APPROVED: Mazhar Siddiqi, Major Professor James Conover, Committee Member Imre Karafiath, Committee Member Robert Pavur, Committee Member Niranjan Tripathy, Committee Member Marcia Staff, Chair, Department of

Finance, Insurance, Real Estate and Law

O. Finley Graves, Dean of the College of Business

James D. Meernik, Acting Dean of the Robert B. Toulouse School of Graduate Studies

THE REASONS FOR THE DIVERGENCE OF IPO LOCKUP AGREEMENTS

Fei Gao, B.A., M.B.A.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

August 2010

Gao, Fei. The reasons for the divergence of IPO lockup agreements. Doctor of

Philosophy (Finance), August 2010, 107 pp., 22 tables, 1 figure, references, 61 titles.

Most initial public offerings (IPOs) feature share lockup agreements, which

prohibit insiders from selling their shares for a specified period of time following the IPO.

However, some IPO firms agree to have a much longer lockup period than other IPO

firms, and some are willing to lockup a much larger proportion of shares. Thus, the

primary research question for this study is: “What are the reasons for the divergence of

the lockup agreements?”

The two main hypotheses that this dissertation investigates are the signaling

hypothesis based on information asymmetry, and the commitment hypothesis based on

agency theory. This study uses methods that have not been applied by previous studies

in the literature relating to IPO lockups.

First, I directly use IPO firms operating performance as a proxy for firm quality.

The results show neither a negative nor a strong positive relationship between lockup

length and firm operating performance. Thus, based on operating performance, the

evidence does not support the agency hypothesis while showing weak support for the

signaling hypothesis.

I then examine the long-run returns for IPO firms with different lockup lengths. I

find that firms with short lockup lengths have much better long-run returns than firms

with long lockup lengths. Therefore, the results reject the signaling hypothesis while

supporting the agency hypothesis. This dissertation further contributes to the IPO long-

run underperformance literature by showing that firms with a high agency problem have

much worse long-run returns than those with a low agency problem.

Finally, I investigate the short-term stock returns around lockup expiry. Generally,

I find that firms with short lockup periods experience better stock returns around lockup

expiry than firms with long lockup periods, though the returns are not significantly

different from one another. Overall, I conclude that the results reject the signaling

hypothesis while partially supporting the agency hypothesis. In addition, I show that

firms with high agency problems have much worse stock returns than those with low

agency problems around lockup expiry, even though the agency variable is not

significant in the regression analysis.

ii

Copyright 2010

by

Fei Gao

iii

ACKNOWLEDGMENTS

I wish to express my deepest gratitude to Dr. Mazhar Siddiqi, my Committee

Chairperson, whose guidance and patience make this dissertation possible. I also want

to thank Dr. James Conover, Dr. Imre Karafiath, Dr. Robert Pavur, and Dr. Niranjan

Tripathy, my committee members, and Dr. John Kensinger, for their contributions. Their

insights and discussions were essential for improving the dissertation.

I would like to thank my wife for her tremendous support and encouragement.

iv

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS………………………………………………………………………..iii

LIST OF TABLES……………………………………………………………………………….v

LIST OF FIGURES……………………………………………………………………….........vi

Chapters

1. INTRODUCTION…………………………………………………………………………1

2. LITERATURE REVIEW………………………………………………………………….5

IPO Underpricing…………………………………………………………………. 5 IPO Long-run Underperformance………………………………………………10 IPO Lockup Agreement……………………………………………………….. 16

3. HYPOTHESIS DEVELOPMENT………………………………………………………27

Signaling Hypothesis…………………………………………………………….27 Agency Hypothesis………………………………………………………………30

4. DATA COLLECTION AND RESEARCH DESIGN…………………………………..35

Signaling Hypothesis…………………………………………………………….35 Agency Hypothesis………………………………………………………………43

5. EMPIRICAL RESULTS………………………………………………………………...50

Summary Statistics……………………………………………………………....50 Signaling Hypothesis…………………………………………………………….51 Agency Hypothesis………………………………………………………………58

6. CONCLUSION AND DISCUSSION…………………………………………………99

REFERENCES…………………………………………………………………………….....

v

LIST OF TABLES

Page

1. Comparisons for the Predictions of Hypotheses…………………………………...34

2. Summary Statistics…………………………………………………………………....64

3. Accounting Numbers and Lockup Length…………………………………………..66

4. Regression for Length of Lockup (OLS)…………………………………………….68

5. Regression for Length of Lockup -- Binary Logistic……………………………….69

6. Regression for Length of Lockup -- Multinomial Logistic…………………………70

7. Accounting Numbers and Lockup Length -- Opaque Firms………………………72

8. Regression for Length of Lockup -- Opaque Firms………………………………..74

9. Accounting Numbers and Lockup Length -- High-tech Firms……………………76

10. Regression for Length of Lockup (High-tech Firms)………………………………77

11. Accounting Numbers and Lockup Length -- High λ Firms………………………..78

12. Regression for Length of Lockup -- high λ…………………………………………80

13. Accounting Numbers and Lockup Length -- Low λ Firms…………………………81

14. Regression for Length of Lockup (Low λ)………………………………………….83

15. Long-run Returns for All IPO Firms…………………………………………………84

16. Abnormal Return around Lockup Expiry……………………………………………86

17. Percentage of Shares Locked……………………………………………………….88

18. Agency Problem and Long-run Return……………………………………………..89

19. Long-run Returns and Underwriter Reputation……………………………………91

20. Venture Capital Backing and Long-run Returns…………………………………..93

21. Auditor Reputation and Long-run Return………………………………………….94

22. Short-run Return and Agency Problem……………………………………………96

vi

LIST OF FIGURES

Page

1. Long-run Return for IPO Firms………………………………………………………97

1

CHAPTER 1

INTRODUCTION

When investment banks take a firm to an initial public offering (IPO), they sign an

underwriter agreement. This contract usually states that without the investment bank‟s

prior written consent, the issuer will not directly or indirectly sell any shares of common

stock for a certain period of time negotiated by the two parties following the public

offering of the stock. Such a contract is known as a share lockup agreement. Most

firms issuing IPOs voluntarily enter into a lockup agreement with their underwriters,

though the contract is not regulated by the Security and Exchange Commission. A

typical lockup lasts for 180 days, and the lockup agreement covers most of the shares

that are not sold in the IPO. The terms of the lockup and its expiration date are

disclosed in the IPO prospectus.

Some IPO firms agree to lockup their shares for a much longer period than other

firms, and some firms lockup more of their shares in the lockup agreement than other

firms. The research interest for this study is to explore the reasons for the divergence of

IPO lockup agreements. Several researchers have examined this topic, and they

approach the issue from two aspects. One aspect is the signaling hypothesis based on

information asymmetry. Courteau (1995) extends Leland and Pyle‟s (1977) signaling

model that focuses on retained ownership by introducing the length of lockup period to

which the owner commits in the prospectus as a signal of firm value. She develops a

model and shows that higher quality firms are more likely to have longer lockups as an

indication of their superior quality. Brav and Gompers (2003) test the signaling

hypothesis developed by Courteau by choosing IPO offer price revision, the probability

of dividend initiation, and frequency of seasoned equity offering (SEO) as measures of

2

firm quality. Their results reject the signaling hypothesis for lockups -- they do not find

that higher quality firms have longer lockup periods. However, Brau, Lambson, and

McQueen (2005) argue that the proxies for firm quality used in Brav et al. (2003) paper

are not appropriate.

Brau et al. (2005) present a theoretical model that shows how the incentives of

insiders, underwriters, and investors can interact with the nature of the firm‟s assets to

explain the existence of lockup agreements. Their results show that larger firms, older

firms, easy to value firms, firms with prestigious investment bankers, firms with venture

capital backing, and firms with well-known auditors have shorter lockup lengths.

However, their empirical evidence indicates only that lockups should be shorter when

the degree of asymmetric information is small. The authors, however, have not shown

that lockup length is a signal for firm quality, which is the main prediction of the signaling

hypothesis by Courteau (1995).

The second way to approach the lockup agreement issue is the commitment

hypothesis based on agency theory. Using Jensen and Meckling‟s (1976) theoretical

model of agency costs, Brav et al. (2003) argue that lockup agreements serve as a

commitment device to alleviate moral hazard problems. As a result, IPOs that have a

higher chance of experiencing agency problems should commit to longer lockup periods

during which the public is convinced to buy their stocks. Brav et al. empirical results

support the commitment hypothesis. But in their paper, the authors use variables of

information asymmetry to test agency hypothesis. For example, they find that smaller

firms, which have high information asymmetry, have longer lockup periods that they

attribute to a higher potential for an agency problem. However, this is not necessarily

3

the case. Insiders of small firms may work hard, while insiders of big firms may be more

likely to take advantage of outside shareholders.

I use different approaches from the papers discussed to test the signaling and

agency hypotheses. First, I directly use firm operating performance several years after

their IPO as a measure of firm quality as in Jain and Kini (1994) and in Zheng and

Stangeland (2007). Then I compare the operating performance for IPOs with long and

short lockup periods to determine whether there is a significant difference between the

two groups. According to the signaling hypothesis, long lockup IPOs, which have higher

quality, should have better operating performance after their offering compared to short

lockup IPOs, which have low quality. On the other hand, according to the agency

hypothesis, firms with longer lockup periods, which have a high agency problem, should

have worse operating performance after their IPO because of their high agency cost

compared to firms with short lockup periods, which have low agency cost.

Second, I investigate the long-run stock returns for IPO firms with different lockup

lengths. According to the signaling hypothesis, firms with longer lockup periods should

have higher quality than firms with shorter lockup periods. However, the information of

the quality of firms imbedded in the length of lockup should be priced into the offer price

of the IPO firms at the time of offerings. Therefore, the long-run returns of the firms with

long and short lockup periods should not be significantly different. Under a signaling

mechanism, only if investors consistently overestimate quality will long-run returns be

worse for longer lockups than shorter lockups. According to the agency hypothesis, in

order to attract investors to buy into their firms‟ shares, companies with a high agency

problem should have a longer lockup period than firms with a low agency problem. This

4

high agency cost will lead to poorer long-run returns for these firms with long lockup

periods compared to firms with short lockup periods.

Third, I examine the short-run stock returns around the lockup expiration date for

IPO firms with long and short lockup lengths. Since all the information about lockup

lengths should be priced into the stock at the time of IPO and since the expiration date

is known to public investors before IPO, there should be no short-run abnormal returns

for firms with long and short lockup lengths according to the signaling hypothesis.

According to the agency hypothesis, on the other hand, insiders of IPO firms may sell

their shares and start to cause an agency problem after lockup expiry. In order not to be

taken advantage of by insiders, investors who hold stocks of firms that have a high

potential for an agency problem will sell their holdings around lockup expiry, leading to

short-term negative returns for these stocks.

In the above mentioned tests, I primarily focus on the firms with short and long

lockup periods: those firms with lockup lengths shorter and longer than 180 days.

However, 75% of the firms in the sample have a lockup period that is exactly 180 days.

In order to investigate why the majority of IPO firms choose 180 days as their lockup

period, I run the multinomial logistic regression, comparing the characteristics of firms

with lockup periods equal to 180 days to those with lockup periods that are shorter and

longer than 180 days.

The remainder of the dissertation is organized as follows: Chapter 2 provides the

literature review. Chapter 3 develops the hypotheses. Chapter 4 discusses the data

collection and research design. Chapter 5 presents the results and chapter 6 concludes.

5

CHAPTER 2

LITERATURE REVIEW

Initial public offerings (IPOs) have attracted much attention from researchers in the

last two decades. Theoretical and empirical literature on IPO-related phenomenon is

extensive. I focus on several main issues of IPOs in this literature review.

IPO Underpricing

Underpricing is the first documented anomaly in the pricing of IPOs of common

stock. It refers to the systematic increase from the offer price to the first day closing

price for firms issuing IPOs. Some early studies documenting positive initial returns

include Ibbotson (1975), Ritter (1984), and Tinic (1988). In a sample of 6,249 IPOs from

1980 to 2001, Ritter and Welch (2002) show that the average first-day return is 18.8%.

Researchers have offered several possible explanations for this short-run IPO

underpricing.

Rock (1986) proposes the winner‟s curse hypothesis. He offers an equilibrium

model in which uninformed investors face a winner‟s curse when they submit an order

for IPO issues because some potential subscribers have superior information. Informed

investors do not subscribe to a new issue when it is priced above its value, leaving the

entire issue to uninformed investors. His model shows that this information asymmetry

may lead to a “lemons problem,” where uninformed investors end up primarily with the

less successful IPOs. Thus, firms are forced to underprice their IPOs in order to

compensate uninformed investors for this adverse selection. Beatty and Ritter (1986)

extend the model to show that the value of information and the necessary underpricing

are higher for issues in which there is greater uncertainty about their value. Michaely

and Shaw (1994) test the empirical implications of the winner‟s curse hypothesis, and

6

consistent with this hypothesis, find that IPOs are not underpriced in markets where

investors know a priori that they do not need to compete with informed investors.

Based on Rock‟s framework, Carter and Manaster (1990) investigate the role of an

investment banker‟s reputation in the IPO market. They show that to maintain their

reputation, prestigious underwriters always choose higher quality and less risky firms,

using information unavailable to the general public. This in turn reduces the uncertainty

and information asymmetry between informed and uninformed investors. Investors

know that by buying IPOs associated with high reputable underwriters, they face less

risk. Using investment bankers‟ capital as a proxy for underwriter reputation, Michaely

and Shaw (1994) show that reputation plays an important role in explaining the initial

day return; that is, IPOs underwritten by reputable investment banks experience less

underpricing.

Because most firms that go public are relatively young and new to general

investors, there are always information asymmetry problems between IPO firms and

outside investors. Welch (1989) presents a signaling model in which high-quality firms

underprice more at the IPO in order to obtain a higher price at a seasoned offering. Low

quality firms cannot imitate high quality firms by having greater underpricing because of

high imitation expenses and the possibility that this imitation is discovered between

offerings. Underpricing by high-quality firms can add sufficient imitation expenses to

induce low-quality firms to reveal their true quality voluntarily. Similarly, Allen and

Faulhaber‟s (1989) model shows that firms with the most favorable prospects will use

the signal of underpricing in an IPO to show their high quality to investors. Their model

predicts that firms that underprice more can recoup the cost of underpricing by going

back to the seasoned offering market more quickly and frequently.

7

In Grinblatt and Hwang‟s (1989) paper, the authors develop a signaling model with

two signals and two attributes to explain new issue underpricing. To overcome the

asymmetric information problem at IPO, the issuer signals the true value of the firm by

offering shares at a discount and by retaining some of the shares of the new issue in

their personal portfolio. This model can be regarded as a generalization of Leland and

Pyle‟s (1977) model, in which the issuer‟s retention ratio of his own company signals the

firm‟s future cash flows. Grinblatt and Hwang‟s results show that a firm‟s intrinsic value

is positively related to the degree its new issue is underpriced.

Underpricing could also be a mechanism to compensate outsiders for the cost of

information production. Chemmanur (1993) presents a model of IPO pricing in which

insiders have private information and sell stock in both the IPO and the secondary

market, and outsiders may engage in costly information production about the firm.

Knowing that they are going to pool with low-value firms, high-value firms induce

outsiders to engage in information production by underpricing, which in turn

compensates outsiders for the cost of producing information. The information is

reflected in the secondary market price of equity, giving a higher expected stock price

for high-value firms.

Michaely and Shaw (1994) test several signaling hypotheses mentioned above,

but do not find evidence supporting the signaling models for IPO underpricing.

Specifically, their results show that firms that underprice more return to the reissue

market less frequently and for lesser amounts than firms that underprice less. Also,

firms that underprice more experience lower earnings and pay fewer dividends.

Baron (1982) assumes that the value of a new issue is affected by market demand

and the investment banker‟s selling effort. In his model, the issuer is less informed

8

relative to its underwriters, not relative to investors. To address this moral hazard and to

induce the underwriter to put in the requisite effort to market shares, it is optimal for the

issuer to permit some underpricing, since the issuer cannot monitor the underwriter

without cost. Muscarella and Vetsuypens (1989) test Baron‟s agency model directly and

find that when underwriters themselves go public, their shares are just as underpriced

even though there is no monitoring problem. This evidence neither favors nor refutes

Baron‟s hypothesis.

There are three types of benefits that issuers find in liquid shares. First, greater

liquidity allows the opportunity to trade retained shares on more favorable terms.

Second, it is often hypothesized that increased liquidity reduces the required return to

investors, thus increasing the price that investors are willing to pay for shares. Third, it

has been shown that increased liquidity can reduce the issuing costs of subsequent

equity offerings. Several studies have investigated the relationship between IPO

underpricing and after-market liquidity.

Booth and Chua (1996) develop an explanation for IPO underpricing in which the

issuers‟ demand for ownership dispersion motivates underpricing and oversubscription.

In their framework, promoting oversubscription can allow broad initial ownership

dispersion and this in turn can achieve a liquid secondary market for the shares.

However, broad initial ownership requires an increase in investor-borne information cost,

which can be compensated by higher underpricing. Their findings suggest that issuers

intentionally underprice to promote oversubscription, which receives a broad initial

ownership, and in turn, increases secondary market liquidity for their stocks.

Reese (1998) studies the relationships between IPO underpricing, investor interest,

and trading volume. He finds that IPOs which appreciate in price during the first two

9

days of trading experience a significantly higher trading volume than those which do not

appreciate in price during the same time period. This difference in trading volume is not

only statistically significant during the first week of trading but persists for more than

three years beyond the issue date. Using the number of newspaper references of a firm

as a proxy for investor interest, Reese finds that there are significant relationships

between the pre-issue market interest in an IPO and its initial return, initial trading

volume, and long-term volume.

