Hard and Soft Information:
Firm Disclosure, SEC Letters, and the JOBS Act∗
Sudip Gupta and Ryan D. Israelsen†
First draft: March 15, 2014
This draft: April 27, 2015
Abstract
This paper evaluates the impact of the recently passed JOBS Act on IPO outcomes. A significant
number of IPO firms use provisions of this act. Using the JOBS Act as a natural experiment
on firms’ choices of the optimal mix of hard and soft information, we find that relative to a
peer group of firms, (1) IPO firms choose a different mix of hard and soft information, (2) the
non-disclosure of certain hard information does not lead to increased cost of capital, and (3) the
choice of soft information reduces underpricing by about 2.7% and bid-ask spreads by about 1
cent. We also find that (4) the SEC changes its behavior post-JOBS Act in responding to firms’
draft registration statements and (5) investors react differently to changes in firms’ hard and
soft information and SEC comments in terms of the probability of informed trading (PIN). A
one standard deviation increase in a proxy for the winner’s curse from the SEC’s signal increases
underpricing by about 5.3%. Overall, the results document an optimal mix of hard and soft
information for IPO firms and their substitutability.
Keywords: Disclosure, SEC, Information Asymmetry, IPO, JOBS Act, Liquidity, Probability
of Informed Trading, Underpricing, Textual Analysis, Topic Models, Hard Information, Soft
Information
∗
We thank Viral Acharya, Reena Agarwal, Azi Ben-Rephael, Matt Billett, Sudheer Chava, Radha Gopalan,
Kathleen Hanley, Gerard Hoberg, Craig Holden, NR Prabhala, Rangarajan Sundaram, and Suresh Sundaresan for
detailed comments and suggestions and participants in seminars at Indiana University and Washington University
at St. Louis for helpful comments. We also thank various anonymous market participants who helped us better
understand the post-JOBS Act IPO process. We thank Jun Wu and Alicia Li for excellent research assistance. A
previous version of this paper circulated under the title “Indirect Costs of the JOBS Act: Disclosures, Information
Asymmetry, and Post-IPO Liquidity.”
†
The authors are at Kelley School of Business, Indiana University. Gupta: sudgupta@indiana.edu, Israelsen:
risraels@indiana.edu
1
Introduction
The impact of information on financial and real efficiency is a long-standing issue in finance and
economics. Disclosure of information is expected to increase financial efficiency and reduce the
cost of capital.1,2 Firms communicate their future prospects and risks using both hard (verifiable)
and soft (non-verifiable) information. While hard information is typically more credible than soft
information, it is costly to produce and disseminate.3,4 Thus, the optimal mix of hard and soft
information disclosure depends on the differential tradeoffs between the marginal costs and benefits
across both types of information, and is contingent upon the equilibrium investor reaction to the
information. The impact of the mix of hard and soft information on the cost of capital is a relatively
underexplored area of research. In this paper, we examine these issues using a natural experiment
provided by the recent passage of the Jumpstart Our Business Startups (JOBS) Act which – in
response to the sluggish IPO market following the recession – relaxed binding constraints on the
mandatory disclosure of hard information by firms undertaking initial public offerings (IPOs).5,6
If a constraint on the disclosure of hard information is binding, its relaxation will reduce the
equilibrium amount of hard information disclosed. Moreover, if there is some level of substitutability
between hard and soft information, disclosures of soft information will also change as a result, even
if there is no change in constraints on soft information. Thus, if the constraint on hard information
was binding, JOBS Act IPO firms should optimally reduce their disclosures of hard information and
change their disclosures of soft information. In equilibrium, rational investors will therefore take
both types of information into account when pricing the stock.
1
Easley and O’Hara (2004) show that the cost of capital should increase as information is shifted from public to
private.
2
Edmans et al. (2013) show that financial efficiency depends on the total amount of information disclosed while
real efficiency depends on the relative mix of hard and soft information.
3
An example of a direct cost is auditor fees. An example of an indirect cost is a reduction in competitive advantage.
4
Petersen (2004) defines hard information as information that is quantitative and easy to store and transmit, and
soft information as information that is communicated in text and difficult to completely summarize in a numeric score.
5
The JOBS Act was passed in response to the weak IPO market in the wake of the financial crisis and recession.
The aim was to encourage small firms to access equity markets by reducing the costs of the IPO process. To achieve
this goal, the JOBS Act reduces the amount of hard information firms are required to disclose. We discuss the details
in Section 3.
6
A few recent papers examine the impact of the JOBS Act on the volume of IPOs and the direct and indirect costs.
Dambra, Field, and Gustafson (2015) analyze the impact of the JOBS Act on IPO decisions and provide evidence
that the JOBS Act led to an increase in the number of IPOs, which they attribute to a reduction in the direct costs of
disclosure. Using a slightly different methodology, Chaplinsky, Hanley, and Moon (2014) provide evidence that the
JOBS Act led neither to a reduction in direct costs nor to an increase in volume.
1
The JOBS Act became law in April 2012 with the goal of reducing the costs and delays of the
IPO filing process. The Act coined a new term for small firms with revenues of less than $1 billion:
emerging growth companies (EGCs).7 IPO firms had been required by regulation to disclose a
significant amount of hard accounting information in order to access equity markets. The production
and filing of such hard information has significant direct costs in the form of various auditor and
legal fees. The JOBS Act enables EGCs to take advantage of “scaled financial disclosure provisions”
and an exemption from an “internal controls audit” helps them to reduce the cost of auditor fees and
the time taken to go public. These provisions therefore ease constraints on disclosure of certain types
of hard information, including the required number of years of audited financial statements, the
number of years of selected financial data, and the amount of disclosure of executive compensation.
Additionally, the JOBS Act permits EGCs to confidentially file their draft registration statement
with the Securities and Exchange Commission (SEC). Firms ultimately choosing to terminate the
confidential filing process may do so without making any public filings.
Using data from a sample of firms before and after the passage of the JOBS Act, we find that
most IPO firms optimally choose to withhold some amount of hard information under the JOBS Act.
In other words, the pre-JOBS Act constraints on the disclosure of hard information were indeed
binding. Moreover, we find that the voluntary withholding of hard information does not directly
affect the cost of capital – using IPO underpricing as a proxy. These two results suggest that the
disclosure requirements on hard information were unnecessarily stringent before the JOBS Act.
Additionally, firms’ disclosures of soft information change under the JOBS Act relative to a matched
set of similar IPO firms prior to the act. However, in contrast to hard information, disclosures of
soft information do affect the cost of capital. Relative to the matched sample, we estimate that the
change in disclosure of soft information under the Jobs Act leads to a 2.7% reduction in underpricing
and a 1 cent reduction in bid-ask spreads following the IPO. Though disclosures of hard information
do not directly impact underpricing, the interaction between disclosures of hard and soft information
does, suggesting that investors take the relative mix of both types of disclosures into account when
interpreting the information.
7
Throughout this paper, we will refer to EGC firms using JOBS Act provisions to go public as JOBS Act IPOs or
EGC IPOs.
2
In addition to the information disclosed by the IPO firms themselves, we examine public
disclosures made by the SEC about the firms. The SEC plays a key role in monitoring firms’ filings
to ensure that they comply with the relevant disclosure and accounting requirements. The SEC
often issues comment letters to IPO firms during the filing process to provide feedback and guidance
on the content of the registration statement.8 These letters are eventually made public.9 We find
that following the introduction of the JOBS Act, the SEC increases the amount of information it
discloses in its comment letters. In particular, SEC comment letters to JOBS Act firms are longer
and include more items than those written to a matched set of IPO firms from before the JOBS Act.
Furthermore, the nature of the soft information in SEC letters to JOBS Act firms differs from those
from before the JOBS Act. Specifically, on average, the soft information includes relatively more
uncertainty words, more weak modal words, and more negative words. Thus, when firms voluntarily
reduce their disclosures of hard information, the SEC provides more soft information and this soft
information tends to be more uncertain and negative.
As the SEC is closely involved in the IPO process of every firm, we use the soft information
from their comment letters as a proxy for the type of information produced by informed investors.
As such, the content of this communication should also influence investors’ behavior. Moreover,
the impact of the content of SEC letters on underpricing will depend on the type of information
disclosed by the firm. We regress IPO underpricing on SEC soft information, as well as firm hard
and soft information and various control variables and find that IPO firms whose SEC comment
letters include more uncertainty words and relatively more weak modal words tend to have more
underpricing. Thus, the SEC plays an important role in the generation and dissemination of
information in the IPO market. While a firm’s decision to reduce disclosure of hard information has
no unconditional impact on underpricing, there is a conditional effect based on the nature of the
soft information in the SEC letters.
While only soft information directly affects underpricing, disclosures of both types of information
affect the amount of informed trading immediately following the IPO. In particular, firms that
voluntarily reduce disclosures of certain types of hard information tend to have more informed
8
See Ertimur and Nondorf (2006) for an analysis of comment letters and the IPO process.
Letters are published to SEC’s EDGAR website no less than 20 days after the SEC has declared the registration
statement effective.
9
3
trading. Similarly, changes in the type of soft information disclosed by firms lead to changes in
informed trading, highlighting an important role of the market in the price discovery process.
In addition to requiring disclosure of hard information, the SEC Regulation S-K sets mandatory
requirements for firms to disclose certain types of soft information in the IPO prospectus. An
important type of soft information mandated by Regulation S-K is the inclusion of a “Risk Factors”
section in the prospectus. The firm is legally liable if they disclose false information or withhold
known risk factors in this section. Thus, this section is mandatory, but open ended. Moreover, in
contrast to the disclosures of hard information, which are typically past accounting data, risk factor
disclosures are, by nature, forward looking. Furthermore, while the JOBS Act changes disclosure
requirements of hard information, there is no change in requirements with respect to the risk factor
section, other than the mandate to disclose that the firm is taking advantage of JOBS Act provisions.
Thus, any additional changes in these soft disclosures are voluntary and will be chosen in conjunction
with the disclosures of hard information.
As our main measure of soft information, we focus on this risk factor section. Several papers
have shown that qualitative disclosures in this section affect IPO underpricing. For example, Beatty
and Welch (1996) provide evidence that the number of risk factors disclosed in a firm’s prospectus
is positively related to IPO underpricing. Hanley and Hoberg (2010) examine four sections of the
prospectus (including the “Risk Factors” section) and find that offers with more informative content
(i.e., words that deviate from those used in other IPOs in the same industry and from recent IPOs)
have more accurate initial offer prices and less underpricing. In his review article, Ljungqvist (2005)
suggests that a “promising approach might be to identify specific . . . risk factors that, if present,
indicate higher uncertainty.” Our approach is consistent with this suggestion. In contrast to the
techniques used in the above papers which examine the number of risks factors or the novelty or
tone of the language, we examine the specific types of risks disclosed by firms in the risk factors
section.
To consistently and systematically measure the types of risks disclosed in the risk factor section of
the prospectus, we use a novel textual analysis technique called topic modeling.10 Topic models are
10
A few recent papers in finance and accounting make use of topic models. Ball et al. (2014) use a topic model on
the MD&A section of firms’ 10-K filings and find that the content is more value relevant when financial statements
are less relevant, and vice-versa. Also focusing on the MD&A section, Hoberg and Lewis (2014) provide evidence
4
used to objectively uncover a set of “topics” in a body of documents by finding latent relationships
between groups of words that tend to appear together. Using a topic model, we examine the
thematic content of IPO firms’ risk disclosures from the risk factors section of their prospectus.
We then test whether variations in the thematic structure of this disclosed risk can explain the
differences in underpricing between EGC firms and a matched sample of their counterparts.
It is the context of the words that matters, not just the words themselves. For example, the
words “product” “drug”, “market”, and “candidate” are very commonly used words in risk factor
disclosures associated with the research and development of new pharmaceutical products. However,
when the words “clinical”, trial”, “approval”, and “FDA” also appear with these words, they tend
to be associated with risks of the product approval process. The word “product” is commonly
found in a few other risk factors. Thus, simply counting the number of times the word “product”
appears will not capture this distinction. Topic models are designed to capture these differences in
an objective way that is not captured by word counts, or even manual classifications.
We show that under the JOBS Act, EGCs change their disclosures of soft information. In
particular, relative to a matched set of firms from before the JOBS Act, EGCs make relatively fewer
disclosures of “new economy” risks (such as the risks associated with intellectual property, systems
failure, and human capital). Moreover, underpricing is 2.7% lower and bid-ask spreads are 1 cent
lower for these firms than it would be were they to make the same risk disclosures as their matched
peers. Thus, in response to the removal of a binding constraint on hard information, firms optimally
change their disclosures of soft information.
The provision allowing firms to confidentially file their draft registration statement also affects
underpricing. This soft information signals to the market that the firm may have been uncertain
about their own IPO prospects. More than 60% of EGCs that eventually have an IPO take advantage
of this provision.11 The SEC publishes confidential draft registration statements before the IPO
roadshow. Thus, investors bidding on shares during the IPO process know that these IPO firms were
relatively uncertain about the overall demand for their shares. This uncertainty about the difficulty
that firms engaging in fraud attempt to deflect attention by reducing disclosure in areas related to the underlying
problems. Fitting a topic model to transcripts from analyst conference calls and analyst reports, Huang et al. (2014)
examine sell-side equity analysts’ information interpretation and discovery roles.
11
Because the process is confidential, EGCs can withdraw from the process without making any public filings. Thus,
the the percentage of firms initiating an IPO process that do so confidentially will be higher than 60%.
5
in finding a counterparty once shares start trading increases the winner’s curse risk. Therefore,
investors should demand an extra premium to subsribe to these shares during the IPO process in
terms of higher underpriing. We find that, all else equal, firms filing confidentially have underpricing
9% to 11% higher than those who do not. This number is much larger than the unconditional 6%
difference in underpricing of EGCs relative to their matched peers.
1.1
Related Literature
Our paper makes several contributions to the literature. We are the first to examine the impact of
mandatory disclosure requirements on IPO firms’ equilibrium mixtures of hard and soft information.
As mentioned in Petersen (2004), the benefits of substitution between hard and soft information is an
empirical question. Edmans et al. (2013) show that improving financial efficiency by providing more
hard information may actually reduce real efficiency. In their model, financial efficiency depends
on total information and real efficiency depends on the relative mix of hard and soft information.
We show empirically that disclosure of soft information is also related to financial efficiency as soft
information disclosure directly affects underpricing. Moreover, the relaxation of a constraint in one
type of information leads to a change in disclosure of the other. Examining the impact of each type
of information on underpricing, we find that most it seems to be driven by the soft information.
When binding constraints on hard information are relaxed firms optimally reduce their direct costs
of hard information disclosure change soft information to reduce overall underpricing.
This paper is related to the literature examining the impact of disclosure on the costs of capital.
Easley and O’Hara (2004) show that the differences in the composition of public and private
information can create asymmetric information induced systematic risk. In their model, uninformed
investors’ perceptions of risk and return differ due to their information disadvantage and, as a
result, they hold too many bad stocks in their portfolios. Anticipating this, these investors require
compensation for holding stocks with more asymmetric information. This risk is the greatest in
stocks with less public information. Thus, if the JOBS Act increases information asymmetry by
reducing publicly available information, this should lead to greater IPO underpricing. They also
show that if more information about an asset is private, the difference between the average holdings
of informed versus uninformed investors will increase. Therefore, if the JOBS Act increases private
6
information, the average shareholding should tilt more towards the informed investors during the
IPO process, which will increase the dispersion of shareholdings. This should in turn lead to
more informed trading post-IPO and the probability of informed trading should be affected by the
disclosure depending on what type of information is withheld.
Bond and Goldstein (2010) show that when the government discloses information about which
it is better informed than speculators, such disclosures reduce speculators’ risk causing them to
trade more. We empirically find a value-enhancing role of the SEC in that its disclosures of soft
information are priced in the market and affect post-IPO informed trading. This highlights the
complementary nature of information that SEC possesses relative to the market.
This paper is also related to the disclosure literature (Verrecchia, 2001; Dye, 2001; Goldstein and
Sapra, 2013) which mostly focused on disclosure of hard information. We show that in a changed
disclosure regime the firms also disclose soft information differently, which has value enhancing
effect in terms of reducing the overall cost of capital.
Our paper is also related to the impact of asymmetric information in the IPO process which has
been a topic of research for decades.12 The issuing firm elicits information from informed investors
during the book-building process via an investment bank (Benveniste and Spindt, 1989) and IPO
underpricing is a fair rent for producing information. The information asymmetry problem among
potential investors – both informed and uninformed – generates the winner’s curse problem. (Rock,
1986) Our paper examines the impact of the JOBS Act and the role of information production
during the IPO process on post-IPO outcomes for EGCs.
