Description
The purpose of this paper is to test the fundamental purpose of the 1992 Small Business
Incentive Act (SBIA) to reduce the regulatory burden for small firms to raise public equity capital
Journal of Financial Economic Policy
Efficacy of the 1992 Small Business Incentive Act
J ames C. Brau J . Troy Carpenter
Article information:
To cite this document:
J ames C. Brau J . Troy Carpenter, (2012),"Efficacy of the 1992 Small Business Incentive Act", J ournal of
Financial Economic Policy, Vol. 4 Iss 3 pp. 204 - 217
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Ef?cacy of the 1992 Small
Business Incentive Act
James C. Brau and J. Troy Carpenter
Department of Finance, Marriott School, Brigham Young University,
Provo, Utah, USA
Abstract
Purpose – The purpose of this paper is to test the fundamental purpose of the 1992 Small Business
Incentive Act (SBIA) to reduce the regulatory burden for small ?rms to raise public equity capital.
Design/methodology/approach – Our research compares initial public offerings (IPOs) that ?led
with the newer SB-2 program to benchmark ?rms that ?led using the traditional S-1 ?ling. The
authors use three proxies to measure success, hypothesizing that, if the regulatory burden has indeed
been reduced for small ?rms, all three variables should be smaller for SB-2 IPOs. Univariate and
multivariate analyses were conducted.
Findings – With regards to easing regulatory costs, it is found that the program has not been
effective. On average, SB-2 IPOs experience larger-scaled offering expenses, and pay higher
underwriter gross spreads compared to S-1 IPOs of similar size. SB-2 IPOs, however, take fewer days
to complete the registration process, when controlling for other relevant factors. In the burden of time,
the SBIA has been effective.
Practical implications – The paper is of value to managers of ?rms desiring to conduct an IPO.
These managers, if they meet the size requirements dictated by the SEC, can elect to use an SB-2 or an
S-1 document. The paper shows that if cost is the primary concern, the S-1 program should be
preferred. If time is the primary consideration, then the SB-2 program is preferred.
Originality/value – To the authors’ knowledge, they are the ?rst to test the ef?cacy of the SBIA
program.
Keywords United States of America, Legislation, Small enterprises, Equity capital,
Small Business Incentive Act of 1992, SEC policy, Initial public offering
Paper type Research paper
Under the US Securities Laws, ?rms may go public by ?ling a number of different
registration forms with the Securities and Exchange Commission (SEC, 1992). The
typical initial public offering (IPO) ?rm ?les an S-1 offering prospectus. Firms that
register with an S-1 have been extensively studied in the ?nancial economics literature.
The theory and empirics of “mainline” IPOs are well developed.
A much lesser-known path to an IPO, designed speci?cally for small businesses, is
to ?le a form SB-2 with the SEC. To qualify for an SB-2 ?ling, a ?rm must be located in
either the USA or Canada, have had less than $25 million in revenues in its last ?scal
year, and have less than a $25 million public ?oat (for seasoned equity offerings). Form
SB-2 permits the raising of an unlimited amount of capital by a small business as long
as the business quali?es to use an SB-2 under the size limitations.
The SB-2 program is not the ?rst attempt by congress to help small ?rms access the
public equity markets. Previous efforts include the Small Corporate Offering
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
The authors thank the Harold and Madeline Ruth Silver Fund for assistance in funding portions
of this research.
JFEP
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Journal of Financial Economic Policy
Vol. 4 No. 3, 2012
pp. 204-217
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211245944
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Registration (SCOR) (Brau and Osteryoung, 2001) and Regulation A (REG-A)
(Coffey and Schier, 1995) ?lings. Unlike SCOR and REG-A, the SB-2 requires audited
?nancial statements. The audited ?nancials requirement provides the researcher
with reliable data. The SB-2 offering prospectuses, though not as tedious as S-1
registrations, are replete with data (generally over 30 pages of offering information,
along with ?nancials).
The SB-2 program was initiated in Senate Hearing 102-779, The Small Business
Incentive Act of 1992 dated March 26, 1992. The headline topic on the cover page of the
printed version from the US Government Printing Of?ce states:
Legislation proposed by the SEC to improve the current system of ?nancing small businesses
that would revitalize the economy to develop and expand, creating job opportunities for our
nation’s workers.
The opening statement by Senator Christopher J. Dodd goes on to say:
Our hearing this morning will focus on the current problems facing small businesses seeking
to raise capital. We will be asking whether the SEC proposals adequately address these
problems [. . .]. The legislation proposed by the SEC, together with proposed regulatory
changes, seeks to reduce the regulatory burdens on offerings of securities by small
businesses.
The purpose of our paper is to directly test the original intent of the SB-2 program to
see if these offerings have “reduce[d] the regulatory burdens on offerings of securities
of small businesses”.
Brie?y summarizing, we provide evidence that the SB-2 program has not fully
reduced the regulatory burden to small ?rms. The SB-2 program is correlated with
shorter times to complete the offering; however, they cost more in terms of underwriter
spreads and offering expenses.
Speci?cally, we ?nd that SB-2 IPOs have higher scaled offering expenses and
underwriting spreads than SB-1 IPOs, contrary to the aims of the legislation. Using
averages (medians) and a pair-matched benchmark sample, SB-2 IPOs pay 7.1 percent
(6.9 percent) in offering expenses, compared to 3.4 percent (2.8 percent) for S-1 IPOs.
The spread is slightly larger using a pooled sample benchmark. SB-2 IPOs pay more
than double in scaled offering expenses. For gross underwriter spreads, SB-2 IPOs pay
an average (median) of 9.0 percent (9.9 percent) compared to 7.1 percent (7.0 percent) for
both pooled and pair-matched benchmark samples. When we examine the number of
days from ?ling to offering, the results are mixed. In a univariate setting, SB-2 IPOs
take an average of eight to ten days longer than S-1 IPOs. In multivariate tests, SB-2
IPOs experience ten to 13 less days in the registration process.
Literature review
The SCOR program mentioned above was a forerunner to the SB-2 program. The
stated purpose of the SCOR program is nearly identical to that of the SB program.
Brau and Osteryoung (2001) study SCOR prospectus companies. In the early 1980s, the
Reagan administration initiated policies to remove regulatory burdens from small
businesses in an attempt to make acquiring capital less costly (the same intent as the
SB-2 program). The SCOR is a direct result of these policies and was originally
designed to enable small business owners to raise external capital by originating their
own offering and selling their own stock with their regular accountants and lawyers.
Ef?cacy of the
1992 SBIA
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Presumably, SCOR would eliminate the need for hiring costly professional securities
practitioners, such as investment banks, thereby lowering the cost of IPOs. In addition,
companies that ?le with the SCOR are not required to ?le with the SEC. Firms ?le Form
U-7 if they are exempt from registering with the SEC under Rule 504 of Regulation
D. Under Rule 504 (Brau and Osteryoung, 2001):
Issuers that are not subject to the reporting obligations of the Securities Exchange Act of 1934
(nonpublic companies) and that are not investment companies may sell up to $1,000,000
worth of securities over a 12-month period to an unlimited number of investors.
Although ?rms ?ling SCOR issues do not have to register with the SEC, they are
expected to provide the SEC with some information (Form D). Once federal exemption
under Rule 504 is granted, state requirements must be met to register securities for sale
in any states where the shares will be sold.
Unlike the SB-2 program which permits an unlimited offer size, a SCOR issue limits
a corporation to raise a maximum of $1,000,000 in a 12-month period. The actual SCOR
form is a 50-question document ?led by the ?rm seeking to register a SCOR offering.
Along with the SCOR form, most states require the submission of company ?nancial
reports. Unlike the SB-2 program, as previously noted, the SCOR program does not
require these ?nancials to be audited.
Brau and Osteryoung (2001) examine marketing mechanisms, expenses, ownership,
governance, offering characteristics, business life stage, and signaling variables for 73
SCORs from the State of Washington. They ?nd the number of directors, size of the
largest block, and early stage offers all impact the success of a SCOR offering.
In a follow-on study, Brau and Gee (2010) use a sample of 339 SCORs from an
expanded national SCOR database. Through univariate and multivariate analyses,
they explore the factors that lead to a successful offer. They ?nd offering marketing
mechanisms, marketing expenses, geographic, ownership, governance, business, and
?rm marketing characteristics impact the success of a SCOR offering. Though Brau
and Osteryoung (2001) and Brau and Gee (2010) do not explicitly show that the SCOR
program has lost popularity and did not fully (or hardly) achieve its original intent,
these views are supported by numerous anecdotal evidences[1].
Brau and Carpenter (2012) provide a direct comparison of the S-1 and SB-2
programs in the context of signaling theory and ?nd that the two types of programs
attract different ?rms. They use four proxies for signaling: lockup length, auditor
reputation, underwriter rank, and venture capital backing. They ?nd that in all cases,
S-1 IPOs send a more positive signal than SB-2 IPOs. When Brau and Carpenter test 11
different control variables, they ?nd that eight (all) of the means (medians) are
signi?cantly different between S-1s and SB-2s, even though they only study S-1’s who
could qualify for the SB-2 issue. We rely on the argument and evidence from Brau and
Carpenter that there is a distinct difference between the S-1 and SB-2 programs to
motivate our testable hypotheses.
Theoretical development and testable hypothesis
Ef?cacy of the 1992 Law
The direct purpose of our testing is to determine if the SB-2 legislation has reduced the
regulatory burdens for small ?rms wishing to raise external equity from the public
markets. We reason that proxies for “regulatory burdens” are the amount of money
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and time needed to get through the registration process. If the SB-2 program has been
effective, we would expect these three hypotheses to be true:
H1. SB-2 IPOs have lower scaled-offering expenses vis-a` -vis S-1 IPOs.
H2. SB-2 IPOs have lower underwriter gross spreads vis-a` -vis S-1 IPOs.
