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College of Business AdministrationUniversity of Rhode Island
2004/2005 No. 1
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Office of the DeanCollege of Business AdministrationBallentine Hall7 Lippitt RoadKingston, RI 02881401-874-2337www.cba.uri.edu
William A. Orme
Tong Yu, LeRoy Brooks, and Xuanjuan Chen
Does Industry Affect the Quality of Seasoned Equity Issuers?
Does Industry Affect the Quality of Seasoned Equity Issuers?
Tong Yu*, LeRoy Brooks**, and Xuanjuan Chen***
September 2004
_______________________ * College of Business Administration, University of Rhode Island, [email protected]. ** John M. and Mary Jo Boler School of Business, John Carroll University, [email protected]. *** College of Business Administration, University of Rhode Island, [email protected]. We thank Chris Anderson, Scott Harrington, Eugene Lee, Bingxuan Lin, Chunlin Liu, Greg Niehaus, Henry Oppenheimer, Zhiyi Song, Tong Yao, Donghang Zhang, and the workshop participants at the University of Rhode Island for helpful comments.
1
Does Industry Affect the Quality of Seasoned Equity Issuers?
Abstract
We analyze how the quality of firms issuing seasoned equity offerings (SEOs)
varies across industries. While the conventional wisdom holds that greater growth
opportunities alleviate the information asymmetry problem for issuers in high-growth
industries, we present a model that introduces a quality screening effect where firms with
negative NPV projects are more likely to be screened out and the average quality of
issuers from lower-growth industries is better. Supportive to our view, the average
announcement period return for the issuers from low-growth industries is 1.5 percent
greater than for issuers from high-growth industries. Further, post-offer operating
performance is better for low-growth industry issuers while they engage in less earnings
management activities prior to the offerings.
2
Numerous studies document that that seasoned equity offering (SEO) issuers have
inferior quality to non-issuers.1 This phenomenon is attributed to Myers and Majluf’s
(1984) information asymmetry argument, where issuers tend to be overvalued because
corporate insiders have more information about issuers than do outside investors. There
is a substantial body of studies that the information asymmetry problem differs across
issuers. In particular, Pilotte (1992) and Jung, Kim and Stulz (1996) highlight the
importance of firm growth opportunities. They find that the announcement period
cumulative abnormal returns (CARs) are higher for issuers with better growth
opportunities.2 In contrast, another strand of studies that investigates the impact of the
general issuance condition on firm incentive to issue SEOs and the announcement period
returns (Choe, Masulis and Nanda, 1993; Bayless and Chaplinsky, 1996).
Given the existence of information asymmetry, issuers have an incentive to
exaggerate their profitability, thus firm-level information may not be adequate for
investors to evaluate issuers. Yet the market-level analysis is also not ideal since the
overall market condition may not be capture the cross-sectional variation of issuers’
quality, especially when issuers’ incentive could vary across industry sectors. It is a
commonplace observation that firms within the same industry are exposed to similar
market conditions, technology innovations and regulatory environment. Motivated by
1 Studies examine the quality of SEO issuers from three aspects. First, Masulis and Korwar (1986) and Asquith and Mullins (1986), for example, find that SEO issuers on average experience negative announcement period returns. Second, Ritter (1991), Spiess and Affleck-Graves (1995) and Loughran and Ritter (1997) report that SEO issuers post-offer stock and operating performance underperforms non-issuers. Third, Rangan (1996) and Teoh, Welch and Wong (1998) show that SEO issuers have stronger incentives to manage earnings prior to SEO offerings. 2 In addition, Korajczyk, Lucas, and McDonald (1990) and Sant and Ferris (1994) examine how issue size affects the market response; Raymar (1993) Masulis and Korwar (1986) examine the impact of leverage; Manuel, Brooks and Schadler (1993) provide evidence on the impact of earnings and dividend announcements.
3
this consideration, we examine the relation between industry growth opportunities and
the quality of SEO issuers.3 More specifically, we investigate how the relative
performance of an issuer’s industry on the overall market on SEO quality.
How does issuers’ quality vary across industries with different growth
opportunities? There are mainly two opposing views to address this question. The first
view holds that high-growth industry issuers are, on average, of higher quality than
issuers in low-growth industries. Stoughton, Wang and Zechner (2001) suggest that
issuers may cluster in particular industries having higher levels of technological
innovation or a positive productivity shock. Superior growth opportunities in high-
growth industries then would alleviate the information asymmetry problem between firms
and investors, thus lowering the information costs. Supportive to this view, Loughran,
Ritter and Rydqvist (1994) suggest a clear tendency for a high volume of IPOs to occur
with market peaks. Ritter (1991) and Spiess and Affleck-Graves (1995) suggest that
equity offerings are concentrated in growth industries. Consistently, Choe, Masulis and
Nanda (1993) and Bayless and Chaplinsky (1996) find that SEO issuers experience more
favorable market responses under improved business conditions and in hot equity
issuance markets.
The opposing view claims that high-growth industry issuers, on average, have a
poorer quality than do low-growth industry issuers. More favorable investment
opportunities in high-growth industries, relative to low-growth industries, would not only
encourage high-quality firms to issue equity, but also provide “windows of opportunities”
3 The importance of analyzing information at the industry level is discussed in the literature. Fama and French (1997) suggest that risk loadings for individual firms are less precise than those of industries. In addition, Maksimovic and Zechner (1991) and Moskowitz and Grinblatt (1999) highlight the importance of industry performance in determining a company’s growth potential.
4
to issue equity for poor-quality firms which do not have access to positive NPV projects.
This is because the more favorable industry condition would lead to higher market
valuation of an issuer; the resulted benefit of being treated as an otherwise firm for the
low-quality firms’ is greater. Conversely, the worse issuance environment in low-growth
industries discourages low-quality issuers from issuing equity. As a result, the quality of
issuers from high-growth industries does not necessarily top that of issuers from low-
growth industries. Consistent with this view, Helwege and Liang (2002) do not find that
hot market IPOs have a better quality than cold market IPOs. Further, Loughran and
Ritter (1995) suggest that hot market SEO firms typically experience a worse post-issue
stock performance.4
This paper strives to disentangle these two views. We first formalize the latter
view with a theoretical model, in which each industry has a good firm with positive NPV
projects and a bad firm with negative NPV projects. Under this setting, both firms play
the mixed strategy and issue equity with some probabilities. In equilibrium, the bad
(good) firm’s issuance probability is positively associated with the good (bad) firm’s
future gains (losses). Intuitively, the positive relation between the bad firm’s issuance
probability and the good firm’s future growth reflects the bad firm’s mimicking
incentive. It confirms the well-documented lemon problem. What is new here is the
positive relation between good firm’s issuance probability and the bad firm’s losses. The
bad firms’ mimicking incentive would be lower as its investment losses are greater. We
refer to the disincentive to issue equity for the bad firm as a quality screening effect.
Further, the issuers in low-growth industries are more likely to be good firms due to a
4 In a study examining the quality of IPO firms across hot and cold markets, Helwege and Liang (2002) do
5
stronger quality screening condition relative to the lemon problem. Our model has two
empirical predictions. First, the announcement period abnormal returns for low-growth
industry issuers would be on average greater than those for high-growth industry issuers.
Second, high-growth industry issuers would underperform low-growth industry issuers in
their post-offer long run operating performance.
We then present evidence on the quality screening argument by examining a
sample of SEOs from January 1980 through December 2002. We evaluate industry
growth opportunities with the industry-wide Tobin’s Q. Supportive to the quality
screening argument, both the equally-weighted and value weighted cumulative abnormal
return (CAR) during days 0 and 1 of the average announcement period for low-growth
industry SEOs is 1.5 percent higher than in high-growth industry SEOs. This
phenomenon is robust and applies to all issuers, not just those in the extreme deciles,
where a 0.8 higher announcement return holds between the bottom five and top five
deciles of industry Q ranked firms.
Subsequently, we perform a series of tests to ensure that the industry quality
effect holds against alternative explanations. First, Are the better market responses to
low-growth industry SEOs actually a firm-size effect? Although low-growth industry
issuers are typically larger in size, higher CARs for low-growth industry issues persist
after controlling for issuers’ market capitalizations. Second, we rule out the possibility
that our result is caused by the greater uncertainty in the growth potentials of high-growth
industries' issuers by showing a still lower CAR in high-growth industries after we sort
CARs jointly by intra-industry standard deviations of Q and industry Q. Third, we report
not find that hot market IPOs have a better quality.
6
that better CARs of low-growth industry issues are neither because issuers have poorer
prior stock performance, nor because their SEOs are more concentrated in hot equity
issuance markets.
We further investigate issuers’ long run operating performance. Using various
measures for operating performance, we find that issuers experience a reversal in their
operating performance: they outperform within-industry non-issuers with similar
characteristics in the pre-offering period while underperform non-issuers in the post-
offering period. In addition, relative to low-growth industry issuers, high-growth
industry issuers experience better ex-post characteristics-adjusted operating performance
after the offerings, but worse ex-ante performance. The worse performance for high-
growth industry issuers can be explained by the quality screening argument, however,
this result may be also due to the managerial optimism because their pre-offering
operating performance may tempt managers to overestimate their future growth rate
(DeLong, et al, 1991).
