal returns for a 36-month (and 24-month) period, as reported in Eq. (3) It measures the long term aftermarket performance of the IPO issued by calculating the buy-and-hold abnormal returns for a 36-month (and 24-month) period, as reported in Eq. (4)

(/)

(/)

(/)

(/)

It evaluates the rigor the financial market where the IPO is issued. It takes value 1 for first-tier stock exchanges, which have much more stringent selection criteria to be listed, and 0 otherwise It describes the listing period when the issue take place. IPO listed in a bullish market get value 1, while those listed in a bearish market get value 0, in line with prior contributions on this topic (e.g. Merikas et al., 2009). The total number of IPOs during the year of listing of each IPO and the total amount of proceedings rised in the process are considered

H.1.1

(+)

H.1.2a H.1.2b

(-/+)

H.2.1

(+)

H.2.2

(+)

It evaluates the nature of the listing company within the overall port governance setting. This dummy variable takes value 1 if the listing company is a (corporatized) Port Authority/port company, or 0 otherwise (e.g. stevedore, terminal operator, etc.) It measures the extent to which the liberalization process within the port domain has been already implemented in the home country of the listing port-related company. The number of years from the first private investment in the local port industry is used as a proxy It assesses the degree of international opening of the listing companies’ home country in the port domain. The number of port terminals of the country, operated by foreign multinational enterprises is used as a proxy

H.3.1

()

H.3.2

()

H.3.3

(+)

It measures the initial (i.e. first-day) abnormal return experienced by the IPO. It is calculated as: ARi,1 = Ri,1 Rmkt,1, where the initial abnormal return of stock ‘‘i” (ARi,1) is the first-day raw return (Ri,1) of stock ‘‘i”, minus the return of the local stock market index of the issue, on the same day (Rmkt,1)

(/)

()

It reflects the size of the company at the listing. It is calculated as the natural logarithm of the total number of employees It measures the weight of the port sector within the listing company’ business model. It is calculated as the percentage of port-related revenues on the total figures in the last year before listing It reflects firms’ profitability in relation to shareholders’ equity. It is measured as the average ROE (return on equity) in the two years before IPO (Source: S&P Capital I-Q)

(/)

(+)

(/)

(+)

(/)

(+)

Long-term aftermarket performance (BHARs)

Independent variables - financial markets FM_ STEX ‘‘First tier” stock exchange

FM_MACO

Market conditions

Independent variables - institutional factors IF_VOAC Voice and accountability

IF_POST

Political stability

Independent variables - industry specific variables IS_PORT Port authority/port company

IS_LIBE

Liberalization process

IS_INOP

International opening

Control variables - initial return C_INRE Initial return

Control variables - firm characteristics C_SIZE Firm size

C_CORE

Shares of port-related revenues

C_PROF

Firm profitability

It refers to the degree of voice and accountability in the host country where the IPO is issued and reflects whether people can hold governments accountable for the political actions taken. It is calculated through the ‘‘voice and accountability” index, included within the World Governance Indicators (WGI) provided by the World Bank It refers to the degree political stability in the host country where the IPO is issued ans is calculated through the ‘‘political stability” index, included within the World Governance Indicators (WGI) provided by the World Bank

(continued on next page)

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G. Satta et al. / Transportation Research Part A 103 (2017) 135–153

Table 3 (continued) Code

Variable

Description and measurement

Hypothesis

Predicted/ expected sign

C_LEVE

Leverage

(/)

()

C_BOOK

Increase in tangible book value

(/)

(+)

C_CASH

Increase in operating cash flow

It measures firm’ financial leverage calculated as the average accounting leverage (i.e. total debt divided by the total equity) in the two years before listing (Source: S&P Capital I-Q) It measures the tangible book value of the listing firm and it is calculated as compound annual growth rate over two years before listing (Source: S&P Capital I-Q) It measures the amount of cash generated by a company’s normal business operations and it is calculated as the compound annual growth rate of cash flows from firm’s operating activities over two years before listing

(/)

(+)

