The Underpricing of Infrastructure IPOs: Evidence from China Qile Tan and Bill Dimovski Deakin University.

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Presentation transcript:

The Underpricing of Infrastructure IPOs: Evidence from China Qile Tan and Bill Dimovski Deakin University

Initial Public Offering (IPO) underpricing: IPOs are common around the world Before 2009, the US was the leading issuer of IPOs in terms of total value. Since then, China has been the leading issuer, raising $73 billion (almost double the amount of IPO money raised on the NYSE and NASDAQ combined) in 2011 Underpricing/first day returns to subscribers on average are also common. Facebook issue $38, close day1 $38.23 Underpricing appears more severe in emerging markets

Australia Lee, Taylor & Walter; Woo; Pham; Ritter 1, % Canada Jog & Riding; Jog & Srivastava; % Kryzanowski, Lazrak & Rakita; Ritter China Chen, Choi, & Jiang; Jia & Zhang 2, % France Husson & Jacquillat; Leleux & Muzyka; % Paliard & Belletante; Derrien & Womack; Chahine; Ritter; Vismara Germany Ljungqvist; Rocholl: Ritter; Vismara % Greece Nounis, Kazantzis & Thomas; % Thomadakis, Gounopoulos & Nounis Hong Kong McGuinness; Zhao & Wu; Ljungqvist & 1, % Yu; Fung, Gul, and Radhakrishnan; Ritter India Marisetty and Subrahmanyam; Ritter 2, % Jordan Al-Ali and Braik % Malaysia Isa; Isa & Yong; Yong; Ma % United Kingdom Dimson; Levis 4, % United States Ibbotson, Sindelar & Ritter; Ritter 12, % source: Ritter (2013) Equally weighted average initial returns for 12 countries

Some theoretical explanations for underpricing Rock (1986) - IPOs need to be underpriced to continually attract uninformed investors Welch(1989) – Underpricing allows for subsequent issues at a higher price Tinic (1988) suggests underpricing is like an insurance and prevents against damages to underwriters/directors Ruud (1993) – underwriters price support the issue for a short time Beatty and Ritter (1986) – greater uncertainty, greater underpricing – older entities, larger entities, those with more reputable auditors and with banking and venture capital relationships – lower underpricing

Underpricing in different industries Dimovski and Brooks (2008) – gold 13.3% (while industrials around 25%) REITs – US – Wang, Chan and Gau(1992) negative 2.82%, Ling and Ryngaert (1997) 3.6%, Bairagi and Dimovski (2011) 4.6% A-REITs – Dimovski (2009) 2.6% J-REITs – Kutsuna et al (2008) 0.5% Infrastructure – Australia 3.5% but not stat sig diff to zero Infrastructure – India 25%

Characteristics of infrastructure: high entry barriers monopoly characteristics long duration large investment scale inelastic demand stable, tax effective and predictable cash flows via government regulation and long-term contracts low correlation with major asset classes low volatility of cash-flows (Newell, Chau and Wong, 2009)

What is infrastructure? It provides the essential services for a community's economic and social needs, eg: – transport: toll roads, airport, sea ports, rail, and bridges; – energy and utilities: electricity, water, and gas; – communications: mobile phone networks, telecommunication networks, and satellite systems; and – social: healthcare, education, and correctional facilities. (RREEF, 2005; and UBS, 2006a)

Previous studies of infrastructure IPOs: Hong Kong Dewenter and Field(2001) , 7 infrastructure IPOs report a 4.67% underpricing (not statistically significant). Australia Dimovski (2011) , 30 infrastructure IPOs report a 3.5% underpricing (not statistically significant). India Ritchie, Dimovski and Deb (2013) , 49 infrastructure IPOs report a 25.4% underpricing (statistically significant).

Previous studies of infrastructure IPOs: Author(s)Market Number of IPOs YearInitial returnSignificance Dewenter and Field (2001) Hong Kong %NO Dimovski (2011)Australia %NO Ritchie, Dimovski and Deb (2013) India %YES Tan and Dimovski (current study) China %YES

Characteristics of the Chinese stock market: Two types of shares:  A-share and B-share Long delay between offering date and listing date  Average 260 calendar days for A-share IPOs (Su and Fleisher, 1999) High government ownership Highly speculate market

Data: 154 A-share infrastructure IPOs issued during1993 to 2012 Prospectuses were downloaded from Shanghai and Shenzhen stock exchanges CSMAR database is used for index returns and in the case of missing data in prospectus

Summary statistics for the Chinese infrastructure IPOs during 1993 to 2012 MeanMedianMax.Min.SD First day return (%) Time delay (days) Offer size(USD) 315,208,54385,050,0008,182,707,9934,453,507863,030,000 Offer price (CYN$) Firm Age Government ownership (%) Market return (%)

Dependent variable: Initial returns Independent variables: – Stock exchange dummy – Market index return – Time lag between offering and listing date – Firm age – Offer price – Logarithm log of issue size – Government ownership after issuing – Pricing method dummy – Underwriter reputation

Methodology: Ordinary Least Square (OLS) Model 1: Utilizing all variables except Time Lag and Market return Model 2: Utilizing all variables

Regression results: In model 1 (LAG and MARKET are not included) offer price, stock exchange and issue size are highly significant with expected signs. In model 2 (LAG and MARKET are included) stock exchange, time lag and issue size are highly significant with expected signs.

Issue size (in log form) is significant with expected sign in all partitions in both models. Underwriter reputation and Government retention are not significant across all partition in both models. Results:

Summary: The average initial return for Chinese infrastructure IPOs during 1993 to 2012 is 91.1%, which is statistically significantly different to zero and relatively higher than in India and Australia. Infrastructure IPOs in China carry less uncertainty in the valuation of the IPO comparing to the whole market.

Summary: In China, the offer price and issue size appear to be helpful in the prediction for the underpricing of infrastructure IPOs. Government retention and underwriter reputation do not have significant predicting power in our study. When time lag and market return being included in the regression model, the time lag and issue size can better explain the underpricing.

Thank you for your time