Finally, we use issuers’ accruals information to differentiate between the quality
screening argument and the behavior explanation based on managerial optimism.
Accruals, the non-cash portion of net incomes, can be decomposed into nondiscretionary
and discretionary components. Nondiscretionary accruals are related to firm growth
while discretionary accruals reflect earnings management activities. If the greater
managerial overconfidence of their future growth opportunities is the culprit of the worse
performance for high-growth industry issuers in the post-offering period, these issuers
would have higher pre-offering non-discretionary accruals than low-growth industry
issuers. Alternatively, a higher pre-offering discretionary accruals for high-growth
7
industry issuers indicate more earnings management activities. Our results support the
quality screening argument by showing a higher pre-offering discretionary accruals for
the high-growth industry issuers while the difference in non-discretionary accruals is
insignificant.
The remainder of our analysis is organized as follows. Section I contains the
model that describes the quality-screening phenomenon and motivates the testable
conditions. Section II describes the data and the measures for announcement period
abnormal returns, characteristics-adjusted operating performance and earnings
management. Section III presents empirical results. Finally, Section IV concludes.
I. The Model
A. Quality Screening in a Single-Industry Economy
We introduce the quality screening effect with a single-industry economy and
then generalize the finding to analyze issuers’ quality in a multi-industry setting. The
model is based on several assumptions. Most important are the following two sets of
assumptions: first, there are two firms in the economy: a good firm and a bad firm. Firm
value consists of existing assets (a) and future growth opportunities (q). For simplicity,
we assume these two firms have the same existing assets, but different growth
opportunities, qg and -qb, where g and b in subscript stand for the good and bad firms.
Both qg and qb are positive indicating that the good firm has positive NPV project and the
bad firm has a negative NPV project.
Second, managers have perfect information of their firms. However, investors do
not know the issuer’s type. Managers act on the interest of existing shareholders. The
8
amount of capital they raise is E and SEOs are offered to new investors. Both managers
and investors are risk neutral and the risk free rate of return is zero.
This setup is similar to Myers and Majluf’s (1984) but they assume both firms
have access to positive NPV projects. Myers and Majluf show that the good firm could
forfeit its positive NPV project while the bad firm would always issue equity since the
good firm is undervalued but the bad firm is overvalued under asymmetric information.
In our setup, the bad firm has negative NPV project. The bad firm will not always issue
equity: it would balance the cost of taking a negative NPV project and the benefit of
being overvalued.5 Consequently, the good firm is likely to issue when the bad firm has
less incentive to issue. We solve the joint decision problem with a mixed strategy
issuance game where each firm issues equity with some probability.6
The normal form game matrix is provided in Figure I. Firm either issue or not
issue, leading to 4 sets of payoffs to existing shareholders of the good and bad firms.
First, in the upper left cell, we show the existing shareholders’ payoff when both firms
issue equity. The good firm’s payoff comes first and then the bad firm’s payoff. When
both firms issue equity, investors do not know the type of the issuer they are dealing with.
Using P to represent the market value of an issuer, the payoff of existing shareholders of
the good firm is )( EqaEP
Pg ++
+, and the payoff of the bad firm is )( Eqa
EPP
b +−+
.
Second, the upper right cell provides the payoffs when only the good firm issues equity.
5 One may argue that the bad firm could stay away from the negative NPV project through an “issuing and not investing” strategy. However this is unlikely. First, issuing equity is costly. Lee et al (1996) report that the average issuance costs are more than 7.11 percent of SEO proceeds. The issuance cost would make this strategy have the same effect as acceptance of a negative NPV investment. Second, the “issuing and not investing” strategy would hurt the issuer’s reputation and their chance to raise capital again in a multi-period environment, which is not examined in our model. 6 It can be easily shown that a pure strategy equilibrium does not exist.
9
The payoff of the good firm is gqa + and the payoff of the bad firm is a. Third, the lower
left cell provides the payoffs when only the bad firm issues equity: the good firm’s payoff
is a and the bad firm’s payoff is bqa − . Finally, in the lower right cell where neither firm
issues, the payoff of both firms is a.
Let pg be the probability that the good firm issues equity; and pb be the bad firm’s
issuance probability. The indifference conditions, the condition when the firms are
indifferent between issuing equity and not issuing equity, for the good firm and the bad
firm are:
apqapEqaEP
Pbgbg =−++++
+)1)(()( (1)
apqapEqaEP
Pgbgb =−−++−
+)1)(()( (2)
The left-hand side of the above indifference conditions provides the payoff when
the good (bad) firm issues equity while the right-hand side is the payoff when it does not
issue equity.
New investors’ breakeven condition is an additional condition of the issuance
game. The payoff to new investors’ payoff is )( EqaEP
Eg ++
+ when they buy shares
from a good issuer, and )( EqaEP
Eb +−
+ when they buy shares from a bad issuer.
Equating E to the weighted average payoffs to new investors of the two firms, we obtain
the following expression:
)](*)()(*)[(bg
bbj
bg
ggj pp
pEqapp
pEqa
EPEE
++−+
+++
+= (3)
Solving (3) for P,
10
bg
bb
bg
gg pp
pqapp
pqaP
+−+
++= )()( (4)
Solving for gp and bp using (1), (2) and (4), we have the following results:
bg qp δ= (5)
gb qp δ= (6)
where )(
)()(
bg
gbbg
qqEqEqaqEqa
−
+−+++=δ .
Equation (5) shows that the good firm issuance probability increases when the bad
firm has a more negative growth opportunity. What is the intuition behind (5)? The bad
firm’s issuance incentive would be lower when its growth opportunities become more
negative since the adoption of a negative NPV project represents an implicit cost for the
bad firm to issue equity. As a result, the good firm’s issuance incentive increases when
the bad firm is screened out of the issuance game. We refer to this as a quality screening
effect. On the other hand, (6) reflects the lemon problem widely documented in the
literature; the bad firm's lemon incentive increases, and thereby their issuance probability
increases, when the good firm has a better growth potential.
Next, we move to the focal point of this analysis: under what condition are we
more likely to observe a good issuer? With gp and bp , we obtain the following
expression for the probabilities of an issuer being a good firm, ρ , or a bad firm, 1- ρ .
bg
b
bg
g
qqq
ppp
+=
+=ρ (7)7
7 We obtain the same expression for ρ when the good firm and the bad firm are not equally probable in the economy. See Appendix.
11
Proposition I: When qg <qb, the probability of observing a good issuer exceeds the
probability of observing a bad issuer, ρ >0.5. When qg >qb, the probability of observing a
good issuer is below the probability of observing a bad issuer, ρ<0.5. When qg =qb, the
probability of observing a good issuer equals the probability of observing a bad issuer, ρ
=0.5.
The proof of this proposition strictly follows (5) and (6). Intuitively, when the
negative NPV of the bad firm exceeds the positive NPV of the good firm (qb > qg), the
bad firm’s benefit from the issue is lower. This leads to a reduced issuance incentive of
the bad firm and investors are more likely to buy shares from a good issuer (ρ >0.5). The
same logic can be applied when interpreting the other two cases.
B. Quality Screening at the Industry Level
Now we examine how the quality screening effect varies across industries having
differential growth opportunities. We define a growth ratio, w=qg/qb. Proposition II
describes the relationship between ρ and w.
Proposition II: The probability of observing a good issuer is a decreasing function of w,
i.e., 0<∂∂wρ .
Proof: see Appendix.
High-growth industry firms on average have better growth opportunities than do
low-growth industries firms. We additionally assume that both the good and bad firms in
12
the high-growth industries are better than those in the low-growth industries. Then we
have
)()( lqhq gg >
)()( lqhq bb < (8)
where h and l in the parentheses represent the high-growth and low-growth industries.
Consequently, w(h) > w(l). According to Proposition II, the probability of observing a
good issuer increases for an issuer from the low-growth industries.
Further, we analyze the relationship between the market value difference in an
issuer and a non-issuer, P∆ )( ni PP −= , and the growth ratio, w. Equation (4) provides
the value of an issuer, Pi, and the value of a non-issuer, Pn, is a. Thus, ∆P is,
bbg qqqP −+=∆ )(ρ (9)
Proposition III: A sufficient condition for 0<∂∂wρ is 0)(
<∂∆∂wP .
Proof: See Appendix.
Proposition III bridges the quality screening argument and the quality difference
between the issuers and non-issuers across industries. Therefore, we can demonstrate the
existence of quality screening by showing an inverse relationship between the value of an
issuer and a non-insurer when there are different industry growth levels.8 This
proposition yields testable conditions.
8 In the appendix, we show that 0)(
<∂∆∂wP
when w>w*=3-2 2 , indicating that the solution for the
inequality is not a null set.
13
First, related to announcement period returns, as high-growth industry issuers
have better quality than low-growth industry issuers, the average announcement period
CAR is greater for low-growth industry issues. Second, our model predicts that low-
growth industry issuers outperform high-growth industry issuers in the post-
announcement period. The second prediction extends Loughran and Ritter (1997)’s
findings that SEO issuers have a poorer operating performance after the offerings by
further linking the magnitude of SEO underperformance with industry growth
opportunities.
