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Institutional investors and long-run return reversals: Insights into post-SEO underperformance
Roger M. Edelen, Özgür Ş. İnce, and Gregory B. Kadlec*
Abstract
Several studies document a positive relation between changes in institutional ownership (∆IO) and short-run returns following seasoned equity offerings (SEOs) and attribute it to an informational role for institutions. However, we find that ∆IO is negatively related to both long run returns and operating performance following SEOs. Indeed, SEO underperformance is almost entirely confined to stocks in the top two quintiles of ∆IO. This result echoes recent findings of long run return reversals in the context of institutional herding, and suggests that the positive link between ∆IO and short run SEO returns found in other studies is a manifestation of destabilizing institutional herding rather than information. More broadly, our evidence establishes a central role for ∆IO in SEO underperformance.
*Edelen is from UC Davis, Ince is from University of South Carolina, and Kadlec is from Virginia Tech. We thank seminar participants at University of Oregon and Virginia Tech for helpful comments. Thanks to Brad Barber, Dave Denis, and Huseyin Gulen for particularly useful suggestions. The authors bear full responsibility for errors.
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1. Introduction
An emerging literature documents an unexpectedly rich temporal pattern to stock returns following
changes in institutional ownership (which we abbreviate ∆IO). Studies have long found that ∆IO
is positively related to future returns in the short run (three to six months), and generally concluded
that the effect is permanent and indicative of informed trading.1 However, more recent studies
have found that ∆IO is negatively related to future returns over the long run (one year or more).
For example, Gutierrez and Kelley (2009) and Dasgupta, Prat, Verardo (2011) examine price
effects of institutional herding and find a long-run return reversal following ∆IO. Taken together,
a positive short-run and negative long-run relation is consistent with institutions having a
destabilizing impact on asset prices (Barberis and Shleifer, 2003; Vayanos and Woolley 2013).
These conflicting views on the role of institutional investors in financial markets are particularly
relevant to the case of seasoned equity offerings (SEOs). On the one hand, institutions are
dominant participants in primary equity markets (SEOs in particular, see Chemmanur, He, and Hu,
2009) who are usually cited as playing an informative role (e.g., IPO bookbuilding models). On
the other hand, many studies argue that SEO underperformance reflects exploitation of overvalued
stock by issuing-firm managers [Loughran and Ritter (1995), Daniel, Hirshleifer, and
Subrahmanyam (1998), Brav, Geczy, and Gompers (2000), and Baker and Wurgler (2002)].
Putting the two together implies that institutions are both informed and exploited.
Several studies reconcile these contradictory views by arguing that institutions are indeed
sophisticated, only buying the ‘right’ SEOs. This assertion is substantiated with evidence of a
positive relation between ∆IO and post-SEO returns [see e.g. Gibson, Safieddine, and Sonti (2004)
1For evidence of a positive relation between institutional ownership and short-horizon returns, see, e.g., Wermers (1999), Coval and Moskowitz (2001), Badrinath and Wahal (2002), Cohen, Gompers, and Vuolteenaho (2002), Parrino, Sias, and Starks (2003), Gibson, Safieddine, and Sonti (2004), and Alti and Sulaeman (2012).
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and Chemmanur, He, and Hu (2009)]. 2 Two considerations suggest caution regarding this
conclusion. First, the return horizon considered in these studies (generally the quarter of or
following the SEO, with robustness checks out to four quarters) does not fully account for the three
to five year horizon that is the norm for underperformance in the SEO literature. Second, as
referenced above, theoretical and empirical studies of herding suggest that the positive relation
between ∆IO and short-horizon returns is followed by a negative relation over longer horizons.
Thus, this study is motivated by the observation that high institutional participation in SEOs
(suggesting herding) combined with a positive relation between ∆IO and short-run SEO returns
(suggesting price impact of the herd) supports an alternative, ‘destabilizing’ hypothesis regarding
institutions’ role in SEOs. Under this alternative hypothesis, institutional buying around an SEO
should be associated with a long-run reversal to fair value as the destabilization runs its course.
Under the informational hypothesis espoused in the aforementioned studies, the relation between
∆IO and long run abnormal returns should be non-negative. This sets up the primary aim of our
study: to analyze the relation between ∆IO and long run post-SEO performance.
We conduct our analysis using a variety of methodologies, and find that not only do institutions
exhibit a herding-like participation in SEO stocks leading up to the SEO (as found in other studies),
they tend to buy SEO stocks that subsequently underperform the most over the long run. Indeed,
virtually all long-run post-SEO underperformance occurs in the top two quintiles of stocks sorted
by ∆IO in the year prior to the SEO -- there is no long-run post-SEO underperformance for stocks
in the bottom three quintiles of ∆IO. Our evidence therefore supports the destabilizing herd rather
2Alti and Sulaeman (2012) examine the relation between changes in institutional holdings and three and five-year post-SEO abnormal returns and conclude that there is little evidence of stronger underperformance for issuers with high institutional demand.
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than informational view of institutions’ role in an SEO setting. Indeed, destabilizing institutional
herds appear to be at the heart of the long run SEO underperformance phenomenon.
In further support of our assertion that ∆IO plays a central role in long-run SEO
underperformance, we examine non-SEO stocks matched to SEO stocks on the basis of ∆IO. We
find long-run return underperformance in the non-SEO sample that is generally statistically
indistinguishable from that in the SEO sample (the difference is significant at 10% in one quintile).
Impressively, we find similar (though somewhat weaker) results for operating performance. Thus,
∆IO is both necessary for long-run underperformance following an SEO, and nearly sufficient for
long-run underperformance without an SEO.
An important consideration in interpreting our results is the holding period of institutional
investors. A negative relation between ∆IO and long-run post-SEO returns rejects an informational
role for institutions only if ∆IO persists throughout the long-run post-SEO return window. We
find that it does (see Figure 1). We emphasize that our analysis operates on aggregate ∆IO. It may
be that there are transfers within the institutional investor universe that benefit an informed subset
of institutions at the expense of others. Even so, our evidence implies that the overall impact of
institutions net of these intra-institutional is destabilizing, not informational.
A key consideration in the SEO literature is operating performance. The herding literature
generally does not considered this dimension of performance, but changes in institutional
ownership could impact a firm’s discount rate, and thus the investment decisions of the firm’s
managers. For example, if herding drives up the share price then the firm’s equity cost of capital
is lower, ceteris paribus. This would imply a lower hurdle rate on real investment. Unfortunately,
internal hurdle rates are not observable. However, if a lower hurdle rate triggers investment and
the firm faces a declining marginal product, then the implied reduction in operating performance
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is observable. We use this reasoning to conduct a parallel analysis of the relation between ∆IO and
operating performance. We find that long-run operating performance of SEO stocks is abnormally
low (as in Loughran and Ritter, 1994), and that high ∆IO firms experience the largest decline in
operating performance; particularly when the firm undertakes real investment along with that ∆IO.
As previously noted, a number of studies suggest that SEOs are used by managers to exploit
equity mispricing. One interpretation of our evidence is that managers time that exploitation to
coincide with the arrival of an institutional herd. This hypothesis dovetails nicely with the analysis
in Alti and Sulaeman (2012), but the interpretation in their study is very different. Alti and
Sulaeman document that a run-up in share price leads to an SEO only when that run up is
accompanied by an increase in institutional ownership. They interpret this as indicating that
institutional certification is a precondition for an offering to be ‘well received.’ An alternative
interpretation, more consistent with our evidence of long run reversals following institutional
buying, is that market-timing managers need more than just overvalued shares to motivate an SEO;
they also need a readily identifiable target to unload those shares upon. Recent herding in the firm’s
stock by institutions would surely satisfy that identification requirement.
Irrespective of the role that ∆IO plays in SEOs – informed certification or destabilizing herd –
our evidence makes it clear that firms undertaking an SEO in the presence of high ∆IO enjoy a low
cost of capital, where low means relative to standard return benchmarks. What is not clear is
whether or not institutions have rational expectations about those low returns. Rational
expectations would imply that the true required return of a very large segment of the market
(institutions in aggregate) is lower than that indicated by standard benchmarks. In other words, the
benchmarks are wrong. But it may be that the benchmarks accurately reflect institutions’ required
returns; they’re just not aware that they’ve pushed prices too high (and expected returns too low).
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In other words, prices are wrong. While we do not claim to resolve this manifestation of Fama’s
joint hypothesis, we provide several analyses to shed some light on the matter.