A few contemporaneous studies examine the impact of the JOBS Act on IPO outcomes. Dambra,
Field, and Gustafson (2015) provide evidence that the IPO volume increased as a result of the
JOBS Act. This increase was the most pronounced in the pharmaceutical industry. The results in
Chaplinsky, Hanley, and Moon (2014) suggest that there was no reduction in direct costs under the
JOBS Act, and that underpricing increased. Barth, Landsman, and Taylor (2014) also examine the
costs of the JOBS Act and find that post-IPO secondary market volatility increases. Dambra, Field,
Gustafson, and Pisciotta (2014) examine the impact of the JOBS Act on the information produced
12
See Ljungqvist (2005) for a survey.
7
by sell-side analysts and find evidence that it reduced the quality of such information and increased
the bias. In contrast to these papers, we examine how the reduction in disclosure requirements in
the JOBS Act affects the relative mix of hard and soft information disclosure and how this affects
underpricing, informed trading, and liquidity. Additionally, we examine how the SEC responds to
the changing information environment in terms of its own production of soft information, and the
response of the market to this information.13
More generally, our paper relates to the literature examining the impact of soft information in
a prospectus on underpricing and uncertainty.14 For example, while they do not identify specific
risk categories, Hanley and Hoberg (2010) examine four sections of the prospectus (including the
“Risk Factors” section) and find that offers with more informative content (i.e., words that deviate
from those used in other IPOs in the same industry and from recent IPOs) have more accurate
initial offer prices and less underpricing. In a related paper, Hanley and Hoberg (2012) examine the
impact of revisions to the prospectus on price revisions during the issue process itself. The tone of
the soft information may affect underpricing. Using a six sentiment word lists created for financial
documents, Loughran and McDonald (2013) find that the frequency of uncertain, weak modal, and
negative words in the prospectus are significantly related to IPO underpricing. In contrast to these
studies, we examine how firms change the types of risks that they disclose following a reduction in
disclosure requirements of hard information, the mixture of hard and soft information, and how this
impacts underpricing.
Our work is also related to the role of soft information in the banking literature. Stein (2002)
analyzes the role that organizational structure plays in evaluating soft information. He shows that
small firms have a comparative advantage in evaluating investment projects when information about
the projects is soft and cannot be credibly communicated. Berger and Udell (1995) and Berger et al.
(2005) show that small banks are better able to collect and act on soft information than large banks.
There are numerous papers in the banking literature which focus on the role of soft information and
relationship lending. Our work complements this literature by documenting that soft information is
13
The content of SEC letters has been shown to be related to the IPO information environment (Ertimur and
Nondorf, 2006).
14
A sample of papers examining the textual content of disclosures to the SEC after the IPO include Li (2008), You
and Zhang (2009), Hoberg and Phillips (2010), Loughran and McDonald (2011), Jegadeesh and Wu (2013), Lawrence
(2013), and Loughran and McDonald (2014).
8
also priced in the equity market and IPO firms optimally chose between hard and soft information
to reduce the overall cost of going public.
Finally, our paper is related to the papers using topic models to quantify soft information. These
models, which are common in computational linguistics, are just beginning to gain traction in the
finance and accounting literature. A few recent papers in finance and accounting make use of topic
models. Ball et al. (2014) use a topic model on the MD&A section of firms’ 10-K filings and find that
the content is more value relevant when financial statements are less relevant, and vice-versa. Also
focusing on the MD&A section, Hoberg and Lewis (2014) provide evidence that firms engaging in
fraud attempt to deflect attention by reducing disclosure in areas related to the underlying problems.
Fitting a topic model to transcripts from analyst conference calls and analyst reports, Huang et al.
(2014) examine sell-side equity analysts’ information interpretation and discovery roles.
The remainder of the paper is laid out as follows. In the following section, we develop our
hypotheses. In Section 3, we describe the JOBS Act. In Section 4, we describe our data. In Section
5, we discuss our empirical methodology and results. We conclude in Section 6.
2
Hypotheses
Firms disclose information when raising capital in order to reduce information asymmetry and hence
the cost of capital. Because such disclosure is costly, firms optimally choose the combination of hard
and soft information to optimize the trade-off between the cost of disclosure and the benefits. When
there is a regulatory restriction on the minimal amount of hard information disclosure then the
firm effectively faces a constrained optimization problem. Marginal costs of the disclosure of hard
information outweigh marginal benefits. Accordingly, any relaxation of the regulatory constraint
should lead to a change in the optimal mix of hard and soft information. The JOBS Act relaxed the
disclosure requirements of hard information for IPO firms. Therefore a firm going public post JOBS
Act should change its disclosures of soft information in the prospectus. This observation provides
our first hypothesis related to the change in the optimal disclosure of hard and soft information
following the JOBS Act.
9
Hypothesis 1 (Optimal disclosure of hard and soft information): The optimal choice of both
hard and soft information disclosed in the IPO prospectus by JOBS Act firms’ will differ from those
of a set of peer firms going public before the JOBS Act.
Disclosure of information in the IPO prospectus helps reduce information asymmetry and the
cost of capital. One proxy for cost of capital is IPO underpricing – defined as the first day stock
return of an IPO firm. Since disclosure of hard information is costly, given the opportunity, an IPO
firm may choose reduce the amount of such disclosures. This reduction of hard information should
affect the underpricing of JOBS Act IPOs relative to a set of similar IPOs prior to the JOBS Act.
Before the JOBS Act, marginal benefits of additional hard information relatively low relative to
the costs. Thus, the reduction of hard information should have a minimal impact on underpricing.
On the other hand, if firms can optimally choose and credibly convey soft information, they may
reduce any additional IPO underpricing under the JOBS Act. We therefore have our second set of
hypotheses.
Hypothesis 2A (IPO Underpricing): Post JOBS Act IPO firms should experience higher
underpricing relative to a peer group from before the JOBS Act.
Hypothesis 2B (IPO Underpricing): The increased underpricing for post JOBS act IPO firms
should disappear once we control for differences in disclosure.
Hypothesis 2C (IPO Underpricing): By choosing a different mix of soft information (relative
to its peer group of firms, pre-JOBS Act), an EGC should be able to reduce its cost of capital
(underpricing).
The SEC plays a key role in monitoring firms’ filings to ensure that they comply with the relevant
disclosure and accounting requirements. After a firm submits its draft registration statement, the
SEC provides the firm with comments and suggestions in order to improve disclosure, which the
firm incorporates into the next revision of the document. Accordingly, the content of SEC comment
letters to IPO firms should reflect the amount of information disclosed by the firm. Though these
comment letters are posted to the EDGAR website after the SEC has declared the registration
statement effective, they they serve as a proxy for the information of an informed investor. Thus,
the content of the letters be related to investors’ behavior. This soft information provided by a
10
regulator may be complimentary to disclosures made by the firm. Thus, the impact of SEC letters
on underpricing should depend on the type of information disclosed by the firm. These arguments
provide us with our third set of hypotheses.
Hypothesis 3A (SEC Soft Information): SEC soft information will be related to IPO underpricing and probability of informed trading.
Hypothesis 3B (SEC Soft Information and the JOBS Act): The SEC will produce more soft
information for JOBS Act IPO firms relative to set of peer firms who went public before the JOBS
Act.
Hypothesis 3C (SEC Soft Information and Underpricing): Investors will place more weight on
SEC soft information for JOBS Act firms relative to a peer group of firms who went public before
the JOBS Act. Moreover the interaction of SEC soft information with the reduction in disclosure of
hard information will be affect IPO underpricing.
Hypothesis 3D (SEC Soft Information and Informed Trading): The additional soft information
provided by the SEC for JOBS Act IPO firms should affect the incentives for information production
for informed investors, post-IPO. Moreover, informed trading should depend on amount of hard
information disclosed. The interaction of SEC soft information with certain non-disclosure of hard
information will lead to higher informed trading.
3
JOBS Act
The Jumpstart Our Business Startups (JOBS) Act became law on April 5, 2012 with the stated
goal “to increase American job creation and economic growth by improving access to the public
capital markets for emerging growth companies” (EGCs).15 Title I of the JOBS Act defines an
EGC as a company with revenues of no more than $1 billion in its most recent fiscal year. It also
delineates certain criteria by which such a company may lose its EGC status.16 Title I also lists a
15
See H.R. 3606 “Jumpstart Our Business Startups Act” http://www.gpo.gov/fdsys/pkg/BILLS-112hr3606enr/
pdf/BILLS-112hr3606enr.pdf
16
Sections 101 (a) and (b) of the JOBS Act state that after the initial determination of EGC status, a company will
remain an EGC until the earliest of:
11
menu of special provisions for the EGCs to chose from. The EGCs may choose all, some, or none of
the special provisions when going public. The main provisions are:
ˆ Confidential SEC review: EGCs may make a confidential submission provided that the initial
confidential submission and all amendments are publicly filed with SEC no later than 21 days
before the commencement of the road show.
ˆ Testing the waters: The EGC (or its underwriter) can engage in oral or written communications
with institutional investors, either before or after filing the first registration statement.
ˆ Reduced financial disclosure: EGCs may disclose only two years of audited financial statements
instead of the three years norm. They may also disclose two years instead of five years of
selected financial data.
ˆ Exemption from internal controls audit: The EGCs are exempt from an internal control audit
as stipulated by the Sarbanes-Oxley Act (2002).
ˆ Reduced disclosure of executive compensation: EGCs are exempt from shareholders advisory
votes on executive compensation required by the Dodd-Frank act of 2010. Moreover, EGCs
may omit a written discussion of executive compensation from its prospectus and may disclose
the compensation of as few as 3 executives instead of the 5 previously required.
ˆ Extended phase-in for new GAAP: EGCs may continue to use private accounting phase-in
periods for new GAAP standards for a period after the IPO.
ˆ Exemption from Public Company Accounting Oversight Board (PCAOB) rules
These provisions are intended to provide the EGC distinct advantages when going public. First,
they improve the ability of firms and underwriters to gather information about the costs/benefits of
going public without committing to an IPO or sending negative signals to the market. For example,
ˆ the last day of any fiscal year in which company earns $1billion revenue or more
ˆ the last date of the fiscal year following the fifth anniversary of IPO date
ˆ the date of issuance, in a three year period of $1 billion or more of non-convertible debt
ˆ the date on which the issuer is a ”large accelerated filer” with at least $ 700 million in public equity float
12
confidentially submitting a draft registration statement to the SEC for comments allows a company
to decide on the optimal timing of an IPO while significantly reducing the glare of publicity and
hiding its intentions from competitors. Similarly, the testing the waters clause enables the issuer
and its underwriter to gauge market sentiment – especially in industries where valuations are highly
uncertain.
Second, many of these provisions reduce the costs of the IPO process. For example, because a
confidential draft registration statement is not considered a filing, no filing fee needs to be paid.
Similarly, providing two years instead of three of audited financial statements may also save the EGC
auditor fees especially if they have recently changed auditors. Moreover, confidential submission
shields the issuer from the Sarbanes-Oxley Act until the registration is publicly filed. The EGCs
may also use private company phase-in period for accounting standards after the IPO.
In addition to the above provisions, the JOBS Act also relaxes standard norms for analyst
research reports of an EGC. Specifically, the JOBS Act suspended the existing FINRA rule of 40 day
of research quiet period immediately following the EGC IPO. It also includes provisions regarding
with pre-deal research communications.
There are minimal additional disclosure requirements for EGCs. For example, if the EGC does
not opt out of the private accounting standard then SEC requires them to mention that as an
additional risk factor in the prospectus to differentiate it from other public companies. EGC are
also required to disclose their EGC status in the prospectus as well as a summary of the exemptions
available to the EGC. Finally, if the EGC decides to opt out of the transitional accounting standards,
it must also mention that it is irrevocable.
In the following section, we examine the frequency with which EGCs take advantage of each of
the provisions of the JOBS Act and describe the data used in the analysis.
13
4
Data
We have five major sources of data in this paper: The SDC Platinum, CRSP, Compustat, TAQ,
and SEC EDGAR databases. The IPO prospectus filed with the SEC provided the filing details of
the firm as well as the pre-IPO accounting data and which (if any) of the JOBS Act provisions are
used by the firm. We hand collect information from the EDGAR website about whether firm filed
confidentially before the IPO. Post-IPO share prices and trading information are collected from
Compustat, CRSP, and TAQ. The IPO prospectuses and SEC comment letters found in the SEC
EDGAR database are the sources of the textual risk factor, and wordcount analyses. The SDC
database provides a list of IPOs as well as information on the underwriters of the IPO, venture
capital backing, fees, and the IPO Offer price. In many of our tests, we include a dummy variable
indicating that the IPO had a star lead underwriter (defined as a lead underwriter with the highest
rating on Jay Ritter’s website, using the methodology in Loughran and Ritter (2004)).
We choose a sample of IPO firms for the years 2010 through 2013. We choose this sample period
for a few reasons. First, because we are creating a matched sample and are examining risk factor
disclosures, we need the set of firms from before the JOBS Act to be relatively similar to our JOBS
Act sample. Over long periods, risk factor disclosures are time varying. For example, in 1999, it
was common for firms to disclose the risk of the “Y2K bug”. We don’t expect large general changes
in shorter periods. Second, we do not want to include the financial crisis in our sample. Thus, we
limit the beginning of the sample to 2010.17 Starting with the calendar years 2010, 2011, 2012, and
2013, we extract 975 IPOs from SDC Platinum. Of these, 806 are for issuers in the USA. Including
only issues listed on the Amex, NYSE and NASDAQ reduces this number to 663. We are able to
match 648 of these to CRSP. Requiring that these are ordinary common stock (CRSP shrcd = 10
or 11), leaves 431 IPOs. Finally, after limiting the sample to firms with revenues of less than $1
Billion in the most recent year, and requiring firms under the JOBS Act to disclose their status as
an EGC in their prospectus leaves us with 129 firms before the JOBS Act and 159 EGCs under the
JOBS Act.18
17
18
This sample period is similar to that used in much of the analysis in Dambra et al. (2015).
See Table A1 in the Appendix.
14
4.1
Summary Statistics
Table 1 presents summary statistics for the variables used in this study for both types of IPO firms.
Panel A presents means, standard deviations, and standard errors for IPO-level variables for both
groups of IPO firms. Variables include the IPO offer price, the size of the offering, the fees, and
the 1- and 3-day IPO returns for the both types of IPO firms. The first variable of interest is the
IPO offer price. The mean IPO offer price for EGC firms is statistically larger than the pre-JOBS
Act IPO firms (as indicated by the asterisks in the table). The average sizes of the offerings are
not statistically distinguishable from each other, at $0.15 Billion for the EGC companies and $0.13
Billion for those IPOs from before the JOBS Act that would have qualified as EGC. Just more than
half of IPOs in both groups have venture capital backing and fees are roughly the same for each
group.
The next two variables are the 1-day IPO return - defined as the return based on the IPO offer
price and the closing price on the first day trading on the exchange - and the 3-day IPO return
- defines using the closing price on the third day. The average 1-day and 3-day IPO returns for
the EGCs are 20.8 and 23.6 percent, respectively. Strikingly, EGC companies, on average, have
larger 1-day and 3-day IPO returns than the pre-JOBS Act firms, suggesting that their IPOs are
underpriced relatively more. Later, we test whether these differences can be explained by differences
in firms’ disclosures of hard and soft information. Underpricing is 5% to 7% higher, on average.
These differences are statistically significant. Additionally, Panel A presents 22-, 44-, and 66-day
PIN for EGCs and their matched peers from before the JOBS Act as well as the total number of
words in the risk factors section of the prospectus. Unconditionally, the PIN measures look roughly
the same across both groups. The length of the risk factor section is larger for JOBS Act firms.
Panel B provides the same metrics as Panel A for a set of accounting variables gathered from
each IPO firm’s prospectus including assets, liabilities, revenues, research & development expense,
and investment in property, plant & equipment. These variables will be used as controls when
examining underpricing and liquidity. As before, asterisks indicate that the mean is statistically
different from the mean of the EGC group.
15
EGCs have an average of $468 million in assets, which is significantly larger than the average of
$250 million for their pre-JOBS Act counterparts. EGCs also have more liabilities than their peers –
$691 million versus $157 million – though the difference is not statistically significant. There are no
statistically significant differences in revenues, R&D expense, or PP&E across the two groups.
4.2
Topic Models and Risk Factors
The Securities and Exchange Commission Regulation S-K instructs filers to provide “provide
under the caption ‘Risk Factors’ a discussion of the most significant factors that make the offering
speculative or risky.” Regulation S-K initially applies to a firm when it files Form S-1 - the
“Registration Statement Under the Securities Act of 1933”, but since 2005 it has also applied to the
10-K. Using the “Risk Factors” section from 27,399 10-Ks and S-1s from 1995–2010 we extract a
set of 30 risk factor “topics” using Latent Dirichlet Allocation (Blei et al., 2003; Ball et al., 2014;
Hoberg and Lewis, 2014).19 We then use the same model trained on the data to fit, out of sample,
the set of risk factors disclosed in the IPO filings of our set of firms. The Appendix contains a
discussion of all 30 risk factors, as well as the methodology used to fit the topic model.
