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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 eects 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 aect 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 eect 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 suered 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 eect 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 suer signi®cant and adverse abnormal returns, while unexposed ®rms experience no such market valuation discount following the hurricane. Angbazo & Narayanan (1996) also examine the eect 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 eciency 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 eect of the catastrophe on stock price, it is necessary to rule out the eect 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 coecient 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 coecient 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 aect 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 aected 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 suered 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 oer an opportunity for management to demonstrate their talent in dealing with dicult circumstances. Eective 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 aecting 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 suering 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 coecients 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 coecient 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 eect 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 coecient 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 coecient 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 coecient 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 eect 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. Coecients 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 coecient 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, oering 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 eect 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 eects 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 insucient protection against the stock price eects 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 eects 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 oer 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 eect on a ®rm's market value. The stock market appears to react eciently to news of a disaster, absorbing the new information quickly and eectively. 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 suering 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 eect is not sustained over the full post-event year. No signi®cant dierence 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 aected 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). REFERENCES Angbazo, L.A. and Narayanan, R. (1996) `Catastrophic Shocks in the Property-Liability Insurance Industry: Evidence on regulatory and contagion eects', Journal of Risk and Insurance, 63. Davidson, W.N. III, Chandy, P.R. and Cross, M. 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