Block Premium and Shareholder Litigation

Block Premium and Shareholder Litigation*

Jaiho ChungKorea University Business School

Joon Ho Hwang**Korea University Business School

Joon-Seok KimKorea Capital Market Institute

Received 17 June 2013; Accepted 7 February 2014

Abstract

The causal relationship between block premium and the likelihood of shareholder litigation

has two possibilities. First, Barclay and Holderness (1989) argue that one of the costs of block

ownership is the expected likelihood of litigation. According to this argument, greater ex ante

litigation risk will reduce the size of block premium. Second, agency theory suggests that

excessive private benefits, the size of which is measured by the block premium, can lead to

litigation by disgruntled shareholders. According to this agency-theory hypothesis, greater

block premium will indicate a greater likelihood of litigation. Using a sample of 593 block

trades in the United States, we find evidence that greater litigation risk at the time of the

block trade lowers the block premium, thus supporting the notion that the expected litigation

risk is one of the costs of block ownership.

Keywords Corporate fraud; Private benefit; Litigation; Block trade; Block premium

JEL Classification: G34, K42, M14

1. Introduction

In a seminal paper on block ownership, Barclay and Holderness (1989) suggest a

method for quantifying the size of private benefits by examining block trades.

Specifically, they measure private benefits using block premium, which is the differ-

ence between the privately negotiated block transaction price and the post-

announcement share price in the stock market. Although many blocks are traded at

a premium to the stock market share price, 20% of the block trades in their sample

*We would like to thank two anonymous referees for their helpful comments. This research

is supported by KUBS Faculty Research Grant.

**Corresponding author: Joon Ho Hwang, Korea University Business School, Anam-dong,

Seongbuk-Gu, Seoul 136-701, Korea. Tel: +82-2-3290-2830, Fax: +82-2-922-7220, email: joon

[email protected].

Asia-Pacific Journal of Financial Studies (2014) 43, 407–431 doi:10.1111/ajfs.12053

© 2014 Korean Securities Association 407

occur at a discount to the post-announcement exchange price.1 Their explanation

for observing negative block premium, or block discount, is that block ownership

incurs not only private benefits but also private costs. One of the costs of block-

holding mentioned in their study, without a formal test, is the pre-existing threat of

litigation.2 For example, when a company is more likely to be a target of share-

holder litigation at the time of the block trade, a potential blockholder will not be

willing to purchase the block unless the block trade price accounts for the expected

cost of litigation. To date, it has not yet been empirically verified whether litigation

costs affect the block premium. We address this research gap by empirically testing

whether litigation risk is indeed one of the costs of block ownership.

The main caveat in empirically testing the above argument is that the causal

relationship between block premium and the likelihood of litigation can go both

ways. While the expected litigation risk at the time of block trade can influence the

size of the block premium on one hand, reverse causality may arise on the other.

As suggested by agency theory, excessive private benefits of control, proxied for by

the size of the block premium, can enhance potential litigation risk. Jensen and

Meckling (1976) argue that people who have controlling power but own only a

fraction of the residual claims on a firm may not work in the best interests of out-

side shareholders. Holderness and Sheehan (1988) show that large blockholders are

sufficiently powerful to exercise control over a firm’s activities. They note that many

blockholders are involved in firm management by appointing themselves or their

friends as directors or officers.3 Therefore, agency problems can arise when private

benefits are pursued by the blockholders to the exclusion of minority shareholders.

When such agency problems become severe, blockholders with large private benefits

may be tempted to abuse their power by engaging in illegal activities. If such illegal

activities significantly undermine shareholders’ wealth, shareholders will react by fil-

ing lawsuits to protect their wealth. Therefore, if blockholders with larger private

benefits have a higher chance to engage in illegal activities to the detriment of

minority shareholders, we will observe more cases of litigation for block trades with

larger premiums.4

1Albuquerque and Schroth (2010) show that this proportion of blocks that are traded at a

discount is even greater in the post Sarbanes-Oxley era.2Other costs mentioned in the study are monitoring costs and the costs of holding an undi-

versified portfolio.3Readers can refer to Holderness (2003) for a review of blockholders and corporate control.4While blockholders themselves may suffer the consequences of shareholder litigation trig-

gered by their own wrongdoing, they may have the motive of defrauding the shareholders ex

ante as long as they do not bear the full costs of litigation. For instance, blockholders may

hold directors and officers (D&O) insurance. As noted by Peng and R€oell (2008), many class

action lawsuits are settled on terms that prescribe insufficient penalties for the executives’

wrongdoing. Further, Agrawal et al. (1999) find no significant management turnover follow-

ing class action lawsuits once firm characteristics have been controlled for.

J. Chung et al.

408 © 2014 Korean Securities Association

Despite the simple conceptual linkage between the size of the private benefits

of control and litigation risk, previous empirical studies on shareholder lawsuits

have not paid due attention to the size of private benefits as one of the determi-

nants of lawsuits. The predictors of shareholder litigation studied in the prior lit-

erature include variables such as firm size, share turnover, stock’s past return,

beta, volatility, and skewness. Accounting variables such as abnormal accruals,

asset tangibility, sales growth, and leverage have also been found to predict share-

holder litigation. However, considering that shareholder litigation is the primary

legal tool to control the agency problem, it is naturally appealing to test whether

the presence of excessive private benefits predicts impending shareholder litiga-

tion.

In this paper, we take into account such endogeneity when examining the

relationship between the block premium and expected litigation risk. We exam-

ine whether (i) the ex ante likelihood of future litigation affects the block pre-

mium size and (ii) the block premium size can be a predictor of future illegal

activities identified through shareholder class action lawsuits. It is important to

note that since the universe of firms in this study is the set of firms the blocks

of shares of which are traded, we do not examine whether block trades per se,

or the existence of blockholders, influences the likelihood of litigation. We ana-

lyze, conditional on the occurrence of block trades and thus the existence of

blockholders, the causal relationship between blockholders’ private benefits and

litigation risk.

We use a sample of 593 block trades in the United States from 1995 to

2004 to examine this relationship. We find that the expected likelihood of litiga-

tion at the time of the block trade negatively affects the block premium size.

That is, blockholders seem to predict future litigation by utilizing public infor-

mation at the time of the block trade, resulting in lower block premium for

firms that are more likely to be targets of litigation. Our results therefore pro-

vide empirical support for the argument of Barclay and Holderness (1989), who

conjecture that litigation risk is one of the costs of being a blockholder. More-

over, we quantify the magnitude of the effect of litigation risk on the cost of

block ownership.

On the other hand, while controlling for other possible determinants of litiga-

tion, we do not find evidence that greater private benefits, as measured by block

premium size, can lead to greater incidence of shareholder lawsuits. Thus, we do

not support the alternative argument that blockholders with greater private benefits

are more likely to engage in corporate misconduct, and that the likelihood of litiga-

tion would be enhanced, is not supported by our study.

The rest of this paper is organized as follows. Section 2 presents the background

for measuring private benefits using the block premium. Section 3 provides an

introduction to securities class action lawsuits. Section 4 describes the sample selec-

tion process and data sources. Section 5 examines how ex ante litigation risk affects

Block Premium and Shareholder Litigation


block premium. Section 6 investigates the effect of block premium on the likelihood

of future litigation. Section 7 concludes with a summary.

