Industry peer firms earnings predictably, financial
crisis and IPO underpricing
Zabihollah Rezaee
[email protected]
Lei Gao
[email protected]
Ji Yu
[email protected]
School of Accountancy
University of Memphis
Memphis, TN 38152, USA
Phone# 901-678-4652
Industry peer firms earnings predictably, financial
crisis and IPO underpricing
ABSTRACT:
Investors have very limited information about firms’ financial condition before firm go initial
public offering (hereafter IPO). Investors and analysts usually analyze the industry peer firms’
financial condition to have better understanding about the IPO firm. However, previous literature
mainly studies firms’ own characteristics effect on IPO underpricing (single-security setting).
This paper extends IPO literature to multi-securities setting by investigating relation between
peer firms earnings predictability and IPO underpricing. By examining peer firms earnings
predictability’s role in IPO underpricing, we find that investors interpret transient (one year and
quarterly) peer firms earnings predictability as managers’ myopic behavior while interpret longrun (five years) peer firms earnings predictability as favorable attribute. This finding supports a
recent study by Francis et al. (2004) that shows an uncertain role of earnings predictability in the
capital market. While our goal is not to cast doubt on previous literature that documents positive
effect of earnings predictability, our finding suggests reconsideration of peer firms earnings
predictability’s role in IPO process. And this study develops understanding about peer firms
earnings predictability’s role by distinguishing different horizons of earnings predictability. Also,
we show that the financial crisis mitigate the positive effect of peer firms earnings predictability
effect on IPO underpricing. Lastly, we show that similar peer firms earnings predictability have
greater effect on IPO underpricing than that influential peer firm. In the additional test, we find
that low peer firms earnings predictability group stock performance outperforms than that of the
high peer firms earnings predictability group in the 12/24 months post-IPO period.
Key words: Industry peer firms earnings predictability, IPO underpricing, financial crisis
Data Availability: Data can be retrieved from sources identified in the article.
1
1. Introduction:
Initial public offering (hereafter IPO) underpricing refers to the phenomenon that the first-day
closing price is usually higher than the initial offering price stated in the prospectus. For example,
Twitter went to public in November 6th 2013 with a price underpricing of 74%. Prior research
shows that on average over the past 50 years, U.S. IPOs were underpriced by 16.8%.1 However,
current IPO research mainly investigates firm’s own characteristics’ effect on IPO underpricing
by using single-security setting. (Teoh et al. 1998, Willenborg et al. 2001, Leone et al. 2006,
Boulton et al. 2011), fewer research studies IPO underpricing phenomenon in a multiplesecurities setting. Our study fills this gap by investigating peer firms earnings predictability’s
effect on IPO underpricing.2
Literature has long documented that stock pricing is affected by forward looking earnings
growth. (Ball and Brown, 1968; Beaver 1968). Theoretically, Lipe (1990) shows that earnings
predictability and earnings persistence are important determinants of stock valuation. In our
study, IPO valuation is one of the most important settings to evaluate a firm’s market value.
Therefore, we expect there is an innate relation between firm’s earnings predictability and IPO
underpricing following Lipe (1990)’s theoretical framework. Earnings predictability in our study
refers to the ability of earnings to predict itself (Lipe 1990). IPO firms are required to disclose
only two years pre-IPO information. Compared with abundant information about IPO firm’s peer
firms, investors know very limited information about the IPO firm3. Thus, it is a common
practice for investors and analysts to understand peer firms’ financial condition to infer IPO
firm’s financial condition. Thus, IPO setting creates a unique setting test peer firms’ impact.
1
Why I.P.O.’s get underpriced by Steven M Davidoff (New York Times, 2011).
We define general peer firms as the other firms in the same industry (except the IPO firm) based on Fama-French
alternative industry classifications.
3
IPO prospectus typically offers little more than two years of pre-IPO information (Boulton et al. 2011)
2
2
Theoretically, Lambert, Leuz and Verrecchia (2007) document peer firms’ effect. Specifically, if
peer firms’ information quality increases (decreases), investors receive less (more) noisy signals
of the firm’s and peer firms’ future cash flow. Those more accurate (inaccurate) signals lead to
lower (higher) investors’ assessed covariance of the firm’s expected future cash flows with the
cash flows of other firms. Finally, those decreased (increased) covariance will impact investors’
perception about firm’s information risk. We further refer that this decreased (increased)
information risk can impact firm’s information asymmetry. IPO literature documents that
information asymmetry “has a first order effect on underpricing.” (Ljungqvist 2007). Specifically,
Rock (1986) suggests that decreased information asymmetry can reduce the IPO underpricing.
Combined with earnings predictability and peer firms effect literature, we suggest that peer firms’
earnings predictability is theoretically associated with IPO underpricing.
Using a sample of 4596 IPOs during the period from January 1976 to December 2012, we
test peer firms earnings predictability’s effect on IPO underpricing. IPO underpricing is defined
as the first day initial return (Leone 2006). Following Lipe (1990) and Francis, we use three
measurements to proxy earnings predictability. They are: 1) standard deviation of the residual
from ten-years rolling regression of earnings per share (hereafter EPS) on previous EPS 2)
analyst forecast error and 3) analyst forecast dispersion.4 We classify industries based on FamaFrench 48 industry classification to group peer firms5. Based on the three measurements of firmspecific earnings predictability, we develop six measurements of equally-weighted or marketshare-weighted peer firms’ earnings predictability following prior studies (Leary and Roberts
2013; Ma 2013). We also establish an aggregate measurement of peer firms’ earnings
4
We use decile rank value of peer firms earnings predictability to avoid outlier effect. High rank value refers to high
earnings predictability.
5
In robustness check, we also use alternative Fama-French industry classification (30 industries and 38 industries).
Results remain qualitatively the same.
3
predictability based on the equally-weighted rank of the three firm-specific earnings
predictability proxies. In total, we establish seven peer firms’ earnings predictability
measurements. We find that transient peer firms earnings predictability is associated with higher
IPO underpricing.6 This result offers new evidence for Francis et al. (2004) argument that
“excessive earnings predictability can be interpreted as earnings management by investors”.
To further investigate peer firms earnings predictability’s effect on IPO underpricing, we
study different time horizons of peer firms earnings predictability. In our hypothesis H1b, shortrun peer firms earnings predictability refers to peer firms’ quarterly forecast accuracy by analysts.
Long-run peer firms earnings predictability refers to peer firms’ five years forecast accuracy by
analysts. If Francis et al. (2004)’s argument about “excessive earnings predictability is an
indication of earnings management” is correct, we expect to observe short-run peer firms
earnings predictability is also positively associated with IPO underpricing while the long-run
peer firms earnings predictability is negatively associated with IPO underpricing. Since it is
easier to commit earnings management to achieve short-run predictable cash flows than that of
long-run, investors are more likely to interpret short-run peer firms earnings predictability as
earnings management. Our result supports this argument and shows that investors and market
reward long-run peer firms earnings predictability by allocating lower IPO underpricing while
they penalize short-run peer firms earnings predictability by allocating higher IPO underpricing.
Financial crisis literature shows that earnings management behavior varies according to
the severity of crisis (Filip and Raffournier 2012; Imperatore and Trombetta 2013). We assume
the general possibility of earnings management behavior in the environment will impact rational
6
In this study, transient peer firms earnings predictability includes one year predictive horizon (tested in the
hypothesis H1a) and quarterly predictive horizon (tested in the hypothesis H 1b). Long-run peer firms earnings
predictability refers to five years horizon peer firms’ earnings predictability (tested in the hypothesis H 1b).
4
investors’ assessment of the association severity between excessive earnings predictability and
earnings management. In other words, firms reduce earnings management in the financial crisis
in general (Filip and Raffournier 2012). This neat environment would alleviate investors’
concern that excessive earnings predictability is an indication of earnings management. The
reduced concern about the association between excessive earnings predictability and earnings
management will finally translate to the phenomenon that peer firms earnings predictability’s
effect on IPO underpricing is reduced in the financial crisis. Therefore, financial crisis mitigates
peer firms earnings predictability’s effect on IPO underpricing by reducing investors’ concern of
the association severity between excessive earnings predictability and earnings management.
Under Imperatore and Trombetta (2013)’s framework, they further suggest that earnings
management behaviors are non-monotonic in crisis. Specifically, earnings management
decreases in small financial crisis, while it increases in big financial crisis. Our empirical result
supports Imperatore and Trombetta (2013)’s finding from another perspective by showing
investors’ different interpretations of excessive earnings predictability in the small crisis and the
big crisis. In other words, we find that rational investors are less likely to interpret excessive
earnings predictability as earnings management in small crisis, while they are more likely to
interpret excessive earnings management as earnings management in big crisis. Besides the
above findings, we also find that financial crisis itself contributes to the IPO underpricing
because of less market liquidity and more information asymmetry in the crisis. Following Lang
and Maffet (2011), we define the general financial crisis as the time period if the market index
falls by more than 1.5 standard deviation of its historical average value in the prior month. Small
financial crisis is defined as the time period if equity market experiences a downturn greater than
1.5 standard deviations below its historical average, but smaller than 2 standard deviations below
5
its historical average in the prior month. Big financial crisis is defined as the time period if equity
market experiences a downturn greater than 2 standard deviations below its historical average in
the prior month. We find that financial crisis in general contributes to the IPO underpricing.
However, we do not observe different levels of financial crisis have different effects on IPO
underpricing.
We also study whether similar peer firms and influential peer firms will have different
effect on IPO underpricing. Similar peer firms refer to the peer firms in the same market-share
quintile in the same industry while influential peer firms refer to the top market-share quintile
peer firms in the same industry (Ma 2013). As expected, we find that both similar peer firms and
influential peer firms have effect on IPO underpricing. More importantly, similar peer firms have
greater effect on IPO underpricing than influential peer firm. This result develops our
understanding of “herd behavior” in the economics literature7 (Scharfstein and Stein1990).
In the additional test, we study the 12 months and 24 months long-run post-IPO
performance of the IPO firms. We classified our sample into low/medium/high three groups
based on peer firms earnings predictability rank value. And we calculate cumulative abnormal
return and buy-and-hold abnormal return as performance measurements. Our result also supports
the “earnings management” argument, that is, low peer firms earnings predictability group
consistently outperform than the high peer firms earnings predictability group.
The contributions of our study can be summarized in the following ways. First, our study
contributes to the literature by extending understanding of IPO underpricing from single security
setting to multi-securities setting. Prior research mainly documents that firm-specific
7
Herd behavior: Scharfstein and Stein (1990) refers to the phenomenon that managers mimic investment decisions
of other managers, ignoring substantive private information.
6
characteristics can impact IPO underpricing while few studies investigate peer firms’ effect on
IPO underpricing. However, it is a common practice for investors and analysts to compare peer
firms financial condition to have a better understanding about the IPO firm. Our study fills the
literature gap and quantifies one attribute of this peer firms effect. Our study shows that
investors do not consider IPO firm in isolation with its peer firms and IPO underpricing is highly
dependent on peer firms’ earnings characteristics.
