Financial Strength and Product Market Competition: Evidence from Asbestos Litigation*
Charles J. Hadlock
Michigan State University
[email protected]
Ramana Sonti
Indian School of Business
[email protected]
First Draft: June 2008
This Draft: September 2009
* Sonti wishes to gratefully acknowledge the assistance and hospitality provided by the faculty
and administration of the Cox School of Business at Southern Methodist University, Dallas,
where much work on this paper was done, while he was temporarily in residence during the
months following Hurricane Katrina. We thank Murillo Campello, Michael Gordy, Peter
MacKay, Sheri Tice, and seminar participants at the Indian School of Business for helpful
comments and suggestions. All errors are our own.
Financial Strength and Product Market Competition: Evidence from Asbestos Litigation
ABSTRACT
We study the role of financial strength on product market competition by examining exogenous
shocks to a firm's liability structure arising from asbestos litigation. We find that unexpected
exogenous increases (decreases) in a firm's asbestos liabilities arising from actions by external
parties are interpreted by the market as negative (positive) news for the firm's close competitors.
Since asbestos liabilities are debt-like claims, these findings support the hypothesis that increases
in fixed liabilities lead to more aggressive competitive interactions. Competitor returns are
particularly negative in events in which one asbestos-tainted firm goes bankrupt and other
asbestos stocks fall on the news, suggesting that non-equity liabilities affect a firm's product
market decisions by causing them to discount default states of the world. The magnitude of the
response of competitor stocks to asbestos liability shocks is not related to most of the firm and
industry characteristics we consider, casting doubt on some possible explanations for our
findings.
JEL Classification: G32; L13
Key words: financial strength; debt; product market competition; asbestos liabilities
1. Introduction
An important issue in corporate finance is the possible relation between a firm's financial
structure and its product market behavior. Several theoretical papers demonstrate that nonequity liabilities may impact a firm's product-market decisions by altering equityholders' relative
returns from different strategic choices (e.g., Brander and Lewis (1986)). The presence of these
liabilities may also expose firms to deliberate actions by competitors who desire to manipulate
the likelihood that a firm is exposed to financial distress risk (e.g., Bolton and Scharfstein
(1990)). Since the strategic dynamics of the product market can have a very large effect on firm
value, any relation between financial strength and product market behavior is likely to have
important implications for our understanding of capital structure choices.
On the empirical front, researchers have examined the relation between a firm's financial
structure and several different dimensions of product market behavior including pricing,
investment, advertising, entry, and exit. Most of these studies use a firm's debt level or a recent
change in debt to study the relation of interest. While it is hard to succinctly characterize the
nuanced results of this literature, the bulk of the evidence appears to indicate that higher levels of
non-equity liabilities, typically in the form of debt, make a firm a weaker competitor.
While this empirical literature has generated many interesting findings, the picture is still
far from complete. One particularly vexing issue arises from the fact that a firm's debt level is
endogenous. Thus, while leverage choices may be associated with certain product market
outcomes, it is often difficult to determine whether an identified association actually reflects a
causal relation. Several authors attempt to address this endogeneity issue, but these attempts are
often indirect and/or rely on identifying assumptions that may not hold. As is the case in many
corporate finance contexts, the fundamental problem is that it is hard to identify truly exogenous
variation in an important strategic choice variable such as debt.
1
In this paper we provide evidence on this issue by considering the competitive effects of
exogenous shocks to a firm's non-equity liability structure. These shocks come in the form of
large discrete pieces of news regarding the expected size of a firm's liability from asbestos
litigation. These liabilities have nothing to do with a sample firm’s current asset or product mix,
since asbestos use today is minimal and the substance is tightly controlled and regulated. Adding
to the exogenous nature of the shocks we consider, the information releases we study are not
controlled by firms. Instead, they reflect news revealed by external agents in the form of (a)
unexpected bankruptcy announcements by other firms with asbestos liabilities, (b) judicial
actions, and (c) legislative developments.
Discrete changes in the expected magnitude of a firm's asbestos liabilities are very much
like changes in other fixed liabilities such as debt. Similar to debtholders, asbestos litigants have
a claim on a firm's assets that is senior to equityholders. These claims can be particularly hard to
renegotiate outside of formal bankruptcy, as individual claimholders can only loosely coordinate
their actions and they cannot freely trade. Thus, in a sense, these claims are very much like
dispersed private debt.
If the size of debt-like claims affects a firm’s competitive position, we would expect to
observe a noticeable market reaction for the firm's closest market competitors in response to an
exogenous change in the size of these claims. In particular, if a weak financial position makes a
firm a weak competitor in terms of a lower ability to cut prices, expand, or respond to
competitive attacks, then increases (decreases) in these claims should be good news (bad news)
for the firm's competition. In a slight abuse of language, we will refer to this as the debt-makesyou-weak hypothesis. Alternatively, if a weak financial position makes a firm an aggressive
competitor in terms of charging low prices or other such actions, then large increases (decreases)
2
in asbestos claims should be viewed as bad news (good news) for the competition. We will refer
to this as the debt-makes-you-aggressive hypothesis.
After thoroughly screening the universe of possible events, we uncover a set of eleven
event dates on which one or more firms experience a large (+/- 10%) change in stock price
purely because of exogenous asbestos news. Identifying close competitors from business
publications, we find that competitor stocks tend to react in sympathy with their asbestos-tainted
competitors on the day of the information revelation. In other words, news about financial
weakness (strength) for the asbestos firm is on average bad news (good news) for the
competition. This finding is robust to a variety of different ways of measuring average
competitor returns. In contrast to much of the prior related evidence, our findings are generally
supportive of the debt-makes-you-aggressive hypothesis. Our results are consistent with the
findings of Busse (2002) who reports that financially weak airlines are more likely to start price
wars.
The distinct advantage of our empirical approach is that we avoid the use of endogenous
financial choices to examine the relation between the presence of non-equity liabilities and
product market interactions. The market reactions we uncover, at the very least, suggest that
something is expected to change for competitors when the financial condition of one market
participant is substantially altered. Given the lack of clean exogenous variation in the prior
related literature, we believe that this is an important finding. An advantage of examining
market returns is that we are able to collapse the possibly multi-dimensional and multi-period
consequences of a change in financial strength into a single metric measuring the expected net
competitive effect of an event. The disadvantage of our approach is that we are limited in our
3
ability to detect the exact channel through which a change in liability structure may lead to
different product market outcomes.1
To provide some evidence on the channel through which large non-equity liabilities are
anticipated to affect product market outcomes, we consider the cross-sectional variation in
competitor abnormal returns both within and across events. Most variables we consider,
including leverage, market concentration, and firm size, do not appear to be closely related to
competitor returns. We do find that competitor returns are substantially more negative in
bankruptcy events compared to judicial and legislative events. We conjecture that these events
may convey more substantive news regarding the possibility of a bankruptcy outcome for a
sample firm, and it is precisely the likelihood of these states of the world that several of the
related theories apply to. In addition, we find that competitors who are more aggressively
increasing capital expenditures are disproportionately negatively affected by an increase in
asbestos liabilities. This is consistent with the possibility that the value of future growth options
falls more sharply than assets in place when a price war or other aggressive behavior is expected
to occur, perhaps because these growth options fall out of the money.
While we motivate our investigation and interpret our results by appealing to established
theories of debt and product market competition, our results could alternatively arise from
anticipated behavior in the market for assets rather than market for products. As illustrated by
Shleifer and Vishny (1992), a change in debt-like liabilities may affect a firm's ability to either
acquire other firms or to be acquired. Our results are consistent with the possibility that an
increase in debt-like liabilities decreases the ability of a firm to acquire one of its competitors, in
which case these events could be bad news for the competition. Since a merger between two
1
As we detail more below, there are many pieces of good and bad news for asbestos firms over a fairly short
horizon. Thus, detecting changes in product market behavior subsequent to a single event is infeasible and we are
restricted to examining event returns. For an interesting direct analysis of the behavior of asbestos-tainted firms in
financial distress, see Taillard (2008).
4
competitors would likely lead to softer competition, this behavior could be interpreted as a
different channel through which debt-like liabilities lead to aggressive competition. However,
the fact that competitor size does not significantly explain competitor event returns does cast
doubt on this channel as the sole explanation for our findings.
The rest of the paper is organized as follows. In section 2 we situate our study in the
context of the prior literature and motivate our empirical strategy. In section 3 we detail our
sample construction and selection procedures. Our main analysis of competitor abnormal returns
in reaction to asbestos events is reported in section 4. Concluding thoughts and reactions are
discussed in section 5.
2. Related literature and empirical strategy
2.1 Theoretical considerations
A seminal paper by Brander and Lewis (1986) demonstrates that the presence of debt can
create incentives for a firm to compete more aggressively when choosing output quantities. This
behavior arises because the possibility of bankruptcy leads the firm to underweight low states of
demand when committing to a production plan. The ensuing literature has extended the Brander
and Lewis (1986) framework to consider variations in competitive choice variables (e.g., prices
versus quantities), variations in the sources and timing of underlying uncertainty, and the
possibility of repeated interaction between competitors.2 Maksimovic (1988) demonstrates that
high debt can harm industry profits by making it harder for firms to credibly commit to tacit
collusion outcomes in a repeated game setting.
2
For surveys of the literature on financial structure and product market competition, see Cestone (2000),
Maksimovic (1995), and Parsons and Titman (2007). Early important papers that motivate this theoretical literature
include Telser (1966) and Titman (1984).
5
While several of the papers in this theoretical stream show that increased debt can lead to
aggressive behavior that is bad for competitors, other models using different assumptions
demonstrate the possibility that debt may actually lead firms to relatively less aggressive
behavior. For example, Showalter (1995) shows that when firms compete in prices rather than
quantities, the presence of debt may lead firms in an industry to charge higher prices because
high debt firms tend to discount low states of demand. In a dynamic setting, Chevalier and
Scharfstein (1996) and Dasgupta and Titman (1998) show that highly levered firms may charge
higher prices in low demand states because they discount the benefits of building future market
share given the firm's fragile financial position and uncertain future.
In a different theoretical vein, Bolton and Scharfstein (1990) demonstrate that a high
level of debt may attract predatory behavior by competitors who want to take advantage of a
firm's relatively precarious financial position. Frictions in financial contracting result in an
inability to completely contract around this predatory outcome. Models along these lines suggest
that high levels of debt may subject firms to aggressive behavior by competitors in the form of
price wars and aggressive entry and expansion decisions.