H3. SB-2 IPOs have shorter ?le-date-to-offer-date spreads vis-a` -vis S-1 IPOs.
We will now consider each of these hypotheses in turn. As part of the offering, ?rms
almost always hire third-party experts, such as securities lawyers and professional
auditors, to prepare the offering prospectus to pass SEC muster (Ritter, 1987). If the
SB-2 program decreases the regulatory burdens on small ?rms, then it stands to reason
that SB-2 IPOs will have lower offering expenses than S-1 IPOs. We de?ne total
offering expenses as the sum of accounting; state’s blue sky; and legal, miscellaneous,
printing, rating, transfer, and trustee expenses (Ang and Brau, 2002). We standardize
total-offering expense by the size of the offering for each IPO. In robustness tests, we
focus on accounting (auditor) expense and legal expense, as several of the other
components of total expense may not depend on type of offering.
Along with paying offering expenses, IPO ?rms typically hire an investment bank
to serve as an underwriter for the offering. Chen and Ritter (2000) document that
mainstream IPOs have clustered at a 7 percent gross spread to the underwriting
syndicate. Although Chen and Ritter (2000) show a clustering of gross spreads,
Bradley et al. (2006) demonstrate signi?cant differences between mainline IPOs and
penny stock IPOs. If the SB-2 program is reducing regulatory red tape for small ?rms,
we would expect less effort required of underwriters and lower gross spreads for SB-2
IPOs vis-a` -vis S-1 IPOs. We de?ne the gross underwriter spread as the aggregate
spread that goes to the underwriting syndicate, standardized by the offering size.
Our third proxy for regulatory burden is the time it takes an IPO to complete the
registration process. It follows that if the regulatory burden is decreased for SB-2 IPOs,
then it should take less time to make it over the SEC ?ling hurdles. We measure time
as the number of days from the original ?ling of the offering prospectus to the IPO
offering date. If the SB-2 program is decreasing regulatory burden, we predict SB-2
IPOs will have a shorter ?ling process vis-a` -vis S-1 IPOs.
A fourth potential proxy for the burden upon issuing ?rms could be the actual
length of the IPO prospectus. We do not include this proxy as one of our testable
hypotheses because a 60-page boiler-plate document may take less time and effort to
complete than a 30-page non-boiler-plate document. For completeness, however, we
examine a random sample of 55 S-1 forms and 55 SB-2 forms, matching by year of
?ling. Of the 55 S-1 (SB-2) forms we are able to locate 45 (47) on EDGAR. S-1s, vis-a` -vis
SB-2s average 92.0 pages compared to 74.6 pages indicating mainline S-1 IPOs are on
average longer by page count. Breaking the prospectuses down to text pages and
?nancial pages, S-1s average 69.9 text (25.9 ?nancial) versus 53.6 text (24.9 ?nancial)
pages for SB-2s. Given the limitations of this proxy, we cannot conclude that SB-2s are
easier to ?le, but we can conclude that the text portion of the average SB-2 prospectus
is shorter than the text portion of the average S-1 prospectus. Although the page
lengths are different on average, Appendix reports two typical tables of contents, one
for each type of offering. As can be seen, the topics are typically very similar.
We inspected dozens of prospectus tables of contents and found this to be the case.
Ef?cacy of the
1992 SBIA
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Sample
Our initial sample of SB-2 and S-1 IPOs is drawn from Securities Data Company’s
(SDC) new issues database. From January 1, 1993 through December 31, 2008, SDC
reports 4,411 IPOs. These dates are chosen because they represent the period in which
the SB-2 program is in place and for which the Center for Research in Security Prices
(CRSP) data is available to create one-year abnormal returns. Next, we merge the
sample with Compustat data to obtain the pre-IPO sales level, the quali?er for the SB-2
program. We successfully match 4,118 of our SDC ?rms with Compustat data. Our
initial desired sample is all SB-2 and S-1 ?rms with less than $25 million in sales in the
?scal year immediately before the IPO. Firms that have less than $25 million in sales at
the IPO can theoretically choose between ?ling an SB-2 or an S-1 form with the SEC[2].
After removing IPOs with greater than $25 million in sales, we are left with 1,930
observations. We then successfully merge all 1,930 of these observations with CRSP
data. We exclude all ?nancial ?rms which leaves 1,899 IPOs in our ?nal pooled sample
with 1,356 S-1 IPOs and 543 SB-2 IPOs. We perform all of our analyses using the
pooled subsamples of all SB-2s and all S-1 IPOs.
Next, we construct a pair-matched sample of SB-2 IPOs and S-1 IPOs based on size
and industry[3,4]. We repeat all of our tests using the pair-matched samples. In our
tables, we report the pooled and pair-matched results to ameliorate any possible
size-effect between SB-2 and S-1 IPOs. Because ?rm size (sales) is often used as a
measure of ?rm quality (Purnanandan and Swaminathan, 2004), we want to make sure
we isolate any SB-2-effects from any possible size-effects.
There are pros and cons to using either the pair-matched or the pooled approach.
For example, a strength of the pair-matched method is that we are able to ensure
the size-effect is ameliorated by matching each SB-2 to the closest-sized S-1.
An example of a strength of the pooling method is that we are able to use all of the S-1
?rms as benchmark ?rms and not just the ones that match in the pair-matched method.
As both methods have strengths and weaknesses, we report results from both in all
of our testing.
Table I, Panel A reports the frequency distribution for the pooled and pair-matched
samples of 1,356 S-1 IPOs and 543 SB-2 IPOs, respectively. For mainline S-1 IPOs, 1999
was the most populated year with 245 (18.1 percent of S-1 sample) occurring. The
second and third most populated years were 2000 with 205 IPOs (15.1 percent) and
1996 with 201 IPOs (14.8 percent). The least populated year for S-1s is 2008, seeing only
four IPOs (0.3 percent). Recall, these counts are not the total number of S-1 IPOs during
these years; they represent the number of S-1 IPOs with less than $25 million in
revenues. The right two columns of Panel A report the frequency for the pair-matched
S-1 benchmark sample. We will leave inspection to the reader. Note that we formed our
pair-match bases on size (sales) and industry (two-digit Kahle and Walkling (1996)
class). As such, the years do not perfectly align between the SB-2 and S-1 pair-matched
sample. For this reason, we include year dummies in our multivariate models and
compute year cluster-adjusted t-statistics for robustness.
Table I, Panel B reports a similar distribution for SB-2 IPOs. The 1993-1996 years
show high frequencies of issues ranging from 82 IPOs (15.1 percent) in 1993 to 128
IPOs (23.6 percent) in 1996. In fact, if we compare 1995 for S-1 and SB-2 IPOs, the count
is 119 S-1s and 100 SB-2s. The SB-2 program seems to have started out with vigor in
1993, peaking in 1996. From 1996 to 2002, we see a monotonic decrease in the number
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of SB-2 IPOs. From 2001 to 2008, the number of SB-2 IPOs only range from zero to six
issues per year, with 2008 seeing zero issues.
Table II, Panels A and B report the sample frequency based on industry. We base our
industry categories on Kahle and Walkling (1996). The table shows that choice of offering
program (SB-2 or S-1) does not seem conditioned by industry. Both samples report
manufacturing and services as the highest-frequency industries, with manufacturing
comprising 42.3 percent of the S-1 sample and 45.5 percent of the SB-2 sample. Services
make up 43.4 percent of the S-1 sample and 35.7 percent of the SB-2 sample. The other
six industry groups are fairly evenly distributed within and between the S-1 and SB-2
samples. The pair-matched S-1s and SB-2s have identical industry frequencies as a result
of our matching algorithm.
Panel A: S-1 IPOs
Pooled sample Pair-matched sample
Issue year Frequency % Frequency %
1993 123 9.1 67 12.3
1994 106 7.8 45 8.3
1995 119 8.8 47 8.7
1996 201 14.8 71 13.1
1997 122 9.0 54 9.9
1998 72 5.3 30 5.5
1999 245 18.1 105 19.3
2000 205 15.1 79 14.6
2001 18 1.3 4 0.7
2002 5 0.4 2 0.4
2003 7 0.5 2 0.4
2004 40 3.0 12 2.2
2005 25 1.8 8 1.5
2006 30 2.2 4 0.7
2007 34 2.5 12 2.2
2008 4 0.3 1 0.2
Total 1,356 543
Panel B: SB-2 IPOs
Issue year Frequency % Cumulative frequency Cumulative percent
1993 82 15.1 82 15.1
1994 83 15.3 165 30.4
1995 100 18.4 265 48.8
1996 128 23.6 393 72.4
1997 64 11.8 457 84.2
1998 31 5.7 488 89.9
1999 15 2.8 503 92.6
2000 10 1.8 513 94.5
2001 4 0.7 517 95.2
2002 3 0.6 520 95.8
2003 4 0.7 524 96.5
2004 6 1.1 530 97.6
2005 2 0.4 532 98.0
2006 6 1.1 538 99.1
2007 5 0.9 543 100
2008 0 0.0 543 100
Table I.
Sample frequencies
by issue year
Ef?cacy of the
1992 SBIA
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Empirical methods and results
Ef?cacy of the 1992 law empirical work
We begin this section by examining our three proxies to measure the easing of
regulatory burdens for small ?rms. Table III reports the descriptive statistics as well as
difference tests for Offering Expenses, Gross Spread, and Days in Process. If the SB-2
program has been effective in decreasing the regulatory burden for registrations, we
would expect all three of these variables to be smaller for SB-2s than for S-1s (H1-H3),
all else equal. Recall, all of the IPOs in each sample have less than $25 million in sales
and the expenses and gross spread are standardized by offering size. As such, any
economy-of-scale effect should be ameliorated, especially in the pair-matched sample.