It measures the length of the initial public offering process. It counts the total number of days between the public offerings initial filing date and the first trading day of the issuance It considers the offer type. It is a dummy variable which takes value 1 if the issuance is a global offering and 0 otherwise It measures the total amount of proceeds raised during the IPO. It is given by the total number of issued shares multiplied by the offering price It is a count variable which considers the total number of underwriters involved in the flotation and underwriting process. It includes lead, co-lead and co-managing underwriters, and excludes accountants and legal advisors. Data have been gathered from S&P Capital I-Q Database and then checked with IPO prospectuses It measures the level of reputation of the lead underwriter. This dummy variable takes 1 if the lead underwriter or at least one of the co-lead underwriters is considered as reputable and 0 otherwise. Consistent with Grammenos and Papapostolou (2012), we use Carter and manaster’s (1990) ranking measures of underwriter reputation, as updated by Loughran and Ritter (2002). Underwriters which hold a rank higher than 1 are coded as reputable (REPU = 1), whereas the others as non-reputable (REPU = 0)

(/)

(+)

(/)

(+)

(/)

()

(/)

(+)

(/)

(+)

Control variables - transaction features C_LITI Listing timeframe

C_TYPE

Offering type

C_PROC

Proceeds

C_UNDE

Number of underwriters

C_REPU

Reputation of the lead underwriter

Table 4 Port-related IPOs’ performance. Source: Authors own elaboration.

Minimum 25th percentile Median 75th percentile Maximum Mean (equally-weighted) S.D. Mean (value-weighted) No. of observations

Initial return (market-adjusted)

24-month BHAR

36-month BHAR

24-month CAR

36-month CAR

87.17% 8.09% 1.52% 20.01% 272.21% 15.31% 54.74% 1.26% 93

161.94% 40.49% 19.13% 21.78% 186.41% 5.13% 67.33% 6.69% 83

219.63% 56.90% 25.75% 2.57% 523.58% 12.18% 102.75% 5.70% 70

151.34% 32.00% 3.56% 27.30% 183.90% 1.27% 59.38% 16.75% 83

195.97% 42.35% 10.21% 23.45% 179.74% 5.28% 63.38% 12.76% 70

consider a 24-month timeframe. Each model is run twice: one time only including the control variables (‘‘a”) and a second time also including the independent variables (‘‘b”). Models 1b and 2b constitute the main findings. They are highly significant (p-value for the F-test of overall significance test <0.001) and hold the best R-squared (0.568 and 0.583, respectively) and Adjusted R-squared (0.394 and 0.414, respectively). The inclusion of independent variables in Model 1b and Model 2b increases Adjusted R-squared in a valuable manner and decreases the Akaike information criterion, supporting the appropriateness of the investigated variables as predictors of port-related IPOs’ long-term performance. In Model 1b, the coefficients of 4 out of 7 variables are significant and correctly signed, whereas in Model 2b there are 6 out of 7 variables with a significant coefficient, consistent with the research hypotheses. All the sub-groups of independent variables (i.e. financial markets, institutional factors, industry-specific variables) emerge as valuable predictors of IPOs’

FM_ STEX FM_MACO IF_VOAC IF_POST IS_PORT IS_LIBE IS_INOP C_INRE C_SIZE C_CORE C_PROF C_LEVE C_BOOK C_CASH C_LITI C_TYPE C_PROC C_UNDE C_REPU

MEAN

SD

VIF

FM_ STEX

0.753 0.697 0.057 0.036 0.180 19.596 14.607 0.090 0.000 0.742 0.266 0.425 0.505 0.438 58.955 0.461 1.836 3.955 0.674

0.434 0.462 0.975 0.875 0.386 12.849 10.940 0.423 1.000 0.305 0.294 0.249 0.686 0.970 64.974 0.501 0.782 4.485 0.471

2.081 1.590 2.467 2.866 2.077 2.880 1.900 1.242 2.063 1.722 2.444 1.376 2.612 1.448 1.406 1.955 3.400 2.203 2.660

1 0.132 0.155 0.137 0.133 0.357*** 0.235* 0.308** 0.105 0.035 0.052 0.112 0.012 0.132 0.166 0.216* 0.384*** 0.164 0.491***

Notes: * p-value < 0.01. ** p-value < 0.005. *** p-value < 0.001.