II. Data and Methodology
A. Data We start with 10,657 seasoned equity issues with their announcement dates
between January 1980 and December 2002 from the Securities Data Corporation (SDC)
database, and then we apply the following selection criteria:
• The SDC database explicitly mentions “Common Stocks” or “Ordinary Stocks” as
the issue type. Issues by closed-end funds, unit investment trusts, real estate
investment trusts (REITS) and American Depository Receipts (ADRs) are
excluded.
• All rights offerings are excluded.9
• The issuer’s CUSIP or TICKER symbol and its SEC filing date are provided; and
it has a nonzero standard industry classification (SIC) code.
9 SEOs are sold to investors in two ways. One is with a general cash offer and the other is with a rights offer. A Cash offer is sold to all interested investors, and a rights offer is sold to existing shareholders. We exclude rights offers because they constitute an apparent violation to our model’s assumption that new shares are offered to new investors.
14
• Only the first SEO of an issuer in a year is included.
• The issuer is included in the Center for Research in Securities Prices (CRSP)
database, and it has at least 60 days’ of stock returns between the period of days -
255 through day -10, relative to the equity issue announcement day 0, and there
are no missing returns on days 0 and +1.
Our final sample includes 8678 offerings from 5480 companies, including 4172
from the NYSE, 517 from AMEX, and 3989 from NASDAQ. Table I reports the
distribution of SEOs across different industry sectors. We follow the 12-industry
classification provided in Ken French’s website. The financial service sector accounts
for the largest percentage of SEO proceeds, followed by business equipments. The
chemical product industry has the smallest percentage of SEO proceeds. Consistent with
the literature, we find that the equally weighted average CAR of all SEOs in our sample
is –1.90 percent, significant at the 1 percent level. The average days (0, 1)
announcement-period cumulative abnormal return (CAR) for each industry sector is
negative. The business equipment industry has the highest average CAR while the
utilities sector has the lowest average CAR.
B. Methodology
B.1. Industry Growth Opportunities
We evaluate industry growth opportunities with Tobin’s Q. The literature
documents that although multiple methods are applied to calculate Tobin’s Q, they yield
similar value (e.g., Perfect and Wiles, 1994; and Chung and Pruitt, 1994). Here we apply
the Q’s measure in Chung and Pruitt’s (1994),
15
Tobin’s Q = (MVE + PS + DEBT)/ TA, (10)
where: MVE (market value of common stocks) = [Closing price at the end of financial
year (Compustat item 24)]*(Number of common shares outstanding (item 25)]; PS =
liquidating value of outstanding preferred stocks (item 10); DEBT = Current liabilities
(item 4) – Current Assets (item 5) + Inventories (item 3) + Long term debt (item 9); and
TA = book value of total assets (item 6).
We calculate the industry average Q using the value-weighted average of firm
growth rate within an industry. Two methods are applied to classify firms into industries.
First, we treat firms with the same first three-digits Standard Industrial Classification
(SIC) codes as an industry. Alternatively, we use the 48-industry classification system in
Fama and French (1997). The CRSP industries are ranked into deciles when we classify
industries with their first 3-digit SIC codes. Because of the much smaller number of
industries with the Fama and French’s 48-industry classification, we then rank industries
into quintiles.
B.2. Cumulative Abnormal Returns
We use the two-day (0, 1) event period cumulative abnormal return (CAR) to
measure the capital market reaction to a firm’s SEO announcement,
∑=
=1
0ttARCAR , (11)
where ARt represents the abnormal returns for event day t. Following the standard event
methodology from Mikkelson and Partch (1986), we apply market model regressions
using returns in days (-255, -10), relative to the SEO announcement day 0, to estimate the
normal return of a stock. The value-weighted market returns are used in the market
16
model estimations and ARs are the differences between realized returns in days (0, 1) and
estimated normal returns.
B.3. Characteristics-Adjusted Operating Performance Measures
We apply three frequently used operating performance measures: (1) operating
income before depreciation (OIBD, item 13) scaled by total assets, (2) return on assets,
i.e. net incomes (item 172) scaled by total assets, and (3) cash flow from operations (item
308) scaled by total assets. Prior to 1987, item 308 is not available as so we calculate
cash flow from operation as the funds from operations (item 110) minus accruals (defined
in Section III.B4)
Barber and Lyon (1996) suggest that matching sample firms to firms in the same
industry and with similar pre-event performance yield well-specified and powerful test
statistics. As firm size and Tobin’s Q may also affect operating performance of both
issuers and non-issuers, we develop a characteristics-adjusted operating performance
measure that matches an issuer with firms having the same 3-digit SIC codes and
comparable prior-year operating performance, size and Tobin’s Q.
To create the characteristics-matching portfolios, within each 3-digit SIC code we
first rank non-issuers by size and create two equally sized big (B) and small (S) sized
portfolios. Using this same procedure we create high (H) and low (L) Tobin’s Q
portfolios and good (G) and poor (P) operating performance portfolios. We then form the
8 possible within-industry matching portfolios combinations of non-issuers (SHG, SHP,
SLG, SLP, BHG, BHP, BLG, BLP). Then during -3 years and 3 years of a SEO, we
identify the matching portfolio for a SEO issuer based on the issuer’s prior year
17
characteristic rankings. For instance, to estimate matching portfolio operating
performance in year -1, we match an issuer’s characteristics in year –2 with non-issuers
having the same characteristic ranks in year –2. An issuer’s characteristics-adjusted
operating performance is the difference between the issuer’s operating performance and
the median performance of its matching portfolio. This measure reflects the quality
difference of an issuer and comparable non-issuers within the same industry.
B.4. Earnings Management
We measure firm earnings management activities using working capital related
accruals, and discretionary accruals. A fairly large literature suggests that firms could
manipulate their earnings through managing accounting accruals, particularly current
accruals (see, e.g., Dechow, 1995; Sloan, 1996; and Toeh, Welch and Wong, 1997).10
We calculates total accruals (TACC) following Toeh, Welch and Wong (1997):
TACC = ∆(CA – CASH) - ∆(CL – CMLTD), (13)
where: CA (current assets) is item 4; CASH is item 1; CL (current liabilities) is item 5;
and CMLTD (current maturity of long-term debt) is item 44.
As total accruals may jointly reflect firms’ growth and earnings management
activities, we further decompose total accruals into discretionary accruals and
nondiscretionary accruals, where nondiscretionary accruals reflect firms’ growth while
discretionary current accruals represent firms’ earnings management activities. We
10 Toeh, Welch and Wong (1997) show that SEO issuers raise reported earnings by altering current accruals, rather than long-term accruals, because manipulating long-term accruals is more visible than managing current accruals (Guenther, 1994).
18
calculate non-discretionary accruals using the cross-sectional Jones (1991) regression
within each industry:
jttj
jt
tjtj
jt
TASALES
aTA
aTA
TACCε+
∆+=
−−−
)()1(1,
11,
01,
, (14)
where firm j are those firms belonging to the same three-digit SIC code, 1, −tjTA is total
assets in year t-1 (item 6), and 1, −∆ tjSALES is the change in sales in year t (∆item 12).
The predicted value of the above model for a firm is nondiscretionary current accruals.
Discretionary accruals are the difference between accruals and nondiscretionary accruals.
We calculate characteristics-adjusted accruals measures by applying the same
characteristics-adjustment procedures to evaluate operating performance. The difference
in the accruals measures reflects the difference in their earnings management efforts
between an issuer and comparable non-issuers within the same industry.
III. Empirical Evidence
In this section, we present the empirical results on the announcement period
cumulative abnormal returns and post-offer long run operating performance across high-
and low-growth industries.
A. Announcement Period Abnormal Returns
A.1. Cumulative Abnormal Returns Based on Industry Performance
Sorted by their growth opportunities, Panel A of Table II contains the average
CARs of days (0, 1) announcement period as well as the numbers of SEOs for each
decile. We classify all firms in CRSP with the same first 3-digit SIC codes into an
19
industry and then rank industries into deciles based on their average Tobin’s Qs.
Consistent with other studies, e.g., Loughran and Ritter (1995), more SEOs are from
high-growth industries than from low-growth industries.
The average CARs, equally or value weighted, are more negative in higher
growth opportunity industry deciles. The equally weighted average CAR of the lowest
growth industry decile (D1) is –1.26 percent, while that of the highest growth industry
decile (D10) is –2.74 percent. The superior performance of the lowest growth industry of
1.48 percent is significant at the 1 percent level. We compare the D1 CAR mean and
D10 mean CAR in each year. Over the 23-year sample period, D1 issuers have
significantly better CARs than do D10 issuers in 15 years; they are not significantly
different in 6 years, and they are only significantly worse in 2 years. Further, the
difference in the average CARs for below median industry-Q issuers and above median
industry-Q issuers is 0.80 percent, also significant at the 1 percent level.