Table III reports the exact opposite of each prediction. Using the pooled benchmark
sample, S-1 IPOs have an average (median) standardized Offering Expense of 3.3 percent
(2.6 percent) compared to an SB-2 average (median) of 7.1 percent (6.9 percent).
Panel A: S-1 IPOs
Pooled sample Pair-matched sample
Industry Frequency % Frequency %
A 1 0.1 1 0.2
B 30 2.2 4 0.7
C 6 0.4 6 1.1
D 574 42.3 247 45.5
E 87 6.4 22 4.1
F 16 1.2 25 4.6
G 46 3.4 37 6.8
I 588 43.4 194 35.7
Missing 8 0.6 7 1.3
Panel B: SB-2 IPOs
Industry Frequency %
Cumulative
frequency
Cumulative
percent
A 1 0.18 1 0.2
B 4 0.74 5 0.9
C 6 1.1 11 2.0
D 247 45.49 258 47.5
E 22 4.05 280 51.6
F 25 4.6 305 56.2
G 37 6.81 342 63.0
I 194 35.73 536 98.7
Missing 7 1.29 543 100.00
Industry description
SIC
manual Two-digit
Agriculture, forestry, and ?shing A 01-09
Mining B 10-14
Construction C 15-17
Manufacturing D 20-39
Transportation, communications, electric,
gas, and sanitary services E 40-49
Wholesale trade F 50-51
Retail trade G 52-59
Finance, insurance, and real estate H 60-67
Services I 70-89
Public administration J 91-97
Table II.
Sample frequencies by
industry group
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The difference in means (medians) 3.8 percent (4.3 percent) is signi?cant with a
p-value of 0.0001 (0.0001). Gross Spread is also signi?cantly higher for SB-2 IPOs
(mean ¼ 9.0 percent, median ¼ 9.9 percent) than S-1 IPOs (mean ¼ 7.1 percent,
median ¼ 7.0 percent) using the pooled sample. The mean (median) difference of
1.9 percent (2.9 percent) is again signi?cant at the 0.0001 level. The ?nal proxy, Days in
Process is signi?cantly greater (parametric p-value ¼ 0.0266, non-parametric
p-value ¼ 0.2136) by an average (median) of 8.7 (6.0) days. The results for the
pair-matched sample are similar to the pooled sample.
From these initial results of our three proxies, we reject hypotheses H1-H3 and
temporarily conclude that the SB-2 program has not decreased the regulatory burden
for small ?rms. At this point, this conclusion must be taken with a grain of salt. So far,
the testing has been in a univariate framework. Next, we conduct multivariate tests to
control for other relevant factors to mitigate a possible omitted variable bias.
To correctly test Offering Expenses, Gross Spread, and Days in Process we must use
tobit methodology. All three of these variables are bounded by zero and, as such, we
need a limited dependent variable model. We estimate a series of three tobit models and
report the results in Tables IV-VI. The base model for each dependent variable is:
ðOffering Expenses or Gross Spread or Days in ProcessÞ
¼ bSB þ b
2
Big Six þ b
3
UWRank þ b
4
logðSalesÞ þ b
5
Cash Flow
þ b
6
Exchange þ b
7
ROA þ b
8
logðAgeÞ þ b
9
VC þ b
10
Debt=Assets
þ b
11
Delaware Corp þ b
12
Offer Size þ b
13
Internet IPO
þ b
14
Dual Share Class þ b
15
Lockup Length þ b
ðyearsÞ
Years
þ b
ðindustriesÞ
Industry
1ðA2IÞ
þ 1
ð1Þ
where the variables are all as de?ned in the hypothesis section and the subscript i for
each ?rm is suppressed[5,6].
To treat for possible endogeneity, we also include two-stage least-square (2SLS)
multivariate analysis. We estimate a two-stage model where the ?rst step is a logit
model modeling the choice of SB-2 or S-1. The dependent variables, which are all
Panel A: SB-2 IPOs
Variable Mean Median
Offering expenses (percent) 7.1 6.9
Gross spread (percent) 9.0 9.9
Days in process (days) 98.1 76.0
Pooled Pair-matched
Variable Mean Median Mean Median
Panel B: S-1 IPOs
Offering expenses (percent) 3.3 2.6 3.4 2.8
Gross spread (percent) 7.1 7.0 7.1 7.0
Days in process (days) 89.4 70.0 87.5 70.0
Panel C: differences (SB-2 minus S-1)
Offering expenses (percent) 3.8 (,0.0001) 4.3 (,0.0001) 3.7 (,0.0001) 4.1 (,0.0001)
Gross spread (percent) 1.9 (,0.0001) 2.9 (,0.0001) 1.9 (,0.0001) 2.9 (,0.0001)
Days in process (days) 8.7 (0.0266) 6.0 (0.2136) 10.6 (0.0192) 6.0 (0.0957)
Table III.
Regulatory proxy
variables and univariate
tests
Ef?cacy of the
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statistically signi?cant, are Big Six, log(Sales), Exchange, Delaware Corp, Offer Size,
and Internet IPO. We estimate an SB-hat from this ?rst step and then use it for the SB
proxy variable in the OLS model with Initial Return as the dependent variable.
Table IV reports that even with all these control variables, many of which are
statistically signi?cant, SB-2 ?rms pay greater scaled Offering Expenses than S-1 IPOs.
Using the pooled (pair-matched) sample, SB-1s pay 2.0 percent (1.9 percent) greater
offering expenses than S-1 IPOs. Considering the mean and median offering expenses,
which are approximately seven percent (Table III), SB-2 IPO’s two-percent higher
expense is economically signi?cant.
Pooled Pair-matched
Estimate p-value Estimate p-value
Intercept 6.9 ,0.0001 6.2 ,0.0001
SB 2.0 ,0.0001 1.9 ,0.0001
Big Six 21.6 ,0.0001 21.3 ,0.0001
UW Rank 211.6 ,0.0001 212.0 0.0001
log(Sales) 20.3 0.0048 20.2 0.0785
Cash Flow 0.01 0.4832 0.04 0.0804
Exchange 21.6 ,0.0001 21.6 ,0.0001
log(Age) 0.2 0.0631 0.3 0.0267
VC 20.6 0.0015 21.0 ,0.0001
Debt/Assets 20.02 0.7547 20.02 0.6595
Delaware Corp 0.5 0.0052 0.7 0.0002
Internet IPO 20.3 0.3067 20.6 0.1417
Dual Share Class 21.0 0.0258 20.8 0.1660
Years Yes Yes
Industry Yes Yes
Table IV.
Offer expenses
multivariate tobit models
Pooled Pair-matched
Estimate p-value Estimate p-value
Intercept 7.5 ,0.0001 7.7 ,0.0001
SB 0.8 ,0.0001 0.8 ,0.0001
Big Six 20.3 ,0.0001 20.4 ,0.0001
UW Rank 20.2 0.7488 21.1 0.2883
log(Sales) 20.1 0.0005 20.1 0.0053
Cash Flow 0.00 0.8097 20.01 0.4832
Exchange 20.6 ,0.0001 20.5 ,0.0001
ROA 20.02 0.0549 20.02 0.2561
log(Age) 0.01 0.8023 0.00 0.9306
VC 20.2 ,0.0001 20.3 ,0.0001
Debt/Assets 20.05 0.0071 20.05 0.0539
Delaware Corp 0.02 0.6797 0.02 0.7802
Internet IPO 20.02 0.7405 20.2 0.1097
Dual Share Class 20.2 0.0213 20.3 0.1413
Lockup Length 0.00 ,0.0001 0.00 ,0.0001
Years Yes Yes
Industry Yes Yes
Table V.
Gross spread
multivariate tobit models
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Table V reports a similar effect for Gross Spread – SB-2s have higher gross spreads
even after employing the control variables. Using the pooled (pair-matched) sample,
SB-1 IPOs have 80 (80) basis points higher gross spread than SB-1s. This gross spread
difference should not be attributed to economies of scale, particularly for the
pair-matched sample, as we match on size and also control for size in the model.
Whereas the results in Panels A and B con?rm the univariate results, the results in
Table VI reverse the interpretation for Days in Progress. After controlling for the other
confounding effects, SB-2 IPOs actually experience between 13.2 (pooled, p ¼ 0.0144)
and 9.8 days (pair-matched, p ¼ 0.1244) “less” in the registration process. Because this
result is surprising vis-a` -vis the univariate results, a discussion of the control variables
is in order.
The control variables that decrease the Days in Progress are Big Six, log(Sales), and
Offer Size. Each of the results for these variables is intuitive in that prestigious auditors
have the experience to prepare the ?nancials such that they do not require corrections
from the SEC. IPOs with relatively high sales and larger offer sizes represent quality
?rms which can pass through the scrutiny of the SEC more quickly[7]. The factor that
increases the Days in Progress is log(Age). Older ?rms take longer in the registration
process perhaps due to having a longer history which must be captured and vetted in the
SEC registration documents.
Summarizing our ?ndings for the ef?cacy of the SB-2 program, both univariate and
multivariate results provide evidence that the program has not decreased the costs for
SB-2 IPOs vis-a` -vis S-1 IPOs of similar size (H1 and H2 are rejected). The evidence
for the time it takes from initial ?ling to IPO date is not as clear. The univariate results
reject H3, suggesting SB-2 ?rms take longer in the registration process. The
multivariate test provides evidence in support of H3, contrary to the univariate
?ndings. The tobit model shows that when we control for confounding factors, the
Pooled Pair-matched
Estimate p-value Estimate p-value
Intercept 121.6 ,0.0001 143.4 ,0.0001
SB 213.2 0.0144 29.8 0.1244
Big Six 228.2 ,0.0001 222.9 0.0015
UW Rank 0.5 0.9926 137.0 0.1389
log(Sales) 26.9 0.0020 213.8 ,0.0001
Cash Flow 20.3 0.4813 0.5 0.5005
Exchange 26.2 0.3010 27.7 0.2601
ROA 0.8 0.4688 1.8 0.1632
log(Age) 11.3 ,0.0001 12.7 0.0003
VC 23.4 0.4331 28.1 0.1804
Debt/Assets 2.1 0.2833 2.8 0.2253
Delaware Corp 2.9 0.4990 6.1 0.2604
Offer Size 20.3 0.0028 20.5 0.0003
Internet IPO 24.8 0.5423 218.8 0.1095
Dual Share Class 211.0 0.3087 213.6 0.3886
Lockup Length 0.0 0.0024 0.0 0.0473
Years Yes Yes
Industry Yes Yes
Table VI.
