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Liquidity benefits from IPO underpricing: ownership dispersion or information effect.

The positive correlation between initial underpricing and liquidity in the secondary market several months after an initial public offering (IPO) has previously been attributed to ownership dispersion induced by underpricing. We find that public information production is another channel by which underpricing improves liquidity. Using a sample of IPOs from Euronext, we find that analyst coverage engendered by initial underpricing reduces information asymmetry costs and illiquidity in the secondary market. The impact of information asymmetry is statistically more significant on measures based on adverse selection costs than on those based on the proportion of informed traders in the market.


The vast majority of empirical studies examining the liquidity of recently listed stocks suggest that initial public offering (IPO) underpricing boosts liquidity in the secondary market. On average, underpriced IPOs exhibit higher trading activity than overpriced IPOs (Miller and Reilly, 1987; Hanley, 1993; Schultz and Zaman, 1994; Zheng and Li, 2008), lower bid-ask spreads (Pham, Kalev, and Steen, 2003; Li, Zheng, and Melancon, 2005), and lower adverse selection costs (Li, McInish, and Wongchoti, 2005). In a study of Australian firms, Pham et al. (2003) link these liquidity gains to increased breadth of ownership. We demonstrate that this relation may also arise from increased information production as a result of increased analyst coverage.

As posited in the theory of Booth and Chua (1996), referred to as the ownership dispersion hypothesis in the remainder of this paper, Pham et al. (2003) find that the positive correlation between initial underpricing and liquidity in the months following an IPO is formed through the creation of a broader ownership structure resulting from the allocation process. In contrast, we determine that a dispersed ownership is not the sole channel through which IPO underpricing may improve secondary market liquidity and that the positive link between initial returns and secondary market liquidity may arise from the information effect of analyst coverage engendered by underpricing. This interpretation of our empirical evidence is based on: 1) the model by Chemmanur (1993) that demonstrates that initial underpricing attracts costly information production by outsiders, a common form of this information production being analyst coverage, and 2) the observation that analyst coverage reduces information asymmetry and, as such, illiquidity (Popescu and Xu, 2011). (1) This theory is further referred to as the information production hypothesis. Finding evidence in favor of this hypothesis emphasizes the relevance of managing information for IPO firms as shown by Arnold, Fische, and North (2010) and Flo et al. (2010).

While the information production hypothesis was never tested in previous research, the literature on financial analysts provides some indications that such an effect could exist for IPOs. IPO underpricing positively correlates to analyst coverage after the IPO. In addition, analyst coverage has been proved to have beneficial effects on liquidity in general. With respect to the correlation between initial returns and analyst coverage, Rajan and Servaes (1997) examine data on analyst following to see how it relates to three well-documented IPO anomalies: 1) underpricing, 2) hot issue markets, and 3) long-run underperformance. They find that higher underpricing leads to increased analyst following. According to Cliff and Denis (2004), the quality of the analysts is also important. They report that IPO underpricing is positively related to analyst coverage by the lead underwriter and to the presence of an all-star analyst on the research staff of the lead underwriter.

Regarding the impact of analyst activity on market prices and market quality, Bradley, Jordan, and Ritter (2003) find abnormal returns at the end of the quiet period for IPO firms subject to analyst coverage initiating, and those abnormal returns are much larger when coverage is initiated by multiple analysts. Irvine (2003) finds that liquidity improves after analyst coverage initiations, but liquidity gains depend upon the nature of the analyst recommendations. The more positive the initial recommendation, the greater the improvement in subsequent liquidity. Ellul and Panayides (2013) obtain the reverse effect for complete coverage termination on stock liquidity. They find that coverage termination deteriorates liquidity and price efficiency, and that it increases information asymmetry with larger impacts for those stocks with a strong insiders' presence. Those works suggest that corporations should encourage analyst coverage to capture liquidity benefits.

However, none of those studies examine the double-stage relation between underpricing and liquidity created by two subsequent effects: 1) underpricing generating greater analyst coverage and 2) the additional coverage triggered by underpricing improving liquidity and reducing information asymmetry in the secondary market. While the literature already provides evidence regarding the positive link between IPO underpricing and analyst coverage (Rajan and Servaes, 1997; Cliff and Denis, 2004) and on the positive link between analyst coverage and liquidity (Irvine, 2003), there is a lack of evidence concerning the twofold effect of underpricing increasing coverage and the coverage attracted by underpricing increasing liquidity.

Our contribution is to test this double link and compare it with the double link involving ownership structure by exploiting IPO data, intraday market data, ownership data, and analyst coverage data for a sample of IPOs undertaken on Euronext Paris from 1995 to 2008. As in previous papers, we find that IPO secondary market liquidity increases with initial underpricing. We find that adverse selection costs and informed trading are lower for more underpriced IPO stocks, suggesting that more public information is produced for these stocks. In contrast to the theory of Booth and Chua (1996) and the empirical findings of Pham et al. (2003), we fail to prove that these effects result from a more diffuse ownership obtained by underpricing the issue. Instead, we find that these results are due to enhanced analyst coverage of IPOs that perform well in the immediate aftermarket. We provide evidence that secondary market information asymmetries are 1 reduced and secondary market liquidity is enhanced through analyst coverage generated by the initial returns.

In other words, we contribute to the literature by demonstrating that analyst coverage is an alternative channel for the underpricing-liquidity link when the underpricing-ownership link is not active. The insignificance of the underpricing-ownership link does not, however, rule out the existence of a positive correlation, after the IPO, between shareholding breadth and liquidity, as appears from our data.

Our Euronext sample is of particular interest as French firms are characterized by concentrated ownership structures in which control is often maintained through a small proportion of cash flow rights. Thus, they are an ideal field of investigation to determine whether the triple relation between IPO underpricing, ownership dispersion, and liquidity holds in a market where corporate control is concentrated, or whether another way of monitoring the liquidity of IPOs in their secondary market can be exploited. In addition, the relative size of the Euronext IPO market makes it a good ground for this investigation. Euronext is the second exchange in Europe in number of IPOs after the London Stock Exchange (LSE) with the majority of those IPOs undertaken on Euronext Paris. In addition, the Euronext primary market raised 245 billion euros from 1995 to 2008, an amount comparable to funds raised by IPOs on the LSE or NASDAQ for the same period.

The remainder of the article is organized as follows. Section I presents the testable hypotheses. Section II describes the institutional settings, the sample, and the data. Empirical measures are laid out in Section III, while Section IV explores the positive link between initial underpricing and liquidity as a prerequisite for our main test. Sections V and VI present the methodology implemented to test our hypotheses and the results, respectively. Several robustness checks are conducted in Section VII, while Section VIII provides our conclusions.

I. Testable Hypotheses

Two theories are considered to explain why initial underpricing would enhance the liquidity of IPOs in their secondary market. One explanation is that underpricing an IPO is a way of attracting more shareholders, as demonstrated by Booth and Chua (1996). The greater dispersed ownership obtained by underpricing the issue would then result in a more liquid secondary market. We refer to this theory as the ownership dispersion hypothesis. A second possible explanation is built from the information model developed by Chemmanur (1993) which indicates that initial underpricing attracts information production. We propose that when initial underpricing attracts information production in the guise of analyst coverage, more public information is produced about the firm that may reduce information asymmetry in the secondary market thereby improving liquidity. We refer to this theory as the information production hypothesis.

A. The Ownership Dispersion Hypothesis

While some IPO candidates may desire concentrated ownership to confer greater monitoring power to pre-IPO or new large shareholders, others may want a diffuse ownership structure in order to obtain higher secondary market liquidity, a factor often considered as an important criterion of the success of an IPO (Corwin, Harris, and Lipson, 2004). Indeed, firms with more dispersed ownership generally have a more liquid stock market (Heflin and Shaw, 2000; Brockman, Chung, and Yan, 2009). This presents several advantages. A more liquid secondary market may make corporate governance more effective (Maug, 1998). In general, greater liquidity contributes to increasing the firm's value and reducing its cost of capital in several ways. It improves the issuing firm's future access to capital markets by attracting investors, reducing transaction costs in future equity raisings (Ibbotson and Ritter, 1995), and lowering gross fees requested by investment banks in subsequent equity offerings (Butler, Grullon, and Weston, 2005). It also reduces the illiquidity premium and, as such, the returns required by investors to hold the firm's shares (Amihud and Mendelson, 1986; Brennan and Subrahmanyam, 1996).

Booth and Chua (1996) demonstrate that IPO firms seeking secondary market liquidity underprice their stock in order to attract a large number of small shareholders and create a more dispersed ownership structure. Consistent with this theory, Michaely and Shaw (1994) and Brennan and Franks (1997) find higher underpricing for IPOs with a more diverse shareholder base. In this study, we test Booth and Chua's (1996) theory by examining not only the relation between initial underpricing and ownership dispersion, but also the subsequent effect of ownership dispersion on secondary market liquidity. These testable hypotheses are expressed as follows;

H1a. Ownership dispersion increases with initial underpricing.

H1b. The ownership dispersion generated by initial underpricing contributes to increasing the liquidity of the IPO stock in the secondary market.

