At-The-Market (ATM) Offerings
Matthew T. Billett, Ioannis V. Floros and Jon A. Garfinkel*
January 2018
ABSTRACT
We study “At-The-Market” (ATM) equity offerings, which are direct share issuances sold in the
secondary market that forgo underwriters and “dribble-out” shares over time rather than raising
them all at once. Enabled in 2008, their use has increased dramatically, and in 2016 their
incidence and total proceeds were respectively 63% and 26% of that for SEOs. Determinants of
firms’ choice between ATMs and SEOs are consistent with the costly certification hypothesis of
Chemmanur and Fulghieri (1994). We also find that 65% of ATM proceeds are used to stockpile
cash compared to 84% of SEO proceeds.
Keywords: At-the-market offering; ATM; seasoned equity offering; equity issuance; certification
JEL Classification: G32, G34, G38
*Billett, mbillett@indiana.edu, Kelley School of Business, Indiana University, Floros,
ivfloros@iastate.edu, Ivy College of Business, Iowa State University, Garfinkel, jongarfinkel@uiowa.edu, Tippie College of Business, The University of Iowa. We thank Audra
Boone, Dave Mauer, Daniel C. de Menocal, Jr., Jay Ritter, Ann Sherman, Joshua White, and
seminar participants at The University of Mississippi for helpful comments. We especially thank
an anonymous referee for many thoughtful suggestions that greatly improved the paper. Part
of the analysis was completed while Ioannis Floros was visiting the Securities and Exchange
Commission. Jeffrey Emrich, Jinsook Lee and Dongping Xie provided excellent research
assistance.
Equity issuance to public market investors in the U.S. has traditionally followed the firm
commitment process (Eckbo, Masulis, Norli (2007)). Explanations for the dominance of this
underwritten offering approach include the certification that investment banks provide to
issuers of uncertain quality, as well as liquidity provision and marketing services (Booth and
Smith (1986) and Gao and Ritter (2010)). While the needs for and costs of certification, liquidity
provision, and marketing likely vary between firms, across time, and with the intended use of
proceeds, few follow-on equity issues bypassed the underwriting services of an investment
bank. However, that changed in 2008 with the introduction of “At-The-Market” (ATM) equity
issues.1
Regulatory changes in 2005 and 2008 (the 2005 Securities Offering Reform (SOR) and
amendments to forms S-3 and F-3 in 2008) facilitated ATM issuances. These are direct-fromshelf placements of non-underwritten shares into the secondary market using a placement
agent strictly as a broker. ATMs offer immediacy at the potential cost of foregone certification
and marketing (thereby relying on existing stock market demand for the firm’s shares). ATMs
also offer two implicit options: the firm may issue less than the authorized amount, and they
may “dribble-out” the shares in smaller and variable quantities over three years.
This paper is the first comprehensive empirical study of ATMs. We offer several lines of
inquiry, beginning with a basic description of the anatomy of the market along with that for
Seasoned Equity Offering (SEO) and Private Investment in Public Equity (PIPE) activity over the
same period. We also describe ex-ante characteristics of ATM and SEO firms and explore firms’
ATMs were actually allowed as early as the 1980s but were rarely used because of institutional restrictions. Prior
research has noted the use of best efforts offerings as an alternative to firm commitment offerings. Our analysis of
the ATM market indicates that they have replaced best efforts deals.
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1
choices between the two issuance approaches. We finish with two ex-post perspectives: ATM
firms’ actual dribble-out activity and their propensity to save cash from the issuance proceeds
(compared to that for SEOs).
Our broad market analysis provides an overall picture of how ATMs have expanded the
equity issuance landscape. We find ATMs are an important and increasingly viable equity
issuance method. They occur frequently among non-regulated and non-financial firms, with 682
programs announcing over $62 billion in (potential) issuance during our 2008-2016 sample
window.2 Their use has also grown in comparison to SEOs and PIPEs. In 2008 ATMs were issued
10% as frequently as SEOs (and 0.7% as frequently as PIPEs). Also in 2008, relative proceeds of
ATMs to SEOs were 1.6% and relative to PIPEs were 1.9%. The relative incidences grew to 63.6%
and 14.4% in 2016, while relative proceeds grew to 25.8% and 40.5% in 2016. It appears that
ATMs have become a common method for publicly traded firms to raise equity capital.
Our comparison of ex-ante firm characteristics for ATM and SEO users indicates several
important differences. ATM firms tend to be smaller with lower sales and profitability than SEO
firms. They also carry larger cash balances and invest more via R&D. They have higher market-
to-book ratios and lower leverage. In short, ATM firms have the markings of growth-orientation
as well as potentially greater asymmetric information concerns. The latter motivates our choice
of theoretical model that underpins our exploration (via logit) of firm issuance technique choice
– either ATM or SEO.3
ATMs are popular among financials – particularly REITs. We eschew analysis of these because they are not
typically included in SEO samples, and we seek comparability.
3
We do not include PIPEs in later analyses because their initial placement is privately negotiated with a typically
small number of qualified institutional buyers (QIBs) – very different from the investor set for ATMs and SEOs.
2
2
Specifically, we test one implication of Chemmanur and Fulghieri (1994). Their model
primarily predicts the well-documented value of greater investment bank reputation in the
form of mitigated adverse selection costs experienced by issuers. However, their implication
number six (6) focuses on firm choice of direct versus underwritten equity issuance. Greater
asymmetric information encourages firms to use underwritten equity offerings, but this
incentive is mitigated by higher costs of certification among lower quality firms. In other words,
the likelihood of an SEO (instead of an ATM) should be increasing in the interaction of
asymmetric information concerns and firm quality. We use financial reporting quality to proxy
for firm-level asymmetric information concerns or opacity (e.g. Lee and Masulis (2009)) and
future analyst recommendations to proxy for firm quality. The empirical likelihood of an SEO
rather than an ATM is increasing in the interaction of firm opacity and quality, consistent with
theory.
Finally, our two ex-post perspectives on ATMs highlight their completion and timing
option benefits. Firms take down less than half of the announced ATM program allocation on
average, but more than one-third of programs are completed (100% take-down). More
generally, ATM take-down activity positively correlates with current quarter stock performance.
Our results on cash savings out of ATM issuance suggest the dribble-out option mitigates the
need to carry financial slack (cash and marketable securities holdings). Regression analysis
implies that ATM firms save 65 cents out of each dollar compared with SEO firms saving 84
cents per dollar of issuance (in our sample).
Overall, our research makes several contributions. As noted above, our primary
contribution is to provide the first comprehensive analysis of this new follow-on equity issuance
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technique. Given their temporal usage growth in both absolute terms and relative to both SEOs
and PIPEs, ATMs are likely a permanent fixture in the equity issuance landscape for publicly
traded firms. Second, we explore the determinants of the firm’s choice between an ATM and an
SEO and test one implication of Chemmanur and Fulghieri (1994). The results are important for
understanding corporate financial policy decisions. We also contribute to the literature on
precautionary savings of corporate issuance (McLean (2011)), showing that the use of ATMs
may mitigate the need for it given their more continuous availability of financing over time.
Our research speaks to the importance of regulatory policy for firm financing policies.
The change in regulations in 2005 (SOR), which allowed for the immediate issuance of shares
off the shelf, opened the door for firms to issue shares under favorable market conditions (a
key motive behind dribbling out shares). The 2008 amendments to Form S-3 increased access to
shelf registration for smaller firms, which also encouraged ATMs. Our research adds to the
literature on the importance of regulation for capital acquisition (e.g. Gustafson and Iliev
(2017)).
Finally, our work extends the analysis of Gao and Ritter (2010), who study the choice of
accelerated SEOs vs. traditional book-built SEOs. Their empirical analysis covers 1996-2007,
which is prior to the emergence of ATMs. They show that inelastic share demand and large
offerings encourage the use of marketing services associated with traditional book-built SEOs.
Our analysis suggests that ATMs offer a viable alternative issuance technique to accelerated
SEOs. Both accelerated SEOs and ATMs eschew marketing efforts that may be used to flatten
the short-run demand curve for shares. The potential advantage of ATMs – particularly of
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stocks with lower institutional demand – is the smaller price impact when fewer shares are
issued at a single point in time, but the firm spreads its total offering over a longer window.
I. Regulatory Reform
The Securities and Exchange Commission’s SOR policy became effective on December 1,
2005. There were several broad motives to the reform. One was to allow more disclosure prior
to follow-on equity offerings and to reduce asymmetric information problems that impede
capital formation (e.g. Clinton, White and Woidtke (2014) and Shroff, Sun, White and Zhang
(2013)). A second listed intent was to define a new category of issuer – a “well-known seasoned
issuer” (WKSI) that meets the following criteria:4
…has worldwide market value (public float) of its outstanding voting and non‐
voting common equity held by non‐affiliates of $700 million or more; or has
issued in the last three years at least $1 billion aggregate principal amount of
non‐convertible securities, other than common equity, in primary offerings for
cash (and not in exchange) registered under the Securities Act, and will register
only non‐convertible securities other than common equity.
A third motive was to provide more timely information to investors without mandating delays
in the offering process. This opened the door to offering securities from the shelf very quickly
after the firm made a decision to do so. In particular, SOR removed requirements to file a posteffective amendment that contained underwriter names.
ATMs were still effectively unused even after SOR. For large firms, issuance size
limitations (when pulling shares off the shelf) were the likely deterrents to ATMs and were a
specific concern noted by the SEC. For small firms, defined as those firms with less than $75
See “Frequently Asked Questions about Communications Issues for Issuers and Financial Intermediaries” by law
firm Morrison and Foerster: https://media2.mofo.com/documents/faq_communications.pdf
4
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million in public float, issuing shares off the shelf (and hence ATMs) were specifically prohibited
(Gustafson and Iliev (2017)).
Both of these concerns were addressed in January 2008 with revisions to requirements
governing issuance via SEC forms S-3 and F-3. Key revisions encouraging ATM activity
broadened the set of companies eligible to issue securities off the shelf and increased the
allowable size of issuances. Regarding the former, the SEC removed the “public float”
restriction to defining WKSI companies, as long as the issuers met other eligibility conditions for
the use of Form S-3. This had the net effect of allowing companies with less than $75 million in
public float to issue via the shelf. Commenters on the SEC’s proposed amendments (governing
S-3 and F-3 policy) welcomed expansion of the eligibility, noting potential enhancement to
smaller companies’ access to capital.
The SEC further amended regulations that had previously restricted the value of
securities that could be sold in an ATM to 10% of the issuer’s aggregate market value of the
outstanding voting stock held by non-affiliates. The new policy allows for fully one third of
public float to be issued within any 12-month period. This latter change opened the door to
larger issues by qualified firms (WKSIs).
In sum, the 2005 SOR potentially sped up issuance times through removal of post-
effective amendment filing. The 2008 changes to forms S-3 and F-3 increased both the breadth
of companies eligible to issue securities and the allowed issuance amount relative to the firm’s
public float. These changes likely broadened and deepened corporate interest in ATM use.
II. Data
A.
ATM and SEO samples
6
Our study is built around two samples of follow-on equity issuance – ATMs and SEOs.
The (main analysis) sample window spans the period of 1/1/2008 to 12/31/2015. 5 Our SEOs are
drawn from the Securities Data Corporation (SDC) database (U.S. common stock issuances). Our
sample of ATMs is primarily hand-collected, but also draws from “The Deal’s” PrivateRaise
database in 2011 through 2015 to confirm our hand-collected data.
For our hand-collection of ATM data, we use the Knowledge Mosaic platform to search
all 8-K and 6-K filings for the keywords: "at-the-market," "at the market," "controlled equity
offering," "sales agency agreement," and "distribution agreement." We also search for
"ordinary brokers" in 8-Ks and 6-Ks that are non-registration statements (to avoid getting
424B2 through 424B5 filings). We do not sample regulated or financial firms. Our initial ATMs
sample includes 682 announced equity agreements.
Our ATM data includes issuer name, closing date, placement status, planned issuance
amount (available for 625 of 682 ATMs), ticker symbol, listing exchange, SIC code, issuer
country and state, closing market price and capitalization, planned use of proceeds, roster of
placement agents, and agent fees charged. We obtain the commitment period within which the
issuing company commits itself to dribble-out some or all of the ATM shares (though this does
not place any legal obligation on the issuer). All ATM specifics are available in Item 1.01 of the
respective 8-K/6-K filing. We gather ATMs’ announcement dates from Factiva.com (only 9 ATMs
have announcement dates preceding their initialization date).
Our SEOs sample is drawn from the SDC database and includes only firm commitment
All but the basic calendar-year data in Table II are based on this sample. For the 2016 ATM, SEO and PIPE activity
information in Table II, we collect only the number of events and quantities raised.