One possible benchmark oversight is an improvement in stock liquidity. Lin and Wu (2013)
find that increases in liquidity around SEOs are related to increases in institutional investors, and
Bilinski, Liu, and Strong (2012) find that post-issuance return performance is related to changes
in liquidity. We confirm this link between ΔIO, liquidity, and post-SEO performance. However,
ΔIO remains strongly related to long run performance after controlling for liquidity using both
Amihud (2002) and Pastor and Stambaughs’ (2003) measures.
Another change in condition that could warrant a lower required return is corporate investment,
e.g., the exercise of real options as in Carlson, Fisher, and Giammarino (2006), or productivity
shocks as in Li, Livdan and Zhang (2009). If institutions are attracted to such firms and benchmarks
are slow to adjust to the change in firm risk, a spurious relation could arise between ΔIO and
benchmarking errors. We observe a small decline in beta estimates following SEOs as in Carlson
et al. (2010), but that decline is uncorrelated with ΔIO. The effect is partly explained by an asset
growth factor, but ΔIO remains statistically significant, whereas asset growth is only weakly so.
Finally, Lehavy and Sloan (2008) and Edelen, Ince, and Kadlec (2015) argue that changes in
institutional ownership might relate to lower required returns by way of a change in market
segmentation (as in Merton (1987), Allen and Gale (1994), Basak and Cuoco (1998), and Shapiro
(2002)). However, calibration suggests that this market-segmentation channel could not generate
the reduction in discount rate we find for high-∆IO SEO firms.
A mispricing interpretation of our evidence seems to contradict the widely held prior belief that
institutions are sophisticated investors. However, even sophisticated investment managers might
play a destabilizing role if forced to do so by investor flows, as in Coval and Stafford (2007),
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Frazzini and Lamont (2008), and Khan, Kogan, and Serafeim (2012). Several factors argue against
this explanation. First, Khan et al. (2012) show that mutual fund inflows typically go towards
expansion of existing positions rather than new positions. We find that long-run returns are more
highly related to the number of institutional shareholders (new positions) rather than the fractions
of shares held by institutions (expansion of existing positions). More directly, we find that the
relation between long-run returns and changes in number of institutions is robust to the exclusion
of SEOs with inflow-induced buying pressure and controlling for changes in ownership by mutual
funds – who are likely to be most sensitive to the effects of flow. Finally, even if the institutional
buying were the result of flow, it is hard to see why a sophisticated institutional investor would
have to concentrate flow-induced stock purchases in the underperforming (SEO) stocks. For
example, we had no trouble finding better performing comparable stock.
Our inability to identify an alternative risk metric to explain the link between ∆IO and SEO
underperformance, or to attribute it to flow, leaves open the possibility that the link arises from the
long-run destabilization that follows in the wake of an institutional herd. While that interpretation
contradicts the widespread view that institutions are sophisticated investors, it certainly provides
an easy fit to our findings. The fact that we observe similar effects at non-SEO firms in the face
of high ∆IO adds credence to this conjecture. Still, at the end of the day, our results remain subject
to Fama’s joint hypothesis caveat.
2. Data sources, variable definitions, and SEO firm characteristics
2.1 Data sources and variable definitions
We obtain data for SEOs from Securities Data Corporation. Our initial sample of issuing firms
includes all SEOs of NYSE, Amex, and NASDAQ stocks between 1981 (Thomson Reuters' 13F
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institutional holdings coverage begins) and 2010 (to allow four years of post-issuance data). We
exclude pure secondary offerings; financial institutions; regulated utilities (SIC codes 4900-4999,
6000-6999); offerings within one year of initial public offering; offerings by the same firm within
five years of a previous SEO; and firm-year observations with incomplete data (must be present
on CRSP, Compustat, and Thomson Reuters 13F).3 Our control sample of non-issuing firms
consists of all NYSE, Amex, or NASDAQ firms with no IPO or SEO within five years, excluding
financial institutions; regulated utilities; and firm-years with incomplete data.
Following Loughran and Ritter (1997) we use OIBD/Assets for operating performance,4 and
Following Cooper, Gulen, and Schill (2008), we use the fiscal-year percent change in total assets
(Compustat data item AT) as our proxy for corporate investment. Monthly returns for the market
and risk-free asset are from Ken French’s website. The liquidity factor of Pastor and Stambaugh
(2003) is from Wharton Research Data Services (WRDS). We construct size (SMB), book-to-
market (HML), and the momentum (MOM) factor portfolios as in Fama and French (1993), but
purge the portfolios of firms that have issued equity in the past five years as in Loughran and Ritter
(2000).
We obtain data on the number of institutional investors and fraction of shares held by
institutional investors for each stock from Thomson Reuters' 13F institutional holdings database.
We focus primarily on the number of institutional investors. The literature uses both measures to
3 Issuers with zero institutional shareholders at the beginning of the fiscal year of the SEO (3.2% of the sample) are excluded from regression analyses because percent change in institutional shareholders is undefined. These issuers are included in quintile sorts by assigning them to the quintile with the highest increase in institutional shareholders if they have non-zero institutional shareholders at the end of the fiscal year. Dropping them does not alter the results. 4OIBD/Assets is operating income before depreciation and amortization divided by the average of beginning and ending period book assets less cash. We subtract cash from book value of assets to prevent operating performance from declining mechanically as a result of cash savings out of offer proceeds.
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proxy for institutional demand but tends to favor the number of institutional investors.5 Moreover,
a count-based measure (number of institutions) is closer to herding measures than quantity based
measure (fraction of shares held). Both measures provide qualitatively similar results but studies
find the fraction of shares held by institutions tends to contain little incremental information for
stock returns relative to number of institutions investors (see, e.g., Sias, Starks, and Titman, 2006
and Jiang, 2010). Nevertheless, we use the fraction of shares held when it appears more directly
connected to the hypothesis being tested.
2.2 Changes in institutional investors surrounding SEOs
Figure 1 provides a timeline of changes in number of institutional investors (∆ #Institutions)
surrounding SEOs starting several quarters before the SEO. In particular, we graph the median of
(#Instst – #Insts-8)/#Insts-8), where t = 0 represents the SEO quarter.
[Figure 1 around here]
Figure 1 indicates a substantial increase in number of institutional investors surrounding SEOs [as
documented in Lehavy and Sloan (2008) and Alti and Sulaeman (2012)]. While a large portion of
the increase occurs during the SEO quarter (quarter 0), roughly half of the change leading up to
the end of the SEO quarter occurs prior to the SEO. For example, SEO firms experience a 91%
increase in number of institutional investors during the four quarters prior to the SEO (quarters -4
to -1). Note also that this increase in number of institutions persists. There is no evidence of a
transitory component (reversal) to changes in institutional investors prior to SEOs.6 Note that
5For studies that use number of institutional investors to proxy for institutional demand see, e.g, Lakonishok et al. (1992); Chen et al. (2002); Sias Starks, and Titman (2006); and Sias, Jiang (2010); Alti and Suleaman (2012), and Edelen, Ince, and Kadlec (2015). 6 Figure 4 in Section 6 examines the long-run persistence of the initial expansion in institutional ownership.
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some of the increase during the offer quarter reflects the SEO-induced increase in float, but a
significant amount does not.
2.3 SEO and control-firm characteristics
Table 1 presents descriptive statistics regarding various characteristics of issuers and non-
issuers (control) firms.
[Table 1 around here]
Consistent with prior studies, SEOs occur following a period of relatively high stock returns and
during years with relatively high operating performance and asset growth. Issuing firms are smaller
than non-issuers in terms of both book assets and market capitalization. Issuers also tend to be
more liquid and have higher CAPM betas and idiosyncratic volatility than non-issuers. Finally,
issuing firms experience a much greater increase in number of institutional investors relative to
non-issuers (147.7% vs. 17.8%) during the fiscal year of the SEO.
3. SEO stock return performance and changes in institutional investors
3.1 Post-issuance stock return performance using standard benchmarks
Table 2 documents the long-run stock return performance of SEO firms following issuance
using standard benchmarks in the literature. In Panel A, we use the time-series factor regression
methodology where monthly returns during the 36 months following the SEO are regressed on
three (Fama and French (1993)), four (Carhart (1997)), and five (Pastor and Stambaugh (2003))
factor portfolio returns. Following Loughran and Ritter (2000) all factors are purged of firms that
have issued equity during the past five years. The regressions are estimated using weighted least
squares with weights equal to the number of firms in the portfolio. From Panel A, this approach
yields an average abnormal return of about -6% per annum, consistent with the literature.
