Are Strategic Disclosure and Underpricing DecisionsInfluenced by Liability Risk?

Kathleen Weiss Hanley and Gerard Hoberg ∗

Current version: March 2010

ABSTRACT

Using word content analysis on IPO prospectuses, we show that the liabil-ity risk associated with strategic non-disclosure of information can be mitigatedby underpricing. This tradeoff explains a significant fraction of the variationin prospectus revision patterns, the partial adjustment phenomenon, and lit-igation outcomes. By examining ex-post litigation and involvement by IPOshareholders, we find that both high initial returns and increased disclosureserve as strong hedges against costly outcomes. Underwriters are the primarybeneficiaries of underpricing as a hedge against litigation risk because they aresubject to penalties beyond monetary damages. Underwriters who fail to ade-quately hedge litigation risk experience subsequent reductions in market share,and increased failure of their brand name.

∗Federal Reserve Board of Governors and University of Maryland, respectively. We thank JeffHarris, Wei Li, Jay Ritter, Robert Savickas, Tracy Wang, and seminar participants at George Wash-ington University, the Securities Exchange Commission, University of Delaware, and WashingtonUniversity. The ideas and opinions expressed in this study are the authors’ and should not be in-terpreted as reflecting the views of either the Board of Governors of the Federal Reserve Systemor its staff. Hanley can be reached at kathleen.hanleyk@frb.gov, and Hoberg can be reached atghoberg@rhsmith.umd.edu. All errors are the authors alone. Copyright c©2009 by Kathleen WeissHanley and Gerard Hoberg. All rights reserved.

I Introduction

During bookbuilding, issuers of initial public offerings (IPOs) often receive new price-

relevant information that results in substantial revisions to the initial price estimate

which affect the final offer price.1 Issuers must decide whether to reveal or disclose

this information to investors in amendments to the initial prospectus. We propose

that this decision is substantially affected by the issuer’s and underwriter’s exposure

to liability risk. Section 11 of the Securities Act of 1933 states “ In case any part of the

registration statement...omitted to state a material fact required to be stated therein

or necessary to make the statements therein not misleading, any person acquiring such

security ... may, either at law or in equity, in any court of competent jurisdiction,

sue every person who signed the registration statement” including the underwriter.

Thus, the challenge facing an issuer is how to strategically reveal (or not reveal, as the

case may be) valuable information and, at the same time, mitigate potential future

liability.2

Issuers have two mechanisms at their disposal to hedge litigation risk: disclosure

strategy or underpricing. This is because lawsuits can only be pursued by investors

if two conditions are met. First, as noted above, investors must be able to produce

evidence of a material omission in the firm’s disclosure that existed at the time of

their initial investment and second, investors must also have suffered damages in the

form of investment losses. Damages are measured by the decline in the aftermarket

trading price relative to either the offer price or the purchase price (for non-IPO

shareholders).3 For example, if an issuer withholds proprietary information, then the

1In our study, for example, the standard deviation of the change in the offer price from the initialestimate to the final offering price is 28%. This standard deviation likely underestimates the truestandard deviation because of the partial adjustment of offer prices to new information (Hanley(1993)).

2For the purposes of this discussion, we will refer only to the issuer rather than the issuer andunderwriter. Later in the paper in Section VII, we show that the issuer’s and underwriter’s incentiveson how to manage litigation risk may differ.

3Section 11 also states “the suit... may be to recover such damages as shall represent the differencebetween the amount paid for the security (not exceeding the price at which the security was offeredto the public) and (1) the value thereof as of the time such suit was brought, or (2) the price atwhich such security shall have been disposed of in the market before suit, or (3) the price at whichsuch security shall have been disposed of after suit but before judgment if such damages shall beless than the damages representing the difference between the amount paid for the security (notexceeding the price at which the security was offered to the public) and the value thereof as of thetime such suit was brought.” Lawsuits by aftermarket investors may also be brought under Section

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first condition, a material omission, may exist. In order to insure that the second

condition, investment losses, is also not present, the issuer could underprice the IPO

to reduce the probability of a decline in price below the offer price. This would

effectively insure the issuer against a subsequent lawsuit because only one condition

would exist, and both conditions are necessary for a lawsuit to succeed.

The issue of liability risk in IPOs is not new. For example, Tinic (1988) and

Hughes and Thakor (1992) argue that underpricing is a result of the issuer’s attempt

to mitigate legal damages. However, there has been mixed empirical evidence in sup-

port of the relationship between initial returns and lawsuits. Drake and Vetsuypens

(1993) find no relation between the incidence of a lawsuit and initial returns. Lowry

and Shu (2002), on the other hand, control for the endogeneity problem in which

initial returns can act as both insurance and a deterrent to litigation and find some

evidence for both.4

Past studies on the nature of IPO liability risk and its effect on underpricing

ignore the important role of strategic disclosure. This is, in part, due to challenges

in assessing how disclosure changes in response to new information learned during

bookbuilding.5 By using a unique methodology, text analysis, to compare the change

in word content from the initial prospectus to the final amendment, we can assess

whether issuers strategically disclose information learned during the bookbuilding

process. This method incorporates both the time series of prospectus amendments

as well as the severity of the revisions to the initial prospectus and is used to classify

issuers into low revisors and high revisors. This classification allows us to examine

if underpricing varies with disclosure strategy and whether this relation is related to

potential liability risk.

We find that IPOs classified as low revisors, those issuers that withhold informa-

tion learned during bookbuilding, use underpricing as a hedge against litigation risk

10b-5 of the Securities Act of 1934 but the plaintiffs must prove that the omission was intended todefraud or mislead.

4Wang, Winton, and Yu (2010) examine the incidence of fraud in IPOs under varying businessconditions but do not look at the impact on pricing.

5Liability risk may also affect disclosure choices in the initial prospectus. However, by concen-trating on the bookbuilding period, we are able to examine how issuers respond to the arrival ofnew information. For additional discussion as to the type of information included in the initialprospectus see Hanley and Hoberg (2009).

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much more aggressively than do IPOs classified as high revisors. This test is particu-

larly relevant as being a low revisor in the presence of new price relevant information

is indicative of a potential material omission. This finding is further strengthened

when the sample is restricted to those IPOs that have information with a positive

mean revealed during bookbuilding and subsequently revise their offer price upward.

For IPOs with the largest change in offer price, low revisors have twice the initial

returns of high revisors. This difference is more pronounced as the issuer’s exposure

to ex ante litigation risk increases even after controlling for the inherent riskiness of

the IPO.

These findings suggest that liability risk is an important determinant of the par-

tial adjustment phenomenon in the IPO literature (Hanley (1993)). Skeptics of book-

building models such as Benveniste and Spindt (1989) as explanations of underpricing,

highlight the extremely high initial returns that are often associated with large pos-

itive changes in offer prices. They suggest that the amount of underpricing “seems

too large to be explained as equilibrium compensation for revealing favorable infor-

mation”(Ritter and Welch (2002)).6 By incorporating the tradeoff between strategic

disclosure and pricing in the context of litigation risk, we show that much of the

partial adjustment can be explained by issuers who do not revise their prospectus in

response to favorable information and instead, underprice as insurance against subse-

quent litigation. Further, those firms with the highest exposure to ex ante litigation

risk also have the largest partial adjustment.

One criticism of liability theories of underpricing is the fact that lawsuits are in-

frequent in countries with well-functioning IPO markets, such as Japan, whose offers

exhibit both partial adjustment and high initial returns (see Kerins, Kutsuna, and

Smith (2007) and Kutsuna, Smith, and Smith (2009)) even though the disclosure

requirements are similar to the U.S.7 There are two possible explanations as to why

6We acknowledge that the optimal level of underpricing even in the presence of litigation riskmay not be as high as that documented in the literature. Thus, other theories of the partialadjustment phenomenon such as Loughran and Ritter (2002) and Edelen and Kadlec (2005) mayalso be important.

7Like the U.S., the Securities and Exchange Law of Japan, Article 18 creates a civil remedyagainst the issuer for investor losses if the prospectus contains a false statement or material omissionand Article 21 extends that liability to the underwriters and auditors. Similar legal constraints alsoexist in the UK where under Section 90 of the Financial Services Market Act of 2000 where “Any

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investor lawsuits alleging material omissions may be rare.8 One is that if reputa-

tional concerns are high then issuers and underwriters will purchase more insurance

(underpricing). For example, Lin, Pukthuanthong-Le, and Walker (2009) examine

cross-country differences in liability and find that IPOs in countries with higher liti-

gation risk have greater underpricing. If the correct amount of insurance is bought,

then we should observe very little litigation because the insurance functioned as ex-

pected.

The second is that the consequences for poor disclosure might include non-legal

penalties such as loss in dealer market share, prestige, and personal societal status. As

long as underpricing can act as insurance against investors extracting these penalties,

our results may shed light on why the partial adjustment phenomenon exists in other

countries whose legal systems are not as well developed as the U.S.

Since we propose that strategic disclosure and underpricing decisions are, in part,

driven by the desire to hedge litigation risk, we examine whether such decisions

have a deterrence effect against future lawsuits. While we document a potential

deterrent effect of increased disclosure, we show no relationship between the level of

instrumented initial returns and the probability of a future lawsuit. This finding is

quite puzzling. If underpricing cannot prevent lawsuits from occurring, why, then, is

there a positive relation between litigation risk and initial returns?

The reason is because underpricing can act as insurance only against litigation

from IPO purchasers but not from aftermarket investors. Because aftermarket in-

vestors’ threshold for damages is lower than that of IPO investors because the after-

market price is often higher than the offer price, they are the most likely plaintiffs

in a lawsuit. Thus, underpricing cannot stop a lawsuit from being brought by after-

market investors but can insure that IPO purchasers will not be part of the class.9

The benefit of excluding IPO purchasers from the class is that the lawsuit is unlikely

person responsible for a prospectus is liable to pay compensation to a person who has acquiredsecurities to which the prospectus applies; and who suffered loss in respect of them as a result of anyuntrue or misleading statement in the prospectus or as a result of the omission from the prospectuswhich should have been included under the duty of disclosure.”

8Note that a recent study by Ikeya and Kishitani (2009) finds that lawsuits in Japan are moreprevalent than expected and that litigation alleging misstatements in Japan is on the rise.

9Note that unlike underpricing, increased disclosure can act as insurance against a lawsuit becauseit is available to both IPO purchasers and aftermarket investors.

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to be brought under Section 11 which reduces the probability that the underwriter

will be named in the suit. The benefits of underpricing as a hedge against insurance,

therefore, accrue primarily to the underwriter. Because the underwriter also plays

a dominant role in setting the final offer price, the impact on underpricing could be

substantial.10

Using a nested logit model, which incorporates both the probability of a lawsuit

and whether IPO purchasers are in the class, we find that the higher the initial return,

the higher is the probability that IPO purchasers will not be part of the class. This is

particularly true in cases where IPO issuers are likely to withhold information learned

during bookbuilding (e.g. have positive changes in offer price). The deterrence effect

of initial returns, therefore, is not in stopping lawsuits from occurring but in limiting

the type of plaintiff that will be involved.

We show that there are significant negative consequences to underwriters be-

yond legal damages if IPO shareholders are included in the class and the litigation is

brought under Section 11. The survivorship of the underwriter brand name as well

as its future market share is significantly affected when IPO purchasers are named

as part of the class but not when such shareholders are excluded. Cheng, Huang, Li,

and Lobo (2009) find that lawsuits with an institutional lead plaintiff are less likely

to be dismissed and have significantly larger settlements. As most IPO investors are

institutional investors (Hanley and Wilhelm (1995), Cornelli and Goldreich (2003),

Aggarwal, Prabhala, and Puri (2002), Ljungqvist and Wilhelm (2002) and Jenkinson

and Jones (2004)) this provides additional motivation as to why underwriters (and

issuers) may be further motivated to exclude IPO shareholders from the class.

This paper contributes to the literature on the legal consequences of voluntary

disclosure. While much of this literature has found that bad information is withheld

and good information is disclosed (for example, Skinner (1994), Skinner (1997), and

Healy and Palepu (2001)) we find the opposite to be true. We suggest that this is

10Section 11 limits damages to underwriters “In no event shall any underwriter ... be liable inany suit or as a consequence of suits authorized under subsection (a) of this section for damages inexcess of the total price at which the securities underwritten by him and distributed to the publicwere offered to the public.” Again, lawsuits against underwriters claiming fraudulent behavior maystill be brought by aftermarket investors under Rule 10b-5.

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likely due to differences in incentives (Kothari, Shu, and Wysocki (2009)) and the

regulatory environment surrounding the IPO process.