Days in registration
multivariate tobit models
Ef?cacy of the
1992 SBIA
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SB-2 program has provided a quicker registration time for SB-2 IPOs. Our overall
conclusion for the ef?cacy of the SB-2 program is that it has not reduced the cost to
go public, but it has decreased the days in registration.
Conclusion
We compare SB-2 IPOs to S-1 IPOs that also had less than $25 million in sales the year
prior to the IPO. We believe we are the ?rst to do so. We test to determine if the legislative
goal of the SB-2 programhas been achieved. We ?nd that SB-2 IPOs have higher-scaled
offering expenses and underwriting spreads, contrary to the aims of the legislation.
Using averages (medians) and a pair-matched benchmark sample, SB-2 IPOs pay
7.1 percent (6.9 percent) in offering expenses, compared to 3.4 percent (2.8 percent) for S-1
IPOs. The spread is slightly larger using a pooled sample benchmark. SB-2 IPOs pay
more than double in scaled offering expenses. For gross underwriter spreads, SB-2 IPOs
pay an average (median) of 9.0 percent (9.9 percent) compared to 7.1 percent (7.0 percent)
for both pooled and pair-matched benchmark samples. When we examine the number of
days from?ling to offering, the results are mixed. In a univariate setting, SB-2 IPOs take
an average of eight to ten days longer than S-1 IPOs. In multivariate tests, SB-2 IPOs
experience ten to 13 less days in the registration process.
Notes
1. Conversation with Tom Steward-Gordon, former editor of the SCOR Report.
2. Firms under $25 million in sales could also choose to ?le form SB-1. SB-1 ?lings, however,
are not comparable to S-1 ?lings because SB-1s are restricted to raising a maximum of
$10 million in any 12-month period.
3. We thank Keith Gamble who served as a discussant for this paper for this suggestion.
4. We match each SB-2 company to the S-1 company closest in size (sales) within the same
industry. We represent size using Compustat annual sales (REVT). Industry classi?cation
follows the Kahle and Walkling (1996) methodology.
5. Our control variables are motivated by the following studies, Big Six and UWRank (Brau
and Johnson, 2009), Sales (Purnanandan and Swaminathan, 2004), Cash ?ows (Livnat and
Lopez-Espinosa, 2008), the Exchange the ?rm is listed on (Bradley and Jordan, 2002), Return
on assets (ROA) (Chen et al., 2011), Age of the ?rm (Loughran and Ritter, 2004), ?rm Debt
ratios (Cotter and Peck, 2001), the state of incorporation (Delaware corp) (Boulton, 2010),
IPO offer size (i.e. the public ?oat) (Bradley et al., 2006), internet-IPO offering (Loughran
and Ritter, 2004), dual-class IPO offering (Smart and Zutter, 2003), and lockup length
(Brau et al., 2005).
6. We customize each model for the speci?c dependent variable by excluding the variables that
theoretically do not motivate inclusion as explanatory variables in a given model. For the
Offering Expense model, we do not include ROA, Offer size, or Lockup Length as they
theoretically (and empirically) do not impact Offer Expense. For the Gross Spread model, we
remove Offer Size, as it is the denominator of the dependent variable, Gross Spread. We use
the complete base model for Days in Progress. If we use the base model for Offering Expenses
and Gross Spread, our reported results are robust.
7. The coef?cient on log(Sales) is statistically signi?cant because the standard errors are so
small. Because we constructed the pooled sample only for IPOs with less than $25 million
and the pair-matched based on size (sales), the small standard errors for sales increase the
t-statistic.
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References
Ang, J. and Brau, J.C. (2002), “Firm transparency and the costs of going public”, Journal of
Financial Research, Vol. 25, pp. 1-18.
Boulton, T.J. (2010), “The impact of the corporate control market on IPO decisions”, Miami
University working paper.
Bradley, D. and Jordan, B. (2002), “Partial adjustment to public information and IPO
underpricing”, Journal of Financial and Quantitative Analysis, Vol. 37, pp. 595-616.
Bradley, D., Cooney, J., Dolvin, S. and Jordan, B. (2006), “Penny stock IPOs”, Financial
Management, Vol. 36, pp. 5-29.
Brau, J. and Carpenter, J.T. (2012), “Small-?rm uniqueness and signaling theory”, Journal of
Business Economics and Finance, Vol. 1 No. 1.
Brau, J. and Gee, G. (2010), “Micro-IPOs: an analysis of the Small Corporate Offering Registration
(SCOR) procedure with national data”, Journal of Entrepreneurial Finance, Vol. 14,
pp. 69-89.
Brau, J. and Johnson, P. (2009), “Earnings management in IPOs: post-engagement third-party
mitigation or issuer signaling?”, Advances in Accounting, Vol. 25, pp. 125-35.
Brau, J. and Osteryoung, J. (2001), “The determinants of successful micro-IPOs: an analysis of
issues made under the Small Corporate Offering Registration (SCOR) procedure”, Journal
of Small Business Management, Vol. 39, pp. 209-27.
Brau, J., Lambson, V. and McQueen, G. (2005), “Lockups revisited”, Journal of Financial and
Quantitative Analysis, Vol. 40, pp. 519-30.
Chen, H. and Ritter, J. (2000), “The seven percent solution”, Journal of Finance, Vol. 55,
pp. 1105-31.
Chen, L., Novy-Marx, R. and Zhang, L. (2011), “An alternative three-factor model”, April,
available at: SSRN: http://ssrn.com/abstract¼1418117; http://dx.doi.org/10.2139/ssrn.
1418117
Coffey, W.J. and Schier, L. (1995), “Small business initiatives under the Securities Acts”, The CPA
Journal, Vol. 65 No. 1, pp. 46-9.
Cotter, J. and Peck, S. (2001), “The structure of debt and active equity investors: the case of the
buyout specialist”, Journal of Financial Economics, Vol. 59, pp. 101-47.
Kahle, K. and Walkling, R. (1996), “The impact of industry classi?cations on ?nancial research”,
Journal of Financial and Quantitative Analysis, Vol. 31, pp. 309-36.
Livnat, J. and Lopez-Espinosa, G. (2008), “Quarterly accruals or cash ?ows in portfolio
construction?”, Financial Analysts Journal, Vol. 64, pp. 67-79.
Loughran, T. and Ritter, J.R. (2004), “Why has IPO underpricing changed over time?”, Financial
Management, Autumn, pp. 5-37.
Purnanandan, A. and Swaminathan, B. (2004), “Are IPO’s really underpriced?”, Review of
Financial Studies, Vol. 17, pp. 811-48.
Ritter, J.R. (1987), “The costs of going public”, Journal of Financial Economics, Vol. 19, pp. 269-81.
Securities and Exchange Commission (SEC) (1992), The Small Business Incentive Act of 1992,
Hearing before the Subcommittee on Securities of the Committee on Banking, Housing,
and Urban Affairs at the Second Session of the One Hundred Second Congress of the
United States Senate, Washington, DC, March.
Smart, S. and Zutter, C. (2003), “Control as a motivation for underpricing: a comparison of dual-
and single-class IPOs”, Journal of Financial Economics, Vol. 69, pp. 85-110.
Ef?cacy of the
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Appendix
Mathstar Inc. (S-1 Sample)
Table of contents
Prospectus Summary 1
Risk Factors 8
Forward-Looking Information 22
Use of Proceeds 23
Dividend Policy 24
Capitalization 25
Dilution 26
Selected Financial Data 28
Management’s Discussion and Analysis of Financial Condition and Results of Operations 30
Business 40
Management 55
Related Party Transactions 63
Principal Stockholders 64
Description of Our Securities 66
Shares Eligible for Future Sale 70
Underwriting 73
Legal Matters 77
Experts 77
Where You Can Find More Information 77
Index to Financial Statements F-1
Marchex Inc. (SB-2 Sample)
Table of contents
Page
Prospectus Summary 1
Summary Consolidated Financial Data 5
Risk Factors 7
Special Note Regarding Forward-Looking Statements 21
Use of Proceeds 22
Determination of Offering Price 23
Capitalization 24
Dilution 26
Dividend Policy 27
Management’s Discussion and Analysis of Financial Condition and Results of Operations 28
Business 51
Management 62
Executive Compensation 66
Security Ownership of Certain Beneficial Owners and Management 70
Certain Relationships and Related Transactions 72
Description of Capital Stock 74
Market for Common Equity and Related Stockholder Matters 79
Underwriting 82
Legal Matters 85
Experts 85
Disclosure of Commission Position on Indemnification for Securities Act Liabilities 85
Where You Can Find More Information 85
Financial Statements
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About the authors
James C. Brau, PhD, CFA, is the Editor of the Journal of Entrepreneurial Finance and a Professor
of Finance at the Brigham Young University Marriott School. He received his PhD from Florida
State University in Business Administration and Finance and has published over
30 peer-reviewed articles in the area of entrepreneurial ?nance.
