IPO waves and the issuance process

Journal of Corporate Finance 25 (2014) 455–473

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Journal of Corporate Finance journal homepage: www.elsevier.com/locate/jcorpfin

IPO waves and the issuance process Kevin Boeh a, Craig Dunbar b,⁎ a b

School of Business, Pacific Lutheran University, United States Western University, Ivey School of Business, Canada

a r t i c l e

i n f o

Article history: Received 9 March 2013 Received in revised form 29 January 2014 Accepted 3 February 2014 Available online 7 February 2014 JEL classification: G24 G32 C2 Keywords: IPOs Waves Spillovers Withdrawals Filings

a b s t r a c t This study examines the impact of institutional features of the IPO market on patterns of IPO activity (waves). Decisions made by firms to enter the market by filing registration documents, adjusting terms while remaining in registration or exiting the market through issuance or withdrawal affect the “value in registration” of issuers seeking capital. We argue that these past decisions convey private information about issuers' collective view on the state of the IPO market (beyond what is indicated by other macroeconomic and market condition indicators), affecting current activity. In addition to considering the role of past activity on issuance decisions, we introduce additional variables to reflect observable IPO market conditions that affect IPO activity: the standard deviation of IPO initial returns; Venture Capital takedown; and the average age of IPOs in registration. Our new variables add substantial explanatory value to prior models of IPO activity. © 2014 Elsevier B.V. All rights reserved.

1. Introduction It is widely accepted that initial public offerings of equity (IPOs) occur in waves. Both the number of IPOs and proceeds raised vary substantially over time. While the existence of “hot” and “cold” IPO markets has been well documented (e.g., Ibbotson and Jaffe, 1975; Ritter, 1984), only recently have alternative theories to explain variation over time in IPO volume been developed and tested. Some theories rely on market inefficiencies in which firms choose to go public when their shares are overvalued by market participants (e.g., issuers are from new and not well understood industries; see Lerner, 1994; Lowry, 2003; Pagano et al., 1998; Rajan and Servaes, 1997; Ritter and Welch, 2002). Other theories have proposed more “rational” explanations for IPO waves by applying real options logic. In these models, waves arise due to time-variation of market-wide information asymmetries (see Choe et al., 1993; Lowry, 2003; Myers and Majluf, 1984), waves of technological shocks and capital needs (Lowry, 2003), and changes over time in market conditions (e.g., Pastor and Veronesi, 2005). Virtually all empirical studies of IPO waves examine the time series variation in the monthly or quarterly (and sometimes annually) number of IPOs or, in a few cases, the total capital raised (e.g., Doidge et al., 2011; Lowry, 2003; Schill, 2004). IPO activity is typically argued to be related to proxies for stock market conditions, investment opportunities, technological innovation (shocks), macroeconomic conditions, the cost of capital, and IPO market conditions. Most empirical models, however, ignore salient institutional features of the IPO market. Importantly, going public is at least a several month process, and so market

⁎ Corresponding author at: Western University, Ivey School of Business, 1255 Western Rd., Room 2309, London, Ontario N6G 0N1, Canada. Tel.: +1 519 661 3716; fax: +1 519 661 3485. E-mail address: [email protected] (C. Dunbar). 0929-1199/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jcorpfin.2014.02.001

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clearing is slow and issuers cannot always act quickly in response to market or economic forces. In this study we examine the extent to which time series variation in IPO activity (waves) is driven by these institutional features. We recognize that in order to go public, an issuer must first decide to enter the IPO market by filing registration documents with appropriate regulatory authorities such as the Securities and Exchange Commission (SEC) in the U.S. Companies that have already filed registration documents must decide (subject to regulatory constraints) whether to remain in registration or exit the IPO market by either raising capital and completing their offerings or formally withdrawing. Those waiting can amend their registration documents adjusting the amount or price at which they seek capital. All of these decisions affect the total capital in registration at any point in time. While some studies have empirically considered the role of past issuances on current issuance activity, our study expands this to consider all past issuance-related activities (filings, issuances, withdrawals and changes to the value of issues remaining in registration) on current activity. We develop a theoretical link between past issuance decisions and current activity relying on information spillover theory (Benveniste et al., 2002). Positive past filings, issuances, and changes to the value of issues remaining in registration occur due to positive (private) information arising in the IPO market while positive past withdrawals occur due to negative information. This positive and negative information should spill over, affecting current activity. We also recognize other theoretical linkages between past and current activity. Given fixed (short-run) underwriting capacity, banks should adjust activities in response to changes in the IPO pipeline. Past activity that has a positive (negative) impact on the pipeline should result in fewer (greater) filings and more negative (positive) adjustments to capital sought in IPOs, while issuances and withdrawals should increase (decrease) so that the pipeline returns to more normal levels. While this recognizes bank “plate clearing” motives, some theories (e.g., Khanna et al., 2008) argue that a growing pipeline of IPOs should attract more filings from marginal firms. Competition for capital arguments (e.g., Hsu et al., 2010) recognizes that the IPO pipeline may shrink following positive spikes in issuance activity given a limited appetite among investors for new issues. Following Rau and Stouraitis (2011) we use vector autoregressive models to estimate the linkage between current and past issuance activity. Overall we find evidence supportive of information spillover and plate clearing arguments. Lagged filings have a positive effect on current filings as well as changes to the value of issues remaining in registration. Lagged issuances have a positive effect on current issuance and lagged withdrawals have a positive effect on current withdrawals. All these relationships are consistent with information spillover but not plate clearing arguments. In contrast, lagged filings positively affect current withdrawals, and lagged changes to the value of issues remaining in registration has a positive effect on current withdrawals and negative effect on current changes to the value of issues remaining in registration. All these findings are consistent with plate clearing and not information spillover arguments. Taken together, this indicates that both theories play a role in explaining issuance activity.1 In addition to considering the linkage between past and current issuance activity, we introduce several new empirical measures to reflect IPO market conditions. The existing literature typically includes past average initial returns as the single IPO market condition variable to explain IPO volume. Complementing this, we consider the volatility of past initial returns. Lowry et al. (2011) find that IPO initial return standard deviation is a good measure of pricing efficiency. Rational issuers should avoid the IPO market when pricing efficiency is low. High IPO initial return standard deviation should lead to less filing, lower issuances, more withdrawals and negative changes to value in registration for IPOs remaining in registration. We also consider the impact of Venture Capitalists on issuance activity by introducing a measure of Venture Capital takedown; the money called from investors by Venture Capitalists to invest. This measure is included to capture the demand to pursue IPOs by private investors seeking liquidity. Higher Venture Capital takedown should lead to more filings, more issuances (given pressure from new filings), fewer withdrawals and positive changes to value in registration for IPOs remaining in registration. Finally we introduce a measure of the (value weighted) average “age” of IPOs in registration where age is the days since an IPO is first filed with regulatory authorities. We argue that IPO age can be thought of as a measure of IPO market risk or deal quality. As the age of the pipeline grows, risk or quality decreases, deterring further activity. We examine monthly issuance activity (the dollar value of filings, issuances, withdrawals and changes to capital sought in issues remaining in registration) for U.S. IPOs between July 1998 and December 2011. Controlling for factors previously considered in explaining IPO waves, we find that firm-level decisions to file, delay, change the amount of capital sought, or seek an outcome are significantly affected by our new measures that reflect IPO market conditions. Initial return standard deviation has a positive effect on withdrawals and a negative effect on filings and issuances. Venture Capital takedown has a positive effect on issuances and capital sought in issues remaining in registration and a negative effect on withdrawals. The age of the IPO pipeline has a negative effect on filings, issuances, and capital sought in issues remaining in registration and a positive effect on withdrawals. While the effects of other control variables on IPO activity variables are generally consistent with existing literature, our new IPO market condition variables add both statistically significant and economically important explanatory value. Model R2s increase from 0.06 to 0.42 when new variables are added to controls from the literature. Overall we believe that this study contributes to the literature on IPO waves by recognizing the impact of past issuance decisions on current activity and also introducing several new variables that more fully reflect the impact of IPO market

1 As discussed in more detail later in the paper, we recognize that our lagged issuance activity variables may simply be capturing “mechanical” effects. For there to be waves in IPO activity, lagged variables resulting in a growing (declining) pipeline should lead to more (fewer) IPO issuances and greater lagged issuances should drive activity that leads to a growing (declining) pipeline. The predictions from this mechanical persistence story are similar to the predictions from the information spillover theory. While the evidence is only partly consistent with these predictions, we identify additional tests in an attempt to disentangle information and mechanical effects. As discussed later in the paper, our additional tests are more supportive of the information spillover theory.

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conditions on issuance activity. Given the importance of these variables in explaining IPO waves, we suspect that they should also be important to explaining other empirical regularities in the IPO market including pricing, issuance costs, and post-IPO performance. The evidence discussed above is based on aggregate IPO activity. As noted in other papers (e.g., Lowry, 2003) industry dynamics are likely to play an important role in IPO issuance decisions. Therefore, we examine issuance decisions at the industry level to consider the impact of both industry and aggregate measures on issuance decisions. Overall the industry-level evidence regarding the impact of our new IPO market condition variables as well as lagged issuance activity variables on current issuance activities is consistent with our findings at the aggregate level. Consistent with intuition, industry factors play a more important role than aggregate factors. Finally this study complements existing literature by considering IPO activity around the 2008 credit crisis (cf., Doidge et al., 2011, a recent sample in the literature although its sample ends in 2007). Our study, therefore, adds by providing new insights into issuance decisions during the crisis. While issuances slowed during 2008, the IPO pipeline remained strong with most firms choosing to wait out what was perceived to be a temporary impediment to capital raising. The remainder of this paper is structured as follows: Section 2 discusses theories linking past and current issuance activity and provides theoretical motivation for our other new IPO market condition variables; in Section 3 we describe the data; Section 4 provides the results of empirical time series models of IPO issuance decisions; Section 5 discusses the industry level evidence; and Section 6 synthesizes the findings.

