* I would like to thank Frank Dobbin, Christopher Marquis, Peter Marsden, Mary Brinton, Filiz Garip, Orlando Patterson, and participants at the Stanford Economic Sociology workshop and ABC Network “Organizing Institutions” conference for their comments and suggestions. Contact Vince Feng, Department of Sociology, Harvard University, William James Hall, 33 Kirkland Street, Cambridge, MA 02138. E-mail: vfeng@fas.harvard.edu.

Reconsidering Price: Institutional Complexity in Initial Public Offering Prices*

Vince Feng Department of Sociology

Harvard University

Word Count: 9,491 excluding end notes Abstract Initial Public Offerings (IPOs) are priced in two stages, with the staged pricing serving as a natural experiment testing economic price theories. IPO second-stage return outcomes (first-day returns, or “underpricing”) directly contradict neoclassical models, with non-rational investor models from behavioral theory addressing second-stage underpricing unable to explain first-stage return outcomes (pricing above the range). This study proposes that first-stage return outcomes isolate issuer-side explanatory factors, with institutional logics explaining variation in pricing above the range for the approximately 800 IPOs from 2001 to 2010. Two logics—Income and Growth—coexist in the private investment field. Investors controlling over half of the IPO companies during this time period perceive companies and IPOs differently due to these two logics. Investor practices emanating from these perceptions differ in resistance to underwriters promoting underpricing, causing variation in IPO first and second-stage return outcomes. Quantitative analysis shows that the Income approach significantly improves, and Growth significantly worsens, IPO pricing even after taking into account behavioral and strategic considerations. Thus, institutional complexity can influence calculative rationality through varying perceptions of the environment, explaining price phenomena poorly understood by rational adaptation and behavioral perspectives. Keywords Institutional Logics, Price Theory, IPOs, Sociology of Markets

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Reconsidering Pricing: Institutional Complexity in Initial Public Offering Prices

Initial Public Offering (IPO) prices exhibit high first-day returns contradicting neoclassical

theory that economists term “underpricing.” IPOs are the initial listing of a private company’s

shares on a public equity market and are priced in two stages, with such staged pricing

representing a natural experiment testing both neoclassical and behavioral price models. Efficient

Market Hypothesis (EMH) asserts that financial market prices reflect all known information. IPO

second-stage returns (first-day returns) directly contradict EMH and neoclassical asset-price

models, with economists attempting to account for second-stage returns by noting information

asymmetries and employing behavioral theories. However, non-rational investor models from

behavioral finance cannot explain IPO first-stage returns (pricing above the range). Despite over

thirty years of research in financial economics, IPO “underpricing” remains an unresolved

research question (Loughran and Ritter 2002; Ritter and Welch 2002). This study proposes that

IPO first-stage return outcomes isolate issuer-side explanatory factors, with institutional logics

explaining variation in pricing above the range.

Modern price theory has ignored sociocultural logics. Neoclassical theory hypothesizes that

market actors rationally update new information to maximize utility against resource constraints

(Manski 2000; Dybvig and Ross 2003). For behavioral theories, systematic cognitive biases

(non-Bayesian updating of information and nonrational preferences) cause divergence from

neoclassical models (Hirshleifer 2001; Barberis and Thaler 2003). Both economic theory groups

assert a universal sociocultural orientation (rationality or systematic cognitive biases,

respectively), denying institutional complexity in logics. Similarly, sociologists examining

prices have not focused on institutional logics but instead followed Granovetter’s lead in linking

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economic action with social structure (1985, 2005), expanding the constraints important for price

determination beyond pecuniary to social structural resources (e.g., Cook and Emerson 1978;

Cook et al. 1983; Podolny 1993, 2001, 2005; Benjamin and Podolny 1999; Uzzi and Lancaster

2004).

While the institutional logics literature has documented the persistence of multiple logics in

the financial sector (Lounsbury 2007; Marquis and Lounsbury 2007), it has not applied these

insights to price analysis. Noncompeting institutional logics encompassing different perceptions

of what constitutes a company coexist in the private investment field. These differing perceptions

of the ontological nature of companies lead strategic actors to exploit different methods of

generating investment gains. As these investment organizations expand their scope of activities,

they extend these logics to markets where the dominant institutional practices and norms may be

detrimental to their interests. In such situations, the strategic actions conditioned by the

perceptions of these coexisting investment logics may differ in resistance to these detrimental

practices and norms.

I study the two dominant institutional logics—Income and Growth—in the institutional

private investment field comprised of the private equity and venture capital industries, and show

how these logics influence IPO first-stage returns. Because Income investors perceive companies

as bundles of cash streams, they develop methods to exploit these cash streams to generate

investment gains. Private equity firms espousing such logic focus strictly on cash considerations

when negotiating financial transactions. In contrast, Growth investors perceive companies as

people turning ideas into paying customers, and accordingly develop methods to exploit

collaboration to generate business growth resulting in investment gains. Private equity firms

apply the beliefs and perceptions of these two differing investment logics to IPO pricing, with

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both groups seeking to generate investment gains from the IPO. Income investors view IPOs as

immediate cash transactions, while Growth investors view IPOs as collaboration with investment

banks (underwriters) to generate long-term growth for their companies that will be reflected in

higher near-term valuations. When companies want to list their shares for the first time, they

must engage an investment bank to underwrite the new issuance of stock. Underwriters have a

vested interest in first and second-stage returns and actively conduct institutional work to price

IPOs attractively for buyers. Whereas the fierce contestation of all cash negotiations benefits

issuers controlled by Income investors, the willingness of Growth investors to accommodate

underwriters results in worse price outcomes for their companies. This study shows that the

beliefs and perceptions of the Income logic significantly improve, while those of the Growth

logic significantly worsen, IPO pricing for issuers even after taking into account behavioral and

strategic considerations.

Thus, institutional pluralism in the private investment field perpetuates differential IPO

pricing, improving our understanding of the variation in returns for the approximately 800

operating company IPOs over the past ten years in the United States. Institutional logics

meaningfully augment our understanding of price phenomena inexplicable for frameworks

lacking institutional complexity and accentuate how cultural orientation matters not only for the

prices under study but also for the study of such prices. Second-stage returns only represent

“underpricing” for a neoclassical framework asserting capital market efficiency. Otherwise, from

the cultural orientation that culture matters, first and second-stage returns are simply natural

outcomes of differences in institutional logics rather than aberrant deviations from the “correct”

value. Hence, understanding differences in sociocultural logics is critical for understanding the

economic action of price determination and the study of such action. My primary contribution is

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the introduction of institutional logics to price theory; in doing so, I explicate a long-standing

unresolved research question in financial economics and demonstrate how logics can influence

calculative rationality through their varying perceptions of objects.

INSTITUTIONAL COMPLEXITY: LOGICS AND RATIONALITY

Institutional logics are cultural assumptions, values, and beliefs that inform how actors

perceive and interpret the environment (Friedland and Alford 1991; Thornton, Ocasio and

Lounsbury 2012). The core hypothesis of the institutional logics perspective is that rationality

and values vary by institutional orders (Thornton, Ocasio and Lounsbury 2012:2-4), or as Weber

termed them “value spheres.” Friedland and Alford’s original concept of an interinstitutional

system of oftentimes contradictory cultural orders resonates with Weber’s work on “social life as

a polytheism of values in combat with one another” (Gerth and Mills [1946] 1958:70; Friedland,

forthcoming). For Weber, “the various value spheres of the world stand in irreconcilable conflict

with each other . . . [quoting John Stuart Mill:] if one proceeds from pure experience, one arrives

at polytheism” (Weber [1918] 1958:147). While Friedland and Alford originally conceived of

conflicting values across fields, research has increasingly pushed the concept of institutional

heterogeneity into the meso-organizational level of analysis (Greenwood et al. 2011; Thornton,

Ocasio and Lounsbury 2012). I apply the institutional logics framework, especially the Weberian

notion of rationality as conditioned and contingent upon value spheres, to the IPO pricing

process.

Some analysts view logics as contradictory to instrumentally rational action, with logics

determining the goals of value-rational action.1 However, logics need not conflict with

calculative rationality, but instead can inform the rational action of calculative actors by

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influencing their perceptions of how objects in the environment will react. As Weber noted,

calculative rationality is “determined by expectations as to the behavior of objects in the

environment and of other human beings; these expectations are used as ‘conditions’ or ‘means’

for the attainment of the actor’s own rationally pursued and calculated ends” (Weber [1922]

1978:24). The ontological perception of what companies are and how they operate obviously

impacts how rational actors generate investment gains. By informing actors how objects react to

their strategic actions, logics become compatible with, rather than opposed to, calculative

rationality. I propose that this aspect of institutional logics could undermine the price predictions

of economic models. The next section outlines the main alternative explanations from

neoclassical and behavioral theory before discussing the IPO process and how logics impact

prices.

Economic Alternatives: Rationality and Non-rationality2

Neoclassical price theory formalizes calculative rationality among atomized actors: market

participants with rational preferences and expectations process information to maximize utility

against resource constraints (Manski 2000; Dybvig and Ross 2003). Rational preferences involve

making “correct” normatively acceptable choices consistent with Savage’s subjective expected

utility (SEU), and rational expectations entail observational learning based on “appropriately”

updating beliefs with new information in accordance with Bayes’ theorem (Barberis and Thaler

2003).3 Applied to stock prices, actors should perform mean-variance optimization: the mean

excess return for each asset should be proportional to the marginal contribution of volatility in

the actor’s optimal portfolio. The resulting asset-price model—Capital Asset Pricing Model

(CAPM)— equates a stock’s excess return over the risk-free rate of return to its exposure to

7

relevant risks (Fama and French 1992).4 Efficient Market Hypothesis (EMH) further asserts that

a functioning market does not require all actors being rational. As long as irrational reactions are

random and follow a normal distribution so that the net price impact cannot be exploited to make

excess returns, then market prices remain the best indicator of intrinsic value. Hence, EMH

predicts that stock prices equal intrinsic value, defined as the discounted present value (DPV)5 of

future dividends (Fama 1965, 1976, 1990). Equivalently, returns from purchasing stock at

prevailing market prices should only reflect compensation for exposure to systematic risks, with

no investor being able to earn excess returns in the long run.

IPO pricing remains a significant area of study after decades of economic research due to its

theoretical import as an ideal natural experiment testing EMH. As discussed more fully in the

“Underwriting Returns” section, underwriters price IPOs in two stages. The second-stage price

outcome (first-day close price) is usually higher than the first-stage price outcome (offer price to

institutional investors). Since the second-stage outcome generally occurs only one day after the

first-stage outcome, IPO first-day returns (second-stage returns, or increase in price between the

two stages of pricing) are purged of the explanatory factors underlying CAPM and EMH. In

other words, each company remains unchanged in its exposure to systematic risks to market

returns, size, and value (CAPM), and no new information on future dividends could cause

rational investors to update their estimates of intrinsic value (EMH). IPO first-day returns should

thus equal zero, with the second-stage price equaling the first-stage price. Numerous studies

over the past three decades have confirmed the persistence of positive first-day returns

contradicting EMH (Ritter and Welch 2002).

Economists have attempted to account for first-day returns by noting agency costs,

substitution costs, information asymmetries, or employing behavioral theories. Agency theory,

8

while espousing EMH, predicts that underpricing may be necessary to align the interests of

underwriters with issuers and may also represent the failure of shareholder monitoring (Jensen

and Meckling 1976; Fama and Jensen 1983). Most analysts utilize the presence of large pre-IPO

institutional shareholders as a proxy for lower agency and monitoring costs; thus private-equity

backed issuers should exhibit lower underpricing. Economists, however, disagree on how

underpricing actually affects post-IPO ownership dispersion and monitoring (Zingales 1995;

Booth and Chua 1996; Brennan and Franks 1997; Mello and Parsons 1998; Stoughton and

Zechner 1998; Field and Sheehan 2004; Zheng and Li 2008). Analysts also disagree as to

whether underpricing is a substitute for litigation costs, either as a form of insurance or

deterrence (Ibbotson 1975; Tinic 1988; Alexander 1993; Drake and Vetsuypens 1993; Lowry

and Shu 2002). If first-day returns were a substitute for litigation costs, companies especially

exposed to litigation risk would exhibit higher underpricing in a bid to please investors and avert

lawsuits. Also, information asymmetry predicts that the reputation of knowledgeable backers

(underwriters and institutional investors) certifies the quality of the issuer to the broader market,

reducing first-day returns (Carter and Manaster 1990; Carter, Dark and Singh 1998).

