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 firststage 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
* 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.
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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
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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,
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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’
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theorem (Odean 1998; Kaustia and Knüpfer 2008). When sentiment levels are high, these nonBayesian 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
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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 threemonth 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 nonrational issuer models within behavioral finance. These models generally relax the assumption of
rational SEU preferences, hypothesizing that issuers either accept or seek underpricing. For
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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 nonSEU 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 nonSEU 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 nonSEU 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,
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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 secondstage 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 nonBayesian 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
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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-
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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 earlystage companies not listed on any exchange and private equity firms invest in companies at all
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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
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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
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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 longterm 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 nearterm 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 nearterm 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 fourdigit 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-àvis 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 closedend 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 CarterManaster 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 fourdigit 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 selfinterest 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
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, nonsuperscripted 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
. Rational actors
relates the conditional probabilities of events A and B: É  É

should update their probability of B occurring when receiving new information A based on
Bayes’ theorem.
4 Formally:                        where R is the risk
f











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

; or for
(DPV) of future dividends. Formally for discrete cash flows:   


1

   .

continuous cash flows:  
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


 
 È ], sentiment investors withdraw from the market
levels [δ < z0 < 0 such that 

and rational investors determine prices:   
 È  . At high sentiment levels [δ > z1 > 0
6
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


      ). The numerator for first-day returns is:
 
by solving



    
             ; 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

      . In such
increase POPR to the maximum price given δ sentiment levels, or 
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 rulebased 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!
0
0
2
5
10
15
Average First-day Returns (percentage)
Loss due to Underpricing (US$ billions)
4
6
8
10
12