Panel C of Table 1 provides the average fraction of the “Risk Factor” section of firms’ prospectuses
dedicated to each of the 30 risk factors for all four groups of IPO firms. As before, asterisks indicate
statistically significant differences with respect to the EGC group of firms. Compared to their
counterparts from before the JOBS Act was in effect, EGC firms make significantly more disclosures
of risk associated with accounting, dividends, financial markets, health care, with legal, regulatory
approval of products, real estate and regulation. On the other hand, they use less space discussing
risks associated with product market competition, human capital, international markets, the internet,
the development of new (non pharmaceutical/biotech) products, and the supply chain.
In addition to examining the effect of individual risk factors on underpricing, we also examine
the impact of their first and second principal components, which together explain almost one-third
of the variation in disclosed risk. Table 2 presents the standardized coefficients of both components.
The first principal component – factor1 – which explains 16% of the variation in the disclosed
19
Israelsen (2014) uses the same set of 30 extracted risk factorto examine how they relate to firm-level risk and
systematic risk.
16
risk factors – has positive coefficients on risk factors associated with the ability to pay dividends,
environmental regulation, access to credit, and the price of oil, and negative coefficients on risks
associated with intellectual property, the development of new products, the need to grow, systems
failure, competition, human capital, and the internet. These may be considered to be old economy
(or low tech) risks versus new economy (or high tech). The second principal component – which
explains 14% of the variation – has positive coefficients on risks asociated with variable demand,
systems failure, competition, and growth and negative coefficients on risks associated with R&D,
product approval, health care and intellectual property. These latter risks tend to be disclosed by
technology firms, particularly biotech or pharmaceutical firms. Interestingly, the latter types of
firms make up a significant portion of EGCs.
5
Empirical Methodology and Results
To test our hypotheses, we proceed as follows. First, we use OLS regressions and propensity score
matching to examine how disclosures of hard and soft information change under the JOBS Act.
We next examine how the information produced by the SEC changes under the JOBS Act. We
then test whether changes in disclosure by firms and information produced by the SEC can explain
variations in underpricing, the probability of informed trading and post-IPO liquidity. Finally, we
use the matched sample to estimate the magnitude of the benefits of the optimal disclosure of soft
information impact on underpricing and liquidity.
5.1
Changes in Disclosure of Hard and Soft Information around the JOBS Act
In this section, we examine Hypothesis 1. Specifically, we examine how disclosures of hard and
soft information change when constraints on the disclosure of hard information are relaxed. More
specifically, we examine the frequency with which EGCs choose to take advantage of the each of the
JOBS Act provisions and examine how risk factor disclosures change. To gather this information,
we read the prospectus of each EGC in our sample to determine whether or not they take advantage
of the continued use of private accounting standards, omit a discussion of executive compensation,
17
provide two years instead of three of audited financial statements, provide fewer than five years
of selected financial data, and provide compensation data on fewer than five executives. The first
several rows of Panel A of Table 3 presents the frequency with which 159 IPO firms from 2012 and
2013 claiming EGC status take advantage of each of these provisions and as well as the correlations
between usage of each provision.
The use of various provisions of the JOBS Act is quite variable. The least used provision – the
choice to continue to use private accounting standards for a period after the IPO – is only used
by 19% of firms. The vast majority choose to opt out, perhaps as a signal of willingness to adopt
better corporate governance practice.
One third of the firms omit a written discussion of their executive compensation from the
prospectus, and 79% report the amount of compensation for less than 5 executives. Surprisingly,
there is no statistically significant correlation between the use of these two accommodations.
61 out of 159 chose to report fewer than five years of selected financial data although almost
half of the firms in our sample chose to provide at least three years of audited financial statements.
Interestingly, firms who disclose fewer than three years of audited statements are more likely to
provide fewer than five years of selected financial data as can be seen from the correlations in Panel
A. Panel B shows that about 69% of firms (67 of 97) providing fewer than five years of selected
financial data also provide fewer than three years of audited financial statements. We find that
70% (21 out of 30) of those who chose to continue to use private accounting standards also chose to
disclose fewer than five years of selected financial data.
61% choose to file a confidential draft registration statement with the SEC. Note that we can
only observe those who chose to go public after a confidential filing, so the true number is certainly
higher.
Taken together, these results suggest that for many firms, the constraints on the production
of hard information prior to the implementation of the JOBS Act were indeed binding. Given
that many firms do reduce the amount of disclosed hard information, we next examine whether
reduction in the disclosure of hard information is associated with a change in the disclosure of soft
information. As shown in the previous subsection, EGCs’ risk disclosures under the JOBS Act differ
18
in many ways from their pre-JOBS Act peers. However, it’s possible that these changes are not due
to changes in disclosure of hard information under the JOBS Act, but due to other differences in
IPO firms. Our next test controls for this possibility.
Table 4 presents results from regressing factor1 or factor2 on the JOBS Act treatment dummy
as well as various sets of control variables, including industry fixed effects. The first column is a
simple regression of factor1 on Offer Size, VCbacked, StarUnderwriter, and the 12 Fama-French
industry fixed effects. Recall that factor1 is greater for firms disclosing relatively more “old economy”
risks and fewer “new economy” risks. Thus, the first regression show that, controlling for industry,
smaller, venture capital backed IPO firms tend to disclose more new economy risks. Column (2)
provides results from running the same regression after adding the JOBS Act Treatment dummy.
The coefficient of -0.11 on the this variable suggests that firms generally include relatively fewer
discussions of “new economy” risks in their risk factor section than their peers going public before
the JOBS Act. The next specification, however, which interacts the treatment dummy with the
control variables shows that the subset of firms with VC backing increase disclosures of new economy
risks. Finally, Specification (4) shows that once measures of the quantity of firm level disclosures are
included, the treatment is no longer statistically significant, suggesting that the general change in
risk factor disclosures following the JOBS Act can be is related to the amount of hard information
disclosed by firms. In other words, the JOBS Act by itself affects disclosures of soft information
through its regulation of hard information disclosures.
The second set of columns repeat the analysis for factor2. Recall that this variable is lower
for firms disclosing more risks related to pharmaceutical product development and approval and
higher for firms disclosing more risks related to non-pharmaceutical technology. Dambra, Field,
and Gustafson (2015) and Chaplinsky, Hanley, and Moon (2014) both show that there is a large
increase in the number of pharmaceutical and biotechnology firms under the JOBS Act. Thus, it is
crucial to control for industry.20 For this second factor, the results are more mixed. In specifications
(5) and (6), only the industry fixed effects are statistically significant. Thus, this factor is more
industry-specific than factor1. Moreover, controlling for the industry of the IPO firm, there does not
appear to be a significant difference in the disclosure of this factor under the JOBS Act. However,
20
These firms fall in Fama-French industry number 10 (Healthcare, Medical Equipment, and Drugs).
19
specification (7) shows that, relative to similar firms before the JOBS Act, VC backed JOBS
Act firms tend to make relatively more non-pharmaceutical high-tech risk disclosures, while those
without VC-backing tend to make more pharmaceutical risk disclosures under the act.
To control for any other changes in firm characteristics that might explain these differences, we
use propensity score matching to match JOBS Act firms with their 5 nearest neighbors based on
various characteristics. To capture the size of firms, we include the variables Offer Size, Lagged
Revenues, and Lagged Assets. To control for the investment opportunity set, we match on Lagged
R&D. To capture the certification effects of financial intermediaries, we include VCbacked and
StarUnderwriter. Finally, to control for industry specific effects, we match on and a high-tech
industry dummy variable.21 After matching firms, we estimate the sample average treatment effect
(SATT) for the JOBS Act firms for both risk factors.22 For factor1, the results consistent with the
above discussion. Specifically, relative to their matched peers, JOBS Act firms make relatively fewer
disclosures of “high tech” risks. The basic result is more nuanced for factor2, which measures the
propensity to disclose pharmaceutical versus non-pharmaceutical high-tech risk. EGCs are more
likely to disclose non-pharmaceutical risk. This differs slightly from the baseline case in Panel A,
which showed no significant effect, but is consistent with the subset of venture capital backed firms
in specification (7).
Thus, there is a difference in the way IPO firms disclose soft information before and after the
enactment of the JOBS Act. Because these regressions control for industry fixed effects, this is
not simply a shift across industries. Within the same industry and controlling for characteristics,
IPO firms’ disclosures of soft information change following the JOBS Act. This change is related to
venture capital backing and the amount of hard information disclosed by the firm.
Next, we examine how the soft information produced by the SEC in its comment letters changes
under the JOBS Act. For each IPO firm in our sample, we search the SEC’s Edgar database for
the first comment letter on the registration statement from the SEC to the IPO firm. We then
create 6 measures based on the content of the letters. The measures are pages (the number of pages
in the letter), items (the number of itemized points discussed by the SEC in the letter), numbers
21
We define high tech industries as the Fama French industries Computers, Software, Electronic Equipment,
Healthcare, Medical Equipment, and Drugs.
22
Table A3 in the Appendix provides more information on the propensity score matching.
20
(the number of numbers appearing in the letter), uncertainty (the number of uncertainty words
divided by the total number of words), weak/strong (the number of weak modal words divided by
the number of strong modal words in the letter), and negative/positive (the number of negative
words divided by the number of positive words in the letter). Uncertainty, weak modal, strong
modal, negative and positive words are those in the Loughran and McDonald (2011) word lists on
Bill McDonald’s website. There are 119 firms from our pre-JOBS Act sample, and 155 firms from
our JOBS Act sample that have at least one comment letter. Panel A of Table 5 provides means,
standard deviations, and standard errors of the means for both groups of firms as well as differences
in means.
As can be seen in the panel, under the JOBS Act, SEC comment letters are longer, and include
more numbers. Additionally, the tone of the comments is different. In particular, they include
relatively more weak modal words and the language is more uncertain and negative. This change in
tone is consistent with a reaction to the change in disclosure observed in Tables 3 and 4. To control
for other changes in firm characteristics that may explain these differences, we return to the matched
sample. Panel B presents sample average treatment effects for the treated IPO firms relative to
their their 5 nearest neighbors based on Offer Size, VCbacked, Lagged Revenues, Lagged Assets, and
a dummy variable indicating the firm is in a high-tech industry. The panel provides SATT estimates
for SEC pages, SEC items, SEC uncertainty, SEC weak/strong, and SEC negative/positive. The
tests confirm the results from Panel A. Specifically, under the JOBS Act, SEC comment letters are
longer, include more items, include more uncertainty words, more weak modal words relative to
strong modal words, and more negative words, relative to positive words. These results suggest
that the SEC does indeed increase the amount of soft information when firms reduce disclosures of
hard information. Moreover, the soft information produced by the SEC tends to be more uncertain,
weak, and negative.
5.2
Hard and Soft Information and IPO Underpricing
Having shown that firms tend to reduce their disclosures of hard information under the JOBS Act
and change their disclosures of soft information, we now examine whether these changes affect the
cost of capital. In particular, we test our second set of hypotheses. We examine whether a reduction
21
in disclosure of hard information following the relaxation of a binding constraint has an impact on
underpricing. Next, we investigate how equilibrium changes in soft information affect underpricing.
Finally, we examine which, if any, of these changes in disclosure can explain the general higher level
of underpricing documented under the JOBS Act.
We begin this analysis using the full sample of 284 firms both prior to, and under the JOBS Act.
Panel A of Table 6 presents these results for 10 specifications. The first column of results presents
coefficient estimates and t-statistics for a regression of IPO underpricing (measured using the first
day IPO return) on a treatment variable indicating that the IPO took place under the JOBS Act
and on a set of control variables including StarUnderwriter, Offer Size, VCbacked, MKT-RF, and 12
Fama French industry fixed effects. The coefficient on the JOBS Act treatment variable is 0.06,
suggesting that under the JOBS Act, underpricing increases by about 6%, on average. This is very
close to the number documented in Chaplinsky, Hanley, and Moon (2014). Generally, firms with a
larger offering, with a star lead underwriter, and wiht venture capital backing tend to have more
underpricing.
To test whether the underpricing can be explained by the reduction in disclosure following the
JOBS Act, the remaining 9 specifications include combinations of the measures of hard and soft
information disclosure. Columns (2) through (6) each include measures of hard information that
were relaxed under the JOBS Act. If these constraints were binding and disclosure was artificially
high, the subsequent reduction in disclosure may not have much of an impact on underpricing. In
other words, relative to the marginal costs, the marginal benefits of additional disclosure of hard
information prior to the JOBS Act were relatively low. The table provides evidence consistent
with this argument. The the amount of hard information disclosed is not generally related to
underpricing. Moreover, with one exception, underpricing is still significantly higher under the JOBS
Act. Column (7) examines whether the confidential submission of a draft registration statement is
related to underpricing. The point estimate is positive, but not statistically significant. However,
the coefficient on the JOBS Act treatment is no longer significantly different from zero.
In the remaining three columns, multiple measures of information are included in each regression.
Columns (8), (9), and (10) report results using all 5 measures of hard information, 3 measures of
soft information – including factor1 and factor2 – and both sets of information, respectively. Across
22
all three columns, the only measures with significant coefficients are Confidential Filing and factor1.
Controlling for industry, firms that disclose more “new economy” risks have more underpricing.
Additionally, underpricing is about 9% higher for IPO firms that choose to confidentially submit
their draft registration statement.
The use of the confidential filing provision sends a signal to investors that the firm was uncertain
about the demand for the IPO. Such uncertainty about the level of demand increases the winner’s
curse risk, since investors who are allocated shares during the book-building process may find it more
difficult to find a counterparty after the shares begin trading. Therefore, during the book-building
process, IPO investors will demand a premium for bearing such risk. Thus, this winner’s curse risk
will increase the IPO underpricing.
Panel B repeats the underpricing regression analysis from Panel A, but restricts the sample to
the subset of 155 EGCs under the JOBS Act. The results look very similar to those for the full
set of firms. In particular, none of the measures of hard information have significant coefficients either individually, or when included as a group (as in column 7). As is the case in the full sample,
underpricing is greater for firms confidentially submitting their draft registration statement, and for
firms disclosing high-tech risks.
5.3
SEC Letters, IPO Underpricing, and PIN
Having shown that the SEC increases the amount of information they produce in their comment
letters to firm, and that this soft information express more uncertainty and negativity, we examine
whether this type of information affects both underpricing during the IPO process and the probability
of informed trading in the secondary market immediately after the IPO. As mentioned, while the
SEC letters do are not necessarily published before the IPO date, the kind of information they
produce is a proxy for the information produced by any informed investor involved in the IPO during
the book-building process. Accordingly, we examine the direct impact of such soft information on
underpricing and PIN, as well as the impact conditional on the amount of information produced by
the firm. Table 7 provides results from these tests.
23
The first column of Panel A presents the results of regressing first day IPO return on measures
of SEC soft information, on firm hard and soft information, and on a set of control variables. The
column shows that firms with more uncertainty words in SEC letters have more underpricing, as do
firms with more weak modal words, relative to strong modal words. Given that this information
is not typically released until after the IPO, this suggests that informed investors participating in
the IPO may possess the same type of soft information. As was the case in Table 6, none of the
measures of hard information have an impact on underpricing, and both Confidential Filing, and
disclosures of “new economy” risks are positively related to the first-day IPO return. Only soft
information affects underpricing.
We next turn our attention to the probability of informed trading in the secondary market.
Reducing the amount of hard and soft information disclosed in the prospectus limits the information
available to the market and potentially increases opacity. As a result, following the IPO, there may
be more informed trading while new information production takes place. In the final three columns
of Panel A in Table 7, we find results consistent with this story. For the subsample of EGCs, we
regress each of our three our three PIN measures (22-, 44-, and 66-day) on each of the measures of
SEC soft information, on each of the disclosure provisions, on the risk factors, and on our set of
control variables. Additionally, to control for changes in the general level of informed trading in the
market, we also include a measure of market pin (MKT-PIN-22, MKT-PIN-44, and MKT-PIN-66)
using data from the SPY ETF during the same window as the dependent variable is measured.
When the SEC includes more numbers (i.e., hard information) in their letters, the probability
of informed trading is reduced over the first 22 and 44 trading days. However, when SEC letters
are longer, informed trading is increased in the first month of trading. Recall that SEC letters are
typically published shortly after the IPO. There is less informed trading when firms voluntarily
include a discussion of executive compensation in their prospectus, and less informed trading for
each additional year of disclosed selected financial data. This latter estimate is consistent – between
-0.019 and -0.022 – across 22-, 44-, and 66-day PIN. There is no direct effect on informed trading of
the measures of firms’ soft information.