B. The Information Production Hypothesis

The idea that attracting information production would be a motivation for underpricing IPOs is a key feature of Chemmanur's (1993) model. In this model, firm insiders sell equity both in the new issues market and in the secondary market. They have private information about their firm's prospects. Firms may be of two types: 1) high value (V = [V.sub.H]) or 2) low value (V = [V.sub.L]) with [V.sub.H] > [V.sub.L] > 0. Insiders know their firm's type, while outsiders only observe the unconditional probability [alpha] of a firm being a high-value type. Prior to bidding in the IPO, outsiders may choose to reduce their informational disadvantage with respect to insiders by paying a cost C to obtain additional information about the firm. The outcome of this information production is an evaluation of the firm which is either good (e = G) or bad (e = B), with the conditional probability P(e - G | V = [V.sub.H]) = [beta] is greater than 0.5 and the conditional probability P(e = G | V = [V.sub.L]) = [theta] is less than 0.5. Insiders set the offering price in order to maximize their expected proceeds from the two sales of equity; 1) the shares sold in the primary market and 2) those sold in the secondary market. While they know their firm type, they do not know how many outsiders will produce information and find e = G. As such, the stock price in the secondary market is a random variable to them. At equilibrium, insiders of high-value firms underprice their IPOs to induce information production thereby obtaining a more precise valuation of their firm in the secondary market. Initial returns compensate outsiders for the cost of producing information. This incentive to underprice should be particularly strong if the firm's managers plan to sell shares sometime after the IPO either by selling their own shares or through a seasoned offering.

This model has several empirical implications. The most relevant for our research is that managers may have an interest in underpricing their IPOs in order to encourage information production by outsiders. First, this implies a positive correlation between underpricing and analyst coverage, which is the common form of costly information production in practice. This leads us to posit the following hypothesis:

H2a. Initial underpricing attracts public information production on the firm following its IPO.

Second, outsiders may decide to pay the cost of information production to reduce their losses related to information asymmetry. This should produce a negative correlation between 1) analyst coverage purchased through underpricing and 2) information asymmetry (H2c), as well as spreads or illiquidity (H2b), leading us to the following two hypotheses:

Nesrine Bouzouita, Jean-Frangois Gajewski, and Carole Gresse *

H2b. The information production generated by initial underpricing contributes to increasing the liquidity of the IPO stock in the secondary market.

H2c. The information production generated by initial underpricing contributes to reducing the information asymmetry on the IPO stock in the secondary market.

Rajan and Servaes (1997) and Cliff and Denis (2004) have already found evidence of a positive link between initial underpricing and analyst coverage measured by the probability of coverage or the number of covering analysts (H2a). For Cliff and Denis (2004), IPO underpricing would be a way of compensating analysts for their efforts to collect information. However, no study has evaluated to what extent the additional coverage related to initial returns enhanced liquidity by increasing interest in the stock and reducing private information (H2b and H2c). We claim that this might be another channel in which liquidity positively correlates to underpricing.

II. Institutional Settings, Sample, and Data

The above-mentioned hypotheses are tested on a sample of IPOs undertaken on Euronext Paris from 1995 to 2008 about which we hold exhaustive data.

A. Institutional Settings

During our sample period, Euronext Paris was restructured. Prior to 2005, it was organized into three regulated market segments: 1) the Premier Marche designed for the listing of large companies, 2) the Second Marche that catered to medium and small companies, and 3) the Nouveau Marche for growth companies. In 2005, Euronext Paris merged the Premier Marche and the Second Marche into a single segment. Eurolist, and the Nouveau Marche was closed and replaced by Alternext. Prior to 2005, most IPOs took place in either the Second Marche or in the Nouveau Marche. Since 2005, they have been distributed evenly between Eurolist and Alternext.

For any new listing, the specificity of Euronext Paris' primary market is to offer and handle a panel of initial offering mechanisms comprising a fixed price offering procedure, a book-building procedure called placement, and three auction mechanisms (direct admission, minimum price offer, and open price offer) in which Euronext is the auctioneer. Fixed price offer and open price offerings can be associated with a placement. In fact, most book-built issues are offered as a double-stage issue whereby in addition to the private book-building process, a separate mechanism offers shares to the public. The simplest and most common technique is to offer shares to the public at a fixed price that is equal to the equilibrium price set during the book-building process. An alternative method is to organize an auction in which individual investors can place limit orders. In this case, the issue price may differ for each category of subscribers, but under Euronext regulations, the issue price paid by institutions in the book-building process may not be lower than the definitive public offer price.

Euronext's secondary markets are all order-driven, yet some trading designs differ according to the market segment and the liquidity of the stock traded. For most liquid stocks, order book trading is continuous, with the trading session commencing and terminating with batch auctions. For less liquid securities, trading is only periodic, with one or two batch auctions a day. In addition, for some securities, liquidity providers (LPs) may act as market makers inside the order book. LPs are brokerage firms that signed a commercial agreement with Euronext to provide liquidity on an instrument. Their role is to: 1) protect against variations in volatility, 2) guarantee transactions at the best price at all times, and 3) support the volume of transactions in the order book. They are required to quote two-way bid and ask prices with a minimum volume size at all times during the trading session, 15 minutes before the opening session, and in batch auctions. In compensation, they are not required to pay trading fees on market-making services. LPs primarily concentrate on small and medium capitalization stocks. For these equities, the agreement with the exchange is often (and always for IPO stocks) combined with a liquidity contract whereby the issuer hires the corporate broker not only to improve the market quality of its stock, but also to provide corporate services, such as listing sponsorship and research.

Regarding the growth market segment, Alternext has a hybrid market structure similar to that of the Nouveau Marche prior to 2005. Two batch auctions are run per day. At the same time, market makers actively participate in the auction procedures and also supply liquidity on a continuous basis between auctions.

B. Sample and Data

This empirical study was conducted using data from four sources. First, we gathered the prospectuses available in the database of the Autorite des Marches Financiers (AMF) for IPOs undertaken on Euronext Paris from 1995 to 2008. (2) After excluding transfers and listings of foreign companies, we obtained a sample of 358 IPOs for which we retrieved post-IPO closing prices from Datastream, high-frequency data from the Euronext Database, and analyst coverage data from Institutional Brokers' Estimate System (I/B/E/S) detail history, over the six months following the IPO. Matching these databases left us with a sample of 326 IPOs. Using their prospectuses, we retrieved the IPO date, the subscription price, the number of shares on sale in the IPO, the number of shares issued in and outstanding after the IPO, the IPO allocation mechanism, the underwriters' identity, the involvement of venture capitalists, and the percentage of shares held by the managers, members of their families, and blockholders before and after the IPO.

Of the 326 IPOs constituting the final sample, 171 were undertaken in the Second Marche or Eurolist and 155 took place in the Nouveau Marche or Alternext. In terms of IPO mechanisms, 275 issues involved a book-building process, 42 were auctioned, and 9 were fixed-priced. Among the 275 book-built IPOs, 14 were exclusively book-built, 153 were associated with an auctioned public offer, and 108 were followed by a fixed-priced offering. All of the auctioned and fixed price IPOs in the sample were undertaken in the 1990s, whereas the IPOs conducted during or after 2000 all used a mixed mechanism that associated the book-building process with either an auction or a fixed price offer.

Among other descriptive statistics on variables further described in Section III, Table I presents general statistics on the characteristics of the issues of our sample. Sampled firms went public after 13 years of existence, on average. They were introduced at an average price close to 20 euros and raised 16.5 million euros per issue, on average, by offering to the public around one-fourth of their post-IPO share quantity. Substantia] discrepancies exist in postlisting market values.

III. Empirical Measures

This study is based on five categories of empirical measures: 1) IPO initial returns, 2) low-and high-frequency liquidity metrics, 3) information asymmetry measures based on intraday data for continuously traded stocks, 4) ownership dispersion indices, and 5) information production measures.

A. Initial Underpricing

Initial performance is the difference between the postlisting equilibrium price ([P.sup.*]) and the IPO price ([P.sub.0]), and it may be adjusted for a market index return. A key issue is the choice of [P.sup.*]. In efficient markets, the first-day closing price can be viewed as the trading price matching offer and demand after the IPO. This is why most studies, particularly those on US IPOs, measure IPO underpricing on the first day of trading, assuming that the postlisting equilibrium price is reached at the first close (Ritter and Welch, 2002; Goergen, Renneboog, and Khurshed, 2006). However, in many markets, particularly in Europe, prices in the first days of trading may be relatively volatile as the matching between offer and demand and the postlisting price discovery process takes more than one day to establish. This is the reason why initial returns are sometimes measured over a five-day horizon as in Brennan and Franks (1997) and Ljungqvist and Wilhelm (2002) for France, Germany, the United Kingdom, and the United States, or over a one-week horizon as in Goergen et al. (2006) for France and Germany.