20
14
Figure 1: Average First-day Returns and Cost of Underpricing (2001-2009)
2002
2004
2006
2008
Year
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).
!
!
Figure 2: Composition of IPO Issuers (2001-2010)
2!
Shareholding
Control
Logic
PE (Income for logic)
Both PE and VC
VC (Growth for logic)
Neither
0
200
400
600
800
Issuers (1 to 813)
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.
!
!
3!
Table 1: Theoretical Import of Initial Public Offerings
IPO
Stage
Secondstage
Firststage
Setting and Process
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.
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 and Return
Outcomes
Price: First-day
close
Return: First-day
returns
Price: Offer price
Return: Pricing
above the range
Purged Factors
for Returns
Changes to
exposure to
systematic market,
size, and value
risk; New
information
Isolated Factors
for Returns
Non-neoclassical
factors
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
Institutional Logics
Income
(1)
(2)
-1.287***
(0.317)
Growth
Power / Agency
Private Equity (PE) Ownership
PE Tie to Underwriter
PE Fund Size ($ billions)
Cost Substitution
Litigation Risk
Non-Bayesian Investor
Shiller Investor Confidence Index
Baker-Wurgler Sentiment Index
(3)
(4)
(5) a
(6) a, b
(7) a, b
1.119***
(0.246)
-0.763*
(0.373)
0.793**
(0.288)
-0.754*
(0.378)
0.850**
(0.298)
-0.834*
(0.406)
0.924**
(0.322)
-0.855*
(0.408)
0.880**
(0.322)
-0.492
(0.434)
0.234
(0.320)
-0.0000329
(0.00124)
-0.0363
(0.453)
0.250
(0.328)
-0.000299
(0.00129)
-0.489
(0.442)
0.237
(0.326)
-0.000440
(0.00131)
-0.242
(0.461)
0.251
(0.330)
-0.000503
(0.00132)
-0.259
(0.467)
0.261
(0.334)
-0.000476
(0.00132)
-0.355
(0.500)
0.321
(0.356)
-0.000353
(0.00141)
-0.355
(0.503)
0.316
(0.358)
-0.000323
(0.00144)
-2.673*
(1.041)
-2.649*
(1.038)
-2.737**
(1.040)
-2.703**
(1.039)
-2.755**
(1.046)
-2.663*
(1.120)
-2.618*
(1.136)
0.0275
(0.0268)
-0.221
(0.270)
0.0294
(0.0273)
-0.212
(0.271)
0.0352
(0.0272)
-0.132
(0.272)
0.0342
(0.0274)
-0.153
(0.272)
0.0250
(0.0473)
-0.854
(0.650)
0.0419
(0.0507)
-0.972
(0.704)
0.0816
(0.0534)
Number of IPOs in Month
-0.00172
(0.0181)
-0.0527
(0.0442)
-1.422
(1.355)
0.0230
(0.0748)
-3.905
(3.659)
Dividend Premium
NYSE Monthly Turnover
Closed-End Fund Discount
Equity Share of Total Issuance
Non-SEU Issuer
Management Ownership
Secondary Portion
Information Asymmetry
Average Underwriter Status
Market Conditions
Dow Jones Industry Sector Index
Market Volatility (VIX)
Log S&P500 Index
S&P500 One-day Return
S&P500 One-month Return
Fundamentals
Log Offering Size ($ millions)
Log Age (years)
Debt to Cap (inverse for negative)
Revenues (standardized)
0.706*
(0.349)
-0.389
(0.383)
0.486
(0.351)
-0.491
(0.386)
1.281***
(0.382)
-0.324
(0.391)
0.984*
(0.402)
-0.396
(0.392)
1.060**
(0.408)
-0.396
(0.400)
1.112*
(0.445)
-0.584
(0.444)
1.076*
(0.445)
-0.606
(0.448)
0.175
(0.105)
0.175
(0.104)
0.106
(0.107)
0.125
(0.107)
0.138
(0.109)
0.0930
(0.119)
0.108
(0.119)
0.0606***
(0.0144)
0.0124
(0.0296)
2.023
(1.153)
0.0180
(0.115)
0.0254
(0.0328)
0.0616***
(0.0147)
0.00175
(0.0301)
2.032
(1.164)
0.0140
(0.116)
0.0144
(0.0334)
0.0586***
(0.0146)
0.00380
(0.0302)
1.788
(1.171)
0.0201
(0.117)
0.0240
(0.0332)
0.0596***
(0.0147)
-0.000120
(0.0303)
1.867
(1.173)
0.0173
(0.118)
0.0179
(0.0335)
0.0613***
(0.0159)
-0.0105
(0.0441)
5.762
(3.101)
0.00364
(0.119)
-0.00535
(0.0386)
0.0620***
(0.0173)
-0.00983
(0.0469)
6.540*
(3.309)
0.0135
(0.128)
0.00309
(0.0412)
0.0712***
(0.0177)
0.0453
(0.0579)
4.765
(3.250)
0.0160
(0.130)
0.0122
(0.0382)
0.561***
(0.136)
-0.313*
(0.134)
-0.0438
(0.0889)
-0.340
0.627***
(0.137)
-0.229
(0.139)
-0.0103
(0.0329)
-0.326
0.796***
(0.148)
-0.230
(0.138)
-0.0165
(0.0415)
-0.359*
0.766***
(0.148)
-0.205
(0.140)
-0.0101
(0.0329)
-0.346*
0.774***
(0.153)
-0.220
(0.141)
-0.00737
(0.0288)
-0.357*
0.861***
(0.175)
-0.280
(0.156)
-0.0100
(0.0357)
-0.404*
0.858***
(0.178)
-0.266
(0.155)
-0.00985
(0.0381)
-0.412*
!
!
Operating Cashflow (standardized)
Positive Earnings
Macroeconomic
Employment Growth
Recession Month
Industrial Production Growth
Real Consumption Growth
Intercept
5!
