Event Study

1. THE EFFECT OFTHE GREAT EAST
JAPAN EARTHQUAKE ONTHE
JAPANESE CHEMICAL INDUSTRY
PANAGIOTIS PANAS

2. THE EVENT STUDY METHODOLOGY
• Popular in the field of accounting & finance as it is widely used in order to measure the
effect of a specific event on the value of a firm
• It is not only restricted to economic events, i.e. earnings announcements or M&A
• It has been also used extensively so as to measure the economic impact of other types of
events like a natural disaster
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3. RELATED STUDIES
• Ferstl, et al. (2012)
• The effect of the Fuckusima nuclear disaster on the Japanese, French, German and the US
stock prices for the alternative energy and the nuclear utility sectors was investigated
• The study shown significant ARs over the one-week event window as well as for the next
four-week post event window for the Japanese firms while significant ARs were observed for
the German and French ones only within the event window
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4. RELATED STUDIES
• Tao (2012)
• The impact of the 2011 Tohuku Earthquake on the Japanese stock market was examined
• The obtained results indicated negative effects on the market, statistically significant at 1%
level, only for the next four days after the event
• Fourteen typical stocks like automotive, electricity, transportation and others,ARs were
negative and statistically significant at 1% level for the next ten days
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5. RELATED STUDIES
• Worthington &Valadkhani (2004)
• The effect of 42 different natural disasters on the Australian stock market over a twenty-year
period was examined
• The most significant outcome of this work was that shocks provoked by natural disasters do
have impact on market returns
• Ceteris paribus, the results pointed out that earthquakes, cyclones and bushfires have a major
impact on the Australian market returns in contrast with other events like floods and sever
storms
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6. IN THIS STUDY…
• The impact of the so called “2011 Great East Japan Earthquake” (GEJE) on the Japanese Chemical
Industry (JCI) is examined
• GEJE occurred in the north-eastern part of the Japanese mainland at 14:46 JST on March 11 and it is the
largest earthquake recorded with a magnitude of 9.1
• The “location” of the earthquake is considered as a low risk region in terms of seismic intensity which
implies that such an extreme event was quite unexpected
• JCI is the 2nd largest industry in Japan and the 4th one globally in terms of shipments, amounting to 286
US billion dollars in 2009
• Due to the importance of JCI for the economy as a whole, it is of particular interest to investigate the
stock reaction of this sector to such an extreme seismic excitation
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7. METHODOLOGY
• The event study methodology is employed in order to measure the abnormal returns
(ARs) in the JCI due to the “GEJE” in March 11, 2011
• Due to the fact that the earthquake occurred only 15 minutes before the closing of the
trading day, it is assumed that there is not enough time for the market to react and hence
the “event date” is taken as the next trading day which is Monday, March 14
• The event window for this study was decided to be from the date of the event, as there
is “no new information” before the occurrence of the earthquake, and the next twenty
trading days [0, 20] = [𝑇1, 𝑇2].
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8. METHODOLOGY
• The normal returns for the securities were calculated over an estimation window of 150
trading days [−160, −11] which is within the range of 100 to 300 days
• Normal returns were obtained through the use of the market model due to its
advantages compared to the constant mean model as follows
• 𝑅𝑖𝑡
= 𝛼𝑖
+ 𝛽𝑖
𝑅 𝑚𝑡
+ 𝜀𝑖𝑡
• where 𝑅𝑖𝑡
, 𝑅 𝑚𝑡
are the returns on stock 𝑖 and the market portfolio respectively, 𝑡 is the
period time of the estimation window, and 𝛼𝑖, 𝛽𝑖
are the OLS parameters of the model.