Finally, our paper reflects a growing interest in the use of word content analysis

to analyze the informativeness of written disclosure and media coverage. Hanley and

Hoberg (2009) examine the information content of IPO initial prospectuses and its

effect on pricing. In the context of managing litigation risk, Nelson and Pritchard

(2008) and Mohan (2007) find that certain word usage is related to the probability

of being sued. Hoberg and Phillips (2008) use text similarity analysis to test theories

of merger incidence and outcomes. Loughran and McDonald (2008) show that firms

using Plain English have greater small investor participation and shareholder-friendly

corporate governance. In other contexts, papers such as Tetlock (2007), Tetlock, Saar-

Tsechanksy, and Macskassy (2008), Li (2006) and Boukus and Rosenberg (2006) find

word content to be informative in predicting stock price movements.

The remainder of the paper is organized as follows: A brief discussion of the

incentive to withhold or disclose information learned during bookbuilding is present

in Section II. The data, word vector construction method and summary statistics are

in Section III. Our method of classifying disclosure strategy is discussed in Section IV.

The relation of disclosure strategy and litigation risk to initial returns (the insurance

effect) is in Section V. How disclosure strategy and initial returns affect the probability

of a lawsuit (the deterrence effect) is in Section VI. The economic consequence of

lawsuits for underwriters is explored in Section VII. The paper concludes in Section

VIII.

II Disclosure Incentives During Bookbuilding

After receiving and addressing comments from the SEC on the initial prospectus, the

underwriter and issuer begin the bookbuilding process. During the road show, the

issuer conveys information regarding the future prospects of the firm (to be limited

to the information in the prospectus) and investors provide feedback on the proposed

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offer price via indications of interest on the proposed price range.11

If the investors’ valuation of the proposed offer price substantially differs from that

of the issuer, the issuer (and underwriter) has an incentive to learn from investors the

nature of the information underlying the difference in order to formulate their disclo-

sure and pricing strategy. If the issuing firm is concerned about potential litigation,

it will choose a combination of disclosure and underpricing that jointly minimize the

two conditions for a lawsuit to be brought: a material omission and damages in the

form of investment losses. Increased disclosure will lower the likelihood of a material

omission while underpricing can reduce the damages of IPO investors and influence

class membership.

Both of these mechanisms, however, are costly. Underpricing leaves money on the

table (Loughran and Ritter (2002)) while enhanced disclosure may reveal proprietary

or strategic information to rivals (Darrough and Stoughton (1990), Bhattacharya

and Chiesa (1995), and Maksimovic and Pichler (2001)). Whether the firm places

greater emphasis on enhanced disclosure or underpricing is likely due to the type of

information revealed during bookbuilding.12

Issuing firms which receive bad information from investors have initial offer prices

that are too high. In order to generate sufficient demand for the IPO, these firms will

need to revise their offer prices downward. By reducing the offer price, the issuing

firm may not have sufficient flexibility to hedge litigation risk with initial returns.13

Instead, IPOs with bad information revealed may decide to increase disclosure to

mitigate liability.

Since bad information was revealed to the issuing firm by investors, it would be

especially risky to withhold such information from the offering document. If the infor-

mation is revealed shortly after the IPO, both conditions for a lawsuit are immediately

11Benveniste and Spindt (1989) and Sherman and Titman (2002) argue that investors are com-pensated for revealing information about the value of the firm to the issuer and underwriter.

12Of course, the issuing firm could both revise the offer price and fully disclose the informationwhich would provide the best hedge against subsequent lawsuits. However, this may over insure theissuer against future liability and reduce the future prospects of the firm.

13In addition, reducing the offer price to increase underpricing may also impact the probability ofwithdrawal of the offering. Dunbar (1998) and Edelen and Kadlec (2005) examine the decision towithdraw an IPO.

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met: investors will experience damages when the stock price declines following the

announcement, and there will be evidence of a material omission. Since bad informa-

tion has potentially low proprietary value to rivals and is unlikely to be concealed for

long, there is little benefit and much cost in withholding negative information.

Further, if offer prices are revised downward, it is also likely that that the issuing

firm will need to file an amendment with the SEC that discusses the effect of lower

than expected proceeds. Regulatory requirements may, therefore, also increase the

propensity to disclose bad information. Such firms may find increased disclosure to

be a more effective mechanism than underpricing in hedging liability risk.

Issuing firms which receive good information have initial offer prices which are

too low. These issuers will want to take advantage of this by revising their offer

prices upward and potentially withholding positive information learned from investors

because it could reveal proprietary value or strategic value to rivals.14 Because IPOs

with good information revealed during bookbuilding have additional flexibility in

pricing, they can easily substitute initial returns for withheld disclosure to mitigate

potential litigation risk.

It may be counterintuitive to think that issuers with good information would be

concerned about litigation risk. After all, the information was unexpectedly positive.

However, the new good information most likely represents a distribution of possible

outcomes some of which may be ex post negative. Indeed, our findings (and those of

Lowry and Shu (2002)) indicate that IPOs which have positive changes in offer price

are more likely to be the subject of a lawsuit.

Consider the following extreme example: an IPO firm in industry X learns from

investors during bookbuilding that its product can be potentially modified to solve a

costly problem in industry Y. The IPO firm might wish to withhold this information

because disclosing it might alert its industry X rivals. However, it is possible that

existing firms in industry Y might solve the problem on their own. If existing firms

14Disclosing new information may also require additional time in registration as the issuer mustfile an amendment with the SEC. If the issuer is concerned about a narrow window of opportunityin going public, as is usually the case in IPOs with upward price revisions, this increase in timemay be costly. Timing issues, therefore, may provide an additional incentive to withhold positiveinformation.

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in industry Y beat the IPO firm to the solution, the IPO firm’s investment would

be lost and its ex-post value will decline. Plaintiffs could argue that the issuing firm

should have disclosed both the good information (new opportunities in industry Y)

and the associated risk factor (industry Y solves the problem before the issuing firm

can act).

However, from a strategic disclosure perspective, neither can be disclosed in the

IPO prospectus without essentially revealing the full information to rivals. In this

case, the issuer faces the possibility that a plaintiff will claim that the issuer had

knowledge of the bad outcome at the time of the IPO which should have been dis-

closed. In order to reduce the plaintiff’s ability to bring the lawsuit, the issuer only

partially adjusts its offer price in response to the new information and thereby, miti-

gates the potential for investment losses.

Our finding that IPO firms are more likely to withhold good information and

disclose bad is opposite to the literature on seasoned non-IPO firms which shows

that such firms are more likely to conceal bad information and disclose good. The

above discussion highlights the complex interactions between incentives, regulation

and the legal environment in the IPO process which may account for the differences

in disclosure strategy between seasoned firms and IPO issuers. Because information

asymmetry is highest when a firm goes public, specific protections have been put into

place to protect IPO investors, such as SEC review and legal recourse for material

omissions in the prospectus. Further, the involvement of an underwriter, who can be

named along with the issuer in a lawsuit, affects the decision of the issuer to disclose

or withhold information. Finally, unlike seasoned firms, IPO issuers have some control

over pricing decisions which can be used to mitigate the litigation risk associated with

the disclosure strategy.

III Data

A Sample and Word Vector Construction

Our initial list and characteristics of all U.S. IPOs issued between January 1, 1996

and October 31, 2005 is from Securities Data Company (SDC) U.S. New Issues

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Database.15 We eliminate ADRs, unit issues, REITs, closed-end funds, financial

firms, and firms with offer prices less than five dollars. A CRSP permno must also

be available for an observation to remain in the sample, and the IPO must also have

a valid founding date, as identified in the Field-Ritter dataset, as used in Field and

Karpoff (2002) and Loughran and Ritter (2004).16 These initial exclusions reduce the

sample to 2,112 IPOs.

For each IPO passing these initial screens, we use a web crawling algorithm to

download the initial prospectus, and all subsequent amendments. In order for an IPO

to remain in our sample, it must have available SEC Edgar filings online, which must

also be machine readable. In order to satisfy our definition of machine readable, a

Table of Contents pagination algorithm must be able to detect, and accurately iden-

tify, the start and end of the entire prospectus. 17 This additional screen eliminates

69 IPOs, leaving us with 2,043 machine readable IPOs. Because these 69 IPOs are a

small fraction of our sample, and because most are also small firms that file using an

SB-2 (larger firms generally file an S-1), we do not believe that omitting these firms

is problematic.

Our estimation of each IPO’s initial prospectus similarity to past sued IPOs re-

quires prospectus information from other IPOs that were sued in the past year. In

order to have sufficient data for the estimation of this key variable, we further restrict

the sample to IPOs that were issued on or after January 1, 1997. IPOs issued prior to

that date (from 1996) are used only to compute starting values for this variable, and

are otherwise discarded. This requirement reduces our sample to 1,623 IPOs which

have a total combined document count (initial prospectus plus amendments) of 8,199.

15Our data begins just after the 1995 Private Securities Litigation Reform Act and before the1998 Securities Litigation Uniform Standards. Certain authors argue, for example Zhu (2009) andPukthuanthong, Walker, and Turtle (2009), that the legal landscape has changed significantly sinceprior studies on IPO litigation risk. Thus, our sample should be relatively unaffected by thesechanges.

16We thank Jay Ritter for generously providing the database of IPO founding dates on his website.17Technically, we require that the algorithm must also be able to detect the start and end of four

sections: Prospectus Summary, Risk Factors, Use of Proceeds and MD&A (see Hanley and Hoberg(2009) for more information). A significant amount of work has been done to maximize the fractionof prospectuses that are deemed machine readable. This includes hand-checking each prospectusfailing our machine readability condition to determine if our document pagination algorithm can beimproved via exception handling. The 69 IPOs failing machine readability generally lack paginationor may even lack a Table of Contents.

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Our algorithm to read each prospectus is written in a combination of PERL and

APL. Once a document is downloaded and paginated, our algorithm’s next step is to

purge the document of attachments, headers, and exhibits so that we can focus on

the prospectus itself. This is achieved using a three prong approach that ensures a

high degree of accuracy: (1) we use the pagination implied by the Table of Contents

to identify the beginning and end of the document, (2) we examine the placement of

the “additional information” statement and the placement of accounting statements

(exhibits) to confirm accuracy,18 and (3) we hand check the algorithm’s accuracy for

most documents and include exception handling where necessary.

For each IPO i, we store the text of the prospectus in separate word vectors,

which we define as wordsi. Our words are based on word roots rather than actual

words and exclude certain types of words such as common words and/or articles. (For

additional information on the word vector construction, see Appendix 1.) Note that

all word vectors have the same length (5,803) as they are based on the same global

word list of 5,803 word roots. Each element of the vector is populated by the count

of the number of times the word is used in the given document.

As an example, consider a simple universe of two prospectuses, one with content

“they sell potatoes and they sell corn”, and one with content “they sold knives”.

Discarding articles, conjunctions and pronouns (the, and, they), there are 4 word

roots in the union of both documents: sell, potato, corn, knife. Thus, in our example,

we have:

words1 = {2, 1, 1, 0} and words2 = {1, 0, 0, 1}

Note that the word vectors, have longer vector lengths when the underlying document

is larger. Hence, these vectors measure the “total amount” of information in the

document.

B IPO and Lawsuit Variables

We collect information on class action lawsuits for up to three years after the IPO

date from Stanford’s Law School Securities Class Action Clearinghouse. We require

18The overwhelming majority of prospectuses filed in our sample have a statement indicatingwhere investors can find additional information toward the end of the prospectus document.

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that the lawsuit be disclosure-based (material omission) which results in 202 IPOs

with a class action lawsuit that meets our criteria. Our class action lawsuit dummy is

one if an IPO is sued based on this sample of lawsuits. Because many of the sample

IPOs occur during the time of increased IPO allocation lawsuits, approximately 10%

of the sample has, in addition to the lawsuit considered here, an additional lawsuit

related to IPO allocation. As we do not examine any ex post price effects of the

lawsuit, we believe these additional lawsuits should not confound our results and

their elimination would severely affect the sample size.

Our sample includes lawsuits that may not have IPO shareholders as part of the

class. This differs from prior work such as Lowry and Shu (2002), who concentrate

only on lawsuits in which IPO shareholders are involved (Section 11 lawsuits). Our

motivation for expanding coverage is twofold. First, we hypothesize that if informa-

tion revealed during bookbuilding is valuable and withheld, the ex post realization of

this information might take time. Thus, the effects of withholding information may

not be observed until well after the IPO date. Second, we hypothesize that underpric-

ing in IPOs only has a deterrent effect on IPO shareholders but not on aftermarket

purchasers. In order to test whether pricing decisions at the time of the IPO has an

effect on the structure of the class, we need to compare lawsuits that have both IPO

and aftermarket investors with lawsuits that have only aftermarket investors.19

We also compute a number of variables that are common to the existing IPO

literature.