Employing 10 measures of liquidity, Hahn and Ligon (2004) explore whether

underpricing of IPOs boosts subsequent secondary market liquidity. They find that, in

general, there is a positive relationship between the two. The positive relationship holds

both prior to and after lockup expiration; thus, the influence of underpricing is not

restricted to the immediate post-issuance period. For three different volume-based

measures of liquidity -- the turnover ratio, Amihud‟s illiquidity measures, and the

average number of trades -- there is a consistent significantly positive relationship

between underpricing and liquidity. They conclude that insiders concerned about future

liquidity for their retained stakes in the issuing firm could benefit from the liquidity-

increasing effects of underpricing.

Zheng and Li (2008) examine a sample of 1,179 Nasdaq IPOs and find that

underpricing is positively related to the number of non-block institutional shareholders

after IPO. The authors also find evidence that the number of non-block institutional

shareholders is positively related to aftermarket liquidity. The relationship is robust and

significant, particularly when liquidity is measured by aftermarket trading volume.

Therefore, they conclude that underpricing is used to help increase the number of non-

block institutional shareholders, which improves secondary market liquidity.

10

Furthermore, the authors show that IPO underpricing is positively related to trading

volume in the secondary market, suggesting that underpricing does have some direct

effect on market liquidity.

In their 2002 paper, Aggarwal, Krigman, and Womack develop a model in which

managers strategically underprice IPOs to maximize personal wealth from selling

shares at lockup expiration. They argue that if IPO firms underprice more, which can

generate more information momentum, they can attract more attention to the firms‟

stock and thereby shift the demand curve for the stock outwards. As a result, this will

allow managers to sell their holdings at a higher price at lockup expiry than they could

otherwise obtain. Their results show that if managers hold more shares of their

company, their stocks will have greater underpricing at IPO. They find also that

underpricing is positively related to research coverage and research coverage is

positively associated with stock returns and insider selling at lockup expiry.

IPO Long-run Underperformance

Long-run underperformance is the second anomaly found in the IPO market, and

the topic has received substantial attention since the early 1990s. Researchers have

tried to find possible explanations for the anomaly by looking at the different

characteristics related to IPO firms.

Ritter (1991) uses a sample of 1,526 IPOs from 1975 to 1984 and finds that IPOs

substantially underperform the sample of matching firms for a three-year period after the

offering. Younger firms and firms going public in heavy volume years do worse than

average. After examining various cross-sectional and time-series patterns, Ritter

attributes this long-run underperformance of IPOs to investors‟ periodically

overoptimistic expectation about the earnings potential of young growth companies.

11

Firms take advantage of these windows of opportunity and go public near the peak of

industry-specific fads.

Schultz (2003) conducts 5,000 simulations of long-run aftermarket abnormal

returns of IPOs and SEOs from 1973 to 2000. He shows that the poor long-run

performance of equity-issuing firms in event-time is real in the sense that IPOs and

SEOs have underperformed relative to their expectations, but that it is not indicative of

any market inefficiency. More firms go public when they can receive a higher price for

their shares. As a result, there are more offerings at peak valuations than at lower

prices. However, the issuing firms do not know prices are at the peak when they issue

stock.

Venture capitalists specialize in providing funds to privately held companies, and

generate their profits from the companies that go public. According to Field and Lowry

(2007) and Megginson and Weiss (1991), prestigious underwriters prefer to deal with

IPOs backed by venture capitalists, as do institutional investors. As a result, the

presence of venture capital may signal a more successful IPO. Brav and Gompers

(1997) investigate the long-run underperformance of IPO firms by comparing a group of

IPOs with venture capital backing to a group without venture capital backing. They find

that the underperformance documented by Ritter comes primarily from small, non-

venture-backed IPOs. Returns on non-venture-backed IPOs are significantly below

those of venture-backed IPOs when returns are weighted equally. Value weighting

significantly reduces underperformance for non-venture-backed IPOs. Carter, Dark, and

Singh (1998) examine the role of underwriter reputation on IPO long-run

underperformance. Reputation concerns force high-ranked investment banks to choose

higher quality and less risky firms with which to do business. Their results show that, on

12

average, the long-run market-adjusted returns are less negative for the IPOs that are

brought to market by more prestigious underwriters.

As an explanation for the IPO long-run underperformance, Teoh, Welch, and

Wong (1998) try to explore a possible source for the over-optimism about the earnings

potential of IPOs found by Ritter. They argue that the IPO process is particularly

susceptible to earnings management, offering entrepreneurs both motivation and

opportunities to manage earnings. Because of information asymmetry between

investors and issuers at the time of the offering, investors must rely on current earnings

reports. As a result, high reported earnings would translate directly into a higher offering

price.

Total accruals can be decomposed into current and long-term components, and

studies have shown that entrepreneurs have more discretion over short-term than over

long-term accruals. Discretionary current accruals are the asset-scaled proxies for

manipulated earnings determined at the discretion of management. So, Teoh, Welch,

and Wong‟s hypothesis is that if marginal investors do not rationally discount for

earnings management in forming expectations about future cash flows, IPOs with

unusually high accruals in the IPO year experience underperformance relative to those

with conservative accruals after three years of the issue. The evidence in their paper

supports this hypothesis.

Miller (1977) argues that uncertainty implies that reasonable investors may differ in

their forecasts. Assuming investors seek to maximize the present value of their

investment, they will have different estimates of expected returns from the investment,

given uncertainty about the true return to the investment in the security. It follows, then,

that the shares will be owned by the investors with the highest evaluation of the return.

13

That is to say, a badly informed or excessively optimistic small group of investors can

bid a stock up to a value that most investors regard as unreasonable. This usually

occurs because they believe that the stock promises substantially better performance

than most other securities available. As a result, the higher the price of a security the

greater the divergence of opinion about the return from the security.

If the divergence of opinion about a stock changes, it follows that the market price

should also change. For instance, if risky stocks become less risky over time, their

prices should drop. This is because the divergence of opinion narrows over time; that is,

the passage of time resolves certain uncertainties about the future of a company. This

can explain the IPOs long-run underperformance. The prices of new issues are set not

by the appraisal of the typical investor, but by the small minority who think highly

enough of the investment merits of the new issue to include it in their portfolio. The

divergence of opinion about the new issue is greatest when the stock is issued,

because the information asymmetry is greatest. Over time, this uncertainty is reduced

as the company discloses more information, and the price of stocks decreases.

In their empirical test for Miller‟s hypothesis, Houge, Loughran, Suchanek, and

Yan (2001) find that all three variables measuring uncertainty -- percentage opening

spread, time of first trade, and flipping ratio (calculated as the sell-side block volume

divided by the total share volume on the IPO day) -- provide significant explanatory

power of IPO returns. A wide opening spread, late opening trade, and a high flipping

ratio are associated with poor long-run returns, suggesting that greater divergence of

opinion or uncertainty about an IPO can generate long-run underperformance.

Chemmanur and Paeglis (2005) empirically examine the relationship between the

quality and reputation of a firm‟s management and various aspects of its IPO return

14

performance. They argue that if a firm has higher management quality and reputation, it

is more likely to attract greater interest from institutional investors, who themselves are

less likely to be subject to over-optimism compared to individual investors. As a result,

such firms are likely to experience a smaller dispersion in beliefs among investors,

which in turn implies that management quality and reputation will be positively

associated with long-run stock price performance following a firm‟s IPO, in accordance

with Miller‟s theory. Their evidence shows that higher management quality is associated

with lower heterogeneity in investor valuations and firms with better managers have

greater long-run stock returns.

Jensen (1986) argues that conflicts of interest between shareholders and

managers over payout policies are particularly severe when the organization generates

substantial free cash flow and managers want to increase managerial benefits like

compensation or power and reputation, implying that there is negative information

contained in the new cash from IPOs. As Miller (1977) shows, the market may not

incorporate the opinion of pessimistic investors into stock valuation because of

divergent investor expectations and short sale constraints. As a result, Zheng (2007)

hypothesizes that the market may not incorporate the negative information contained in

the new cash into stock valuation; that is, the market may under-react to the free cash

flows from an IPO. Thus, Zheng predicts that firms getting more new cash from IPOs

should have poor long-term stock returns. Results indicate that raising more new cash

in an IPO is related to poorer long-term stock performance. IPO firms that receive more

new free cash flow and have large divergences of investor opinion would tend to be

more overvalued, leading to poor long-term returns.

15

Although studies have documented the IPO long-run underperformance, others

question the existence of such an anomaly. These researchers criticize the

methodology of measuring the long-run returns and conclude that different data, a

different study period, or a different methodology will give different answers. They not

only show that abnormal performance measurement is conditional on an asset-pricing

model, but also recognize that the method of measurement of abnormal performance

affects inferences. The method influences both the magnitude of the measured

abnormal performance as well as the size and power of the statistical test. Brav, Geczy,

and Gompers (2000) reexamine the robustness of IPO underperformance with respect

to various model specifications. In measuring event time returns, the authors use

various benchmarks such as the S&P (Standard & Poor‟s) 500, the Nasdaq composite

index, the CRSP (Center for Research in Security Prices) value weight index, and the

CRSP equal weight index. Size and book-to-market portfolios are also formed. The

paper computes the Fama-French three-factor model, the excess return on the value

weighted market portfolio, the return on a zero investment portfolio, and the return on a

portfolio of high book-to-market stocks less the return on a portfolio of low book-to-

market stocks. The authors conclude that value weighting cuts in half the

underperformance calculated from equal-weighting. Once IPO firm returns are matched

to size and book-to-market portfolios, there is no underperformance. Underperformance

is concentrated primarily in small issuing firms with low book-to-market ratios. Model

mis-specification is an important consideration in long horizon performance tests.

16

IPO Lockup Agreement

Rule 144 limits insider selling. It requires that restricted shares (unregistered

shares acquired directly or indirectly from the issuer from non-public offerings) be held

for a minimum of one year from the time that the shares were originally acquired. Before

1997, this minimum holding period was two years.

When an issuing firm and an investment bank enter into an agreement to offer

securities in an IPO, they sign an underwriter agreement. This contract usually states

that without the investment bank‟s prior written consent, the issuer will not directly or

indirectly sell any shares of common stock for a certain period of time negotiated by the

two parties following the commencement of the public offering of the stock. It is typically

a voluntarily agreement, and is not mandated by any SEC or state securities laws that

regulate insider trading.

Most IPOs feature share lockup agreements, which prohibit insiders and other pre-

IPO shareholders from selling any of their shares for a specified period. The typical

lockup lasts for 180 days, though lockups may range anywhere from three months to

three years. The lockup agreement covers most of the shares that are not sold in the

IPO. The terms and the expiration date of lockup are disclosed in the IPO prospectus.

Earlier, I mentioned that IPO underpricing could either be a device to solve the

information asymmetry problem or a device to reduce the agency problem. As outlined

in the papers reviewed below, IPO lockup agreements may also serve as either a

signaling device or a commitment device to solve the asymmetric information problem

and the agency problem in addition to underpricing. As a result, we can regard lockup

agreements as a complementary tool to underpricing that underwriters and issuers can

choose in the IPO process. Further, I try to shed light on the reasons for an IPO long-

17

run underperformance by examining the long-term stock returns for IPOs with different

lockup characteristics.

IPO Lockup Related Literature

Field and Hanka (2001) examine the IPO‟s stock price and trading volume around

the lockup expiration day. They find that while lockups are in effect, there is little selling

by insiders. Around the scheduled unlock day, there is on average a permanent 40%

increase in trading volume and a statistically prominent three-day abnormal return of

1.5%. Both of these effects are roughly three times larger in venture-backed firms than

in non-venture-backed firms. Venture capital investors sell more aggressively than other

pre-IPO shareholders. The authors find limited support for several hypotheses that may

explain the abnormal return, but do not provide a complete explanation. The abnormal

return is not caused by a change in the proportion of trades at the bid price, temporary

price pressure, or increased trading costs. Also, the abnormal return may be partly

caused by downward sloping demand curves or by consistently larger-than-expected

insider sales.

By examining market reaction to the expiration of IPO lockup, Brau, Carter,

Christophe, and Key (2004) find that the expiration of share lockups has important

share-price implications. Results show statistically significant negative abnormal returns

surrounding the lockup expiration. The authors argue that the negative abnormal returns

are consistent with theoretical predictions based on information asymmetries and

decreased incentive alignment between insiders and general shareholders. The results

of the cross-sectional regression also shed light on characteristics that affect market

returns around the lockup expiration date. The paper finds that greater uncertainty

about insiders‟ future actions is related to negative abnormal returns. Specifically,

18

percentage of shares in lockup, venture capital backing, the percentage of management

ownership in the firm after the offer, and the size of the firm are significantly related to

the cumulative abnormal returns.

Ofek and Richardson (2000) conduct a similar study about the volume and price

patterns around the lockup expiration day. Consistent with the previously mentioned

papers, the authors provide evidence that stock prices fall around the end of their IPO

lock-up period, but they also provide evidence that the lockup effect is not arbitrageable.

Trading costs, the difficulty of shorting newly-public stocks, and short-term capital gains

faced by original shareholder can help explain this fact. Furthermore, the paper argues

that the stock price fall is somewhat consistent with a downward sloping curve for

shares and certain variables, such as stock price volatility, have clear predictive power

for the magnitude of the fall.

Bradley, Jordan, and Yi (2001) find that the average abnormal return on the lockup

expiration day is -0.74%, and the cumulative abnormal return over the five-day

surrounding period is -1.61%. However, the negative abnormal returns are largely

concentrated in the 45% of the firms with venture capital backing. Such firms lose, on

average, 3% to 4% of their value. For the venture-capital-backed group, the largest

losses occur for high-tech firms and firms with the greatest post-IPO stock price

increases, the largest relative trading volume in the period surrounding expiration, and

the highest quality underwriters.

Cao, Field, and Hanka (2004) explore whether insider trading impairs market

liquidity around IPO lockup expiration. They show that officers and directors sell

substantial shares of their own at lockup expiration, but those selling have little effect on

effective spreads. Instead they find that quote depth -- the average of ask depth (the

19

number of shares offered for sale at the ask price) and bid depth (the number of shares

offered for sale at the bid price) -- average trade size, and number of trades per day all

increase. Overall, lockup expiration seems to improve liquidity. The authors explain that

the increase in asymmetric information costs is obscured by the liquidity benefits from

increased trading volume.

Using a different approach, Gao (2005) uses intraday data to explore the trading

activity and the information environment around IPO lockup expiration. He finds that the

price drop around lockup expiration is significantly positively correlated with venture

backing, analyst earnings forecast bias, and forecast dispersion. Further, results show

that information asymmetry of IPO stocks experience little change after the unlock day.

This suggests that insider trading on lockup expiry is unlikely to be driven by private

information; instead, insiders sell their holding for the purpose of diversification.

Even though a normal lockup agreement lasts for several months, in some

situations, some underwriters do allow restricted shareholders to sell a small portion of

the restricted shares early according to the lockup agreement between underwriter and

issuers. While the scheduled lockup expiration day is stated in the prospectus prior to

the firms‟ IPO, the early release represents new information to the market. Keasler

(2001) examines the influence of an underwriter‟s early lockup release on shareholders

wealth. The author finds that most of the firms receiving early release are backed by

venture capital and experience an increase in market capitalization after their IPO.

There are significantly negative abnormal returns associated with the early lockup

release announcement, and negative returns are greater for venture backed IPOs.

Further, negative abnormal returns at the lockup expiration day are reduced for firms

announcing the early lockup release.

20

Brav and Gompers (2003) also explore the extent of insider equity sales prior to

lockup expiration. They argue that if lockup agreement is a commitment device, then

only those firms that have greatly reduced the potential for insiders to take advantage of

shareholders will be released from the lockup restriction. Their results show that firms

that are associated with less moral hazard, such as larger firms, firms with higher

turnover, firms backed by venture capitalists, firms with high reputable underwriters, and

firms with higher post-IPO abnormal returns, are more likely to have an early release.

Both of the above papers conclude that their findings are consistent with the

commitment hypothesis.

The Reasons for the Divergence of IPO Lockup Agreements

Some IPOs have a three-month lockup period, while others lockup their shares for

more than three years. Some IPOs lock 60% of their shares, while others lock only 10%.

Thus far, researchers exploring the reasons for the existence of lockup agreements

have focused on two hypotheses, the signaling hypothesis and the agency hypothesis.

In the market for real assets where the quality of projects is highly variable, we

observe a scenario in which entrepreneurs know the quality of their own projects, while

lenders cannot distinguish between them. In order to solve this information asymmetry

problem, Leland and Pyle (1977) show that if the owner remains under-diversified, this

may communicate private information about the value of the firm to prospective

investors. The willingness of the owner to invest in his firm may serve as a signal to the

lending market of the true quality of the project. Lenders will place a value on the project

that reflects the information transferred by the signal. The authors use a signaling model

to come up with the conclusion that the market reads higher entrepreneurial ownership

as a signal of a more favorable future project.

21

However, Gale and Stiglitz (1989) argue that under-diversification at the time of

IPO is not sufficient for fully communicating private information. If the entrepreneur can

sell the retained shares on the secondary market immediately after the issue, the

signaling strategy will not be convincing to investors. Courteau (1995) extends Leland

and Pyle‟s signaling model that focuses on the retained ownership, and introduces the

length of lockup period to which the owner commits in the prospectus as a signal of firm

value. She develops a model and shows that the length of the holding period, in a

signaling mechanism, complements ownership retention. Also, she finds that higher

quality firms are more likely to have longer lockups to show their quality.