5
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common stock offerings in the U.S. Our specific filtering criteria are: (with available number of
observations after imposing each criterion in parentheses) a) all firm commitment follow-on
offerings between 1/1/2008 and 12/31/2015 (18,157), b) no rights issues (15,174), c) issuer SIC
code not including regulated or financial industry or REIT firms (11,185), d) issuer is traded on
any of the main U.S. stock exchanges (NYSE (now NYSE MKT LLC), NASDAQ, NASDAQ SmallCap)
(2,842), e) no unit issues (2,842), f) no LBO or RLBO firms (2,837), g) no limited partnerships
(2,612), h) no ADRs (2,540), i) no simultaneous international offerings (2,524), and finally j) with
an offering price exceeding $5 (1,950). These screens are broadly consistent with the extant
literature studying SEO activity.
B.
Market Differences
Table I, Panel A lists the different procedures for issuing equity via ATMs and SEOs. ATM
offering shares are sold strictly into the secondary market. A placement agent is chosen by the
firm and essentially acts as a broker of the shares in sales on the open secondary market. SEO
shares are sold in primary market transactions and typically involve a firm commitment by the
underwriter. ATM issuance programs may be executed over time with only a fraction of shares
sold during each visit (by the placement agent) to the secondary market. By contrast, (firm
commitment) SEOs involve the issuance of shares in a single transaction.
PIPE issuance procedures are described in Panel B of Table I. Buyers are institutional or
accredited investors. The firm uses a placement agent rather than an underwriter, and this
agent does not engage in any market stabilization. PIPEs are often placed with hedge funds,
other corporations, and private equity firms, and the PIPE contracts often contain provisions
with specific investor protections and/or control rights (see Billett, Elkamhi, and Floros, 2015).
8
The difference between unregistered PIPEs and “Registered Direct” (RD) PIPEs is that the latter
are pre-registered and thus available for sale to retail investors immediately after placement.
This enhances the liquidity of such shares.
The different issuance mechanisms present an equity capital raising landscape that is
more complete than before the new regulations and particularly offers additional equity raising
opportunities to smaller firms. Gustafson and Iliev (2017) note this increase in opportunities,
and our evidence is complementary to theirs. We offer evidence in Table II, Panel A.
Beginning with 2008 (the start of ATM market activity), we present equity issuance
statistics for the three major techniques. In both frequency and proceeds, ATM activity is
clearly gaining in importance. From 2008 through 2016, announced ATM issuance programs (by
non-regulated and non-financial firms) grew monotonically from 8 programs to 163 programs.
Total proceeds grew from a mere $0.5 billion to nearly $20 billion during the same period.
SEO activity shows a rather different pattern over time. First of all, there is a dearth of
SEOs in 2008 (due to the financial crisis). From 2009 through 2015, we see a general trend
upward with some variability in SEO activity. Then we observe a clear reduction in activity in
2016. PIPE activity (again by non-regulated, non-financial firms) resembles the pattern of SEOs
more than ATMs. The general trend is upward but with several different years of declines.
Notably though, 2016 sees a pronounced rise in PIPE use.
The three equity raising techniques combine to show a more consistent pattern of
expansion in equity issuance over our sample period. Unreported analysis indicates a nearly
monotonic rise in total equity issuance between 2008 and 2015. There is a significant drop in
total equity issuance by public firms in 2016, driven by much lower SEO activity. Overall, the
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data mostly supports our inference of an expanding equity issuance landscape. Firms take
advantage by raising more equity using all three techniques.
To better understand the role of ATMs in this expansion, we also present relative ATM
(to SEO and PIPE) activity measures. Compared to SEO activity both relative count and relative
proceeds of ATM/SEO peak in 2016, with a pronounced rise over the last three years (20142016). The same pattern is evident for ATM activity relative to PIPE activity. These numbers
strongly suggest the viability of ATMs as a permanent fixture in the U.S. equity issuance
landscape.
Finally, in Panel B of Table II we show PIPE issuance activity classified by whether the
issue was RD or not. There is a clear increase in the use of RD PIPE offerings after 2008 with
both the number of issues and the proceeds essentially doubling. Given prior works’ different
sampling (on both time period and inclusion of RDs), this new information indicates greater
expansion of equity issuance in the post-2008 era than previously thought.
III. Ex-ante Characteristic Differences and Selection of ATM vs. SEO Issuance
A.
Descriptive Statistics
In addition to differences between ATM and SEO issuance technique and growth, there
are important firm characteristic differences between them. Table III presents and compares
the ATM and SEO samples. We define all variables used in our analysis in the respective table
legends. ATM firms are smaller (MARKET_VALUE_OF_EQUITY) than SEO firms, and they have
lower leverage. They also expend more on R&D_TA_RATIO and show a higher TOBINS_Q while
investing less via CAPEX. ATM firms have lower SALES_TA_RATIO and lower EBITDA_TA_RATIO,
and correspondingly. They carry more cash relative to assets and burn it more quickly. They
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also need more external funds (when EFN is negative, increases in CAPEX and net working
capital are larger than EBITDA). Finally, despite lower apparent average FEES on ATMs than on
SEOs, this is per dollar of announced proceeds. Accounting for ATMs’ actual takedown implies
higher fees per dollar of issuance on ATMs compared to SEOs. Note that these differences are
likely attributable to variation in timing, industry clustering, and firm conditions.
In section III.B. we discuss our choice of proxies for the two key variables explaining
ATM/SEO issuance technique choice, motivated by Chemmanur and Fulghieri (1994).
Asymmetric information is proxied with ACCRUALS_QUALITY (a.k.a. opacity).
ACCRUALS_QUALITY’s construction (as the standard deviation of residuals from a regression
explaining accruals) implies that larger values represent increases in asymmetric information.
ATM firms have average ACCRUALS_QUALITY of 0.010, while SEO firms’ average is 0.002,
significantly smaller. The medians show a similar pattern. SEO firms have lower asymmetric
information than ATM firms.
Our proxy for (ex-ante) unobservable firm quality is future analyst recommendations
(ANALYST_RECOMMENDATIONS) and changes in them from before to after the event. The
coding of ANALYST_RECOMMENDATIONS is 1 to 5 for strongest to weakest (strong buy to
strong sell). The average recommendation value (across analysts) for SEOs in our sample (2.03)
is more bullish than for ATMs (2.45), implying SEOs associate with higher quality firms than
ATMs do. The same is true when we compare cross-analyst averages of changes in
recommendations (ATMs equal 0.44, while SEOs equal 0.06).
We also require TOTAL_INSTITUTIONAL_HOLDINGS to control for the potential influence
of share demand elasticity on issuance method choice (Gao and Ritter (2010)). Our institutional
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holdings data are from 13F quarterly filings and are collected for the quarter prior to the
announcement (of either ATM or SEO). Both mean and median values of
TOTAL_INSTITUTIONAL_HOLDINGS among ATM firms are significantly lower than among SEO
firms. As we discuss below in our results, the dribble-out nature of ATMs potentially reduces
the need for strong institutional demand for shares.
Since ATMs involve secondary market issuance, we also examine trading-related
variables. We focus on factors that are likely to correlate with the optionality in dribble-out
(take-down) amount and flexible timing of ATMs, particularly those that proxy for existing
secondary market demand. TURNOVER is similar in the median across the two samples, while
mean TURNOVER is slightly higher for ATMs – loosely consistent with some practitioners’ views
that ATMs offer a liquidity-timing option to issuing firms (whenever they deem that liquidity is
favorable). STOCK_RETURN_VOLATILITY is higher before ATMs than SEOs, also consistent with
ATMs being used potentially to exploit timing options.
B.
The Choice Between ATM and SEO
Given clear differences in the characteristics of ATM and SEO users – particularly
suggesting that ATM users are more opaque and of lower quality than SEO users– we explore
their influence on firm choice of issuance technique. We specifically test Chemmanur and
Fulghieri’s (1994) implication that firms prefer underwritten equity issuance unless it is too
costly. Underwriter certification is most expensive when there is asymmetric information about
firm quality, and an investment bank’s certification of high quality is likely to have negative
reputation repercussions – i.e. when the bank perceives that the firm is likely to be of lower
quality. Chemmanur and Fulghieri (1994) predict that the likelihood of an equity offering being
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underwritten is increasing in the interaction between asymmetric information and unobserved
issuer quality.
We test the joint influence of asymmetric information and (unobserved) firm quality
using a logit regression. The dependent variable equals one if the firm conducts an ATM and
zero if an SEO. Our general specification is as follows:
(1)
y = α + δ×X + β1×AI + β2×Quality + β3×AI×Quality + ε
where X is a matrix of control variables, AI is the proxy for asymmetric information, and Quality
is the proxy for unobserved firm quality (that the underwriter certifies in an SEO). The key
coefficient is β3. Since the product of AI and Quality is more positive among firms that are both
higher quality and have higher asymmetric information (who are expected to use an
underwriter), we expect β3<0.
We follow Lee and Masulis (2009) (who use the modified Dechow and Dichev (2002)
model as applied in Francis, Lafond, Olsson and Shipper (2005)) and measure asymmetric
information between insiders and outsiders with accruals quality (a.k.a. opacity). These papers
argue that opacity measures the difference between the information set of outside investors
who rely on financial reporting information and that of insiders who do not have to rely on
financial reporting. Lee and Masulis (2009) show that both SEO announcement returns
decrease and gross spreads charged by underwriters increase in opacity. Billett and Yu (2016)
show that stock price reactions to open-market share repurchases increase in this measure.
For unobserved firm quality we use future analyst recommendations and future changes
in recommendations. Underwriter certification is valuable in Chemmanur and Fulghieri’s (1994)
model when it clarifies firm quality to asymmetrically informed investors. More bullish
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recommendations indicate higher expected value than currently available, and the ex-post
version is unobserved at the time of issuance. Because Table III indicates a high standard
deviation relative to the average recommendation, we use a dummy variable for our proxy. The
dummy equals one if the firm’s future consensus analyst recommendation is more bullish (than
the sample median recommendation value) and zero otherwise. A parallel approach is used
when recommendation changes proxy unobserved firm quality.
We present our logit estimates in the first two columns of Table IV. The models only
differ by their proxy for firm quality. In model (1), we use the dummy based strictly on ex-post
analyst recommendation, while model (2) uses the change in analyst recommendation dummy.
The latter two columns in Table IV offer corresponding estimations using a linear probability
model (LPM). This facilitates economic interpretation of factors influencing issuance technique.
The LPM models also allow for inclusion of industry fixed-effects that help control for
systematic differences in, for example, opacity across industries.
The logit results support the prediction of Chemmanur and Fulghieri (1994) that higher
asymmetric information along with higher firm quality increases the likelihood of an SEO
instead of an ATM. The coefficient on the interactive of accruals quality and the firm quality
dummy is significantly negative (recall that SEOs carry a lower dependent variable value) in
both specifications.6 To ascertain the economic effect of the interactive variable, we use the
LPM (in columns (3) and (4)). The simplest interpretation is to consider conditioning on higher
quality firms (dummy = 1). In this case the coefficient on the interactive variable (-0.46 in
The Online Appendix (using the methodology of Ai and Norton (2003)) confirms the negative coefficient on the
logistic regressions’ interaction term of firm quality with asymmetric information.
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column 3) may be interpreted as follows. For high quality firms, a one standard deviation
increase in opacity (0.05) associates with a 2.3% increase in the probability of a firm choosing to
issue via an SEO instead of an ATM. Given the relative number of occurrences of ATMs to SEOs
in our sample of 30.9%, the 2.3% increase amounts to 7.4% of the sample average probability.
When we proxy firm quality with changes in recommendation that exceed the median (column
4), the coefficient on the interactive (-0.80) term implies that a one standard deviation increase
in opacity (0.05) associates with a 4.0% increase in the probability of an SEO.
A potential concern with the results from the Linear Probability Model (LPM) with
industry fixed-effects is that some industries have only one firm conducting an ATM and are
thus dropped from the analysis. We assess the robustness of our results by removing the
industry dummies and include all ATMs in the model (untabulated but available by request).
The coefficients on the interactives in specifications mirroring (3) and (4) are remarkably similar
to the reported ones. Asymmetric information combined with high firm quality robustly
correlates with SEO choice (over ATM).
Returning to the logits, a few control variables are significant and noteworthy. We see a
negative coefficient on TOBINS_Q, suggesting that more growth opportunities encourage an
SEO as opposed to an ATM. Firms with more growth opportunities may need more capital in
general. Indeed, the negative and significant coefficients on PROCEEDS_MVEQ_RATIO and
CAPEX_TA_RATIO suggest this. We also see that SEO choice is more likely when
EBITDA_TA_RATIO is higher in the prior year. Larger RUN_UP associates with greater likelihood
of an SEO as opposed to ATM, consistent with the extant SEO literature, which documents
strong average stock performance before equity issuances.