The same results hold when using value-weighted means to aggregate CARs in
each industry decile and when applying the Fama-French (1997) 48-industry
classification system for industries. Our findings fail to support the conventional view
that better growth prospects in outperforming industries ameliorate the asymmetric
information problem. The Table II results follow from our model and support the quality
screening argument, which predicts a better announcement period return for low-growth
industry issuers.
A.2. Do Alternative Explanations Substitute for Quality Screening?
20
We test the robustness of the quality screening effect by controlling for several
alternatively explanations that could be leading to difference in CARs across different
growth industries. We consider the following factors:
• Issuers’ firm size: measured by an issuer’s market value of equity in June of the
year of a SEO announcement;
• Issuers’ leverage: equals the sum of debt in current liability (item 34) and long-
term debt (item 9) scaled by total assets (item 6) in the year of issuance;
• Intra-industry standard deviation of Q: the stand deviation of Tobin’s Q for firms
with the same first three-digit SIC codes;
• Issuers’ ex-ante stock performance: the intercept from the CAPM-based one-
factor model regression using 36-month pre-announcement period stock returns;
• The change in the issuers’ Tobin’s Q.
In each year, we sort industry Q into deciles where firms with the same first 3-
digit SIC codes are grouped into industries. Different from Table II that includes all
CRSP industries in the ranking process, here we only involve industries having SEO
issuers in a year. Table III contains issuers’ characteristics across industry deciles ranked
by industry Q. Subsequently, in Table IV we provide results from double sorts on
industry Q and each variable that could possibly be providing an alternative explanation.
First, relative to high-growth industry issuers, issuers from low-growth industries
have a lower Q (due to our decile ranking on this variable), are larger and are more
financially leveraged. All three of these factors are characteristics of more mature
industries. There is typically less information asymmetry with larger firms that generally
have higher proportions of assets-in-place, cash flows that can be estimated with greater
21
accuracy and lower future growth opportunities. In Panel A of Table IV, the average Q1
CARs are significantly lower than Q5 CARs in 4 of the 5 size ranked quintiles.
Additionally, Panel B shows that double ranks of issuers by their industry Q and leverage
provide similar results. Differences in firm size and financial leverage, likely indicators
of company maturity, do not eliminate the quality screening explanation.
Second, Table III also shows that the average intra-industry standard deviation of
firm Q for high-growth industry issuers is much higher than that for low-growth industry
issuers. The higher intra-industry dispersion in firm quality for the high-growth
industries leads to greater uncertainty on issuers’ quality, thus offering another alternative
explanation for the poorer market response to high-growth industry SEO announcements.
We address this concern in Panel C of Table IV, where we do a double sort by the intra-
industry standard deviation of Q and by industry Q. The differences in the average CARs
between Q1 and Q5 issuers ranked by industry Q are significantly positive in all five
industry dispersion breakdowns. The poorer CAR in high-growth industry issues is
unlikely attributable to a greater dispersion of intra-industry Qs in high-growth industries.
Third, shown in the fifth column of Table III, the low-growth issuers’ ex-ante
stock performance is poorer than the high-growth issuers. Lucas and MacDonald (1991)
suggest that SEOs are overvalued firms. If so, the more negative SEO price reactions
from the better pre-offer stock-performing high-growth issuers could be explained by a
greater market correction coming from a greater likely lemon condition of overvaluation.
Consequently, the less negative average D1 CAR than the average D10 issue CAR could
reflect different levels of stock overvaluation in D1 over D10.11 This concern is
11 Supportive to the overvaluation conjecture, Cornett and Tehranian’s (1994) find the market responds more favorably when commercial banks involuntarily issue equity to meet reserve requirements than to
22
addressed in Panel D of Table IV. Consistent with the overvaluation expectation, when
we break down issuers’ ex-ante stock performance into quintiles, the average CAR
becomes more negative in groups with better ex-ante stock performance. Nevertheless,
the differences between the average CARs between Q1 and Q5 issuers are positive and
significant in 4 of 5 quintiles, further supporting the quality screening conjecture.
Finally, shown in Table III, issuers experience a decline in their Qs during the
SEO year while the reduction in Q is greatest for D10 issuers (-0.325 in D10 versus –
0.006 in D1). Prior studies, e.g., Pilotte, 1992; Bayless and Chaplinsky, 1996; and Jung,
Kim and Stulz, 1996, suggest that the market would less negatively respond to SEO
announcements of high-growth firms. Following this view, the more negative D10 CARs
could be reflecting investors’ rational expectation in changes of firms’ growth
opportunities, rather than the differential quality screening effects across industries. To
address this possible condition, we report the double-sort results of industry Q and the
changes in issuers’ Q during the year of issuance in Panel E of Table IV. We find that
the average Q1 CARs are significantly higher than Q5 CARs in 4 out of 5 quintiles
ranked by the change in issuers’ Q. This result is supportive to the quality screening
argument.
We address one more question not previously addressed; is the difference in the
average CARs between the high- and low-growth industry issuers attributable to the
difference in market conditions? To address this issue, we follow Bayless and
Chaplinsky (1996) by classifying the issuance market into hot, normal and cold markets.
An issuance market is hot when there are at least three contiguous months where equity
meet voluntary financing needs. In a subsequent study, Cornett, Mehran, and Tehranian (1998) find that the post-offer stock performance of involuntary issues is also better. They argue that this is due to a less
23
volume is in the highest quartile of equity volume, cold when the issue volume falls in
the lowest quartile for at least three contiguous months, and normal otherwise.12 Panel F
of Table IV further demonstrates that the market condition does not explain the
difference in average CARs between Q1 and Q5 issuers. The market responds to the Q1
industry issues more favorably under each of the three market conditions. The results in
Panel F also strongly favor the existence of a significant industry effect on SEO
announcement CARs that is not fully captured by overall market conditions.
Further, we jointly consider the impact of industry Q and other factors through
cross-sectional regressions. Table V reports the coefficient estimates with t-statistics in
the parentheses below the coefficient estimates. Column A includes all the explanation
variables in Table V except the hot market dummy is defined slightly differently. It
equals 1 in a hot market, and 0 in a normal or cold market. In Column B, we additionally
include a product term of Industry Q and a hot market dummy. The coefficients on
industry Q are negative and significant. In addition, we find that CARs are negatively
related to the standard deviation of intra-industry Q and issuers’ pre-offer stock
performance, but positively related to the change in firm Q. However, the coefficient on
the interactive term is insignificant, suggesting that issuance conditions have little impact
on the negative relationship between CARs and Industry Q.13
severe overvaluation problem in the involuntary issues. 12 Following Bayless and Chaplinsky (1996), we compute equity volume by deflating and detrending three month moving averages of monthly equity issue proceeds, including both IPOs and SEOs. 13 We conduct a series of robustness checks on the results. First, we remove financial institutions and utilities from our sample. Second, we alternatively use the pre-issue industry stock performance and sales growth rate as the proxy for industry growth opportunities. Third, we separate our sample into pre-1990 and post-1991 periods. None of these modifications change the basic results.
24
In sum, our evidence shows that the average announcement-period CAR of low-
growth industries is higher than the average CAR of high-growth industry SEOs. This
result supports quality screening: the average quality of low-growth industry issuers is
better than that of high-growth industry issuers.
B. Operating Performance and Earnings Management Around Announcement
B.1. Operating Performance
We now examine to see if the long run post-offer operating performance across
high- and low-growth industry issuers is related to a quality screening effect. All the
industries having SEO issuers are separated into quintiles based on Tobin’s Q. Table VI
reports the equally-weighted averaged characteristics-adjusted operating performance in
each industry quintile for each year from year -3 through year 3 surrounding a SEO
announcement year 0.14
Panel A reports the operating performance results based on issuers’ yearly
operating income before depreciations (OIBD) scaled by total assets. Aggregating
issuers’ operating performance across all growth quintiles, the average characteristics-
adjusted operating performance is positive in the four-year period leading up to and
including the year of issue while the post-offer operating performance is negative. The
characteristic-adjusted operating performance reflects the relative performance of an
issuer to non-issuers with comparable characteristics within the same industry, so that our
results confirm the operating performance pattern documented in Loughran and Ritter
14 We also conduct tests based on the value-weighted mean abnormal operating performance measures. They yield similar results.
25
(1997); SEOs typically follow periods of overperformance and are followed by periods of
underperformance.
Further, Panel A reveals interesting patterns in the post-offering period. The
magnitude of underperformance in the high-growth industries is much greater than that in
the low-growth industries. In year 1 the Q1 issuers slightly underperform and in year 2
slightly outperform non-issuers, however, the average operating performance for Q5
issuers is -3.65 percent and -3.32 percent, respectively. Q5 issuers underperform Q1
issuers by 3.61 percent and 3.33 percent in the first two years after the offerings, both
significant at the 1 percent level. This result is consistent with the quality screening
argument under which low-growth industry issuers have better quality than high-growth
industry issuers after the issue.