FM_MACO

IF_VOAC

IF_POST

IS_PORT

IS_LIBE

IS_INOP

C_INRE

C_SIZE

C_CORE

C_PROF

C_LEVE

C_BOOK

C_CASH

C_LITI

C_TYPE

C_PROC

C_UNDE

C_REPU

1 0.068 0.006 0.118 0.057 0.032 0.172 0.070 0.035 0.004 0.081 0.101 0.019 0.025 0.169 0.347*** 0.059 0.011

1 0.435*** 0.435*** 0.031 0.073 0.014 0.262* 0.330** 0.030 0.028 0.017 0.035 0.118 0.047 0.118 0.093 0.070

1 0.085 0.408*** 0.012 0.154 0.058 0.295** 0.217* 0.045 0.030 0.087 0.309** 0.094 0.089 0.013 0.185.

1 0.049 0.063 0.065 0.247* 0.364*** 0.236* 0.026 0.288** 0.147 0.108 0.154 0.262* 0.313** 0.076

1 0.5380*** 0.0910 0.0440 0.0240 0.1480 0.1600 0.2460* 0.1500 0.1560 0.0060 0.0900 0.0490 0.1940.

1 0.083 0.082 0.010 0.024 0.157 0.023 0.156 0.024 0.087 0.004 0.076 0.242*

1 0.058 0.221* 0.074 0.005 0.039 0.102 0.051 0.177. 0.211* 0.010 0.202.

1 0.0280 0.1060 0.0840 0.0600 0.1640 0.1200 0.3500*** 0.5210*** 0.4040*** 0.1820.

1 0.064 0.003 0.078 0.066 0.017 0.052 0.103 0.161 0.111

1 0.140 0.514*** 0.380*** 0.070 0.114 0.053 0.010 0.087

1 0.110 0.164 0.080 0.176. 0.158 0.182. 0.231*

1 0.2480* 0.0640 0.2170* 0.2620* 0.0860 0.1550

1 0.044 0.002 0.052 0.005 0.013

1 0.125 0.018 0.086 0.068

1 0.562*** 0.378*** 0.402***

1 0.516*** 0.311**

1 0.434***

1

G. Satta et al. / Transportation Research Part A 103 (2017) 135–153

Table 5 Descriptive statistics and correlation matrix. Source: own elaboration by authors.

147

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G. Satta et al. / Transportation Research Part A 103 (2017) 135–153

Table 6 OLS regression results. BHAR 36

Intercept

Model 2b

Model 3a

Model 3b

Model 4a

Model 4b

3.27E01 4.41E01

7.64E01. 4.37E01

2.11E01 3.02E01

6.47E01* 2.63E01

2.33E02 2.74E01

2.57E01 2.73E01

2.39E02 2.44E01

3.29E01 2.32E01

IF_POST IS_PORT IS_LIBE IS_INOP

C_PROF C_LEVE C_BOOK C_CASH C_LITI C_TYPE C_PROC C_UNDE C_REPU Number of observations Rsquared Rsquared adjusted F statistic AIC KolmogorovSmirnov test BreuschPagan test Maximum VIF

5.95E01** 2.14E01 6.09E01*** 1.67E01 7.42E02 9.15E02 3.81E01** 1.19E01 5.09E01* 2.13E01 2.70E02*** 7.60E03 1.67E02* 7.19E03

4.44E01 3.54E01 5.44E01. 2.76E01 2.28E01 1.52E01 5.72E01** 1.97E01 9.23E01* 3.54E01 3.19E02* 1.26E02 1.50E02 1.19E02

IF_VOAC

C_CORE

CAR 24

Model 2a

FM_MACO

C_SIZE

BHAR 24

Model 1b

Independent variables FM_ STEX

Control variable C_INRE

CAR 36

Model 1a

3.31E01. 1.69E01 3.48E02 1.31E01 6.86E02 7.73E02 9.93E02 9.65E02 9.77E02 1.75E01 2.68E02*** 6.41E03 2.59E02*** 6.19E03

1.20E01 1.99E01 4.31E02 1.55E01 5.51E02 9.10E02 1.09E01 1.14E01 2.29E01 2.06E01 2.63E02*** 7.55E03 2.61E02*** 7.28E03

7.27E01* 3.21E01 3.20E01* 1.23E01 1.53E03 3.59E01 1.09E01 4.48E01 1.06E + 00* 5.12E01 3.97E01* 1.95E01 1.31E01 1.09E01 1.79E03 1.70E03 3.36E01 2.77E01 2.76E01 2.01E01 4.68E02 2.83E02 5.35E02 2.95E01 67 0.428 0.300 3.362** 182.535 0.088