Regarding the market portfolio, bothTOPIX and Nikkei 225 indices were used so as to
compare the similarity of the results
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9. METHODOLOGY
• The abnormal returns can be measured and analysed over the event window by taking
the difference between the actual returns and the market model estimates as follows
where 𝜏 refers to event time [0, 20]
• 𝐴𝑅𝑖𝜏
= 𝑅𝑖𝜏
– ( ො𝑎𝑖 + መ𝛽𝑖 𝑅 𝑚𝜏)
• The aggregation of the AR through the event window is needed so as to conclude overall
inferences for the event of the earthquake. Cumulative abnormal returns (CARs) are
given by the following equation where 𝑇1 ≤ 𝜏1 ≤ 𝜏2 ≤ 𝑇2
• 𝐶𝐴𝑅𝑖 𝜏1, 𝜏2 = σ 𝜏=𝜏1
𝜏2
𝐴𝑅𝑖𝜏
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10. METHODOLOGY
• CARs are calculated for 𝜏1 = 𝑇1 = 0 and 𝜏2 = 𝑇2 = 20 for the chemical industry as well
as for each subsector within the industry. In order to test the null hypothesis “Ho:The
abnormal returns are zero” the following test statistics in equations (Mackinlay, 1997) and
2.5 (Patell, 1976) have to be constructed:
• 𝐽1 =
𝐶𝐴𝑅 𝜏1,𝜏2
𝑣𝑎𝑟(𝐶𝐴𝑅 𝜏1,𝜏2 )
~ 𝑁(0, 1)
• 𝐽2 =
𝑁(𝐿1−4)
𝐿1−2
𝑆𝐶𝐴𝑅𝑖 𝜏1, 𝜏2 ~ 𝑁(0, 1)
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11. METHODOLOGY
• Where:
• 𝐶𝐴𝑅 𝜏1, 𝜏2 =
1
𝑁
σ𝑖=1
𝑁
𝐶𝐴𝑅𝑖 𝜏1, 𝜏2
• 𝐴𝑅 𝜏 =
1
𝛮
σ𝑖=1
𝑁
𝐴𝑅𝑖𝜏
• 𝑣𝑎𝑟 𝐶𝐴𝑅 𝜏1, 𝜏2 = σ 𝜏=𝜏1
𝜏2
𝑣𝑎𝑟(𝐴𝑅 𝜏)
• ෣𝑆𝐶𝐴𝑅𝑖 𝜏1, 𝜏2 =
෣𝐶 𝐴𝑅 𝑖 𝜏1,𝜏2
𝑣𝑎𝑟(𝐶𝐴𝑅 𝑖 𝜏1,𝜏2
• 𝑆𝐶𝐴𝑅𝑖 𝜏1, 𝜏2 =
1
𝑁
σ𝑖=1
𝑁 ෣𝑆𝐶𝐴𝑅𝑖 𝜏1, 𝜏2
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12. METHODOLOGY
• The null hypothesis is defined as: the abnormal returns are equal to zero.
• The two sided hypothesis test for this study is formally given below where if the null
hypothesis is rejected in favour of the alternative (for different significance levels: 10%, 5%
and 1%) then the event does have impact on the stock returns
• 𝐻0: 𝐶𝐴𝑅 𝜏1, 𝜏2 = 0
• 𝐻1: 𝐶𝐴𝑅 𝜏1, 𝜏2 ≠ 0
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13. DATA DESCRIPTION
• TheThomson Reuters Database was used so as to gain access to the financial data
needed for the sample construction
• The initial sample consists of 159 companies of the JCI which are divided into 4 different
chemical subsectors including agricultural, diversified, commodity and specialty chemicals
• The sample comprises in total 77571 observations as it consists of 159 companies times
the number of the trading days for each company
• Trading days ranging from 5/01/2010 to 30/12/2011
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14. DATA DESCRIPTION
• Three variables were generated in total for each company which are the stock returns,
the market returns which are theTopix and Nikkei225 indices
• The returns were calculated in the same way for these variables using the “logarithmic
difference” as follows
• 𝑅𝑖𝑡
= 𝑙𝑜𝑔 (𝑃𝑖𝑡
) – 𝑙𝑜𝑔 (𝑃𝑖𝑡
−
1
)
• where 𝑅𝑖𝑡
is for example the stock return and 𝑃𝑖𝑡
is the closing price of the stock 𝑖 on
trading day 𝑡
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15. DATA DESCRIPTION
• The next step is to correct for missing data as some of the companies in the sample are
not listed and therefore stock returns are not available
• Nine companies in total were dropped from the sample as they were identified without
returns
• The sample consists of 150 companies and 73200 observations
• The observations for the day of “05/01/2010” were dropped for every company “ticker”
as there was not a lag price for the calculation of the returns
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16. DATA DESCRIPTION
• There are not problems with data availability or missing values as well as that the number
of the companies is very sufficient for the purposes of this study
• In terms of subsectors the sample includes 5 companies falling within the agricultural
chemicals, 63 in commodity chemicals as well as 22 and 60 companies in diversified and
specialty chemicals respectively
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17. ANALYSIS OF RESULTS
• Table 1 presents the results obtained from analysing a portfolio of 150 chemical firms
• At first glance, it is obvious that ARs are negative for the first two days; negative (-6.92%) and
highly significant (1% level) ARs are observed at the day of the event (day 0) which is also the
case for the first day after the earthquake where ARs are less negative (-5.72%) but even
more significant
• Beyond these two days, JCI started to recover as it is illustrated in figure 8.1 where CAR is
positively sloping with test statistics 𝐽1
and 𝐽2
to suggest significance at 1% level up to day 7
and day 5 respectively.According to the results obtained, CARs stop to be statistically
significant after 11 and 7 days for 𝐽1
and 𝐽2
respectively
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18. ANALYSIS OF RESULTS
• Figure 1 indicates that after day 8, CAR is relatively stable compared to the previous
response as it would be expected but it becomes statistically insignificant
• After day 16 statistically significant ARs are observed suggesting probably the presence of