∆P =Pipo − Pmid

Pmid

, IR =Pmkt − Pipo

Pipo

. (1)

Pmid, Pipo, and Pmkt are the filing date midpoint, the IPO price, and the after-

market trading price, respectively. ∆P is underwriter’s price adjustment from the

filing date to the IPO date, and IR (initial return) is the market’s price adjustment

from Pipo to Pmkt. Investors who purchase shares at the IPO price, Pipo, can realize

returns equal to IR by selling their shares at the closing price on the first day of

public trading.

19A small portion of the sample, seven lawsuits, contain only IPO purchasers and no aftermarketinvestors.

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We also control for the following variables identified in the existing IPO literature:

∆P+: The positive component of ∆P equal to max[∆P, 0]. This variable controls

for the partial adjustment phenomenon documented in Hanley (1993) and was

first used in Lowry and Schwert (2002).

∆P–: The negative component of ∆P equal to min[∆P, 0].

Firm Age: IPO year minus the firm’s founding date, where founding dates are

obtained from the Field-Ritter dataset, as used in Field and Karpoff (2002) and

Loughran and Ritter (2004).

Lead UW $ Market Share: Lead underwriter’s dollar market share in the past cal-

endar year as calculated by Megginson and Weiss (1991).

Law $ Market Share: The dollar market share of legal counsel in the past calen-

dar year and a separate variable is constructed for the lead underwriter’s legal

counsel and the issuer firm’s legal counsel.

VC Dummy: Dummy variable equal to one if the firm is VC-backed, and zero oth-

erwise as in Barry, Muscarella, Peavy, and Vetsuypens (1990).

Nasdaq Return: We construct two measures of this variable. Our first is the NAS-

DAQ return for the 30 trading days preceding the filing date. Our second is the

NASDAQ return for the 30 trading days preceding the issue date. Logue (1973)

first examined whether past market returns can predict future underpricing,

and this measure has been used more recently by Loughran and Ritter (2002).

IPO Size: We construct two measures of this variable. Our first is the natural

logarithm of the original filing amount. Our second is the natural logarithm of

the offering amount.

Tech Dummy: Dummy variable equal to one if a firm resides in a technology industry

as identified in Loughran and Ritter (2004).

Risk: Equal to (1/Pmid) as in Bradley and Jordan (2002).

Volatility: Firm risk using the matching method in Lowry and Shu (2002).

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C Summary Statistics

Table I presents summary statistics on the various measures we employ in this paper.

Panel A has information on the price variables, and our sample is similar to other

studies that include the bubble period of 1999 and 2000. On average, this sample

of IPOs has an average initial return of 37.9% with a much lower median of 14.6%.

The average change in the offer price from the first initial price range midpoint to the

final offer price is 5.0%. ∆P+, the positive component of offer price changes, averages

12.2% while ∆P-, the negative component of offer price changes, averages -7.2%.

Panel B displays statistics for IPO characteristics. The mean IPO files an offer

amount of approximately $214 million. The average age of the firm is almost 14 years

but the median is significantly smaller at 7 years. Almost fifty percent of the IPOs

have venture capital backing and 46% is classified as tech firms as defined in Loughran

and Ritter (2004). The average market share of the underwriter in the year prior to

the offer is 3.0%. Consistent with Lowry and Schwert (2002), IPOs are brought to

market when prior returns are high, with an average return in the thirty days prior

to filing of approximately 5%.

Panel C presents summary statistics describing the prospectus and revision vari-

ables. The average document has a total of almost 10,000 root words. Since the

number of possible unique root words is 5,803, an average number of root words for

the document as a whole of almost 10,000 means that some root words appear more

frequently. The average issuer files four amendments to the initial prospectus for a

total of five prospectus filings. Approximately 38% of the sample is classified as a low

revisor (discussed in the next section, Section IV).

Finally, Panel D presents the proportion of the sample, 10%, which are subse-

quently involved in a shareholder lawsuit. Because we include lawsuits both with and

without IPO shareholders, our sample of sued IPOs is larger than Lowry and Shu

(2002). About half of our lawsuits include IPO investors in the class.

15

IV Classification of Disclosure Strategy

Our measure of disclosure is based on classifying how intensely an issuer revises its

prospectus during bookbuilding. We suggest that the greater is the revision intensity,

the higher is the issuer’s disclosure of new information learned after the filing of the

initial prospectus. The issuer’s revision intensity incorporates both the time series

of prospectus amendments and the severity of the revisions to the initial prospectus

and each amendment.

Consistent with Hanley and Hoberg (2009) and Hoberg and Phillips (2008), we

measure how similar document content is using the cosine similarity method. Its

opposite, one minus the document similarity is how dissimilar or distant is the content

between two documents. This method is also widely used in studies of information

processing (see Kwon and Lee (2003) for more information), and its name is due to

its measuring the angle between two word vectors on a unit sphere (see Appendix 1

for more details).

In order to characterize revision intensity , we must first expand our notation. Let

wordsi,1 denote the word usage in IPO i’s initial prospectus, and wordsi,n is analo-

gously defined for IPO i’s n-th prospectus. An IPO with N total filings (including

the initial prospectus and all amendments with the exception of the final prospec-

tus filed after the IPO date) is thus described by the series of vectors {wordsi,1, ...,

wordsi,N }. We denote the series of N − 1 document distances (which is simply one

minus document similarity) summarizing the time series of revisions from the initial

prospectus to the final version as {Di,1, ..., Di,N−1}. Since distance is measured using

two adjacent pairs of documents in a given time series, Di,j is the document distance

between IPO i’s jth filing and its j + 1th filing.

Table II presents a summary of prospectus and amendment filing patterns. As

can be seen in Panel A, the majority of IPOs in the sample have an initial prospectus

and at least three amendments. The total distance from the previous amendment

which is measured as Di,j, is highest for the first revision after the initial prospectus.

By the second and third amendment, approximately 94% of change in content has

occurred.

16

After the filing of the initial prospectus with the SEC, there are two primary

reasons for a substantial prospectus revision:20 1) regulators request revisions through

the comment letter process and 2) the issuer can decide to revise the prospectus

voluntarily. We refer to the former type as “RD-revisions” (regulation-driven) and

the latter type as “ID-revisions” (issuer-driven). This dichotomy is important because

our primary hypothesis relates to the voluntary, rather than involuntary or potentially

SEC-driven, component of disclosure during the IPO process.21 Conversations with

practitioners indicate that the first major revision (usually appearing as the first or

second amendment to the initial filing) is the primary RD-revision in the U.S. That

is, the SEC generally comments on every IPO, and their requests are usually factored

in by issuers in amendments filed soon after the initial prospectus.22

We define the major RD-revision in each IPO’s time series as the largest revi-

sion among the first two revisions (where RDi=MAX[Di,1, Di,2]).23 Because issuers

generally address SEC comments prior to distributing the prospectus to prospective

investors, the variable RDi which focuses on the first two revisions, likely captures

the issuer’s response to these comment letters. We omit this revision from our series

of ID-revisions as our hypothesis only relates to voluntary revisions based upon infor-

mation generated during bookbuilding. Because each series is likely to contain a large

firm-specific revision effect, we scale the series of ID-revisions by RDi. This controls

for firm characteristics and writing style in the measurement of specific ID-revisions.24

We denote ID-revisions for each IPO i’s j-th time series pair of amendments (not in-

cluding the RD-revision) as:

20There may be many minor reasons for a revision or amendment to a prospectus and as mentionedlater, our method would essentially classify such changes in the documents as insignificant.

21For the potential effect of SEC comment letters on the IPO process, see Ertimur and Nondorf(2009).

22Preliminary prospectuses are generally not circulated until comments from the SEC are ad-dressed and further material revisions to the prospectus are unlikely. After a preliminary prospectushas been circulated, any material revisions would necessitate a new prospectus which must be re-printed and re-circulated to investors which is costly in terms of both time and money.

23Our results do not change materially if we simply use the first amendment rather than themaximum of the first two.

24This scaling removes potentially substantial author-specific fixed effects from each time seriesof revisions. For example, a long-winded author might write 50 sentences to explain a new businessopportunity, whereas a concise writer might use only 5 sentences. In addition, this scaling has thenice property that the regulator (the SEC) is held constant across all IPOs in our sample.

17

IDi,j =Di,j

RDi

(2)

with a maximum of N-2 possible IDi,js.

As can be seen from Figure 1, there is a significant amount of clustering close to

zero for the value of any individual IDi,j. For example, a large number of revisions

are near zero, but the median normalized revision is between .05 and .06. In order to

control for this clustering, we classify whether an issuer is a “low” revisor or “high”

revisor using a dummy variable. The low revisor dummy takes the value of one if

at least two-thirds of the given IPO’s ID-revisions are below the median among all

ID-revisions for all IPOs issued in the same year. The high revisor dummy is equal

to (1-low revisor dummy). The value of two-thirds is based upon Table II in which

many IPOs in our sample have at least three revisions. Table III presents summary

statistics on the revisor dummy and the interaction terms.

Since litigation risk has been proposed as an explanation for underpricing, our

primary interest is in assessing the tradeoff between high initial returns and low,

rather than high, disclosure. Thus, our focus is slightly different from the voluntary

disclosure literature because we are interested in situations in which there is a lack of

disclosure rather than enhanced disclosure.

The main idea behind the revisor dummy is to identify issuers that do not revise

their prospectus as they learn new information during bookbuilding and issuers that

do. An issuer that files mainly price change only amendments, for example, will have

ID-revisions below the median size, and will, thus, be categorized as a low revisor. A

key idea is that an issuer that has a large price adjustment, but is also a low revisor,

is likely to have a material omission in the prospectus as they did not explain the

information underlying the price change.

Interesting differences in this pattern are apparent from the next two panels in

Table II, which show low revisors (Panel B) and high revisors (Panel C). Low revisors

have higher content revisions on the first amendment but converge much quicker

to a final document than high revisors. By the fourth amendment after the initial

prospectus, low revisors have almost completely converged to the final amendment.

18

In contrast, high revisors take until the sixth amendment to reach the same degree

of convergence. From a statistical standpoint, the t-stat of the difference in means

of the cumulative convergence by the second filing between high and low revisors is

16.36. The t-stats on the differences in convergence from the third to sixth filings are

14.76 (third), 8.95 (fourth), 6.13 (fifth), and 3.88 (sixth). These statistics suggest a

marked difference in prospectus revision strategy between our classifications of high

and low revisors.

Table IV examines differences in IPO characteristics based on whether the issuer

is a low or high revisor. The table presents evidence of the strong relationship be-

tween disclosure strategy, ∆P, initial returns, and litigation outcomes. IPOs that

are low revisors, those that withhold information learned during bookbuilding, have

significantly higher initial returns and are more likely to have positive changes in the

offer price. Low revisors have an average initial return of 46.6% compared to 32.6%

for high revisors and low revisors also have a higher likelihood of a future lawsuit.

Firms with ∆P ≤ 0 have a statistically higher proportion of high revisors than

low revisors which supports our initial conjecture that firms with negative informa-

tion generated during bookbuilding have little incentive to withhold information. In

contrast, firms with ∆P > 0 have a higher proportion of low revisors than high revi-

sors and this result is consistent with the incentives to withhold good information for

proprietary or strategic reasons. Other firm characteristics, such as venture capital

backing, underwriter market share and whether or not the IPO is a tech firm do not

differ. These relationships confirm our finding that the results of the paper are robust

to including numerous controls including technology firms, venture capital backing,

and industry and time fixed effects.

V The Effect of Disclosure Strategy and Litigation

Risk on Underpricing

The prior literature on liability risk and underpricing has documented a positive re-

lation between initial returns and subsequent lawsuits Lowry and Shu (2002) term

the “insurance effect”. We conjecture, however, that insurance in the form of initial

19

returns is only needed when the issuer withholds information learned during book-

building. The amount of insurance should be related to both the issuer’s disclosure

strategy as well as its exposure to liability risk.

We begin by replicating the traditional initial return regression that includes a

dummy variable indicating whether or not the IPO had a subsequent lawsuit as an

independent variable. This regression is presented in Panel A of Table V. Like Drake

and Vetsuypens (1993) and Lowry and Shu (2002), we find no difference in initial

returns between sued IPOs and non-sued IPOs using an ex post class action lawsuit

dummy. However, as Lowry and Shu (2002) correctly note, the relation between

initial returns and liability risk is endogenous. Firms with greater liability risk will

underprice more (positive relation between liability risk and initial returns) while

firms that underprice more will have a lower incidence of lawsuits (negative relation

between liability risk and initial returns).

We control for this endogeneity by following the simultaneous-equation approach

of Lowry and Shu (2002) for assessing the effect of litigation risk on initial returns.

Instead of using turnover as an instrument for litigation risk as they do, we determine

whether an IPO is more likely to be affected by litigation risk by using content

analysis. We measure the similarity of the current IPO’s prospectus content to the

average prospectus content of IPOs that were sued in the year preceding the current

IPO’s initial filing date using the cosine similarity method described in Section IV.