J. Troy Carpenter is a Research Associate and Data Analyst for the Marriott School Research
Center at Brigham Young University. He received his MBA from Brigham Young University and
has 20 years of experience in Corporate America and Asia. In addition to his research activities
he is the Director of the Marriott School’s Asia Study Abroad program. J. Troy Carpenter is the
corresponding author and can be contacted at: [email protected]
Ef?cacy of the
1992 SBIA
217
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doc_716162205.pdf
The purpose of this paper is to test the fundamental purpose of the 1992 Small Business
Incentive Act (SBIA) to reduce the regulatory burden for small firms to raise public equity capital
Journal of Financial Economic Policy
Efficacy of the 1992 Small Business Incentive Act
J ames C. Brau J . Troy Carpenter
Article information:
To cite this document:
J ames C. Brau J . Troy Carpenter, (2012),"Efficacy of the 1992 Small Business Incentive Act", J ournal of
Financial Economic Policy, Vol. 4 Iss 3 pp. 204 - 217
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Ef?cacy of the 1992 Small
Business Incentive Act
James C. Brau and J. Troy Carpenter
Department of Finance, Marriott School, Brigham Young University,
Provo, Utah, USA
Abstract
Purpose – The purpose of this paper is to test the fundamental purpose of the 1992 Small Business
Incentive Act (SBIA) to reduce the regulatory burden for small ?rms to raise public equity capital.
Design/methodology/approach – Our research compares initial public offerings (IPOs) that ?led
with the newer SB-2 program to benchmark ?rms that ?led using the traditional S-1 ?ling. The
authors use three proxies to measure success, hypothesizing that, if the regulatory burden has indeed
been reduced for small ?rms, all three variables should be smaller for SB-2 IPOs. Univariate and
multivariate analyses were conducted.
Findings – With regards to easing regulatory costs, it is found that the program has not been
effective. On average, SB-2 IPOs experience larger-scaled offering expenses, and pay higher
underwriter gross spreads compared to S-1 IPOs of similar size. SB-2 IPOs, however, take fewer days
to complete the registration process, when controlling for other relevant factors. In the burden of time,
the SBIA has been effective.
Practical implications – The paper is of value to managers of ?rms desiring to conduct an IPO.
These managers, if they meet the size requirements dictated by the SEC, can elect to use an SB-2 or an
S-1 document. The paper shows that if cost is the primary concern, the S-1 program should be
preferred. If time is the primary consideration, then the SB-2 program is preferred.
Originality/value – To the authors’ knowledge, they are the ?rst to test the ef?cacy of the SBIA
program.
Keywords United States of America, Legislation, Small enterprises, Equity capital,
Small Business Incentive Act of 1992, SEC policy, Initial public offering
Paper type Research paper
Under the US Securities Laws, ?rms may go public by ?ling a number of different
registration forms with the Securities and Exchange Commission (SEC, 1992). The
typical initial public offering (IPO) ?rm ?les an S-1 offering prospectus. Firms that
register with an S-1 have been extensively studied in the ?nancial economics literature.
The theory and empirics of “mainline” IPOs are well developed.
A much lesser-known path to an IPO, designed speci?cally for small businesses, is
to ?le a form SB-2 with the SEC. To qualify for an SB-2 ?ling, a ?rm must be located in
either the USA or Canada, have had less than $25 million in revenues in its last ?scal
year, and have less than a $25 million public ?oat (for seasoned equity offerings). Form
SB-2 permits the raising of an unlimited amount of capital by a small business as long
as the business quali?es to use an SB-2 under the size limitations.
The SB-2 program is not the ?rst attempt by congress to help small ?rms access the
public equity markets. Previous efforts include the Small Corporate Offering
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
The authors thank the Harold and Madeline Ruth Silver Fund for assistance in funding portions
of this research.
JFEP
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Journal of Financial Economic Policy
Vol. 4 No. 3, 2012
pp. 204-217
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211245944
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Registration (SCOR) (Brau and Osteryoung, 2001) and Regulation A (REG-A)
(Coffey and Schier, 1995) ?lings. Unlike SCOR and REG-A, the SB-2 requires audited
?nancial statements. The audited ?nancials requirement provides the researcher
with reliable data. The SB-2 offering prospectuses, though not as tedious as S-1
registrations, are replete with data (generally over 30 pages of offering information,
along with ?nancials).
The SB-2 program was initiated in Senate Hearing 102-779, The Small Business
Incentive Act of 1992 dated March 26, 1992. The headline topic on the cover page of the
printed version from the US Government Printing Of?ce states:
Legislation proposed by the SEC to improve the current system of ?nancing small businesses
that would revitalize the economy to develop and expand, creating job opportunities for our
nation’s workers.
The opening statement by Senator Christopher J. Dodd goes on to say:
Our hearing this morning will focus on the current problems facing small businesses seeking
to raise capital. We will be asking whether the SEC proposals adequately address these
problems [. . .]. The legislation proposed by the SEC, together with proposed regulatory
changes, seeks to reduce the regulatory burdens on offerings of securities by small
businesses.
The purpose of our paper is to directly test the original intent of the SB-2 program to
see if these offerings have “reduce[d] the regulatory burdens on offerings of securities
of small businesses”.
Brie?y summarizing, we provide evidence that the SB-2 program has not fully
reduced the regulatory burden to small ?rms. The SB-2 program is correlated with
shorter times to complete the offering; however, they cost more in terms of underwriter
spreads and offering expenses.
Speci?cally, we ?nd that SB-2 IPOs have higher scaled offering expenses and
underwriting spreads than SB-1 IPOs, contrary to the aims of the legislation. Using
averages (medians) and a pair-matched benchmark sample, SB-2 IPOs pay 7.1 percent
(6.9 percent) in offering expenses, compared to 3.4 percent (2.8 percent) for S-1 IPOs.
The spread is slightly larger using a pooled sample benchmark. SB-2 IPOs pay more
than double in scaled offering expenses. For gross underwriter spreads, SB-2 IPOs pay
an average (median) of 9.0 percent (9.9 percent) compared to 7.1 percent (7.0 percent) for
both pooled and pair-matched benchmark samples. When we examine the number of
days from ?ling to offering, the results are mixed. In a univariate setting, SB-2 IPOs
take an average of eight to ten days longer than S-1 IPOs. In multivariate tests, SB-2
IPOs experience ten to 13 less days in the registration process.
Literature review
The SCOR program mentioned above was a forerunner to the SB-2 program. The
stated purpose of the SCOR program is nearly identical to that of the SB program.
Brau and Osteryoung (2001) study SCOR prospectus companies. In the early 1980s, the
Reagan administration initiated policies to remove regulatory burdens from small
businesses in an attempt to make acquiring capital less costly (the same intent as the
SB-2 program). The SCOR is a direct result of these policies and was originally
designed to enable small business owners to raise external capital by originating their
own offering and selling their own stock with their regular accountants and lawyers.
Ef?cacy of the
1992 SBIA
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Presumably, SCOR would eliminate the need for hiring costly professional securities
practitioners, such as investment banks, thereby lowering the cost of IPOs. In addition,
companies that ?le with the SCOR are not required to ?le with the SEC. Firms ?le Form
U-7 if they are exempt from registering with the SEC under Rule 504 of Regulation
D. Under Rule 504 (Brau and Osteryoung, 2001):
Issuers that are not subject to the reporting obligations of the Securities Exchange Act of 1934
(nonpublic companies) and that are not investment companies may sell up to $1,000,000
worth of securities over a 12-month period to an unlimited number of investors.
Although ?rms ?ling SCOR issues do not have to register with the SEC, they are
expected to provide the SEC with some information (Form D). Once federal exemption
under Rule 504 is granted, state requirements must be met to register securities for sale
in any states where the shares will be sold.
Unlike the SB-2 program which permits an unlimited offer size, a SCOR issue limits
a corporation to raise a maximum of $1,000,000 in a 12-month period. The actual SCOR
form is a 50-question document ?led by the ?rm seeking to register a SCOR offering.
Along with the SCOR form, most states require the submission of company ?nancial
reports. Unlike the SB-2 program, as previously noted, the SCOR program does not
require these ?nancials to be audited.
Brau and Osteryoung (2001) examine marketing mechanisms, expenses, ownership,
governance, offering characteristics, business life stage, and signaling variables for 73
SCORs from the State of Washington. They ?nd the number of directors, size of the
largest block, and early stage offers all impact the success of a SCOR offering.
In a follow-on study, Brau and Gee (2010) use a sample of 339 SCORs from an
expanded national SCOR database. Through univariate and multivariate analyses,
they explore the factors that lead to a successful offer. They ?nd offering marketing
mechanisms, marketing expenses, geographic, ownership, governance, business, and
?rm marketing characteristics impact the success of a SCOR offering. Though Brau
and Osteryoung (2001) and Brau and Gee (2010) do not explicitly show that the SCOR
program has lost popularity and did not fully (or hardly) achieve its original intent,
these views are supported by numerous anecdotal evidences[1].
Brau and Carpenter (2012) provide a direct comparison of the S-1 and SB-2
programs in the context of signaling theory and ?nd that the two types of programs
attract different ?rms. They use four proxies for signaling: lockup length, auditor
reputation, underwriter rank, and venture capital backing. They ?nd that in all cases,
S-1 IPOs send a more positive signal than SB-2 IPOs. When Brau and Carpenter test 11
different control variables, they ?nd that eight (all) of the means (medians) are
signi?cantly different between S-1s and SB-2s, even though they only study S-1’s who
could qualify for the SB-2 issue. We rely on the argument and evidence from Brau and
Carpenter that there is a distinct difference between the S-1 and SB-2 programs to
motivate our testable hypotheses.
Theoretical development and testable hypothesis
Ef?cacy of the 1992 Law
The direct purpose of our testing is to determine if the SB-2 legislation has reduced the
regulatory burdens for small ?rms wishing to raise external equity from the public
markets. We reason that proxies for “regulatory burdens” are the amount of money
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and time needed to get through the registration process. If the SB-2 program has been
effective, we would expect these three hypotheses to be true:
H1. SB-2 IPOs have lower scaled-offering expenses vis-a` -vis S-1 IPOs.