2. Measuring Private Benefits using Block Trades

Prior literature suggests that blockholders may be motivated by the private benefits

of control, and may use their voting power to consume corporate resources or

enjoy corporate benefits that are not shared with minority shareholders. Following

Barclay and Holderness (1989), we measure private benefits using block premium

as follows:

Block Premiumð%Þ� ðPrivately negotiated block trade priceÞ �ðone day after exchange priceÞone day after exchange price

� ��100: ð1Þ

In the corporate finance literature, block trades are defined as trades that

involve 5% or more of a firm’s stock. Block trades typically occur in the upstairs

market and the terms of the transaction are privately negotiated between the trans-

acting parties. If all shareholders are to receive benefits in proportion to their frac-

tional ownership, blocks have to be traded at the exchange price. However, if

blockholders enjoy private benefits that do not accrue to minority shareholders,

blocks will be traded at a premium to the post-announcement exchange price. It is

important to note that the benchmark price used for measuring the block premium

is the post-announcement price rather than the pre-announcement price. The rea-

son is that the post-announcement price incorporates the informational content

and the expected effect of the transaction. For instance, if a blockholder seeks to

improve the performance of a severely mismanaged firm, he or she may be willing

to pay more than the pre-announcement share price for the block since the transac-

tion will generate public benefits. That is, while the post-announcement exchange

price reflects only the shared or public benefits of the block trade, the privately-

negotiated block trade price will reflect both private and public benefits. Thus, the

difference between the block trade price and the post-announcement exchange price

can measure only the private benefits.

According to Barclay and Holderness (1989), blockholding also incurs costs

such as litigation risk, monitoring costs, and the costs of possibly carrying an undi-

versified portfolio. If the costs of being a blockholder outweigh the benefits thereof,

a block may trade at a discount. Therefore, the size of the block premium is a mea-

sure of the net private benefits. That is, if a block of shares is traded at a premium,

it indicates that the blockholder expects to gain from the private benefits net of any

costs of block ownership.

Based on a sample of 63 block trades in the United States from 1978 to 1982,

Barclay and Holderness (1989) find that the block premium averages 16% of the

post-announcement exchange price and 4.3% of the total market value of the firm’s

equity. In a subsequent study measuring block premiums, Dyck and Zingales

(2004) examine 412 control transactions in 39 countries over 1990–2000 and show

J. Chung et al.


that, on an average, the size of private benefits is worth 14% of the firm’s equity

value.

3. Securities Class Action Lawsuits

For minority shareholders, the incentive to check for fraudulent behavior by the

controlling stakeholders is reduced by the free-rider problem (Shleifer and Vishny,

1997). This free-rider problem can be addressed by the ability of lawyers to organize

a class of shareholders and litigate for that class. Lawyers have an incentive to col-

lect costly information because they typically receive one-third of the settlement

(Martin et al., 1999). Therefore, securities class actions are initiated by the plaintiffs’

attorneys who file suits on behalf of shareholders. Typically, a filing is triggered by

an information release, such as the revelation of an accounting scandal or disap-

pointing earnings announcement that causes a firm’s stock price to drop substan-

tially. The plaintiffs’ attorneys will allege that the managers or executives in charge

are guilty of fraud as they have directly engaged in wrongdoing or concealed nega-

tive information. Further, they would argue that because the firm’s stock price did

not reflect the negative information during the class period (i.e. the period during

which the fraudulent activities are alleged to have taken place), investors who pur-

chased shares during the class period paid artificially inflated prices. Moreover,

shareholders who had held the stock until the negative information was released

would have suffered losses and, therefore, would be eligible for compensation.

The securities laws relevant to class action lawsuits include the Securities Act of

1933 and the Securities Exchange Act of 1934. The former regulates the process

through which companies make offerings of securities, while the latter covers all

aspects of securities trading for firms, the securities of which are traded in the sec-

ondary markets. Sections 11 and 12 of the Securities Act of 1933 cover fraudulent

registration statements, noncompliance with registration rules, and misrepresenta-

tion. As per Rule 10b-5 under Section 10(b) of the Securities Exchange Act of 1934,

it is unlawful to disseminate false information regarding a material fact or fail to

disclose materially relevant information to investors. Many class action lawsuits base

their case under Rule 10b-5.5

4. Sample Selection and Data Sources

We collect samples of block trades of United States companies from 1995 to 2004

from the SDC Platinum Mergers and Acquisitions database. We start our sample

period from 1995 because that year saw a structural change in the legal framework

due to the passage of the Private Securities Litigation Reform Act. This coincides

5This occurs because plaintiffs are not required to prove that they relied on the misinforma-

tion under Rule 10b-5. Further, this misinformation is not restricted to the company’s SEC

filings and can include false press releases and statements made by the company officials.



with the commencement of the Stanford Securities Class Action Clearinghouse

database that was designated as the official Internet site under Rule 23-2 of the

Private Securities Litigation Reform Act of 1995. At the other end of our timeline,

we examine block trades until 2004 because we use a five-year window for searching

class action lawsuits. Class action lawsuits span several years from the start of the

class period until the date of filing of the lawsuit. In our search for class action law-

suits, we found that the longest time span between the class period start date and

filing date is 7 years. However, the further we extend this period for the lawsuit

search, the more we must shorten the period for our sample. As a tradeoff, we use

block trades until 2004 and search for filings of class action lawsuits within 5 years

of the block trade. Figure 1 illustrates the timeline for our analysis.

We collect block purchases that comprise 5% or more of the respective firms’

outstanding shares from the SDC Platinum Mergers and Acquisitions database.

These trades are classified as block purchases in the acquisition technique category

of the database. The cutoff point used for measuring the block premium is 5% —the threshold level that triggers a mandatory filing with the SEC regarding block

transactions. Our initial search yielded 4240 cases of block trades.

For our empirical analysis, we require data regarding the price paid per share

for the block transaction and the exchange-traded price of the stock on the day fol-

lowing the block trade announcement. Thus, from our initial sample, we exclude

cases where the price paid per share may not be valued objectively, such as transac-

tions that involve convertible bonds, liabilities, options, warrants, and so forth. We

also omit cases where the transaction price may not reflect private benefits and

exclude cases where either the target or the acquirer is either a subsidiary of the

acquiring company or a government agency. We further exclude transactions that

involve open-market repurchases, tender offers, spinoffs, recapitalizations, self-ten-

ders, exchange offers, repurchases, and acquisitions of remaining interest. After

these selection criteria, our sample comprises 2072 block trades.

Finally, for each block trade, we require the availability of stock market data

such as the stock price and daily trading volume from CRSP and accounting data

such as abnormal accruals, tangibility, leverage, and sales from COMPUSTAT for

companies where block is traded. We exclude companies with negative book value

of equity, obtaining a final sample of 593 block trades. To minimize the effect of

outliers, we winsorize all the explanatory variables at the lower and upper 1% tails.6

Next, we use the Stanford Securities Class Action Clearinghouse database to find

cases of securities class action lawsuits in the sample of block-traded companies.

This database provides detailed information on federal class action lawsuits from

1995 onwards.

Table 1 presents the results of the univariate tests of the differences between

sued and non-sued firms for our main variables. A firm belongs to the sued firms

6Results are qualitatively similar if we drop observations at the 1% tails instead of winsorizing

them.

J. Chung et al.


category if the block-traded company is subsequently involved in class action share-

holder lawsuits within 5 years of the block transaction. There are 47 such cases in

our sample between 1995 and 2004. An average firm in our sample has a total asset

size of US$1398 million, market-to-book ratio of 3.49, and leverage ratio of 0.19.