Behavioral finance relaxes either the assumption of Bayesian updating or rational SEU

preferences, claiming that groups of actors are universally biased or non-rational in the same

manner (Hirshleifer 2001; Barberis and Thaler 2003). Investor models within behavioral finance

rely on the presence of non-Bayesian investors, generally referred to as “noise traders” or

“sentiment investors.” In particular, Baker and Stein (2004) demonstrate that sentiment investors

can dictate prices by driving rational investors out of the market due to short-sale constraints.

These sentiment investors are prone to sentiment (non-rational optimism or pessimism in

pricing) and underweighting information relevant to DPV intrinsic values, violating Bayes’

9

theorem (Odean 1998; Kaustia and Knüpfer 2008). When sentiment levels are high, these non-

Bayesian sentiment investors increase both market liquidity (trading volume) and prices beyond

rational levels, as rational investors cannot short sell the overpriced shares.6 Derrien (2005)

builds on the work of Miller (1977), Benveniste and Spindt (1989), De Long et al. (1990), and

Welch (1992) to model how underwriters price IPOs in the presence of sentiment investors in

France. Derrien hypothesizes that underwriters rationally maximize profits, consisting of the

underwriting fee as a percentage of the offer price (first-stage price outcome) less the cost of

price support post-IPO. In the model, underwriters buy the offering from the issuer for resale to

two groups of investors with different price predictions, rational and sentiment. Underwriters

themselves do not know the intrinsic value of the issuer, so they conduct a two-stage IPO process,

soliciting price predictions from the group of rational investors first. Rational investors must be

enticed to disclose private information on their estimation of the intrinsic value of the issuer, so

underwriters price the offering below the irrationally inflated price predictions of sentiment

investors. Given such a model, the offer price to rational investors (first-stage price) increases

with investor sentiment, but usually does not reach the price predictions of sentiment investors,

thus explaining the persistence of first-day returns.7 Furthermore, issuers are not upset with this

underpricing, since the offer price is priced above the intrinsic value of the firm, or the predicted

price by rational investors (Purnanandam and Swaminathan 2004). Finally, rational investors are

happy to sell the shares to sentiment investors on the first day of trading for a quick profit. I will

discuss in the “Underwriting Returns” section how U.S. IPO practices violate this model’s core

assumptions. Nevertheless, this model represents one of the most complete sentiment investor

accounts of IPO first-day returns and has been extended to explain long-run IPO

10

underperformance (Ljungqvist, Nanda and Singh 2006) and why underwriters induce sentiment

investors into the market (Cook, Kieschnick and Van Ness 2006).

Corroborating behavioral price theories, little evidence exists that institutional investors

make better IPO investments than retail investors due to superior monitoring or private

information; instead, better use of publicly available fundamental information explains almost all

of their outperformance relative to retail investors (Field and Lowry 2009). Since sentiment

investors underweight public information, fundamental information about issuers (e.g., offering

size, revenues, earnings, etc.) could predict pricing. Given non-Bayesian investor models of

pricing, we should also include appropriate measures of investor sentiment. Baker and Stein

initially recommended market liquidity (NYSE share turnover) as a suitable proxy for investor

sentiment, but have since developed improved composite indices of sentiment (Baker and

Wurgler 2006, 2007). Derrien (2005) recommends “market conditions” (defined as the three-

month moving average return on industry sector indices) as a suitable proxy for sector-specific

sentiment. I will address endogeniety and other concerns when regressing pricing above the

range on these sentiment proxies in the “Data and Methods” section. Less problematic are

investor surveys that attempt to measure sentiment levels directly by sampling both institutional

(rational) and retail (sentiment) investors, such as those conducted by Robert Shiller since 1989.

While Shiller did not formalize how investor sentiment impacts IPO first-day returns as later

analysts did, he demonstrated how excess volatility in stock prices relative to dividends directly

implies the predictability of long-run returns, contradicting EMH.8

More importantly for IPO first-stage return outcomes (pricing above the range) are non-

rational issuer models within behavioral finance. These models generally relax the assumption of

rational SEU preferences, hypothesizing that issuers either accept or seek underpricing. For

11

instance, professional managers with lower share ownership versus owner-managers may be less

vigilant against higher first-day returns (Ljungqvist and Wilhelm 2003): higher management

ownership should thus predict lower underpricing. Prospect theory remains the dominant non-

SEU theory of preferences within behavioral finance (Kahneman and Tversky 1979; Barberis

and Thaler 2003). Instead of normative preferences, prospect theory utilizes cognitive

psychology to argue that utility is defined relative to an arbitrary reference level, with actors

being risk-averse when above such a level but risk-seeking when below. Given these empirical

preferences, framing and mental accounting effects matter since the utility of actors are

reference-level dependent and non-linear.9 Applied to IPO pricing, prospect theory predicts that

issuers integrate wealth gain from first-day returns with the loss of underpricing (Habib and

Ljungqvist 2001; Loughran and Ritter 2002). The portion of the offering consisting of secondary

(existing shares sold by pre-IPO shareholders) rather than primary shares (new shares issued by

the company for the IPO) should thus impact first-day returns. A high secondary portion reduces

the mental integration effects of prospect theory since issuers have already sold their shares at

the offer price and do not benefit from the wealth gain created by first-day returns. Given non-

SEU issuer models of pricing, we should include management ownership and secondary portion

in our analyses. As I will elaborate in the next section, both non-Bayesian investors and non-

SEU issuers should impact first-day returns, but only non-SEU issuers could theoretically impact

pricing above the range.

Underwriting Returns

When a company wants to access the U.S. equity capital markets for the first time, they must

engage an investment bank to underwrite the new issuance of stock. In the United States,

12

underwriters price IPOs in a two-stage process. In the first stage, only certain institutional

investors—primarily hedge, mutual, pension, and private equity funds—negotiate with issuers to

determine offer prices through the underwriter mediated order collection and allocation

(bookbuilding) process. Behavioral theorists designate these institutional investors as rational

investors. Underwriters recommend an indicative price range incorporating the IPO discount to

approach investors with, and issuers either acquiesce or negotiate the price range with the lead

underwriter. Once the price range is agreed upon, meetings with institutional investors

(roadshow) commence and bookbuilding demand informs the determination of the final offer

price to institutional investors.10 This final offer price can be priced above, within, or below the

price range, representing the first-stage return outcome.

After underwriters complete this first stage allocating and selling the offering to institutional

investors, the stock begins trading on the general market, and retail investors (sentiment

investors for behavioral theorists) can purchase shares from these institutional investors or from

the lead underwriter in the second stage of IPO pricing. The first-day return is the increase in

share price from the offer to the closing price on the first day of trading (i.e., the difference

between the two stages of pricing, or the second-stage return outcome). IPO first-day returns

averaged 21.4 percent from 1990 to 2008, costing issuers a cumulative total of $122 billion, or

on average $6.4 billion a year. Figure 1 shows the average first-day returns and cost of

“underpricing” over the time period of this study.

[Insert Figure 1]

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As discussed earlier, IPO pricing is of keen theoretical import to price theorists precisely

because the second-stage outcome (first-day returns) purges the returns of all neoclassical

explanatory factors. With the failure of EMH to explain IPO underpricing, economists have

attempted to offer explanations that explain second-stage returns while recognizing that pricing

above the range (first-stage returns) is the key determinant of such returns (Ritter and Welch

2002). The failure of economists to investigate first-stage return outcomes is telling, as

behavioral theories have little to say about pricing above the range. Indeed, non-Bayesian

investor models cannot explain first-stage return outcomes as they only involve rational investors

in the first stage.11 Non-SEU issuer models, however, might be able to explain pricing above the

range, as non-rational issuers may be seeking underpricing both in the second stage (first-day

returns) as well as in the first stage (pricing above the range). In other words, just as second-

stage outcomes purge returns of changes to systematic risk exposure and new information,

isolating non-neoclassical explanatory factors; so too, first-stage outcomes purge returns of non-

Bayesian investor factors, isolating issuer and underwriter motives as the key explanatory

factors. The following table summaries the explanatory factors isolated by each stage of IPO

pricing.

[Insert Table 1]

Underwriters have a vested interest in first-day returns and actively conduct institutional

work to ensure the realization of first-day returns in the IPO market. While investment banks

earn less on the underwriting fee with increased first-day returns as fees are always a percentage

of the offer price, they more than make up for reduced fees in increased stabilization trading

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profits, rent-seeking behavior from investors during the allocation process (spinning), and quid

pro quo with institutional buyers of IPOs. Economic studies have shown that high first-day

returns lead to increased volatility, translating into significantly increased trading profits for the

lead underwriter approximating two percent of the offering during the first three months of the

price support, or stabilization, period (Aggarwal 2000; Ellis, Michaely and O’Hara 2000, 2002;

Ritter and Welch 2002).12 As a senior trader at a large institutional buyer of IPOs states on the

quid pro quo aspect: “The way it really works is like this. If . . . the investment bank keeps on

pricing things that make no money, they fuck their clients [the IPO investors], their clients won’t

trade with them, it has much bigger repercussions to other parts of the business. Right? So if

they price it cheap, their clients make money, like put it this way. If [a leading investment bank]

comes out with an IPO, I make money because day one it goes up 16 percent, what am I gonna

do? I’m gonna give them a couple more trades and say thank you for the IPO, thanks for the

allocation, right?”13

Non-Bayesian investor models generally assume that underwriters buy and resell (hard

underwrite) the offering to investors, the underwriting fee is their primary source of income, and

post-IPO price support is costly. However, U.S. underwriters do not hard underwrite IPOs, but

act as agents helping issuers sell shares on a best-efforts basis. Furthermore, the alternative

sources of income from an offering (spinning, quid pro quo with investors, and stabilization

profits) could rival or exceed that from the underwriting fee. Far from being costly, post-IPO

stabilization activity generates significant trading volume and profit for the lead underwriter if

the IPO experiences high first-day returns (Booth and Chua 1996; Aggarwal 2000; Ellis,

Michaely and O’Hara 2000, 2002; Boehmer and Fishe 2004). These practices fundamentally

15

alter the profit-maximization constraints facing underwriters and the predictive efficacy of

investor sentiment.14

Economists note that fundamentals cannot explain second-stage return outcomes (first-day

returns) and speculate that the cause remains hidden in first-stage return outcomes, in other

words the “setting of the offer price, where the normal interplay of supply and demand is

suppressed by the underwriter” (Ritter and Welch 2002:1803). Underwriters apply normative

pressure on issuers to accept an “IPO discount” on the offer price. As a global divisional head at

a leading underwriter explains: “There’s got to be some discount, right? The so-called IPO

discount. IPO discount is a function of a few things, I think of it as sort of the price of

admission . . . they’re [issuers] not in the IPO market everyday, so yeah it definitely requires

some education about the whole process.” For the IPO discount institutional norm, a properly

priced offering needs to be underpriced relative to comparable companies. The IPO discount has

developed into common practice over time, resulting in mimetic as well as normative pressure

for compliance.

Importantly, setting a low price range to increase the likelihood of the offer price being

priced above the range significantly impacts first-day returns due to the social good mechanism.

For IPOs, the first and second-stage return outcomes are linked. Economists denote this as a

positively sloping demand curve for IPOs, whereby excess demand in the first stage of pricing

results in greater demand on the first day of trading (Loughran and Ritter 2002). Investors value

stocks as a social good with investor demand dependent on the demand by other investors

(Zuckerman 1999). This social good mechanism is a variation on the mechanism implicated in

critical mass and tipping (Schelling [1978] 2006), threshold models of behavior (Granovetter

1978; Granovetter and Soong 1983, 1986), and Merton’s self-fulfilling prophecy: a belief-

16

formation mechanism whereby the actions of observed others influence goal directed actors

(Hedstrom and Swedberg 1998). The social good mechanism presupposes uncertainty about the

underlying quality of the product, a shared assumption of both information asymmetry and status

signal theories. During the bookbuilding and first-stage pricing process, underwriting practices

and norms work to set a low price range. Setting a low price range leads to increased pricing

above the range, resulting in higher demand on the first trading day. By influencing pricing

above the range outcomes, issuers can impact first-day returns. I hypothesize that institutional

pluralism in the investment field will result in differing levels of resistance to normative and

mimetic pressures for an IPO discount, predicting variation in pricing above the range, and hence

first-day returns, over the past ten years.