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ATMs are also more likely to be chosen over SEOs when institutional ownership is ex-
ante lower. Gao and Ritter (2010) argue that low institutional ownership weakens short-run
elasticity of demand for shares. Large placements of shares (SEOs) incur significant price
discounts in such cases unless marketing flattens the short-run demand curve, thus
encouraging fully marketed SEOs. But we observe the opposite, raising the question of why.
The advantage of ATMs when institutional ownership is low is the opportunity to issue smaller
share quantities several times over a longer time-span, moving along the long‐run demand
curve. This allows the firm to potentially take advantage of greater demand elasticity at each
step. Put differently, a firm issuing shares amounting to 20% of outstanding shares is likely to
face greater discounting or market impact than a firm issuing 5% of shares outstanding at four
widely-spaced intervals.
IV. Actual Issuance Behavior in ATM Programs
Given ATMs’ flexibility to be executed in a dribble-out fashion, we investigate firms’
actual issuance behavior under their ATM programs. Firms report their issuance of equity under
the ATM program on their 10-Q (or 10-K in the case of the fiscal year-end) filing. The 10-Q
provides only aggregated (across all of the firm’s issues in the quarter) information on actual
issuance activity. The sample comprises ATMs that we are also able to find price data for (from
CRSP), and we only hand-collected the take-down information through December 2015.
A.
Univariate Statistics
We focus on two firm-level measures of actual issuance activity.
FRACTION_OF_ANNOUNCED_ISSUANCE equals the total number of shares actually issued
during the ATM program, divided by the announced number of shares that the firm planned to
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issue under the ATM program. COMPLETION_DURATION is conditional on a firm completing full
issuance of the announced number of shares under the ATM and equals the number of
quarters it took the firm to do so. For this variable, even if a firm did not issue shares during a
particular quarter during its ATM program, we count that quarter as long as it occurs before the
ATM is completed.
We also present two measures of “price efficiency” of firm take-down. We scale the
firm’s reported “weighted average actual issuance price”7 by two different measures of market
price: the end-of-quarter price and the average (time-series) quarter’s price (using daily closing
prices). The former measure may be viewed as the benefit of doing an ATM relative to an SEO
executed strictly on the last day of the quarter. While not all firms would choose to do an SEO
at the end of the quarter, it is one possible view of the snapshot that would occur on any
particular day of the quarter. The latter measure may be viewed as the benefit/cost of picking
various days/times to dribble-out (perhaps on the basis of firm expectations that it’s a favorable
moment), relative to a rather uninformed approach of dribbling out an equal amount each day
of the quarter. This latter methodology mirrors recent work in the stock repurchase literature
(Bonaimé, Hankins and Jordan (2016), and Dittmar and Field (2015)). Both papers report
average price paid during a buyback exceeds average stock price smoothed over the repurchase
window.
Table V presents means, medians, and standard deviations of the above variables across
various samples. For the full sample of firms (in Panel A), average actual issuance is slightly less
This is a weighted average of the daily prices that shares were issued at (across all dribble-outs that quarter) with
weighting by number of shares issued at each day’s price.
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than half of the announced plan size (43% in the mean), while the median firm issues just over
one quarter (27%). There is substantial variation in execution across firms (42% standard
deviation). The typical firm does not issue the full amount of announced ATM planned shares
and often takes down substantially less than even half the announced amount. Confirming
evidence is seen in the roughly one third ratio of firms (191 out of 515) taking down the full
planned / announced amount. Also in Panel A we see that the cross-sectional mean
WEIGHTED_AVERAGE_ISSUE_PRICE_ENDOFQUARTER_PRICE_RATIO is 1.37 and the median is
1.02. The final row of Panel A compares the average firm issuance price to the average daily
stock price during the quarter. The mean (median) of this ratio equals 1.64 (1.02). 8
How should we interpret these relative price patterns and (lack of) completion
behavior? One plausible explanation is that firms follow a mechanical rule: issue shares until a
price drop and then stop. If stock price rises again, the firm reengages in issuance. Such
behavior yields average issue prices that exceed average quarter prices and also the end-of-
quarter price. It also potentially explains failure to complete the issuance of all ATM-announced
shares. However, it’s important to note that none of this necessarily implies managers have
market timing ability; prediction of future price patterns is not necessary under this mechanical
rule.
Staged investment patterns may also explain the price patterns and lack of complete
take-down. If the size of the announced ATM program is an upper bound on the firm's expected
future financing needs, then as the investment unfolds - and more is learned about the
While the mean values of pricing efficiency may seem large, they possess equal-weight across all observations. If
we value-weight according to market cap of each firm at the previous quarter-end, the results indicate between
5% and 6% premia of average take-down price relative to quarter-end or average quarterly price.
8
18
opportunity - the firm either continues to invest and expand or curtails investment. This could
lead to uncompleted programs if market feedback is negative, which would likely be
accompanied by lower returns in the final quarter of take-down.
Finally, untabulated results indicate that firms that do not complete take-down show
worse stock performance in the final quarter of take-down activity but rarely are acquired or
dropped from the exchange. Perhaps the most surprising implication of our results is that we
don’t see mirror images of mechanical behavior in average firm repurchasing.
Panels B and C of Table V split our full sample based on whether the firm has above or
below median inflation-adjusted sales. The key difference in take-down behavior between firms
in the higher and lower sales groups is cumulative execution. Firms with sales greater than the
sample median take down a larger percentage of the announced plan (48% vs. 43% for the
means, and 34% vs. 13% for the medians). However, this does not imply an obvious difference
in actual completion ratios (37% of firms in each sub-sample completely issue announced takedown). The time to completion is slightly longer for firms with greater sales but only by one
calendar quarter, and while average measures of price efficiency are “better” among larger
sales firms, the medians are not. While it is difficult to draw firm conclusions, the evidence is
consistent with larger sales firms being more likely to take advantage of the optionality in ATM
issues.
B.
Censored quantile regressions
Numerous factors may influence firms’ preferences to execute actual issuances in ATM
programs. In Table VI we investigate the effects of time, stock volatility, and stock returns on
dribble-out activity. The regressions treat each quarter of potential actual issuance by a firm as
19
a separate observation, resulting in 1,436 firm/quarter observations (despite only 515 firms
with actual issuance information). While this suggests a small average number of quarters per
firm, recall that a large number of ATM programs were announced in 2014 and 2015. Since we
only collect dribble-out data through December 31, 2015, many of our observations will have
limited data available. We also stop collecting dribble-out information upon completion of a
firm’s ATM program, and we require sufficient data to calculate our regressors.
We use censored quantile regression (CQR) because of the highly censored nature of
actual issuance activity (fully 59.9% of our firm/quarters have zero dribble-out executed). 9 Put
differently, since Ordinary Least Squares (OLS) minimizes the sum of squared deviations from
the mean, significant mass of the distribution at one tail (zero in our case since many firm-
quarters show zero take-down) can unduly influence coefficients. We would learn less about
factors that influence variation of take-down conditional on issuance and more about the
decision to take-down or not (which was explored in the prior section). We estimate the CQR
centering our analysis on the 80th, 85th and 90th quantiles. Varying the center-point around
these higher percentiles provides roughly similar numbers of observations on either side of the
centering quantile. It also increases the efficiency of our estimates in the minimization of sum
of squared errors.
Our results indicate the following factors correlate significantly with firms’ actual
issuance: the number of quarters since ATM announcement (QUARTER_COUNTER); the firm’s
stock return during the quarter (CURRENT_QUARTER_BHARS); and the cumulative amount of
A Tobit regression also handles censored data; however, our data on actual take-down fails to satisfy the
normality and homoscedasticity assumptions of the Tobit.
9
20
take-down in prior quarters (PRIOR_CUMULATIVE_TAKEDOWN_FRACTION). Firms take down
less in later quarters, consistent with fewer shares remaining on the shelf. However, controlling
for time elapsed, greater past take-down implies more current-quarter take-down. Finally,
greater dribble-out during quarters of higher (abnormal) stock returns is consistent with firms
potentially following the earlier-mentioned mechanical rule.
C.
Cash Savings Behavior out of ATMs and SEOs
Given ATMs’ flexibility in issuance timing, firms may view them as a substitute for
alternative flexible financing. Huang and Ritter (2016) report that net debt issuers spend 86
cents of every new dollar raised. To the extent that a meaningful proportion of net debt
issuance is bank debt, this is consistent with viewing bank loans (usually take-downs of
revolving credit lines) as a more continuous form of financing. We may interpret ATMs similarly,
especially in comparison to SEOs, which raise lumpy quantities of equity (much like public debt
issues). This raises the question of whether firms will tend to spend a greater proportion of
ATM issuance proceeds immediately relative to that seen among SEOs.
Table VII tests this using cash savings regressions following McLean (2011). The
dependent variable is the change in cash (quarterly). The regressors are stock issue proceeds
(STOCK_ISSUE), debt issue proceeds (DEBT_ISSUE), CASH_FLOW, and other sources of cash
(OTHER_SOURCES), all scaled by lagged total assets; and a control variable of TOTAL_ASSETS.
We run the regression on two separate samples: ATM events over the eight quarters following
(because two years out is when most take-down has occurred); and SEO events over one
quarter from the pre-event quarter end to the post-event quarter-end. These are panel
regressions with Rogers’ standard errors (see Petersen (2009)).
21
Across the two regressions we see a majority of equity issuance proceeds saved. For
ATMs the coefficient on STOCK_ISSUE is 0.65, while for SEOs the coefficient is 0.84. The latter is
significantly higher indicating a greater propensity of firms to immediately spend ATM proceeds
compared to SEO proceeds. An F-test confirms statistical significance of this difference.
Economically the difference is also important. A 65% savings rate for the average ATM
announcement ($92 million) combined with an average takedown of 43% implies a $26 million
cash savings. The same calculation implies $215 million of average firm SEO proceeds are saved.
Firms issuing equity via SEO appear to hoard a greater proportion of the proceeds than firms
issuing equity via ATM, consistent with the more continuous nature of ATM financing.
The coefficients on the control variables are “in the neighborhood” of McLean’s (2011)
for SEOs.10 Notably though, the coefficient on debt issue proceeds is quite different for ATM
firms than for SEO firms. This too may reflect the more continuous nature of ATM financing,
encouraging less spending of net debt issuances. ATMs may substitute for other forms of
continuous financing.
V. Conclusions
We study the anatomy of a new approach to offering equity. ATM offerings came into
fashion starting in 2008, driven by regulatory changes that made such offerings feasible. Over
our sample period (2008 – 2016), there were 31% as many ATM issuance programs as there
While our coefficients may be somewhat larger than McLean’s (2011), we have a later sample period, and we
focus on just SEOs in the second specification. McLean also includes private placements, rights offerings, stock
sales through direct purchase plans, preferred stock issues, conversions of debt and preferred stock and employee
options, grants and benefit plans.
10
22
were SEOs, with the percentage growing over time. ATMs comprise a meaningful portion of the
follow-on equity issuance market.
ATM firms’ ex-ante characteristics differ from those of SEO firms. They tend to be
smaller with higher growth opportunities but are less profitable, with a notable concentration
among money-losing biotech companies (see the Online Appendix for details). ATM firms also
have lower leverage and carry more cash. In short, they have the markings of greater
asymmetric information problems. This implies potential benefits to the certification associated
with using an underwriter, but also potential costs in doing so. Theory by Chemmanur and
Fulghieri (1994) predicts that higher asymmetric information encourages underwriting (instead
of direct placement of equity as in ATMs) when the bank’s potential reputational costs of doing
so are small – in other words, among higher quality firms. We document that SEOs are more
likely among higher asymmetric information firms, but when they are also of higher quality.
We explore firms’ actual issuance behavior in their ATM programs. The data are
consistent with firms following a mechanical rule of issuing when the stock price is rising and
not doing so in the face of falling prices. Moreover, these results are consistent with patterns
resulting from staged investments. Further exploration of this issue would require more precise
information on the daily issuance activity under ATM programs. Currently such data is not
available.
Many avenues for future research remain. For example, given that underwritten offers
are aimed at institutions, prior work on SEOs largely ignores retail investor considerations.
Given our results that ATMs are more likely when institutional demand for shares is lower,
ATMs may rely more on retail demand for shares. Another potential area of exploration with
23
ATMs is the capital structure literature on partial adjustment towards a target. ATMs offer
another avenue for leverage reduction that may optimally involve timing options. Overall, our
conclusion that ATMs are a permanent fixture in the equity issuance landscape augurs new
opportunities for research in the corporate financing arena.