Brav and Gompers (2003) test the firm quality signaling hypothesis. The authors

designate the IPO offer price revision, the probability of dividend initiations, and the

frequency of seasoned equity offerings (SEOs) as measures of firm quality. They argue

that firms that signal their higher quality through longer lockups would be more likely to

raise their offering price in order to garner greater proceeds at the time of their IPO, or

they would have a higher chance to initiate dividend payment after IPO, or else they

would be more likely to issue equity in a subsequent seasoned equity offering. The

authors‟ results reject the signaling hypothesis of lockup, because they do not find that

higher-quality firms possess longer lockup periods. However, as argued by Brau,

Lambson, and McQueen (2005), the proxies for firm quality used in Brav et al. paper are

not appropriate.

Brau, Lambson, and McQueen (2005) present a theoretical model that shows how

the incentives of insiders, underwriters, and investors can interact with the nature of the

firm‟s assets to explain the existence of lockup agreements. Their model shows how

lockups can be a signaling solution to the adverse selection problem resulting from

22

information asymmetries at the time of the stock issue. Specifically, insiders cannot only

retain some fraction of shares of their firm, but also sign a lockup agreement not to sell

the shares for a period of time. Their results indicate that lockups should be shorter

when the degree of asymmetric information is small, and when the cost of mimicking is

high. Specifically, larger firms, older firms, easy to value firms, firms with prestigious

investment bankers, firms with venture capital backing, and firms with well-known

auditors have shorter lengths of lockup.

The problem in the findings of Brau et al. (2005) is that the signal is not a valid

signal by definition. For a signal to be valid, there should be a higher cost to mimickers

for sending a false signal. If their conclusion is true, then opaque firms can mimic

transparent firms by setting a shorter lockup period, a false signal, at a lower cost. This

is because with a shorter lockup period, they can cash out, and make themselves

diversified sooner. The authors realize this problem, and try to solve it by showing that

shorter lockup periods are associated with higher idiosyncratic risk. However, the result

for the test is just marginally significant (p-value = 0.0954). Even if their conclusion is

true, the authors have not shown whether lockup length is a signal for firm quality.

Jensen and Meckling (1976) develop a theoretical model of agency costs. Agency

costs arise when the manager‟s interests are not aligned with firm owners‟ interests,

and these costs increase as the equity share of the manager declines. Managers can

have on-the-job perks, shirking, and self-interested and entrenched decisions that might

reduce shareholders wealth.

In their paper, Brav and Gompers (2003) argue that these lockup agreements

serve as a commitment device to alleviate moral hazard problems. After a firm‟s IPO,

interests of insiders may not align with interests of outside shareholders due to the

23

separation of ownership and management. As a result, IPOs that have a higher chance

of having agency problems should have a longer lockup period to convince the public to

buy their stocks. The authors argue that for a firm with high information asymmetry, the

agency problem should be high because outsiders do not know the actions of the

managers. The results support the commitment hypothesis that unprofitable firms, low

book-to-market ratios firms, firms with low reputation underwriters, and firms without

venture backing have significantly longer lockup periods. These firms suffer from a

greater potential for insiders to take advantage of shareholders, therefore they need a

longer lockup to induce investors to buy into the offering. One concern regarding their

conclusion is that the authors use variables of information asymmetry to test agency

hypothesis. For example, they find that smaller firms, which have high information

asymmetry, have longer lockup period because of higher potential for an agency

problem. This is not necessarily true. Insiders of small firms may work hard, and

insiders of big firms may be more likely to take advantage of outside shareholders.

Using underwriter reputation, Yung and Zender (2008) separate their IPO sample

into two groups, one group with high reputation underwriters and the other with low

reputation underwriters, and test whether signaling hypothesis and agency hypothesis

work separately for these different groups. For the group of high reputable underwriters,

the authors argue that these IPOs will have low information asymmetry. Therefore, the

lockup is a device to solve agency problem for these firms. For the group of low

reputable underwriters, the authors argue that these IPOs cannot find high reputable

underwriters to solve information asymmetry problem, so they must use lockups to

solve this problem. One potential problem for this paper is choosing underwriter

reputation as a standard to divide the whole sample into two groups. Yung and Zender

24

(2008) argue that “underwriter certification has been argued to reduce information

asymmetry; however, this certification should have no impact on moral hazard

problems.”

But underwriters do have a monitoring function in reducing the firms‟ agency

problems after the IPO. According to Krigman, Shaw, and Womack (2001), only 30% of

firms completing an SEO within three years of their IPO switched their lead underwriters.

In addition, in the event of a switch, firms will choose more reputable underwriters. Thus,

we can see that IPO firms keep a relatively long and stable relationship with their

underwriter in order to extract more services from these investment banks. As a result,

a firm with a reputable underwriter will have fewer agency problems even after their IPO

due to the monitoring function of underwriters. To keep their reputation, highly reputable

underwriters will set stricter standards to do business with firms. If a firm has a high

agency problem, which lowers operating performance, a highly reputable underwriter

may refuse to do business with this firm rather than hurting its own reputation. This

could motivate such firms to reduce agency problems in order to keep and/or acquire

the services of highly reputable underwriters. In this way, a highly reputable underwriter

could serve as both a resolution to the information asymmetry and the agency problem

(at least to some extent). Thus, choosing this as a standard to divide the whole sample

into two groups is not appropriate.

My Approach to the Reasons of Divergence of IPO Lockup

Even though several studies mentioned above have investigated the reasons for

the divergence of IPO lockup, the results are mixed. Further, those studies have limited

their research to examining the relationships between lockup length and proxies for

information asymmetry and agency problems. They completely ignore both short-term

25

and long-term stock price behaviors from which we can get rich insight for the reasons

in the divergence of IPO lockup agreements. For example, according to signaling

hypothesis, all the information about the lockup length should be valued into the offering

price of firms at the time of their IPO, thus there should not be a significant difference

between the long-run stock returns for IPOs with long and short lockup lengths. On the

other hand, if the agency hypothesis is true, then firms with a long lockup period, which

have high potential for an agency problem will experience long-run underperformance

due to the high agency cost. Therefore, examining long-run stock returns may give us a

clearer idea for the reasons in the divergence of IPO lockup agreements.

At the lockup expiration day, insiders are given their first chance to sell a

substantial proportion of the shares they hold. Since the lockup expiration day is known

to the public, the short-term returns around lockup expiry should not be different for

stocks with long and short lockup lengths according to signaling hypothesis. But for

agency hypothesis, investors will heavily sell their holdings for firms with high potential

for agency problems, in order not to be taken advantage of by insiders of the firms after

the lockup expires. As a result, there should be a greater negative short-term return for

this group of stocks than those with fewer agency problems. Thus, the short-run returns

around lockup expiry may allow us to differentiate between the signaling and agency

hypotheses.

In choosing the proxies for firm quality, prior studies ignore the most direct

measures, the operating performance of IPO firms. Thus we can examine the operating

performance several years after firms‟ offerings to see whether there is a significant

difference between the group with long lockup periods and the group with short lockup

periods. By doing this, we can determine whether lockup length is a signal for firm

26

quality or is a mechanism to solve an agency problem. According to the signaling

hypothesis, firms with long lockup periods should have better operating performances

since they have better quality than short lockup IPO firms. But according to the agency

hypothesis, firms with long lockup periods should have worse operating performances

after their offering because of their high agency cost.

27

CHAPTER 3

HYPOTHESES DEVELOPMENT

Signaling Hypothesis

By signing a lockup agreement with underwriters, insiders of initial public offering

(IPO) firms bear the cost of non-diversification. Insiders of higher quality firms can bear

these costs to show their firms‟ quality to outside investors, while insiders of low quality

firms cannot give the false signal of committing to a long lockup period because of their

expected negative future performance. Therefore, we can predict that high quality firms

should have a long lockup period, while low quality firms should have a short lockup

period. Brav and Gompers (2003) use the probability of SEOs, offer price revision, and

the probability of dividend initiations as proxies for firm quality in their testing of this IPO

lockup signaling hypothesis. But, as argued by Brau et al. (2005), their measures of firm

quality are not appropriate.

Jain and Kini (1994) investigate whether equity retained by the original

entrepreneurs is a signal of firm quality. The authors use firms‟ operating performance

after the IPO as a proxy for firm quality. They find that there is a positive relationship

between the two variables – a higher retention ratio means a higher operating

performance after the firm‟s IPO. Zheng and Stangeland (2007) test whether

underpricing is a signal for firm quality. They also use the operating performance as a

proxy for firm quality and conclude that IPOs with greater underpricing are of better

quality. This dissertation intends to examine whether lockup is a signal for firm quality.

Therefore I use operating performance as a measure of firm quality, a proxy not

previously used in the literature of IPO lockups, thus leading to my first hypothesis.

28

Hypothesis 1: Firms with a longer lockup period should have higher quality, and

therefore higher operating performance for several years after their IPO.

Brau et al. (2005) find that lockups should be shorter when the degree of

asymmetric information is small. I can extend their paper, and partition the whole

sample into two groups, with one group including transparent IPOs, the other including

opaque IPOs. Both groups include high quality firms and low quality firms, because both

types of firms can be either opaque or transparent. Since opaque firms have a higher

degree of asymmetric information problems, high quality firms belonging to this group

have a strong motive to use lockup length to differentiate their quality from other firms.

On the other hand, for transparent firms, it is easier for investors to differentiate high

quality and low quality firms because of less information asymmetry, meaning there may

not be a strong relationship between firm quality and lockup length. Therefore, we

should observe that the relationship between firm quality and lockup length is stronger

for opaque firms than for transparent firms.

Hypothesis 1A: The positive relationship between operating performance and lockup

length (Hypothesis 1) is stronger for opaque firms than for transparent firms.

When partitioning the whole sample into opaque and transparent firms in

Hypothesis 1A, I use the asymmetric-information variables according to Brau et al.

(2005). However, some of those variables are proxies for both information asymmetry

and agency problem. For example, the reputation of an underwriter, as a third party

certification, is a good proxy for information asymmetry (Ritter, 1986; Michaely and

Shaw, 1995; Megginson and Weiss, 1991). But underwriters do have a monitoring

function in reducing the firms‟ agency problems after the IPO. To keep their reputation,

highly reputable underwriters will set strict standards to do business with firms. This

29

could motivate such firms to reduce agency problems in order to keep and/or acquire

the services from highly reputable underwriters. As a result, a firm with a reputable

underwriter will have fewer agency problems. Instead, I will use “high-tech” as a

criterion to separate the sample into opaque and transparent firms. High-tech firms will

have more research and development and know-how, which makes them hard to be

valued due to high information asymmetry. On the other hand, it is not necessarily true

that the managers of high-tech firms will cause more agency problems compared to

non-high-tech firms. Thus, high-tech is a good proxy for information asymmetry, and

can be used to partition the whole sample into opaque and transparent firms, providing

the impetus for my third hypothesis:

Hypothesis 1B: The positive relationship between operating performance and lockup

length (Hypothesis 1) is stronger for high-tech firms than for non-high-tech firms.

A third criterion may be used to separate firms with high information asymmetry

from firms with low information asymmetry. This third criterion is λ, the proportion of the

bid-ask spread due to adverse selection. Firms with a high λ have high information

asymmetry and firms with a low λ have low information asymmetry, therefore:

Hypothesis 1C: The positive relationship between operating performance and lockup

length (Hypothesis 1) is stronger for firms with a high λ than for firms with a low λ.

Brau et al. (2005) argue that the reason they do not examine the long-term stock

returns is that at the time of IPO good firms and bad firms are both fairly priced.

According to the signaling hypothesis, firms with a longer lockup period should have

higher quality than firms with a shorter lockup period. But the information of firm quality

imbedded in the lockup lengths has been incorporated into the stock price at the time of

a firm‟s IPO. In other words, the offer prices of IPO stocks are set according to their

30

quality. As a result, even though IPO firms experience long-run underperformance

compared to the market, we should not observe a difference between long-run stock

returns for firms with long lockup periods, which have high quality, and short lockup

periods, which have low quality. Previous research has not examined the long-run stock

returns for IPOs with different lockup lengths. I propose the following hypothesis:

Hypothesis 2: If investors incorporate the signal of lockup into the IPO offer price, there

will be no difference between the long-run returns for long and short lockups.

Starting from the lockup expiration day, insiders of the IPO firms who are

previously restricted from selling their holdings have the first chance to sell a large

proportion of their shares. However, the dates of IPO lockup expiration and the number

of shares that can then be sold by insiders are well known by investors at the time of

firms‟ IPO from their prospectus. In addition, as Field and Hanka (2001) point out, IPO

lockup expiry is a relatively clean event to study because few companies make

important announcements around lockup expiry, such as earnings, dividend, mergers or

acquisitions. Therefore, there should be no significant abnormal returns for IPO firms

around their lockup expiry. The hypothesis is as follows:

Hypothesis 3: There are no significant abnormal returns for IPO firms around their

lockup expiration dates.

Agency Hypothesis

For IPOs with a high potential for agency problems, investors require more shares

to be locked to avoid being taking advantage of by insiders. This is because if insiders

of high agency firms hold only a very small portion of the firm then even with the lockup

agreement it is hard to align the interests of insiders with the interests of shareholders.

Brav and Gompers (2003) use variables such as firm size and industry to measure

31

agency costs. These variables are not appropriate because they are also proxies for

information asymmetry. Instead, I will use free cash flow, expense ratio, asset utilization

ratio, and debt level to measure the degree of agency problem (McKnight and Weir,

2008) in testing the following:

Hypothesis 4: Higher agency problem IPOs should lock up more shares.

Agency costs arise when the manager‟s interests are not aligned with firm owners‟

interests, and these costs increase as the equity share of the managers declines

(Jensen and Meckling, 1976). As the insiders of high agency IPO firms sell their shares

and therefore reduce their interests in the firm after lockup expiry, they can start to have

on-the-job perks, shirking, and self-interested and entrenched decisions. These agency

costs have a negative impact on the firm‟s operating performance and reduce

shareholders‟ wealth (Jain et al., 1994). As a result, I predict that firms with longer

lockup periods will have greater agency problems, which will lead to worse operating

performance after their IPO.

Hypothesis 5: IPO firms with longer lockup periods will have worse operating

performance for several years after their offerings compared to firms with shorter lockup

periods.

According to the agency hypothesis, firms with a high agency problem should

have a longer lockup period than firms with a low agency problem. At the time of lockup

expiry, Insiders have the chance to sell their holdings of the firm and to cause agency

problems thereafter. In order not to be taken advantage of by insiders, some investors

will sell their shares of high agency firms around lockup expiry, leading to a price drop.

Other investors may still hold their shares, probably due to their underestimate of the

agency problem of the firm. As insiders of these firms continuously cause agency

32

problems, leading to the deterioration of operating performance, more investors will sell

the firms‟ shares. Thus, this high agency cost will lead to poor long-run returns (Harris

and Glegg, 2009) for firms with long lockup periods. On the other hand, for firms with a

low agency problem, investors may not worry too much about the agency problem.

Therefore, they may not sell the shares of the firm as intensively as investors do in high

agency firms.

Hypothesis 6: Firms with long lockup periods will experience worse long-run stock

returns after their offerings compared to firms with short lockups.

As discussed in Hypothesis 1B, high reputation underwriters may be good

monitors and therefore reduce the agency problem for IPO firms. As a result, for firms

with high reputation underwriters, there should be a low agency problem. The lockup

length of these firms is not used to differentiate the agency problem among them. I

predict that there will be no difference between the long-run returns for this group of IPO

firms with long and short lockup lengths. On the other hand, for firms with low reputation

underwriters, agency problems may not be effectively reduced, so these firms still need

to use lockup length to differentiate the agency problems among them. Thus I predict

that the long-run returns for firms with short lockups will be better than firms with long

lockups. The reputation of underwriters works as a complimentary tool for lockup length.

By the same reasoning, venture capital backing and the reputation of auditors may also

work as a mechanism to reduce agency problems, so I have the following hypotheses:

Hypothesis 6A: For firms with high reputation underwriters, auditors, or with venture

capital backing, there is no difference between long-run stock returns for long and short

lockups.

33

Hypothesis 6B: For firms with low reputation underwriters, auditors, or without venture

capital backing, longer lockups are associated with worse long-run stock returns than

shorter lockups.

On the IPO lockup expiration date, insiders of the firm have the chance to sell their

holdings of the firm. Under agency hypothesis, if insiders sell their shares at the lockup

expiration day, they have more incentive to cause agency problems and may start to

expropriate wealth from shareholders. For long lockup IPOs, which have higher

chances of experiencing agency problems, investors will sell their shares around IPO

lockup expiry to avoid wealth expropriated by insiders. Therefore, we should observe a

big price drop for firms with long lockup periods around lockup expiration. On the other

hand, for IPOs with short lockup periods, we should observe no price drop or a smaller

price drop. This is because insiders in these firms will always try to maximize

shareholders wealth, and investors know that there are fewer agency problems in these

firms. As a result, investors will sell the shares for these IPO firms less intensively.

Previous research has not explored the short-run price behavior of IPOs around lockup

expiration for IPOs with different lockup lengths. Therefore, I propose:

Hypothesis 7: There are significant negative abnormal returns for IPO firms around their

lockup expiry, and firms with longer lockups, which have high chances of experiencing

agency problems, will experience worse returns than firms with shorter lockups.

I can use these hypotheses to examine whether the IPO lockup length is a solution

to the agency problem or a signaling mechanism, as shown in Table 1. The predictions

for the signaling hypothesis and the agency hypothesis are quite different. For example,

when examining the operating performance after firms‟ IPOs, the signaling hypothesis

predicts that firms with long lockups will have better performances than firms with short

34

lockups, while the agency hypothesis predicts that, on the contrary, short lockups will

have better performances. For the long-run stock returns, the signaling hypothesis

predicts that there are no differences between the long-run returns for firms with long

and short lockup periods, while the agency hypothesis predicts that firms with short

lockup periods will have better returns than firms with long lockup periods. For the short-

term returns around lockup expiry, the signaling hypothesis predicts that there are no

abnormal returns for IPO firms, while the agency hypothesis predicts that there should

be bigger negative abnormal returns for firms with long lockup periods than firms with

short lockup periods.