(0.185)
-0.100
(0.106)
0.499*
(0.223)
(0.175)
-0.103
(0.0996)
0.534*
(0.226)
(0.179)
-0.110
(0.102)
0.561*
(0.229)
(0.175)
-0.110
(0.0997)
0.561*
(0.229)
(0.178)
-0.110
(0.0986)
0.584*
(0.232)
(0.190)
-0.0750
(0.104)
0.562*
(0.255)
(0.192)
-0.0652
(0.105)
0.548*
(0.256)
0.0256*
(0.0123)
0.465
(0.482)
0.00268
(0.00209)
-0.0000944
(0.00219)
-22.45*
(10.03)
0.0252*
(0.0125)
0.531
(0.485)
0.00238
(0.00210)
-0.000449
(0.00224)
-22.99*
(10.15)
0.0266*
(0.0126)
0.499
(0.489)
0.00297
(0.00214)
-0.000287
(0.00222)
-22.78*
(10.18)
0.0260*
(0.0126)
0.526
(0.488)
0.00269
(0.00213)
-0.000450
(0.00224)
-23.03*
(10.21)
0.0282
(0.0146)
0.527
(0.580)
0.00270
(0.00226)
-0.000517
(0.00247)
-61.56
(474.2)
0.0324*
(0.0159)
0.585
(0.614)
0.00303
(0.00244)
-0.000787
(0.00268)
-55.54*
(24.26)
0.0241
(0.0167)
0.701
(0.698)
0.00314
(0.00261)
-0.000801
(0.00274)
-48.31
(24.87)
-0.260
-0.252
808
0.181
-333.8
741.6
915.3
808
0.184
-332.5
747.1
939.6
RANDOM EFFECTS
SIC Log Standard Deviation
Observations
Pseudo R2
Log-likelihood
AIC
BIC
808
0.133
-353.3
756.6
873.9
808
0.155
-344.4
740.8
862.8
808
0.160
-342.6
737.2
859.2
808
0.165
-340.5
734.9
861.7
808
0.173
-336.9
745.9
914.9
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
Institutional Logics
Income
(1)
(2)
-5.263***
(1.370)
Growth
Power / Agency
Private Equity (PE) Ownership
PE Tie to Underwriter
PE Fund Size (bn)
Cost Substitution
Litigation Risk
Non-Bayesian Investor
Shiller Investor Confidence Index
Baker-Wurgler Sentiment Index
(3)
(4)
(5) a
(6) a, b
(7) a, b
4.270***
(1.123)
-3.457*
(1.625)
2.739*
(1.332)
-3.275*
(1.631)
2.778*
(1.350)
-3.548*
(1.583)
2.812*
(1.308)
-3.502*
(1.578)
2.765*
(1.299)
-2.869
(1.983)
0.386
(1.502)
0.00351
(0.00544)
-0.491
(2.061)
0.189
(1.490)
0.00354
(0.00539)
-2.705
(1.966)
0.367
(1.489)
0.00305
(0.00540)
-1.202
(2.085)
0.244
(1.487)
0.00323
(0.00538)
-1.527
(2.095)
0.466
(1.494)
0.00351
(0.00540)
-2.104
(2.019)
0.792
(1.439)
0.00399
(0.00521)
-2.060
(2.016)
0.773
(1.437)
0.00342
(0.00520)
-6.374**
(2.107)
-6.499**
(2.089)
-6.695**
(2.091)
-6.662**
(2.086)
-6.519**
(2.113)
-3.943
(2.134)
-3.647
(2.128)
0.273*
(0.130)
2.142
(1.179)
0.290*
(0.129)
2.211
(1.169)
0.307*
(0.129)
2.532*
(1.174)
0.306*
(0.129)
2.437*
(1.172)
0.0381
(0.211)
1.006
(2.821)
0.0916
(0.204)
0.696
(2.738)
0.0954
(0.217)
Number of IPOs in Month
-0.104
(0.0746)
-0.175
(0.174)
-0.446
(5.069)
-0.0696
(0.295)
-19.57
(10.93)
Dividend Premium
NYSE Monthly Turnover
Closed-End Fund Discount
Equity Share of Total Issuance
Non-SEU Issuer
Management Ownership
Secondary Portion
Information Asymmetry
Average Underwriter Status
Market Conditions
Dow Jones Industry Sector Index
Market Volatility (VIX)