Second, our study develops understanding of earnings predictability’s effect in capital
market. A large body of literature shows earnings attributes can impact capital market. Prior
literature has examined conservatism, accrual quality, earnings smoothness and earnings
management’s effect in capital market (Teoh et al. 1998; Boulton et al. 2011; Zhang 2008; Ferris
et al. 2012). However, fewer studies investigate earnings predictability’s effect. In this study, we
fill this gap by using IPO setting to examine earnings predictability’s effect on IPO underpricing.
Furthermore, extant literature assumes that earnings predictability has positive effect in capital
market. For example, Graves et al. (2002) find that earnings predictability increases firm’s
liquidity thus decreases information asymmetry. And they connect this decreased information
asymmetry to lower cost of equity. However, recent study such as Francis et al. (2004) shows
that high earnings predictability is significantly associated with high cost of equity. But this
relation is sensitive to alternative proxies of earnings predictability and cost of equity. They
further conclude that there is no reliable association between earnings predictability and cost of
equity. And they suggest that “earnings predictability is not in and of itself desirable, in the sense
of reducing information risk; in fact, predictability that exists over and above that implied by
innate determinants contributes to information risk”. While we do not cast doubt on previous
literature that documents earnings predictability’s favorable effect on market liquidity (Graves et
7
al. 2002), our result suggests that market has different interpretation about transient (or short-run)
earnings predictability and long-run (five years) earnings predictability. IPO setting is a unique
setting to examine Francis et al. (2004)’s argument since investors are more likely to interpret
earnings predictability as earnings management in the IPO process. For example, Ducharme et al.
(2004) show that abnormal accruals are unusually high around stock offers and firms are more
likely to opportunistically manipulate earnings upward before IPO. Teoh et al. (1998) shows that
firms tend to inflate their earnings before IPO and seasoned equity offering. Our result suggests
that investors can distinguish transient (or short-run) earnings predictability with long-run (five
years) earnings predictability. Specifically, we find that investors interpret transient (or short-run)
earnings predictability as a unfavorable attribute in the IPO process while consider long-run (five
years) earnings predictability as favorable attribute since long-run earnings predictability refers
to “real” steady future cash flow. In summary, our study further develops understanding about
earnings predictability’s role in capital market.
Third, our study is important for investors, IPO issuers and standard setters such as FASB.
Our result shows that market does not always consider earnings predictability as a favorable
earnings attribute. In IPO process, market can distinguish transient (or short-run) earnings
predictability with long-run earnings predictability. And market interprets transient (or short-run)
earnings predictability as managers’ myopic behavior which increases information asymmetry in
the IPO process. This result offers new evidence to support Francis et al. (2004) argument that
investors do not constantly value earnings predictability in capital market, excessive earnings
predictability can also negatively impact the capital market. In summary, our new finding
suggests reconsideration of earnings predictability’s role in IPO process by investors, issuers and
standard setters.
8
Last, few research investigates the impact of a macro-economic change’s impact in IPO
setting. Our study fills this gap by developing the understanding of IPO activity in the financial
crisis. We show that IPO underpricing tends to be higher in the financial crisis and peer firms
earnings predictability’s effect on IPO underpricing is mitigated by financial crisis. Moreover,
our result supports recent studies’ result that investigate financial crisis’ impact on earnings
management from another perspective (Filip and Raffournier, 2012; Imperatore and Trombetta,
2013). We show investors understand that earnings management behaviors are fewer in the crisis,
and investors are less likely to associate earnings predictability with earnings management in the
crisis. This is consistent with Filip and Raffournier (2012)’s finding that earnings management
significantly decreases in the crisis. We also find that investors are less likely to associate
excessive earnings predictability with earnings management in small financial crisis, while they
are more likely to associate excessive earnings predictability with earnings management in big
crisis. This finding is consistent with Imperatore and Trombetta (2013)’s result that firms commit
less earnings management in the small crisis while commit more earnings management in the big
crisis. We also show that among all the peer firms, similar peer firms have greater impact on IPO
firm than influential firms. This result offers new insight about herd behavior in economics
literature (Scharfstein and stein1990).
We must mention there is one potential omitted correlated problem in our study.
Specifically, we do not control firm-specific earnings predictability in our model when we
examine peer firms earnings predictability’s role on IPO underpricing. We explain the potential
caveat as follows: first, because of data limitation of two years pre-IPO financial information, it
is acceptable that IPO literature does not control unavailable firm specific characteristics. For
example, Boulton et al. (2011) investigates country level earnings quality’s effect on firm level
9
IPO underpricing. In their model, they include proxies for country level earnings quality but do
not include firm-specific earnings quality because of limited pre-IPO information. Second, in
our robustness test, we add more control variables related with firm-specific characteristics to
alleviate omitted variable problem. These innate firm specific controls include natural logarithm
value of firm asset, return on asset ratio, and book to market ratio.
Section 2 surveys the hypothesis development and related literature. Section 3 describes
the variable measures, sample and descriptive statistics. Section 4 describes empirical analysis.
Section 5 provides additional test and robustness check. Section 6 concludes.
2. Theories, Hypothesis Development and Related Literature:
2.1 Theories and Hypotheses Development:
2.1.1 Peer firms earning predictability effect on IPO underpricing hypothesis
Large body of research shows that earnings contain information used by the market in
assessing the value of a firm. Lipe (1990) further suggests that earnings persistence and earnings
predictability are two important characteristics that can impact firm’s valuation. Specifically,
Lipe shows that stock return is a function of exogenous expected return, accounting earnings,
and alternative information.
Rt=f (
, Xt,, It, Xt,-1, It-1, Xt,-2, It-2,……)
(1)
ΔXt=∑
(2)
It=Xt+1+nt+1
(3)
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is the exogenous expected return, Xt is the unexpected return due to the release of accounting
earnings, and It is the alternative information. Equation (1) shows that stock return is a function
of exogenous expected return, accounting earnings, and alternative information. Equation (2) and
(3) describe the structure of the model. Equation (2) specifies the characteristics of earnings. The
refers to the autoregressive coefficients (earnings persistence) and
refers to standard
deviation of forecast errors (earnings predictability). Equation (3) indicates that the alternative
information equals next period’s earnings plus noise. Specifically, equation (1) and equation (2)
show that the stock return should be a function of earnings characteristics such as earnings
persistence and earnings predictability. Based on Lipe’s theoretical framework that earnings
predictability is associated with firm’s valuation, we suggest that earnings predictability can
impact firm’s valuation in a unique setting—IPO process.
But empirical studies about the role of earnings predictability in capital market are mixed.
Traditional studies consider earnings predictability as favorable attribute valued by investors and
market. For example, Graves et al. (2002) finds that firms with higher earnings predictability are
associated with lower information asymmetry and higher market liquidity. And they infer that
this improved market liquidity is further associated with lower cost of equity. However, recent
studies, such as Francis et al. (2004), indicate that excessive earnings predictability beyond
innate determinants might refer to more earnings manipulations and opportunistic behavior. This
opportunistic behavior increases firm’s information asymmetry for investors. And their empirical
result further shows that earnings predictability is associated with higher cost of equity. Francis
et al. (2004)’s “earnings management” argument is especially more likely to be supported in IPO
setting. For example, Ducharme et al. (2004) show that abnormal accruals are unusually high
around stock offers and firms are more likely to opportunistically manipulate earnings upward
11
before IPO. It is very likely that investors interpret earnings predictability differently in the IPO
process than in the regular period. Especially, Investors probably consider short-run or mediumrun excessive earnings predictability in IPO process as issuer’s earnings opportunistic
manipulations to impress investors thus boost the initial offer price.
Turning to the peer firm effect, Leary and Roberts (2013) show that firms in the industry
are highly interdependent and peer firm has large impact on firm-specific financial decisions and
capital structure. They specify two explanations for the peer firm effect. The first explanation is
that firms in the same industry face similar institution environment or have similar firm
characteristics, such as production technology and investment opportunities. Inefficiency to
perfectly proxy these determinants generates a role for peer firm effect. The second explanation
is that firms’ financial policies are partly driven by a response to their peers. This response
impact the firm-specific policy through two channels. The first channel is the action channel.
Through actions, firms respond to their peers’ financial policies. The second channel is
characteristics channel. Through comparison of characteristics, firms respond to changes in the
characteristics of their peers. Foster (1981) shows that investors’ assessment of a firm’s value is
impacted by information contained both in the firm’s and also peer firms financial report.
Lambert, Leuz and Verrecchia (2007) build an analytical model to show peer firms effect.
Specifically, they show that firm j’s expected return is decomposed into two components: 1) the
firm’s own variance and 2) the firm’s covariance with other firms.
Cov ( ̃ ∑
̃
E (̃
)=
(̃ ∑
(̃ ̃)
̃
∑
̃)
(4)
(5)
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(̃ ̃
In equation (4),
)
(̃ ̃ )
(̃)
is firm j’s end-of-period cash flow,
is the sum of all firms’ end-of-
period cash flows. In equation (5), E is the expected return,
is ex ante expected value,
ex ante precision of the end-of-period cash flow, and
refers that investors receive Q
pieces of information. In equation (6),
(6)
̃
is
refers to the error contained in firm j’s information set.
As we discussed above, investors are more likely to interpret high earnings predictability as
oppornustic behavior in the IPO process. That is, variable
in equation (5), which is the
precision of the Q pieces of information, decreases. Lambert, Leuz and Verrecchia (2007) show
that the firm j’s own variance is a negative function of
Thus, as Q pieces of information
precision decreases, firm j’s variance increases. In equation (6), Lambert, Leuz and Verrecchia
suppose there are only two securities in the market.
is an inverse measure of firm j’s
information precision. Empirical research also supports that peer firms’ future cash flows are on
average positive correlated. (Foster 1981). Therefore,
( ̃ ̃ ) part in equation (6) is
considered as positive. I expect that decreases in firm j’s information precision will increase firm
k’s covariance with firm j. Based on the expectation of both the firm j’s variance (equation 5)
and covariance of firm j with firm k (equation 6) increases because of high peer firms earnings
predictability (high opportunistic behavior), we conclude that firm j will have higher information
asymmetry in the IPO process. Enhanced information asymmetry for firm j will finally lead to a
higher firm j’s IPO underpricing. In summary, while we do not cast doubt on extant explanations
about earnings predictability’s favorable or unreliable effect in regular periods, we argue that
investors might have different views about earnings predictability’s role in a unique setting--IPO
process. It is highly possible that investors become more sensitive and cautious about transient
13
and short-run earnings predictability in IPO process than in other periods. Based on the evidence
that IPO firms commit unusual high accruals before IPO to impress the market, investors might
interpret excessive transient earnings predictability as manager’s myopia behavior in the IPO
process thus has negative impact on IPO pricing. Combined with our argument with peer firm
effect, we formalize our hypothesis H1a as:
H1a: Transient peer firms earnings predictability is associated with higher IPO
underpricing.8
To further support our H1a, we infer that investors in the IPO process can distinguish
short-run(quarterly) and long-run (five years) peer firms earnings predictability’s different role.