7.45E01* 3.04E01 2.62E01* 1.28E01 6.73E01 4.03E01 6.85E01 4.70E01 9.31E01. 4.89E01 6.22E01** 2.14E01 2.18E01* 1.08E01 3.17E03. 1.79E03 3.86E01 2.72E01 3.62E01 2.28E01 8.37E02** 3.01E02 2.07E01 3.46E01 67 0.568 0.394 3.253*** 177.670 0.080

3.64E01 2.20E01 1.66E01. 8.38E02 5.56E03 2.46E01 1.61E01 3.07E01 3.14E01 3.50E01 3.38E02 1.34E01 4.67E02 7.45E02 2.88E03* 1.16E03 3.47E01. 1.90E01 1.13E01 1.37E01 2.28E02 1.93E02 1.45E01 2.02E01 67 0.288 0.131 1.828* 131.623 0.102.

3.31E01. 1.83E01 1.73E01* 7.73E02 4.69E01. 2.43E01 1.24E01 2.83E01 2.24E01 2.95E01 2.07E01 1.29E01 1.18E01. 6.51E02 3.59E03** 1.08E03 4.34E01* 1.64E01 2.80E01* 1.38E01 5.45E02** 1.81E02 1.92E01 2.09E01 67 0.583 0.414 3.458*** 109.865 0.097

4.76E01* 2.10E01 2.36E01** 7.57E02 2.17E02 2.21E01 2.18E01 2.82E01 2.50E01 3.04E01 1.38E02 1.23E01 3.65E02 7.11E02 1.37E03 1.04E03 4.71E01** 1.74E01 2.17E01. 1.26E01 2.13E02 1.76E02 2.70E01 1.75E01 79 0.333 0.211 2.740** 148.181 0.059

4.78E01* 1.97E01 2.33E01** 7.70E02 3.29E01 2.41E01 1.35E01 2.82E01 2.38E01 2.84E01 1.92E01 1.29E01 7.61E02 6.88E02 5.73E04 1.10E03 6.03E01*** 1.66E01 8.86E02 1.34E01 2.19E02 1.77E02 1.67E01 1.96E01 79 0.492 0.329 3.009*** 140.595 0.093.

3.10E01 1.87E01 1.43E01* 6.73E02 8.76E02 1.96E01 2.14E01 2.51E01 1.07E01 2.70E01 1.41E02 1.09E01 4.05E02 6.32E02 2.32E03* 9.22E04 3.44E01* 1.55E01 1.91E01. 1.12E01 2.16E02 1.57E02 1.94E01 1.56E01 79 0.280 0.149 2.142** 129.621 0.083

2.98E01. 1.68E01 1.47E01* 6.54E02 3.53E01. 2.05E01 1.68E01 2.39E01 1.16E01 2.41E01 1.63E01 1.10E01 8.59E02 5.84E02 1.73E03. 9.32E04 4.58E01** 1.41E01 1.12E01 1.14E01 2.29E02 1.50E02 8.24E03 1.66E01 79 0.500 0.339 3.106*** 114.830 0.062

28.559** 2.287

33.640** 3.400

16.148 2.287

23.104 3.400

12.261 2.290

14.484 3.062

9.952 2.290

12.487 3.062

Notes: standard errors are in italics; p-values: . p < 0.10; . p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001.

long-term aftermarket performance. In particular, the coefficient of variables FM_MACO and IF_POST in Model 1b are positive and significant (p-value < 0.1 and 0.01, respectively), whereas the coefficient of variables IS_PORT and IS_LIBE are negatively signed and statistically significant (p-value < 0.05). Outcomes, therefore, support H1.2a, H2.2, H3.1 and H3.2, whereas H1.1, H2.1, H3.3 do not find empirical evidence when applying Model 1b. The results suggest that port-related IPOs issued during bullish cycles or in host capital markets with a high level of political stability experience higher aftermarket performance, measured in terms of BHARs. Conversely, IPOs issued by port authorities and by companies headquartered in countries which started port liberalization and privatization processes a long time ago, tend to obtain lower 36-month BAHRs.

