Download Corporate Catastrophes, Stock Returns, and Trading Volume

Corporate Reputation Review
Volume 2 Number 4
Corporate Catastrophes, Stock Returns, and
Trading Volume
Rory F. Knight and Deborah J. Pretty
Templeton College, University of Oxford
INTRODUCTION
Catastrophes are phenomena which provide
a unique opportunity to evaluate how
®nancial markets respond when major risks
become reality. Recent empirical studies
have examined the e€ects of fortuitous
losses on ®rm value. Shelor, Anderson &
Cross (1992), Lamb (1995), and Angbazo &
Narayanan (1996) consider the impact of
natural disasters upon property-liability
insurers' stock values, while Davidson,
Chandy & Cross (1987) and Marcus,
Bromiley & Goodman (1987) study
industry-speci®c corporate catastrophes.
Sprecher & Pertl (1983) focus on the impact
of large losses on the stock price of a crosssection of industries in the United States.
This paper extends the literature to a
rede®nition of catastrophes, takes it international, and attempts to model the impact
on stock price, stock price volatility, and
trading volume. The preliminary ®ndings
indicate that the impact of catastrophes on
stock returns is not strongly in¯uenced by
the existence of catastrophe insurance.
Catastrophes appear to a€ect returns in
rather complex ways which seem to result
in a re-evaluation of management Ð
which may be positive or negative. This
result is largely consistent with modern
®nancial theory which suggests that stock
valuation is based on ex ante risk assessments in the context of large portfolios. In
such a setting, much of the idiosyncratic
risk associated with a particular company is
diversi®ed away. Further hedging of risk
by management may be redundant from
the view of shareholders.
This paper aims to identify the impact of
catastrophes by focusing on 15 major corporate catastrophes and tracing their impact on
stock returns and trading volume. As would
be expected, in all cases the catastrophe had
a signi®cant negative initial impact on stock
returns. Figure 1 shows the average impact
of all the catastrophes on stock returns.
However, after a sharp initial negative
impact amounting to almost 8 per cent of
stock value, there is on average an apparent
full recovery in just over 50 trading days.
This suggests that the net impact on stock
returns is negligible. However, as will be
demonstrated, the ability to recover the lost
shareholder value over the long term varies
considerably among ®rms.
In addition to the direct impact on stock
price, catastrophes also have a highly
signi®cant impact on the level of trading in
shares. Figure 2 shows that trading in
shares in these corporations is more than
four times the usual level in the days
immediately after the catastrophe. On
average, trading settles down to normal
levels around a month afterwards. Thus,
the immediate and negative impact on
value not surprisingly coincides with
abnormally high levels of trading activity.
By contrast, the drift back in stock value
occurs at a normal level of trading activity.
Figure 3 illustates the impact of catastrophes on the volatility of stock returns
indicating that, although volatility increases
initially, it does settle down soon after the
event. This result suggests that no signi®cant sustained impact on stock price volatility is induced by catastrophes.
Corporate Reputation Review,
Vol. 2, No. 4, 1999, pp. 363±378
# Henry Stewart Publications,
1363±3589
Page 363
Corporate Catastrophes, Stock Returns, and Trading Volume
Figure 1
Impact of Catastrophes on Stock Returns
Figure 2
Impact of Catastrophes on Stock Trading Volume
OVERVIEW OF LITERATURE
Sprecher & Pertl (1983) examine a crosssection of industries in the United States,
and measure the e€ect of large losses on
stock prices, for the period 1969 to 1978.
The authors de®ne large losses as being at
least 10 per cent of the ®rm's net worth,
and ®nd that, although there is an initial
Page 364
negative impact of 4 per cent on stock
prices, the impact is not sustained beyond
two or three days after the event. Marcus,
Bromiley & Goodman (1987) ®nd similar
results in their study of automobile safety
recalls. In addition, Marcus et al. measure
stock market reactions to Union Carbide's
(1984) poisonous gas leak in Bhopal, India.
Knight and Pretty
Impact of Catastrophes on Stock Price Volatility
Following the Bhopal disaster, Union
Carbide stock su€ered dramatic adverse
performance which continued until
rumours of takeover began to restore the
price. Davidson, Chandy & Cross (1987)
focus their attention on the airline industry.
No signi®cant long-term abnormal returns
are found for airlines around the date of an
air crash. They suggest that the airline
industry may have some unique characteristics which make these results industryspeci®c.
More recent studies by Shelor, Anderson
& Cross (1992) and Lamb (1995) examine
the impact of a natural disaster on
property-liability insurers' stock values.
Shelor, Anderson & Cross evaluate the
price reaction of the 1989 Californian
earthquake, and ®nd that the bene®t to
insurers from increased demand outweighs
the discount investors may attribute to
insurers for incurring sudden claims
payments. In contrast, Lamb estimates the
e€ect of Hurricane Andrew (1992) on
insurers' stock returns, and demonstrates
that the price impact varies, depending on
the insurer's book of business and the
implied exposure in policies underwritten.
The stockmarket is shown to classify insur-
Figure 3
ance companies correctly, according to
their exposure; exposed ®rms su€er signi®cant and adverse abnormal returns, while
unexposed ®rms experience no such market
valuation discount following the hurricane.