While investors are especially concerned about transient peer firms earnings predictability in the
IPO process, they possibly favor long-run (five years) peer firms earnings predictability. We
suggest that it is more difficult for myopic managers to commit earnings management to achieve
long-run earnings predictability than short-run earnings predictability. Investors’ caution about
myopic behavior in the IPO process reduces when they receive long-run predictable earnings
signal. Furthermore, investors might consider long-run earnings predictability as more steady
future cash flow and less uncertainty for the firm. Combined with peer firm effect, we suggest
our H1b as:
H1b: Short-run (quarterly) peer firms earnings predictability is associated with higher IPO
underpricing, while long-run (five years) peer firms earnings predictability is associated
with lower IPO underpricing.
2.1.2 Financial Crisis Effect
8
In hypothesis H1a’s empirical test, transient earnings specifically refer to one year predictive horizon of earnings
predictability.
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Lang and Maffet (2011) show that market has significantly lower liquidity and higher
information asymmetry during the market downturn. Thus, the higher information asymmetry
can be translated into higher IPO underpricing in the financial crisis. Filip and Raffournier (2012)
find that earnings management has significantly decreased in the financial crisis 2008-2009 by
examining 16 European countries data. They suggest three explanations for the reduced earnings
management in crisis: 1) managers possibly have less incentive to manipulate earnings in crisis
periods due to a higher market tolerance for poor performance. 2) Litigation risk increases
significantly during crisis, which restricts managers’ motivation to engage in earnings
management. 3) The change in the behavior of companies may respond to a higher demand for
more timely earnings in crisis. During a time period that has less earnings management in
general, we assume investors can understand this reduced earnings management trend and
interpret this trend as that excessive earnings predictability is less likely to be associated with
earnings management in crisis. In other words, investors are less likely to relate excessive
earnings predictability with earnings management in the crisis. Therefore, peer firms earnings
predictability’s effect on IPO underpricing is reduced in crisis because of the decreased investors’
perception of association between excessive earnings predictability and earnings management.
Furthermore, Imperatore and Trombetta (2013) suggest that financial crisis has non-monotonic
effect on earnings management. They show that earnings management decreases when the crisis
is small and earnings management increases when the crisis is big. Following their finding, we
should observe the phenomenon that investors are less likely to relate excessive earnings
predictability as earnings management in the small crisis, while they are more likely to relate
excessive earnings predictability with earnings management in the big crisis. In other words, we
would expect that peer firms earnings predictability’s effect on IPO underpricing is significantly
15
reduced in the small crisis, while peer firms earnings predictability’s effect on IPO underpricing
do not differ in big crisis. 9 In general, we suggest that financial crisis can mitigate peer firms
earnings predictability’s effect on IPO underpricing. Thus, we suggest our hypothesis H2 as:
H2: Peer firms earnings predictability effect on IPO underpricing is reduced in the
financial crisis.
2.1.3 Similar Peer Firms VS Influential Peer Firms
Leary and Roberts (2013) show that more financially constrained firms with lower paid
and less experienced CEOs are more likely to mimic their peers. Furthermore, Ma (2013) shows
that similar peer firm have greater effect on firm’s cost of equity than influential peer firm. In our
IPO setting, investors and analysts are more likely to compare IPO firm’s financial condition
with its peer firm’s financial condition than influential firm in common practice. Thus, we
concludes hypothesis H3 as:
H3: Similar peer firms earnings predictability’s effect on IPO underpricing is greater than
that of influential peer firms earnings predictability.
2.2 Related Literature
2.2.1 Information Asymmetry and IPO Underpricing
Information asymmetry theory plays a key role to explain IPO underpricing in finance
literature. Rock (1986) shows that there are two types of investors of in the market: informed
investors and uninformed investors. Underpricing exists to induce uninformed investors to buy
9
Our empirical result strongly supports these two expectations. Specifically, interaction term between peer firms
earnings predictability and small crisis is significantly negative in Table 5 Panel C. Interaction term between peer
firms earnings predictability and big crisis is not significant in Table 5 Panel C.
16
IPO shares. In Ljungqvist (2007) survey, he suggests that information asymmetry “has a first
order effect on underpricing. Boulton et al. (2010) find that country’s legal environment can
also affect IPO underpricing. They use several governance legal indices to capture capital market
related legal system development. Boulton et al. (2011) find that country level earnings quality
can mitigate the information asymmetry in IPO process internationally. They construct the
earnings quality proxy by taking consideration of earnings manipulation, accruals quality, and
earnings smoothness. They show that higher earnings quality can lower the IPO underpricing
after controlling country, economic development and other variables’ effect by using a sample of
34 countries. They find that underpricing will be higher in countries with stronger legal
environments. Teoh et al. (1998) show that most aggressive quartile IPO firms have a three-year
20 percent less stock return than the most conservative quartile. Also, conservative firms issue
about 20 percent more seasoned equity offering than aggressive firms. Ferris et al. (2012) use
IPO prospectus textual tone as proxies for the uncertainty. By analyzing the frequency of
negative words in the different sections of prospectus, they find that more negative tone is related
with higher IPO underpricing and less operating performance. Lin et al. (2012) examines
Chinese data, and they show that accounting conservatism is negatively associated with IPO
underpricing. Leone et al. (2006) find that disclosure of intended use of proceeds can decrease
IPO underpricing significantly. Moreover, they break down disclosure of intended use into two
categories: debt category and non-debt category. They document these specific disclosures can
also decrease IPO underpricing. Willenborg et al. (2001) find that going concern auditing report
can reduce the IPO underpricing. They argue that going concern can reduce the uninformed
investors ex ante uncertainty about the firm, thus reduce the information asymmetry in IPO
process. The reduction of uncertainty benefit the IPO process and supply certainty to the
17
investors then reduce the IPO underpricing. In summary, previous literature suggests that firms
in the IPO process that have higher information asymmetry tend to have higher IPO underpricing.
2.2.2 Peer Firm Effect
Foster (1981) find that peer firms’ expected cash flows are significantly positive
correlated. Kim and Ritter(1999) suggests that adjusted peer firms’ accounting multiples such as
price-earnings ratio, market-to-book ratio and price-to-sales ratio have higher explanation power
to value IPO than unadjusted accounting multiples. Mackay and Philips (2005) suggest that
firm’s financial structure depends on a firm’s position within its industry. Leary and Roberts
(2013) further suggest that corporate financial policies are highly interdependent and firms make
financing decisions in large part by responding to the financial decisions of their peers. Ma (2013)
suggests that beyond firm-specific earnings quality’s effect on cost of equity, peer firm’s
earnings quality can also impact cost of equity. Specifically, he shows that both firm specific and
peer firm’s earnings quality are negatively associated with cost of equity. In summary, prior
literature suggests that peer firms’ financial characteristics are correlated and single firm’s
financial decision is impacted by its peer firms.
3. Data and Research Design
3.1 Data
We obtain the IPO underpricing information and IPO deal related information from
Thomson Financial Securities Data new issues database (SDC). We obtain financial statement
information and financial market information from Compustat and CRSP. And we obtain analyst
forecast information from I/B/E/S. We original sample from SDC consists of 11539 IPOs. We
exclude financial firms, rights offerings, unit offerings, closed-end funds, trusts, limited
18
partnerships, and depository receipts. And we require firms to be covered by the Field-Ritter data
set of IPO founding dates. We further eliminate observations that lack control variables from
SDC/Compustat/CRSP. Our final sample consists of 4596 IPOs from January 1, 1976 to
December 31, 2012.
3.2 Earnings Predictability Measurement
Since earnings predictability is the most important interest variable in this study, we
employ autoregressive model of order one for split-adjusted earnings per share (Lipe 1990 and
Francis et al. 2004), analyst forecast accuracy and analyst forecast dispersion (Kerr et al. 2013)
three proxies to measure individual firms’ earnings predictability. In the autoregressive model
(AR1), we use a rolling ten-years window to run firm-year level regressions of annual splitadjusted earnings per share (Xj,t) on its previous year’s split-adjusted earnings per share (Xj,t-1) .
And we use the square root of the error variance from AR1 model to proxy the earnings
predictability. In order to avoid extreme value’s effect, we use decile ranks to replace the raw
value of earnings predictability from AR1 model. We assign large rank value to firms with the
large earnings predictability (small value square root of error variance from AR1). In equation
(1), we specify the AR1 model.
Xj,t=β0,j + β1,j Xj,t-1 +єj,t
(1)
Where:
Xi,t = firm’s split-adjusted earnings per share at year t;
Xi,t-1 = firm’s split-adjusted earnings per share at year t-1;
19
For the analyst forecast accuracy measurement, we calculate analyst forecast errors as the
absolute value of the difference between the actual earnings per share (EPS) and the most recent
mean analyst EPS forecast that is available prior to the actual EPS announcement, both taken
from I/B/E/S. We then scale this difference by the firm-specific stock price as of the end of the
return window for year t-1. As our measure of analyst forecast dispersion, we use the standard
deviation of the most recent analyst EPS forecast for year t, also scaled by the firms’ stock price
as of the end of the return window for year t-1. We also use decile ranks to replace the raw value
of earnings predictability to avoid outliers. We assign large rank value to firms with large analyst
forecast accuracy and small analyst forecast dispersion, so large rank value implies more
predictable earnings.
3.3 Industry Peer Firms Earnings Predictability
Following Fama-French industry classification, we categorize 48 industries in our sample.
Industry peer firms earnings predictability refers to equally weighted or market-share-weighted
(revenue) average earnings predictability of all the other firms in the same industry except the
IPO firm in the fiscal year. Equally weighted measurements assigns the same weight to each
observation, thus are more likely to be affected by small firms with less economic importance.
Market-share-weighted puts more weight on large firms, which are considered more important to
affect peer firms by Bratten et al. (2012). We assign decile ranks to peer firms earnings
predictability to avoid outlier effect. Large value of industry peer firms earnings predictability
rank refers to more predictable earnings of other firms in the industry. Since we have three
measurements for individual firm earnings predictability, we have six measurements for peer
firms earnings predictability. Finally, we have an aggregate rank measurement of the three
individual firm earnings predictability measures (forecast accuracy, forecast dispersion and
20
rolling regression) as our last proxy for peer firms earnings predictability. In summary, we have
seven peer firms earnings predictability proxies.
Where:
Ind_er1=peer firms earnings predictability rank based on equally-weighted earnings
predictability of all other firms in the same industry during the IPO fiscal year. (Other
firms earnings predictability is based on analyst forecast accuracy measurement).
Ind_dp1= peer firms earnings predictability rank based on equally-weighted earnings
predictability of all other firms in the same industry during the IPO fiscal year. (Other
firms earnings predictability is based on analyst forecast dispersion measurement).
Ind_rlerror= peer firms earnings predictability rank based on equally-weighted earnings
predictability of all other firms in the same industry during the IPO fiscal year. (Other
firms earnings predictability is based on AR1 ten-years rolling regression).
Indw_er1=peer firms earnings predictability rank based on market-share-weighted
earnings predictability of all other firms in the same industry during the IPO fiscal year.
(Other firms earnings predictability is based on analyst forecast accuracy measurement).
Indw_dp1= peer firms earnings predictability rank based on market-share-weighted
earnings predictability of all other firms in the same industry during the IPO fiscal year.
