The relation between underwriter’s prestige and IPO underpricing in
the United States for the period from
1985 to 2016.
Bachelor Thesis of Laura Klaver
Abstract
This thesis discusses the underpricing of Initial Public Offerings (IPO) and examines whether prestigious underwriters guiding the IPOs have a significant effect on the level of underpricing. The dataset consists of IPOs in the United States between January 1, 1985 and December 31, 2016. For ranking the underwriters, the adjusted Carter and Manaster rank is used. This study finds that underwriter’s prestige does not reduced the level of underpricing for IPOs in the United States for the period from 1985 to 2016.
January 31, 2017
Supervisor: Dhr. dr. Vladimir N. Vladimirov
Name: Laura Klaver
Student number: 10597352
Statement of Originality
This document is written by Student Laura Klaver who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of Content Introduction ... 4 Literature review ... 5 Hypothesis ... 8 Methodology ... 8 Data ... 11 Results ... 14
Conclusion and discussion ... 18
References ... 20
Introduction
When a private company decides to raise external capital through the public market by getting listed to a stock exchange market, it will perform an Initial Public Offering (IPO). Within the process of this new offering, investors are able to make significant short term returns while the issuing company could have raised more capital. This important anomaly is known as underpricing and is discussed in many studies.
Underpricing occurs when the price of the newly available shares, also known as the offering price, is lower than the closing price of the first trading day (Eckbo, 2008). Furthermore, underpricing also shows the valuation of the firm is difficult to predict by the valuation models used by economics and investment bank(s) evaluating the value of company going public. According to Ibbotson (1975) and Lowry & Schwert (2002), companies tend to sell shares which are underpriced to generate interests of investors, which thus can lead to significant gains for investors but could also generate future gains for the company.
A high level of investors’ interest increases the amount of publicity and will increase the proceeds after the IPO. Johnston and Roten (2015) show that investors interest has a positive relation with initial return. However, according to Eckbo (2008), underpricing leaves firms billions of money “left on the table” for shares that should have been sold at the aftermarket trading price every year. Money “left on the table” is therefore the amount of wealth loss for the issuing company when the IPO is underpriced.
An example of underpricing is the IPO of LinkedIn which occurred at May 19, 2011 (business insider). The initial offer price was 45 dollars, but at the end of the first trading day, the price increased to 94,25 dollars per share. In the case of LinkedIn, investors were able to earn high short term gains while LinkedIn could have acquired more capital. The “underpricing discount” was more than 50 percent and this can explain the missed capital gain when companies underprice their IPO. Media argues that the underwriters of LinkedIn left around 130 million dollars “on the table” (Ramsinghani, 2014).
The New York times (http://www.wsj.com) states “LinkedIn's was scammed by its bankers”, which in the case of LinkedIn were Morgan Stanley, Bank of America Merrill Lynch and J.P. Morgan Chase. Within a scale from zero to nine, all these underwriters were ranked with a nine on the Carter and Manaster (1990) rank and are therefore seen as the most prestigious underwriters. In this extreme case of LinkedIn, the question raises why high-ranked underwriters allow underpricing and leaves LinkedIn with “money left on the table”.
According to Beatty and Ritter (1986), ex-ante uncertainty is the most prominent cause of underpricing. The definition of ex-ante uncertainty is uncertainty about the intrinsic value of the shares when the company is getting listed to a stock exchange market (Clarkson & Merkley, 1994). Also, underpricing increases when there appears ex-ante uncertainty about the true value of the company (Ritter, 1986). Popular proxies to use for decreasing this ex-ante uncertainty is the level of underwriter prestige, age, gross proceeds and more. Hiring prestigious underwriters mitigates ex-ante uncertainty since they certify the quality of the company.
In this thesis, I would like to study the relation between IPO underpricing and underwriter’s prestige. Existing evidence isn’t consistent about this relation. Carter & Manaster (1990) show a negative relation between IPO underpricing and underwriter’s prestige until the 1990. However, more recent studies, including the research of Loughran & Ritter (2004), show a positive relation. Therefore, I find it interesting to study whether this relation is either positive or negative over a longer time period.
The aim of the thesis is to study the relation between underwriter prestige and IPO underpricing. For ranking the underwriting prestige, the adjusted Carter and Manaster (1990) ranking is used. The research question is stated as follows:
“Do prestigious underwriters reduce the level of underpricing for IPOs in the United States for the period from 1985 to 2016?”
This thesis is structured as follows: the second chapter discusses theories and existing empirical research on underpricing and the role of underwriters in the IPO process. Chapter three introduces the hypotheses based on the discussed literature and the method used to empirically examine the research question. Chapter four will present the sources and data used to test the hypothesis while the fifth chapter describes and explains the outcomes of the methods used. In the last chapter, a brief summary and conclusion of the empirical research conducted will be presented.
Literature review
Initial Public Offering
An IPO is the event where a private company sell shares (or stocks) to the public for the first time. Exciting research show that an underwriter can support the company by setting the best offer price. Going public offers the company the opportunity to seek capital to expand at lower costs (Eckbo, 2008). The capital raised can be used to invest and stimulate growth of the
company, according to Brau and Fawcett (2006) it gives the company a platform and can stimulate future acquisitions and mergers. Another advantage of an IPO is that it gives existing shareholders the opportunity to diversify their investment and turn it into cash(Ritter and Welch 2002). However, there are also several costs associated with an IPO. Examples are the direct costs, namely legal and tax expenses and costs of hiring an underwriter.
Underpricing
The study of Ibbotson (1975), which is one of the first regarding the underpricing of IPOs, explains underpricing influences awareness of the company and makes future offerings of the issuer more profitable. Research also shows several other theories which explain the reason of underpricing like the winner’s curse hypothesis, signaling theory and Baron models which are explained later on. Furthermore, Loughran and Ritter (2004) found that there are periods where the average initial return of an IPO is exceptional high. An example is the period of the dot-com bubble where the initial average return was more than 60 percent, but examples can also be found in the beginning of the 1970s and mid-1980s. This phenomenon is defined as a ‘’hot issue’’ market (Ibbotson and Jaffe, 1975). Lowry and Schwertz (2002) states this is caused by ex-ante uncertainty though asymmetric information created by investment bankers.
Ex-ante uncertainty and underwriter prestige
Clarkson & Merkley (1994) state the amount of ex-ante uncertainty of the firm’s value is mainly determined by the degree of underpricing in IPOs. They build on Rock (1986), who states in the winner’s curse hypothesis that some investors are better informed about the intrinsic value of the firm. Informed investors bid only to attractive priced IPOs, where uninformed investors don’t. Therefore, uninformed investors must be compensated through underpricing, and by doing so, both types of investors continue to participate in the same IPO. On the other hand, underpricing is costly for the firm going public.
Allen and Faulhaber (1989) introduces the signaling model, which explains underpricing is a positive signal to the investors. Reason is good companies can handle the amount of ‘’money left on the table’’ while bad companies know their expected performance cannot handle initial losses from underpricing, so they don’t send the signal. In the signaling models some signals dominate other signals and one of these signals can be the underwriter’s prestige (Eckbo, 2008). According to Jones and Swaleheen (2010) the best way to reduce adverse selection problems between informed and uninformed investors, is to hire a prestige underwriter. The choice of the underwriter gives a signal to potential investors. This signal
should reduce the incentive for potential investors to gather information, which can be erroneous (Eckbo, 2008). Jones and Swaleheen (2010) states a prestigious underwriter is more effective to estimate the impact of the ex-ante uncertainty, resulting in a better first-day performance after the IPO issue. Thereby, they suggest hiring a less prestigious underwriter is a bad signal to the investors, and is associated with a higher degree of underpricing.