2. IPO market conditions and issuance decisions 2.1. Past issuance decisions In modeling IPO volume (the number of IPOs over a given period), Pastor and Veronesi (2005) include lagged IPO volume as an independent variable. They indicate that this is included, “to capture persistence in IPO volume that is unexplained due to any potential misspecification in the regression” (pp. 1741–1742). An alternative motivation can be developed building on the literature which examines the role of public and private information in IPO pricing (e.g., Edelen and Kadlec, 2005). Controlling for publicly observable factors, past IPO volume may capture private information acquired through primary market interactions between issuers, investment banks, and investors. Higher lagged IPO volume presumably occurs when positive private information arises and this good news should “spill over”, leading to higher current volume (see Benveniste et al., 2002, 2003, for theoretical development and empirical support for spillover theory). While past IPO volume should contain private information relevant to current IPO issuance activity, other past issuance decisions should also contain relevant and potentially complementary/independent private information. Lagged IPO filings and lagged changes to the value of filings remaining in registration should also be higher when private information regarding IPO firm prospects is positive. The private information contained in recent (lagged) filings and changes to filings in registration could be different from recent IPO volume and each other, however. Lagged IPO volume may be a noisy measure of IPO prospects as volume can be higher during less positive periods when banks simply are attempting to clear out an overly large pipeline of deals. Because issuers infrequently change filing terms (see Boeh and Dunbar, 2013; Bradley and Jordan, 2002), changes that do occur may be stronger signals of private information than new filings. We, therefore, believe that all lagged issuance activity variables should be included in models of current issuance activity to capture potentially independent information. While IPO issuance volume, filings, and changes to the value of issues remaining in registration should be higher when IPO prospects are more positive, lagged IPO withdrawals, in contrast, should be higher when private information regarding IPO prospects are less positive. Given the significant costs to withdrawing an IPO (see Dunbar and Foerster, 2008), the information revealed by increased withdrawals may be more significant than the information revealed by other activities that reduce the IPO pipeline (e.g., reductions to the value of IPO already in registration). Again, we believe that including past withdrawal activity along with other issuance measures can provide a richer picture regarding private information arising in the IPO market. To summarize, lagged filings, lagged IPOs, and lagged changes to the value of issues remaining in registration should reflect positive private information while lagged withdrawals should reflect negative information in the IPO market. Current filings should, therefore, be positively related to lagged filings, lagged issuances and lagged change to the value of issues remaining in registration, and negatively related to lagged withdrawals. Current IPO issuances and current changes to the value of issues remaining in registration should similarly be positively related to lagged filings, lagged issuances and lagged change to the value of issues remaining in registration and negatively related to lagged withdrawals. Finally, current withdrawals should be positively related to lagged withdrawals and negatively related to lagged filings, lagged issuances and lagged change to the value of issues remaining in registration. While inclusion of lagged issuance activity variables in current activity models can be motivated using information spillover theory, other theoretical motivations are possible. Khanna et al. (2008) develop a model where the supply of banking services is inelastic in the short run. As the pipeline of IPOs grows, there should be pressure on banks to “clear their plate” by either completing or withdrawing deals in process in order to bring the pipeline back to a more manageable level. Lagged issuance activity leading to a growing pipeline (lagged filings and lagged changes to the value of issues remaining in registration) should lead to fewer current filings and negative changes to the value of issues remaining in registration but more current issuances and withdrawals. Conversely, lagged issuance activity leading to a shrinking pipeline (lagged issuances and withdrawals) should lead to fewer current issuances and withdrawals and more current filings and positive changes to the value of issues remaining in registration.

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While these predictions recognize how banks should try to affect the supply of IPOs given a changing pipeline, Khanna, Noe and Sonti also note that a changing pipeline should affect the demand from issuers to go public. Positive shocks to the IPO pipeline make it more challenging for the relatively fixed supply of bankers to screen candidate firms, attracting marginal firms to the IPO market2. This “IPO quality” argument suggests that current filings should be positively related to lagged issuance activity which has led to a growing pipeline (lagged filings and lagged changes to the value of issues remaining in registration) and negatively related to lagged issuance activity which has led to a shrinking pipeline (lagged issuances and withdrawals). Finally competition for capital arguments can also be used to link past IPO issuances to current activity (see Hsu et al., 2010). If there is a limited demand for new issues by investors, lagged IPO issuances should be negatively related to current issuances. Current filings and changes to the value of issues remaining in registration should also be negatively related to lagged issuances as high past activity discourages new entrants to the IPO market. Finally, current withdrawals should be positively related to lagged IPO issuances given the lower current investor demand for IPOs. Table 1 summarizes predictions from alternative theories regarding the relationships among lagged and current IPO activity variables. While consideration of lagged issuance activity variables in models of current issuance activity can be motivated by information spillover, plate clearing, IPO quality and competition for capital arguments, we recognize that significant relationships could arise due to “mechanical” persistence in the time series. For there to be a “wave” of IPOs (as already documented in the literature), a spike to issuances should lead to a spike in the IPO pipeline (increases to new filings or increases to the value of registrations already in registration and decreases to withdrawals) and that spike to the IPO pipeline (increases to new filings or increases to the value of registrations already in registration and decreases to withdrawals) should in turn drive future IPOs. The predictions from this mechanical persistence story regarding the impact of lagged issuance activity variables on current issuance activity are the same as those arising from the information spillover theory. We, therefore, explore additional implications of these theories in an attempt to disentangle effects. The mechanical persistence theory argues that the impact of a change to the IPO pipeline on issuances should not depend on its source. A dollar withdrawn from the pipeline should have the same effect as a decrease by one dollar in the value of filings remaining in registration or a decrease by one dollar in new filings. For models of current issuance activity, mechanical persistence therefore predicts that the coefficient on lagged IPO filings should equal the coefficient on lagged changes to the value of issues in registration. The coefficient on lagged withdrawals should be equal in magnitude but opposite in sign. Similarly IPO issuances should have the same effect (in absolute value) on all other activities that impact the IPO pipeline. The coefficient on lagged IPO issuances in a model of IPO filings should equal the coefficient on lagged IPO issuance in a model of changes to the value of issues in registrations. The coefficient on lagged IPO issuances in a model of IPO withdrawals should be equal in magnitude but opposite in sign. Information spillover theory, in contrast, argues that past filings, issuances, withdrawals, and changes to the value of issuances in registration can (and should) convey different information. The coefficients on lagged filings, withdrawals and changes to the value of issues remaining in registration should, therefore, be different in absolute magnitude in a model of current issuance activity and the coefficient on lagged issuance activity variables should be different in absolute magnitude in models of filings, changes to the value of issues in registration and withdrawals. 2.2. IPO market conditions In addition to considering the impact of lagged IPO activity on current activity, we introduce a number of new IPO market condition variables to explain IPO waves that have not previously been considered in the literature. In this subsection we provide theoretical motivation for these new variables and identify related empirical predictions. Detailed variable definitions and descriptive statistics are provided in Appendix A. First, we include the standard deviation of initial returns for IPOs issued over the prior three months and the monthly change in this measure as independent variables in our models of issuance activity (alternative periods are considered but results are not materially impacted).3 While not included in previous empirical studies of IPO waves, Lowry et al. (2011) argue that the standard deviation of IPO initial returns captures pricing efficiency of the IPO market. Periods with high standard deviation of IPO initial returns are times when banks find it difficult to value issues, ex ante. Rational issuers should avoid periods in which the standard deviation of IPO initial returns is high or increasing. These standard deviations of IPO initial return measures should, therefore, have a negative effect on current filings, changes to the value of issues remaining in registration, and current issuances and should have a positive effect on current withdrawals. We also introduce new measures to reflect the impact of Venture Capital on issuance activity. Specifically we include measures of prior Venture Capital “takedown” and changes to takedown as independent variables in our models of issuance activity. Takedown is the money called from investors by Venture Capitalists based on prior agreements to invest in the Venture Capital fund. Venture Capitalists access this money in order to invest in new ventures or add capital to later stage companies. Monthly data on Venture Capital takedown are from Thomson Financial Securities Data's Venture Economics database. We measure Venture Capital takedown over the prior six months and also consider monthly changes in this variable.4 When takedown is high 2

Yung et al.'s (2008) time varying adverse selection model of the IPO market leads to a similar prediction. The measurement period for this variable (and many variables discussed later) is arguably arbitrary. For measures based on pricing of past IPOs we focus on three months to ensure that there are generally enough IPOs to allow measure measurement (all periods have at least 3 IPOs). In all cases, we consider an alternative measurement period and results are qualitatively unaffected. 4 We initially focus on a longer measurement window in order to reduce volatility caused by short term spikes in drawdown. Results using measurement windows from one to twelve months are qualitatively similar, although the statistical and economic significance of this variable drops when considering windows over one year. 3

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Table 1 Predictions from alternative theories. This table presents a summary of the signs for the predicted relationships between lagged activity variables (listed in table columns) and current IPO activity (listed in table rows) emerging from alternative theories. Lagged filings

Lagged IPO issuances

Lagged withdrawals

Lagged changes to the value of issues remaining in registration

Information spillover theory IPO filings IPO issuances IPO withdrawals Changes to the value of issues in registration

+ + − +

+ + − +

− − + −

+ + − +

Bank ‘plate clearing’ theory IPO filings IPO issuances IPO withdrawals Changes to the value of issues in registration

− + + −

+ − − +

+ − − +

− + + −

−

+

Competition for capital IPO filings IPO issuances IPO withdrawals Changes to the value of issues in registration IPO quality theory IPO filings

− − + − +

−

or increasing, more issuers should be attracted to the IPO market5. Our takedown measures should, therefore, have a positive effect on current filings, changes to the value of issues remaining in registration, and current issuances and should have a negative effect on current withdrawals. Finally we introduce measures to capture the “age” of IPOs in registration. Specifically we measure the value weighted average age (in days) of IPOs in registration at the end of the month preceding issuance decision month t (AGEt − 1), defined as, AGEt−1 ¼

X i¼1;I

hn .X o i x ti V i;t−1 V i¼1;I i;t−1

ð1Þ

where Vi,t − 1 is the value of each IPO (i) on file at the end of month t − 1, there are I IPOs on file, and ti is the number of days issue i has been in registration at the end of month t − 1. We also measure changes in this measure from month t − 2 to t − 1. Consideration of these times in registration variables can be motivated using different theories. One view is that the age of IPOs in registration can be thought of as a measure of primary market uncertainty. Registration periods can stretch for several reasons. First, regulatory review can be delayed due to an unusually large or complex pipeline. Second, issuers can file amendments (in response to regulatory demands or material business events in a dynamic market) resulting in delay. As well, issuers may encounter markets (investors) that demand more information revelation in order to reduce uncertainty — driving issuers to stretch the time on file. In all of these cases, longer registration periods should be associated with higher primary market uncertainty. Adverse selection theories (e.g., Beatty and Ritter, 1986; Booth and Chua, 1996; Myers and Majluf, 1984) predict that issuers will avoid raising capital when uncertainty increases, as this uncertainty should lead to greater issuance costs. Thus, higher uncertainty associated with an older pipeline should deter new filers and also have a negative effect on issuances as well as changes to the value of issues remaining in registration but should have a positive effect on IPO withdrawals. Real option theory (e.g., Busaba et al., 2009) instead argues that filing creates an option and that option is more valuable with greater uncertainty. In this case, higher uncertainty associated with an older pipeline should lead to more filings and positive changes to the value of issues remaining in registration but fewer issuance and withdrawals (as issuers choose to keep options alive). Consideration of these ages of IPOs in registration variables can also be motivated using bank capability theories discussed previously. An older pipeline is more likely to arise when banks find it more challenging to move issuers through the IPO process. Given inelastic (quasi-fixed in the short run) underwriting capacity, banks are less likely to file new issues or increase capital sought for issues in registration when the IPO pipeline age is high or growing. Banks are, however, more likely to complete the IPO process for existing deals either through issuance or withdrawal given a high or growing pipeline age. Finally, the average age of IPOs in registration could be a proxy for issue quality. Lower quality issues raise more red flags resulting in a longer average filing period6. As discussed previously, different theories predict that a decline in quality of firms in 5 Venture Capital takedown is not solely for new investments and instead those dollars are available for later stage funding, including firms currently on file with the SEC. Venture Capitalists presumably take down additional capital when positive investment opportunities exist, resulting in more positive IPO activity. 6 In the absence of more transparent information, investors might interpret longer registration as being consistent with early demand for the offering being weak, resulting in negative “cascades” (see Welch, 1992. In this scenario, issuers with longer registration periods are viewed by investors as if they are of lower quality.