The above quintile approach shows a decreasing mean in the OIBD operating
performance measure as the average industry Q increases. Further, we perform
multivariate regressions to see if the relationship between industry growth opportunities
and post-offering operating performance holds against other variables. We include the
same set of regressors used in the CAR regression. In Column A of Table VII, we
measure operating performance by the ratio of operating income before depreciation to
total assets (OIBD/TA) in the year before the offerings without characteristics
adjustments. The result supports the quality screening argument; issuers’ operating
performance is negatively correlated with industry Q. Consistent with our findings in
Table IV and the conventional wisdom, we find that operating performance is better
when: (1) the intra-industry standard deviation of Q is lower; (2) the issuer’s pre-issue Q
26
is lower; (3) issuer size is higher; (4) the change in Q around the SEO is higher; and (5)
or the issue is in a hot issuance market.
In sharp contrast to the pattern of post-announcement operating performance,
low-growth industry issuers underperform high-growth industry issuers in the pre-
offering period and the year of offering. For example, in year –1, the average
characteristic-adjusted OIBD performance of Q1 issuers is 1.36 percent and is
significantly lower than the Q5 issuers’ 4.67 percent. Graph A of Figure II depicts the
characteristics-adjusted OIBD performance for Q1 and Q5 issuers. Q5 issuers’ operating
performance peaks in year –1 then drops from the issuance year to its lowest level in year
2. By contrast, Q1 issuers’ operating performance is much less volatile, and declines at a
near constant level per year during the 7-year observation period. The material difference
in pre- and post-announcement operating performance rules out the possibility that the
more severe post-announcement underperformance of the high-growth industry issuers is
a simple continuation of their pre-announcement performance pattern. This result offers
additional support to our quality screening argument.
In Panel B, we measure operating performance using return on assets (ROA).
Similar to the results when using the OIBD operating performance measure, the average
characteristics-adjusted ROA for high-growth industry issuers peaks in year -1 and is
lower in post-announcement periods.
What could account for, or at least contribute to, the documented difference in
pre-announcement and post-announcement operating performance for the high-growth
and low-growth industry issuers? Rangan (1997) and Teoh, Welch and Wong (1998)
suggest that seasoned equity issuers have a greater incentive to manager their earnings
27
prior to the offerings than non-issuing firms and that firms and that firms have more
earnings management have greater future underperformance.
According to their view, more earnings management activities for the high-
growth industry issuers would be expected. Issuers’ earnings can be decomposed into
cash flow and accruals. Cash flow is generally viewed as a more reliable estimate of the
non-managed part of firm earnings. Based on this view, we further examine the cash
flow performance of different types of issuers in Panel C of Table VI. Although the
differences in the cash flow performance for Q1 and Q5 issuers are negative in years 0
and -1, neither of the differences is significant. As the potentially more earnings
managed operating income based measure is significantly higher for high growth industry
issuers in Panels A and B relative to low-growth industry issuers in the years –1 and 0,
this finding can be viewed as indirect evidence that high-growth industry issuers engage
in earnings management.
B.2. Earnings Management Activities
In this section, we examine the cross-industry earnings management efforts of
SEO issuers. Panel A of Table VIII reports the average characteristics-adjusted accruals
of issuers in high-growth industries and low-growth industries. Similar to the pattern
documented in Teoh, Welch and Wong (1998), the all-rank aggregation reveals an
interesting pattern in issuers’ earnings management activities, where total accruals
increase in the years before SEOs and during the SEO year, and then they quickly drop
over the post-issue period. The peak appears in the issuance year 0. The reversed U-
shaped patterns in total accruals are also depicted in Figure III (a).
28
In addition, the high-growth industry issuers engage in more earnings
management activities than the low-growth industry issuers in year 0 and year -1. The
differences in the average characteristics-adjusted total accruals between Q1 and Q5
issuers are significantly negative in years -1 and 0. Especially in year 0, the average
adjusted current accruals for Q5 issuers are 4.20 percent, more than double the total
accruals for Q1 issuers (2.02 percent). The pattern of a peak occurring in all quintiles in
the issuance year also indicates that earnings management appears to be systematically
occurring across all SEO issue quintiles in Panel A of Table VIII.15
We decompose the total accruals into discretionary accruals and nondiscretionary
accruals. The results for characteristics-adjusted discretionary accruals are reported in
Panel B. In year 0, the average discretionary accruals of low-growth industry issuers are
significantly higher than those of high-growth industry issuers. Figure III (b) shows that
the pattern in issuers’ discretionary accruals is similar to that in total accruals. Panel 3 of
Table VIII and Figure III (c) provide the results for characteristics-adjusted
nondiscretionary accruals. High-growth industry issuers have higher nondiscretionary
accruals prior to SEO announcement, but the difference in the nondiscretionary accruals
between high-growth and low-growth industry issuers in the year –3 to year 0 period is
insignificant, suggesting discretionary accruals account more for the operating
performance difference across issuers in different industry groups.
In sum, issuers’ earnings management is more severe in high-growth industry
issuers, indicating the high-growth industry issuers are more likely to have a greater
“lemons” problem. The earnings management evidence appears to at least materially
15 In the tests not reported here, we alternatively use Sloan’s (1996) and Chan et al’s (2003) measures of accruals and corresponding discretionary accruals measures. We obtain qualitatively similar results.
29
contribute to the cross-industry differences of operating performance between high and
low growth firms before and after the offerings, while also further supporting the quality
screening argument.
IV. Conclusions
We examine the relationship between SEO issuers’ quality and industry growth
opportunities. Differing from the conventional view that better growth opportunities
alleviate the information asymmetry problem, we introduce a quality screening effect that
predicts low-growth industry issuers’ quality is better than the high-growth industry
issuers. Consistent with the quality-screening hypothesis, we find that SEOs’ two-day
announcement abnormal returns are nearly 1.5 percent higher in the bottom growth
industry decile than in the top growth industry decile. Higher CARs for low-growth
industry issuers are also robust after we control for size, leverage and other behavioral
factors, such as possible investors’ overvaluation and issuance market conditions at the
time of the SEO announcement,
The quality screening argument is further supported by our long-run operating
performance analyses. High-growth industry issuers have poorer characteristics-adjusted
operating performance after SEO offerings even though they have better characteristics-
adjusted operating performance in years before the offerings and during the offering year.
We further analyze issuers’ earnings management activities by comparing total accruals
and discretionary accruals for high-growth and low-growth issuers. We find that all
issuers appear to have greater earnings management than non-issuers, and the magnitude
of pre-issue earnings management in high-growth industry issuers is higher. In all,
30
support for a quality screening condition is found in the difference between high and low
growth industrial companies’ SEO announcement CARs, their pre- to post-operating
performance and their apparent earnings management behavior.
31
Appendix
A. The Case When Good and Bad Firms are not Equally Probable
Assuming the proportion of good firms is θ, the game tree remains the same as
Figure 1. In addition, the indifference conditions for the good and bad firms are identical
to (1) and (2). As we need consider θ in the breakeven condition of the new investors,
the expected value of an issuer, P, can be expressed as the following:
bg
bb
bg
gg pp
pqapp
pqaP
)1()1()(
)1()(
θθθ
θθθ
−+−
−+−+
+= (A1)
Jointly solving for pg and pb using (1), (2) and (A1), we obtain the following
results:
bg qp 'δ= (A2)
gb qp 'δ= (A3)
where )][(
)1()()('
bg
gbbbg
qqE
qEqaqEqa
−
−+−+++=
θ
θθδ .
Thus, the expression for the probability of an issuer being a good firm, ρ, is same as (7).
Q.E.D.
B. Proof of Proposition II:
wqq
q
bg
b
+=
+=
11ρ where
b
g
w = . (A4)
www 2)1(2
1+
−=∂∂ρ <0 (A5)
Q.E.D.
32
C. Proof of Proposition III:
bbbg qwqqqP ]1)1([)( −+=−+=∆ ρρ
wqw
wP b
∂−+∂
=∂∆∂ }]1)1({[)( ρ
bb qw
wqw ])1('[]1)1([ ρρρ +++
∂∂
−+= (A6)
g
b
b
b
qww
q 21−=
∂∂
=∂∂ (A7)
Insert A7 to A6, we have
=∂∆∂wP)(
ww
wρρ −
++∂∂ 1)1( (A8)
As ρ <1, the right-hand side second term in (A8), w
ρ−1 , is always positive. As a
result, a necessary way to ensure 0)(<
∂∆∂wP is 0<
∂∂wρ .
Q.E.D.
D. Condition for 0)(<
∂∆∂wP :
Inserting A4 to A8 and simplifying the expression, we have w<w*=3-2 2 .
33
References
Akerlof, G., 1970, The market for lemons: quality uncertainty and the market mechanism, Quarterly Journal of Economics 84, 488-500.
Asquith, P., Mullins D., 1986, Equity issues and offering dilution, Journal of Financial
Economics 15, 61-89. Baker, M., Wurgler, J., 2000, The equity share in new issues and aggregate stock returns,
Journal of Finance 55, 2219-2257. Barber, B.M. and Lyon, J.D., 1996, Detecting abnormal operating performance: The
empirical power and specification of test statistics, Journal of Financial Economics 41, 359-399.