According to the statistics on initial returns reported in Table I, the mean and median values of the initial returns increase substantially from 2.80% and 0.05% to 15.63% and 6.13%, respectively, over the first week of trading for our sample, while the difference between the initial returns at the one-week horizon and the initial returns at the one-month horizon remain small in comparison, particularly when considering the median values. This suggests that post-IPO prices take longer than one day to stabilize on Euronext. Thus, we follow Goergen et al. (2006) and base our measure of underpricing on the return between the IPO price and the closing price observed five business days (one week) after the IPO date, adjusted for the SBF 250 index return as follows: (3)

[IR.sub.1w] = [P.sup.*]/[P.sub.0] - [I.sub.1w]/[I.sub.0], (1)

where [P.sup.*] is the closing price on the fifth business day following the IPO date, [P.sub.0] is the IPO price, [I.sub.1w] is the closing value of the SBF 250 index on the fifth day following the IPO date, and [I.sub.0] is the closing value of the index on the day immediately preceding the IPO. An IPO is underpriced when [IR.sub.1w] > 0 and the measure of underpricing that we use in our empirical tests is [U.sub.1w] = max([IR.sub.1w]; 0).

B. Liquidity Measures

Liquidity is measured over the six-month period that begins five trading days after the IPO date. This five-day gap is meant to eliminate the effect of abnormal trading activity generally observed in the first days following a primary listing. Among the 326 IPOs in our sample, 154 were traded continuously during the six-month observation period. The remaining 172 stocks were traded in batch auctions only (one or two per day).

For the entire sample, market liquidity over the six months following the IPO is measured by the average daily turnover, the Amihud's (2002) illiquidity ratio, and the zero-return ratio of Lesmond, Ogden, and Trzcinka (1999). The average daily turnover, denoted as TURN, is the average daily volume in percentage of the number of shares sold in the IPO. The Amihud (2002) ratio, denoted as AMIH, is a price sensitivity proxy calculated as the average of the daily absolute return divided by the volume traded on the same day. Lesmond et al.'s (1999) measure (L_O_T) is the ratio of zero-return days to the total number of trading days in the observation period. The assumption behind this measure is that no informed trading occurs when trading costs are high enough to offset trading gains, which leads to zero daily returns. For the subsample of continuously traded stocks, we compute time-weighted average quoted spreads and average effective spreads. We also calculate the average realized spreads at the 30-minute horizon following Bessembinder and Kaufman (1997). (4) Statistics calculated for those liquidity measures indicate that average liquidity levels are those typically observed for middle capitalization stocks (cf. Table I).

C. Measures of Information Asymmetry

Measures of information asymmetry are derived over the same six-month observation period as that chosen to measure liquidity. The magnitude of information asymmetry is estimated with four methodologies: 1) the average 30-minute price impact of Bessembinder and Kaufman (1997) denoted by PIMP, 2) the adverse-selection spread component of Lin, Sanger, and Booth (1995) denoted by [[alpha].sub.lsb], 3) the probability of informed trading (PIN) of Easley et al. (1996), and the adjusted PIN of Duarte and Young (2009) denoted by AdjPIN.

D. Measures of Ownership Structure

Ownership structure data were extracted from IPO prospectuses. In order to estimate the ownership concentration, we identify all of the blockholders who possessed at least 5% of the firm's shares and compute their total holding in percentage (BLOCK). We also calculate the Herfindhal-Hirschmann index (HERF) by summing squared shareholdings of the five largest shareholders:

HERF = [5.summation over (i=1)] [s.sup.2.sub.i], (2)

where [s.sub.i] is the part that belongs to the ith largest shareholder (i = 1, ..., 5). Ownership statistics displayed in Table I indicate that blockholders still own a greater proportion of most firms' shares after the IPO. On average, after the IPO, around 67% of the shares are retained by shareholders who own more than 5% of the shares, more than 40% of the shares are retained by managers, and nearly 23% are still owned by their families. Those observations are consistent with previous studies (La Porta, Lopez-de-Silanes, and Shleifer, 1999; Faccio and Lang, 2002) who report that French firms' ownership is characterized by high concentrations and family ownership.

E. Measures of Information Production

We measure information production by considering the information produced by financial analysts over the first six months following the IPO date. I/B/E/S data were collected for this observation period to measure the intensity of analyst coverage by three metrics taken in logarithm: 1) the number of brokerage firms covering the IPO (#BR), 2) the number of analysts (#AN), and 3) the number of recommendations they issued (#REC). The average number of brokers and the average number of analysts per IPO both range between one and two, and the average number of recommendations they issue within the six months following the initial offering exceeds two, with large cross-sectional discrepancies (cf. Table I).

IV. The Positive Relation between Initial Underpricing and Secondary Market Liquidity

As a preliminary step, we measure to what extent secondary market liquidity and information asymmetry are positively correlated with initial underpricing. This preliminary step cannot be forgone as finding a positive correlation is prerequisite to our main empirical tests and because Ellul and Pagano (2006) confirm that there exists exceptions to this widely documented positive link. They demonstrate that initial underpricing can be an increasing function of aftermarket illiquidity due to asymmetric information after the IPO. They provide empirical evidence in support of their theory using a sample of IPOs in the United Kingdom.

We regress low- and high-frequency liquidity measures and information asymmetry measures on the level of underpricing in the following way:

Liquidity or InfoAsym = [a.sub.0] + [summation over (i)] [a.sub.i]C [V.sub.i] + a[U.sub.1w] + [??], (3)

where Liquidity is alternatively the average daily turnover (TURN), the Amihud (2002) illiquidity ratio (AMIH), the zero-return ratio (L_O_T), the average time-weighted quoted spread (QS), the average effective spread (ES), or the average realized spread calculated at the 30-minute horizon (RS); InfoAsym is alternatively the average price impact calculated at the 30-minute horizon (PIMP), the adverse selection cost component of the effective spread following Lin et al. (1995) ([[alpha].sub.lsb]), the probability of informed trading (PIN) estimated following Easley et al. (1996), and the adjusted PIN measure (AdjPIN) of Duarte and Young (2009); [{C [V.sub.i]}.sub.i] is a set of control variables; [U.sub.1w] is the initial adjusted return of the stock over the first week of trading when this return is positive and zero otherwise; and [??] is the error term.

For low-frequency liquidity measures, the set of control variables, [{C [V.sub.i]}.sub.i], comprises the volatility of the daily closing returns ([sigma]), firm size measured by the logarithm of the firm's market value at the IPO date (lnMV), price level measured by the average closing price during the liquidity observation period (lnP), ownership concentration measured by the post-IPO Herfindhal-Hirschmann index (HERF), a binary variable set to one if there was a lockup agreement during the liquidity observation period and zero otherwise (Lock-up), and a binary variable that is equal to one if the IPO firm signed a liquidity contract (LP). (5) We use the same control variables in the regressions of spreads except for the firm's initial market value (lnMV) which is replaced by the average daily trading volume taken in logarithm (lnF). In the regressions of information asymmetry variables, we control for volatility, market value, and price level as in the spread regressions. We also expect the information asymmetry to be greater if: 1) the managers retained a greater shareholding after the listing, 2) the firm was introduced on a growth market, and 3) it belongs to the new technology sector. Thus, we add as controls the shareholding of the manager after the IPO (MAN), the market segment represented by dummy GM that is equal to one when the IPO was undertaken in the growth market (Nouveau Marche or Alternext) and zero otherwise, and the NTIC dummy set to one for new technologies IPO firms (and zero for others). (6)

Table II presents the results. In accordance with most of the previous studies, the estimations indicate that secondary market liquidity positively correlates with initial underpricing. The statistical significance of the underpricing variable coefficients reaches 1% in all regressions of the liquidity variables with the exception of realized spreads in which the significance is still 5%. This liquidity effect is supported by an information asymmetry reduction effect. Information asymmetry significantly decreases with initial underpricing at the 1% level when measured by price impact (PIMP), at the 10% level when measured by [[alpha].sub.lsb] and the PIN, and at the 5% level when measured by the adjusted PIN (AdjPIN). This suggests that liquidity gains due to underpricing could be supported by information effects.

Regarding the control variables, the traditional effects of volatility, size, trading volume, and price level on liquidity are found. We obtain clear evidence that liquidity decreases with ownership concentration. Liquidity is not clearly affected by the existence of a lockup agreement or a liquidity provision contract. Asymmetric information does not relate to either the managers' shareholdings or the new technologies dummy, but it is stronger for IPOs undertaken on the Nouveau Marche and its successor, Alternext, the French growth markets.

Additionally, we perform the same analysis by measuring underpricing over distinct horizons from the first day to the first week of trading. The estimates reported in Table III indicate that the impact of initial underpricing on liquidity and information asymmetry is not significant prior to the fifth trading day. As such, the underpricing measure used to investigate the formation of this impact should be based on prices observed at the end of the first five days or the first week of trading.

V. Main Test Design

In this section, we test both the ownership dispersion hypothesis and the information production hypothesis using a two-stage regression model. The first stage consists of estimating the impact of initial underpricing on ownership concentration and on information production by analysts. The second stage involves estimating how the values of ownership concentration and information production predicted in the first-stage influence secondary market liquidity. With regard to the information production hypothesis, the second stage also includes a test to determine how the first-stage predicted information variables correlate with the level of information asymmetry in the secondary market. This two-stage approach is designed to distinguish the impact of the underpricing-generated component from the impact of the exogenous component of the variable under consideration. This distinction is achieved by including both the predicted values and the residual terms estimated from first-stage regressions in the second-stage regressions. The comparison of the coefficients of the two variables allows us to determine to what extent the relation between liquidity and underpricing is formed through an ownership or information effect actually produced by underpricing.