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The outcomes of Model 2b, further confirm these findings, and, in addition, provide empirical support to H1.1 and H3.3. In fact, the coefficients of all independent variables, except IF_VOAC, are significant and correctly signed. Model 2b, therefore, demonstrates that IPOs issued on first tier stock exchanges have higher CARs in the long run in comparison to those listed on markets with less stringent selection criteria for listing. Hypothesis 1.1 is therefore accepted. Moreover, consistent with hypothesis H3.3, IPOs issued by companies headquartered in countries characterized by internationally open port systems experience higher aftermarket performance in the long run, measured as 36-month CARs. Overall, the empirical outcomes provide support to accept 4 out of the 7 research hypotheses (H1.2a, H2.2, H3.1 and H3.2) when considering BHARs, and 6 out of 7 research hypotheses (H1.1, H1.2a, H2.2, H3.1 and H3.2, H3.3) when CARs are used as dependent variable. The results of the models have been further tested to verify the assumptions of linear regression. In particular, the Kolmogorov-Smirnov normality test (p-value > 0.05) shows that model residuals are normally distributed. The BreuschPagan tests unveil the absence of heteroscedasticity for all the models, except for Model 1a and 1b. The violation of the assumption of constant variance makes test results unreliable; to overcome this problem the heteroscedasticity-corrected covariance matrix is used to correct coefficients standard errors. The new results confirm the significance of coefficients in Model 1a and 1b. In Models 3a-4b, we replicate the regression analysis using 24-month BHARs and CARs as dependent variables. The outcomes are in line with the previous results, but the statistical significance of the models decreases and the coefficients of a number of independent variables lose their statistical significance. In particular, in Model 3b and 4b, only industry specific variables are good predictors of IPOs long term aftermarket performance in line with the two-year perspective, whereas hypotheses related to both financial markets and institutional factors of the host countries are not empirically supported. These findings suggest that variables concerning financial markets and host country’s institutional settings tend to exert their impact on port-related IPOs aftermarket performance only when a reasonably long timeframe is considered. 4.3. Robustness checks To validate the empirical findings and test their consistency, a number of robustness checks have been carried out, although data are not reported for parsimony. The analysis has been performed in three directions. First, we ironed out any bias arising from capitalization differences, as capitalization may influence trading volume, stock volatility and therefore IPO’s long term performance (Keim, 1999). As a result, we reran the regression analysis by splitting the sample into two sub-samples of IPOs (Model 5a and Model 5b) according to the size of the financial transaction. In particular, Model 5a includes all IPOs with a first-day capitalization lower than USD 286.88 million (i.e. the capitalization mean) and uses the 36-month BHARs as dependent variable. The model is highly significant (F-statistics = 3.804; p-value < 0.01) and confirms the outcomes of Model 1b, except for the variables FM_MACO and IS_LIBE, which are not significant. Conversely, Model 5b considers issues with a first-day capitalization higher than USD 286.88 million. The model is still significant (F-statistics = 2.439; p-value < 0.05), but most independent variables lose their statistical significance. Nevertheless, FM_MACO and IS_LIBE remain significant and correctly signed, suggesting that the timing hypothesis better fits large capitalized companies. Models 5c and 5d are performed on the two aforementioned sub-samples, using 36-month CARs as dependent variable. Model 5d is statistically significant and basically confirms the outputs of Model 2b, whereas Model 5c is not significant. Second, we also considered the country of origin in our analysis. The institutional theory, in fact, suggests that the home country’s corporate settings and institutional environment may impact on risk perception and IPO performance (Moshirian et al., 2010). Therefore, we split the sample according to the issuer’s country of origin. Models 6a and 6c refer to IPOs issued by companies headquartered in developed countries (as defined by the World Bank), whereas Model 6b and 6d focus on developing countries. The outcomes suggest that the proposed conceptual framework is a better fit for IPOs issued by port companies coming from developing countries. In particular, Model 6b is highly significant (F-statistic = 4.997, pvalue < 0.001) whereas Model 6a is not significant. Third, in order to scrutinize the impact of the 2009 crisis, we further divided the sample in two subsamples, according to the year of listing. In particular, Models 7a and 7b include IPOs taking place before 2009, while Models 7c and 7d refer to post-crisis IPOs. Models 7a and 7c use BHARs as dependent variable, whereas in Model 7b and 7d the impact of independent variables on CARs is evaluate. The results confirm prior findings, as all the models maintain their statistical significance, as well as coefficient and sign of the independent variables corroborate the main models. Among them, in Model 7d (CARs of pre-crisis IPOs), all independent variables keep the same explanatory power. Moreover, the coefficients of the control variables C_CORE and C_LEVE are positive and significant. The findings demonstrate that, before the explosion of the bubble, leveraged companies enjoyed higher performance as, under confident market conditions, investors were eager to exploit aggressive Debt/Equity ratios. 5. Conclusions This paper proposed an overarching conceptual framework for capturing the determinants of the long-term aftermarket performance of port-related IPOs. The proposed approach not only focuses on the predictive role of firm characteristics and

