The Channels of Capital Supply, Financial Intermediaries, and Competition in Peer Effects

by

Matthew T. Billetta Jon A. Garfinkelb

Yi Jiangc

Current draft: July 2017

ABSTRACT

We explore three jointly related channels in peer effects by studying SEOs. Constrained firms’ SEO timing accelerates in their peers’ prior SEO activity. Plausibly exogenous positive shocks to a firm’s capital supply diminish the effect, and vice-versa. Shared intermediaries between constrained firm and peers accelerate firm SEO timing. SEO peer effects are not apparent when the firm and peers are direct product market competitors, consistent with competitive dynamics (Asker and Ljungqvist (2010)). We conclude that capital supply, financial intermediaries, and competition play key roles in the transmission of peer-to-peer financial policies.

We thank Wolfang Bessler, Azi Ben-Rephael, Redouane Elkamhi, Janet Gao, Nandini Gupta, Isaac Hacamo, Dave Mauer, Jay Ritter, Tong Yao, Yilei Zhang, and seminar participants at Bilkent University, City University of Hong Kong, Florida State University, Indiana University, Hong Kong Polytechnic University, Kent State University, Sabanci University, Santa Clara University, SKK Graduate School of Business, Tsinghua University PBCSF, University of Florida, University of Hong Kong, University of Illinois Chicago, University of New Hampshire, University of North Carolina-Charlotte, University of Saskatchewan, University of Toronto, as well as the 2014 FMA and 2015 MFA conferences, for many helpful comments and suggestions. This paper previously circulated under the titles: “The Role of Financial Intermediaries in the Transmission of Peer-to-Peer Financial Policies” and “Capital Supply, Financial Intermediaries, and Corporate Peer Effects.”

a Richard E. Jacobs Chair in Finance, Kelley School of Business, Indiana University, mbillett@indiana.edu b Tippie College of Business, University of Iowa, jon-garfinkel@uiowa.edu c Mihaylo College of Business & Economics, California State University at Fullerton, yjiang@exchange.fullerton.edu

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Peer effects in corporate policies are a well-documented phenomenon.1 The general view is either

that firms directly copy peers’ actions, or that they learn from their peers’ actions or characteristics and

respond accordingly. For example, Leary and Roberts (2014) conclude that peers’ capital structure

changes inform related firms who subsequently alter their own capital structure.

If these peer-motivated changes are relatively minor then it may be sufficient for managers to

observe their peers and take appropriate actions. On the other hand, peer effects of a larger scale may

require additional resources, inviting the importance of capital supply as well as the attendant financial

market participants (particularly underwriters). In turn, the central positioning/role of underwriters in

capital acquisition can raise (competitive) concerns over information spillover or leakage from peers to

responding firms (see Asker and Ljungqvist (2010)). However, the extant peer-effects literature is largely

silent on these channels of influence.2 We are the first paper to jointly explore all three considerations.

We investigate the roles of capital supply, financial intermediaries (such as underwriters), and

competitive dynamics, in the propagation of peer effects. Our analysis revolves around seasoned equity

offerings (SEOs). We select this event for two primary reasons and a couple of ancillary ones. SEOs raise

significant quantities of capital, implying the importance of capital supply as well as of financial

intermediaries. Equity is also an information-sensitive security, increasing potential information spillovers,

and any associated competitive concerns (again along the lines of Asker and Ljungqvist (2010)). Additional

benefits of studying SEOs include that they have been shown to cluster in waves, suggesting that firms

may indeed react to their peers (see Lucas and McDonald (1990), Choe, Masulis, and Nanda (1993),

1 Leary and Roberts (2014) document that firms’ capital structure and financial policies are influenced by peers’ financing. Foucault and Fresard (2014) show that a rival’s investment depends on their peers’ valuation. Hoberg and Phillips (2015) find that following a negative exogenous shock to demand, firms invest in R&D and advertising to differentiate their products from those of their peers. Kaustia and Rantala (2015) find that firms are more likely to split their stock after seeing a peer firm do the same. Servaes and Tamayo (2014) find that firms mimic the changes by peer firms who are responding to activist investors. 2 Both Asker and Ljungqvist (2010) and De Franco et al. (2016) note trade-offs due to product market peer concerns over information leakage, but their papers do not seek to explain peer effects.

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Bayless and Chaplinsky (1996), and Rau and Stouraitis (2011)). Also, they are publicly announced events,

allowing the researcher to exploit the timing and sequencing among peers, as well as document

information effects (via event study).

We begin by exploring the timing and sequencing of SEOs for two separate groups of firms –

financially constrained and financially unconstrained – using a hazard model.3 Among financially

constrained firms, the conditional probability of an SEO increases in the number of their peers that

conducted SEOs within the prior six months. Financially unconstrained firms show no such sensitivity.

These two results are consistent with the inference from Leary and Roberts (2014) that constrained firms’

financial policies are sensitive to those of their peers, while unconstrained firms’ are not. It meets the

necessary (though not sufficient) condition of correlation between the firm’s and their peers’ actions, to

potentially imply response of one to the other.

We then offer our three main innovations. We begin with our strategy to jointly infer

identification of both the concept of peer effects – a constrained firm’s SEO timing is actually affected by

[elements in the process of] the peers’ SEOs – as well as the relevance of financial capital supply to such

effects. We use the plausibly exogenous shocks described in Chang, Hong and Liskovich (2015) for our

identification strategy. Firms that migrate from the bottom of the Russell 1000 to the top of the Russell

2000 experience positive shocks to equity capital supply; and vice-versa.

Our identifying logic is as follows. Financial constraints inhibit firm capital raising through either

the price or availability of capital. Our result that SEO hazards for constrained firms are increasing in prior

peer SEOs could be interpreted as prior peer equity issuance easing (mitigating) a later-moving firm’s

constraints, either through a reduction in the cost of capital or an increase in the willingness of investors

to supply it. The exogenous shock of Chang, Hong, and Liskovich (2015) provides identification because it

3 Grouping by constrained firm status is motivated by Leary and Roberts’ (2014) recognition that follower firms tend to be smaller, unrated, with no payout and high Whited-Wu index values.

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affects investors’ willingness to supply equity capital, as well as the measured hazard sensitivity.4

Constrained firms experiencing a positive shock to their equity capital supply (and concomitant decline in

cost) show less sensitivity (of conditional probability of SEO) to prior peer SEO activity. We also find the

reverse for firms experiencing negative exogenous shocks to equity capital supply. Firms migrating “up”

from the Russell 2000 to the Russell 1000 show greater sensitivities.

We further show the importance of capital supply, and highlight the role of peer SEOs in catalyzing

followers, via event study. While the average constrained firm experiences positive returns to the event

of unconstrained peer SEOs, the effect is significantly stronger among firms that experienced negative

shocks to equity capital supply via index migration. Overall, exogenous shocks to capital supply provide a

lens through which to identify peer effects and the importance of capital supply to them.

Our second innovation is to highlight the role of financial intermediaries – particularly

underwriters – in the documented peer effects. Constrained firms’ SEO hazards increase in the number of

peers’ SEOs that the (constrained) firm’s underwriter has recently brought to market. One explanation for

the positive effects of this underwriter overlap is greater efficiency in the underwriter’s certification

efforts. We find two pieces of evidence on this. The average spread an underwriter charges the

constrained firm decreases in the number of (recent) SEOs that underwriter did for the firm’s peers.

Second, the constrained firm’s own SEO event return increases in the same underwriter experience

variable.5

Our third main innovation is to recognize the influence of competitive dynamics on peer effects

in SEOs. Asker and Ljungqvist (2010) find that direct competitors avoid sharing an investment bank. This

raises the question of why we see peer effects more pronounced through common underwriters. The

4 Importantly, the shock is presumably independent from (or even negatively related to) firm investment opportunities. We discuss our identification in greater detail in section 1.1. 5 We offer ancillary exploration of other financial market participants’ influence on constrained firm SEO hazards, in the Online Appendix. Greater overlap in institutional ownership and (separately) in analyst coverage – between the constrained firm and its prior issuing peers – raises constrained firm SEO hazards.

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answer is that our results are concentrated where the firm and peers likely share a common investor

sector (e.g. consumer durables), but not where the firm and peers are direct product market rivals.6 This

is consistent with Asker and Ljungqvist (2010), and also consistent with firms eschewing or delaying their

own SEO when it could benefit their product market rivals.

A by-product of our competition-related result is the doubt/question that it raises over extant

interpretations of peer effects. Most papers in the literature focus on industry peers at the 3-digit SIC

level. Our evidence is relatively weak in this sample, presumably because of the offsetting effect of

strategic behavior among direct rivals. A second by-product contribution of this result is to alleviate a

typical endogeneity concern. It belies the notion that (unobservable) shared investment opportunity sets

by the firm and peers drives our measured correlation/sequencing. Since the firm and peers driving our

results are not rivals, they are unlikely to face identical investment opportunities.7

Overall, we offer new perspectives on corporate peer effects. For those (mimicking) actions that

require significant financial resources, supply of capital is integral. For peer effects involving issuance of

securities, overlap in intermediaries (particularly underwriters) between the following firm and peers,

appears important. Finally, concerns over the competitive implications of peer effects may enter the

objective function of the leader. These results augur for a more nuanced approach to studying corporate

peer effects. But we hasten to add the following caveat. Our results are for SEOs, while other work studies

different corporate policies and finds that peer effects exist among rivals. Perhaps the competitive

concerns we highlight are pronounced because scale of SEO policy changes and the crucial role of both

underwriters and financial constraints in the context of SEOs.

6 We discuss our delineation of peers into direct rivals and otherwise, below. 7 One may plausibly argue that the firm and non-rival peers are vertically related, and that this could drive our sequencing results. Controlling for vertical relatedness between them does not alter our conclusions.

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1. Drivers of Peer Effects in Corporate Behavior

This section further develops the logic underlying our three peer effect channels of influence. It also

positions our work in the related extant literatures and highlights connections between them.

1.1 Prior Analyses of Peer Effects

Extant literature on corporate peer effects takes an essentially demand-oriented view; firms observe peer

actions or characteristics and this motivates the firm’s own behavior. Examples include major corporate

finance decision arenas: investment, financing, compensation, as well as others. But all of this work is

essentially silent on the relevance of the supply of capital to the manifestation of peer effects. A brief

sampling of these papers is as follows.

Studying corporate investment behavior, Foucault and Fresard (2014) show the importance of

peers’ stock prices in conveying information that helps a firm learn about optimal investment policy.

When an investing firm’s peers have higher valuations, the firm invests significantly more. Hoberg and

Phillips (2016) examine shocks to two industries (military goods and services, and software), and show

that firms respond to their product-market peers when making product offering decisions. Faulkender

and Yang (2010) illustrate a strong peer influence on CEO compensation policy.8 Kaustia and Rantala

(2015) show that firms are more likely to split their stock when peers have done so. Perhaps most closely

related to our work is that of Leary and Roberts (2014).9 They document sensitivity of firm leverage and

financing decisions to peers’ exogenous variation in characteristics or behaviors.10 Notably, their results

could also be driven by mimicry of relatively small changes in capital structure. One important way our

research complements all of the above papers is that we study SEOs, which entail demonstrative capital

structure decisions that require the participation of outside intermediaries and stakeholders.

8 See also Bizjak, Lemmon and Nguyen (2011). 9 We view their paper as a starting point for our analysis. They call for identification of channels in peer effects. 10 They isolate peers’ exogenous variation using an augmented market model, with industry-peer average excess returns as an additional regressor. The resulting measures of idiosyncratic equity returns among the peers (the residuals from the augmented market model), have a significant influence on firms’ leverage and financing policies.

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1.2 Identifying a Capital Supply Channel in Peer Effects

The challenge of illustrating a causal effect of financial capital supply on SEO peer effects is

endogeneity. Constrained firms’ sensitivity of issuance timing to prior peer SEO activity might be driven

by the peer effect; the firm responds to peer action or at least the firm’s SEO is facilitated by [elements in

the process of] the peers’ SEOs. Alternatively, the sensitivity could be due to an omitted common factor

with no facilitation due to peers’ actions.11 To invalidate this alternative, we require a shock to treatment

firms that affects their sensitivity while untreated firms show a different sensitivity.

We rely on the plausibly exogenous shocks studied in Chang, Hong, and Liskovich (2015) to aid in

our identification. They find that firms migrating from the Russell 1000 to the Russell 2000 experience a

significant increase in indexers’ demand for the firm’s shares, i.e. an increase in capital supply. This is

because the indices are value-weighted, so the weights of the largest stocks in the Russell 2000 are much

larger than the weights of the smallest stocks in the Russell 1000 (even though the former are actually

smaller stocks in market value than the latter). Particularly in the context of SEOs by constrained firms,

the “requirement” that index funds track closely the index’s performance, implies a ready and willing

participant in the firm’s SEO. This exogenously driven increase in capital supply may substitute for the

importance of prior peer SEOs in facilitating constrained firm issuance, as long as the prior peer SEOs were

important because of capital supply considerations.

Chang et al. (2015) also find a symmetric response to firm migration “up” from the Russell 2000

into the Russell 1000. Such moves reduce the firm’s weight in the index and thereby decrease indexers’

demand. This represents a negative exogenous shock to capital supply, potentially increasing a

constrained firm’s sensitivity of issuance to its peers’ recent issuance activity (as long as the sensitivity is

a peer effect driven by the importance of capital supply).

11 This latter explanation also requires that the delay in constrained firms’ responses to the omitted common factor not be influenced by the peers’ earlier SEOs.