Log S&P500 Index
S&P500 One-day Return
S&P500 One-month Return
Fundamentals
Log Offering Size (mm)
Log Age (years)
Debt to Cap (inverse for negative)
4.370*
(1.755)
-2.029
(1.899)
3.431
(1.757)
-2.530
(1.887)
6.514***
(1.829)
-1.794
(1.884)
5.129**
(1.938)
-2.207
(1.890)
5.292**
(1.955)
-2.347
(1.899)
4.406*
(1.902)
-2.864
(1.832)
4.323*
(1.887)
-2.761
(1.827)
0.344
(0.447)
0.380
(0.443)
0.0944
(0.448)
0.208
(0.450)
0.231
(0.452)
0.142
(0.441)
0.215
(0.441)
0.214**
(0.0655)
0.0868
(0.131)
3.351
(5.473)
-0.828
(0.499)
0.273
(0.147)
0.217***
(0.0649)
0.0526
(0.130)
3.439
(5.426)
-0.755
(0.495)
0.221
(0.146)
0.198**
(0.0650)
0.0554
(0.130)
2.513
(5.431)
-0.776
(0.495)
0.277
(0.146)
0.205**
(0.0650)
0.0442
(0.130)
2.871
(5.421)
-0.746
(0.494)
0.242
(0.146)
0.213**
(0.0703)
0.0197
(0.188)
17.68
(13.57)
-0.908
(0.507)
0.186
(0.163)
0.200**
(0.0681)
0.0252
(0.180)
19.26
(13.02)
-0.972*
(0.490)
0.219
(0.157)
0.224***
(0.0674)
0.202
(0.216)
29.96*
(13.07)
-0.993*
(0.489)
0.141
(0.146)
4.968***
(0.650)
-2.361***
(0.645)
-0.00420
(0.0632)
5.323***
(0.651)
-1.960**
(0.648)
0.0192
(0.0629)
5.810***
(0.681)
-1.992**
(0.647)
0.0118
(0.0628)
5.741***
(0.681)
-1.861**
(0.648)
0.0214
(0.0628)
5.710***
(0.694)
-1.783**
(0.652)
0.0172
(0.0629)
5.592***
(0.684)
-1.820**
(0.636)
0.00816
(0.0613)
5.591***
(0.680)
-1.846**
(0.634)
0.0192
(0.0613)
!
Revenues (standardized)
Operating Cashflow (standardized)
Positive Earnings
Macroeconomic
Employment Growth
Recession Month
Industrial Production Growth
Real Consumption Growth
Intercept
**
!
***
***
***
***
-1.832
(0.577)
-0.744
(0.493)
5.637***
(1.051)
-1.940
(0.573)
-0.769
(0.489)
5.739***
(1.042)
-1.977
(0.574)
-0.807
(0.489)
5.792***
(1.042)
-1.996
(0.572)
-0.801
(0.488)
5.803***
(1.040)
-1.986
(0.578)
-0.787
(0.491)
5.897***
(1.050)
-1.980***
(0.564)
-0.752
(0.477)
5.028***
(1.044)
-1.929***
(0.562)
-0.772
(0.475)
4.861***
(1.042)
0.106
(0.0578)
0.259
(2.135)
0.00294
(0.00927)
-0.00300
(0.00958)
-74.72
(47.51)
0.103
(0.0573)
0.537
(2.117)
0.00295
(0.00919)
-0.00414
(0.00950)
-78.91
(47.11)
0.103
(0.0573)
0.207
(2.117)
0.00494
(0.00921)
-0.00423
(0.00950)
-76.48
(47.11)
0.102
(0.0572)
0.408
(2.114)
0.00423
(0.00919)
-0.00454
(0.00948)
-78.60
(47.02)
0.0877
(0.0648)
0.240
(2.545)
0.00691
(0.00982)
-0.00531
(0.00993)
-157.0
(101.6)
0.0987
(0.0622)
-0.228
(2.452)
0.00871
(0.00950)
-0.00871
(0.00961)
-169.7
(97.49)
0.103
(0.0637)
1.359
(2.555)
0.0117
(0.00971)
-0.00800
(0.00961)
-247.6*
(100.4)
1.186***
2.481***
1.187***
2.476***
803
803
-3154.4
6384.8
6562.9
-3150.5
6385.0
6581.9
RANDOM EFFECTS
SIC Log Standard Deviation
Residual Log Standard Deviation
Observations
R2
Adjusted R2
Log-likelihood
AIC
BIC
7!