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process should attract more firms to the IPO market (Khanna et al., 2008; Yung et al., 2008). Filings should, therefore, increase when the age of IPOs in registration is high or growing. 3. Data We initially obtain data on all firm commitment IPOs from Thomson Financial Securities Data's (TFSD) databases. Identifying all IPOs in registration at a point in time requires data from several sources. First, we identify all successful IPOs through a search of the TFSD New Issues database. We download details from TFSD on all IPOs issued between January 1, 1998 and December 31, 2011. Consistent with most IPO research we exclude unit offerings (combinations of equity and warrants), REITs, American Depositary Receipts or Global Depositary Receipts, IPOs from foreign issuers, spinoffs, and closed-end funds7. We identify 1979 successful IPOs that were in the registration process at some time between January 1, 1998 and December 31, 2011. While we collect data on IPOs starting January 1, 1998 our time series analysis starts in July 1998 to ensure early data are complete.8 TFSD's New Issues database contains information on the initial filing size (expected number of shares to be offered and the minimum and maximum filing prices) and any changes to the filing size made in amendments through the IPO process. These data have numerous errors (incorrect amendment dates, missing price or share adjustments, etc.), and so we obtain data on filing terms through manual searches of prospectuses filed on the SEC's Edgar system. For each IPO we track the maximum filing size (expected shares to be offered, times the maximum filing price) from initial filing through each amendment leading up to the final prospectus. While issuers are required to indicate a bona fide range of prices (high and low) in preliminary prospectuses, they generally do not do so in early filings instead noting a range in a subsequent amendment. The initial filing does indicate the maximum proceeds for the IPO in order to compute a filing fee. To ensure that we identify capital in registration consistently throughout the process, we focus on this maximum filing size. As noted by Dunbar and Foerster (2008) and others (e.g., Boeh and Southam, 2011; Busaba et al., 2001; Dunbar, 1998) a large number of IPOs are filed with the SEC but are later withdrawn prior to successful issuance. We obtain data from TFSD on all IPOs in registration between January 1, 1998 and December 31, 2011 that are later withdrawn (with the same screening as successful IPOs). For each withdrawn IPO, we collect data on the maximum filing size from the initial filing date through all amendments leading up to withdrawal. Finally, there are a number of IPOs that are filed with the SEC but are neither successful nor formally withdrawn. We also obtain information on these from TFSD (screened as for successful IPOs). For each filing identified, we obtain data on the maximum filing size from prospectuses obtained from the Edgar system. Consistent with Rule 230.479 of the Exchange Act of 1933 concerning abandoned offerings we assume that the IPO is in registration until nine months after its last filing.9 We identify 911 IPOs in registration between January 1, 1998 and December 31, 2011 that are later withdrawn and 62 that are abandoned. Finally, we identify 9 IPOs registered between January 1, 1998 and December 31, 2011 that are neither completed, withdrawn nor abandoned. At the end of each month we aggregate the value of IPOs in registration from the successful, withdrawn and abandoned IPO data sets. We also track the value of new filings at the end of each month (based on the latest amendment), the value of IPOs that are withdrawn from registration (based on the latest amendment) and the changes in the value of IPOs that were in registration at the start of the month and remain on file (neither were completed nor withdrawn). Finally, we sum the capital raised (excluding overallotments) for issuers that successfully complete IPOs during the month.10 Summary statistics on these monthly data are reported in Table 2 without adjustment for inflation. The mean month end value in registration over this over thirteen year period is $14.18 billion. The value in registration is highly variable with a standard deviation of approximately $6.0 billion. The mean monthly change in value in registration is approximately $97 million. The mean value of new filings each month is approximately $2.2 billion and is also highly variable, ranging from $0 to over $10 billion. The capital raised through IPOs each month follows a similar pattern (mean of approximately $1.7 billion, ranging from $0 to $17.4 billion). The value of IPOs withdrawn or abandoned each month is approximately $0.6 billion and ranges from $0 to $5.1 billion. Finally, the mean change in monthly value of IPOs in registration for those that remained in registration averaged a little over $0.1 billion while the range of value adjustments is large, from −$2.2 billion to + $4.6 billion. To provide a comparison of value changes to the series of completed or withdrawn IPOs, we also compute the absolute value of IPO size adjustments over each month (for an IPO that makes a size adjustment, we compute the absolute value of that size adjustment and sum over all IPOs for the month). The average absolute value of changes to the size of IPOs remaining in registration each month is approximately $0.26 billion, a little less than half the value of IPOs withdrawn or abandoned each month and about 10% of the value of new filings, suggesting a significant variation in the demand for capital after initial filing. Fig. 1 plots the time series of monthly value of IPOs in registration over the more than 13 year period showing a steady rise from early 1998, peaking in May 2000. This run-up corresponds to the dot-com bubble and burst in early 2000. The value in registration falls steeply and remains at a low level for 2002 and 2003. In early 2004 the value in registration bounces back to a 7

We also drop the $15.8 billion IPO for General Motors filed in August 2010 and completed on November 17, 2010 as an extreme outlier. Some independent variables in our models use information (e.g., initial returns) from IPOs that are completed up to six months before the measurement month. Because our IPO data is complete beginning January 1998 we begin our time series analysis six months later. 9 According to Rule 230.479, when a registration statement has been on file with the SEC for a period of nine months and has not become effective the Commission may determine that such registration statement has been abandoned. The nine-month period is computed from the date of the latest amendment. 10 Our analysis focuses on the time series of issuance activities defined at the monthly level primarily to give us sufficient power to detect significant relationships given our relatively short sample period. We also believe that predictions from all theories regarding the impact of past issuance activity on current activity are likely to be weaker with larger measurement windows. 8

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Table 2 Descriptive statistics — IPO market conditions. This table provides descriptive statistics on monthly IPO market conditions between 1998 and 2011. Amounts are in millions of US dollars. Value in registration is the sum of maximum size of the last filing for all filings remaining in registration at the end of the month. The change of value in registration is the value in registration at the end of a month less the value in registration at the end of the prior month. The value of new filings is the sum of the maximum offering size in the IPO prospectus for all new filings in the month (if an issuer adjusts the offering size over the month, the last offering size is used). The value of IPOs is the sum of capital raised through IPOs (excluding overallotments) over the month. The value of withdrawn offerings is the sum of the value of offerings (based on the maximum size of last filing) that are either withdrawn or abandoned during the month. The change in value for offerings that remain in registration is the sum of any changes in maximum filing size across issuers that remained in registration for the month. The absolute value of change of value for offerings that remain in registration is the sum of the absolute value of any changes in the maximum filing size across issuers that remained in registration for the month. Data on capital raised and in registration are obtained from Thomson Financial Securities Data and confirmed through prospectuses obtained through the SEC Edgar system (www.sec.gov/edgar).

Value in registration Change of value in registration Value of new filings Value of issuances Value of withdrawn offerings Change of value for offerings that remained in registration Absolute value of change of value for offerings that remain in registration

Mean

Median

Standard deviation

Minimum

Maximum

14,183.74 96.55 2240.14 1730.96 609.95 136.75 255.16

14,087.89 −14.00 1914.62 1245.74 409.90 29.77 105.14

6040.63 2008.00 1822.22 2081.43 671.23 541.23 496.22

3082.42 −6031.18 0.00 0.00 0.00 −2204.33 0.00

30,280.46 6050.51 10,152.79 17,446.30 5070.00 4626.92 4626.92

level in the $10 to $15 billion range and remains in that range until the end of 2010 with some significant variation. Starting in 2010 the value in registration increases, again with variation, to a range between $15 and $25 billion. To put this time series in context, Fig. 1 also plots the time series of monthly capital raised through successful IPOs. The successful IPO time series also peaks in late 1999 and falls off to lower levels until late 2004. Capital raised in completed IPOs also falls dramatically during the credit crisis between June 2008 and October 2009. In general, the capital raised is much less than the value in registration and the patterns differ.11 Thus, neither individually is always reflective of the health of the IPO markets. For example in the late 1990s the value of IPO issuances was growing along with the value of IPOs in registration, as many new issuers were joining the market when others were exiting, suggesting a healthy market. In late 2001, however, there was a sudden increase in the value of completed IPOs but the value of IPOs in registration plummeted. Rather than indicative of a “healthy” market, the spike in IPO issuance appears to be a final “clearing out” of inventory of a market in decline. The sharp decline in completed IPOs during the credit crisis also does not provide a complete picture of the health of the IPO market. While few IPOs were completed, the IPO pipeline remained strong. The dollar value of IPOs in registration did not drop dramatically and in fact grew. Most issuers chose to wait, suggesting that the “crisis” was likely thought to only be a temporary impediment to capital raising. This view of issuer reaction to the ebbs and flows of the IPO market is confirmed in Fig. 2, which plots the time series of average days in registration. During “normal” periods the days in registration ranges between 100 and 200 days. In mid-2000 the average days in registration began to grow peaking at over 350 days in late 2002. The average then fell as the pipeline cleared mostly through withdrawals. From 2004 to 2008 the days in registration returned to the normal range only to spike again with the onset of the credit crisis. The spike is more pronounced and short-lived, returning by the end of 2009 to normal levels. This picture again suggests that the dot-com bust had a more prolonged and significant effect on the IPO market than the credit crisis. 4. Empirical analysis of IPO activity 4.1. Base-case time series regression models As noted previously, an existing literature examines the time series variation in IPO activity. Most studies estimate time series regression models where the dependent variable is the monthly (or quarterly and sometimes annually) number of IPOs (e.g., Busaba et al., 2009; Doidge et al., 2011; Lowry, 2003; Pastor and Veronesi, 2005; Rau and Stouraitis, 2011, and Schill, 2004). Following Schill (2004) we model the dollar value of IPOs issued each month. Because we track the flows of issuers through the registration process, we also consider the dollar value of IPOs filed each month (as in Busaba et al., 2009), the dollar value of IPOs withdrawn (based on the latest filing terms) each month (as in Schill, 2004) and the change in the dollar value of IPOs remaining in registration (based on latest filing terms) each month as separate dependent variables. For time series regression models to be well specified, dependent variables need to be stationary. Unadjusted, stationarity of our four activity time series is rejected using the augmented Dickey–Fuller and Phillips–Perron unit root tests. To account for this non-stationarity (following the convention in the literature) we deflate our monthly activity variables by the lagged aggregate dollar value of CRSP-listed firms (in our regressions each time series data point is then multiplied by 10,000 for ease of reporting).12 Once adjusted in this way, stationarity of each time series cannot be rejected. While the focus of our study is on the role of past issuance activity as well as new IPO market condition variables, we control for independent variables that have been found in the literature to play a significant role in explaining monthly IPO activity. 11 12

While the correlation between the time series of completed IPOs and IPOs in registration is positive, it is not highly significant (ρ = 0.25). To be more precise, the activity in month t is deflated by the market value of CRSP-listed firms at the end of month t-1.

35,000

14,000

30,000

12,000

25,000

10,000

20,000

8,000

15,000

6,000

10,000

4,000

5,000

2,000

0

Value of IPOs ($M) during month

K. Boeh, C. Dunbar / Journal of Corporate Finance 25 (2014) 455–473

Value in registration ($M) at end of month

462

0

Fig. 1. IPO capital markets — value in registration and issued (1998–2011). The value of IPOs in registration at the end of each month (US$ millions) is depicted by the solid line. Capital raised through IPO issuances (excluding overallotments, in millions of dollars) is depicted by the dashed line. Data on capital raised and in registration are from Thomson Financial Securities Data and confirmed through prospectuses on the SEC Edgar system (www.sec.gov/edgar).