Institutional Pluralism within the Private Investment Field

Institutional private investment firms invest in private companies that have yet to list their

shares on public equity markets and are often involved in the subsequent IPOs of these

companies. Two large groups of institutional investors actively invest in such private

opportunities: venture capital and private equity firms. Both groups of investors are legally

organized as “general partners” managing a fund structured as a legal partnership and capitalized

by outside investors, or “limited partners” (for a description of the venture capital industry, see

Podolny 2001; generally, the same legal structure and investor dynamics apply to the private

equity industry). As distinct from other professional institutional investors, venture capital and

private equity firms primarily invest in private opportunities not available to the general public

trading equity shares listed on public exchanges. Hence, venture capital firms invest in early-

stage companies not listed on any exchange and private equity firms invest in companies at all

17

stages of development, including public companies. However, private equity firms often

negotiate private transactions with such public companies that are not available to other public

market investors, such as Private Investments in Public Equities (PIPEs) or Leveraged Buy-Outs

(LBOs). Such negotiated investments usually entail some combination of management control,

board representation, preference shares, assumption of debt obligations by the public company,

or even the complete delisting of the company’s shares (transforming the public corporation back

into a private company). Unlike venture capital firms, private equity firms often assume control

of the companies they invest in and usually manage far larger pools of capital given their focus

on later-stage or even public company investments. However, both venture capital and private

equity firms are often involved in IPOs of their companies given their investments in private

companies, and for private equity firms the large LBO investments that delist the shares of public

corporations.

As evidenced by the longstanding self-designated distinction between “LBO” and “growth

capital” firms, two logics have coexisted within the private equity industry. The chairman of a

leading growth capital private equity firm described, “the buy-out firms are single-mindedly

focused on cashflows and leveraging the balance sheet to generate returns . . . they view

companies as a stream of cashflows to support debt whereas the growth firms focus on the

company’s management and people, and work with them to figure out a long-term strategy for

revenue growth . . . we make our money from business growth and hence equity growth, not

from leverage.” I term these two coexisting logics “Income” and “Growth.” Income investors

view companies as streams of income and cash (perception) that can be used to borrow money

that is paid out to shareholders but remains the obligation of the company to repay (belief), and

accordingly focus on negotiating financial instruments without regard to other aspects of the

18

issuer’s business relations. The practice of forcing companies to borrow against their assets and

future income to pay shareholders is often referred to as “leveraged recapitalization.” Examples

of private equity firms espousing an Income logic include LBO firms such as Texas Pacific

Group and Kohlberg Kravis Roberts (for a description of the emergence of the LBO industry

from fringe actors within the broader socio-political context of the 1980’s merger wave, see

Stearns and Allan 1996).

In contrast, Growth investors view companies as a nexus of business partners and

relationships (perception) that can generate long-term business growth and hence equity returns

(belief), and accordingly focus on long-term strategic planning and exploiting business

partnerships. Examples of Growth investors include venture capital firms and such large private

equity investors like General Atlantic and Warburg Pincus. While the private equity industry

exhibits both institutional logics, the venture capital industry is dominated by the Growth logic

given the early-stage nature of its investments, when most companies lack stable near-term

income. Thus, rational adaptation could explain the emergence of these institutional logics since

narrowly viewing early stage companies without revenues or income as streams of cash is

farfetched.

Importantly, both Income and Growth investors are focused on maximizing time-weighted

returns. The private equity and venture capital industries are highly competitive, especially with

regard to raising capital from limited partners. These limited partners focus strictly on

performance as measured by internal rate of returns (IRRs, equivalent to the discount rate in

DPV calculations) net of fees and carried interest (generally 2 percent of funds under

management and 20 percent of investment gains generated). IRRs increase with increased

investment gains and decreased investment period.15 Hence, the difference in ontological

19

perceptions of companies is not a difference between short and long-term investment horizons.

As both groups of investors are focused on maximizing IRRs, any increase in investment holding

period requires an increase in investment gains to offset the decrease to IRRs. Critically, the

perception of companies as growth vehicles when combined with the acceptance of DPV

intrinsic values as rational could lead Growth investors to believe that improving the growth

prospects of their companies (future value) will increase their near-term valuations (DPV).

Growth investors focus on long-term strategy and business growth not because they are long-

term investors, but because they believe that is the best way to increase the present value of their

investments. Nothing about the differing logics necessitates or suggests that Growth investors

would hold onto investments longer than Income investors; instead, differing ontological

perceptions of companies drives differing methods of generating maximum investment gains in

the shortest time period possible (leveraged recapitalization generating near-term cash for

Income investors and business growth generating near-term increases in DPV intrinsic value for

Growth investors).16

Figure 2 details the compositional breakdown of issuers by shareholding, control, and logics.

Of the 813 operating company IPOs over the past ten years, institutional private investors

controlled 58 percent of all issuers (control defined by the shareholding group holding the largest

block of voting shares). Of these 470 issuers, private equity and venture capital firms controlled

281 (60 percent) and 189 (40 percent), respectively. Based on my operationalization (as

discussed in the “Data and Methods” section), over one-third of the 281 private equity controlled

issuers espoused a Growth institutional logic (95 versus 186 Income). Including venture capital

firms, Growth and Income logics accounted for 284 and 186, respectively, of the 813 IPOs.

20

[Figure 2]

INSTITUTIONAL COMPLEXITY HYPOTHESES

Income investors view companies as streams of cash and accordingly focus on cash

considerations when making investments. Just as they view companies as streams of cash, they

view IPO transactions as simply streams of cash. Given such a perception, their strategic

imperative in dealing with underwriters is to maximize the cash received from the IPO.

Therefore, we would expect firms controlled by Income investors to more successfully and

persistently refute the IPO discount relative to other logics.

H1 Issuers controlled by private equity firms espousing a Income institutional logic experience lower pricing above the range relative to other issuers.

Many private equity and venture capital firms, however, view companies as collaboration to

generate increasing sales and accordingly focus on business growth rather than short-term

income considerations. As a senior partner at a leading growth capital private equity firm

remarked on the difference between the two logics: “[for LBO investors] there is much more of a

focus on optimizing the profitability and cashflow of companies . . . there is a bias of making

decisions that optimize nearer-term profitability over longer-term growth . . . we focus on the

top-line growth and value over time of working with the management team and others, rather

than fixating on near-term cash and profit.” Again, this preference for long-term growth over

near-term income is not related to investment holding period, but instead centered on the belief

that long-term growth prospects improve near-term DPV valuations more than near-term income.

Just as they view their companies as growth vehicles, Growth investors view IPO transactions as

21

vehicles to generate long-term share-price growth that will also be reflected in increased near-

term prices. Consequently, Growth investors are more willing to work with underwriters,

acquiescing to the IPO discount presumably in the hopes of improving the prospects for near-

term share price appreciation by appeasing initial public market investors in the stock with strong

first-day gains.

H2 Issuers controlled by venture capital and private equity firms espousing a Growth institutional logic experience higher pricing above the range relative to other issuers.

I will elaborate in the “Discussion” section following the presentation of the findings how

econometric studies of the relation between first-day and longer-term returns, differences in

holding and lock-up periods, industry sector specialization, and other strategic considerations do

not account for these predictions.

DATA AND METHODS

I analyze Thomson Reuters’s Securities Data Company (SDC) database on the 813 operating

company IPOs over the past ten years (January 1, 2001 to April 30, 2010) to examine empirical

support for the institutional complexity hypotheses on IPO pricing. For the first-stage return

outcome, I conduct standard logistic regression of pricing above the range on institutional logic

and controls for alternative hypotheses. We can parameterize first-stage returns as a continuous

variable by calculating the percentage increase from the midpoint of the price range. I conduct

standard ordinary least squares (OLS) and two-limit Tobit regressions of these “offer-price

returns” to corroborate the logistic analysis of pricing above the range. I then conduct OLS

regression of first-day returns on pricing above the range, institutional logic, and controls for

22

alternative hypotheses. Additionally, I control for the fixed effects of offering year while also

controlling for industry sector variation in IPO return outcomes by clustering issuers within four-

digit Standard Industrial Classification (SIC) codes utilizing restricted maximum likelihood

estimation (REML).

The sample under study is the universe of IPOs over the past ten years with offer prices

greater than $5.00 and offering sizes greater than US$30 million, excluding American

Depositary Receipts of foreign issuers (ADRs), unit offers, closed-end funds, Real Estate

Investment Trusts (REITs), non-operating partnerships, banks, savings and loans (S&Ls), and

acquisition companies. Most economists study a similar sample of IPOs. Industry veterans in

investment banking and private equity believe that offering sizes below US$30 million represent

a distinctively separate population exhibiting different dynamics from most operating company

IPOs.

Response Variables

Pricing above the range is a dichotomous variable coded one for offer prices exceeding the

high-end of the price range and zero otherwise. The price range is almost universally quoted as

$2 per share around a midpoint share price. The median midpoint in the sample is $15 per share

with a corresponding high-end of $16 and a low-end of $14. Offer-price returns are defined as

the percentage point increase in share price from the midpoint of the price range to the final offer

price to institutional investors. First-day returns are defined as the percentage point increase in

share price from the offer to the first-day close.

Explanatory Variables

23

The explanatory variables are institutional logics: Income and Growth. Based on information

from prospectuses obtained from SEC Edgar online on shareholding, board membership, and

senior management backgrounds, I code issuer institutional logic for every IPO in the sample.

Issuers controlled by founders or senior managers (control defined by the shareholding group

owning the largest block of voting shares) are the reference group. Issuers controlled by venture

capital and private equity firms are coded as either Growth or Income. Given institutional

pluralism, we must look not only at private equity control when operationalizing the Income

logic, but also the tangible actions flowing from such an institutional logic: a history of LBOs,

leveraged recapitalizations, or high debt-to-capitalization levels immediately prior to going

public. High debt-to-capitalization ratios are particularly relevant since LBOs and leveraged

recapitalizations necessarily increase debt levels. Furthermore, Income investors often force their

companies to undertake new leveraged recapitalizations prior to IPOs if the company has already

paid down its previous debt. By borrowing money against the IPO, these private equity firms can

pay out the proceeds to themselves pre-IPO without impacting the share price performance as

debt ratios do not affect IPO pricing whereas dividends post-IPO do depress share prices (please

refer to “Findings” section for analysis of the effect of debt on IPO pricing).17

Investors who view companies as streams of cash prefer extracting the IPO proceeds through

the circuitous method of borrowing against the offering prior to the IPO, while those who view

companies as growth vehicles would prefer investing the IPO proceeds to grow the issuer’s

business.18 All venture capital controlled issuers are coded as Growth logic. I code private equity

controlled issuers as Income logic if they have debt-to-capitalization ratios greater than 59

percent. All other private equity controlled issuers are coded as Growth logic. I select 59 percent

as the threshold based on the minimum debt-to-capitalization level carried by issuers controlled

24

by Income logic private equity firms as identified through a history of leveraged recapitalizations

revealed in the prospectuses. The mean and median debt-to-capitalization levels are 0.89 and

0.44 for the sample of issuers under study, with a wide standard deviation of 7.23 (excluding

Income logic issuers 0.53, 0.26, and 2.46, respectively).19 Theoretically, elevated levels of debt

could not explain why Income logic issuers experience lower first and second-stage returns as

financial economics predicts the exact opposite outcome for high debt issuers.

Control Variables From Alternative Hypotheses

Private equity ownership (dichotomous variable indicating private equity investment in the

issuer) serves as a proxy for lower agency and monitoring costs. However, private equity

ownership could also proxy for information asymmetry, or for greater negotiating power vis-à-

vis underwriters (Baker 1990). All three theories make the same directional prediction for return

outcomes. I include additional measures of power (ties to underwriters and private equity fund

size) in order to distinguish between agency and resource dependence predictions. Ties to

underwriters are coded as zero for no identifiable ties to the underwriting syndicate based on the

prospectus and publicly available information, one for normal relations, and two when the

relationship is so close that it creates a conflict of interest requiring legal disclosure in the

prospectus. An example of the latter are situations where the underwriters are also investors in

the funds managed by the private equity firm, or private equity firms that are institutionally

affiliated with the underwriters. For substitution costs, I include a dichotomous variable

indicating if the issuer is especially exposed to litigation risk as identified in the prospectus by a

lack of revenues, on-going or recent major litigation, or previous criminal record of the owners

or management team.

25

For non-Bayesian investor models, I include the Shiller one-year confidence retail investor

survey (percentage of respondents who believe the Dow Jones Industrial Average will increase

over the next year). This survey is conducted monthly beginning in July 2001 and bi-annually

(October and April) previously; I utilize linear extrapolation for the five months of missing data

in 2001. Valuation, crash, and buy-on-dips confidence indices produce similar results, as do

institutional investor surveys (available upon request). I also include the monthly Baker-Wurgler

orthogonalized sentiment index (Baker and Wurgler 2006, 2007). This composite index of

sentiment is based on the common variation in six underlying proxies that have been Winsorized

(0.05 and 0.95 levels) and orthogonalized against four macroeconomic variables to remove

business cycle covariation. Principal component analysis of the six residual proxies and their

lagged counterparts results in a final composite index based on the first principal component.