24
References
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(2003), 123-129.
Billett, M. T., and M. Yu. “Asymmetric Information, Financial Reporting, and Open-Market Share
Repurchases.” Journal of Financial and Quantitative Analysis, 51 (2016), 1165-1192.
Billett, M. T.; Elkamhi, R.; and I. V. Floros. “The influence of investor identity and contract terms
on firm value: Evidence from PIPEs.” Journal of Financial Intermediation, 24 (2015), 564-589.
Bonaimé, A. A.; Hankins, K. W.; and B. D. Jordan. “The Cost of Financial Flexibility: Evidence
from Share Repurchases.” Journal of Corporate Finance, 38 (2016), 345-362.
Booth, J. R., and R. L. Smith. “Capital Raising, Underwriting, and the Certification Hypothesis.”
Journal of Financial Economics, 15 (1986), 261-281.
Chaplinsky, S. and D. Haushalter. “Financing Under Extreme Risk: Contract Terms and Returns
to Private Investments in Public Equity.” Review of Financial Studies, 23 (2010), 2789-2820.
Chemmanur, T. J., and P. Fulghieri. “Investment Bank Reputation, Information Production, and
Financial Intermediation.” Journal of Finance, 49 (1994), 57-79.
Clinton, S.; White, J.; and T. Woidtke. “Differences in the Information Environment Prior to
Seasoned Equity Offerings Under Relaxed Disclosure Regulation.” Journal of Accounting and
Economics, 58 (2014), 59-78.
Dechow, P. M., and I. D. Dichev. “The Quality of Accruals and Earnings: The Role of Accrual
Estimation Errors.” Accounting Review, 77 (2002), 35-59.
Dittmar, A., and L. C. Field. “Can Managers Time the Market? Evidence Using Repurchase Price
Data.” Journal of Financial Economics, 115 (2015), 261-282.
Eckbo, E. B.; Masulis, R. W.; and O. Norli. “Security Offerings.” In Handbook of Corporate
Finance: Empirical Corporate Finance, Vol. I, Elsevier/North-Holland (2007), Chapter 6.
Francis, J.; Lafond, R.; Olsson, P.; and K. Schipper. “The Market Pricing of Accruals Quality.”
Journal of Accounting Economics, 39 (2005), 295-327.
Gao, X., and J. R. Ritter. “The Marketing of Seasoned Equity Offerings.” Journal of Financial
Economics, 97 (2010), 33-52.
Gustafson, M. T., and P. Iliev. “The Effects of Removing Barriers to Equity Issuance.” Journal of
Financial Economics, 124 (2017), 580-598.
25
Huang, R., and J. R. Ritter. “Corporate Cash Shortfalls and Financing Decisions.” Working Paper,
University of Florida (2016).
Lee, G., and R. W. Masulis. “Seasoned Equity Offerings: Quality of Accounting Information and
Expected Flotation Costs.” Journal of Financial Economics, 92 (2009), 443-469.
McLean, D. R. “Share Issuance and Cash Savings.” Journal of Financial Economics, 99 (2011),
693-715.
Morrison & Foerster LLP. Frequently Asked Questions About At-the-Market Offerings, White
Paper (2013).
Shroff, N.; Sun, A. X.; White, H. D.; and W. Zhang. “Voluntary Disclosure and Information
Asymmetry: Evidence From the 2005 Securities Offering Reform.” Journal of Accounting
Research, 51 (2013), 1299-1345.
26
Table I
Institutional Characteristics of ATMs, SEOs and PIPEs
Table I (Panel A) describes the main characteristics of two types of equity offerings: ATMs and common stock SEOs.
Panel B describes the main characteristics of PIPEs. The source for SEOs is the Securities Data Corporation (SDC)
database, for ATMs it is both The Deal's PrivateRaise and our hand-collection. The data source for PIPEs is The Deal’s
PrivateRaise. The announcement date for ATMs is hand-gathered from factiva.com, as well as 8-K statements. Event
dates for SEOs are drawn from SDC. Announcement dates for PIPEs (both unregistered stock and registered stock)
come from The Deal’s PrivateRaise.
Panel A: ATMs and SEOs
Announced
before
completion
Sold in the
secondary market
Sold in
increments
ATMs
We find that
95.4% of ATMs
programs are
announced on
(or after) the
closing date of
the
commencement
of the program.
The dates of the
securities' sale
are not
announced.
Through ATMs,
newly-issued
shares are sold to
the secondary
markets.
Yes
SEOs
We find that
89.1% of SEO
issuances have
their filing date
preceding the
issue date.
Through SEOs,
pure primary or
combined
primary and
secondary shares
(with the primary
shares proportion
being at least
50% of the entire
offering) are sold
to the secondary
markets.
No
Underwritten
Through ATMs, newly-issued shares
are dribbled out into the trading
market through a designated brokerdealer at prevailing market prices.
There is a placement agent used that
acts on a best efforts basis. In the rare
case that the placement agent
commits to purchase the issuer's
securities for its own account with a
view to reselling securities, he does
not conduct any roadshows or other
solicitations. The placement agent is
still liable with respect to material
misstatements or omissions in the
accompanying shelf registration
statement.
SEO issuers use underwriters that act
on a firm commitment basis.
27
Panel B: PIPEs
PIPEs –
Unregistere
d stock
We find that
61.9% of
unregistered
stock PIPEs have
their first
announcement
date preceding
the closing date
PIPEs –
Registered
stock
(Registered
Directs)
We find that
52.4% of
registered stock
PIPEs
(Registered
Directs) have
their first
announcement
date preceding
the closing date
Unregistered
stock PIPEs refer
to private
placements of
either newly
issued shares of
common stock or
shares of
common stock
held by selling
stockholders (or a
combination of
primary and
secondary shares)
offered primarily
to accredited
investors
Registered stock
PIPEs (Registered
Directs) refer to
private
placements of
either newly
issued shares of
common stock or
shares of
common stock
held by selling
stockholders (or a
combination of
primary and
secondary shares)
offered primarily
to accredited
investors
No
In unregistered stock PIPEs 46.0% use
the services of a placement agent. The
placement agent is not obligated to
buy any shares that are offered and
cannot engage in market stabilizing
transactions. The placement agent
acts as a distribution participant and
could be considered an underwriter
only in the sense of introducing new
securities to the market. The
placement agent is simply
intermediating the sale to
institutional investors as no retail
investors participate in registered
stock PIPEs (Registered Directs).
No
In registered stock PIPEs (Registered
Directs) 89.9% use the services of a
placement agent. The placement
agent is not obligated to buy any
shares that are offered and cannot
engage in market stabilizing
transactions. The placement agent
acts as a distribution participant and
could be considered an underwriter
only in the sense of introducing new
securities to the market. The
placement agent is simply
intermediating the sale to
institutional investors as no retail
investors participate in registered
stock PIPEs (Registered Directs).
28
Table II
Comparison of ATMs, SEOs and PIPEs
Table II presents the main characteristics of ATMs, SEOs, and PIPEs for the years from 2008 to 2016. Panel A reports
the distribution of completed transactions as well as the total gross proceeds amounts raised for all ATMs (excluding
REITs and regulated industries). Information on the number of SEOs and PIPEs spans the same time period and again
excludes REITs and regulated industries. The source for SEOs is the Securities Data Corporation (SDC) database. Our
ATMs come from both The Deal's PrivateRaise (for the years 2011, 2012, 2013, 2014, 2015 and 2016) and handcollection (for the years 2008, 2009 and 2010). Our PIPEs come from the Deal’s PrivateRaise. Panel B focuses on
PIPEs. PIPEs include two different offering types: the PIPEs offering unregistered stock and the PIPEs offering
registered stock (Registered Directs). These two types of PIPEs have the following characteristics: PIPEs offering
unregistered stock: These are placements involving equity and/or equity-linked securities under the Securities Act
of 1933, as amended - Section 4(2). Unregistered stock PIPEs are not immediately resalable to the public upon
transaction closing. PIPEs offering pre-registered stock (Registered Directs): These are placements that involve the
issuance of pre-registered equity and equity-linked securities (e.g., shelf sale) by an Issuer to an unlimited number
of accredited Investors. Registered Directs are immediately resalable to the public upon transaction closing.
Panel A: All ATMs, all SEOs and all PIPEs
All ATMs
YEAR
2008
8
0.49
47
1.50
27
2011
56
2012
2013
2014
2015
2016
All
years
Comparing ATMs and
SEOs
All PIPEs
Comparing ATMs and
PIPEs
#_OF_ PROCEEDS #_OF_I PROCEEDS #_OF_ISSUES: PROCEEDS: #_OF_I PROCEEDS #_OF_ISSUES:
ISSUES ($BILLIONS) SSUES ($BILLIONS)
ATM/SEO ATM/SEO SSUES ($BILLIONS)
ATM/PIPE
2009
2010
All SEOs
62
87
10.5%
1.6%
1167
26.11
0.7%
1.9%
234
34.76
20.1%
4.3%
1372
24.95
3.4%
6.0%
2.30
176
4.03
10.06
163
19.53
682
30.74
218
97
135
76
3.30
5.25
15.96
PROCEEDS:
ATM/PIPE
212
333
329
39.07
46.50
43.76
98.31
84.02
372
111.35
62.42 2,206
564.1
256
75.59
12.4%
31.8%
8.4%
4.9%
29.2%
12.0%
29.5%
12.0%
63.7%
25.8%
26.1%
36.3%
30.9%
4.1%
14.3%
1151
1134
1030
1023
1085
1020
1132
11.1% 10,114
34.59
19.79
33.48
20.40
32.22
2.3%
4.9%
11.6%
8.5%
19.8%
6.0%
8.9%
38.89
13.2%
278.62
6.7%
48.19
9.5%
14.4%
15.7%
31.2%
41.0%
40.5%
22.4%
29
Panel B: All PIPEs, all unregistered stock PIPEs and all registered stock PIPEs (Registered Directs)
Pipes by stock registration status
Registered Directs
All PIPEs
Unregistered stock
YEAR
2008
2009
2010
2011
2012
2013
2014
2015
2016
All years
#_OF_I
SSUES
1167
1151
1372
1134
1030
1023
1085
1020
1132
10114
PROCEEDS
($BILLIONS)
26.11
34.59
24.95
19.79
33.48
20.40
32.22
38.89
48.19
278.62
#_OF_ISSU
ES
1067
901
1114
942
825
780
866
780
856
8131
PROCEEDS
($BILLIONS)
23.02
28.40
19.28
14.61
26.68
13.81
26.62
32.65
40.39
225.46
#_OF_ISSUES
100
250
258
192
205
243
219
240
276
1983
PROCEEDS
($BILLIONS)
3.09
6.19
5.67
5.18
6.80
6.59
5.60
6.24
7.80
53.16
30
Table III
Issuer Characteristics
Table II presents and compares the mean and the median values of annual financials, annual trading-related and recommendationrelated information for all ATMs and SEOs in our sample. Transaction specific information is also reported (FEES and
PROCEEDS_MVEQ_RATIO). Financial data (from Compustat Fundamentals Annual) is as of the fiscal year-end preceding the ATM
announcement date or SEO issue date. MARKET_VALUE_OF_EQUITY is the product of shares outstanding and closing price from two
trading days prior to the ATM announcement or the SEO issuance date, expressed in $millions. LEVERAGE is the sum of short-term
and long-term debt, all divided by total assets. R&D_TA_RATIO is R&D expenditures divided by total assets; if R&D is missing we set
it equal to zero. CASH_TA_RATIO is cash divided by total assets. SALES_TA_RATIO is revenues divided by total assets. CASH_BURN is
the absolute value of operating income before depreciation divided by the sum of cash and cash equivalents; when the income
number is positive cash burn is set equal to zero. This follows Chaplinsky and Haushalter (2010). EBITDA_TA_RATIO is EBITDA divided
by total assets. TOBINS_Q is the book value of total assets minus the book value of equity plus the market value of equity, all divided
by the book value of assets. EFN_TA_RATIO is external financing needs and equals EBITDA minus change in CAPEX minus change in
net working capital, all divided by total assets. CAPEX_TA_RATIO is capital expenditures divided by total assets.
TOTAL_INSTITUTIONAL_HOLDINGS is the fraction of total shares outstanding owned by institutions taken from 13F filings.