803
0.196
0.171
-3176.4
6402.8
6520.0
803
0.211
0.185
-3168.8
6389.6
6511.5
803
0.210
0.185
-3169.0
6390.0
6511.9
803
0.215
0.189
-3166.6
6387.3
6513.9
803
0.225
0.189
-3161.6
6395.1
6563.9
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)
!
!
Table 5: First-Day Return – OLS Regressions (10 year period 2001-2010)
FIXED EFFECTS
Priced Above the Range
(1)
(2)
-5.053
(2.829)
2.289
(2.131)
0.00339
(0.00774)
-13.01***
(3.173)
0.230
(0.227)
-0.0738
(0.0995)
-0.227
(0.179)
-3.118
(5.236)
0.0202
(0.354)
-13.72
(14.78)
2.386
(2.497)
-3.198
(2.698)
1.980**
(0.646)
0.261**
(0.0973)
0.636**
(0.221)
17.97
(10.66)
-0.380
(0.693)
0.653**
(0.208)
1.821*
(0.927)
-1.651
(0.920)
-0.0468
(0.0900)
-2.627**
(0.821)
0.156
(0.700)
3.196*
(1.517)
0.127
(0.0856)
3.588
-7.506**
(2.298)
4.561*
(1.879)
-1.521
(2.947)
1.960
(2.089)
0.00276
(0.00758)
-13.88***
(3.111)
0.346
(0.223)
-0.0888
(0.0975)
-0.188
(0.176)
-4.210
(5.144)
0.185
(0.348)
-15.67
(14.50)
3.367
(2.734)
-3.734
(2.656)
1.771**
(0.643)
0.243*
(0.0956)
0.539*
(0.217)
22.64*
(10.47)
-0.246
(0.679)
0.567**
(0.204)
3.225***
(0.962)
-0.662
(0.916)
0.00183
(0.0885)
-2.936***
(0.806)
0.0739
(0.686)
3.323*
(1.485)
0.123
(0.0838)
4.262
Income
Growth
Private Equity (PE) Ownership
PE Tie to Underwriter
PE Fund Size (bn)
Litigation Risk
Shiller Investor Confidence Index
Number of IPOs in Month
Dividend Premium
NYSE Monthly Turnover
Closed-End Fund Discount
Equity Share of Total Issuance
Management Ownership
Secondary Portion
Average Underwriter Status
Dow Jones Industry Sector Index
Market Volatility (VIX)
Log S&P500 Index
S&P500 One-day Return
S&P500 One-month Return
Log Offering Size (mm)
Log Age (years)
Debt to Cap (inverse for negative)
Revenues (standardized)
Operating Cashflow (standardized)
Positive Earnings
Employment Growth
Recession Month
(3)
17.86***
(1.570)
-5.594**
(2.133)
2.489
(1.748)
-1.365
(2.726)
1.629
(1.933)
0.00344
(0.00702)
-10.22***
(2.896)
0.307
(0.206)
-0.0805
(0.0902)
-0.198
(0.163)
-2.480
(4.762)
0.180
(0.322)
-8.927
(13.43)
0.847
(2.539)
-2.409
(2.460)
1.428*
(0.596)
0.0884
(0.0895)
0.477*
(0.201)
17.49
(9.697)
-0.253
(0.628)
0.537**
(0.189)
1.133
(0.909)
-0.146
(0.849)
0.0107
(0.0819)
-2.068**
(0.749)
0.350
(0.635)
2.005
(1.379)
0.0513
(0.0778)
3.290
(4) a
17.87***
(1.569)
-5.964**
(2.130)
2.041
(1.760)
-0.809
(2.730)
1.318
(1.939)
0.00267
(0.00701)
-10.13***