Angbazo & Narayanan (1996) also examine
the e€ect of Hurricane Andrew on insurers'
stock returns. They con®rm the earlier
negative stock return results reported by
Lamb but, in contrast, do not ®nd eciency by the stockmarket in discriminating
between exposed and unexposed ®rms.
DATA AND METHODS
We selected a set of corporate catastrophes
based on four criteria: the disasters are
man-made as opposed to natural; each
involves a publicly-quoted company; each
has received headline coverage in world
news; and each has occurred since 1980.
Six of the disasters pro®led were in the
oil/petrochemical/chemical industries, and
six were product-related incidents (see
Table 1). Overall, four events were attributable to deliberate acts of tampering or
terrorism, and sabotage was suspected in
two of them. Eight of the 15 catastrophes
occurred during the period 1988±90,
which is consistent with the results of
Page 365
Corporate Catastrophes, Stock Returns, and Trading Volume
Table 1: Catastrophe portfolio
Date
Company name
Catastrophe
Type of Catastrophe
Financial
estimate (US$)*
09/30/82
12/03/84
02/11/86
11/01/86
03/06/87
05/05/88
07/06/88
12/21/88
03/24/89
09/19/89
10/23/89
02/10/90
07/17/90
04/10/92
08/25/93
Johnson & Johnson
Union Carbide
Johnson & Johnson
Sandoz
P&O
Shell Oil
Occidental
Pan Am
Exxon
Upjohn
Phillips Petroleum
Source Perrier
Eli Lilly
Commercial Union
Heineken
Tylenol
Bhopal
Tylenol
Rhine
Zeebrugge
Norco
Piper Alpha
Lockerbie
Valdez
Halcion
Pasadena
Benzene
Prozac
Baltic Exchange
Glass
Product tamper & recall
Liability
Product tamper & recall
Fire & ollution
Liability
Explosion ®re
Fire & explosion
Terrorism
Pollution
Liability
Explosion & ®re
Product recall
Liability
Terrorism
Product recall
150m
527m (min.)
150m
85m
70m (min.)
706m
1,400m
652m
11,500m (max.)
23m
1,300m
263m
0
2,170m
10±50m
*These estimates represent those publicly available at the time of the loss
Lloyd's of London, the largest single
provider of catastrophe insurance worldwide. Eight of the companies are American
and the remaining six are European Ð
British, Dutch, French and Swiss. Thus,
this catastrophe portfolio is international
and constitutes a representative sample
across industries and across the major
classes of loss worldwide.
STOCK PRICE IMPACT
In order to isolate the e€ect of the catastrophe on stock price, it is necessary to
rule out the e€ect of other events that
may impact stock prices simultaneously.
This process involves the estimation of socalled abnormal returns for a period immediately after the catastrophe, followed by
the alignment of these abnormal returns on
the catastrophe (day 0) and an averaging
across the total sample. These average
abnormal returns are then accumulated
over what is now catastrophe time, resulting in a set of portfolio returns from day 0
known as cumulative abnormal returns
(CAR).
Page 366
Figures 1 and 4 show the CARs for
portfolios of the total sample and the portfolios of recoverers and non-recoverers.
The CAR charts re¯ect the impact on
stock returns in percentage terms.
More formally, the abnormal return, on
stock i on day t, is de®ned as:
Uit = Rit ± E(Rit)
(1)
where:
Rit = the return on stock i on day t.
Rit = log(Pit / Pit-1)
(2)
E = the expected value operator.
Pit = market price of stock i on day t.
The expected return is modelled, using the
risk-adjusted market model of the form:
E(Rit) = ai + biRmt
(3)
Rmt = the return on the market
portfolio on day t.
(3)
where:
The model parameters, ai and bi, represent
Knight and Pretty
the intercept and slope coecient respectively, estimated from a market model
regression of the following form:
metric to evaluate the impact on trading
volume is de®ned relative to the average
trading volume in the stock.2 Formally;
Rit = ai + biRmt + eit
UTVit= TVit/ATVi
(4)
The risk-adjustment procedure follows the
work of Sharpe (1964) and Lintner (1965)
on the well-known Capital Asset Pricing
Model (CAPM). The systematic risk parameter, beta, is calculated for each individual
company, and is equal to the slope coecient in a time series regression of the return
on stock i (Rit) on the return on the market
portfolio (Rmt). In this way, the results are
controlled for market-wide in¯uences.
The abnormal returns for each ®rm are
accumulated over the event window as
follows:
1 N t
CAR pt = Ð ~ ~ Uit
N i=1 t=0
(5)
where:
CARpt = the cumulative abnormal return
on portfolio p on day t, relative to the day
of the catastrophe (t = 0).
N = the number of corporate catastrophes
in portfolio p.
The data are daily and relate to trading
days.1 The analysis is conducted relative to
a common event time, rather than in
calendar time. In the case of each catastrophe, abnormal returns are calculated in
the local currency of the parent company,
and the market index chosen varies according to the market in which the stocks are
traded. Since the abnormal returns on all
stocks are measured in real terms, their
additivity across numeraires appeals to
Purchasing Power Parity.