A. Test of the Ownership Dispersion Hypothesis

In the first stage, we examine whether underpricing affects ownership structure by modeling our measures of ownership concentration (OwnConc representing HERF and BLOCK alternatively) as a function of the underpricing level:

OwnConc = [b.sub.0] + [b.sub.1] ln MV + [b.sub.2]SaleRatio + [b.sub.3]FAM + [b.sub.4]GM + [b.sub.5]VC + [b.sub.6]BB + [b.sub.7][U.sub.1w] + [[??].sub.OwnConc]. (4)

We control for market size (lnMV) and for the rate of newly issued shares (SaleRatio), calculated as the number of new shares divided by the total number of shares outstanding after the IPO, as large IPO firms and IPOs with a greater proportion of newly issued shares are expected to have more dispersed ownership. We also control for the percentage of shares retained by the manager's family after the IPO (FAM) and the segment of listing (binary GM), as family owned companies and growth firms usually have more concentrated shareholding structures. Finally, book-built IPOs, as well as venture-backed IPOs, may have different ownership structures than others. Therefore, we include two dummies to control for those effects: 1) VC that is equal to one for venture-backed IPOs (and zero for others) and 2) BB that is equal to one for book-built IPOs (and zero for others).

In the second stage, liquidity measures are regressed on the ownership concentration measures as predicted in first-stage regressions (2) and on their unpredicted component:


OwnConc is the first-stage predicted value of the concentration ownership index (HERF) or, alternatively, the blockholders' shareholdings (BLOCK). [[??].sub.OwnConc] is the first-stage residual values of those variables. The same control variables as in model (3), with the exception of the HERF concentration index, are used. Given that two regressors are estimated from a previous regression, p-values and adjusted [R.sup.2] are established by increasing the degree of freedom by two. Finding significantly positive values for [b.sub.7] and [c.sub.p] coefficients would be in support of the ownership dispersion hypothesis.

B. Test of the Information Production Hypothesis

The test of the information production hypothesis is designed in the same way as that of the ownership dispersion hypothesis. In the first stage, we regress measures of information production on the underpricing measure [U.sub.1w]:

InfoProd = [d.sub.0] + [d.sub.1]SaleRatio + [d.sub.2]HM + [d.sub.3]GM + [d.sub.4]REP + [d.sub.5][U.sub.1w] + [[??].sub.InfoProd]. (6)

The measures of information production, generically denoted as InfoProd, are composed of the number of brokers covering the IPO firm (#BR), the number of analysts (#AN), and the number of recommendations issued over the postlisting six-month period (#REC). These three variables are primarily determined by the size of the firm, but controlling for lnMV in Regression (6) would be a source of collinearity in the second-stage regression. For this reason, we choose to regress the previously mentioned measures of information production, taken in logarithm, on lnMV. We then use the residuals of these preliminary regressions as the dependent variables in Regression (6). IPOs with a higher sale ratio usually receive more coverage, while growth IPOs receive less. IPO cyclicality is likely to impact the level of coverage. The reputation of the lead underwriter may also positively determine the probability of coverage, as demonstrated by Cliff and Denis (2004). This leads us to control for the sale ratio (SaleRatio), the listing market segment (GM), the logarithm of the number of IPOs undertaken during the three months preceding the IPO (HM), and the lead underwriter's reputation (REP) measured as its market share, following the approach of Megginson and Weiss (1991). This market share is calculated as the amount of funds in euros raised in the IPOs underwritten by the considered investment bank as a percentage of the total euro amount brought to market for all IPOs in the sample. If an IPO has more than one lead underwriter, the average of the underwriters' market shares is used as the measure of reputation. REP is taken in logarithm. (7)

At the second-stage level, all liquidity and information asymmetry measures are regressed on the predicted and residual values of the first-stage regression as follows:


The control variable [CV.sub.i] is the same as those used in Regression (3). InfoProd is the first-stage predicted value of, alternatively, the number of brokers covering the firm (#BR), the number of analysts (#AN), and the number of recommendations issued on the stock (#REC). [[??].sub.InfoProd] denotes the first-stage residual of each of those three variables. The degree of freedom is increased by the number of estimated regressors to calculate p-values and adjusted [R.sup.2]. Significant positive values for coefficients [d.sub.5] and [e.sub.p] would support the information production hypothesis.

VI. Results

The methodology described in the previous section is meant to determine which of the ownership dispersion hypothesis or the information production hypothesis best explains liquidity benefits from underpricing. First, we report the findings for the test of the ownership dispersion hypothesis. We recall that this test consists of regressing ownership concentration on underpricing in the first stage and regressing liquidity measures on the first-stage predicted and unpredicted values of ownership concentration in the second stage. Table IV presents the estimations for the first-stage regressions of the shareholding structure variables on underpricing. The results indicate that ownership dispersion is unrelated to initial underpricing. As such, the liquidity effect cannot be viewed as formed through the mediation of the ownership structure, in contrast with the findings of Pham et al. (2003), based on a sample of Australian firms. We reject hypothesis Hla. (8) The rejection of hypothesis Hla makes testing hypothesis Hlb unnecessary. Thus, the second-stage regressions are not reported. Regarding the control variables in Regression (4), Table IV indicates that venture-backed IPOs, book-built IPOs, and IPOs with more shares sold during the offering have a more dispersed ownership after the IPO. Conversely, family ownership and firm size favor ownership concentration after the IPO.

In addition, we report the findings for the test of the information production hypothesis. This hypothesis is tested by regressing the measures of information production on underpricing in the first-stage and regressing liquidity measures on the first-stage predicted and unpredicted values of information production in the second stage. The estimates obtained from regressing the information production measures on IPO underpricing are provided in Table V. They indicate that underpricing stimulates analyst coverage very significantly. Thus, we fail to reject hypothesis H2a for public analyst-produced information. We also note that analyst coverage increases with the percentage of shares sold in the IPO. The results for binary variable GM indicate that financial analysts pay less attention to IPOs conducted on growth markets. The results in Table V also reveal that information production does not intensify during hot market periods.

In the second stage, liquidity positively correlates with the analyst coverage intensity predicted by underpricing (cf. Table VI). This finding holds at the 1% significance threshold according to all liquidity metrics except the Amihud (2002) ratio for which #REC is significant at the 5% level, #AN is significant at the 10% level, and #BR fails to be significant. In contrast, the component of analyst coverage that is unrelated to underpricing (first-stage residual terms) does not significantly affect most liquidity measures. It only impacts turnover and zero-return ratios, but with a much lower economic significance than the predicted component does. This provides support for hypothesis H2b. With respect to the control variables, the traditional effects of volatility, size, and price level on liquidity are found. (9) IPO stocks with a lockup period appear to be more liquid and those with a liquidity provision contract are not.

Finally, all information asymmetry measures are significantly reduced by the first-stage predicted analyst coverage (cf. Table VI), while none of them are significantly reduced by the exogenous component of analyst coverage. This provides evidence in support of hypothesis H2c. The statistical significance of underpricing-generated coverage is greater when information asymmetry is measured by price impact metrics (1 % level of significance for PIMP and [[alpha].sub.lsb]) than when it is measured by the probabilities of informed trading (10% or 5% level of significance for PIN and AdjPIN). Marginally, the coefficients of the control variables, not reported for sake of brevity, indicate that the presence of informed traders is greater in growth markets. (10) Asymmetric information costs increase with volatility, but the proportion of informed traders in the market does not. Information asymmetry is inversely linked to firm size if we consider asymmetric information costs, but the probability of informed trading is positively correlated with firm size.

In summary, our findings do not support the ownership dispersion hypothesis (rejection of H1a and H1b), but strongly support the information production hypothesis (no rejection of H2a, H2b, and H2c). When comparing the impact of our information production measures, the number of analyst recommendations issued after the IPO plays the most significant role. This is consistent with the idea that the component of analyst coverage that is relevant for improving liquidity is the component that conveys firm-level information rather than industry- or market-level information. Crawford, Roulstone, and So (2012) demonstrate that this type of information is more likely produced by analysts initiating coverage on firms with existing coverage, while pioneer analysts (i.e., analysts initiating coverage on firms with no prior analyst) produce industry-or market-wide information. Linking our results with those of Crawford et al. (2012) suggests that initial underpricing will generate more liquidity benefits if it succeeds in attracting more than a first-round analyst and if a substantial number of recommendations are issued.

VII. Robustness Checks

This section is dedicated to determining whether our results are biased by selectivity or whether they may be driven by specific groups of IPOs or outliers.

A. Selection Bias

It could be argued that our results suffer from a selection bias resulting from underpricing caused by factors that determine the liquidity of the stock after the IPO. For example, IPOs with greater sale ratios may be underpriced more to attract larger demand. They may also appear to be more liquid, not because they were initially underpriced, but due to their larger market size. This would result in a nonrandom sample of underpriced IPOs in the sense that observations of overpriced IPOs with large-sale ratios would be none or scarce. This nonrandom sample selection would then likely bias our estimations.