(2.905)
0.258
(0.291)
-0.212*
(0.101)
-0.250
(0.235)
2.419
(6.875)
0.178
(0.401)
-15.40
(14.69)
0.850
(2.545)
-2.396
(2.466)
1.372*
(0.597)
0.111
(0.0920)
0.767**
(0.292)
57.56**
(17.71)
-0.250
(0.635)
0.469*
(0.195)
1.303
(0.917)
-0.101
(0.851)
0.00925
(0.0818)
-1.979**
(0.753)
0.196
(0.637)
1.774
(1.382)
0.0486
(0.0868)
8.812*
(5) a, b
17.17***
(1.516)
-5.801**
(2.057)
1.612
(1.693)
-1.531
(2.611)
1.495
(1.852)
0.00355
(0.00673)
-8.037**
(2.875)
0.386
(0.281)
-0.232*
(0.0969)
-0.275
(0.227)
2.026
(6.538)
0.0848
(0.383)
-12.84
(14.14)
0.272
(2.461)
-3.560
(2.362)
1.070
(0.581)
0.110
(0.0883)
0.846**
(0.279)
63.64***
(16.96)
-0.216
(0.610)
0.491**
(0.187)
1.632
(0.901)
-0.227
(0.825)
0.00549
(0.0794)
-1.989**
(0.732)
0.182
(0.615)
1.756
(1.362)
0.0655
(0.0827)
8.158*
8!
!
!
Industrial Production Growth
Real Consumption Growth
Intercept
(3.149)
0.0273*
(0.0137)
0.00987
(0.0138)
-164.3*
(83.03)
9!
(3.085)
0.0294*
(0.0134)
0.00654
(0.0136)
-213.7**
(81.76)
(2.856)
0.0217
(0.0124)
0.00682
(0.0126)
-164.9*
(75.77)
(3.433)
0.0213
(0.0131)
0.00380
(0.0129)
-459.6***
(135.9)
RANDOM EFFECTS
SIC Log Standard Deviation
Residual Log Standard Deviation
Observations
R2
Adjusted R2
Log-likelihood
AIC
BIC
(3.291)
0.0226
(0.0126)
0.00343
(0.0124)
-513.9***
(130.0)
1.527***
2.729***
795
0.121
0.088
-3420.9
6899.8
7035.4
795
0.159
0.126
-3403.0
6868.0
7013.0
795
0.281
0.252
-3340.7
6745.4
6895.1
795
0.295
0.258
-3332.8
6747.5
6939.4
795
-3323.8
6733.7
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
Institutional Logics
Income
Growth
Power / Agency
Private Equity (PE) Ownership
PE Tie to Underwriter
PE Fund Size ($ billions)
Cost Substitution
Litigation Risk
Non-Bayesian Investor
Shiller Investor Confidence Index
Number of IPOs in Month
Dividend Premium
NYSE Monthly Turnover
Closed-End Fund Discount
Equity Share of Total Issuance
Non-SEU Issuer
Management Ownership
Secondary Portion
Information Asymmetry
Average Underwriter Status
Market Conditions
Dow Jones Industry Sector Index
Market Volatility (VIX)
Log S&P500 Index
S&P500 One-day Return
S&P500 One-month Return
Fundamentals
Log Offering Size ($ millions)
Log Age (years)
Debt to Cap (inverse for negative)
Revenues (standardized)
(1)
(2)
(3)
(4)
-0.818*
(0.385)
0.855**
(0.303)
-0.921*
(0.438)
0.852**
(0.304)
-0.824*
(0.385)
0.863**
(0.307)
-0.962*
(0.483)
0.915*
(0.356)