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In Table 2 Panel B we use the matched-sample methodology to benchmark post-issuance
returns to size, book-to-market, and liquidity reference portfolios. Matched-sample returns are
calculated as issuers’ three-year buy-and-hold returns from July of year t+1 through June of year
t+4 in excess of the returns earned by a reference portfolio of control firms with similar
characteristics but no equity issuance during the prior five years.7,8 From panel B, this approach
yields an average three year buy-and-hold return abnormal return of -9.0%, also consistent with
the literature.
[Table 2 around here]
Table 2, Panel C compares characteristics of SEO and control firms matched on size and book-
to-market, as in much of the literature. The comparison indicates that issuing firms have a much
larger increase in institutional investors, higher idiosyncratic volatility, and a greater increase in
liquidity than non-issuing firms matched on standard dimensions.
3.2. SEO stock return performance and changes in institutional investors
In this section we evaluate the stock return performance of equity issuers. In section 3.2.1, we
use (i) an event-study approach with reference portfolios, and (ii) calendar-time time-series factor
regression approach to investigate the relation between changes in institutional investors and post-
SEO stock returns. In section 3.2.2, we use Fama-MacBeth (1973) cross-sectional regressions on
7 Size control portfolios are constructed each June using NYSE market capitalization decile breakpoints. Size and book-to-market (liquidity) control portfolios are constructed using annual, independent sorts on size and book-to-market (Amihud (2002) illiquidity ratio). Missing monthly returns are set equal to the mean monthly returns of the remaining stocks in the portfolio. Following Lyon et al. (1999), we calculate the benchmark return by first compounding the returns and then summing across securities in the reference portfolio, which prevents new listing and rebalancing biases. We estimate p-values using the bootstrapped distribution of abnormal returns from simulated pseudo-portfolios to avoid skewness bias. 8 The three-year buy-and-hold return window in the matching-firm methodology starts in July after the fiscal year spanning the SEO, which permits the use of the most up-to-date post-SEO B/M ratio in constructing the reference portfolios where the book value of equity is from the fiscal year-end following the SEO. Starting the return window earlier would necessitate either using the pre-SEO B/M ratio that is out-of-date or the post-SEO B/M ratio yet unknown to the market, potentially causing a forward-looking bias.
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the full sample including both equity issuers and non-issuers to investigate whether changes in
institutional investors explain the underperformance of equity issuers relative to non-issuers.
3.2.1. Stock return performance of SEO firms
Table 3 presents a matched-sample analysis of post-issuance returns partitioned by the change
in number of institutional investors during the fiscal year spanning the SEO (∆ #Institutions). We
report average abnormal returns of SEO firms matched on Size, Size+B/M, and Size+Liquidity
partitioned into quintiles by ∆ #Institutions. The results show that ∆ #Institution provides a near
monotonic sort of post-issuance abnormal returns, with the highest ∆ #Institution quintiles yielding
the lowest returns. In particular, the three-year buy-and-hold abnormal returns for stocks in the
highest ∆ #Institution quintile are -25.6%, -18.9%, and -25.9% versus 13.4%, 14.5%, and 11.1%
for stocks in the lowest quintile using Size, Size+B/M, and Size+Liquidity reference portfolios,
respectively. The negative abnormal returns of issuers in the highest quintile are all statistically
significant at the 1% level while the positive abnormal returns of issuers in the lowest quintile are
significant at the 10% level for Size matching; the 1% level for Size+B/M matching; and the 10%
level for Size+Liquidity matching. Thus, conventional matched-samples fail to capture a strong
dependence of post-issuance returns on ∆ #Institutions.
[Table 3 around here]
In Table 4 we form calendar-time portfolios of SEO firms sorted by ∆ #Institutions over the four
quarters prior to the SEO.9 Each month we form an equal-weighted portfolio of issuers from the
past 36 months in each ∆ #Institutions quintile. Portfolio returns are then regressed in time series
9 In the calendar-time factor regressions, the use of factor returns instead of firm characteristics as benchmarks enables us to start the performance evaluation window immediately following the SEO (e.g., no gap needed to allow the market to observe the post-SEO B/M.) and sort issuers by changes in institutions strictly prior to the SEO.
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on the Fama-French (1994) three-factor model augmented with the Carhart (1997) momentum
factor and the Pastor and Stambaugh (2003) liquidity factor. SEO stocks in the lowest ∆
#Institutions quintiles do not exhibit significant abnormal returns, whereas SEO stocks in the
higher ∆ #Institutions quintiles exhibit substantial underperformance. The t-statistic for the
abnormal return of the highest ∆ #Institutions quintile is -4.2 and the t-statistic for the difference
between the abnormal returns of the highest and lowest quintiles is -2.4. This again demonstrates
that the change in institutional investors is an important determinant of post-issuance return
performance. Moreover, the fact that changes in institutional investors are measured prior to the
SEO establishes that the effects are not due to a reverse causality (i.e., an SEO event causing the
change institutional investors).
[Table 4 around here]
3.2.2. Stock returns of issuers versus non-issuers
In Table 5 we present Fama-MacBeth (1973) cross-sectional regressions of stock returns on ∆
#Institutions and a dummy indicating that the firm has issued equity during the previous fiscal
year, along with a variety of control variables from the literature. Following Cooper et al. (2008),
the dependent variable is the compounded raw monthly stock returns between July of calendar
year t+1 and June of year t+2, where the SEO indicator equals one if the firm had an SEO during
the fiscal year ending in calendar year t. Control variables include asset growth to capture the
relation between corporate investment and performance [Cooper et al. (2008) and Lyandres et al.
(2009)]; the change in Amihud’s (2002) illiquidity ratio and change in share turnover to capture
liquidity effects; the change in Dimson’s (1979) beta to capture the effect of real options exercise
[Carlson et al. (2006, 2010)]; the level of Baker and Wurgler’s (2006) sentiment index to capture
sentiment-related mispricing and accruals to capture mispricing associated with earnings
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management [Teoh et al. (1998)]. Control variables are observed from the fiscal year ending in
calendar year t. We also includebut do not tabulatethree standard regressors used in the
literature to capture expected performance: log market capitalization at the end of June of year t,
log book-to-market ratio (book value of equity as of the fiscal year ending in calendar year t
divided by market capitalization as of the end of calendar year t); and the six-month buy-and-hold
return from January to June in year t (momentum).
[Table 5 around here]
Regression 1 of Table 5 shows that equity issuers earn 4.4% less during the twelve months
starting in July of year t+1 controlling for size, book-to-market, and past returns, with a t-statistic
of -2.2. Comparing regressions 1 and 2 confirms the importance of ∆ #Institutions in explaining
post-issuance returns. ∆ #Institutions is significantly negatively related to future stock returns in
the full sample (t-statistic of -2.1) while the SEO indicator variable loses significance in the
presence of ∆ #Institutions. Moreover, when we interact ∆ #Institutions with the SEO indicator in
regression 3, the coefficient on the interaction term is indistinguishable from zero, indicating that
the impact of a change in institutional investors on long-horizon stock return performance is
similar for both issuers and non-issuers irrespective of an SEO event.
Finally, Table 5 regression 4 includes all control variables as well as two alternative
specifications of investor demand (change in fraction of shares held by institutions and change in
the number of all shareholders). The evidence suggests that the relation between ∆ #Institutions
and long-run returns does not seem to be attributable to standard factor models or standard control
variables used in SEO studies.
3.3. Long vs. short-horizon relations between institutions and post-issuance performance
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The negative relation between ∆ IO and long-run post-SEO returns we document appears to
contradict the notion that institutions are relatively informed investors, and the evidence in several
studies that institutional investors are "smart" when it comes to SEOs. For example, Gibson,
Safieddine, and Sonti (2004), Chemmanur, He, and Hu (2009), and Alti and Sulaeman (2012) all
find a positive relation between offer-period institutional demand and post-offer stock returns.
However, these studies relate short-horizon changes in institutional holdings to short-horizon
returns (horizons of 3-6 months), whereas our analysis relates long-horizon changes in institutional
investors to long-horizon returns (horizons of 1-3 years). In Table 6 we examine the effect of time
horizon on the relation between changes in institutional holdings and post-SEO stock return
performance.