Table 1

Comparisons for the Predictions of Hypotheses

Hypotheses Signaling Agency

Operating performance after

IPO

H1: Long lockups will have

better performance

H5: Short lockups will

have better performance

Long-run returns after IPO H2: No difference between

long and short lockups

H6: Short lockups have

better long-run return

Short-run return around

lockup expiry

H3: No short-term

abnormal return

H7: There are short-term

abnormal returns and

long lockups have a

worse return

35

CHAPTER 4

DATA COLLECTION AND RESEARCH DESIGN

The data for this study is from the Thompson Securities Data Corporation (SDC)

database and consists of initial public offerings (IPOs) of equity for the period from 1989

through 2004. The end year, 2004, was chosen to ensure that four years worth of

operating performance data is available on Compustat. Information about each IPO is

collected, such as the IPO date, issuer name, symbol ticker, lead underwriter, IPO

proceeds, offer price, the number of primary shares and secondary shares, percentage

of shares locked up, auditor name, lockup expiration date, and total debt at the time of

the IPO. When SDC misses some data fields, the individual firm prospectus is searched

for the relevant information. Information on underwriter rankings is collected from Jay

Ritter‟s website based on Carter-Manaster (1990). Consistent with current research,

American depository receipts (ADRs), units offerings, closed end funds, real estate

investment fund (REITs), reverse leveraged buyouts (LBOs), and equity carve outs are

excluded. IPOs with offer price below $5 are excluded. Further firms must be listed on

the Center for Research in Security Prices (CRSP) after their offering. Thus daily stock

price, trading volume, and bid-ask spread for each IPO can be collected. Accounting

data is obtained from Compustat.

Signaling Hypotheses

Hypothesis 1 predicts that firms with a longer lockup period should have higher

quality, and therefore have higher operating performance for several years after their

IPO. Following Jain and Kini (1994) and Zheng and Stangeland (2007), growth rates of

several accounting variables are used to measure the operating performance of IPO

firms, which in turn proxy for firm quality. Specifically, two cash flow variables are

36

calculated. First is operating return on assets (OR), which is defined as operating

income before depreciation and taxes divided by total assets at the end of the fiscal

year. This variable provides a measure of the efficiency of asset utilization. The second

operating performance measure is defined as operating cash flows deflated by total

assets (OCF) at the end of the fiscal year. This ratio equals operating income minus

capital expenditures divided by total assets, and is a useful measure since operating

cash flow is a primary component in net present value calculations used to value a firm.

Other performance measures included are sales (SALE), operating income (OI), and

asset turnover (AT), which is defined as the ratio of sales to total assets. Earnings is not

used as a performance variable since managers may adopt a strategy that inflates

earnings initially at the expense of earnings growth rates in future years (Zheng et al.,

2007).

Growth rates are calculated for each performance measure by comparing their

values at the end of each fiscal year following the IPO (Year +2 to +4) to their values at

the end of the fiscal year of IPO (Year +1). The changes in operating performance are

measured as the median changes because the performance measures may be skewed

and the mean is particularly sensitive to outliers (Jain et al., 1994). Only those firms

with positive measures in Year +1 are included in our calculation of growth rates.

Different industries grow at different rates in the economic cycle. IPO firms tend to be

concentrated in high growth industries, thus their growth rate may reflect industry-wide

growth patterns. Industry-adjusted changes are calculated to reduce the effect of such

patterns in IPO growth rates. The industry-adjusted performance for a particular IPO

firm is the difference between its growth rate and the median growth rate in all firms in

37

its industry during the same period. Each IPO firm is matched with firms in the same

industry based on three-digit Standard Industrial Classification (SIC) codes.

Performance growth rates are compared between firms with long lockups and

firms with short lockups in order to evaluate whether long lockup periods are associated

with higher quality. To do this, the entire sample is split into quartiles according to the

length of firm‟s lockup. Then the median growth rates are compared for the first and

fourth quartiles to see whether the growth rates for the fourth quartile (long lockup group)

are significantly higher than those for the first quartile (short lockup group). The

hypotheses are:

H0 : Ms ≥ Ml

H1 : Ms < Ml

where Ms is the median growth rate of performance measure for the short lockup group,

and Ml is the median growth rate of performance measure for the long lockup group.

Regression analysis is performed to control for other variables that may affect the

length of lockup (LOL).

(1). LOLi = β0 + β1 ORi + β2 OCFi + β3 SALEi + β4 OIi + β5 ATi + β6 SIZEi +

β7 AGEi + β8 TECHi + β9 UWi + β10 VCi + β11 AUDIi + εi

where

LOL: length of lockup (number of days);

OR: operating return on asset;

OCF: operating cash flow on asset;

SALE: net total revenue;

OI: operating income;

AT: asset turnover;

38

SIZE: proceeds of a firm‟s IPO;

AGE: the years from a firm‟s inception till IPO;

TECH: a dummy variable for high-tech firms;

UW: Carter-Manaster ranking for underwriter;

VC: a dummy variable for venture capital backing;

AUDI: a dummy variable for the well-known top six auditors.

Length of lockup is the number of days in a firm‟s lockup agreement. Besides

growth rates of the five accounting numbers, several variables that may affect the

lockup length of IPO firms are included. Large firms and older firms will have less

information asymmetry, so they may have a shorter lockup period (Brau et al., 2005).

Following Brau et al., (2007), proceeds from a firm‟s IPO are used as a proxy for firm

size. It is calculated by multiplying the number of shares offered in the IPO with the offer

price. Nominal dollar values are converted to 2004 dollars by using the consumer price

index. Firm ages (AGE) are obtained from Jay Ritter‟s website. When the founding year

is the same as the offering year, 0.5 is assigned as the age for the firm (Brau et al.,

2007). High-tech firms may have higher information asymmetry; therefore, an industry

dummy variable (TECH) is included to catch this effect. TECH equals 1 for high-

technology firms and 0 otherwise. Following Field et al., (2001), high-tech firms are

identified by using three-digit SIC codes of 357, 367, 369, 382, 384, and 737. Following

Beatty and Ritter (1986), Michaely and Shaw (1995), and Megginson and Weiss (1991),

three variables for third party certifications are also included in the regression --

reputation of underwriters (UW), auditors (AUDI), and presence of venture capitalists

(VC). Carter and Manaster (1990) use underwriters‟ relative placements in stock

offering tombstone announcements as the proxy for underwriter reputation. Briefly, the

39

measure is constructed by examining tombstone advertisements and comparing the

relative placement of investment banks in the advertisements. The rankings range from

1 to 9, with higher ranking indicating higher reputation underwriters. AUDI and VC are

set as dummy variables. AUDI equals 1 if the firm‟s auditor is one of the top six auditors,

otherwise it is 0. The big six accounting firms are Arthur Andersen, Coopers & Lybrand,

Deloitte Touche, Ernst & Young, KPMG Peat Marwick, and Price Waterhouse. Firms

backed with venture capital are assigned a VC value of 1, otherwise it is 0. Since firm

quality and lockup length are expected to be positively related, the hypotheses are:

H0 : β1 and β2 and β3 and β4 and β5 ≤ 0

H1 : β1 and β2 and β3 and β4 and β5 > 0

Hypothesis 1A predicts that the positive relationship between operating

performance and lockup length (Hypothesis 1) is stronger for opaque firms than for

transparent firms. Brau et al., (2005) use several variables to measure information

asymmetry, such as firm size, whether or not the firm is in a high tech field, underwriter

reputation, presence of venture capital, and reputation of auditor. Using these variables,

the sample is partitioned into two groups (transparency and opaqueness) by creating

the following scoring scheme. A value of one is assigned to firms with high information

asymmetry -- high tech firms, firms without presence of venture capital, and firms not

using the big 6 auditor firms. Otherwise, 0 is assigned to a firm. For firm size and

underwriter reputation, 0 is assigned to firms that have values above the 75th percentile,

1 to firms below the 25th percentile, and 0.5 to the remaining firms. By summing up all

these scores, each firm in the sample will get a total score. The score can range from 0

to 5. Firms with a high score are opaque firms, while firms with a low score are

transparent firms. Thus, it is possible to test whether there is a stronger relationship

40

between operating performance and lockup length for opaque firms than for transparent

firms. Specifically, the entire sample is partitioned into quartiles according to these

scores and the same regression analysis is performed as in Hypothesis 1 for firms in

quartile 1 (opaque firms) and for quartile 4 (transparent firms). The regressions and

hypotheses are as follows:

(2). LOLoi = βo0 + βo1 ORoi + βo2 OCFoi + βo3 SALEoi + βo4 OIoi + βo5 AToi +

βo6 SIZEoi + βo7 AGEoi + βo8 TECHoi + βo9 UWoi + βo10 VCoi + βo11 AUDIoi +εoi

(3). LOLti = βt0 + βt1 ORti + βt2 OCFti + βt3 SALEti + βt4 OIti + βt5 ATti + βt6 SIZEti +

Βt7 AGEti + βt8 TECHti + βt9 UWti + βt10 VCti + βt11 AUDIti +εti

where equation (2) is the regression for opaque firms (subscript o), and equation (3) is

the regression for transparent firms (subscript t). For opaque firms, a stronger relation

between firm quality and lockup length is expected; therefore, the hypotheses are:

H0 : βo1 ≤ βt1 and βo2 ≤ βt2 and βo3 ≤ βt3 and βo4 ≤ βt4 and βo5 ≤ βt5

H1 : βo1 > βt1 and βo2 > βt2 and βo3 > βt3 and βo4 > βt4 and βo5 > βt5

Hypothesis 1B predicts that the positive relationship between operating

performance and lockup length (Hypothesis 1) is stronger for high-tech firms than for

non-high-tech firms. I partition the sample into high-tech firms and non-high-tech firms

and run tests similar to Hypothesis 1.

(4). LOLhi = βh0 + βh1 ORhi + βh2 OCFhi + βh3 SALEhi + βh4 OIhi + βh5 AThi +

βh6 SIZEhi + βh7 AGEhi + βh8 TECHhi + βh9 UWhi + βh10 VChi + βh11 AUDIhi +εhi

(5). LOLni = βn0 + βn1 ORni + βn2 OCFni + βn3 SALEni + βn4 OIni + βn5 ATni +

βn6 SIZEni + βn7 AGEni + βn8 TECHni + βn9 UWni + βn10 VCni + βn11 AUDIni +εni

where equation (4) is the regression for high-tech firms (subscript h), and equation (5) is

the regression for non-high-tech firms (subscript n). For high-tech firms, a stronger

41

relation between firm quality and lockup length is expected; therefore, the hypotheses

are:

H0 : βh1 ≤ βn1 and βh2 ≤ βn2 and βh3 ≤ βn3 and βh4 ≤ βn4 and βh5 ≤ βn5

H1 : βh1 > βn1 and βh2 > βn2 and βh3 > βn3 and βh4 > βn4 and βh5 > βn5

Hypothesis 1C predicts that the positive relationship between operating

performance and lockup length (Hypothesis 1) is stronger for firms with a high λ than for

firms with a low λ. The bid-ask spread has three components: order processing costs,

inventory holding costs, and adverse information costs. Since the asymmetric

information component is of interest in this paper, this component needs to be extracted

from the total spread. Following Chazi and Tripathy (2007), one generally accepted

measure, λ, will be used to proxy for the adverse selection component. The measure λ

is from a modification of Lin, Sanger, and Booth‟s (1995) model, which is based on the

model of Huang and Stoll (1994). In the following regression, λ represents the portion of

the spread due to adverse selection.

(6). Qt+1 – Qt = λzt + et+1

where

Qt = (Askt + Bidt)/2, is the quote midpoint at time t;

zt = Pricet – Qt , is the effective half-spread.

Chazi et al. use t as a measure of daily changes and extract λ from yearly

regression by firm. Thus, in their model, λ measures the firm‟s yearly adverse selection.

I am interested in the information asymmetry around IPO lockup expiration, so λ will be

extracted from the trading data 30 days after the expiration day. Daily stock closing

numbers are used for price, bid, and ask values. After partitioning the sample into high-λ

firms and low-λ firms, I run tests similar to Hypothesis 1.

42

(7). LOLhi = βh0 + βh1 ORhi + βh2 OCFhi + βh3 SALEhi + βh4 OIhi + βh5 AThi +

βh6 SIZEhi + βh7 AGEhi + βh8 TECHhi + βh9 UWhi + βh10 VChi + βh11 AUDIhi +εhi

(8). LOLli = βl0 + βl1 ORli + βl2 OCFli + βl3 SALEli + βl4 OIli + βl5 ATli +

βl6 SIZEli + βl7 AGEli + βl8 TECHli + βl9 UWli + βl10 VCli + βl11 AUDIni +εli

where equation (7) is the regression for high-λ firms (subscript h), and equation (8) is

the regression for low-λ firms (subscript l). For high-λ firms, a stronger relation between

firm quality and lockup length is expected; therefore, the hypotheses are:

H0 : βh1 ≤ βl1 and βh2 ≤ βl2 and βh3 ≤ βl3 and βh4 ≤ βl4 and βh5 ≤ βl5

H1 : βh1 > βl1 and βh2 > βl2 and βh3 > βl3 and βh4 > βl4 and βh5 > βl5

Hypothesis 2 predicts that if investors incorporate the signal conveyed in lockup

length into the IPO offer price, there will be no difference between the long-run stock

returns for long and short lockups. Because model mis-specification is an important

consideration in long horizon stock price performance tests (Brav, Geczy, and Gompers,

2000), when testing for hypotheses 2, two alternative methods are used to measure the

long-run stock returns: equally-weighted market-adjusted excess returns, and value-

weighted market-adjusted excess returns. Long-run returns are defined as the three-

year holding period return following a firm‟s IPO. All the returns are calculated starting at

the 26th day after firms‟ IPOs to avoid the effect of earlier aftermarket activities such as

stabilization and quiet period (Brau et al., 2007). Market adjusted return (MAR) is

defined as the firm‟s buy and hold return (BAH) minus the market return from CRSP.

Buy and hold return is defined as the geometrically compounded return:

(9). BAH = (1 +𝑀𝑡=𝑗 ri,t )-1

where ri,t is the daily return for stock i on day t, j is the starting day, and M is the ending

day for a calculating period. Market adjusted return is calculated as:

43

(10). MAR = (1 +𝑀𝑡=𝑗 ri,t ) - (1 +𝑀

𝑡=𝑗 rm,t )

where rm,t is the equally-weighted or value-weighted daily market return from the CRSP.

After obtaining the long-run returns for each firm, univariate tests are conducted to

see whether the mean and median returns are significantly different for firms with a long

lockup period and firms with a short lockup period.

H0 : µs = µl or Ms = Ml

H1 : µs ≠ µl or Ms ≠ Ml

Where µs and Ms are the average and median long-run returns, respectively, for

short lockups, and µl and Ml are the average and median long-run returns, respectively,

for long lockups. The power of these tests is low because the null hypothesis represents

the signaling model. However, when testing the agency hypothesis, I set the null

hypothesis in the normal way, and therefore provide the counterparts of these tests,

increasing the test power. This is the case also for a few of the hypotheses that follow.

Further, regression analysis is conducted by fitting the long-run returns (LR)

against lockup lengths while controlling for other variables.

(11). LRi = β0 + β1 IRi + β2 LOLi + β3 SIZEi + β4 AGEi

+ β5 TECHi + β6 UWi + β7 VCi + β8 AUDIi +εi

Houge et al. (2001) find that initial return (IR) is negatively correlated with long-run

performance. Therefore, this variable is included in our regression. Other variables that

might affect the IPO long-run returns - SIZE, AGE, TECH, UW, VC, and AUDI - are

defined the same as in equation (1). There should be no significant relationship

between LR and LOL. The hypotheses are:

H0 : β2 = 0

H1 : β2 ≠ 0

44

Hypothesis 3 predicts that there are no significant abnormal returns for IPO firms

around their lockup expiration dates. Event study methodology is used to examine the

stock prices behavior around IPO lockup expiration. The market model is specified as

follows:

(12). Rit = αi + βi Rmt + εit,

where

Rit is the return for firm i on day t in estimation period;

Rmt is the average return for all firms in the stock market on day t (CRSP

value-weighted index is used as the market index);

αi and βi are the intercept and the slope parameters for firm i;

εit represents the error term for firm i on day t.

αi and βi will be estimated over T trading days in the estimation period, where T

varies according to the length of lockup. For IPOs having a lockup period between 3 to

5 months, the estimation period will start at the first day of the IPO and end 10 days

before the event day (lockup expiry). If an IPO has a 6 month or longer lockup period,

the estimation period will start 130 days before the event day and end 10 days before

the event day. EVENTUS software will be used for these tests. The average 7-day

abnormal return (3 days before and after the IPO lockup expiration day) is calculated.

Univariate tests will be conducted and the hypotheses are:

H0 : µs = 0 and µl = 0

H1 : µs ≠ 0 and µl ≠ 0

where µs is the average abnormal return for short lockup IPOs, and µl is the average

abnormal return for long lockup IPOs. Further the means of the two groups of stocks are

compared, and the hypotheses are:

45

H0 : µs = µl

H1 : µs ≠ µl

Agency Hypotheses

Hypothesis 4 predicts that higher agency problem IPOs should lock more shares.