-0.312
(0.480)
0.295
(0.343)
-0.000
(0.001)
-0.305
(0.483)
0.282
(0.346)
-0.000
(0.001)
-0.308
(0.480)
0.292
(0.343)
-0.000
(0.001)
-0.358
(0.485)
0.286
(0.349)
-0.000
(0.001)
-2.421*
(1.070)
-2.420*
(1.073)
-2.406*
(1.070)
-2.521*
(1.080)
0.070
(0.051)
-0.002
(0.017)
-0.045
(0.041)
-1.297
(1.302)
0.013
(0.072)
-4.539
(3.629)
0.076
(0.051)
-0.002
(0.017)
-0.044
(0.042)
-1.432
(1.312)
0.016
(0.072)
-4.611
(3.644)
0.071
(0.051)
-0.002
(0.017)
-0.045
(0.042)
-1.302
(1.304)
0.015
(0.072)
-4.477
(3.627)
0.073
(0.051)
-0.001
(0.017)
-0.045
(0.042)
-1.433
(1.321)
0.011
(0.072)
-4.529
(3.649)
1.032*
(0.416)
-0.525
(0.413)
1.047*
(0.419)
-0.535
(0.416)
1.021*
(0.416)
-0.526
(0.413)
1.093**
(0.422)
-0.545
(0.418)
0.122
(0.112)
0.121
(0.112)
0.122
(0.112)
0.126
(0.113)
0.077***
(0.017)
0.058
(0.055)
5.000
(3.135)
0.009
(0.121)
0.000
(0.036)
0.078***
(0.018)
0.064
(0.055)
5.159
(3.152)
0.010
(0.121)
0.001
(0.036)
0.076***
(0.017)
0.058
(0.055)
4.987
(3.135)
0.012
(0.121)
0.001
(0.036)
0.080***
(0.018)
0.063
(0.055)
5.078
(3.167)
0.024
(0.122)
0.002
(0.036)
0.798***
(0.162)
-0.231
(0.143)
-0.015
(0.045)
-0.402*
(0.179)
0.804***
(0.163)
-0.229
(0.145)
-0.015
(0.046)
-0.416*
(0.180)
0.793***
(0.162)
-0.231
(0.143)
-0.016
(0.045)
-0.401*
(0.179)
0.831***
(0.164)
-0.231
(0.145)
-0.018
(0.046)
-0.434*
(0.183)
!
!
Operating Cashflow (standardized)
Positive Earnings
Macroeconomic
Employment Growth
Recession Month
Industrial Production Growth
Real Consumption Growth
Intercept
RANDOM EFFECTS
Sector Standard Deviation
Income Standard Deviation
Growth Standard Deviation
Observations
Pseudo R2
Log-likelihood
AIC
BIC
11!
-0.092
(0.099)
0.513*
(0.245)
-0.091
(0.099)
0.529*
(0.248)
-0.091
(0.099)
0.503*
(0.246)
-0.099
(0.099)
0.503*
(0.247)
0.023
(0.016)
0.707
(0.681)
0.003
(0.003)
-0.001
(0.003)
-48.873*
(23.857)
0.024
(0.016)
0.707
(0.685)
0.003
(0.003)
-0.002
(0.003)
-50.547*
(23.988)
0.023
(0.016)
0.682
(0.680)
0.003
(0.003)
-0.001
(0.003)
-48.826*
(23.859)
0.024
(0.016)
0.752
(0.692)
0.003
(0.003)
-0.002
(0.003)
-49.911*
(24.099)
0.535
0.618
0.583
0.507
0.104
0.641
0.832
0.499
808
0.201
-325.796
737.591
939.458
808
0.218
-318.751
729.503
945.452
808
0.187
-331.573
745.146
937.623
808
0.202
-325.455
736.909
938.776
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: listwise 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)
!