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A benefit of using these shocks is that their logic runs counter to one alternative explanation for

the treated firm’s SEO – demand for more capital. Since the shock that increases capital supply is due to

a drop in the firm’s market-value-based ranking, the firm is underperforming relative to unshocked firms.

It is therefore harder to argue for the alternative view that positive capital supply shock firms (with falling

relative market value) seek additional capital due to relative improvements in investment opportunities.

A further way to test our interpretation of the importance of capital supply is via event study.

Bradley and Yuan (2013) show that announcements of rival SEOs are met with positive abnormal returns

for non-issuing firms – i.e. contagion. They interpret this as positive signals regarding industry growth

opportunities.12 Our alternative interpretation is that the positive response signals reduction of barriers

to issuance. We can again lean on the exogenous shock of Chang et al. to confirm. Particularly for firms

negatively shocked in exogenous capital supply (migration from the Russell 2000 to the Russell 1000), the

contagion effect should be more positive than the average. Overall, we view supply of financial capital as

an integral connector of peer effects.

1.3 The Importance of the Constrained Firm Using the Same Underwriter as Peer(s)

The significance of capital supply in SEO peer effects naturally suggests a role for underwriters who acquire

and disseminate information between issuers and potential investors. Such activities produce information

advantages that are (at least) two-fold. First, prior experience with peers alerts the underwriter to

common concerns raised by potential institutional investors in similar firms. Second (and relatedly), the

underwriter learns about institutional investor interest in this sector, and may even catalyze institutional

demand through its underwriting and marketing efforts (e.g. Gao and Ritter (2010)). The information

benefits to underwriting the current firm’s SEO should be clearest when the common (to both constrained

firm and peers) investment bank has more experience recently underwriting peer SEOs.

12 Although our later analysis showing concentration of peer effects in the non-rivals sub-sample is inconsistent with this interpretation.

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We assess the importance of commonality of underwriter between the firm and peers in two

ways. First, beneficial effects of more (common) underwriter experience should generally increase the

constrained firm’s SEO hazard. However, if underwriters specialize in a particular sector, and if

constrained firm following of unconstrained peers’ SEOs is not due to peer effects, then such evidence is

potentially inconclusive.13 Therefore, we buttress our common underwriter importance conclusions by

focusing more precisely on the specific mechanism of information advantages. Prior work characterizes

underwriter spreads as compensation for asymmetric information (e.g. Lee and Masulis (2009)). The

overlapping underwriter’s greater acquisition of information through more prior-peer-underwriting

activity should reduce spreads. Furthermore, constrained firms’ own SEO event returns should be higher

when asymmetric information costs are mitigated by underwriter overlap (again similar to Lee and

Masulis (2009)).

1.4 The Hindering Effect of Competition

Asker and Ljungqvist (2010) explain investment bank client exclusivity with rival concerns over information

leakage. When investment banks merge, customer firms are more likely to switch underwriters when their

current bank (suddenly) serves a top-3 firm within the industry. If our sample shares such concerns, this

should reduce underwriter overlap (between the firm and rivals), and its associated effects on measured

hazard sensitivities. Alternatively (but with the same eventual effect), a competitive effect may emerge if

unconstrained rivals are concerned with catalyzing constrained rival SEOs because external financing

enables competition (Bolton and Scharfstein (1990)).

To address potential competitive effects on our hazard sensitivities, we require our groupings of

firms and their peers to reflect anticipated peer-sensitivity benefits while varying the costs according to

the level of competitive concern.14 Our first pass grouping – to capture benefits – is all firms within the

13 Notwithstanding our evidence that constrained firm SEO timing is sensitive to prior peer SEOs. 14 This logic mirrors Asker and Ljungqvist (2010).

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same Fama/French (FF)-49 set. This grouping typically belongs to the same sector from an

investor’s/supply-of-capital perspective, but may or may not be direct competitors/rivals (in the product

market).15 Our second pass segments FF-49 peers into those firms more vs. less likely to be rivals. For

example, Intel and Cisco belong to the same FF-49 group but they are not primary product market rivals.

On the other hand, Intel and AMD are direct rivals. Thus while Intel and Cisco may face common capital

supply effects (like increased investor capital dedicated to the tech sector), Intel and AMD face

competitive concerns in addition to capital supply effects. We deem the Intel/Cisco pairing of non-rivals

as “less” related peers, and the Intel/AMD pairing of rivals as “more” related peers. If competitive

concerns of rivals are germane to (i.e hinder) peer effects, the hazard sensitivities should be more

pronounced for less related peers .

1.5 Other Explanations Requiring Controls (from the SEO Literature)

The literature on SEOs has identified numerous factors influencing firms’ issuance decisions and their

pricing, which we must control for. Key to this literature is the notion that firms attempt to time the

market and issue when prices (their own or the market’s) are at a relative high. Asquith and Mullins (1986),

Masulis and Korwar (1986), Loughran and Ritter (1995, 1997) and many others suggest this.16 Baker and

Wurgler (2002) argue for a particular underpinning of this relationship – high investor sentiment. Our tests

control for pre-issuance stock price runup.

On the other hand, recent work by DeAngelo, DeAngelo and Stulz (2010) questions the

importance of the market timing hypothesis in the context of SEOs. While they do show that the likelihood

of an SEO is increasing in the firm’s M/B ratio and prior three-year abnormal stock return, they highlight

that the economic effect of market timing is less pronounced than the life-cycle effect. Moreover, both of

these pale in comparison to their “cash needs” argument as a motive for the SEO. In particular, 63% of

15 Such a broad amalgamation of firms is questioned as a technique for peer group formation by Kaustia and Rantala (2013, 2015). But we use this grouping so that we can further discriminate between rival and non-rival firms. 16 Schultz (2003) suggests return patterns around IPOs (not SEOs) reflect pseudo market timing.

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SEO firms (in their sample) would have run out of cash and 81.1% would have shown below normal cash

holdings without the offer proceeds, in the year after the offer. Therefore, our tests also control for the

cash needs proxy offered by DeAngelo et al. (2010).

Alti and Sulaeman (2012) provide a more supply-of-capital perspective on SEO issuance, by noting

another consideration when testing the market timing motivation for SEOs. They show that high stock

returns trigger SEOs only when they coincide with strong market reception. They proxy for this with

institutional investor demand, Instidem, which we also include in our tests. Given all of these controls, our

results may be viewed as incrementally important.

2. Data and Methods

2.1 Base Sample of SEOs

Our analysis revolves around SEOs. We begin with Thomson Financial’s SDC Global New Issues database

to identify firms that conduct SEOs during 1970–2010. Our sample satisfy the following criteria: (1) We

include only common share offers listed on NYSE (the New York Stock Exchange), AMEX (the American

Stock Exchange) or NASDAQ; (2) We exclude financial companies, such as bans, insurance companies and

REITs (SIC codes between 6000–6999) and utility companies (SIC codes 4900–4999); (3) We exclude unit

offers, spinoffs, carve–outs, rights, and shelf offerings17; (4) We include only firms with stock return data

available in CRSP and with financial data available in COMPUSTAT; (4) We include only issues that are

more than 50% primary offering, and (5) We exclude firms with a market cap of less than $10 million

during 1970–2010 to minimize the influence of outliers in the analysis. The resulting sample consists of

7,973 SEOs.

Table A-1, Panel A, of the Online Appendix reports the distribution of our sample SEO firms by

year. It shows that the number of SEO firms fluctuates over the years, suggesting that SEOs tend to occur

17 A shelf SEO is defined as an SEO whose issue date is at least 60 days after the filing date. Following Altinkilic and Hansen (2003) and Huang and Zhang (2011), we exclude shelf registered offers.

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in waves.18 Table A-1, Panel B, of the Online Appendix confirms this. We identify SEO waves following the

moving-average method of Helwege and Liang (2004). There are five SEO waves during our sample period,

with the number of SEOs in a wave ranging between 500 and 1,226. Finally, in Panel C of Table A-1 (in the

Online Appendix), we explore issuance timing within each wave separately for constrained and

unconstrained firms. A constrained firm has a consistent history of zero payout (neither dividend

distribution nor share repurchase) since the previous issue. Unconstrained firms refers to the complement

sample. We consistently observe a greater proportion of unconstrained firms issuing earlier in the wave

and a greater proportion of constrained firms issuing later in the wave. For example, there is a much

higher percentage of unconstrained early movers in wave one, but a much larger percentage of

constrained late movers in the same wave. Unconstrained firms appear to lead; constrained firms appear

to follow. We provide more formal evidence of such sequencing below.

2.2 Construction of Exogenous Shock Variables

Key to our identification strategy is the pair of variables Index Shock Pos and Index Shock Neg. We follow

the procedures described in Chang et al. (2015) as well as Schmidt and Fahlenbrach (2017) to construct

them. Our data begin in 1984, because that is the launch year of the Russell Indices. First we build the

theoretical Russell 1000 and 2000 based on raw market capitalizations of firms. Prior to 2007, index

membership is strictly classified on the basis of ranked market value of equity. For the top 3000 stocks

that meet Russell’s index inclusion criteria, the largest 1000 are assigned to the Russell 1000 and the next

largest 2000 stocks are assigned to the Russell 2000. These assignments are made once per year, on the

basis of end-of-May market values of equity.

Stocks that move “down” from the Russell 1000 to the Russell 2000 based on these end-of-May

(year t) rankings, are given a value of one for Index Shock Pos over the ensuing year (June 1 of year t

through May 31 of year t+1). All other stocks receive a zero for Index Shock Pos over the same period.

18 See Figure 1 as well.

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Stocks that move “up” from the Russell 2000 to the Russell 1000 have Index Shock Neg = 1 over the ensuing

year (June 1 of year t through May 31 of year t+1), zero otherwise. Obviously, stocks that do not “migrate”

between indices based on the May 31 ranking date, are assigned a value of zero for both shock dummies.19

Beginning in 2007, Russell introduced “banding” policies to reduce index migration that reflected

“smaller” relative changes in market value of equity. The policy restricted migration to those stocks

moving more than 2.5% (in either direction) of the 1000th (in the rankings) stock’s market value of equity.

We assess the robustness of our results to this change in Russell policy by running our tests for two

samples: the full sample through 2010 but using the non-banding approach to index formation; and the

sample of SEOs only through 2006 (again using the non-banding approach).20

2.3 Descriptive Statistics

Table 1 reports ex-ante descriptive statistics for sample SEO firms (over 1970-2010) classified by financial

constraint status. For each group, we report mean, median and standard deviation of firm characteristics

at the fiscal year-end prior to the SEO, as well as issuance characteristics. We also report peer group and

market conditions, as well as the incidence of positive and negative capital supply shocks (within the year

prior to SEO) among our sample firms. We test the significance of differences in means and medians across

groups. All variable name definitions are also included in Appendix 1.

Unconstrained firms are on average larger firms with higher book-to-market and larger SEO offer

size. Also, both constrained and unconstrained firms face a cash shortfall around the time of the SEO, but

it is significantly larger for the constrained sample.21 Whether institutional investor demand is high

19 Chang et al. (2015) actually restrict their attention to the top 100 stocks of the Russell 2000 and the bottom 100 stocks of the Russell 1000. Schmidt and Fahlenbrach (2017) sample on all index migrations for their main tests, as do we. To address lingering endogenous omitted variable concerns (due to index migrations by firms outside the 100 “bordering” each side of the cut-line), we re-run our analysis for the 200 firms analyzed by Chang et al. Our results are robust. See the Online Appendix. 20 Despite the weaker identification (less randomness to the shock) in our full sample results, our inferences are no different across the two sampling approaches. See the Online Appendix. 21 Near-term cash needs (Cashneeds), constructed as in DeAngelo et al. (2010), proxies for cash shortfall.

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(Dinstidem, as in Alti and Sulaeman (2012)) appears similar across the two groups. But this calculation

ignores whether institutional ownership of the firm and its peers overlaps (which we show is important in

later tests reported in the Online Appendix).

All event study returns use standard market model methodology over the 3-day window

surrounding the event. Our data conform with others in terms of announcement returns to SEOs. The

average SEO announcement is met with a significantly negative response (-1.47%). It is statistically worse

for constrained firms than for unconstrained firms (-1.7% vs. -1.2%). We explore the variation in SEO

announcement reactions in later tests.

There are significant differences between the index migration behaviors prior to constrained vs.

unconstrained firm SEOs. Those by unconstrained firms are preceded by both upward and downward

index migrations (about 9%) more often than are SEOs by constrained firms. Nevertheless, we show below

that the shocks have stronger effects on constrained firms’ SEO timing and peer effects.

Our variable Peer SEO equals the number of firms in the same FF-49 peer group that conduct an

SEO in the prior six months. We also construct Market SEO to measure the intensity of market-wide SEO

activity, and define it as the number of firms (outside of the peer group but in the overall market)

conducting SEOs in the prior six months. On average, there are 18.47 (38.12) firms in a peer group issuing

SEOs in the six months preceding a constrained (unconstrained) firm’s SEO announcement. By contrast,

the average number of SEOs conducted in the (rest of the) market in the prior six months is significantly

higher for constrained (59.31) than for unconstrained firms (34.94). One potential reason for the fewer

peer SEOs before constrained firm SEOs is that constraints sensitize firms to “more effective” triggers. We

use Meyer’s (1990) hazard model to capture the sensitivity of constrained firm SEOs to peer activity.