We term this similarity “Sued IPO Similarity”. The greater the similarity to past

sued IPOs, the greater is the IPO’s exposure to future liability.25

Because this variable is based on public information known at the time of initial

filing, its impact should be factored into the initial offer price (or range). This is a key

requirement making it a valid instrument for litigation risk in regressions examining

initial returns. In addition, this variable significantly predicts ex-post litigation ac-

tivity, satisfying a second key requirement. A logistic regression with the class action

lawsuit dummy variable as the dependent variable and Sued IPO Similarity plus our

25Lowry and Shu (2002) use the turnover of a matched sample of firms and Field, Lowry, and Shu(2005) use a dummy variable equal to one if the firm is in a high legal exposure industry (as definedas above median lawsuit rates in the six years prior to their sample period) as their instrumentedlitigation risk variable.

20

control variables as independent variables yields a significant coefficient, at the 1%

level, on the Sued IPO Similarity variable.

In Row 2 of Panel A of Table V, we confirm the Lowry and Shu (2002) insurance

effect as there is a significant positive relation between our instrumented measure of

ex ante liability risk and initial returns. Thus, these findings indicate that initial

returns are influenced by potential litigation.

However, we conjecture that initial returns are needed as insurance only when

there exists the potential for a material omission in the prospectus. Row 2 of Panel A

shows that initial returns are highest both when litigation risk is high and when the

issuer is classified as a low revisor. Thus, the findings on initial returns confirms not

only the presence of an insurance effect in IPO pricing but also a potential substitution

effect between pricing and disclosure in hedging against liability risk.26

Since lawsuits are predicated on two conditions, the effect of liability risk should

only be present in IPOs that are more likely to have material omissions in the prospec-

tus. For IPOs with negative changes in offer price, there is little incentive to withhold

information learned during bookbuilding and we predict that such firms are unlikely to

need the insurance effect provided by initial returns. In contrast, IPOs with positive

changes in offer price have incentives both from a timing and proprietary information

perspective to withhold information learned during bookbuilding. Although such in-

formation has a mean positive effect on offer prices, it is not completely certain, at

the time of the offering, that the information learned will result in an ex post positive

outcome.27 In the event that it does not, the firm is potentially exposed to liability

risk whose effect could be mitigated by a combination of underpricing and disclosure.

Row 3 confirms the conjecture that IPOs with negative price changes and high

revisions in the prospectus have significantly lower initial returns and thus, purchase

less insurance in the form of underpricing. The largest effect on initial returns is

associated with IPOs with positive price changes who do not revise the prospectus

26We also show in this table a relation between the 30 day Nasdaq return and initial returns.This relationship is important because this variable is used as an instrument (as in Lowry and Shu(2002)) for initial returns later in the paper.

27As discussed in Section II, one can think of the withholding not only the good informationlearned during bookbuilding but also withholding any potential risk factors associated with thatinformation.

21

(Low Revisor Dummy x ∆P+). The standard deviation of the interaction between

the revisor dummy and ∆P+ in Panel A, for the full sample (see Table III), is almost

identical to that for the subsample of non-tech IPOs in Panel B. Thus, one need only

compare the coefficients to ascertain the differences in economic magnitude. IPOs

with good information that do not revise the prospectus have 44% greater initial

returns than IPOs that do revise the prospectus in response to new information.

This relationship is robust to the exclusion of tech firms (Panel B).

We further parse the sample into IPOs that are hypothesized to be most affected

by litigation risk in Panels C and D. We define an IPO as having high ex ante litigation

risk if its Sued IPO Similarity is above the median. The difference in the amount of

underpricing between high and low revisors with ∆P+ for a one standard deviation

change is 60% in Panel C and 125% in Panel D.28 Thus, a significant portion of the

positive relation between ∆P and underpricing can be attributed to IPOs that are low

revisors. This suggests that the partial adjustment phenomenon is, in part, due to a

tradeoff between disclosure and pricing which reflects the issuer’s efforts to mitigate

litigation risk.

Further evidence on the economic magnitudes of these findings are presented in

Table VI. This table includes only IPOs with ∆P > 0 and the sample is broken into

terciles of ∆P+ as well as whether or not the IPO is classified as a low revisor or high

revisor. In addition to raw initial returns, we also show the residual initial return

which are the residuals from a regression of raw initial returns on industry and year

fixed effects, as well as the control variables from Table V: 30 day Nasdaq return,

VC dummy, log issue size, log firm age, UW $ market share, risk (1/offer price), and

volatility. The residual initial return is a measure of the unexpected initial return

after controlling for firm, market and offering characteristics.

Within each tercile and across all panels, low revisors with positive price changes

have much higher initial returns than high revisors. In addition, for almost all subsets

of low and medium ∆P+, the residual initial return is either negative or close to

28These differences are slightly larger than a comparison of the coefficients would indicate becausethe standard deviations in these subsamples now differ between ∆P+ x Low Revision Dummy (0.200in Panel C and 0.149 in Panel D) and ∆P+ x High Revision Dummy (0.179 in Panel C and 0.117in Panel D).

22

zero. We interpret this to mean that when there is only a small amount of positive

information generated during bookbuilding, there is little incentive to provide high

initial returns and/or to revise the prospectus in response to new information.

This is not the case for IPOs that increase their offer price substantially. These

IPOs are likely to have a significant amount of unexpected information generated

during bookbuilding that is incorporated into the final offer price. Since this infor-

mation is new and not in the initial prospectus, the issuer/underwriter must determine

whether or not to increase disclosure in response. If they revise the prospectus sub-

stantially, they may not only reveal potentially proprietary information to rivals but

also have to file an amendment with the SEC which could lengthen the time before

going public. Not revising the prospectus, however, exposes the firm to potential al-

legations of a material omission in the future should the ex post outcome be negative

and may, thus, require increasing the level of initial returns needed as insurance.

The differences in initial returns between low and high revisors for the tercile with

the largest ∆P+ is significant for all ∆P+ IPOs (Panel A) and when excluding tech

firms (Panel B). Residual initial returns for low revisors are twice as large as those

for high revisors for the full sample of IPOs with ∆P > 0 and four times as great

after excluding tech firms. Raw initial returns follow a similar pattern but with lower

magnitude. Of particular interest are Panels C and D which include only IPOs with

∆P > 0 that are most likely to be exposed to litigation risk. Low revisors in these

panels not only have the highest amount of residual initial return but also the largest

divergence from high revisors. Like Panel D of Table V, low revisor non-tech IPOs

that have the highest exposure to ex ante litigation risk have residual initial returns

of almost 47% compared to a negative 2% for high revision IPOs. Raw initial returns

for low revisors are over 100% while raw initial returns for high revisors are 46%.

Overall, we find evidence in support of our hypothesis that firms that have positive

information revealed during bookbuilding will be more likely to hedge the potential

litigation risk associated with their disclosure strategy through initial returns. In

other words, only those IPOs that do not revise their prospectus in response to new

information need to use underpricing as insurance against future lawsuits. We confirm

and strengthen Lowry and Shu (2002)’s results in that we find that only firms with

23

the potential for a material omission in the prospectus have a strong insurance effect

in initial returns.

VI The Probability of a Lawsuit, Disclosure Strat-

egy and Initial Returns (Deterrence Effect)

One of the challenges in the IPO literature regarding litigation risk has been the

puzzling lack of a strong deterrence effect of initial returns. Using the full sample of

Section 11 lawsuits, Lowry and Shu (2002) do not find a relation between instrumented

initial returns and the probability of subsequent lawsuit. It is only when they exclude

dismissals that a weak “deterrence effect” can be detected.

Table VII presents a series of logistic regressions that examine the effect initial

returns have on the probability of a subsequent lawsuit. As in Lowry and Shu (2002),

we instrument initial returns using the 30 day Nasdaq return prior to the filing of the

initial prospectus along with other control variables. Row 1 replicates the well-known

result from Drake and Vetsuypens (1993), that the level of initial returns are unrelated

to the presence of subsequent lawsuits. Once initial returns are instrumented to

control for endogeneity in Row 2, we still do not find a significant relation between

instrumented initial returns and whether or not the IPO was subject to ex post

litigation.

In Row 3 we include the disclosure strategy of the IPO. Issuers classified as low

revisors are significantly more likely to have a subsequent lawsuit. Withholding in-

formation learned during bookbuilding, thus, increases the probability of a future

lawsuit. Analogously, issuers that disclose more information, high revisors, are signif-

icantly less likely to be sued after the IPO. Unlike underpricing, disclosure strategy

can act as a deterrent to a future lawsuit.

Row 4 presents the results related to the interaction between the type of informa-

tion revealed during bookbuilding and the disclosure strategy. Consistent with our

prior findings, IPOs with negative changes in offer price are less likely to be involved

in subsequent lawsuits. In contrast, IPOs with positive changes in offer price, par-

ticularly those that do not revise their prospectus, are significantly more likely to be

24

subject to litigation.

The fact that these IPOs are more likely to be sued may seem counterintuitive

as one might conjecture that firms learning bad information should be more likely to

be involved in a lawsuit. However, this ignores the response of the issuing firms to

both the information and the legal environment. If bad information is revealed, there

is little benefit to withholding information, particularly if it will be revealed in the

short term and the stock price will fall. As a result, firms learning bad information

avoid this scenario by revising the prospectus and disclosing the information.

When good information is revealed, the issuing firm must choose to disclose the

information or withhold it for strategic reasons. Since information revealed is only the

mean estimate of the value of the information, the distribution of possible outcomes,

even with good information, can include an ex post negative realization. Those IPOs

with good information that revise their prospectus significantly and hence, reduce

their chance of a material omission are not at a greater risk of a lawsuit. Only those

IPOs that do not revise their prospectus are more likely to be sued. These findings

are consistent for the remaining panels of the table (Panels B, C and D) that either

exclude tech firms and/or include IPOs which are more likely to have high ex ante

litigation risk.29

Overall, lawsuits are more likely the higher the likelihood of a material omission

(as proxied for Low Revisor Dummy x ∆P+) and the more the IPO looks like an

IPO that was sued in the past. Unlike disclosure, however, initial returns have low

power to deter subsequent lawsuits. What then, does underpricing deter? In the next

section, we show that, in the event of litigation, underpricing can be a strong hedge

against IPO shareholder involvement in the lawsuit which can prevent substantial

damage to underwriter reputation.

29Also note that our measure of ex ante litigation risk, Sued IPO Similarity, is also positivelyrelated to the probability of a lawsuit which confirms its use as an instrumental variable for lawsuitprobability.

25

A What Does Underpricing Deter?

Prior studies that hypothesize that underpricing is a hedge against litigation risk

fail to consider that the primary plaintiff in most ex post lawsuits is an aftermarket

shareholder. Indeed, in our sample of over 200 lawsuits, just seven lawsuits include

only IPO purchasers but not aftermarket shareholders as plaintiffs.

Aftermarket investors are more likely to bring a lawsuit because they often buy at

higher prices than IPO purchasers and therefore, their threshold for claiming damages

is lower. Since the price aftermarket investors pay is unaffected by and does not

include any underpricing, it cannot insure against the incidence of litigation. What

underpricing can do, however, is deter IPO purchasers from joining the class. Thus,

the lawsuit can no longer be brought under Section 11 which significantly reduces the

probability the underwriter will be named in the suit.

Prior examinations of litigation risk in IPOs have restricted their sample to Section

11 lawsuits or lawsuits that only include IPO shareholders as part of the class. By

expanding our sample to include all disclosure-based class action lawsuits within

three years of the IPO, we can test whether IPO shareholders can be deterred from

joining the class by high initial returns. In Table VIII using a nested logit model, we

simultaneously estimate the affect of initial returns on the incidence of a lawsuit and

the probability that IPO shareholders will be part of the class. We use a nested logit

to incorporate the joint determination of the decision to sue and who joins the class.

Because these decisions share unobserved attributes, a key assumption of independent

errors is violated and a nested logit allows error terms to be correlated within the

nest.30

Panel A of Table VIII reflects the first node of the model, whether or not the IPO

is involved in a lawsuit, and essentially finds similar results to Table VII. In this case,

initial returns, whether instrumented or not, have no effect on a whether a lawsuit

occurs. Again, IPOs that do not revise the prospectus and who are more similar to

past sued IPOs are more likely to be sued. Consistent with the incentives mentioned

previously, this is mainly limited to IPOs with positive changes in offer price.

30Yasuda (1995) uses a nested logit to examine bank relationships and underwriter choice.

26

Panel B examines the likelihood that IPO shareholders will be included in the

lawsuit given that a lawsuit has occurred in the first node. (Note that if an IPO does

not have a lawsuit the node ends.) The results indicate a strong deterrence effect for

underpricing. Initial returns are significant and negatively related to the probability

that IPO shareholders will be a part of the class and this relation is present only for

IPOs with upward changes in offer price.