PROCEEDS_MVEQ_RATIO is the ATM (or SEO) proceeds divided by the market value of equity (computed on the event day). FEES is
the gross spread over proceeds for SEOs, and it is agent cash fees over planned proceeds for ATMs. ANALYST_RECOMMENDATIONS
is the firm’s cross-analyst average of future consensus analyst recommendation. Consensus analyst recommendation comes from
Thomson Reuters the quarter following the ATM announcement quarter or SEO issuance quarter. Analyst recommendations scale is
as follows: “strong buy” = 1, “buy” = 2, “hold” = 3, “underperform” = 4 and “sell” = 5. ANALYST_REVISIONS is the firm’s (cross-analyst)
average of the difference between future and lagged consensus analyst recommendations. ACCRUALS_QUALITY is the standard
deviation of residuals from the estimation model explaining total current accruals with lagged, contemporaneous and lead cash flows
from operations as well as change in sales and property, plant and equipment. RUN_UP is the stock’s daily cumulative marketadjusted return over the window [-252,-3]. TURNOVER is the trading volume divided by shares outstanding, per day (averaged over
trading days -25 to -3 where the ATM announcement date or SEO issue date is trading day 0). STOCK_RETURN_VOLATILITY is the
annualized standard deviation of daily stock returns over the prior trading year, specifically [-252,-2], where day 0 is the
announcement date of ATM program or issuance date of SEO. Trading-related and recommendations-related information is drawn
from various sources: PROCEEDS and FEES (ATMs) from PrivateRaise, TURNOVER, MARKET_VALUE_OF_EQUITY,
STOCK_RETURN_VOLATILITY from CRSP, FEES (SEOs) from SDC, ANALYST_RECOMMENDATIONS and ANALYST_REVISIONS from
I/B/E/S.
31
ATMs
SEOs
Variable
mean
median
St. Dev.
Obs.
mean
median
St. Dev.
LEVERAGE
0.39
0.34
0.31
439
0.48
0.45
0.34
MARKET_VALUE_OF_EQUITY ($ B)
R&D_TA_RATIO
1.52
0.42
0.18
6.78
444
0.3
0.68
414
0.35
0.16
0.50
446
EBITDA_TA_RATIO
-0.40
-0.28
0.75
EFN_TA_RATIO
-0.45
-0.3
0.64
CASH_TA_RATIO
0.50
CASH_BURN
0.81
SALES_TA_RATIO
TOBINS_Q
CAPEX_TA_RATIO
TOTAL_INSTITUTIONAL_
HOLDINGS
3.46
0.56
0.42
2.38
0.05
0.01
PROCEEDS_MVEQ_RATIO
0.20
0.15
ANALYST_RECOMMENDATIONS
2.45
ACCRUALS_QUALITY
FEES
ANALYST_REVISIONS
RUN_UP
TURNOVER
STOCK_RETURN_VOLATILITY
0.34
0.36
2.25
4.63
0.09
0.3
0.26
3.00
0.01
446
2.94
0.18
0.29
0.85
0.07
0.11
0.7
0.50
445
-0.04
0.09
432
-0.13
0.05
390
0.60
0.64
442
442
442
0.41
2.96
0.07
12.41
1550
0.37
1463
0.33
1565
1634
0.76
1631
0.46
1626
0.47
1534
0.3
1567
0.00
2.30
1.87
3.5
1630
1525
0.03
0.11
1628
0.11
0.55
1251
2.00
0.45
1658
0.20
423
2.50
0.61
394
0.01
0.007
0.05
402
0.002
0.0008
0.04
1483
0.02
0.008
0.04
448
0.01
0.008
0.03
1744
2.94
0.44
0.02
0.67
0.27
0.03
0.56
0.72
1.63
0.44
460
368
448
449
0.18
Obs.
4.53
2.03
0.06
0.21
0.53
4.75
0.02
0.13
0.43
0.02
0.51
1.12
0.60
1201
1658
1741
1743
32
Table IV
Logistic and Linear Probability Regressions Explaining ATM vs. SEO Event
Table IV models (1) and (2) report log odds estimates from logistic regressions explaining the incidence of ATM or
SEO where the dependent variable equals one for an ATM and zero for an SEO. Models (3) and (4) report the marginal
effects from linear probability model regressions where the dependent variable equals one for an ATM and zero for
an SEO. ANALYST_RECOMMENDATIONS_DUMMY is equal to one if the firm’s future consensus analyst
recommendation is lower than the sample median value and zero otherwise. Consensus (average) analyst
recommendation comes from Thomson Reuters the quarter following the ATM announcement quarter or SEO
issuance quarter. ANALYST_REVISIONS_DUMMY is equal to one if the firm’s difference between future and lagged
consensus analyst recommendations is lower than the sample median value and zero otherwise. Consensus
(average) analyst recommendation comes from Thomson Reuters the quarter following as well as the quarter
preceding the ATM announcement or SEO issuance quarter. ACCRUALS_QUALITY is the standard deviation of
residuals from the estimation model explaining total current accruals with lagged, contemporaneous and lead cash
flows from operations as well as change in sales and property, plant and equipment. We also include the interaction
variable
ANALYST_RECOMMENDATIONS_DUMMY_ACCRUALS_QUALITY_INT
and
ANALYST_REVISIONS_DUMMY_ACCRUALS_QUALITY_INT. LN_MARKET_VALUE_OF_EQUITY is the natural logarithm
of the market value of equity. R&D_TA_RATIO is R&D expenditures divided by total assets. If R&D is missing, we set
it equal to zero. CAPEX_TA_RATIO is capital expenditures divided by total assets. TOBINS_Q is the book value of total
assets minus the book value of equity plus the market value of equity, all divided by the book value of assets.
TURNOVER is trading volume divided by shares outstanding, per day (averaged over trading days -25 and -3 when
the ATM announcement date or SEO issue date is trading day 0). STOCK_RETURN_VOLATILITY is the annualized
standard deviation of daily stock returns over prior trading year, specifically [-252,-2], where day 0 is the
announcement date of ATM program or issuance date of SEO. PROCEEDS_MVEQ_RATIO is the ATM/SEO proceeds
divided by the market value of equity (computed on the event day). RUN_UP is the stock’s daily cumulative marketadjusted return over the window [-252,-3]. EFN_TA_RATIO is external financing needs and equals EBITDA minus
change in CAPEX minus change in net working capital. SALES_TA_RATIO is revenues divided by total assets.
LEVERAGE is the sum of short-term and long-term debt divided by total assets. EBITDA_TA_RATIO is EBITDA divided
by total assets. CASH_TA_RATIO is cash and equivalents all divided by total assets.
TOTAL_INSTITUTIONAL_HOLDINGS is the fraction of total shares outstanding owned by institutions taken from 13F
filings. Institutional holdings are available from Thomson Reuters and are reported on a quarterly basis. We use
analyst recommendations and Analyst revisions as a proxy for firm’s quality and accruals quality as a proxy for
information asymmetry, respectively. The linear probability models 3 and 4 contain industry fixed effects defined by
Fama-French 49 industry classifications. The values in parentheses across all four regression models denote p-values
for statistical significance levels and a,b,c denote levels of 10%, 5% and 1%, respectively.
33
Intercept
ANALYST_RECOMMENDATIONS_DUMMY
ACCRUALS_QUALITY
ANALYST_RECOMMENDATIONS_DUMMY
_ACCRUALS_QUALITY_INT
ANALYST_REVISIONS_DUMMY
ANALYST_REVISIONS_DUMMY
_ACCRUALS_QUALITY_INT
LN_MARKET_VALUE_OF_EQUITY
R&D_TA_RATIO
CAPEX_TA_RATIO
TOBINS_Q
TURNOVER
STOCK_RETURN_VOLATILITY
PROCEEDS_MVEQ_RATIO
RUN_UP
EFN_TA_RATIO
SALES_TA_RATIO
LEVERAGE
EBITDA_TA_RATIO
CASH_TA_RATIO
TOTAL_INSTITUTIONAL_HOLDINGS
Industry Fixed Effects
-2 Log Likelihood (models 1 & 2) and F-Value
(models 3 & 4)
Max-rescaled R-Square (models 1 & 2) and
Adjusted R-Square (models 3 & 4)
Number of observations
(1)
7.07c
(0.000)
1.64c
(0.000)
4.88b
(0.018)
-1.17c
(0.001)
(2)
7.26c
(0.000)
-1.09c
(0.000)
-0.56
(0.550)
-7.14a
(0.055)
-0.35c
(0.000)
-7.16
(0.408)
-0.47
(0.555)
-2.92c
(0.004)
-0.20b
(0.060)
-0.22
(0.684)
-0.46
(0.184)
0.70
(0.136)
-2.53c
(0.002)
0.24
(0.768)
-1.88c
(0.006)
No
733.67
-1.06c
(0.000)
-1.11
(0.179)
-7.10b
(0.025)
-0.37c
(0.000)
-10.97
(0.145)
0.18
(0.747)
-3.11c
(0.003)
-0.21a
(0.082)
-0.08
(0.870)
-0.27
(0.372)
0.61
(0.192)
-3.07c
(0.000)
-0.07
(0.927)
-2.62c
(0.000)
No
733.98
-0.07c
(0.000)
-0.25c
(0.000)
-0.30b
(0.014)
-0.03c
(0.000)
-0.72
(0.212)
-0.01
(0.785)
-0.05b
(0.036)
-0.03c
(0.002)
-0.06
(0.169)
-0.02
(0.298)
0.06
(0.138)
-0.30c
(0.000)
0.10a
(0.078)
-0.24c
(0.000)
Yes
35.17
769
769
0.68
769
1.91c
(0.006)
0.63b
(0.003)
-1.14b
(0.024)
0.66
(3)
0.88c
(0.000)
0.10c
(0.006)
0.42c (0.001)
-0.46c
(0.000)
0.43
(4)
0.83c
(0.000)
0.63c
(0.001)
0.04b
(0.03)
-0.80b
(0.011)
-0.06c
(0.000)
-0.28c
(0.000)
-0.34c
(0.006)
-0.03c
(0.000)
-0.44
(0.447)
-0.01
(0.701)
-0.06b
(0.015)
-0.04c
(0.001)
-0.05
(0.293)
-0.02
(0.309)
0.05
(0.205)
-0.34c
(0.000)
0.11a
(0.060)
-0.27c
(0.000)
Yes
33.11
0.42
769
34
Table V
Univariate Statistics on Firm Dribble-out Behavior (of ATM Shares)
Table V presents univariate statistics on firms’ actual issuance behavior of ATM offerings. Panel A presents
summary statistics for all ATM issuers, Panel B (Panel C) presents all ATM issuers that belong to the ATMs that
exhibit greater (lower) than the ATM sample median inflation-adjusted sales. Data come from firms’ 10-Q filings
(for the ending quarter of each fiscal year we use the 10-K filing). FRACTION_OF_ANNOUNCED_ISSUANCE is the
fraction of the announced equity that was actually taken down. COMPLETION_DURATION is the number of
quarters that it took the ATM issuer to take down all shares announced in the ATM program. We report two
different weighted average market prices based on actual issuance activity reported by ATM issuers. The first
(WEIGHTED_AVERAGE_ISSUE_PRICE_ENDOFQUARTER_PRICE_RATIO) is the average market price ATM issuers
receive
divided
by
the
end-of-quarter
closing
price.
The
second
(WEIGHTED_AVERAGE_ISSUE_PRICE_QUARTERAVERAGECLOSING_PRICE_RATIO) is the average market price
ATM issuers receive divided by the average contemporaneous quarter’s closing price. For these measures, we
only consider the ATM issuances for which we have the weighted average market price documented in the
issuer’s 10-Q or 10-K, respectively. Annual Consumer Price Index comes from the Federal Reserve Bank of
Minneapolis website https://www.minneapolisfed.org/community/financial-and-economic-education/cpicalculator-information/consumer-price-index-and-inflation-rates-1913 and all sales are adjusted for inflation
using the year 2008 as the basis.