2.2 Empirical evidence
The theoretical models discussed above can loosely be grouped into a debt-makes-youaggressive group and a debt-makes-you-weak group. The distinct differences between these
groups of theories have naturally led to empirical tests of the role of debt on product market
competition. In interpreting evidence on this issue, it is worth noting that what will be identified
is the effect of perturbing a firm's debt level from a starting point which may have already been
6
optimally determined.3 Since competitive interactions can be difficult to characterize, most of
these studies focus on a single industry/market and examine a limited number of dimensions on
which firms compete.
Several of these studies demonstrate that firms with high or increased levels of leverage
tend to be relatively weak competitors. For example, Chevalier (1995a, 1995b) presents
evidence indicating that leveraged buyouts (LBOs) in the supermarket industry lead to
aggressive entry and high product prices. Similarly, Phillips (1995) finds that LBOs are
associated with lower output levels and higher output prices in three of the four industries he
studies. Khanna and Tice (2000) demonstrate that highly levered firms appear to be at a
disadvantage in responding to aggressive entry, while Khanna and Tice (2005) uncover pricing
patterns that are consistent with low debt firms attempting to deliberately induce highly levered
firms to exit the market. Finally, Zingales (1998) shows that highly levered firms are less likely
to survive following deregulation in the trucking industry.
While the aforementioned studies restrict attention to single industries, similar results
have been reported for broader samples of firms. Campello (2003, 2006) studies a large set of
firms on the Compustat database and finds that leverage, at least over some ranges, is associated
with less aggressive behavior in terms of higher prices in recessions and less aggressive
expansion plans. Kovenock and Phillips (1997) report a similar finding for the expansion
activities of highly levered firms using plant-level data. Adding to this evidence, Grullon,
Kanatas, and Kumar (2006) find that higher leverage is associated with less aggressive
advertising strategies.
3
Several authors in this literature explore the implications of the strategic role of debt on capital structure choices at
the industry level. See, for example, the industry models of Maksimovic and Zechner (1991) and Williams (1995)
and the associated empirical tests of MacKay and Phillips (2005).
7
This empirical literature largely supports the debt-makes-you-weak hypothesis, which
would imply that increases in debt should be good news for a firm's competition. Consistent
with this implication, Chevalier (1995a) reports that competitors exhibit positive abnormal
returns when a firm announces it is undertaking an LBO. There are, however, a few empirical
results in the literature that suggest that debt may, at times, make firms more aggressive. In one
of the industries studied by Phillips (1995), LBOs are associated with more aggressive
competition. In addition, Campello (2006) reports that increases in leverage below a moderate
level are associated with market share gains. Finally, Busse (2002) reports that financially weak
airlines are more likely than other airlines to start price wars.
The principal interpretation problem in this prior work is that a firm's leverage choice is
endogenous. Thus, it is possible that firms with higher levels or increases in debt are
systematically different from other firms, and it could be these differences that actually drive the
product market behavior. While most authors attempt to address this issue by ruling out
alternative interpretations for their findings, it is difficult to rule out all possible alternatives
without identifying clean exogenous variation in a firm’s financial structure. Given this
limitation, while existing findings certainly strongly suggest that financial strength affects
product market interactions, the evidence is not yet definitive. Most of the received evidence,
but not all, appears to more strongly support the debt-makes-you-weak hypothesis.
In addition to the studies discussed above that directly examine debt and competitive
interactions, a related literature examines the effect of bankruptcy or distress announcements on
competitor stock returns (e.g., Lang and Stulz (1992), Ferris, Jayaraman, and Makhija (1997),
and Hertzel, Li, Officer, and Rodgers (2008)). These studies show that distress or bankruptcy
announcements are generally bad news for a firm’s competitors. However, since the events
examined in these studies contain news both about future industry prospects (the contagion
8
effect) and possible future competitive dynamics (the competitive effect), it is difficult to cleanly
interpret these results with regard to the competitive effects we are interested in understanding.
Since we choose a set of events in which news about future industry prospects should be zero,
we can more cleanly identify the sign of the competitive effect of an increase in the likelihood of
future bankruptcy for one firm on other product market participants.4
2.3 Asset market considerations
While a firm's liability structure may directly affect product markets via strategic
operating decisions, the work of Shleifer and Vishny (1992) and Hege and Hennessy (2007)
suggests an alternative channel. In particular, debt and the possibility of financial distress can
have an important effect on asset transfers within an industry. Increased debt can raise the
likelihood that a firm defaults and consequently is sold to a new owner. While competitors will
often tend to be the natural buyers of liquidated assets, they will at times be unable to
consummate a purchase because of financial constraints, particularly if they are highly levered.
These considerations suggest that an increase in a particular firm’s debt-like liabilities may have
an effect on competitors since it affects the likelihood that competitors will be on either the buy
side or sell side of a control transaction with the firm.
If a competitor were a potential future target of a firm that experiences a large increase in
debt-like liabilities, this logic suggests that the change would be bad news for the competitor
since its likelihood of being acquired would be reduced. In this case fixed liabilities act as an
impediment to a cooperative transaction (i.e., a merger) that could raise joint profits, and thus
4
While some authors in this literature (e.g., Lang and Stulz (1992)) assume that the competitive effect must be
positive (i.e., bankruptcy is good for the competition), the indirect evidence on this issue across these studies is quite
mixed. Our more direct evidence suggests that the effect may in fact be negative. Using information from a single
litigation episode, Hertzel and Smith (1993) report evidence that is broadly consistent with our findings in the sense
that an exogenous increase in distress risk is associated with negative announcement returns for competitors.
9
this can be viewed as a different flavor of the debt-makes-you-aggressive hypothesis. We can
attempt to distinguish this scenario from the other debt-makes-you-aggressive possibilities by
considering whether any of the effects that we identify are particularly pronounced for
competitors that are more likely to be acquisition targets.
If a competitor were a potential future purchaser of a firm that experiences a large
increase in debt-like liabilities, this change could be good news for the competitor since the
likelihood of being able to purchase these assets at possibly discounted prices in distress may
have increased. This can be viewed as a variant of the debt-makes-you-weak hypothesis, since
the presence of large liabilities increases the likelihood of subsequently being absorbed into a
competitor and presumably softening competition.
2.4 Empirical strategy
As we discuss above, an important limitation of prior research arises from the difficulty
in identifying exogenous variation in a firm's debt usage. Given that debt is a choice variable, it
is hard to envision circumstances under which leverage will have a truly exogenous component.
To overcome this problem, we identify major shocks to a type of debt-like claim, a firm's
asbestos liability, which is not under the control of the firm. As long as we can assume that these
shocks are not systematically related to firm or industry characteristics, these changes should
satisfy the exogeneity requirement. Since asbestos is now a tightly controlled and regulated
substance that none of our sample firms use or sell in material quantities, this would appear to be
a very reasonable assumption.
As we discuss below, asbestos claims can reasonably be viewed as a special type of debt
in that they represent a claim that is senior to equity (and some debt) in the event of default.
Asbestos liabilities are owned by a disperse group of individuals who cannot easily trade their
10
claims. Consequently, these liabilities should be particularly hard to renegotiate outside of
formal Chapter 11 proceedings. This is a particularly advantageous feature, since many of the
theories we discuss above assume/require that debt be non-renegotiable.
Our strategy is to select a set of asbestos liability shocks that are clearly material to the
firm's overall financial strength but are unrelated to any other firm or industry news. After
identifying these events, we examine the effect of these shocks on competitors by examining
competitor announcement-date abnormal stock returns. Under the debt-makes-you-weak
scenarios, we would expect increases (decreases) in debt-like asbestos liabilities to be good news
for competitors. In contrast, under the debt-makes-you-aggressive scenarios, an increase
(decrease) in these debt-like liabilities should be bad news for competitors.5
To maximize the power of our tests, it is important that we select firms that actively
compete with a firm that experiences the shock. Consequently, we select firms that are identified
in the business press as competitors of the affected firm. This should provide a more powerful
test than the conventional approach of selecting firms based on relatively noisy SIC codes.
One advantage of studying competitor returns is that we can measure the present value
consequences of perhaps a large number of anticipated future changes in the competitive
landscape. For example, suppose an increase in debt-like liabilities is anticipated to lead to a
short-term industry price war and a long-run price increase. Using our approach, we will capture
this in a single metric that gauges the overall impact of the liability change on anticipated
competitor profitability.
5
An increase in liabilities could also be bad news for competitors if they share a common supplier with the asbestos
firm and if distress would negatively impact these supply relationships (see Hertzel and Smith (1993)). Using the
Fee and Thomas (2004) algorithm to identify suppliers, we find only one supplier that had a disclosed relationship
with both an asbestos firm and one of its competitors. The announcement returns of the supplier are generally not
consistent with this hypothesis. Moreover, if we exclude this asbestos firm from the analysis, the main results we
report below are substantively unchanged. Thus, this possibility does not appear to be relevant in our sample.
11
While our empirical strategy does have the distinct advantage that the changes we
identify are exogenous, there are some limitations. In particular, we cannot detect the actual
channel through which a change in financial strength may affect competitive outcomes. While it
is tempting to search for evidence on this issue in our sample, there are numerous pieces of
positive and negative asbestos news in a short time period, and thus cleanly tracing the impact of
one change on subsequent product market behavior is not practical.
In an indirect attempt to understand how financial strength may affect competition, we do
examine the cross-sectional variation in competitor announcement returns. Thus, for example,
we are able to assess whether smaller competitors who are more likely to be potential acquisition
targets of the asbestos-tainted firms are more adversely affected by an increase in asbestos
liabilities. This allows us to offer some insights on what may be driving some of our findings.
However, given the indirect nature of these tests, this evidence cannot be viewed as conclusive.
Before concluding this discussion, it is worth noting an interesting related study by
Taillard (2008). He examines the operating performance and choices of asbestos-tainted firms
who experience financial distress. In many cases he finds that these firms performed relatively
well (at an enterprise level) throughout their distress episode. This suggests that the total indirect
costs of pure financial distress may be fairly small, or alternatively that the compensating
benefits of distress may be substantial. In our study we use asbestos liabilities to consider the
effect of the probability of distress on one dimension of a firm’s environment, namely its
anticipated competitive interactions as perceived by the market. Viewed in this way, our
investigation complements the findings of Taillard (2008) by offering some specific insights into
how pure financial distress may alter firm behavior.
2.5 A brief overview of asbestos liabilities
12
Before proceeding with our analysis, we provide a brief overview of the most salient
facts regarding asbestos liabilities that are relevant for our study.6 Asbestos is a naturally
occurring substance with certain desirable natural properties. It was widely used in a variety of
industries for much of the twentieth century. Over time, it became evident that asbestos could
lead to several fatal health conditions related primarily to the chest and lungs. This has led to
large legal liabilities for former producers of asbestos, and most of the major producers filed for
bankruptcy in the 1980s and 1990s. Given the risks from litigation and a set of increasingly
stringent regulatory rules, the use and production of asbestos dropped precipitously by the early
1990s.