2.4 Hazard

Our primary analysis technique is a hazard. We adopt Meyer’s (1990) approach because it is particularly

well-suited for studying data with similar characteristics to ours. It estimates the shape of the hazard non-

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parametrically, it incorporates time-varying covariates, and it handles unobserved heterogeneity.22 Our

basic specification (where i indexes firms and j indexes spells for firm i) is as follows:

hij(t) = h0(t)exp[Xij(t)’β] (1)

The h0(t) are the baseline hazards, while the Xij(t) are the time-varying covariates. Our focus is on the

coefficients (the vector β) on the time-varying covariates. The presence of peer effects along with the

capital supply channel are tested with coefficients on Ln(Peer SEO), the shock dummies, and the shocks’

interactions with Ln(Peer SEO).23 The common underwriter channel is tied to its namesake variable. The

competition channel is assessed via the delineation of covariates (and coefficients) by less versus more

related peers (i.e., rival vs. non-rival status). Positive coefficients indicate raised hazards or sped up SEOs,

implying the covariate catalyzes the test firm’s SEO; and vice-versa.

The dependent variable in our hazard analysis is Spell Length, defined as the number of days

between consecutive SEOs or between IPO and first SEO. On average, unconstrained firms have longer

spells than constrained firms (about 20% longer, see Panel B of Table 1). This reflects the fact that

unconstrained firms have ample internally generated funds and access to debt markets, resulting in a

reduced reliance on external equity capital. The longer Spell Length for unconstrained firms highlights the

concern with comparing baseline hazard rates; the probability of doing an SEO conditional on how long

the spell has been, is misleading. Rather than comparing baseline hazard rates across constrained and

unconstrained groups, we focus on the determinants of within group variation to identify the important

drivers of the timing of the group’s SEOs.

22 Whited (2006) provides a detailed discussion of these benefits in the context of large (spike) investment hazards. For us, SEOs are the significant (infrequent) corporate event similar to the large investments in Whited (2006). 23 We take the natural log of (1+Peer SEO) [and of (1+Market SEO)] to form the related covariates in the hazard. For ease of presentation, we denote these Ln(Peer SEO) and Ln(Market SEO). This recognizes that an additional prior SEO is likely more important when there were few before, than when there were many before. Results are robust to using the simple count rather than taking the log of (1+count), available upon request. Results are also robust to using proceeds-based estimates of prior peer issuance activity (Table A-2 Online Appendix).

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Fraction Censored refers to the percentage of right censored spells in the sample. We right censor

firms whose SEO dates are after 2010. For example, if a firm went public (IPO) in 2004 and it never issued

an SEO before 2010 (when our sample ends), the censoring time is six years. There are 19% (29%) censored

SEOs for constrained (unconstrained) firms. We also left-truncate our sample for firms whose IPO dates

precede 1970. For example, if a firm’s SEO date is 1980, and the IPO date is 1965, then the spell length is

ten years.24 Each SEO spell is treated as an independent event. We exclude firms with only a single

censored spell. We also exclude those with one censored spell and one uncensored spell, if the censored

spell is shorter than the uncensored spell.25 This is a repeated event study.

3. Results

Our results are presented as follows. Table 2 provides evidence of sequencing in SEOs. Table 3 presents

hazard results with identification via the exogenous capital supply shocks of Chang et al. (2015). Table 4

illustrates the information content of peer SEOs, also with capital-supply-channel identification. Table 5

emphasizes the importance of overlap (between firm and peers) in underwriters to propel peer effects.

Table 6 addresses the competition channel of influence on peer effects.

3.1 SEO Sequencing (Hazard) Results, without Identification

Table 2 presents estimates from hazard analysis of SEO issuance decisions by constrained and

(separately) unconstrained firms.26 Dependence of constrained firms’ SEO hazards on prior peer SEO

activity suggests sensitivity to peers in the spirit of Leary and Roberts (2014).

The hazard for constrained firms in the first column of Table 2 indicates that sequencing is evident

in our data. The constrained firm’s SEO decision is highly sensitive to prior issuance activity by peers; the

coefficient on Ln(Peer SEO) is positive and significant. In the spirit of Whited (2006), a factor that raises

24 Hazard models are effective at handling left-truncated and right-censored data (see Allison (1995)). 25 Again see Allison (1995). 26 Table A-3 (in the Online Appendix) illustrates the robustness of those results to alternative definitions of ‘financially constrained’ firms.

16

the hazard aids in/speeds up the process of overcoming barriers (such as financial constraints) to the

event. More peer SEO activity recently, associates with earlier SEOs by firms in the constrained sample –

i.e. the spells between issues are shorter. This is incremental to more typical results found in the literature,

in particular that firms’ SEO hazards increase in stock returns (firm and industry). It is also noteworthy

that the coefficients on prior peer vs. market issuance activity (in the constrained sample hazard) differ

significantly. Prior peer issuance activity has a stronger influence on the hazard (0.099) than prior market

activity does (0.031); the coefficients are statistically different at the 1% level. The incremental importance

of prior peer issuance activity to a firm’s own SEO decision is an important perspective on SEOs and

highlights the potential role of peer actions (not just characteristics) for financing policy.

The second column presents hazard estimates for our sample of unconstrained firms (those that

had positive payout since their previous issue). There is little evidence of sensitivity to prior peer activity

or returns in this sample. Moreover, the coefficient on prior market equity issuance activity is negative,

consistent with unconstrained firms having superior access to equity markets. On the other hand, we see

the typical result that firm-specific returns (net of industry average) correlate positively with

unconstrained firms’ SEO timing. The positive coefficient (0.311) indicates that unconstrained firms speed

up their issuance in the face of recent runup. This effect is much stronger than we saw among constrained

firms (note the significant difference in coefficients documented in column 3), indicating greater SEO

timing sensitivity to own-firm returns among unconstrained firms.

Overall, our hazard results highlight the importance of peers’ prior SEOs to constrained firms’ SEO

timing decisions. The timing result is new; the measured effects are incremental to known determinants

of SEO decisions; and there is a suggestion of causality that we explore next.

3.2 SEO Sequencing (Hazard) Results, with Identification Shocks

Table 3 presents results from estimating hazards including capital supply shocks built on Chang et al.’s

(2015) approach. Firms migrating “down” from the Russell 1000 to the Russell 2000 attract more indexing

17

dollars into the firm’s equity, potentially mitigating capital supply barriers (i.e. financial constraints). These

shocks should reduce constrained firms’ SEO (hazard) sensitivities to peer SEOs, as long as the sensitivity

reflects the importance of capital supply to peer effects. We repeat the hazard specification of Table 2,

with several added variables: the two dummies for positive and negative exogenous capital supply shocks,

and each dummy’s interactives with prior peer SEO activity and prior peer (average) returns.27 As before,

the first column presents results for constrained firms, while the second speaks to influences on

unconstrained firms’ SEO timing.

The hazard results confirm our earlier hinted at inferences. Capital supply is a key channel in peer

effects. In column one, the positive coefficient on Index Shock Pos indicates that greater capital supply

accelerates constrained firms’ SEOs, while the negative coefficient on Index Shock Neg indicates that a

dearth of capital acts to slow these firms’ SEOs. More importantly, the coefficients on both interactives of

the shocks with prior peer activity are of the expected sign and significant. For example, when the capital

supply shock is positive, prior peer SEO activity has a (significantly) less positive influence on constrained

firms’ SEO hazards. The positive capital supply shock substitutes for the facilitation effect of prior peer

equity issuance, on constrained firms’ ability to bring seasoned equity to market.

Notably, the coefficients on the other interactives (of shocks with prior peer returns) are

insignificant. This further highlights the importance of peer actions rather than returns for constrained

firms’ SEO timing. Also supporting our inferences, in column two, neither of the shock dummies nor their

interactives appear to influence unconstrained firms’ SEO timing decisions. Overall, our results support

the channel of capital supply in SEO peer effects.

3.3 Contagion Effects

27 Russell launched its indices in 1984. We therefore check the robustness of our Table 2 hazard results to sampling only 1984-2010. Our Table 2 inferences are unchanged under this sampling. Results are available from the authors upon request.

18

We further highlight the capital supply channel in SEO peer effects with event study analysis. If peers’

SEOs catalyze constrained firm SEOs because they ease capital supply, then we should observe several

relationships. First, the implied easing of constraints is positive news, so constrained firms should

experience positive abnormal returns to the announcement of their peers’ SEOs. Second, this information

effect should be weaker if the firm has recently conducted their own SEO. Third, the positive effect should

be stronger (weaker) for firms that experienced negative (positive) exogenous capital supply shocks due

to index migration. Our evidence supports (all but one of) these implications.

The events we analyze are SEOs by constrained firms’ unconstrained peers.28 We calculate

abnormal returns to the constrained firm around these events. The constrained firms’ abnormal returns

are then regressed on dummies associated with the constrained firm: both positive and negative

exogenous capital supply shock dummies, as well as a dummy for having conducted their own SEO

recently. We offer three different specifications that recognize whether the constrained firm did a prior

SEO within the last several months. Specifications 1, 2, and 3 include (respectively) dummies equal to one

if the SEO was within the last 6, 9 or 12 months, zero otherwise. The intercept illustrates the average

contagion effect absent recent index migrations or own recent SEO. Standard errors are clustered at the

unconstrained SEO event level because constrained firms may enter the regression multiple times (once

for each unconstrained peer’s SEO), but such observations are not independent.

Table 4 presents results from these regressions. The intercept is positive and significant in all three

specifications, indicating that the average constrained firm’s response29 to its unconstrained peers’ SEO

events is positive. This is consistent with unconstrained peer SEOs reducing barriers to constrained firms’

SEOs. The more precise measure of how this reflects easing of capital supply constraints, is the (uniformly)

positive coefficient on the exogenous shock dummy for negative capital supply (index migration) events.

28 We also present results for the corollary sample of unconstrained firms’ responses to their constrained peers’ SEOs. 29 Uncontaminated by either recent index migration or own SEO.

19

In other words, if the unconstrained peer’s SEO occurs within the year following a constrained firm’s

migration “up” (from the Russell 2000 to 1000), the reduction in indexer-based supply of equity capital to

the constrained firm is potentially offset by the capital supply catalyzing effects of peer SEOs. On the other

hand, the coefficient on the positive exogenous capital supply shock dummy is not significant. Finally, the

coefficient on prior SEO by the constrained firm is marginally significant (and negative) in all specifications.

The positive news regarding potentially easier issuance is moot for firms that recently conducted an SEO.30

The last three columns of Table 4 study unconstrained firm responses to announcements of SEOs

by their constrained peers. The evidence indicates no significant response. The intercept and all

coefficients on dummies are uniformly insignificant. Overall, our evidence highlights the value-implying

catalysis of constrained firms’ SEOs by their peers’ earlier SEOs.

3.4 The Importance of Common Underwriters in Propelling Peer Effects

When the bank that underwrites a constrained firm’s SEO has prior experience with peer SEOs, the

information collected during prior activities can mitigate asymmetric information costs for the current

SEO. This should raise a constrained firm’s hazard sensitivity to prior peer SEOs. We test this using the

hazard framework presented in Table 3, but with the inclusion of a new variable measuring the quantity

of prior peer SEO experience by the underwriter. The new variable, Ln(Common Underwriter), is the

natural log of one plus the number of SEOs by peers that were underwritten by the same investment bank

(that the constrained firm uses) over the last six months. The effect of the Ln(Common Underwriter)

variable is incremental to the effect of Ln(Peer SEO).31

Table 5 Panel A indicates the results from the hazards including Ln(Common Underwriter). For

constrained firms, we continue to see the importance of Ln(Peer SEO). Now we also see incremental

importance of Ln(Common Underwriter), significant at the 1% level. When the constrained firm’s

30 The sum of the intercept and negative coefficient on prior own SEO is very close to zero and is not significant. 31 The incremental importance of other financial intermediaries (such as analysts and institutional investors) to constrained firm SEO hazards, is explored in the Online Appendix (Tables A-6 and A-7).

20

underwriter has led more peer SEOs to market recently, this further increases the hazard for the

constrained firm. Notably, there is no such influence on unconstrained firms. The difference in results

suggests a more pronounced reduction in asymmetric information costs, among constrained firms. We

explore this next.

We delve more specifically into indicators of reduced asymmetric information costs associated

with more underwriter experience with recent peer SEOs, as follows. We explore announcement returns

to, and gross spreads charged on, SEOs by constrained firms. If underwriters learn from their prior

activities underwriting SEOs of its peers, then we expect asymmetric information related costs to decline

in our common underwriter variable.

Table 5 Panel B presents regression results32 for these tests. Constrained firms’ SEO

announcement abnormal returns are increasing (less negative) in Ln(Common Underwriter). More

underwriting of peers’ SEOs recently, improves the bank’s information set and this knowledge can be used

to reduce asymmetric information related costs at the constrained firm’s SEO. This also manifests in the

gross spread fees charged on constrained firms’ SEOs which are decreasing in Ln(Common Underwriter).

3.5 Competition Channel Influence on Peer Effects

Concerns over competition can enter the SEO peer effects relationship in (at least) two ways. Firms may

be wary of sharing an underwriter due to the information-leakage concerns highlighted in Asker and

Ljungqvist (2010). It is also possible that unconstrained firms recognize the catalyzing effects of their own

SEO on constrained peers’ SEOs. If those constrained peers are rivals, facilitating their equity issuance

could introduce more competition in the industry – an outcome that the leader may wish to avoid. We

test each of these ideas below.