The deterrence effect in initial returns is not against a lawsuit, per se, but in

providing a disincentive for IPO investors to enter into the class. If IPO purchasers

are not in the class, the suit is unlikely to be brought under Section 11 and this reduces

the probability that the underwriter will be named in the suit. Thus, the underwriter

has an incentive to use initial returns as insurance to reduce their potential damages

and protect their reputation. The next section examines how underwriters might be

damaged by IPO lawsuits and in particular, if damages are a function of whether or

not IPO shareholders are in the class.

VII Economic Effect of Lawsuits

Given the considerable cost of insuring against potential lawsuits, we consider whether

it is worthwhile for the underwriter, in particular, to underprice an issue as a hedge

against litigation.31 Given the competitive nature of the market for investment bank-

ing services, it would seem logical that competitors would use the existence of a

lawsuit as a basis for gaining market share. Using a sample of 29 investigations,

Beatty, Bunsis, and Hand (1998) document significant declines in IPO market share

for underwriters after the announcement of an SEC investigation.32 We examine a

hierarchy of possible outcomes related to market share that incorporates merger and

acquisition activity (de-branding) in the underwriting industry based on Ljungqvist,

31Note that we do not examine the outcome of a lawsuit. Lowry and Shu (2002) point to thepotentially expensive settlements that occur in their sample. We argue, however, that the cost oflitigation is likely to be high even if a settlement is not reached. Even frivolous lawsuits will haveserious economic consequences to underwriter reputations (as shown in this section) and therefore,we do not consider whether a lawsuit has merit or not.

32Interestingly, they also find that “the number of trading days between the registration and offerdates of IPOs on which a sanctioned large or small underwriter was the lead underwriter increasesafter the SEC investigation is made public.”

27

Marston, and Wilhelm (2003). In particular, we consider three different consequences

or dependent variables for an underwriter involved in litigation. Beginning with the

most severe:

Complete De-Branding: A dummy variable indicating if the given lead under-

writer was involved in a restructuring transaction that involved an end to its

pre-restructuring brand (name) in year t+1 (i.e., if the post-restructuring firm’s

branding differs completely from the pre-merger firm’s branding). For example,

DLJ was purchased by CSFB and the post-restructuring firm was CSFB (in

this example, DLJ was de-branded and CSFB was not). Includes 15 Complete

De-Brandings.

Partial De-Branding: A dummy variable indicating if the given lead underwriter

was involved in a restructuring transaction that involved at least some re-

branding in year t + 1. For example, Cruttenden Capital and Roth Capital

merged to become Cruttenden Roth (in this example, both had at least some

re-branding). Includes 33 Partial De-Brandings which incorporate the 15 Com-

plete De-Brandings above.

Change in Underwriter Dollar Market Share: The change in proceeds weighted

by market share from year t to year t + 1.

We include as independent variables the lagged market share of the underwriter

to control for changes in market share outside of the class action lawsuit. The main

independent variable is Class Action Lawsuits, which is the natural logarithm of one

plus the number of IPOs by the given underwriter that were involved in class action

lawsuits in the previous three years. A different form of this variable is used in each

of the four different specifications for each consequence considered: 1) all lawsuits, 2)

non-tech lawsuits only, 3) lawsuits which have IPO shareholders in the class and 4)

lawsuits which do not have IPO shareholders in the class.

The results in Table IX show a strong relation between the incidence of a class ac-

tion lawsuit and subsequent restructuring and de-branding. The only type of lawsuit

that has no effect on underwriter restructure and de-branding are those that do not

28

include IPO shareholders. Therefore, the consequences for lawsuits that include IPO

shareholders in the class is strong motivation to use initial returns as a deterrent. If

underwriters can ensure that IPO shareholders will not participate in a subsequent

lawsuit, they can limit the damage to their reputation in the event one of their IPO

firms is sued.

Like Beatty, Bunsis, and Hand (1998)’s findings on SEC investigations, we find

that underwriters lose a significant amount of market share in the year after a class

action lawsuit is initiated but only if IPO shareholders are named as part of the class.

This finding is robust to including only non-technology lawsuits as the independent

variable.

In another context, Cheng, Huang, Li, and Lobo (2009) show that lawsuits with

an institutional lead plaintiff are less likely to be dismissed and have significantly

larger settlements. As most IPO investors are institutional investors (Hanley and

Wilhelm (1995), Cornelli and Goldreich (2003), Aggarwal, Prabhala, and Puri (2002),

Ljungqvist and Wilhelm (2002) and Jenkinson and Jones (2004)) this provides addi-

tional motivation as to why underwriters (and issuers) may be further motivated to

exclude IPO shareholders from the class. Thus, exposure to litigation risk and dis-

agreement regarding the optimal manner in which it is to be hedged may contribute

to the potential agency problem between the underwriter and the issuing firm.

VIII Conclusion

By using word content analysis, we are able to assess the disclosure strategy of IPO

firms in response to information learned during bookbuilding. We examine whether

issuing firms trade off disclosure with underpricing to hedge litigation risk. Our find-

ings suggest that firms with good information revealed during bookbuilding have an

incentive to withhold information for proprietary reasons and to, instead, use under-

pricing as a hedge against lawsuits. Conversely, there is little benefit to withholding

bad information as it as it may have lower proprietary value to rivals and might be

difficult to conceal for any length of time. In addition, the issuing firm has less flexibil-

ity substituting initial returns for disclosure. Thus, firms that have bad information

29

revealed are more likely to use disclosure rather than initial returns in mitigating

litigation risk. We show that these relationships are more pronounced the greater the

ex ante litigation risk exposure of the firm.

Consistent with existing literature, we find an insurance effect in initial returns,

but our findings differ in two ways. First, we add to the literature by showing that

disclosure during bookbuilding has a strong deterrence effect. Second, we find that

initial returns do not deter class action lawsuit incidence, but they do deter IPO

shareholders from joining class action lawsuits. Because aftermarket shareholders

almost always pay higher prices than IPO shareholders, these investors cannot be

deterred from suing using initial returns.

Importantly, we find that deterring IPO shareholders from joining class action

lawsuits is valuable because it limits the plaintiff’s ability to bring the suit under

Section 11 and naming the underwriter as defendant. As the primary beneficiary of

deterring IPO shareholders from the class, underwriters have an incentive to under-

price the issue aggressively to avoid the loss of their brand name and decline market

share in the event of a lawsuit.

Overall, our findings suggest that a good portion of the partial adjustment phe-

nomenon can be attributed to the issuer and underwriter’s efforts to mitigate their

exposure to litigation risk. In particular, partial adjustment arises as underwriters

require very high levels of underpricing to preserve their reputation capital should

issuers decide not to revise their prospectus after learning new information. Because

these tradeoffs are based on economic incentives inherent to the legal system, our re-

sults provide an explanation as to why the partial adjustment phenomenon continues

to remain robust.

30

Appendix 1

This Appendix explains how we compute the “document similarity” between two

documents i and j. We first take the text in each document and construct a numerical

vector summarizing the counts of its English Language word roots. This vector has

a number of elements equal to the number of word roots, and one element is the

number of times the given word root appears in the document. Word roots are

identified by Webster.com, and we use a web crawling algorithm to build a database

of the unique word roots that correspond to all English Language words that appear

in the universe of all IPO prospectuses. For example, the words display, displayed,

and display all have the same word root “display”.33 In order to conserve computing

space, we exclude articles, conjunctions, personal pronouns, abbreviations, compound

words and any words that appear fewer than a total of five times in the universe of

all words from these counts because they are not informative regarding content. This

leaves a vector of 5,803 possible words. We define this vector for the total document

as wordsi as the total number of root words used.

To measure the degree of similarity of documents i and j, we simply take the dot

product of the two word vectors normalized by their vector lengths. This quantity

is the widely used in studies of information processing and is known as the “cosine

similarity” method (see Kwon and Lee (2003) for more information), because it mea-

sures the angle between two word vectors on a unit sphere. We refer to this quantity

as “document similarity”.

Document Similarityi,j =wordsi · wordsj

‖wordsi‖ ‖wordsj‖(3)

Because all word vectors wordsi have elements that are non-negative, this measure

of document similarity has the nice property of being bounded in the interval (0,1).

Intuitively, the similarity between two documents is closer to one when they are

more similar and can never be less than zero if they are entirely different. We define

document distance as one minus document similarity.

33Methodologically, we first create a vector of all word counts in the document, and we thenreplace each word with its word root. We then tabulate the frequency vector for the given documentbased on the total counts of each word root.

31

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34

Table I: Sample Summary Statistics

Summary statistics are reported for 1,623 IPOs issued in the US from November 1996 to October 2005 excluding:firms with an issue price less than five dollars, ADRs, financial firms, unit IPOs, dual class IPOs, and REITs.Initial Return is the actual return from the IPO offer price to the first CRSP reported closing price. ∆P is thereturn from the filing date midpoint to the IPO offer price, and ∆P+ and ∆P- are its positive and negativetruncated components. The IPO Size at Filing is the original filing amount in millions. Firm Age is the IPOyear minus the firm’s founding date, where founding dates are obtained from the Field-Ritter dataset, as used inField and Karpoff (2002) and Loughran and Ritter (2004). The VC Dummy is equal to one if a firm is VCfinanced. The Tech Dummy is equal to one if a firm resides in a technology industry as identified in Loughran andRitter (2004). Lead UW $ Market Share is the lead underwriter’s dollar market share in the past calendar year.Pre-Issue Nasdaq Returns are returns for the 30 trading days preceding the issue date. Volatility is the log offirm risk as measured using the matching method in Lowry and Shu (2002). Risk equal to (1/Pmid) as in Bradleyand Jordan (2002). Document Root Words (wordsi) is the number of root words used in the prospectus. TheNumber of Prospectus Filings is the number of amendments in the given IPO’s sequence of filings. The ClassAction Lawsuit Dummy is one if a class action lawsuit is filed against the IPO firm in the three year periodfollowing its IPO, and Class Action w/IPO Investors is a dummy variable indicating when IPO shareholders areamong the class of shareholders in the lawsuit.

Std.

Variable Mean Dev. Minimum Median Maximum Obs.

Panel A: Price Variables

Initial Return (IR) 0.379 0.709 -0.399 0.146 6.267 1,623

Price Adjustment(∆P ) 0.050 0.284 -0.657 0.000 2.200 1,623

∆P+ = Max[0, ∆P ] 0.122 0.219 0.000 0.000 2.200 1,623

∆P− = Min[0, ∆P ] -0.072 0.122 0.000 0.000 -0.657 1,623

Panel B: IPO Variables

IPO Size at Filing 213.57 1293.6 3.75 64.000 46,926.1 1,623

Firm Age 13.744 20.365 0.000 7.000 165.0 1,623

VC Dummy 0.496 0.500 0.000 0.000 1.000 1,623

Tech Dummy 0.455 0.498 0.000 0.000 1.000 1,623

Underwriter Dollar Mkt Share 0.030 0.026 0.000 0.024 0.147 1,623

30 Day Pre-Offer Nasdaq Return 0.053 0.088 -0.265 0.057 0.359 1,623

Risk 0.083 0.029 0.008 0.077 0.250 1,623

Volatility -1.675 0.420 -3.448 -1.645 -0.148 1,623

Panel C: Prospectus Variables

Document Root Words (wordsi) 9968.94 3290.62 4338.00 9341.00 35942.0 1,623

Number of Prospectus Filings 5.052 1.575 1.000 5.000 12.000 1,623

Panel D: Lawsuit Variables

Class Action Lawsuit Dummy 0.102 0.302 0.000 0.000 1.000 1,623

Class Action w/ IPO Investors 0.049 0.215 0.000 0.000 1.000 1,623

35

Table II: Summary of Prospectus and Amendment Filing Patterns

The table reports the average number of raw words and the severity of revision since the last amendment for eachseries of prospectus amendments for each IPO. Panel A is based on all IPOs, and Panels B and C based on low andhigh revision IPOs. To categorize low and high revisors (used to create the subsamples used in Panels B and C,respectively), we first compute the raw Revision Distance for each prospectus amendment as one minus the similarity(based on cosine similarities) between the given prospectus and the preceding one. The normalized revision distanceis this distance scaled by the maximum distance among the first two revisions (which is likely regulation-driven). AnIPO is a Low Revisor if at least two thirds of its normalized revisions exceed the cross sectional median normalizedrevision. Otherwise, it is deemed a High Revisor. The total distance from previous is the raw revision distancebetween the current amendment and the previous filing. We report this as a cumulative fraction in the cumulativedistance column. The days since last amendment is the number of days that have elapsed between the previousprospectus and the current amendment. The total number of IPOs for which the given number of prospectuses arefiled is reported in the last column. All columns are based on the actual order in which amendments are made.