While asbestos is no longer widely used or produced, the legal liabilities from its past
usage have continued to grow over time and constitute the longest-running mass tort in U.S.
history. This growth has arisen from the fact that many asbestos related diseases have a very long
dormant period, coupled with some of the peculiarities of how asbestos liabilities have been
settled by the courts. As many of the major asbestos producers went bankrupt in the 1980s and
early 1990s, plaintiffs searched for deeper pockets. Since asbestos defendants often have "joint
and several" liability, plaintiffs have been successful in obtaining large settlements from firms
that had only minimal involvement with the use or production of asbestos. Carroll et al. (2005)
report that 730,000 individuals have sued 8,400 firms for asbestos damages with a total expected
liability exceeding $200 billion.
The size and peculiar nature of asbestos liabilities is well illustrated by the case of one of
our sample firms, Crown Cork and Seal, a metals and plastics packaging firm. In 1963, Crown
Cork and Seal purchased Mundet, a cork bottle cap manufacturer, for $7 million. Mundet had a
6
This description draws heavily from White (2004, 2006) and Carroll et. al. (2002, 2005) who provide detailed
overviews of asbestos litigation. The reader is referred to these sources for additional details and context.
13
small insulation division that was sold by Crown Cork and Seal 93 days after consummating the
purchase. Despite this very brief attachment to an asbestos-tainted insulation producer, by 2002,
Crown Cork and Seal had paid over $350 million in asbestos claims and the otherwise healthy
firm was at the brink of bankruptcy. As is the case of many firms with asbestos liabilities,
Crown Cork and Seal experienced pure financial distress without any economic distress.
Two major legal judgments in the late 1990s [Amchem Products v. Windsor (1997) and
Ortiz v. Fibreboard Corp. (1999)] significantly expanded the possibility that firms with a limited
connection to asbestos could experience large financial losses from asbestos litigation.
Subsequent to these decisions, firms with some asbestos exposure often experienced dramatic
movements in their stock price as legal developments and legislative initiatives unfolded that
could drastically alter the market's assessment of the firm's future asbestos liability. It is these
news events that form the basis of our study.
From an economic perspective, a firm's asbestos claims are similar to senior debt and
asbestos claimants are at times referred to as "involuntary creditors". If a firm goes bankrupt,
asbestos claims are grouped together and have the same seniority as unsecured senior debt.7 In
most cases, claims in bankruptcy are settled by creating a trust that pays current and future
claims at some fraction of the assessed value of the claim. In a bankruptcy reorganization, 75%
of the class of current asbestos claimants must approve of a proposed plan. While individual
asbestos claimants are dispersed, there is often some coordination amongst these claimants (both
inside and outside of Chapter 11) owing to the presence of lawyers who jointly represent many
claimants and at times organize class action lawsuits.
7
Legal scholars have debated whether tort claims should be treated as unsecured debt, since this provides an
incentive for firms to issue secured debt that is effectively more senior (e.g., Bebchuk and Fried (1996) and Spier
and Sykes (1998)). While this is an interesting theoretical issue, Listokin (2008) shows that this behavior is not an
important empirical phenomenon for firms with asbestos liabilities.
14
Given this description, it is evident that asbestos claims are similar to debt in the sense
that they represent a fixed non-equity claim on the firm's assets. These debt-like claims are hard
to renegotiate as they are generally dispersed and non-tradable. Given these features, the effects
of changes in these liabilities on firm and competitor behavior in the presence of limited liability
should be similar to changes in the presence of dispersed senior debt. By studying shocks to this
debt-like liability, we hope to learn something about the role of debt-like liabilities and financial
strength on product market competition.
3. Sample selection
3.1 Identifying asbestos events
To select events that represent large exogenous changes in asbestos liabilities, we first
identify firms that reveal a material asbestos liability at some point between the Ortiz v.
Fibreboard decision (June 24, 1999) and the start of our sample collection efforts (June 2004).
We begin this identification by reading every Wall Street Journal article that mentions the phrase
"asbestos litigation." All firms identified in these articles are placed onto an initial list of
candidate firms. From this initial list we select for further study all firms that are listed on the
Compustat and CRSP databases and indicate a material asbestos liability in at least one of their
10-K filings.
For the resulting sample of firms, we carefully investigate the firm's asbestos liability
history by searching through all news articles listed in the Factiva database that include the
firm's name and the word “asbestos”. We also search electronically for the term asbestos in all
of the firm's available 10-K filings. If these sources clearly indicate a firm had only a very minor
exposure to asbestos liabilities, we exclude the firm from further consideration. The remaining
sample includes 31 firms with possibly substantial liabilities.
15
Since we are searching for cases in which a firm experiences a sharp revision in its
expected liabilities, we identify from the CRSP file all days between June 30, 1999 and June 30,
2004 on which a sample firm's stock price increases or decreases by at least 10%. For firms that
eventually declare bankruptcy, we exclude from consideration all dates that occur subsequent to
the bankruptcy filing. For all other identified dates, we read news articles on Factiva in a
window of three trading days centered on the identified date to determine if the stock price
revision was related to asbestos news.
In many cases, a sharp stock price revision appears to be connected to asbestos news, but
it is also possible that the price movement is partially related to other news regarding the firm
and/or the industry. For example, if an analyst downgrades a stock because of concerns about
asbestos, we suspect that most of the downward price movement in the stock will be due to the
asbestos news. However, the analyst's decision may implicitly contain some additional
information regarding the health of the firm and/or the industry. Consequently, this type of event
is not sufficiently clean for the purposes of our investigation, since this residual information may
also drive competitor stock returns. Similar concerns apply to any voluntary revelation by the
firm regarding the size of its asbestos liability.
To select events that are clearly not contaminated by news regarding the value of real
assets, we impose a condition that the asbestos news associated with an event must be directly
related to the actions of an external party. In addition, we require that the news be free of any
statements regarding the overall value, prospects, or earnings of the firm. Practically speaking,
this leaves us with three types of events. One type of event is the unexpected announcement of a
bankruptcy by a firm with known asbestos liabilities which causes the market to revise its
estimate of the size of other firms' asbestos liabilities. In what follows, we refer to these as
bankruptcy events. The second type of event, which we refer to as a judicial event, is a ruling by
16
a court regarding asbestos litigation. Typically these events affect many firms as the market
again revises its estimate of the general magnitude of asbestos liabilities. Finally, the third type
of event represents cases in which a legislative development at either the state or federal level
causes a revision in the market's assessment of asbestos liabilities. We refer to these as
legislative events.
After identifying events that satisfy these criteria, we proceed to eliminate events with
confounding news for the firm reported on Factiva on the announcement day. We also eliminate
events for which we could identify no competitors for the firm (details on competitors below).
The resulting sample includes 17 stock price revision events for 7 asbestos-tainted firms on 11
distinct event dates. A brief description of these events and the asbestos history of sample firms
is reported in Appendix A. There are substantially more stock price revision events than event
dates, because in many cases several asbestos-tainted stocks all move on the same day in
response to the same piece of news.8 One of the event dates falls immediately after a prior event
date (October 5 and 6, 2000). In this case, the announcement of the Owens Corning bankruptcy
filing battered asbestos stocks on both the day of the announcement and the subsequent day, with
no apparent confounding news. Thus, we use both of these event dates in our sample, but take
care in our estimation to account for the possible correlation in announcement returns on
adjacent dates (see details below and also Appendix B).
3.2 Identifying competitors
For each sample firm, we attempt to identify the firm's close competitors. A common
approach for identifying competitors is to use SIC industry codes. However, many firms in the
8
Many of our event dates with only a single asbestos firm in the sample were days on which several other asbestos
stocks also changed substantially, but not by enough to satisfy the +/- 10% selection criterion.
17
same SIC industry often do not directly compete, either because the industry codes are only
coarse indicators of a firm's business activity or because product markets are segmented by
geographic or product niche. This is a major cause of concern for us, since noise in our choice of
competitors may result in tests of low power.
In light of these concerns, we turn to the business press to identify competitors. In
particular, we exploit the fact that Hoover's Inc. (now a subsidiary of Dun and Bradstreet) has
included a list of a firm's top three competitors in its company capsules on the Factiva database
starting in September of 2001. We electronically search through this source and identify all
firms that list a sample asbestos firm as a top competitor as of the third quarter of 2001. These
are firms that outside business observers have identified as competing closely with a sample
firm. If the change in financial strength of an asbestos firm has any competitive effects, these
competitors are likely to be the ones who are most affected.
Since we seek to identify the effect of an exogenous change in asbestos liabilities on
competitors via their expected future interaction with the asbestos firm, we need to make sure
that the competitors themselves do not have any material asbestos exposure. Thus, we
immediately exclude from the set of competitors all firms that appear on any of our lists of firms
with possible asbestos exposure. We then proceed to electronically search each competitor firm's
10K statements for all sample period years for any reference to asbestos liability arising from the
firm's past use or production of asbestos products. If the firm reveals any material liability in
these filings, we exclude it from the list of competitors.9 Thus, the remaining list should include
firms that compete directly with an asbestos firm and have no material asbestos liabilities of their
9
Some firms mention that they have asbestos in some of their buildings, as would be expected from standard
construction practices in the U.S. prior to the late 1970s. Since the liability from this asbestos presence is expected
to be minimal and should also affect the benchmark firms used for calculating abnormal returns below, we do not
exclude competitor firms with this type of routine disclosure. Whenever a firm's disclosure is unclear about the
presence of material asbestos liabilities, we supplement the 10K disclosure with information from a Factiva search.
18
own. If two asbestos firms have a common competitor, we assign the competitor solely to the
firm that is closer in size based on book assets.
A few of the asbestos firms in our sample have a large set of identified competitors.
Since we want to limit the competitor list to firms that most closely compete with an asbestos
firm, in cases in which more than ten competitors are identified, we restrict attention to the ten
largest competitors based on book assets. Since competitors are almost always smaller than the
asbestos firm they are matched with, the top ten competitors will be the ones who are effectively
closest in size to the asbestos-tainted firm. In some of our robustness checks, we consider the
effect of including all competitors, even those below the top ten, in the analysis. After imposing
these criteria, the mean and median numbers of competitors computed across all events are both
equal to 6.0.