Asker and Ljungqvist (2010) identify their information leakage concern by holding the benefits of

information sharing constant while varying the costs. We operationalize this approach by splitting our

32 Again with standard errors clustered at the unconstrained SEO event level.

21

peers into groups that are more or less likely to be rivals. Fama/French-49 peers can experience shared

information benefits associated with a common underwriter because of the bank’s experience interacting

with investors that have shown interest in this particular investment sector. On the other hand, the subset

of rivals within this peer group is likely to face greater costs of information sharing than is the subset of

non-rivals. We define FF-49 peers as rivals if they share the same 2-digit SIC code with the firm, and non-

rivals as those with the same FF-49 but different 2-digit SIC codes. For robustness, we also segment by

same vs. different 3-digit SIC code and (separately) by “nearest peers” based on Hoberg and Phillips’

(2016) TNIC measure, to parse peers. We use the moniker more to indicate rivals, given that rivals are

more related, and less to indicate non-rivals, given non-rivals are less related than rivals.

The TNIC measure deserves discussion. We begin by obtaining data from the Hoberg-Phillips Data

Library.33 TNIC score is calculated as the cosine similarity of product descriptions provided by firms k and

j in year t. In our case TNIC is measured between the peer firm and the SEO issuing firm, in the event year.

This variable ranges between zero and one, with larger values indicating greater similarity between the

product descriptions of the two firms. We then use TNIC to rank all of the peers for a given SEO issuing

firm (i.e., all firms in the same FF-49). We define peer firms as more/less likely to be rivals when their

TNIC sore is above/below the median of TNIC scores of all the SEO firm’s peers.

We expect our peer effects to be clearer among non-rivals because of the compromising effects

of competition highlighted by Asker and Ljungqvist (2010). Table 6 presents hazard regressions resembling

those in Table 5 Panel A, but parsing peer effects and (incremental) common underwriter effects by rival

and non-rival status. Specifically, we create two versions of our Ln(Peer SEO) variable (Ln(Peer SEO more)

and Ln(Peer SEO less)), and two versions of our Ln(Common Underwriter) variable (again with the more

and less addenda).

33 We are grateful to them for making it available at http://cwis.usc.edu/projects/industrydata/

22

There are three sets of hazard regression results (a set is comprised of constrained firm results

and unconstrained firm results and their difference). The three sets are for 2-digit, 3-digit, and TNIC

parsing of peers. Regardless of which method we use to parse peers, the coefficients on Peer SEO less are

positive and significant. By contrast, in two of the three sets of results (the exception being TNIC parsing),

coefficients on Ln(Peer SEO more) are insignificant. And in all cases, Ln(Peer SEO less) has significantly

greater explanatory power for constrained firm SEO hazards, than Ln(Peer SEO more). Similar (if not

stronger) patterns are seen in the importance of the Ln(Common Underwriter) variable. The hazard-

increasing effect of underwriter overlap is clear and significant when the firm and peers (that share the

same underwriter) are not rivals. There is no evidence of hazard-increasing benefit to overlapping

underwriter, when the firm and peers are rivals. Overall, these results support the concerns of Asker and

Ljungqvist (2010).

The other mechanism for rival competition concerns to mitigate peer effects is via the logic of

Bolton and Scharfstein (1990). Releasing financial constraints that typically inhibit constrained firm

competitiveness, can increase competition. If unconstrained firms understand that their own SEO may

facilitate an SEO by constrained rivals, then the unconstrained firm may delay their issue until rival

spillover benefits are smaller. We illustrate this alternative as follows.

3.6 Strategic Behavior by Unconstrained Firms

We explore time-series patterns of average cash ratios for constrained firms, around SEOs by

unconstrained firms. The formation of the average cash ratio is done on a quarterly basis, and in event

time relative to the unconstrained peer firm’s SEO. Thus for each unconstrained firm SEO, we calculate

the average cash ratio across all of the unconstrained firm’s (constrained) peers, in each quarter of [-4, 4]

(where 0 is the quarter containing the unconstrained firm’s SEO). Given one average cash ratio of

constrained firms for each unconstrained SEO event, we then average across all unconstrained SEO

events.

23

We present Figure 2, showing the time-series of quarterly average cash ratios of constrained

firms, around the SEOs of unconstrained firms. There are three lines: blue (solid), red (dashed) and green

(small dashed) for the sample of constrained firms whose cash ratios are averaged around the SEO events

of all, rival, and non-rival unconstrained peer firms.34 We first discuss the full sample results illustrated

with the blue line.

The data show an interesting trend in built-up slack by constrained firms in event time. One year

prior to the average unconstrained peer’s SEO, the average constrained firm cash ratio is about 17% of

assets. This does not change meaningfully for six months. Then cash build-up begins and steadily climbs,

and reaches (a local) peak in the quarter after the average unconstrained peer’s SEO. Afterwards, it

appears to flatten again.

The build-up is the key. One interpretation is that unconstrained firms wait until their constrained

peers had built up enough slack, such that it reduces the benefits these constrained firms would get from

released financial constraints (driven by the unconstrained firm’s SEO). In other words, the constrained

firms were nearing the point of ability to compete effectively with the unconstrained firms, due to

sufficient slack, even though they might have difficulty conducting an SEO (or it would be expensive).

Therefore, the unconstrained firm does not experience the strategic cost of releasing its peers’ financial

constraints, because they have effectively been released through the constrained firm’s build-up of slack.

The lean towards strategic considerations suggests that the pattern should be more pronounced

among rival peers (i.e. rivals). Figure 2 indeed shows this to be the case. The slope of the red line is steeper

than the slope(s) of (both) the green (and blue) line(s).35 In other words, the build-up in slack of

constrained firms, preceding unconstrained firms’ SEOs, is more pronounced among the more related

peers. This is consistent with strategic behavior by the average unconstrained firm, because the

34 Rival vs. non-rival status is determined by matching vs. non-matching 3-digit SIC code. 35 Parenthetical results are obvious since the blue should be the average of red and green results.

24

opportunity cost of reducing asymmetric information / financial constraint barriers of constrained firms

is smaller, when these constrained firms have acquired sufficient slack to compete as rivals in the product

market.

3.7 Additional Controls for Alternative Interpretations

Our concentration of results among non-rivals may still reflect “relatedness” between firm and

peers, if there is a vertical relationship between the two. Controlling for this is important to buttress our

claim that (unmeasured) shocks to investment opportunities do not fully explain our results. We measure

vertical relatedness using the same approach as Fan and Goyal (2006) with data from the BEA Input-

Output tables. Two BEA industries are considered vertically related if one industry’s output is at least 1%

contributory to the other industry’s output. Specifically this is the case, if the amount of output from

industry i required to produce a dollar of output from industry j is at least 1% of total industry j output (or

vice-versa).

Our two controls for vertical relatedness are VR_same and VR_diff, mimicking the perspective of

“more” and “less” related (above), but with the vertical relatedness interaction. If the firm and its peer

are in the same (2-digit, 3-digit, or TNIC) industry and vertically related, then VR_same equals one (else

zero). If the firm and its peer are in different industries but are vertically related, then VR_diff equals one

(else zero). We include all of these controls in our Table 6 hazard models. Overall, our main inferences

regarding the three channels of influence in peer effects persist.

The other controls we acknowledge are two variables from Leary and Roberts (2014): their

instrument for peer financial policies and its corollary; peer firm average equity shock (Peer Idio. Ret.) and

own firm idiosyncratic return (Own Idio. Ret.). Since these mimic Leary and Roberts’ (2014) industry-based

variables, we do not construct separate versions for more- vs. non-rival. Inclusion of these controls also

does not change our inferences.

25

4. Conclusions

Peer effects are known to be influential in corporate finance policy. Investment, capital structure,

executive compensation, all show some form of sensitivity to peer characteristics and/or actions. We

explore three channels through which these peer effects may be influenced: capital supply, financial

intermediaries, and competition.

We study SEO timing decisions using hazards. Constrained firms’ SEO hazards are increasing in

prior SEO activity by peers. Positive exogenous shocks to capital supply reduce this sensitivity, suggesting

that the sensitivity proxies for the importance of capital supply and that such shocks are substitutes for it.

We also examine constrained firm abnormal returns to peers’ SEO announcements. We find positive

responses, and they are incrementally stronger among firms that experienced negative exogenous capital

supply shocks (due to index migration). This too suggests substitution between peer SEOs and exogenous

capital supply shocks, for catalyzing (constrained) firm SEOs.

Financial intermediaries’ channel of influence is indicated in the role of underwriter experience in

constrained firms’ SEO timing. When the constrained firm’s underwriter has (recently) more experience

with peer SEOs, the hazard increases. Two results support the hypothesis that asymmetric information

costs (faced by the constrained firm) are mitigated through underwriter experience. Spreads charged by

the underwriter are decreasing in their experience with peers’ SEOs. And constrained firms’ own-SEO

event returns are increasing in the same experience variable.

Finally, one or two competitive channels appear to interrupt peer effects in SEO financing.

Constrained firm hazards are increasing in “non-rival” peers’ SEOs, but not in rivals’ SEOs. Two possible

mechanisms show consistent evidence. The importance of underwriter overlap (between the constrained

firm and peers) is absent when the peers are rivals, consistent with Asker and Ljungqvist’s (2010)

information leakage concerns. And unconstrained peers that are rivals appear to delay their SEOs until

their constrained peers show significant runup in slack that would otherwise enable them to compete.

26

Overall, our research highlights the critical channels of capital supply, intermediaries, and

competitive effects in the transmittal of peer-to-peer financial policies. Yet our focus has been on equity

and therefore sidesteps the potential for peer effect transmittal in debt financing. And while De Franco,

Edwards and Liao (2016) explore the information leakage concern (of Asker and Ljungqvist (2010)) in bank

lending, future research on peer effects in public debt financing may be fruitful.

27

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Table 1. Descriptive Statistics The table reports descriptive statistics for sample SEO firms classified by finance constraints over 1970-2010. Firms are classified into 49 industries following Fama and French (1997). Constrained firms refer to a sample of firms that have a consistent history of zero dividend distribution & share repurchase since their previous issue. Unconstrained firms refer to the complement sample. Peer SEO is the number of firms conducting SEOs in the industry in the prior 6 months. Market SEO is defined as the number of firms conducting SEOs in the market in the prior 6 months, minus the number of firms conducting SEOs in the industry in the prior 6 months. Appendix 1 contains all variable definitions. Panel A reports the firm and deal characteristics for constrained and unconstrained SEOs separately. Panel B reports the spell characteristics. Asterisks indicate significant difference across subsamples. The difference in means t-test assumes unequal variances across groups when a test of equal variances is rejected at the 10% level. The significance level of the difference in medians is based on a Wilcoxon sum-rank test. A ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Variables Constrained SEOs Unconstrained SEOs Mean Median Std. Mean Median Std.

Panel A. Firm and Deal Characteristics Peer SEO 18.47 11.00 22.98 38.12*** 27.00*** 35.05 Market SEO 59.31 64.00 26.58 34.94*** 32.00*** 35.84 Firm_indret (%) 6.67 1.31 30.02 1.68*** -0.03*** 19.13 Ind_mktret (%) 5.08 2.91 14.09 1.78*** 1.39*** 15.16 Mktret (%) 4.88 5.17 6.37 5.67*** 4.95 12.12 Index Shock Pos 0.15 0 0.36 0.24*** 0 0.43 Index Shock Neg 0.20 0 0.40 0.29*** 0 0.45 Ln(MV) 5.79 5.79 1.34 6.12*** 6.12*** 1.48 Book-to-market 0.49 0.46 0.26 0.74*** 0.78*** 0.30 Cashneeds -0.15 -0.07 0.36 -0.07*** -0.02*** 0.19 Dinstidem 0.20 0.00 0.40 0.20 0.00 0.40 Forecast dispersion 0.010 0.005 0.021 0.005*** 0.002*** 0.015 Proceeds 90.79 51.85 150.22 101.83** 46.80*** 267.07 Event abnormal return (%) -0.017 -0.018 0.071 -0.012*** -0.009*** 0.050 Number of obs. 3964 4009 Panel B. Spell Characteristics Spell Length (days) 1920.64 902.00 2401.81 2510.5*** 1205.50*** 2962.41 Fraction Censored 0.19 0.29

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Table 2. Semiparametric Hazard Model Estimates The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over 1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β. The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 contains all variable definitions. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Constrained Firms Unconstrained firms Difference Ln(Peer SEO) 0.099*** 0.007 0.092** (0.022) (0.029) [2.512] Ln(Market SEO) 0.031*** -0.068*** 0.099*** (0.009) (0.010) [7.501] Book-to-market -0.983*** -0.079 -0.903*** (0.069) (0.064) [-9.598] Firm_indret 0.119** 0.311*** -0.193** (0.042) (0.087) [-1.999] Ind_mktret 0.636*** -0.115 0.751*** (0.104) (0.142) [4.261] Mktret -0.331 -0.157 -0.174 (0.292) (0.185) [-0.504] Cashneeds -0.075*** -0.042** -0.033 (0.022) (0.018) [-1.127] Dinstidem 0.407*** 0.155*** 0.251*** (0.048) (0.048) [3.710] Ln(MV) -0.002 -0.079*** 0.079*** (0.015) (0.012) [4.161] Proceeds 0.007 0.011** -0.004 (0.010) (0.004) [-0.360] Log Likelihood -30695.267 -32005.302 Likelihood Ratio test 490.938*** 316.649*** Diff test: Peer SEO 0.068*** 0.075*** −Market SEO (0.005) (0.001)