In order to correct this potential bias, we use a Heckman (1979) correction by adding a preliminary stage in which the probability of underpricing is estimated in a probit model. In this preliminary stage, the probability for an issue to be underpriced, denoted as P([U.sub.1W] > 0), is modeled as a function of the pre-IPO managers' holdings (MAN_preIPO), a binary equal to one for VC-backed IPOs (VC), the percentage of shares sold in the IPO (SaleRatio), the IPO cycle measured by the number of offerings in the preceding three months (HM), underwriter quality measured by IPO market share (REP), and the LP binary variable that is equal to one for IPOs with a LP contract. (11) Pre-IPO managers' holdings are used as a proxy for pre-IPO adverse selection. Regarding the impact of venture capitalists (VC) on IPO underpricing, there are two opposing theories. The certification hypothesis states that venture capitalists help certify the true value of the firm (Barry et al., 1990; Megginson and Weiss, 1991). This certification effect induces a reduction in underpricing when the firm goes public. In contrast, Gompers (1993) develops a theoretical model in which young venture capitalists are ready to support additional costs in order to introduce firms. As such, VC-backed IPOs should be more underpriced. This is supported by the findings of Gompers (1996) and Lee and Wahal (2004). The sales ratio has been used in previous studies to explain underpricing (Aggarwal, Krigman, and Womack, 2002) as it is an indicator of the offer size relative to the size of the firm. We expect a greater sales ratio to be associated with greater underpricing. Carter and Manaster (1990) find underwriter reputation to be negatively correlated with underpricing for US IPOs in the 1970s and 1980s, but this correlation becomes positive in the 1990s according to Beatty and Welch (1996). The HM variable is included in the model as there is empirical evidence in Europe that underpricing is greater during hot market periods (Gajewski and Gresse, 2006). Finally, the existence of a liquidity contract (LP) is controlled for as underpricing of the issue may be in the interest of the LP.

Our results, not reported here for the sake of brevity, support the certification hypothesis that predicts a reduction in underpricing when the IPO is venture-backed. In addition, higher underpricing is observed when the firm has signed a liquidity contract. All of the other variables have no significant influence on initial underpricing.

The inverse Mills ratios produced by the probit estimation are then included as an additional regressor controlling for selection bias in the regressions of liquidity and information asymmetry on underpricing (Regression (3)) and in the regressions of ownership concentration and analyst coverage on underpricing (Regressions (4) and (6)). The coefficients estimated in those regressions for the underpricing measure and the Mills ratios and their associated p-values are displayed in Table VII. They confirm that for most dependent variables, the Mills ratio lacks significance indicating an absence of selectivity bias. The exceptions are turnover, the PIN measure, and the measures of ownership concentration. However, the Heckman (1979) correction does not alter our conclusions regarding the impact of underpricing on those variables. In a last stage, the liquidity and information asymmetry metrics are regressed on predicted and unpredicted analyst coverage as estimated in Regressions (4) and (6). The results are very similar to those of Section VI and our conclusions remain unchanged.

B. Underpricing Measuring

We address two issues related to the way we measure underpricing. First, we determine whether our findings are created by a dichotomy between underpriced IPOs and others, or whether the level of underpricing is important. This question is answered by running double-stage regressions on the subsample of underpriced IPOs (231 firms of which 114 have continuously traded shares). We derive the same conclusions as that of the entire sample, with the statistical significance of the impact of underpricing-predicted coverage on the probability of informed trading increasing to 5% for both the PIN and the AdjPIN measures. This leads us to consider that not only the occurrence of underpricing, but also the magnitude of the initial returns influences secondary market liquidity through additional information production.

The second issue we address is whether the truncation of our underpricing measure introduces a bias by smoothing the impact of less successful IPOs. Our measure of underpricing is set to zero when the initial return is negative as the theories that we test in the paper are supportive of an impact of positive initial returns on liquidity, but tell us nothing about the impact of negative initial returns. However, for a robustness check, we also ran the tests using the initial return instead of the truncated measure. The findings are alike in all dimensions and our conclusions are unchanged. (12)

C. Book-Building

It is often argued that the discretionary power of underwriters characterizing the share allocation in book-built offerings is used to shape ownership structure, either by allocating to buy-and-hold investors (Cornelli and Goldreich, 2001; Jenkinson and Jones, 2004) or by favoring small or large shareholders (Brennan and Franks, 1997; Stoughton and Zechner, 1998). Further, Degeorge, Derrien, and Womack (2007) find that underwriters use discretion to allocate additional shares to banks that will provide positive analyst coverage. These practices may strengthen or weaken the relations between underpricing, ownership concentration, analyst coverage, and liquidity for book-built IPOs. Thus, we repeat the tests over the subsample of book-built offers comprised 247 IPO stocks, 147 of which are continuously traded. All of our findings are similar with the following exceptions: 1) the statistical significance of the negative correlation between underpricing and the Herfindhal-Hirschmann concentration index of postlisting ownership increases to 10%, and 2) the impact of the coverage generated by underpricing on information asymmetry, while still significant for the LSB measure and price impact, is not significant for informed trading probabilities.

D. Primary Offerings

We consider the subgroup of IPOs for which there is a primary offer, as ownership and coverage issues may be more relevant for IPOs issuing new shares. This subgroup contains 261 stocks, 135 of which are continuously traded. The findings are exactly the same as those for the book-built subsample.

E. Underwriter Reputation

Underwriter quality may change the triple relation linking underpricing, information production, and liquidity, as high-quality underwriters are more inclined to cover their IPOs (Cliff and Denis, 2004), while IPO stocks underwritten by more reputed banks have been found to be more liquid (Popescu and Xu, 2011) independent of their initial returns. To test this hypothesis, we reduced the sample to IPOs underwritten by more reputed banks, defined as those having a greater market share (REP) than the median market share. This criterion produced a group of 247 IPOs (130 of which were continuously traded), from which we drew the same conclusions as those drawn from the complete sample. A noted difference was that the impact of underpricing-induced coverage on informed trading probabilities is slightly more significant.

F. Times of Extreme Illiquidity

Stock markets experienced a strong liquidity dry up at the end of our observation period when the subprime crisis occurred. To determine whether our findings are driven by outliers in the illiquid period, we shortened the observation period and dropped the IPOs undertaken after June 30, 2007, from the sample. Our conclusions remain unchanged although, due to sample size reduction, we lose the statistical significance of the coverage variable coefficients for the PIN and AdjPIN measures in the regressions of Table VI.

G. Stabilization Contracts

One last concern is whether price stabilization activity by the underwriter impairs our findings. As coined by Ellul and Pagano (2006), stabilization can artificially enhance liquidity by reducing price volatility, thereby generating a spurious relation between underpricing and liquidity. Our concern regarding this matter is rather limited as contrary to the practice in the United States, underwriter stabilization is relatively infrequent and recent in the French IPO market. No stabilization contracts were ever used prior to 2005 and 54 firms of the 120 introduced from 2005 to 2008 had a stabilization agreement for a period of one month following the IPO. However, we conduct a robustness check by including in the regressions of liquidity and adverse selection measures as a dummy equal to one when the firm has contracted a stabilization agreement and zero otherwise. We find that stabilized IPOs have greater turnover, greater zero-return ratios, and smaller spreads, but that stabilization contracts do not significantly affect either Amihud (2002) ratios or information asymmetry. Therefore, we reestimate our two-stage model with a stabilization dummy in the regression of liquidity metrics only. Although the level of significance of predicted coverage intensity measures decline from 1% to 5% for most liquidity variables in the second stage, our conclusions are unchanged. We also examined whether underpricing occurrence depended upon stabilization, as demonstrated by Ruud (1993) with US data, by including the stabilization dummy in the probit regression of the Heckman (1979) procedure presented in subsection VILA. No significant impact is found.

VIII. Conclusion

Using a sample of Euronext IPOs issued from 1995 to 2008, we find that more underpriced IPO stocks are more intensively traded and have lower liquidity costs in the secondary market. Further, adverse selection costs and informed trading are lower for more underpriced IPO stocks, suggesting that more public information is produced for these stocks. In contrast to Booth and Chua (1996)'s theory and Pham et al. (2003)'s empirical evidence, we fail to prove that these effects result from a more diffuse ownership obtained by underpricing the issue. Instead, we find that they are the result of the information production modeled by Chemmanur (1993) or of analyst coverage attracted by the better performance of underpriced IPO stocks in the immediate aftermarket. The increased analyst coverage generated about a firm by underpricing its IPO contributes to reducing informed trading and adverse selection costs. While the impact on adverse selection costs is very significant and robust, the impact on the proportion of informed traders has a weaker statistical significance and is not robust to all sample distortions. Among analyst coverage variables, the most significant is the number of recommendations issued after the IPO.

We conjecture that the difference between our findings based on French IPOs and those of Pham et al. (2003) based on Australian IPOs relates to the legal and institutional differences of comparing common law and civil law countries. According to La Porta et al. (1998), in common law countries (e.g., Australia), investor protection is relatively high and firms are widely held. In contrast, in French civil law countries, investor protection is weaker and ownership is substantially concentrated, with family and state shareholdings occurring frequently. As an illustration, while Pham et al. (2003) report 45% of shareholdings owned by blockholders after issue in their IPO sample, we find a percentage of 67%. We suspect that in economies with structurally concentrated shareholdings, the post-IPO ownership dispersion remains relatively insensitive to the initial underpricing. As such, the triple relation between underpricing, ownership dispersion, and post-IPO liquidity cannot form. Further, while enlarging the shareholder basis may be greater motivation for going public in common law countries, it may not be central in civil law countries where pre-IPO shareholders do not necessarily seek to exit in the IPO. For example, Pagano, Panetta, and Zingales (1998) find that Italian firms go public to rebalance their accounts and not to finance future investment projects. These differences in ownership structure and IPO motivation may explain why in some cases, the liquidity benefit from underpricing is an outcome of shareholding broadness, while in others, the double-stage relation linking underpricing, analyst following, and liquidity diminishes the impact of ownership dispersion. This interpretation based on contrasting common law and civil law countries would, however, deserve to be tested with international ownership and IPO data.