H2. SB-2 IPOs have lower underwriter gross spreads vis-a` -vis S-1 IPOs.
H3. SB-2 IPOs have shorter ?le-date-to-offer-date spreads vis-a` -vis S-1 IPOs.
We will now consider each of these hypotheses in turn. As part of the offering, ?rms
almost always hire third-party experts, such as securities lawyers and professional
auditors, to prepare the offering prospectus to pass SEC muster (Ritter, 1987). If the
SB-2 program decreases the regulatory burdens on small ?rms, then it stands to reason
that SB-2 IPOs will have lower offering expenses than S-1 IPOs. We de?ne total
offering expenses as the sum of accounting; state’s blue sky; and legal, miscellaneous,
printing, rating, transfer, and trustee expenses (Ang and Brau, 2002). We standardize
total-offering expense by the size of the offering for each IPO. In robustness tests, we
focus on accounting (auditor) expense and legal expense, as several of the other
components of total expense may not depend on type of offering.
Along with paying offering expenses, IPO ?rms typically hire an investment bank
to serve as an underwriter for the offering. Chen and Ritter (2000) document that
mainstream IPOs have clustered at a 7 percent gross spread to the underwriting
syndicate. Although Chen and Ritter (2000) show a clustering of gross spreads,
Bradley et al. (2006) demonstrate signi?cant differences between mainline IPOs and
penny stock IPOs. If the SB-2 program is reducing regulatory red tape for small ?rms,
we would expect less effort required of underwriters and lower gross spreads for SB-2
IPOs vis-a` -vis S-1 IPOs. We de?ne the gross underwriter spread as the aggregate
spread that goes to the underwriting syndicate, standardized by the offering size.
Our third proxy for regulatory burden is the time it takes an IPO to complete the
registration process. It follows that if the regulatory burden is decreased for SB-2 IPOs,
then it should take less time to make it over the SEC ?ling hurdles. We measure time
as the number of days from the original ?ling of the offering prospectus to the IPO
offering date. If the SB-2 program is decreasing regulatory burden, we predict SB-2
IPOs will have a shorter ?ling process vis-a` -vis S-1 IPOs.
A fourth potential proxy for the burden upon issuing ?rms could be the actual
length of the IPO prospectus. We do not include this proxy as one of our testable
hypotheses because a 60-page boiler-plate document may take less time and effort to
complete than a 30-page non-boiler-plate document. For completeness, however, we
examine a random sample of 55 S-1 forms and 55 SB-2 forms, matching by year of
?ling. Of the 55 S-1 (SB-2) forms we are able to locate 45 (47) on EDGAR. S-1s, vis-a` -vis
SB-2s average 92.0 pages compared to 74.6 pages indicating mainline S-1 IPOs are on
average longer by page count. Breaking the prospectuses down to text pages and
?nancial pages, S-1s average 69.9 text (25.9 ?nancial) versus 53.6 text (24.9 ?nancial)
pages for SB-2s. Given the limitations of this proxy, we cannot conclude that SB-2s are
easier to ?le, but we can conclude that the text portion of the average SB-2 prospectus
is shorter than the text portion of the average S-1 prospectus. Although the page
lengths are different on average, Appendix reports two typical tables of contents, one
for each type of offering. As can be seen, the topics are typically very similar.
We inspected dozens of prospectus tables of contents and found this to be the case.
Ef?cacy of the
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Sample
Our initial sample of SB-2 and S-1 IPOs is drawn from Securities Data Company’s
(SDC) new issues database. From January 1, 1993 through December 31, 2008, SDC
reports 4,411 IPOs. These dates are chosen because they represent the period in which
the SB-2 program is in place and for which the Center for Research in Security Prices
(CRSP) data is available to create one-year abnormal returns. Next, we merge the
sample with Compustat data to obtain the pre-IPO sales level, the quali?er for the SB-2
program. We successfully match 4,118 of our SDC ?rms with Compustat data. Our
initial desired sample is all SB-2 and S-1 ?rms with less than $25 million in sales in the
?scal year immediately before the IPO. Firms that have less than $25 million in sales at
the IPO can theoretically choose between ?ling an SB-2 or an S-1 form with the SEC[2].
After removing IPOs with greater than $25 million in sales, we are left with 1,930
observations. We then successfully merge all 1,930 of these observations with CRSP
data. We exclude all ?nancial ?rms which leaves 1,899 IPOs in our ?nal pooled sample
with 1,356 S-1 IPOs and 543 SB-2 IPOs. We perform all of our analyses using the
pooled subsamples of all SB-2s and all S-1 IPOs.
Next, we construct a pair-matched sample of SB-2 IPOs and S-1 IPOs based on size
and industry[3,4]. We repeat all of our tests using the pair-matched samples. In our
tables, we report the pooled and pair-matched results to ameliorate any possible
size-effect between SB-2 and S-1 IPOs. Because ?rm size (sales) is often used as a
measure of ?rm quality (Purnanandan and Swaminathan, 2004), we want to make sure
we isolate any SB-2-effects from any possible size-effects.
There are pros and cons to using either the pair-matched or the pooled approach.
For example, a strength of the pair-matched method is that we are able to ensure
the size-effect is ameliorated by matching each SB-2 to the closest-sized S-1.
An example of a strength of the pooling method is that we are able to use all of the S-1
?rms as benchmark ?rms and not just the ones that match in the pair-matched method.
As both methods have strengths and weaknesses, we report results from both in all
of our testing.
Table I, Panel A reports the frequency distribution for the pooled and pair-matched
samples of 1,356 S-1 IPOs and 543 SB-2 IPOs, respectively. For mainline S-1 IPOs, 1999
was the most populated year with 245 (18.1 percent of S-1 sample) occurring. The
second and third most populated years were 2000 with 205 IPOs (15.1 percent) and
1996 with 201 IPOs (14.8 percent). The least populated year for S-1s is 2008, seeing only
four IPOs (0.3 percent). Recall, these counts are not the total number of S-1 IPOs during
these years; they represent the number of S-1 IPOs with less than $25 million in
revenues. The right two columns of Panel A report the frequency for the pair-matched
S-1 benchmark sample. We will leave inspection to the reader. Note that we formed our
pair-match bases on size (sales) and industry (two-digit Kahle and Walkling (1996)
class). As such, the years do not perfectly align between the SB-2 and S-1 pair-matched
sample. For this reason, we include year dummies in our multivariate models and
compute year cluster-adjusted t-statistics for robustness.
Table I, Panel B reports a similar distribution for SB-2 IPOs. The 1993-1996 years
show high frequencies of issues ranging from 82 IPOs (15.1 percent) in 1993 to 128
IPOs (23.6 percent) in 1996. In fact, if we compare 1995 for S-1 and SB-2 IPOs, the count
is 119 S-1s and 100 SB-2s. The SB-2 program seems to have started out with vigor in
1993, peaking in 1996. From 1996 to 2002, we see a monotonic decrease in the number
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of SB-2 IPOs. From 2001 to 2008, the number of SB-2 IPOs only range from zero to six
issues per year, with 2008 seeing zero issues.
Table II, Panels A and B report the sample frequency based on industry. We base our
industry categories on Kahle and Walkling (1996). The table shows that choice of offering
program (SB-2 or S-1) does not seem conditioned by industry. Both samples report
manufacturing and services as the highest-frequency industries, with manufacturing
comprising 42.3 percent of the S-1 sample and 45.5 percent of the SB-2 sample. Services
make up 43.4 percent of the S-1 sample and 35.7 percent of the SB-2 sample. The other
six industry groups are fairly evenly distributed within and between the S-1 and SB-2
samples. The pair-matched S-1s and SB-2s have identical industry frequencies as a result
of our matching algorithm.
Panel A: S-1 IPOs
Pooled sample Pair-matched sample
Issue year Frequency % Frequency %
1993 123 9.1 67 12.3
1994 106 7.8 45 8.3
1995 119 8.8 47 8.7
1996 201 14.8 71 13.1
1997 122 9.0 54 9.9
1998 72 5.3 30 5.5
1999 245 18.1 105 19.3
2000 205 15.1 79 14.6
2001 18 1.3 4 0.7
2002 5 0.4 2 0.4
2003 7 0.5 2 0.4
2004 40 3.0 12 2.2
2005 25 1.8 8 1.5
2006 30 2.2 4 0.7
2007 34 2.5 12 2.2
2008 4 0.3 1 0.2
Total 1,356 543
Panel B: SB-2 IPOs
Issue year Frequency % Cumulative frequency Cumulative percent
1993 82 15.1 82 15.1
1994 83 15.3 165 30.4
1995 100 18.4 265 48.8
1996 128 23.6 393 72.4
1997 64 11.8 457 84.2
1998 31 5.7 488 89.9
1999 15 2.8 503 92.6
2000 10 1.8 513 94.5
2001 4 0.7 517 95.2
2002 3 0.6 520 95.8
2003 4 0.7 524 96.5
2004 6 1.1 530 97.6
2005 2 0.4 532 98.0
2006 6 1.1 538 99.1
2007 5 0.9 543 100
2008 0 0.0 543 100
Table I.
Sample frequencies
by issue year
Ef?cacy of the
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Empirical methods and results
Ef?cacy of the 1992 law empirical work
We begin this section by examining our three proxies to measure the easing of
regulatory burdens for small ?rms. Table III reports the descriptive statistics as well as
difference tests for Offering Expenses, Gross Spread, and Days in Process. If the SB-2
program has been effective in decreasing the regulatory burden for registrations, we
would expect all three of these variables to be smaller for SB-2s than for S-1s (H1-H3),
all else equal. Recall, all of the IPOs in each sample have less than $25 million in sales
and the expenses and gross spread are standardized by offering size. As such, any
economy-of-scale effect should be ameliorated, especially in the pair-matched sample.