24
There tends to be less informed trading for firms with a star lead underwriter. This is consistent
with the story that the underwriter is skilled at providing information to the market. This remains
after controlling for variation in risk factors.
Next, we examine whether investors condition on one type of information when interpreting
another. In particular, we examine how the interaction of the measures of hard information and the
SEC Letters and disclosed risk factors affects underpricing and PIN. Panel B of Table 7 provides
these regression. For the sake of brevity, some variables with statistically insignificant coefficients
are not reported. For underpricing, as shown by the interaction of the disclosed risk factors and the
treatment, the market interprets the firms’ soft information differently under the JOBS Act, and
hence, the coefficients are significant.
When firms reduce disclosures of hard information, the SEC soft information plays a complimentary role. For example, when the firm provides fewer than 3 years of audited financial statements,
and the SEC includes relatively more uncertainty words in its letters, underpricing in increased.
Similar results can be seen with the interaction of the number of executives and SEC weak/strong.
Once shares are trading, there are similar interactions. For example, when firms signal that they
are uncertain about investor demand and confidentially file their registration statement and the
SEC includes more uncertainty words in their letters, there tends to be more informed trading in
the first month following the IPO.
Finally, as seen by the coefficient on 1-day IPO return, when there is more underpricing,
the probability of informed trading decreases. This is consistent with the substitutability of the
production of information during the IPO process and following the IPO.
5.4
Post-IPO Liquidity
We have established that changes in disclosures of hard and soft information by EGCs as well as
the SEC affect IPO underpricing. Moreover, this information is also related to the probability of
informed trading in the secondary market, following the IPO. To the extent that these differences in
disclosure under the JOBS Act are associated with an increased degree of asymmetry of information
25
among IPO investors, these results may show up in the secondary market in the form of larger
bid-ask spreads. To test this, we estimate measures of liquidity using TAQ data for several days of
trading following the IPO and examine whether changes in disclosure have an impact on these costs.
We use TAQ data to estimate effective spreads and realized spreads using two weighting methods:
dollar weighted (denoted with a subscript DW), and share weighted (denoted with a subscript SW).
We use the Lee and Ready (1991) method to classify the midpoint.23 Table 8 presents regressions of
these four bid-ask spread measures on the same set of disclosure and control variables as in the
previous tests for 244 firms before and after the enactment of the JOBS Act. Bid-ask spreads are
averaged across the first 22 trading days following the IPO.
The table provides evidence that both hard and soft information are related to liquidity.
Realized bid-ask spreads are smaller when firms provide information on the compensation of more
key executives and fewer years of selected financial data, thought these are only significant at the
10% and not significant using effective spreads. The confidential submission of a draft registration
statement is related to effective spreads, but there is no statistically significant impact on realized
spreads. As was the case with underpricing and the probability of informed trading, factor1 is
related to liquidity. In particular, firms disclosing less (more) new economy risk have smaller (larger)
spreads. These coefficients are significant at the 5% level across all four measures of spreads and are
estimated after controlling for industry.
5.5
Benefits of Optimal Disclosure of Soft Information
Under the JOBS Act, firms reduce their disclosures of hard information. There is also a change
in the way they disclose soft information. We now turn to the question of whether this change
in soft information results in more or less underpricing and post-IPO liquidity. More specifically,
we examine the magnitude of the impact of the optimal disclosure of soft information by JOBS
Act firms on their underpricing and liquidity. The answer to this question relies on an estimate of
the counterfactual underpricing and liquidity that would have occurred had the disclosure of soft
information remained the same. This is a difficult task. Because firms only have one IPO, we cannot
23
See the Appendix for a detailed description of the methodology.
26
use a difference in differences approach directly. Instead, we return to the matched sample we used
to show that disclosures of soft information change for both the firm and the SEC under the JOBS
Act. We used propensity scores to match JOBS Act firms with their 5 nearest pre-JOBS Act peers
based on Offer Size, VCbacked, StarUnderwriter, Lagged Revenues, Lagged Assets, Lagged R&D,
and a high-tech industry dummy.24 We use this same methodology to estimate the counterfactual
disclosure of soft information in section 5.1.
Recall that, on average, JOBS Act firms reduce their emphasis on “new economy” risks relative
to “old economy” risks, compared to their matched peers. For each JOBS Act firm, we create a
hypothetical reduction in underpricing based on the difference in disclosure of factor1 relative to its
matched pre-JOBS Act peers. This is calculated by multiplying the difference in factor1 with the
coefficient of factor1 in the underpricing regression in column (10) of Panel A in Table 6. Table 9
provides these results. The first row of Panel A shows the cross-sectional the mean and percentiles
of the reduction in underpricing, and Figure 1 provides a histogram of these individual values.
On average, JOBS Act IPO underpricing is 2.7% lower than it would have been had firms risk
factor disclosures been similar to those of their matched peers. This difference in underpricing
ranges between -30% and 10% with a median difference of -1.5%. These numbers suggest that firms
optimally reduce their discussion of “new economy” risks relative to other risks and this results in a
relative reduction of underpricing.
The second row of Panel A repeats the analysis for the realized spread. Here, we multiply
differences in disclosure of factor1 with the regression coefficient in the liquidity regression in column
3 of Panel A in Table 8. Realized spreads are 1.1% lower on average than they would be had
disclosures of soft information not changed. Figure 2 is a histogram of these hypothetical differences
in liquidity.
An alternative way to estimate the impact of the optimal choice of soft information on underpricing and liquidity is to match on soft information. The first column of Panel B presents the
difference in underpricing for JOBS Act firms relative to the matched sample. Without controlling
for differences in disclosure between EGCs and their matched peers, underpricing is 6.8% higher
24
Table A3 in the Appendix provides more information on the propensity score matching.
27
for EGCs. The regression results in Table 6 suggest that much of this can be explained by EGCs’
usage of the confidential filing process. This accommodation increases the winner’s curse and should
increase underpricing. This may also reflect a change in the degree of investors’ risk aversion.25
If the JOBS Act reduces the cost of going public then, ceteris paribus, it likely reduces the lower
threshold of the quality of the IPO firms. Hence, faced with this new distribution of quality of IPO
firms, investors should become more risk averse and demand more average underpricing. Propensity
score matching may control for quality, but cannot control for changes in risk aversion.
Because the confidential IPO process was not an option before the JOBS Act, we cannot match
on this variable. However, Table 6 also shows that factor1 is related to underpricing. In particular,
firms making relatively more disclosures of “new economy” risks have more underpricing. Because
there is variation in this risk factor both before and after the introduction of the JOBS Act, we
might be tempted to match on this choice variable, as well as on factor2. The first row of the second
column of Panel B in Table 9 presents the average difference in underpricing when matching on both
risk factors as well as the same characteristics used before. Forcing the disclosures to be similar
would have resulted in more general underpricing. This difference of about 2% is not surprising,
given that (1) JOBS Act firms change their soft information disclosures and, (2) these disclosures
are related to underpricing, controlling for industry. However, because firms optimally choose their
disclosures of both soft information and hard information, matching on the former may not give an
accurate estimate.26 There do not seem to be significant differences in liquidity between the EGCs
and their matched peers across these two matching schemes.
6
Conclusion
This paper evaluates the impact of the recently passed JOBS Act on the optimal disclosure of hard
and soft information. Because the JOBS Act relaxes binding constraints on IPO firms’ disclosures of
hard information, it provides a nice way to examine the substitutability of hard and soft information.
By carefully matching JOBS Act firms to a set of peer IPO firms from just prior to the act, we
25
See Ritter (1984).
In fact, propensity scores estimated mathing on characteristics and soft information exhibit unappealing characteristics for matching. In particular, some characteristics are significantly different between EGCs and their matched
peers.
26
28
show that relaxation of the constraints result in fewer disclosures of hard information and a change
in disclosures of soft information. Moreover, of the two types of information, only soft information
directly affects underpricing. This suggests that constraints on hard information were indeed binding.
The overall amount of underpricing under the JOBS Act increases – largely due to the provision that
allows firms to confidentially submit their draft registration statement, which increases the winner’s
curse risk. We show, however, that firms’ optimal choice of soft information reduces underpricing
by about 2.7% and bid-ask spready by about 1 cent relative to the counterfactual.
Additionally, we show that the SEC plays an important role in terms of its comment letters
to IPO firms. Under the JOBS Act, the soft information produced by the SEC increases, and is
generally more uncertain and negative in tone. Moreover, both underpricing and the probability of
informed trading in the post-IPO secondary market are related to this information. Overall, the
results document an optimal mix of hard and soft information for IPO firms and highlight their
substitutability.
29
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32
A
Appendix
In this section, we provide discussion of the 30 disclosed risk factors extracted from the Risk Factors
sections of 27,399 10-K and S-1 filings from 1996 through 2010 using Latent Dirichlet Allocation
(Blei et al., 2003) on the risk factor section of firms’ S-1 (1996-2005) and 10-K filings (2006-2012).
These are the same factors that are extracted (out-of-sample) from each firm’s prospectus and used
in this paper.
A.1
Latent Dirichlet Allocation
Topic Models are probabilistic generative models used to describe a set of latent “topics” that occur
in a given collection of documents. Documents are modeled as mixtures of a smaller number of
topics and topics as probability distributions over words. In generating a document, words are
assumed to have been randomly chosen from the topics given the specific document’s distribution
over topics, θ(d) , and the word distribution for the drawn topics, φ. Based on these models, Bayesian
statistics can be used to “uncover” the latent document-topic and topic-word distributions. These
models are particularly useful when corpora are large and when there are many topics. One benefit
of using a topic model is the classification technique is standardized and objective. The results of a
manual classifications may depend on the individual performing the classification and also may not
be internally consistent. Topic models allow the data to determine the classifications, which may
include latent connections that a researcher might otherwise miss.
One of the most commonly used topic models in machine learning and computational linguistics
is Latent Dirichlet Allocation (LDA) (Blei et al., 2003).27 LDA is a hierarchical model which chooses
the latent topics as well as probability distributions in order to maximize the likelihood of observing
a given set of D documents. In particular, if we have K topics, we can write the probability of the
ith word in the dth document as
P (wdi ) =
K
X
P (wdi |zd = k)P (zd = k).
(1)
k=1
Here zd is a latent variable indicating the topic from which the ith word in document d was
potentially drawn and P (wdi |zd = k) is the probability of the word wi under the kth topic. P (zi = k)
gives the probability of drawing a word from topic j in the current document, which will vary across
documents.
The K topics are defined as categorical distributions28 over W words with parameter vector φ,
(k)
such that P (w|z = k) = φw , and the the dth document is defined as multinomial distributions
over the K topics with parameter vector θd , such that P (z) = θd . LDA augments the model with
the addition of Dirichlet priors on both θ and φ Specifically, θ ∼ Dirichlet(α) and φ ∼ Dirichlet(β)
27
See Chang et al. (2009) who provide evidence from experiments that both humans and topic models arrive at
similar topics.
28
The categorical distribution is equivalent to the multinomial distribution with one trial.
33
where α and β are hyperparameters. Dirichlet priors are assumed. These priors are conjugate to the
categorical distributions which means posterior distributions are Dirichlet. The inferential problem
that needs to be solved is to analyze the posterior distribution
P (z, θ, φ|w, α, η).
(2)
With the introduction of the Dirichlet priors, estimation is a matter of maximizing the total
probability of the model
P (W, Z, θ, φ; α, β) =
K
Y
i=1
P (φi ; β)
M
Y
P (θj ; α)
j=1
Nj
Y
P (Zj,t |θj )P (Wj,t |φZj,t )
(3)
t=1
by choosing the set of parameter vectors θ and φ. These two sets of estimates provide the
document’s probability distribution across topics, and the topic’s probability distribution across
words, respectively.
A.2
Disclosed Risk Factors Description
Table A2 lists the 20 most common words for each of the 30 extracted risk factor topics. For each
of the 30 extracted risk factors, a name is chosen based on the words and industries most closely
associated with these factors and based on reading through a sample of disclosures assigned to each
topic. However, it is the words that define the topics, not the title. In this subsection, we briefly
examine each of the 30 disclosed risk factors. The discussion is based on the word distributions
from Table A2, on the distribution of disclosures (in the 10-Ks) across industries. The extracted
risk factors are listed in alphabetical order.
Accounting contains words like reporting, accounting, result, statement, material and require
and deals generally with the risk that statements may be incorrect. It is most likely to be disclosed
in the 10-K in industries such as “measuring and control equipment” (LabEq), “Recreation” (T oys),
and “Business Services” (BusSv) as well as firms that are not easily classified into an industry
(Other). Industries that are unlikely to disclose this risk in their 10-K are “Agriculture” (Agric),
“Coal” (Coal), and “Utilities” (U til).
Competition contains words associated with competition or product market competition like
company, competition, compete, market, product and services. It is most commonly disclosed in
industries where product market competition is most likely, such as “Textiles” (T xtls), “Candy &
Soda” (Soda), “Beer & Liquor” (Beer), and “Food Products” (F ood). Industries that are least
likely to disclose competition risk in their 10-Ks are industries in which there are larger barriers to
entry such as “Petroleum and Natural Gas” (Oil), “Utilities” (U til), “Non-Metallic and Industrial
Metal Mining” (M ines), “Precious Metals” (Gold), Coal, and “Pharmaceutical Products” (Drugs).
Contractual contains words associated with contract risk such as agreement, contract, terminate,
terminate, obligation, and renew. This risk is commonly disclosed in firms 10-Ks in industries that
rely on government contracts such as “Defense” (Guns), “Restaurants, Hotels, Motels” (M eals),
34
Coal, and the “Aircraft” (Aero). At the other extreme are industries like “Banking” (Bank),
“Medical Equipment” (M edEq), “Measuring and Control Equipment” (LabEq) and Gold.
Costs contains words associated with the risk that input prices may change such as costs,
increase, labor, equipment, adversely, and affect. This risk is commonly disclosed in 10-Ks in
industries such as “Transportation” (T rans), “Shipbuilding, Railroad Equipment” (Ships), Aero,
“Construction” (Cnstr), and Guns . At the other extreme are industries like “Banking” (Bank),
“Medical Equipment” (M edEq), “Measuring and Control Equipment” (LabEq) and Gold.
Credit contains words associated with credit or the ability to raise capital such as credit, debt,
ability, capital, additional, and require. This risk is the most prevalent in industries like F un, F abP r,
T xtls, and Coal. The industries least likely to disclose Credit risk are Comps, M edEq, Insur,
Banks, and Drugs.
Demand contains words associated with changes in demand or the economic environment such
as economic, conditions, affect, result, customer, and demand. This is most commonly disclosed in
the 10-K in industries with seasonal or fluctuating demand such as Smoke. M eals, Clths, Rtail,
Beer. Industries with inelastic demand such as Oil, M edEq, Hlth, and Drugs are less likely to
disclose this risk. Gold is the industry least likely to disclose Demand risk.
Disclosure contains words such as risk, statement, forward, looking, uncertainty. This risk is
most commonly included in the 10-K as a disclaimer about relying on forward-looking statements.
Industries most likely to emphasize this risk are Smoke, Soda, F abP r, Rubbr, and Books. At the
other extreme lie T elcm, T rans, M edEq, Drugs, and Coal.
Dividends contains words such as dividend, pay, distribution, capital, subsidiary. The industries
Insur, Smoke, Banks, RlEst, and F in are the industries most likely to disclose Dividend risk in
the 10-K while M edEq, Soda, Clths, LabEq, Guns are the industries least likely to disclose this
risk.
EnviroReg contains words associated with environmental regulation or impact such as environmental, regulation, state, federal, permit, emission. The firms most likely to emphasize this risk in
the 10-K are those operating in industries subject to environmental regulation such as Coal, Gold,
U til, and M ines. At the other extreme lie Clths, Smoke, Comps, Banks, Insur.
Financing contains words dealing with the need to acquire additional capital such as acquisition,
acquire, additional, capital, operations, and need. Firms in RlEst, Boxes, Aero, and M ines are
especially likely to disclose this risk in the 10-K, while firms in Rtail, M eals, Clths, Insur, Smoke
are less likely.
FinMarket contains words associated with aggregate financial market risk such as such as
investment, financial, market, rates, credit, affect. Firm in industries such as Banks, F in, Insur,
U til, and RlEst are the most likely to disclose F inM arket risk in the 10-K. Industries which place
less emphasis on this risk are F abP r, T oys, M edEq, Drugs, and Guns. F inM arket risk is the
second most likely risk to be discussed, representing 5.3% of the Risk Factor section, on average.