3.3 Description of the sample
In Table 1 we present basic summary statistics for sample asbestos firms. These statistics
are calculated as of the end of fiscal 1999, the last fiscal year ending before any of the sample
event dates. As is evident from the statistics on book assets, sales, and market equity, asbestos
firms in our sample are typically substantially larger than their median competitor. The median
asbestos firm has sales at the 85th percentile of its 4-digit Compustat industry, indicating that
these are big firms relative to their industry. It is worth noting that competitor firms are, in
aggregate, also of substantial size. As we report in column 4 of the table, the median asbestos
firm is slightly larger than the sum of its competitors using book assets as a size criterion
(relative size ratio of 1.329), but slightly smaller using either sales or market equity as a size
measure (relative size ratios of 0.763 and 0.702 respectively). Given the substantial size of the
19
competitors in aggregate, the economic consequences of any change in product market behavior
on asbestos firms and their closest competitors could be substantial.
Turning to the rows on various growth rates, the figures in Table 1 indicate that asbestos
firms tend to be growing more quickly than their median competitor in the period prior to the
sample events. However, the evidence on profitability is more mixed. Asbestos firm's have
lower accounting profitability than their median competitor and a similar Tobin's Q. Only when
we consider the ratio of market equity to book equity do the asbestos firms appear to outperform
their median competitor (median relative ratio of 1.168).
With regard to financial structure, the asbestos firms in our sample appear fairly highly
levered. As we report in the table, the median ratio of debt to assets is 0.381, a figure which
substantially exceeds the Compustat median in 1999 of 0.211. Additionally, using two different
measure of leverage, the figures in Table 1 indicate that the asbestos-exposed firms in our sample
are more levered than their median industry competitor (median ratios of 1.498 and 1.395 using
alternative measures of leverage).
Finally, we present in the last rows of Table 1, statistics on the size of the typical
announcement return for sample events. As we report, the absolute value of the announcement
return for asbestos firms has a mean and median that exceeds 14%. If we instead normalize the
absolute value of the change in equity value on the announcement date by book debt plus book
equity, the mean and median change are on the order of 5% (see final row of Table 1). Using
either type of measure, it is apparent that sample events represent a substantial change in a firm's
expected liability structure. We now turn to examining how these events are expected to affect
competitors.10
10
We have experimented with normalizing the statistics in Table 1 with end of fiscal 2002 data (near the end of the
sample period) rather than end of fiscal 1999 data. Not surprisingly, these statistics reveal a sharp decline over time
20
4. Asbestos news and competitor returns
4.1 Competitor Abnormal Returns
For each event, we are interested in the abnormal returns of the competitors of asbestostainted firms. Our sample includes a mix of bad news and good news events (i.e., expected
asbestos liabilities increasing and decreasing respectively). Since the impact of a change in
asbestos liabilities on competitors should be exactly opposite for these two types of events, we
can maximize the power of our tests by combining information from both types of events. To
accomplish this, we use the following sign convention. We make bad news events (i.e., events in
which an asbestos stock falls sharply) the default case and consider the (unadjusted) abnormal
returns of competitors for these events. In the case of good news events, we sign adjust
competitor abnormal returns by multiplying them by -1. In what follows, when we refer to
competitor abnormal returns, we are referring to returns after this sign adjustment has been
made. Thus, the results we report should represent the impact on competitors from a bad news
event in which an asbestos-tainted competitor experiences a sharp increase in its asbestos
liabilities. The predicted impact of a sharp decrease in asbestos liabilities would be exactly the
opposite of what we report.
To obtain an initial picture of how competitor stocks react in sample events, we first
calculate the abnormal returns of competitors on sample event dates. Letting day 0 be an event
date for a given asbestos firm, we calculate abnormal returns by estimating a market model for
each identified competitor of this firm over the period between trading days -260 and -11. We
exclude from this estimation period any dates that represent other asbestos event dates. The
in equity values for asbestos firms relative to competitors. On the operating side (e.g., sales, book assets), asbestos
firms continue to appear substantially larger than competitors, but their growth slows relative to the competition and
thus the reported size differentials becomes somewhat smaller.
21
abnormal return for a competitor is then calculated as the difference between the observed eventdate return and the predicted return using the parameters of the estimated market model.
In row 1 of Table 2 we report the mean competitor abnormal return across all competitors
and events. We also report the associated p-value which is calculated by first standardizing each
abnormal return by its estimated standard deviation and then aggregating across events assuming
independence of the standardized returns.11 As is illustrated in the table, the average competitor
abnormal return of -0.00891 is negative and highly significant (p=0.0024). This indicates that
the typical competitor experiences a downward value revision of almost 1% when their asbestostainted competitor experiences a major increase in asbestos liabilities.12 This finding is
consistent with the notion that an increase in debt-like liabilities will tend to toughen product
market competition, thus hurting competitors. The results are quite similar in row 2 when we use
an even simpler model to calculate abnormal returns by simply subtracting the market return
from the competitor’s return on the event date to derive a simple market-adjusted event return
(mean return of -0.00824, p=0.0052).
While these initial results are suggestive, there are some important statistical issues to
address. Treating individual competitor stocks as independent is almost surely inappropriate,
since we would expect a fair amount of correlation in competitor returns. To adjust for this
possibility, for each event date we create a portfolio composed of the set of all competitors of all
asbestos firms that experience a sharp stock price revision on that date.13 If there is a single
asbestos firm on a given event date, we use an equally weighted portfolio of all its competitors to
11
Precise details on the construction of each estimate and the associated p-values are reported in Appendix B.
12
Recall that given our sign conventions, this result really represents a more general phenomenon in which
competitor stocks move in sympathy with asbestos stocks: down (up) when asbestos firms get bad (good) asbestos
liability news.
13
See Eckbo (1983) and Stillman (1983) for similar approaches to estimating event returns for portfolios of
competitors.
22
create the competitor portfolio for that date. When there are multiple asbestos firms on a single
event date, we first form equally weighted portfolios of the competitors corresponding to each
asbestos firm and then we equally weight each of these portfolios to create a final competitor
portfolio for that date.
After creating competitor portfolios, we estimate a market model for each portfolio and
calculate the portfolio abnormal return on the event date. The estimation period, abnormal return
calculation, sign convention, and p-values are calculated in an analogous way to our earlier
treatment of individual competitor stocks. As we report in row 3 of Table 2, the mean
competitor portfolio abnormal return using this approach of -0.01139 is negative, significant
(p=0.028), and slightly larger in magnitude than our corresponding earlier estimates. This again
indicates that competitors appear to be harmed on average when their asbestos-tainted competitor
experiences a sharp increase in its liabilities. The overall magnitude of the estimated effect is
slightly more than 1% of the competitor portfolio's total equity value.
These findings strongly suggest that the presence of debt-like liabilities can have an
important effect on expected product market outcomes. However, some statistical issues still
remain. Of particular concern is the fact, described earlier, that two of our event dates are
immediately adjacent to each other (October 5 and 6, 2000). While the results above assume that
portfolio returns are independent across all event dates, given the close proximity of these two
dates, it is possible that common market model estimation error in calculating portfolio abnormal
returns for these dates could lead to some serial correlation.
To address this issue, for these adjacent dates we calculate a standardized cumulative
abnormal return by adding the two portfolio abnormal returns and then dividing by the estimated
standard deviation of this cumulative return. This estimated standard deviation accounts for the
common estimation error in the calculation of the adjacent-day abnormal returns. After
23
calculating the standardized cumulative abnormal return in this way, we add this to the
standardized abnormal returns for all of the other event dates and calculate an associated p-value.
As we report in row 4 of Table 2, the resulting mean abnormal return is of similar magnitude to
the preceding estimate (-0.01253) and it remains significant at the 5% level (p=0.0439). This
estimation approach appears reasonably conservative to us, and thus in much of our subsequent
analysis we will treat this as the default or baseline specification. This evidence indicates fairly
strongly that competitors' stocks move in sympathy with asbestos firm stocks on days of
important asbestos news. This finding supports the debt-makes-you-aggressive hypothesis
outlined in the introductory sections of the paper and it constitutes the key result of our paper.
4.2 Robustness
In this subsection, we consider the robustness of our main empirical findings regarding
competitor abnormal returns. One concern with the preceding results is that they may reflect a
common industry effect that is partially driving the returns of both the asbestos firms and their
close competitors for reasons that have nothing to do with asbestos liabilities. While our event
selection criteria should alleviate much of this concern, given the surprising nature of our results,
it is worth considering this possibility further.14
To investigate, we consider a multifactor model in which competitor portfolio returns are
modeled over the pre-event estimation period as a linear function of the market portfolio return
and industry returns (plus a constant). In this modeling, we include separate terms for the returns
of each 4-digit SIC industry that at least one competitor portfolio firm identifies as its main
14
We have checked whether our sample of events would be altered if we require an asbestos firm's abnormal return
to cross the +/- 10% threshold with abnormal returns calculated using a single factor industry model in which the
sole factor is the return of the firm's 4-digit SIC industry (equal or value weighted). The final sample of events is
identical using this slightly modified selection criterion. Thus, small pieces of industry news do not appear to
materially affect entry of events into the sample, indicating that correlation between asbestos firms and competitors
induced by the +/- 10% selection condition is not a concern.
24
industry. In calculating industry returns, we exclude all sample asbestos firms and all firms in
the competitor portfolio. Using the derived loadings, we calculate the competitor portfolio
abnormal return on each event date and standardize these returns using the model residuals in an
analogous way to our earlier treatment.
As we report in rows 5 and 6 of Table 2, whether we use equal or value weighted
industry returns, the results with this modification are changed little from our earlier findings. In
particular, average competitor abnormal portfolio returns on the event dates are around -1.2%,
and these figures remain significant at the 5% level. The fact that our results change little with
the addition of industry controls indicates that common industry shocks that affect both asbestos
firms and competitor firms on sample event dates are a very unlikely explanation for our
findings.
In the case of the two event dates that are adjacent to one another (October 5 and 6,
2000), the competitor portfolios for these two dates are different since only one of the three
asbestos firms that satisfy the selection criteria on the first date also satisfies the selection criteria
on the second date. To check whether this has any effect on the results, we re-estimate the
baseline model by using a competitor portfolio derived from all three asbestos firms for both
dates. As we report in row 7 of Table 2, this alteration has no substantive effect on our
estimates.
One may be concerned that some of the events that we study are fairly close in time to
each other, and thus there is some overlap in the estimation period for the pre-event market
model estimates. This could possibly generate a small correlation in abnormal returns across
events because of common estimation error. To alleviate these concerns, we re-estimate our
baseline model by restricting the estimation period for each event to be disjoint from all other
25
events.15 As we report in row 8 of Table 2, the results with this modification are again
substantively unchanged. Average (sign adjusted) competitor portfolio returns remain negative
and similar in magnitude and statistical significance.