TRADING VOLUME IMPACT
In addition to examining the direct impact
of the catastrophe on stock prices, Figures
2 and 3 report the impact on trading
volume and volatility respectively. The
(6)
Where:
TVit = trading volume of stock i on day t.
ATVi = 12 month daily average trading
volume of stock i, over event
months ±6 to 0 and 1 to 7.
UTVit was calculated for each stock for the
®rst month following the event. It is
assumed that whereas a corporate catastrophe may a€ect stock price behaviour
throughout the entire post-event year, any
impact on trading activity will be evident
primarily in the ®rst post-event month only.
STOCK PRICE VOLATILITY IMPACT
Volatility is measured as the volatility in
the daily stock returns over a two year
interval surrounding the catastrophe. These
were then averaged across catastrophes.
Pre-event and post-event variances were
calculated for each catastrophe as follows:
i
~ (Rit ± Rit ± 1)
t=1
2
s = ÐÐÐÐÐÐ
n±1
(7)
where:
n = the number of trading days in the
event window.
All other data were obtained from the
annual reports and accounts of the portfolio companies, and from Reuters
Textline, the international newspaper and
newswire archive.
RESULTS AND ANALYSIS
This section provides empirical results of
®rm's stock price reaction to corporate
catastrophes. The cumulative abnormal
Page 367
Corporate Catastrophes, Stock Returns, and Trading Volume
Table 2: Cumulative Abnormal Return Results (%)
Event trading
day
±5
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
Full sample
Recovery
portfolio
Non-recovery
portfolio
0.92415
±1.80667
±6.65200
±5.16314
±3.39533
±1.18718
±0.15328
1.65588
2.44450
1.21453
1.70340
2.59587
1.02089
±0.60765
±0.64762
0.04329
±0.36429
±0.95114
±1.17698
±0.21654
0.52994
0.56943
±0.46912
±0.58046
±0.28734
±0.57994
±1.97566
±1.56662
1.35012
±1.08976
±3.24463
±1.52898
1.11236
5.43327
7.81739
8.73402
11.45457
11.13390
13.79843
15.89015
14.27933
12.11651
11.50994
12.89291
11.22245
9.90388
9.82500
12.49328
12.13299
12.24353
11.07653
10.92583
10.81818
9.97495
8.39896
8.56897
0.43733
±2.62599
±10.54614
±9.31647
±8.54697
±8.75341
±9.26261
±6.43344
±7.85273
±10.12189
±12.11950
±12.59760
±14.13162
±15.14956
±14.54198
±14.64200
±13.60627
±13.35688
±13.75067
±14.74204
±12.73068
±12.77239
±13.66414
±13.73051
±12.97936
±12.64267
±13.83237
±13.15015
returns (CARs) were calculated on a daily
basis, and are reported selectively in Table
2 in percentage form.
RECOVERERS AND NON-RECOVERERS
Interestingly, ®rms a€ected by catastrophes
fall into two relatively distinct groups Ð
recoverers and non-recoverers.3 The initial
loss of stock value is approximately 5 per
cesnt on average for recoverers and about
11 per cent for non-recoverers. Figure 4
shows that by the 50th trading day, the
average cumulative impact on stock
returns for the recoverers was 5 per cent
Page 368
plus. So the net impact on stock returns by
this stage was actually positive. The nonrecoverers remained more or less unchangesd between days 5 and 50 but su€ered a
net negative cumulative impact of almost
15 per cent up to one year after the catastrophe.
Why would some catastrophes lead to an
increase in stock returns? One explanation
from our research is that there are two
elements to the catastrophic impact. The
®rst is the immediate estimate of the associated economic loss. The second hinges on
management's ability to deal with the
Knight and Pretty
Recoverers' and Non-Recoverers' Abnormal Returns
aftermath. Although all catastrophes have
an initial negative impact on price, paradoxically they o€er an opportunity for
management to demonstrate their talent in
dealing with dicult circumstances. E€ective management of the consequences of
catastrophes would appear to be a more
signi®cant factor than whether catastrophe
insurance hedges the economic impact of
the catastrophe. Figure 5 shows that the
Figure 4
abnormal trading (shown earlier in Figure
2) is predominately caused by nonrecoverers. Thus, an absence of frenetic
trading around the time of a catastrophe is
usually associated with a subsequent
recovery in stock price. Interestingly, the
research reveals that the volatility impact is
almost identical for both recoverers and
nonrecoverers.
The essential distinctions between reco-
Recoverers' and Non-Recoverers' Stock Trading Volume
Figure 5
Page 369
Corporate Catastrophes, Stock Returns, and Trading Volume
verers and non-recoverers appear to be
that:
Ð There is among non-recoverers an
initial negative response of over 10 per
cent of market capitalization.
Ð In the ®rst two or three months the
magnitude of the estimated ®nancial
loss is signi®cant among non-recoverers.
Ð There are a large number of fatalities.
This seems to govern recovery in the
®rst two or three months.
Ð Thereafter, the issue of management's
responsibility for accident or safety
lapses appears to explain the shareholder value response.