Other asymmetric information models
Besides the winner’s curse hypothesis and signaling models, there are other asymmetric information models which explain the relation between underpricing and underwriter prestige. Baron (1982) was one of the first to study information asymmetry as an explanation for IPO underpricing and explained this phenomenon with the principal-agency problem theory. According to the principle-agency model, underwriters have more knowledge about the condition of the IPO than the issuer itself and make advantage of this position. The incentive for the underwriter to underprice the IPO is to increase interests of investors, which can lead to significant gains since underwriter’s fees are proportional to IPO gross proceeds. Eckbo (2008) states that proceeds are inverse related to underpricing and so the incentive appears to keep underpricing low. According to Biais, Bossaerts, and Rochet (2002), higher offer prices means fewer shares are issued. For maximizing the gross proceeds underwriters have to set the offer price as low as possible.
Loughran and Ritter (2004) introduce the spinning hypothesis, that involves a conflict of interest between the underwriters and the company. Again, this hypothesis assumes that underwriters possess more information. In this theory, the underwriter advices the decision makers (CEOs) to underpricing the IPO. In this case companies choose to leave money on the table through underpricing. According to Loughran and Ritter (2004) spinning of IPOs is partly responsible for the underpricing found during the technology bubble. Likewise, Ljungqvist and Wilhelm (2003) argue that CEOs, in agreement with the underwriter, use a high-level of underpricing during the internet bubble.
Underwriter prestige
Underwriter prestige is defined as a certified intermediary which monitors the quality of the issue (Carter, 1990). To measure the underwriter prestige several measures can be used. One measure is the Carter and Manaster (1990) rank. They developed a ranking from zero to nine, based on the exposures about IPOs released through the financial media.
The study of Lowry & Schwert (2002) show underwriter prestige has a significant negative relation with underpricing. They find that high-ranked underwriters have experience and resources to set the right price. However, Edelen and Kadlec (2005), Zhang (2012) and Johnston and Roten (2015) find a positive relation between underwriter prestige and underpricing for IPOs. In addition, this research contributes to the literature by studying the effect of prestigious underwriters on the level of underpricing over a longer timeframe, namely from 1985 to 2016. Hereby an extended version of the Carter and Manaster (1990) rank is used.
Hypothesis
In this paper, I aim to examine the relation between the underwriter’s prestige and the level of underpricing. Existing literature does not show a consistent relation between underpriced IPOs and prestigious underwriters. I expect IPOs with prestigious underwriters are less underpriced than IPOs with non-prestigious underwriters because of the signaling theory. Reason is prestigious underwriters are better capable of setting the right price and provide a positive signal to potential investors which decreases the ex-ante uncertainty and therefore lower the level of underpricing. The hypothesis that will be tested is as follows:
H1: IPO underpricing is higher for prestigious underwriters than for non-prestigious
underwriters, measured on the adjusted Carter and Manaster (1990) rank.
Methodology
In this research the ordinary least squares (OLS) model is used to explain the relation between the dependent and independent variables. The model is based on the studies of Loughran and Ritter (2004) and Johnston and Roten (2015). Loughran and Ritter (2004) shows underpricing has changed over time while the study of Johnston and Roten (2015) focused on whether underpricing changed after controlling for other variables that may influence underpricing. Both studies answer to different hypotheses, but most of the variables in their models match with each other. Over the past decades many studies discuss the relation between underpricing and prestigious underwriters, but use different models to examine this relation. In this research, the model for testing is defined as:
Underpriced= β0 + β1 * underwriter dummy+ β2 *ln(1+age) + +β3* size + β4* VC backed Dummy + ε.
This model is useful because it is based on many models used in various studies like the research of Jay Ritter. Within in the field of studying IPOs, he conducted many studies where other researchers based their studies upon. All implemented variables in the model are examined in previous research and found significant to underpricing. I narrowed my model with the use of three control variables which can influence the level of omitted variable bias. Loughran and Ritter (2004) and Johnston and Roten (2015) include more control variables in their models and according to Eckbo (2008), there are many variables influencing the level of underpricing. However, Keynes (1939) shows in the Keynes-Tinbergen debate that it is a difficult search to find the correct model. He shows that the best fitting model could be the model with less explanatory variables. According to Keynes, in some cases explanatory variables are not independent of each other and do not influence the dependent variable in a certain model.
Dependent and independent variables
The similarity between the research of Loughran and Ritter (2004) and Johnston and Roten (2015) is that both studied the relation between underpricing and underwriter prestige. Also, both studies defined underpricing as percentage change between the offer price and closing price of the first trading day.
For the ranking of underwriter prestige, I use a similar approach of these studies. Both use the Carter and Manaster (1990) rank which is a great foundation for reputation frameworks in existing literature. The Carter and Manaster (1990) rank has a scale from zero till nine, where zero means the underwriter isn’t prestigious and nine tells the underwriter is very prestigious. This rank has some disadvantages, namely the rank is integer-valued and strictly ordinal. The rank has limited ability to discriminate between underwriters with the same level of prestige. However, this rank is used in most of the studies determining the relation between underpricing and underwriter prestige. Other proxies for measuring the underwriter’s prestige are the Megginson and Weiss (1991) rank and the Johnson and Miller (1988) rank. However, according to Carter, Dark and Singh (1998) there is no significant differences between the various proxies.
Loughran and Ritter (2004) used a top-tier underwriter dummy, which is set equal to one if the underwriter has a rank of eight or more, and zero if the underwriter has a rank lower than eight. In this research, the variable underwriter dummy is formulated like this research. Furthermore, most IPOs have more than one underwriter assigned, where in this thesis, only the lead underwriter is used. Based on previous research of Carter and Manaster (1990), Loughran and Ritter (2004) and Johnston and Roten (2015), the coefficient of this underwriter dummy is expected to be negative.
Control variables
The control variables of both models differ from each other, but both aim to control for ex-ante uncertainty. Loughran and Ritter (2004) include the variable age as a control variable because the cross-sectional pattern between age and first-day returns is studied. Results show there is more underpricing of younger firms than older ones. According to Chen, Firth and Kim (2004) information asymmetry is less for older companies. Older Companies have more information available which reduces ex-ante uncertainty. The variable age is defined as the difference between founded date and date of issue (Loughran and Ritter, 2004). The control variable age is listed as Ln(1+age) which is the natural logarithm of 1 plus the years of the founding date of the IPO. Loughran and Ritter (2004) states there is a negative significant relation between the ln(1+age) of the company and underpricing. Therefore, a negative relation between ln(1+age) and underpricing is expected.
Johnston and Roten (2015) includes the variable size in their model and shows a negative relation between underpricing and size. Size is the gross amount of capital the company raises through the IPO and the relation between underpricing and size is expected to be negative. According to Leone, Rock & Willenborg (2007) the size of the IPO is a signaling mechanism, because more established companies raise higher amounts through an IPO and are associated with lower risk.
Another control variable is the VC backed dummy, where zero stands for an IPO which isn’t VC-backed and one otherwise. Venture capital backed companies are companies which are financed with venture capital money. According to Cotei and Farhat (2011) venture capitalists invest in promising young companies which have a high ability to grow. Megginson and Weiss (1991) compared venture capital (VC) backed IPOs with comparable non-VC backed IPOs. They state venture capitalists certify the true value of the company, which reduces the level of underpricing. Also, Loughran and Ritter (2004) state VC backed IPOs are more competent to attract a prestige underwriters than non-VC backed, but raise less gross proceeds than non-VC backed IPOs. However, according to Rossetto (2008) VC backed IPOs experience higher underpricing than non-VC backed IPOs. This is in line with the grandstanding hypothesis, which states underpricing is necessary for future fund-raising activities (Gompers, 1996). The company is willing to bear the cost of underpricing in order to establish its reputation.