While the results we report above are for the ten largest competitors, a few asbestos firms
have considerably more than ten firms that are identified as competitors. The results when we
include all of these competitors rather than just the top ten are reported in row 9 of Table 2. The
results here are similar to, but weaker than, our earlier findings. The average competitor
portfolio abnormal return of -0.010 is similar in magnitude to the earlier estimates, but this
estimate is not quite significant at conventional levels (p=0.129). This weakening of the results
suggests that very small competitors are less affected than larger competitors when an asbestos
firm experiences a shock to its financial strength. We will search systematically for the presence
of this type of effect later when we consider cross-sectional variation in competitor abnormal
returns. At this stage, it is simply worth emphasizing that our results are stronger when we
restrict attention to competitors that are more similar in size to the asbestos-tainted firm.
Finally, one may be concerned that the average effect we detect for competitors is driven
by a small number of competitors with large reactions on asbestos event dates rather than broad
movement amongst many competitors’ stocks in the same direction on the event dates. To
investigate, we compute the fraction of competitor abnormal returns that are below zero on the
event date. We continue to use the same sign adjustment convention as earlier so that competitor
abnormal returns in good news asbestos events are multiplied by -1. As we report in the final
row of Table 2, fully two thirds (60 out of 90) of the competitor abnormal returns are below 0, a
15
We choose non-overlapping estimation periods by first selecting the 90 trading day period ending day -11 before
each event date. If this window does not overlap with the 90 day window for a prior event, we use it for the given
event date. If there is an overlap, we move backward in time and select the latest non-intersecting contiguous 90
day trading window that precedes the given event date. In implementing this algorithm, we start with the later
events and move backward in chronological order by event date.
26
fraction that is highly significant.16 These figures indicate that there is a broad movement in
competitor stocks in the same direction as the asbestos-tainted firm on sample event dates, thus
increasing our confidence in our main findings.
4.3 Extensions
The preceding results provide compelling evidence that competitor stocks tend to move
in the same general direction as asbestos firms in response to asbestos liability news. Thus, an
increase (decrease) in a debt-like liability appears to be bad news (good news) for a firm’s close
competitors. This is highly consistent with the debt-makes-you-aggressive scenarios outlined
earlier, an important finding in our view. It would be informative, however, to also understand
the precise channel through which a firm's liability structure may affect product market
outcomes. To provide some insights on this issue, we turn to exploring the determinants of the
cross-sectional variation in the magnitude of competitor returns in response to asbestos liability
shocks.
4.3.1 Event and asbestos-firm characteristics
One distinguishing feature of the events in our sample is the type of news released on the
event date. Sample events fall into three categories: bankruptcy events, judicial events, and
legislative events. As we detail in Appendix A, bankruptcy events tend to be earlier in the
sample period and are always bad news events. The judicial and legislative events fall later in
the sample period and are generally good news events. It is reasonable to suspect that
bankruptcy events, which are cases in which one asbestos firm goes bankrupt and other asbestos
16
This test necessarily assumes independence across competitor stocks and thus should be interpreted cautiously.
The results are quite similar if we consider the fraction of competitor stocks with an abnormal return that lies below
the stock's median abnormal return derived from the pre-event estimation period.
27
firms’ stock prices fall in response, may contain more marginal information about changes in the
probability of an eventual bankruptcy filing by a sample firm. Since many of the theories that
motivate our analysis depend importantly on the likelihood of an actual default, this would
suggest that the general effect we identify above for competitor stocks may be most pronounced
in the bankruptcy events.
To investigate, we calculate mean (sign adjusted) competitor portfolio returns using our
baseline modeling assumptions described earlier for each of the three types of events. As we
report in the first three rows of Table 3, there are in fact sharp differences between events. While
the mean competitor return is in all cases negative, it is small in magnitude and insignificant for
the judicial and legislative events. In contrast, for the bankruptcy events, the mean return is
large (-2.99%), highly significant (p=0.013), and significantly different from the other events.
Thus, it does appear that the general effect on competitors we identify earlier is closely related to
the likelihood of bankruptcy.
While all events in our sample represent substantial (+/- 10%) movements in an asbestos
firm's stock, there is some variation in the size of these returns. One might suspect that
competitor returns would react more in events in which the asbestos stock changes more sharply,
since presumably these are events with more significant news regarding expected future
liabilities. To investigate, we separate events into cases in which the absolute value of the
announcement return for the asbestos firm falls above or below the sample median. As we report
in rows 4 and 5 of Table 3, we find no significant difference in the mean return of competitor
portfolios sorted by this criterion.17 This indicates that the magnitude of the news released in the
event is not closely related to competitor returns. While we have no clear explanation for this
17
Since the magnitude of announcement returns may be affected by recent changes in market equity values, we have
repeated this analysis separating events by the change in the asbestos firm's dollar value of equity on the event date
divided by the end-of-prior-year book value of assets. We continue to find no significant difference in mean
competitor portfolio returns for subsamples of events sorted in this way.
28
result, it is certainly possible that the larger news events happen to occur in cases in which the
role of debt-like liabilities on product market competition is more limited.
Since most of the theories of financial strength and product market competition depend
on the strategic interaction between a limited number of competitors, it is possible that the effects
we identify depend on measures of market concentration. To investigate, we divide the asbestos
events into two groups based on whether the sales-based Herfindahl index of the asbestos firm's
4-digit SIC industry is above or below the sample median. In rows 6 and 7 of Table 3 we report
the mean competitor portfolio return for these two subsamples. As the figures reveal, there do
not appear to be any substantial differences between these groups. Thus, we are unable to
uncover evidence indicating that market concentration is an important determinant of the effect
of debt-like liabilities on expected competitive outcomes. However, this examination may suffer
from low power given the inherent noisiness in the Herfindahl index and the fact that we select
for investigation firms that are identified as closely competing with an associated asbestos firm.
Finally, given the prominent role of firm leverage in the theories that motivate our
analysis, we separate the sample based on whether the leverage of the asbestos firm falls above
or below the sample median. If asbestos news has a different impact on the likelihood of
bankruptcy depending on leverage, we may observe differences in competitor abnormal returns
for these two groups. As we report in the final two rows of Table 3, the figures do suggest some
possible differences. The mean competitor portfolio abnormal return is negative but small and
insignificant for asbestos firms that are highly levered. In contrast, the corresponding figure for
competitors of less levered asbestos firms is negative, substantial in magnitude (-1.66%), and
highly significant (p=0.015). However, the difference in abnormal returns between the two
groups of competitors is not significant at conventional levels (p=0.1776). We believe that a
reasonable interpretation of this evidence is that it hints at the possibility that changes in the
29
aggressiveness of product market behavior depend on a firm's initial level of leverage, but the
evidence cannot be viewed as conclusive.
Summarizing these findings, using several sample cuts related to the underlying theory,
we are unable to detect many differences in competitor returns across different sub-samples of
events. We caution, however, that many of these comparisons are likely to have limited power
given the small number of events in our sample. The one event characteristic that emerges as
particularly important is whether the news event driving the returns of the asbestos firm is related
to a bankruptcy filing. While the evidence is indirect, the relatively more negative competitor
returns in these events is broadly supportive of the idea that debt-like liabilities affect product
market behavior by increasing the likelihood of default.
4.3.2 Competitor characteristics
While we have a small number of sample events, the number of competitor-event dates is
substantially larger. Thus, we may be able to learn more about how debt-like liabilities affect
product market outcomes by examining the cross-sectional variation in individual competitor
returns. Our approach here is to regress individual (sign-adjusted) competitor abnormal event
returns against various competitor firm characteristics that may be expected to affect these
returns. As is common in these types of regressions, we choose to use a variance weighted-leastsquares (VWLS) estimation procedure, where the analytical weights employed in the regressions
are the inverse of the estimated standard deviation of the abnormal returns derived from the
corresponding market model. We first investigate one firm characteristic at a time, since this
approach should maximize our power to detect the presence of any underlying effects given the
small sample sizes and possible multicollinearity between different variables.
30
One possibly relevant characteristic is firm size. Larger firms may occupy relatively
more profitable niches and thus may be hurt more than others if an industry-price war
commences. Alternatively, small firms may have more valuable growth options that will expire
out of the money in an aggressive competitive environment. To investigate, we use the log of a
competitor firm's book assets as a measure of firm size and consider its role in explaining a
competitor's abnormal return on the event date. As we report in column 1 of Table 4, the
estimated coefficient on this variable is small in magnitude and insignificant. We have also
experimented with replacing this variable with the log of a firm's sales and with a measure of
relative size, namely the ratio of a competitor firm's sales to the corresponding asbestos firm's
sales. In all cases the explanatory variable remains small and insignificant. Thus, we uncover no
evidence that competitor returns are closely related to measures of size or relative size.
These findings on firm size cast some doubt on the asset market explanation for our
findings as outlined in the introductory sections above. If an increase in liabilities lowers the
likelihood that an asbestos firm makes an acquisition, this should be relatively worse news for
small competitors who are generally more likely to be acquisition targets (see Palepu (1986) and
Mikkelson and Partch (1989)). However, the negative and insignificant coefficient on firm size
in the model of Table 4 is completely inconsistent with this expectation.18
Another factor that may be relevant for competitor returns is their level of financial
leverage. More highly levered firms may be in a poorer position to capitalize on a competitor's
weakness and they may be more exposed to aggressive product market actions by other firms.
However, when we use leverage (debt/assets) as an explanatory variable as in column 2 of Table
4, the coefficient is small and insignificant. Thus, it does not appear that leverage is an important
18
Given the fact that almost all competitors are smaller than asbestos firms, it would appear unlikely that asbestos
firms would be on the selling end of a control transaction. Given the possibly unbounded nature of asbestos
liabilities, any control transaction combining an asbestos-tainted firm with one of the sample competitors may be
value destroying, in which case any asset-market explanation for our findings would also be ruled out.
31
determinant of the relative impact of the anticipated new product market environment on future
competitor profits.
We now turn to various measures of future growth prospects. If a competitor has strong
growth prospects, they may be better positioned to capitalize on the asbestos-firm's relative
weakness when liabilities increase sharply. In this case, competitor returns would be positively
related to growth prospects. Alternatively, growth options that are currently in-the-money may
become out-of-the money if product market competition becomes more intense. If this occurs,
growth option values could fall much more sharply than existing assets in place. Under this
scenario, competitor returns would be negatively related to growth prospects.
We use three different measures of growth to investigate this issue: Tobin's Q, the firm's
most recent three-year sales growth rate, and the firm’s most recent three-year growth in capital
expenditures. As we report in columns 3 and 4 in Table 4, the coefficients on Q and sales
growth are negative but insignificant. In contrast, the coefficient on capital expenditure growth
in column 5 is negative and significant at the 5% level. This provides some evidence that firms
that are aggressively exercising growth options are hurt relatively more by an anticipated
increase in aggressive product market competition.