[Table 6 around here]
To allow a more direct comparison with the aforementioned studies we focus on changes in
the fraction of shares held by institutions during the offer quarter (denoted ∆ %Institutions).10 We
confirm that ∆ %Institutions is associated with higher stock returns during the three months
immediately following the SEO. However, the relation turns negative for horizons beyond the first
quarter (i.e., months 4-6, 7-9, 10-12, and 1-36 post-SEO) after the offering. Table 6 Panel B shows
that this reversal is most prominent in higher quintiles of ∆ %Institutions. Issuers with the largest
increases in ∆ %Institutions out-perform all other quintiles in the first few months post-SEO but
under-perform in the long-run; significantly so versus quintile 2. Consequently, the positive short-
10 Gibson et al. (2004) and Chemmanur et al. (2009) focus primarily on changes in the fraction of shares held by institutions whereas Alti and Sulaeman (2012) examine changes in the number of institutional investors. In untabulated results, we find that the relation between institutional interest and returns is positive in the short-run and negative in the long-run using either the percent held by institutions or the number of institutions.
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run relations of Gibson et al. (2004), Chemmanur et al. (2009), and Alti and Sulaeman (2012) do
not persist over the longer-horizon typically associated with the post-SEO puzzle.
4. SEO firm operating performance and changes in institutional ownership
An important dimension to the post-SEO puzzle is that the poor stock return performance of
issuers is accompanied by poor operating performance [Loughran and Ritter (1997)]. In this
section we investigate whether the negative relation between ΔIO and subsequent stock return
performance documented in Section 3 extends to operating performance.
4.1. Post-issuance operating performance using standard benchmarks
Table 7 parallels the analysis of Table 2, focusing on operating performance rather than stock
returns. Panel A shows the operating performance of SEO firms from years t-4 through t+4. Two
measures of operating performance are reported: OIBD/Assets and return on assets (ROA).11
Consistent with prior studies, operating performance declines significantly after issuance. In
results not tabulated, the median difference between average operating performance in years t-3
through t-1 and years t+1 through t+3 has a Z-statistic of -3.4 for OIBD/Assets and -2.8 for ROA
(both significant at less than 1%).
[Table 7 around here]
Table 7, Panel B presents matched-sample, difference-in-differences analyses of operating
performance for SEO firms matched to non-issuing firms on operating and liquidity
characteristics.12 The first row presents the unmatched change in operating performance for SEO
11 Loughran and Ritter (1997) and Denis and Sarin (2001) find that post-SEO abnormal operating performance is robust to alternative measures for firms’ earnings. 12 Following Barber and Lyon (1996), we evaluate abnormal operating performance by matching sample firms to control firms with similar initial operating performance and we use non-parametric Wilcoxon tests to evaluate statistical significance.
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firms year t to t+4 with the remaining rows showing benchmark performance. In all eight matched-
sample comparisons the post issuance decline in operating performance is significantly larger for
SEO firms than non-issuing firms, consistent with Loughran and Ritter (1997).
Table 7, Panel C compares various characteristics of SEO and control firms matched on initial
operating performance in year t (as is standard in the literature). As in the case of return
performance (Table 2, Panel C), matched firms again fail to capture SEO firms’ larger increase in
institutional investors, higher idiosyncratic volatility, and greater increase in liquidity.
4.2. SEO operating performance and changes in institutional investors
In this subsection, we evaluate the abnormal operating performance of SEO firms using (i)
matched-sample analysis with portfolio sorts, and (ii) Fama-MacBeth (1973) cross-sectional
quantile regressions.
4.2.1 Matched-sample analysis
Table 8, Panel A sorts on ∆ #institutions during the fiscal year of the SEO, using two matching
controls (operating performance and liquidity). Note that the operating performance of issuers in
the low quintile of ∆ #institutions does not underperform that of matching non-issuers during the
four fiscal years after the offering. By contrast, the operating performance of issuers in the high
quintile underperforms substantially (t-statistics ranging from -10.5 to -4.3). Table 8 Panel B
presents the same analysis as Panel A sorting on changes in institutional investors strictly prior to
the SEO event. The post-SEO decline in operating performance is even more strongly associated
with the pre-SEO ∆ #institutions. For example, the t-statistic on the high-low difference is 3.3
(versus 2.3 in Panel A).
[Table 8 around here]
17
4.2.2 Regression Analysis
Table 9 reports quantile regressions of the change in the OIBD/Assets of all firms (issuers and
non-issuers) over four fiscal years (fiscal years 0 through 4) using the Fama-MacBeth
methodology. All regressions include the lagged change in operating performance measured
during fiscal year -1 to control for mean reversion in accounting data (Barber and Lyon (1996)).
[Table 9 around here]
Regression 1 of Table 9 uses an indicator variable to show the effect of an SEO event on
operating performance. The significantly negative coefficient indicates a decrease in operating
performance following SEOs. Regression 2 includes ∆ #Institutions and its interaction with the
SEO Indicator. The coefficient on ∆ #Institutions is significantly negative (t-statistic of -8.5),
indicating that changes in institutional ownership are negatively related to future operating
performance. Moreover, the coefficient on the ‘∆#Institutions x SEO ind.’ interactive term is
statistically insignificant, indicating that the impact of ∆#Institutions on operating performance is
statistically indistinguishable in the issuer and non-issuer samples. However, the SEO indicator
remains significant (t-statistic -2.1). Finally, from regression 3, which includes the same control
variables as the Fama-MacBeth stock return regression from Table 5, ∆#Institutions remains
statistically significantly negatively related to future operating performance (t-statistic of -6.9).
Note that the coefficient on the SEO Indicator turns significantly positive (t-statistic of 2.7),
indicating that SEO firms exhibit significantly better operating performance compared to non-
issuers after controlling for a host of factors including changes in institutional ownership.
5. Non-issuing Firms and Changes in Institutional Ownership
18
The results of sections 3 and 4 imply that an increase in number of institutions is a necessary
condition for underperformance of SEOs firms. The regressions of Tables 4 and 9 establish that
the negative relation between ∆#Institutions and future return and operating performance is not
isolated to SEO firms. In this section, we investigate whether ∆#Institutions is a sufficient
condition for post-issuance-like underperformance in firms without an SEO.
In Table 10 we sort all non-issuing firms (no SEO in the past five years) by ∆#Institutions during
the previous year and examine their subsequent long-run stock return and operating performance.
We use breakpoints from the population of SEO firms to sort non-issuers. We place non-issuing
firms with negative ∆#Institutions in a separate group as they are not directly comparable with
issuing firms, which rarely experience a contraction in institutional ownership during the year of
the SEO (6.8% of issuers vs. 34.5% of non-issuers).
[Table 10 around here]
Table 10 documents a significantly negative relation between ∆ #Institutions and long-run
performance very similar to that of SEO firms, for both return (see Table 3) and operating (see
Table 8) performance. Non-issuers with a large increase in institutional investors earn significantly
lower three-year buy-and-hold stock returns based on raw as well as size-matched and size+B/M
matched abnormal returns compared to non-issuers with a small increase in institutional investors.
Similarly, the future four-year operating performance of non-issuers with a large increase in
institutional investors during the prior year is significantly worse compared to non-issuers with a
small increase. The differences are significant at less than 0.1% level; moreover, they are on a par
with that seen at SEO firms.
Thus, the anomalous long-run under-performance of SEO firms appears more to do with ∆IO
than the SEO itself. This evidence sharpens the conclusion of Bessembinder and Zheng (2013)
19
who argue that abnormal performance following corporate events has more to do with firm
characteristics than the event itself – we show that in the context of SEOs a necessary and sufficient
firm characteristic is ∆IO.
6. Implications for the role of institutional investors in asset pricing
The analyses of Sections 3-5 suggests that the long-run underperformance following SEOs has
little to do with the SEO per se but is instead a manifestation of a more general effect associated
with changes in the institutional interest in a firm’s stock. This evidence places an important
restriction on both mispricing and risk-based explanations of post-SEO underperformance: in
particular, the explanation should include a central role for ∆IO. In this section we consider
potential implications regarding institutions’ role in asset pricing.
6.1. Mispricing
Under a mispricing interpretation of our evidence, agency conflicts, behavioral biases, or
investor flow may drive institutional investors to make poor investment decisions. While this
interpretation is at odds with the notion that institutions are relatively informed investors, it is
consistent with our evidence that institutions increase their participation in SEO stocks with the
poorest long-run post-issuance performance, prior to the SEO. An important matter relevant to a
mispricing interpretation of our results is the holding period of institutional investors. Our analysis
relates ∆IO prior to the SEO to returns over the three-year period following the offering. Perhaps
institutions buy prior to the SEO and sell shortly after the SEO—capturing the positive short-run
returns (as documented in prior studies) while avoiding the negative long-run returns—leaving
their reputation as sophisticated investors intact. While the evidence in Figure 1 suggests that
20
changes in institutional shareholders prior to SEOs are relatively long lived, Figure 2 sheds
additional light on this matter.