(Other firms earnings predictability is based on analyst forecast dispersion measurement).
Indw_rlerror= peer firms earnings predictability rank based on market-share-weighted
earnings predictability of all other firms in the same industry during the IPO fiscal year.
(Other firms earnings predictability is based on AR1 ten-years rolling regression).
21
Ind_predict= peer firms earnings predictability rank based on aggregate rank
measurement of the three individual firms earnings predictability measures (forecast
accuracy, forecast dispersion and rolling regression) .
3.4 Short-run and Long-run Industry Peer Firms Earnings Predictability
To further understand peer firms earnings predictability’s effect on IPO underpricing, we
break down the peer firms earnings predictability into two categories: short-run (quarterly) peer
firms earnings predictability and long-run (five years) peer firms earnings predictability.
For the short-run peer firms earnings predictability, we first calculate the short-run
individual firm earnings predictability. We employ two measurements to calculate short-run
individual firm earnings predictability (short-run analyst forecast accuracy and short-run analyst
forecast dispersion). Specifically, short-run individual firm analyst forecast accuracy refers to
calculation of quarterly analyst forecast error. This analyst forecast error is based on the value of
the difference between the actual quarterly EPS and the most recent mean analyst EPS forecast
that is available prior to the actual quarterly EPS announcement. We then scale this difference by
the firm-specific stock price as of the end of the return window for year t-1. For the short-run
individual firm analyst forecast dispersion, , we use the standard deviation of the most recent
analyst EPS forecast for quarter t, also scaled by the firms’ stock price as of the end of the return
window for year t-1. We use the mean value of four firm-quarter levels short-run earnings
predictability within one year as the firm-year level short-run individual earnings predictability.
Second, we use equally weighted or market-share-weighted (revenue) average quarterly earnings
predictability of all the other firms in the same industry except the IPO firm to calculate peer
firms earnings predictability. The mean value of four equally weighted and market-share-
22
weighted measurements is our final proxy for short-run peer firms earnings predictability. We
use decile ranks of short-run peer firms earnings predictability to avoid outlier effect. Large
value of short-run peer firms earnings predictability rank refers to more predictable earnings of
other firms in the industry. Thus we form the short-run peer firms earnings predictability proxy:
Stpredictrk=short-run (quarterly) peer firms earnings predictability based on mean value
of equally weighted and market-share-weighted earnings predictability of all other firms
in the same industry during the IPO fiscal year.
For the long-run (five years) peer firms earnings predictability, we first calculate the
long-run individual firm earnings predictability. We employ two measurements to calculate longrun individual firm earnings predictability (long-run analyst forecast accuracy and long-run
analyst forecast dispersion). Specifically, long-run individual firm analyst forecast accuracy
refers to calculation of five years analyst forecast error. This analyst forecast error is based on
the value of the difference between the actual five years EPS and the most recent mean analyst
EPS forecast that is available prior to the actual five years EPS announcement. We then scale
this difference by the firm-specific stock price as of the end of the return window for year t-1.
For the long-run individual firm analyst forecast dispersion, , we use the standard deviation of
the most recent analyst EPS forecast for year t+5, also scaled by the firms’ stock price as of the
end of the return window for year t-1. Second, we use equally weighted and market-shareweighted (revenue) average five years earnings predictability of all the other firms in the same
industry except the IPO firm to calculate long-run peer firms earnings predictability. The mean
value of four equally weighted and market-share-weighted measurements is our final proxy for
long-run peer firms earnings predictability. We use decile ranks of long-run peer firms earnings
predictability to avoid outlier effect. Large value of long-run peer firms earnings predictability
23
rank refers to more predictable earnings of other firms in the industry. Thus we form the longrun peer firms earnings predictability proxy:
Lgpredictrk=long-run (five years) peer firms earnings predictability based on mean value
of equally weighted and market-share-weighted earnings predictability of all other firms
in the same industry during the IPO fiscal year.
3.5 Financial Crisis
Following Lang and Maffet (2011), we use market index Standard&Poor’s 500 index
(S&P500) standard deviation to indicate financial crisis. If the equity market experiences a
downturn greater than 1.5 standard deviations below its historical average, we consider it as
general financial crisis and code Crisis_Big dummy variable as one, otherwise zero. In order to
understand different levels of financial crisis effect on IPO underpricing, we break down the
financial crisis into two more specific groups. If the equity market experiences a downturn
greater than 1.5 standard deviations below its historical average, but smaller than 2 standard
deviations below its historical average, we consider it as small financial crisis and code
Crisis_big1 dummy variable as one, otherwise zero. If the equity market experiences a downturn
greater than 2 standard deviations below its historical average, we consider it as big financial
crisis and code Crisis_big2 dummy variable as one, otherwise zero.
3.6 Similar Peer Firms and Influential Peer firms
In order to further understand different types of peer firm effect on IPO underpricing, we
break down the peer firm into two specific categories: similar peers and influential peers. For
each industry year, we first calculate the quintile rank of my sample firms based on market value.
Then we define IPO firm’s similar peers to other peer firms in the same quintile and influential
24
peer s in the top quintile (with the largest market value). Ma (2013) finds that similar peers’
earnings quality plays a more important role in determining the cost of equity than influential
peers.
3.7 Initial Public Offering Underpricing
Initial public offering underpricing (IPO underpricing) is the dependent variable in this
study. IPO underpricing is considered as indirect cost of IPO process. While the direct cost
includes IPO registration, underwriting, attorney and auditing fees, the indirect cost, IPO
underpricing, is a widely observed phenomenon. When firms go public, the offer price set by
investment bank is usually lower than the first day closing price. The difference is the IPO
underpricing, which is considered as an incentive for investors to first bear the risks to buy the
shares. Equation (2) specifies the formula for IPO underpricing:
IPO Underpricing= (first day closing price-offer price)/offer price
(2)
In the regression model, we take the logarithm value of IPO underpricing to avoid extreme
observations’ effect.
3.8 Control Variables
Following the prior literature, our control variables include firm characteristics variables,
and deal characteristics variables. Firm characteristics include firm age, exchange market where
the IPO listed, and whether the firm belongs to high technology firms. Longer established firms
have better available information and are expected to have lower IPO underpricing (Beatty and
Ritter 1986). We include logarithm value of age (Lnage) in the model. IPO firms listed in NYSE
and AMEX are considered listing in the renowned stock exchange market, and tend to have
25
lower IPO underpricing (Lowry et al. 2010). We include a dummy variable (NYSEAMEX)
coded as one if the stock is listed in NYSE or AMEX. High technology firms are considered to
have more information asymmetry and involve more risk. Thus high technology firms tend to
have higher IPO underpricing. We include a dummy variable (Hightech) coded as one if the firm
is considered as high technology firms.
Deal characteristics include IPO proceed, IPO underwriter bank rank, whether the firm’s
offer price is an integer, and auditors rank. Bradley et al. (2004) report that IPOs with integer
prices experience higher underpricing. Bradley et al. (2004) also suggest that underwriters
bargain power of a finer offer price increases as the uncertainty surrounding firm value declines.
Thus, for IPOs that are particularly difficult to value tend to have integer offer price. We include
a dummy variable (Intpdummy) coded as 1 if the offer price is an integer. Previous literature
finds that high rank of underwriter and auditing firms tends to have lower underpricing, which is
called certification effect. (Carter and Manaster 1990). But recent literature finds that high rank
of auditor and underwriter might have a positive relationship with IPO underpricing, which is
called reverse certification effect. (Loughran and Ritter 2004). We include a dummy variable
(Topunderwriter) coded as 1 if the lead underwriter’s Carter and Manster (1990) rank is greater
than or equal to eight. We also include a dummy variable (VC) if the issuing firm has venture
capital banking. We include a variable (Priceupdate) to indicate the percent change between the
offer price and the average of the high and low initial filing prices. We calculated it as IPO share
price / mid-point of initial share price range as filed with SEC. And we expect it has positive
relation with the IPO underpricing. We include a dummy variable (Pureprimary) to indicate
whether offering consists of primary shares only. And we expect it has positive relation with
IPO underpricing. Finally, we include a dummy variable (Topauditor) to indicate whether the
26
firm’s auditor belongs to big4/big6/big8. We expect it has negative relation with IPO
underpricing.
3.9 Descriptive Statistics
Table 1 reports industry distribution of IPOs from 1976 to 2012. Our Sample includes
4585 IPO observations from 44 industries. Underprice is the percentage IPO underpricing for
each industry across sample time period. Among the 44 industries, 42 industries have average
IPO underpricing in the first trading day. There are two exception industries (Agriculture and
Textiles) that have overpricing in the first trading day. During the sample period, business
service industry has the greatest number of IPOs, which amounts to 1030 IPOs. And this industry
also has the highest average IPO underpricing, which is 36.8%. Tobacco products industry has
the lowest number of IPOs during the sample period, which only has 3 IPOs in our sample. The
remaining three columns (Ind_predict/ Stpredict/ Lgpredict) refers to aggregate transient (one
year) peer firms earnings predictability, aggregate short-run(quarterly) peer firms earnings
predictability and aggregate long-run (five years) peer firms earnings predictability mean value.
Table 2 lists descriptive statistics of all variables used in the main regression model. The
mean underpricing in our sample is 20%. The mean age of the firm in years at the time of the
IPO is 17.6 years. The aggregate short-run (quarterly) peer firms earnings predictability value is
greater than aggregate transient (one year) peer firms earnings predictability value. And the
aggregate transient (one year) peer firms earnings predictability value is greater than aggregate
long-run (five years) peer firms earnings predictability value.
Table 3 lists the correlation matrix among all the variables. The interested variables in
our study are peer firms earnings predictability proxies, and financial crisis. We find that three
27
aggregate peer firms earnings predictability are significantly correlated with each other (0.114
for the correlation of long-run peer firms earnings predictability with transient (one year) peer
firms earnings predictability and 0.174 for the correlation of long-run peer firms earnings
predictability with short-run peer firms earnings predictability). Without controlling other
factors, three aggregate peer firms earnings predictability are significantly positive correlated
with IPO underpricing (0.141 for transient [one year] peer firms earnings predictability, 0.104 for
short-run peer firms earnings predictability, and 0.118 for long-run peer firms earnings
predictability). The financial crisis variable is positively correlated with IPO underpricing, but
not significant.
4. Empirical Analyses
4.1 Test Effect of Transient (One Year) Peer firms earnings Predictability on IPO Underpricing：
We first set up equation (5) to examine the effect of transient (one year) peer firms
earnings predictability effect on IPO underpricing.
Lnunder=β0+ β1 Intpdummy+β2 NYSEAMEX +β3 Hightech +β4 Topauditor+β5VC
+β6 Priceupdate +β7 Lnprcd +β8 Pureprimary +β9 Lnage + β10 Topunderwriter + β11
Transient (one year) peer firms earnings predictability+ ε
(5)
In equation 5, the dependent variable is the natural logarithm value of IPO underpricing.