35
Growth contains words associated with growth or expansion such as growth, new, business,
ability, and expand. Firms in industries like Rtail, M eals, Clths, Hshld, and W hlsl are most
associated with this risk disclosure in the 10-K, while those in Aero, Boxes, Coal, Gold, Oil are
the least likely to disclose.
HealthCare contains words associated with healthcare and healthcare programs such as healthcare, program, reimbursement, medical, state. Not surprisingly, the industries most associated with
these disclosures in the 10-K are Hlth, M edEq, Drugs, Insur, and P erSv. At the other extreme
are the industries F un, T xtls, Oil, Clths, and Gold.
HumanCapital contains words associated with the risk of hiring and retaining key employees
such as retain, key, personnel, ability, attract, depend. It is most commonly disclosed in the 10-Ks of
human-capital intensive industries like BusSv, F in, Hlth, P erSv, and LabEq and least commonly
disclosed in Chems, Beer, Boxes, Smoke, U til.
Insurance contains words dealing with risk associated with insurance such as insurance, claim,
liability, coverage, losses. Firms in the industry Insur are those most likely to disclose this risk.
Other industries associated with this risk are Ships, F abP r, Guns, and Cnstr. The industries
whose firms are least likely to disclose this risk in the 10-K are Comps, Books, Clths, Banks, and
T elcm.
IntellProp contains words about the risks associated with intellectual property and patents as
patent, intellectual, property, infringement, application, and license. Reassuringly, the firms most
likely to discuss IntellP rop risk in their 10-Ks are those in the industries Drugs, M edEq, LabEq,
Comps, and Chips. Industries whose firms make very little mention of this risk are T rans, Insur,
U til, and Banks.
International contains words dealing with risks associated with foreign exchange risk and
international markets such as foreign, currency, exchange, political, tax, and china. The firms most
likely to make these disclosures in their 10-Ks are found in industries like Boxes, M ach, T xtls,
Rubbr, and Clths, where international markets are more likely to play significant roles. At the other
extreme lie firms in more localized or service industries such as M eals, U til, Banks, and Hlth.
Internet contains words such as internet, advertising, sales, content, increase, and revenue.
Internet risk is the disclosure least likely to be made. The median firm only dedicates 0.2% of
the words in its risk factors disclosures to this topic. The industries with 10-K disclosures most
closely associated with this risk are Books, P erSv, T elcm, F un, and BusSv. Industries such as
Oil, Ships, Boxes, F abP r, and Steel are those least likely to make disclosures about internet risk.
Legal contains words about litigation and legal risk such as court, action, federal, lawsuit, claim,
and law. The industries whose firms are most likely to discuss this risk in their 10-Ks are Smoke,
Hlth, Guns, Gold, and Insur. This is the 4th least likely risk to be discussed. Many of these firms
and industries are heavily regulated and have been subjects of lawsuits and legal action. Firms least
likely to disclose Legal risk tend to lie in the industries LabEq, T oys, T xtls, Soda, and Clths.
Oil contains words associated with energy prices and production such as oil, natural, gas, price,
production, and reserves. Not surprisingly, firms making these 10-K risk disclosures are more likely
36
to be in industries that either produce energy or extract precious metals such as Oil, Gold, M ines,
U til, and Coal. Firms in the industries Banks, M edEq, Hlth, Comps, and Drugs are the least
likely to disclose Oil risk.
ProdApproval contains words associated with the product approval process such as product,
approval, clinical, trial, fda, and regulatory. Not surprisingly this risk is most commonly disclosed in
the 10-K of firms in regulated industries such as Drugs, M edEq, Smoke, Agric, Hlth. Firms in
service industries such as F in and Banks and less innovative industries such as Steel, Books, and
Ships are much less likely to make this disclosure.
ProductDev1 contains words about the development of new products, technologies, and services
such as product, new, technology, market, development, ability and introduce. Firms in high tech
industries such as Comps, Chips, and LabEq are the most likely to disclose this risk in their 10-Ks.
Firms in the BusSv are also likely to disclose this risk. At the other extreme lie firms in industries
associated with older technologies such as T rans, M ines, U til, Coal, and Gold.
Like the previous risk, ProductDev2 contains words associated with the development of new
products such as product, development, candidate, market, commercialization. However, these
disclosures tend to be made in the 10-Ks of research and development-intensive industries such as
Drugs, M edEq, LabEq, Agric, and ElcEq. As was the case with the previous risk factor, firms
in “old economy” industries such as Insur, T rans, Coal, T xtls, Banks are those least likely to
disclose this risk. This is much less commonly disclosed in the 10-K than the risk P rodDev1.
RealEstate contains words associated with real estate and mortgages such as loan, real, estate,
losses, property, and mortgage. This risk the most likely to be disclosed in 10-Ks in industries that
rely heavily on real estate such as RlEst, U til, Coal, Gold or in industries that are affected by
securitized claims on real estate such as Banks. Manufacturing industries such as Clths, Rubbr,
Comps, M edEq, and Drugs are the least likely to discuss RealEstate risk.
Regulation contains words such as regulation, state, federal, subject, change, and compliance.
Firms in highly regulated the highly regulated industries such as Beer, Insur, Hlth, Banks, and
Guns tend to focus more attention on this risk in their 10-K filings. Firms in the less-regulated
manufacturing industries Chips, P aper, Rubbr, Steel, and Boxes, are less likely to emphasize this
risk.
Revenue contains words associated with items on the income statement such as revenue, tax,
net, future, income, and estimate. The firms emphasizing Revenue risk in their 10-Ks tend to cluster
in the industries Cnstr, Aero, Insur, and BusSv, while those in M edEq, Rubbr, Drugs, Soda,
and M eals are the least likely to emphasize this risk.
Stakeholder contains words associated with corporate control and ownership such as stock,
share, board, right, control. Firms in the industries F un, F in, Gold, T elcm, and Rubbr are those
most commonly discussing this risk in their 10-Ks. At the other extreme are the industries Hshld,
Aero, F abP r, U til, and Boxes.
StockPrice contains words associated with stock market or stock price risk such as stock, price,
market, decline, fluctuation, and affect. Firms in “high beta” industries, such as M ines, Comps,
37
Drugs, and Gold, or those directly impacted by stock markets as is the case with the industry
F in, are the most likely to emphasize this risk in their 10-Ks. Firms in the industries Insur, Soda,
Guns, U til, and Aero, are less likely to disclose StockP rice risk.
SupplyChain contains words associated with the risk associated with supply chains such as
product, customer, supplier, manufacturing, material, component, and delay. Firms disclosing this
risk in their 10-Ks tend to fall in the manufacturing industries Chips, Steel, Agric, Clths, and
Soda. These industries generally rely on suppliers when manufacturing goods. Disclosing firms may
be suppliers, themselves. Firms that are less likely to emphasize SupplyChain risk cluster in the
industries Gold, F un, RlEst, Insur, and Banks.
The final disclosed risk factor, Systems, contains words associated with the risk of systems
interruption or data failure such as system, information, failure, data, disruption, and security.
Firms in data intensive industries such as BusSv, F in, Rtail, Comps, and T elcm are those most
commonly discussing Systems risk in their 10-Ks. Firms in manufacturing and commodity industries
like Autos, Coal, M ines, Gold, and Smoke, are much less likely to emphasize this risk.
A.3
Propensity Score Matching
For much of the analysis, we use propensity score matching to form a control group for our set of
JOBS Act IPO firms. Specifically, we estimate a probit model on the likelihood of a JOBS Act IPO
based on the variables Offer Size, VCbacked, Lagged Revenues, Lagged Assets, and a dummy variable
indicating the firm is in a high-tech industry (Healthcare, Medial Equipment, or Drugs). Panel A of
Table A3 provides coefficients estimates from this probit model. The pseudo R-squared statistic is
0.05. In general, the results show that there are not many differences between the pre-JOBS Act
IPO firms in our sample and EGCs under the JOBS Act. One exception is the industry classification.
Dambra et al. (2015) and Chaplinsky et al. (2014) both document that there are relatively more
pharmaceutical IPOs under the JOBS Act than before the act. In Panels B and C, groups of firms
are broken into blocks based on propensity score. Panel B provides t-statistics for differences in
mean characteristics across the control and treatment groups. There is no statistically significant
difference in characteristics within either group. Panel C tests whether propensity scores across the
control and treatment groups differ within a given block. The results suggest that within blocks,
propensity scores are not different across groups. Together, these results suggest that propensity
scores will provide a good match across these characteristics.
Using propensity scores from this probit model, for each JOBS Act firm, we choose their 5
nearest neighbors from before the JOBS Act. We then calculate Sample Average Treatment effects
for the Treated (SATT) for factor1 and factor2 (Panel B of Table 4), for various measures from
SEC comment letters (Panel B of Table 5), and underpricing (Table 9).
38
A.4
Liquidity Measures
Realized and effective spreads are estimated as follows. The bid and ask price is calculated using
National Best Bid and Offer (NBBO) quotes, following Holden and Jacobsen (2014). All spreads have
two versions (share-weighted (SW) and dollar-weighted (DW)). For example, the dollar-weighted
percent effective spread for security i on a given day (variable “effective DW ” is measured as follows:
Percent Effective Spread DW =
X
wk × Percent Effective Spread k
(4)
k∈i
where
Dollar volume for trade k
k∈i Dollar volume for trade k
wk = P
(5)
and Dollar volume for trade k = (Number of shares in trade k ) × (Price of trade k ).
When using share-weighted (SW) measures, the weight factor is:
Number of shares for trade k
t∈i Number of shares for trade k
wk = P
(6)
We assign trade directions using the Lee and Ready (1991) algorithm: the trade is a buy if
the price (Pk ) is greater than the midpoint (Mk ), a sell if Pk < Mk , and a tick test is used when
P k = Mk .
We calculate four basic measures of liquidity for every stock in the 22 trading days following the
IPO. The spread is measured at each trade as:
Percent Effective Spread k = (2Dk Pk − Mk )/Mk
Dollar Realized Spread k = 2Dk Pk − Mk+5
(7)
(8)
(9)
where Dk is the indicator variable that equals +1 if the k th trade is a buy and −1 if the k th
trade is a sell. Pk is the price of the k th trade. Mk+5 is the midpoint five minutes after the
k th trade. These individual spreads are aggregated over the 22 days using equations (4)-(6).
39
Table A1:
Sample Selection
Filter:
IPOs
1 SDC: US Common Stock IPOs from 2010 to 2014
2 Issuer in the USA
3 Listed on AMEX, NASDAQ and NYSE
4 Matching stock in CRSP database
5 Listed as Common Stock (shrcd = 10 or 11) in CRSP
6 Disclosing EGC status after the JOBS Act, or less than $1 Billion
in revenues reported in their prospectus for the most recent fiscal
year before the JOBS Act
Final Sample:
Pre JOBS Act
JOBS Act
975
806
663
648
431
285
129
156
40
Table A2:
Word-Topic Distributions
The table shows the 20 most likely words across the 30 extracted risk factor topics, along with the word-topic probabilities, p. Topics are extracted
using Latent Dirichlet Allocation on the risk factor section of firms’ 10-K filings from 2006-2012.
41
rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Accounting
word
financial
control
internal
reporting
accounting
result
report
require
company
impairment
assets
goodwill
management
material
public
statement
standard
intangible
independent
procedure
p
4.2
3.5
2.5
2.3
2.2
1.8
1.7
1.7
1.5
1.5
1.2
1.2
1.2
1.2
0.9
0.9
0.9
0.8
0.8
0.8
Competition
word
p
company
7.7
customer
2.9
competitor
2.6
market
2.3
competition
2.2
services
2.1
compete
2.0
competitive
1.7
business
1.6
financial
1.4
product
1.3
industry
1.2
result
1.1
greater
1.1
service
0.9
resource
0.9
price
0.9
increase
0.8
operate
0.7
offer
0.7
Contractual
word
p
agreement
5.9
contract
4.9
terms
1.6
terminate
1.5
lease
1.3
plan
1.3
provide
1.2
obligation
1.1
certain
1.1
enter
1.1
term
1.0
termination
1.0
arrangement
0.9
require
0.8
renew
0.8
purchase
0.8
franchise
0.8
party
0.7
restaurant
0.7
pension
0.6
Costs
word
costs
increase
cost
project
result
labor
equipment
operations
employee
affect
work
operate
construction
facility
adversely
delay
company
contract
vessel
material
p
3.8
2.9
2.1
2.0
1.9
1.7
1.4
1.3
1.2
1.1
1.1
1.0
1.0
0.9
0.9
0.9
0.9
0.8
0.8
0.8
Credit
word
credit
debt
facility
cash
note
indebtedness
ability
capital
financial
covenant
senior
additional
obligation
flow
require
financing
agreement
business
default
operations
p
3.4
3.3
2.3
2.0
1.9
1.9
1.8
1.8
1.4
1.3
1.2
1.1
1.1
1.1
1.0
1.0
1.0
1.0
1.0
0.9
Demand
word
result
economic
affect
conditions
business
customer
sales
demand
impact
adversely
market
operations
product
operate
financial
change
industry
factor
revenue
consumer
rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
EnviroReg
word
environmental
regulation
laws
operations
costs
material
subject
facility
require
state
hazardous
use
result
permit
emission
safety
federal
include
waste
water
p
3.2
2.6
2.0
1.6
1.4
1.4
1.2
1.2
1.1
1.1
1.1
0.9
0.9
0.9
0.9
0.8
0.8
0.8
0.8
0.8
F inancing
word
acquisition
business
acquire
additional
capital
operations
company
financing
future
result
terms
need
equity
raise
require
available
include
able
obtain
risk
F inM arket
word
investment
financial
market
rates
credit
security
loan
rate
affect
bank
increase
institution
deposit
result
company
risk
value
change
assets
rating
Growth
word
growth
business
new
manage
operations
sales
expand
ability
increase
management
continue
strategy
financial
operate
market
system
result
effectively
maintain
expansion
p
3.4
2.5
2.3
2.2
1.8
1.7
1.7
1.7
1.5
1.4
1.4
1.3
1.3
1.2
1.1
1.1
1.1
1.1
1.0
1.0
HealthCare
word
health
care
healthcare
program
reimbursement
product
government
medical
medicare
patient
physician
services
state
party
hospital
payors
payment
drug
medicaid
provider
p
2.6
2.2
2.1
2.1
2.1
2.0
1.6
1.4
1.3
1.2
1.2
1.1
1.1
1.0
0.9
0.9
0.8
0.8
0.8
0.8
HumanCapital
word
p
personnel
4.9
key
3.8
retain
3.6
employee
3.5
management
2.8
business
2.7
attract
2.3
officer
2.3
executive
2.1
qualify
2.0
ability
1.8
company
1.5
depend
1.3
loss
1.3
services
1.3
success
1.2
senior
1.1
chief
1.1
continue
1.0
hire
0.9
Table A2 continues on the next page.
p
5.1
4.9
2.7
2.4
2.4
1.9
1.8
1.6
1.5
1.3
1.1
1.0
0.9
0.9
0.9
0.9
0.9
0.9
0.8
0.8
p
2.7
2.6
2.5
2.0
1.7
1.5
1.5
1.4
1.4
1.3
1.3
1.2
1.2
1.1
1.1
1.1
1.0
1.0
1.0
1.0
p
3.7
2.4
2.4
2.1
2.1
2.0
1.8
1.4
1.4
1.4
1.4
1.4
1.4
1.3
1.2
1.2
1.2
1.1
1.1
1.1
Disclosure
word
p
risk
6.5
statement
4.6
result
3.4
forward
2.8
factor
2.6
looking
2.5
financial
2.4
business
2.4
operations
1.9
uncertainty
1.9
include
1.6
information
1.6
condition
1.6
future
1.4
materially
1.4
report
1.2
prospectus
1.2
actual
1.1
following
1.1
affect
1.1
Dividends
word
dividend
subsidiary
pay
company
payment
distribution
holding
cash
capital
venture
trust
future
ability
joint
receive
obligation
investment
make
funds
bank
p
4.6
4.0
3.6
3.3
2.0
1.8
1.6
1.6
1.3
1.2
1.1
1.1
1.1
1.0
0.9
0.8
0.8
0.8
0.8
0.7
Insurance
word
insurance
claim
liability
result
business
product
coverage
risk
financial
operations
adverse
material
litigation
subject
condition
losses
liabilities
company
reinsurance
costs
IntellP rop
word
patent
right
property
intellectual
license
technology
party
proprietary
product
protect
claim
litigation
use
protection
infringement
infringe
application
result
trademark
obtain
p
5.2
3.8
3.4
3.1
2.8
2.0
2.0
1.8
1.8
1.6
1.6
1.2
1.1
1.0
1.0
1.0
1.0
1.0
0.9
0.9
p
5.1
4.2
3.1
2.4
1.7
1.7
1.6
1.6
1.3
1.1
1.1
1.1
1.0
0.9
0.9
0.9
0.9
0.9
0.8
0.8
Table A2 continued from the previous page.