Specific independent variables are introduced below while detailed variable definitions and descriptive statistics are provided in Appendix A. Prior empirical studies of IPO waves show an association between IPO volume and market returns (e.g., Loughran et al., 1994). This association is consistent with rational and irrational explanations. Stock returns may increase with investor optimism. Issuers choose to issue equity after high market returns (and in advance of low returns) in order to time the market and issue equity at opportunistically high prices — good for issuers, but bad for IPO investors. This relationship between issuance decisions and market returns can also be explained by more rational theories. Pastor and Veronesi (2005) develop a real options model in which issuers time IPOs during periods when investment opportunities are more favorable, such as periods when expected market returns are lower. This theory also suggests that IPO volume is highest after positive market returns and in advance of less positive returns. To account for the impact of the market return, empirical studies often include both contemporary and lagged returns as independent variables (see Busaba et al., 2009; Lowry, 2003; Pastor and Veronesi, 2005; Rau and Stouraitis, 2011, and Schill, 2004). Consistent with this, we include in our models the daily average two month lagged CRSP value weighted index return (alternative lag periods are considered but results are not materially impacted) as well as the daily average CRSP value weighted index return over the measurement month as independent variables.

400

Average Calendar Days in Registration

350 300 250 200 150 100 50 0

Fig. 2. IPO capital markets — average time in registration of U.S. IPOs (1998–2011). Weighted average (by maximum filing size) time in registration (days from initial filing) for all IPOs in registration, monthly, 1998–2011.

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Several studies also include market volatility measures to explain IPO activity. Schill (2004) argues that issue risk (offer price, distributional, after-market, and reputational) increases with market volatility, resulting in lower issuance volume. Pastor and Veronesi (2005) note that market volatility can be thought of as a proxy for the equity risk premium. As market volatility increases, the risk premium also increases resulting in lower issuance volume. Finally, Busaba et al. (2009) argue that the “option value” of filing for an IPO increases when market volatility increases. The option logic suggests filing volume should be positively related to market volatility while issuance volume is negatively related (issuers choose an outcome when the option value of keeping the IPO process alive decreases). Empirically, Schill (2004) finds past changes in market volatility to be significantly negatively related to IPO issuances (as does Pastor and Veronesi, 2005) and positively related to IPO withdrawals. Busaba et al. (2009) find changes in market volatility to be positively related to IPO filing volume. Consistent with existing literature, we include changes in market volatility over the two months preceding the measurement period as well as changes in volatility over the measurement month as independent variables. Volatility measures are calculated as the standard deviation of daily market (CRSP value weighted index) returns. Consistent with some studies (e.g., Busaba et al., 2009) we also consider the VIX index as an alternative measure and find qualitatively similar results.13 Several studies of equity financing patterns over time focus on the role of investment opportunities and alternative sources of capital. The change in industrial production is often included as an independent variable to capture changes to investment opportunities facing issuing companies (e.g., Busaba et al., 2009; Lowry, 2003). IPO filings, issuances and changes to the value of issues remaining in registration should be positively related to the change in industrial production and withdrawals should be negatively related. The change in the spread between Baa and Aaa rated corporate debt is also a common variable included to capture changes in aggregate default risk (e.g., Choe et al., 1993; Busaba et al., 2009, and Rau and Stouraitis, 2011). IPO filings, issuances and changes to the value of issues remaining in registration should be negatively related to the change in Baa–Aaa yield spread and withdrawals should be positively related.14 Finally, the change in spread between long-term and short-term Treasury yields is argued in several studies to capture the relative cost of alternative (debt) financing (e.g., Busaba et al., 2009; Choe et al., 1993; Pastor and Veronesi, 2005). When the long-term/short-term Treasury yield spread increases, equity issuance becomes attractive relative to debt. IPO filings, issuances and changes to the value of issues remaining in registration should, therefore be positively related to the change in term/short-term Treasury yield spreads and withdrawals should be negatively related. It should be noted that the government bond–bill term spread is also considered to be a significant indicator of economic activity with widening spreads being a predictor for recessions. This interpretation of term spread would lead to the opposite predictions. While we introduce several new IPO market condition variables, existing studies typically include lagged average IPO initial return as an independent variable to capture IPO market conditions in IPO wave regressions. There are several theories linking initial returns and IPO volume. Loughran and Ritter (2004) argue that conflicts of interest can arise between issuing firms and underwriters in which underwriters prefer greater initial returns as indirect compensation. Periods with higher initial returns, therefore, may correspond to periods in which underwriters have greater bargaining power relative to issuers. Given this bargaining power, underwriters should attempt to bring more companies public during these periods, resulting in higher IPO volumes. Yung et al. (2008) in contrast develop a theoretical model in which time-varying investment opportunities result in a positive association between IPO volume and initial returns.15 Empirically, Lowry (2003) confirms this positive association between past initial returns and IPO issuance volume. Busaba et al. (2009) also find a positive association between IPO initial returns and IPO filings. Consistent with these studies we include lagged average IPO initial returns over the three months prior to the value in registration measurement as an independent variable in our regressions (alternative periods are considered but results are not materially impacted). We also consider the change in this variable during the activity month as an independent variable. Our base-case time series IPO activity regression models are reported in Table 3.16 We begin by discussing results for models that only include independent variables examined in prior research. In column (1) of Table 3 the dependent variable is the value of IPO filings each month. Consistent with existing research (Busaba et al., 2009), lagged change in market return standard deviation has a significantly positive effect on filings, consistent with the notion that filing creates an option which is more valuable with increased volatility. Average prior IPO initial returns also have a significantly positive effect on IPO filings, consistent with the existing theoretical and empirical literature. The change in Treasury bond–bill spread has a significantly negative effect on IPO filings. This is consistent with the term spread being an economic activity indicator. A widening spread occurs in advance of economic downturns, resulting in fewer IPO filings. The most economically significant variable in the IPO filing regression is the average prior IPO initial return. A one standard deviation change in this variables results in an approximately 0.42 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $740 million change. In column (3) of Table 3 the dependent variable is the value of IPO issuances each month. Consistent with existing research (Lowry, 2003; Pastor and Veronesi, 2005; Schill, 2004) lagged market return and change in industrial production have significantly

13 While the two month measurement period for average market returns and return standard deviation is selected based on prior research we considered other periods up to six months and results are qualitatively unaffected. 14 Again, the opposite predictions result if risk is viewed from a real options perspective. 15 In Yung et al.'s (2008) theory, shocks to the economy result in the start of a wave in which many firms attempt to raise capital. The start of a wave attracts followers, including poorer quality firms attempting to pool. This increases adverse selection, and therefore equilibrium subsequently returns. 16 Standard errors used to compute t-statistics are based on White (1980), accounting for heteroskedasticity. For all time series models we test for residual autocorrelation. In all cases models are well specified (the hypothesis of no residual autocorrelation cannot be rejected).

464

IPO filings (1) Constant Market return variables Lagged market return Market return Lagged change in market return standard deviation Change in market return standard deviation Macroeconomic condition variables Change in Baa–Aaa spread Change in Treasury bond–bill spread Change in industrial production IPO market condition variables Average prior IPO initial returns Change in average prior initial return Standard deviation of prior IPO initial return Change in standard deviation of initial return Venture Capital takedown Change in Venture Capital takedown Average days in registration Change in average days in registration R2

IPO issuances (2)

(3)

IPO withdrawals (4)

(5)

Changes to filings in registration (6)

(7)

(8)

1.217

(6.71)

2.832

(11.75)

0.934

(5.56)

1.769

(5.76)

0.459

(6.90)

0.158

(0.78)

0.061

(2.02)

0.244

(4.47)

0.194 0.101 0.277 0.288

(0.54) (0.25) (2.00) (1.59)

−0.150 −0.057 0.024 −0.038

(−0.57) (−0.20) (0.25) (−0.34)

1.223 0.309 0.241 0.054

(3.03) (1.03) (1.46) (0.36)

1.398 0.437 0.228 0.125

(3.57) (1.83) (1.42) (0.79)

−0.265 −0.085 −0.056 −0.018

(−1.23) (−0.47) (−0.69) (−0.22)

−0.217 −0.063 −0.019 0.002

(−1.18) (−0.39) (−0.25) (0.03)

−0.025 0.147 0.107 0.057

(−0.16) (0.95) (2.46) (0.78)

0.027 0.174 0.110 0.064

(0.20) (1.08) (2.19) (0.87)

0.296 −0.634 0.243

(0.47) (−2.42) (1.49)

0.527 −0.441 0.107

(1.14) (−1.94) (0.98)

0.100 0.065 0.330

(0.11) (0.11) (1.62)

0.013 0.167 0.246

(0.02) (0.29) (1.15)

−0.044 0.206 −0.141

(−0.19) (1.53) (−3.49)

−0.097 0.215 −0.103

(−0.48) (2.07) (−2.27)

−0.343 −0.091 0.033

(−1.59) (−1.19) (1.15)

−0.489 −0.042 0.024

(−1.94) (−0.60) (1.04)

2.168 0.325

(2.28) (0.50)

5.165 3.052 −2.793 −1.960 −0.013 0.413 −0.007 −0.031 0.624

(2.94) (2.36) (−2.38) (−2.26) (−0.31) (1.83) (−9.12) (−5.16)

1.515 0.499

(1.47) (0.65)

3.993 2.195 −2.368 −1.463 0.037 0.405 −0.005 0.005 0.241

(2.13) (1.28) (−1.84) (−1.40) (0.92) (2.15) (−3.18) (0.93)

0.093 −0.060

(0.61) (−0.41)

−0.624 −0.823 0.550 0.552 0.035 −0.183 0.001 0.000 0.149

(−1.29) (−1.06) (1.51) (1.10) (1.10) (−2.20) (0.74) (0.09)

0.181 0.112

(1.34) (0.52)

−0.147 −0.291 0.056 0.223 0.019 0.084 −0.001 −0.001 0.115

(−0.23) (−0.34) (0.15) (0.33) (2.36) (1.35) (−6.73) (−0.59)

0.241

0.154

0.083

0.055

K. Boeh, C. Dunbar / Journal of Corporate Finance 25 (2014) 455–473

Table 3 Monthly aggregate time-series analysis of IPO activity. This table reports monthly regressions. In models (1) and (2) the dependent variable is the value of new filings (based on the maximum offering size in the IPO prospectus; if an issuer adjusts the offering size over the month, the last offering size is used) times 10,000 divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month (http://mba.tuck.dartmouth.edu/pages/ faculty/ken.french/data_library.html). In models (3) and (4) the dependent variable is the value of IPOs (without overallotments) over the month times 10,000 divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month. In models (5) and (6) the dependent variable is the value of withdrawn offerings (based on the maximum size in the last filing) over the month times 10,000 divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month. In models (7) and (8) the dependent variable is the change in value of offerings remaining in registration (the maximum size in the last filing in the month less the maximum size in the last filing in the prior month or the previous filing if there were no filings in the prior month) times 10,000 divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month. Independent variables are defined in Appendix A. Significant variables (at the 10% level or better) are bolded and italicized (t-statistics, based on White (1980) heteroskedasticity-consistent standard errors, are reported in parentheses). Each model has 163 observations.