Table 1 indicates that sued firms have lower block premium (mean of �5.19% and

median of �5.88%) than non-sued firms (mean of 6.72% and median of �1.99%).

This univariate comparison supports the argument that the expected cost of litiga-

tion can be a cost of block ownership, thus lowering the block premium. Table 1

also shows that sued firms have fewer cash holdings and greater beta, leverage, and

sales growth than non-sued firms. Interestingly, sued firms have higher stock

returns in the year before the block trade than non-sued firms, possibly because this

period for calculating stock returns coincides with that when the alleged wrongdo-

ings have taken place and when the stock price was inflated. For the other variables

such as block size, stock return, ROA, stock return volatility, market-to-book ratio,

and tangibility, the means of sued and non-sued firms are statistically indistinguish-

able from each other.

Many blocks are traded at a discount rather than a premium. Barclay and Hol-

derness (1989, 1991) show that block discounts comprise 20% (in their 1989

study) and 15% (in their 1991 study) of all block trades. In more recent studies,

Dyck and Zingales (2004) report that 41% of blocks trades occur at a discount.

Albuquerque and Schroth (2010) show that block discounts comprise about half of

their sample. These findings support Barclay and Holderness’s (1989) argument that

block ownership entails not just private benefits but also costs. Insofar as block

trades that occur at discounts are more likely to be associated with greater costs of

block ownership, one of such costs can be costs related to litigation risk. Thus, we

Figure 1 Timeline of block trades and lawsuits

Firm characteristics andother control variables regarding litigation risk

Block tradeLawsuit filed

Within 5 years

This figure illustrates the timeline of block trades and shareholder class-action lawsuits used in the study.

Block trades are identified through the SDC Platinum’s Mergers and Acquisitions database. For each

block trade, we search for securities class action litigation over 5 years after the block trade. The identifi-

cation of cases of securities class action lawsuits is undertaken through the Stanford Securities Class

Action Clearinghouse. All the control variables are measured by using the most recent information avail-

able at the time of the block trade announcement.



examine block trades occurring at a discount in detail. Table 2 compares the

characteristics of firms the blocks of which are traded at a non-negative premium

and those where blocks are traded at a discount.

Table 1 Summary statistics: Sued firms versus non-sued firms

This table gives the means and medians of several variables for 593 firms the blocks of which were traded

between 1995 and 2004. A firm belongs to the sued firms category if the company is involved in class

action shareholder lawsuits within 5 years of the block trade. The block premium is defined as {(pricepaid per share of the block) – (exchange price on the day following the announcement of the transac-

tion)} / (exchange price on the day following the announcement of the transaction). Block size is the

percentage of shares acquired in the block transaction. Firm size is the size of total assets for the most

recent fiscal year before the block trade announcement. Cash is defined as the ratio of cash and market-

able securities to total assets before the block trade announcement. Liquidity is the average daily trading

volume divided by the number of shares outstanding for the 12 months ending 1 month before the block

trade announcement day. Return is the one-year cumulative stock return ending 1 month before the

block trade announcement. ROA is the net income divided by total assets for the most recent fiscal year

before the block trade announcement. Beta is measured relative to the CRSP NYSE/Amex/Nasdaq equally

weighted index using 12-month daily data ending 1 month before the block trade announcement. Vola-

tility is the standard deviation of the daily stock returns for the 12 months ending 1 month before the block

trade announcement. Skewness is the stock’s daily return skewness for the 12 months ending 1 month

before the block trade announcement. M/B is the firm’s market value of equity divided by the book value of

equity; both these values are for the most recent fiscal year end before the block trade announcement. Tangi-

bility is the proportion of property, plant, and equipment relative to total assets for the fiscal year end before

the block trade. Leverage is the proportion of long-term debt to total assets for the fiscal year end before the

block trade. Sales growth is the ratio of sales in the fiscal year before the block trade to the sales in the year

before that fiscal year. Individual acquirer takes the value of one if the block purchaser is an individual. Each

variable is winsorized at the lower and upper 1% tails. *, **, and *** denote significant differences for the

two groups at the 10%, 5%, and 1% levels, respectively, according to the t-test.

Whole sample Sued firms Non-sued firmsDifference

in meanMean Median Mean Median Mean Median

Block

premium (%)

5.77 �2.26 �5.19 �5.88 6.72 �1.99 �11.91***

Block size (%) 15.27 10.46 15.12 10.00 15.28 10.56 �0.16

Firm size (mil) 1398.47 185.13 3067.16 653.60 1254.83 174.31 1812.34

Cash 0.53 0.62 0.41 0.42 0.54 0.63 �0.13**Liquidity (%) 0.55 0.31 0.64 0.57 0.54 0.30 0.10

Return (%) 0.16 0.06 0.44 0.13 0.13 0.04 0.31*ROA (%) �0.14 0.01 �0.09 0.01 �0.14 0.01 0.05

Beta 1.15 0.96 1.54 1.64 1.12 0.89 0.42***Volatility (%) 3.99 3.55 3.81 3.78 4.01 3.53 �0.20

Skewness 0.55 0.40 0.41 0.40 0.56 0.41 �0.15

M/B 3.49 1.55 5.35 2.09 3.33 1.52 2.02

Tangibility 0.22 0.16 0.26 0.25 0.22 0.14 0.04

Leverage 0.19 0.11 0.31 0.20 0.18 0.10 0.13***Sales growth 1.82 1.10 3.24 1.20 1.70 1.09 1.54**Individual acquirer 0.11 0.00 0.09 0.00 0.11 0.00 �0.02

Sample size 593 47 546

J. Chung et al.


Table 2 Summary statistics: Block premium versus block discount

This table gives the means and medians of several variables for the 593 firms the blocks of which were

traded between 1995 and 2004. All the variables are defined in Table 1. In Panel B, a firm belongs to the

sued firms category if the company is involved in class action shareholder lawsuits within 5 years of the

block trade. *, **, and *** denote significant differences for the two groups at the 10%, 5%, and 1% lev-

els, respectively, according to the t-test.

Whole sample

Block trades

with non-nega-

tive premiums

Block trades with

discountsDifference

in meanMean Median Mean Median Mean Median

Panel A: Block premium versus Block discount

Block premium (%) 5.77 �2.26 33.59 11.72 �13.81 �8.88 47.40***Block size (%) 15.27 10.46 15.86 10.95 14.85 10.11 1.01

Firm size (mil) 1398.47 185.13 812.05 168.36 1811.32 222.54 �999.27***Cash 0.53 0.62 0.50 0.57 0.55 0.65 �0.05

Liquidity (%) 0.55 0.31 0.55 0.29 0.55 0.33 0.00

Return (%) 0.16 0.06 0.06 �0.04 0.23 0.10 �0.17

ROA (%) �0.14 0.01 �0.08 0.01 �0.17 0.01 0.09

Beta 1.15 0.96 1.03 0.86 1.24 1.03 �0.21***Volatility (%) 3.99 3.55 4.05 3.66 3.95 3.44 0.00

Skewness 0.55 0.40 0.55 0.43 0.54 0.39 0.01

M/B 3.49 1.55 2.21 1.45 4.39 1.62 �2.19***Tangibility 0.22 0.16 0.23 0.15 0.22 0.16 0.01