By contrast, whether the losses were fully
covered by insurance does not appear to
have signi®cant in¯uence.
MODELLING THE IMPACT
In an attempt to evaluate the determinants
of ®rms' abnormal return performance
following a catastrophe, multivariate
regression analysis is performed.
Abnormal returns are assumed to be a
function of: the ®rm's size (SIZE), the
®nancial impact of loss (IMPACT), the
number of fatalities resulting from the catastrophe (FATALITIES) and the publicity
which the disaster attracted (PUBLICITY).
The following dummy variables are
included also: the ®rm's industry
(INDUSTRY), the ®rm's insurance
coverage (INSURANCE), the parent
company's
country
of
residence
(COUNTRY), the class of loss (CLASS)
and whether or not the ®rm was held
responsible for the loss (RESPONSIBLE).
In order to minimise bias and distortion
from company size, industry of operation
and parent country a€ecting the regression
parameter estimates, SIZE, INDUSTRY
and COUNTRY are included as controlling explanatory variables.
Page 370
It is hypothesised that the market may
react more negatively to companies whose
catastrophes produce a larger ®nancial dent
in their market value, hence the inclusion of
IMPACT. If, however, the ®rm is compensated ®nancially for the direct loss by the
commercial insurance market, it is possible
that the market will adopt a more lenient
stance, hence INSURANCE as a variable.
The class of loss may play a role also in the
market judgment. If, for example, a ®rm
loses a warehouse, the value of loss tends to
be estimated and absorbed by a ®rm with
more con®dence and over a shorter time
period than with a liability loss.
The explanatory variable, RESPONSIBLE is included in order to evaluate
whether or not the market pays heed to the
issue of `blame' when estimating the future
cash¯ow of a company su€ering from
disaster. Finally, it is suggested that the
market may be swayed in its judgment by
the number of human deaths which result
from the catastrophe, and perhaps also by the
media hype surrounding the event; captured
by the explanatory variables FATALITIES
and PUBLICITY respectively.
The model thus controls for SIZE,
INDUSTRY and COUNTRY, and seeks
to explain the variation in abnormal
returns in terms of IMPACT, FATALITIES, PUBLICITY, INSURANCE,
CLASS and RESPONSIBLE.
The regression equation assumes the
following form:
CARt = at + b1tSIZE + b2tIMPACT
+ b3tINDUSTRY
+ b4tINSURANCE
+ b5tFATALITIES
+ b6tCOUNTRY
+ b7tRESPONSIBLE
+ b8tCLASS + b9tPUBLICITY
+et
where:
at = the intercept term on day t.
Knight and Pretty
bkt = the explanatory variable coecients
for k = 1 to 9 on day t.
et = the random error term on day t.
The nine independent variables of the
regression equation are de®ned in Table 3.
Each cross-sectional regression was
repeated for each week in the post-catastrophe year; on the day of the disaster and
on every ®fth trading day thereafter. Thus,
53 weekly regressions were performed.
The slope coecient of IMPACT is
signi®cant and positive almost throughout
the period from post-event trading day 30
until 215 inclusive at con®dence levels of
over 90 per cent. This indicates that,
during this period, the ®nancial impact of
the catastrophe upon a ®rm's market capitalisation is signi®cantly and directly
related to the ®rm's abnormal return.4
The result is counter-intuitive since it
signals a market penalty for a small ®nancial impact and a market bonus for a large
impact.
The e€ect of IMPACT appears to be
irrespective of whether or not the
company is compensated ®nancially for the
loss by the commercial insurance market;
suggested by the statistical insigni®cance of
the independent variable, INSURANCE.
It is possible that the explanatory power of
INSURANCE could be improved if data
were available to reveal the proportion of
Table 3: De®nition of Independent Regression Variables
Variable
De®nition
SIZE
The ®rm's annual turnover (US$m) in the year prior to the catastrophe
IMPACT
The direct ®nancial cost of the catastrophe (US$m) as a proportion of the
®rm's market capitalisation, taken immediately prior to the disaster. The
cost of the event includes the latest (to date) published estimates of the
three main constitutents of loss, where applicable: property damage,
business interruption and liability costs.
INDUSTRY
1 if the ®rm operated primarily in the oil/gas/petrochemical/chemical
industrial sectors; 0 otherwise.
INSURANCE
I if the company has insurance for the major proportion of the loss; 0
otherwise. Where the ®rm is believed to have insurance, it is recognised
that this will be subject to a deductible and, in most cases, also subject to
an additional layer of risk retention, perhaps funded by captive insurance.
FATALITIES
The number of human deaths resulting as an immediate consequence of
the tragedy.
COUNTRY
1 if the parent ®rm's domestic country is the USA; 0 otherwise.
RESPONSIBLE
1 if responsibility for the catastrophe rests with the company; 0 otherwise.
In cases where there remains dispute, the latest Court verdict is taken.
CLASS
1 if the major proportion of the ®nancial loss was due to property damage/
business interruption; 0 if the major proportion was a liability loss.
PUBLICITY
The number of headlines generated by the disaster worldwide, calculated
on a cumulative daily basis.