Free cash flow, growth rate, expense ratio, asset utilization ratio, and the amount of

debt at the time of a firm‟s IPO are used as proxies for agency cost (McKnight and Weir,

2008). If a firm has high free cash flow and low growth opportunity, then the firm has a

higher chance of having an agency problem. Following McKnight et al. (2008) and

Zheng (2007), the sum of two values standardized by total assets is used as the proxy

for free cash flow. The first value is defined as operating income before depreciation

minus the sum of taxes plus interest expense and dividends paid at the time of IPO

(Lehn and Poulsen, 1989). This value represents the free cash flow a firm has before its

IPO. The second value is the cash that a firm raises in the IPO. The sum of the two

numbers then is divided by the total assets at the time of a firm‟s IPO to measure the

free cash flow IPO firms have. Market-to-book ratio is used to measure the growth

opportunity for IPO firms. The ratio is calculated by using the market value of a firm

divided by its book value, where market value is the product of the firm‟s stock price and

the number of shares outstanding, and book value is the difference between the firm‟s

asset and total liability. A firm‟s growth opportunity is an increasing function of its

market-to-book ratio. Firms with a market-to-book ratio lower than the median are

assigned a 1 and are seen as low growth opportunity firms. A value of 0.5 is assigned to

firms with a market-to-book ratio greater than the median and these firms are seen as

high growth opportunity firms. Each firm‟s market-to-book value is then multiplied by its

free cash flow value. Firms with high free cash flow and low growth opportunities

46

receive higher values, which could indicate a higher chance of an agency problem.

Expense ratios are defined as operating expenses scaled by annual sales. It is

positively related to the agency cost (Mcknight et al., 2008). Asset utilization ratio is

calculated as annual sales divided by total assets, and it is negatively related to the

agency cost (Mcknight et al., 2008). Debt holders could be good monitors, so the higher

total debt a firm has at the time of IPO, the lower the potential for agency problem.

The sample is partitioned into a high agency cost group and a low agency cost

group by creating a scoring scheme similar to that used for information asymmetry. One

is assigned to the firms with high potential for an agency problem -- firms with values

above the 75th percentile for the four variables measuring agency problem. Zero is

assigned to the firms that have values below the 25th percentile, and 0.5 to the

remaining firms. By summing all these scores for each firm, a total score can be

obtained for every firm in the sample. The scores range from 0 to 4. Firms with a high

score will have a greater potential for an agency problem. By partitioning the entire

sample into quartiles according to these scores, IPOs with a high agency problem

(quartile 4) can be compared to IPOs with a low agency problem (quartile 1) to see

whether the former group locks up more shares. Hypotheses are:

H0 : µsha ≤ µsla

H1 : µsha > µsla

where µsha is the mean percentage of shares locked up for high agency IPOs, and µsla is

the mean percentage of shares locked up for low agency IPOs.

Hypothesis 5 predicts that IPO firms with longer lockup periods will have worse

operating performances several years after their IPOs compared to firms with shorter

47

lockup periods. The same methodology is applied as was done for Hypothesis 1. The

hypotheses are:

H0 : Ms ≤ Ml

H1 : Ms > Ml

where Ms is the median growth rate of performance measure for short lockup group, and

Ml is the median growth rate of performance measure for long lockup group.

Regression analysis is performed to control for other variables that may affect the

length of lockup (LOL).

(13). LOLi = β0 + β1 ORi + β2 OCFi + β3 SALEi + β4 OIi + β5 ATi + β6 SIZEi +

β7 AGEi + β8 TECHi + β9 UWi + β10 VCi + β11 AUDIi + εi

According to Hypothesis 5, there should be a negative relationship between

growth rates of operating performance and lockup length.

H0 : β1 and β2 and β3 and β4 and β5 ≥ 0

H1 : β1 and β2 and β3 and β4 and β5 < 0

Hypothesis 6 predicts that firms with long lockup periods will experience worse

long-run stock returns after their IPOs compared to firms with short lockup periods. The

same methodology is applied as was done for Hypothesis 2. After obtaining the long-run

returns for each firm, univariate tests are conducted to see whether the mean and

median returns are significantly higher for groups with short lockup periods than for

firms with long lockup periods.

H0 : µs ≤ µl or Ms ≤ Ml

H1 : µs > µl or Ms > Ml

where µs and Ms are the average and median long-run returns for short lockups, and µl

and Ml are the average and median long-run returns for long lockups.

48

Further, regression analysis is conducted by fitting long-run returns (LR) against

lockup lengths.

(14). LRi = β0 + β1 IRi + β2 LOLi + β3 SIZEi + β4 AGEi

+ β5 TECHi + β6 UWi + β7 VCi + β8 AUDIi +εi

According to Hypothesis 6, there should be a significant negative relationship

between LR and LOL. The hypotheses are:

H0 : β2 ≥ 0

H1 : β2 < 0

Hypothesis 6A predicts that firms with high reputation underwriters, auditors, or

venture capital backing will experience no difference between long-run stock returns for

long and short lockups, while Hypothesis 6B predicts that longer lockups are associated

with worse long-run stock returns than shorter lockups for firms with low reputation

underwriters, auditors, or no venture capital backing. After obtaining the long-run stock

returns, the sample is divided into firms with high reputation underwriters and low

reputation underwriters, firms with venture capital backing and no venture capital

backing, and firms with well-known auditors and without well-known auditors. Then the

long-run returns of firms with different lockup lengths are compared within each group.

For firms with high reputation underwriters, auditors, or with venture capital

backing:

H0 : µs = µl or Ms = Ml

H1 : µs ≠ µl or Ms ≠ Ml

For firms with low reputation underwriters, auditors, or no venture capital backing:

H0 : µs = µl or Ms = Ml

H1 : µs > µl or Ms > Ml

49

where µs and Ms are the average and median long-run returns for short lockups, and µl

and Ml are the average and median long-run returns for long lockups.

Hypothesis 7 predicts that there are significant negative abnormal returns for IPO

firms around their lockup expiry, and firms with longer lockups, which have high

chances of experiencing agency problems, will experience worse returns than firms with

shorter lockups. The same methodology is applied as was done for Hypothesis 3.

Univariate tests are conducted to compare the short-run abnormal returns around

lockup expiry for firms with long and short lockup periods. The hypotheses are:

H0 : µs = 0 and µl = 0

H1 : µs < 0 and µl < 0

where µs is the average short-term abnormal return for short lockup IPOs, and µl is the

average short-term abnormal return for long lockup IPOs. Further the means of the two

groups of stocks are compared, and the hypotheses are:

H0 : µs ≤ µl

H1 : µs > µl

50

CHAPTER 5

EMPIRICAL RESULTS

Table 2 provides summary characteristics of the sample. Panel A provides

information on the full sample. There are 3980 initial public offering (IPO) firms that

have lockup lengths available from 1989 to 2004. The mean lockup length is 220 days,

while the median is 180 days. The average underpricing for the sample is 21.19%,

consistent with the literature (Ibbotson 1975; Ritter 1984; Tinic 1988).

Panel B lists sub-periods and shows the variation of lockup length and other IPO

characteristics over time. In 1995 and 1996 there were 948 companies that went to IPO.

The number of IPOs went down after the bust of the high-tech bubble in the early 2000s.

More and more IPO firms choose to use 180-day as their lockup length. In 1989 and

1990, 54% of IPO firms chose lockup lengths other than 180 days, while this number

decreased to 22% in the 1995 – 1996 period, and further dropped to 12% in 2001 and

2002. As a result, the mean lockup length decreased from 223 days in 1989 - 1990 to

186 days in 2001 - 2002. Researchers have noticed this standardization of the lockup

length in recent years; but no reasons have been found to explain it (Field and Hanka

2001; Bradley, Jordan, and Yi 2001). The percentage of IPO firms that are backed by

venture capital is relatively stable across time, with the highest 55% in the 1999 – 2000

period. Almost half of the firms that went public in this period were high-tech firms. This

period also has the highest underpricing -- 54.2% -- as investors appeared to have a

high level of passion for these high-tech firms. The percentage of IPO firms that use the

top six auditors is relatively stable over the 1989-2004 period.

In Panel C, I partition the sample into three groups – IPOs with lockup length

shorter than 180 days (define as short lockup group), those with lockup length equal to

51

180 days, and those with lockup longer than 180 days (define as long lockup group).

About 1000 firms (25% of the sample) use lockup lengths not equal to 180 days. I find

some differences when comparing the characteristics of the three groups. First, 77% of

IPO firms with lockup lengths shorter than 180 days have venture capital backing, while

only 18% of firms with lockup lengths longer than 180 days are backed by venture

capital. Thirty-five percent of IPO firms with short lockups are high-tech firms, while only

7% of IPO firms with long lockups are high-tech firms. The reputation of underwriters for

the group with short lockups and the group with a lockup length equal to 180 days is

much higher than that for the group with long lockups. Similarly, in comparison to the

other groups, the long-lockup group contains a smaller proportion of firms that use a

top-six auditor. Lastly, firms with a 180-day lockup length have the highest offer price

and raise the most funds among the three groups.

Tests for the Signaling Hypothesis

Hypothesis 1: Firms with a longer lockup period should have higher quality, and

therefore a higher operating performance for several years after their IPO.

The results of the test for Hypothesis 1 are contained in Table 3. Before I discuss

these, an explanation of the missing data is in order. I use the growth rates of five

accounting variables to proxy for firm quality. There are 264 firms that do not have the

needed accounting data in Compustat for the four years after the IPO. In addition, for

IPO years, 1052 firms have negative operating returns on assets, 1052 firms have

negative operating income, and 1448 firms have negative operating cash flows. It does

not make sense to calculate the growth rates based on the negative number of the base

year. Thus, following Jain and Kini (1994) I do not calculate the growth rates of these

three accounting variables for firms with negative numbers in IPO years. There are 272

52

firms that do not have sales data available for IPO years. Growth rates for sales and

asset turnover are not calculated for these firms because of missing base year data.

For the second post-IPO year, there are 267 firms without data for operating

returns on assets and operating income, 240 firms without data for operating cash flow,

and 82 firms without sales data. For the third post-IPO year, there are 511 firms without

data for operating returns on assets and operating income, 441 firms without data for

operating cash flow, and 484 firms without sales data. For the fourth post-IPO year,

there are 999 firms without data for operating returns on assets and operating income,

857 firms without data for operating cash flow, and 1178 firms without sales data. As a

result, Table 3 shows that the numbers of observations dropped year by year.

The results from the test of Hypothesis 1, as shown in Table 3, are mixed.

Operating returns on assets and operating cash flow on assets get worse as time goes

by. For example, the growth rate of operating returns on assets for firms with lockup

lengths shorter than 180 days decreases from -7% in the 1-2 year period to -27% in the

1-4 year period. On the other hand, sales, operating income, and asset turnover

improve over time. For example, the growth rate of sales for firms with a 180 day lockup

period increases from 33% in the 1-2 year period to 93% in the 1-4 year period. When

comparing the medians for firms with long and short lockups, only the growth rates of

asset turnover support Hypothesis 1. For example, the growth rate of asset turnover for

firms with long lockups in the 1-2 year period is 10%, compared to 3% for firms with

short lockups. The difference between the two growth rates is significant at the 1% level.

The 1-3 and 1-4 periods show similar results. When comparing firms with and without a

180-day lockup period, I find that the growth rates of operating return on asset and

operating income for firms with a lockup length equal to 180 days are better than those

53

for firms with lockup lengths other than 180 days for all three periods. In sum, from the

univariate tests, only one variable among the five – asset turnover – supports

Hypothesis 1.

In the OLS regression shown in Table 4, I control for other variables that may

affect the length of lockup. Among the five accounting variables, operating return on

asset is significantly negatively correlated with lockup length. The coefficient is -0.118,

and it is significant at the 1% level (p-value of 0.003). At the same time, asset turnover

is significantly positively correlated with lockup length. Its coefficient is 0.069, and it is

significant at the 5% level (p-value of 0.048). Though not reported, when using growth

rates of year 1-2 and 1-4 as independent variables instead of year 1-3, none of the

accounting variables are significant. Since the majority of the sample is firms with a

lockup length of 180 days, I also ran the OLS regression including only firms with lockup

lengths other than 180 days. The results are very similar with those using all three

groups. Thus, consistent with the univariate tests, I do not find any clear relationship

between the operating performance and lockup lengths.

Because the relationship between operating performance and lockup length may

not be linear, I also use a non-linear model – binary logistic model – to run the

regression. As Table 5 shows, among the five accounting variables, asset turnover is

marginally significant. It has a coefficient of 0.710, an odds ratio of 2.033, and is

significant at the 10% level (p-value of 0.076). It means that with each 1% increase in

the growth rate of asset turnover, the odds of a firm having a lockup length greater than

180 days increases by 2.033 times. In other words, firms with a lockup length greater

than 180 days tend to have a higher growth rate of asset turnover. When using years 1-

2 and 1-3 as independent variables, all accounting variables are not significant.

54

Therefore, I conclude that there is a weak non-linear relationship between operating

performance and lockup length since only one out of the five accounting variables has a

marginal significance.

In order to examine why firms choose 180 days as their lockup period, I run a

multinomial logistic regression to compare the characteristics of firms with a 180-day

lockup length to firms with a lockup length longer and shorter than 180 days. Table 6

shows the results. When comparing firms with a 180-day lockup period to firms with a

lockup period shorter than 180 days, size has a coefficient of -0.85, an odds ratio of

0.427, and it is significant at the 1% level (p-value of 0.000). Similarly, when comparing

firms with a 180-day lockup period to firms with a lockup period longer than 180 days,

size has a coefficient of -0.959, an odds ratio of 0.383, and it is significant at the 1%

level (p-value of 0.000). The results indicate that as firms‟ sizes increase, firms have a

higher chance to choose 180 days as their lockup length. In other words, because I use

proceeds from IPO as the proxy for firm size, firms choosing 180 days as their lockup

period raise more money in their IPO than other firms do. When using 1-3 and 1-4 year

growth rates in the regression, I get similar results.

Hypothesis 1A: The positive relationship between operating performance and

lockup length (Hypothesis 1) is stronger for opaque firms than for transparent firms.

Table 7 contains the results from the univariate tests for accounting numbers for

opaque firms. Among the five accounting variables, only asset turnover is significantly

greater for firms with long lockups than that for firms with short lockups. Table 8 shows

the results of OLS regression for opaque firms that include lockup lengths equal to 180

days, longer than 180 days, and shorter than 180 days. Among the five accounting

variables, four are significant. But two variables are positively related with lockup length

55

while the other two are negatively related with lockup length. When using 1-2 and 1-4

year periods, none of the accounting variables are significant. I further run the OLS

regression by excluding firms with a lockup length equal to 180 days and find similar

results. In addition, the binary logistic regressions testing the non-linear relationship

between operating performance and lockup length show no significant results. For

transparent firms, I repeat all the tests mentioned above, but find no significant results.

In sum, the evidence does not support Hypothesis 1A.

Hypothesis 1B: The positive relationship between operating performance and

lockup length (Hypothesis 1) is stronger for high-tech firms than for non-high-tech firms.

The growth rates of the five accounting variables for high-tech firms are shown in

Table 9. Similar with opaque firms in Hypothesis 1A, only asset turnover shows a

superior growth rate for high-tech firms with long lockups. However, in the regression

results shown in Table 10, none of the accounting variables are significantly and

positively related to lockup length. I also conduct other similar regression analysis in

Hypothesis 1A for high-tech firms and non-high-tech firms, and no further significant

results are found. Therefore, the evidence does not support Hypothesis 1B.

Hypothesis 1C: The positive relationship between operating performance and

lockup length (Hypothesis 1) is stronger for firms with a high λ than for firms with a low λ.

Table 11 contains the results of univariate tests for firms with a top 20% of λ. As

one can see from the table, consistent with Hypothesis 1A and 1B, only asset turnover

shows a high growth rate for firms with a high λ. Table 12 shows the OLS regression

results for firms with a top 20% of λ. Asset turnover is significant at the 1% level (p-

value of 0.007) with a coefficient of 0.867. Operating return on asset is also significant

(p-value of 0.042), but with a coefficient of -2.052. The results show no clear

56

relationship between operating performance and lockup length. Other tests similar with

Hypothesis 1A show no significant results for accounting variables for top λ firms.

I compare the growth rates for these five accounting variables for firms with a

bottom 20% of λ in Table 13. The same pattern as before is found. Asset turnover is the

only significant variable. Table 14 shows the OLS regression results for firms with a

bottom 20% of λ. Asset turnover is significant at 5% level (p-value of 0.036) with a

coefficient of 0.248. Other tests show no significant results for accounting variables for

bottom λ. Therefore, there is no evidence to support Hypothesis 1C.

Hypothesis 2: If investors incorporate the signal conveyed in lockup length into the

IPO offer price, there will be no difference between the long-run stock returns for long

and short lockups.

There are 3980 IPO firms that have lockup lengths available from SDC, among

which only 3813 firms have available stock return data from the CRSP. The difference

may be due to the fact that some IPO firms included in Securities Data Corporation

(SDC) are listed on the Pink Sheet or Small Capital Market where no stock price data is

available from CRSP.

Table 15 shows the long-run returns for IPO firms with different lockup lengths.

Panel A shows that, generally, IPO firms experience long-run stock return

underperformance compared to the market, consistent with the literature (Ritter 1991;

Loughran and Ritter 1995). When I divide the whole sample into three groups according

to the length of lockup, some differences appear among groups. For example, the

median 6-month value-weighted stock return for firms with a lockup length shorter than

180 days is -7%, higher than the -11% for firms with a 180-day lockup period, and -15%

for firms with a lockup length longer than 180 days. The differences are significant at the

57

1% level. The 1-year, 2-year, and 3-year returns give similar results. The evidence

shows that firms with a shorter lockup length experience better long-run stock returns

than firms with a longer lockup length.