Data
The dataset of United States IPOs between January 1, 1985 and December 31, 2016 is obtained from the Thomson One database. A long timeframe is chosen where different periods of expansion and recession are included. The data extract shows a sample of companies which are listed in the United States and offers various information about the IPOs: name of the company, type of industry, issue date, date founded, offer price, closing price of the first trading day, VC-backed flag, name of the underwriter and the (gross) proceeded amount.
The data of the underwriter’s rank is obtained from the website of Ritter. This ranking method was initiated by Carter and Manaster (1990) and is completed by Ritter. The rank is divided in different periods from 1985 till 2015. Appendix A contains the underwriter’s rank from the website of Ritter. For the unranked years, the most recent known rank is used and for the year 2016, where no rank is available yet, ranks of 2015 are used.
The raw data extract had a sample size of 31.620 companies. However, to perform the analysis stated in this study, data exclusions are made based on: Depository Receipts (ADR), closed-end funds, investment trusts, real-estate investment trusts (REITs), partnerships and all issues with an offer price below five dollars are excluded. Furthermore, IPOs with missing data regarding at least one variable tested were also excluded from the sample. Besides these exclusions, outliers, especially for the variable size, were deleted. The sample size after this reduction consist of 3.430 companies. Because of the missing information certain level of selection bias appears.
Descriptive summary
Table I shows a summary of statistics and the distribution for the variables used in the sample. The IPOs in the sample are underpriced by 22,4 percent on average which shows investing in all IPOs in this dataset means a short-term gain of more than twenty percent. Investors purchasing shares of the company at offering price therefore see a significant increase in share price the first day of trading while the company could have acquired more capital. The standard deviation of underpricing is 59,5 percent and has minimum of -99,4 and a maximum of 2.246,70 percent. Although the average underpricing is positive, 25,8 percent of the sample is zero or negative, which means the IPO is overpriced so investing in IPOs is not without risk. The remaining 73,9 percent has an offer price lower than the first-day closing price and is therefore underpriced.
Table I: Summary Statistics – All Variables
The summary statistics includes 3.430 IPO observations from 1985 to 2016. Data is extracted from the Thomson One database. ADR, closed-end funds, investment trusts, REITs, partnerships and all issues with an offer below five dollars are excluded. Underpricing is the first-day closing price minus the offer price divided by the offer price. The underwriter dummy is based on the Carter and Manaster (1990) rank found on the Ritter website and has a scale from zero till nine. Underwriter dummy is one (zero otherwise) if the underwriter’s prestige is ranked with an 8 or higher. Ln(1+age) is the natural logarithm of the firms age plus 1. Size is the gross proceeds offered. VC backed is a dummy that equals one (zero otherwise) if the IPO is backed by venture capital.
Variable Obs. Mean Std. Dev. Min. Max.
Underpricing 3.430 22,402% 59,46 7% -99,400% 2.246,667%
Underwriter dummy 3.430 0,668 0,471 0,000 1,000
Ln(1+age) 3.430 2.164 0.960 0.000 4.663
Size 3.430 $102,358 $ 238,794 $ 2,405 $ 4.600,000
VC backed dummy 3.430 0,430 0,495 0,000 1,000
The underwriter dummy has a minimum of zero and a maximum of one. The average is 0,668 with a standard deviation of 0,471. The average rank on the Carter and Manaster (1990) rank is 7,4 which means that on average underwriter’s prestige is low-ranked. Table II shows a summary of statistics and the distribution for the variables used in the sample where a distinction is made between low- and high-ranked underwriter’s prestige. 33,2 percent (1.138 companies) of the IPOs have a low-ranked underwriter while 66,8 percent (2.292 companies) have a high- ranked underwriter. Low-ranked underwriters are on average underpriced with 13,8 percent while high-ranked underwriters are on average underpriced with 26,7 percent.
Table III shows the number of underwriters on the Carter and Manaster (1990) rank from zero to nine. Also, this table shows that most of the IPOs are assigned through a high-ranked underwriter. 36,1 percent (1.239 companies) of the IPOs are high-ranked with a nine and 30,7 percent (1.053 companies) of the IPOs are ranked with an eight. Low-ranked underwriters are mostly ranked with a seven, in total 12,0 percent (419 companies). Hereafter most underwriters are ranked with a five (5,8 percent, 198 companies) of the sample. The remaining 15,2 percent (541 companies) are distributed over the other ranks.
The variable ln(1+age) is the natural logarithm of age plus one, but for better understanding I explain the descriptive summaries in terms of age. The average age of an IPO is 13 years and has a standard deviation of 16,6 years. The minimum years between the founded date and issue date is zero and the maximum is 104,9. On average, high-ranked underwriters are about one year older than low-ranked, namely 13,4 years to 12,4 years. Both have a
minimum age of zero years, but the maximum age for a high-ranked underwriter is 104,9 years while low-ranked has a maximum age of 100,3 years. The average size of an IPO is $102,4 million and has a standard deviation of $437,6 million. The minimum is $1,8 million and the maximum is $4.600,0 million. IPOs with high-ranked underwriter have a higher gross proceed. IPOs with a low-ranked underwriter have average gross proceeds of $33,4 million while IPOs with a high-ranked underwriter have average gross proceeds of $136,6 million.
Table II: Summary Statistics – low- and high-ranked underwriter prestige
The summary statistics includes 3.430 IPO observations from 1985 to 2016. Data is extracted from the Thomson One database. ADR, closed-end funds, investment trusts, REITs, partnerships and all issues with an offer below five dollars are excluded. Underpricing is the first-day closing price minus the offer price divided by the offer price. The underwriter dummy is based on the Carter and Manaster (1990) rank found on the Ritter website and has a scale from zero till nine. Ln(1+age) is the natural logarithm of the firms age plus 1. Size is the gross proceeds offered. VC backed is a dummy that equals one (zero otherwise) if the IPO is backed by venture capital.
Low-ranked underwriter’s prestige
Variable Obs. Mean Std. Dev. Min. Max.
Underpricing 1.138 13,770% 32,540% -99,400% 380,000%
Ln(1+age) 1.138 2,122 0,975 0,003 4,618
Size (in $ million) 1.138 $ 33,356 $ 51,206 $ 2,500 $ 1.001,385
VC backed dummy 1.138 0,293 0,455 0,000 1,000
VC-backed dummy defined by non-VC-backed and VC-backed IPOs
Non-VC backed 805
VC backed 333
High-ranked underwriter’s prestige
Variable Obs. Mean Std. Dev. Min. Max.
Underpricing 2.292 26,723% 68,702% -98,529% 2.246,667%
Ln(1+age) 2.292 2,190 0,943 0,000 4,663
Size (in $ million) 2.292 $ 136,618 $ 283,737 $ 2,405 $ 4.600,000
VC backed dummy 2.292 0,500 0,500 0,000 1,000
VC-backed dummy defined by non-VC-backed and VC-backed IPOs
Non-VC backed 1.149
Table III: Summary Statics – Distribution of number of IPOs on the Carter-Manaster (1990) from Ritter website
The summary statistics includes 3.430 IPO observations from 1985 to 2016. Data is extracted from the Thomson One database. ADR, closed-end funds, investment trusts, REITs, partnerships and all issues with an offer below five dollars are excluded. The Carter and Manaster (1990) rank of the Ritter website has a scale from zero till nine.