Page 371
Corporate Catastrophes, Stock Returns, and Trading Volume
the ®nancial loss which was covered by
commercial insurance.
The human death toll resulting immediately from the catastrophe appears as a
signi®cant independent variable at levels
exceeding 90 per cent and, as might be
expected, is inversely related to abnormal
returns. The slope coecient of FATALITIES remains signi®cant and negative
sporadically during the period from two
calendar months until ®ve calendar months
after the tragedy. It is likely that the low
coecient is due to the scale in which
FATALITIES is measured. In Bhopal, for
example, approximately 3,000 people died
in the disaster yet the dependent variable,
CARit remains measured in logarithmic
scale. It is suggested that some transformation of the variable is necessary.
From post-event trading days 165 to 200
inclusive, INDUSTRY and RESPONSIBLE are found to be signi®cant explanatory variables at levels over 9 per cent
positively and negatively respectively. A
corporate catastrophe occurring within the
oil/gas/petrochemical/chemical industries,
therefore, is associated with abnormal
returns during this period, and there is a
strong association also of abnormal returns
with not being responsible for the catastrophe, during the same period. It would
seem, therefore, that approximately seven
and a half calendar months following the
catastrophe, and for a duration of about
two months, the market begins to judge the
®rm on longer-term, commercial criteria.
More speci®cally, the results appear to
suggest that three models might be appropriate to explain the variation in abnormal
returns. The ®rst model would be applied
to post-event trading days 25 to 110 inclusive, the second, to trading days 115 to 160
inclusive, and the third, to trading days 165
to 215 inclusive. Potentially, such partitioning of data across time periods will yield
more powerful results for each model and,
thus, overall.
Page 372
Curiously, the slope coecient of SIZE
is highly signi®cant and negative on a
single day Ð post-event trading day 210
Ð at a 99 per cent level. Abnormal returns
on this day, therefore, are inversely
proportional to a ®rm's turnover. It is
unclear why the larger companies should
have a poorer stock performance on this
particular day, and is likely to be an
anomaly in the data, resulting from the use
of a small sample size. Similarly, CLASS
appears as signi®cant and positive at a 90
per cent level on trading day 25 only,
suggesting that physical damage/business
interruption losses, rather than liability
losses, are associated with abnormal returns
on this day. This ®nding is likely to be
spurious also.
The lack of statistical signi®cance in the
explanatory variable, PUBLICITY, indicates that the market is not swayed in its
reaction by the intensity of media attention. It suggests that abnormal stock
returns are not correlated with the
frequency of Press articles, indicating
perhaps minimal information content from
this particular source.
None of the explanatory variables is
signi®cant for the ®rst 20 post-event
trading days or after event day 215. This is
suggestive of the complexity of information digestion by the market and the high
level of informational uncertainty which
surrounds the event in the ®rst month Ð
consistent with the observations made
relating to abnormal trading volume Ð
and, by 10 months after the catastrophe,
other events take priority in judging the
company. Alternatively, there could be
omitted variables which might be responsible for the variation.
The intercept term, while being negative
until event trading day 90 (4 calendar
months) Ð consistent with the results
shown in Figure 1 Ð is not signi®cantly
so. This indicates that, on average, catastrophes have an adverse e€ect on
Knight and Pretty
abnormal returns in the ®rst four months,
but not to a substantial level. The lack of
statistical signi®cance in the intercept in the
early weeks is surprising and may require
further examination.
Given that there are nine independent
variables in the regression, the adjusted Rsquared values are considered to be more
accurate than R-squared values in re¯ecting the explanatory power of the regression equation. Mean adjusted R-squared,
across the period from post-event trading
day 25 to 215 inclusive is 62 per cent,
reaching a peak around event trading day
60 where it assumes levels of 84 per cent.
Values for adjusted R-squared are low
particularly in the ®rst calendar month
following the catastrophe and beyond ten
calendar months, consistent with the statistical insigni®cance of the explanatory variables during these periods. Future research
might indicate that it is possible for the
mean adjusted R-squared to be improved
by substituting for the least signi®cant
explanatory variables Ð INSURANCE,
CLASS, COUNTRY, PUBLICITY Ð
alternatives, such as debt ratios, pro®tability, extent of company diversi®cation or
market share.
Examination of the F-statistics reveals
that the variance is signi®cant from postevent trading day 25 until 200 inclusive,
and again on event day 210. That the
variance should be insigni®cant outside this
period is unsurprising, given the corresponding insigni®cance of the explanatory
variables. The result does suggest,
however, that there would be little value
in attempting to explain the variation in
CARpt before day 25 or after day 210,
lending support to earlier comments
regarding a three-model explanation for
the period from day 25 to 215 inclusive.
Selected results across the full post-event
year are reported in Table 4, where t-statistics are presented in parentheses. Coecients signi®cant at a 99 per cent
con®dence level are highlighted, and those
signi®cant at 90 per cent are reported in
bold type.