[Figure 2]
Figure 2 depicts the long-term cumulative changes in institutional investors for SEO firms
sorted by the initial change spanning the fiscal year of the SEO. Two interesting facts emerge from
Figure 2: (i) the number of institutional investors in SEO firms continues to expand during the
three years following the offering, and (ii) the post-issuance expansion is especially pronounced
for firms with large pre-SEO expansion — the firms with the poorest post-issuance stock return
and operating performance. Thus, to the extent that post-issuance underperformance is due to
mispricing, our evidence suggests that institutions are particularly prone to these pricing errors.
The negative relation between ∆IO and long-run returns we document parallels evidence in
several herding studies [Wermers (1999), and Sias (2004), Gutierrez and Kelley (2009) and
Dasgupta, Prat, Verardo (2011)]. 13 Collectively, the positive short-run and negative long-run
relation between ∆IO and returns is consistent with herding models in which institutions play a
destabilizing role in asset pricing. There are many potential motives for such herding, including
manager reputation [Scharfstein and Stein (1990)], tracking of common firm characteristics
[Lakonishok, Shlefer, and Vishney (1994), Del Guercio (1996), Falkenstein (1999), Barberis and
Shleifer (2003)], and correlated investor flow [Coval and Stafford (2007) Frazzini and Lamont
(2008) and Khan, Kogan, and Serafeim (2012)].
13Evidence that the relation between ∆IO and future returns turns negative over longer horizons also appears in Wermers (1999) [Table VI] and Sias (2004) [Table 5] though it is not a point of emphasis in these studies and no statistics regarding the reversal are provided.
21
Prior studies have examined the potential role of investor flow in institutional herding around
SEOs. In particular, Frazzini and Lamont (2008) and Khan, Kogan, and Serafeim (2012) find
evidence of price pressure prior to SEOs from mutual funds experiencing large investor inflows.
In both cases the changes in institutional holdings may correlate with concurrent price pressure
and subsequent long-run stock return reversals. Khan et al. (2012) show that mutual fund inflows
typically go towards expansion of existing positions (fraction of shares held) rather than new
positbions. However, we find that long-run returns are more highly related to the number of
institutional shareholders (new positions) rather than the fractions of shares held by institutions
(expansion of existing positions). In addition, in untabulated results, we find that the relation
between long-run returns and changes in number of institutions is robust to the exclusion of SEOs
with inflow-induced buying pressure and controlling for changes in ownership by mutual funds –
who are likely to be most sensitive to the effects of flow. Hence this evidence suggests casts doubt
on flow as a basis for a price pressure explanation. Thus, the negative relation between ∆IO and
long-run returns seems more consistent with managerial herding due to agency conflicts or
behavioral biases as opposed to mutual fund flow.
6.2. Benchmarking-errors hypothesis
An alternative to the mispricing interpretation is that long-run post-SEO underperformance
reflects benchmarking errors that correlate with institutional preferences. We consider four
potential sources: real options, Q-theory, liquidity, and market segmentation.
A risk-based interpretation for the negative relation between ∆IO and long-run returns argues
that institutions are not duped by the long-run underperformance but rather accept it as a reduction
in discount rates that is associated with firms conducting SEO -- a reduction that standard return
benchmarks fail to capture. Our evidence narrows this broad explanation to something closely
22
related to institutions, because lower long-run returns (discount rates, under this alternative
interpretation) occur only in conjunction with ∆IO.
Carlson, Fisher, Giammarino (2006) argue that firm risk decreases following SEOs due to the
exercise of real options. If benchmarks are slow to adjust to this decrease in risk it would lead to
overstated benchmark returns. Institutions may be attracted to changes in firm characteristics that
trigger real option exercise [Falkenstein (1996)], giving rise to a spurious positive relation between
changes in institutional investors and these benchmark errors. Under this hypothesis, we should
observe a correlation between SEO underperformance and changes in risk. As in Carlson et al.
(2010), we find a small decline in beta estimates following SEOs on average [see Table 1].
However, we find no correlation between post-issuance performance and the magnitude of changes
in systematic risk [see Table 5].
Related to the real options literature, Li, Livdan, and Zhang (2009) and Chan and Zhang (2010)
argue that Q-theory can explain the financing-based anomalies. In particular, a downward shift in
the discount rate leads to new financing and investment along with lower future stock returns and
operating performance (see also Cochrane (1991, 2011) and Lamont (2000)). Thus, investment
forms a proxy for managers’ perceived discount rate. Consistent with this view, we find evidence
that asset growth has a contributing role in SEO firms’ long-run underperformance (Tables 5 and
9). But, changes in institutional investors remain a significant predictor.
Changes in stock liquidity surrounding SEOs is another potential source of benchmarking error
related to changes in institutional investors. While asset-pricing theories relate required returns to
liquidity [Amihud and Mendelson (1989), Vayanos (1998), Acharya and Pedersen (2005)], this
factor is typically omitted from standard performance benchmarks. Lin and Wu (2013) find that
increases in liquidity around SEOs are related to increases in institutional investors and Bilinski,
23
Liu, and Strong (2012) find that post-issue return performance is related to changes in liquidity.
While it is difficult to isolate the effects of variables as empirically close as liquidity and
institutional ownership, our tests indicate that changes in institutional investors have stronger
effects than that of changes in liquidity. First, our matched-sample analyses in Tables 3 and 8
include matching on liquidity to evaluate abnormal performance using Amihud’s (2002) illiquidity
ratio and the institutional investor results stand. Second, our calendar-time portfolio abnormal
stock return analysis in Table 4 includes the Pastor and Stambaugh (2003) liquidity factor, and
again the results stand. Finally, our cross-sectional regressions in Tables 5 and 9 include control
variables for changes in Amihud’s illiquidity ratio and changes in turnover. Again, the change in
institutional ownership appears to be the dominant factor.
Finally, Lehavy and Sloan (2008) and Edelen, Ince, and Kadlec (2013) argue that changes in
institutional ownership surrounding SEOs might relate to lower required returns by way of a
change in market segmentation (as in Merton (1987), Allen and Gale (1994), Basak and Cuoco
(1998), and Shapiro (2002)). While our evidence is also consistent with this line of argument, we
find that changes in discount rate via this market-segmentation channel do not appear to be capable
of accounting for the magnitude of long-run post-SEO performance.
7. Conclusion
We analyze the relation between changes in institutional ownership and long-run post-SEO
returns. We find that institutions exhibit a herding-like participation in SEO stocks despite their
well-documented long-run underperformance. Moreover, institutions tend to buy SEO stocks with
the worst subsequent long-run stock return and operating performance. Indeed, virtually all long-
run post-SEO underperformance occurs in the top two quintiles of stocks sorted by ∆IO in the year
24
prior to the SEO. In short, long-run SEO underperformance occurs only when accompanied by
high ∆IO.
We find that non-SEO firms with large increases in institutional ownership exhibit long-run
underperformance (both stock return and operating performance) similar to that of SEO firms. We
thus have the surprising conclusion that ΔIO is both necessary for long-run underperformance
following an SEO, and sufficient for long-run underperformance without an SEO. This conclusion
complements that of Bessembinder and Zhang (2013) who make the general point that long-run
abnormal performance following corporate events has more to do with firm characteristics than
the event itself.
Both the fact that post-SEO underperformance is concentrated in high ΔIO stocks and the fact
that non-SEO firms parallel effect of ΔIO for SEO firms suggests that herding behavior of
institutional investors plays a central role in the long-run post-SEO underperformance. Whether
that herding has its origin in agency conflict or behavioral bias and whether it relates to mispricing
or time-varying discount rates is less clear. It is possible that institutional investors have
preferences for stock characteristics that identify equilibrium expected returns relating to non-
standard asset-pricing factors (e.g., liquidity or investment). However, we are unable to find strong
evidence for a link to expected returns of sufficient magnitude to account for the post-issuance
phenomenon. The most plausible interpretation of our results is that institutional herding
destabilizes equity prices, with a subsequent long-run reversal to equilibrium valuations.
25
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31
Figure 2The Investor Base of Issuing Firms in the Long-Run
This figure presents the median cumulative percentage change in the number of institutional investors holding the issuing firm's shares during the fiscal year spanning the SEO and the three subsequent fiscal years. Issuers aresorted into quintiles based on the fractional change in the number of institutional investors during the fiscal yearspanning the SEO.