Our results emphasize the importance of analyst activity around IPOs not only for aftermarket pricing, but also for the liquidity of the secondary market in the months following an IPO. IPOs gain in liquidity from analyst coverage invited by underpricing. Whether this liquidity effect produced by underpricing purchased analyst coverage persists in the long run is still an open question. Another question of interest for future research is whether this liquidity effect impacts long-term performance and the market conditions of future seasoned equity offerings.


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Nesrine Bouzoulta, Jean-Francois Gajewski, and Carole Gresse *

* Nesrine Bouzouita is an Associate Professor at the Paris School of Business, Paris, France. Jean-Francois Gajewski is a Professor at the Universite Savoie Mont Blanc, IREGE, IAE Savoie Mont-Blanc, Annecy, France. Carole Gresse is a Professor at the Universite Paris-Dauphine, DRM, Paris, France.

We gratefully acknowledge financial support from ECMI. We are thankful to Vikas Agarwal, Ali Akyol, Noel Amenc, Olivier Brandouy, Hsin-Hui Chapman, Mark Chen, Serge Darolles, Carolina Da Silva, Michel Dubois, Sonia Falconieri, Gerald Gay, Edith Ginglinger, Dimitrios Gounopoulos, Steven Jones, Jayant Kale, Petko Kalev, Omesh Kini, Bill Megginson, Helene Rainelli, Ronnie Sadka, Alain Schatt, Fabrice Riva, Chip Ryan, Kent Womack, as well as to the participants at the AFBC in Sydney, FMA meetings, the AMF scientific board seminar, and seminars at the Cass Business School. Georgia State University, and the universities of Grenoble, Neuchatel, Paris-Est Creteil, Paris-Dauphine, and Paris-Pantheon Sorbonne for insightful comments. We also thank an anonymous referee, an associate editor, and Marc Lipson (Editor) for their valuable input.

(1) Popescu and Xu (2011) find that some characteristics of the underwriting syndicate, as well as the number of analyst recommendations are associated with lower spreads and lower information asymmetry after the primary listing.

(2) AMF is the French financial markets regulator.

(3) The SBF 250 is a stock index of Euronext that is comprised of the 250 largest capitalization stocks of Euronext Paris. It includes large, mid, and small caps and it is the most representative index of the French stock market.

(4) Bessembinder and Kaufman (1997) decompose the effective spread between the realized spread, which can be interpreted as the spread actually earned by the liquidity provider in a trade, and the price impact of that trade on the midquote interpreted as an indicator of information asymmetry.

(5) Findings by Cao, Field, and Hanka (2004) suggest that lockup agreements restrict liquidity. They find substantial improvements in trading volumes and depth at lockup expirations although well-informed traders enter the market.

(6) Firms introduced on growth markets usually exhibit a higher degree of information asymmetry as they are generally younger firms with a higher potential for innovation or growth. Young firms lack recognition from the market and have a shorter history of financial statements. For firms with a high potential for innovation or growth, the information asymmetry between insiders and outsiders is greater by nature.

(7) We also estimate the market share of an underwriter by the percentage of offerings it underwrote in the sample. The results remain unchanged.

(8) As no significant relation is found between ownership concentration and underpricing, we cannot support the opposite theory of Stoughton and Zechner (1998), who suggest that IPO firms underprice their stocks at issuance to create a more concentrated ownership structure.

(9) Estimates for the control variables are not reported for the sake of brevity, but are available on request.

(10) Unreported estimates are available upon request.

(11) We also tried to include the age of the firm, the debt leverage at the time of the IPO, dummy GM that is equal to one for IPOs undertaken in the growth market (Nouveau Marche or Alternext), and the NTIC dummy set to one for new-technologies IPO firms in the probit model. Age and debt leverage were tested as risk proxies. Other authors using them as potential determinants of underpricing include Chan, Wang, and Wei (2004), Hill (2006), and Loughran and Ritter (2004) for age and Pham et al. (2003) for debt leverage. A high-tech sector variable is also used in regressions of underpricing by Aggarwal et al. (2002), Loughran and Ritter (2004), and Pham et al. (2003). Growth market segment is controlled for in underpricing regressions by Chan et al. (2004) and Hill (2006). We abandoned those variables as they were deemed to have no statistical significance and they decreased the quality of the model according to the AIC criterion.

(12) Those estimates, not provided for the sake of brevity, are available on request.
Table I. Descriptive Statistics on Sample Firms

AGE is the age of the firm in number of years at the time of the IPO.
[P.sub.0] is the IPO price. IPO_size is the issue size and is equal
to the number of shares on sale times the IPO price. MV is the market
value of the firm in million euros. Sharesonsale is the number of
shares sold in the IPO. SaleRatio is the ratio of shares sold in the
IPO divided by the number of outstanding shares following the IPO.
[IR.sub.1], [IR.sub.1w], and [IR.sub.1m] are adjusted returns
observed over the first day, the first week, and the first month,
respectively. All trading measures are estimated over the six months
following the IPO date. V is the average daily trading volume in
[euro] over this postlisting period. TURN is the average daily
turnover, which is the average daily volume in percentage of the
number of shares sold in the IPO. [sigma] is the closing return
volatility. QS is the time-weighted average quoted and ES is the
average effective spreads. BLOCK is the percentage of shares
controlled by blockholders. MAN is the percentage of shares retained
by the managers. FAM is the percentage of shares controlled by the
family. HERF is the Herfindhal-Hirschmann index of ownership
concentration. #AN, #BR, and #REC are the number of analysts,
brokers, and recommendations during the postlisting period,

Variable             # Obs.      Mean        Median        Min

AGE                   326       13.33         9.00         0.25
[P.sub.0]             326       20.01        17.50         2.00
IPO_size              326     16,473,742   16,224,447   13,841,845
MV                    326      444.61        50.73         3.81
Sharesonsale          326     3,654,526     729,356       83,482
SaleRatio             326       25.50%       24.13%        3.60%
[IR.sub.1]            326        2.80%        0.05%      -27.61%
[IR.sub.1w]           326       15.63%        6.13%      -28.77%
[IR.sub.1m]           326       20.06%        5.22%      -32.35%
V                     326      817,243       62,824         38
TURN                  326        0.135%       0.100%       0.001%
[sigma]               326        3.164%       2.724%       0.764%
QS                    154        1.685%       1.354%       0.077%
ES                    154        1.361%       1.124%       0.076%
BLOCK (before IPO)    326       89.48%       92.92%       37.00%
BLOCK (after IPO)     326       67.16%       68.53%        0.00%
HERF (before IPO)     326        0.438        0.373        0.001
HERF (after IPO)      326        0.270        0.230        0.002
MAN (before IPO)      326       50.75%       52.36%        0.00%
MAN (after IPO)       326       40.51%       42.82%        0.00%
FAM (before IPO)      326       28.60%        0.00%        0.00%
FAM (after IPO)       326       22.86%        0.00%        0.00%
#AN                   326        1.79          1            0
#BR                   326        1.78          1            0
#REC                  326        2.31          1            0

Variable                 Max       Standard Deviation

AGE                   139.00             14.75
[P.sub.0]             113.00             12.22
IPO_size             22,547,763        1,337,213
MV                    58,037.37         3,493.40
Sharesonsale         187,869,028       15,632,285
SaleRatio             145.25%            13.66%
[IR.sub.1]            132.72%            13.25%
[IR.sub.1w]           260.33%            31.40%
[IR.sub.1m]           438.09%            50.70%
V                    104,282,174       6,213,134
TURN                    1.402%            0.131%
[sigma]                11.073%            1.645%
QS                      7.324%            1.168%
ES                      6.716%            0.907%
BLOCK (before IPO)    100.00%            11.71%
BLOCK (after IPO)      98.63%            14.04%
HERF (before IPO)       1.000             0.277
HERF (after IPO)        0.823             0.186
MAN (before IPO)      100.00%            36.16%
MAN (after IPO)        97.06%            29.79%
FAM (before IPO)      100.00%            37.89%
FAM (after IPO)        99.94%            30.92%
#AN                      23               3.31
#BR                      23               3.22
#REC                     39               4.79

Table II. Impact of Underpricing on Liquidity and Information

This table reports the estimates of regressions of liquidity and
information asymmetry measures on initial underpricing. Underpricing
is measured as the adjusted return ([U.sub.1w]) observed over the
first week of trading for underpriced issues and is set to zero for
others. Liquidity and information asymmetry measures are estimated
over a six-month period following the IPO date. Liquidity measures
comprise the average daily turnover (TURN), the Amihud illiquidity
ratio (AMIH), and the Lesmond et al. (1999)'s zero-return ratio
(L_O_T), the time-weighted average quoted spread (QS), the average
effective spread (ES), and the average realized spread (RS).
Information asymmetry metrics [[alpha]lsb], PIMP, PIN, and
AdjPINdenote the Lin et al. (1995)'s alpha coefficient, the average
30-minute price impact, the PIN measure, and the adjusted PIN
measure, respectively. Control variables comprise the closing return
volatility ([sigma]), the market value in logarithm (InMV), the
logarithm of the average trading volume in [euro] (InV), the average
postlisting closing price in logarithm (InP), the Herfindhal-
Hirschmann index of ownership concentration measured after the IPO
(HERF), a binary variable equal to one if there is a lockup period
(Lock-up), a binary variable equal to one if there is a liquidity
provider contract (LP), the managers' holdings after the IPO (MAN), a
binary variable equal to one for Nouveau Marche or Altemext issues
(GM), and a binary variable equal to one for new technologies firms
(NTIC). p-Values are reported in parentheses.