The primary interested variable is transient (one year) peer firms earnings predictability. In table
4, we include eight specifications to apply ordinary least square (OLS) regression of the equation
(5). The first six specifications in table 4 employ six separate transient (one year) peer firms
earnings predictability proxies without industry fixed effect. Specification seven includes the
aggregate transient (one year) peer firms earnings predictability proxies without industry fixed
28
effect. Specification eight includes the aggregate transient (one year) peer firms earnings
predictability proxies with industry fixed effect. Among the six separate transient (one year) peer
firms earnings predictability proxies (specification 1 to specification 6), five of the proxy
coefficients are significantly positive. The only one exception is the market-share-weighted
analyst forecast accuracy proxy (Indw_er1 in specification 4). Specification 4 coefficient is
negative, but not significant. The aggregate transient (one year) peer firms earnings predictability
coefficients (specification 7 and 8) are also positively significant (at the 1% level) with and
without industry fixed effect. Adding industry fixed effect, the last column adjusted R2 increases
from around 19% to 23.2%. This strong positive relation in the seven specifications shows that
transient (one year) peer firms earnings predictability is positively associated with IPO
underpricing. In other words, more predictable one year peer firms earnings predictability is
associated with higher IPO underpricing. This result is consistent with our hypothesis 1 and
Francis et al. (2004) argument. Francis et al. (2004) show that firm earnings predictability proxy
is positively associated with firm cost of equity capital. They further indicate that although
earnings predictability seems a favorable attribute by analyst and managers, excessive earnings
predictability probably refer to unnecessary earnings management that will negatively impact
firm’s future cash flow thus actually increase firm’s cost of equity capital. In our IPO setting,
investors have limited information about the IPO firms before firms are publicly listed. Investors
probably consider high transient (one year) peer firms earnings predictability as managers’
excessive earnings management to have a better initial offer price. This excessive earnings
management increases the information asymmetry during the IPO process. Thus, transient (one
year) peer firms earnings predictability is positively associated with IPO underpricing.
29
Turing to the control variables, we find that integer offer price dummy (Intpdummy),
priceupdate dummy (Priceupdate), IPO proceed variables (Lnprcd), and pureprimary dummy
(Pureprimary) , high technology variable (Hightech) are strongly positively associated with IPO
underpricing, consistent with Bradley et al. (2004). Top underwriter variable (Topunderwriter)
is also positively significant at 5% level, consistent with Loughran and Ritter (2004)’s reverse
certification argument. NYSEAMEX and age variable (Lnage) are negatively associated with
IPO underpricing at the 1% level, consistent with the expectation that firms listed on renowned
exchange and firms with long history have lower IPO underpricing. Venture capital (VC) is
positively associated with IPO underpricing, consistent with Hwang (2011). Auditor of the firm
(Topauditor) is also positively associated with IPO underpricing, but it is not significant.
4.2 Test Effect of Short-run and Long-run peer firms Earnings Predictability on IPO
Underpricing：
We set up equation (7) to examine the effect of short-run and long-run peer firms
earnings predictability effect on IPO underpricing.
Lnunder=β0+ β1 Intpdummy+β2 NYSEAMEX +β3 Hightech +β4 Topauditor+β5VC
+β6 Priceupdate +β7 Lnprcd +β8 Pureprimary +β9 Lnage + β10 Topunderwriter + β11
Stpredictrk+ β12 Lgpredictrk + β13 Crisis_big + β13 (Stpredictrk or Lgpredictrk)
*Crisis_big + ε
(7)
In equation (7), we consider other two types time horizon of earnings predictability:
short-run peer firms earnings predictability (Stpredictrk) and long-run peer firms earnings
predictability (Lgpredictrk). We also include the interaction term between the two predictability
types and financial crisis (Crisis_big). Consistent with our hypothesis development, we find
30
short-run peer firms earnings predictability is positively associated with IPO underpricing (all
specifications). This result shows that investors interpret short-short-runrun peer firms earnings
predictability as excessive and intentional earnings management. And thus investors price higher
IPO underpricing for IPO firms that have short-run peer firms earnings predictability. On the
other hand, long-run peer firms earnings predictability is considered as good attribute by
investors. It is hard for low quality firms to mimic long-run peer firms earnings predictability by
committing excessive earnings management. In order to achieve long-run peer firms earnings
predictability, firms have to maintain real predictable future cash flow. In table 5, long-run peer
firms earnings predictability in all the five specifications is negatively significant at the 1% level.
Specifications3 and 4 in table 6 include both short-run peer firms earnings predictability and
long-run peer firms earnings predictability in the same regression model. Result also shows that
investors are in favor of long-run peer firms earnings predictability while investors are not in
favor of short-run peer firms earnings predictability. Specification 5 adds industry fixed effect
and the adjusted R2 increases to 27%. The financial crisis variable and interaction term between
short/long-run peer firms earnings predictability coefficients’ signs are consistent with
expectation, but insignificant.
4.3 Test Effect of Transient (one year) Peer firms Earnings Predictability and Financial Crisis on
IPO Underpricing：
We set up equation (6) to examine the effect of transient (one year) peer firms earnings
predictability effect on IPO underpricing.
Lnunder=β0+ β1 Intpdummy+β2 NYSEAMEX +β3 Hightech +β4 Topauditor+β5VC
+β6 Priceupdate +β7 Lnprcd +β8 Pureprimary +β9 Lnage + β10 Topunderwriter + β11
31
Transient (one year) peer firms earnings predictability+ β12 Crisis_big + β13
Transient (one year) peer firms earnings predictability*Crisis_big + ε
(6)
In equation (6), the dependent variable is the logarithm value of IPO underpricing. The
primary interested variables are transient (one year) peer firms earnings predictability, financial
crisis (Crisis_big/ Crisis_big1/ Crisis_big2) and the interaction term between peer firms earnings
predictability and financial crisis. In table 6, Panel A and Panel B include seven specifications to
apply OLS regression of equation 6. The seven specifications include six specification of
separate transient (one year) peer firms earnings predictability proxy and one specification of
aggregate transient (one year) peer firms earnings predictability proxy. Panel A lists all the
controls variables in equation (6). The control variables sign and significance level are generally
the same with table 4, consistent with the literature discussion. Table 6 panel B lists the
interested independent variables in equation (6). The financial crisis variables (Crisis_big) are
positively significant at least 5% level in all the seven specifications. These positive coefficients
strongly support that financial crisis is positively associated with IPO underpricing. In the
financial crisis, market liquidity is low, and both investors and IPO issuers lack confidence to
invest in the market. Issuers consider that the pessimistic macroeconomic environment would
negatively impact IPO initiation. And investors consider the lack of market liquidity would
negatively affect IPO firms’ performance. Thus the information asymmetry in the IPO process
increases because of financial crisis. Turning to the transient (one year) peer firms earnings
predictability proxies coefficients, six specifications coefficients are positively significant. The
only one exception is the market-share-weighted analyst forecast accuracy proxy (Indw_er1 in
specification 4). Specification 4 coefficient is -0.002, but not significant. This result shows that
transient (one year) peer firms earnings predictability is positively associated with IPO
32
underpricing. In other words, the more predictable of peer firms earnings, the higher
underpricing of the IPO firm. For the interaction between transient (one year) peer firms earnings
predictability and financial crisis, we find that five of the seven specifications interaction term
coefficients are negatively significant at the 10% level or above. The other two specification
interaction term coefficients are also negative (-0.02 in specification 2 and-0.021in specification
3) but not significant. This result shows that transient (one year) peer firms earnings
predictability’s effect on IPO underpricing is mitigated by the financial crisis. In other words,
transient (one year) peer firms earnings predictability’s effect on IPO underpricing is smaller if
the firm goes IPO in the financial crisis. This result is consistent with Filip and Raffournier
(2012)’s finding. Filip and Raffournier (2012) suggests that there are three possible explanations
for reduced earnings management in the crisis: 1) investors’ higher tolerance for poor
performance 2) higher litigation risk of earnings management 3) investors’ higher demand for
accounting conservatism in crisis. Investors in the reduced earnings management environment
would have the rational expectation that excessive peer firms earnings predictability is less likely
to be interpreted as earnings management. Therefore, peer firms earnings predictability’s effect
on IPO underpricing is reduced since investors’ perception of association between excessive
earnings predictability and earnings management is reduced. Thus, we observe that the
interaction terms between predictability and crisis in Panel B are negatively significant.
Table 6 Panel C breaks down the general financial crisis into two more specific
categories: small financial crisis (Crisis_Big1) and big financial crisis (Crisis_Big1) (Lang and
Maffet 2011). Control variables are omitted for brevity. Among the interested independent
variables, peer firm (one year) earnings predictability remains positively significant at the 1%
level. The two financial crisis variables are positive (consistent with Panel A and Panel B) but
33
insignificant. Thus, we do not find that magnitude of financial crisis will impact IPO
underpricing. Interaction term between peer firm (one year) earnings predictability and small
crisis is negatively significant (-0.09). This result is consistent with Imperatore and Trombetta
(2013). Specifically, they show that earnings management decreases when the crisis is small
while it increases when the crisis is big. We assume rational investors interpret the reduced
earnings management environment (small crisis) as a weaker association between earnings
predictability and earnings management, while interpret the increased earnings management
environment (big crisis) as a stronger association between earnings predictability and earnings
management. The weaker association between earnings predictability and small crisis is
translated to reduced earnings predictability’s role in the financial crisis. (Significant interaction
term coefficient -0.09). The stronger association between earnings predictability and big crisis
refers that earnings predictability plays no different roles in big crisis or not in big crisis.
(Insignificant interaction term). In summary, Panel B result is consistent with Filip and
Raffournier (2012)’s finding about earnings management in crisis. Panel C result is consistent
with Imperatore and Trombetta (2013)’s finding about earnings management in crisis. Therefore,
Table 6’s result not only supports hypothesis 2, but also supports the basic assumption of this
paper “investors interpret excessive earnings predictability as earnings management in IPO
process”.
4.4 Test Effect of Similar Peer firm and Influential Peer Firm on IPO Underpricing：
We set up equation (8) to examine the effect of similar peer firm and influential peer firm
on IPO underpricing.
34
Lnunder=β0+ β1 Intpdummy+β2 NYSEAMEX +β3 Hightech +β4 Topauditor+β5VC
+β6 Priceupdate +β7 Lnprcd +β8 Pureprimary +β9 Lnage + β10 Topunderwriter + β11
Sim_predict + β12 Influ_predict + ε
(8)
In equation (8), Sim_predict refers to earnings predictability of peers that are in the same
market share quintile. Influ_predict refers to earnings predictability of peers that are in the top
market share quintile. In table 7, specifications 1 and 3 include similar peer firms earnings
predictability (Sim_predict) and influential peer firm (Influ_predict) earnings predictability
separately in the regression model. Both coefficients of those two variables are positively
significant. This result shows that both similar peer earnings predictability and influential peer
earnings predictability can impact IPO firms’ underpricing.