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positive effects on issuances. Of these variables, the most economically significant is the lagged market return. A one standard deviation change in this variables results in an approximately 0.19 standard deviation change in the value of issuances. Measured at the average market capitalization over the period this represents a $390 million change.17 In column (5) of Table 3 the dependent variable is the value of IPOs withdrawn each month. The only statistically significant variable in this regression is the change in industrial production, which has a negative effect on withdrawals, as predicted. A one standard deviation change in this variable results in an approximately 0.07 standard deviation change in the value of withdrawals. Measured at the average market capitalization over the period this represents a $45 million change.18 Finally the dependent variable in column (7) of Table 3 is the change to the value of issues remaining in registration. The lagged change in market return standard deviation has a significantly positive effect on changes to the value of filings remaining in registration. This is consistent with the finding for new filings (filing creates an option which is more valuable with increased volatility). A one standard deviation change in this variables results in an approximately 0.13 standard deviation change in the value of issues remaining in registration. Measured at the average market capitalization over the period this represents a $70 million change. Table 3 also presents time series regression estimates where we add our new IPO market condition variables. In column (2) of Table 3 the dependent variable is the value of IPO filings each month. First it should be noted that the explanatory power of the model increases dramatically with inclusion of new variables (the R2 increases from 0.241 to 0.624). Five of the six additional variables have significant effects on IPO filings. IPO filings are significantly negatively related to the standard deviation of prior IPO initial returns and the change in standard deviation of initial returns. Issuers tend to rationally avoid the IPO market when pricing efficiency is low and declining. The average days in registration and the change in average days in registration have significantly negative effects on IPO filings. This is consistent with bank capability theories. It is also consistent with the age of the pipeline being a measure of IPO market risk, deterring issuers concerned with increased adverse selection costs. Finally the change in Venture Capital takedown has a positive effect on filings as predicted. The signs and significance of market and macroeconomic variables considered previously do not change materially except for lagged market return standard deviation which is no longer statistically (or economically) significant. The most economically significant variables in this expanded specification are average prior IPO initial returns and standard deviation of prior IPO initial returns. A one standard deviation change in these variables results in an approximately 0.8 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $1.4 billion change. In column (4) of Table 3 the dependent variable is the value of IPO issuances each month. The explanatory power of the model again increases dramatically with the inclusion of new variables (the R2 increases from 0.154 to 0.241). Coefficients on three of the six additional variables are statistically significant. IPO issuances are significantly negatively related to the standard deviation of prior IPO initial returns. Issuers tend to rationally avoid issuing an IPO when pricing efficiency is low. The average days in registration has a significantly negative effect on IPO issuances. This is consistent with the age of the pipeline being a measure of IPO market risk (both adverse selection and real options theory would predict that issuers avoid completing an IPO when risk is higher). Finally the change in Venture Capital takedown has a positive effect on issuances as predicted. The signs and significance of market and macroeconomic variables considered previously do not change materially in this expanded model (market return and change of industrial production change statistical significance but point estimates do not change materially). The most economically significant variables in this expanded specification are average prior IPO initial returns and standard deviation of prior IPO initial returns. A one standard deviation change in these variables results in an approximately 0.6 standard deviation change in the value of issuances. Measured at the average market capitalization over the period this represents a $1.2 billion change. In column (6) of Table 3 the dependent variable is the monthly value of IPO withdrawals. The explanatory power of the model again increases with the inclusion of new variables (the R2 increases from 0.083 to 0.149). Two of the six additional variables have significant effects on IPO withdrawals. IPO withdrawals are significantly positively related to the standard deviation of prior IPO initial returns. Issuers tend to rationally pull out of the IPO market when pricing efficiency is low. The change in Venture Capital takedown has a significantly negative effect on IPO withdrawals, as predicted. The signs and significance of market and macroeconomic variables considered previously do not change materially in this expanded model (the change in Treasury bond–bill spread now has a statistically significantly positive effect on withdrawals, consistent with the spread being an economic indicator for recessions, although the point estimate on this variable does not change dramatically). The most economically significant variables in this expanded specification are average prior IPO initial returns and standard deviation of prior IPO initial returns. A one standard deviation change in these variables results in an approximately 0.15 standard deviation change in the value of withdrawals. Measured at the average market capitalization over the period this represents a $100 million change. In column (8) of Table 3 the dependent variable is the change in the value of issues remaining in registration each month. The explanatory power of the model again increases with inclusion of new variables (the R2 increases from 0.055 to 0.115). Two of the six additional variables have significant effects. The change in value of IPOs remaining in registration is significantly positively related to Venture Capital takedown, as predicted. The average days in registration has a significantly negative effect on the

17 While not statistically significant at conventional levels, the coefficient estimate for average prior IPO initial returns indicates that this variable has the economically most significant effect on issuances. A one standard deviation change in this variables results in an approximately 0.25 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $515 million change. 18 Consistent with Schill (2004), the explanatory power of this model is quite low with an R2 of 0.083. The most significant variable in Schill's model is lagged market return, which has a marginally significantly negative effect on withdrawals. We similarly find that lagged market returns negatively affect withdrawals, although the relationship is not statistically significant.

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change in value of IPOs remaining in registration. This is consistent with bank capacity theories. It is also consistent with the age of the pipeline being a measure of IPO market risk, deterring issuers concerned with increased adverse selection costs. The signs and significance of market and macroeconomic variables considered previously do not change materially in this expanded specification. The most economically significant variable is the average days in registration. A one standard deviation change in this variables results in an approximately 0.25 standard deviation change in the change in the value issues remaining in registration. Measured at the average market capitalization over the period this represents a $130 million change. 4.2. Vector autoregressive models In this section we explore the effect of past issuance decisions on current decisions. We model the four issuance decisions (filing, issuance, withdrawal, and change to the value of issues remaining in registration) as a vector autoregressive system with exogenous variables (VARX). Formally, we estimate the following equations: Ft ¼ α F þ It ¼ α I þ

X i¼1;T

X i¼1;T

W t ¼ αW þ Ct ¼ αC þ

  X   X   X   β F;i  F t−i þ δ F;i  I t−i þ γ F;i  W t−i þ θ F;i  C t−i þ ζ F  X þ ε F;t i¼1;T i¼1;T i¼1;T

  X   X   X   βI;i  F t−i þ δI;i  It−i þ γI;i  W t−i þ θI;i  C t−i þ ζ I  X þ εI;t i¼1;T i¼1;T i¼1;T 

X i¼1;T

X i¼1;T

 X   X   X   βW;i  F t−i þ δW;i  It−i þ γW;i  W t−i þ θW;i  C t−i þ ζ W  X þ εW;t i¼1;T i¼1;T i¼1;T

  X   X   X   βC;i  F t−i þ δC;i  It−i þ γC;i  W t−i þ θC;i  C t−i þ ζ C  X þ εC;t i¼1;T i¼1;T i¼1;T

ð2Þ ð3Þ ð4Þ ð5Þ

where Ft, It, Wt and Ct represent the value of filings, issuances, withdrawals and changes to the value of issues remaining in registration at time t, respectively, and X is a vector of exogenous variables. This general model structure conforms to the theory outlined in Subsection 2.1. This approach is similar to that applied by Rau and Stouraitis (2011) who examine waves across corporate events (mergers, repurchases, seasoned equity offerings, and initial public offerings). Empirically, a VARX model is well specified if none of the dependent variables has a unit root, residuals exhibit no autocorrelation, and model errors are uncorrelated with dependent variables (see Hamilton, 1994). All conditions hold for the IPO market activity model estimates we report. Because theory is silent with respect to the appropriate number of lags (T) in the model, we consider several alternatives but report only results for the best model based on the Akaike, Hannan–Quinn and Schwartz (or Bayesian) information criteria (see Bierens, 2006). Our one-lag (T = 1) VARX model estimates are reported in Table 4. In column (1) the dependent variable is the value of IPO filings each month. First it should be noted that the explanatory power of this model (R2 of 0.659) exceeds that of the time series models reported in Table 3 (R2 ranging from 0.241 to 0.624). Most significant variables from Table 3 remain significant in the VARX model. Change in Treasury bond–bill spread and change in Venture Capital takedown are no longer statistically significant but point estimates are not dramatically changed. In general the economic significance variables considered in Table 3 do not materially change in this model. Two of the four lagged activity variables, lagged filings and lagged IPO issuances, have statistically significantly positive effects on current filings. These results are mostly consistent with the information spillover theory (see Table 1). The positive sign on lagged filings is not consistent with plate clearing arguments. Also, the positive coefficient on lagged issuances is not consistent with competition for capital or quality arguments. Lagged filings is the economically most significant of the lagged activity variables in this model. A one standard deviation change in this variable results in an approximately 0.24 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $400 million change. In column (2) of Table 4, the dependent variable is the value of IPO issuances each month. The explanatory power of this model (R2 of 0.337) exceeds that of the time series models reported in Table 3 (R2s range from 0.154 to 0.241). All significant variables from Table 3 remain significant in the VARX model with no dramatic changes to coefficient estimates (and, therefore, little change to economic significance). Lagged issuances and lagged changes to the value of issues remaining in registration have significantly positive effects on current issuances. This is mostly consistent with the information spillover theory. The positive coefficient on lagged issuances is not consistent with plate clearing or competition for capital arguments. Lagged changes to the value of issues remaining in registration is the economically most significant of the lagged activity variables in this model. A one standard deviation change in this variable results in an approximately 0.31 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $600 million change. In column (3) of Table 4 the dependent variable is the value of IPO withdrawals each month. The explanatory power of this model (R2 of 0.365) far exceeds that of the time series models reported in Table 3 (R2s range from 0.083 to 0.149). The economic and statistical significance of variables from Table 3 does not change significantly in the VARX model although the coefficient on average prior IPO initial returns is statistically significantly negative and the coefficients on the standard deviation of prior IPO initial returns and average days in registration are statistically significantly positive. Lagged withdrawals, lagged filings and lagged changes to the value of filings remaining in registration all have statistically significantly positive effects on withdrawals. The positive coefficient on lagged withdrawals is consistent with the information spillover theory and not plate clearing arguments. In stark contrast, the positive coefficients on lagged filings and lagged changes to the value of issues remaining in

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Table 4 Monthly vector auto-regression with exogenous variables (VARX) models of IPO activity. This table reports results for first order vector auto-regressive models with exogenous variables (VARX). In model (1) the dependent variable is the value of new filings (based on the maximum offering size in the IPO prospectus; if an issuer adjusts the offering size over the month, the last offering size is used). In model (2) the dependent variable is the value of IPOs (without overallotments) over the month. In model (3) the dependent variable is the value of withdrawn offerings (based on the maximum size in the last filing) over the month. In model (4) the dependent variable is the change in value of offerings remaining in registration (the maximum size in the last filing in the month less the maximum size in the last filing in the prior month or the previous filing if there were no filings in the prior month). All activity variables are multiplied by 10,000 and divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). Independent variables are lagged values of dependent variables plus exogenous variable that are defined in Appendix A. Significant variables (at the 10% level or better) are bolded and italicized (t-statistics, based on White (1980) heteroskedasticity-consistent standard errors, are reported in parentheses). Each model has 163 observations. IPO filings