As one of the six proxies in the Baker-Wurgler index is average IPO first-day returns, the

index could introduce endogeneity into both first and second-stage return models. I address this

concern by replacing the index and with the five remaining monthly constituent proxies for

sentiment: number of IPOs, the dividend premium, NYSE share turnover, closed-end fund

discount, and equity share in new issues. The dividend premium is the log difference of the

average market-to-book ratios of dividend payers and non-payers. NYSE share turnover is total

share volume of trades divided by average shares listed from the NYSE Fact Book. The closed-

end fund discount is the value-weighted average difference between the net asset value (NAV) of

listed closed-end funds and their market prices. Finally, equity share is the gross equity issuance

divided by the total gross issuance of equity and long-term debt for the month, using data from

the Federal Reserve Bulletin. Of course, we could also reconstruct the composite index using

principal component analysis of the five proxies, Winsorized and orthogonalized against

26

macroeconomic variables. I have done so and the inferences remain unchanged from those

presented in the “Findings” section (available upon request).

For non-SEU issuers, I code from the prospectuses secondary portion (secondary shares

divided by total offering shares) and management ownership (percentage of total shares

outstanding held by senior management and board members unaffiliated with institutional

shareholders prior to the offering). For information asymmetry, I measure underwriter status with

the average Carter-Manaster rank for the lead underwriters. I use Carter-Manaster ranks (the

standard economics reputation score for the investment banking industry) rather than the

Eigenvector centrality measure due to the availability of periodically updated rank lists of Carter-

Manaster scores throughout the period of study. Carter-Manaster ranks are calculated based on

the same tombstone advertising data as Eigenvector centrality, and attempts to measure the same

hierarchical placement of underwriters relative to each other.20 Carter-Manaster ranks range

from 0 to 9, with investment banks scoring 8 or higher generally considered to be high-status

lead underwriters and those scoring 6 or less low-status co-managers. Status signal theory also

predicts that underwriter status should lower the cost of underwriting (Podolny 1993, 2005).

High-status investment banks can syndicate offerings at lower cost, and these efficiency gains

can be traced to the effectiveness of expanded syndicates in marketing and distributing stock.21

I control for broader market price movements with same-day and one-month returns to the

Standard and Poor (S&P) 500 index, the log S&P 500 index, and the three-month return on the

Dow Jones industry sector index relevant for the issuer based on SIC code. Dow Jones industry

sector indices include: basic materials, consumer goods, consumer services, financials,

healthcare, industrials, energy, technology, telecommunications, and utilities. To measure market

volatility, I utilize the Chicago Board Options Exchange Volatility Index (VIX), which captures

27

the implied volatility of index futures reflecting forward-looking investor risk sentiment.22 Some

analysts believe VIX and sector-specific index returns could also proxy for sentiment.

I also code fundamental issuer characteristics from prospectuses, including offering size (log

millions), revenues (standardized), operating cashflow23 (standardized), positive earnings

(dichotomous), age since founding (log years), debt-to-capitalization ratio (total debt divided by

capitalization, inverse coded for negative), and SIC code. Finally, I include the four

macroeconomic variables orthogonal to the Baker-Wurgler index: employment growth, industrial

production growth, total consumption growth, and a dummy variable for National Bureau of

Economic Research (NBER) recession month. Employment growth is the percentage increase in

monthly total nonfarm payroll employment from the Bureau of Labor Statistics. Industrial

production growth is the percentage increase in the monthly industrial production index from the

Federal Reserve Statistical Release. Finally, total consumption growth is the percentage increase

in monthly consumer durables, non-durables, and services data deflated by CPI from the BEA

National Income Accounts.

FINDINGS

Regression analyses of IPO price outcomes over the past ten years strongly support the

institutional logic hypotheses. The following table presents Pearson correlations and descriptive

statistics for the 34 variables of interest.

[Insert Table 2]

28

I present seven logit models for pricing above the range: model 1 with only control variables,

model 2 with Income logic, model 3 with Growth logic, model 4 representing the full model with

both institutional logic variables, model 5 controlling for offering year fixed effects, model 6

additionally clustering issuers by four-digit SIC codes with REML estimation, and model 7

replacing the Baker-Wurgler index with five constituent proxies. As indicated by the logistic

regression of pricing above the range (see Table 3), the Income institutional logic significantly

reduces the log odds of pricing above the range (estimated coefficients of -0.75 to -1.29 across

all five models, significant at either p<0.05 or p<0.001), corroborating H1. This represents a

multiplicative odds ratio of 0.28 to 0.47, meaning Income logic issuers experience approximately

60 percent lower odds of pricing above the range relative to issuers not controlled by private

equity or venture capital firms. Furthermore, the Growth institutional logic significantly

increases the log odds of pricing above the range (estimated coefficients of 0.79 to 1.12 across all

five models, significant at either p<0.01 or p<0.001), corroborating H2. This represents a

multiplicative odds ratio of 2.20 to 3.06; Growth logic issuers experience at least twice the odds

of pricing above the range relative to issuers not controlled by private equity or venture capital

firms. As shown in models 6 and 7, clustering issuers by four-digit SIC codes in addition to

controlling for offering year fixed effects does not alter these findings.

[Insert Table 3]

I also conduct regression analyses of the continuous variable parameterization of pricing

above the range (offer-price returns; see Table 4). Again, the Income institutional logic

significantly reduces offer-price returns (estimated coefficients of -3.3 to -5.3 across all five

29

models, significant at p<0.05 or p<0.001), corroborating H1. Income logic issuers experience

offer-price returns of up to 5 percentage points lower than issuers not controlled by institutional

investors. Likewise, the Growth institutional logic significantly increases offer-price returns

(estimated coefficients of 2.7 to 4.3 across all five models, significant at p<0.05 or p<0.001),

corroborating H2. Growth logic issuers experience offer-price returns of up to 4 percentage

points higher than issuers not controlled by institutional investors.

[Insert Table 4]

Regression analysis of first-day returns confirms that the social good mechanism operates

between the two stages of IPO pricing, enabling institutional logics to influence first-day returns

through pricing above the range (see Table 5). I present five models for the second-stage return

outcome given the different pricing dynamics from the first stage: model 1 with only control

variables, model 2 with institutional logics, model 3 adding pricing above the range, model 4

controlling for offering year fixed effects, and model 5 additionally clustering issuers by four-

digit SIC codes with REML estimation. As expected, first-stage returns predict second-stage

returns, with pricing above the range increasing first-day returns by over 17 percentage points

controlling for all alternative hypotheses (p<0.001).

[Insert Table 5]

Robustness Analyses

30

Clustering issuers by SIC code is important as issuers from different industry sectors may

experience systematically different return outcomes. The fixed effect coefficient estimates may

change substantially due to covariance with issuer industry sector, such as industry specialization

in the case of private equity and venture capital investors. For example, perhaps the Growth logic

exerts its effect primarily because venture capital firms disproportionately invest in technology

companies, and technology companies as a group experience higher pricing above the range?

Furthermore, the effects of the institutional logics may themselves vary by industry sector, with

no fixed effect remaining after controlling for these random effects. For example, perhaps the

Income logic exerts its effect primarily because LBO firms primarily target sectors they can

successfully perform leveraged recapitalizations (e.g., utilities), but when they invest outside

these preferred sectors (e.g., technology), they are unable to avoid pricing above the range.

While we have clustered issuers by four-digit SIC codes (in models 6 and 7 of Tables 3 and 4),

we have not allowed the logic coefficients to vary by cluster. Also, more aggregated sector

clusters would simplify the analysis for potential bias due to industry specialization by

investment firms. I use the ten main industry sectors for the Dow Jones sector indices to cluster

issuers, conducting four additional pricing above the range hierarchical mixed effect logit models

(see Table 6). Model 1 clusters issuers by Dow Jones industry sector, model 2 further allows the

Income logic coefficient to vary by sector, model 3 allows Growth to vary, and model 4 allows

both institutional logic coefficients to vary. All models also control for offering year fixed effects.

The mixed effect logit models with random slopes for the institutional logic variables strongly

confirm hypotheses H1 and H2, with the fixed effect coefficient estimates of Income (-0.82 to -

0.96; p<0.05) and Growth (0.85 to 0.92; p<0.01 or p<0.05) retaining significance throughout.

31

Hence, the impact of institutional logics on pricing above the range is not due to sector-level

variation in issuers.

[Insert Table 6]

We can also utilize SEC filing rules to mitigate another source of potential bias: unforeseen

circumstances arising during the roadshow. Underwriters must re-file with the SEC if the offer

price is priced more than 20 percent outside the price range; issuers and underwriters seek to

avoid re-filing as it significantly delays the IPO process. As such, issuers pricing more than 20

percent outside the price range are doing so due to unforeseen circumstances. While not outliers

in the statistical sense, these extreme situations could nevertheless unduly influence our

coefficient estimates for the logic variables. I conduct two-limit Tobit regressions restricting the

offer-price return to +/-30 percent to test the robustness of our inferences to these situations and

the exogenous shocks they may represent (see Table 7). For the majority of price ranges in the

data, +/-30 percent offer-price returns approximates pricing more than 20 percent outside the

price range. Again, the analyses strongly confirm hypotheses H1 and H2. Of particular note the

proxy for prospect theory (secondary portion) becomes significant once we limit the response

variable.

[Insert Table 7]

In sum, the findings are decidedly mixed for the alternative hypotheses from behavioral

finance for all five sets of regressions, but in particular for our primary response variable of

32

interest: first-stage returns (pricing above the range, or its parameterization as offer-price returns).

Table 8 summarizes the key findings for the alternative hypotheses tested in the final models for

first-stage return outcomes (models 6 and 7 in Tables 3 and 4, and all models from Tables 6 and

7). As discussed earlier, pricing above the range isolates issuer-side factors, with return outcomes

purged of neoclassical and non-Bayesian investor explanatory factors. Thus, it is not surprising

that sentiment indicators do not influence first-stage return outcomes as predicted. Secondary

selling weakly supports non-SEU issuer hypotheses based on prospect theory. Power, agency,

litigation-risk, and information asymmetry hypotheses all find no support in the data. Generic

rational risk-aversion hypotheses for market conditions and issuer fundamentals are supported.

The findings strongly support the institutional logics hypotheses.

[Table 8]

DISCUSSION

Calculative rationality cannot explain these findings. Shareholders always gain from lower

first-day returns. Whether issuers borrow against the offering to pay their shareholders or invest

the offering to grow their companies does not change the strategic motivation to avoid pricing

their shares lower than necessary to first-stage investors. Obviously, Growth investors have as

much to gain from lower first and second-stage returns as do Income investors, as the extra

proceeds could be invested in growing the issuer’s business, generating stronger future organic

growth and (near-term) share price performance.

We could question whether strategic constraints account for the pricing differences between

Income and Growth logic investors. For instance, if Growth investors hold onto investments

33

longer post-IPO than Income investors, they may care less about short-term returns. However,

the opposite is actually true, with venture capital firms selling shares post-IPO faster than any

other institutional shareholding group (Field and Hanka 2001). Furthermore, Growth investors

actually do not gain from higher first-day returns with regard to longer-term share price growth.

Econometric studies show that first-day returns are negatively correlated with longer-term

returns (Ritter 1991; Krigman, Shaw and Womack 1999): acquiescing to the IPO discount is

detrimental both in the immediate and longer-term for issuers and their backers. Also, lock-up

expiration provisions cannot account for differences between the two sets of investors. The

regulations encouraging the industry practice of lock-up provisions for large pre-IPO

institutional investors apply equally to both Income and Growth logic investors, with lock-up

provisions becoming increasingly standardized over time (Bradley et al. 2001; Brav and

Gompers 2003; Cao, Field and Hanka 2004). 24 Hence, differences in selling shortly after the IPO

cannot explain the divergence in outcomes. Power and agency theories tested in the models share

a close affinity with strategic explanations for variation in IPO pricing, but also fail to explain

the dramatic differences between Income and Growth logic investors, whether ties to

underwriters or negotiating power due to the size of funds under management. We have also

controlled for offering year fixed effects, so differences in the timing of IPOs by year of offering

and all annualized proxies cannot account for the divergence. Finally, we have controlled for the

issuer’s industry classification, whether fine-grained at the four-digit SIC level or aggregated at

the Dow Jones industry sector level, ruling out strategic differences due to choice of industry

specialization by private equity and venture capital firms.