Total 1 2 3 4 5 6 7 8 9
Number 3430 50 98 152 75 198 146 419 1053 1239
Percentages 100% 1,5% 2,9% 4,4% 2,2% 5,8% 4,3% 12,2% 30,7% 36,1%
Results
This section presents the main results of the OLS regression and will answer to the hypotheses. Table IV shows the correlation between all the variables in a matrix. The correlation coefficient represents the degree to which two variables are associated to move together and in which direction. The range of values for the correlation coefficient is between minus one to plus one, where zero means there is no correlation and (minus) one means there is a perfect correlation. Most of the variables are significant at a level of five percent and the majority is significant under the one percent. Only the correlation between underpricing and size is not significant.
According to Farrar and Glauber (1967) correlations between explanatory variables should be smaller than 0,8 otherwise multicollinearity occurs. In this case, no variables have a correlation that exceeds the threshold of 0,8. The highest occurring correlation is 0,204 which is between variables underwriter dummy and size. In response to the rule of thumb set by Farrar and Glauber (1967), a correlation of 0,204 does not develop a problem for the multicollinearity.
Table IV shows the correlation between size and ln(1+age) is positive. This is as expected since the variable size is higher for aged firms because of longer operation and finance history (Rossotto, 2010). Furthermore, the matrix shows a negative correlation between VC backed dummy and ln(1+age) which means VC backed companies are younger which is in line with the study of Cotei and Farhat (2011) stating venture capitalists especially invest in promising young companies. Furthermore, underwriter dummy is positive correlated with VC backed IPOs. According to Wang, Wang and Lu (2003), this is in line with previous research, showing VC backed companies are able to attract more prestigious underwriters. They suggest VC backed IPOs need a prestigious underwriter for certifying the IPO. Furthermore, underwriter dummy has a negative correlation with size and this matches with the research of (Gompers, 1996). In this study the grandstanding hypothesis of Gompers does not apply.
Table IV: Correlation Matrix of dependent and independent variables
The summary statistics includes 3.430 IPO observations from 1985 to 2016. Data is extracted from the Thomson One database. ADR, closed-end funds, investment trusts, REITs, partnerships and all issues with an offer below five dollars are excluded.
Variable Underpricing Underwriters
dummy
Ln (1+age) Size VC-backed dummy Underpricing 1,000 Underwriters dummy 0,103*** 1,000 Ln(1+age) -0,090*** 0,034** 1,000 Size -0,009 0,204*** 0,066*** 1,000 VC-backed dummy 0,128*** 0,196*** -0,105** -0,122** 1,000
Table V shows the result of the univariable OLS regressions. Regression one is a univariate regression between the underpricing and underwriter’s dummy. In regressions two, three and four, the OLS regressions are between the independent and the control variables ln(1+age), size and VC-backed dummy. These regressions are to test whether all variables influence underpricing like the theory predicts.
In the first univariate regression, there is a significant positive relation between underpricing and the underwriter dummy, which suggests that underwriter prestige causes higher underpricing. This is in line with the previously viewed correlation and the existing literature of Edelen and Kadlec (2005), Zhang (2012) and Johnston and Roten (2015), who also find a positive relation between underwriter prestige and underpricing for IPOs. These papers show us reasons of this relation can be found in personal gains for higher management, the need for positive analysis and upfront agreements with investors who do not exclusively invest in the offering but also purchase shares of the company in the aftermarket. The relation has a significant level of one percent.
Regressions of the control variables ln(1+age) and VC-backed both show significance at a level of one percent. Like expected in response to the research of Loughran and Ritter (2004), there is a negative relation between underpricing and ln(1+age). So, the negative relation between underpricing and ln(1+age) matches the expectation and means that when age increases the level of underpricing decreases. Furthermore, there is a positive relation between underpricing and VC backed IPOs. This means that VC backed IPOs experience higher underpricing than non-VC backed IPOs which is contrary to the expectations. However, this is
in line with study of Rossette (2008). They give the explanation that VC companies on average don’t establish their reputation and are more prepared to bear the cost of underpricing.
Table V: results of the univariate ordinary least square analysis
The summary statistics includes 3.430 IPO observations from 1985 to 2016. Data is extracted from the Thomson One database. ADR, closed-end funds, investment trusts, REITs, partnerships and all issues with an offer below five dollars are excluded. Underpricing is the first-day closing price minus the offer price divided by the offer price. The underwriter dummy is based on the Carter and Manaster (1990) rank found on the Ritter website and has a scale from zero till nine. Underwriter dummy is one (zero otherwise) if the underwriter’s prestige is ranked with an 8 or higher. Ln(1+age) is the natural logarithm of the firms age plus 1. Size is the gross proceeds offered. VC backed is a dummy that equals one (zero otherwise) if the IPO is backed by venture capital. T-statistics are computed using heteroscedasticity-consistent standard errors. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively. Underpricing 1 2 3 4 Intercept 0,138*** (0,010) 0,345*** (0,028) 0,226*** (0,011) 0,158*** (0,014) Underwriter’s dummy 0,130*** (0,017) Ln(1+age) -0,056*** (0,009) Size -0,000 (0,000) VC-backed dummy 0,154*** (0,020) N 3.430 3.430 3.430 3.430
Regressions of the control variables ln(1+age) and VC-backed both show significance at a level of one percent. Like expected in response to the research of Loughran and Ritter (2004), there is a negative relation between underpricing and ln(1+age). So, the negative relation between underpricing and ln(1+age) matches the expectation and means that when age increases the level of underpricing decreases. Furthermore, there is a positive relation between underpricing and VC backed IPOs. This means that VC backed IPOs experience higher underpricing than non-VC backed IPOs which is contrary to the expectations. However, this is in line with study of Rossette (2008). They give the explanation that VC companies on average don’t establish their reputation and are more prepared to bear the cost of underpricing.
As expected, underpricing has a negative relation with size, however this relation is not significant. The negative relation is in line with the study of Leone, Rock & Willenborg (2007),
which states the size reduces ex-ante uncertainty and shows there is a negative relation between underpricing and size.
Table VI shows multivariate OLS regressions. Regression one shows the relation between underpricing, underwriter’s dummy and ln(1+age). After regression one the regression is extended with variable size to verify whether the regression fits better. Regression three shows the regression between the independent, dependent and the control variables.
Table VI: results of the multivariate Ordinary Least Square analysis
The summary statistics includes 3.430 IPO observations from 1985 to 2016. Data is extracted from the Thomson One database. ADR, closed-end funds, investment trusts, REITs, partnerships and all issues with an offer below five dollars are excluded. Underpricing is the first-day closing price minus the offer price divided by the offer price. The underwriter dummy is based on the Carter and Manaster (1990) rank found on the Ritter website and has a scale from zero till nine. Underwriter dummy is one (zero otherwise) if the underwriter’s prestige is ranked with an 8 or higher. Ln(1+age) is the natural logarithm of the firms age plus 1. Size is the gross proceeds offered. VC backed is a dummy that equals one (zero otherwise) if the IPO is backed by venture capital. T-statistics are computed using heteroscedasticity-consistent standard errors. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively. Underpricing 1 2 3 Intercept 0,140*** (0,018) 0,261*** (0,223) 0,210*** (0,027) Underwriter’s dummy 0,137*** (0,018) 0,140*** (0,019) 0,110*** (0,021) Ln(1+age) -0.058*** (0.009) -0,057*** (0,009) -0,051*** (0,009) Size -0,000*** (0,000) -0,000 (0,000) VC-backed dummy 0,122*** (0,027) N 3.430 3.430 3.430 R2 0,019 0,019 0,028 F 33,487*** 34,25*** 34,25***
The adjusted R-squared (R2) shows the best fit and is modified from R-squared adjusted
for the number of variables. The R2 only increases if a new variable improves the model. According to table VI the R2 has the highest value for the regression with independent,
dependent and control variables. Also, the F-test for this regression is significant. However, the variable size isn’t significant, but shows significance in regression one and two. Furthermore, all variables, except VC backed, behave as expected in response to literature.