IPO issuances

(1) Constant Endogenous Variables Lagged filings Lagged IPOs Lagged withdrawals Lagged changes to the value of issues remaining in registration Exogenous Variables Lagged market return Market return Lagged change in market return standard deviation. Change in market return standard deviation. Change in Baa-Aaa spread Change in Treasury bond-bill spread Change in industrial production Average prior IPO initial returns Change in average prior initial return Standard deviation of prior IPO initial return Change in standard deviation. of initial return Venture Capital takedown Change in Venture Capital takedown Average days in registration Change in average days in registration 2 R

(2)

IPO withdrawals

Changes to the value of issues in registration

(3)

(4)

1.933

(4.76)

1.438

(3.07)

-0.208

(-1.05)

0.086

(0.82)

0.235 0.064 -0.095 0.230

(2.27) (2.53) (-1.20) (1.24)

-0.064 0.105 0.053 1.215

(-0.85) (2.31) (0.30) (3.77)

0.040 0.115 0.270 0.148

(1.70) (1.44) (5.47) (2.35)

0.073 -0.021 -0.030 -0.137

(1.72) (-1.19) (-0.74) (-3.66)

-0.197 -0.080 -0.019 -0.081 0.486 -0.269 0.052 3.794 3.234 -1.941 -2.048 -0.017 0.205 -0.005 -0.029 0.659

(-0.73) (-0.28) (-0.19) (-0.73) (1.06) (-1.47) (0.47) (2.12) (2.22) (-1.66) (-2.03) (-0.58) (0.89) (-3.75) (-4.85)

1.243 0.436 0.298 0.191 0.467 0.292 0.230 4.275 1.410 -2.546 -1.034 0.003 0.325 -0.003 0.008 0.337

(3.26) (2.08) (1.47) (1.27) (0.76) (0.59) (0.97) (2.45) (1.14) (-2.23) (-1.31) (0.09) (1.73) (-2.62) (1.49)

-0.267 -0.085 -0.037 0.008 0.063 0.246 -0.100 -0.908 -0.783 0.668 0.484 0.016 -0.279 0.001 -0.001 0.365

(-1.61) (-0.60) (-0.47) (0.13) (0.25) (2.44) (-2.11) (-1.96) (-1.19) (2.56) (1.17) (0.69) (-2.90) (1.70) (-0.42)

0.048 0.179 0.088 0.045 -0.578 -0.024 0.014 -0.522 -0.150 0.292 0.149 0.025 0.065 -0.001 -0.001 0.167

(0.36) (1.13) (1.89) (0.58) (-2.00) (-0.37) (0.51) (-0.61) (-0.22) (0.57) (0.27) (2.67) (1.44) (-2.54) (-0.76)

registration are consistent with plate clearing arguments and not the information spillover theory. Lagged withdrawals is the economically most significant of the lagged activity variables in this model. A one standard deviation change in this variable results in an approximately 0.27 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $172 million change. Finally in column (4) of Table 4 the dependent variable is changes to the value of issues remaining in registration. The explanatory power of this model (R2 of 0.167) exceeds that of the time series models reported in Table 3 (R2s range from 0.055 to 0.155). The economic and statistical significance of variables from Table 3 does not change significantly in the VARX. The coefficient on lagged filings is statistically significantly positive and the coefficient on lagged changes to the value of filings remaining in registration is statistically significantly negative in this model. The positive coefficient on lagged filings is consistent with the information spillover theory (and IPO quality theory) but not plate clearing arguments. In contrast, the negative coefficient on lagged changes to the value of issues remaining in registration is consistent with plate clearing and not the information spillover theory. Lagged filings is the economically most significant of the lagged activity variables in this model. A one standard deviation change in this variable results in an approximately 0.37 standard deviation change in the value of filings. Measured at the average market capitalization over the period this represents a $200 million change. Overall the evidence in Table 4 indicates that past issuance decisions significantly impact current issuance decisions. Consideration of our new IPO market condition variables as well as past issuance decisions, therefore, adds significantly to our ability to explain IPO waves. Models of IPO issuances that only account for past issuances and ignore other important issuance decisions are not well specified and ignore important details of the issuance process. Some evidence is consistent with both plate clearing and information spillover theories indicating that both play a role in explaining issuance activity. As noted previously, any evidence consistent with information spillover theory is potentially also consistent with mechanical persistence arguments. To disentangle the relative importance of these arguments, we test for equality (in absolute magnitude) of coefficients on lagged filings, lagged withdrawals and lagged changes to the value of issuances already in registration in model (2) in Table 4 where the dependent variable is the value of IPO issuances. We also test for equality (in absolute magnitude) of the coefficients on lagged IPO issuances in models (1), (3) and (4) (where the dependent variables are filings, withdrawals and changes to the value of issues remaining in registration). We reject theses hypotheses of equal coefficients

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(in absolute value) at better than the 1% level. This suggests that the evidence is mostly consistent with information effects and not mechanical persistence.19

5. Industry analysis Industry dynamics are likely to play an important role in IPO issuance decisions. As there are numerous industry classification systems we consider several alternatives. While finer classification systems are likely to capture important firm differences, issue volume for most industry groups is quite sparse. We report evidence based on Fama and French's seventeen industry classification system (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) as several industry groupings have sufficient activity to allow time series modeling. Eight of the 17 industries have sufficient IPO activity over time including Chemicals (industry 6), Drugs, Soap, Perfumes and Tobacco (industry 7), Machinery and Business Equipment (industry 11), Transportation (industry 13), Utilities (industry 14), Retail Stores (industry 15), Financials (industry 16) and Other (industry 17).20 We measure IPO issuance decision dependent variables for each industry, deflating each variable by the lagged aggregate dollar value of CRSP-listed firms in the same industry (as for aggregate activity models, time series data points are then multiplied by 10,000 for ease of reporting). While several studies pool industries in a single model, we find that time series model requirements (e.g., stationarity) are better satisfied when industries are modeled separately. In Table 5 we report VARX model estimates for Fama–French industry group 11 (Machinery and Business Equipment). We select this grouping to report as it is one the more active industry groups in this period. The evidence from this group is also representative of average effects across industry groups. For models in Table 5 we include all independent variables considered in the models reported in Table 4. Where possible we also include variables defined at the industry level (industry return measures, industry IPO initial return measures, and industry IPO time in registration). Rather than discussing all significant findings, we focus on changes relative to the significant findings previously discussed. In column (1) of Table 5 the dependent variable is the value of industry IPO filings each month. Of the general economic measures only the change in Venture Capital takedown has a statistically significant (positive) effect on filings (consistent with evidence in Table 3 at the aggregate level). None of the aggregate primary and secondary market exogenous variables are significant in this model while two measures defined at the industry level (industry days in registration and change in industry days in registration) have a significant impact on filing activity, consistent with the notion that industry factors are most important. Of the lagged activity variables only the lagged changes to filings remaining in registration has a significant (positive) effect on industry filings, consistent with the information spillover and IPO quality arguments. In column (2) of Table 5, the dependent variable is the value of industry IPO issuances each month. None of the general economic measures have significant coefficients in this model. Of the aggregate primary and secondary market exogenous variables only the change in average three month initial return has a significant effect on issuances. The coefficient on this variable is negative, which is the opposite of that found in prior models. It should be noted that the coefficient on industry average prior initial return is significantly positive. Taken together, firms are more likely to issue equity in an industry when initial return is high in that industry relative to initial return at the aggregate level. The lagged industry return standard deviation also has a significantly negative effect on industry issuances (firms are more likely to issue when industry returns are less volatile, consistent with real options and adverse selection arguments). As for the industry filing model, more measures defined at the industry than aggregate level are statistically significant. Of the lagged activity variables only the lagged changes to filings remaining in registration has a significant (positive) effect on industry issuances, consistent with both information spillover and plate clearing arguments. In column (3) of Table 5, the dependent variable is the value of industry IPO withdrawals each month. None of the general economic measures or aggregate primary and secondary market variables is significant in this model. Several measures defined at the industry level are significant however. Lagged industry return standard deviation has a significantly negative effect on withdrawals consistent with real options arguments (issuers are less likely to withdraw when riskiness, and real option value, is high). The standard deviations of industry underpricing measures have significantly positively effects on withdrawals. Issuers are more likely to withdraw when industry pricing efficiency declines. Of the lagged activity variables only lagged industry withdrawals has a significant (positive) effect on industry withdrawals, consistent with information spillover but not plate clearing arguments. 19 In unreported evidence we consider an alternative VARX model where there are only two dependent variables: issuances and “net” filings. The latter variable is defined as filings plus changes to the value of issues remaining in registration subtract withdrawals. Under the mechanical persistence hypothesis, net filings captures all important information regarding the impact of decisions made to alter the IPO pipeline so combining three activity variables does not “throw away” any important information. Under the information spillover theory, the different activity variables potentially convey different information so combining them into a single net filings variable does throw away information. In this case, the explanatory power of VARX models should be significantly reduced. The R2 in the VARX model with issuances as the dependent variable is 0.251, significantly lower than the R2 from model (2) of Table 4. This suggests that the three activity variables convey independent information, consistent with the information spillover theory. While not a formal test of information spillover versus mechanical persistence, it should also be noted that the impact of exogenous variables on filings, withdrawals and changes to the value of issues in registration is quite different, suggesting that “waves” for these activities do not move in lock step. 20 For these industries, there are IPOs in registration in over 90% of the months for the sample period. More formally, we consider all industries but do not report evidence for those where statistical properties (e.g. stationarity) are satisfied. We consider alternative industry groupings based on different Fama–French classifications. As we move to more finely grained classifications, fewer industry groupings satisfy the condition that there is sufficient activity and the resulting time series have appropriate statistical properties. We considered “coarser” classifications including the Fama–French 10 industry grouping. While statistical properties are also satisfied for 8 groups, we find weaker evidence using this system. We conjecture that in moving to coarser classification systems, we lose the power to identify “unique” industry effects.

