Institutional investors and long-run return reversals ... · post-SEO abnormal returns and conclude that there is little evidence of stronger underperformance for issuers with high

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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, 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.


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).


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).


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.


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, 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).


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)).


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 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

Documents

Institutional investors and long-run return reversals ... · post-SEO abnormal returns and conclude that there is little evidence of stronger underperformance for issuers with high