Simple
partial
regressions
were
conducted to test for independence
between explanatory variables. The relationship between RESPONSIBLE and
PUBLICITY is signi®cant and positive at a
98 per cent level; mean R2 = 0.44 across
the selected event trading days shown
above, considered acceptable. This signals
that there is some correlation between the
number of headlines a corporate catastrophe generates and the onus of responsibility lying with the company. This could
be interpreted as an indication of public
demand for managerial accountability.
The relationship between SIZE and
INDUSTRY was signi®cant and positive
at a 97 per cent level (R2 = 0.31), re¯ecting the large turnovers typical of the major
players in the oil and chemical sectors.
Correlations between all other paired
explanatory variables Ð except in cases of
two dummy variables which are not tested
Ð are below 0.24; considered acceptable
levels to assume independence.
Regressions between IMPACT and
FATALITIES, IMPACT and RESPONSIBLE, and FATALITIES and RESPONSIBLE yielded values for R-squared of
0.001, 0.047 and 0.072 respectively; all
extremely low, indicating the absence of
multicollinearity between the most
powerful explanatory variables.
In order to test the regression assumptions relating to the disturbance term, et,
plots of each independent variable against
the residuals were analysed. In the case of
FATALITIES, variance of the residuals
appeared to be inversely proportional to
the variable, suggesting nonlinear dependence. The nonlinearity might explain the
weak coecient of FATALITIES. A
possible transformation which might
improve the value of the parameter and
the value of adjusted R-squared in the ®rst
Page 373
Corporate Catastrophes, Stock Returns, and Trading Volume
Table 4: Selected Results of Cross Sectional Regressions
Variable
Day 30
Day 60
Day 90
Day 120
Day 150
Day 180
Day 210
Intercept
-0.01427
(-0.66084)
-0.00343
(-0.17252)
-0.01465
(-0.42786)
0.00372
(0.10290)
0.01357
(0.40784)
0.03280
(0.82460)
0.03423
(0.89083)
SIZE
-1.28E-07
(-0.24112)
-3.14E-09
(-0.00592)
-6.60E-07
(-0.72007)
±8.91E-07
(±0.92079)
0.08430
(2.90457)
0.12976
(4.20063)
0.12976
(2.27438)
0.18382
(2.92884)
0.11837
(2.04286)
0.26615
(3.76414)
0.17834
(2.60851)
INDUSTRY
-0.01622
(-0.44612)
-0.05991
(-1.50056)
-0.05831
(-0.82234)
0.04287
(0.55238)
0.05821
(0.64348)
0.23053
(2.65453)
0.16102
(1.90558)
INSURANCE
±0.01415
(-0.36597)
-0.04454
(-1.20366)
0.02634
(0.39037)
±0.00737
(±0.10099)
FATALITIES
-2.52E-05
(-1.81586)
-2.58E-05
(-1.88883)
-4.13E-05
(-1.68260)
±4.16E-05
(±1.60037)
±3.11E-05 ±1.83E-05
(±1.30103) (±0.63935)
COUNTRY
-0.00156
(-0.07874)
0.00446
(0.22530)
0.00374
(0.10150)
±0.01176
(±0.29486)
±0.02310 ±0.05470 ±0.02926
(±0.62262) (±1.21397) (±0.66768)
RESPONSIBLE
0.00213
(0.05501)
0.04777
(1.10968)
0.02335
(0.34190)
±0.05351
(±0.73969)
±0.08055 ±0.18472 ±0.04719
(±1.20970) (±2.32261) (±0.61298)
CLASS
0.03901
(1.54922)
0.03649
(1.46541)
0.02521
(0.56404)
0.01247
(0.26362)
-0.00013
(-0.46275)
-0.00011
(-0.50871)
-7.44E-05
(-0.28638)
±5.22E-05
(±0.22101)
IMPACT
PUBLICITY
®ve post-catastrophe calendar months
would be to use either the square root or
cube root of FATALITIES, or to scale the
variable down by a constant. Alternative
transformations would include calculating
the natural logarithm or reciprocal of the
variable. However, neither of these is
de®ned for zero and, in some cases,
nobody died as a result of the catastrophe.
Examination of residual plots for the
other explanatory variables reveals no
consistent pattern, supporting the multiple
regression assumptions of linearity between
the dependent and independent variables,
and homogeneity of variance, s2, among
the residuals, et. In addition, the normal
probability plots reveal an approximately
linear relationship between the disturbances, indicating that the sample residuals
have come from a normally distributed
population. Throughout the entire post-
Page 374
±1.12E-06 ±8.28E-07 ±4.02E-06
(±1.24483) (±0.77787) (±3.90917)
0.04599 ±0.042213 ±0.04408
(0.84570) (±0.51599) (±0.55742)
±0.00954 ±0.02079
(±0.21914) (±0.39930)
3.66E-06
(0.13247)
0.05607
(1.11495)
±2.03E-05 ±1.26E±05 ±0.00033
(±0.10229) (±0.05479) (±1.54578)
event period, the standardised residuals lie
between +2 and ±2 and, in 93 per cent of
cases, lie between +1 and ±1, o€ering
further support to the normality assumption.