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Table 3: Hazard with identification via Capital Supply Shocks The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over 1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β. The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 contains all variable definitions. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Constrained Firms Unconstrained firms Difference Ln(Peer SEO) 0.082*** -0.040 0.121** (0.023) (0.032) [3.064] Ln(Market SEO) 0.028*** -0.008 0.036*** (0.008) (0.010) [2.696] Book-to-market -0.448*** -0.620*** 0.171 (0.073) (0.085) [1.532] Firm_indret 0.060 0.165* -0.105 (0.054) (0.091) [-0.994] Ind_mktret 0.110 0.051*** 0.059 (0.137) (0.018) [0.424] Mktret 0.144 -0.086 0.230 (0.265) (0.272) [0.606] Cashneeds -0.312*** -0.373*** 0.061 (0.042) (0.078) [0.689] Dinstidem 0.313*** 0.280*** 0.033 (0.050) (0.058) [0.429] Ln(MV) 0.038** -0.083*** 0.121*** (0.018) (0.018) [4.741] Proceeds -0.0001 0.00002 -0.0001 (0.0001) (0.0001) [-0.768] Index Shock Pos 0.595*** -0.075 0.670*** (0.162) (0.141) [3.112] Ln(Peer SEO) * Index Shock Pos -0.250*** -0.069 -0.181** (0.062) (0.050) [-2.281] Ind_mktret * Index Shock Pos -0.064 -0.281 0.217 (0.324) (0.571) [0.331] Index Shock Neg -0.354** -0.250* -0.103 (0.149) (0.140) [-0.505] Ln(Peer SEO) * Index Shock Neg 0.160*** 0.070 0.090 (0.059) (0.049) [1.177] Ind_mktret * Index Shock Neg 0.096 0.579 -0.483 (0.324) (0.539) [-0.768] Log Likelihood -26779.972 -18082.309 Likelihood Ratio test 227.481*** 228.437*** Diff test: Peer SEO 0.054*** -0.032*** −Market SEO (0.005) (0.007)

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Table 4: Contagion Effects of Peer SEOs This table reports the reactions to peer SEO announcements, for unconstrained and constrained firms separately. 3-day CAR is the SEO 3-day announcement period abnormal returns calculated using standard market model over the event days –1, 0, and +1, where day 0 is the filing date. Constrained firms are firms that have a consistent history of zero distribution & share repurchase since their previous issue. Unconstrained firms refer to the complement sample. SEO 3-day CAR is the dependent variable. Prior 6, 9 or 12 month issue is a dummy variable that equals to one if a constrained firm issue SEO in prior 6, 9, or12 month for column (1)-(3); and that equals to one if a unconstrained firm issue SEO in prior 6, 9, or12 month for column (4)-(6). ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

When unconstrained firms issue SEO When constrained firms issue SEO (1) (2) (3) (4) (5) (6) Intercept 0.0036*** 0.0036*** 0.0037*** 0.0004 0.0004 0.0004

(3.02) (3.04) (3.03) (0.67) (0.69) (0.67)

Index Shock Pos -0.0027 -0.0027 -0.0027 -0.0051 -0.0051 -0.0051

(-0.31) (-0.30) (-0.30) (-0.28) (-0.28) (-0.28)

Index Shock Neg 0.0227*** 0.0227*** 0.0227*** 0.0166 0.0166 0.0166

(3.02) (3.07) (3.07) (0.76) (0.76) (0.76)

Prior 6 month issue -0.0031* 0.0011

(-1.77) (0.53)

Prior 9 month issue -0.0033** -0.0001

(-2.11) (-0.08)

Prior 12 month issue -0.0029* 0.0009 (-1.93) (0.52)

No. of obs 230,655 230,655 230,655 167,733 167,733 167,733 No. of clusters in event date

636 636 636 1,219 1,219 1,219

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Table 5. “Common” Underwriter Influence Panel A of this table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over 1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)ex(t)β.The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs Appendix 1 contains all variable definitions. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Panel A. Hazard Analysis

Constrained Firms Unconstrained firms Difference Ln(Peer SEO) 0.072*** -0.005 0.078** (0.024) (0.030) [2.019] Ln(Market SEO) 0.034*** -0.001 0.035*** (0.009) (0.001) [4.048] Ln(Common Underwriter) 0.013** -0.016*** 0.028*** (0.006) (0.005) [3.645] Book-to-market -0.437*** -0.669*** 0.232** (0.075) (0.089) [1.989] Firm_indret 0.117* 0.177* -0.060 (0.062) (0.096) [-0.525] Ind_mktret 0.117 0.496** -0.379 (0.141) (0.213) [-1.487] Mktret 0.354 0.219 0.135 (0.306) (0.321) [0.305] Cashneeds -0.315*** -0.352** -0.037 (0.042) (0.080) [0.411] Dinstidem 0.335*** 0.296*** 0.038 (0.051) (0.061) [0.482] Ln(MV) 0.039** -0.095*** 0.134*** (0.019) (0.018) [5.111] Proceeds -0.0002 0.0000 -0.0000 (0.0001) (0.0001) [-0.117] Index Shock Pos 0.653*** -0.147 0.800*** (0.165) (0.147) [3.621] Ln(Peer SEO) * Index Shock Pos -0.269*** -0.038 -0.232*** (0.063) (0.052) [-2.838] Ind_mktret * Index Shock Pos -0.109 -0.263 0.155 (0.326) (0.579) [0.233] Index Shock Neg -0.415*** -0.258* -0.157 (0.152) (0.145) [-0.748] Ln(Peer SEO) * Index Shock Neg 0.187*** 0.078 0.09 (0.059) (0.051) [1.394] Ind_mktret * Index Shock Neg 0.062 0.576 -0.514 (0.327) (0.551) [-0.803] Log Likelihood -25576.372 -16627.016 Likelihood Ratio test 231.867*** 244.432***

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Panel B: Asymmetric Information Indicators The panel reports regression results for the sample of constrained firms. Constrained firms are firms that have a consistent history of zero distribution & share repurchase since their previous issue. In column (1), the dependent variable is the SEO 3-day announcement period abnormal returns, calculated using standard market model over the event days –1, 0, and +1, where day 0 is the filing date. In column (2), the dependent variable is the Gross spread, calculated as the dollar amount of the underwriter gross spread scaled by the principal amount, multiplied by 100 to be a percentage. Appendix 1 contains all variable definitions. The t-statistics are in the square brackets. A ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) Announcement

abnormal return Gross spread

Intercept -0.013 2.111*** [-1.08] [5.37] Ln(Peer SEO) 0.331* -0.437*** [1.88] [-8.52] Ln((Market SEO) -0.017 0.013 [-0.17] [0.45] Ln(Common Underwriter) 0.286** -0.669*** [2.25] [-9.32] Book-to-market 0.013*** -0.112 [2.86] [-0.89] Firm_indret 0.031*** 0.063 [8.30] [0.55] Ind_mktret 0.028*** 0.415 [3.33] [1.61] Mktret 0.016 0.564 [0.74] [0.92] Cashneeds 0.005** -0.094 [2.04] [-0.97] Dinstidem 0.005 0.021 [1.47] [1.24] Ln(MV) 0.003*** -0.065* [2.65] [-1.78] Proceeds -0.001 -0.004 [-0.65] [-1.39] Year dummy Yes Yes

Industry dummy Yes Yes

No. of obs 4,864 3,853

R-squared 0.040 0.121

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Table 6. Competition Channel in Peer Effects The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms for the sample period 1984-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β. Ln(Peer SEO_More) is the natural logarithm of one plus the number of firms conducting SEOs in the industry in the prior 6 months that has the same 2-digit SIC code as the issuing firm in Panel A, same 3-digit SIC code as the issuing firm in Panel B, and more related TNIC score to the issuing firm in Panel C. Ln(Peer SEO_Less) is the natural logarithm of one plus the number of firms conducting SEOs in the industry in the prior 6 months that has different 2-digit SIC code as the issuing firm in Columns (1), different 3-digit SIC code as the issuing firm in Columns (2), and less related TNIC score to the issuing firm in Columns (3). Common Undwrt_More/Less is the number of "same-industry" peer firms that used the same lead underwriter as the issuing firm in the prior 6 months. Those peers also have the same/different 2-digit code as the issuing firm in columns (1), same/different 3-digit code as the issuing firm in columns (2), and more/less related TNIC score to the issuing firm in columns (3). Ln(Common Undwrt_More/Less) is the natural logarithm of one plus Common Undwrt_More/Less. The sample period is 1996-2010 for the analysis involving TNIC score. Peer idio. ret is industry peer’s cumulative idiosyncratic stock return over the prior 6 months. Own idio. ret is firm’s own cumulative idiosyncratic stock return over the prior 6 months. Following Leary and Roberts (2014), for each peer firm, we use the prior 60 months of peer firm’s historical data (require at least 24 months of historical data available) to estimate βM and βIND, using the following monthly regressions: R = α + βM(Rm− Rf) + βIND (Rind− Rf) + η, where Rm-Rf is the excess monthly return on the market, and Rind –Rf is the excess monthly return on an equal weighted industry portfolio excluding the firm. Once we obtain estimates of α, βM and βIND, we calculate expected return ˆR, and then compute Idiosyncratic Return = R − ˆR for each peer firm. The peer firms’ average idiosyncratic return is the average of idiosyncratic stock returns over peer firms for each month. Peer idio. ret is calculated as industry peer’s cumulative idiosyncratic stock return over the prior 6 months. VR same is a dummy variable that takes on the value of one if both the SEO firm and peer firm are in the same industry and are vertically related, and zero otherwise. VR diff is a dummy variable that takes on the value of one if the SEO firm and peer firm are in different industries but are vertically related, and zero otherwise. Following Fan and Goyal (2006), the SIC classification is linked to BEA data to define vertical relatedness. We define any two BEA industry with a relatedness coefficient >0.01 as being “related”. Index Shock Pos is a dummy variable that equals to one if the firm migrated from the Russell 1000 to the Russell 2000 within the last 365 days, zero otherwise. Peer SEO_more * Index Shock Pos is the interaction term of Peer SEO_more and Index Shock Pos. Peer SEO_less * Index Shock Pos is the interaction term of Peer SEO_less and Index Shock Pos. Ind_mktret * Index Shock Pos is the interaction term of Ind_mktret and Index Shock Pos. Index Shock Neg is a dummy variable that equals to one if the firm migrated from the Russell 2000 to the Russell 1000 within the last 365 days, zero otherwise. Peer SEO_more * Index Shock Neg is the interaction term of Peer SEO_more and Index Shock Neg. Peer SEO_less * Index Shock Neg is the interaction term of Peer SEO_less and Index Shock Neg. Ind_mktret * Index Shock Neg is the interaction term of Ind_mktret and Index Shock Neg. All other variables are defined as in Appendix 1. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

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Panel A: 2-digit SIC code Constrained firms Unconstrained firms Difference Ln(Peer SEO_more) 0.003 0.013 -0.010 (0.007) (0.009) [-0.862] Ln(Peer SEO_less) 0.093*** 0.008*** 0.086*** (0.014) (0.001) [5.909] Ln(Market SEO) 0.041*** -0.001 0.041*** (0.011) (0.001) [3.711] Ln(Common undwrt_more) -0.347 0.074** -0.421 (0.493) (0.031) [-0.853] Ln(Common undwrt_less) 0.232*** 0.045*** 0.187*** (0.016) (0.003) [11.282] Book-to-mkt -1.029*** -0.658*** -0.371*** (0.096) (0.108) [-2.565] Firm_indret 0.121*** 0.120 0.001 (0.046) (0.085) [0.005] Ind_mktret 0.305** 0.361** -0.057 (0.135) (0.164) [-0.267] Mktret -0.052 -0.283 0.231 (0.309) (0.308) [0.530] Cashneeds -0.070*** -0.013 -0.057 (0.025) (0.029) [-1.500] Dinstidem 0.313*** 0.468*** -0.154** (0.050) (0.056) [-2.051] Lnmv 0.013 -0.164*** 0.176*** (0.018) (0.015) [7.521] Offer size -0.0001 0.0002*** -0.0002** (0.0001) (0.0001) [-2.030] Peer idio. ret 1.096*** 0.864*** 0.233 (0.188) (0.209) [0.828] Own idio. ret 0.192** 1.832 -1.639 (0.076) (1.457) [-1.123] VR_same -0.004 0.024 -0.028 (0.010) (0.016) [-1.480] VR_diff 0.159** 0.042*** 0.117* (0.068) (0.010) [1.693] Index Shock Pos 0.155 0.246** -0.091 (0.099) (0.109) [-0.616] Ln(Peer SEO_more) * Index Shock Pos -0.003 -0.046 0.043 (0.002) (0.030) [1.433] Ln(Peer SEO_less) * Index Shock Pos -0.060** -0.004* -0.057** (0.027) (0.002) [-2.076] Ind_mktret * Index Shock Pos 0.103 -0.585 0.688 (0.343) (0.625) [0.965] Index Shock Neg -0.207** -0.138 -0.069 (0.090) (0.104) [-0.505] Ln(Peer SEO_more)* Index Shock Neg 0.006** 0.007 -0.001 (0.003) (0.027) [-0.020] Ln(Peer SEO_less) * Index Shock Neg 0.031* 0.003 0.028 (0.019) (0.002) [1.452] Ind_mktret * Index Shock Neg 0.440 0.462 -0.022 (0.338) (0.596) [-0.033] Log Likelihood -26093.241 -20203.143 Likelihood Ratio test 728.843*** 703.229***