Total Total Cum- Days

Number Dist uative Since

Amend- Raw from Dist- Last

mend Words prev ance Amendment Obs

Panel A: All IPOs

Initial 34,749 0.000 0.000 0.0 1623

2 36,725 0.032 0.612 45.3 1620

3 37,841 0.014 0.844 24.1 1599

4 38,925 0.009 0.939 18.9 1376

5 40,410 0.006 0.975 14.0 984

6 42,425 0.004 0.991 12.0 530

7 42,578 0.004 0.997 10.7 277

8 43,438 0.002 0.999 7.9 123

9 49,100 0.002 1.000 8.7 45

10 48,415 0.001 1.000 7.3 15

11 47,068 0.001 1.000 11.8 4

12 50,033 0.000 1.000 2.3 3

Panel B: Low Revisors

Initial 33,574 0.000 0.000 0.0 610

2 35,415 0.047 0.759 48.9 607

3 36,183 0.013 0.941 22.0 586

4 36,952 0.004 0.978 13.2 501

5 37,779 0.002 0.992 8.4 416

6 39,758 0.002 0.997 7.7 167

7 40,212 0.001 0.999 6.4 82

8 41,632 0.001 1.000 5.0 47

9 48,534 0.001 1.000 3.2 13

10 46,863 0.000 1.000 1.0 5

11 38,050 0.000 1.000 1.0 1

12 1.000

Panel C: High Revisors

Initial 35,457 0.000 0.000 0.0 1013

2 37,510 0.023 0.524 43.2 1013

3 38,801 0.014 0.787 25.4 1013

4 40,055 0.012 0.916 22.1 875

5 42,336 0.009 0.965 18.1 568

6 43,652 0.005 0.987 14.0 363

7 43,573 0.006 0.996 12.4 195

8 44,554 0.003 0.999 9.7 76

9 49,330 0.003 1.000 11.0 32

10 49,191 0.002 1.000 10.4 10

11 50,073 0.001 1.000 15.3 3

12 50,033 0.000 1.000 2.3 3

36

Table III: Summary Statistics on Disclosure Strategy

Summary statistics are reported for 1,623 IPOs issued in the US from November 1996 to October 2005 excluding:firms with an issue price less than five dollars, ADRs, financial firms, unit IPOs, dual class IPOs, and REITs. ∆P isthe return from the filing date midpoint to the IPO offer price, and ∆P+ and ∆P- are its positive and negativetruncated components. Th To categorize firms by revision intensity, we first compute Revision Distance as oneminus the similarity for each prospectus amendment in the time series of amendments for each IPO. The normalizedrevision distance is this raw distance scaled by the maximum raw distance among the first two revisions (which islikely regulation-driven). The Low Revisor Dummy is one for a given IPO if at least two thirds of its normalizedrevisions exceed the cross sectional median normalized revision. We also consider cross terms of this variable withthe upward and downward price adjustment variables ∆P+ and | ∆P− |. The High Revisor Dummy is (1-LowRevisor Dummy).

Std.

Variable Mean Dev. Minimum Median Maximum Obs.

∆P+ = Max[0, ∆P ] 0.122 0.219 0.000 0.000 2.200 1,623

∆P− = Min[0, ∆P ] -0.072 0.122 0.000 0.000 -0.657 1,623

Low Revisor Dummy 0.376 0.484 0.000 0.000 1.000 1,623

∆P+ x Low Revisor Dummy 0.056 0.166 0.000 0.000 2.200 1,623

∆P+ x High Revisor Dummy 0.066 0.167 0.000 0.000 2.000 1,623

| ∆P− | x Low Revisor Dummy 0.023 0.077 0.000 0.000 0.657 1,623

| ∆P− | x High Revisor Dummy 0.049 0.106 0.000 0.000 0.583 1,623

37

Table IV: Difference in Means By Revision Intensity

Summary statistics are reported for various subsamples of 1,623 IPOs issued in the US from November 1996 toOctober 2005 excluding: firms with an issue price less than five dollars, ADRs, financial firms, unit IPOs, dual classIPOs, and REITs. Variable descriptions are summarized in Table I. To identify high and low revisors, we firstcompute the raw Revision Distance for each prospectus amendment as one minus the similarity (based on cosinesimilarities) between the given prospectus and the preceding one. The normalized revision distance is this distancescaled by the maximum distance among the first two revisions (which is likely regulation-driven). The LowRevisor Dummy is one for a given IPO if at least two thirds of its normalized revisions exceed the cross sectionalmedian normalized revision. We also consider cross terms of this variable with the upward and downward priceadjustment variables ∆P+ and | ∆P− |. The High Revisor Dummy is (1-Low Revisor Dummy).

Low High Difference

Variable Revisor Revisor t-stat

Initial Return 0.466 0.326 3.872

∆P 0.086 0.028 4.033

∆P+ 0.148 0.105 3.810

abs∆P− 0.062 0.078 -2.520

Class Action Lawsuit Dummy 0.125 0.088 2.374

Log IPO Proceeds 4.277 4.339 -1.060

VC Dummy 0.482 0.504 -0.877

Technology Dummy 0.467 0.448 0.746

UW $ Market Share 0.028 0.030 -1.669

38

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P+

|∆P−|

|∆P−|

Du

mm

yS

ize

Du

mm

yR

etu

rnR

2O

bs

PanelA

:A

llIP

Os

(1)

0.0

59

0.0

83

-0.0

21

0.2

07

1.2

53

0.2

60

1,6

23

(0.9

4)

(3.2

0)

(-1.2

1)

(4.5

7)

(4.5

9)

(2)

1.4

82

0.0

83

-0.0

33

0.1

40

1.1

50

0.2

63

1,6

23

(3.4

8)

(3.1

2)

(-1.8

9)

(3.2

7)

(4.4

5)

(3)

0.8

12

2.1

83

1.4

99

-0.1

75

-0.3

52

-0.0

69

-0.0

37

0.0

69

-0.1

84

0.5

13

1,6

23

(2.4

2)

(38.5

2)

(13.3

7)

(-1.3

8)

(-4.3

9)

(-2.6

1)

(-2.1

5)

(2.6

2)

(-0.9

6)

PanelB:N

on-T

ech

IPO

s

(4)

0.7

53

0.0

70

-0.0

20

0.1

13

0.9

60

0.2

39

884

(2.3

9)

(2.4

8)

(-1.2

1)

(2.1

8)

(3.3

8)

(5)

0.3

62

2.1

55

1.3

42

-0.1

59

-0.2

51

-0.0

23

-0.0

01

0.0

73

0.0

76

0.4

96

884

(1.2

9)

(14.0

5)

(5.5

1)

(-1.0

8)

(-3.3

1)

(-0.9

2)

(-0.0

6)

(1.8

0)

(0.3

8)

PanelC:H

igh

Ex

Ante

Litig

ation

Ris

kIP

Os

(6)

0.8

20

0.0

85

-0.0

39

0.1

83

1.6

93

0.3

23

814

(1.1

3)

(1.4

9)

(-1.2

6)

(3.6

2)

(4.4

2)

(7)

1.1

98

2.0

80

1.4

70

-0.2

83

-0.2

62

-0.0

82

-0.0

64

0.0

52

-0.1

47

0.5

82

814

(1.9

0)

(30.6

8)

(10.6

4)

(-1.2

1)

(-2.2

8)

(-2.5

6)

(-1.6

6)

(1.6

6)

(-0.5

8)

PanelD

:N

on-T

ech

Hig

hEx

Ante

Litig

ation

Ris

kIP

Os

(8)

-0.2

83

0.0

98

-0.1

13

0.2

02

1.1

58

0.3

33

340

(-0.4

5)

(1.6

5)

(-2.6

5)

(2.0

2)

(2.6

3)

(9)

0.0

53

2.2

62

1.2

57

-0.2

37

0.0

55

-0.0

04

-0.0

60

0.0

83

-0.0

19

0.6

36

340

(0.1

0)

(10.5

2)

(4.6

6)

(-0.8

2)

(0.4

3)

(-0.1

1)

(-1.4

1)

(1.3

1)

(-0.0

9)

39

Tab

leV

I:R

evis

orIn

tensi

tyan

dP

arti

alA

dju

stm

ent

Eco

nom

icM

agnit

udes

Su

mm

ary

stati

stic

sare

rep

ort

edfo

rvari

ou

ssu

bsa

mp

les

of

1,6

23

IPO

sis

sued

inth

eU

Sfr

om

Novem

ber

1996

toO

ctob

er2005

excl

ud

ing:

firm

sw

ith

an

issu

ep

rice

less

than

five

dollars

,A

DR

s,fi

nan

cial

firm

s,u

nit

IPO

s,d

ual

class

IPO

s,an

dR

EIT

s.P

an

elA

rep

ort

sre

sult

sfo

rall

Del

taP

+IP

Os,

an

dP

an

elB

for

non

-tec

hn

olo

gy

Del

taP

+IP

Os.

Pan

elC

incl

ud

esD

elta

P+

IPO

sw

ith

hig

hex

-ante

liti

gati

on

risk

(ab

ove

med

ian

Su

edIP

OS

imilari

ty).

Pan

elD

furt

her

rest

rict

sth

esa

mp

lein

Pan

elC

ton

on

-tec

hn

olo

gy

firm

s.T

he

tab

led

isp

lays

aver

age

∆P

,in

itia

lre

turn

s,an

dre

sid

ual

init

ial

retu

rns

for

terc

ile-

sub

sam

ple

sw

ith

low

,m

ediu

m,

an

dh

igh

off

erp

rice

ad

just

men

ts(∆

P).

Ter

cile

sare

form

edin

each

yea

r.R

esid

ual

init

ial

retu

rns

are

the

resi

du

als

from

are

gre

ssio

nof

raw

init

ial

retu

rns

on

ind

ust

ryan

dyea

rfi

xed

effec

ts,

as

wel

las

the

contr

ol

vari

ab

les

from

Tab

leV

:30

day

Nasd

aq

retu

rn,

VC

du

mm

y,lo

gis

sue

size

,lo

gfi

rmage,

UW

$m

ark

etsh

are

,ri

sk(1

/off

erp

rice

),an

dvola

tili

ty.

All

contr

ol

vari

ab

les

are

des

crib

edin

Tab

leI.

To

iden

tify

hig

han

dlo

wre

vis

ors

,w

efi

rst

com

pu

teth

era

wR

evis

ion

Dis

tan

cefo

rea

chp

rosp

ectu

sam

end

men

tas

on

em

inu

sth

esi

milari

ty(b

ase

don

cosi

ne

sim

ilari

ties

)b

etw

een

the

giv

enp

rosp

ectu

san

dth

ep

rece

din

gon

e.T

he

norm

alize

dre

vis

ion

dis

tan

ceis

this

dis

tan

cesc

ale

dby

the

maxim

um

dis

tan

ceam

on

gth

efi

rst

two

revis

ion

s(w

hic

his

likel

yre

gu

lati

on

-dri

ven

).A

nIP

Ois

aLow

Revis

or

ifat

least

two

thir

ds

of

its

norm

ali

zed

revis

ion

sex

ceed

the

cross

sect

ion

al

med

ian

norm

alize

dre

vis

ion

.O

ther

wis

e,it

isd

eem

eda

Hig

hR

evis

or.