                     Measures of Liquidity

                 TURN         AMIH        L_0_T

Intercept      0.106        7.067 ***   85.672 ***
              (0.284)      (0.000)      (0.000)
[sigma]        0.008 *      0.203 **    -2.681 ***
              (0.063)      (0.012)      (0.000)
InMV          -0.005       -0.286 ***   -2.589 ***
              (0.320)      (0.003)      (0.000)

InP            0.031 ***   -0.585 ***   -1.796 **
              (0.001)      (0.001)      (0.043)
HERF          -0.106 ***    1.126 *      3.031
              (0.003)      (0.074)      (0.357)
Lock-up        0.025 *     -0.258       -1.268
              (0.084)      (0.320)      (0.349)
LP            -0.008        0.012       -1.775
              (0.599)      (0.964)      (0.193)
[U.sub.1w]     0.001 ***   -0.010 **    -0.063 ***
              (0.000)      (0.030)      (0.009)
# Obs            326          326          326
Adj.            26.19%       13.06%       26.09%

                     Measures of Liquidity

                  QS           ES           RS

Intercept      5.815 ***    4.826 ***    1.650 ***
              (0.000)      (0.000)      (0.000)
[sigma]        0.427 ***    0.309 ***    0.113 ***
              (0.000)      (0.000)      (0.000)
InV           -0.367 ***   -0.312 ***   -0.106 ***
              (0.000)      (0.000)      (0.000)
InP           -0.177       -0.103       -0.034
              (0.135)      (0.262)      (0.403)
HERF           0.697 *      0.553 **     0.213 *
              (0.050)      (0.046)      (0.078)
Lock-up       -0.301 *     -0.174       -0.117 **
              (0.071)      (0.178)      (0.040)
LP            -0.228       -0.213 *     -0.079
              (0.142)      (0.079)      (0.137)
[U.sub.1w]    -0.008 ***   -0.006 ***   -0.002 **
              (0.002)      (0.006)      (0.023)
# Obs            154          154          153
Adj.            50.55%       50.22%       39.22%

                          Information Asymmetry Measures

                 PIMP      [[alpha].sub.lsb]      PIN        AdjPIN

Intercept      0.709 ***       1.447 ***       -0.216       -0.042
              (0.000)         (0.000)          (0.183)      (0.774)
[sigma]        0.030 ***       0.032 *         -0.008       -0.009
              (0.000)         (0.058)          (0.294)      (0.213)
InMV          -0.024 ***      -0.052 ***        0.025 ***    0.011
              (0.001)         (0.004)          (0.003)      (0.143)
InP           -0.044 ***      -0.080 **         0.007        0.028 *
              (0.002)         (0.028)          (0.666)      (0.069)

MAN            0.032          -0.035            0.051        0.029
              (0.277)         (0.642)          (0.136)      (0.344)
GM             0.035 *        -0.013            0.076 ***    0.044 *
              (0.098)         (0.813)          (0.003)      (0.050)
NTIC           0.008          -0.061           -0.004       -0.006
              (0.694)         (0.259)          (0.876)      (0.774)
[U.sub.1w]    -0.001 ***      -0.001 *         -0.001 *     -0.001 **
              (0.001)         (0.077)          (0.083)      (0.031)
# Obs            153              153             148          150
Adj.            40.54%          11.54%           9.24%        5.51%

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table III. Impact of Underpricing on Liquidity and Information
Asymmetry According to Initial Return Horizon

This table reports the estimates for the underpricing regressor
in first-stage regressions of liquidity and asymmetry information
measures with various measures of underpricing. [U.sub.xd] is the
adjusted return observed at the close of the xth trading day for
underpriced issues and is set to zero for others. [U.sub.1w] is the
same measure calculated after one week of trading. Liquidity and
information asymmetry measures are estimated over a six-month period
following the IPO date. TURN is the average daily turnover. AMIH is
the Amihud illiquidity ratio. L_O_T is the zero-return ratio of
Lesmond et al. (1999). QS is the time-weighted average quoted
spreads, ES is the average effective spreads, and RS is the average
realized. [[alpha].sub.lsb], PIMP, PIN, and AdjPIN denote the Lin et
al. (1995)'s alpha coefficient, the average 30-minute price impact,
the PIN measure, and the adjusted PIN measure, respectively. Control
variables are those of Table II. p-Values are reported in parentheses.

                    [U.sub.1d]   [U.sub.2d]   [U.sub.3d]

TURN                 0.001       -0.000        0.000
                    (0.358)      (0.706)      (0.989)
AMIH                -0.003       -0.008       -0.007
                    (0.766)      (0.355)      (0.218)
L_O_T               -0.111 **    -0.133 ***   -0.079 ***
                    (0.023)      (0.003)      (0.009)
QS                  -0.000        0.001       -0.001
                    (0.994)      (0.839)      (0.641)
ES                  -0.001       -0.002       -0.002
                    (0.821)      (0.734)      (0.532)
RS                  -0.001       -0.001       -0.000
                    (0.738)      (0.806)      (0.669)
[[alpha].sub.lsb]   -0.001       -0.001       -0.001
                    (0.651)      (0.589)      (0.320)
PIMP                 0.000       -0.000       -0.000
                    (0.756)      (0.745)      (0.315)
PIN                 -0.001       -0.001       -0.001
                    (0.442)      (0.136)      (0.093)
AdjPIN              -0.001       -0.001       -0.001
                    (0.309)      (0.262)      (0.124)

                    [U.sub.4d]   [U.sub.5d]   [U.sub.1w]

TURN                 0.000        0.001 ***    0.001 ***
                    (0.471)      (0.000)      (0.000)
AMIH                -0.008       -0.010 **    -0.010 **
                    (0.110)      (0.036)      (0.030)
L_O_T               -0.077 ***   -0.069 ***   -0.063 ***
                    (0.003)      (0.006)      (0.009)
QS                  -0.001       -0.006 **    -0.008 ***
                    (0.723)      (0.039)      (0.002)
ES                  -0.001       -0.004 **    -0.006 ***
                    (0.540)      (0.050)      (0.006)
RS                  -0.000       -0.001       -0.002 **
                    (0.685)      (0.133)      (0.023)
[[alpha].sub.lsb]   -0.001       -0.001       -0.001 *
                    (0.312)      (0.109)      (0.077)
PIMP                -0.000       -0.001 ***   -0.001 ***
                    (0.214)      (0.004)      (0.001)
PIN                 -0.001 *     -0.001 *     -0.001 *
                    (0.089)      (0.078)      (0.083)
AdjPIN              -0.001 *     -0.001 **    -0.001 **
                    (0.066)      (0.020)      (0.031)

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table IV. Impact of Initial Underpricing on Ownership Concentration

This table displays the results obtained from regressing ownership
concentration measures on underpricing. Dependent variables are the
Herfindhal-Hirschmann index of the ownership structure (HERF) and the
percentage of shares controlled by the blockholders (BLOCK) after the
IPO. IPO underpricing is measured as the adjusted return ([U.sub.1w])
observed over the first week of trading for underpriced issues and is
set to zero for others. Control variables include the market value in
logarithm (InMV), the ratio of shares sold in the IPO divided by the
number of outstanding shares following the IPO (SaleRatio), the
percentage of shares controlled by the family after the IPO (FAM), a
binary variable equal to one for Nouveau Marche or Alternext issues
(GM), a binary variable equal to one if the firm is VC-backed (VC),
and a binary variable equal to one for book-buildings (BB). p-Values
are reported in parentheses.

                    HERF        BLOCK

Intercept         0.107        0.725 ***
                 (0.431)      (0.000)
InMV              0.021 ***    0.008
                 (0.005)      (0.122)
SaleRatio        -0.288 ***   -0.442 ***
                 (0.000)      (0.000)
FAM               0.057 *      0.040 *
                 (0.061)      (0.058)
GM               -0.033       -0.026 *
                 (0.115)      (0.071)
VC               -0.135 ***   -0.068 ***
                 (0.000)      (0.000)
BB               -0.077 ***   -0.058 ***
                 (0.008)      (0.005)
[U.sub.1w]       -0.000        0.000
                 (0.255)      (0.872)
#Obs.               326          326
Adj. [R.sup.2]   31.80%       40.13%

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table V. Impact of Initial Underpricing on Public Information

This table displays the estimations of information production
measures on underpricing. #AN, #BR, and #REC are the number of
analysts, brokers, and recommendations during the postlisting six-
month period (in logarithm), respectively. IPO underpricing is
measured as the adjusted return ([U.sub.1w]) observed over the first
week of trading for underpriced issues and is set to zero for others.
Control variables include the ratio of shares sold in the IPO divided
by the number of outstanding shares following the IPO (SaleRatio), a
binary variable equal to one for Nouveau Marche or Alternext issues
(GM), the number of IPOs over the preceding three months (HM) in
logarithm, and REP is the market share of all lead underwriters for
an IPO (in logarithm). p-Values are reported in parentheses.