With previously discussed results the hypothesis can be answered. The null hypothesis is rejected. Within a 95 percent confidence interval, IPO underpricing is higher for prestigious underwriters than for non-prestigious underwriters, measured on the adjusted Carter and Manaster (1990) rank. All regression analysis show a positive significant effect of the underwriter dummy on underpricing. The model has a higher R2 when the control variables are included. Finally, when the dependent, independent and control variables are present, the model fits best.
Conclusion and discussion
This thesis discussed the underpricing of Initial Public Offerings (IPO) and examined whether prestigious underwriters guiding the IPOs have a significant effect on the level of underpricing. An IPO is carried out by a company when it decides to raise external capital throughout the public market. This process of getting listed to a stock exchange market is guided by investment banks which are also known as underwriters. They are responsible for setting the right offering price of the shares which can then be purchased by investors. However, after the first day of trading, the share price could be significant different (mostly higher) than the offering price. This phenomenon is called underpricing, which leaves the company with missed capital, also known as leaving money at the table, but creates high returns for investors.
The performed tests in this study examined the effect of underwriter’s prestige on underpricing for initial issues in the United States for the period between January 1985 and December 2016. Existing literature shows mixed results but most studies find a negative effect of prestigious underwriters on underpricing. Prestigious underwriters should therefore be better capable of setting the right offer price than lower ranked underwriters. Based on the reviewed literature, the research question in this thesis states:
Do prestigious underwriters reduce the level of underpricing for IPOs in the United States for the period from 1985 to 2016?
In order to perform the required tests, a raw dataset with a sample size of 31.620 IPOs was extracted from the Thomson One database. After excluding IPOs with missing values and multiple variables like ADR’s and REIT’s, the dataset was reduced to 3.430 companies which went public within the defined period.
For testing the relation between prestigious underwriters and underpricing, first the descriptive statistics were computed and the correlation between the variables was studied. This
was followed up by performing the students T-test and F-test in an Ordinary Least Square (OLS) model. The dependent variable underpricing, independent variable underwriter and control variables age, size and venture capital backed were tested as follows:
Underpriced= β0 + β1 * underwriter dummy+ β2 *ln(1+age) + +β3* size + β4* VC backed Dummy + ε.
Results show 66,8 percent of the hired underwriters were ranked with an eight or higher on the Carter and Manaster (1990) rank, which means more than two-third of the underwriters in the sample are prestigious. Low-ranked underwriters score a lower average underpricing (13,8 percent) than high-ranked underwriters (26,7 percent). Furthermore, both the univariate as the multivariate regression show that the positive effect of underwriter’s prestige on underpricing is significant. This indicates IPO’s guided by prestigious underwriters are more underpriced than underwriters who are not prestigious or lower ranked. The conclusion to the research question therefore states underwriter’s prestige does not reduced the level of underpricing for IPOs in the United States for the period from 1985 to 2016. Although many studies show contrary results, the outcomes of this thesis are in line with the more recent studies of Zhang (2012) and Johnston and Roten (2015). They also find a positive relation between underwriter prestige and underpricing for IPOs. One of the reasons for this outcome could possibly be found in the used timeframe. Even when testing takes place over more than three decades, short periods of extreme anomalies in the IPO market could have influenced the results. Therefore, a recommendation for further research will be the exclusion of periods where exceptional high averages initial returns of IPOs are made, like the years during the dot-com bubble. Another suggestion is to test for endogeneity which could lead to biased results. Habib and Ljungqvist (2001) states that it is important to controlling for endogeneity, because underwriter’s prestige could be endogenous in a regression analysis with underpricing as the dependent variable.
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Appendix A Carter and Manaster rank
Underwriter Name '85-'91 '92-'00 '01-'04 '05-'07 '08-'09 '10-'11 '12-'16
Citigroup 9,001 9,001 9,001 9,001 9,001
Citigroup Global Markets Inc 9,001 9,001 9,001 9,001 9,001
Citigroup/Salomon Smith Barney 9,001 9,001 9,001 9,001 9,001
CS First Boston 9,001 9,001 9,001 9,001 9,001 9,001
Deutsche Banc Alex Brown 9,001 9,001 9,001 9,001 9,001 9,001
Deutsche Morgan Grenfell 9,001 9,001 9,001 9,001 9,001 9,001
Donaldson Lufkin & Jenrette 8,750 9,001 9,001 9,001 9,001 9,001 9,001
First Boston Corp 9,000 9,001 9,001 9,001 9,001 9,001 9,001
Goldman Sachs & Co 9,000 9,001 9,001 9,001 9,001 9,001 9,001
JP Morgan (JPM) 9,001 9,001 9,001 9,001 9,001 9,001
JP Morgan Securities Inc 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Merrill Lynch Capital Markets 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Merrill Lynch, Pierce, Fenner 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Morgan Stanley 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Morgan Stanley & Co 8,875 9,001 9,001 9,001 9,001 9,001 9,001