Figure 1 plots the 1-year return for these three groups, with all three groups

showing similar patterns. For firms with a lockup length shorter than 180 days, 62% of

firms have a negative 1-year return, compared to 73% for firms with a lockup length

longer than 180 days. Thirty-two percent of firms with short lockups have returns less

than -50%, while 45% of firms with long lockups have returns below -50%. Twenty-one

percent of firms with short lockups have returns greater than 50%, while only 12% of

firms with long lockups have returns greater than 50%. Thus, the poor long-run returns

for long lockups are not driven by some extreme numbers; on the contrary, they are

driven by general poor returns for the group.

In the regression analysis in Panel B, I control for other variables that may affect

the long-run returns. The dependent variable is the 3-year long-run stock return. The

lockup length variable has a coefficient of -0.15, and it is significant at the 1% level (p-

value of 0.00). It indicates that lockup length and IPO long-run returns are significantly

negatively related. The long-run returns for 6-month, 1-year, and 2-year periods give

similar results. In sum, I conclude that the long-run returns are significantly different for

firms with long and short lockups; therefore, I find no support for Hypothesis 2.

Hypothesis 3: There are no significant abnormal returns for IPO firms around their

lockup expiration dates.

There are 3249 firms that have short-term returns available around lockup expiry

from EVENTUS. Some possible reasons for the missing data are: data end before

lockup expiration day, no data available for estimation period, and data start after

58

expiration day. Panel A of Table 16 shows the short-run returns for the whole sample.

The mean seven-day return for firms with a lockup length shorter than 180 days is -

1.4%, while the return for firms with a lockup length longer than 180 days is -1.91%.

Both of them are significantly different from zero. However, the difference between the

two groups of returns is not significant.

Next, I examine the short-run returns for firms with high information asymmetry

and low information asymmetry. Panel B shows that transparent firms have significantly

negative abnormal returns around lockup expiry. Panel C indicates that high-tech firms

experience a much worse short-run return around lockup expiry than non-high-tech

firms. The difference is significant at the 1% level. This finding is consistent with

literature (Field and Hanka, 2001; Bradley, Jordan, and Yi, 2001), but no explanations

have been found. Panel D shows that for firms with high adverse selection, those that

have a lockup period shorter than 180 days experience a positive abnormal return. In

sum, because firms do have significant abnormal returns around lockup expiry, I reject

Hypothesis 3.

Tests for the Agency Hypothesis

Hypothesis 4: Higher agency problem IPOs should lock up more shares.

As shown in Table 17, the mean and median percentage of shares locked for firms

with a high agency problem are 60.28% and 67.25%, respectively. The mean and

median percentage of shares locked for firms with a low agency problem are 60.02%

and 64.88%, respectively. The means and medians are very close and are not

significantly different. Because the agency hypothesis says firms with a high agency

problem should have a longer lockup period, I further compare the percentage of shares

locked between firms with long and short lockup periods. The mean and median

59

percentage of shares locked for firms with a lockup length shorter than 180 days are

50.45% and 57.58%, respectively. The mean and median percentage of shares locked

for firms with a lockup length longer than 180 days are 52.29% and 56.79%,

respectively. The means and medians are not significantly different between the two

groups. In sum, there is no evidence to support Hypothesis 4.

Hypothesis 5: IPO firms with longer lockup periods will have worse operating

performance several years after their offerings compared to firms with shorter lockup

periods.

The same tests are applied as for Hypothesis 1. As shown in Table 3, among the

five accounting variables, only the growth rates of operating return on asset and

operating income for firms with short lockups are higher than those for firms with long

lockups in the 1-2 and 1-3 year periods. Further, the OLS regressions and logistic

regressions do not show a clear relationship between operating performance and

lockup length as shown in Hypothesis 1. Thus, I find no support for Hypothesis 5.

Hypothesis 6: Firms with long lockup periods will experience worse long-run stock

returns after their offerings compared to firms with short lockup periods.

The same tests are applied as for Hypothesis 2. As shown in Table 10, the long-

run returns for firms with a short lockup period are significantly better than firms with a

long lockup period. Therefore, the evidence supports Hypothesis 6.

Further, I compare the long-run returns for firms with high agency problems and

low agency problems. Table 18 shows the results. In Panel A, the median 2-year long-

run return for firms with a low agency problem is -28%, which is significantly greater

than the -64% for firms with a high agency problem. Other returns show similar results.

In the regression analysis in Panel B, the 1-year stock return is the dependent variable,

60

and the independent variables are factors that may affect the long-run return. The

agency score has a coefficient of -0.23, and it is significant at the 1% level. The returns

for 6-month, 2-year, and 3-year give similar results. Thus, the evidence indicates that

high agency firms experience much worse long-run stock returns compared to firms with

low agency problems. Note that the R-squared is 4.4%. This is normal because the

studies investigating IPO long-run returns by using regressions analysis normally show

an R-squared around 5%. However, when examining the relationship between lockup

length and long-run returns for firms with high and low agency problems, both

regressions generate a negative but insignificant coefficient for lockup length.

Hypothesis 6A: For firms with high reputation underwriters, auditors, or venture

capital backing, there is no difference between long-run stock returns for long and short

lockups.

Hypothesis 6B: For firms with low reputation underwriters, auditors, or no venture

capital backing, longer lockups are associated with worse long-run stock returns than

shorter lockups.

Table 19 shows the long-run returns for high- and low-reputation underwriters. In

Panel A, it shows that the median long-run return for firms with high-reputation

underwriters is much higher than that for firms with low-reputation underwriters,

consistent with the literature (Carter, Dark, and Singh 1998). For example, the median

1-year returns are -11% and -55% for firms with high-reputation underwriters and low-

reputation underwriters, respectively. The difference is significant at the 1% level. The

returns for other time periods also show similar results. Panel B shows the results for

firms with high-ranking underwriters. The long-run returns for firms with long and short

lockups are not significantly different from one another. This is because high ranking

61

underwriters are good monitors of agency problems, so firms with reputable

underwriters will have low agency problems. As a result, these firms do not need to use

lockup length to indicate their potential for agency problems. On the contrary, in Panel C,

because firms do not have high-reputation underwriters as monitors of agency problems,

they still need to use lockup length to differentiate the agency problem. Thus, the long-

run returns for firms with short lockups, which have low agency problems, are

significantly better than firms with long lockups, which have high agency problems.

Table 20 shows the long-run returns for firms with and without venture capital

backing. Panel A shows that all the returns in different time periods for firms with

venture capital backing are significantly better than those for firms without venture

capital backing, consistent with the literature (Brav and Gompers 1997). When looking

at Panel B and C, I find that with or without venture capital backing, firms with short

lockups have significantly better long-run returns for all the time periods than firms with

long lockups. The result is different from that of underwriter. This may be because

venture capital is not a good monitor of agency problems, thus firms still need to use

lockup length to differentiate their agency problems.

Table 21 shows the long-run returns for firm with high- and low-ranking auditors. I

find that, in Panel A, firms with a high-ranking auditor experience much better long-run

stock returns than firms with a low-ranking auditor. As with venture capital, I find that

auditors, regardless of rankings, are not good monitors of firm agency problems. As a

result, the long-run returns for firms with short lockups are always better than those for

firms with long lockups, not matter whether they have high-ranking or low-ranking

auditors.

62

Thus, the agency problem appears to be closely related to firms‟ lockup length and

long-run stock returns. If firms already have a good mechanism to control for agency

problems, lockup length does not mean a great deal to them. Thus, their long-run stock

returns, regardless of lockup lengths, are not different. On the other hand, firms lacking

a good mechanism to control for agency problems need to use lockup lengths to

differentiate their agency problems. Therefore, high agency problem firms, which have

longer lockup lengths, experience much worse long-run stock returns than low agency

problem firms, which have shorter lockup lengths.

Hypothesis 7: There are significant negative abnormal returns for IPO firms around

their lockup expiry, and firms with longer lockups, which have a high probability of

experiencing agency problems, will experience worse returns than firms with shorter

lockups.

The same tests are conducted as for Hypothesis 3. As Panel A of Table 11 shows,

firms with short lockups and long lockups have significant negative short-term abnormal

returns around their lockup expiry. The negative return is greater for long lockups

compared to short lockups, but the difference is not significant. Thus, Hypothesis 7 is

partially supported.

Further, I examine the relationship between short-run return around lockup expiry

and agency problem. In Panel A of Table 22, I find that firms with high agency problems

experience a much higher negative abnormal return than firms with low agency

problems. For instance, the mean 7-day return around lockup expiry for firms with a low

agency problem is -0.58%, and it is not significantly different from zero. While the return

for firms with a high agency problem is -3.34% with a significant level of 1%. The

evidence indicates that investors sell their holdings for firms with high agency problems

63

around lockup expiry to avoid being taking advantage of by insiders after lockup expiry.

In the regression analysis in Panel B, the agency score variable is not significant, while

venture capital is significantly negatively related to short-run stock return around lockup

expiry. Field and Hanka (2001) and Bradley, Jordan, and Yi (2001) also find that firms

with venture capital backing experience a much worse return than non-venture backed

firms, but no reasonable explanations have been found yet.

64

Table 2

Summary Statistics

Panel A: Full Sample (N = 3980)

Mean Median

Number of Lockup days 220 180

Underwriter Ranking(UR) 6.8 8

Offer Price(OP) $11.85 $11.50

Underpricing(UP) 21.19% 11.11%

Panel B: Distribution of IPOs by Year and Characteristics

Lockup Length IPO Characteristics

Period N Non-180 Mean VC TECH UP UR AUDIT

1989-1990 186 54% 223 41% 26% 13.2% 7.3 58%

1991-1992 598 32% 221 44% 24% 16.5% 7.1 82%

1993-1994 822 31% 242 37% 20% 13.9% 6.5 77%

1995-1996 948 22% 228 43% 37% 20.8% 6.7 79%

1997-1998 605 26% 224 36% 29% 18.6% 6.4 78%

1999-2000 425 14% 184 55% 48% 54.2% 6.6 63%

2001-2002 117 12% 186 36% 30% 15.5% 7.9 77%

2003-2004 279 13% 183 43% 22% 12.7% 7.6 72% (table continues)

65

Table 2 (continued).

Panel C: IPOs with Different Lockup Lengths and Their Characteristics

Lockup Length IPO Characteristics

Length N Mean VC TECH UP UR AUDIT SIZE OP

<180 309(7.6) 108 77% 35% 16% 6.8 76% 45 M 11.56

=180 2979(74.8%) 180 45% 31% 22% 7.3 79% 68 M 12.65

>180 692(17.4%) 443 18% 7% 19% 4.4 59% 30 M 8.54

Note: Underwriter ranking (UR) is based on Carter-Manaster (1990), and the higher the score, the higher the reputation. Venture Capital (VC) is the percentage of IPOs backed by venture capital, TECH is the percentage of IPOs that are high-tech firms, and AUDIT is the percentage of IPOs using top six auditors. SIZE is the product of number of shares offered and offer price. Underpricing (UP) is the average percentage price change from offer price to the closing price of the first day after IPO.

66

Table 3

Accounting Numbers and Lockup Length

Panel A: Operating Return On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.07 204 -0.05*** 1892 -0.15*** 301 NA NA 0.00

Yr 1 to 3 -0.2*** 183 -0.13*** 1702 -0.31*** 268 NA NA 0.00

Yr 1 to 4 -0.27*** 147 -0.19*** 1309 -0.3*** 209 NA NA 0.03

Panel B: Operating Cash Flow On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.17*** 176 -0.16*** 1617 -0.29*** 235 NA NA 0.02

Yr 1 to 3 -0.40*** 162 -0.27*** 1457 -0.40*** 208 NA NA 0.02

Yr 1 to 4 -0.44*** 130 -0.35*** 1120 -0.42*** 161 NA NA NA

Panel C: Sales

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.3*** 257 0.33*** 2572 0.36*** 533 0.06 NA NA

Yr 1 to 3 0.69*** 229 0.63*** 2276 0.76*** 455 NA NA NA

Yr 1 to 4 1.00*** 180 0.93*** 1739 1.05*** 347 NA NA NA (table continues)

67

Table 3 (continued).

Panel D: Operating Income

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.29*** 204 0.28*** 1892 0.17*** 301 NA NA 0.00

Yr 1 to 3 0.47*** 183 0.45*** 1702 0.21 268 NA NA 0.00

Yr 1 to 4 0.53*** 147 0.56*** 1309 0.25** 209 NA NA 0.06

Panel E: Asset Turnover

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.03 257 0.05*** 2572 0.10*** 533 0.00 NA NA

Yr 1 to 3 0.06 229 0.06*** 2276 0.15*** 455 0.00 0.08 NA

Yr 1 to 4 0.02 180 0.07*** 1739 0.17*** 347 0.00 0.02 NA

Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period.

68

Table 4

Regression for Length of Lockup (OLS)

Estimated Coefficient P-Value

Operating Retn 1-3 Year Growth Rate -0.118 0.003

Cash Flow 1-3 Year Growth Rate 0.005 0.813

Sales 1-3 Year Growth Rate -0.039 0.347

Opera Income 1-3 Year Growth Rate 0.064 0.164

Asset Turnover 1-3 Year Growth Rate 0.069 0.048

Size -0.063 0.021

Age -0.017 0.453

High-tech -0.037 0.105

Underwriter Ranking -0.335 0.000

Venture Capital Backing -0.094 0.000

Auditor Ranking -0.054 0.013

Adjusted R Square 18.10

Note: Number of lockup days is the dependent variable. Independent variables are listed in the table. Size is the natural logarithm of the product of the number of shares offered and offer price. Age is defined as the years from a firm‟s inception till IPO. High-tech is a dummy variable, and it equals 1 for high-tech firms and 0 otherwise. Venture Capital Backing is a dummy variable, and it equals 1 for firms with venture capital backing and 0 otherwise. Auditor Ranking is a dummy variable, and it equals 1 for firms use top six auditors and 0 otherwise. All other independent variables are defined as before. The sample includes firms with lockup lengths longer than 180 days, equal to 180 days, and shorter than 180 days. Ordinary Least Square (OLS) is used.

69

Table 5

Regression for Length of Lockup -- Binary Logistic

Estimated Coefficient P-Value Exp(B)

Operating Retn 1-4 Year Growth Rate -0.096 0.647 0.908

Cash Flow 1-4 Year Growth Rate -0.017 0.602 0.983

Sales 1-4 Year Growth Rate -0.052 0.629 0.949

Opera Income 1-4 Year Growth Rate 0.075 0.362 1.077

Asset Turnover 1-4 Year Growth Rate 0.710 0.076 2.033

Size 0.465 0.044 1.592

Age -0.004 0.649 0.996

High-tech -0.405 0.226 0.667

Underwriter Ranking -0.436 0.000 0.647

Venture Capital Backing -1.243 0.000 0.288

Auditor Ranking 0.220 0.513 1.246

Adjusted R Square 35.30

Note: The sample only includes firms with lockup lengths other than 180 days. Binary logistic test is used. Dependent variable is 0 for firms with a lockup length shorter than 180 days, and 1 for firms with a lockup length longer than 180 days. Independent variables are the same as defined before. Exp(B), or odds ratio, is calculated by raising e to the power of logistic coefficient.

70

Table 6

Regression for Length of Lockup -- Multinomial Logistic

Panel A: Compare Length <180 and =180

Estimated Coefficient P-Value Exp(B)

Operating Retn 1-3 Year Growth Rate -0.008 0.503 0.992

Cash Flow 1-3 Year Growth Rate 0.010 0.190 1.010

Sales 1-3 Year Growth Rate 0.001 0.992 0.959

Opera Income 1-3 Year Growth Rate 0.005 0.582 1.005

Asset Turnover 1-3 Year Growth Rate 0.026 0.339 1.027

Size -0.851 0.000 0.427

Age 0.002 0.582 1.002

High-tech 0.330 0.027 1.390

Underwriter Ranking 0.032 0.472 1.033

Venture Capital Backing 0.001 0.992 1.001

Auditor Ranking -0.168 0.306 0.845

Panel B: Compare Length >180 and =180

Estimated Coefficient P-Value Exp(B)

Operating Retn 1-3 Year Growth Rate -0.006 0.516 0.994

Cash Flow 1-3 Year Growth Rate -0.001 0.903 0.999

Sales 1-3 Year Growth Rate 0.007 0.757 1.007

Opera Income 1-3 Year Growth Rate 0.004 0.671 1.004

Asset Turnover 1-3 Year Growth Rate 0.010 0.710 1.010

(table continues)

71

Table 6 (continued).

Estimated Coefficient P-Value Exp(B)

Size -0.959 0.000 0.383

Age -0.008 0.077 0.992

High-tech -0.137 0.334 0.872

Underwriter Ranking -0.379 0.000 0.682

Venture Capital Backing -0.540 0.000 0.583

Auditor Ranking -0.330 0.015 0.719

Note: The sample is partitioned into three groups – firms with lockup length equal to 180 days, shorter than 180 days, and longer than 180 days. The group with a 180-day lockup length is a reference group, and its characteristics are compared to those of the other two groups. Dependent variable is 0, 1, and 2, representing each of the three groups, respectively. Independent variables are defined as before. Multinomial logistic regression is conducted. Exp(B), or odds ratio, is calculated by raising e to the power of logistic coefficient.

72

Table 7

Accounting Numbers and Lockup Length -- Opaque Firms

Panel A: Operating Return On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.15* 19 -0.09 39 -0.40*** 89 NA NA 0.00

Yr 1 to 3 -0.29** 17 -0.17 36 -0.52** 74 NA NA 0.06

Yr 1 to 4 -0.32** 16 -0.48*** 31 -0.47*** 58 NA NA NA

Panel B: Operating Cash Flow On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.40 15 -0.08 36 -0.57*** 65 NA NA 0.00

Yr 1 to 3 -0.48* 14 -0.11 33 -0.58*** 53 NA NA 0.06

Yr 1 to 4 -0.30** 13 -0.73*** 29 -0.52*** 39 NA NA NA

Panel C: Sales

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.39*** 28 0.22*** 61 0.35*** 180 NA NA NA

Yr 1 to 3 0.98*** 22 0.46*** 50 0.76*** 144 NA NA NA

Yr 1 to 4 1.73*** 21 0.79*** 43 1.12*** 107 NA NA NA

(table continues)

73

Table 7 (continued).