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Panel B: 3-digit SIC code Constrained firms Unconstrained firms Difference Ln(Peer SEO_more) 0.002 -0.007 0.009 (0.007) (0.009) [0.837] Ln(Peer SEO_less) 0.092*** 0.008*** 0.085*** (0.014) (0.001) [5.854] Ln(Market SEO) 0.039*** -0.001 0.039*** (0.011) (0.001) [3.505] Ln(Common undwrt_more) -0.361 0.072** -0.434 (0.494) (0.031) [-0.875] Ln(Common undwrt_less) 0.228*** 0.041*** 0.188*** (0.016) (0.003) [11.535] Book-to-mkt -1.036*** -0.660*** -0.376*** (0.095) (0.109) [-2.598] Firm_indret 0.122*** 0.139* -0.017 (0.046) (0.085) [-0.177] Ind_mktret 0.316** 0.385** -0.069 (0.135) (0.164) [-0.325] Mktret -0.051 -0.271 0.220 (0.309) (0.310) [0.502] Cashneeds -0.071*** -0.014 -0.057 (0.025) (0.028) [-1.512] Dinstidem 0.318*** 0.473*** -0.155** (0.050) (0.056) [-2.071] Lnmv 0.011 -0.169*** 0.180*** (0.018) (0.015) [7.702] Offer size -0.0001 0.0002*** -0.0003** (0.0001) (0.000) [-2.079] Peer idio. ret 1.119*** 0.941*** 0.178 (0.188) (0.208) [0.634] Own idio. ret 0.187** 1.819 -1.632 (0.076) (1.446) [-1.127] VR_same -0.004 0.028* -0.032* (0.010) (0.016) [-1.705] VR_diff 0.155** 0.044*** 0.111 (0.068) (0.010) [1.615] Index Shock Pos 0.154 0.252** -0.098 (0.099) (0.109) [-0.666] Ln(Peer SEO_more) * Index Shock Pos -0.003 -0.043 0.041 (0.002) (0.030) [1.343] Ln(Peer SEO_less) * Index Shock Pos -0.067** -0.004* -0.063** (0.030) (0.002) [-2.087] Ind_mktret * Index Shock Pos 0.111 -0.637 0.748 (0.343) (0.624) [1.050] Index Shock Neg -0.204** -0.138 -0.066 (0.090) (0.104) [-0.478] Ln(Peer SEO_more)* Index Shock Neg 0.006** 0.006 0.001 (0.003) (0.027) [0.035] Ln(Peer SEO_less) * Index Shock Neg 0.032* 0.003 0.028 (0.019) (0.002) [1.481] Ind_mktret * Index Shock Neg 0.434 0.513 -0.079 (0.338) (0.595) [-0.116] Log Likelihood -27432.782 -21092.639 Likelihood Ratio test 730.681*** 673.283***

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Panel C: TNIC Constrained firms Unconstrained firms Difference Ln(Peer SEO_more) 0.017*** 0.022** -0.005 (0.007) (0.009) [-0.433] Ln(Peer SEO_less) 0.118*** 0.009*** 0.108*** (0.014) (0.001) [7.704] Ln(Market SEO) 0.027** 0.025** 0.002 (0.011) (0.012) [0.149] Ln(Common undwrt_more) 0.007 0.105*** -0.098*** (0.005) (0.030) [-3.197] Ln(Common undwrt_less) 0.583*** 0.086*** 0.497*** (0.026) (0.003) [19.289] Book-to-mkt -0.933*** -0.620*** -0.313** (0.096) (0.106) [-2.192] Firm_indret 0.103** 0.092 0.011 (0.048) (0.084) [0.110] Ind_mktret 0.237* 0.242 -0.005 (0.137) (0.162) [-0.024] Mktret -0.185 -0.244 0.059 (0.296) (0.283) [0.145] Cashneeds -0.088*** -0.018 -0.070* (0.029) (0.031) [-1.651] Dinstidem 0.325*** 0.458*** -0.133* (0.050) (0.056) [-1.779] Lnmv 0.031* -0.145*** 0.176*** (0.018) (0.015) [7.432] Offer size -0.0002** 0.0001*** -0.0004*** (0.0001) (0.0001) [-3.010] Peer idio. ret 1.085*** 0.749*** 0.336 (0.186) (0.206) [1.212] Own idio. ret 0.085 1.114 -1.028 (0.076) (1.487) [-0.690] VR_same -0.006 0.039** -0.046** (0.010) (0.015) [-2.476] VR_diff 0.141** 0.026** 0.115* (0.067) (0.010) [1.688] Index Shock Pos 0.143 0.191* -0.048 (0.099) (0.110) [-0.322] Ln(Peer SEO_more) * Index Shock Pos -0.002 -0.071** 0.069** (0.002) (0.032) [2.159] Ln(Peer SEO_less) * Index Shock Pos -0.066** -0.003 -0.063** (0.030) (0.002) [-2.096] Ind_mktret * Index Shock Pos 0.249 -0.313 0.562 (0.386) (0.627) [0.763] Index Shock Neg -0.193** -0.155 -0.038 (0.090) (0.104) [-0.273] Ln(Peer SEO_more)* Index Shock Neg 0.006** 0.010 -0.004 (0.003) (0.028) [-0.146] Ln(Peer SEO_less) * Index Shock Neg 0.033* 0.004* 0.030 (0.019) (0.002) [1.540] Ind_mktret * Index Shock Neg 0.587 0.452 0.136 (0.374) (0.591) [0.194] Log Likelihood -26264.654 -20899.927 Likelihood Ratio test 1033.427*** 1085.065***

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Figure 1. Number of all SEOs, Constrained SEOs, and Unconstrained SEOs by Year

Figure 2. The cash holdings of constrained firms in the four quarters before (-4 to -1) and four quarters after (+1 to +4) the unconstrained firms’ SEO announcement (quarter t=0)

0

50

100

150

200

250

300

1970 1975 1980 1985 1990 1995 2000 2005 2010

# of constrained SEOs # of unconstrained SEOs # of SEOs

15.00%15.50%16.00%16.50%17.00%17.50%18.00%18.50%19.00%19.50%

-4 -3 -2 -1 0 1 2 3 4Fiscal Quarter

mean cash/asset ratioAll More related Peer Less related Peer

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Appendix 1: Variable Definitions

3-day CAR: the SEO 3-day announcement period abnormal returns calculated using standard market model over the event days –1, 0, and +1, where day 0 is the filing date.

Age: the number of years since founding.

Book-to-market: the ratio of book equity (CEQ) of fiscal year ending in year t-1 to market equity (from CRSP) at the end of year t-1.

Cashneeds: the Pro Forma Cash/TA ratio = (Cash t+1- SEO proceeds from primary shares)/(Total Assets t+1 - SEO proceeds from primary shares), which follows DeAngelo, DeAngelo and Stulz (2010).

Common Analysts: the number of analysts providing EPS forecasts for an SEO firm as well as on any of the peers that issued an SEO in the prior 6 months, divided by the number of analysts covering the SEO firms. The data are from the IBES Detailed History File. This closely follows the definition from Cohen and Frazzini (2008).

Common Institutions: the number of institutional investors reporting holdings on an SEO firm as well as on any of the peers that issued an SEO in the prior 6 months, divided by the number of institutional investors holding the SEO firm. Data are from Thomson-Reuters institutional holdings (i.e., 13F). This closely follows the definition from Cohen and Frazzini (2008).

Common Underwriter is defined as the number of “same industry” firms that used the same lead underwriter as the issuing firm in the prior 6 months.

Common Undwrt_More/Less is the number of "same-industry" more/less related peer firms that used the same lead underwriter as the issuing firm in the prior 6 months. More/less related Peers are those peers that have the same/different 2-digit SIC code/3-digit SIC code as the issuing firm, or more/less related TNIC score to the issuing firm. Peer firms are more/less related to the SEO issuing firms when their TNIC sore is above/below the median of the TNIC score of all peers that are deemed related to the issuing firm using this TNIC data.

Constrained firms: firms that have a consistent history of zero distribution & share repurchase since their previous issue.

Dinstidem: a dummy variable that takes the value of one if the institutional demand variable (new holdings) is in its highest quintile, which follows Alti and Sulaeman (2012).

Firm_indret: the firm’s cumulative return in the prior 6 months, minus industry cumulative return in the prior six months before the SEO.

Firm size: the natural logarithm of the book value of assets (Compustat item at).

Fraction Censored: the percentage of (right-)censored spells in the sample.

Index Shock Neg: equal to one if the firm migrated from the Russell 2000 to the Russell 1000 within the last 365 days, zero otherwise.

Index Shock Pos: equal to one if the firm migrated from the Russell 1000 to the Russell 2000 within the last 365 days, zero otherwise.

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KZ Index: it is constructed following Lamont, Polk, and Saa-Requejo (2001) as –1.001909[(ib + dp)/lagged ppent] + 0.2826389[ (at + prcc_f×csho - ceq - txdb)/at] + 3.139193[(dltt + dlc)/(dltt + dlc + seq)] – 39.3678[(dvc + dvp)/lagged ppent]–1.314759[che/lagged ppent], where all variables in italics are Compustat data items.

Length Censored (uncensored): the censored (uncensored) number of days between issues (spells).

Less-related Peer: a dummy variable that equals one if constrained (unconstrained) firm has different 2-digit SIC code/different 3-digit SIC code/non-rival TNIC as SEO-issuing unconstrained (constrained) peers.

Ln(Common Underwriter) is the natural logarithm of (1+Common Underwriter).

Ln(Common Undwrt_More/Less) is the natural logarithm of (1+ Common Undwrt_More/Less).

Ln(Market SEO): the natural logarithm of (1+Market SEO).

Ln(Peer SEO): the natural logarithm of (1+Peer SEO).

Ln(Peer SEO($)): the natural logarithm of (1+Peer SEO($)).

Ln(Peer SEO_More): the natural logarithm of one plus the number of peers conducting SEOs in the prior 6 months that had the same 2-digit SIC code / same 3-digit SIC code / more related TNIC score as the issuing firm.

Ln(Peer SEO_Less): the natural logarithm of one plus the number of peers conducting SEOs in the prior 6 months that had different 2-digit SIC code/different 3-digit SIC code/less related TNIC score than the issuing firm.

Ind_mktret: the industry cumulative return in the prior 6 months, minus NYSE/Amex value weighted cumulative return in the prior six months before the SEO.

Lnmv: the Natural logarithm of market capitalization, computed as share price times shares outstanding (both from CRSP) as of the end of June of year t.

Market SEO: the number of firms conducting SEOs in the market in the prior 6 months, minus the number of peer firms conducting SEOs in the prior 6 months.

Mktret: the NYSE/Amex value weighted cumulative return in the prior 6 months before the SEO.

Non-rated firms: firms that do not have a credit rating from S&P, using data obtained from Compustat (variable splticrm).

Offer size: the total proceeds raised by the firm in the SEO offering.

Own idio. Ret: firm’s own cumulative idiosyncratic stock return over the prior 6 months. Following Leary and Roberts (2014), for each firm, we use the prior 60 months of firm’s historical data (require at least 24 months of historical data available) to estimate βM and βIND, using the following monthly regressions: R = α + βM(Rm− Rf) + βIND (Rind− Rf) + η, , where Rm-Rf is the excess monthly return on the market, and Rind –Rf is the excess monthly return on an equal weighted industry portfolio excluding the firm. Once we obtain estimates of α, βM and βIND, we calculate expected return ˆR, and then compute own Idiosyncratic Return = R − ˆR.

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Peer idio. Ret: industry peer’s cumulative idiosyncratic stock return over the prior 6 months. Following Leary and Roberts (2014), for each peer firm, we use the prior 60 months of peer firm’s historical data (require at least 24 months of historical data available) to estimate βM

and βIND, using the following monthly regressions: R = α + βM(Rm− Rf) + βIND (Rind− Rf) + η, , where Rm-Rf is the excess monthly return on the market, and Rind –Rf is the excess monthly return on an equal weighted industry portfolio excluding the firm. Once we obtain estimates of α, βM and βIND, we calculate expected return ˆR, and then compute Idiosyncratic Return = R − ˆR for each peer firm. The peer firms’ average idiosyncratic return is the average of idiosyncratic stock returns over peer firms for each month. Peer idio. ret is calculated as industry peer’s cumulative idiosyncratic stock return over the prior 6 months.

Peer SEO: the number of firms conducting SEOs in the same FF-49 industry in the prior 6 months.

Peer SEO ($): the sum of proceeds (scaled by market value of equity) across all SEOs of the peer group in the last six months.

Prior 6 month issue: a dummy variable that equals to one if a constrained firm issued SEO in prior 6 months.

Spell Length: the number of days between IPOs and first SEOs and between consecutive SEOs.

The percentage of constrained SEOs in the wave: the number of constrained SEOs in the corresponding 6-month window period of the wave, divided by the total number of constrained SEOs in the wave.

The percentage of unconstrained SEOs in the wave: the number of unconstrained SEOs in the corresponding 6-month window period of the wave, divided by the total number of unconstrained SEOs in the wave.

Unconstrained firms: Unconstrained firms are the complement sample to constrained firms.

VR same: a dummy variable that takes on the value of one if both the SEO firm and peer firm are in the same industry and are vertically related, and zero otherwise. Following Fan and Goyal (2006), the SIC classification is linked to BEA data to define vertical relatedness. We define any two BEA industry with a relatedness coefficient >0.01 as being “related.”

VR diff: a dummy variable that takes on the value of one if the SEO firm and peer firm are in different industries but are vertically related, and zero otherwise. Following Fan and Goyal (2006), the SIC classification is linked to BEA data to define vertical relatedness. We define any two BEA industry with a relatedness coefficient >0.01 as being “related.”

WW Index: it is constructed following Whited and Wu (2006) and Hennessy and Whited (2007) as –0.091 [(ib + dp)/at] – 0.062[indicator set to one if dvc + dvp is positive, and zero otherwise] + 0.021[dltt/at] – 0.044[log(at)] + 0.102[average industry sales growth, estimated separately for each three-digit SIC industry and each year, with sales growth defined as above] – 0.035[sales growth], where all variables in italics are Compustat data items.