Leverage 0.19 0.11 0.21 0.14 0.18 0.09 0.03

Sales growth 1.82 1.10 1.26 1.09 2.21 1.11 �0.95

Individual acquirer 0.11 0.00 0.09 0.00 0.12 0.00 �0.03

Sample size 593 245 348

Sued firms with

non-negative

premiums

Sued firms with

discounts

Non-sued firms

with non-nega-

tive premiums

Non-sued

firms with

discounts

Mean Median Mean Median Mean Median Mean Median

Panel B: Sued firms versus Non-sued firms and Block premiums versus Block Discounts

Block

premium (%)

15.38 7.42 �13.92 �9.45 34.70 11.90 �13.80 �8.83

Block size (%) 14.66 8.22 15.32 10.08 15.93 11.22 14.80 10.14

Firm size (mil) 1197.45 436.14 3860.38 974.42 788.69 158.67 1596.66 196.95

Cash 0.53 0.64 0.36 0.37 0.50 0.57 0.57 0.69

Liquidity (%) 0.66 0.50 0.64 0.65 0.54 0.28 0.54 0.31

Return (%) 0.40 0.25 0.46 0.06 0.04 �0.05 0.20 0.10



In our sample, 348 block trades occurred at a discount. Therefore, while the

average block premium is positive, there are more cases of block trades occurring at

a discount than those occurring at a premium, indicating that block ownership

incurs not only private benefits but also sizable costs. Panel A, Table 2, shows that

firms with block discounts are more likely to be larger firms. Blockholders of larger

firms incur greater private benefits as well as costs of becoming a blockholder.

Table 2 also indicates that riskier and high-growth firms, as evidenced by larger

beta and market-to-book ratios are more likely to incur greater costs of block own-

ership, as evidenced by block discounts.

In Panel B of Table 2, we divide the sample of firms based on two different

dimensions: (i) sued firms versus non-sued firms; and (ii) block trades with non-

negative premiums versus block trades with discounts. We find that firm size is

the greatest for the subsample of sued firms with block discounts and the smallest

for the subsample of non-sued firms with non-negative premiums. The difference

between the two subsamples is almost five-fold based on the average firm size and

more than six-fold based on the median firm size. This suggests that larger firms

with greater litigation risk incur greater costs of block ownership as indicated by

the block discounts. The same relationship is observed for the firms’ betas, mar-

ket-to-book ratios, and sales growth. This implies that riskier firms and high

growth firms with greater litigation risk are associated with greater costs of block

ownership.

Table 3 presents the distribution of allegations in shareholder lawsuits of block-

traded companies in the final sample. Many lawsuits cite multiple causes of action.

The most common type of allegation is issuing materially false or misleading

Table 2 (Continued)

Sued firms with

non-negative

premiums

Sued firms with

discounts

Non-sued firms

with non-nega-

tive premiums

Non-sued

firms with

discounts

Mean Median Mean Median Mean Median Mean Median

ROA (%) �0.05 �0.01 �0.11 0.02 �0.09 0.01 �0.18 0.01

Beta 1.52 1.47 1.55 1.64 1.00 0.81 1.21 0.98

Volatility (%) 3.04 2.88 4.13 4.28 4.11 3.69 3.94 3.33

Skewness 0.41 0.41 0.40 0.36 0.56 0.44 0.56 0.40

M/B 3.80 1.96 6.01 2.09 2.11 1.38 4.22 1.58

Tangibility 0.20 0.20 0.28 0.26 0.23 0.14 0.21 0.13

Leverage 0.24 0.15 0.34 0.30 0.21 0.13 0.16 0.08

Sales growth 2.10 1.13 3.76 1.24 1.22 1.09 2.05 1.10

Individual

acquirer

0.14 0.00 0.06 0.00 0.09 0.00 0.12 0.00

Sample size 14 33 231 315

J. Chung et al.


financial statements or estimates.7 This implies that if excessive private benefits lead

to various types of illegal activities, the most common case of such wrongdoing is

issuing false or misleading financial statements or estimates. However, if a firm faces

greater litigation risk ex ante, such risk can become a cost of block ownership, lead-

ing to lower block premium or block discounts. Table 3 also shows that other

examples of litigation type are the omission of material information, unfair stock or

bond issues, unfair merger or tender offers, unfair buyouts, market manipulation or

improper trading practices, and embezzlement.

5. Effect of Ex Ante Litigation Risk on Block Premium

We first test the argument of Barclay and Holderness (1989) that ex ante litigation

risk can be one of the costs of block ownership. That is, we examine the possible

causality from the ex ante litigation risk to the block premium by using various sets

of explanatory variables to measure the ex ante litigation risk at the time of the

block trade.

Our two-stage regression model is shown in equation (2). In the first equation,

we model litigation risk. Following the literature on the determinants of litigation,

we use three different models of litigation risk. In the second equation, we explain

the block premium by using the predicted litigation risk from the first equation,

along with other explanatory variables of block premium.

Table 3 Types of allegations in securities class action litigation

This table shows the distribution of allegations in securities class action lawsuits for 593 block trades dur-

ing 1995–2004. For each block trade, we search for securities class action litigation within 5 years of the

block trade. Block trades are identified through the SDC Platinum’s Mergers and Acquisitions database.

Cases of securities class action lawsuits are identified through the Stanford Securities Class Action Clear-

inghouse. The numbers do not add up to the total number of firms because many lawsuits cite multiple

causes of action.

Type of allegation Number of cases

False or misleading financial statements or estimates 29

Nondisclosure of material information 13

Unfair stock or bond issues 5

Unfair merger or tender offer 4

Unfair buyout 3

Market manipulation or improper trading practices 2

Embezzlement 2

Other frauds 3

Total number of block trades with class action lawsuits 47

7Examples in this category of litigation include: (i) artificial inflation of earnings, revenue,

sales, or assets; (ii) over-hyping of technology, product, or business success; and (iii) the dis-

semination of misleading remarks to analysts or investors.



First equation :

Model1 :

Litigation riski ¼ aþ b1 logðfirm sizeÞi þ b2ðturnoverÞiþ b3ðreturnÞi þ b4ðbetaÞi þ b5ðvolatilityÞiþ b6ðskewnessÞi þ b7ðM/BÞi þ b8ðabnormal accrualsÞiþX

bjðindustry dummiesÞiþX

bhðyear dummiesÞiModel 2 :

Litigation riski ¼ aþ b1 logðfirm sizeÞiþ b2ðturnoverÞi þ b3ðreturnÞi þ b4ðbetaÞi þ b5ðvolatilityÞiþ b6ðskewnessÞi þ b7ðM=BÞi þ b8ðtangibilityÞiþ b9ðleverageÞi þ b10ðsales growthÞiþ b11ðacquisition dummyÞi þ b12ðequity issue dummyÞiþX


bhðyear dummiesÞiModel 3 :

Litigation riski ¼ aþ b1 logðfirm sizeÞi þ b2ðturnoverÞi þ b3ðreturnÞiþ b4ðvolatilityÞi þ b5ðskewnessÞib9ðsales growthÞiþX


bhðyear dummiesÞiSecond equation :

Block premiumi ¼ aþ b1ðpredicted litigation riskÞi þ b2ðblock sizeÞiþ b3ðreturnÞi þ b4 logðfirm sizeÞi þ b5ðleverageÞiþ b6ðtangibilityÞiþb7ðcashÞiþb8ðindividual acquirer dummyÞiþ b9ðsame industry acquirer dummyÞi þ b10ðrelative cashÞþ b11ðactive shareholder dummyÞi þ b12ðSOX dummyÞiþX


bhðyear dummiesÞið2Þ

5.1. Determinants of Shareholder Litigation

The first equation in equation (2) is a probit model that examines the likelihood of

class action lawsuits, while controlling for the other possible determinants of

litigation.8 In this equation, the dependent variable equals one if the block-traded

company is sued after the block trade (specifically, if the lawsuit is filed within

5 years of the block trade announcement date) and zero otherwise. Previous empiri-

cal research has identified a number of predictors of shareholder litigation. We

8Studies such as Peng and R€oell (2008) and Gande and Lewis (2009) use probit regression,

while studies such as DuCharme et al. (2004) and Barabanov et al. (2008) utilize logit regres-

sion to estimate the probability of litigation. Our results are similar when we use logit regres-

sion.