                    #REC         #BR          #AN

Intercept        -0.1742      -0.052       -0.112
                 (0.279)      (0.706)      (0.415)
SaleRatio         0.776 ***    0.625 ***    0.715 ***
                 (0.002)      (0.004)      (0.001)
GM               -0.165 **    -0.205 ***   -0.178 ***
                 (0.0211)     (0.001)      (0.004)
HM                0.016       -0.007       -0.005
                 (0.691)      (0.841)      (0.882)
REP               0.017        0.016        0.014
                 (0.456)      (0.430)      (0.487)
[U.sub.1w]        0.006 ***    0.005 ***    0.005 ***
                 (0.000)      (0.000)      (0.000)
# Obs.              326          326          326
Adj. [R.sup.2]    9.07%        9.38%       10.88%

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table VI. Impact of Underpricing-Generated Analyst Coverage on
Liquidity and Information Asymmetry

This table reports the results for the regressions of liquidity
measures (TURN, AMIH, L_O_T, QS, ES, and RS) and information
asymmetry measures ([[alpha].sub.lsb] PIMP, PIN, and AdjPIN) on the
predicted values of information production variables [??], [??], and
[??] and on their exogenous component [[??].sub.AN], [[??].sub.BR],
and [[??].sub.REC]. Regressions of low-frequency liquidity measures
control for volatility, market value, price level, post-IPO ownership
concentration, the existence of a lockup period, and the presence of
liquidity providers. Regressions of high-frequency liquidity control
for the same factors except market value, but include volume as a
control. Regressions of information asymmetry measures control for
volatility, market value, price level, post-IPO manager's holdings,
market segment, and NTIC sector, p-Values, reported in parentheses,
and adjusted [R.sup.2] are computed by increasing the degree of
freedom by two to account for the estimation of two regressors.

                         [??]        [[??].sub.AN]

TURN                   0.391 ***         0.032 ***
                      (0.000)           (0.003)
                    Adj. [R.sup.2]      44.28%
AMIH                  -1.189 *           0.224
                      (0.078)           (0.314)
                    Adj. [R.sup.2]      11.91%
LOT                  -11.308 ***        -2.684 **
                      (0.001)           (0.020)
                    Adj. [R.sup.2]      26.92%
QS                    -1.153 ***         0.034
                      (0.003)           (0.775)
                    Adj. [R.sup.2]      48.97%
ES                    -0.931 ***        -0.009
                      (0.002)           (0.921)
                    Adj. [R.sup.2]      49.46%
RS                    -0.373 ***        -0.001
                      (0.005)           (0.973)
                    Adj. [R.sup.2]      38.78%
[[alpha].sub.lsb]     -0.346 ***        -0.035
                      (0.005)           (0.324)
                    Adj. [R.sup.2]      12.63%
PIMP                  -0.181 ***        -0.023
                      (0.000)           (0.096)
                    Adj. [R.sup.2]      41.19%
PIN                   -0.106 *          -0.010
                      (0.067)           (0.545)
                    Adj. [R.sup.2]       7.07%
AdjPIN                -0.085            -0.013
                      (0.101)           (0.374)
                    Adj. [R.sup.2]       1.95%

                         [??]        [[??].sub.BR]

TURN                   0.403 ***         0.026 **
                      (0.000)           (0.016)
                    Adj. [R.sup.2]      41.78%
AMIH                  -1.128             0.195
                      (0.121)           (0.377)
                    Adj. [R.sup.2]      11.63%
LOT                  -11.685 ***        -2.824 **
                      (0.002)           (0.014)
                    Adj. [R.sup.2]      26.86%
QS                    -1.165 ***         0.038
                      (0.007)           (0.752)
                    Adj. [R.sup.2]      48.52%
ES                    -0.969 ***        -0.007
                      (0.004)           (0.940)
                    Adj. [R.sup.2]      49.13%
RS                    -0.389 ***         0.000
                      (0.007)           (0.986)
                    Adj. [R.sup.2]      38.47%
[[alpha].sub.lsb]     -0.399 ***        -0.040
                      (0.004)           (0.249)
                    Adj. [R.sup.2]      12.99%
PIMP                  -0.205 ***        -0.024
                      (0.000)           (0.080)
                    Adj. [R.sup.2]      41.26%
PIN                   -0.121 *          -0.013
                      (0.061)           (0.428)
                    Adj. [R.sup.2]       7.28%
AdjPIN                -0.099 *          -0.013
                      (0.091)           (0.363)
                    Adj. [R.sup.2]       2.04%

                         [??]        [[??].sub.REC]   #obs.

TURN                   0.374 ***         0.036 ***
                      (0.000)           (0.000)
                    Adj. [R.sup.2]      46.33%         326
AMIH                  -1.285 **          0.115
                      (0.041)           (0.543)
                    Adj. [R.sup.2]      11.99%         326
LOT                  -11.337 ***        -2.817 ***
                      (0.001)           (0.004)
                    Adj. [R.sup.2]      27.88%         326
QS                    -1.121 ***        -0.024
                      (0.002)           (0.821)
                    Adj. [R.sup.2]      49.13%         154
ES                    -0.894 ***        -0.032
                      (0.002)           (0.695)
                    Adj. [R.sup.2]      49.67%         154
RS                    -0.361 ***        -0.016
                      (0.003)           (0.639)
                    Adj. [R.sup.2]      39.04%         153
[[alpha].sub.lsb]     -0.318 ***        -0.007
                      (0.005)           (0.825)
                    Adj. [R.sup.2]      12.10%         153
PIMP                  -0.167 ***        -0.018
                      (0.000)           (0.131)
                    Adj. [R.sup.2]      41.13%         153
PIN                   -0.104 **         -0.015
                      (0.048)           (0.298)
                    Adj. [R.sup.2]       7.89%         148
AdjPIN                -0.081 *          -0.010
                      (0.091)           (0.424)
                    Adj. [R.sup.2]       1.98%         150

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table VII. Regressions Conducted on Underpricing with a Heckman

This table reports the estimates for the underpricing variable and
the inverse Mills ratio in the regressions of low-frequency liquidity
measures (TURN, AMIH, and L_O_T), high-frequency measures (QS, ES,
and RS), information asymmetry measures ([[alpha].sub.lsb], PIMP,
PIN, and AdjPIN), shareholding concentration variables (HERF and
BLOCK), and analyst coverage variables (#AN, #BR, and #REC) on
underpricing, when con-ducting a Heckman correction by estimating a
probit model of the probability of underpricing at a previous stage.
In the first-stage probit regression, underpricing measured at the
close of the first week of trading ([U.sub.Iw]) is modeled as a
function of pre-IPO managers' holdings, a binary for VC-backed IPOs,
the sales ratio, the IPO cycle measured by the number of offerings in
the preceding three months, underwriter quality, and a dummy for IPOs
with a liquidity contract. p-Values are reported in parentheses.

Dependent                    Inverse       Adj.
Variable      [U.sub.1w]   Mills Ratio   [R.sup.2]   # Obs.

TURN           0.001 ***    0.236 ***     31.18%      326
              (0.000)      (0.000)
AMIH          -0.010 **    -1.052         12.91%      326
              (0.029)      (0.227)
L_O_T         -0.063 ***    0.351         25.62%      326
              (0.009)      (0.939)
QS            -0.008 ***   -0.264         49.95%      154
              (0.002)      (0.616)
ES            -0.005 ***   -0.319         49.74%      154
              (0.006)      (0.437)
RS            -0.002 **    -0.174         38.77%      153
              (0.025)      (0.334)
[a.sub.lsb]   -0.001 *     -0.068         10.42%      153
              (0.077)      (0.676)
PIMP          -0.001 ***   -0.025         39.78%      153
              (0.001)      (0.697)
PIN           -0.001 *     -0.132 *       10.00%      148
              (0.077)      (0.076)
AdjPIN        -0.001 **    -0.070          4.91%      150
              (0.030)      (0.292)
HERF          -0.000       -0.139 *       32.08%      326
              (0.214)      (0.070)
BLOCK          0.000       -0.137 **      40.96%      326
              (0.988)      (0.011)
#AN            0.005 ***   -0.015         10.33%      326
              (0.000)      (0.941)
#BR            0.005 ***   -0.031          8.82%      326
              (0.000)      (0.880)
#REC           0.006 ***   -0.134          8.59%      326
              (0.000)      (0.575)

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.
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Title Annotation:initial public offering
Author:Bouzouita, Nesrine; Gajewski, Jean-Francois; Gresse, Carole
Publication:Financial Management
Article Type:Report
Geographic Code:1USA
Date:Dec 18, 2015
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