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Online Appendix

This appendix contains three sections. Section A1 contains Table A-1, delineating (in three panels) our SEO data by year, by wave and by wave and firm-type (constrained vs. unconstrained). Section A2 focuses on robustness checks of our main hazard results. Section A3 provides analysis of analysts’ and institutional investors’ incremental roles in our hazards.

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A1. SEO sample patterns

Table A-1, Panel A. Number of SEOs by year The table reports 7,973 uncensored SEOs from January 1970 to December 2010 in the U.S. The sample excludes financials, utilities, and shelf registrations and those that are comprised of less than 50% primary shares.

Year Number of SEOs 1971 24 1972 59 1973 77 1974 72 1975 126 1976 112 1977 82 1978 109 1979 93 1980 173 1981 165 1982 309 1983 275 1984 129 1985 251 1986 249 1987 154 1988 64 1989 122 1990 85 1991 305 1992 249 1993 360 1994 265 1995 357 1996 449 1997 420 1998 233 1999 285 2000 305 2001 189 2002 182 2003 215 2004 261 2005 215 2006 207 2007 215 2008 99 2009 213 2010 219

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Table A-1, Panels B and C. Summary of SEO Waves 7,973 uncensored SEOs from January 1970 to December 2010 in U.S. market are used to identify SEO waves for each decade. SEO waves are identified following the moving-average method of Helwege and Liang (2004). Panel A reports start and end time and total number of SEOs of each SEO wave. There are five SEO waves from January 1970 to December 2010. Panel B reports the number and percentage of constrained /unconstrained firms of each SEO wave. Constrained firms are firms that have a consistent history of zero dividend distribution & share repurchase since their previous issue. Unconstrained firms refer to the complement sample. The percentage of constrained SEOs in the wave is number of constrained SEOs in the corresponding 6-month window period of the wave, divided by the total number of constrained SEOs in the wave. The percentage of unconstrained SEOs in the wave is number of unconstrained SEOs in the corresponding 6-month window period of the wave, divided by the total number of unconstrained SEOs in the wave.

Panel B: SEO Waves from 1970 to 2010 Waves Period Total Number of SEOs Wave 1 1982-1983 584 Wave 2 1985-1986 500 Wave 3 1991-1993 914 Wave 4 1995-1997 1226 Wave 5 1999-2000 590

Panel C: Constrained and unconstrained SEOs during waves

Waves Period # of SEOs constrained

# of SEOs unconstrained

% of constrained SEOs in the wave

% of unconstrained SEOs in the wave

Wave 1 Jan.1982-June 1982 7 138 8.64% 27.44% July 1982-Dec.1982 7 157 8.64% 31.21% Jan.1983-June 1983 38 111 46.91% 22.07% July 1983-Dec.1983 29 97 35.80% 19.28% Wave 2 Jan.1985-June 1985 41 75 20.40% 25.08% July 1985-Dec.1985 32 103 15.92% 34.45% Jan.1986-June 1986 76 61 37.81% 20.40% July 1986-Dec.1986 52 60 25.87% 20.07% Wave 3 Jan.1991-June 1991 79 75 14.31% 20.72% July 1991-Dec.1991 80 71 14.49% 19.61% Jan.1992-June 1992 87 72 15.76% 19.89% July 1992-Dec.1992 53 37 9.60% 10.22% Jan.1993-June 1993 117 54 21.20% 14.92% July 1993-Dec.1993 136 53 24.64% 14.64% Wave 4 Jan.1995-June 1995 99 69 11.88% 17.56% July 1995-Dec.1995 113 76 13.57% 19.34% Jan.1996-June 1996 187 70 22.45% 17.81% July 1996-Dec.1996 123 69 14.77% 17.56% Jan.1997-June 1997 162 58 19.45% 14.76% July 1997-Dec.1997 149 51 17.89% 12.98% Wave 5 Jan.1999-June 1999 68 74 18.48% 33.33% July 1999-Dec.1999 84 59 22.83% 26.58% Jan.2000-June 2000 128 54 34.78% 24.32% July 2000-Dec.2000 88 35 23.91% 15.77%

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A2. Robustness of Hazard Results

A2.1 Value-weighted Peer SEO Covariate

An alternative to counting all SEOs by peers within the prior six months (for our Peer SEO covariate) is to value-weight. We calculate proceeds-based peer SEO activity (denoted Peer SEO ($)) as follows: it equals the sum of proceeds (scaled by market value of equity) across all SEOs of the peer group in the last six months. Table A-2 presents the results. Constrained firms’ SEO hazards are significantly increasing in prior peer SEO issuance activity and more so than unconstrained firms’ sensitivity (which is now also significant). The peer issuance sensitivity is also significantly larger than market issuance sensitivity, in the sample of constrained firms. Our main inferences from Table 2 persist, when we value-weight peer SEO activity.

A2.2 Alternative Proxies for Financial Constraints

Table A-3 offers robustness checks of our Table 2 results under different constraint proxies. Given the debate over measurement of financial constraints,36 we offer five alternatives. Whether we proxy for constraints with firm Age (above or below sample median), firm Size (top and bottom quartile and middle 50%), KZ index, Whited-Wu Index, or the existence of a long term credit rating, our inferences do not change. Constrained firms show a stronger and significant influence of prior peer SEO activity on their SEO hazard than unconstrained firms do. A2.3 Robustness of Capital Supply Channel to Variations in Sampling We re-estimate the Table 3 hazards under two different sampling approaches. First, we recognize the potential influence of “banding” in the formation of the Russell indices, after 2006. Table A4 contains estimates from the same hazard specification of Table 3, but sampling only through 2006. Our inferences persist. Positive (negative) capital supply shocks reduce (increase) constrained firms’ SEO hazard sensitivity to prior peer SEOs. Our second sampling adjustment is to the construction of the shocks. We focus solely on the bottom 100 firms (by market value rank) of the Russell 1000 and top 100 firms (by market value rank) of the Russell 2000, when looking for migration between indices. In other words, even if a firm migrates between indices based on May 31 market value rank, it gets a zero-value for either shock dummy if it was not within 100 firms (either side) of the cut-line on May 31. Table A5 presents results of the Table 3 hazards, using these new shock values and associated interactions. Again our conclusions are robust. Positive (negative) capital supply shocks reduce (increase) constrained firms’ SEO hazard sensitivity to prior peer SEOs.

36 See Fazzari, Hubbard and Petersen (1988), Kaplan and Zingales (1997), Whited and Wu (2006), Hadlock and Pierce (2010) and Farre-Mensa and Ljungqvist (2016) in particular. There is also the cash savings perspective in Almeida, Campello and Weisbach (2004) and McLean (2011).

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Table A-2. Robustness Check: Hazard with Proceeds-Weighted Prior SEO Activity The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over

1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β.The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 (of the main paper) contains all variable definitions. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Constrained Firms Unconstrained firms Difference Ln(Peer SEO ($)) 0.032*** 0.015*** 0.017*** (0.003) (0.002) [5.187] Market SEO ($) 0.017*** 0.007*** 0.010*** (0.001) (0.001) [7.273] Book-to-market -0.988*** 0.149** -1.138*** (0.068) (0.062) [-12.365] Firm_indret 0.122** 0.288*** -0.166* (0.044) (0.086) [-1.722] Ind_mktret 0.584*** -0.304** 0.888*** (0.107) (0.141) [5.02] Mktret -0.140 0.064 -0.204 (0.283) (0.183) [-0.605] Cashneeds -0.087*** -0.037* -0.050 (0.024) (0.020) [-1.607] Dinstidem 0.430*** 0.161*** 0.269*** (0.048) (0.047) [3.974] Ln(MV) 0.048*** -0.078*** 0.126*** (0.016) (0.012) [6.431] Proceeds -0.008 0.005 0.000 (0.011) (0.005) [-1.138] Log Likelihood -30298.796 -31904.730 Likelihood Ratio test 792.943*** 201.134*** Diff test: Peer SEO 0.015*** 0.008*** ($)−Market SEO ($) (0.001) (0.001)

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Table A-3. Robustness Checks: Alternative Proxies for Financial Constraints Hazard Model Estimates The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over

1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β. The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 (of the main paper) contains all variable definitions, except as follows. In Panel A, Age is years since founding. Constrained (Unconstrained) firms are firms whose age is below (above) the median age group in the sample. In Panel B, Firm size is defined as the natural logarithm of the book value of assets (Compustat item at). Constrained (Unconstrained) firms are firms whose size is ranked in the bottom 25% (top 25%) in the sample. In Panel C, KZ Index is constructed following Lamont, Polk, and Saa-Requejo (2001) as –1.001909[(ib + dp)/lagged ppent] + 0.2826389[ (at + prcc_f×csho - ceq - txdb)/at] + 3.139193[(dltt + dlc)/(dltt + dlc + seq)] – 39.3678[(dvc + dvp)/lagged ppent]–1.314759[che/lagged ppent], where all variables in italics are Compustat data items. Following convention, firms are sorted into terciles based on their index values in the previous year. Firms in the top tercile are coded as constrained and those in bottom tercile are coded as unconstrained. In Panel D, WW Index is constructed following Whited and Wu (2006) and Hennessy and Whited (2007) as –0.091 [(ib + dp)/at] – 0.062[indicator set to one if dvc + dvp is positive, and zero otherwise] + 0.021[dltt/at] – 0.044[log(at)] + 0.102[average industry sales growth, estimated separately for each three-digit SIC industry and each year, with sales growth defined as above] – 0.035[sales growth], where all variables in italics are Compustat data items. Following convention, firms are sorted into terciles based on their index values in the previous year. Firms in the top tercile are coded as constrained and those in bottom tercile are coded as unconstrained. In Panel E, Non-rated firms are those that do not have a credit rating from S&P, using data obtained from Compustat (variable splticrm). Non-rated firms are coded as constrained and rated firms are coded as unconstrained. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A. Proxy: Age Below median age Above median age Difference Ln(Peer SEO) 0.088*** 0.042 0.046* (0.028) (0.038) [1.777] Ln(Market SEO) 0.018* -0.020 0.038** (0.011) (0.014) [2.206] Book-to-market -0.563*** -1.182*** 0.620*** (0.083) (0.092) [5.003] Firm_indret 0.183*** 0.208*** -0.025 (0.011) (0.069) [-0.306] Ind_mktret 0.705*** 0.755*** -0.050 (0.129) (0.132) [-0.273] Mktret -1.044* 0.038 -1.082* (0.406) (0.417) [-1.859] Cashneeds -0.586*** 0.247*** -0.833*** (0.029) (0.063) [-12.067] Dinstidem 0.164*** 0.363*** -0.199** (0.059) (0.066) [-2.245] Ln(MV) 0.080*** -0.194*** 0.275*** (0.019) (0.021) [9.812] Proceeds 0.005*** 0.003*** 0.002 (0.001) (0.001) [0.928] Log Likelihood -18094.732 -14816.627 Likelihood Ratio test 538.376*** 288.582***

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Panel B. Proxy: Firm Size (1) Bottom 25% (2) Middle Group (3) Top 25% (1)-(2) (1)-(3) (2-3) Ln(Peer SEO) 0.222*** 0.160*** 0.096 0.061 0.126*** 0.065* (0.034) (0.022) (0.032) [1.526] [2.697] [1.661] Ln(Market SEO) 0.030** 0.013 -0.016 0.017 0.046** 0.028** (0.014) (0.008) (0.012) [1.042] [2.486] [1.998] Book-to-market -0.431*** -0.434*** -0.546*** 0.003 0.115 0.112 (0.099) (0.062) (0.084) [0.027] [0.890] [1.075] Firm_indret 0.309*** 0.291*** 0.007 0.018 0.302*** 0.284*** (0.067) (0.066) (0.081) [0.192] [2.873] [2.718] Ind_mktret 0.601*** 0.145 0.189 0.456** 0.412* -0.044 (0.159) (0.117) (0.191) [2.315] [1.662] [-0.196] Mktret -0.506 0.331* -1.466*** -0.838** 0.959* 1.797*** (0.328) (0.196) (0.370) [-2.192] [1.940] [4.287] Cashneeds -1.079*** -0.364*** 0.038 -0.715*** -1.117*** -0.402*** (0.084) (0.057) (0.032) [-7.022] [-12.436] [-6.150] Dinstidem 0.329*** 0.429*** 0.874*** -0.100 -0.546** -0.446** (0.051) (0.045) (0.217) [-1.471] [-2.448] [-2.009] Ln(MV) -0.391*** 0.097*** 0.221*** -0.488*** -0.612*** -0.124*** (0.033) (0.032) (0.036) [-10.645] [-12.474] [-2.569] Proceeds 0.003*** 0.000 0.040*** 0.003 -0.037*** -0.040*** (0.000) (0.003) (0.001) [0.934] [-3.707] [-3.962] Log Likelihood -13016.094 -32622.56 -14439.687 Likelihood Ratio test 579.647*** 353.320*** 194.283***

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Panel C. Proxy: KZ index Constrained Unconstrained Difference Ln(Peer SEO) 0.152*** 0.073 0.079* (0.028) (0.033) [1.812] Ln(Market SEO) 0.036*** -0.033*** 0.069*** (0.011) (0.011) [4.506] Book-to-market -0.047 -0.803*** 0.756*** (0.066) (0.085) [6.991] Firm_indret 0.333*** 0.015 0.318*** (0.059) (0.061) [3.739] Ind_mktret 0.223* 0.390*** -0.167 (0.142) (0.141) [-0.835] Mktret 0.028 -0.268 0.296 (0.203) (0.353) [0.726] Cashneeds -0.028 -0.556*** 0.528*** (0.021) (0.097) [5.311] Dinstidem 0.193*** 0.256*** -0.063 (0.055) (0.059) [-0.787] Ln(MV) -0.022** -0.019 -0.003 (0.012) (0.020) [-0.116] Proceeds 0.001*** 0.000 0.001 (0.000) (0.001) [0.952] Log Likelihood -27353.129 -17442.259 Likelihood Ratio test 297.026*** 247.407***