J. Chung et al.


categorize these predictors into stock market variables and accounting variables.9

Regarding stock market variables, Francis et al. (1994), Jones and Weingram

(1996), Field et al. (2005), Peng and R€oell (2008), and Gande and Lewis (2009)

show that both the firm size and share turnover are positively associated with the

incidence of lawsuits. This is because larger companies have more assets available

for the recovery of damages, and shareholder damages generally increase with the

number of shares traded. We define firm size as the size of the total assets in the

most recent fiscal year before the block trade announcement day.10 We measure

share turnover as the average of the daily trading volume divided by the number of

shares outstanding for the 12 months ending 1 month before the block trade

announcement day.

Consistent with the expectation that firms with poor stock price performance are

more likely to get sued, Alexander (1991), Jones and Weingram (1996), Lowry and

Shu (2002), DuCharme et al. (2004), Barabanov et al. (2008), and Gande and Lewis

(2009) show that the recent period cumulative return is negatively related to the inci-

dence of lawsuits.11 We measure the recent period cumulative return as the 12-month

cumulative stock return ending 1 month before the block trade announcement.

Some previous studies also include firms’ systematic risk, stock return volatility,

and skewness to control for the ex ante probability of large price drops. Jones and

Weingram (1996), Beck and Bhagat (1997), and Barabanov et al. (2008) show that

a firm’s systematic risk, measured by the firm’s beta, is positively related to the like-

lihood of litigation. With regard to stock volatility, Strahan (1998) finds a positive

relationship between volatility and litigation risk. Jones and Weingram (1996) and

Barabanov et al. (2008) show that the skewness of stock returns is negatively related

to the likelihood of litigation. For systematic risk, we use the firm’s beta relative to

the CRSP NYSE/Amex/Nasdaq equally weighted index using 12-month daily data

ending 1 month before the block trade announcement. For stock return volatility,

9We do not include variables that are not found significant by previous studies for predicting

shareholder litigation. For example, Johnson et al. (2007) find little evidence of association

between governance structure (for example, average tenure, external holdings, and indepen-

dence of outside directors) and lawsuit filings. Schrand and Zechman (2012) report similar

results. Peng and R€oell (2008) show that the governance index measure given by Gompers

et al. (2003) is insignificant in predicting the likelihood of shareholder litigation. Regarding

variables pertaining to CEO characteristics, McTier and Wald (2011) find that the explana-

tory powers of compensation, ownership, age, and tenure are insignificant. Regarding vari-

ables that measure information opaqueness, McTier and Wald (2011) note that analyst

dispersion and analyst forecast error are insignificant in explaining future litigation. Baraba-

nov et al. (2008) find that whether a firm is traded on a major exchange does not affect the

likelihood of shareholder litigation.10The results are similar when we measure firm size using the market value of common

equity.11Based on a similar line of reasoning, Peng and R€oell (2008) use ROA instead of stock

returns and find that ROA is negatively related to the likelihood of litigation.



we measure the standard deviation of the daily stock returns for the 12 months

ending 1 month before the block trade announcement. For stock return skewness,

we measure the stock’s daily return skewness for the 12 months ending 1 month

before the block trade announcement.

Strahan (1998) shows that firms with lower market-to-book ratios are more

likely to face lawsuits. He reasons that since the market-to-book equity ratio cap-

tures managerial quality (Morck et al., 1988), firms with high market-to-book ratios

are well-managed and, therefore, less likely to be sued. We measure a firm’s mar-

ket-to-book ratio as the market value of its equity divided by the book value of its

equity for the most recent fiscal year end before the block trade announcement.

Our second set of control variables for litigation risk comprises accounting vari-

ables. In Model 1, we use the abnormal accounting variable as a control variable. In

Model 2, we use other predictors of aggressive accounting choices as control variables

for litigation. Alternating the use of different accounting variables is in line with the

previous studies. Some studies, including DuCharme et al. (2004) and Gong et al.

(2008), use abnormal accruals as a direct measure of earnings management. However,

others, including DeFond and Jiambalvo (1994), Teoh et al. (1998), Erickson and

Wang (1999), Johnson et al. (2007), Barabanov et al. (2008), and Peng and R€oell

(2008), test the significance of alternative predictors of aggressive accounting choices

such as the tangibility of assets, leverage, sales growth, acquisition dummy, and equity

issue dummy, instead of an abnormal accruals variable. Specifically, Peng and R€oell

(2008) find that the tangibility of assets within a firm is negatively related to litigation

risk, and conclude that companies with more intangible assets have greater agency

problems because intangible assets are more difficult to value and monitor. DeFond

and Jiambalvo (1994), Strahan (1998), and Peng and R€oell (2008) show that leverage

is positively associated with litigation risk. Peng and R€oell (2008) note that high lever-

age may reflect a recent history of poor performance, asset write-downs, or forced

heavy borrowing, which may lead to shareholder dissatisfaction. Previous studies also

find that firms with high sales growth (Beneish, 1997; Johnson et al., 2007; Peng and

R€oell, 2008), firms making acquisitions (Erickson and Wang, 1999; Peng and R€oell,

2008), and firms issuing equity (Teoh et al., 1998; Barabanov et al., 2008) are more

likely to engage in aggressive accounting practices or manage earnings that may sub-

sequently trigger shareholder litigation.

We estimate abnormal accruals for the most recent fiscal year end before the

block trade announcement, similar to Gong et al. (2008) who base their estimation

on Kothari et al. (2005). We measure the tangibility of assets as the size of property,

plant, and equipment divided by the size of the total assets, measured at the most

recent fiscal year before the block trade announcement. We measure leverage as

short-term debt plus long-term debt divided by total assets, measured at the most

recent fiscal year before the block trade.12 We measure sales growth by dividing the

12The results are qualitatively similar when we measure leverage as long-term debt divided by

total assets.

J. Chung et al.


sales in the fiscal year before the block trade by the sales during the year before

that fiscal year. For the variable regarding whether the firm has made any acquisi-

tion, we use a dummy variable that equals one if the firm has made business acqui-

sitions in the fiscal year before the block trade (specifically, if Compustat data item

129 is greater than 0 or shows a combined code). For the equity issue variable, we

use a dummy variable that equals one if the number of shares outstanding, adjusted

for splits and dividends, increases by more than 10% in the fiscal year before the

block trade.

In their recent study on litigation risk, Kim and Skinner (2012, pp.13–18) com-

pare different models of litigation risk and identify the set of explanatory variables

that have the highest predictive power of litigation — firm size, sales growth, stock

returns, stock return skewness, standard deviation of stock returns, and stock turn-

over. Based on these variables, we provide another form of estimating the litigation

risk in Model 3 in equation (2).