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transaction features, but also explicitly considers a range of macro-economic variables. The consideration of such determinants constitutes one of the main methodological contributions of this research. Macro-economic variables, which include financial markets, institutional factors and industry-specific variables (Grammenos and Arkoulis, 2002) are expected to hold a valuable explanatory role, when assessing IPOs’ performance in the long run or when focusing on high-regulated and local-embedded industries, such as transport and ports (Ng and Pallis, 2010; Parola et al., 2013a). For the aims of the study, a dataset of 93 port-related IPOs issued worldwide in the 2000–2015 timeframe was used and ordinary least squares (OLS) regression analysis was performed for testing a set of seven research hypotheses. The results shed light on the performance of port-related IPOs. In particular, IPOs are found to experience heterogeneous performance in their first trading day, whereas the IPOs’ underpricing phenomenon does not seem to be a main concern in the port domain. In this perspective, the outcomes suggest that, as port terminals usually produce stable cash flows, due to a sort of guaranteed location-bound ‘‘quasi-monopolistic” status, these assets are perceived as less risky investments by financial operators, and low initial returns are sufficient for compensating ex-ante uncertainty. Nonetheless, the sample IPOs experience poor long-term aftermarket performance, as both BHARs and CARs are negative for 24- and a 36-month timeframes. A number of extant contributions already demonstrated that IPOs tend to underperform the market as well as comparable peers (Schultz, 2003; Grammenos and Arkoulis, 1999). Still, we argue that the outcomes could be significantly affected by the extraordinary financial market conditions in the sample timeframe. The bursting of the financial bubble in 2008 considerably devaluated port-related assets (Rodrigue et al., 2011), and drove to the collapse of stock prices of several port companies, seriously hitting the long-term performance of a number of IPOs. Further, the analysis of the determinants of IPOs’ long term aftermarket performance demonstrates that all the macroeconomic variables, i.e. financial markets, institutional factors, industry-specific variables, hold valuable explanatory power. As concerns financial markets, the findings prove that IPOs issued during bullish cycles experience higher aftermarket performance, measured in terms of BHARs and CARs, in the long run. Favorable market conditions which characterize bullish cycles, in fact, are demonstrated to support stock prices and to reduce risk perception in the port industry, where a number of institutional investors operate (Rodrigue et al., 2011), thus determining higher long term performances. Relatedly, outcomes show that IPOs issued on first tier stock exchanges have higher CARs in the long run, compared to those listed on markets characterized by less stringent selection criteria to be listed, whereas BHARs do not seem to be affected by this variable. When it comes to the institutional factors, IPOs issued in host capital markets with a high level of political stability report more satisfying long-term performance, whereas the level of voice and accountability characterizing the host country is not found to affect IPOs’ performance significantly. In addition, industry-specific variables emerge as valuable determinants. In particular, IPOs issued by port authorities and by companies headquartered in countries, which started port liberalization and privatization processes a long time ago, tend to obtain lower 36-month BAHRs and CARs. In the case of port authorities, major pre-IPO shareholders, typically central or local governmental bodies, are predominantly interested in the success of the port restructuring process rather than the IPO’s financial return. In addition, port authorities, due to their ‘‘hybrid nature” heritage, tend to purpose ‘‘stakeholder-ori ented” strategies (Notteboom et al., 2015). Therefore, they are less devoted to the shareholder value maximization paradigm and experience a lower financial performance in the long run. When it comes to the port liberalization timeframe, the empirical findings show that port-related companies operating in highly liberalized markets, where monopolistic rents are lower, reach second-rate profitability and lower financial returns (Turkisch, 2011). Finally, port-related IPOs issued by companies headquartered in countries, which have an internationally open port system, experience higher aftermarket performance in the long run, in terms of 36-month CARs. Pioneering countries, which were able to reform port governance settings and to open the market to foreign investors, captured the commitment of high skilled terminal operators which, by investing significant amount of financial resources in the host port systems, fostered port attractiveness and business opportunities. The paper provides insightful implications for both academics and practitioners. First, the manuscript adds to the extant debate of the financialization process within the port domain, as it investigates for the first time the long term aftermarket performance of port-related IPOs. The study goes beyond general finance theories on IPO performance and places institutional theory side by side with informational asymmetry theories and symmetric information theories (Ritter and Welch, 2002) to assess the determinants of long term IPOs’ success in the port domain. Following an institutional approach, institutional factors in the home and host countries are suggested to be valuable predictors of IPOs long-term aftermarket performance. An overarching conceptual framework for assessing IPO’s success in the long run is tested, stressing the explanatory power of ‘‘financial markets”, ‘‘institutional factors” and ‘‘industry specific variables”. Moreover, by assuming an investor perspective, the paper strengthens the nexus between finance and the port industry, and suggests some new pathways for further studies in this area. Notably, both shipping and port industries have been interconnected with the (international) financial sector (Stopford, 2009). Nonetheless, this relation became much more intense over the last two decades (Rodrigue et al., 2011; Notteboom and Rodrigue, 2012) and that calls for a deeper understanding of the leverages by which financial objectives and motivations, financial markets, models and institutions shape the evolutionary trajectories of the maritime and port sector.