To improve the explanatory power of
the model and to examine more closely the
results revealed above, several additional
reduced regressions were performed for
trading days throughout the entire postevent year, guided by the results emerging
from the above analysis. Presented in Table
5 are the regression results for a representative trading day; approximately six
calendar months after the catastrophe. The
column on the far right of Table 5 represents the regression model which yields the
highest value for adjusted Rsquared on the
trading day indicated.
The reduced regressions results are
consistent with those of the full regression
Knight and Pretty
model and reveal that, for the ®rst six
calendar months, the stockmarket appears
to judge the company primarily on the
basis of the impact the ®nancial loss has
upon the ®rm's average market capitalisation. This judgment is reinforced by the
number of deaths arising as an immediate
consequence of the catastrophe.
Beyond six months, the market pays
increasingly less attention to the ®nancial
and human costs, and begins to judge the
®rm much more according to whether or
not it was held responsible for the disaster.
By this time, however, the split between
recoverers and nonrecoverers has largely
taken place, although the discrepancy
between the two groups does reduce.
In addition to the above analysis, further
regression analysis was undertaken to
explore the counter-intuitive signi®cance in
correlation between IMPACT and positive
abnormal returns. It is found that the result
is due to two prominent outliers in observations for Pan Am and the Commercial
Union. Selected models which exclude
these observations are analysed, and it is
found that the signi®cance in IMPACT
disappears Ð at any reasonable level of
con®dence Ð across the post-event year.5
This is an interesting result because it
reveals that a ®rm's abnormal return
performance is not associated with the size
of ®nancial dent made by a corporate catastrophe to the ®rm's market value.
INTERPRETATION OF RESULTS
This research presents evidence which
suggests that a ®rm's recovery of stock
returns immediately following a catastrophic loss is independent of the presence
of insurance cover. This raises interesting
issues for the consumers (companies) and
the providers (insurers and brokers) of risk
management services.
The empirical results we present suggest
that the impact of a catastrophe on stock
price derives from two sets of factors. The
®rst is the direct ®nancial consequences of
the catastrophe. What will be the impact
of the catastrophe on the ®rm's future cash
¯ows? Although the cash ¯ow impact is
not known with certainty at the time of
the catastrophe, the stock market will form
a collective opinion and adjust price
accordingly. These direct factors usually
will have a negative impact on stock
returns, but this impact will be cushioned
by the extent to which insurance recoveries
reduce the cash out¯ows.
The second set of factors are what may
be described as the indirect factors. These
factors have an impact on stock returns
which springs from what catastrophes
reveal about management skills not
hitherto re¯ected in value. A re-evaluation
of management by the stock market is
likely to result in a re-assessment of the
®rm's future cash ¯ows in terms of both
magnitude and con®dence. This, in turn,
would have potentially large implications
for stock returns. Management is placed in
the spotlight and has an opportunity to
demonstrate its skill or otherwise in an
extreme situation. The indirect factors are,
therefore, able to have a large negative or
positive impact on returns.
The combined e€ect of the two sets of
factors could therefore be either positive or
negative: positive, in circumstances where
the bene®ts of what is revealed about
management outweigh the net ®nancial
loss of the catastrophe; unfavourable, if the
revelation e€ects are negative, since this
will amplify the negative impact of the
®nancial loss.
The results of this study suggest that it is
the indirect factors which dominate the
impact of catastrophes on stock returns.
The net ®nancial loss has a relatively minor
impact on the full change of stock returns
associated with catastrophes.
The message is clear: catastrophe insurance cover is insucient protection against
the stock price e€ects of catastrophes. This
Page 375
Corporate Catastrophes, Stock Returns, and Trading Volume
suggests that a company's insurance strategy
should not be considered in isolation and
should not be viewed as a substitute for
high quality risk management and contingency planning systems and procedures.
The results suggest further that there
may be considerable demand from the
corporate sector for the unbundling of
traditional insurance products in future.
Frequently, the insurance premium paid by
a corporate includes a fairly modest
element to cover a catastrophic loss, the
balance of the premium relating to claims
handling and management services. In
addition, there appear to be signi®cant
opportunities on the supply-side for the
insurance providers to expand their services
in the latter end, namely by providing
more extensive risk management and catastrophe management services. There
appears from these results to be considerable value-adding potential in this domain.
An unbundling of the insurance products
would allow ®rms to disentangle their
decision to insure losses from their decision
to purchase risk management services and
claims handling.
The results suggest that the ®nancial loss
is a small part of the stock price e€ects of a
catastrophe. The crisp issues facing
management are:
Ð Is the insurance cover value for money?
Ð Is there any value in outsourcing the
management of catastrophes?
The varying responses to these issues will
shape the demand for insurance services.
The of insuring the ®nancial loss is being
questioned seriously by many ®rms. What
are the bene®ts to well diversi®ed shareholders who also hold shares in insurance
companies? At best a zero-sum game
perhaps? There are unlikely to be any free
lunches on o€er from the insurance
industry. British Petroleum's (BP) historic
decision in 1990 to retain the bulk of its
Page 376
exposures, including catastrophe exposure,
is an example of this logic.6 Interestingly,
BP management continues to purchase risk
management services from the insurance
industry. The results presented here seem
to indicate that more corporates may adopt
this approach in future.