If case-to-case strategic constraints drive behavior rather than the perceived constraints

embodied in institutional logics, venture capital firms should behave differently when they

34

occasionally control companies with high debt levels. If strategic constraints such as debt trump

logics, venture capital controlled issuers with high debt levels should operate as Income issuers;

however, they clearly do not, but act as Growth issuers increasing first-stage return outcomes.

As shown in Table 9, venture capital firms do not differ in return outcomes based on differences

in debt levels. A two sample t-test confirms that there is no reason to reject the null hypothesis

of equal means between the two populations (t[df=186]=0.20, p<0.84), and a variance test

indicates no reason to reject the null hypothesis of equal variances (F[df=159,27] = 0.92, p<0.72).

While these two groups of venture capital firms face very different strategic constraints (debt

levels held by their portfolio company going public), they strategically pursue the same course of

action because their perceived constraints remain the same (their companies are not bundles of

cash streams but instead people organizations striving to generate business growth).

[Table 9]

The ontological perception of objects in the environment influences instrumentally rational

action. Calculative actors take into account the perceived responses and constraints in the

environment to develop a course of action. Here, investment firms attempt to generate

investment gains in the best way possible given their perceptions of what companies are. Both

Income and Growth logic investors are trying to maximize value from the IPO, but they act

differently based on their perception of how companies respond. Extracting the IPO proceeds

and angering initial investors in the company’s stock appear harmful to Growth investors, but do

not concern Income investors. As we have demonstrated, this cannot be due to differences in

35

holding period, sales restrictions, industry specialization, year of offering, negotiating power,

issuer debt levels, or broader market and macroeconomic conditions.

I am challenging both behavioral and rational price theories. Behavioral finance relies upon

individualistic biases, not sociocultural logics, and cannot explain first-stage return outcomes:

pricing above the range. Pricing above the range dwarves all other effect sizes in determining

first-day returns. The social influence of first-stage return outcomes on second-stage return

outcomes is itself a sociological rather than an atomized actor (whether rational or non-rational)

outcome. Market struggles are not only about calculative actors pursuing instrumentally rational

action oriented towards self-interest, or non-rational actors pursuing action oriented towards self-

interest skewed by irrational calculations. Instead, market struggles entail purposive actors

pursuing instrumentally rational action oriented towards self-interest but influenced by their

perception of how other actors and objects will react. Importantly, institutional logics do not

override strategic concerns, but instead coexist with calculative rationality. IPO returns are the

outcome of sociological (social good and institutional logics), behavioral (prospect theory), and

rational risk-aversion (market conditions and issuer fundamentals) processes.

Institutional logics are critical for the study of prices. Prices directly impact the distribution

of resources in a market economy, and are vital for an understanding of social phenomenon such

as stratification and inequality. IPO first-day returns ultimately represent an inequitable

allocation of rewards between financial market participants, highlighting how modern markets

can perpetuate inequitable outcomes. The relevance of price theory to economic and

organizational sociology is even more self-evident. Despite this, mainstream research in the

sociology of markets has heretofore not focused on the core market mechanism of price

determination (Swedberg 2005), with the literature instead focusing on how social actors create

36

and sustain markets. If markets facilitate price determination, we should be able to link our

insights on how social actors create and sustain markets to tangible price outcomes. I suggest that

institutional complexity is one of several ways we can link our insights on how actors participate

in markets to prices.

37

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ENDNOTES 1 Instrumentally rational action (zweckrational) is social action by purposive actors who, based upon expectations of the behavior of other actors and objects, calculate an expedient means to achieve rationally pursued end goals. Value-rational action (wertrational) is social action by purposive actors who strive to achieve end goals oriented towards an “ethical” standard valued for its own sake. Action may be instrumentally rational in the choice of means, but value-rational in the choice of end goals (Weber [1922] 1978:24-36). 2 I have altered the original notations in the economic models to present a consistent set of notations throughout this paper. P refers to price, superscript * refers to predictions, non-superscripted P are observed prices, subscript t refers to time period, subscript or superscript R and S refer to rational and sentiment investors, respectively; p or P() refers to probability; R refers to returns; Q refers to quantity; δ refers to sentiment; and ε to uncorrelated disturbance terms. 3 Subjective expected utility (SEU) describes how rational actors choose between decision alternatives; formally, actors maximize SEU value

where U(.) is the individual’s utility function, xi is the vectors of goods in the ith state of the world, and pi is the probability of the ith state of the world occurring. Savage ([1954] 1972) demonstrated that preferences should adhere to seven axioms for SEU maximization to occur. Bayes’ theorem relates the conditional probabilities of events A and B: É É

. Rational actors should update their probability of B occurring when receiving new information A based on Bayes’ theorem. 4 Formally: where Rf is the risk free rate, Km is the market rate of return, SMB (small minus big) and HML (high minus low) are the differences in returns between portfolios by market capitalization and book-to-market (value) ratios, and the relevant coefficients are firm-specific exposures to such risks (market, size, and value risk). 5 Discounted cashflow analysis derives stock valuations by estimating the stream of dividends accruing to shareholders over the entire future life-course of the company and discounting that stream of payments back to the present. Intrinsic value is equal to the discounted present value (DPV) of future dividends. Formally for discrete cash flows:

; or for

continuous cash flows: .

6 Formally, rational investors initially value a company based on the DPV of its future dividend stream: while sentiment investors value the same company: ; where δ represents investor sentiment and DPV1 is the correct discounted present value of future dividends at time=1. At time=2, new information is revealed and rational investors correctly update their price expectations: while sentiment investors incorrectly update their price expectations: where ξ* is the expected change to future dividends revealed and ½ < θ < 1 represents the underweighting of such information by sentiment investors. Demand for shares is: S where and ψ is the risk tolerance factor for that type of investor (rational or sentiment). At low sentiment levels [δ < z0 < 0 such that È], sentiment investors withdraw from the market and rational investors determine prices: È. At high sentiment levels [δ > z1 > 0

45

such that È], rational investors are driven from the market due to short-sale constraints and sentiment investors determine pricing: È. At intermediate sentiment levels [z0 < δ < z1 such that È È], rational and sentiment investors jointly determine price, which approximates a risk-tolerance weighted average of price expectations: È È È . 7 Formally, the first-day close price for the issuer’s shares fully incorporating sentiment investors’s expectations is: where is the rational predicted price and is the intensity of noise trader sentiment at t=1 and is a random variable uniformly distributed on so É [integrating Baker and Stein’s formulation in footnote 6 to Derrien’s model, if δ < z0 then sentiment investors withdraw from the market, , and ; if δ > z0, P1 is strictly increasing with increasing δ]. The underwriters set the offer price POPR by maximizing fees earned against the cost of price support activities post-IPO. Total fees are the gross spread times the offer price: , where f is the gross spread. Cost of price support is: É

; underwriters maximize earnings by pricing the IPO offer price at: (obtained by solving

). The numerator for first-day returns is:

; as f is generally fixed at 0.07, investor sentiment should predict first-day returns (if δ > z0, Pfdr is strictly increasing with increasing δ). 8 Formally, the predicted price equals the actual price plus an uncorrelated disturbance term: . As ε is by definition uncorrelated with P*, the variance of predicted prices must equal the sum of the variances for observed prices and the disturbance term:

; as variances cannot be lower than zero, the variance of predicted prices cannot be lower than the variance of observed prices:

. Conversely, if the variance of predicted prices is lower than that for observed prices, then must be predictable. Since EMH asserts that predicted prices are the DPV of future dividends, the volatility of dividends should be greater than or equal to that of observed prices. Shiller demonstrates that is not the case (equivalently by showing that log dividend-price ratios are more variable than present value models), and that the excess volatility of stock prices directly implies predictability of long-run returns (LeRoy and Porter 1981; Shiller 1981, 1984; Campbell and Shiller 1987, 1988). 9 Formally, prospect theory hypothesizes that people assign gambles the value , where if x ≥ 0 and if x < 0; ;

; and p*

is the probability that the gamble will yield outcomes at least as good as x. A wide range of studies supports λ ≈ 2 (coefficient of loss aversion, a measure of relative sensitivity to gains and losses), violating SEU preference axioms since the sensitivity to gains and losses should be uniform. 10 One could justifiably ask why an investment bank must underwrite IPOs and price them in a two-stage bookbuilding process. While non-Bayesian investor models generally assert that underwriters are soliciting private price information from rational investors, IPOs have been priced by auction methods or sold directly to public investors in the United States and other countries, with a specialized investment bank, WR Hambrecht + Co, championing Dutch auction

46

IPOs in the U.S. since the late 1990s. These non-bookbuilt IPOs exhibit far lower first-day returns (Purnanandam and Swaminathan 2004). However, such non-bookbuilt IPOs remain a rarity. An exposition on the institutionalization of current U.S. IPO practices following the aftermath of the Great Depression is beyond the scope of this paper. 11 For instance, examination of Derrien’s (2005) model reveals that and second-stage return outcomes are: . However, nothing can be said about first-stage return outcomes, as this formulation cannot address the relation of to the price range with regard to the underwriter’s profit-maximization motive. This again highlights the incompleteness of this model of first-day returns, as studies indicate that pricing above the range is far and away the most important factor in determining first-day returns. 12 The lead underwriter serves as a primary trader for the newly listed stock with the goal of stabilizing the price for the first several months following the IPO. This is the only legal exception to anti-manipulation trading laws allowed by the SEC. The stabilization agent can short sell the stock if necessary, and cover the shorts by calling on more shares from the issuer through a “greenshoe” mechanism. Named after the first company to grant an over-allotment option to its underwriters (Green Shoe Manufacturing Company, since renamed Stride Rite Corporation), the greenshoe is basically a free call option for an additional 15 percent of the original number of IPO shares at the final offer price to institutional investors granted to the lead underwriter by the issuer. 13 All quotes by industry practitioners are drawn from a qualitative study of IPO processes conducted between September 2009 and August 2010. The study interviewed senior managers of key IPO participants (underwriters, institutional investors, and private-equity backed issuers) selected from a stratified random sample of top-tier firms, achieving a combined response rate of 75 percent. See Feng (forthcoming) for details on the data and qualitative analysis procedures. 14 If we take Derrien’s (2005) model of underwriter profit-maximization, the lack of a costly price support É

means underwriters should increase POPR to the maximum price given δ sentiment levels, or . In such a formulation, δ would predict price level but not returns, as P1 = POPR and first-day returns should again equal zero, with underwriters effectively selling to sentiment investors when δ > z0. Furthermore, if underwriters do not seek private information to estimate the intrinsic value of the issuer, there would be no need to sell to rational investors, unless the underwriters are purposively trying to generate profits from spinning, quid pro quo, and stabilization activity from high first-day returns, or when sentiment levels are sufficiently low: δ < z0. 15 If we take the discrete DPV equation from footnote 5:

; IRR is i, the initial

funding provided by limited partners is DPV, the investment gain is FVt-DPV, and we can rewrite the equation as: (for simplicity, assuming gains gross of fees and carried interest for a one investment discrete fund), and , so for , IRRs decrease with increasing time to realization t. 16 As the partner of a leading Silicon Valley venture capital firm remarked: “We’re not angels. We’re here to make a buck like everyone else . . . its dog eat dog, every VC for himself. Believe me, if I can get out, I sell fast, but I want to sell dear, so I have to make sure what I’m selling has a future, otherwise what idiot is going to buy it from me?” A senior managing director at a leading LBO firm corroborates this viewpoint from the other side: “The media is so biased, all