Low

Rev

isors

Hig

hR

evis

ors

Res

idu

al

Res

idu

al

Init

ial

Init

ial

Nu

mb

erof

Init

ial

Init

ial

Nu

mb

erof

Vari

ab

le∆

PR

etu

rnR

etu

rnO

bs

∆P

Ret

urn

Ret

urn

Ob

s

PanelA

:A

llIP

Os

with

∆P

>0

Low

∆P

+0.0

83

0.3

36

-0.3

03

110

0.0

70

0.2

87

-0.2

37

153

Med

ium

∆P

+0.2

16

0.6

41

-0.0

35

111

0.1

70

0.5

46

-0.0

75

165

Hig

h∆

P+

0.5

21

1.3

91

0.4

83

110

0.4

45

0.9

42

0.2

13

153

PanelB:N

on-T

ech

IPO

swith

∆P

>0

Low

∆P

+0.0

67

0.2

29

-0.1

61

44

0.0

56

0.1

41

-0.2

04

74

Med

ium

∆P

+0.1

36

0.3

57

-0.0

59

56

0.1

25

0.3

69

-0.0

31

87

Hig

h∆

P+

0.3

59

1.0

35

0.4

15

49

0.3

02

0.5

86

0.0

97

80

PanelC:H

igh

Ex

Ante

Litig

ation

Ris

kIP

Os

with

∆P

>0

Low

∆P

+0.1

07

0.4

54

-0.3

55

54

0.0

81

0.4

63

-0.1

20

75

Med

ium

∆P

+0.2

48

0.7

32

0.0

06

60

0.1

89

0.5

74

-0.1

00

83

Hig

h∆

P+

0.6

17

1.5

66

0.4

78

57

0.4

60

0.8

96

0.1

15

77

PanelD

:N

on-T

ech

Hig

hEx

Ante

Litig

ation

Ris

kIP

Os

with

∆P

>0

Low

∆P

+0.0

74

0.1

62

-0.2

75

15

0.0

60

0.1

58

-0.0

99

29

Med

ium

∆P

+0.1

52

0.4

64

0.0

31

23

0.1

37

0.3

43

-0.0

93

32

Hig

h∆

P+

0.3

76

1.1

34

0.4

69

21

0.2

86

0.4

60

-0.0

20

30

40

Tab

leV

II:

Law

suit

Pro

bab

ilit

y

Logit

regre

ssio

ns

wit

hin

du

stry

and

yea

rly

fixed

effec

tsare

pre

sente

dfo

r1,6

23

IPO

sis

sued

inth

eU

Sfr

om

Novem

ber

1996

toO

ctob

er2005

excl

ud

ing:

firm

sw

ith

an

issu

ep

rice

less

than

five

dollars

,A

DR

s,fi

nan

cial

firm

s,u

nit

IPO

s,d

ual

class

IPO

s,an

dR

EIT

s.T

he

dep

end

ent

vari

ab

leis

ad

um

my

vari

ab

leeq

ual

toon

eif

the

IPO

firm

was

sued

ina

class

act

ion

law

suit

.P

an

elA

incl

ud

esall

IPO

san

dP

an

elB

incl

ud

eson

lyn

on

-tec

hIP

Os.

Pan

els

Can

dD

resp

ecti

vel

yre

stri

ctth

esa

mp

les

inP

an

els

Aan

dB

toIP

Os

wit

hh

igh

ex-a

nte

liti

gati

on

risk

,as

mea

sure

dby

the

med

ian

ex-a

nte

Su

edIP

OS

imilari

ty.

Th

ein

dep

end

ent

vari

ab

les

incl

ud

eth

eIn

itia

lR

etu

rn

,th

ein

stru

men

ted

init

ial

retu

rn,

an

dth

eu

nd

erw

rite

r’s

upw

ard

pri

cead

just

men

t(∆

P+

).W

eco

nsi

der

both

raw

init

ial

retu

rns

an

din

stru

men

ted

init

ial

retu

rns

from

atw

ost

age

regre

ssio

nm

od

elas

inL

ow

ryan

dS

hu

(2002).

Ou

rin

stru

men

tal

vari

ab

leis

the

30

Day

Nasd

aq

Retu

rn

(th

eN

AS

DA

Qre

turn

for

the

30

trad

ing

days

pre

ced

ing

the

off

erd

ate

).Sued

IPO

Sim

ilarity

isth

eaver

age

docu

men

tsi

milari

tyb

etw

een

the

curr

ent

IPO

’sin

itia

lp

rosp

ectu

san

dth

ep

rosp

ectu

ses

of

IPO

firm

sth

at

wer

esu

edin

the

yea

rp

rior

toth

ecu

rren

tIP

O’s

filin

gd

ate

.T

oca

tegori

zefi

rms

by

revis

ion

inte

nsi

ty,

we

firs

tco

mp

ute

Rev

isio

nD

ista

nce

as

on

em

inu

sth

esi

milari

tyfo

rea

chp

rosp

ectu

sam

end

men

tin

the

tim

ese

ries

of

am

end

men

tsfo

rea

chIP

O.

Th

en

orm

alize

dre

vis

ion

dis

tan

ceis

this

raw

dis

tan

cesc

ale

dby

the

maxim

um

raw

dis

tan

ceam

on

gth

efi

rst

two

revis

ion

s(w

hic

his

likel

yre

gu

lati

on

-dri

ven

).T

he

Hig

hR

evis

or

Dum

my

ison

efo

ra

giv

enIP

Oif

more

than

on

eth

ird

of

its

norm

alize

dre

vis

ion

sex

ceed

the

cross

sect

ion

al

med

ian

norm

alize

dre

vis

ion

inre

vis

ion

inte

nsi

ty.

We

als

oco

nsi

der

cross

term

sof

this

vari

ab

lew

ith

the

pri

cead

just

men

tvari

ab

les

∆P

+an

d|∆

P−|.

Contr

ols

for

ind

ust

ryan

dyea

rfi

xed

effec

ts,

tech

nolo

gy

IPO

s,u

nd

erw

rite

r$

mark

etsh

are

,lo

gfi

rmage,

firm

risk

(1/off

erp

rice

),an

dlo

gvola

tility

are

als

oin

clu

ded

inth

ere

gre

ssio

n,

bu

tn

ot

dis

pla

yed

toco

nse

rve

space

.A

llco

ntr

ol

vari

ab

les

are

des

crib

edin

Tab

leI.

All

stan

dard

erro

rsare

ad

just

edfo

rcl

ust

erin

gw

ith

inyea

ran

din

du

stry

.

Inst

ru.

Low

Hig

hL

ow

Hig

hS

ued

men

ted

Rev

isor

Rev

isor

Rev

isor

Rev

isor

Low

IPO

Log

VC

-

Init

ial

Init

ial

Du

mm

yx

Du

mm

yx

Du

mm

yx

Du

mm

yx

Rev

isor

Sim

il-

Issu

eback

edP

seu

do

Row

Ret

urn

Ret

urn

∆P

+∆

P+

|∆P−|

|∆P−|

Du

mm

yari

tyS

ize

Du

mm

yR

2O

bs

PanelA

:A

llIP

Os

(1)

0.1

22

4.5

68

0.1

16

0.4

33

0.1

14

1,6

23

(1.1

1)

(2.7

3)

(1.0

8)

(1.6

8)

(2)

1.3

69

3.9

51

0.1

40

0.1

88

0.1

17

1,6

23

(1.6

2)

(2.6

0)

(1.2

4)

(0.6

5)

(3)

0.4

05

4.6

04

0.1

19

0.4

91

0.1

19

1,6

23

(2.0

1)

(2.7

2)

(1.1

5)

(2.0

1)

(4)

0.7

98

0.1

30

-1.4

37

-1.4

56

0.2

51

4.5

53

0.1

32

0.4

48

0.1

27

1,6

23

(2.7

1)

(0.2

4)

(-0.8

6)

(-1.1

4)

(0.9

8)

(2.6

8)

(1.2

0)

(1.7

4)

PanelB:N

on-tec

hIP

Os

(5)

0.3

59

3.9

49

0.2

21

0.4

57

0.1

72

884

(1.2

2)

(2.0

6)

(1.2

7)

(1.1

1)

(6)

1.8

13

-0.4

89

-0.2

58

-2.0

28

0.0

05

3.7

65

0.2

81

0.4

13

0.1

86

884

(3.8

1)

(-0.4

0)

(-0.1

2)

(-1.1

1)

(0.0

1)

(1.9

8)

(1.6

5)

(0.9

5)

PanelC:H

igh

Ex

Ante

Litig

ation

Ris

kIP

Os

(7)

0.4

61

6.2

69

0.0

99

0.1

74

0.1

53

814

(1.9

7)

(1.4

2)

(0.5

4)

(0.6

2)

(8)

0.7

93

0.0

04

-0.5

50

-1.4

07

0.2

11

6.5

39

0.1

02

0.1

04

0.1

61

814

(2.6

2)

(0.0

1)

(-0.2

2)

(-0.8

6)

(0.5

9)

(1.4

6)

(0.5

3)

(0.3

6)

PanelD

:N

on-T

ech

Hig

hEx

Ante

Litig

ation

Ris

kIP

Os

(9)

0.8

00

-5.2

73

-0.0

71

-0.1

29

0.3

20

340

(1.6

1)

(-0.5

7)

(-0.1

5)

(-0.1

9)

(10)

1.7

66

-4.9

59

4.0

40

-2.5

54

-0.1

42

-3.7

10

0.0

89

-0.1

04

0.3

49

340

(2.7

7)

(-1.1

3)

(1.0

7)

(-0.8

8)

(-0.2

2)

(-0.4

1)

(0.1

9)

(-0.1

4)

41

Tab

leV

III:

Nes

ted

Log

isti

cL

awsu

itM

odel

Nes

ted

Logis

tic

regre

ssio

ns

wit

hin

du

stry

an

dyea

rly

fixed

effec

tsare

pre

sente

dfo

r1,6

23

IPO

sis

sued

inth

eU

Sfr

om

Novem

ber

1996

toO

ctob

er2005

excl

ud

ing:

firm

sw

ith

an

issu

ep

rice

less

than

five

dollars

,A

DR

s,fi

nan

cial

firm

s,u

nit

IPO

s,d

ual

class

IPO

s,an

dR

EIT

s.R

ob

ust

stan

dard

erro

rsare

rep

ort

ed.

Th

efi

rst

nod

eof

the

nes

ted

tree

isb

inary

an

dth

ed

epen

den

tvari

ab

leis

ad

um

my

vari

ab

leeq

ual

toon

eif

the

IPO

firm

was

sued

ina

class

act

ion

law

suit

.If

the

firm

was

not

sued

,th

etr

eeen

ds.

Ifth

efi

rmw

as

sued

,th

eou

ter

nod

ed

epen

den

tvari

ab

leis

an

ind

icato

req

ual

toon

eif

IPO

share

hold

ers

join

edth

ecl

ass

act

ion

law

suit

.P

an

elA

rep

ort

sth

ere

sult

sfo

rth

ein

ner

nod

em

od

el,

wh

ich

isan

alo

gou

sto

the

logis

tic

mod

elin

Tab

leV

II.

Pan

elB

rep

ort

sth

ere

sult

sfo

rth

eou

ter

nod

evari

ab

les.

Th

ein

dep

end

ent

vari

ab

les

incl

ud

eth

eIn

itia

lR

etu

rn

an

dth

ein

stru

men

ted

init

ial

retu

rn.

We

con

sid

erb

oth

raw

init

ial

retu

rns

an

din

stru

men

ted

init

ial

retu

rns

from

atw

ost

age

regre

ssio

nm

od

elas

inL

ow

ryan

dS

hu

(2002).

Ou

rin

stru

men

tal

vari

ab

leis

the

30

Day

Nasd

aq

Retu

rn

(th

eN

AS

DA

Qre

turn

for

the

30

trad

ing

days

pre

ced

ing

the

off

erd

ate

).Sued

IPO

Sim

ilarity

isth

eaver

age

docu

men

tsi

milari

tyb

etw

een

the

curr

ent

IPO

’sin

itia

lp

rosp

ectu

san

dth

ep

rosp

ectu

ses

of

IPO

firm

sth

at

wer

esu

edin

the

yea

rp

rior

toth

ecu

rren

tIP

O’s

filin

gd

ate

.T

oca

tegori

zefi

rms

by

revis

ion

inte

nsi

ty,

we

firs

tco

mp

ute

Rev

isio

nD

ista

nce

as

on

em

inu

sth

esi

milari

tyfo

rea

chp

rosp

ectu

sam

end

men

tin

the

tim

ese

ries

of

am

end

men

tsfo

rea

chIP

O.

Th

en

orm

alize

dre

vis

ion

dis

tan

ceis

this

raw

dis

tan

cesc

ale

dby

the

maxim

um

raw

dis

tan

ceam

on

gth

efi

rst

two

revis

ion

s(w

hic

his

likel

yre

gu

lati

on

-dri

ven

).T

he

Hig

hR

evis

or

Dum

my

ison

efo

ra

giv

enIP

Oif

more

than

on

eth

ird

of

its

norm

alize

dre

vis

ion

sex

ceed

the

cross

sect

ion

al

med

ian

norm

alize

dre

vis

ion

inre

vis

ion

inte

nsi

ty.

We

als

oco

nsi

der

cross

term

sof

this

vari

ab

lew

ith

the

upw

ard

pri

cead

just

men

tvari

ab

le∆

P+

.C

ontr

ols

for

ind

ust

ryan

dyea

rfi

xed

effec

ts,

tech

nolo

gy

IPO

s,u

nd

erw

rite

r$

mark

etsh

are

,lo

gfi

rmage,

firm

risk

(1/off

erp

rice

),an

dlo

gvola

tility

are

als

oin

clu

ded

inth

ere

gre

ssio

n,

bu

tn

ot

dis

pla

yed

toco

nse

rve

space

.A

llco

ntr

ol

vari

ab

les

are

des

crib

edin

Tab

leI.

PanelA

(Inner

Node):

DependentVari

able

=Cla

ssAction

Lawsu

itD

um

my

Inst

ru.