Morgan Stanley Dean Witter 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Morgan Stanley International 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Nomura Secs Intl 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Nomura Securities 8,250 9,001 9,001 9,001 9,001 9,001 9,001
Salomon Brothers 9,000 9,001 9,001 9,001 9,001 9,001 9,001
Salomon Smith Barney 9,000 9,001 9,001 9,001 9,001 9,001 9,001
Shearson Lehman Brothers 8,833 9,001 9,001 9,001 9,001 9,001 9,001
Shearson Lehman Hutton 9,001 9,001 9,001 9,001 9,001 9,001 9,001
Prudential-Bache Capital Fund 8,750 8,750 8,750 8,750 8,750 8,750 8,750
Robertson, Colman & Stephens 8,750 8,750 8,750 8,750 8,750 8,750 8,750
Bank of America-Merrill Lynch 8,501 8,501 8,501 8,501 8,501 8,501 8,501
Credit Suisse First Boston 9,001 9,001 9,001 9,001 8,501 8,501
Deutsche Bank Securities Corp 9,001 9,001 9,001 9,001 8,501 8,501
Merrill Lynch & Co Inc 9,001 9,001 9,001 9,001 9,001 8,501 8,501
UBS Investment Bank 8,001 8,001 8,001 8,501 8,501
UBS Securities Inc 8,001 8,001 8,001 8,001 8,501 8,501
Credit Suisse 8,500 8,500 8,500 8,500 8,500 8,500 8,500
ABN-AMRO Holding NV 8,001 8,001 8,001 8,001 8,001 8,001
Alex Brown & Sons Inc 8,875 8,001 8,001 8,001 8,001 8,001 8,001
Allen & Co Inc 7,000 7,001 7,001 7,001 7,001 8,001 8,001
Banc of America Securities LLC 8,001 8,001 8,001 8,001 8,001 8,001
BancAmerica Robertson Stephens 8,001 8,001 8,001 8,001 8,001 8,001
BancBoston Robertson Stephens 8,001 8,001 8,001 8,001 8,001 8,001
Barclays Capital 8,001 8,001 8,001 8,001
Bear Stearns & Co Inc 8,750 8,001 8,001 8,001 8,001 8,001 8,001
Bear Stearns International 8,001 8,001 8,001 8,001 8,001 8,001
BT Alex Brown Inc 8,001 8,001 8,001 8,001 8,001 8,001
Chase H&Q 8,001 8,001 8,001 8,001 8,001 8,001
Citicorp Securities Inc 8,001 8,001 8,001 8,001 8,001 8,001
Daiwa Securities America 8,125 8,001 8,001 8,001 8,001 8,001 8,001
Dean Witter Reynolds Inc 8,500 8,001 8,001 8,001 8,001 8,001 8,001
Dillon, Read & Co Inc 8,625 8,001 8,001 8,001 8,001 8,001 8,001
Underwriter Name '85-'91 '92-'00 '01-'04 '05-'07 '08-'09 '10-'11 '12-'16
Hambrecht & Quist 8,000 8,001 8,001 8,001 8,001 8,001 8,001
Jefferies & Co Inc 5,333 5,001 5,001 5,001 6,001 8,001 8,001
Kidder Peabody & Co Inc 8,833 8,001 8,001 8,001 8,001 8,001 8,001
Lazard Capital Markets 9,001 8,001 8,001 8,001
Lazard Freres & Co LLC 8,750 9,001 9,001 9,001 8,001 8,001 8,001
Lehman Brothers 9,001 9,001 8,001 8,001 8,001 8,001 8,001
Montgomery Securities 8,750 8,001 8,001 8,001 8,001 8,001 8,001
NationsBanc Montgomery Sec 8,001 8,001 8,001 8,001 8,001 8,001
NatWest Securities 8,001 8,001 8,001 8,001 8,001 8,001
PaineWebber 8,001 8,001 8,001 8,001 8,001 8,001 8,001
Prudential Vector Healthcare 8,001 8,001 8,001 8,001 8,001 8,001
Prudential-Bache Securities 8,001 8,001 8,001 8,001 8,001 8,001 8,001
RBC Capital Markets 7,001 7,001 8,001 8,001 8,001
Robertson Stephens & Co 8,750 8,001 8,001 8,001 8,001 8,001 8,001
SBC Warburg Dillon Read Inc 8,001 8,001 8,001 8,001 8,001 8,001
Schroder & Co Inc 8,001 8,001 8,001 8,001 8,001 8,001
Schroder Wertheim & Co 8,001 8,001 8,001 8,001 8,001 8,001
Smith Barney Inc 8,001 8,001 8,001 8,001 8,001 8,001
Smith Barney Shearson 8,001 8,001 8,001 8,001 8,001 8,001
Smith Barney, Harris Upham 8,750 8,001 8,001 8,001 8,001 8,001 8,001
UBS Warburg 8,001 8,001 8,001 8,001 8,001 8,001
Warburg Dillon Read 8,001 8,001 8,001 8,001 8,001 8,001
Wells Fargo 7,001 8,001 8,001
Wertheim Schroder 8,830 8,001 8,001 8,001 8,001 8,001 8,001
EF Hutton & Co Inc 8,000 8,000 8,000 8,000 8,000 8,000 8,000
Thomson McKinnon Securities 7,750 7,750 7,750 7,750 7,750 7,750 7,750
CIBC Oppenheimer 8,001 8,001 8,001 8,001 7,501 7,501
CIBC World Markets 8,001 8,001 8,001 8,001 7,501 7,501
Piper Jaffray Cos 7,001 7,001 7,001 7,501 7,501
Piper Jaffray Inc 7,001 7,001 7,001 7,001 7,501 7,501
Raymond James & Associates Inc 5,625 7,001 7,001 7,001 7,001 7,001 7,501
Sandler O'Neill Partners 8,001 8,001 8,001 8,001 7,501 7,501
Blunt Ellis & Loewi Inc 7,167 7,167 7,167 7,167 7,167 7,167 7,167
AG Edwards & Sons Inc 8,000 7,001 7,001 7,001 7,001 7,001 7,001
C.J. Lawrence/Deutsche Bank 7,001 7,001 7,001 7,001 7,001 7,001
County NatWest Securities Ltd 7,001 7,001 7,001 7,001 7,001 7,001
Cowen 5,500 7,001 7,001 7,001 7,001 6,501 7,001
Dain Bosworth Inc 7,625 7,001 7,001 7,001 7,001 7,001 7,001
Dain Rauscher Corp 7,001 7,001 7,001 7,001 7,001 7,001
Dain Rauscher Wessels 7,001 7,001 7,001 7,001 7,001 7,001
Fleet Boston Corp 7,001 7,001 7,001 7,001 7,001 7,001
Furman Selz LLC 7,001 7,001 7,001 7,001 7,001 7,001
Furman Selz Mager Dietz Birney 6,375 7,001 7,001 7,001 7,001 7,001 7,001
Hornblower Weeks Noyes & Trask 7,001 7,001 7,001 7,001 7,001 7,001
ING Baring Furman Selz LLC 7,001 7,001 7,001 7,001 7,001 7,001
ING Barings 7,001 7,001 7,001 7,001 7,001 7,001
JC Bradford & Co 7,375 7,001 7,001 7,001 7,001 7,001 7,001
JMP-Sec 5,001 7,001 7,001 7,001
Keefe Bruyette & Woods Inc 8,333 7,001 7,001 7,001 7,001 7,001 7,001
Underwriter Name '85-'91 '92-'00 '01-'04 '05-'07 '08-'09 '10-'11 '12-'16
Legg Mason Wood Walker 7,125 7,001 7,001 7,001 7,001 7,001 7,001
LF Rothschild Unterberg Towbin 7,001 7,001 7,001 7,001 7,001 7,001 7,001
Morgan Keegan Inc 6,625 7,001 7,001 7,001 7,001 7,001 7,001
Oppenheimer & Co Inc 7,875 8,001 8,001 7,001 7,001 7,001 7,001
Pacific Crest Securities Inc 1,001 1,001 1,001 1,001 1,001 7,001
Piper Jaffray & Hopwood Inc 7,001 7,001 7,001 7,001 7,001 7,001 7,001
Prudential Securities Inc 6,001 6,001 7,001 7,001 7,001 7,001 7,001
Rauscher Pierce Refsnes Inc 6,250 7,001 7,001 7,001 7,001 7,001 7,001
Robert W Baird & Co Inc 5,750 7,001 7,001 7,001 7,001 7,001 7,001
SG Warburg & Co Inc (SZ) 7,001 7,001 7,001 7,001 7,001 7,001