Finally, we consider the role of competitor profitability on event returns. As we report in
column 6 of Table 4, profitability, as measured by return on assets, does not appear to be
significantly related to announcement returns. This suggests that any ensuing changes in the
product market behavior are not expected to differentially impact firms with higher or lower
profit rates.
For completeness, in column 7 we report results for a multivariate regression model that
includes all of the explanatory variables from the other columns. The results here are similar to
what we report above. The coefficients on all explanatory variables are insignificant, except for
32
the coefficient on capital expenditure growth, which maintains is negative sign and remains
significant at the 5% level. We have also experimented with measuring all of the explanatory
variables in Table 4 on an industry-relative basis by subtracting off industry medians. The
results with this modification, which we do not tabulate, are substantively that same as what we
report in the table.
The explanatory variables we use in Table 4 exhibit both within-event and across-event
variation. It is possible that one of these sources of variation matters more than the other, for
example it may be that a firm's leverage relative to its direct competitors is more important than
its leverage relative to competitors of other asbestos firms. To investigate, we have
experimented with modifying the Table 4 regressions by including event-specific fixed effects.
The results with this modification are generally similar to what we report in Table 4. In
particular, the coefficients that are insignificant in the table continue to be insignificant. In the
case of capital expenditure growth, the coefficient on this variable is negative but insignificant
using the event-specific fixed effects specification (p=0.264). This drop in significance is not
surprising, as there are only a small number of competitors for each event. The actual magnitude
of the estimated coefficient in the fixed-effects model is actually very similar to what we report
in column 5 of Table 4. Thus, our results offer little evidence that within-event variation in the
capital expenditure growth variable matters more or less than the across-event variation.
Consequently, we interpret the significant coefficient in column 5 of Table 4 as indicating the
presence of a general effect of capital expenditure growth on competitor returns in the presence
of an asbestos liability shock.
These findings on competitor returns are informative in several ways. First, they suggest
that there is a fairly broad effect in which a shock to the asbestos-liabilities of one firm is
transmitted to expected lower future profits for a handful of close competitors. In addition, the
33
fact that most competitor-specific characteristics do not explain the cross-sectional variation in
competitor returns casts doubt on some theories that may potentially explain our findings, most
notably models that emphasize the role of debt-like liabilities on anticipated future intra-industry
corporate control activity. The data do hint at the possibility that competitors with valuable
growth options are hurt more in these events, a finding that is consistent with the notion that
growth options may expire out-of-the money in a more intensely competitive environment.19
5. Conclusion
In this paper we provide evidence supporting the hypothesis that a firm's financial
strength is related to product market behavior. In particular, we find that unexpected exogenous
increases (decreases) in a firm's asbestos liabilities are interpreted by the market as negative
(positive) news for the firm's close competitors. Since asbestos liabilities are debt-like claims,
our findings support the general hypothesis that increases in debt can lead a firm to anticipated
aggressive future product market actions. The key distinguishing feature of our empirical
strategy is that we are able to exploit exogenous variation in a firm's liability structure arising
from decisions by external actors including courts, legislators, and other firms. We argue that
this feature represents an important improvement over studies that rely on a firm's endogenous
debt choices to study the role of financial strength on product market competition.
Our evidence indicates that something is expected to change in the product market when
the financial strength of a firm changes unexpectedly. However, we are unable to directly
identify the exact channel through which a change in liability structure leads to different product
19
We have also considered the possibility that firm’s that compete more closely with the asbestos firm may be more
affected by the asbestos news. In particular, using segment data, we calculate the fraction of a competitor’s assets
that are in the same 4, 3, or 2 digit industry as one of the asbestos firm’s segments. When we use these variables in
regressions analogous to the models reported in Table 4, they are in all cases insignificant. This may reflect the fact
that the Hoover’s information we use to identify close competitors may be substantially more refined than SIC code
information.
34
market behavior. We do find that competitor returns are particularly negative in events in which
one asbestos-tainted firm goes bankrupt and other asbestos stocks fall on the news. We suspect
that these events may contain more marginal information about changes in the probability of an
eventual bankruptcy filing by other asbestos firms. Thus, consistent with many models of debt
and product market competition, this finding suggests that non-equity liabilities affect a firm's
product market decisions by causing them to discount default states of the world.
Most of the firm and industry characteristics we consider including measures of leverage,
market concentration, and firm size, are not related to the magnitude of the response of
competitor stocks to asbestos liability shocks. There is information content in this evidence,
since it casts doubt on some explanations for our findings, including theories that emphasize the
role of debt-like liabilities on future within-industry acquisition activity. We do find that
competitors who are more aggressively increasing capital expenditures are more negatively
impacted by negative asbestos liability news. This is consistent with the possibility that the
value of future growth options falls more sharply than assets in place when price wars or related
aggressive behavior is anticipated.
While our study offers some interesting evidence that financial strength is related to
product market behavior, much work still remains to be done. In particular, it would be useful to
understand why the character of our results differs from many prior studies. One possibility is
that endogenous variations in debt are quite different from exogenous shocks to a firm's liability
structure. In addition, as Campello (2006) emphasizes, it is possible that the role of debt on
product market choices is non-monotonic, so the starting point for any debt or liability
perturbation will effect the direction of the observed effect. Finally, it seems entirely possible
that the role of debt on product market outcomes could be industry or context specific. Since
many studies consider either specific industries or small samples of firms, it is possible that these
35
sampling choices limit the generality of reported findings to other settings. This limitation, most
notably with regard to sample size issues, certainly also applies to our study. Hopefully, future
research will lead to a more complete understanding of the general circumstances under which
the results that we identify will be observed.
36
Appendix A: Sample description
In this appendix, we briefly detail the asbestos history of the seven asbestos-tainted firms in our
sample. In addition, we describe the eleven sample event dates on which at least one of these
firms experiences a sharp (+/- 10%) stock price revision related to asbestos news.
Sample companies
Crown Cork and Seal
In 1963, Crown Cork and Seal, a company in the consumer goods packaging business, bought
Mundet, a North Bergen, N.J. firm that made cork bottle caps and insulation products which
contained asbestos. Crown sold off the insulation business part of Mundet, which it never
operated, just 93 days later. However, as a result of this short-lived ownership, Crown Cork
eventually paid millions of dollars to settle some 70,000 asbestos-related claims, bringing the
company to the edge of bankruptcy.
Federal Mogul
Federal-Mogul Corp., a Michigan based automotive parts supplier, grew rapidly in the late 1990s
in part through aggressive acquisitions. The company was never in the asbestos business, but in
1998 it bought T&N PLC of Manchester, England, an auto-parts maker that once used asbestos
extensively in a separate building-supplies business. At the time of the acquisition, FederalMogul set aside $2.1 billion for anticipated future asbestos claims to supplement its insurance
coverage. The company experienced a higher than expected number of claims, which eventually
forced the firm to file for reorganization under Chapter 11 in October 2001.
Foster Wheeler
Foster Wheeler, a New Jersey based company, was in the business of building refineries and
power plants. The asbestos lawsuits against the company held it responsible for asbestos related
injuries suffered by workers who used or were exposed to asbestos before and during the 1970s.
Georgia Pacific
Georgia-Pacific, a paper and building products company, discontinued using asbestos in the
manufacturing of all of its products in 1977. Beginning in the mid 1980s, lawsuits were filed
against Georgia-Pacific at an approximate annual rate of 40,000 by plaintiffs alleging that they
had suffered lung and other diseases as a result of exposure to Georgia-Pacific products.
Halliburton
Halliburton, the world’s largest oilfield services company, faced legal claims related to the use of
asbestos made by a former subsidiary, Harbison-Walker Refractories. The Harbison claims date
back to before 1992, when Dresser Industries, which Halliburton acquired in 1998, spun off
Harbison, a refractory manufacturing business. Under the terms of the spin-off, Dresser and
Harbison agreed to allocate responsibilities for asbestos claims related to the refractory business
between them. Dresser agreed to retain claims filed prior to the spin-off, while Harbison agreed
to assume claims filed after the spin-off and to indemnify and defend Dresser against those
claims. Eventually these claims led to a substantial liability for Halliburton.
37
Owens-Illinois
Owens-Illinois, a glass and plastic bottle maker, faced lawsuits from individuals claiming that
they developed health problems from inhaled asbestos fragments generated from Owens-Illinois'
pipe insulation products which the company produced from 1948 to 1958. By the end of 2000,
223,000 claims had been filed against Owens-Illinois seeking over $1 billion in damages.
USG Corp
USG Corp., the world's largest supplier of gypsum wallboard, faced asbestos litigation arising
from the firm’s U.S. Gypsum subsidiary which had incorporated asbestos into its joint compound
products from the 1930s through the 1970s. Facing 50,000 asbestos-related personal injury
claims since 1994, and having paid more than $450 million for litigation, the company filed for
reorganization under Chapter 11 in June 2001.
Sample Events
(sample firms for each event are denoted in bold type)
Event type: Bad news; Bankruptcy
Event date 1: October 5, 2000
Owens Corning Fiberglass, a former manufacturer of asbestos products, filed for Chapter 11
bankruptcy citing asbestos claims. Owens Corning had been battling asbestos litigation for
nearly twenty years. On the announcement of this news, virtually every stock tainted by asbestos
litigation was negatively affected. Three of these reactions (Federal Mogul, Owens-Illinois,
and USG Corp.) are large enough and clean enough to survive our data filters.
Event date 2: October 6, 2000
Event type: Bad news; Bankruptcy
Asbestos companies continued to be negatively affected following the Owens Corning
announcement of Chapter 11 from the previous day (see event date 1 above). USG Corp is the
only asbestos company represented in the sample on this event date.
Event date 3: April 2, 2001
Event type: Bad news; Bankruptcy
W. R. Grace and Co., a former manufacturer of asbestos based products, filed for Chapter 11
bankruptcy citing asbestos claims. W. R. Grace had been battling asbestos litigation for nearly
fifteen years. On the announcement of this news, several asbestos-tainted stocks were negatively
affected. Two of the reactions (Owens-Illinois and USG Corp.) are large enough and clean
enough to survive our data filters.
Event type: Bad news; Bankruptcy
Event date 4: June 25, 2001
USG Corp., a packaging products company and former manufacturer of asbestos based products,
filed for Chapter 11 bankruptcy citing asbestos claims after battling asbestos litigation for nearly
fifteen years. On the announcement of this news, Foster Wheeler stock was negatively affected,
along with the stocks of several other companies involved in asbestos litigation. Only the Foster
Wheeler return is uncontaminated and large enough to satisfy our sampling criteria.