Panel A: All firms
FRACTION_OF_ANNOUNCED_ISSUANCE
COMPLETION_DURATION (completed only)
WEIGHTED_AVERAGE_ISSUE_PRICE_
ENDOFQUARTER_PRICE_RATIO
WEIGHTED_AVERAGE_ISSUE_PRICE_
QUARTERAVERAGECLOSING_PRICE_RATIO
Mean
0.43
6.21
Median
0.27
6.00
StdDev
0.42
3.36
N
515
191
1.64
1.02
7.74
762
Median
0.34
7.00
StdDev
0.44
3.48
N
258
96
2.23
381
Median
0.13
6.00
StdDev
0.39
3.17
N
257
95
1.04
1.41
381
1.37
Panel B: Greater Than Median Inflation-adjusted Sales Firms
Mean
FRACTION_OF_ANNOUNCED_ISSUANCE
0.48
COMPLETION_DURATION (completed only)
6.74
WEIGHTED_AVERAGE_ISSUE_PRICE_
ENDOFQUARTER_PRICE_RATIO
1.42
WEIGHTED_AVERAGE_ISSUE_PRICE_
QUARTERAVERAGECLOSING_PRICE_RATIO
1.69
Panel C: Lower Than Median Inflation-adjusted Sales Firms
Mean
FRACTION_OF_ANNOUNCED_ISSUANCE
0.33
COMPLETION_DURATION (completed only)
5.85
WEIGHTED_AVERAGE_ISSUE_PRICE_
ENDOFQUARTER_PRICE_RATIO
1.28
WEIGHTED_AVERAGE_ISSUE_PRICE_
QUARTERAVERAGECLOSING_PRICE_RATIO
1.58
1.02
1.01
1.00
1.04
4.39
2.14
1.14
998
499
499
35
Table VI
Regressions Explaining Dribble-out Behavior
Table VI presents estimates from censored quantile regressions of the actual cumulative issuance (the total number
of shares issued from start of program through this quarter, relative to the number of shares the firm announced it
planned to issue in the original filing) on a set of explanatory variables. QUARTER_COUNTER is the number of
quarters since the ATM announcement. STOCK_RETURN_VOLATILITY is the contemporaneous quarter’s standard
deviation of daily stock returns. PRIOR_QUARTER_BHARS is the buy-and-hold abnormal returns minus the return of
the CRSP value-weighted index including dividends over the quarter preceding the actual cumulative issuance
measure’s quarter. CURRENT_QUARTER_BHARS is the buy-and-hold abnormal returns minus the CRSP valueweighted return index including dividends over the quarter of the actual cumulative issuance measure’s quarter.
COMMITMENT_DUMMY is equal to one if the firm announces a commitment period for issuance (rather than
allowing it to expire at the end of the three-year shelf registration period), and zero otherwise.
PRIOR_CUMULATIVE_TAKEDOWN_FRACTION is the total fraction of shares taken down over the announced total,
across all preceding quarters of the ATM program. The sample is all firm/quarters (1,436) with sufficient data to run
the regression. In separate models, we focus on the following quantiles of the fraction of actual issuance: 80 th (Q80),
85th (Q85), and 90th (Q90). p-values are reported underneath coefficients, in parentheses. a,b,c denote significance
levels of 10%, 5%, 1% respectively. For each ATM program, the actual issuance activity is computed up to
12/31/2015.
Parameters
Intercept
QUARTER_COUNTER
STOCK_RETURN_VOLATILITY
PRIOR_QUARTER_BHARS
CURRENT_QUARTER_BHARS
COMMITMENT_DUMMY
PRIOR_CUMULATIVE_TAKEDOWN_
FRACTION
Predicted
Mean
Value
of
Dependent Variable
Number of firm/quarters
Parameter Estimates
(1)
Q80
0.16c
(0.000)
-0.01c
(0.000)
0.01
(0.423)
0.06
(0.297)
0.12c
(0.000)
0.01
(0.631)
0.04c
(0.000)
0.176
1,436
Parameter Estimates
(2)
Q85
0.20c
(0.000)
-0.02c
(0.003)
0.04b
(0.049)
0.07
(0.193)
0.20c
(0.000)
0.01
(0.735)
0.06c
(0.000)
0.257
Parameter Estimates
(3)
Q90
0.26c
(0.000)
-0.02c
(0.000)
0.06b
(0.037)
0.11
(0.147)
0.23c
(0.000)
0.02
(0.362)
0.07c
(0.000)
0.388
36
Table VII
Regressions of Change in Cash on Source(s) of Cash
[Cash Savings Behavior out of ATMs and SEOs]
Table VII presents the linear regression estimates explaining the difference between cash at the end of the year “t”
and cash at the end of the year “t-1”. Sources of cash (i.e., stock issue, debt issue, cash flow, other sources) are
measured as of year “t” and are scaled by lagged total assets. Following McLean (2011), STOCK_ISSUE is cash
proceeds from share issuance. DEBT_ISSUE is cash proceeds from debt issuances, CASH_FLOW is cash flow from
operations. OTHER_SOURCES is all other cash sources, which includes the sales of assets and investments.
TOTAL_ASSETS equals the total book value of assets. All are measured as of year “t”. There are two estimation
models presented, one for ATMs and one for SEOs. Standard errors are clustered at the firm level. P-values are
reported in parentheses underneath coefficients. a,b,c denote significance levels of 10%, 5%, 1%, respectively.
Parameters
Intercept
STOCK_ISSUE
DEBT_ISSUE
CASH_FLOW
OTHER_SOURCES
TOTAL_ASSETS
R-square
Number of firm/quarters
Number of clusters (firm level)
Parameter Estimates/ATMs
(1)
-0.04
(0.183)
0.65c
(0.000)
0.61c
(0.000)
-0.40
(0.145)
0.11
(0.257)
-0.00
(0.637)
0.229
1,394
347
Parameter Estimates/SEOs
(2)
-0.14c
(0.000)
0.84c
(0.000)
0.10
(0.104)
-0.47b
(0.015)
0.02
(0.749)
0.00a
(0.059)
0.821
966
651
37
Online Appendix for “At-The-Market” (ATM) Offerings
We present three distinct sections to accompany the manuscript. The first section builds
on the analysis of take-down behavior. In addition to exploring (in the main manuscript) the
average firm’s cash-savings behavior from ATMs, this appendix shows changes in other “spending
accounts” as firms take down shares. The second offers pictorial representation of Ai and Norton
analysis of the coefficient(s) on the interaction variable AI*Quality in the (two) logit(s) [of Table
IV]. Last, the third section provides a list of the firms in our sample that conduct ATMs and SEOs
in 2012 to provide more detail on the companies conducting these types of follow-on offerings.
Section A.I.
The 65% average cash-savings rate out of ATM issuances (over the first two years
following announcement) implied 35% spending. This section illustrates the allocation of ATM
issuance proceeds.
Table A.I presents changes in most potential uses of ATM issuance proceeds, along with
the actual issuance amount. We do so on a quarterly basis, for the quarter of the ATM
announcement as well as two years (eight quarters) after.11 We also take the “cumulative”
perspective: each presented “change in” quarterly variable is the quarter “t” variable value minus
the variable value from the quarter preceding the ATM announcement. Each variable (with
exceptions noted) is also relative to total assets, so that the common scaling allows us to speak
to the magnitude of the allocation of proceeds to various categories, in common size terms.
The potential destinations of proceeds we investigate (i.e. the ratios of the following
variables relative to total assets) are: accounts receivable, cash, inventories, net PP&E,
11
Our analysis indicates that most take-down is executed in those first two years.
38
intangibles, R&D expenditures, advertising expenditures, leverage (short plus long-term debt,
which is divided by total assets), and dividends. We also present the quarterly cumulative (ATM)
issuance proceeds, relative to assets in the quarter preceding the ATM announcement. Finally,
we show the percentage growth in the total assets quarter by quarter.
The results can be summarized easily. Despite several variables showing increases
contemporaneously with proceeds taken down, there are only two variables that positively
covary with proceeds by a similar magnitude; total assets and cash. To illustrate pictorially, we
present Figure A.I. The three lines that obviously rise together are the total assets growth
variable, the cumulative change in cash variable, and the gross proceeds taken down variable.
No other category shows any such consistent pattern. Firms appear to hoard the cash proceeds
from ATM issuance, consistent with the precautionary motive for issuing equity (McLean (2011)).
39
Table A.I.
Financial Ratios Across Quarters – Uses of ATM Proceeds
Table A.I. presents the mean values of changes in quarterly financial ratios for our sample of ATMs. Each
variable listed (except total assets growth) is first scaled by total assets. This ratio is calculated in quarter -1 (the
quarter preceding the event) and in quarter “t” (0, 1, … , 8). The “change variable” is the difference between the
quarter “t” ratio and the quarter “-1” ratio. In other words, these are cumulative ratio changes. Total assets growth
is the percentage difference between quarter “t” total assets and quarter “-1” total assets. Financials are drawn from
Compustat (Fundamentals Quarterly) database. The number of observations for each of the quarters is as follows:
Q0: 595 obs, Q1: 488 obs, Q2: 445 obs, Q3: 424 obs, Q4: 400 obs, Q5: 389 obs, Q6: 371 obs, Q7: 345 obs and Q8:
323 obs. All mean values are winsorized at the 1% and the 99% level.
Financial ratios trend across quarters
0
1
2
3
4
5
6
7
8
1)
2)
3)
4)
5)
6)
7)
8)
9)
.010
.007
.013
.014
.020
.023
.022
.033
.039
.020
.104
.105
.132
.176
.212
.191
.211
.230
.000
.002
.004
.008
.007
.008
.009
.017
.007
Change in
intangibles
.010
.022
.033
.047
.060
.070
.088
.117
.124
.004
.008
.013
.019
.015
.020
.040
.028
.050
Change in R&D
expenditures
0.004
0.006
0.010
0.010
0.013
0.014
0.015
0.022
0.023
.004
.007
.016
.020
.025
.021
.023
.026
.021
Gross proceeds
adjusted
.046
.083
.101
.118
.131
.143
.160
.166
.168
Change in total
leverage
.020
.025
.055
.095
.106
.111
.138
.223
.227
.000
.000
.000
.001
.002
.002
.002
.002
.003
.051
.161
.198
.327
.398
.469
.520
.603
.670
Change in
receivables
Change in cash
Change in
inventories
Change in net
PPE
Change in
advertising expenditures
Change in total
dividends
Total assets
growth
40
Figure I
Changes in firm characteristics during the ATM take-down period
Cumulative change from quarter -1
Figure I presents the quarterly cumulative trends for the financials’ and gross proceeds’ percentage changes appearing in Table V. Quarterly
cumulative trends span the time period from quarter -1 to quarter +8 when quarter 0 is the quarter of the announcement of the ATM initialization.
Figure I
0.5
0.4
0.3
0.2
0.1
0
-0.1
Q0
Q1
Q2
Q3
Q4
Q5
Quarter number relative to ATM announcement quarter
Q6
Change in receivables
Change in cash
Change in inventories
Change in advertising expenditures
Change in total leverage
Change in dividends
Change in net PPE
Total assets growth
Change in intangibles
Gross proceeds adjusted
Q7
Q8
Change in R&D expenditures
41
Section A.II.
Ai and Norton
We present figures of the variation in the coefficient on the AI*Quality interaction term (in models 1
and 2 of the logit regression in Table IV of the paper). Each figure shows the interaction effect as a function of
the predicted probability of the dependent latent variable. The presentation of the variation of the interaction
effects across all predicted probability values is motivated by the Ai and Norton (2004) correction when
interaction effects are present in non-linear estimation models.
Interaction effect of Analyst recommendations with accruals quality (Table IV, model 1)
estimates of Interaction
Effects
z-statisticsLogit
of Interaction
Effects after
Logit
z-statistic
Logit
10
5
0
-5
0
.2
.4
.6
Predicted Probability that y = 1
.8
1
42
Interaction effect of Analyst recommendations’ revisions with accruals quality (Table IV, model 2)
estimates of Interaction Effects
z-statisticsLogit
of Interaction
Effects after Logit
z-statistic
Logit
10
5
0
-5
0
.2
.4
.6
Predicted Probability that y = 1
.8
1
43
Section A.III.
List of 2012 ATMs and SEOs
This table lists our sample of ATM offerings announced in 2012 (Panel A) and our SEO issues in 2012 (Panel
B). Data for ATMs come from The Deal’s PrivateRaise and SEOs are collected from SDC. We report the issuer’s name
and trading symbol, the announcement date and gross proceeds (announced in the case of ATMs and issued in the
case of SEOs), including both primary and secondary shares, the four-digit SIC code and the respective 49 Fama
French industry. 12 Four-digit SIC codes are collected from EDGAR.
Trading
symbol
ASTI
ZLCS
MHR
RVXCF
THLD
VTUS
WAVX
ONTY
TXCC
PTX
MXWL
ROYL
GALE
AGEN
GNOM
LGND
HNR
ACAD
12
Panel A. 2012 ATMs
Issuer name
Ascent Solar
Technologies Inc.
Epirus
Biopharmaceuticals Inc.
Blue Ridge
Mountain Resources Inc.
Resverlogix Corp.
Molecular
Templates Inc.
Assembly
Biosciences Inc.
Wave Systems
Corp.
Cascadian
Therapeutics Inc.
Transwitch Corp.
Pernix
Therapeutics Holdings Inc.
Maxwell
Technologies Inc.
Royale Energy
Inc.
Galena
Biopharma Inc.
Agenus Inc.
Complete
Genomics Inc.
Ligand
Pharmaceuticals Inc.
Harvest Natural
Resources Inc.
Acadia
Pharmaceuticals Inc.