42
rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
International
word
p
foreign
4.4
currency
3.0
international
2.6
operations
2.4
tax
2.2
country
2.0
exchange
2.0
unite
2.0
risk
1.8
dollar
1.5
state
1.4
china
1.3
result
1.3
fluctuation
1.2
political
1.1
sales
1.0
business
1.0
rates
0.9
subject
0.9
change
0.9
Internet
word
internet
services
advertising
brand
program
online
content
business
user
use
service
consumer
increase
revenue
web
medium
site
network
provide
website
rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
RealEstate
word
loan
real
estate
losses
increase
power
energy
property
commercial
risk
portfolio
market
allowance
coal
mortgage
home
result
borrower
construction
residential
Regulation
word
p
regulation
5.9
laws
3.5
state
3.0
business
2.4
subject
2.2
regulatory
2.0
change
1.9
federal
1.8
requirement
1.5
government
1.4
operations
1.2
comply
1.2
affect
1.2
compliance
1.1
include
1.0
result
1.0
require
0.9
act
0.9
new
0.8
applicable
0.8
p
7.0
2.6
2.4
1.7
1.4
1.3
1.3
1.2
1.2
1.1
1.1
1.1
1.0
1.0
0.9
0.9
0.8
0.8
0.8
0.8
p
3.2
2.3
1.6
1.5
1.3
1.3
1.3
1.2
1.1
1.0
1.0
1.0
0.9
0.9
0.9
0.8
0.8
0.8
0.7
0.7
Legal
word
action
state
court
company
prc
law
federal
file
claim
laws
investigation
certain
information
security
legal
lawsuit
include
unite
act
relate
p
1.5
1.5
1.3
1.1
0.9
0.9
0.9
0.8
0.8
0.8
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
Oil
word
gas
oil
natural
price
production
reserves
drilling
property
fuel
result
operations
future
produce
costs
affect
exploration
energy
commodity
estimate
activity
p
5.1
4.0
3.7
3.3
2.3
1.8
1.4
1.2
1.2
1.1
1.0
0.9
0.9
0.9
0.9
0.9
0.9
0.8
0.8
0.8
P rodApproval
word
p
product
4.4
approval
3.6
clinical
3.5
trial
2.9
fda
2.7
regulatory
2.6
drug
1.5
obtain
1.4
candidate
1.4
require
1.1
result
1.1
delay
1.0
study
0.9
market
0.9
approve
0.8
use
0.8
include
0.8
patient
0.7
process
0.7
safety
0.6
P roductDev1
word
product
new
technology
market
develop
change
customer
services
development
software
technological
industry
acceptance
ability
exist
competitor
competitive
introduce
standard
solution
p
9.5
4.5
4.1
2.9
1.8
1.7
1.5
1.4
1.4
1.2
1.1
1.1
1.0
0.9
0.9
0.9
0.8
0.8
0.8
0.8
P roductDev2
word
product
development
candidate
research
develop
marketing
partner
sales
party
commercialize
commercialization
collaboration
market
commercial
license
drug
effort
revenue
collaborator
technology
p
7.1
4.4
2.3
2.1
1.7
1.4
1.3
1.2
1.0
1.0
1.0
0.9
0.9
0.9
0.9
0.9
0.8
0.8
0.8
0.8
Revenue
word
revenue
tax
net
result
future
income
year
expense
operate
losses
period
estimate
end
december
approximately
base
significant
fiscal
change
incur
p
3.8
2.5
2.4
2.2
1.8
1.7
1.6
1.6
1.6
1.5
1.5
1.3
1.3
1.2
1.2
1.1
1.1
1.0
1.0
1.0
Stakeholder
word
p
stock
5.2
share
4.5
common
3.5
stockholder
2.7
director
2.4
board
1.4
control
1.3
right
1.2
company
1.2
offering
1.2
provisions
1.1
outstanding
1.1
shareholder
1.0
prefer
1.0
price
0.9
issue
0.9
option
0.8
holder
0.8
change
0.8
exercise
0.8
StockP rice
word
p
stock
9.0
price
7.1
common
6.5
market
6.3
security
2.2
trading
2.1
share
1.8
company
1.7
result
1.5
public
1.2
decline
1.2
offering
1.1
fluctuation
1.1
operate
1.0
future
1.0
factor
1.0
investor
0.9
affect
0.9
analyst
0.8
sales
0.8
SupplyChain
word
product
customer
supplier
manufacturing
supply
material
manufacturer
component
result
increase
manufacture
production
price
delay
purchase
order
raw
source
costs
inventory
p
6.2
2.5
2.4
2.2
1.9
1.9
1.6
1.5
1.4
1.3
1.2
1.2
1.1
1.0
0.9
0.9
0.9
0.9
0.9
0.8
Systems
word
system
business
information
customer
result
failure
operations
security
services
client
data
party
service
interruption
damage
disruption
loss
financial
cause
reputation
p
4.6
2.8
2.1
2.1
2.0
1.8
1.7
1.4
1.3
1.3
1.3
1.1
1.0
1.0
0.9
0.9
0.9
0.9
0.8
0.8
Table A3:
Propensity Score Matching
The table presents coefficient estimates and diagnostics from probit regressions of JOBS Act IPO status on
on Star Underwriter, VCbacked, Offer Size, Lagged Revenues, Lagged R&D, Lagged Assets, and a dummy
variable indicating the firm is in a high-tech industry. The dependent variable is a 1 if the IPO firm is
an EGC under the JOBS Act and a 0 otherwise. Panel A provides these coefficients. Panel B presents
t-statistics for differences in these variables across the treatment and control based for firms with similar
propensity scores. Blocks 1 and 2 are the blocks with enough observations to run the difference in means
tests. Panel C provides test statistics for differences in mean propensity scores between the treatment and
control group by block.
Panel A: Probit Regression: JOBS Act IPOs
Panel B: Differences in Variables by Block
Variable
Coeff.
Std. Err.
z
P>z
Variable
StarUnderwriter
VCbacked
Offer Size ($B)
Revenues ($B)
R&D ($B)
Assets ($B)
Industry
Constant
-0.19
-0.13
1.01
81.80
94.20
246.00
0.08
0.01
0.19
0.20
0.79
602.00
1130.00
301.00
0.03
0.25
-1.03
-0.65
1.27
0.14
0.08
0.82
2.31
0.05
0.31
0.52
0.20
0.89
0.93
0.42
0.02
0.96
StarUnderwriter
VCbacked
Offer Size ($B)
Revenues ($B)
R&D ($B)
Assets ($B)
Industry
Block 1
Block 2
-0.82
1.45
-0.49
0.87
0.86
0.08
1.57
0.22
-0.11
-0.22
-0.17
-0.17
-0.89
-0.75
Panel C: Propensity Score Differences by Block
H0 : diff = 0
t-stat
Ha : diff < 0
Pr(T < t)
Ha : diff 6= 0
Pr(|T | > |t|)
Ha : diff > 0
Pr(T > t)
-1.09
-1.24
0.14
0.11
0.28
0.22
0.86
0.89
Block
1
2
43
Table 1:
Summary Statistics
The table provides summary statistics for IPO-level variables (Panel A), firm-level variables (Panel B), secondary
market liquidity and informed trading (Panel C) and prospectus-level risk factor variables (Panel D) for 285 firms
with IPOs between 2010 and 2013. 129 firms have IPOs before the implementation of the JOBS Act, and 156
have IPOs under the JOBS Act. IPO-level variables include the IPO Offer price, the size of the offering, whether
the IPO firm had venture capital backing, whether the firm has a star lead underwriter has the highest rating on
Jay Ritter’s website (StarUnderwriter, using the methodology in Loughran and Ritter (2004)), the gross spread
as percentage of the offering, as well as the 1-day and 3-day IPO return, and the number of words in the risk
factors section of the prospectus. Firm-level variables from the last fiscal year prior to the IPO include total assets,
Total Liabilities, revenues, R&D Expense and Investment in PP&E. Secondary market variables are 22-, 44-, and
66-day probability of informed trading (PIN) and four bid-ask spread measures defined in the Appendix. See the
Appendix for a description of the individual risk factors in Panel C. Means are listed as well as standard deviations
(in parentheses), and standard errors of the mean (in square brackets). *, **, and *** denote that the mean of the
EGC group is statistically different from the mean of the pre-JOBS Act group at the 10%, 5%, and 1% level of
significance, respectively, using a one-tailed test.
Panel A: IPO-level variables
Variable
Pre JOBS Act
JOBS Act
13.27
(5.01)
[0.44]
0.13
(0.13)
[0.01]
0.40
0.49
[0.04]
0.56
(0.50)
[0.04]
6.91
(0.87)
[0.08]
15.7
(21.5)
[1.9]
16.8
(23.0)
[2.0]
7212
(2158)
[190]
129
15.02***
(6.37)
[0.51]
0.15
(0.21)
[0.02]
0.38
0.49
[0.04]
0.54
(0.50)
[0.04]
6.80
(0.53)
[0.04]
20.9*
(29.0)
[2.3]
23.6**
(32.6)
[2.6]
8638***
(2498)
[200]
156
IPO Offer Price
Offer Size ($B)
StarUnderwriter
VCbacked
Fees (% Gross Spread)
1-day IPO ret
3-day IPO ret
Number of Words in
Risk Factors Section
N
Table 1 continues on the next page.
44
Table 1 continued from the previous page.
Panel B: Firm-level variables
Variable
Pre JOBS Act
JOBS Act
250
(642)
[57]
157
(375)
[33]
139
(173)
[15]
22
(24)
[3]
14
(46)
[4]
129
468*
(1344)
[108]
691
(4451)
[356]
146
(263)
[21]
19
(75)
[6]
31
(204)
[16]
156
Total Assets ($M)
Total Liabilities ($B)
Revenues ($M)
R&D Expenses ($M)
Investment in PP&E ($M)
N
Panel C: Informed Trading and Liquidity
Variable
Pre JOBS Act
JOBS Act
PIN-22
0.197
(0.071)
[0.006]
0.193
(0.067)
[0.006]
0.194
(0.063)
[0.006]
0.0048
(0.0040)
[0.0004]
0.0049
(0.0041)
[0.0004]
0.0432
(0.1524)
[0.0134]
0.0419
(0.1552)
[0.0137]
0.202
(0.088)
[0.007]
0.203
(0.101)
[0.009]
0.204
(0.078)
[0.007]
0.0055
(0.0092)
[0.0007]
0.0056
(0.0094)
[0.0007]
0.0733
(0.2049)
[0.0164]
0.0716
(0.2056)
[0.0165]
PIN-44
PIN-66
Pct. Effective Spread (SW)
Pct. Effective Spread (DW)
Dollar Realized Spread (SW)
Dollar Realized Spread (DW)
Table 1 continues on the next page.
45
Table 1 continued from the previous page.
Panel D: Disclosed Risk Factors
Pre JOBS ACT
Risk Factor
Accounting
Competition
Contractual
Costs
Credit
Demand
Disclosure
Dividends
EnviroReg
Financing
FinMarket
Growth
HealthCare
HumanCapital
Insurance
IntellProp
International
Internet
Legal
Oil
ProdApproval
ProductDev1
ProductDev2
RealEstate
Regulation
Revenue
Stakeholder
StockPrice
SupplyChain
Systems
JOBS ACT
Mean
Std. Dev.
Mean
Std. Dev.
Diff
4.6
3.0
2.3
1.2
3.0
4.0
2.9
1.2
1.3
3.3
1.2
4.8
1.6
2.7
2.2
6.4
2.0
3.6
1.6
2.3
4.2
4.8
3.5
0.7
4.6
3.6
8.8
5.4
3.5
4.7
2.1
2.1
1.8
2.2
3.0
3.3
1.1
1.1
2.3
1.6
3.1
2.9
2.5
1.3
0.9
3.8
2.0
5.6
1.5
6.7
9.0
4.1
5.8
1.9
2.5
1.7
2.9
1.7
4.2
3.5
5.4
2.4
2.3
0.9
2.5
3.5
3.4
1.6
1.0
3.2
2.0
4.5
2.3
2.4
2.2
6.0
1.5
2.2
2.2
1.2
6.4
3.9
4.4
1.6
5.2
3.9
9.2
5.2
2.5
4.2
2.3
1.7
1.7
1.5
2.6
3.4
3.8
1.7
1.8
2.0
4.9
2.9
3.3
0.8
1.4
4.4
1.5
4.2
2.5
5.4
10.2
4.1
6.5
3.7
2.4
2.2
4.9
1.9
3.3
3.4
0.8***
-0.6***
0.0
-0.3
-0.5
-0.5
0.5
0.4***
-0.3
-0.1
0.8*
-0.3
0.7**
-0.3**
0.0
-0.4
-0.5***
-1.4***
0.6***
-1.1
2.2*
-0.9*
0.9
0.9***
0.6**
0.3
0.4
-0.2
-1.0**
-0.5
46
Table 2:
Principal Component Analysis on Disclosed Risk Factors
The table presents standardized coefficients for the first (factor1) and second (factor2) principal components of 30
disclosed risk factors from the risk factors section of IPO firms’ registration statements. Risk factors are sorted
in descending order for each of the principal components. The disclosed risk factors are estimated using Latent
Dirichlet Allocation on a training set of risk factor sections from 10-Ks and registration statements from 2006–2012
and are fit out-of-sample. See the appendix for a list of words associated with each risk factor topic.
First Principal Component (factor1)
Topic
Dividends
EnviroReg
Credit
Oil
Contractual
RealEstate
Revenue
Costs
FinMarket
Stakeholder
Insurance
Financing
Legal
Disclosure
International
Demand
Regulation
HealthCare
StockPrice
Accounting
ProdApproval
SupplyChain
ProductDev2
Internet
HumanCapital
Competition
Systems
Growth
ProductDev1
IntellProp
Standardized Coeff.
0.150
0.134
0.118
0.117
0.082
0.081
0.073
0.071
0.062
0.042
0.039
0.024
0.023
-0.003
-0.013
-0.015
-0.024
-0.035
-0.044
-0.049
-0.050
-0.054
-0.058
-0.079
-0.081
-0.102
-0.113
-0.131
-0.141
-0.144
Second Principal Component (factor2)
Topic
Demand
Systems
Competition
Growth
Internet
Revenue
ProductDev1
Regulation
Contractual
International
Accounting
HumanCapital
Costs
Credit
StockPrice
FinMarket
RealEstate
Stakeholder
Dividends
Legal
Insurance
Financing
Disclosure
SupplyChain
Oil
EnviroReg
IntellProp
HealthCare
ProductDev2
ProdApproval
47
Standardized Coeff.
0.129
0.127
0.122
0.108
0.078
0.077
0.076
0.075
0.074
0.069
0.067
0.056
0.045
0.045
0.040
0.032
0.031
0.021
0.018
0.017
0.010
0.009
0.006
-0.004
-0.012
-0.016
-0.126
-0.160
-0.205
-0.208
Table 3:
Usage of JOBS Act Provisions by EGCs
The table provides the frequency with which 220 IPO firms claiming EGC status make use of five JOBS ACT provisions as well as correlation
coefficients and cross tabulations between usage of the provisions. The JOBS Act provisions are: (1) “Confidential Filing” (abbreviated “Confid.
Filing”): An EGC may confidentially submit its draft registration statement to the SEC, (2) “No Discussion of Exec. Comp” (abbreviated “ExecComp
Discuss”): An EGC may omit a discussion of executive compensation, (3) “Private Accounting Standards” (abbreviated “Private Acctg”): EGCs
may take advantage of an extended transition period using private accounting standards, (4) “<3 Yrs of Audited Statements” (abbreviated “<3
yrs Audited”): An EGC can disclose as few as 2 years of audited financial statements in its registration statement, 5) “<5 Yrs of Selected Fin.
Data” (abbreviated “<5 yrs Fin Data”): An EGC can disclose as few as 2 years of selected financial data in its registration statement, and (6) “<5
Executives’ Compensation”(abbreviated “<5 Exec”): An EGC can disclose the compensation of as few as 3 executives. Panel A provides frequencies
and correlation coefficients. Panel B provides cross tabulations for selected pairs of accommodations. Correlation coefficients greater (in absolute
value) than 0.13, 0.16, and 0.21 are significant at the 10%, 5%, and 1% level of significance, respectively.
Panel A: Summary Statistics and Correlations
48
JOBS Act
Provisions / Factors
Hard Information
No Discussion of Exec. Comp.
Private Accounting Standards
<3 Yrs of Audited Statements
<5 Yrs of Selected Fin. Data
<5 Executives’ Compensation
Soft Information
Confidential Filing
factor1
factor2
Table 3 continues on the next page.