SG Warburg Securities 7,001 7,001 7,001 7,001 7,001 7,001 7,001
Soundview Financial Group Inc 7,001 7,001 7,001 7,001 7,001 7,001
Stephens Inc 6,750 7,001 7,001 7,001 7,001 7,001 7,001
Stifel Nicolaus & Co Inc 5,750 6,001 5,001 5,001 5,001 7,001 7,001
Thomas Weisel Partners LLC 8,001 8,001 8,001 7,001 7,001 7,001
US Bancorp Piper Jaffray 7,001 7,001 7,001 7,001 7,001 7,001
Volpe Brown Whelan & Co 7,001 7,001 7,001 7,001 7,001 7,001
Volpe Welty & Co 5,000 7,001 7,001 7,001 7,001 7,001 7,001
W.R. Hambrecht & Company 7,001 7,001 7,001 7,001 7,001 7,001
Wachovia Securities Inc 7,001 7,001 7,001 7,001 7,001 7,001
Wessels Arnold & Henderson LLC 5,333 7,001 7,001 7,001 7,001 7,001 7,001
William Blair & Co 7,875 7,001 7,001 7,001 7,001 7,001 7,001
William R. Hough 7,001 7,001 7,001 7,001 7,001 7,001
Butcher & Singer Inc 6,750 6,750 6,750 6,750 6,750 6,750 6,750
BMO Capital Markets 5,001 5,001 6,001 6,501
SunTrust Robinson Humphrey 6,001 6,001 6,001 6,501 6,501
Laidlaw Adams & Peck 6,500 6,500 6,500 6,500 6,500 6,500 6,500
Eppler Guerin & Turner Inc 6,250 6,250 6,250 6,250 6,250 6,250 6,250
Advest Inc 7,125 6,001 6,001 6,001 6,001 6,001 6,001
BB&T Capital Markets 7,001 7,001 6,001 6,001 6,001 6,001
BMO Nesbitt Thomson Ltd 6,001 6,001 6,001 6,001 6,001 6,001
Canaccord Genuity 6,001 6,001
CE Unterberg Towbin 6,001 6,001 6,001 6,001 6,001 6,001
Chicago Corp 6,001 6,001 6,001 6,001 6,001 6,001 6,001
Davenport 4,875 6,001 6,001 6,001 6,001 6,001 6,001
Equitable Securities Corp 6,001 6,001 6,001 6,001 6,001 6,001 6,001
Invemed Associates Inc 6,500 6,001 6,001 6,001 6,001 6,001 6,001
Kemper Securities 4,001 6,001 6,001 6,001 6,001 6,001 6,001
Ladenburg Thalmann & Co 6,000 6,001 6,001 6,001 6,001 6,001 6,001
Robinson-Humphrey Co 6,001 6,001 6,001 6,001 6,001 6,001
Roney Capital Markets 6,001 6,001 6,001 6,001 6,001 6,001
Scott & Stringfellow Financial 5,500 6,001 6,001 6,001 6,001 6,001 6,001
SG Cowen Securities Corp 6,001 6,001 6,001 6,001 6,001 6,001
Sutro & Co Inc 6,000 6,001 6,001 6,001 6,001 6,001 6,001
Tucker Anthony Inc 7,000 6,001 6,001 6,001 6,001 6,001 6,001
Unterberg Harris 4,000 5,001 5,001 6,001 6,001 6,001 6,001
Wheat First Butcher & Singer 6,001 6,001 6,001 6,001 6,001 6,001 6,001
Boettcher 6,000 6,000 6,000 6,000 6,000 6,000 6,000
Moseley, Hallgarten, Estabrook 5,750 5,750 5,750 5,750 5,750 5,750 5,750
Underwriter Name '85-'91 '92-'00 '01-'04 '05-'07 '08-'09 '10-'11 '12-'16
Rotan Mosle Inc 5,667 5,667 5,667 5,667 5,667 5,667 5,667
Lovett Mitchell Webb Garrison 5,500 5,500 5,500 5,500 5,500 5,500 5,500
Seidler Amdec Securities Inc 5,125 5,125 5,125 5,125 5,125 5,125 5,125
Adams Harkness & Hill Inc 5,001 5,001 5,001 5,001 5,001 5,001 5,001
Auerbach Pollak & Richardson 5,001 5,001 5,001 5,001 5,001 5,001
CL King & Associates Inc 5,001 5,001 5,001 5,001 5,001 5,001
Cohig & Associates 5,001 5,001 5,001 5,001 5,001 5,001 5,001
DA Davidson & Co Inc 4,375 5,001 5,001 4,001 4,001 4,001 5,001
Dickinson & Co 5,500 5,001 5,001 5,001 5,001 5,001 5,001
EVEREN Securities Inc 5,001 5,001 5,001 5,001 5,001 5,001
Fahnestock & Co 4,250 5,001 5,001 5,001 5,001 5,001 5,001
fbr.com (Friedman Billings Ramsey) 5,001 5,001 5,001 5,001 5,001 5,001
Ferris, Baker Watts 5,001 5,001 5,001 5,001 5,001 5,001
First of Michigan Corp 5,625 5,001 5,001 5,001 5,001 5,001 5,001
First Union Capital Markets 5,001 5,001 5,001 5,001 5,001 5,001
Friedman Billings Ramsey & Co 5,001 5,001 5,001 5,001 5,001
Friedman Billings Ramsey Group 5,001 5,001 5,001 5,001 5,001 5,001
Gerard Klauer Mattison & Co 5,001 5,001 5,001 5,001 5,001 5,001
Gruntal & Co Inc 5,875 5,001 5,001 5,001 5,001 5,001 5,001
Hanifen Imhoff Inc 5,000 5,001 5,001 5,001 5,001 5,001 5,001
HC Wainwright & Co Inc 5,001 5,001 5,001 5,001 5,001 5,001
Hoak Breedlove Wesneski & Co 5,001 5,001 5,001 5,001 5,001 5,001
Howard Weil Labouisse Freid 5,750 5,001 5,001 5,001 5,001 5,001 5,001
Interstate Securities 5,001 5,001 5,001 5,001 5,001 5,001 5,001
Interstate/Johnson Lane Inc 5,001 5,001 5,001 5,001 5,001 5,001
Interstate/Johnson Lane Inc 6,000 5,001 5,001 5,001 5,001 5,001 5,001
Janney Montgomery Scott Inc 6,000 7,001 7,001 5,001 5,001 5,001 5,001
John G Kinnard & Co 5,001 5,001 5,001 5,001 5,001 5,001 5,001
Keycorp/McDonald Investments 5,001 5,001 5,001 5,001 5,001
Mabon Securities Corp 5,001 5,001 5,001 5,001 5,001 5,001
May Davis Group Inc 5,001 5,001 5,001 5,001 5,001 5,001
McDonald Investments 6,001 6,001 5,001 5,001 5,001 5,001 5,001
Needham & Co Inc 6,000 5,001 5,001 5,001 5,001 5,001 5,001
Ohio Co 5,001 5,001 5,001 5,001 5,001 5,001 5,001
Parker-Hunter Inc 4,875 5,001 5,001 5,001 5,001 5,001 5,001
Pennsylvania Merchant Group 3,833 5,001 5,001 5,001 5,001 5,001 5,001
Raffensperger, Hughes 5,001 5,001 5,001 5,001 5,001 5,001 5,001
Roney & Co 4,750 5,001 5,001 5,001 5,001 5,001 5,001
Roth Capital Partners Inc 4,001 4,001 4,001 4,001 5,001 5,001
Ryan Beck & Co 7,000 5,001 5,001 5,001 5,001 5,001 5,001
Sanders Morris Harris Inc 5,001 5,001 5,001 5,001 5,001
Southwest Securities 5,001 5,001 5,001 5,001 5,001 5,001 5,001
Sterne Agee & Leach Inc 4,001 5,001 5,001 5,001 5,001 5,001 5,001
Strasbourger Pearson Tulcin 5,500 5,001 5,001 5,001 5,001 5,001 5,001
Thinkequity Partners LLC 5,001 5,001 5,001 5,001
Van Kasper & Co 3,500 5,001 5,001 5,001 5,001 5,001 5,001
Vector Securities Intl 1,500 5,001 5,001 5,001 5,001 5,001 5,001
Wedbush Securities 5,001 5,001 5,001 5,001 5,001 5,001 5,001
First Affiliated Securities 5,000 5,000 5,000 5,000 5,000 5,000 5,000
Underwriter Name '85-'91 '92-'00 '01-'04 '05-'07 '08-'09 '10-'11 '12-'16
Carolina Securities 4,250 4,250 4,250 4,250 4,250 4,250 4,250
Black & Co Inc 4,001 4,001 4,001 4,001 4,001 4,001
Craig-Hallum, Inc. 4,500 4,001 4,001 4,001 4,001 4,001 4,001
D. H. Blair 4,001 4,001 4,001 4,001 4,001 4,001 4,001