In specification 5, we include
similar peer and influential peer proxies in the same model. The similar peer firm coefficient
magnitude is larger than influential peer firm coefficient (0.01>0.009). And the coefficient
significance level for similar peer firm is larger than influential peer firm (t-stat 2.52>2.24). This
result shows that compared with influential peer firms impact on IPO underpricing, similar peer
firms impact on IPO underpricing is larger. Specifications 2, 4 and 6 add industry fixed effect
based on specifications 1, 3 and 5. Result with industry fixed effect is consistent with
specifications without fixed effect.
5. Additional Test
5.1. Long-run Stock Returns Performance
In order to show that investors do not consider transient (one year) peer firms earnings
predictability as favorable earnings attribute, we study the long-run performance of IPO firms
based on the transient (one year) peer firms earnings predictability. We separate the sample into
35
three categories for each industry-year: high transient (one year) peer firms earnings
predictability portfolio, medium transient (one year) peer firms earnings predictability portfolio,
and low transient (one year) peer firms earnings predictability portfolio. The high (low) transient
(one year) peer firms earnings predictability portfolio refers to the IPO firms that have more (less)
predictable peer firms earnings.
We use cumulative abnormal return and buy-and-hold abnormal return to measure postIPO long-run performance of IPO firms. We calculate the twelve months post-IPO return and
twenty four months post-IPO return to test whether peer firms earnings predictability have
impact on stock’s long-run performance.
Cumulative abnormal returns
Buy-and-hold abnormal returns
∑
∑
∑
∏
(3)
(
) ∏
(
)
(4)
ri,t and mi,t are monthly raw and benchmark returns. We use all available stocks market adjusted
value weighted return as the bench mark return. T is the time period months after IPO. (T=12
months and T=24 months). N is the stock numbers in the portfolio based on earnings
predictability.
Table 8 shows the result of long-run performance of IPO firms based on peer firms
earnings predictability. The difference within each portfolio is the IPO firms’ net return after
considering the market adjusted value weighted return. One notable phenomenon is that IPO
firms perform worse than the market adjusted benchmark for both 12 months post-IPO and 24
months post-IPO in all the low, medium and high earnings predictability groups. The difference
across low and high portfolios is our interested observation. Panel A uses cumulative abnormal
36
return to calculate post-IPO return. In Panel A, low peer firms earnings predictability group
significantly outperform 2.9% than the high peer firms earnings predictability group after 12
month of IPO. The second column (24 months) in Panel A result is consistent with column one,
but the difference is not significant. This panel in general shows that low peer firms earnings
predictability group long-run performance outperforms high peer firms earnings predictability
group. Panel B employ buy-and-hold adjusted return to calculate post-IPO return. Both the 12
months column and 24 months column results are consistent with Panel A’s result.
5.2 Robustness Check
Our results are robust to a wide range of alternative specifications. First, we use
alternative of industry classification. Since our industry peer firms earnings predictability
measurements highly depend on the classification of industries, we classify our sample by FamaFrench 30 industries and 38 industries. We have similar results for all the hypotheses.
Second, in order to avoid omitted correlated variable problem, we add more firm-specific
control variables in the main model. We add natural logarithm value of asset, return on asset
ratio, and book to market ratio. Our results remain qualitatively similar.
Third, I add additional sample selection requirements: 1) I truncate the sample at the top
and bottom 5% levels for all continuous firm-level variables. 2) I use SIC code from 6000-6999
to refer to banking industries, and I delete this broad defined banking industry in my sample.
6. Conclusion
This study contributes to the literature by extending IPO underpricing literature from
single security settings to multi-securities settings. We find that peer firms earnings predictability
37
is positively associated with IPO underpricing. Our result supports Francis et al. (2004)’s
argument that “earnings predictability is not in and of itself desirable, in the sense of reducing
information risk; in fact, predictability that exists over and above that implied by innate
determinants contributes to information risk”. And our result raises demand of reconsideration
about earnings predictability’s role in IPO process. Specifically, we find that investors interpret
short-run peer firms earnings predictability as an unfavorable attribute in the IPO process since
investors consider them as possible indication of earnings management. Investors consider longrun peer firms earnings predictability as favorable attribute in the IPO process based on the
assumption that it is difficult to commit earnings management to achieve long-run predictable
earnings than short-run. We also find that financial crisis mitigate peer firms earnings
predictability effect on IPO underpricing. We show that investors are less likely to interpret
excessive earnings predictability as earnings management in the crisis. This result supports
recent literature findings that earnings management decreases in the crisis (Filip and Raffournier
2012; Imperatore and Trombetta 2013). Lastly, we find that similar peer firms have greater
effect than influential peer firms on IPO underpricing. In the additional test, we find that low
peer firms earnings predictability group’s performance significantly outperform than that of high
peer firms earnings predictability group in the post-IPO 12 months and 24 months. This
additional test supports Francis et al. (2004)’s argument that “excessive earnings predictability is
interpreted as earnings management”.
38
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41
Appendix A: Variables Definitions
Variables
Lnunder
Definitions
natural logarithm of IPO underpricing. IPO Underpricing= (first day
closing price-offer price)/offer price
Intpdummy
NYSEAMEX
dummy one if the IPO initial offer price is an integer, zero otherwise
dummy one if the IPO are listed in the NYSE or AMEX stock
exchange, zero otherwise
Hightech
dummy one if the firms SIC code are classified as high technology
firms following Loughran and Ritter 2004, zero otherwise
Topauditor
dummy one if IPO firm' auditor belongs to big4/big6/big8, zero
otherwise
dummy one if the IPO firm is backed by venture capitalists and zero
otherwise
IPO initial offer price / mid-point of initial share price range as filed
with SEC
natural logarithm of total proceeds (in millions of dollars)
dummy one if the IPO consists of primary shares only
the age of the firm in years at the time of the IPO
dummy one if IPO underwriter prestige ranking is higher or equal to
eight, zero otherwise. The prestige ranking is based on Carter and
Manaster 1990 reputation rank
VC
Priceupdate
Lnprcd
Pureprimary
Age
Topunderwriter
Crisis_big
dummy one if the equity market experiences a downturn greater than
1.5 standard deviations below its historical average, zero otherwise
Crisis_big1
dummy one if the equity market experiences a downturn greater than
1.5 standard deviations below its historical average, but smaller than 2
standard deviations below its historical average, zero otherwise
Crisis_big2
dummy one If the equity market experiences a downturn greater than 2
standard deviations below its historical average, zero otherwise
Ind_er1
peer firms earnings predictability rank based on equally-weighted
earnings predictability of all other firms in the same industry during
the IPO fiscal year. (Other firms earnings predictability is based on
analyst forecast accuracy measurement)
Ind_dp1
peer firms earnings predictability rank based on equally-weighted
earnings predictability of all other firmss in the same industry during
the IPO fiscal year. (Other firmss earnings predictability is based on
analyst forecast dispersion measurement)
Ind_rlerror
peer firms earnings predictability rank based on equally-weighted
earnings predictability of all other firms in the same industry during
the IPO fiscal year. (Other firms earnings predictability is based on
AR1 ten-years rolling regression)
Indw_er1
peer firms earnings predictability rank based on market-share-weighted
earnings predictability of all other firms in the same industry during
the IPO fiscal year. (Other firms earnings predictability is based on
analyst forecast accuracy measurement)
42
Indw_dp1
peer firms earnings predictability rank based on market-share-weighted
earnings predictability of all other firms in the same industry during
the IPO fiscal year. (Other firms earnings predictability is based on
analyst forecast dispersion measurement)
Indw_rlerror
peer firms earnings predictability rank based on market-share-weighted
earnings predictability of all other firms in the same industry during
the IPO fiscal year. (Other firms earnings predictability is based on
AR1 ten-years rolling regression)
Ind_predict
peer firms earnings predictability rank based on aggregate rank
measurement of the three individual firms earnings predictability
measures (forecast accuracy, forecast dispersion and rolling regression)
Stpredictrk
short-run (quarterly) peer firms earnings predictability based on mean
value of equally weighted and market-share-weighted earnings
predictability of all other firms in the same industry during the IPO
fiscal year
long-run (five years) peer firms earnings predictability based on mean
value of equally weighted and market-share-weighted earnings
predictability of all other firms in the same industry during the IPO
fiscal year
Lgpredictrk
43
Table 1:Industry distribution of IPOs from 1976 to 2012
Industry
N
Underprice Ind_predict Stpredict Lgpredict
1 Agriculture
17
-6.66%
-3.152
-0.005
-0.006
2 Aircraft
20
10.13%
-0.912
-0.002
-0.045
3 Apparel
60
10.62%
-0.836
-0.003
-0.127
4 Automobiles and Trucks
46
8.39%
-0.961
-0.004
-0.164
5 Beer and Liquor
8
12.53%
-0.723
-0.001
-0.009
6 Business Services
1030
36.80%
-0.439
-0.002
-0.069
7 Business Supplies
35
12.59%
-0.785
-0.005
-0.118
8 Candy and Soda
15
12.17%
-0.874
-0.004
9 Chemicals
62
6.40%
-0.657
-0.002
-0.075
10 Coal
8
24.26%
-1.910
-0.029
-0.075
11 Communication
201
17.77%
-0.904
-0.002
-0.034
12 Computers
272
27.29%
-0.610
-0.005
-0.233
13 Construction
54
7.14%
-1.080
-0.005
-0.121
14 Construction Materials
51
11.50%
-0.818
-0.003
-0.062
15 Consumer Goods
76
12.29%
-0.625
-0.002
-0.059
16 Defense
6
0.26%
-1.058
-0.019
-0.181
17 Electrical Equipment
38
13.50%
-0.642
-0.002
-0.033
18 Electronic Equipment
371
34.30%
-0.502
-0.003
-0.073
19 Entertainment
62
12.51%
-0.769
-0.005
-0.039
20 Fabricated Products
10
11.56%
-0.748
-0.003
21 Food Products
42
11.55%
-0.746
-0.002
-0.016
22 Healthcare
155
8.89%
-0.492
-0.002
-0.049
23 Machinery
101
8.49%
-0.668
-0.005
-0.062
24 Measuring and Control Equipment
96
14.98%
-0.415
-0.003
-0.025
25 Medical Equipment
209
11.60%
-0.357
-0.002
-0.064
26 Metallic and Industrial Metal Mining
6
10.48%
-0.805
-0.003
-0.052
27 Other
35
11.69%
-0.639
-0.002
-0.135
28 Personal Services
69
15.09%
-2.879
-0.002
-0.057
29 Petroleum and Natural Gas
133
3.50%
-0.641
-0.004
-0.054
30 Pharmaceutical Products
309
7.67%
-0.353
-0.003
-0.055
31 Precious Metals
5
2.09%
-0.784
-0.006
32 Printing and Publishing
28
29.46%
-0.629
-0.002
-0.023
33 Recreation
46
13.88%
-0.777
-0.007
-0.027
34 Restaurants, Hotels, Motels
122
19.55%
-0.904
-0.002
-0.017
35 Retail
339
13.24%
-0.668
-0.003
-0.032
36 Rubber and Plastic Products
25
11.09%
-0.896
-0.003
-0.036
37 Shipbuilding, Railroad Equipment
10
17.94%
-1.470
-0.006
-0.062
38 Shipping Containers
15
18.55%
-0.873
-0.003
39 Steel Works Etc
54
14.27%
-1.297
-0.009
-0.092
40 Textiles
24
-0.34%
-1.206
-0.006
41 Tobacco Products
3
17.54%
-0.802
0.000
-0.010
44
42 Transportation
122
9.37%
-1.206
-0.008
-0.093
43 Utilities
34
10.64%
-0.514
-0.003
-0.026
44 Wholesale
161
12.19%
-0.588
-0.003
-0.054
Note: Table 1 reports industry distribution of IPOs from 1976 to 2012. Sample includes 4585 IPO
observations from 44 industries. Underprice is the percentage IPO underpricing for each industry across
sample time period. Ind_predict is the aggregate measure for one year horizon earnings predictability
(based on negative value of raw earnings predictability). Stpredict is the aggregate measure for quarterly
horizon earnings predictability (based on negative value of raw earnings predictability). Lgpredict is the
aggregate measure for five years horizon earnings predictability (based on negative value of raw earnings
predictability).