Barclay, M.J. and Litzenberger R.H., 1988, Announcement effects of new equity issues
and the use of intraday price data, Journal of Financial Economics 21, 71-99. Bayless, M., Chaplinsky, S., 1996, Is there a window of opportunity for seasoned equity
issuance, Journal of Finance 51, 253-278. Barberis, N., Shleifer, A., Vishny, R., 1998, A model of investor sentiment, Journal of
Financial Economics 49, 307-343. Carhart, M. M., 1997, On persistence in mutual fund performance, Journal of Finance
52, 57-83. Chan, Konan, Louis K.C.Chan, Narasimhan Jegadeesh and Josef Lakonishok, 2003,
“Earnings Quality and Stock Returns,” Working Paper. Choe, H., R. Masulis, W., Nanda, V., 1993, Common stock offerings across the business
cycle: theory and evidence, Journal of Empirical Finance 1, 3-31. Cooney, J. W., Kalay, A., 1993, Positive information from equity issue announcement,
Journal of Financial Economics 33, 149-172. Cornett, M. M., Tehranian, H., 1994, An examination of voluntary versus involuntary
security issues by commercial banks, Journal of Financial Economics 35, 99-122. De Bondt, W., R. Thaler, 1985, Does the stock market overreact? Journal of Finance 40,
793-805. De Bondt, W., Thaler, R., 1987, Further evidence on investor overraction and stock
market seasonality, Journal of Finance 42, 557-581.
34
Dechow, Patricia, Richard Sloan, and Amy Sweeney, 1996, Causes and consequences of earnings manip-ulation: An analysis of firms subject to enforcement actions by the SEC, Contemporary Accounting Research 13, 1–36.
Fama, E. F., French, K. R., 1997, Industry costs of equity, Journal of Financial
Economics 43, 153-193. Fama, E. F., French, K. R., 1993, Common risk factors in the returns on stocks and
bonds, Journal of Financial Economics 33, 3-56. Helwege, Jean, and Nellie Liang, 2004, Initial Public Offerings in Hot and Cold Markets,
Journal of Financial and Quantitative Analysis, Forthcoming. Jones, J., 1991, Earnings management during import relief investigations, Journal of
Accounting Research 29, 193-228. Jung, K., Kim, Y. and Stulz, R.M., 1996, Timing, investment opportunities, managerial
discretion, and the security issue decision, Journal of Financial Economics 42, 159-185.
Korajczyk, R.A., Lucas D.J. and MaDonald R. L. 1990, Understanding stock price
behavior around the time of equity issue, in R. Glenn Hubbard (ed.) Asymmetric Information, Corporate Finance and Investment, University of Chicago Press, 257-277.
Lakonishok, J., Shleifer, A., Vishny, R. W., 1994, Contrarian investment, extrapolation,
and risk, Journal of Finance 49, 1541-1578. Lee, I., Lochhead, S., Ritter, J., Zhao, Q., 1996, The costs of raising capital, Journal of
Financial Research 19, 59-74. Lang, L., Ofek, E., and Stulz, R. M., 1996, Leverage, investment, and firm growth,
Journal of Financial Economics 40, 3-29. Loughran, T. and J. R. Ritter, 1997, The Operating Performance Firms Conducting
Seasoned Equity Offerings, Journal of Finance, 52, 1823-1850. Loughran, T. and J. R. Ritter, 1995, The New Issue Puzzle, Journal of Finance, 50, 23-
51. Initial Public Offerings in Hot and Cold Markets Journal of Financial and Quantitative Analysis, Vol.
39, p. 541, September 2004 Lucas, D., McDonald. R., 1990, Equity issues and stock price dynamics, Journal of
Finance 45, 1019-1043. Masulis, R., Korwar A., 1986, Seasoned equity offerings, Journal of Financial
Economics 15, 91-118.
35
Mikkelson, W. H., Partch, M. M., 1986, Valuation effects of security offerings and the
issuance process, Journal of Financial Economics 15, 31-60. Moskowitz, T.J. and Grinblatt, M., 1999, Do industries explain momentum? Journal of
Finance 54, 1249-1290. Myers, S. C., 1977, Determinants of corporate borrowing, Journal of Financial
Economics 5, 147-175. Myers, S. C., and Majluf, N. S., 1984, Corporate financing and investment decisions
when firms have information that investors do not have, Journal of Financial Economics 13, 187-221.
Perry, S, and Williams, T., 1994. Earnings management preceding management buyout
offers, Journal of Accounting and Economics 18, 157-179. Pilotte, E., 1992, Growth opportunities and the stock price response to new financing, Journal of Business 65, 371-394. Rangan, S., 1997. Earnings around seasoned equity offerings: Are they overstated?
Journal of Financial Economics 50, 101-122. Ritter, J. R., 1991, The long-run performance of initial public offerings, Journal of
Finance, 46, 3-27. Ross, S. A., 1977, The determinants of financial structure: The incentive-signaling
approach, The Bell Journal of Economics 8, 23-40. Raymar, S., 1993, The financing and investment of A levered firm under asymmetric
information, Journal of Financial Research 16, 321-336. Sant, R. and Ferris S.P., 1994, Seasoned equity offerings: the case of all-equity firms, Journal of Business Finance and Accounting 20, 115-124. Sloan, Richard G., 1996, “Do Stock Prices Fully Reflect Information in Accruals and
Cash Flows about Future Earnings?” The Accounting Review 71, 289-315. Slovin, M., Sushka, M., and Polonchek, J., 1992, Informational externalities of seasoned
equity issues, Journal of Financial Economics 32, 87-101. Spiess, D. K. and J. Affleck-Graves, 1995, Underperformance in long-run stock returns
following seasoned equity offerings, Journal of Financial Economics, 38, 243-267. Stoughton, Neal M., Kit P. Wong, and Jesef Zechner, 2001, IPO and Product Quality,
Journal of Business, 74, 375-408.
36
Szewczyk, S. H., 1992, The intra-industry transfer of information inferred from
announcements of corporate security offerings, Journal of Finance 47, 1935-1945. Teoh, S. H., Ivo, W. and Wong, T.J., 1998, Earnings management and the
underperformance of seasoned equity offerings, Journal of Financial Economics 50, 63-99.
37
Table I SEO Characteristics across Industries
Table I reports the numbers of SEO issues, the percentage of the total issues within each industry, aggregate proceeds, and the two-day days (0, 1) average cumulative abnormal returns (CARs) of seasoned equity offerings (SEOs) for 12 industrial sectors over the period of 1980-2002. The industry classification follows Fama and French (1997) industrial sector classifications. The mean CAR in the full sample period is calculated as the average of all SEO CARs in our sample. The t-statistics of the mean CARs are in parentheses.
Name of Industry
Number Percentage of the total issues (%)
Proceeds ($Billion)
CAR (%)
Consumer Non-durable
353 4.07 24.8 -2.36 (-9.45)
Consumer Durable
180 2.07 21.5 -1.94 (-5.46)
Manufacturing
753 8.68 58.7 -2.04 (-11.41)
Energy
389 4.48 29.7 -1.92 (-6.90)
Chemical Products
126 1.45 10.6 -1.86 (-5.67)
Business Equipments
1443 16.63 118.6 -2.41 (-13.67)
Telecommunication Products
306 3.53 59.2 -2.06 (-5.92)
Utilities
663 7.64 57.6 -0.61 (-7.06)
Wholesales and Retail Businesses
960 11.06 60.2 -2.18 (-15.68)
Healthcare
827 9.53 53.9 -2.72 (-12.52)
Financial Institutions
1554 17.91 134.3 -1.12 (-8.74)
Others
1123 12.94 148.9 -2.03 (-14.93)
1980-2002 8677 778.1 -1.90
(-47.06)
38
Table II Cumulative Abnormal Returns Sorted by Industry Q
Panel A of Table II reports the equally- and value-weighted average two-day (0, 1) CARs of SEOs across industry deciles. In each year, we group all CRSP firms with the same first 3-digit SIC codes into industries and then rank the industries into deciles based on value-weighted average Qs. We pool all issuers with the same industry ranking into the same group and calculate the equally-weighted average CARs for issuers in each decile. D1-D10 is the differences in CARs of SEOs between the lowest-growth industry decile (D1) and the highest-growth industry decile (D10). D(1-5) – D(6-10) is the difference in CARs of SEOs between below-median industries and above-median industries. Panel B of Table II is similarly constructed except firms are grouped based on the Fama and French (1997) 48-industry classifications and all industries are broken down into quintiles. The t-Statistics of the differences in mean CARs are in parentheses. Panel A: Using 3-digit SIC Codes to Classify Industries
Industry Q Rank Number of SEOs Equally-Weighted CAR Value-Weighted CAR D1 (low) 832 -1.26 -0.77
2 859 -1.30 -0.96 3 472 -1.51 -1.10 4 698 -1.67 -1.44 5 776 -1.53 -1.47 6 760 -1.80 -1.61 7 684 -2.22 -1.25 8 899 -2.24 -1.72 9 1209 -2.04 -1.89
D10 (high) 1490 -2.74 -2.25 D1 – D10 1.48
(5.71) 1.48
(7.77) D(1-5) – D(6-10) 0.80
(7.15) 0.92
(9.74) Panel B: Using Fama-French Definition to Classify Industries
Industry Q Rank Number of SEOs Equally-Weighted CAR Value-Weighted CAR Q1 1897 -1.30 -0.58 2 1532 -1.50 -0.76 3 1315 -1.77 -1.29 4 1522 -2.31 -1.78
Q5 2487 -2.50 -1.74 Q1 – Q5 1.20
(6.88) 1.16
(8.66) Q(1-2) – Q(4-5) 1.00
(8.34) 0.76
(8.30)
39
Table III Issuers’ Characteristics Sorted by Industry Q
Table III reports the value-weighted issuers’ characteristic measures for each industry decile in the offering year sorted by industry Q. Industry Q is the value-weighted average Q of stocks having the same first three-digit SIC codes. Issuer size is an issuer’s market capitalization in June of the holding year. Leverage is the sum of debt in current liability and long-term debt scaled by total assets. The intra-industry standard deviation of Q is the standard deviation of firm Q within the same three-digit SIC code. Issuers’ performance is the intercept from the CAPM-based one-factor model regression using 36-month pre-issue stock returns. Change in issuer Q is the change of firm Q from the end of the prior SEO year to the end of the SEO year.