Number
Pct
ExecComp
Discuss
Private
Acctg
<3 yrs
Audited
<5 yrs
Fin Data
52
30
74
61
124
33%
19%
47%
39%
79%
0
-0.11
-0.17
0.05
-0.05
0.06
-0.07
0.58
0.24
0.2
95
–
–
61%
–
–
-0.11
0.11
0.22
0.02
0.05
-0.09
0.32
0.07
-0.36
0.44
0.12
-0.28
<5
ExecComp
Confid.
Filing
factor1
0.24
0.00
-0.15
-0.02
-0.09
-0.06
Table 3 continued from the previous page.
Panel B: Selected Cross Tabulations
<3 Yrs of Audited Statements
<5 Yrs of Selected Fin. Data
No
Yes
Total
No
52
30
82
Private Accounting Standard
No
Yes
Total
No
66
16
82
Yes
7
67
74
Total
59
97
156
<3 Yrs of Audited Statements
Yes
60
14
74
Total
126
30
155
<5 Yrs of Selected Fin. Data
Private Accounting Standard
No
Yes
Total
No
50
9
59
49
Yes
76
21
97
Total
126
30
155
Table 4:
Changes in Disclosed Risk Factors around the JOBS Act
Panel A of the table presents results from regressing the first principal component, factor1, of 30 disclosed risk
factors from the risk factors section of IPO firms’ registration statements on a dummy variable equal to 1 if the
firm is an EGC under the JOBS Act, the offer size, a dummy variable indicating that the lead underwriter has the
highest rating on Jay Ritter’s website (StarUnderwriter, using the methodology in Loughran and Ritter (2004)), the
JOBS Act accommodations, and a set of 12 industry fixed effects (using the 12 Fama French industry definitions
from Ken French’s website), and an intercept. There are 285 observations of firms with less than $1 Billion in
revenues from 2010–2013. The disclosed risk factors are estimated using Latent Dirichlet Allocation on a training
set of risk factor sections from S-1s from 1996–2005 and 10-Ks and from 2006–2012 and are fit out-of-sample. The
first principal component is defined in Table 2. *, **, and *** indicate statistical significance at the 10%, 5%, and
1% levels, repsectively. t-statistics are in parentheses. Panel B presents sample average treatment effects for the
treated IPO firms (SATT) relative to a matched set of pre-JOBS Act IPO firms. JOBS Act firms are matched to
their 5 nearest neighbors based on a propensity score matching on Offer Size, StarUnderwriter, VCbacked, Lagged
Revenues, Lagged R&D, Lagged Assets, and a dummy variable indicating the firm is in a high-tech industry. Panel
B provides the SATT estimates for factor1 and factor2.
Panel A: Regressions
Dependent Variable
factor1
Variable
(1)
(2)
(3)
(4)
0.54
(2.23)**
-0.47
(6.66)***
-0.08
(-1.45)
0.11
(2.05)**
0.51
(2.09)**
-0.47
(6.68)***
-0.08
(-1.37)
0.26
(2.28)**
0.50
(2.12)**
-0.32
(3.44)***
-0.10
(-1.19)
-0.29
(-2.35)**
0.04
(0.36)
0.10
(1.03)
0.52
(2.09)**
-0.47
(6.57)***
-0.08
(1.34)
Treatment
Offer Size ($Billions)
VCbacked
StarUnderwriter
factor2
VCbacked × Treatment
StarUnderwriter × Treatment
< 3 Yrs of Audited Statements
Discussion of Exec. Comp.
Private Accounting Standards
Num Exec. Comp. Reported
Num Yrs of Selected Fin Data
Confidential Filing
Industry Fixed Effects
N
Adj R-Sq
Y
285
0.64
Y
285
0.64
Y
285
0.65
Table 4 continues on the next page.
50
0.07
(0.74)
-0.04
(-0.45)
0.05
(0.58)
-0.04
(-1.81)*
0.06
(1.69)*
-0.02
(-0.25)
Y
285
0.64
(5)
(6)
(7)
(8)
-0.01
(-0.09)
-0.06
(-0.74)
0.08
(1.36)
-0.01
(-0.24)
-0.01
(-0.05)
-0.06
(-0.74)
0.08
(1.34)
-0.26
(-2.96)***
-0.01
(-0.12)
-0.22
(-2.41)**
-0.01
(-0.15)
0.32
(2.46)**
0.17
(1.48)
0.01
(0.11)
0.02
(0.22)
-0.07
(0.89)
0.08
(1.34)
Y
285
0.84
Y
285
0.83
Y
285
0.84
-0.13
(-1.45)
0.03
(0.40)
-0.17
(-1.45)
-0.01
(-0.32)
0.02
(0.48)
0.09
(0.90)
Y
285
0.83
Table 4 continued from the previous page.
Panel B: Disclosed Risk Factors - Matched Sample
SATT
factor1
factor2
0.28
(3.30)***
0.47
(3.36)***
51
Table 5:
Changes in SEC Letters around the JOBS Act
Panel A of the table presents differences in characteristics of SEC comments letters to IPO firms before and after
the introduction of the JOBS Act. Panel A provides means, standard deviations (in parentheses), and standard
errors of the mean (in square brackets) for firms before and under the JOBS Act. There are 119 observations of
IPO firms with less than $1 Billion in revenues and with SEC comment letters from the beginning of 2010 up
until the enactment of the JOBS Act and 155 EGCs with comment letters under the JOBS Act. The SEC letters
textual measures are from the first letter from the SEC to the IPO firm. The measures are numbers (the number
of numbers appearing in the letter), uncertainty (the number of uncertainty words in the letter divided by the
total number of words) multiplied by 1,000 (for expositional purposes), weak/strong (the number of weak modal
words divided by the number of strong modal words in the letter), negative/positive (the number of negative words
divided by the number of positive words in the letter), item (the number of itemized points discussed by the SEC
in the letter), and pages (the number of pages in the letter). Uncertainty, weak modal, strong modal, negative and
positive words are those in the Loughran and McDonald (2011) word lists on Bill McDonald’s website. SEC letters
are downloaded from the Securities and Exchange Commission Edgar website. Panel B presents sample average
treatment effects for the treated IPO firms (SATT) relative to a matched set of pre-JOBS Act IPO firms. JOBS Act
firms are matched to their 5 nearest neighbors based on Offer Size, StarUnderwriter, VCbacked, Lagged Revenues,
Lagged R&D, Lagged Assets, and a dummy variable indicating the firm is in a high-tech industry. Panel B provides
SATT estimates for SEC pages, SEC items, SEC uncertainty, SEC weak/strong, and SEC negative/positive.
Panel A: Summary Statistics
Comment Letter Variable
pages
items
numbers
uncertainty (× 1,000)
weak/strong
negative/positive
N
Pre JOBS Act
5.32
(3.52)
[0.32]
18.14
(18.94)
[1.74]
101.48
(73.72)
[6.76]
4.53
(4.32)
[0.40]
0.49
(0.43)
[0.04]
1.70
(1.20)
[0.11]
119
Post JOBS Act
8.08
(3.43)
[0.28]
34.94
(18.63)
[1.50]
163.57
(79.46)
[6.38]
6.86
(9.60)
[0.77]
0.70
(0.52)
[0.04]
2.93
(2.30)
[0.19]
155
Difference
2.76 ***
16.80 ***
62.09 ***
2.33 ***
0.20 ***
1.23 ***
Panel B: SEC Letters - Matched Sample
SATT
pages
items
numbers
uncertainty (× 1,000)
weak/strong
negative/positive
2.53
(4.56)***
16.12
(5.49)***
53.74
(4.48)***
2.61
(2.11)**
0.14
(1.75)*
1.32
(4.30)***
52
Table 6:
Hard and Soft Information and IPO Underpricing
The table presents results from regressing first-day IPO returns on various combinations of a set dummy variables
indicating that the IPO firm has taken advantage of various accommodations of the JOBS Act along with the
size of the offering (in billions of dollars), on the first (factor1) and second (factor2) principal components of 30
disclosed risk factors from the risk factors section of IPO firms’ registration statements, the return of the CRSP
value weighted market portfolio in excess of the risk free rate, StarUnderwriter, that is equal to 1 if the lead
underwriter has the highest rating on Jay Ritter’s website (using the methodology in Loughran and Ritter (2004)),
a dummy variable indicating the IPO was backed by venture capital investors, a set of 12 industry fixed effects
(using the 12 Fama French industry definitions from Ken French’s website). The disclosed risk factors used in
the first and second principal components are estimated using Latent Dirichlet Allocation on a training set of
risk factor sections from 10-Ks and registration statements from 2006–2012 and are fit out-of-sample. factor1 and
factor2 are defined in Table 2. The JOBS Act provisions are: (1) “Confidential Filing” (abbreviated “Confid.
Filing”): An EGC may confidentially submit its draft registration statement to the SEC, (2) “No Discussion of
Exec. Comp” (abbreviated “ExecComp Discuss”): An EGC may omit a discussion of executive compensation,
(3) “Private Accounting Standard” (abbreviated “Private Acctg”): EGCs may take advantage of an extended
transition period using private accounting standards, (4) “<3 Yrs of Audited Statements” (abbreviated “<3 yrs
Audited”): An EGC can disclose as few as 2 years of audited financial statements in its registration statement,
5) “<5 Yrs of Selected Fin. Data” (abbreviated “<5 yrs Fin Data”): An EGC can disclose as few as 2 years of
selected financial data in its registration statement, and (6) “<5 Executives’ Comp.”(abbreviated “<5 Exec”): An
EGC can disclose the compensation of as few as 3 executives. Panel A presents results using data from 129 IPOs
from before the JOBS Act and 155 IPOs under the JOBS Act, while Panel B is limited to the 155 JOBS Act
firms. Included in Panel A is Treatment dummy variable indicating that the IPO took place under the JOBS Act.
First day IPO returns are measured as the change in price form the IPO offer price to the closing price on the first
day of trading on the exchange. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels,
repsectively. t-statistics are in parentheses.
53
Panel A: Full Sample
Treatment
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.06
(2.23)**
0.08
(2.34)**
0.03
(0.75)
0.08
(2.66)***
0.08
(2.46)**
0.08
(2.64)***
0.03
(0.83)
0.08
(1.45)
0.04
(1.29)
0.05
(1.01)
Hard Information:
< 3 Yrs of Audited Statements
0.03
(0.79)
Discussion of Executive Compensation
0.03
(0.64)
0.05
(1.05)
0.07
(1.42)
0.00
(0.35)
0.01
(0.32)
0.05
(1.09)
Private Accounting Standards
0.07
(1.51)
Num Executives’ Compensation Reported
0.01
(1.05)
Num Yrs of Selected Fin Data
0.02
(1.19)
Soft Information:
Confidential Filing
0.06
(1.35)
factor1
54
factor2
Control Variables:
StarUnderwriter
Offer Size ($Billions)
VCbacked
Mkt-RF
Adj R-Sq
Industry FE?
N
0.15
Table 6 continues on the next page.
0.07
(2.06)**
0.15
(1.92)*
0.09
(2.41)**
0.00
(0.05)
Y
284
0.14
0.07
(2.09)**
0.16
(1.94)*
0.09
(2.35)**
0.00
(0.03)
Y
284
0.15
0.06
(1.95)*
0.15
(1.93)*
0.08
(2.30)**
0.00
(0.07)
Y
284
0.16
0.07
(2.30)**
0.15
(1.93)*
0.09
(2.52)**
0.00
(0.03)
Y
284
0.14
0.07
(1.97)**
0.15
(1.83)*
0.09
(2.37)**
0.00
(0.02)
Y
284
0.15
0.06
(1.93)*
0.15
(1.95)*
0.08
(2.35)**
0.00
(0.09)
Y
284
0.15
0.06
(1.99)**
0.15
(1.91)*
0.09
(2.45)**
0.00
(0.12)
Y
284
0.16
0.07
(2.11)**
0.15
(1.95)*
0.08
(2.32)**
0.00
(0.14)
Y
284
0.19
0.04
(0.77)
0.04
(0.83)
0.06
(1.26)
0.00
(0.02)
0.02
(1.00)
0.05
(1.25)
-0.10
(-3.61)***
0.03
(1.33)
0.09
(1.95)*
-0.10
(-3.51)***
0.03
(0.94)
0.05
(1.71)*
0.20
(2.57)**
0.04
(1.09)
0.00
(0.27)
Y
284
0.20
0.06
(1.74)*
0.20
(2.71)***
0.04
(0.96)
0.00
(0.01)
Y
284
Table 6 continued from the previous page.
Panel B: JOBS Act Firms
(1)
Hard Information:
< 3 Yrs of Audited Statements
(2)
(3)
(4)
(5)
(6)
-0.04
(-0.77)
Num Executives’ Compensation Reported
0.05
(0.90)
Private Accounting Standards
-0.08
(-1.38)
Num Yrs of Selected Fin Data
0.01
(0.70)
55
Soft Information:
Confidential Filing
0.07
(1.52)
factor1
factor2
Control Variables:
StarUnderwriter
Offer Size ($Billions)
Mkt-RF
VCbacked
Industry FE?
N
Adj R-Sq
0.04
(0.86)
0.28
(2.35)**
0.00
(0.03)
0.12
(2.01)**
Y
155
0.11
0.04
(0.83)
0.27
(2.29)**
0.00
(0.05)
0.12
(2.18)**
Y
155
0.11
0.03
(0.70)
0.27
(2.27)**
0.00
(0.07)
0.11
(2.00)**
Y
155
0.11
0.03
(0.74)
0.27
(2.34)**
0.00
(0.09)
0.13
(2.34)**
Y
155
0.13
0.04
(0.83)
0.27
(2.29)**
0.00
(0.03)
0.12
(2.05)**
Y
155
0.11
(8)
-0.06
(-0.94)
-0.01
(-0.46)
0.06
(1.05)
-0.08
(-1.39)
0.00
(0.14)
0.00
(0.10)
Discussion of Executive Compensation
(7)
0.04
(0.82)
0.26
(2.25)**
0.00
(0.17)
0.13
(2.25)**
Y
155
0.12
0.03
(0.61)
0.28
(2.39)**
-0.01
(-0.19)
0.11
(1.88)*
Y
155
0.12
(9)
-0.08
(-1.29)
-0.01
(-0.74)
0.05
(0.92)
-0.09
(-1.52)
0.01
(0.47)
0.07
(1.43)
-0.09
(-1.68)*
0.01
(0.21)
0.11
(2.10)**
-0.09
(-1.72)*
-0.01
(-0.25)
0.04
(0.91)
0.29
(2.45)**
0.00
(0.14)
0.08
(1.30)
Y
155
0.13
0.04
(0.75)
0.30
(2.58)**
0.00
(0.12)
0.06
(0.91)
Y
155
0.14
Table 7:
SEC Letters, Underpricing, and PIN
The table presents results from regressing first-day IPO returns, 22-day, 44-day, and 66-day PIN (Probability
of Informed Trading, Easley et al. (1996)) on the first (factor1) and second (factor2) principal components
of 30 disclosed risk factors from the risk factors section of IPO firms’ registration statements, textual
measures from SEC letters to the firm during the IPO process, six JOBS Act accommodations, the offer
size, a dummy variable indicating that the lead underwriter has the highest rating on Jay Ritter’s website
(StarUnderwriter, using the methodology in Loughran and Ritter (2004)), a dummy variable indicating the
IPO firm had venture capital backing (VCbacked), the return of the CRSP value weighted market portfolio
in excess of the risk free rate (Mkt-RF), and a set of 12 industry fixed effects (the 12 industry portfolios
from Ken French’s website). Panel A presents results for 150 JOBS Act EGCs from 2012 and 2013 for
underpricing, and 149, 133, and 116 of these firms for which sufficient data are available to estimate 22-,
44-, and 66-day PIN, respectively. Panel B presents results for 243 IPO firms from 2010 through 2013 for
undepricing, and 243, 227, and 210 of these firms for 22-, 44-, and 66-day PIN, respectively. The SEC
letters textual measures are from the first letter from the SEC to the IPO firm. The measures are numbers
(the number of numbers appearing in the letter), uncertainty (the number of uncertainty words in the
letter divided by the total number of words), weak/strong (the number of weak modal words divided
by the number of strong modal words in the letter), negative/positive (the number of negative words
divided by the number of positive words in the letter), item (the number of itemized points discussed by
the SEC in the letter), and pages (the number of pages in the letter). Uncertainty, weak modal, strong
modal, negative and positive words are those in the Loughran and McDonald (2011) word lists on Bill
McDonald’s website. SEC letters are downloaded from the Securities and Exchange Commission Edgar
website. The disclosed risk factors used in the first and second principal components are estimated using
Latent Dirichlet Allocation on a training set of risk factor sections from 10-Ks and registration statements
from 2006–2012 and are fit out-of-sample. factor1 and factor2 are defined in Table 2. The JOBS Act
provisions are: (1) “Confidential Filing” (abbreviated “Confid. Filing”): An EGC may confidentially
submit its draft registration statement to the SEC, (2) “No Discussion of Exec. Comp” (abbreviated
“ExecComp Discuss”): An EGC may omit a discussion of executive compensation, (3) “Private Accounting
Standard” (abbreviated “Private Acctg”): EGCs may take advantage of an extended transition period
using private accounting standards, (4) “<3 Yrs of Audited Statements” (abbreviated “<3 yrs Audited”):
An EGC can disclose as few as 2 years of audited financial statements in its registration statement, 5)
“<5 Yrs of Selected Fin. Data” (abbreviated “<5 yrs Fin Data”): An EGC can disclose as few as 2 years
of selected financial data in its registration statement, and (6) “<5 Executives’ Comp.”(abbreviated “<5
Exec”): An EGC can disclose the compensation of as few as 3 executives. Additionally, the regression
in Panel B includes interactions of factor1, factor2, SEC uncertainty, and SEC weak/strong on the
firm-level measures of hard and soft information. For the sake of brevity, some variables with statistically
insignificant coefficients are not reported. *, **, and *** indicate statistical significance at the 10%, 5%,
and 1% levels, repsectively. t-statistics are in parentheses.