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Table 5 Monthly vector auto-regression with exogenous variables (VARX) models of industry IPO activity. This table reports results for industry level first order vector auto-regressive models with exogenous variables (VARX). In model (1) the dependent variable is the value of new filings in Fama-French Industry 11, Machinery and Business Equipment (based on the maximum offering size in the IPO prospectus; if an issuer adjusts the offering size over the month, the last offering size is used). In model (2) the dependent variable is the value of IPOs in Fama-French Industry 11 (without overallotments) over the month. In model (3) the dependent variable is the value of withdrawn offerings in Fama-French Industry 11 (based on the maximum size in the last filing) over the month. In model (4) the dependent variable is the change in value of offerings remaining in registration in Fama-French Industry 11 (the maximum size in the last filing in the month less the maximum size in the last filing in the prior month or the previous filing if there were no filings in the prior month). All activity variables are multiplied by 10,000 and divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month in Fama-French Industry 11 (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). Independent variables are lagged values of dependent variables plus exogenous variable that are defined in Appendix A. Significant variables (at the 10% level or better) are bolded and italicized (t-statistics, based on White (1980) heteroskedasticity-consistent standard errors, are reported in parentheses). Each model has 163 observations. IPO filings

IPO completions

(1) Constant Endogenous Variables Lagged filings Lagged IPOs Lagged withdrawals Lagged changes to filings remaining in registration Exogenous Variables - General Economic Measures Change in Baa-Aaa spread Change in Treasury bond-bill spread Change in industrial production Venture Capital takedown Change in Venture Capital takedown Aggregate Primary and Secondary Market Exogenous Variables One month lagged market return Market return One month lagged market return standard deviation Market return standard deviation Average 3 month prior IPO initial return Change in average 3 month initial return Standard deviation of prior IPO initial return Change in standard deviation of initial return Average days in registration Change in average days in registration Industry-Level Primary and Secondary Market Exogenous Variables One month lagged industry return Industry return One month lagged industry return standard deviation Industry return standard deviation Average 6 month prior industry IPO initial return Change in average 6 month industry initial return Standard deviation of prior industry IPO initial return Change in standard deviation of industry initial return Industry average days in registration Change in industry average days in registration R2

Constant Endogenous Variables Lagged filings Lagged IPOs Lagged withdrawals Lagged changes to filings remaining in registration Exogenous Variables - General Economic Measures Change in Baa-Aaa spread Change in Treasury bond-bill spread Change in industrial production Venture Capital takedown Change in Venture Capital takedown Aggregate Primary and Secondary Market Exogenous Variables Lagged market return Market return

(2)

0.798

(2.02)

0.775

(2.07)

0.075 -0.066 0.115 0.992

(1.36) (-1.16) (1.30) (2.30)

0.042 -0.041 -0.075 2.983

(0.58) (-0.89) (-1.14) (8.17)

-0.708 -0.743 0.084 -0.055 0.883

(-1.04) (-1.60) (0.29) (-0.97) (2.49)

-0.065 -0.207 -0.045 -0.017 -0.488

(-0.12) (-0.80) (-0.28) (-0.26) (-1.08)

-0.793 0.147 0.123 0.473 -3.788 -1.767 1.838 1.137 0.003 -0.008

(-0.95) (0.17) (0.30) (1.12) (-1.05) (-1.10) (1.54) (1.00) (1.40) (-1.37)

-0.054 0.406 0.419 0.116 -0.967 -3.233 0.143 1.976 -0.001 -0.001

(-0.10) (0.80) (1.64) (0.30) (-0.78) (-2.14) (0.21) (1.61) (-0.77) (-0.23)

0.118 -0.028 -0.339 -0.136 1.524 -0.547 0.623 -0.415 -0.001 -0.005

(0.31) (-0.06) (-1.29) (-0.41) (1.38) (-0.61) (1.00) (-0.62) (-2.98) (-2.83) 0.401

0.284 -0.129 -0.448 -0.282 2.141 1.268 -0.438 0.699 0.000 0.000

(1.22) (-0.39) (-1.89) (-1.11) (2.45) (1.32) (-0.69) (0.92) (-0.55) (0.91) 0.577

IPO withdrawals

Changes to filings in registration

(3)

(4)

0.255

(1.26)

-0.438

(-2.34)

0.029 0.004 0.068 -0.004

(0.75) (0.12) (1.90) (-0.03)

0.076 -0.011 -0.034 -0.101

(1.68) (-1.01) (-1.54) (-1.96)

-0.016 0.278 -0.019 -0.025 -0.093

(-0.04) (1.32) (-0.28) (-1.00) (-0.66)

-0.273 -0.116 0.014 0.064 0.114

(-1.24) (-2.18) (0.49) (2.77) (1.36)

0.078 -0.268

(0.21) (-0.52)

-0.297 -0.303

(-1.68) (-1.29)

(continued on next page)

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Table 5 (continued)

Aggregate Primary and Secondary Market Exogenous Variables One month lagged market return standard deviation Market return standard deviation Average 3 month prior IPO initial return Change in average 3 month initial return Standard deviation of prior IPO initial return Change in standard deviation of initial return Average days in registration Change in average days in registration Industry-Level Primary and Secondary Market Exogenous Variables One month lagged industry return Industry return One month lagged industry return standard deviation Industry return standard deviation Average 6 month prior industry IPO initial return Change in average 6 month industry initial return Standard deviation of prior industry IPO initial return Change in standard deviation of industry initial return Industry average days in registration Change in industry average days in registration R2

IPO withdrawals

Changes to filings in registration

(3)

(4)

0.384 0.207 -1.226 -0.512 0.660 0.361 0.001 -0.005

(1.38) (0.73) (-0.88) (-0.36) (0.88) (0.38) (1.33) (-1.21)

-0.078 0.028 0.313 0.445 -0.185 -0.321 0.001 0.000

(-0.74) (0.34) (1.14) (0.92) (-1.10) (-0.98) (1.50) (-0.11)

-0.122 -0.086 -0.376 -0.155 -0.687 -0.457 0.759 0.616 0.000 -0.001

(-0.44) (-0.32) (-1.71) (-0.86) (-1.04) (-1.12) (2.73) (1.98) (-1.19) (-1.52) 0.160

0.200 0.346 0.091 0.020 -0.063 0.294 -0.138 0.068 0.000 0.000

(2.06) (2.27) (0.92) (0.28) (-0.30) (1.05) (-0.93) (0.47) (-2.02) (1.40) 0.377

Finally, in column (4) of Table 5, the dependent variable is changes to the value of industry issues remaining in registration. Two of the general economic measures are significant in this model (Treasury bond–bill spread and Venture Capital takedown) with both having signs consistent with prior evidence. Of the aggregate primary and secondary market variables only the lagged market return has a significant coefficient. The negative sign on this variable is not consistent with prior evidence. It should be noted that the coefficients on industry return variables are significantly positive. Taken together this suggests that issuers are more likely to increase the value sought when their industry performance is high relative to the overall market. In addition to industry return measures, the industry average days in registration is also significant in this model (the negative coefficient is consistent with prior evidence indicating that issuers are more likely to cut offering sizes when the industry pipeline ages). Of the lagged activity variables, lagged filings has a significantly positive effect and lagged changes to the value of issues remaining in registration has a significantly negative effect. The former is consistent with information spillover arguments and the latter is consistent with plate clearing arguments. Overall the evidence in Table 5 is consistent with evidence at the aggregate level in Table 4 in showing that past issuance decisions significantly impact current issuance decisions. Consideration of our new IPO market condition variables as well as past issuance decisions, therefore, adds significantly to our ability to explain industry IPO waves. Some industry evidence is consistent with both plate clearing and information spillover theories indicating that both play a role in explaining industry issuance activity. As noted previously, any evidence consistent with the information spillover theory is potentially also consistent with mechanical persistence arguments. To disentangle the relative importance of these arguments, we again test for equality (in absolute magnitude) of coefficients on lagged filings, lagged withdrawals and lagged changes to the value of issuances already in registration in model (2) of Table 5 where the dependent variable is the value of IPO issuances. We also test for equality (in absolute magnitude) of the coefficients on lagged IPO issuances in models (1), (3) and (4) (where the dependent variables are filings, withdrawals and changes to the value of issues remaining in registration). We reject theses hypotheses of equal coefficients (in absolute value) at better than the 1% level. This suggests that the evidence is mostly consistent with information effects and not mechanical persistence. The industry evidence also suggests that secondary and IPO market condition variables defined at the industry level play a more significant role on issuance activity decisions than variables defined at the aggregate-level, given that many more industry level variables are statistically significant. Similar patterns emerge when VARX models are estimated for other industry groups. To more formally compare the role of industry and aggregate level factors we follow the approach used by Hoberg (2007) and examine the relative contribution to model R2 by aggregate and industry-level variables in Table 6. For each industry we estimate three VARX models. The first, referred to as the base case model, includes lagged activity variables plus general economic measures. The second model adds primary and secondary market condition variables defined at the aggregate level to the base case model. The third model adds primary and secondary market condition variables defined at the industry level. Median (across industry group) model R2s for these three models are reported in the second to fourth columns of Table 6. Model R2s are highest in the fourth column, when industry factors are added to the base case model. We define the incremental percent explained by aggregate and industry factors as the R2 from the expanded model less the R2 from the base case model. The incremental percent explained by industry variables (the fifth column) is the incremental percent explained by the industry model divided by the sum

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Table 6 Impact on R2 in Industry VARX models due to aggregate and industry exogenous variables. This table reports R2 results from various VARX models at the industry level (each model has 163 observations). Dependent variables are the value of new filings in a Fama-French Industry (based on the maximum offering size in the IPO prospectus; if an issuer adjusts the offering size over the month, the last offering size is used) over a month, the value of IPOs in a Fama-French Industry (without overallotments) over the month, the value of withdrawn offerings in a Fama-French Industry (based on the maximum size in the last filing) over the month, and the change in value of offerings remaining in registration in a Fama-French Industry (the maximum size in the last filing in the month less the maximum size in the last filing in the prior month or the previous filing if there were no filings in the prior month) over a month. Industries considered include Fama-French industries 6, 7, 11, 13, 14, 16 and 17. All activity variables are multiplied by 10,000 and divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of the prior month in the respective Fama-French Industry (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). Independent variables are lagged values of dependent variables plus exogenous variables that are defined in Appendix A. The base case models with R2s reported in the second column include as right hand side variables lagged dependent variables plus variables labeled in Table 5 as “Exogenous Variables — General Economic Measures”. The third column R2s are based on models that add to the base case model variables label in Table 5 as “Aggregate Primary and Secondary Market Exogenous Variables”. The fourth column R2s are from models that add to the base case model variables label in Table 5 as “Industry-level Primary and Secondary Market Exogenous Variables”. To determine the incremental percent explained by industry variables (column 5) we first define the incremental percent explained due to aggregate primary and secondary market variables as the R2 from the expanded model (column 3) less the R2 from the base case model. We then define the incremental percent explained due to industry-level primary and secondary market variables as the R2 from the expanded model (column 4) less the R2 from the base case model. The incremental percent explained by industry variables is the incremental percent explained due to industry-level primary and secondary market variables divided by the sum of the incremental percent explained due to industry-level primary and secondary market variables and the incremental percent explained due to aggregate primary and secondary market variables. Dependent variable

Median base case R2

Median R2 when adding aggregate primary and secondary market exogenous variables

Median R2 when adding industry-level primary and secondary market exogenous variables

Median incremental percent explained by industry variables

IPO filings IPO issuances IPO withdrawals Changes to the value of issues in registration

0.118 0.510 0.051 0.028

0.154 0.548 0.149 0.085

0.299 0.560 0.177 0.157

83.2% 57.0% 56.2% 69.4%

of the incremental percent explained by aggregate and industry factors. Industry factors explain from 56.2% of the variation in IPO withdrawals to over 83% of the variation in IPO filing activity. Overall, this is consistent with industry factors having a more important impact on issuance decisions than aggregate factors. 6. Conclusion This study tracks the monthly value of IPO activity and IPOs in registration over a greater than 13 year period from 1998 to 2011 in which the value in registration is the sum of the maximum proceeds indicated in IPO prospectuses. The monthly value in registration is large and highly variable. The average monthly value in registration is approximately six to seven times the average monthly capital raised in successful IPO and the average capital sought in new filings. The value in registration peaked in May 2000 at $30.3 billion and then steadily declined to its low of $3.1 billion in April 2003. This study is the first to consider IPO market measures through the 2008 credit crisis. While issuances declined significantly during 2008, the IPO pipeline (value of IPOs in registration) remained strong. Most firms chose to wait out what was perceived to be a temporary impediment to capital raising.21 The monthly value of IPOs in registration changes as new offerings are filed, some already in registration are priced and issued to the public, some in registration are withdrawn, and some in registration have their offering sizes amended. We examine the factors affecting each of these issuance decisions. Consistent with prior research on IPO volume we consider the effect of market return and macroeconomic factors on issuance decisions. We also introduce new IPO market condition variables that have not been considered previously including the standard deviation of prior IPO initial returns, Venture Capital takedown and the average age (days in registration) of IPOs in registration. Further we consider how past issuance decisions (filings, issuances, withdrawals, and changes to the capital sought in issues remaining in registration) affect current issue decisions. Collectively, the new IPO market condition variables have a significant impact on issuance decisions. The standard deviation of IPO initial returns has a significantly positive effect on IPO withdrawals and a significantly negative effect on IPO issuances and filings. This is consistent with issuers rationally avoiding the IPO market when pricing efficiency is low. Venture Capital takedown has a significantly positive effect on issuances and capital sought in issues remaining in registration and a negative effect on withdrawals. The age of the IPO pipeline has negative effect on filings, issuances and capital sought in issues remaining in registration and a positive effect on withdrawals. Using vector autoregressive models, we examine the impact of past issuance activity on current activity. The evidence is consistent with both information spillover and plate clearing arguments. Lagged filings have a positive effect on current filings as well as changes to the value of issues remaining in registration. Lagged issuances have a positive effect on current issuance and lagged withdrawals have a positive effect on current withdrawals. All these relationships are consistent with information spillover but not plate clearing arguments. In contrast, lagged filings positively affect current withdrawals, and lagged changes to 21 Because our sample period included two significant shocks (the credit crisis and the dot-com bubble), we replicated our findings in Tables 3 to 6 using various sub periods. While evidence generally weakened given smaller sample sizes, the most significant findings of our models were qualitatively unaffected.