47

those propaganda films about how evil PE [private equity, here referring to LBO] firms are. I mean, what the hell, you know who the real assholes are? It's the damn vulture capitalists, the VCs are the first ones looking to get out of Dodge when the shit hits the fan. We’re in there for the long haul . . . so we leverage these companies, make them leaner, more productive, so what? That’s better than the vultures selling before any of the heavy lifting.” 17 As the co-managing partner of a leading global LBO firm commented: “Hey, we’re not the growth guys, in the buy-out business we get what we can whenever we can, and we generate damn good returns without growth . . . if you can squeeze the lemon before the offering and have the IPO pay back the debt, hell, why not?” He had previously referred to dividends to shareholders from leveraged recapitalizations of companies as making lemonade, since you retain share ownership without dilution and simply have to “rehydrate” the dry lemons to squeeze again. Here, the IPO investors are rehydrating the lemons. 18 As a senior partner at a leading global growth capital private equity firm remarked: “we don’t get into the business of layering on debt prior to the offering, unlike the buy-out shops, our companies are growing rapidly. There are far better places to put the [IPO] money than in our own pockets . . . putting it [IPO proceeds] back to work by reinvesting in future growth more than makes up for passing on an immediate payday.” 19 Negative values of debt-to-capitalization where negative equity is greater than debt are inverse coded as one plus the absolute value of the debt ratio. Equity can become negative due to accumulated losses. I add one to preserve the correct relative ranking of leverage since a negative debt ratio represents a higher debt level than a debt ratio of one (i.e., debt equals capitalization because equity is zero). Unadjusted for negative values, the mean, median and standard deviation for debt in the full sample are: 0.79, 0.40, 7.23 (excluding Income logic: 0.41, 0.21, and 2.46, respectively). All analyses utilize inverse coded debt, but use of unadjusted debt does not alter the substantive results. Debt (unadjusted or inverse coded) is never a significant predictor of any response variable. 20 Investment banks conform to a strict status hierarchy best documented by the stringent rule-based name placement ceremony involved in advertising completed transactions. These advertisements are known as “tombstones” because they were originally published in the Wall Street Journal facing the obituaries section. The top-tier underwriters form a distinct group and are referred to as the “bulge-bracket” due to their type font and placement on the tombstones. 21 High-status underwriters achieve greater efficiency by their enhanced ability to assemble a syndicate of co-managers that help sell the stock. Podolny showed that underwriter status lowered debt-underwriting fees, with underwriters passing on cost savings to issuers (1993, 2005). For IPO underwriting, fees are fixed at 7 percent for the majority of offerings, so underwriter status cannot influence fees. Instead, status signal effects could only operate through the price outcome, with underwriters passing along efficiency savings in marketing and distributing the stock back to the issuer in the form of lower first-day returns. 22 VIX was originally introduced in a finance journal article as an index of the implied volatility on the S&P 100 (Whaley 1993). In 2003, a new VIX was developed for the S&P 500. The VIX is the square root of the risk neutral expectation of S&P 500 variance over the next 30 calendar days quoted on an annualized variance basis. 23 EBITDA: earnings before interest, depreciation, and amortization. 24 Rule 144 applies equally to all 5 percent blockholders and investors holding shares issued outside a registered offering (e.g., pre-IPO shares). Such shares cannot be sold until a one-year

48

holding period has elapsed (which may already be satisfied at the time of the IPO). There are numerous regulations and reporting requirements for the sale of such shares even after the company goes public. Hence all private equity and venture capital investments pre-IPO are subject to Rule 144. Underwriters do not distinguish between private equity and venture capital firms in trying to secure a lock-up provision for the IPO.

! ! 1! Figure 1: Average First-day Returns and Cost of Underpricing (2001-2009)

Note: Bars represent cost of underpricing to issuers due to foregone Initial Public Offering (IPO) proceeds in US dollar billions. Lines represent average IPO first-day returns (second-stage return outcome: percent increase in share price from offer price to first-day close).

Year

Loss

due

to U

nder

pric

ing

(US

$ bi

llion

s)

2002 2004 2006 2008

02

46

810

1214

05

1015

20A

vera

ge F

irst-d

ay R

etur

ns (p

erce

ntag

e)

! ! 2!Figure 2: Composition of IPO Issuers (2001-2010)

Note: 813 total IPO issuers: 586 (72%) backed by Private Equity (PE) or Venture Capital (VC) firms. Of these 586 issuers, 95 had both PE and VC backing, 299 PE backing only, and 192 VC backing only. 470 IPO issuers are controlled by either PE (281) or VC firms (189). Of these 470 PE or VC-controlled issuers, Income and Growth logics account for 186 and 284 of the issuers, respectively.

Shareholding

Control

Logic

PE (Income for logic)Both PE and VCVC (Growth for logic)Neither

Issuers (1 to 813)

0 200 400 600 800

! ! 3! Table 1: Theoretical Import of Initial Public Offerings IPO Stage

Setting and Process Price and Return Outcomes

Purged Factors for Returns

Isolated Factors for Returns

Second-stage

First day of trading immediately following the allocation of shares to institutional investors. Retail investors (sentiment investors of behavioral theory) can purchase shares from first-stage buyers (rational investors) or from the underwriter.

Price: First-day close Return: First-day returns

Changes to exposure to systematic market, size, and value risk; New information

Non-neoclassical factors

First-stage

Underwriters and issuers negotiate indicative price range to approach institutional investors (rational investors of behavioral theory) in the roadshow and bookbuilding process. Bookbuilding demand determines final offer price and whether the IPO is priced above the high end of the price range.

Price: Offer price Return: Pricing above the range

Non-Bayesian investors

Issuer and underwriter motivation and sociocultural factors

! ! 4!Table 3: Pricing Above the Range – Logistic Regression (10 year period 2001-2010) FIXED EFFECTS (1) (2) (3) (4) (5) a (6) a, b (7) a, b Institutional Logics Income -1.287*** -0.763* -0.754* -0.834* -0.855* (0.317) (0.373) (0.378) (0.406) (0.408) Growth 1.119*** 0.793** 0.850** 0.924** 0.880** (0.246) (0.288) (0.298) (0.322) (0.322) Power / Agency Private Equity (PE) Ownership -0.492 -0.0363 -0.489 -0.242 -0.259 -0.355 -0.355 (0.434) (0.453) (0.442) (0.461) (0.467) (0.500) (0.503) PE Tie to Underwriter 0.234 0.250 0.237 0.251 0.261 0.321 0.316 (0.320) (0.328) (0.326) (0.330) (0.334) (0.356) (0.358) PE Fund Size ($ billions) -0.0000329 -0.000299 -0.000440 -0.000503 -0.000476 -0.000353 -0.000323 (0.00124) (0.00129) (0.00131) (0.00132) (0.00132) (0.00141) (0.00144) Cost Substitution Litigation Risk -2.673* -2.649* -2.737** -2.703** -2.755** -2.663* -2.618* (1.041) (1.038) (1.040) (1.039) (1.046) (1.120) (1.136) Non-Bayesian Investor Shiller Investor Confidence Index 0.0275 0.0294 0.0352 0.0342 0.0250 0.0419 0.0816 (0.0268) (0.0273) (0.0272) (0.0274) (0.0473) (0.0507) (0.0534) Baker-Wurgler Sentiment Index -0.221 -0.212 -0.132 -0.153 -0.854 -0.972 (0.270) (0.271) (0.272) (0.272) (0.650) (0.704) Number of IPOs in Month -0.00172 (0.0181) Dividend Premium -0.0527 (0.0442) NYSE Monthly Turnover -1.422 (1.355) Closed-End Fund Discount 0.0230 (0.0748) Equity Share of Total Issuance -3.905 (3.659) Non-SEU Issuer Management Ownership 0.706* 0.486 1.281*** 0.984* 1.060** 1.112* 1.076* (0.349) (0.351) (0.382) (0.402) (0.408) (0.445) (0.445) Secondary Portion -0.389 -0.491 -0.324 -0.396 -0.396 -0.584 -0.606 (0.383) (0.386) (0.391) (0.392) (0.400) (0.444) (0.448) Information Asymmetry Average Underwriter Status 0.175 0.175 0.106 0.125 0.138 0.0930 0.108 (0.105) (0.104) (0.107) (0.107) (0.109) (0.119) (0.119) Market Conditions Dow Jones Industry Sector Index 0.0606*** 0.0616*** 0.0586*** 0.0596*** 0.0613*** 0.0620*** 0.0712*** (0.0144) (0.0147) (0.0146) (0.0147) (0.0159) (0.0173) (0.0177) Market Volatility (VIX) 0.0124 0.00175 0.00380 -0.000120 -0.0105 -0.00983 0.0453 (0.0296) (0.0301) (0.0302) (0.0303) (0.0441) (0.0469) (0.0579) Log S&P500 Index 2.023 2.032 1.788 1.867 5.762 6.540* 4.765 (1.153) (1.164) (1.171) (1.173) (3.101) (3.309) (3.250) S&P500 One-day Return 0.0180 0.0140 0.0201 0.0173 0.00364 0.0135 0.0160 (0.115) (0.116) (0.117) (0.118) (0.119) (0.128) (0.130) S&P500 One-month Return 0.0254 0.0144 0.0240 0.0179 -0.00535 0.00309 0.0122 (0.0328) (0.0334) (0.0332) (0.0335) (0.0386) (0.0412) (0.0382) Fundamentals Log Offering Size ($ millions) 0.561*** 0.627*** 0.796*** 0.766*** 0.774*** 0.861*** 0.858*** (0.136) (0.137) (0.148) (0.148) (0.153) (0.175) (0.178) Log Age (years) -0.313* -0.229 -0.230 -0.205 -0.220 -0.280 -0.266 (0.134) (0.139) (0.138) (0.140) (0.141) (0.156) (0.155) Debt to Cap (inverse for negative) -0.0438 -0.0103 -0.0165 -0.0101 -0.00737 -0.0100 -0.00985 (0.0889) (0.0329) (0.0415) (0.0329) (0.0288) (0.0357) (0.0381) Revenues (standardized) -0.340 -0.326 -0.359* -0.346* -0.357* -0.404* -0.412*

! ! 5! (0.185) (0.175) (0.179) (0.175) (0.178) (0.190) (0.192) Operating Cashflow (standardized) -0.100 -0.103 -0.110 -0.110 -0.110 -0.0750 -0.0652 (0.106) (0.0996) (0.102) (0.0997) (0.0986) (0.104) (0.105) Positive Earnings 0.499* 0.534* 0.561* 0.561* 0.584* 0.562* 0.548* (0.223) (0.226) (0.229) (0.229) (0.232) (0.255) (0.256) Macroeconomic Employment Growth 0.0256* 0.0252* 0.0266* 0.0260* 0.0282 0.0324* 0.0241 (0.0123) (0.0125) (0.0126) (0.0126) (0.0146) (0.0159) (0.0167) Recession Month 0.465 0.531 0.499 0.526 0.527 0.585 0.701 (0.482) (0.485) (0.489) (0.488) (0.580) (0.614) (0.698) Industrial Production Growth 0.00268 0.00238 0.00297 0.00269 0.00270 0.00303 0.00314 (0.00209) (0.00210) (0.00214) (0.00213) (0.00226) (0.00244) (0.00261) Real Consumption Growth -0.0000944 -0.000449 -0.000287 -0.000450 -0.000517 -0.000787 -0.000801 (0.00219) (0.00224) (0.00222) (0.00224) (0.00247) (0.00268) (0.00274) Intercept -22.45* -22.99* -22.78* -23.03* -61.56 -55.54* -48.31 (10.03) (10.15) (10.18) (10.21) (474.2) (24.26) (24.87) RANDOM EFFECTS SIC Log Standard Deviation -0.260 -0.252 Observations 808 808 808 808 808 808 808 Pseudo R2 0.133 0.155 0.160 0.165 0.173 0.181 0.184 Log-likelihood -353.3 -344.4 -342.6 -340.5 -336.9 -333.8 -332.5 AIC 756.6 740.8 737.2 734.9 745.9 741.6 747.1 BIC 873.9 862.8 859.2 861.7 914.9 915.3 939.6 Note: Unadjusted standard errors in parentheses. Missing data: list-wise deletion applied to 5 cases missing underwriter status or pricing above the range information (total sample 813). Multiple imputation of missing data does not alter results (implemented in R with Amelia and Zelig). McFadden pseudo-R2 calculated on log likelihood of -407.6 for null model. (PE = Private Equity). a Models 5, 6 and 7: Logit models controlling for offering year fixed effects. Offering years’ fixed effects not shown. b Models 6 and 7: Mixed effect logit models simultaneously clustering issuers by four-digit SIC codes (estimation fit using adaptive Gauss-Hermite approximation with 7 integration points) and controlling for offering year fixed effects with dummy variables. Offering years’ fixed effects not shown. * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests)