Table III reports the exact opposite of each prediction. Using the pooled benchmark
sample, S-1 IPOs have an average (median) standardized Offering Expense of 3.3 percent
(2.6 percent) compared to an SB-2 average (median) of 7.1 percent (6.9 percent).
Panel A: S-1 IPOs
Pooled sample Pair-matched sample
Industry Frequency % Frequency %
A 1 0.1 1 0.2
B 30 2.2 4 0.7
C 6 0.4 6 1.1
D 574 42.3 247 45.5
E 87 6.4 22 4.1
F 16 1.2 25 4.6
G 46 3.4 37 6.8
I 588 43.4 194 35.7
Missing 8 0.6 7 1.3
Panel B: SB-2 IPOs
Industry Frequency %
Cumulative
frequency
Cumulative
percent
A 1 0.18 1 0.2
B 4 0.74 5 0.9
C 6 1.1 11 2.0
D 247 45.49 258 47.5
E 22 4.05 280 51.6
F 25 4.6 305 56.2
G 37 6.81 342 63.0
I 194 35.73 536 98.7
Missing 7 1.29 543 100.00
Industry description
SIC
manual Two-digit
Agriculture, forestry, and ?shing A 01-09
Mining B 10-14
Construction C 15-17
Manufacturing D 20-39
Transportation, communications, electric,
gas, and sanitary services E 40-49
Wholesale trade F 50-51
Retail trade G 52-59
Finance, insurance, and real estate H 60-67
Services I 70-89
Public administration J 91-97
Table II.
Sample frequencies by
industry group
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The difference in means (medians) 3.8 percent (4.3 percent) is signi?cant with a
p-value of 0.0001 (0.0001). Gross Spread is also signi?cantly higher for SB-2 IPOs
(mean ¼ 9.0 percent, median ¼ 9.9 percent) than S-1 IPOs (mean ¼ 7.1 percent,
median ¼ 7.0 percent) using the pooled sample. The mean (median) difference of
1.9 percent (2.9 percent) is again signi?cant at the 0.0001 level. The ?nal proxy, Days in
Process is signi?cantly greater (parametric p-value ¼ 0.0266, non-parametric
p-value ¼ 0.2136) by an average (median) of 8.7 (6.0) days. The results for the
pair-matched sample are similar to the pooled sample.
From these initial results of our three proxies, we reject hypotheses H1-H3 and
temporarily conclude that the SB-2 program has not decreased the regulatory burden
for small ?rms. At this point, this conclusion must be taken with a grain of salt. So far,
the testing has been in a univariate framework. Next, we conduct multivariate tests to
control for other relevant factors to mitigate a possible omitted variable bias.
To correctly test Offering Expenses, Gross Spread, and Days in Process we must use
tobit methodology. All three of these variables are bounded by zero and, as such, we
need a limited dependent variable model. We estimate a series of three tobit models and
report the results in Tables IV-VI. The base model for each dependent variable is:
ðOffering Expenses or Gross Spread or Days in ProcessÞ
¼ bSB þ b
2
Big Six þ b
3
UWRank þ b
4
logðSalesÞ þ b
5
Cash Flow
þ b
6
Exchange þ b
7
ROA þ b
8
logðAgeÞ þ b
9
VC þ b
10
Debt=Assets
þ b
11
Delaware Corp þ b
12
Offer Size þ b
13
Internet IPO
þ b
14
Dual Share Class þ b
15
Lockup Length þ b
ðyearsÞ
Years
þ b
ðindustriesÞ
Industry
1ðA2IÞ
þ 1
ð1Þ
where the variables are all as de?ned in the hypothesis section and the subscript i for
each ?rm is suppressed[5,6].
To treat for possible endogeneity, we also include two-stage least-square (2SLS)
multivariate analysis. We estimate a two-stage model where the ?rst step is a logit
model modeling the choice of SB-2 or S-1. The dependent variables, which are all
Panel A: SB-2 IPOs
Variable Mean Median
Offering expenses (percent) 7.1 6.9
Gross spread (percent) 9.0 9.9
Days in process (days) 98.1 76.0
Pooled Pair-matched
Variable Mean Median Mean Median
Panel B: S-1 IPOs
Offering expenses (percent) 3.3 2.6 3.4 2.8
Gross spread (percent) 7.1 7.0 7.1 7.0
Days in process (days) 89.4 70.0 87.5 70.0
Panel C: differences (SB-2 minus S-1)
Offering expenses (percent) 3.8 (,0.0001) 4.3 (,0.0001) 3.7 (,0.0001) 4.1 (,0.0001)
Gross spread (percent) 1.9 (,0.0001) 2.9 (,0.0001) 1.9 (,0.0001) 2.9 (,0.0001)
Days in process (days) 8.7 (0.0266) 6.0 (0.2136) 10.6 (0.0192) 6.0 (0.0957)
Table III.
Regulatory proxy
variables and univariate
tests
Ef?cacy of the
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statistically signi?cant, are Big Six, log(Sales), Exchange, Delaware Corp, Offer Size,
and Internet IPO. We estimate an SB-hat from this ?rst step and then use it for the SB
proxy variable in the OLS model with Initial Return as the dependent variable.
Table IV reports that even with all these control variables, many of which are
statistically signi?cant, SB-2 ?rms pay greater scaled Offering Expenses than S-1 IPOs.
Using the pooled (pair-matched) sample, SB-1s pay 2.0 percent (1.9 percent) greater
offering expenses than S-1 IPOs. Considering the mean and median offering expenses,
which are approximately seven percent (Table III), SB-2 IPO’s two-percent higher
expense is economically signi?cant.
Pooled Pair-matched
Estimate p-value Estimate p-value
Intercept 6.9 ,0.0001 6.2 ,0.0001
SB 2.0 ,0.0001 1.9 ,0.0001
Big Six 21.6 ,0.0001 21.3 ,0.0001
UW Rank 211.6 ,0.0001 212.0 0.0001
log(Sales) 20.3 0.0048 20.2 0.0785
Cash Flow 0.01 0.4832 0.04 0.0804
Exchange 21.6 ,0.0001 21.6 ,0.0001
log(Age) 0.2 0.0631 0.3 0.0267
VC 20.6 0.0015 21.0 ,0.0001
Debt/Assets 20.02 0.7547 20.02 0.6595
Delaware Corp 0.5 0.0052 0.7 0.0002
Internet IPO 20.3 0.3067 20.6 0.1417
Dual Share Class 21.0 0.0258 20.8 0.1660
Years Yes Yes
Industry Yes Yes
Table IV.
Offer expenses
multivariate tobit models
Pooled Pair-matched
Estimate p-value Estimate p-value
Intercept 7.5 ,0.0001 7.7 ,0.0001
SB 0.8 ,0.0001 0.8 ,0.0001
Big Six 20.3 ,0.0001 20.4 ,0.0001
UW Rank 20.2 0.7488 21.1 0.2883
log(Sales) 20.1 0.0005 20.1 0.0053
Cash Flow 0.00 0.8097 20.01 0.4832
Exchange 20.6 ,0.0001 20.5 ,0.0001
ROA 20.02 0.0549 20.02 0.2561
log(Age) 0.01 0.8023 0.00 0.9306
VC 20.2 ,0.0001 20.3 ,0.0001
Debt/Assets 20.05 0.0071 20.05 0.0539
Delaware Corp 0.02 0.6797 0.02 0.7802
Internet IPO 20.02 0.7405 20.2 0.1097
Dual Share Class 20.2 0.0213 20.3 0.1413
Lockup Length 0.00 ,0.0001 0.00 ,0.0001
Years Yes Yes
Industry Yes Yes
Table V.
Gross spread
multivariate tobit models
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Table V reports a similar effect for Gross Spread – SB-2s have higher gross spreads
even after employing the control variables. Using the pooled (pair-matched) sample,
SB-1 IPOs have 80 (80) basis points higher gross spread than SB-1s. This gross spread
difference should not be attributed to economies of scale, particularly for the
pair-matched sample, as we match on size and also control for size in the model.
Whereas the results in Panels A and B con?rm the univariate results, the results in
Table VI reverse the interpretation for Days in Progress. After controlling for the other
confounding effects, SB-2 IPOs actually experience between 13.2 (pooled, p ¼ 0.0144)
and 9.8 days (pair-matched, p ¼ 0.1244) “less” in the registration process. Because this
result is surprising vis-a` -vis the univariate results, a discussion of the control variables
is in order.
The control variables that decrease the Days in Progress are Big Six, log(Sales), and
Offer Size. Each of the results for these variables is intuitive in that prestigious auditors
have the experience to prepare the ?nancials such that they do not require corrections
from the SEC. IPOs with relatively high sales and larger offer sizes represent quality
?rms which can pass through the scrutiny of the SEC more quickly[7]. The factor that
increases the Days in Progress is log(Age). Older ?rms take longer in the registration
process perhaps due to having a longer history which must be captured and vetted in the
SEC registration documents.
Summarizing our ?ndings for the ef?cacy of the SB-2 program, both univariate and
multivariate results provide evidence that the program has not decreased the costs for
SB-2 IPOs vis-a` -vis S-1 IPOs of similar size (H1 and H2 are rejected). The evidence
for the time it takes from initial ?ling to IPO date is not as clear. The univariate results
reject H3, suggesting SB-2 ?rms take longer in the registration process. The
multivariate test provides evidence in support of H3, contrary to the univariate
?ndings. The tobit model shows that when we control for confounding factors, the
Pooled Pair-matched
Estimate p-value Estimate p-value
Intercept 121.6 ,0.0001 143.4 ,0.0001
SB 213.2 0.0144 29.8 0.1244
Big Six 228.2 ,0.0001 222.9 0.0015
UW Rank 0.5 0.9926 137.0 0.1389
log(Sales) 26.9 0.0020 213.8 ,0.0001
Cash Flow 20.3 0.4813 0.5 0.5005
Exchange 26.2 0.3010 27.7 0.2601
ROA 0.8 0.4688 1.8 0.1632
log(Age) 11.3 ,0.0001 12.7 0.0003
VC 23.4 0.4331 28.1 0.1804
Debt/Assets 2.1 0.2833 2.8 0.2253
Delaware Corp 2.9 0.4990 6.1 0.2604
Offer Size 20.3 0.0028 20.5 0.0003
Internet IPO 24.8 0.5423 218.8 0.1095
Dual Share Class 211.0 0.3087 213.6 0.3886
Lockup Length 0.0 0.0024 0.0 0.0473
Years Yes Yes
Industry Yes Yes
Table VI.