!
Table 7: Offer-Price Return –Tobit Models (10 year period 2001-2010)
Institutional Logics
Income
Growth
Power / Agency
Private Equity (PE) Ownership
PE Tie to Underwriter
PE Fund Size (bn)
Cost Substitution
Litigation Risk
Non-Bayesian Investor
Shiller Investor Confidence Index
Number of IPOs in Month
Dividend Premium
NYSE Monthly Turnover
Closed-End Fund Discount
Equity Share of Total Issuance
Non-SEU Issuer
Management Ownership
Secondary Portion
Information Asymmetry
Average Underwriter Status
Market Conditions
Dow Jones Industry Sector Index
Market Volatility (VIX)
Log S&P500 Index
S&P500 One-day Return
S&P500 One-month Return
Fundamentals
Log Offering Size (mm)
Log Age (years)
Debt to Cap (inverse for negative)
Revenues (standardized)
Operating Cashflow (standardized)
(1) a
(2) b
-3.758**
(1.453)
2.399*
(1.196)
-3.521*
(1.453)
2.475*
(1.197)
-2.477
(1.855)
1.162
(1.320)
0.00288
(0.00478)
-1.949
(1.851)
0.854
(1.321)
0.00161
(0.00478)
-2.651
(2.021)
-3.388
(1.929)
0.213
(0.200)
-0.0792
(0.0687)
-0.160
(0.160)
-1.227
(4.662)
-0.0477
(0.272)
-19.94*
(10.05)
0.221
(0.199)
-0.0701
(0.0688)
-0.218
(0.159)
-0.843
(4.683)
-0.119
(0.272)
-20.10*
(9.979)
2.480
(1.742)
-4.067*
(1.684)
2.840
(1.727)
-3.895*
(1.680)
0.249
(0.406)
0.182
(0.405)
0.233***
(0.0619)
0.172
(0.199)
25.11*
(12.04)
-0.807
(0.450)
0.112
(0.135)
0.259***
(0.0634)
0.251
(0.199)
26.48*
(12.12)
-0.653
(0.448)
0.0772
(0.134)
4.782***
(0.627)
-1.681**
(0.584)
0.0196
(0.0564)
-1.726***
(0.517)
-0.665
4.794***
(0.636)
-1.624**
(0.583)
0.0410
(0.0555)
-1.838***
(0.512)
-0.656
12!
!
!
13!
(0.436)
4.714***
(0.976)
0.0885
(0.0586)
(0.433)
4.857***
(0.977)
0.0816
(0.0589)
3.282
(2.354)
0.0153
(0.00898)
-0.00783
(0.00888)
-221.1*
(92.37)
3.136
(2.356)
0.0138
(0.00893)
-0.00539
(0.00876)
-233.7*
(92.91)
RANDOM EFFECTS
Sector Standard Deviation
Residual Standard Deviation
3.273***
10.88***
2.517**
11.13***
Observations
Wald χ2
Log-likelihood
AIC
BIC
803
202.0***
-3013.9
6111.8
6308.7
803
197.7***
-3013.3
6110.6
6307.5
Positive Earnings
Employment Growth
Macroeconomic
Recession Month
Industrial Production Growth
Real Consumption Growth
Intercept
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)
!
!
Table 8: Predictions of Alternative Hypotheses for First-Stage Returns
14!
Theory
Institutional Logics
Coefficient Prediction
Income < 0
Growth > 0
Coefficient Estimates
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
VC-controlled issuer with debt ≤0.59
N
Offer-price return
160 mean -2.79 (s.d. 16.04)
VC-controlled issuer with with debt >0.59 28
t-Test Difference in Means
mean -3.45 (s.d. 16.25)
95% Confidence Interval
[-5.29, -0.28]
[-9.94, 3.04]
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).