Su

edL

ead

men

ted

Low

IPO

UW

Log

Log

VC

-L

og

Init

ial

Init

ial

Rev

isor

Sim

il-

Mark

etIs

sue

Fir

mb

ack

edV

ola

-

Row

Mod

el:

Sam

ple

Ret

urn

Ret

urn

Du

mm

yari

tyS

hare

Siz

eA

ge

Du

mm

yR

isk

tility

Ob

s

(1)

Mod

el1:

All

IPO

s0.1

30.4

04.8

1-0

.86

0.1

2-0

.13

0.5

1-5

.93

-0.3

91,6

23

(0.0

9)

(2.3

4)

(2.6

5)

(-0.2

2)

(1.1

1)

(-1.3

2)

(2.1

4)

(-1.2

2)

(-1.0

9)

(2)

Mod

el2:

All

IPO

s0.4

30.3

44.4

3-4

.23

0.1

4-0

.09

0.3

6-6

.95

-0.2

91,6

23

(0.0

2)

(1.4

2)

(2.6

2)

(-0.6

3)

(1.1

2)

(-0.7

7)

(1.3

6)

(-1.1

3)

(-0.5

4)

(3)

Mod

el3:

∆P≤

0IP

Os

0.5

10.1

90.9

7-1

.67

0.1

8-0

.13

0.4

5-3

.49

-0.4

0821

(0.1

0)

(0.6

8)

(0.4

6)

(-0.2

7)

(1.1

7)

(-0.8

7)

(1.2

3)

(-0.4

3)

(-0.9

0)

(4)

Mod

el4:

∆P≤

0IP

Os

-1.9

3-0

.18

2.3

8-3

.30

0.2

7-0

.11

-0.0

5-3

.04

-0.2

5821

(-0.1

5)

(-0.2

5)

(0.1

4)

(-0.3

6)

(0.2

2)

(-0.6

5)

(0.0

6)

(-0.0

6)

(-0.1

2)

(5)

Mod

el5:

∆P

>0

IPO

s0.2

10.5

49.7

4-2

.52

0.1

3-0

.11

0.6

0-4

.04

-0.4

3802

(0.1

7)

(2.1

5)

(3.1

6)

(-0.4

7)

(0.8

6)

(-0.7

9)

(1.9

4)

(-0.6

4)

(-0.8

5)

(6)

Mod

el6:

∆P

>0

IPO

s0.0

90.5

79.7

5-0

.60

0.1

4-0

.14

0.6

8-3

.86

-0.4

7802

(0.0

1)

(2.0

4)

(3.9

3)

(-0.0

7)

(0.7

9)

(-0.7

0)

(1.5

9)

(-0.5

5)

(-0.7

8)

PanelB

(Oute

rN

ode):

IPO

Share

hold

ers

Inclu

ded

Inst

rum

ente

d

Row

Mod

el:

Sam

ple

Init

ial

Ret

urn

Init

ial

Ret

urn

(1)

Mod

el1:

All

IPO

s-1

.15

(-3.0

4)

(2)

Mod

el2:

All

IPO

s-1

.90

(-1.9

5)

(3)

Mod

el3:

∆P≤

0IP

Os

-3.9

9

(-0.8

9)

(4)

Mod

el4:

∆P≤

0IP

Os

-0.6

4

(-0.0

8)

(5)

Mod

el3:

∆P

>0

IPO

s-1

.28

(-2.5

7)

(6)

Mod

el4:

∆P

>0

IPO

s-3

.06

(-3.0

3)

42

Tab

leIX

:E

ffec

tof

Obse

rved

Lit

igat

ion

onF

utu

reU

nder

wri

ter

Mar

ket

Shar

ean

dD

e-bra

ndin

gR

estr

uct

uri

ng

Logis

tic

(for

du

mm

yvari

ab

les)

or

OL

S(f

or

chan

ge

inm

ark

etsh

are

)re

gre

ssio

ns

wit

hyea

rly

fixed

effec

tsfo

r643

chan

ges

inu

nd

erw

rite

rm

ark

etsh

are

ob

serv

ati

on

sfr

om

Novem

ber

1996

toO

ctob

er2005

excl

ud

ing:

firm

sw

ith

an

issu

ep

rice

less

than

five

dollars

,A

DR

s,fi

nan

cial

firm

s,u

nit

IPO

s,d

ual

class

IPO

s,an

dR

EIT

s.A

llst

an

dard

erro

rsare

ad

just

edfo

rcl

ust

erin

gw

ith

inyea

ran

dby

un

der

wri

ter.

Th

ed

epen

den

tvari

ab

leis

note

din

the

firs

tco

lum

n.

Th

eC

om

ple

teD

e-B

randin

g(R

est

ructu

re)

vari

ab

leis

ad

um

my

ind

icati

ng

ifth

egiv

enle

ad

un

der

wri

ter

was

involv

edin

are

stru

ctu

rin

gtr

an

sact

ion

that

involv

edan

end

toit

sp

re-r

estr

uct

uri

ng

bra

nd

inyea

rt

+1

(i.e

.,if

the

post

-res

tru

ctu

rin

gfi

rm’s

bra

nd

ing

diff

ers

com

ple

tely

from

the

pre

-mer

ger

firm

’sb

ran

din

g).

For

exam

ple

,D

LJ

was

pu

rch

ase

dby

CS

FB

an

dth

ep

ost

-res

tru

ctu

rin

gfi

rmw

as

CS

FB

(in

this

exam

ple

,D

LJ

was

de-

bra

nd

edan

dC

SF

Bw

as

not)

.T

he

Parti

alD

e-B

randin

g(R

est

ructu

re)

vari

ab

leis

ad

um

my

ind

icati

ng

ifth

egiv

enle

ad

und

erw

rite

rw

as

involv

edin

are

stru

ctu

rin

gtr

an

sact

ion

that

involv

edat

least

som

ere

-bra

nd

ing

inyea

rt

+1.

For

exam

ple

,C

rutt

end

enC

ap

ital

an

dR

oth

Cap

ital

mer

ged

tob

ecom

eC

rutt

end

enR

oth

(in

this

exam

ple

,b

oth

had

at

least

som

ere

-bra

nd

ing).

Th

eC

hange

inU

nderw

rit

er

Dollar

Market

Share

isth

ech

an

ge

inp

roce

eds

wei

ghte

dm

ark

etsh

are

from

yea

rt

toyea

rt

+1.

Th

ein

dep

end

ent

vari

ab

les

incl

ud

eth

eC

lass

Acti

on

Law

suit

s,w

hic

his

the

natu

ral

logari

thm

of

on

ep

lus

the

nu

mb

erof

IPO

sp

revio

usl

yle

ad

un

der

wri

tten

by

the

giv

enu

nd

erw

rite

rth

at

wer

ein

volv

edin

class

act

ion

law

suit

sin

the

pre

vio

us

inth

ep

ast

thre

eyea

rs.

We

con

sid

erfu

rth

erlim

itin

gth

issa

mp

leof

law

suit

sin

vari

ou

sw

ays,

as

note

din

the

seco

nd

colu

mn

an

dco

nsi

der

the

follow

ing

gro

up

s:all

class

act

ion

law

suit

s,n

on

-tec

hn

olo

gy

firm

law

suit

son

ly,

law

suit

sin

wh

ich

IPO

inves

tors

join

edth

ecl

ass

,an

dth

ose

that

did

not

incl

ud

eIP

Oin

ves

tors

inth

ecl

ass

.O

ther

ind

epen

den

tvari

ab

les

incl

ud

eth

egiv

enle

ad

un

der

wri

ter’

sm

ark

etsh

are

inth

ep

revio

us

thre

eyea

rs.

Yea

rfi

xed

effec

tsare

als

oin

clu

ded

,b

ut

not

rep

ort

ed.

Past

Tw

ice

Th

rice

Th

ree-

Yea

rL

agged

Lagged

Lagged

R2

or

Cla

ssA

ctio

nM

ark

etM

ark

etM

ark

etP

seu

do

Row

Dep

end

ent

Vari

ab

leL

aw

suit

Sam

ple

Law

suit

sS

hare

Sh

are

Sh

are

R2

Ob

s

(1)

Com

ple

teD

e-B

ran

din

g(R

estr

uct

ure

)A

llL

aw

suit

s2.3

24

-43.9

15

3.6

22

0.6

00

0.2

40

643

(2.9

8)

(-2.4

6)

(0.1

1)

(0.0

2)

(2)

Com

ple

teD

e-B

ran

din

g(R

estr

uct

ure

)N

on

-Tec

hL

aw

suit

s2.7

27

-52.0

55

13.6

97

0.4

86

0.2

34

643

(2.2

8)

(-2.6

2)

(0.4

8)

(0.0

2)

(3)

Com

ple

teD

e-B

ran

din

g(R

estr

uct

ure

)IP

O-i

n-C

lass

Law

suit

s3.5

82

-56.1

14

4.4

57

-1.0

70

0.2

67

643

(3.5

4)

(-2.5

5)

(0.1

6)

(-0.0

3)

(4)

Com

ple

teD

e-B

ran

din

g(R

estr

uct

ure

)IP

O-N

ot-

in-C

lass

Law

suit

s-0

.399

-52.4

54

41.5

27

10.4

12

0.1

93

643

(-0.2

4)

(-1.7

6)

(1.1

0)

(0.3

5)

(5)

Part

ial

De-

Bra

nd

ing

(Res

tru

ctu

re)

All

Law

suit

s1.6

02

-35.0

53

9.0

58

5.7

54

0.1

75

643

(2.6

2)

(-1.8

3)

(0.5

4)

(0.3

0)

(6)

Part

ial

De-

Bra

nd

ing

(Res

tru

ctu

re)

Non

-Tec

hL

aw

suit

s1.2

07

-37.6

83

18.1

43

10.4

28

0.1

54

643

(1.1

1)

(-1.8

1)

(1.0

6)

(0.4

5)

(7)

Part

ial

De-

Bra

nd

ing

(Res

tru

ctu

re)

IPO

-in

-Cla

ssL

aw

suit

s2.5

64

-38.9

95

9.3

10

0.3

80

0.1

94

643

(3.1

5)

(-1.6

3)

(0.6

1)

(0.0

2)

(8)

Part

ial

De-

Bra

nd

ing

(Res

tru

ctu

re)

IPO

-Not-

in-C

lass

Law

suit

s-0

.028

-36.2

20

25.4

86

15.1

21

0.1

38

643

(-0.0

3)

(-1.8

5)

(1.2

6)

(0.8

2)

(9)

∆$

UW

mark

etsh

are

All

Law

suit

s-0

.521

-42.8

92

3.5

02

37.0

55

0.2

04

643

(-1.4

8)

(-5.0

2)

(0.3

9)

(3.8

8)

(10)

∆$

UW

mark

etsh

are

Non

-Tec

hL

aw

suit

s-0

.809

-42.9

48

3.6

12

37.0

43

0.2

09

643

(-1.8

1)

(-5.0

6)

(0.4

1)

(3.9

7)

(11)

∆$

UW

mark

etsh

are

IPO

-in

-Cla

ssL

aw

suit

s-0

.846

-43.1

65

4.0

84

38.0

87

0.2

09

643

(-1.8

5)

(-5.1

1)

(0.4

5)

(3.9

3)

(12)

∆$

UW

mark

etsh

are

IPO

-Not-

in-C

lass

Law

suit

s-0

.045

-39.7

85

3.3

90

23.7

34

0.1

56

592

(-0.0

8)

(-3.5

6)

(0.2

8)

(1.9

4)

43

Fig

ure

1:T

hefig

ure

disp

lays

the

empi

rica

lde

nsit

yof

norm

aliz

edre

visi

ons.

One

obse

rvat

ion

ison

eam

endm

ent

toth

epr

ospe

ctus

(exc

ludi

ngth

ela

rges

tre

visi

onam

ong

the

first

two

revi

sion

s,w

hich

islik

ely

tobe

the

resp

onse

toth

ere

gula

tory

com

men

ts).

Defi

neth

ela

rges

tre

visi

onam

ong

the

first

two

revi

sion

sas

the

regu

lato

ryre

visi

on,

and

defin

eth

isre

visi

on’s

docu

men

tdi

stan

cefr

omth

epr

evio

usfil

ing

asth

ere

gula

tory

revi

sion

dist

ance

.T

heno

rmal

ized

revi

sion

isth

edo

cum

ent

dist

ance

betw

een

the

give

nre

vise

dpr

ospe

ctus

and

the

prec

edin

gve

rsio

n,sc

aled

byth

ere

gula

tory

revi

sion

dist

ance

.

0.0%

2.0%

4.0%

6.0%

8.0%

10.0

%

12.0

%

14.0

%

16.0

%

18.0

%

20.0

% 0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0.32

0.36

0.40

0.44

0.48

0.52

0.56

0.60

0.64

0.68

0.72

0.76

0.80

0.84

0.88

0.92

0.96

1.00

Nor

mal

ized

Rev

isio

n

Probability

44