Event date 5: December 12, 2001
Event type: Good news; Legislative
The State Senate of Pennsylvania gave its final approval to a law limiting the asbestos liability of
Crown Cork and Seal, a company domiciled in that state. Although news of the passage of this
bill through the senate was keenly followed, the news of the final approval resulted in a large
38
positive return to shares of Crown Cork on this date. Eventually the Pennsylvania Supreme
Court would rule this law unconstitutional and reinstate the company’s large asbestos liabilities.
Event type: Good news; Judicial
Event date 6: January 9, 2002
A U.S. district court in Louisiana dismissed a declaratory judgment action filed by insurers
against McDermott International, a company with substantial asbestos liabilities at the time.
Upon announcement of this news (good for asbestos companies and bad news for their insurers),
the stock price of Halliburton Industries appreciated significantly.
Event date 7: January 25, 2002
Event type: Good news; Legislative
News reports speculate that ongoing discussions at the level of the Senate Judiciary committee
would result in a legislative cap on asbestos liabilities. Stocks of many asbestos-tainted
companies, among them Crown Cork and Seal and Foster Wheeler, rose significantly.
Event date 8: February 15, 2002
Event type: Good news; Judicial
A court rules to stay all asbestos claims against Dresser Industries arising from a dispute with a
former subsidiary that had been spun-off. On the announcement of this news, the shares of
parent company Halliburton appreciated significantly.
Event date 9: November 29, 2002 Event type: Good news; Judicial
Sealed Air Corp., a company involved in asbestos litigation and looking to merge with W. R.
Grace, announced a settlement of $500 million with plaintiffs for all asbestos-related claims. On
the announcement of this news, shares of Georgia Pacific Corp. and Halliburton Industries
rose, along with several other asbestos-tainted firms that did not enter the final sample.
Event date 10: June 24, 2003
Event type: Good news; Legislative
Stock prices of several asbestos-tainted companies rose after the Senate Judiciary Committee
announced that it had reached agreement on medical criteria in asbestos related suits. Among the
firms that experienced an increase in stock price, Crown Cork and Seal and Owens-Illinois are
represented in the sample.
Event type: Bad news; Legislative
Event date 11: July 10, 2003
The Senate judiciary committee approved a asbestos litigation reform bill by a slim majority of
10-8. News reports indicate high levels of uncertainty about the bill's fate on Senate floor, which
led to decreases in the stock prices of several asbestos-tainted companies. Of these, the returns
of Owens-Illinois survived the filters required to be in our sample.
39
Appendix B: Additional details on estimation and test statistics
Initial approach: no adjustment for contiguous dates
Our estimation and testing strategy borrows from the standard event study methodology
described in Campbell, Lo, and MacKinlay (1997) [hereafter, CLM] and we use parallel notation
here. For each event date, we choose an estimation window of L1=250 days ending 11 days prior
to the event date. We exclude from the estimation period any prior asbestos event dates. For
each sample observation j (i.e., a competitor firm or competitor portfolio on an identified
asbestos event date), we estimate a market model over the 250 day estimation window. We then
calculate the abnormal return for the observation, ARj, on the event date in the standard way by
using residuals from the market model predicted value. Under standard assumptions, the variance
of this abnormal return is (CLM, equation 4.4.9)
σ 2j = σ ε2 [1 + X *j ( X 'j X j ) −1 X *'j ],
(A1)
j
where Xj is a 250×2 matrix consisting of ones in the first column and CRSP value-weighted
market index returns for the 250 estimation days in the second column, and the starred vector
represents these values on the actual event date. To obtain an estimator of the above
variance, σˆ 2j , we substitute the following estimator of σ ε2j into equation (A1) above
σˆ ε2 =
j
1
εˆ 'j εˆ j ,
L1 − 2
(A2)
where ε̂ j is the vector of market model residuals over the estimation period. Using the identified
abnormal return and the corresponding estimate of its standard error, we create a standardized
abnormal return, SARj, for the observation given by SARj = ARj / σˆ j . As described in the text, we
sign adjust the ARj and SARj by multiplying the value by -1 if the event in question is a “good
news” event (see Section 4.1. for details). We denote these sign-adjusted abnormal returns and
standardized abnormal returns as AARj and ASARj respectively. To construct test statistics for the
null hypothesis that event abnormal returns are zero, we aggregate across events j=1,2,…,N by
calculating the J2 statistic given by
⎛ N ( L1 − 4) ⎞
⎟⎟
J 2 = ⎜⎜
−
2
L
1
⎝
⎠
1/ 2
ASAR , where ASAR =
1
N
N
∑ ASAR
j =1
j
.
(A3)
Under the null hypothesis of zero abnormal returns, this J2 statistic will be distributed
asymptotically according to a standard normal distribution. The mean abnormal returns we
report in models 1-3 of Table 2 and models 4-9 of Table 3 refer to the mean sign-adjusted
abnormal return ( AAR ) and the associated p-value is derived from the corresponding J2 statistic
given in (A3).
Refined approach: adjusting for contiguous dates
40
For all other models in Tables 2 and 3 that include the two contiguous sample event dates
(October 5 and 6 of 2000), we adjust for possible correlation in the abnormal returns on these
two dates. We note that the competitor portfolios for these dates, P1 and P2, are different. We
estimate the abnormal event date portfolio returns, AR1 and AR2, in the standard way by running
a one-factor market model for each portfolio using a common estimation period of L1=250 days.
We calculate a composite abnormal return for these two contiguous event dates by summing the
above two abnormal returns. We obtain an estimate of the composite variance of this composite
abnormal return over the two day interval as σ 2j = V11 + 2V12 + V22 where
[
]
[
]
[
]
V11 = σ ε21 1 + X1* ( X ' X) −1 X1*' , V22 = σ ε22 1 + X *2 ( X ' X) −1 X *'2 , and V12 = σ ε1 ,ε 2 X1* ( X ' X) −1 X *'2 . (A4)
In these expressions X is a 250×2 matrix consisting of ones in the first column and market index
returns for the (common) 250 estimation days in the second column, while the starred vectors
represent these values on the two (contiguous) event dates. Note that this composite variance
includes a component due to cross-correlation between portfolios P1 and P2. We derive a
consistent estimator of this composite variance by using the analogs of (A2) for the two variance
terms and the following estimator for the cross-correlation
1
σˆ ε1 ,ε 2 =
εˆ 1' εˆ 2 ,
(A5)
L1 − 2
where ε̂ 1 and ε̂ 2 are the vectors of market model residuals over the estimation period for
portfolios P1 and P2. We sign-adjust the composite return for these contiguous events and divide
this sign-adjusted return by the estimated composite standard deviation to create an adjusted
standardized composite return for the two-day contiguous event. We treat this adjusted
composite return (adjusted standardized composite return) as the AARj (ASARj) for the two-day
event period and aggregate this with the other event dates in the manner described earlier.
Modification for multifactor models
The multifactor models we utilize in rows 5 and 6 of Table 2 are a straightforward extension of
the single-factor market model and all calculations and test statistics are constructed in an
analogous manner to what we describe above. In this modeling, we include separate terms for
the returns of each 4-digit (SIC) industry that at least one competitor portfolio firm identifies as
its main industry. In calculating industry returns, we exclude all sample asbestos firms and all
firms in the competitor portfolio. The matrices in the formulas described above are extended to
include not only a constant and the market return, but also the returns for each of the industries to
which a competitor portfolio firm belongs. The variance formulas given above for the
contiguous event dates must be modified to reflect the fact that the included industries for the
two dates differ and the new terms in the calculation are given by V11 = σ ε21 1 + X1* ( X1' X1 ) −1 X1*' ,
[
]
[
[
]
]
V22 = σ ε22 1 + X *2 ( X '2 X 2 ) −1 X *'2 , and V12 = σ ε1 ,ε 2 X1* ( X1' X1 ) −1 ( X1' X 2 )( X '2 X 2 ) −1 X *'2 . In addition,
since there is no unambiguous degrees of freedom correction for estimating the cross-covariance
for the contiguous dates, given the differing number of explanatory variables, we use L1 =250
with no degree of freedom correction to estimate all components of the composite variance. For
consistency, in the rows for these models only, we make no degrees of freedom correction for the
41
standard errors for any of the other event dates. We then appeal to asymptotics and use the test
statistic J 2 = N 1 / 2 ( ASAR) ~ N (0, 1) to construct p-values for the mean event-date abnormal
return and we report these figures in rows 5 and 6 of Table 2.
In the interest of conservatism, we have also derived a slightly more conservative degrees
of freedom correction for the counterpart to the J2 statistic in these multifactor models. In
particular, we use an analog of equation (A5) to estimate the cross-covariance by subtracting in
the denominator from L1 an adjustment k, where k=max(k1,k2) if εˆ 1εˆ 2 > 0 , and k=min(k1,k2)
otherwise. This adjustment results in the most conservative estimate of the standard error, while
taking into account the contiguous nature of two of the event dates. In this more conservative
approach, we derive the J2 statistic by explicitly recognizing that this particular event has an
L1 − k
( L − 2)
abnormal return with variance
instead of the usual 1
. This modification has
L1 − k − 2
L1 − 4
only a negligible effect on the results we report. In particular, the p-value in row 5 (row 6) of
Table 2 increases only slightly from 0.0484 to 0.0509 (0.0438 to 0.0462).
42
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46
Table 1: Characteristics of Asbestos Firms
Relative to
Median
Competitor
(3)
9.025
10.521
8.530
1.138
2.087
1.212
2.152
0.719
0.977
1.168
1.498
1.395
-
Relative to
Sum of all
competitors
(4)
1.329
0.763
0.702
-
Percent of
competitors
below
(5)
92.857
100.000
88.095
61.905
57.143
47.619
54.762
52.381
47.619
59.524
64.286
71.429
-
Mean
Median
(1)
(2)
Variable
Book Assets
9,223.66
10,728.00
Sales
8,538.63
6,487.50
Market Equity
5,285.85
2,709.19
Industry Sales Rank
74.757
85.800
Capital expenditure growth
0.481
0.204
Book asset growth
1.234
0.525
Sales growth
0.615
0.390
Return on assets
0.093
0.069
Tobin's Q
1.316
1.166
(Market equity)/(Book equity)
1.972
1.944
Debt/(Book assets)
0.367
0.381
Debt/(Debt+Book equity)
0.603
0.650
Absolute Value Event Return
0.152
0.140
(Abs. Value of change in equity value)/
0.057
0.048
(Debt plus book equity)
Note.- All firm characteristics are calculated as of the end of fiscal 1999. The sample includes 17 asbestos events on
11 distinct event dates with 7 distinct sample firms having a total of 90 competitors. Book assets are given by
Compustat data item #6. Sales are given by item #12. Industry sales rank is the percentile ranking of the firm's
sales relative to all firms in the same 4-digit SIC code in the same year. Market equity is defined as (item #25 x item
#199). Book equity is defined as item #60. Tobin's Q is defined as (book assets minus book equity plus market
equity) / (book assets). Debt is defined as debt in current liabilities (item #34) plus long term debt (item #9).