5.2. Determinants of Block Premium

Previous studies show that the block premium is affected by certain characteristics

of both the firm where the block is traded and the acquirer.

Of primary interest is the coefficient b1 that shows how the expected likeli-

hood of litigation at the time of a block trade affects the block premium.

Barclay and Holderness (1989) argue, without a formal test, that the likelihood

of litigation at the time of a block trade is one of the costs of block ownership.

As described in Section 2, block premium measures the size of private benefits,

net of any costs. Therefore, if a firm has high ex ante litigation risk, a potential

blockholder may hesitate to purchase a block of its shares or may be willing

to purchase it only at a discounted price. In this case, the firm’s underlying

litigation risk at the time of the block trade can affect the size of the block

premium.

Other explanatory variables for the block premium are taken from Barclay

and Holderness (1989), Burkart et al. (2000), Dyck and Zingales (2004), and

Albuquerque and Schroth (2010). Regarding the block size variable, a blockhold-

er who acquires a large block of shares has more voting power and greater pro-

tection from a hostile takeover or proxy contest.13 Regarding the prior stock

return variable, the opportunity of enjoying private benefits will be greater (smal-

ler) if the firm is performing well (poorly). As for firm size, larger firms offer

potentially greater benefits, both pecuniary and nonpecuniary. Leverage may

affect the block premium in two distinct ways. First, debt may have a negative

effect on private benefits by constraining access to free cash flow (Jensen, 1986).

In contrast, debt may increase one’s effective control over corporate assets

13We measure block size as a continuous variable in our analysis. We found qualitatively sim-

ilar results for an alternative measure of block size — an indicator variable for blocks com-

prising 25% or more of a company’s equity (Barclay and Holderness, 1989).



(Harris and Raviv, 1988; Stulz, 1988), increasing the size of private benefits.

Private benefits may also be related to the tangibility of assets because acquirers

of a block may find it difficult to divert resources if assets are tied down and

easily observable. Similarly, when a firm has more cash, blockholders can spend

more on perquisites (Jensen, 1986). Therefore, we include the target firm’s cash

holdings, defined as the ratio of cash and marketable securities to total assets

before the block trade announcement.

Further, we use the following control variables to capture the characteristics of

the acquirer. First, we include a dummy variable for acquisition by individuals

because, as compared to corporate blockholders, individuals enjoy the added bene-

fit of consuming perquisites (Demsetz and Lehn, 1985). Second, in cases where

the acquirer is a corporation rather than an individual, the acquirer may enjoy

greater private benefits if it is in the same industry as the target company. Thus,

we include a dummy variable that takes the value of one when the acquiring com-

pany is in the same industry group as the target company based on the two-digit

SIC code. Albuquerque and Schroth (2010) note that a blockholder may enjoy

more private benefits if she has already acquired knowledge about how to extract

those benefits from the company. Therefore, we include the active shareholder

indicator variable, which takes the value of one if the acquirer already owned a

toehold of between 5%–10% of the target firm’s shares before the block trade

announcement.

Finally, we control for institutional and regulatory factors. Burkart et al. (2000)

suggest that institutional and regulatory factors can affect the ability of firms to

extract private benefits. Based on this reasoning and consistent with Albuquerque

and Schroth (2010), we control for the impact of the Sarbanes-Oxley Act (SOX) by

including an indicator variable that takes the value of one for block trades after July

2002 when SOX became effective. We also control for industry characteristics

because the size of private benefits may differ across industries. Demsetz and Lehn

(1985) suggest that owners of companies in the media, entertainment, and sports

industries enjoy greater private benefits. We capture industry differences by catego-

rizing companies, the blocks of which are traded into their major industry groups,

based on the two-digit SIC code.

5.3. Results of the Two-stage Regression

We estimate equation (2) using a two-stage estimation technique (Maddala, 1983).

We use instrumental variables to identify both the equations in equation (2).

Research shows that variables such as turnover, beta, volatility, skewness, market-to-

book ratio, sales growth, acquisition dummy, and equity issue dummy are directly

related to the possibility of litigation. These variables appear in the first equation of

equation (2). Further, previous studies show that variables such as block size, asset

tangibility, cash holdings, individual acquirer dummy, and same-industry dummy are

directly related to the size of private benefits. These variables appear in the second

J. Chung et al.


equation of equation (2). Explanatory variables that are common to both equations

include firm size, leverage, tangibility of assets, industry dummies, and year dummies.

In the first-stage regression, we estimate the probability of lawsuits by regressing

the litigation dummy on all exogenous variables using probit regression. Since all

the exogenous variables are observable at the time of the block transaction, our esti-

mations from the first-stage regression represent the ex ante likelihood of litigation.

In the second-stage regression, we use the predicted values from the first-stage

regression as explanatory variables.

Table 4 presents the results of the two-stage regression. Panel A shows the

results of the first-stage probit regression where we attempt to determine the pre-

dicted value of litigation at the time of the block trade. In Model 1, we use the

abnormal accounting variable as a control variable. In Model 2, we use other pre-

dictors of aggressive accounting choices as control variables for litigation. In Model

3, we use predictors of litigation based on Kim and Skinner (2012).

Panel A of Table 4 shows that the firm size, which factors into the determi-

nation of potential damage, is positively associated with the incidence of lawsuits.

The estimated coefficient of the prior stock return is positive, suggesting the per-

iod that we measure performance (12-month period ending 1 month before the

block trade announcement) can coincide with the period during which the

alleged wrongdoing takes place, which may inflate the stock price. The coefficient

of beta is also positive, suggesting that firms with greater systematic risk are

more likely to be sued. In Model 2, the coefficient of leverage is positive, which

indicates that firms with heavy borrowings are more likely to be sued by share-

holders.

Panel B of Table 4 presents the results for the second-stage regression where

our focus is on examining the effect of predicted litigation on the block pre-

mium. The main variable of interest is the predicted value of litigation, which is

the fitted value of litigation based on the instruments of litigation in the first-

stage equation. In Models 1, 2, and 3, respectively, in Panel B, the predicted liti-

gation variable is the predicted value of the litigation dummy from Models 1, 2,

and 3, respectively, in Panel A. The results show that after controlling for a

variety of other factors, the size of the expected litigation risk at the time of the

block trade negatively affects the size of the block premium. This is in line with

the view of Barclay and Holderness (1989) that the threat of litigation is one of

the costs of block ownership. Our result also suggests that blockholders seem to

predict litigation by utilizing public information at the time of the block trade.

In a hypothetical case where the expected probability of litigation jumps from

0% to 100%, the block premium (discount) decreases (increases) by 10.5%

according to Model 1, 11.9% according to Model 2, and 10.6% according to

Model 3. The magnitude of the decrease (increase) in block premium (discount)

is economically significant given that the average block premium is 4.27% in

our sample.



Table

4Effectofex

ante

litigationrisk

onblock

premium

Thistableprovides

theresultsoftheanalysisregardingtheeffect

ofthelikelihoodoflawsuitsontheblock

premium.PanelAshowstheresultsofthefirst-stageregres-

sionthat

explainsthelikelihoodoflawsuits.Panel

Bshowstheresultsofthesecond-stage

regressionthat

explainstheblock

premium

byusingthepredictedvalueof

thelitigationrisk

from

thefirst-stageregressionandother

controlvariablesfortheblock

premium.In

Panel

B,thepredictedvalues

oflitigationin

Models1,

2,and3

correspondto

thefitted

values

oflitigationin

Models1,

2,and3ofPanel

A,respectively.Thecoefficients

inPanel

Baremultiplied

by100forexpositional

purposes.