! ! 6! Table 4: Offer-Price Return – OLS Regressions (10 year period 2001-2010) FIXED EFFECTS (1) (2) (3) (4) (5) a (6) a, b (7) a, b Institutional Logics Income -5.263*** -3.457* -3.275* -3.548* -3.502* (1.370) (1.625) (1.631) (1.583) (1.578) Growth 4.270*** 2.739* 2.778* 2.812* 2.765* (1.123) (1.332) (1.350) (1.308) (1.299) Power / Agency Private Equity (PE) Ownership -2.869 -0.491 -2.705 -1.202 -1.527 -2.104 -2.060 (1.983) (2.061) (1.966) (2.085) (2.095) (2.019) (2.016) PE Tie to Underwriter 0.386 0.189 0.367 0.244 0.466 0.792 0.773 (1.502) (1.490) (1.489) (1.487) (1.494) (1.439) (1.437) PE Fund Size (bn) 0.00351 0.00354 0.00305 0.00323 0.00351 0.00399 0.00342 (0.00544) (0.00539) (0.00540) (0.00538) (0.00540) (0.00521) (0.00520) Cost Substitution Litigation Risk -6.374** -6.499** -6.695** -6.662** -6.519** -3.943 -3.647 (2.107) (2.089) (2.091) (2.086) (2.113) (2.134) (2.128) Non-Bayesian Investor Shiller Investor Confidence Index 0.273* 0.290* 0.307* 0.306* 0.0381 0.0916 0.0954 (0.130) (0.129) (0.129) (0.129) (0.211) (0.204) (0.217) Baker-Wurgler Sentiment Index 2.142 2.211 2.532* 2.437* 1.006 0.696 (1.179) (1.169) (1.174) (1.172) (2.821) (2.738) Number of IPOs in Month -0.104 (0.0746) Dividend Premium -0.175 (0.174) NYSE Monthly Turnover -0.446 (5.069) Closed-End Fund Discount -0.0696 (0.295) Equity Share of Total Issuance -19.57 (10.93) Non-SEU Issuer Management Ownership 4.370* 3.431 6.514*** 5.129** 5.292** 4.406* 4.323* (1.755) (1.757) (1.829) (1.938) (1.955) (1.902) (1.887) Secondary Portion -2.029 -2.530 -1.794 -2.207 -2.347 -2.864 -2.761 (1.899) (1.887) (1.884) (1.890) (1.899) (1.832) (1.827) Information Asymmetry Average Underwriter Status 0.344 0.380 0.0944 0.208 0.231 0.142 0.215 (0.447) (0.443) (0.448) (0.450) (0.452) (0.441) (0.441) Market Conditions Dow Jones Industry Sector Index 0.214** 0.217*** 0.198** 0.205** 0.213** 0.200** 0.224*** (0.0655) (0.0649) (0.0650) (0.0650) (0.0703) (0.0681) (0.0674) Market Volatility (VIX) 0.0868 0.0526 0.0554 0.0442 0.0197 0.0252 0.202 (0.131) (0.130) (0.130) (0.130) (0.188) (0.180) (0.216) Log S&P500 Index 3.351 3.439 2.513 2.871 17.68 19.26 29.96* (5.473) (5.426) (5.431) (5.421) (13.57) (13.02) (13.07) S&P500 One-day Return -0.828 -0.755 -0.776 -0.746 -0.908 -0.972* -0.993* (0.499) (0.495) (0.495) (0.494) (0.507) (0.490) (0.489) S&P500 One-month Return 0.273 0.221 0.277 0.242 0.186 0.219 0.141 (0.147) (0.146) (0.146) (0.146) (0.163) (0.157) (0.146) Fundamentals Log Offering Size (mm) 4.968*** 5.323*** 5.810*** 5.741*** 5.710*** 5.592*** 5.591*** (0.650) (0.651) (0.681) (0.681) (0.694) (0.684) (0.680) Log Age (years) -2.361*** -1.960** -1.992** -1.861** -1.783** -1.820** -1.846** (0.645) (0.648) (0.647) (0.648) (0.652) (0.636) (0.634) Debt to Cap (inverse for negative) -0.00420 0.0192 0.0118 0.0214 0.0172 0.00816 0.0192 (0.0632) (0.0629) (0.0628) (0.0628) (0.0629) (0.0613) (0.0613)

! ! 7!Revenues (standardized) -1.832** -1.940*** -1.977*** -1.996*** -1.986*** -1.980*** -1.929*** (0.577) (0.573) (0.574) (0.572) (0.578) (0.564) (0.562) Operating Cashflow (standardized) -0.744 -0.769 -0.807 -0.801 -0.787 -0.752 -0.772 (0.493) (0.489) (0.489) (0.488) (0.491) (0.477) (0.475) Positive Earnings 5.637*** 5.739*** 5.792*** 5.803*** 5.897*** 5.028*** 4.861*** (1.051) (1.042) (1.042) (1.040) (1.050) (1.044) (1.042) Macroeconomic Employment Growth 0.106 0.103 0.103 0.102 0.0877 0.0987 0.103 (0.0578) (0.0573) (0.0573) (0.0572) (0.0648) (0.0622) (0.0637) Recession Month 0.259 0.537 0.207 0.408 0.240 -0.228 1.359 (2.135) (2.117) (2.117) (2.114) (2.545) (2.452) (2.555) Industrial Production Growth 0.00294 0.00295 0.00494 0.00423 0.00691 0.00871 0.0117 (0.00927) (0.00919) (0.00921) (0.00919) (0.00982) (0.00950) (0.00971) Real Consumption Growth -0.00300 -0.00414 -0.00423 -0.00454 -0.00531 -0.00871 -0.00800 (0.00958) (0.00950) (0.00950) (0.00948) (0.00993) (0.00961) (0.00961) Intercept -74.72 -78.91 -76.48 -78.60 -157.0 -169.7 -247.6* (47.51) (47.11) (47.11) (47.02) (101.6) (97.49) (100.4) RANDOM EFFECTS SIC Log Standard Deviation 1.186*** 1.187*** Residual Log Standard Deviation 2.481*** 2.476*** Observations 803 803 803 803 803 803 803 R2 0.196 0.211 0.210 0.215 0.225 Adjusted R2 0.171 0.185 0.185 0.189 0.189 Log-likelihood -3176.4 -3168.8 -3169.0 -3166.6 -3161.6 -3154.4 -3150.5 AIC 6402.8 6389.6 6390.0 6387.3 6395.1 6384.8 6385.0 BIC 6520.0 6511.5 6511.9 6513.9 6563.9 6562.9 6581.9 Note: Unadjusted standard errors in parentheses. Missing data: list-wise deletion applied to 10 cases missing underwriter status or offer price information (total sample 813). Multiple imputation of missing data does not alter results (implemented in R with Amelia and Zelig). (PE = Private Equity). a Models 5, 6 and 7: Fixed effect models controlling for offering year. Offering years’ fixed effects not shown. b Models 6 and 7: Mixed effect models simultaneously clustering issuers by four-digit SIC codes (estimation fit using REML) and controlling for offering year fixed effect with dummy variables. Offering years’ fixed effects not shown. * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests)

! ! 8!Table 5: First-Day Return – OLS Regressions (10 year period 2001-2010) FIXED EFFECTS (1) (2) (3) (4) a (5) a, b Priced Above the Range 17.86*** 17.87*** 17.17*** (1.570) (1.569) (1.516) Income -7.506** -5.594** -5.964** -5.801** (2.298) (2.133) (2.130) (2.057) Growth 4.561* 2.489 2.041 1.612 (1.879) (1.748) (1.760) (1.693) Private Equity (PE) Ownership -5.053 -1.521 -1.365 -0.809 -1.531 (2.829) (2.947) (2.726) (2.730) (2.611) PE Tie to Underwriter 2.289 1.960 1.629 1.318 1.495 (2.131) (2.089) (1.933) (1.939) (1.852) PE Fund Size (bn) 0.00339 0.00276 0.00344 0.00267 0.00355 (0.00774) (0.00758) (0.00702) (0.00701) (0.00673) Litigation Risk -13.01*** -13.88*** -10.22*** -10.13*** -8.037** (3.173) (3.111) (2.896) (2.905) (2.875) Shiller Investor Confidence Index 0.230 0.346 0.307 0.258 0.386 (0.227) (0.223) (0.206) (0.291) (0.281) Number of IPOs in Month -0.0738 -0.0888 -0.0805 -0.212* -0.232* (0.0995) (0.0975) (0.0902) (0.101) (0.0969) Dividend Premium -0.227 -0.188 -0.198 -0.250 -0.275 (0.179) (0.176) (0.163) (0.235) (0.227) NYSE Monthly Turnover -3.118 -4.210 -2.480 2.419 2.026 (5.236) (5.144) (4.762) (6.875) (6.538) Closed-End Fund Discount 0.0202 0.185 0.180 0.178 0.0848 (0.354) (0.348) (0.322) (0.401) (0.383) Equity Share of Total Issuance -13.72 -15.67 -8.927 -15.40 -12.84 (14.78) (14.50) (13.43) (14.69) (14.14) Management Ownership 2.386 3.367 0.847 0.850 0.272 (2.497) (2.734) (2.539) (2.545) (2.461) Secondary Portion -3.198 -3.734 -2.409 -2.396 -3.560 (2.698) (2.656) (2.460) (2.466) (2.362) Average Underwriter Status 1.980** 1.771** 1.428* 1.372* 1.070 (0.646) (0.643) (0.596) (0.597) (0.581) Dow Jones Industry Sector Index 0.261** 0.243* 0.0884 0.111 0.110 (0.0973) (0.0956) (0.0895) (0.0920) (0.0883) Market Volatility (VIX) 0.636** 0.539* 0.477* 0.767** 0.846** (0.221) (0.217) (0.201) (0.292) (0.279) Log S&P500 Index 17.97 22.64* 17.49 57.56** 63.64*** (10.66) (10.47) (9.697) (17.71) (16.96) S&P500 One-day Return -0.380 -0.246 -0.253 -0.250 -0.216 (0.693) (0.679) (0.628) (0.635) (0.610) S&P500 One-month Return 0.653** 0.567** 0.537** 0.469* 0.491** (0.208) (0.204) (0.189) (0.195) (0.187) Log Offering Size (mm) 1.821* 3.225*** 1.133 1.303 1.632 (0.927) (0.962) (0.909) (0.917) (0.901) Log Age (years) -1.651 -0.662 -0.146 -0.101 -0.227 (0.920) (0.916) (0.849) (0.851) (0.825) Debt to Cap (inverse for negative) -0.0468 0.00183 0.0107 0.00925 0.00549 (0.0900) (0.0885) (0.0819) (0.0818) (0.0794) Revenues (standardized) -2.627** -2.936*** -2.068** -1.979** -1.989** (0.821) (0.806) (0.749) (0.753) (0.732) Operating Cashflow (standardized) 0.156 0.0739 0.350 0.196 0.182 (0.700) (0.686) (0.635) (0.637) (0.615) Positive Earnings 3.196* 3.323* 2.005 1.774 1.756 (1.517) (1.485) (1.379) (1.382) (1.362) Employment Growth 0.127 0.123 0.0513 0.0486 0.0655 (0.0856) (0.0838) (0.0778) (0.0868) (0.0827) Recession Month 3.588 4.262 3.290 8.812* 8.158*

! ! 9! (3.149) (3.085) (2.856) (3.433) (3.291) Industrial Production Growth 0.0273* 0.0294* 0.0217 0.0213 0.0226 (0.0137) (0.0134) (0.0124) (0.0131) (0.0126) Real Consumption Growth 0.00987 0.00654 0.00682 0.00380 0.00343 (0.0138) (0.0136) (0.0126) (0.0129) (0.0124) Intercept -164.3* -213.7** -164.9* -459.6*** -513.9*** (83.03) (81.76) (75.77) (135.9) (130.0) RANDOM EFFECTS SIC Log Standard Deviation 1.527*** Residual Log Standard Deviation 2.729*** Observations 795 795 795 795 795 R2 0.121 0.159 0.281 0.295 Adjusted R2 0.088 0.126 0.252 0.258 Log-likelihood -3420.9 -3403.0 -3340.7 -3332.8 -3323.8 AIC 6899.8 6868.0 6745.4 6747.5 6733.7 BIC 7035.4 7013.0 6895.1 6939.4 6934.8 Note: Unadjusted standard errors in parentheses. Missing data: list-wise deletion applied to 18 cases missing underwriter status, offer price, or first-day close price information (total sample 813). Multiple imputation of missing data does not alter results (implemented in R with Amelia and Zelig). (PE = Private Equity). a Models 4 and 5: Fixed effect models controlling for offering year. Offering years’ fixed effects not shown. b Model 5: Mixed effect model simultaneously clustering issuers by four-digit SIC codes (estimation fit using REML) and controlling for offering year fixed effect with dummy variables. Offering years’ fixed effects not shown. * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests)

! ! 10!Table 6: Pricing Above the Range – Alternative Sector Clustering (10 year period 2001-2010)