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the value of issues remaining in registration has a positive effect on current withdrawals and negative effect on current changes to the value of issues remaining in registration. All these findings are consistent with plate clearing and not information spillover arguments. Taken together, this indicates that both theories play a role in explaining issuance activity. As noted previously, any evidence consistent with the information spillover theory is potentially also consistent with mechanical persistence arguments. Further tests of implications from mechanical persistence suggest that information effects are more likely driving the finding in our autoregressive models. While the effects of other control variables on IPO activity variables are generally consistent with those found in the existing literature, we find that the new IPO market condition and past issuance activity variables play a more significant role, economically and statistically, on issuance decisions. Overall we believe that our broader consideration of IPO market activity and the issuance process adds substantially to our understanding of IPO issuance decisions at the firm level and IPO waves at the aggregate level. Given the importance of IPO market activity variables in issuance decisions we expect that these measures will affect IPO pricing, issuance costs, and post-IPO performance. We leave the exploration of these issues to future research.

Acknowledgments For helpful comments, we are grateful to Steve Foerster, Raghu Rau and an anonymous referee as well as the participants at the 2012 Financial Association Meeting in Atlanta.

Appendix A. Variable definitions and descriptive statistics

Variable Name

Definition

Lagged market return

For observations in month t, the average daily return on the CRSP value weighted index 1 is computed over months t − 1 and t − 2 (in percent). For observations in month t, the average daily return on the CRSP value weighted index 1 is computed month t (in percent). The standard deviation of daily returns is computed for the CRSP value weighted index 1 over each month. For observations in month t the lagged change in standard deviation is the standard deviation in month t − 1 less the standard deviation in months t − 2 (in percent). For observations in month t, the change in standard deviation is the standard deviation during month t less the standard deviation during month t − 1 (in percent). The Baa–Aaa spread is measured as Moody's Seasoned Baa Corporate Bond Yield less the Seasoned Aaa Corporate Bond Yield 2. For observations in month t, the change in Baa–Aaa spread is the Baa–Aaa spread at the beginning of month t + 1 less the Baa–Aaa spread at the start of month t in percent. The Treasury bond–bill spread is measured as the 10-Year Treasury constant maturity rate less the 3-Month Treasury Bill (secondary market) rate2, in percent. For observations in month t, The Change in Treasury bond–bill spread is then measured as the Treasury bond– bill spread at the start of the month t + 1 less the Treasury bond–bill spread at the start of t. For observations in month t, the change in industrial production is the industrial production index (Federal Reserve index INDPRO) at the beginning of month t + 1 2 less the Industrial production index at the start of month t. For each IPO, initial return is defined as closing price on the first day of public trading (from CRSP) divided by the IPO offer price, subtract 1. For observations in month t, the average prior IPO initial returns is the average initial return for all successful IPO listed on CRSP that went public during months t − 3 through t − 1 (in percent). For observations in month t, the change in average prior initial return is the average prior IPO initial return variable (as defined above) in month t less the average prior IPO initial return variable in month t − 1. For observations in month t, the standard deviation of prior IPO initial return is defined as the standard deviation of individual IPO initial returns (as defined above) for all successful IPOs listed on CRSP that went public during months t − 3 through t − 1 (in percent). For observations in month t, the change in standard deviation of initial return is the standard deviation of prior IPO initial return variable (as defined above) in month t less the standard deviation of prior IPO initial return variable in month t − 1. For observations in month t, Venture Capital takedown is the sum of monthly Venture Capital takedown over months t − 6 through t − 1 (from Thompson Financial's Venture Economics Database) multiplied by 10,000 and then divided by the total value of all publicly traded companies on the NYSE, AMEX or Nasdaq exchanges at the end of month t − 1. For observations in month t, the change in Venture Capital takedown is the Venture Capital takedown variable (as defined above) in month t less the Venture Capital takedown variable in month t − 1.

Market return Lagged change in market return standard deviation

Change in market return standard deviation Change in Baa–Aaa spread

Change in Treasury bond–bill spread

Change in industrial production

Average prior IPO initial returns

Change in average prior initial return

Standard deviation of prior IPO initial return Change in standard deviation of initial return Venture Capital takedown

Change in Venture Capital takedown

Mean

Standard deviation

0.020

0.242

0.021

0.242

0.007

0.492

0.004

0.495

0.005

0.141

0.009

0.269

0.072

0.682

0.195

0.258

−0.001

0.163

0.264

0.380

−0.001

0.237

−0.004

0.398

−0.004

0.398

K. Boeh, C. Dunbar / Journal of Corporate Finance 25 (2014) 455–473

473

Appendix (continued) A (continued) Variable Name

Definition

Mean

Standard deviation

Average days in registration

For observations in month t, we examine all IPOs in registration at the start of month t and determine the time (calendar days) that the offering has been in registration. Average days in registration is the weighted average (based on the value of the most recent filing) days that all IPOs have been in registration. For observations in month t, the change in average days in registration is the average days in registration variable (as defined above) in month t less the average days in registration variable in month t − 1.

179.24

68.51

1.32

21.66

Change in average days in registration

1 2

Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. Source: http://research.stlouisfed.org/fred2/.

References Beatty, R., Ritter, J., 1986. Investment banking, reputation, and the underpricing of initial public offerings. J. Financ. Econ. 15, 213–232. Benveniste, L., Busaba, W., Wilhelm, W., 2002. Information externalities and the role of underwriters in primary equity markets. J. Financ. Intermed. 11, 61–86. Benveniste, L., Ljungqvist, A., Wilhelm, W., Yu, X., 2003. Evidence of information spillovers in the production of investment banking services. J. Financ. 58, 577–608. Bierens, H., 2006. Information Criteria and Model Selection. Pennsylvania State University (Unpublished working paper). Boeh, K., Dunbar, C., 2013. Deal Flow and the Pricing of IPOs. Western University (Ivey Business School) (Unpublished working paper). Boeh, K., Southam, C., 2011. Impact of initial public offering coalition on deal completion. Ventur. Cap. 13, 313–336. Booth, J., Chua, L., 1996. Ownership dispersion, costly information and IPO underpricing. J. Financ. Econ. 41, 291–310. Bradley, D., Jordan, B., 2002. Partial adjustment to public information and IPO underpricing. J. Financ. Quant. Anal. 37, 595–616. Busaba, W., Benveniste, L., Guo, R.-J., 2001. The option to withdraw IPOs during the premarket: empirical analysis. J. Financ. Econ. 60, 73–102. Busaba, W., Li, D., Yang, G., 2009. Market Volatility and the Timing of IPO Filings. Western University (Ivey Business School) (Unpublished working paper). Choe, H., Masulis, R., Nanda, V., 1993. Common stock offerings across the business cycle: theory and evidence. J. Empir. Financ. 1, 3–31. Doidge, C., Karolyi, A., Stulz, R., 2011. The U.S. left behind: the rise of IPO activity around the world. Charles A Dice Center Working Paper No. 2011-8. Dunbar, C., 1998. The choice between firm-commitment and best-efforts offering methods in IPOs: the effect of unsuccessful offers. J. Financ. Intermed. 7, 60–90. Dunbar, C., Foerster, S., 2008. Second time lucky? Withdrawn IPOs that return to the market. J. Financ. Econ. 87, 610–635. Edelen, R., Kadlec, G., 2005. Issuer surplus and the partial adjustment of IPO prices to public information. J. Financ. Econ. 77, 347–373. Hamilton, J., 1994. Times Series Analysis. Princeton University Press, Princeton, New Jersey. Hoberg, G., 2007. The underwriter persistence phenomenon. J. Financ. 62, 1169–1206. Hsu, H.-C., Reed, A., Rocholl, J., 2010. The new game in town: competitive effects of IPOs. J. Financ. 65, 495–528. Ibbotson, R., Jaffe, J., 1975. Hot issue markets. J. Financ. 30, 1027–1042. Khanna, N., Noe, T., Sonti, R., 2008. Good IPOs draw in bad: inelastic banking capacity in hot markets. Rev. Financ. Stud. 21, 1873–1906. Lerner, J., 1994. Venture capitalists and the decision to go public. J. Financ. Econ. 35, 293–316. Loughran, T., Ritter, J., 2004. Why has IPO underpricing changed over time? Financ. Manag. 33, 5–37. Loughran, T., Ritter, J., Rydqvist, K., 1994. Initial public offerings: international insights. Pac. Basin Financ. J. 2, 165–199. Lowry, M., 2003. Why does IPO volume fluctuate so much? J. Financ. Econ. 67, 3–40. Lowry, M., Officer, M., Schwert, G.W., 2011. The variability of IPO initial returns. J. Financ. 65, 425–465. Myers, S., Majluf, N., 1984. Corporate financing and investment decisions when firms have information that investors do not have. J. Financ. Econ. 13, 187–221. Pagano, M., Panetta, F., Zingales, L., 1998. Why do companies go public? An empirical analysis. J. Financ. 53, 27–64. Pastor, L., Veronesi, P., 2005. Rational IPO waves. J. Financ. 60, 1713–1757. Rajan, R., Servaes, H., 1997. Analyst following of initial public offerings. J. Financ. 52, 507–530. Rau, R., Stouraitis, A., 2011. Patterns in the timing of corporate event waves. J. Financ. Quant. Anal. 46, 209–246. Ritter, J., 1984. The ‘hot issue’ market of 1980. J. Bus. 57, 215–240. Ritter, J., Welch, I., 2002. A review of IPO activity, pricing, and allocations. J. Financ. 57, 1795–1828. Schill, M., 2004. Sailing in rough water: market volatility and corporate finance. J. Corp. Financ. 10, 659–681. Welch, I., 1992. Sequential sales, learning and cascades. J. Financ. 47, 695–732. White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817–838. Yung, C., Colak, G., Wang, W., 2008. Cycles in the IPO market. J. Financ. Econ. 89, 192–208.