32
Sample: SEO Non-issuersNumber of Issuance events 2,966 0Number of Firms 2,559 7,023Number of Annual observations 52,745Market capitalization (2009 $ million) 1,153.2 1,959.0Book assets (2009 $ million) 1,102.9 1,546.7OIBD / Book assets (median) 16.7% 13.5%Prior fiscal year change in OIBD / Book assets 3.4% -0.3%Prior fiscal year raw stock returns 70.7% 17.2%Asset growth 82.2% 9.5%Accruals 17.6% -4.0%Amihud's illiquidity ratio ( x106) (median) 0.05 0.34% change in Amihud's illiquidity (median) -62.4% -7.2%Turnover 17.8% 8.0%% change in turnover 113.8% 20.0%Beta 1.81 1.23Change in beta -0.15 -0.01Annualized idiosyncratic volatility 49.8% 45.4%
# of institutional investors 42.8 58.5% change in # of institutional investors 147.7% 17.8%% change in # of institutional investors (pre-SEO) 90.9% N/AInstitutional ownership 31.4% 29.0%% change in % institutional ownership 189.9% 28.6%% change in % institutional ownership (pre-SEO) 125.6% N/A# of shareholders 4,237.8 7,100.6% change in # of shareholders 52.2% 3.2%
Table 1Summary statistics
All variables are winsorized means (1% both tails) unless indicated otherwise. The full sample is all NYSE, AMEX, andNasdaq firms excluding regulated utilities (SIC 49**) and financials (SIC 6***) from 1977 - 2010. The Seasoned equityofferings (SEO) sample is from 1981 - 2006 excluding SEOs that are pure secondary or within one year of IPO or five years ofa previous SEO. The non-issuer sample is all valid CRSP/Compustat/CDA observations with no SEO in the preceding fiveyears. Variables in the SEO sample are measured during the fiscal year of the SEO. Market capitalization is at the end of Junefollowing the end of the SEO fiscal year. OIBD/Book Assets refers to operating income before depreciation and amortizationdivided by the average of beginning and ending period book assets less cash. Amihud's illiquidity ratio is the average ratioof a stock's daily return to daily dollar trading volume over the fiscal year. Turnover is the average of the monthly sharestraded divided by shares outstanding over the fiscal year. Beta is the stock's Dimson (1979) beta estimated as the sum ofcoefficients from the one-factor CAPM model of weekly returns against contemporaneous and four lags of weekly marketreturns over the fiscal year of the SEO (change calculated as the beta during the fiscal year post-SEO minus the beta duringthe fiscal year of the SEO). Asset Growth is the percentage change in book assets. Accruals are measures as the change innon-cash current assets, less the change in current liabilities exclusive of short-term debt and taxes payable, lessdepreciation expense, all divided by lagged book assets. Idiosyncratic volatility is the standard deviation of monthlyresiduals from the Fama-French three-factor model over the three years preceding the fiscal year (set to missing if fewer than 12 observations). Ownership variables are observed as of the beginning of the fiscal year; market capitalization, bookassets, and OIBD/Assets are as of the end of the fiscal year; asset growth, accruals, turnover, illiquidity, and the change inthe number of shareholders are measured during the fiscal year, and changes in institutional ownership are measuredthrough the four quarters starting with the calendar quarter-end that falls on or immediately before the beginning of thefiscal year. Pre-SEO refers to the four quarters preceding the SEO.
Panel A. Data and control variables
Panel B. Variables Relating to Investor Base
33
Factor: Intercept Market HML MOM
One-factor Regression -0.52% 1.44 73.7%(-2.5) (30.6)
Three-factor Regression -0.64% 1.42 -0.17 88.6%(-4.6) (43.7) (-2.5)
Four-factor Regression -0.48% 1.39 -0.22 -0.29 90.7%(-3.8) (46.8) (-3.5) (-8.6)
Five-factor Regression -0.44% 1.39 -0.21 -0.29 -0.06 90.7%(-3.4) (46.9) (-3.4) (-8.7) (-1.7)
Issuer 38.1% # Inst (Pre) 23 32 *** 28 ***
# Inst (Post) 44 37 *** 33 ***
Reference 46.9% -8.8% *** ∆ # Inst 73.8% 7.1% *** 9.6% ***
Inst %held (Pre) 27.0% 34.6% *** 32.4% ***
Reference 46.9% -7.9% *** Inst %held (Post) 45.3% 37.6% *** 35.7% ***
(Size+B/M) ∆ Inst %held 49.1% 4.3% *** 5.5% ***
Reference 49.4% -10.4% *** # Shareholders (Pre) 1,049 2,000 *** 1,728 ***
(Size+Liq) # Shareholders (Post) 1,222 1,960 *** 1,750 ***
∆ # Shareholders 3.6% -3.1% *** -2.6% ***
Amihud's Illiquidity (Pre) 0.16 0.10 *** 0.13 ***
Amihud's Illiquidity (Post) 0.05 0.08 *** 0.09 ***
∆ Amihud's Illiquidity -62.4% -15.5% *** -19.0% ***
Asset Growth 49.5% 6.4% *** 8.8% ***
Idio. Volatility 43.9% 33.6% *** 35.5% ***
Prior Fiscal Year Raw Stock Returns 37.9% 9.5% *** 12.5% ***
Table 2Preliminary Analysis of Return Performance
Panel A presents time-series regressions of equal-weighted monthly returns of 2,966 firms with an SEO during the prior 36months (1981 - 2006). Size (SMB), book-to-market (HML), and momentum (MOM) factors are purged of firms that have issuedequity in the past five years. LIQ refers to the Pastor and Stambaugh (2003) liquidity factor. The coefficients are estimatedusing weighted least squares, with weights equal to the number of issuers during the month. Matching firms (Panels B & C)have no SEO within five years, selected based on annual, independent characteristics sorting rebalanced each June. The"Size" reference portfolio uses NYSE market capitalization decile breakpoints. The "Size+B/M" reference portfolio uses NYSEbook-to-market ratio decile breakpoints. The "Size+Liq" reference portfolio uses NYSE Amihud's illiquidity ratio decilebreakpoints. Test statistics in parentheses.
Panel A. Regressions
The panel presents equal-weighted average three-year buy and hold returns starting in July followingthe fiscal year-end of the SEO. ***, **, and *indicate statistical significance at the 1%, 5%, and10% levels, respectively, calculated using theempirical distribution of simulated pseudo-portfolios.
Variable definitions are as in Table 1. Changes correspond to thefiscal year of the SEO. 'Inst' refers to institutional ownership (#number and % percent of shares outstanding). Pre (post) refers tothe beginning (end) of the fiscal-year of the SEO. ***, **, and *indicate that the issuer's characteristic is statistically significantlydifferent from the matching firm's at the 1%, 5%, and 10% levels,respectively.
1.07(20.7)
1.12(23.8)
1.12(23.6)
SMB LIQ R-square
Panel B. Matched-sample Analysis Panel C. Sample Characteristics (Medians)
Sample: Issuers Reference PortfolioSize Size+B/M
(Size)
Returns: Raw vs. reference
34
Ref Abn Ref Abn Ref Abn
Low 60.2 46.8 13.4 * 50.1 14.5 *** 51.4 11.1 *2 42.9 46.5 -3.6 50.8 -7.7 51.0 -7.43 43.8 47.1 -3.3 49.5 -4.5 49.0 -3.74 29.6 46.9 -17.3 *** 46.4 -17.7 *** 48.9 -20.0 ***High 21.4 47.0 -25.6 *** 40.6 -18.9 *** 47.4 -25.9 ***
High - Lowp-value 0.0000.001
Table 3Buy-and-Hold Matched-sample Returns
This table presents mean buy-and-hold returns (raw and net of matched reference portfolio returns, as in Lyon, Barber, and Tsai,1999) during the three years starting in July of year t+1, of firms that issued seasoned equity during the fiscal year ending incalendar year t. The column labels "Ref" and "Abn" refer to the reference portfolio return and the issuer portfolio return less thecontrol, respectively. The "Size" reference portfolio is constructed each June using market capitalization deciles (in all cases, weuse NYSE breakpoints). B/M and Liquidity refer to book-to-market ratio and Amihud illiquidity measure as of fiscal year t(spanning the SEO), respectively. Issuers are sorted into quintiles based on the change in number of institutions owning the stockduring the fiscal year of the offering. Quintile breakpoints are determined excluding firms with four or fewer institutional investors,however those observations are included in the analysis. Asterisks indicate p-value significance: ***, **, and * for less than 1%,5%, and 10%, respectively (calculated using the empirical distribution of simulated pseudo-portfolios).