Industry Q Rank
Size ($Million) Leverage
Intra-industry Std. Dev. of Q
Issuer Performance (%)
Change in Issuer Q
1 1709 0.28 0.39 0.88 -0.006 2 1552 0.34 0.84 1.14 -0.025 3 1623 0.30 0.62 1.01 -0.031 4 1248 0.32 0.65 1.15 -0.019 5 1362 0.31 0.94 1.47 -0.060 6 862 0.25 1.13 2.25 -0.115 7 1130 0.21 1.51 2.18 -0.114 8 941 0.19 1.70 2.49 -0.103 9 669 0.15 2.14 2.43 -0.151 10 879 0.14 2.34 3.06 -0.325
40
Table IV Double Sorted Cumulative Abnormal Returns
Table IV reports the equally-weighted average cumulative abnormal returns (CARs) for SEOs first sorted into industry Q quintiles and then into quintiles based on alternative control measures: by the issuer’s firm size quintile in Panel A, by the issuer’s leverage in Panel B, by the intra-industry standard deviation of Q in Panel C, by firm performance in Panel D, by change in issuer Q in Panel E, and by hot, cold, and normal issuance market in Panel F. The last row of each panel reports the differences in CARs between SEOs issued by firms from the bottom industry growth portfolio (Q1) and from the top industry growth portfolio (Q5). The t-statistics are in parentheses. A. Firm Size B. Leverage
Industry Q Ranking
Q1 (low)
Q2 Q3 Q4 Q5 (high)
Q1 (low)
Q2 Q3 Q4 Q5 (high)
All -1.99 -2.24 -1.96 -1.86 -1.61 -2.18 -1.97 -1.84 -1.97 -2.07 Q1 (low) -0.93 -1.71 -1.01 -1.25 -0.81 -1.23 -1.55 -1.34 -1.19 -1.59
2 -1.63 -2.25 -1.72 -1.41 -1.54 -2.46 -2.16 -1.59 -1.19 -1.26 3 -2.25 -2.10 -1.98 -1.69 -1.82 -2.56 -1.95 -1.88 -1.46 -2.26 4 -2.57 -2.86 -2.18 -2.27 -1.78 -1.85 -2.27 -1.92 -3.22 -2.47
Q5 (high) -2.51 -2.24 -2.89 -2.68 -2.10 -2.76 -1.94 -2.46 -2.73 -2.75 Q1-Q5 (t-stat)
1.55 (2.84)
0.92 (2.22)
1.67 (4.44)
1.83 (4.34)
0.75 (1.35)
1.54 (3.31)
0.39 (0.65)
1.11 (2.81)
1.55 (3.80)
1.16 (2.70)
C. Intra-industry Std. Dev. of Issuer Q D. Issuer Performance
Industry Q Ranking
Q1 (low)
Q2 Q3 Q4 Q5 (high)
Q1 (low)
Q2 Q3 Q4 Q5 (high)
All -1.86 -1.63 -1.95 -2.05 -2.11 -1.36 -1.48 -1.84 -1.92 -2.25 Q1 (low) -1.16 -0.67 -1.31 -0.97 -1.23 -0.35 -0.94 -0.93 -1.02 -1.80
2 -1.50 -1.29 -1.64 -2.34 -1.95 -1.31 -1.09 -1.05 -1.73 -2.41 3 -1.72 -1.74 -2.24 -2.19 -1.91 -1.61 -1.32 -2.01 -2.06 -2.58 4 -2.87 -2.05 -2.03 -2.51 -2.41 -1.62 -2.20 -2.62 -1.94 -2.11
Q5 (high) -2.03 -2.50 -2.52 -2.52 -2.93 -1.90 -1.86 -2.59 -2.85 -2.38 Q1-Q5 (t-stat)
0.87 (2.12)
1.83 (3.76)
1.21 (2.96)
1.55 (3.82)
1.71 (3.86)
1.58 (2.41)
0.54 (1.21)
1.87 (5.20)
1.43 (3.80)
1.29 (4.04)
E. Change in Issuer Q F. Market Conditions
Industry Q Ranking
Q1 (low)
2 3 4 Q5 (high)
Cold Normal Hot
All -2.13 -1.89 -1.46 -2.25 -2.03 -2.19 -1.76 -2.35 Q1 (low) -1.51 -1.79 -0.97 -1.52 -1.03 -1.29 -1.00 -1.56
2 -1.97 -1.29 -1.13 -1.30 -2.59 -1.61 -1.58 -2.01 3 -2.45 -2.03 -1.59 -1.89 -2.18 -1.69 -1.80 -2.59 4 -2.20 -2.22 -2.17 -2.88 -1.92 -2.54 -2.09 -2.97
Q5 (high) -2.47 -2.02 -2.55 -3.20 -2.05 -3.45 -2.35 -2.51 Q1-Q5 (t-stat)
0.96 (1.76)
0.23 (0.44)
1.58 (4.88)
1.69 (3.39)
1.02 (1.99)
2.16 (3.85)
1.35 (5.66)
0.95 (2.95)
41
Table V Regressions of Announcement Period CARs
Table V reports the estimated coefficients of regressions of announcement period abnormal returns times 100. Independent variables include: (a) Industry Q; (b) Intra-industry Std. Dev. of Issuer Q; (c) Hot market, dummy variable equals 1 if when there are at least three contiguous months where equity volume is in the highest quartile of equity volume and 0 otherwise; (d) the product of industry Q and the hot market dummy; (e) the change in issuer Q; (f) the risk-adjusted firm performance estimated from the four-factor regression using 36-month return prior to SEOs; (g) the logarithm of a firm’s market value of equity in June of the year prior to a SEO announcement; and (h) firm leverage of the issuer in the year prior to its SEO announcement (Leverage). The t-statistics are in parentheses.