Announcement
date
Announced gross
proceeds amount
SIC
49
Fama French
industry
20120105
5,000,000.00
3674
CHIPS
20120110
15,000,000.00
2834
DRUGS
20120118
20120119
Not disclosed
Not disclosed
1311
2834
OIL
DRUGS
20120120
2,578,960.00
2834
DRUGS
20120130
20,000,000.00
2834
20120130
20,000,000.00
3577
HARDW
20120203
20120210
50,000,000.00
10,000,000.00
25,000,000.00
8731
3674
2834
BUSSV
CHIPS
DRUGS
20120216
30,000,000.00
3690
ELCEQ
20120217
50,000,000.00
1311
OIL
20120210
DRUGS
20120217
20120302
10,000,000.00
Not disclosed
2834
2836
DRUGS
DRUGS
20120308
30,000,000.00
8731
BUSSV
20120326
30,000,000.00
2834
DRUGS
20120330
20,000,000.00
2834
DRUGS
20120330
75,000,000.00
1311
OIL
Using Fama/French 49 groupings (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)
44
IDRA
CZR
RPTP
SNTA
KMR
CLWR
TZYM
EXM
ECTE
PAA
FPP
EROC
EPD
INO
MELA
RGP
ZLCS
SGYP
HEB
AVNR
HZNP
CERS
ACRX
OPXA
PAA
CLDX
NVAX
Idera
Pharmaceuticals Inc.
Caesars
Entertainment Corp.
Raptor
Pharmaceuticals Corp.
Madrigal
Pharmaceuticals Inc.
Kinder Morgan
Management Inc.
Clearwire Corp.
Ocera
Therapeutics Inc.
Excel Maritime
Carriers Ltd.
Echo
Therapeutics Inc.
Plains All
American Pipeline L.P.
FieldPoint
Petroleum Corp.
Eagle Rock Energy
Partners L.P.
Enterprise
Products Partners L.P.
Inovio
Pharmaceuticals Inc.
Strata Skin
Sciences Inc.
Regency Energy
Partners L.P.
Epirus
Biopharmaceuticals Inc.
Synergy
Pharmaceuticals Inc.
Hemispherx
Biopharma Inc.
Avanir
Pharmaceuticals Inc.
Horizon Pharma
Plc.
Cerus Corp.
AcelRx
Pharmaceuticals Inc.
Opexa
Therapeutics Inc.
Plains All
American Pipeline L.P.
Celldex
Therapeutics Inc.
Novavax Inc.
20120412
10,000,000.00
2836
DRUGS
20120412
Not disclosed
7011
MEALS
20120430
50,000,000.00
2834
DRUGS
20120504
20120504
500,000,000.00
300,000,000.00
4610
4899
TRANS
TELCM
20120507
25,000,000.00
2834
DRUGS
20120509
20,000,000.00
3845
MEDEQ
20120509
300,000,000.00
4610
TRANS
20120530
100,000,000.00
1311
OIL
20120502
20120507
20120516
35,000,000.00
35,000,000.00
Not disclosed
2834
DRUGS
4412
TRANS
1311
OIL
20120531
1,000,000,000.00
1311
OIL
20120601
25,000,000.00
3841
MEDEQ
20120615
20,000,000.00
3841
MEDEQ
20120619
200,000,000.00
1311
20120619
15,000,000.00
2834
DRUGS
20120723
75,000,000.00
2836
DRUGS
20120808
25,000,000.00
2834
DRUGS
20120831
30,000,000.00
7,500,000.00
3841
2834
MEDEQ
20120906
Not disclosed
2834
DRUGS
DRUGS
DRUGS
20120621
20120814
20120831
30,000,000.00
75,000,000.00
OIL
2834
DRUGS
2834
20120913
500,000,000.00
4610
20120920
20121001
44,000,000.00
50,000,000.00
2835
2836
DRUGS
DRUGS
TRANS
45
UNIS
CNDO
PACB
GERN
MILL
MWE
VICL
Unilife Corp.
Fortress Biotech
Inc.
Pacific
Biosciences of California
Inc.
Geron Corp.
Miller Energy
Resources Inc.
Markwest Energy
Partners L.P.
ACHN
ANTH
GENE
OMER
SCMP
PSTI
PPHM
STEM
QTWW
WES
Vical Inc.
Achillion
Pharmaceuticals Inc.
Anthera
Pharmaceuticals Inc.
Genetic
Technologies Ltd.
Omeros Corp.
Sucampo
Pharmaceuticals Inc.
Pluristem
Therapeutics Inc.
Peregrine
Pharmaceuticals Inc.
Microbot Medical
Inc.
Quantum Fuel
Systems Technologies
Worldwide Inc.
Western Gas
Partners L.P.
20121003
45,000,000.00
3826
MEDEQ
20121005
30,000,000.00
2834
DRUGS
20121005
20121008
30,000,000.00
50,000,000.00
3826
2834
LABEQ
DRUGS
20121012
100,000,000.00
1311
OIL
20121107
600,000,000.00
1311
20121108
50,000,000.00
2834
DRUGS
20121108
25,000,000.00
2834
DRUGS
20121214
60,000,000.00
2834
DRUGS
20121226
95,000,000.00
2836
DRUGS
20121228
30,000,000.00
2836
DRUGS
20121228
5,000,000.00
3714
AUTOS
20121228
125,000,000.00
5,254,078,960.00
1311
OIL
SIC
49
Fama French
industry
OIL
20121107
20121116
20121221
20121227
50,000,000.00
Not disclosed
20,000,000.00
75,000,000.00
OIL
2836
DRUGS
2836
DRUGS
2834
DRUGS
2834
DRUGS
Panel B. 2012 SEOs
Trading
symbol
GEVA
VNR
NAT
SAVE
NBIX
ZIOP
Issuer name
Synageva
BioPharma Corp
Vanguard Natural
Resources LLC
Nordic American
Tankers Ltd
Spirit Airlines Inc
Neurocrine
Biosciences Inc
ZIOPHARM
Oncology Inc
Issue date
Gross proceeds
78,261,000.00
2836
20120118
197,773,000.00
1311
20120119
159,500,000.00
4512
20120120
50,180,000.00
20120105
20120119
20120119
77,550,000.00
76,950,000.00
DRUGS
4400
TRANS
2836
DRUGS
2834
DRUGS
TRANS
46
TSRX
FRAN
REXX
RDEA
OCZ
SBH
ANAC
RMTI
WNS
EXEL
IRWD
HALO
ALNY
PMFG
OYOG
BDE
PLX
ACHN
GNRC
CIE
GNK
CFX
VVUS
LUFK
FOLD
GPRE
IPGP
TAL
Trius
Therapeutics Inc
Francesca's
Holdings Corp
Rex Energy Corp
Ardea Biosciences
Inc
OCZ Technology
Group Inc
Sally Beauty
Holdings Inc
Anacor
Pharmaceuticals Inc
Rockwell Medical
Technologies
WNS(Holdings)Ltd
Exelixis Inc
Ironwood
Pharmaceuticals Inc
Halozyme
Therapeutics Inc
Alnylam
Pharmaceuticals Inc
PMFG Inc
Oyo Geospace
Corp
Black Diamond
Inc
Protalix
Biotherapeutics Inc
Achillion
Pharmaceuticals Inc
Generac Holdings
Inc
Cobalt Intl Energy
Inc
Genco Shipping &
Trading Ltd
Colfax Corp
VIVUS Inc
Lufkin Industries
Inc
Amicus
Therapeutics Inc
Green Plains
Renewable Energy
IPG Photonics
Corp
TAL International
Group Inc
20120126
20120126
20120131
45,150,000.00
239,200,000.00
64,750,000.00
2834
DRUGS
20120201
144,500,000.00
2834
DRUGS
20120207
108,000,000.00
3572
HARDW
20120208
21,450,000.00
2834
DRUGS
20120209
20120209
98,513,000.00
60,500,000.00
7389
8731
20120207
20120209
20120210
422,000,000.00
17,528,000.00
79,223,000.00
5600
1311
RTAIL
OIL
5990
RTAIL
3845
MEDEQ
2834
DRUGS
BUSSV
BUSSV
20120210
72,148,000.00
2836
DRUGS
20120214
20120215
20120215
80,625,000.00
41,600,000.00
106,644,000.00
2834
3589
DRUGS
MACH
20120216
58,125,000.00
3949
TOYS
20120216
20120221
23,625,000.00
250,336,000.00
2836
DRUGS
20120222
59,000,000.00
3621
ELCEQ
20120223
1,456,000,000.00
1311
4412
3561
2834
OIL
TRANS
MACH
DRUGS
20120229
198,125,000.00
3560
MACH
20120301
57,000,000.00
2834
2860
DRUGS
CHEMS
20120301
162,900,000.00
3674
CHIPS
20120302
110,700,000.00
7359
BUSSV
20120223
20120228
20120229
20120301
53,250,000.00
272,000,000.00
202,500,000.00
31,230,000.00
3829
LABEQ
2834
DRUGS
47
JAZZ
INVN
AMLN
QLTY
SBAC
AYR
ALKS
NOR
SB
VLCCF
GNC
LNG
XPO
FLT
OGXI
ARGN
NLSN
FRGI
GALT
CMRE
HERO
KORS
DG
TNGO
ZNGA
DNKN
DSCI
SBAC
CLVS
SKM
Jazz
Pharmaceuticals PLC
InvenSense Inc
Amylin
Pharmaceuticals Inc
Quality
Distribution Inc
SBA
Communications Corp
Aircastle Ltd
Alkermes PLC
Noranda
Aluminum Holding Corp
Safe Bulkers Inc
Knightsbridge
Tankers Ltd
GNC Holdings Inc
Cheniere Energy
Inc
XPO Logistics Inc
FleetCor
Technologies Inc
OncoGenex
Pharmaceuticals Inc
Amerigon Inc
Nielsen Holdings
NV
Fiesta Restaurant
Group Inc
Galectin
Therapeutics Inc
Costamare Inc
Hercules Offshore
Inc
Michael Kors
Holdings Ltd
Dollar General
Corp
Tangoe Inc
Zynga Inc
Dunkin Brands
Group Inc
Derma Sciences
Inc
SBA
Communications Corp
Clovis Oncology
Inc
SK Telecom Co
Ltd
20120305
20120306
393,774,000.00
96,525,000.00
2834
3674
DRUGS
CHIPS
20120307
65,000,000.00
4213
TRANS
20120307
20120307
20120308
285,238,000.00
65,500,000.00
346,500,000.00
4890
7359
2834
TELCM
BUSSV
DRUGS
20120313
20120313
107,100,000.00
32,500,000.00
3334
4412
STEEL
TRANS
20120313
569,500,000.00
5400
RTAIL
20120314
20120315
20120320
20120307
20120313
20120314
20120314
203,060,000.00
34,744,000.00
DRUGS
4400
TRANS
1311
4700
OIL
TRANS
219,000,000.00
7389
BUSSV
20120320
49,980,000.00
70,150,000.00
907,500,000.00
2835
3714
DRUGS
AUTOS
20120321
72,500,000.00
5812
MEALS
20120322
20120322
10,435,000.00
105,750,000.00
2834
4412
DRUGS
TRANS
20120322
1,175,000,000.00
3100
CLTHS
20120327
20120328
20120328
1,131,250,000.00
148,000,000.00
515,630,000.00
5331
7372
7374
RTAIL
SOFTW
BUSSV
20120329
778,800,000.00
5810
3841
MEALS
MEDEQ
20120402
263,813,000.00
4890
2834
TELCM
DRUGS
20120403
285,187,000.00
4812
CHIPS
20120322
20120402
20120403
317,100,000.00
126,000,000.00
2834
102,000,000.00
19,656,000.00
75,000,000.00
7389
BUSSV
1381
OIL
48
CKEC
PCRX
FRAN
TNP
GWRE
PKT
PHMD
TSPT
MX
IGTE
TRS
NMM
TAL
SBH
KTOS
ULTA
SXCI
WWWW
PETD
ANGI
ACHC
ARNA
SANW
CSII
ALXN
GMAN
BMRN
ELGX
Carmike Cinemas
Inc
Pacira
Pharmaceuticals Inc
Francesca's
Holdings Corp
Tsakos Energy
Navigation Ltd
Guidewire
Software Inc
Procera Networks
Inc
PhotoMedex Inc
Transcept
Pharmaceuticals Inc
MagnaChip
Semiconductor Corp
iGATE Corp
TriMas Corp
Navios Maritime
Partners LP
TAL International
Group Inc
Sally Beauty
Holdings Inc
Kratos Defense &
Security
Ulta Salon
Cosmetic &
SXC Health
Solutions Corp
Web.com Group
Inc
Petroleum
Development Corp
Angie's List Inc
Acadia Healthcare
Co Inc
Arena