∆ # Inst
Sort Bin Issuers Size match Size+B/M match Size+Liquidity match
-37.00.000
-39.0 -33.4
35
Intercept Mkt - rf SMB HML MOM LIQ
-0.21 1.29 1.08 0.18 -0.24 -0.08(-1.15) (30.29) (15.73) (2.03) (-4.89) (-1.61)
-0.20 1.27 0.86 0.01 -0.36 -0.13(-1.33) (36.45) (15.82) (0.17) (-9.43) (-3.16)
-0.34 1.47 1.06 -0.20 -0.31 -0.08(-1.94) (36.25) (16.39) (-2.31) (-6.86) (-1.70)
-0.50 1.43 1.29 -0.36 -0.26 0.00(-3.01) (37.94) (21.43) (-4.49) (-5.95) (0.02)
-0.73 1.46 1.26 -0.48 -0.30 -0.05(-4.17) (36.31) (19.17) (-5.59) (-6.32) (-1.07)
High - Low -0.51(-2.37)
Table 4Calendar-time Issuer Portfolio Abnormal Returns
Each fiscal year we sort issuers into quintile portfolios based on the change in number of institutional shareholders during thefour calendar quarters prior to the offering. Each month between 1982 and 2009 we form an equal-weighted portfolio of all firmsthat issued equity within the past 36 months seperately for each quintile. Monthly returns are then regressed in time-series onFama and French's (1993) market, size, and book-to-market factors along with Carhart's (1997) momentum and Pastor andStambaugh's (2003) liquidity factors, using weighted least squares with weights equal to the number of portfolio firms during themonth. The size, book-to-market, and momentum factors are purged of firms that have issued equity in the past five years. T-statistics are in the parenthesis.
∆ # Institutions (Pre-SEO)
Low
2
3
4
High
36
1 2 3 4SEO indicator -0.044 -0.018 -0.030 0.013
(-2.2) (-1.0) (-1.2) (0.5)Δ #Institutions -0.029 -0.030 -0.020
(-2.1) (-1.9) (-2.5)Δ #Institutions * SEO ind. 0.011
(0.5)Δ #Shareholders -0.001
(-0.1)Δ Institutions (% held) 0.002
(0.3)Asset Growth -0.035
(-1.7)Accruals -0.041
(-0.8)Δ Illiquidity 0.004
(0.5)-0.01(-1.3)0.009(1.6)0.068(0.9)
Past return 0.042 0.041 0.042 0.054(2.0) (1.9) (1.9) (2.2)
Intercept 0.346 0.351 0.349 0.394(2.7) (2.7) (2.7) (2.4)
R-square 3.9% 4.1% 4.2% 6.0%
Table 5Fama-MacBeth Regressions with Annual Returns
The sample includes both issuers and nonissuers. The dependent variable is the annual buy-and-hold return from July of year t+1 to June of year t+2, 1982 to 2007. SEO indicator equals one if thefirm issued equity during the fiscal year ending in calendar year t. '%held' refers to sharesoutstanding. Asset Growth is the fractional change in book assets during fiscal year ending in year t.Accruals is the change in non-cash current assets, less the change in current liabilities, lessdepreciation expense during fiscal year ending in t, divided by lagged book assets. Δ Illiquidity is thefractional change in the Amihud illiquidity ratio between fiscal years ending in t-1 and t. Δ Turnover is the fractional change in the average daily share turnover between fiscal years ending in t-1 and t. ΔBeta is the change in the stock's CAPM Dimson beta using weekly returns with four lags betweenfiscal years ending in t and t+1. Sentiment index (Baker and Wurgler (2006)) is measured during themonth of the issuance for SEO firms and is the average during the fiscal year ending in year t for non-issuing firms. Past return is the six-month buy-and-hold return from January to June in year t+1(momentum control). Two additional control regressors are not reported: the natural logarithm of thefirm's market capitalization at the end of June of year t+1; the natural logarithm of the book-to-marketratio. T-statistics adjusted for autocorrelation for one lag using the Newey-West procedure are inparentheses.
Δ Beta
Sentiment Index
Δ Turnover
37
Raw Abnormal Raw Abnormal Raw Abnormal Raw Abnormal Raw Abnormal
All 3.5 1.2 1.6 -1.3 0.8 -3.3 13.3 -12.1 21.3 -15.9(p-value) (0.021) (0.013) (0.001) (0.001) (0.001)
Low 1.5 -0.7 -0.1 -1.9 -0.6 -4.7 10.2 -15.7 23.8 -16.32 1.7 -0.2 1.6 -0.6 3.0 -1.7 18.2 -5.1 25.2 -10.53 3.6 1.9 1.6 -1.3 1.0 -2.1 14.5 -11.2 24.3 -14.14 3.9 1.5 1.4 -1.7 -0.8 -4.4 13.9 -11.5 19.5 -15.0High 6.7 3.5 2.2 -0.4 0.6 -4.0 9.5 -16.9 13.9 -23.6
High - Low 4.2 1.5 0.7 -1.2 -7.3(p-value) (0.021) (0.355) (0.787) (0.839) (0.344)
High - 2 3.7 0.2 -2.3 -11.8 -13.1(p-value) (0.040) (0.879) (0.379) (0.043) (0.090)
Panel B. Sorted by the Change in % Held by Institutions During the SEO Quarter
Panel A. All SEOs
Table 6Short-run Stock Returns and Institutional Demand at the Offer
This table presents mean buy-and-hold returns of issuers (raw and net of matched-portfolio returns, as in Lyon, Barber, and Tsai, 1999) during the three yearsstarting with the calendar quarter immediately following the SEO. The column labels "Raw" and "Abnormal" refer to the mean issuer return and the mean issuerreturn less the reference portfolio return, respectively. The reference portfolios are constructed each June using market capitalization and B/M deciles (in allcases, we use NYSE breakpoints). Panel A reports the average return performance of all issuers. Panel B sorts issuers into quintiles based on the change in thepercentage of the issuers' shares held by institutions during the calendar quarter spanning the SEO (Change in % Held), which is calculated as the percentage ofthe shares held by institutions at the end of the SEO quarter less the percentage held at the beginning of the SEO quarter.
BinMonths 1:3 Months 7:12 Months 1:24 Months 1:36Months 4:6
38
Fiscal year -4 -3 -2 -1 1 2 3 4OIBD/Assets 14.7% 14.4% 14.9% 16.5% 14.9% 13.2% 13.0% 12.7%ROA 4.2% 3.7% 4.1% 4.8% 4.5% 3.3% 3.0% 2.9%
Statistic:Performance: OIBD/A ROA OIBD/A ROA Sample: Issuers Matched Diff'nce
Issuers -4.5% -2.9% -7.5% -5.0% # Inst (Pre) 24 18 *(-18.4) (-17.9) (-19.0) (-18.8) # Inst (Post) 46 21 ***
∆ # Inst 75.0% 9.1% ***
vs. Match on -2.2% -1.3% -3.3% -1.7% Inst %held (Pre) 27.8% 25.8% ***OIBD/Assets (-6.8) (-5.2) (-6.5) (-4.0) Inst %held (Post) 46.1% 28.2% ***
∆ Inst %held 45.8% 4.3% ***
vs. Match on -3.0% -1.5% -5.6% -2.7% # Shareholders (Pre) 1,176 1,815 ***Liquidity (-8.5) (-6.6) (-9.8) (-6.7) # Shareholders (Post) 1,400 1,850 ***
∆ # Shareholders 3.9% -2.3% ***
Amihud's Illiquidity (Pre) 0.15 0.25 ***Amihud's Illiquidity (Post) 0.05 0.19 ***
∆ Amihud's Illiquidity -62.0% -23.8% ***
Asset Growth 46.9% 8.2% ***Idio. Volatility 41.7% 37.0% ***
Book Assets (2009 $ million) 271.8 181.3 ***Market Capitalization (2009 $ million) 405.6 182.7 ***Prior Fiscal Year Raw Stock Returns 38.6% 13.2% ***
0
Table 7Preliminary Analysis of Operating Performance
Panel A. Operating Performance in Event Time (Medians)
This table reports on a sample of 2,966 seasoned equity offerings (SEOs), 1981 - 2006. OIBD/Assets refers to operating incomebefore depreciation scaled by book assets; ROA is return (net income) on book assets. Matching firms (Panel B & C) are fromthe same 2-digit SIC code that have no SEO in the past five years by choosing the firm with (i) the closest OIBD/Assets ratio asof the end of the fiscal year of the SEO, with the requirment that OIBD/Assets is within 90% to 110% of the issuing firm, or (ii) theclosest Amihud's illiquidity ratio as of the end of the fiscal year of the SEO. If no match, then we search at the same one-digit SICcode level (14% of the issuers), and then without regard to industry (11% of the issuers). Disappearing matched firms arereplaced with the next-best original match.