Panel D. Proxy: Whited-Wu Index Constrained Unconstrained Difference Ln(Peer SEO) 0.186*** 0.059 0.127** (0.036) (0.024) [2.120] Ln(Market SEO) 0.034** 0.005 0.029* (0.014) (0.008) [1.733] Book-to-market -1.405*** -0.149** -1.256*** (0.092) (0.073) [-10.710] Firm_indret 0.229*** 0.026 0.203** (0.048) (0.067) [2.441] Ind_mktret 0.913*** -0.324** 1.237*** (0.137) (0.126) [6.653] Mktret -0.044 0.011 -0.054 (0.406) (0.189) [-0.121] Cashneeds -0.013 -0.361*** 0.349*** (0.021) (0.064) [5.196] Dinstidem 0.487*** 0.155*** 0.332*** (0.069) (0.054) [3.802] Ln(MV) -0.171*** 0.103*** -0.273*** (0.017) (0.016) [-11.622] Proceeds 0.004*** -0.002** 0.006*** (0.001) (0.001) [4.118] Log Likelihood -15044.411 -23825.647 Likelihood Ratio test 570.724*** 171.503***

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Panel E. Proxy: Credit Rating

Constrained Unconstrained Difference Ln(Peer SEO) 0.149*** 0.010 0.139*** (0.023) (0.027) [3.916] Ln(Market SEO) 0.020** -0.040*** 0.060*** (0.009) (0.009) [4.778] Book-to-market -1.068*** -0.274*** -0.794*** (0.068) (0.067) [-8.300] Firm_indret 0.119*** 0.406*** -0.287*** (0.043) (0.084) [-3.049] Ind_mktret 0.635*** -0.096 0.731*** (0.101) (0.138) [4.271] Mktret -0.741** -0.008 -0.733** (0.300) (0.178) [-2.103] Cashneeds -0.014 -0.769*** 0.755*** (0.018) (0.090) [8.299] Dinstidem 0.321*** 0.270*** 0.050 (0.054) (0.043) [0.727] Ln(MV) -0.089** -0.133*** 0.044** (0.015) (0.013) [2.155] Proceeds 0.001*** 0.000 0.001*** (0.001) (0.001) [7.320] Log Likelihood -30845.775 -31935.986 Likelihood Ratio test 536.899*** 370.891***

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Table A-4: Hazard with Identification via Capital Supply Shocks Robustness Check Sampling Through 2006 The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over

1970-2006. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β.The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 (of the main paper) contains all variable definitions. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Constrained Firms Unconstrained firms Difference Ln(Peer SEO) 0.095*** -0.014 0.109*** (0.024) (0.034) [2.58] Ln(Market SEO) 0.031*** -0.004 0.035** (0.008) (0.011) [2.525] Book-to-market -0.487*** -0.643*** 0.156 (0.078) (0.090) [1.308] Firm_indret 0.116* 0.158 -0.042 (0.065) (0.100) [-0.355] Ind_mktret 0.139 0.562*** -0.423 (0.142) (0.216) [-1.638] Mktret 0.437 0.085 0.352 (0.313) (0.333) [0.771] Cashneeds -0.317*** -0.355*** 0.038 (0.043) (0.081) [0.41] Dinstidem 0.329*** 0.228*** 0.101 (0.055) (0.063) [1.205] Ln(MV) 0.029 -0.079*** 0.109*** (0.019) (0.019) [4.063] Proceeds -0.0002 -0.00006 -0.0002 (0.0001) (0.0001) [-1.112] Index Shock Pos 0.674*** -0.102 0.776*** (0.172) (0.148) [3.425] Peer SEO * Index Shock Pos -0.271*** -0.058 -0.213*** (0.064) (0.051) [-2.603] Ind_mktret * Index Shock Pos -0.084 -0.237 0.152 (0.326) (0.593) [0.226] Index Shock Neg -0.439*** -0.221 -0.218 (0.158) (0.148) [-1.003] Peer SEO * Index Shock Neg 0.180*** 0.06 0.120 (0.061) (0.051) [1.51] Ind_mktret * Index Shock Neg 0.079 0.461 -0.382 (0.329) (0.562) [-0.586] Log Likelihood -24062.693 -16214.888 Likelihood Ratio test 226.949*** 198.124*** Diff test: Peer SEO 0.064*** -0.010*** −Market SEO (0.004) (0.003)

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Table A-5: Hazard with Identification via Capital Supply Shocks Robustness Check with Shocks Limited to Firms Migrating Between Bottom 100 of Russell 1000 and Top 100 of Russell 2000 The table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over

1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)eX(t)β.The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 (of the main paper) contains all variable definitions. Index shock variables are restricted to equal zero for all observations “outside” of the 100 observations above and 100 observations below the demarcation line separating the two indices. Standard errors are in parentheses. The t-statistics are in the square brackets. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Constrained

Firms Unconstrained firms Difference

Ln(Peer SEO) 0.086*** -0.036 0.122*** (0.024) (0.033) [3.011] Ln(Market SEO) 0.027*** -0.008 0.035*** (0.008) (0.011) [2.621] Book-to-market -0.434*** -0.605*** 0.171 (0.075) (0.087) [1.495] Firm_indret 0.110* 0.187** -0.077 (0.062) (0.095) [-0.679] Ind_mktret 0.099 0.052** 0.046 (0.141) (0.020) [0.326] Mktret 0.365 0.062 0.303 (0.304) (0.312) [0.694] Cashneeds -0.315*** -0.372*** 0.057 (0.042) (0.079) [0.638] Dinstidem 0.332*** 0.278*** 0.054 (0.051) (0.060) [0.681] Ln(MV) 0.035* -0.078*** 0.113*** (0.019) (0.018) [4.401] Proceeds -0.0001 -0.00002 -0.0001 (0.0001) (0.0001) [-0.83] Index Shock Pos 0.639*** -0.112 0.751*** (0.166) (0.144) [3.42] Peer SEO * Index Shock Pos -0.262*** -0.060 -0.202** (0.063) (0.050) [-2.521] Ind_mktret * Index Shock Pos -0.076 -0.307 0.232 (0.326) (0.580) [0.348] Index Shock Neg -0.396*** -0.237* -0.159 (0.153) (0.143) [-0.758] Peer SEO * Index Shock Neg 0.177*** 0.064 0.113 (0.060) (0.050) [1.457] Ind_mktret * Index Shock Neg 0.043 0.590 -0.547 (0.328) (0.550) [-0.856] Log Likelihood -25767.591 -17419.699 Likelihood Ratio test 226.354*** 219.950*** Diff test: Peer SEO 0.060*** -0.028*** −Market SEO (0.006) (0.002)

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A3. Other Financial Market Participants’ Influences on SEO Hazards

The positive effects of common underwriters on constrained firms’ SEO hazards may extend to other financial market participants. A3.1 Institutional Investors Institutional investors that own both the constrained firm and peers may also raise constrained firm SEO hazards. This need not solely reflect indexer behavior (particularly shocks). Active institutions can also play a role, perhaps through the decision to diversify within a sector, or because their ownership of peers implies information acquisition that encourages them to buy (additional) shares of the constrained firm. To test the influence of common institutional ownership (of constrained firm and peers) on constrained firm SEO hazards, we construct a common institutional ownership variable from 13F filings analogous to Cohen and Frazzini (2008). Common Institutions equals the number of institutional investors reporting holdings of a constrained firm as well as of any peers that issued an SEO in the prior 6 months, divided by the number of institutional investors holding the SEO firm. We transform this by taking the natural log of one plus it. For our unconstrained firm SEO hazard estimation, a corresponding measure is formed. Table A-6 presents the results from hazards mirroring Table 5 Panel A, but replacing the common underwriter variable with Ln(Common Institutions). In the constrained firm hazard, the coefficient on the new variable is positive and marginally significant (p-value = .068). Other inferences, regarding peer effects and the importance of the capital supply channel, persist. In the unconstrained firm hazard, the Ln(Common Institutions) variable has a reducing effect on the hazard. A3.2 Analysts The main paper infers that reduced asymmetric information costs may explain the importance of underwriter overlap for constrained firm hazards. Analysts are another intermediary that communicate information and can reduce asymmetric information costs. To test the influence of common analyst coverage (of constrained firm and peers) on constrained firm SEO hazards, we construct a common analyst variable as follows. Each constrained firm is (potentially) covered by several analysts. For each analyst that follows a constrained firm, we determine whether that analyst follows any of the firm’s peers that have conducted SEOs in the last six months. We then divide the number (of analysts) that did so, by the total number of analysts that follow the constrained firm, to create Common Analysts. We transform this by taking the natural log of one plus it. For our unconstrained firm SEO hazard estimation, a corresponding measure is formed. Table A-7 presents the results from hazards mirroring Table 5 Panel A, but replacing the common underwriter variable with Ln(Common Analysts). The coefficient on the new variable is positive and significant. Other inferences, regarding peer effects and the importance of the capital supply channel, persist. In the unconstrained firm hazard, the Ln(Common Analysts) variable has an insignificant effect.

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Table A-6: Hazard with Common Institutional Ownership Incremental Effects This table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over 1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)ex(t)β.The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 (of the main paper) contains all variable definitions.

Common Institutional Holdings Constrained

firms Unconstrained firms Difference

Ln(Peer SEO) 0.076*** -0.001** 0.076*** (0.024) (0.000) [3.118] Ln(Market SEO) 0.030*** 0.005 0.025** (0.008) (0.010) [1.969] Ln(Common Institutions) 0.003* -0.007*** 0.010*** (0.002) (0.002) [4.018] Book-to-mkt -0.444*** -0.621*** 0.178 (0.075) (0.086) [1.55] Firm_indret 0.112* 0.180* -0.067 (0.062) (0.095) [-0.59] Ind_mktret 0.106 0.502** -0.340 (0.141) (0.209) [-1.57] Mktret 0.344 0.135 0.208 (0.304) (0.317) [0.475] Cashneeds -0.313*** -0.370*** 0.056 (0.042) (0.078) [0.633] Dinstidem 0.331*** 0.281*** 0.050 (0.051) (0.060) [0.64] Lnmv 0.035* -0.079*** 0.113*** (0.019) (0.018) [4.391] Offer size -0.0001 -0.00003 -0.0001 (0.0001) (0.00007) [-0.909] Index Shock Pos 0.642*** -0.118 0.759*** (0.165) (0.148) [3.43] Peer SEO*Index Shock Pos -0.266*** -0.052 -0.213*** (0.062) (0.052) [-2.635] Ind_mktret*Index Shock Pos -0.086 -0.239 0.152 (0.326) (0.575) [0.23] Index Shock Neg -0.412*** -0.203 -0.209 (0.152) (0.146) [-0.989] Peer SEO*Index Shock Neg 0.187*** 0.051 0.136* (0.059) (0.052) [1.727] Ind_mktret*Index Shock Neg 0.037 0.518 -0.481 (0.328) (0.546) [-0.756] Log Likelihood -25257.097 -17080.129 Likelihood Ratio test 234.136*** 238.480***

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Table A-7: Hazard with Common Analyst Incremental Effects This table reports semiparametric hazard parameter estimates for constrained versus unconstrained firms over 1970-2010. Hazard model with time-dependent covariates is specified as hi(t)=h0(t)ex(t)β.The dependent variable is the number of days between IPOs and first SEOs and between consecutive SEOs. Appendix 1 (of the main paper) contains all variable definitions.

Constrained Firms Unconstrained firms Difference Ln(Peer SEO) 0.082*** -0.011 0.093*** (0.024) (0.027) [2.559] Ln(Market SEO) 0.041*** 0.007 0.047*** (0.009) (0.001) [5.335] Ln(Common Analysts) 0.015*** 0.002 0.013*** (0.004) (0.002) [2.733] Book-to-market -1.009*** -0.166** -0.084*** (0.070) (0.066) [-8.8] Firm_indret 0.132*** 0.329*** -0.197** (0.042) (0.085) [-2.08] Ind_mktret 0.706*** -0.369** 1.075*** (0.119) (0.162) [5.342] Mktret -0.301 -0.111 -0.189 (0.300) (0.342) [-0.539] Cashneeds -0.075*** -0.039** -0.037 (0.022) (0.019) [-1.255] Dinstidem 0.399*** 0.169*** 0.230*** (0.048) (0.048) [3.377] Ln(MV) -0.016 -0.087*** 0.071*** (0.016) (0.012) [3.621] Proceeds 0.0001 0.0001*** -0.0000 (0.0001) (0.0000) [-0.177] Index Shock Pos 0.580*** 0.121 0.458** (0.163) (0.129) [2.203] Peer SEO * Index Shock Pos -0.282*** -0.004 -0.279*** (0.063) (0.040) [-3.717] Ind_mktret * Index Shock Pos -0.395 -0.405 0.010 (0.301) (0.542) [0.016] Index Shock Neg -0.474*** -0.270** -0.204 (0.150) (0.127) [-1.039] Peer SEO * Index Shock Neg 0.168*** 0.022 0.146** (0.060) (0.042) [1.99] Ind_mktret * Index Shock Neg 0.184 1.817*** -1.633*** (0.301) (0.531) [-2.674] Log Likelihood -30495.999 -29772.714 Likelihood Ratio test 538.262*** 315.586***