Another trend evident in the last decade
has been for large industrial corporations to
adopt captive and/or self-insurance while
continuing to purchase catastrophe insurance cover. This is in sharp contrast to the
BP philosophy in that these corporates
have perceived value in the cover but not
the service. This presents the insurance
industry with considerable opportunities
and threats. Considering the results of this
study, it is likely that the opportunities
could be larger than the threats for the
responsive and innovative.
On the supply-side, the response of the
global brokers to the changes in the
purchasing and risk management philosophy of their major clients has been to
move progressively away from a transaction-based culture, with remuneration by
brokerage, towards the provision of an
increasingly wide range of risk management, insurance and consultancy services,
remunerated by fees for work undertaken
and added-value provided. These results
support the wisdom of such a strategy.
Finally, an additional response to the
changing patterns of behaviour seen
recently in the commercial insurance
markets has been the establishment of
Bermuda-based catastrophe reinsurance
ventures. In 1992±93 these companies
attracted more than US$4bn of capital in
the private and public markets. Recent
years have seen also a marked growth in
hybrid funding mechanisms such as the
catastrophe insurance futures and options
traded in Chicago. It is possible that these
new instruments will have other rami®cations for post-catastrophe stock price
response.
Knight and Pretty
SUMMARY AND CONCLUSIONS
The study demonstrates clearly to the
stakeholders of the ®rm Ð managers,
shareholders, bondholders, for example Ð
the potential for considerable and
enduring, adverse implications of catastrophes for market valuation.
The results illustrate that, on average,
corporate catastrophes have a substantial
and adverse e€ect on a ®rm's market value.
The stock market appears to react eciently to news of a disaster, absorbing the
new information quickly and e€ectively.
The digestion period for the catastropherelated information is shown to be
approximately three months from the day
of the disaster. It would seem, however,
that the negative impact is not necessarily
sustained; some ®rms su€ering from catastrophe proceed to outperform market
expectations signi®cantly, on average,
while others underperform signi®cantly.
The variance of stock returns for the full
catastrophe portfolio is found to be signi®cantly greater one month after the event
than one month before, but this e€ect is
not sustained over the full post-event year.
No signi®cant di€erence in volatility
impact appears amongst recoverers and
non-recoverers. Trading activity for the
stocks of crisis-struck ®rms increases radically, on average, after a catastrophe, and
positive abnormal trading volume is shown
to continue throughout the post-event
month. Trading activity is above average
in this month particularly for those nonrecovering ®rms.
The complex nature of information
absorption by the stock market is highlighted by the regression analyses from
which no clear picture emerges for the ®rst
month following the disaster. It is found,
however, that the number of human deaths
resulting from a disaster appears as a significant and negative explanatory variable
from event calendar month one to month
six while, from event calendar month
seven and a half to month nine and a half,
the stockmarket's criteria for judging the
company appears to be whether or not it
was responsible for the catastrophe, and
whether the ®rm operated primarily in the
oil, gas, petrochemical or chemical industries. Abnormal returns were associated
with a lower number of fatalities, an oil/
gas/petrochemical/chemical company and a
®rm which is believed not to be responsible for the disaster. In addition, a surprising
result emerged pertaining to the signi®cance of the impact of ®nancial loss upon a
®rm's annual average market capitalisation
in explaining abnormal returns. It is shown
that this variable is not associated with a
®rm's abnormal stock performance following a catastrophe.
It remains unclear what additional variables may be signi®cant in driving the
behaviour in abnormal stock performance.
Factors relating to a ®rm's crisis management procedures are possibilities. This
represents potentially a fruitful area for
future research.
ENDNOTES
1 The raw data on stock prices, wading volume and
market capitaiisation underlying this study were
obtained from the Datastream ®nancial database.
2 Data on trading volume were unavailable for
Sandoz and Perrier. Consequently, the catastrophe
portfolio comprises 13 catastrophes where trading
volume is analyzed. On days where there was no
trading, the data points were removed from the
analysis and the average ®gures were adjusted
accordingly.
3 Throughout this paper, the term, `recovery', refers
to the extent to which a ®rm's abnormal returns
recover to market expectations during the postevent year. The term does not refer to whether or
not a ®rm incurs bankruptcy following a catastrophic loss.
4 `Firm value' in this paper does not include the
value of debt. If `value' were to include debt, and
book value surrogates were used, the research
results would not be a€ected since there would be
no market reaction. In contrast, if a market value
of debt were used, it is likely that any impact
from the catastrophe would be in the same direction as that on equity. The analysis ignores any
Page 377
Corporate Catastrophes, Stock Returns, and Trading Volume
game theoretic options open potentially to shareholders and bondholders who face a con¯ict of
interests.
5 The regression statistics of IMPACT were also
compared with those of a new variable:
AVGIMPACT = the direct ®nancial cost (US$m)
of the catastrophe as a proportion of the
®rm's market capitalisation, averaged over
the year (261 trading days) prior to the
disaster. Across the full post-event year,
AVGIMPACT appears as almost exclusively,
though only marginally, more signi®cant
than IMPACT.
6 For a detailed analysis of BP's decision, see
Doherty & Smith (1992).
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