Median Winsorized mean
17.5%5.6%
Panel B. Matched-sample Analysis Panel C. Sample Characteristics (Medians)The panel presents the median and winzorized mean (5% on eachtail) four-year changes in raw and abnormal operating performance, starting at the end of the fiscal-year of the SEO. Test statistics(Wilcoxon signed-rank Z-statistic for the medians, t-statistics forthe means) are in parentheses.
Issuers are matched with non-issuers with the same 2-digit SIC code and the closest OIBD/Assets ratio as ofthe end of the fiscal year of the SEO. Changescorrespond to the fiscal year of the SEO. 'Inst' refers toinstitutional shareholders. Variable definitions are as inTable 1. Pre (post) refers to the beginning (end) of thefiscal-year of the SEO. ***, **, and * indicates 1%, 5%,and 10% statistical significance for the difference.
39
Quintile: Low 2 3 4 High
# Institutions Pre-SEO 64 49 32 19 7 Post-SEO 65 67 52 42 28 % Change 2% 37% 63% 121% 300%
∆ OIBD/Assets, t = 0 to 4 -0.7% -2.8% -4.3% -4.9% -7.7% -7.0%(Raw) (-0.9) (-5.3) (-6.3) (-7.4) (-10.5) (-7.8)
-0.7% -2.0% -1.7% -3.2% -3.3% -2.6%(-1.2) (-3.4) (-1.9) (-3.8) (-4.3) (-2.3)
0.2% -0.7% -2.6% -4.5% -6.0% -6.2%(1.9) (-0.9) (-2.6) (-3.8) (-5.4) (-5.4)
-6% 14% 37% 73% 200%
∆ OIBD/Assets, t = 0 to 4 -0.4% -1.5% -2.5% -2.7% -4.1% -3.7%Matched on OIBD/Assets (-0.8) (-1.3) (-3.0) (-3.7) (-5.5) (-3.3)
Matched on Liquidity
Panel B. Sort by Percentage Change in Number of Institutions Pre-SEO (Qtr. -5 to -1)
% Change in # Institutions
Matched on OIBD/Assets
Table 8Operating Performance of Issuers
The table presents four-year change in operating performance, beginning with the fiscal year of the SEO. Issuing firmsare sorted into quintiles by the percentage change in the number of institutional investors during the fiscal year of theSEO. OIBD/Assets is operating income before depreciation scaled by book assets (raw is nonmatched, t refers to yearspost-SEO). Non-issuing matching firms (no SEO in the past 5 years) are from the same 2-digit SIC code, by choosing thefirm with: [OIBD/Assets ] the closest OIBD/Assets ratio as of the end of the fiscal year of the SEO, with the requirmentthat OIBD/Assets is within 90% to 110% of the issuing firm; or [Liquidity ] the closest Amihud's illiquidity ratio duringthe fiscal year of the SEO. If no match, then we search at the same one-digit SIC code level (14% of the issuers), and thenwithout regard to industry (11% of the issuers). Matching firms that disappear before the performance window ends arereplaced from that point forward by the next best firm from the initial match. In Panel A the sort is based on ∆ #Institutions during the fiscal year spanning the SEO, starting in the calendar quarter-end that falls on or immediatelybefore the fiscal year-end preceding the SEO, through the four subsequent quarters. In Panel B the sort is based onchanges from five to one quarter prior to the SEO. Test statistics (in parenthesis) are from Wilcoxon signed-rank tests.All figures except counts and test statistics are medians.
High - LowPanel A. Sort by Percentage Change in Number of Institution Spanning the SEO
40
1 2 3SEO Indicator -0.030 -0.010 0.009
(-9.0) (-2.1) (2.7)Δ #Institutions -0.028 -0.019
(-8.5) (-6.9)Δ #Institutions x SEO ind. 0.001
(0.3)Δ #Shareholders -0.004
(-1.1)Δ Institutions (% held) 0.003
(2.9)Asset Growth -0.042
(-5.9)Accruals -0.120
(-10.3)Δ Illiquidity 0.007
(7.3)Δ Turnover 0.003
(1.7)Δ Beta -0.001
(-0.6)Lagged ΔOIBD/A -0.220 -0.206 -0.172
(-9.9) (-8.9) (-6.9)Intercept -0.008 -0.005 -0.010
(-2.5) (-1.5) (-3.1)Pseudo R-square 4.0% 5.1% 8.5%
Table 9Operating Performance: Cross-Sectional Regression Evidence
This table presents a quantile (median) regression analysis of changes in OIBD/Assets from fiscal year 0 (theSEO year) through 4, using the Fama-MacBeth methodology. OIBD/Assets is operating income beforedepreciation scaled by book assets. Δ #Institutions is the fractional change in the number of institutionalinvestors during the fiscal year spanning the SEO (year 0). The analysis excludes firms with zero institutionalownership at the beginning of fiscal year 0. Δ #Shareholders is the fractional change in the number ofshareholders of record during fiscal year 0. '%held' refers to shares outstanding. Asset Growth is thefractional change in book assets during fiscal year 0. Idio. Vol. is the standard deviation of monthly residualsfrom the Fama-French three-factor model over the three years preceding fiscal year 0. Accruals is the changein non-cash current assets, less the change in current liabilities, less depreciation expense during the fiscalyear ending in t, divided by lagged book assets. Δ Illiquidity is the fractional change in the Amihud illiquidityratio between fiscal years -1 and 0. Δ Turnover is the fractional change in the average daily share turnoverbetween fiscal years -1 and 0. Δ Beta is the change in the stock's CAPM Dimson beta using weekly returnswith four lags between fiscal years 0 and +1. Lagged ΔOIBD/A is the change in OIBD/A during fiscal year -1.Standard errors corrected for autocorrelation (Newey-West); t-statistics are in parentheses.
41
∆ #Inst<0 34.5% 52.1 -2.2 -5.6 *** 12.1 13.1 1.0 ***
Low 33.2% 46.6 1.5 -0.4 15.8 15.1 -0.7 ***2 15.5% 49.5 -4.4*** -5.1 * 16.7 15.2 -1.5 ***3 7.0% 52.5 -2.7 -3.3 16.7 14.5 -2.2 ***4 5.9% 43.5 -14.3*** -19.2 ** 15.4 12.8 -2.6 ***High 3.9% 36.9 -30.0*** -19.6 *** 16.6 11.9 -4.7 ***
H-L -9.7(p-value) (0.003)
% of non-issuers
Operating PerformanceThree-year B&H returns (%)
Change
-4.0(0.000)
Table 10Performance of Non-issuers and Changes in Investor Base
(% OIBD/A)
Sort Bin Raw
This table presents the three-year mean buy-and-hold stocks returns and four-year median operating performance of firmswith no equity issuance in the past five years categorized by the fractional change in the number of institutions owning thestock (∆ #Institutions) during fiscal year 0. Stock returns (raw and net of reference portfolio returns, as in Lyon, Barber, andTsai, 1999) are measured during the three years starting in July of calendar year +1. The size reference portfolio isconstructed using market capitalization deciles in June of year +1 (in all cases, we use NYSE breakpoints). B/M refers tobook-to-market ratio as of fiscal year 0. Reference portfolios are purged of matching firms in the same ∆ #Institutionsquintile as that of the event firm. Operating perfromance is measured as the change in the operating income beforedepreciation and amortization divided by the average of beginning and ending period book assets less cash (OIBD/Assets)during the four fiscal years following year 0. Non-issuers are grouped into quintiles using breakpoints from the populationof SEO firms. Non-issuers with a decline in the number of shareholders are placed in a seperate group. ***, **, and *indicates statistical significance at the 1%, 5%, and 10% levels, respectively.
Year 0 Year 4Size-matched
Size+B/M matched
∆ # Inst
-31.5(0.000)
-19.2(0.000)
42
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