45
Table 2: Variables descriptive statistics
Variable
N
Mean
Std Dev
25th
Pctl
50th
Pctl
75th
Pctl
Controls variable
Intpdummy
4596
0.792
0.406
1.000
1.000
1.000
NYSEAMEX
4596
0.217
0.412
0.000
0.000
0.000
Hightech
4596
0.420
0.494
0.000
0.000
1.000
Topauditor
4596
0.768
0.422
1.000
1.000
1.000
VC
4596
0.438
0.496
0.000
0.000
1.000
Priceupdate
4347
0.996
0.147
0.917
1.000
1.081
Proceed
4596
89.951
395.936
18.000
38.400
76.500
Pureprimary
4596
0.562
0.496
0.000
1.000
1.000
Age
4525
17.602
21.823
5.000
9.000
19.000
Topunderwriter
4596
0.547
0.498
0.000
1.000
1.000
Crisis_big
4596
0.012
0.107
0.000
0.000
0.000
Crisis_big1
4596
0.010
0.098
0.000
0.000
0.000
Crisis_big2
4596
0.002
0.042
0.000
0.000
0.000
Dependent and interested independent variables
Underprice
4596
0.200
0.454
0.000
0.071
0.235
Ind_er1
4215
4.479
2.343
3.000
5.000
6.000
Ind_dp1
4215
4.289
2.365
2.000
5.000
6.000
Ind_rlerror
4585
6.139
2.705
4.000
7.000
9.000
Indw_er1
4204
5.025
2.383
3.000
6.000
7.000
Indw_dp1
4204
4.993
2.410
3.000
5.000
7.000
Indw_rlerror
4585
5.464
2.608
4.000
6.000
8.000
Ind_predict
4596
4.971
0.963
4.339
5.087
5.715
Stpredictrk
4188
5.098
2.482
3.000
5.000
7.000
Lgpredictrk
2438
3.845
2.754
1.000
3.000
6.000
Note: Table 2 reports the variables descriptive statistics based on 4596 IPO firms during the period 19762012. To remove outliers, the continuous variables are truncated at 1% and 99% level. Refer to Appendix
A for variable descriptions.
46
Table 3: Correlation matrix
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Lnunder
1 1.000
Ind_Predict
2 0.141 1.000
Stpredictrk
3 0.104 0.419 1.000
Lgpredictrk
4 0.118 0.114 0.174 1.000
Crisis_Big
5 0.006 0.018 0.024 0.029 1.000
Intpdummy
6 0.130 0.098 0.077 0.036 0.005 1.000
NYSEAMEX
7 0.128 0.202 0.101 0.067 0.052 0.051 1.000
Hightech
8 0.139 0.398 0.155 0.115 0.034 0.082 0.226 1.000
Topauditor
9 0.101 0.150 0.106 0.039 0.047 0.055 0.056 0.054 1.000
VC
10 0.170 0.286 0.099 0.051 0.007 0.105 0.274 0.386 0.102 1.000
Priceupdate
11 0.368 0.075 0.094 0.078 0.063 0.082 0.052 0.094 0.065 0.069 1.000
Lnprcd
12 0.129 0.019 0.117 0.049 0.009 0.161 0.321 0.009 0.232 0.017 0.201 1.000
Pureprimary
13 0.069 0.136 0.086 0.012 0.020 0.037 0.033 0.076 0.105 0.051 0.045 0.003 1.000
Lnage
14 0.127 0.187 0.079 0.038 0.011 0.038 0.221 0.228 0.013 0.236 0.066 0.195 0.169 1.000
Topunderwriter 15 0.090 0.005 0.024 0.018 0.025 0.072 0.200 0.031 0.131 0.085 0.080 0.508 0.026 0.085 1.000
Note: Table 3 is the Pearson correlation coefficients for variables in our primary analysis. The correlations are based on 4596 IPO firms during the period 19762012. Correlations that are significant at the 5% level or better are presented in bold. Refer to Appendix A for variable descriptions.
47
Table 4: IPO underpricing and industry peer firms' earnings predictability
Variables
1
2
3
4
5
0.036***
0.036***
0.035***
0.038***
0.036***
Intpdummy
(3.600)
(3.620)
(3.540)
(3.740)
(3.580)
-0.063*** -0.063*** -0.06***
-0.067*** -0.062***
NYSEAMEX
(-5.600)
(-5.630)
(-5.550)
(-5.890)
(-5.450)
0.016*
0.017*
0.003
0.019**
0.014
Hightech
(1.850)
(1.930)
(0.340)
(2.090)
(1.570)
0.015
0.016
0.015
0.016
0.015
Topauditor
(1.390)
(1.440)
(1.500)
(1.490)
(1.420)
0.046***
0.046***
0.037***
0.046***
0.044***
VC
(5.090)
(5.010)
(4.260)
(5.090)
(4.850)
0.627***
0.63***
0.619***
0.63***
0.63***
Priceupdate
(22.570)
(22.700)
(23.200)
(22.670)
(22.690)
6
0.035***
(3.640)
-0.061***
(-5.610)
0.011
(1.220)
0.017
(1.670)
0.04***
(4.570)
0.619***
(23.170)
7
0.035***
(3.590)
-0.059***
(-5.480)
0.009
(1.010)
0.014
(1.410)
0.04***
(4.520)
0.622***
(23.320)
8
0.027***
(2.800)
-0.055***
(-5.060
0.044***
(3.120)
0.005
(0.490)
0.042***
(4.770)
0.561***
(21.060)
Lnprcd
0.025***
(5.440)
0.025***
(5.400)
0.026***
(5.880)
0.027***
(5.790)
0.024***
(5.120)
0.025***
(5.640)
0.024***
(5.490)
0.025***
(5.470)
Pureprimary
0.033***
(4.090)
0.033***
(4.070)
0.03***
(3.850)
0.035***
(4.280)
0.031***
(3.810)
0.031***
(3.990)
0.029***
(3.690)
0.048***
(6.130)
Lnage
-0.022***
(-5.130)
-0.022***
(-5.200)
-0.021***
(-5.100)
-0.022***
(-5.220)
-0.022***
(-5.160)
-0.021***
(-5.230)
-0.021***
(-5.030)
-0.023***
(-5.500)
Topunderwriter 0.021**
(2.290)
0.004**
Ind_er1
(2.170)
0.021**
(2.270)
0.021**
(2.330)
0.021**
(2.230)
0.022**
(2.350)
0.021**
(2.310)
0.021**
(2.350)
0.021**
(2.360)
Ind_dp1
0.003*
(1.930)
0.015***
(3.290)
0.031***
(3.560)
0.007***
(4.420)
Ind_rlerror
-0.002
(-1.310)
Indw_er1
0.004**
(2.170)
Indw_dp1
0.005***
(3.060)
Indw_rlerror
Ind_predict
Industry Fixed
Effect
Adj. R-Squared
Observations
No
0.194
4123
No
0.194
4123
No
0.197
4278
No
0.194
4112
No
0.194
4112
No
0.195
4278
No
0.195
4289
Yes
0.232
4289
Note: Table 4 reports coefficient estimates from OLS regression of IPO underpricing (dependent) on independent variables of industry
peer firms’ earnings predictability. The IPO underpricing is the logarithm value of firm first day trading return. For each IPO, industry
peer firms’ earnings predictability is the average one-year earnings predictability value of its peer firms. Among eight specifications,
seven industry peer firms’ earnings predictability measurements include six individual proxies and one aggregate proxy. Industry peer
firms’ earnings predictability measures use decile ranks to avoid outlier values. T-statistics are presented in parentheses. ***, ** and *
separately refer to significance (two tailed) at the 1%, 5%, and 10% level. Refer to Appendix A for variable descriptions.
48
Table 5: IPO underpricing, industry peer firms' long-run and short-run earnings
predictability, and financial crisis
Variables
1
2
3
4
5
0.038***
0.054***
0.054***
0.054***
0.042***
Intpdummy
(3.700)
(3.540)
(3.500)
(3.500)
(2.770)
-0.063***
-0.099***
-0.097***
-0.097***
-0.102***
NYSEAMEX
(-5.540)
(-5.760)
(-5.630)
(-5.610)
(-5.890)
0.016*
-0.006
-0.011
-0.011
0.041**
Hightech
(1.770)
(-0.520)
(-0.880)
(-0.890)
(2.220)
0.014
0.064***
0.061***
0.061***
0.038**
Topauditor
(1.250)
(3.850)
(3.660)
(3.670)
(2.310)
VC
0.045
(5.040)
0.045
(3.660)
0.045
(3.670)
0.045
(3.650)
0.045
(3.640)
Priceupdate
0.631***
(22.620)
0.654***
(17.740)
0.648***
(17.530)
0.649***
(17.550)
0.538***
(14.490)
Lnprcd
0.025***
(5.340)
0.033***
(4.540)
0.033***
(4.530)
0.033***
(4.530)
0.045***
(6.160)
Pureprimary
0.032***
(3.880)
0.034***
(2.900)
0.033***
(2.800)
0.033***
(2.800)
0.068***
(5.780)
Lnage
-0.022***
(-5.110)
-0.03***
(-4.730)
-0.03***
(-4.700)
-0.03***
(-4.680)
-0.031***
(-4.750)
Topunderwriter
0.023**
(2.430)
0.003
(1.510)
0.042***
(3.250)
0.043***
(3.290)
0.043***
(3.280)
0.04***
(3.120)
0.006**
(2.380)
-0.010***
(-4.900)
-0.003
(-0.010)
0.023
(0.730)
-0.017
(-0.560)
0.004
(1.530)
-0.006***
(-2.560)
0.067
(0.280)
0.029
(0.940)
-0.038
(-1.250)
No
0.223
2398
Yes
0.269
2398
Stpredictrk
Lgpredictrk
-0.009***
(-4.540)
Stpredictrk
0.006**
(2.460)
-0.010***
(-4.960)
Lgpredictrk
Stpredictrk
Lgpredictrk
Crisis_Big
Stpredictrk_Crisis
Lgpredictrk_Crisis
Industry Fixed Effect
Adj. R-Squared
Observations
No
0.194
4098
No
0.221
2398
No
0.223
2398
49
Note: Table 5 reports coefficient estimates from OLS regression of IPO underpricing(dependent) on independent variables
of long-run and short-run industry peer firms’ earnings predictability, financial crisis and interaction between those
interested variables. The IPO underpricing is the logarithm value of firm first day trading return. For each IPO, short-run
industry peer firms’ earnings predictability (Stpredictrk) is the average quarterly earnings predictability value of its peer
firms. Long-run industry peer firms’ earnings predictability (Lgpredictrk) is the average value of five year peer firms
earnings predictability. Both short-run and long-run earnings predictability use decile ranks to avoid outlier values. Tstatistics are presented in parentheses. ***, ** and * separately refer to significance (two tailed) at the 1%, 5%, and 10%
level. Refer to Appendix A for variable descriptions.