Weestimatetheabnorm

alaccrualsforthemost

recentfiscal

year

endbefore

theblock

tradeannouncementusingthemethodsuggestedbyKothariet

al.(2005).The

acquisitiondummyisavariable

that

equalsoneifthefirm

madebusinessacquisitions(specifically,

ifCompustat

dataitem

129is

greaterthan

zero

orshowsacom-

bined

code)

intheyear

before

theblock

trade.Equityissuedummyisavariable

that

equalsoneifthenumber

ofshares

outstanding,

adjusted

forsplits

anddividends,

increasesbymore

than

10%

intheyear

before

theblock

trade.

Thesame-industry

acquirer

dummyisadummyvariable

that

equalsoneiftheacquiringfirm

has

the

sametwo-digitSIC

codeas

thetarget

firm

theblock

ofwhichis

traded.Activeshareholder

variable

isan

indicatorthat

takesthevalueofoneiftheacquirer

already

owned

between5%

to10%

ofthetarget

firm

’sshares

before

theblock

tradeannouncement.SO

Xis

anindicatorvariable

that

takesavalueofoneforblock

trades

occurringafterJuly

2002

when

theSO

Xbecam

eeffective.

Thedefinitionsoftheother

variablesaregivenin

Table

1.Industry

dummiesbased

onthetwo-digitSIC

codeandyear

dummiesareincluded

asregressors.p-values

arein

parentheses.*,

**,and***denote

significantcoefficientsat

the10%,5%

,and1%

levels,respectively.

Category

ofindependent

variables

Independent

variable

Model

1Model

2Model

3

Panel

A:First-stage

Regression(D

ependentVariable:LitigationDummy)

Stock

marketvariables

Log(Firm

size)

0.105(0.028)**

0.082(0.105)

0.138(0.002)***

Turnover

�0.127

(0.387)

�0.104

(0.494)

0.009(0.918)

Return

0.146(0.097)*

0.117(0.196)

0.163(0.043)**

Beta

0.313(0.008)***

0.299(0.013)**

Volatility

�0.025

(0.655)

�0.043

(0.459)

0.038(0.390)

Skew

ness

�0.068

(0.401)

�0.085

(0.309)

�0.085

(0.286)

M/B

0.001(0.845)

0.000(0.965)

J. Chung et al.


Table

4(C

ontinued)

Category

ofindependent

variables

Independent

variable

Model

1Model

2Model

3

Accountingvariables

Abnorm

alaccruals

0.033(0.844)

Tangibility

0.138(0.707)

Leverage

0.739(0.024)**

Salesgrowth

0.014(0.170)

0.015(0.118)

Acquisitiondummy

0.074(0.708)

Equityissuedummy

0.064(0.746)

PseudoR2

0.072

0.097

0.055

Panel

B:Second-stage

Regression(D

ependentVariable:Block

Premium)

Litigation

Predictedlitigation

�10.483(0.042)**

�11.894(0.038)**

�10.580(0.040)**

Targetcharacteristics

Block

size

0.149(0.447)

0.142(0.466)

0.142(0.467)

Return

�6.115

(0.076)*

�5.255

(0.106)

�4.442

(0.247)

Log(Firm

size)

�0.747

(0.654)

�0.329

(0.824)

0.190(0.921)

Leverage

�23.182(0.324)

�18.158(0.469)

�23.069(0.325)

Tangibility

8.130(0.726)

9.634(0.679)

8.510(0.713)

Cash

�14.82

(0.482)

�13.649(0.518)

�14.595(0.487)

Acquirer

characteristics

Individual

acquirer

�19.174(0.022)**

�19.689(0.019)**

�19.513(0.020)**

Same-industry

acquirer

�4.883

(0.382)

�5.394

(0.332)

�5.631

(0.315)

Activeshareholder

3.481(0.070)*

3.210(0.068)*

3.411(0.073)*

Regulation

SOX

4.507(0.563)

4.375(0.574)

4.381(0.574)

Adjusted

R2

0.027

0.027

0.028



6. Effect of Block Premium on Ex-Post Litigation

In this section, we test the agency theory hypothesis regarding block premium.

According to this hypothesis, blockholders with excessive private benefits of control

will be sued more often by the shareholders. First, we employ a simple regression of

the future litigation on the block premium to examine if the block premium has

explanatory power in predicting the future likelihood of litigation. Second, we ana-

lyze the relationship between private benefits and the future likelihood of litigation

by allowing for the endogeneity of the block premium variable. We do this by

designing a set of two-stage equations that explains the predicted block premium.

Equation (3) shows our model. The ex-post litigation dummy takes the value of

one if the block-traded firm was involved in a class-action lawsuit, as defined in the

previous section. We estimate this likelihood of litigation using the block premium

variable besides the other explanatory variables of litigation. As explained earlier, we

use three different models of litigation following different strands of literature on

the determinants of litigation.

Model 1: Ex-post litigationi ¼ aþ b1ðblock premiumÞiþ b2logðfirm sizeÞi þ b3ðturnoverÞi þ b4ðreturnÞi þ b5ðbetaÞiþ b6ðvolatilityÞi þ b7ðskewnessÞi þ b8ðM/BÞi þ b9ðabnormal accrualsÞiþX


bhðyear dummiesÞiModel 2: Ex-post litigationi ¼ aþ b1ðblock premiumÞiþ b2 logðfirm sizeÞi þ b3ðturnoverÞi þ b4ðreturnÞi þ b5ðbetaÞiþ b6ðvolatilityÞi þ b7ðskewnessÞi þ b8ðM/BÞi þ b9ðtangibilityÞiþ b10ðleverageÞi þ b11ðsales growthÞiþ b12ðacquisition dummyÞi þ b13ðequity issue dummyÞiþX


bhðyear dummiesÞiModel 3: Ex-post litigationi ¼ aþ b1ðblock premiumÞiþ b2logðfirm sizeÞi þ b3ðturnoverÞi þ b4ðreturnÞi þ b5ðvolatilityÞiþ b6ðskewnessÞi þ b7ðsales growthÞiþX


bhðyear dummiesÞi

ð3Þ

The results in Table 5 show the possible effect of the block premium on the

likelihood of future litigation. We find that the coefficient of the block premium

variable is statistically insignificant in all three models.14 Therefore, these results do

14Acknowledging the possibility that block trades at a discount may have unique characteris-

tics as compared to block trades at a premium, we also performed a subsample analyses on

the former. However, the key effects of the block discount variable in the subsample analyses

were statistically insignificant.

J. Chung et al.


not support the agency hypothesis that greater private benefits (as measured by the

block premium) lead to greater incidence of corporate misconduct (as detected

through shareholder litigation). Regarding the control variables of litigation, we find

that larger firms and those with higher beta and leverage are more likely to be

engaged in future litigation. These results are consistent with our conjecture that

firms with more assets that can be recovered through lawsuits and riskier firms are

more likely to be targets of litigation.