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In this perspective, the paper points to circumstances that make the port terminal asset attractive for private (institutional and retail) investors, in line with a long-run perspective, which is consistent with port business’ innate characteristics, and discourages purely speculative approaches in the sector. Second, the manuscript provides insights for port managers who face port finance issues. The performance of extant portrelated IPOs is argued to impact both the capacity of port and terminal companies to gather additional financial resources from equity markets in the future, and to affect the cost of funding. A conscious assessment of IPOs’ performance offers useful tools for lowering the weighted average cost of capital (WACC) for port companies and for maximizing gross proceeds raised through IPOs. The findings provide insights for specialized financial intermediaries when managing private portfolios on behalf of institutional and retail investors. The analysis of both BHARs and CARs suggests that a proactive financial strategy to port-related equity shares is more effective and profitable compared to a passive one. Moreover, data on port-related IPOs initial returns and long-term performance could further support specialists in providing well-suited asset allocation and wealth management financial services to institutional and retail investors. The manuscript suffers from some inherent limitations. First, some data constraints emerged in the analysis due to difficulties in tracking stock prices of the companies headquartered in Vietnam during the sample period. Therefore, further research is needed to provide more insight on Vietnamese IPOs and their long-term performance. Second, the manuscript studies port-related IPOs in isolation, and does not compare them with non-port IPOs. The lower R-squared adjusted of Models 2a, 3a and 4a (which include only control variables from general finance literature), coupled with the fair statistics related to Models 2b, 3b and 4b (which introduce the independent variables) suggest that the performance of port-related IPOs tends to follow unique patterns. Hence, extant knowledge might benefit from cross-sectorial comparisons which, however, fall beyond the scope of the presented study. Third, the paper assumes a long-term perspective for investigating IPOs’ performance. We considered 24-month and 36month BHARs and CARs as dependent variables as a significant number of port-related IPOs in the sample are of recent date. Nonetheless, a longer timeframe should be included in the analysis by evaluating IPOs’ performance in a 5-year and 10-year perspective. In a few years, additional contributions focusing on longer timeframes can deepen the analysis and present further tests on the robustness of our findings. Fourth, as the analytical method used for calculating IPO performance has been argued to affect the magnitude of the findings and their statistical significance, further contributions can include also the Fama and French Three Factor Model in the analysis. Next to this, by enlarging the sample size and introducing more sophisticated statistical techniques, capable to assess time varying stock performance, e.g. Fama-MacBeth panel regressions (Teoh et al., 1998), future studies may pursue more robust outcomes. Fifth, the manuscript only introduces the institutional theory as a valuable ground for assessing port-related IPOs’ success, without providing an in-depth examination of the possible relations between the institutional factors of both host and home countries. 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