Growth rates are calculated as (current year value minus lagged three year value)/(lagged three year value). Return
on assets is defined as operating income after depreciation divided by end of year book assets. Event return
information is calculated based on the absolute value of the raw daily return for each asbestos event in the sample
The change in equity value variable is calculated using the absolute value of the raw change in equity value for the
asbestos firm on the event date normalized by the book value of debt plus equity as of the end of fiscal 1999. The
figures in column 1 (column 2) are mean (median) values for all asbestos firm treating each firm that has at least one
asbestos event in the final sample as a single observation. The figures in column 3 are constructed by first
calculating the median value for an asbestos firm's set of competitors for the indicated variable and then calculating
the ratio of the asbestos firm's value for this variable relative to the competitor median. If one of these ratios has a
negative denominator the ratio is set equal to the maximum of the rest of the sample. The statistic we report in
column 3 is the median level of this relative-to-competitor ratio. In column 4 we report the median level of the ratio
of each asbestos firm's value for the indicated variable relative to the sum of the values of the firm's identified
competitors. In column 5 we report the fraction of all competitors across all asbestos firms that have a value for the
indicated variable that lies below the corresponding value for the asbestos firm.
47
Table 2: Competitor Returns and Asbestos News
Model
Mean Abnormal
Return
p-value
-0.00891
0.0024
Individual competitors,
simple market adjusted returns
-0.00824
0.0052
Competitor portfolios,
market model abnormal returns
-0.01139
0.0280
-0.01253
0.0439
-0.01233
0.0484
-0.01211
0.0438
-0.01279
0.0181
Panel A: Alternative models of abnormal returns
(1)
Individual competitors,
market model abnormal returns
(2)
(3)
(4)
(5)
(6)
Competitor portfolios,
market model abnormal returns,
adjusting for correlation on October 5/6 [Baseline Model]
Competitor portfolios,
multifactor model of abnormal returns including
equally-weighted industry factors
Competitor portfolios,
multifactor model of abnormal returns including
value-weighted industry factors
Panel B: Robustness tests using baseline model
(7)
Use all October 5, 2000 competitors for October 6, 2000
(8)
Use 90-day non-overlapping estimation windows
-0.01307
0.0379
(9)
Use all competitors by dropping top 10 restriction
-0.01004
0.1293
Percent Negative
p-value
0.6667
0.0021
Panel C: Robustness test using signs
(10)
Individual competitors,
market model abnormal returns
Note.- This table reports abnormal returns for competitors of asbestos firms for dates on which the asbestos firm
they compete with experiences a sharp (+/- 10%) revision in its stock price because of exogenous news regarding
asbestos liabilities. If liabilities increase and thus the asbestos stock falls in value, we directly use the competitor
abnormal returns in our calculations. If liabilities decrease and the asbestos stock rises in value, we sign adjust the
competitor abnormal returns by multiplying these returns by -1. In rows 1 and 2 we report the mean (sign adjusted)
abnormal return across all event dates treating each asbestos firm competitor as a single independent observation. In
rows 3-9 we report the mean (sign adjusted) abnormal return across all event dates treating all competitors of
asbestos firms on a given date as a single portfolio. The portfolios are formed by equally weighting the competitors
of each asbestos firm and then, where necessary, equally weighting these portfolios across asbestos firms if multiple
asbestos firms are affected on a given day. All p-values for mean returns are calculated by dividing daily abnormal
return estimates by the corresponding estimated standard deviation from the market model, simple market
adjustment model, or multifactor model and then aggregating across events. In rows 4-9 we first aggregate the
abnormal returns of the two adjacent sample event dates before standardizing the return for this event. All abnormal
return estimates except those in rows 2, 5 and 6 are derived from a one-factor value-weighted market model
estimated over days -260 to -10. The estimates in row 2 are for simple market-adjustment model in which we
subtract the market return from the competitor’s return on the event date. The estimates in rows 5 and 6 are derived
from a multifactor model that augments the market model with equally-weighted (row 5) or value-weighted (row 6)
industry controls. The estimates in rows 7-9 use the same modeling specification as in row 4 (the baseline model)
but with the indicated alternative treatment of the data (additional details in the text). In row 10 we report the
fraction of (sign adjusted) abnormal returns that are negative across all event dates treating each individual
competitor-event date as a single independent observation. The associated p-value is from a simple two-sided
binomial test. Additional details on the sample of events and estimation procedures are reported in Appendix B.
48
Table 3: Competitor Returns for Selected Subsamples
Subsample
(1)
Bankruptcy Events
(2)
Judicial Events
(3)
Legislative Events
(4)
Events with abs. value asbestos firm return > median
(5)
Events with abs. value asbestos firm return ≤ median
(6)
Events with Herfindahl index > median
(7)
Events with Herfindahl index ≤ median
(8)
Events with asbestos firm leverage > median
(9)
Events with asbestos firm leverage ≤ median
Mean
Abnormal
Return
Return = 0
p-value
Return equal
across groups
p-value
-0.02991
0.0134
0.0483
-0.00919
0.2598
0.9764
-0.00201
0.9460
0.0641
-0.01266
0.0806
-
-0.00935
0.1709
0.7098
-0.01381
0.0529
-
-0.00834
0.2335
0.5050
-0.00281
0.6083
-
-0.01658
0.0150
0.1776
Note.- This table reports abnormal returns for competitors in selected sub-samples of events in which an asbestos
firm experiences a sharp (+/- 10%) revision in its stock price because of exogenous news regarding asbestos
liabilities. If liabilities increase and thus the asbestos stock falls in value, we directly use the competitor abnormal
returns in our calculations. If liabilities decrease and the asbestos stock rises in value, we sign adjust the competitor
abnormal returns by multiplying these returns by -1. The events in row 1 are cases in which an asbestos-tainted firm
files for bankruptcy and this in turn causes other asbestos stocks to fall in value. The events in row 2 (row 3) are
cases in which a liability ruling by a judge or court (action on a piece of liability legislation) sharply impacts the
market's assessment of the value of one more asbestos firms. Events in row 4 (row 5) are cases in which the
absolute value of the raw percentage change in an asbestos firm's stock on the event date is greater than (less than or
equal to) the sample median. Events in row 6 (row 7) are cases in which the sales-based Herfindahl index of the
asbestos firm's 4-digit Compustat SIC industry in the fiscal year prior to the event is greater than (less than or equal
to) the sample median. Events in row 8 (row 9) are cases in which leverage (book debt/book assets) of the asbestos
firm in the fiscal year prior to the event is greater than (less than or equal to) the sample median. In all rows we
report the mean (sign adjusted) abnormal return across all events that satisfy the indicated selection criterion. In
rows 1-3 we treat all competitors of all asbestos firms on a given event date as a single portfolio. In rows 4-9 we
treat the competitors of each asbestos firm as a separate portfolio and include the return of the portfolio if the event
satisfies the indicated selection criterion. In rows 1-3 portfolios are formed by equally weighting the competitors of
each asbestos firm and then, when necessary, equally weighting these portfolios across asbestos firms if multiple
asbestos firms satisfying the selection criterion are affected on a given day. In rows 4-9 each competitor portfolio is
formed by equally weighting the competitors of a single asbestos firm. All portfolio abnormal return estimates are
from a one-factor value-weighted market model estimated over days -260 to -11. All p-values for whether (signadjusted) mean returns equal zero are calculated by dividing daily abnormal return estimates by the corresponding
estimator of the standard deviation from the market model and then aggregating across events. If a model in rows 13 contains the two adjacent event dates in the sample (Oct. 5 and 6, 2000), we first aggregate the competitor
abnormal returns of the two adjacent dates before standardizing the return for these dates. The p-values for returns
being equal across groups tests whether the mean return for competitor portfolios in the indicated row is equal to the
mean return for competitor portfolios for all portfolios that are not included in that row. This p-value is derived
from a simple t-test comparing (sign-adjusted) standardized abnormal returns assuming equal variances.
49
Table 4: Competitor Event Returns and Competitor Characteristics
(1)
Log(book assets)
(2)
(3)
(4)
(5)
0.01497
(0.0198)
0.00243
(0.0068)
Tobin's Q
-0.00158
(0.0103)
-0.00629
(0.0064)
Sales growth
0.00096
(0.0074)
-0.00717 **
(0.0035)
Capital expend.
growth
Return on assets
(ROA)
Number of
Observations
Chi-sqaured
goodness of fit
(p-value)
(7)
-0.00068
(0.0024)
0.01079
(0.0172)
Debt/Book assets
Constant
(6)
-0.00058
(0.0018)
-0.0118**
(0.0054)
0.00872
(0.0313)
0.08547
(0.0665)
-0.00531
(0.0134)
-0.01314
(0.0064)
-0.01283
(0.0100)
-0.00862
(0.0028)
-0.00905
(0.0027)
-0.01032
(0.0041)
90
90
89
86
88
90
86
0.10
(0.7512)
0.39
(0.5308)
0.13
(0.7219)
0.97
(0.3239)
4.17
(0.0411)
0.08
(0.7804)
7.38
(0.2868)
-0.01533
(0.0177)
Note.- Each column reports estimates from a regression model in which each competitor's (sign adjusted) abnormal
return on the event date is the dependent variable. The sign adjustment multiplies the competitor's abnormal return by -1
for events in which their asbestos-tainted competitor experiences good asbestos news. In bad news events no adjustment
is made. The sample includes the abnormal returns of all competitors on all sample dates in which their asbestos-tainted
competitor experiences a sharp (+/- 10%) revision in its stock price because of exogenous news regarding asbestos
liabilities. All estimates are VWLS estimates with analytical weights being the estimated standard deviations of
abnormal returns from the underlying market model for each competitor estimated over days -260 through -11. Standard
errors are reported in parentheses under each coefficient estimate. All explanatory variables are based on characteristics
of the competitor derived from Compustat data for the last full fiscal year before the event date. All explanatory
variables are winsorized at the 1% and 99% points. Debt is defined as debt in current liabilities (item #34) plus long
term debt (item #9). Tobin's Q is defined as (book assets minus book equity plus market equity)/(book assets). Sales
growth is salest - salest-3 / salest-3 where year t is the fiscal year immediately prior to the event date and all sales figures
are inflation adjusted. Capital expenditure growth is defined in an analogous manner. ROA is defined as operating
income after depreciation divided by end of year book assets.
*Significant at the 10% level
**Significant at the 5% level
***Significant at the 1% level
50