56
Panel A: Direct Effects of Risk Factors and SEC Letters
Dependent variable
SEC Soft Information:
SEC numbers
SEC uncertainty
SEC weak/strong
SEC negative/positive
SEC items
SEC pages
Firm Hard Information:
< 3 Yrs of Audited Statements
Num Exec Comp Reported
Discussion of Executive Compensation
Private Accounting Standards
Num Yrs of Selected Fin Data
Firm Soft Information:
Confidential Filing
factor1
factor2
1-day IPO return
PIN-22
PIN-44
PIN-66
-0.001
(-0.83)
5.195
(1.71)*
0.103
(2.22)**
-0.008
(-0.74)
-0.002
(-0.38)
0.034
(1.14)
-0.001
(-2.63)***
-0.572
(-0.79)
0.012
(1.03)
0.004
(1.47)
-0.001
(-1.28)
0.021
(2.95)***
-0.001
(-2.09)**
0.142
(0.15)
0.001
(0.05)
0.001
(0.35)
0.001
(0.63)
0.007
(0.68)
-0.001
(-1.56)
0.042
(0.04)
-0.007
(-0.26)
0.001
(0.33)
0.001
(0.77)
0.007
(0.59)
-0.085
(-1.39)
-0.012
(-0.64)
0.025
(0.48)
-0.083
(-1.39)
0.010
(0.39)
0.001
(0.06)
-0.002
(-0.55)
-0.024
(-1.85)*
0.018
(1.30)
-0.019
(-3.08)***
-0.014
(-0.61)
0.010
(1.54)
-0.030
(-1.67)*
0.031
(1.64)
-0.022
(-2.27)**
-0.002
(-0.07)
0.014
(2.07)**
-0.024
(-1.18)
0.038
(1.60)
-0.019
(-1.77)*
0.135
(2.44)**
-0.117
(-2.25)**
-0.024
(-0.51)
-0.017
(-1.28)
-0.020
(-1.56)
-0.007
(-0.62)
0.000
(0.01)
0.019
(1.02)
0.004
(0.23)
-0.005
(-0.25)
0.033
(1.52)
0.011
(0.62)
Control Variables:
MKT-PIN-22
-0.098
(-0.21)
MKT-PIN-44
-1.604
(-1.39)
MKT-PIN-66
1-day IPO return
StarUnderwriter
Offer Size ($Billions)
VCbacked
MKT-RF
Industry Fixed Effects?
N
Adj R-Sq
0.064
(1.28)
0.284
(2.42)**
0.066
(1.03)
-0.019
(-0.66)
Y
150
0.19
Table 7 continues on the next page.
57
-0.050
(-2.32)**
-0.009
(-0.75)
-0.065
(-2.30)**
-0.032
(-2.05)**
-0.040
(-1.35)
-0.056
(-3.35)***
-0.086
(-1.59)
0.021
(0.94)
-0.306
(-0.46)
-0.019
(-0.52)
-0.049
(-2.62)**
-0.181
(-1.60)
0.017
(0.72)
Y
149
0.34
Y
133
0.21
Y
116
0.16
Table 7 continued from the previous page.
Panel B: Direct Effects and Interactions of Risk Factors and SEC Letters
Dependent variable
1-day IPO return
PIN-22
PIN-44
PIN-66
0.056
(0.97)
-0.004
(-0.23)
-0.044
(-2.19)**
-0.045
(-1.59)
0.116
(3.18)***
-0.011
(-1.15)
-0.031
(-2.40)**
-0.025
(-0.81)
-3.315
(-0.45)
-0.087
(-0.63)
0.005
(0.11)
0.010
(0.34)
0.013
(0.61)
0.018
(1.23)
0.000
(0.73)
1.637
(0.50)
-12.137
(-2.82)***
0.658
(0.39)
4.962
(1.72)*
-0.087
(-2.30)**
0.015
(1.23)
-0.008
(-0.86)
0.016
(1.69)*
Y
Y
243
0.27
-0.001
(-0.02)
-0.074
(-2.97)***
-0.022
(-0.56)
0.038
(0.74)
-0.017
(-1.35)
-0.013
(-0.70)
-0.038
(-0.75)
11.760
(1.07)
-0.117
(-0.83)
0.137
(1.94)*
-0.017
(-0.53)
-0.016
(-0.50)
-0.019
(-0.67)
0.000
(0.44)
-4.465
(-1.05)
1.675
(0.27)
-4.052
(-1.58)
0.889
(0.16)
-0.070
(-1.49)
0.031
(2.06)**
-0.032
(-2.37)**
0.009
(0.54)
Y
Y
227
0.23
-0.004
(-0.15)
-0.036
(-1.62)
-0.034
(-0.93)
0.095
(1.45)
-0.004
(-0.36)
-0.037
(-2.00)**
0.035
(0.93)
8.219
(1.01)
-0.164
(-1.16)
0.165
(2.11)**
0.019
(0.51)
0.003
(0.12)
-0.003
(-0.13)
0.000
(1.01)
-1.794
(-0.52)
-4.375
(-0.51)
-0.726
(-0.32)
-2.838
(-0.62)
-0.072
(-0.90)
0.015
(1.13)
-0.032
(-1.98)*
0.025
(1.32)
Y
Y
210
0.22
Treatment
1-day IPO return
< 3 Yrs of Audited Statements
-0.077
(-0.75)
0.001
(0.01)
0.049
(1.80)*
-0.049
(-1.30)
-0.007
(-0.07)
-66.370
(-2.80)***
0.624
(1.37)
-0.090
(-1.80)*
0.030
(0.65)
0.110
(2.41)**
-0.037
(-1.66)*
0.002
(2.51)**
32.879
(3.19)***
-3.602
(-0.26)
15.184
(2.53)**
24.115
(1.92)*
-0.055
(-0.40)
-0.083
(-2.31)**
Private Accounting Standards
Num Exec Comp Reported
Num Yrs of Selected Fin Data
Confidential Filing
SEC uncertainty
SEC weak/strong
factor1
factor2
factor1 × Treatment
factor2 × Treatment
factor2 × SEC uncertainty
< 3 Yrs of Audited Statements × SEC uncertainty
Private Accounting Standards × SEC uncertainty
Num Yrs of Selected Fin Data × SEC uncertainty
Confidential Filing × SEC uncertainty
Private Accounting Standards × SEC weak/strong
Num Exec Comp Reported × SEC weak/strong
Num Yrs of Selected Fin Data × factor1
Private Accounting Standards × factor2
Additional Controls/Interactions?
Firm Fixed Effects?
N
Adj R-Sq
Y
Y
243
0.26
58
Table 8:
JOBS Act and Liquidity
The table presents results from regressing 22-day average bid-ask spreads on the first (factor1) and second
(factor2) principal components of 30 disclosed risk factors from the risk factors section of IPO firms’
registration statements, textual measures from SEC letters to the firm during the IPO process, six JOBS
Act accommodations, the offer size, a dummy variable indicating that the lead underwriter has the
highest rating on Jay Ritter’s website (StarUnderwriter, using the methodology in Loughran and Ritter
(2004)), a dummy variable indicating the IPO firm had venture capital backing (VCbacked), the return
of the CRSP value weighted market portfolio in excess of the risk free rate (Mkt-RF), and a set of 12
industry fixed effects (the 12 industry portfolios from Ken French’s website). Bid-ask spreads used in the
regressions are dollar effective bid-ask spreads and percent realized spreads are estimated for each firm
in the 22 trading days following the IPO. Both share-weighted and dollar-weighted spreads are used for
each type of spread. Details are in the Appendix. Regressions include observations for 244 IPO firms
from 2010 through 2013 for are presented. The SEC letters textual measures are from the first letter
from the SEC to the IPO firm. The measures are numbers (the number of numbers appearing in the
letter), uncertainty (the number of uncertainty words in the letter divided by the total number of words),
weak/strong (the number of weak modal words divided by the number of strong modal words in the
letter), negative/positive (the number of negative words divided by the number of positive words in the
letter), item (the number of itemized points discussed by the SEC in the letter), and pages (the number
of pages in the letter). Uncertainty, weak modal, strong modal, negative and positive words are those in
the Loughran and McDonald (2011) word lists on Bill McDonald’s website. SEC letters are downloaded
from the Securities and Exchange Commission Edgar website. The disclosed risk factors used in the first
and second principal components are estimated using Latent Dirichlet Allocation on a training set of risk
factor sections from 10-Ks and registration statements from 2006–2012 and are fit out-of-sample. factor1
and factor2 are defined in Table 2. The JOBS Act provisions are: (1) “Confidential Filing” (abbreviated
“Confid. Filing”): An EGC may confidentially submit its draft registration statement to the SEC, (2)
“No Discussion of Exec. Comp” (abbreviated “ExecComp Discuss”): An EGC may omit a discussion
of executive compensation, (3) “Private Accounting Standard” (abbreviated “Private Acctg”): EGCs
may take advantage of an extended transition period using private accounting standards, (4) “<3 Yrs of
Audited Statements” (abbreviated “<3 yrs Audited”): An EGC can disclose as few as 2 years of audited
financial statements in its registration statement, 5) “<5 Yrs of Selected Fin. Data” (abbreviated “<5 yrs
Fin Data”): An EGC can disclose as few as 2 years of selected financial data in its registration statement,
and (6) “<5 Executives’ Comp.”(abbreviated “<5 Exec”): An EGC can disclose the compensation of as
few as 3 executives. For the sake of brevity, control variables are included, but coefficients are not reported.
*, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, repsectively. t-statistics are
in parentheses.
59
Percent Effective Spread
Treatment
Firm Hard Information:
< 3 Yrs of Audited Statements
Discussion of Executive Compensation
Private Accounting Standards
Num Exec Comp Reported
Num Yrs of Selected Fin Data
Firm Soft Information:
Confidential filing
factor1
factor2
SEC Soft Information:
SEC numbers
SEC uncertainty
SEC weak/strong
SEC negative/positive
SEC items
SEC pages
Control Variables?
Industry Fixed Effects?
N
Adj R-Sq
Dollar Realized Spread
Share-weighted
Dollar-weighted
Share-weighted
Dollar-weighted
0.001
(0.410)
0.001
(0.390)
0.018
(0.410)
0.014
(0.330)
0.000
(0.120)
0.000
(0.090)
-0.001
(-0.610)
0.000
(0.260)
-0.001
(-1.270)
0.000
(0.140)
0.000
(0.050)
-0.001
(-0.620)
0.000
(0.270)
-0.001
(-1.300)
-0.043
(-1.040)
0.017
(0.430)
-0.009
(-0.240)
-0.012
(-1.78)*
0.022
(1.87)*
-0.040
(-0.970)
0.020
(0.530)
-0.008
(-0.230)
-0.012
(-1.72)*
0.022
(1.88)*
-0.003
(-1.98)**
-0.001
(-2.09)**
-0.001
(-1.240)
-0.003
(-1.97)*
-0.001
(-2.03)**
-0.001
(-1.250)
0.037
(0.980)
-0.040
(-2.26)**
-0.045
(-2.37)**
0.039
(1.030)
-0.037
(-2.07)**
-0.046
(-2.34)**
0.000
(1.410)
-0.017
(-0.590)
0.002
(1.160)
0.000
(0.720)
0.000
(0.990)
-0.001
(-1.330)
Y
Y
244
0.08
0.000
(1.430)
-0.016
(-0.530)
0.002
(1.130)
0.000
(0.720)
0.000
(0.990)
-0.001
(-1.330)
Y
Y
244
0.08
0.000
(0.050)
-0.634
(-0.760)
-0.003
(-0.120)
-0.001
(-0.140)
-0.001
(-0.240)
0.010
(0.620)
Y
Y
244
-0.01
0.000
(0.140)
-0.548
(-0.630)
-0.005
(-0.210)
-0.001
(-0.140)
-0.001
(-0.300)
0.012
(0.740)
Y
Y
244
-0.01
60
Table 9:
Benefits of Optimal Disclosure of Soft Information
The table presents differences in underpricing and bid-ask spreads for JOBS Act IPO firms relative to a matched sample of IPO firms from
before the JOBS Act. Each JOBS Act firm is matched to its 5 nearest neighbors based on Offer Size, StarUnderwriter, VCbacked, Lagged
Revenues, Lagged R&D, Lagged Assets, and a dummy variable indicating the firm is in a high-tech industry. Where indicated, firms are also
matched on factor1 and factor2. Panel A presents summary statistics for differences in underpricing and spreads based on the differences
in factor1 for JOBS Act firms and their matched peers from the first column of panel A. Differences in underpricing(spreads) are defined
as the difference in factor1 for the JOBS Act firm and factor1 for its matched peer multiplied by the coefficient on factor1 from the
underpricing (liquidity) regression in column 10 of Panel A in Table 6 (column 3 of Panel A in Table 8). Panel B provides Sample Average
Treatment effect for the Treated (SATT) JOBS Act firms for underpricing and realized spreads based on matching with and without the
risk factors. Underpricing is defined as the first day IPO return, measured as the change in price form the IPO offer price to the closing
price on the first day of trading on the exchange. The measure of bid-ask spread is share-weighted dollar realized spread and is defined in
the Appendix. factor1 and factor2 are the first and second principal components are estimated using Latent Dirichlet Allocation on a
training set of risk factor sections from 10-Ks and registration statements from 2006–2012 and are fitted out-of-sample to the text of the
risk factor section from the IPO firm’s prospectus. Other matching variables are gathered from the accounting disclosures in the prospectus,
from SDC platinum, and from CRSP. A total of 129 firms from before the JOBS Act and 156 JOBS Act firms are used in the matching.
61
Panel A: Impact of factor1 on Outcomes
Panel B: Matched Sample - SATT
Matching on factor1 and factor2?
Difference in:
Min
P25
Mean
Median
P75
Max
Variable:
Underpricing
-0.301
-0.062
-0.027
-0.005
0.021
0.163
Underpricing
Realized Spread
-0.120
-0.025
-0.011
-0.002
0.008
0.065
Realized Spread
No
Yes
0.068
(1.86)*
0.044
-(1.42)
0.084
(2.36)**
0.044
-(1.51)
Figure 1: Risk Factor 1 and Differences in Underpricing
The figure displays a histogram of differences in underpricing for JOBS Act firms based on differences in
disclosure of factor1 relative to matched firms. Each JOBS Act firm is matched to its 5 nearest neighbors
based on Offer Size, StarUnderwriter, VCbacked, Lagged Revenues, Lagged R&D, Lagged Assets, and a
dummy variable indicating the firm is in a high-tech industry. Then, the difference in underpricing is
defined as the difference in factor1 for the JOBS Act firm and factor1 for its matched peer multiplied by
the coefficient on factor1 from the underpricing regression results in column (10) of Panel A in Table 6.
62
Figure 2: Risk Factor 1 and Differences in Liquidity
The figure displays a histogram of differences in bid-ask spreads for JOBS Act firms based on differences
in disclosure of factor1 relative to matched firms. Each JOBS Act firm is matched to its 5 nearest
neighbors based on Offer Size, StarUnderwriter, VCbacked, Lagged Revenues, Lagged R&D, Lagged
Assets, and a dummy variable indicating the firm is in a high-tech industry. Then, the difference in
realized spread is defined as the difference in factor1 for the JOBS Act firm and factor1 for its matched
peer multiplied by the coefficient on factor1 from the bid-ask spread regression results in the third
column of Panel A in Table 8.
63