FIXED EFFECTS (1) (2) (3) (4) Institutional Logics Income -0.818* -0.921* -0.824* -0.962* (0.385) (0.438) (0.385) (0.483) Growth 0.855** 0.852** 0.863** 0.915* (0.303) (0.304) (0.307) (0.356) Power / Agency Private Equity (PE) Ownership -0.312 -0.305 -0.308 -0.358 (0.480) (0.483) (0.480) (0.485) PE Tie to Underwriter 0.295 0.282 0.292 0.286 (0.343) (0.346) (0.343) (0.349) PE Fund Size ($ billions) -0.000 -0.000 -0.000 -0.000 (0.001) (0.001) (0.001) (0.001) Cost Substitution Litigation Risk -2.421* -2.420* -2.406* -2.521* (1.070) (1.073) (1.070) (1.080) Non-Bayesian Investor Shiller Investor Confidence Index 0.070 0.076 0.071 0.073 (0.051) (0.051) (0.051) (0.051) Number of IPOs in Month -0.002 -0.002 -0.002 -0.001 (0.017) (0.017) (0.017) (0.017) Dividend Premium -0.045 -0.044 -0.045 -0.045 (0.041) (0.042) (0.042) (0.042) NYSE Monthly Turnover -1.297 -1.432 -1.302 -1.433 (1.302) (1.312) (1.304) (1.321) Closed-End Fund Discount 0.013 0.016 0.015 0.011 (0.072) (0.072) (0.072) (0.072) Equity Share of Total Issuance -4.539 -4.611 -4.477 -4.529 (3.629) (3.644) (3.627) (3.649) Non-SEU Issuer Management Ownership 1.032* 1.047* 1.021* 1.093** (0.416) (0.419) (0.416) (0.422) Secondary Portion -0.525 -0.535 -0.526 -0.545 (0.413) (0.416) (0.413) (0.418) Information Asymmetry Average Underwriter Status 0.122 0.121 0.122 0.126 (0.112) (0.112) (0.112) (0.113) Market Conditions Dow Jones Industry Sector Index 0.077*** 0.078*** 0.076*** 0.080*** (0.017) (0.018) (0.017) (0.018) Market Volatility (VIX) 0.058 0.064 0.058 0.063 (0.055) (0.055) (0.055) (0.055) Log S&P500 Index 5.000 5.159 4.987 5.078 (3.135) (3.152) (3.135) (3.167) S&P500 One-day Return 0.009 0.010 0.012 0.024 (0.121) (0.121) (0.121) (0.122) S&P500 One-month Return 0.000 0.001 0.001 0.002 (0.036) (0.036) (0.036) (0.036) Fundamentals Log Offering Size ($ millions) 0.798*** 0.804*** 0.793*** 0.831*** (0.162) (0.163) (0.162) (0.164) Log Age (years) -0.231 -0.229 -0.231 -0.231 (0.143) (0.145) (0.143) (0.145) Debt to Cap (inverse for negative) -0.015 -0.015 -0.016 -0.018 (0.045) (0.046) (0.045) (0.046) Revenues (standardized) -0.402* -0.416* -0.401* -0.434* (0.179) (0.180) (0.179) (0.183)

! ! 11!Operating Cashflow (standardized) -0.092 -0.091 -0.091 -0.099 (0.099) (0.099) (0.099) (0.099) Positive Earnings 0.513* 0.529* 0.503* 0.503* (0.245) (0.248) (0.246) (0.247) Macroeconomic Employment Growth 0.023 0.024 0.023 0.024 (0.016) (0.016) (0.016) (0.016) Recession Month 0.707 0.707 0.682 0.752 (0.681) (0.685) (0.680) (0.692) Industrial Production Growth 0.003 0.003 0.003 0.003 (0.003) (0.003) (0.003) (0.003) Real Consumption Growth -0.001 -0.002 -0.001 -0.002 (0.003) (0.003) (0.003) (0.003) Intercept -48.873* -50.547* -48.826* -49.911* (23.857) (23.988) (23.859) (24.099) RANDOM EFFECTS Sector Standard Deviation 0.535 0.618 0.507 0.641 Income Standard Deviation 0.583 0.832 Growth Standard Deviation 0.104 0.499 Observations 808 808 808 808 Pseudo R2 0.187 0.202 0.201 0.218 Log-likelihood -331.573 -325.455 -325.796 -318.751 AIC 745.146 736.909 737.591 729.503 BIC 937.623 938.776 939.458 945.452 Note: Mixed effect logit models controlling for offering year fixed effect and clustering issuers by ten Dow Jones Industry Sectors: Basic Materials, Consumer Goods, Consumer Services, Industrials, Oil and Gas, Financials, Healthcare, Technology, Telecommunications, and Utilities. Estimation fit using adaptive Gauss-Hermite approximation with 7 integration points; covariance structure for random effect coefficients are not assumed to be independent. Offering years’ fixed effects not shown. Missing data: list-wise deletion applied to [5] cases missing underwriter status or pricing above the range information (total sample [813]). Multiple imputation of missing data does not alter results (implemented in R with Amelia and Zelig). McFadden pseudo-R2 calculated on log likelihood of -407.6 for null model. (PE = Private Equity). * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests)

! ! 12!Table 7: Offer-Price Return –Tobit Models (10 year period 2001-2010)

(1) a (2) b Institutional Logics Income -3.758** -3.521* (1.453) (1.453) Growth 2.399* 2.475* (1.196) (1.197) Power / Agency Private Equity (PE) Ownership -2.477 -1.949 (1.855) (1.851) PE Tie to Underwriter 1.162 0.854 (1.320) (1.321) PE Fund Size (bn) 0.00288 0.00161 (0.00478) (0.00478) Cost Substitution Litigation Risk -2.651 -3.388 (2.021) (1.929) Non-Bayesian Investor Shiller Investor Confidence Index 0.213 0.221 (0.200) (0.199) Number of IPOs in Month -0.0792 -0.0701 (0.0687) (0.0688) Dividend Premium -0.160 -0.218 (0.160) (0.159) NYSE Monthly Turnover -1.227 -0.843 (4.662) (4.683) Closed-End Fund Discount -0.0477 -0.119 (0.272) (0.272) Equity Share of Total Issuance -19.94* -20.10* (10.05) (9.979) Non-SEU Issuer Management Ownership 2.480 2.840 (1.742) (1.727) Secondary Portion -4.067* -3.895* (1.684) (1.680) Information Asymmetry Average Underwriter Status 0.249 0.182 (0.406) (0.405) Market Conditions Dow Jones Industry Sector Index 0.233*** 0.259*** (0.0619) (0.0634) Market Volatility (VIX) 0.172 0.251 (0.199) (0.199) Log S&P500 Index 25.11* 26.48* (12.04) (12.12) S&P500 One-day Return -0.807 -0.653 (0.450) (0.448) S&P500 One-month Return 0.112 0.0772 (0.135) (0.134) Fundamentals Log Offering Size (mm) 4.782*** 4.794*** (0.627) (0.636) Log Age (years) -1.681** -1.624** (0.584) (0.583) Debt to Cap (inverse for negative) 0.0196 0.0410 (0.0564) (0.0555) Revenues (standardized) -1.726*** -1.838*** (0.517) (0.512) Operating Cashflow (standardized) -0.665 -0.656

! ! 13! (0.436) (0.433) Positive Earnings 4.714*** 4.857*** (0.976) (0.977) Employment Growth 0.0885 0.0816 (0.0586) (0.0589) Macroeconomic Recession Month 3.282 3.136 (2.354) (2.356) Industrial Production Growth 0.0153 0.0138 (0.00898) (0.00893) Real Consumption Growth -0.00783 -0.00539 (0.00888) (0.00876) Intercept -221.1* -233.7* (92.37) (92.91) RANDOM EFFECTS Sector Standard Deviation 3.273*** 2.517** Residual Standard Deviation 10.88*** 11.13*** Observations 803 803 Wald χ2 202.0*** 197.7*** Log-likelihood -3013.9 -3013.3 AIC 6111.8 6110.6 BIC 6308.7 6307.5 Note: Two-limit Tobit models with upper and lower bounds of +/-30 percent controlling for offering year fixed effect. Issuers must refile with the SEC if they price the offering more than 20 percent outside the price range; with the bounds of the price range primarily between $10 to $20, this approximates an offer-price return of +/-30 percent. Offering years’ fixed effects not shown. Missing data: list-wise deletion applied to 10 cases missing underwriter status or pricing above the range information (total sample 813). (PE = Private Equity). a Model 1: Two-limit Tobit model controlling for offering year fixed effects and clustering issuers by four-digit SIC codes. b Model 2: Two-limit Tobit model controlling for offering year fixed effects and clustering issuers by ten Dow Jones Industry Sectors: Basic Materials, Consumer Goods, Consumer Services, Industrials, Oil and Gas, Financials, Healthcare, Technology, Telecommunications, and Utilities. * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests)

! ! 14!Table 8: Predictions of Alternative Hypotheses for First-Stage Returns

Theory Coefficient Prediction Coefficient Estimates Institutional Logics Income < 0

Growth > 0

Income < 0 Growth > 0

Power and Agency PE Indicatorsa < 0

PE Indicatorsa = 0

Substitution Costs Litigaton Risk > 0

Litigation Risk ≤ 0

Non-Bayesian Investorb Baker-Wurgler Index > 0 Shiller Index > 0 Sentiment Proxiesc > 0

Baker-Wurgler Index = 0 Shiller Index = 0 Sentiment Proxiesc ≤ 0

Non-SEU Issuer Management Ownership < 0 Secondary Portion < 0

Management Ownership ≥ 0 Secondary Portion ≤ 0

Information Asymmetry Underwriter Status < 0

Underwriter Status = 0

Market Conditions Dow Jones Sector Returns > 0 Market Volatility > 0 S&P 500 Index > 0 S&P 500 Returns > 0

Dow Jones Sector Returns > 0 Market Volatility = 0 S&P 500 Index ≥ 0 S&P 500 Returns ≤ 0

Fundamentals Log Offering Size < 0 Log Age < 0 Debt to Cap > 0 Revenues < 0 Operating Cashflow < 0 Positive Earnings < 0

Log Offering Size > 0 Log Age ≤ 0 Debt to Cap = 0 Revenues < 0 Operating Cashflow = 0 Positive Earnings > 0

Note: Coefficient estimates refer to estimates across final models for first-stage return outcomes controlling for offering year fixed effects and issuer industry sector random effects (models 6 and 7 in Tables 3 and 4, and all models from Tables 6 and 7). If coefficient estimate is never significant in any final model, then coefficient is regarded as equal to zero. Inequality signs refer to coefficient estimates that are statistically significantly in all final models. Finally, ≥ and ≤ refer to coefficient estimates that are consistently greater than or less than zero, respectively, but whose statistical significance is sometimes above the p<0.05 level. Of the statistically significant coefficient estimates, Income, Growth, Litigation Risk, Log Offering Size, Log Age, Revenues, and Positive Earnings are economically significant (greater than 50 percent change in return outcome at mean levels). Corroborated coefficient predictions are bolded. a Private Equity (PE) indicators are: PE ownership (dichotomous), PE ties to underwriters, PE fund size (US$ billions). b As discussed in the “Underwriting Returns” section, non-Bayesian investor factors should have no explanatory power for pricing above the range outcomes based on an inspection of the formal economic models. The coefficient predictions presented are for the predicted effect of sentiment on the second-stage return outcome (first-day returns), in case analysts wish to test whether such predictions hold for the first-stage return outcome. Corroborating our understanding of the non-Bayesian investor models, sentiment has no impact on first-stage return outcomes based on the coefficient estimates from the final models. c Other proxies for investor sentiment are: number of IPOs, dividend premium, NYSE turnover, closed-end fund discount, and equity share. Dividend premium and closed-end fund discount should vary inversely with investor sentiment. Equity share is predictive of offer-price returns in two-limit Tobit final models (but in the “wrong” direction).

! ! 15! Table 9: Venture Capital-controlled Issuer Debt Comparison

N Offer-price return 95% Confidence Interval

VC-controlled issuer with debt ≤0.59 160 mean -2.79 (s.d. 16.04)

[-5.29, -0.28]

VC-controlled issuer with with debt >0.59 28 mean -3.45 (s.d. 16.25)

[-9.94, 3.04]

t-Test Difference in Means 188 t =0.20 (d.f. 186); p < 0.84

Note: Debt refers to debt-to-capitalization ratio, inverse coded for negative values. One VC-controlled issuer missing offer price information. (VC = Venture Capital).