(A) (B) Constant -1.99
(-3.87) -1.99
(-3.80) Industry Q -0.18
(-2.33) -0.19
(-2.40) Intra-industry Std. Dev. of Q -0.17
(-2.55) -0.16
(-2.54) Hot Market -0.28
(-1.87) -0.28
(-1.75) Industry Q * Hot Market 0.06
(0.65) Change in firm Q 0.04
(1.97) 0.04
(1.99) Firm Performance -13.33
(-5.68) -13.27 (-5.65)
Log Firm Size 0.057 (1.46)
0.052 (1.32)
Leverage 0.13 (0.68)
0.13 (0.41)
Number of Observations
6640 6640
Adjusted R2 0.058
0.062
42
Table VI Characteristics-Adjusted Operating Performance Sorted by Industry Q
Table VI reports mean abnormal operating performance in the three years surrounding the SEO announcement year for each industry Q quintile. The COMPUSTAT data items to measure operating performance are operating income before depreciation (OIBD)/Total Assets (item 13/item 6), return on assets (item 172/item 6), and cash flow income/total assets (item 308/item 6). The abnormal operating performance is the difference of operating performance between a SEO firm and the median performance of matched firms with the same three-digit SIC codes, comparable firm sizes, Qs, and operating performance in the prior year. The t-statistics are in parentheses. Panel A. Operating Income before Depreciation/Total Assets
Industry Rank -3 -2 -1 0 1 2 3 All Ranks 0.90 1.43 2.26 1.18 -1.11 -0.75 -0.61
Q1 1.61 1.30 1.36 0.47 -0.04 0.01 -0.43 2 -0.66 1.25 0.18 1.14 0.60 0.11 0.31 3 0.15 0.73 1.48 1.72 -0.26 -0.29 -0.38 4 1.53 2.87 3.66 0.67 -2.23 -0.95 -0.47
Q5 2.07 1.97 4.67 1.90 -3.65 -3.32 -2.13 Q1-Q5 -0.46 -0.67 -3.31 -1.50 3.61 3.33 1.70 (t-stat) (-0.18) (-0.66) (-2.51) (-1.72) (3.90) (3.24) (1.53)
Panel B: Return of Assets Industry Rank -3 -2 -1 0 1 2 3
All Ranks 1.06 1.33 1.93 1.90 -2.20 -2.37 -1.30 Q1 1.04 1.02 1.04 0.52 -0.41 -0.71 -1.06 2 -0.64 0.56 0.36 1.57 -0.22 -0.30 -0.16 3 1.88 0.47 2.12 3.33 -0.56 -1.99 0.21 4 2.50 3.36 2.48 1.28 -4.30 -2.08 -1.63
Q5 0.46 1.21 3.61 2.77 -5.49 -6.69 -3.82 Q1-Q5 0.57 -0.19 -2.57 -2.25 5.08 5.98 2.76 (t-stat) (0.32) (-0.11) (-1.96) (-2.08) (3.75) (4.23) (1.92)
Panel C: Cash Flow Income/Total Assets
Industry Rank -3 -2 -1 0 1 2 3 All Ranks 0.58 0.85 1.12 -0.06 -1.10 -1.17 -0.49
Q1 0.23 0.52 -0.08 -0.28 -0.13 0.09 -0.66 2 -0.30 0.41 0.33 -0.03 0.14 -0.57 -0.26 3 0.69 1.17 0.96 0.03 -1.09 -0.13 0.35 4 1.92 2.46 1.31 -0.57 -0.58 -0.84 0.50
Q5 0.41 0.11 1.36 0.52 -3.83 -4.40 -2.31 Q1-Q5 -0.18 0.41 -1.44 -0.80 3.70 4.49 1.65 (t-stat) (-0.13) (0.37) (-1.45) (-0.81) (3.98) (3.99) (1.60)
43
Table VII Regressions of Issuers’ Post-Announcement Operating Performance
Table V reports the estimated coefficients of regressions of year 1 operating performance. Independent variables include: (a) Industry Q; (b) Intra-industry Std. Dev. of Q; (c) Hot market, dummy variable equals 1 if when there are at least three contiguous months where equity volume is in the highest quartile of equity volume and 0 otherwise; (d) the change in issuer’s Q; (e) the issuer’s pre-issue Q; (f) the logarithm of a firm’s market value of equity in June of the SEO year; and (g) the issuer’s leverage in the SEO year. Operating performance is calculated without characteristics-adjustment. Column (A) measures operating performance with the ratio of operating income before depreciation to total assets (OIBD/TA); Column (B) measures operating performance with return on assets (ROA); Column (C) measures operating performance with cash flow performance to total assets. The t-statistics are in parentheses.
(A) OIBD/TA
(B) ROA
(C) Cash Flow
Constant -0.204 (-9.22)
-0.270 (-10.33)
-0.24 (-11.49)
Industry Q -0.017 (-5.59)
-0.018 (-5.28)
-0.008 (-3.13)
Intra-industry Std. Dev. of Q -0.010 (-2.29)
-0.015 (-3.02)
-0.020 (-4.91)
Hot Market -0.011 (-2.19)
-0.007 (-1.26)
-0.012 (-2.58)
Change in firm Q 0.011 (2.60)
0.023 (4.46)
0.006 (1.52)
Pre-issue Firm Q -0.009 (-4.51)
-0.016 (-6.54)
-0.017 (-8.90)
Log Firm Size 0.026 (15.58)
0.027 (13.80)
0.028 (17.45)
Leverage 0.13 (0.68)
-0.068 (-4.02)
-0.039 (-2.84)
Number of Observations
5787 6011 5604
Adjusted R2 0.108
0.097 0.121
44
Table VIII Characteristics-Adjusted Accruals Sorted by Industry Q
Table VII reports the mean accounting accruals for the three years surrounding the SEO announcement for each quintile sorted by the industry Q in the SEO year. Firms’ total accruals are calculated as the change in non-cash current assets (∆ item 4-∆item 1), less the change in current liabilities (exclusive of short-term debt and taxes payable) (∆ item 5-∆ item 34-∆ item 71). Discretionary accruals are the difference between total accruals and non-discretionary accruals. Non-discretionary accruals are estimated using the Jones (1991) model. All accruals are scaled by the beginning-period total assets (item 6). The t-statistics are in parentheses. Panel A: Total Accruals
Industry Rank -3 -2 -1 0 1 2 3 All Ranks 0.54 1.08 1.79 3.86 1.47 0.28 0.21
Q1 0.13 1.25 1.22 2.02 1.08 0.29 0.25 2 0.27 0.73 1.13 1.93 1.00 0.21 0.66 3 1.42 1.33 1.73 5.38 1.38 0.14 -0.41 4 0.43 1.28 2.17 5.74 2.73 0.73 1.02
Q5 0.48 0.80 2.49 4.20 1.13 0.03 -0.50 Q1-Q5 -0.35 0.45 -1.27 -2.18 -0.05 0.26 0.75 (t-stat) (-0.36) (0.47) (-1.81) (-2.94) (-0.06) (0.42) (1.04)
Panel B: Discretionary Accruals
Industry Rank -3 -2 -1 0 1 2 3 All Ranks 0.10 -0.06 0.38 1.93 0.80 0.03 0.18
Q1 -0.06 0.15 0.50 0.83 0.44 -0.07 0.52 2 0.17 0.02 0.04 1.20 0.19 0.15 0.81 3 0.93 0.25 1.09 3.02 1.08 0.33 -1.03 4 0.54 -0.48 0.23 2.65 1.42 -0.49 0.69
Q5 -1.07 -0.20 0.96 1.99 0.84 0.23 -0.15 Q1-Q5 1.01 0.35 -0.46 -1.16 0.40 -0.30 0.67 (t-stat) (1.22) (0.43) (-0.58) (-1.91) (-0.71) (-0.54) (1.02)
Panel C: Non-discretionary Accruals
Industry Rank -3 -2 -1 0 1 2 3 All Ranks 0.28 0.77 1.11 1.73 0.53 0.34 0.23
Q1 -0.23 0.40 1.05 1.38 0.54 0.23 0.12 2 0.37 0.49 0.56 1.36 0.50 0.42 0.26 3 0.56 1.21 1.01 2.13 0.75 0.28 0.14 4 0.53 0.90 1.40 1.88 0.49 0.78 0.47
Q5 0.18 0.87 1.53 1.88 0.36 -0.04 0.15 Q1-Q5 -0.41 -0.47 -0.48 -0.50 0.18 0.37 -0.03 (t-stat) (-0.78) (-1.01) (-1.06) (-1.26) (0.48) (1.05) (-0.12)
45
Figure 1 Existing Shareholders’ Payoff in the Issuing Game
There are one good firm and one bad firm. Each firm has two strategies, either issue seasoned equity or do not issue. The good firm issues with probability of pg and the bad firm issues with probability of pb. The cells present the payoffs of the existing shareholders of the good firm and then the bad firm.
Bad Firm
Good Firm Issue Not Issue
Issue
))(),(( EqaEP
PEqaEP
Pbg +−
+++
+
),( aqa g+
Not Issue
),( bqaa −
),( aa
46
Figure II Operating Performance of Q1 and Q5 Industry Issuers
Operating performance is scaled by the beginning-period total assets for bottom and top industry growth quintiles and is plotted for 3 years plus and minus the SEO announcement year. Operating income before depreciation is plotted (a); return on assets is plotted in (b) and cash flow of operations is plotted in (c).
(a) Operating Performance Before Depreciations
-8.00%
-6.00%
-4.00%
-2.00%
0.00%
2.00%
4.00%
6.00%
-3 -2 -1 0 1 2 3
Year Relative to SEO Announcement
OIB
D (%
)
Bottom Industry Quintile Top Industry Quintile
(b) Return on Assets
-8.00%
-6.00%
-4.00%
-2.00%
0.00%
2.00%
4.00%
6.00%
-3 -2 -1 0 1 2 3
Fiscal Year Relative to SEO
Ret
urn
on A
sset
s (%
)
Bottom Industry Quintile Top Industry Quintile
47
(c) Cash Flows from Opertions
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
-3 -2 -1 0 1 2 3
Fiscal Year Relative to SEO
Cas
h Fl
ow fr
om O
pera
tion
(%)
Bottom Industry Quintile Top Industry Quintile
48
Figure III Earnings Management for Q1 and Q5 Industry Issuers
Total accruals and its two components, discretionary and non-discretionary accruals, are scaled by the beginning-period total assets for bottom and top industry performance quintiles and are plotted for 3 years plus and minus the SEO announcement year.
(a) Total Accruals
-1.00%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
-3 -2 -1 0 1 2 3
Fiscal Year Relative to SEO
Cur
rent
Acc
rual
s
Bottom Industry Quintile Top Industry Quintile
(b) Discretionary Accruals
-1.50%-1.00%-0.50%0.00%0.50%1.00%1.50%2.00%2.50%
-3 -2 -1 0 1 2 3
Fiscal Year Relative to SEO
Dis
cret
iona
ry C
urre
nt
Acc
rual
s
Bottom Industry Quintile Top Industry Quintile
49
(c) Nondiscretionary Accruals
-0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
-3 -2 -1 0 1 2 3
Fiscal Year Relative to SEO
Non
disc
retio
nary
A
ccru
als
Bottom Industry Quintile Top Industry Quintile
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