Panel D: Operating Income

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.25 19 0.27* 39 -0.22 89 NA NA 0.03

Yr 1 to 3 0.43** 17 0.54** 36 0.04 74 NA NA 0.02

Yr 1 to 4 0.24 16 0.32 31 0.00 58 NA NA NA

Panel E: Asset Turnover

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.03 28 0.01 61 0.19*** 180 0.05 NA NA

Yr 1 to 3 0.07 22 -0.09 50 0.29*** 144 0.06 NA NA

Yr 1 to 4 -0.03 20 -0.13*** 43 0.39*** 107 0.04 NA NA

Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period.

74

Table 8

Regression for Length of Lockup -- Opaque Firms

Estimated Coefficient P-Value

Operating Retn 1-3 Year Growth Rate -0.448 0.002

Cash Flow 1-3 Year Growth Rate -0.152 0.257

Sales 1-3 Year Growth Rate -1.166 0.002

Opera Income 1-3 Year Growth Rate 0.602 0.007

Asset Turnover 1-3 Year Growth Rate 0.960 0.007

Size -0.189 0.207

Age -0.078 0.443

High-tech 0.045 0.733

Underwriter Ranking -0.226 0.133

Venture Capital Backing -0.043 0.674

Auditor Ranking -0.182 0.146

Adjusted R Square 24.20

Note: The regression is for opaque firms that include lockup lengths equal to 180 days, longer than, and shorter than 180 days. OLS is used. All variables are defined as before.

75

Table 9

Accounting Numbers and Lockup Length -- High-tech Firms

Panel A: Operating Return On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.08 76 -0.01 523 -0.20 64 NA NA 0.00

Yr 1 to 3 -0.19* 72 -0.13** 468 -0.42*** 58 NA NA 0.00

Yr 1 to 4 -0.43** 60 -0.37*** 360 -0.55** 45 NA NA NA

Panel B: Operating Cash Flow On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.25*** 70 -0.11*** 467 -0.24*** 53 NA NA NA

Yr 1 to 3 -0.46*** 67 -0.28*** 419 -0.46*** 47 NA NA NA

Yr 1 to 4 -0.57*** 56 -0.58*** 319 -0.64*** 36 NA NA NA

Panel C: Sales

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.35*** 98 0.37*** 817 0.37*** 131 NA NA NA

Yr 1 to 3 0.76*** 91 0.68*** 705 0.66*** 108 NA NA NA

Yr 1 to 4 1.17*** 72 0.99*** 539 1.04*** 80 NA NA NA

(table continues)

76

Table 9 (continued).

Panel D: Operating Income

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.29* 76 0.34*** 522 0.17* 64 NA NA 0.03

Yr 1 to 3 0.58** 72 0.43*** 468 0.09 58 NA NA 0.02

Yr 1 to 4 0.02 60 0.21*** 360 0.10 45 NA NA NA

Panel E: Asset Turnover

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.04 98 0.13*** 817 0.14*** 131 0.01 0.00 NA

Yr 1 to 3 0.09 91 0.16*** 705 0.22*** 108 0.03 0.00 NA

Yr 1 to 4 0.10 72 0.19*** 539 0.30*** 80 0.02 0.02 NA

Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period.

77

Table 10

Regression for Length of Lockup (High-tech Firms)

Estimated Coefficient P-Value

Operating Retn 1-3 Year Growth Rate -0.005 0.935

Cash Flow 1-3 Year Growth Rate 0.010 0.813

Sales 1-3 Year Growth Rate -0.118 0.063

Opera Income 1-3 Year Growth Rate 0.050 0.531

Asset Turnover 1-3 Year Growth Rate 0.062 0.164

Size -0.136 0.004

Age -0.024 0.563

Underwriter Ranking -0.260 0.000

Venture Capital Backing -0.141 0.000

Auditor Ranking -0.083 0.041

Adjusted R Square 16.3

Note: The regression is for opaque firms that include lockup lengths equal to 180 days, longer than, and shorter than 180 days. OLS is used. All variables are defined as before.

78

Table11

Accounting Numbers and Lockup Length -- High λ Firms

Panel A: Operating Return On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.41** 17 0.02 340 -0.14** 25 NA NA 0.00

Yr 1 to 3 0.05 17 -0.09* 301 -0.24** 23 NA NA 0.00

Yr 1 to 4 -0.74** 11 -0.18*** 265 -0.28** 17 NA NA NA

Panel B: Operating Cash Flow On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.20* 17 -0.02 290 -0.02 20 NA NA NA

Yr 1 to 3 -0.34** 17 -0.16** 257 -0.15* 18 NA 0.04 NA

Yr 1 to 4 -1.02*** 11 -0.31*** 215 -0.30** 13 NA 0.06 NA

Panel C: Sales

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.58** 25 0.30*** 542 0.35** 46 NA NA NA

Yr 1 to 3 1.45*** 21 0.54*** 463 0.65*** 39 NA NA NA

Yr 1 to 4 2.24*** 12 0.68*** 401 0.67*** 28 NA NA NA

(table continues)

79

Table11 (continued).

Panel D: Operating Income

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 1.20** 17 0.26** 340 0.07 25 NA NA 0.03

Yr 1 to 3 1.82** 16 0.40*** 301 0.21** 23 NA NA 0.02

Yr 1 to 4 0.63* 11 0.35*** 265 -0.05 17 NA NA 0.07

Panel E: Asset Turnover

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.07 25 0.07** 542 0.10** 46 0.05 NA NA

Yr 1 to 3 0.00 21 0.08** 463 0.23** 39 0.03 0.06 NA

Yr 1 to 4 -0.19 12 0.09** 401 0.13** 28 0.02 0.02 NA

Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period. High λ firms are defined as those firms with top 20% of λ calculated in the one month period after firms‟ lockup expiry.

80

Table 12

Regression for Length of Lockup -- high λ

Estimated Coefficient P-Value

Operating Retn 1-4 Year Growth Rate -2.052 0.042

Cash Flow 1-4 Year Growth Rate 0.370 0.269

Sales 1-4 Year Growth Rate -0.140 0.597

Opera Income 1-4 Year Growth Rate 1.110 0.095

Asset Turnover 1-4 Year Growth Rate 0.867 0.007

Size 0.584 0.465

Age -0.071 0.694

High-tech -0.415 0.064

Underwriter Ranking 0.325 0.489

Venture Capital Backing -0.432 0.285

Auditor Ranking -0.241 0.257

Adjusted R Square 61.3

Note: The OLS regression excludes firms with lockup length equal to 180 days and includes firms with top 20% λ.

81

Table13

Accounting Numbers and Lockup Length -- Low λ Firms

Panel A: Operating Return On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.09 38 -0.07 387 -0.06 78 NA NA NA

Yr 1 to 3 -0.09 35 -0.14** 360 -0.19** 68 NA NA NA

Yr 1 to 4 -0.25** 33 -0.15** 292 -0.20** 60 NA NA NA

Panel B: Operating Cash Flow On Asset

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.17** 32 -0.17*** 336 -0.04 62 NA NA NA

Yr 1 to 3 -0.44** 29 -0.25*** 310 -0.23** 52 0.08 0.04 NA

Yr 1 to 4 -0.43*** 28 -0.27*** 250 -0.20** 45 NA 0.03 NA

Panel C: Sales

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.26*** 45 0.28*** 477 0.37*** 134 NA NA NA

Yr 1 to 3 0.69*** 40 0.60*** 439 0.73*** 121 NA NA NA

Yr 1 to 4 0.66*** 33 0.94*** 355 1.05*** 99 0.05 0.02 NA

(table continues)

82

Table13 (continued).

Panel D: Operating Income

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 0.21** 39 0.22** 387 0.22* 78 NA NA NA

Yr 1 to 3 0.49*** 35 0.49*** 360 0.31** 68 NA NA 0.06

Yr 1 to 4 0.62*** 29 0.67*** 292 0.71*** 60 NA NA NA

Panel E: Asset Turnover

GR <180 (1) N =180 (2) N >180 (3) N Comparison(p-value)

3&1 2&1 2&3

Yr 1 to 2 -0.02 45 0.02 477 0.12*** 134 0.01 NA NA

Yr 1 to 3 0.00 40 0.02 439 0.13** 121 0.01 NA NA

Yr 1 to 4 -0.03 37 0.05** 355 0.17*** 99 0.02 NA NA

Note: Operating Return on Asset is defined as operating income before depreciation and taxes divided by total assets, Operating Cash Flow on Asset equals operating income minus capital expenditures, divided by total assets, and Asset Turnover is defined as the ratio of sales to total assets. GR is the median growth rate of a post-IPO year relative to the IPO year. Industry-adjusted growth rates are used. *, **, and *** denote significant different from zero at 10%, 5%, and 1% level, respectively. Mann-Whitney test is used to compare the medians for two groups. When p-value is greater than 0.10, I use NA instead of the real p-value. The null hypothesis for the comparison of 3&1 is that the median for firms with short lockups is greater than or equal to that for firms with long lockups. The null hypothesis for the comparison of 2&1 is that the median for firms with short lockups is greater than or equal to that for firms with a 180-day lockup period. The null hypothesis for the comparison of 2&3 is that the median for firms with long lockups is greater than or equal to that for firms with a 180-day lockup period. High λ firms are defined as those firms with bottom 20% of λ calculated in the one month period after firms‟ lockup expiry.

83

Table 14

Regression for Length of Lockup (Low λ)

Estimated Coefficient P-Value

Operating Retn 1-3 Year Growth Rate -0.391 0.220

Cash Flow 1-3 Year Growth Rate 0.165 0.305

Sales 1-3 Year Growth Rate -0.030 0.856

Opera Income 1-3 Year Growth Rate 0.118 0.711

Asset Turnover 1-3 Year Growth Rate 0.248 0.036

Size -0.119 0.353

Age -0.174 0.086

High-tech -0.093 0.384

Underwriter Ranking -0.281 0.032

Venture Capital Backing -0.190 0.096

Auditor Ranking 0.058 0.581

Adjusted R Square 27.10

Note: The OLS regression excludes firms with a 180-day lockup period, and includes firms with bottom 20% of λ.

84

Table 15

Long-run Returns for All IPO Firms

Panel A: Univariate Test

Long-run Returns (Median)

Return Period <180 N =180 N >180 N Difference(p-value)

6-month -0.07** 292 -0.11*** 2878 -0.15*** 643 0.00

1-year -0.19** 292 -0.30*** 2878 -0.42*** 643 0.00

2-year -0.33** 292 -0.57*** 2878 -0.84*** 643 0.00

3-year -0.61** 292 -0.72*** 2878 -1.07*** 643 0.00

Panel B: Regression Results

Estimated Coefficient P-value

Underpricing

-0.057

0.122

Lockup Length

-0.15

0.000

Size

0.033

0.523

Age

-0.009

0.816

High-tech

0.125

0.001

Underwriter

0.091

0.100

Venture Capital

-0.008

0.839

Auditor

-0.052

0.179

Adjusted R Square 6.3

(table continues)

85

Table 15 (continued).

Note: Long-run returns are defined as the 6-month, 1-year, 2-year, and 3-year holding period return following a firm‟s IPO. All the returns are calculated starting at the 26th day after firms‟ IPO to avoid the effect of earlier aftermarket activities such as stabilization and quiet period (Brau et al. 2007). Value-weighted and equally-weighted (not shown) market-adjusted excess returns are calculated. Market adjusted return (MAR) is defined as the firm‟s buy and hold return (BAH) minus the market return from CRSP. Buy and

hold return is defined as the geometrically compounded return BAH = (1 +𝑀𝑡=𝑗 ri,t )-1,

where ri,t is the daily return for stock I on day t, j is the starting day and M is the ending

day for a calculating period. Market adjusted return is calculated as MAR = (1 +𝑀𝑡=𝑗 ri,t )

– (1 +𝑀𝑡=𝑗 rm,t ), where rm,t is the equally-weighted or value-weighted daily market return

from the CRSP. In the OLS regression, 3-year long-run return is the dependent variable, and all the independent variables are defined as before.

86

Table 16

Abnormal Return around Lockup Expiry

Panel A: All Data

Short-run Returns (Mean, %)

Return Period <180 N =180 N >180 N Difference(p-value)

Day (-3,3) -1.4*** 280 -1.85*** 2391 -1.91*** 578 0.12

Day (-4,4) -0.91** 280 -1.79*** 2391 -2.52*** 578 0.16

Panel B: Opaque and Transparent

Short-run Return (Mean, %)

Return Period Opaque N Transparent N Difference(P-value)

Day(-3,3) -1.64* 402 -2.23*** 925 0.058

Day(-4,4) -2.31 402 -2.1*** 925 0.16

Panel C: High-tech and Non-high-tech

Short-run Return (Mean, %)

Return Period High-tech N Non-high-tech N Difference(P-value)

Day(-3,3) -2.82*** 1104 -1.28*** 2503 0.00

Day(-4,4) -3.20*** 1104 -1.19*** 2503 0.00

Panel D: Top 20 Adverse Selection

Short-run Return (Mean, %)

Return Period <180 N >180 N Difference(P-value)

Day(-3,3) 3.62* 30 -0.27 53 0.058

Day(-4,4) 5.33** 30 -0.50 53 0.16

(table continues)

87

Table 16 (continued).

Note: The market model is specified as follows: Rit = αi + βi Rmt + εit, where Rit is the return for firm I on day t in estimation period; Rmt is the average return for all firms in the stock market on day t (CRSP value-weighted index is used as the market index); αi and βi are the intercept and the slope parameters for firm I; αi and βi will be estimated over T trading days in the estimation period, where T varies according to the length of lockup. For IPOs having a lockup period between 3 to 5 months, the estimation period will start at the first day of its IPO, and end 10 days before the event day (lockup expiry). If an IPO has a 6 month or longer lockup period, the estimation period will start 130 days before the event day and end 10 days before the event day. The average 7-day and 9-day abnormal returns (3 days and 4 days before and after the IPO lockup expiration day) are calculated.

88

Table 17

Percentage of Shares Locked

Percent of Shared Locked (%)

High Agency Low Agency Difference (P-value)

Mean 60.28 60.02 0.39

Median 67.25 64.88 0.21

<180 >180

Mean 50.45 52.29 0.19

Median 57.58 56.79 0.3

Note: Free cash flow, growth rate, expense ratio, asset utilization ratio, and the amount of debt at the time of a firm‟s IPO are used as proxies for agency cost to partition the sample into high and low agency firms. Percent of share locked is defined as number of shares locked in the lockup agreement divided by the number of shares outstanding after a firm‟s IPO.

89

Table 18

Agency Problem and Long-run Return

Panel A: Univariate Test

Long-run Return (Median, %)

Return Period Low Agency N High Agency N Difference(P-value)

6-month 0.014 545 -0.25*** 292 0.00

1-year -0.05* 545 -0.48*** 292 0.00

2-year -0.28*** 545 -0.64*** 292 0.00

3-year -0.42*** 545 -0.73*** 292 0.00

Panel B: Regression Results

Estimated Coefficient P-value

Agency Score

-0.232

0.000

Underpricing

-0.071

0.138

Lockup Length

-0.061

0.228

Size

-0.068

0.231

Age

-0.09

0.076

High-tech

0.092

0.064

Underwriter

0.011

0.856

Venture Capital

-0.035

0.522

Auditor

0.007

0.881

Adjusted R Square 4.4

(table continues)

90

Table 18 (continued).

Note: The sample is partitioned into high and low agency groups by using the scoring scheme discussed. The dependent variable is the 1-year stock return. Six-month, 2-year, and 3-year returns give similar results. Higher agency score indicates a higher agency problem. Other variables are defined as before. OLS is used.

91

Table 19

Long-run Returns and Underwriter Reputation

Panel A: High and Low Reputation

Long-run Return (Median, %)

Return Period High N Low N Difference(P-value)

6-month 0.002 290 -0.24*** 292 0.00

1-year -0.11** 290 -0.55*** 292 0.00

2-year -0.27*** 290 -1.01*** 292 0.00

3-year -0.51*** 290 -1.23*** 292 0.00

Panel B: High Reputation

Long-run Return (Median, %)

Return Period <180 N >180 N Difference(P-value)

6-month 0.026 154 -0.019 135 0.48

1-year -0.054 154 -0.18*** 135 0.09

2-year -0.26*** 154 -0.28*** 135 0.31

3-year -0.52*** 154 -0.51*** 135 0.29

Panel C: Low Reputation

Long-run Return (Median, %)

Return Period <180 N >180 N Difference(P-value)

6-month -0.22 39 -0.24*** 306 0.11

1-year -0.33** 39 -0.55*** 306 0.06

2-year -0.65** 39 -1.05*** 306 0.00

3-year -0.86** 39 -1.26*** 306 0.00

(table continues)

92

Table 19 (continued).

Note: If an underwriter has a ranking of 8 or above, I define it as a high reputation underwriter. If an underwriter has a ranking of 4 or below, I define it as a low reputation underwriter.