50
Table 6: IPO underpricing, industry peer firms' earnings predictability and financial crisis
Variables
1
2
3
4
5
6
Panel A: Controls
7
Intpdummy
0.037***
(3.64)
0.037***
(3.66)
0.034***
(3.53)
0.038***
(3.75)
0.036***
(3.59)
0.036***
(3.66)
0.027***
(2.82)
NYSEAMEX
-0.064***
(-5.72)
-0.065***
(-5.74)
-0.062***
(-5.67)
-0.068***
(-6.01)
-0.064***
(-5.61)
-0.062***
(-5.73)
-0.056***
(-5.2)
Hightech
0.017*
(1.87)
0.017*
(1.95)
0.003
(0.37)
0.019**
(2.1)
0.014
(1.56)
0.011
(1.26)
0.044***
(3.13)
Topauditor
0.016
(1.49)
0.017
(1.54)
0.015
(1.52)
0.017
(1.54)
0.016
(1.5)
0.017*
(1.74)
0.005
(0.51)
VC
0.045***
(4.97)
0.044***
(4.88)
0.037***
(4.19)
0.045***
(4.96)
0.042***
(4.67)
0.039***
(4.49)
0.041***
(4.69)
Priceupdate
0.629***
(22.62)
0.632***
(22.76)
0.62***
(23.2)
0.632***
(22.72)
0.632***
(22.74)
0.621***
(23.18)
0.562***
(21.09)
Lnprcd
0.025***
(5.49)
0.025***
(5.43)
0.026***
(5.91)
0.027***
(5.85)
0.024***
(5.2)
0.025***
(5.66)
0.025***
(5.57)
Pureprimary
0.034***
(4.17)
0.034***
(4.15)
0.031***
(3.9)
0.035***
(4.35)
0.032***
(3.88)
0.032***
(4.06)
0.049***
(6.24)
Lnage
-0.021***
(-5.12)
0.021**
(2.3)
-0.022***
(-5.19)
0.021**
(2.29)
-0.02***
(-5.01)
0.021**
(2.3)
-0.022***
(-5.23)
0.021**
(2.23)
-0.022***
(-5.16)
0.022**
(2.38)
-0.021***
(-5.12)
0.021**
(2.3)
-0.023***
(-5.46)
0.02**
(2.32)
0.133**
(2.25)
0.204***
(2.58)
0.14**
(2.19)
0.185***
(2.99)
0.188**
(2.55)
0.451**
(2.29)
Topunderwriter
Panel B: Interested Variables
Crisis_big
0.147**
(2.4)
0.004**
Ind_er1
(2.41)
-0.024*
Ind_er1_crisis
(-1.8)
Ind_dp1
Ind_dp1_crisis
Ind_rlerror
Ind_rlerror_crisis
Indw_er1
Indw_er1_crisis
Indw_dp1
Indw_dp1_crisis
0.004**
(2.16)
-0.020
(-1.61)
0.008***
(4.63)
-0.027**
(-2.08)
-0.002
(-1.06)
-0.021
(-1.61)
0.004**
(2.51)
-0.030**
(-2.52)
51
Variables
Indw_rlerror
Indw_rlerror_crisis
Ind_predict
Ind_predict_crisis
1
2
3
4
5
6
0.005***
(3.28)
-0.027**
(-2.04)
7
0.031***
(3.61)
-0.081**
(-2.03)
Industry Fixed
Effect
No
No
No
No
No
No
Yes
Adj. R-Squared
0.195
0.195
0.198
0.195
0.196
0.196
0.23
Observations
4123
4123
4278
4112
4112
4278
4289
Panel C: Interested Variables
Ind_Predict
0.031***
(3.600)
Crisis_Big1
0.481
(2.330)
Crisis_Big2
0.180
(0.280)
Ind_Predict_Crisis1 -0.09**
(-2.140)
Ind_Predict_Crisis2 -0.010
(-0.080)
Industry Fixed
Effect
Yes
Adj. R-Squared
0.23
Observations
4289
Note: Table 6 reports coefficient estimates from OLS regression of IPO underpricing (dependent) on independent
variables of industry peer firms’ earnings predictability, financial crisis and interaction between those two variables. The
IPO underpricing is the logarithm value of firm first day trading return. For each IPO, industry peer firms’ earnings
predictability is the average one-year earnings predictability value of its peer firms. Seven industry peer firms’ earnings
predictability measurements are used in the regression models, which includes six individual proxies and one aggregate
proxy. Industry peer firms’ earnings predictability measures use decile ranks to avoid outlier values. Panel A shows the
control variables coefficients in the main regression model. Panel B shows the interested variables’ coefficients in the
main regression model. Panel C runs an additional regression to show the impact of different levels of financial crisis.
Controls variables in Panels C are omitted for brevity. T-statistics are presented in parentheses. ***, ** and * separately
refer to significance (two tailed) at the 1%, 5%, and 10% level. Refer to Appendix A for variable descriptions.
52
Table 7: IPO underpricing, similar industry peer firms and influential peer firms' earnings predictability
Variables
1
2
3
4
5
6
Intpdummy
0.036***
(3.44)
0.027***
(2.68)
0.034***
(3.49)
0.026***
(2.73)
0.035***
(3.38)
0.028***
(2.68)
NYSEAMEX
-0.066***
(-5.67)
-0.061***
(-5.28)
-0.059***
(-5.36)
-0.055***
(-5.04)
-0.06***
(-5.06)
-0.055***
(-4.65)
Hightech
0.013
(1.39)
0.02
(1.54)
0.012
(1.33)
0.045***
(3.18)
0.008
(0.88)
0.041***
(2.72)
Topauditor
0.019*
(1.76)
0.013
(1.26)
0.015
(1.54)
0.009
(0.95)
0.013
(1.25)
0.005
(0.48)
VC
0.04***
(4.33)
0.042***
(4.56)
0.04***
(4.54)
0.042***
(4.75)
0.038***
(4.02)
0.038***
(4.11)
Priceupdate
0.62***
(22)
0.545***
(19.34)
0.62***
(23.19)
0.561***
(21.03)
0.621***
(21.93)
0.553***
(19.58)
Lnprcd
0.025***
(5.21)
0.028***
(5.86)
0.022***
(5.02)
0.022***
(4.63)
0.023***
(4.75)
0.026***
(5.01)
Pureprimary
0.027***
(3.21)
0.05***
(5.9)
0.028***
(3.6)
0.048***
(6.05)
0.027***
(3.11)
0.048***
(5.64)
Lnage
-0.018***
(-4.05)
-0.019***
(-4.26)
-0.021***
(-5.13)
-0.022***
(-5.36)
-0.021***
(-4.69)
-0.021***
(-4.65)
Topunderwriter
0.027***
(2.8)
0.025***
(2.67)
0.02**
(2.28)
0.02**
(2.23)
0.026***
(2.76)
0.025***
(2.64)
Sim_predict
0.012***
(3.52)
Sim_predict
0.01**
(2.52)
0.016***
(3.9)
Influ_predict
0.009**
(2.24)
0.012*
(1.83)
Sim_predict
Influ_predict
Influ_predict
0.015***
(3.57)
0.013***
(3.72)
0.02***
(3.37)
Industry Fixed Effect No
Yes
No
Yes
No
Yes
Adj. R-Squared
0.196
0.234
0.196
0.233
0.198
0.238
Observations
3903
3903
4264
4264
3864
3864
Note: Table 7 reports coefficient estimates from OLS regression of IPO underpricing (dependent) on independent
variables of similar industry peer firms and influential peer firms earnings predictability. The IPO underpricing is the
logarithm value of firm first day trading return. Similar peer firms refer to the peer firms in the same quintile based on
rank of the industry-year level firms’ revenue. Influential peer firms refer to the peer firms in the top quintile based on
rank of the industry-year level firms’ revenue. Industry peer firms’ earnings predictability measures use decile ranks to
avoid outlier values. T-statistics are presented in parentheses. ***, ** and * separately refer to significance (two tailed) at
the 1%, 5%, and 10% level. Refer to Appendix A for variable descriptions.
53
Table 8: Industry peer firms' earnings predictability and long-run performance of IPOs
Panel A: Cumulative abnormal return
Peers earnings predictability
Low
IPO firms
Market-adj value weighted return
Difference
Medium
IPO firms
Market-adj value weighted return
Difference
High
IPO firms
Market-adj value weighted return
Difference
Difference(low)-Difference(high)
T-statistics
Panel B: Buy-and-hold abnormal return
Peer earnings predictability
Low
IPO firms
Market-adj return (value weighted)
Difference
Medium
IPO firms
Market-adj value weighted return
Difference
High
IPO firms
Market-adj value weighted return
Difference
Difference(low)-Difference(high)
T-statistics
12 Months (%)
24 Months (%)
0.055
0.115
-0.060
0.154
0.257
-0.104
0.041
0.099
-0.058
0.164
0.231
-0.066
0.040
0.129
-0.089
0.136
0.247
-0.111
0.029**
(2.000)
0.007
(0.350)
12 Months (%)
24 Months (%)
0.075
0.120
-0.045
0.148
0.280
-0.133
0.074
0.104
-0.030
0.190
0.258
-0.068
0.052
0.135
-0.082
0.140
0.282
-0.143
0.037**
(2.19)
0.01
(0.31)
Note: Table 8 reports the performance of IPOs and market adjusted value weighted benchmark return. We assign IPOs to low,
medium and high groups based on industry peer firms’ earnings predictability. We calculate post 12 months and post 24 months’
return after firms’ IPO dates. Panel A shows the post 12/24 months cumulative abnormal return. Panel B shows the post 12/24 months
buy and hold abnormal return. T-statistics for return difference between low peer firms’ earnings predictability group and high peer
firms’ earnings predictability group are presented in parentheses. Industry peer firms’ earnings predictability measures use decile
ranks to avoid outlier values. ***, ** and * separately refer to significance (two tailed) at the 1%, 5%, and 10% level. Refer to
Appendix A for variable descriptions. Cumulative abnormal returns and buy-and-hold abnormal returns formulas are: (r i,t and mi,t are
monthly raw and benchmark returns, T is the time period months after IPO, N is the stock numbers in the portfolio based on earnings
predictability)
Cumulative abnormal returns
Buy-and-hold abnormal returns
∑
∑
∑
∏
(Panel A)
(
) ∏
(
)
(Panel B)
54