D. H. Blair Investment Banking 4,001 4,001 4,001 4,001 4,001 4,001
D. H. Wallach 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Dirks & Company Inc 4,001 4,001 4,001 4,001 4,001 4,001
Feltl & Co 4,001 4,001 4,001 4,001 4,001
First Albany 6,000 5,001 5,001 4,001 4,001 4,001 4,001
Hamilton Investments 4,001 4,001 4,001 4,001 4,001 4,001
Hoefer & Arnett Inc 4,001 4,001 4,001 4,001 4,001 4,001
Imperial-Cap 4,001 4,001
Johnson Rice & Co 4,001 4,001 4,001 4,001 4,001 4,001 4,001
L.H. Alton & Co. 4,001 4,001 4,001 4,001 4,001 4,001
Laidlaw Global Securities 4,001 4,001 4,001 4,001 4,001 4,001
MDB-Capital 2,001 4,001
Merriman Curhan Ford & Co. 4,001 4,001 4,001 4,001
Milwaukee 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Moseley Securities Corporation 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Nutmeg Securities Ltd 4,001 4,001 4,001 4,001 4,001 4,001
Pacific Growth Equities Inc 1,001 4,001 4,001 4,001 4,001 4,001
Pauli Johnson Capital & Resch 4,001 4,001 4,001 4,001 4,001 4,001
Prime Charter Ltd 4,001 4,001 4,001 4,001 4,001 4,001
Principal Financial Securities 4,001 4,001 4,001 4,001 4,001 4,001
Punk, Ziegel & Company 6,001 4,001 4,001 4,001 4,001 4,001
R. G. Dickinson 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Reich 4,000 4,001 4,001 4,001 4,001 4,001 4,001
Royce Investment Group Inc 4,001 4,001 4,001 4,001 4,001 4,001
Sands Brothers & Co Ltd 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Seidler Corp 4,001 4,001 4,001 4,001 4,001 4,001
Shemano Group 4,001 4,001 4,001 4,001 4,001
Southcoast Capital 3,001 4,001 4,001 4,001 4,001 4,001 4,001
Tejas Securities Inc 4,001 4,001 4,001 4,001 4,001 4,001
W. E. Kaufman 4,001 4,001 4,001 4,001 4,001 4,001
Waldron & Co 4,001 4,001 4,001 4,001 4,001 4,001
Werbel-Roth Securities 4,000 4,001 4,001 4,001 4,001 4,001 4,001
William K Woodruffe & Co Inc 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Wm. C. Roney 4,001 4,001 4,001 4,001 4,001 4,001 4,001
Woolcott 3,750 3,750 3,750 3,750 3,750 3,750 3,750
AB Capital Markets 3,001 3,001 3,001 3,001 3,001 3,001
Aegis Capital 4,001 4,001 4,001 4,001 3,001 3,001
AmeriCorp Securities Services 3,001 3,001 3,001 3,001 3,001 3,001
Anderson & Strudwick 4,001 4,001 3,001 3,001 3,001 3,001
Argent Securities, Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Baraban Securities Inc 3,001 3,001 3,001 3,001 3,001 3,001
Berthel Fisher & Co Financial 3,001 3,001 3,001 3,001 3,001 3,001
Boston Group 3,001 3,001 3,001 3,001 3,001 3,001
Brauer & Associates 3,001 3,001 3,001 3,001 3,001 3,001
Burnham Securities Inc 5,001 5,001 5,001 5,001 5,001 3,001
Underwriter Name '85-'91 '92-'00 '01-'04 '05-'07 '08-'09 '10-'11 '12-'16
Capital West Securities 3,001 3,001 3,001 3,001 3,001 3,001
Cardinal Capital Management 3,001 3,001 3,001 3,001 3,001 3,001
Chardan Capital Markets, LLC 2,001 2,001 3,001 3,001
Coleman & Company 3,001 3,001 3,001 3,001 3,001 3,001
Commonwealth Securities, Utah 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Cruttenden & Co. Inc. 2,500 3,001 3,001 3,001 3,001 3,001 3,001
Cruttenden Roth Inc 3,001 3,001 3,001 3,001 3,001 3,001
Dominick 3,001 3,001
Donald & Co. Securities 3,001 3,001 3,001 3,001 3,001 3,001 3,001
EKN Financial Services, Inc. 3,001 3,001 3,001 3,001
EuroAtlantic Securities, Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Farrell 3,001 3,001 3,001 3,001 3,001 3,001
First Cambridge Securities Co 3,001 3,001 3,001 3,001 3,001 3,001
First London Securities Corp 3,001 3,001 3,001 3,001 3,001 3,001
Franklin-Lord, Inc. 3,001 3,001 3,001 3,001 3,001 3,001
GunnAllen Financial Inc 3,001 3,001 3,001 3,001 3,001 3,001
H. D. Brous 3,001 3,001 3,001 3,001 3,001 3,001
Hampshire Securities Corp 3,001 3,001 3,001 3,001 3,001 3,001
Howe Barnes Investments Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Huberman, Margaretten & Straus 3,001 3,001 3,001 3,001 3,001 3,001 3,001
I. M. Simon 3,001 3,001 3,001 3,001 3,001 3,001 3,001
IAR Securities Corp 3,001 3,001 3,001 3,001 3,001 3,001
J.W. Barclay 3,001 3,001 3,001 3,001 3,001 3,001
James J. Duane 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Joseph Stevens & Company 3,001 3,001 3,001 3,001 3,001 3,001
Kashner Davidson Securities 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Keane Securities 3,000 3,001 3,001 3,001 3,001 3,001 3,001
Kenneth Jerome & Co. Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Kensington Securities, Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Kirlin Securities Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Laidlaw Equities Inc 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Life Planning 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Mathews, Holmquist & Assoc. 3,001 3,001 3,001 3,001 3,001 3,001
Maxim Group LLC 2,001 3,001 3,001 3,001 3,001
Maxwell Capital 3,001 3,001 3,001 3,001 3,001 3,001
Meridian Capital Markets 3,001 3,001 3,001 3,001 3,001 3,001
Miller, Johnson & Kuehn, Inc. 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Nationwide Securities, Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Neidiger, Tucker, Bruner Inc. 2,500 3,001 3,001 3,001 3,001 3,001 3,001
Network 1 Financial Securities 3,001 3,001 3,001 3,001 3,001 3,001
Noble International Investment 3,001 3,001 3,001 3,001 3,001 3,001
Noble Investment Co 3,001 3,001 3,001 3,001 3,001 3,001 3,001
Norcross Securities, Inc. 3,001 3,001 3,001 3,001 3,001 3,001
Oak Ridge Investments 3,001 3,001 3,001 3,001 3,001 3,001
Paulson Investment Co 5,000 3,001 3,001 3,001 3,001 3,001 3,001
R. B. Marich 3,001 3,001 3,001 3,001 3,001 3,001 3,001
RAF Financial 3,001 3,001 3,001 3,001 3,001 3,001 3,001
RAS Securities Corporation 3,001 3,001 3,001 3,001 3,001 3,001
Robert Todd Financial Corp. 3,001 3,001 3,001 3,001 3,001 3,001 3,001