93

Table 20

Venture Capital Backing and Long-run Returns

Panel A: With and without VC Backing

Long-run Return (Median, %)

Return Period VC N No VC N Difference(P-value)

6-month -0.10** 253 -0.13*** 687 0.05

1-year -0.32*** 253 -0.36*** 687 0.04

2-year -0.61*** 253 -0.74*** 687 0.03

3-year -0.87*** 253 -0.93*** 687 0.03

Panel B: VC Backing

Long-run Return (Median, %)

Return Period <180 N >180 N Difference(P-value)

6-month -0.01 129 -0.15*** 124 0.02

1-year -0.14** 129 -0.43*** 124 0.00

2-year -0.29*** 129 -0.84*** 124 0.00

3-year -0.60*** 129 -1.07*** 124 0.00

Panel C: No VC Backing

Long-run Return (Median, %)

Return Period <180 N >180 N Difference(P-value)

6-month -0.07** 165 -0.16*** 521 0.03

1-year -0.19*** 165 -0.42*** 521 0.00

2-year -0.34*** 165 -0.85*** 521 0.00

3-year -0.64*** 165 -1.06*** 521 0.00

94

Table 21

Auditor Reputation and Long-run Return

Panel A: High and Low Ranking Auditor

Long-run Return (Median, %)

Return Period High N Low N Difference(P-value)

6-month -0.10*** 602 -0.19*** 322 0.01

1-year -0.27*** 602 -0.47*** 322 0.00

2-year -0.59*** 602 -0.88*** 322 0.00

3-year -0.84*** 602 -1.04*** 322 0.02

Panel B: High Ranking Auditor

Long-run Return (Median, %)

Return Period <180 N >180 N Difference(P-value)

6-month -0.08* 384 -0.10*** 220 0.11

1-year -0.20*** 384 -0.32*** 220 0.03

2-year -0.37*** 384 -0.71*** 220 0.01

3-year -0.63*** 384 -0.98*** 220 0.00

Panel C: Low Ranking Auditor

Long-run Return (Median, %)

Return Period <180 N >180 N Difference(P-value)

6-month -0.006 70 -0.25*** 251 0.01

1-year -0.14** 70 -0.61*** 251 0.00

2-year -0.28* 70 -0.98*** 251 0.00

3-year -0.61*** 70 -1.13*** 251 0.00

(table continues)

95

Table 21 (continued).

Note: The top six auditors are defined as reputable auditors, and the remaining auditors are defined as low ranking auditors.

96

Table 22

Short-run Return and Agency Problem

Panel A: Low and High Agency

Short-run Return (Mean, %)

Return Period Low Agency N High Agency N Difference(P-value)

Day(-3,3) -0.58 529 -3.34*** 281 0.00

Day(-4,4) -0.53 529 -3.13*** 281 0.00

Panel B: Regression Results

Estimated Coefficient P-value

Agency Score

0.24

0.666

Size

0.096

0.087

Insider Holding Before IPO

0.053

0.276

High-tech

-0.065

0.201

Underwriter

-0.045

0.433

Venture Capital

-0.147

0.009

Adjusted R Square 2.7

Note: Low and high agency firms are defined as before. OLS regression is used in panel B. Dependent variable is the short-run return around lockup expiry, and independent variables are some factors that may affect the short-run abnormal returns.

97

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Figure 1: Long-run Return for IPO Firms. The figure shows the long-run returns for firms with lockup lengths shorter than, equal to, and longer than 180 days. Horizontal axis is the number of observations, and the vertical axis is the median 1-year returns.

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CHAPTER 6

CONCLUSION AND DISCUSSION

This dissertation investigates the reasons for the divergence of initial public

offering (IPO) lockup agreements. Previous studies exploring this topic chose

inappropriate proxies for firm quality, information asymmetry, and agency problems.

They also ignored long-term stock returns after firms‟ IPO, and short-term stock returns

after firms‟ lockup expiry. These return behaviors may give us some insight about the

reasons for the existence of IPO lockup agreements. In this dissertation, I try to fill in

some of the gaps in existing literature.

I use the growth rate of IPO firms‟ operating performance as a proxy for firm

quality to examine whether there is a relationship between lockup length and firm quality.

I also study this relationship for firms with different levels of information asymmetry. I

partition the sample into firms with high and low information asymmetry by using two

new proxies for information asymmetry – high-tech firms and high-adverse selection

firms.

I find that, among the five accounting variables used, only asset turnover shows a

positive relationship with lockup length for some time periods. This is true for both the

whole sample and for sub-samples containing only firms with high information

asymmetry. Since there are not consistently strong positive relationships between

operating performance and lockup length, I conclude that there is only weak evidence to

support the notion that lockup length is used to signal firms‟ quality. In other words,

there is weak evidence that high-quality firms use a longer lockup length to differentiate

their quality from low-quality firms. On the other hand, I do not find a significant negative

relationship between operating performance and lockup length. Thus, based on

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operating performance, there is no evidence to support the notion that lockup length is

used to differentiate firms with low agency problems from firms with high agency

problems.

There are two possible reasons why I do not find a strong relationship between

lockup length and firm quality. First, even though the growth rate of operating

performance has been used as a proxy for firm quality in the literature, it may not be a

good one. Further, choosing different accounting variables may give different results.

Second, for some of the tests, the number of observations is too small to get a

significant result due to missing data or negative accounting values in the base year.

Future research should focus on searching for better proxies for firm quality and test

their relationship with lockup length.

I then examine the long-run stock returns for IPO firms. For the whole sample, I

find that IPOs with short lockups experience a much better long-run return than that for

IPOs with long lockups. For instance, the median 2-year stock return for firms with short

lockups is -33%, while it is -84% for firms with long lockups. The difference is significant

at the 1% level. This result rejects the signaling hypothesis, which predicts no difference

between the long-run returns of the two groups, and is consistent with the agency

hypothesis. According to the agency hypothesis, as insiders of high agency firms

continuously cause agency problems after lockup expiry, firms‟ operating performance

will deteriorate. As a result, more investors will sell the firms‟ shares. Thus, this high

agency cost will lead to poor long-run returns for firms with long lockup periods.

Further, I find that among firms with low-reputation underwriters, the long-run

returns of short lockups are consistently higher than long-run returns for long lockups.

Among firms with high-reputation underwriters, on the other hand, I find no difference

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between long-run returns for short and long lockups. This finding is consistent with the

agency explanation and suggests strongly that underwriter reputation and lockup length

are substitute methods for controlling the agency problem.

When I examine long-run returns for firms that have venture capital backing and

for firms that use a top six auditor, I find no relationship between lockup length and long-

run returns. Unlike underwriter reputation, venture capital backing and auditor quality do

not appear to be substitutes for lockup length in controlling agency problems.

I further contribute to the IPO long-run return literature by finding that firms with

high agency problems experience much worse long-run returns than firms with low

agency problems. The sample is partitioned into high and low agency firms by using five

agency variables from the literature: free cash flow, growth rate, expense ratio, asset

utilization ratio, and the amount of debt. The results show that firms with low agency

problems have a median 3-year stock return of -42%, which is significantly higher than

the -73% for firms with high agency problems.

Finally, I investigate the short-run returns for IPO firms around their lockup

expiration day. For the whole sample, I find that firms with long lockups and short

lockups do have significant negative abnormal returns, even though they are not

significantly different from each other. Thus, I reject the signaling hypothesis which

predicts that there should be no abnormal returns. However, the results do not fully

support the agency hypothesis either. The agency hypothesis predicts that short-run

returns for short lockups should be better than those for long lockups. Consistent with

the literature, I find that high-tech firms experience a much worse short-run abnormal

return than non-high-tech firms, and venture capital backed firms experience a much

worse short-run abnormal return than non-venture-backed firms. I, like previous

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researchers, am unable to provide an explanation for these unusual returns. In sum, the

evidence from the short-run returns around the lockup expiration date rejects the

signaling hypothesis while partially supporting the agency hypothesis (negative short-

run returns at lockup expiry). The possible reason for not finding a full support for the

agency hypothesis is that there may be other unknowns, and therefore factors not

controlled for that also affect the short-term stock behavior. Future research can be

focused on the possible reasons for the lack of significant difference in short-run returns

for short and long lockups at lockup expiry.

My examination of short-run returns at lockup expiry also shows that firms with

high agency problems experience a much worse short-run return than firms with low

agency problems. For instance, the average 7-day abnormal return around lockup

expiry for firms with low agency problems is -0.58%, which is insignificant different from

zero. This is significantly better than the -3.34% for firms with high agency problems.

However, in the regression analysis, the agency variable is not significant while venture

capital is significantly negatively related to short-run returns.

Interestingly, I do not find a significant relationship between the percentage of

shares locked and lockup length. But I do find that firms with a 180-day lockup period

have bigger size, which is proxied by the proceeds from the IPO. In other words, firms

with a 180-day lockup period raise more money in their IPOs than firms with a lockup

period shorter or longer than 180 days. The average proceeds for firms with a 180-day

lockup period is $68 million, compared to $30 million for long lockups and $45 million for

short lockups.

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REFERENCES

Aggarwal, R., Krigman, L., & Womack, K. (2002). Strategic IPO underpricing, information momentum, and lockup expiration selling. Journal of Financial Economics, 66, 105-137.

Allen, F., & Faulhaber, G. (1989). Signaling by underpricing in the IPO market. Journal

of Financial Economics, 23, 303-323. Ang, J., Cole, R., & Lin, J. (2000, Feb). Agency costs and ownership structure. Journal

of Finance, 81-105. Baron, D. (1982). A model of the emand for investment banking advising and

distribution services for new issues. Journal of Finance, 37, 955-976. Beatty, R., & Ritter, J. (1986). Investment banking, reputation, and the underpricing of

initial public offerings. Journal of Financial Economics, 15, 213-232. Booth, J., & Chua, L. (1996). Ownership dispersion, costly information, and IPO

underpricing. Journal of Financial Economics, 41, 291-310. Bradley, D., Jordan, B., Roten, I., & Yi, H. (2001). Venture capital and IPO lockup

expiration: An empirical analysis. Journal of Financial Research, 24, 465-493. Brau, J., Carter, D., Christophe, S., & Key, K. (2004). Market reaction to the expiration

of IPO lockup provisions. Managerial Finance, 30, 75-91. Brau, J., Lambson, V., & McQueen, G. (2005). Lockups revisited. Journal of Financial

and Quantitative Analysis, 40, 519-530. Brau, J., Li, M., & Shi, J. (2007). Do secondary shares in the IPO process have a

negative effect on aftermarket performance? Journal of Banking & Finance, 31, 2612-2631.

Brav, A., Geczy, C., & Gompers, P. (2000). Is the abnormal return following equity

issuances anomalous? Journal of Financial Economics, 56, 209-249. Brav, A., & Gompers, P. (2003). The role of lockups in initial public offerings. Review of

Financial Studies, 16, 1-29. Brav, A., & Gompers, P. (1997). Myth or reality? The long-run underperformance of

initial public offerings: Evidence from venture and non-venture capital-backed companies. Journal of Finance, 5, 1791-1821.

Cao, C., Field, L., & Hanka, G. (2004). Does insider trading impair market liqudity?

Evidence from IPO lockup expirations. Journal of Financial and Quantitative Analysis, 39, 25-46.

104

Carter, R., Dark, F., & Singh, A. (1998). Underwriter reputation, initial returns, and the long-run performance of IPO stocks. Journal of Finance, 1, 285-311.

Carter, R., & Manaster, S. (1990). Initial public offerings and underwriter reputation.

Journal of Finance, 45, 1045-1067. Chazi, A., & Tripathy, N. (2007). Which version of equity market timing affects capital

structure? Journal of Applied Finance, 17, 1, 70-81. Chemmanur, T. (1993). The pricing of initial public offerings: A dynamic model with

information production. Journal of Finance, 1, 285-304. Chemmanur, T., & Paeglis, I. (2005). Management quality, certification, and initial public

offerings. Journal of Financial Economics, 76, 331-368. Courteau, L. (1995). Under-diversification and retention commitments in IPOs. Journal

of Financial and Quantitative Analysis, 30, 487-517. Ertimur, Y., Sletten, E., & Sunder, J. (2008). Voluntary disclosure strategy around IPO

lockup expirations. Unpublished Manuscript. Department of Finance, Northwestern University, Evanston, Illinois.

Field, L., & Hanka, G. (2001). The expiration of IPO share lockups. Journal of Finance,

56, 471-500. Field, L., & Lowry, M. (2007). Institutional versus individual investment in IPOs: The

importance of firm fundamentals. Unpublished Manuscript. Department of Finance, Penn State University, University Park, Pennsylvania.

Gale, I., & Stiglitz, J.E. (1989). The informational content of initial public offerings.

Journal of Finance, 44, 469-477. Gao, Y. (2005). Trading and the information environment of IPO stocks around lockup

expiration: Evidence from intraday data. Unpublished Manuscript. Department of Finance, Cornell University, Ithaca, New York.

Grinblatt, M., & Hwang, C. (1989). Signaling and the pricing of new issues. Journal of

Finance, 44, 393-420. Hahn, T., & Ligon, J. (2004). Liquidity and initial public offering underpricing.

Unpublished Manuscript. Department of Finance, Auburn University at Montgomery, Montgomery, Alabama.

Harris, O., & Glegg, C. (2009). Government quality and privately negotiated stock

repurchase: Evidence of agency conflict. Journal of Banking and Finance, 33, 317-325.

105

Houge, T., Loughran, T., Suchanek, G., & Yan, X. (2001). Divergence of opinion, uncertainty, and the quality of initial public offerings. Financial Management, 4, 5-23.

Huang, R., & Stoll, H. (1994). Market microstructure and stock predictions. Review of

Financial Studies, 7, 179-213. Ibbotson, R. (1975). Price performance of common stock new issues. Journal of

Financial Economics, 3, 235-272. Jain, B., & Kini, O. (1994, Dec). The post-issue operating performance of IPO firms.

Journal of Finance, 1699-1726. Jensen, M. (1986). Agency costs of free cash flow, corporate finance, and takeovers.

American Economic Review, 37, 525-550. Jensen, M., & Meckling, W. (1976). Theory of the firm: Managerial behavior, agency

costs and ownership structure. Journal of Financial Economics, 3, 305-360. Krigman, L., Shaw, W., & Womack, K. (2001). Why do firms switch underwriters?

Journal of Financial Economics, 60, 245-284. Lehn, K., & Poulsen, A. (1989). Free cash flow and stock holder gains in going private

transactions. Journal of Finance, 44, 774-789. Leland, H., & Pyle, D. (1977). Informational asymmetries, financial structure, and

financial intermediation. Journal of Finance, 32, 371-387. Lin, J., Sanger, G., & Booth, G. (1995). Trade size and components of bid-ask spread.

Review of Financial Studies, 8, 1153-1183. McKnight, P., & Weir, C. (2008). Agency cost, corporate governance mechanisms and

ownership structure in large UK publicly quoted companies: A panel data analysis. Quarterly Review of Economics and Finance, 49, 2, 139-158.

Megginson, W., & Weiss, K. (1991). Venture capitalist certification in initial public offers.

Journal of Finance, 46, 879-903. Michaely, R., & Shaw, W. (1995). Does the choice of auditor convey quality in and initial

public offering? Financial Management, 24, 15-30. Michaely, R., & Shaw, W. (1995). The pricing of initial public offerings: Tests of adverse

selection and signaling theories. Review of Financial Studies, 7, 279-319. Miller, E. (1977). Risk, uncertainty, and divergence of opinion. Journal of Finance, 4,

1151-1168.

106

Muscarella, C., & Vetsuypens, M. (1989). The underpricing of „second‟ initial public offerings. Journal of Financial Research, 12, 183-192.

Ness, B., Ness, R., & Warr, R. (2001, Aug). How well do adverse selection components

measure adverse selection? Financial Management, 5-30. Ofek, E., & Richardson, M. (2000). The IPO lock-up period: Implications for market

efficiency and downward sloping demand curves. Unpublished Manuscript. Department of Finance, New York University, New York City, New Jersey.

Reese, W. (1998). IPO underpricing, trading volume and investor interest. Unpublished

Manuscript. Department of Finance, Tulane University, New Orleans, Louisiana. Ritter, J. (1984). The hot issue market of 1980. Journal of Business, 32, 215-240. Ritter, J., & Welch, I. (2002). A review of IPO activity, pricing, and allocations. Journal of

Finance, 4, 1795-1828. Ritter, J. (1991). The long-run performance of initial public offerings, Journal of Finance,

1, 3-27. Rock, K. (1986). Why new issues are underpriced. Journal of Financial Economics, 15,

187-212. Singh, M., & Davidson, W. (2003). Agency costs, ownership structure and corporate

governance mechanisms. Journal of Banking and Finance, 27, 793-816. Schultz, P. (2003). Pseudo market timing and the long-run underperformance of IPOs.

Journal of Finance, 2, 483-517. Teoh, S., Welch, I., & Wong, T.J. (1998). Earnings management and the long-run

market performance of initial public offerings. Journal of Finance, 6, 1935-1974. Tinic, S. (1988). Anatomy of initial public offerings of common stock. Journal of Finance,

43, 789-822. Venkatesh, P., & Chiang, R. (1986). Information asymmetry and the dealer‟s bid-ask

spread: A case study of earnings and dividend announcements. Journal of Finance, 41, 1089-1102.

Welch, I. (1989). Seasoned offerings, imitation costs and the underpricing of initial

public offerings. Journal of Finance, 44, 421-449. Yung, C., & Zender, J. (2008). Moral hazard, asymmetric information and IPO lockups.

Unpublished Manuscript. Department of Finance, University of Colorado at Boulder, Boulder, Colorado.

107

Zheng, S., & Stangeland, D. (2007). IPO underpricing, firm quality, and analyst forecasts. Financial Management, 36, 45-64.

Zheng, S., & Li, M. (2008). Underpricng, ownership dispersion, and aftermarket liquidity

of IPO stocks. Journal of Empirical Finance, 15, 436-454. Zheng, X. (2007). Market underreaction to free cash flows from IPOs. The Financial

Review, 42, 75-97.