Days in registration
multivariate tobit models
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SB-2 program has provided a quicker registration time for SB-2 IPOs. Our overall
conclusion for the ef?cacy of the SB-2 program is that it has not reduced the cost to
go public, but it has decreased the days in registration.
Conclusion
We compare SB-2 IPOs to S-1 IPOs that also had less than $25 million in sales the year
prior to the IPO. We believe we are the ?rst to do so. We test to determine if the legislative
goal of the SB-2 programhas been achieved. We ?nd that SB-2 IPOs have higher-scaled
offering expenses and underwriting spreads, contrary to the aims of the legislation.
Using averages (medians) and a pair-matched benchmark sample, SB-2 IPOs pay
7.1 percent (6.9 percent) in offering expenses, compared to 3.4 percent (2.8 percent) for S-1
IPOs. The spread is slightly larger using a pooled sample benchmark. SB-2 IPOs pay
more than double in scaled offering expenses. For gross underwriter spreads, SB-2 IPOs
pay an average (median) of 9.0 percent (9.9 percent) compared to 7.1 percent (7.0 percent)
for both pooled and pair-matched benchmark samples. When we examine the number of
days from?ling to offering, the results are mixed. In a univariate setting, SB-2 IPOs take
an average of eight to ten days longer than S-1 IPOs. In multivariate tests, SB-2 IPOs
experience ten to 13 less days in the registration process.
Notes
1. Conversation with Tom Steward-Gordon, former editor of the SCOR Report.
2. Firms under $25 million in sales could also choose to ?le form SB-1. SB-1 ?lings, however,
are not comparable to S-1 ?lings because SB-1s are restricted to raising a maximum of
$10 million in any 12-month period.
3. We thank Keith Gamble who served as a discussant for this paper for this suggestion.
4. We match each SB-2 company to the S-1 company closest in size (sales) within the same
industry. We represent size using Compustat annual sales (REVT). Industry classi?cation
follows the Kahle and Walkling (1996) methodology.
5. Our control variables are motivated by the following studies, Big Six and UWRank (Brau
and Johnson, 2009), Sales (Purnanandan and Swaminathan, 2004), Cash ?ows (Livnat and
Lopez-Espinosa, 2008), the Exchange the ?rm is listed on (Bradley and Jordan, 2002), Return
on assets (ROA) (Chen et al., 2011), Age of the ?rm (Loughran and Ritter, 2004), ?rm Debt
ratios (Cotter and Peck, 2001), the state of incorporation (Delaware corp) (Boulton, 2010),
IPO offer size (i.e. the public ?oat) (Bradley et al., 2006), internet-IPO offering (Loughran
and Ritter, 2004), dual-class IPO offering (Smart and Zutter, 2003), and lockup length
(Brau et al., 2005).
6. We customize each model for the speci?c dependent variable by excluding the variables that
theoretically do not motivate inclusion as explanatory variables in a given model. For the
Offering Expense model, we do not include ROA, Offer size, or Lockup Length as they
theoretically (and empirically) do not impact Offer Expense. For the Gross Spread model, we
remove Offer Size, as it is the denominator of the dependent variable, Gross Spread. We use
the complete base model for Days in Progress. If we use the base model for Offering Expenses
and Gross Spread, our reported results are robust.
7. The coef?cient on log(Sales) is statistically signi?cant because the standard errors are so
small. Because we constructed the pooled sample only for IPOs with less than $25 million
and the pair-matched based on size (sales), the small standard errors for sales increase the
t-statistic.
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References
Ang, J. and Brau, J.C. (2002), “Firm transparency and the costs of going public”, Journal of
Financial Research, Vol. 25, pp. 1-18.
Boulton, T.J. (2010), “The impact of the corporate control market on IPO decisions”, Miami
University working paper.
Bradley, D. and Jordan, B. (2002), “Partial adjustment to public information and IPO
underpricing”, Journal of Financial and Quantitative Analysis, Vol. 37, pp. 595-616.
Bradley, D., Cooney, J., Dolvin, S. and Jordan, B. (2006), “Penny stock IPOs”, Financial
Management, Vol. 36, pp. 5-29.
Brau, J. and Carpenter, J.T. (2012), “Small-?rm uniqueness and signaling theory”, Journal of
Business Economics and Finance, Vol. 1 No. 1.
Brau, J. and Gee, G. (2010), “Micro-IPOs: an analysis of the Small Corporate Offering Registration
(SCOR) procedure with national data”, Journal of Entrepreneurial Finance, Vol. 14,
pp. 69-89.
Brau, J. and Johnson, P. (2009), “Earnings management in IPOs: post-engagement third-party
mitigation or issuer signaling?”, Advances in Accounting, Vol. 25, pp. 125-35.
Brau, J. and Osteryoung, J. (2001), “The determinants of successful micro-IPOs: an analysis of
issues made under the Small Corporate Offering Registration (SCOR) procedure”, Journal
of Small Business Management, Vol. 39, pp. 209-27.
Brau, J., Lambson, V. and McQueen, G. (2005), “Lockups revisited”, Journal of Financial and
Quantitative Analysis, Vol. 40, pp. 519-30.
Chen, H. and Ritter, J. (2000), “The seven percent solution”, Journal of Finance, Vol. 55,
pp. 1105-31.
Chen, L., Novy-Marx, R. and Zhang, L. (2011), “An alternative three-factor model”, April,
available at: SSRN: http://ssrn.com/abstract¼1418117; http://dx.doi.org/10.2139/ssrn.
1418117
Coffey, W.J. and Schier, L. (1995), “Small business initiatives under the Securities Acts”, The CPA
Journal, Vol. 65 No. 1, pp. 46-9.
Cotter, J. and Peck, S. (2001), “The structure of debt and active equity investors: the case of the
buyout specialist”, Journal of Financial Economics, Vol. 59, pp. 101-47.
Kahle, K. and Walkling, R. (1996), “The impact of industry classi?cations on ?nancial research”,
Journal of Financial and Quantitative Analysis, Vol. 31, pp. 309-36.
Livnat, J. and Lopez-Espinosa, G. (2008), “Quarterly accruals or cash ?ows in portfolio
construction?”, Financial Analysts Journal, Vol. 64, pp. 67-79.
Loughran, T. and Ritter, J.R. (2004), “Why has IPO underpricing changed over time?”, Financial
Management, Autumn, pp. 5-37.
Purnanandan, A. and Swaminathan, B. (2004), “Are IPO’s really underpriced?”, Review of
Financial Studies, Vol. 17, pp. 811-48.
Ritter, J.R. (1987), “The costs of going public”, Journal of Financial Economics, Vol. 19, pp. 269-81.
Securities and Exchange Commission (SEC) (1992), The Small Business Incentive Act of 1992,
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and Urban Affairs at the Second Session of the One Hundred Second Congress of the
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and single-class IPOs”, Journal of Financial Economics, Vol. 69, pp. 85-110.
Ef?cacy of the
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Appendix
Mathstar Inc. (S-1 Sample)
Table of contents
Prospectus Summary 1
Risk Factors 8
Forward-Looking Information 22
Use of Proceeds 23
Dividend Policy 24
Capitalization 25
Dilution 26
Selected Financial Data 28
Management’s Discussion and Analysis of Financial Condition and Results of Operations 30
Business 40
Management 55
Related Party Transactions 63
Principal Stockholders 64
Description of Our Securities 66
Shares Eligible for Future Sale 70
Underwriting 73
Legal Matters 77
Experts 77
Where You Can Find More Information 77
Index to Financial Statements F-1
Marchex Inc. (SB-2 Sample)
Table of contents
Page
Prospectus Summary 1
Summary Consolidated Financial Data 5
Risk Factors 7
Special Note Regarding Forward-Looking Statements 21
Use of Proceeds 22
Determination of Offering Price 23
Capitalization 24
Dilution 26
Dividend Policy 27
Management’s Discussion and Analysis of Financial Condition and Results of Operations 28
Business 51
Management 62
Executive Compensation 66
Security Ownership of Certain Beneficial Owners and Management 70
Certain Relationships and Related Transactions 72
Description of Capital Stock 74
Market for Common Equity and Related Stockholder Matters 79
Underwriting 82
Legal Matters 85
Experts 85
Disclosure of Commission Position on Indemnification for Securities Act Liabilities 85
Where You Can Find More Information 85
Financial Statements
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About the authors
James C. Brau, PhD, CFA, is the Editor of the Journal of Entrepreneurial Finance and a Professor
of Finance at the Brigham Young University Marriott School. He received his PhD from Florida
State University in Business Administration and Finance and has published over
30 peer-reviewed articles in the area of entrepreneurial ?nance.
J. Troy Carpenter is a Research Associate and Data Analyst for the Marriott School Research
Center at Brigham Young University. He received his MBA from Brigham Young University and
has 20 years of experience in Corporate America and Asia. In addition to his research activities
he is the Director of the Marriott School’s Asia Study Abroad program. J. Troy Carpenter is the
corresponding author and can be contacted at: [email protected]
Ef?cacy of the
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