To incorporate the endogeneity of the block premium variable, we use the

predicted block premium value instead of the actual block premium. We find the

predicted block premium using the following regression:

Block premiumi ¼aþ b1ðblock sizeÞi þ b2ðreturnÞi þ b3logðfirm sizeÞiþ b4ðleverageÞi þ b5ðtangibilityÞi þ b6ðcashÞi

Table 5 Effect of block premium on the ex-post likelihood of lawsuits

This table presents the results of the analysis regarding the effect of the block premium on the likelihood

of lawsuits. The coefficients are multiplied by 100 for expositional purposes. The definitions of the vari-

ables are presented in Tables 1 and 3. Industry dummies based on the two-digit SIC code and year dum-

mies are included as regressors. p-values are in parentheses. *, **, and *** denote significant coefficients

at the 10%, 5%, and 1% levels, respectively.

Category of

independent

variables

Independent

variable Model 1 Model 2 Model 3

Block premium Block premium �0.004 (0.144) �0.003 (0.197) �0.004 (0.199)

Stock market

variables

Log (Firm size) 0.103 (0.032)** 0.080 (0.114) 0.136 (0.003)***

Turnover �0.125 (0.403) �0.102 (0.509) 0.016 (0.861)

Return 0.141 (0.112) 0.114 (0.212) 0.153 (0.060)*

Beta 0.310 (0.010)*** 0.296 (0.015)**

Volatility �0.027 (0.629) �0.045 (0.442) 0.035 (0.428)

Skewness �0.074 (0.365) �0.089 (0.287) �0.087 (0.282)

M/B 0.001 (0.904) �0.000 (0.989)

Accounting

variables

Abnormal

accruals

0.018 (0.912)

Tangibility 0.155 (0.676)

Leverage 0.712 (0.031)**

Sales growth 0.014 (0.201) 0.014 (0.156)

Acquisition

dummy

0.075 (0.705)

Equity issue

dummy

0.073 (0.711)

Pseudo R2 0.079 0.102 0.062



þ b7ðindividual acquirer dummyÞiþ b8ðsame industry acquirer dummyÞi þ b9ðrelative cashÞþ b10ðactive shareholder dummyÞi þ b11ðSOX dummyÞiþX


bhðyear dummiesÞi

ð4Þ

Table 6 shows the results of the effect of the predicted block premium on the like-

lihood of future litigation. Panel A shows the results of the first-stage regression where

we regress the block premium variable on the right-hand side variables shown in

equation (4). Panel B shows the results of the second-stage regression where we

regress the litigation dummy on the predicted block premium variable from the first-

stage regression. Model 1 uses the abnormal accrual variable as proxy for the level of

aggressive accounting, whereas Model 2 uses other predictors of aggressive accounting

in lieu of the abnormal accrual variable. Model 3 includes only those variables identi-

fied by Kim and Skinner (2012) as having the best predictive power of litigation.

In all three models, the coefficients of the predicted block premium are not statisti-

cally significant, suggesting that once we incorporate the possible endogeneity of the

block premium, greater private benefits do not increase the likelihood of the new block-

holder engaging in corporate misconduct, as indicated by the securities class action law-

suits. Regarding the other control variables that predict shareholder lawsuits, firms with

greater size, beta, and leverage are more likely to be sued by shareholders. These results

are consistent with those of previous studies and of Panel A, Table 3, where we deter-

mined the likelihood of shareholder litigation in the first-stage regression.

Overall, we do not find evidence that the size of private benefits, measured by

the block premium, presents a warning flag to the diffuse shareholders of the firm

in terms of increasing the likelihood of class action lawsuits. However, we do find

that the ex ante likelihood of litigation at the time of the block trade is incorpo-

rated into the block premium measure. This result provides empirical support to

the argument of Barclay and Holderness (1989) that one of the costs of block own-

ership is the threat of litigation.

7. Conclusion

We examine the causal relationship between block premium size and the likelihood of

shareholder litigation. Specifically, we ask the questions of whether (i) the ex ante likeli-

hood of litigation is incorporated into the block premium (test of Barclay and Holder-

ness (1989)) and (ii) the size of private benefits enhances the likelihood of self-dealing,

ultimately triggering class action lawsuits (agency theory hypothesis). Using a sample

of 593 block trades in the United States in the period 1995–2004, we examine the above

questions while considering the endogeneity of litigation and the block premium.

Our results suggest that the ex ante likelihood of litigation is incorporated into the

block-traded price. In a hypothetical case where the expected probability of litigation

changes from 0% to 100%, the block premium decreases by 10.5%, 11.9%, and 10.6%

based on different model specifications. On the other hand, we find no empirical

J. Chung et al.


Table 6 Effect of the predicted block premium on the ex-post likelihood of lawsuits

This table provides the results of examining the effect of the block premium on the likelihood of lawsuits

using a two-stage equation for the block premium and class action lawsuits. Panel A shows the results of

the first-stage regression that explains the block premium. The coefficients are multiplied by 100 for

expositional purposes. Panel B presents the results of the second-stage regression that explains the likeli-

hood of securities class action lawsuits using the predicted value of the block premium from the first-

stage regression and other control variables for the likelihood of litigation. The definitions of the variables

are presented in Tables 1 and 3. Industry dummies based on the two-digit SIC code and year dummies

are included as regressors. p-values are in parentheses. *, **, and *** denote significant coefficients at the

10%, 5%, and 1% levels, respectively.

Category of independent variables Independent variable Coefficient

Panel A: First-stage Regression (Dependent Variable: Block Premium)

Target characteristics Block size 0.148 (0.446)

Return �6.068 (0.037)**

Log (Firm size) �0.721 (0.582)

Leverage �23.137 (0.324)


Cash �14.786 (0.481)

Acquirer characteristics Individual acquirer �19.191 (0.021)**

Same-industry acquirer �4.910 (0.371)

Active shareholder 3.346 (0.073)*

Regulation SOX �3.478 (0.563)

Adjusted R2 0.03

Category of

independent

variables

Independent

variable Model 1 Model 2 Model 3

Panel B: Second-stage Regression (Dependent Variable: Litigation Dummy)

Block

premium

Predicted block

premium

0.005 (0.666) 0.006 (0.665) 0.005 (0.643)

Stock

market

variables

Log (Firm size) 0.108 (0.025)** 0.085 (0.096)* 0.141 (0.002)***

Turnover �0.122 (0.402) �0.102 (0.500) 0.010 (0.913)

Return 0.175 (0.116) 0.154 (0.214) 0.196 (0.066)**

Beta 0.312 (0.008)*** 0.296 (0.014)**

Volatility �0.026 (0.644) �0.043 (0.455) 0.036 (0.406)

Skewness �0.070 (0.389) �0.086 (0.300) �0.088 (0.275)

M/B 0.001 (0.842) 0.000 (0.957)

Accounting

variables

Abnormal

accruals

0.033 (0.842)


Leverage 0.783 (0.023)**

Sales growth 0.015 (0.169) 0.015 (0.119)

Acquisition dummy 0.076 (0.698)

Equity issue dummy 0.070 (0.723)

Pseudo R2 0.073 0.097 0.056



evidence that the magnitude of private benefits, as indicated by the size of the block pre-

mium, is an indication of engagement in wrongdoings that trigger shareholder lawsuits.

Our study is the first to provide empirical evidence that one of the costs of

block ownership is litigation risk. Testing for other costs of block ownership men-

tioned by Barclay and Holderness (1989), such as the cost of carrying an undiversi-

fied personal portfolio and monitoring costs, is left for future research.

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Block Premium and Shareholder Litigation