Pharmaceuticals Inc
S&W Seed Co
Cardiovascular
Systems Inc
Alexion
Pharmaceuticals Inc
Gordmans Stores
Inc
BioMarin
Pharmaceutical Inc
Endologix Inc
20120404
52,000,000.00
7830
2834
DRUGS
20120417
248,400,000.00
5600
4412
RTAIL
TRANS
20120418
226,000,000.00
7371
SOFTW
20120419
20120423
94,500,000.00
27,552,000.00
7371
3845
SOFTW
MEDEQ
20120501
20120501
20120502
79,800,000.00
27,450,000.00
83,000,000.00
3674
7371
3460
CHIPS
SOFTW
FABPR
20120503
62,720,000.00
4412
TRANS
20120503
193,750,000.00
7359
BUSSV
20120509
100,000,000.00
3760
GUNS
20120509
588,000,000.00
5990
7372
RTAIL
SOFTW
20120510
122,000,000.00
7372
SOFTW
20120515
109,725,000.00
7310
BUSSV
20120412
20120418
20120426
20120508
20120510
20120515
20120515
58,500,000.00
65,000,000.00
40,500,000.00
399,000,000.00
471,120,000.00
172,250,000.00
127,875,000.00
FUN
2834
DRUGS
5990
RTAIL
1311
OIL
8093
HLTH
20120516
20120518
60,500,000.00
5,500,000.00
2834
100
DRUGS
AGRIC
20120522
20120524
16,020,000.00
465,100,000.00
3841
MEDEQ
20120525
50,979,000.00
5600
RTAIL
36,180,000.00
3841
20120531
20120531
235,820,000.00
2834
DRUGS
2834
DRUGS
MEDEQ
49
DG
TGI
FLT
TFM
DDD
AEGR
SHO
IHS
OMER
ELLI
TMH
OSUR
GEVA
IMGN
TEU
SUSS
BV
SBH
DCIX
SPLK
SSNC
IMMY
XNPT
SPF
BNNY
MAPP
SAVE
IDIX
ROSG
AYR
CHTR
Dollar General
Corp
Triumph Group
Inc
FleetCor
Technologies Inc
Fresh Market Inc
3D Systems Corp
Aegerion
Pharmaceuticals Inc
Sunstone Hotel
Investors Inc
IHS Inc
Omeros Corp
Ellie Mae Inc
Team Health
Holdings Inc
Orasure
Technologies Inc
Synageva
BioPharma Corp
ImmunoGen Inc
Box Ships Inc
Susser Holdings
Corp
Bazaarvoice Inc
Sally Beauty
Holdings Inc
Diana
Containerships Inc
Splunk Inc
SS&C
Technologies Holdings Inc
Imprimis
Pharmaceuticals
XenoPort Inc
Standard Pacific
Corp
Annies Inc
MAP
Pharmaceuticals Inc
Spirit Airlines Inc
Idenix
Pharmaceuticals Inc
Rosetta Genomics
Ltd
Aircastle Ltd
Charter
Communications Inc
20120605
1,402,500,000.00
5331
RTAIL
20120607
20120612
20120613
249,750,000.00
506,674,000.00
99,900,000.00
7389
5411
7372
BUSSV
RTAIL
SOFTW
20120614
50,150,000.00
2834
DRUGS
869,565,000.00
30,000,000.00
52,728,000.00
7370
2834
7372
SOFTW
DRUGS
SOFTW
7363
BUSSV
20120606
274,461,000.00
3720
AERO
20120619
114,950,000.00
20120629
191,600,000.00
75,030,000.00
3841
MEDEQ
20120710
20120712
20120713
100,034,000.00
100,000,000.00
30,000,000.00
2836
2834
4412
DRUGS
DRUGS
TRANS
20120717
20120717
180,000,000.00
130,900,000.00
5412
7372
RTAIL
SOFTW
20120719
20120719
50,625,000.00
331,770,000.00
4412
7372
TRANS
SOFTW
20120719
173,250,000.00
7372
SOFTW
20120725
20120725
267,837,000.00
40,000,000.00
2834
2834
DRUGS
DRUGS
20120801
20120801
52,000,000.00
193,535,000.00
2834
4512
DRUGS
TRANS
20120802
176,000,000.00
2834
DRUGS
20120807
106,375,000.00
7359
BUSSV
20120621
20120627
20120627
20120710
20120718
20120731
20120731
20120802
20120808
582,295,000.00
70,875,000.00
124,575,000.00
27,500,000.00
368,750,000.00
7011
MEALS
5990
RTAIL
1531
2000
CNSTR
FOOD
2834
DRUGS
4841
TELCM
50
EXAS
INFI
UNXL
DNKN
GNC
TRS
GET
FLDM
MCP
MHO
WCRX
SPSC
Z
VCRA
ET
VNR
HK
TMH
GWR
SAAS
TGH
ASGN
RP
AREX
THR
KORS
KEYW
EXP
POST
DG
Exact Sciences
Corp
Infinity
Pharmaceuticals Inc
Uni-Pixel Inc
Dunkin Brands
Group Inc
GNC Holdings Inc
TriMas Corp
Gaylord
Entertainment Co
Fluidigm Corp
Molycorp Inc
M/I Homes Inc
Warner Chilcott
PLC
SPS Commerce
Inc
Zillow Inc
Vocera
Communications Inc
ExactTarget Inc
Vanguard Natural
Resources LLC
Halcon Resources
Corp
Team Health
Holdings Inc
Genesee &
Wyoming Inc
inContact Inc
Textainer Grp
Hldg Ltd
On Assignment
Inc
RealPage Inc
Approach
Resources Inc
Thermon Group
Holdings Inc
Michael Kors
Holdings Ltd
The KEYW
Holding Corp
Eagle Materials
Inc
Post Holdings Inc
Dollar General
Corp
20120808
53,625,000.00
8731
2834
3679
DRUGS
CHIPS
20120809
20120809
20120813
661,342,000.00
387,500,000.00
32,715,000.00
5810
5400
3460
MEALS
RTAIL
FABPR
20120813
20120816
20120817
20120905
225,725,000.00
52,155,000.00
120,000,000.00
38,786,000.00
4833
3826
1000
1531
TELCM
LABEQ
MINES
CNSTR
20120905
563,673,000.00
2834
DRUGS
20120906
20120906
53,600,000.00
172,000,000.00
7372
7389
SOFTW
BUSSV
20120911
168,750,000.00
7372
SOFTW
OIL
20120809
20120809
20120906
20120912
76,850,000.00
16,315,000.00
138,719,000.00
165,060,000.00
BUSSV
3669
CHIPS
1311
OIL
20120912
245,000,000.00
1311
7363
BUSSV
20120913
20120913
226,625,000.00
35,000,000.00
4011
7372
TRANS
SOFTW
20120913
236,250,000.00
7359
BUSSV
20120913
20120914
20120919
87,944,000.00
99,400,000.00
152,500,000.00
7363
7372
BUSSV
SOFTW
20120920
220,000,000.00
3620
ELCEQ
20120924
1,219,000,000.00
3100
7373
CLTHS
SOFTW
20120927
20120927
139,500,000.00
206,668,000.00
3241
2040
BLDMT
FOOD
20120927
1,863,000,000.00
5331
RTAIL
20120912
20120924
224,400,000.00
86,950,000.00
1311
OIL
51
EMKR
TSLA
WAIR
SBAC
BGS
SFL
MFRM
WAGE
RIGL
BLOX
LPI
EEQ
CMRE
ANAC
PANW
PBYI
MM
NAV
OREX
IMOSD
AWI
GNC
MRC
TUMI
MAKO
ROC
SEE
NOW
VGR
PRLB
CYNO
EMCORE Corp
Tesla Motors Inc
Wesco Aircraft
Holdings Inc
SBA
Communications Corp
B&G Foods Inc
Ship Finance
International Ltd
Mattress Firm
Holding Corp
WageWorks Inc
Rigel
Pharmaceuticals Inc
Infoblox Inc
Laredo Petroleum
Holdings Inc
Enbridge Energy
Management Llc
Costamare Inc
Anacor
Pharmaceuticals Inc
Palo Alto
Networks Inc
Puma
Biotechnology Inc
Millennial Media
Inc
Navistar
International Corp
Orexigen
Therapeutics Inc
ChipMOS
Tech(Bermuda)Ltd
Armstrong World
Industries Inc
GNC Holdings Inc
MRC Global Inc
Tumi Holdings Inc
MAKO Surgical
Corp
Rockwood
Holdings Inc
Sealed Air Corp
ServiceNow Inc
Vector Group Ltd
Proto Labs Inc
Cynosure Inc
20120928
20120928
8,700,000.00
195,652,000.00
3674
3711
CHIPS
AUTOS
20120928
24,570,000.00
5072
WHLSL
20121001
20121002
285,426,000.00
109,782,000.00
4890
2000
TELCM
FOOD
20121003
20121003
141,091,000.00
105,000,000.00
5712
7389
RTAIL
BUSSV
20121003
20121004
130,008,000.00
100,000,000.00
2834
7374
DRUGS
SOFTW
20121011
253,125,000.00
1311
1311
4412
OIL
OIL
TRANS
20121017
24,000,000.00
2834
DRUGS
20121017
302,400,000.00
3577
HARDW
20121023
141,500,000.00
7311
3711
BUSSV
AUTOS
20121025
60,500,000.00
2834
DRUGS
20121026
25,250,000.00
3674
CHIPS
440,000,000.00
213,110,000.00
5084
3100
20121002
20121012
20121016
20121018
20121024
91,500,000.00
40,000,000.00
98,000,000.00
120,000,000.00
200,000,000.00
20121107
20121107
265,200,000.00
412,983,000.00
20121113
40,002,000.00
20121108
20121108
20121114
20121114
20121114
20121115
20121115
20121116
296,391,000.00
250,945,000.00
392,000,000.00
90,487,000.00
111,600,000.00
65,600,000.00
4412
TRANS
2834
DRUGS
3089
5400
RUBBR
RTAIL
3842
MEDEQ
2800
3089
7372
2111
CHEMS
RUBBR
SOFTW
SMOKE
WHLSL
CLTHS
3440
3845
BLDMT
MEDEQ
52
CJES
CHTR
FLT
CAP
NXST
SUPN
RRTS
KAR
PRGX
MHR
DSCI
AVGO
ACHC
CONN
HTZ
HCA
ST
G
YNDX
TTS
KOS
ZAP
INFI
RBC
SRPT
LPTN
TMH
CLDX
C&J Energy
Services Inc
Charter
Communications Inc
FleetCor
Technologies Inc
CAI International
Inc
Nexstar
Broadcasting Group Inc
Supernus
Pharmaceuticals Inc
Roadrunner
Transp Sys Inc
KAR Auction
Services Inc
PRGX Global Inc
Magnum Hunter
Resources Corp
Derma Sciences
Inc
Avago
Technologies Ltd
Acadia Healthcare
Co Inc
Conn's Inc
Hertz Global
Holdings Inc
HCA Holdings Inc
Sensata Tech Hldg
NV
Genpact Ltd
Yandex NV
Tile Shop
Holdings Inc
Kosmos Energy
Ltd
Harbinger Group
Inc
Infinity
Pharmaceuticals Inc
Regal Beloit Corp
Sarepta
Therapeutics Inc
Lpath Inc
Team Health
Holdings Inc
Celldex
Therapeutics Inc
20121119
20121120
60,986,000.00
213,590,000.00
1389
4841
TELCM
20121127
235,125,000.00
7389
BUSSV
20121129
74,000,000.00
4833
TELCM
20121130
48,000,000.00
2834
DRUGS
20121205
20121205
236,250,000.00
39,933,000.00
5500
8700
RTAIL
BUSSV
20121206
23,500,000.00
1311
3841
OIL
MEDEQ
20121206
741,406,000.00
3674
CHIPS
20121206
20121207
237,974,000.00
147,125,000.00
8093
5731
HLTH
RTAIL
20121210
1,059,200,000.00
8062
20121212
20121128
20121205
20121206
20121210
75,000,000.00
60,375,000.00
31,661,000.00
7359
BUSSV
4731
TRANS
7510
PERSV
3823
8742
7370
LABEQ
BUSSV
SOFTW
67,500,000.00
5700
RTAIL
20121213
112,850,000.00
1311
3690
OIL
ELCEQ
20121213
20121213
150,000,000.00
184,250,000.00
2834
3621
DRUGS
ELCEQ
20121214
20121214
125,000,000.00
11,830,000.00
2834
3841
DRUGS
MEDEQ
20121219
231,600,000.00
7363
BUSSV
20121211
20121211
20121211
20121213
20121221
790,000,000.00
OIL
299,500,000.00
170,212,000.00
106,700,000.00
150,000,000.00
79,925,000.00
43,759,264,000.00
HLTH
2835
DRUGS
53
54