another event like the accident occurred in the Fuckusima nuclear plant on 5th of April
and the announcement of high levels of radiation and/or the strong aftershock on the 7th
of the same month
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19. ANALYSIS OF RESULTS
• The CAR plots for each of the four subsectors of JCI are presented in figure 8.2 as well as in
figure 8.3 where a relative comparison is being made while all the results are reported in the
rest tables
• The same trends with JCI are observed in the CAR plots for all subsectors with Commodity
and Diversified Chemicals to be the most affected ones with statistically significant at 1% level
ARs for the first day after the earthquake, equal to -7.59% and -6.88% respectively
• All subsectors started to response positively in the second day after the event (day 2) but this
does not hold for the agricultural sector which experiences negative ARs (-1.21%) and starts
to recover a day after
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20. ANALYSIS OF RESULTS
• Regarding the significance of the results, test statistics suggest similar trends like the JCI with
some minor differences among the subsectors. For example, CARs for the agricultural sector
are statistically significant for 5 days (at 1% for the first four days and at 5% for the fifth one)
while for Commodity chemicals significance lasts for 8 days (at 1% for the first 6 days and at
10% for the last 2 days)
• Similarly, statistical significance is observed for Specialty and Diversified chemicals for 6 and 7
days respectively. Generally, significance increases for the whole industry as well as for the
subsectors as we add up more and more companies
• It has to be mentioned that identical results are obtained when the Nikkei 225 index is used
in the market model instead of the Topix index for the calculation of the normal returns.
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21. LIMITATIONS
• Overall, the outcome of this work is in an agreement with other studies from the
existing literature, being referred in the introduction, as it is confirmed that extreme
natural disasters do have impact on the stock market
• Although different industries have different reactions under an event, the response of the
chemical sector in terms of significant ARs and the persistence of the earthquake effect
on the “chemical” stocks is consistent with other sectors examined in previous studies
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22. LIMITATIONS
• The most usual problem that arises in the application of the event study method is the so
called “clustering” which in this work is unavoidable
• The event windows for all stocks included in the analysis overlap in calendar time (total
clustering – same day of event for all firms) and hence the aggregation of AR is no longer
valid as the covariances across the stocks are not zero
• The latter results in violation of the test statistics which may overstate the significance of
the results, leading frequently to rejection of the null hypothesis while this might not be
the case
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23. LIMITATIONS
• The most common way to deal with the issue of the total clustering is to analyse AR
without aggregation by testing the null hypothesis using unaggregated stock by stock data
through the use of a multivariate regression model including a dummy variable for the
date of the event
• In addition, one should consider other factors that may have an effect on the obtained
results like the Fuckusima nuclear blast, occurred just a day after the earthquake, which
could have a further impact generally on the stock market as well as in the chemical
industry
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24. LIMITATIONS
• Events which are not related with the seismic action like the increased supply of money
by the Bank of Japan on the 15th of March or the extra liquidity offered by the Japanese
government through the repurchasing of bonds on 16th of March could affect the
outcome of this study
• A much shorter event window could be appropriate in order to “isolate” the effect of
the earthquake but this would be of one day duration
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25. RESULTS
JCI - Plot of CAR for
earthquake from event
day 0 to event day 20
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26. RESULTS
JCI Subsectors - Plots of
CAR for earthquake from
event day 0 to event day 20
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27. RESULTS
Chemical Subsectors – CAR
Comparisons for earthquake
from event day 0 to event day 20
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28. RESULTS
Table 1
Reaction to GEJE: Daily &
Cumulative Abnormal Returns
for JCI
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29. RESULTS
Table 2
Daily & Cumulative Abnormal
Returns for Agricultural
Chemicals
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30. RESULTS
Table 3
Daily & Cumulative Abnormal
Returns for Commodity
Chemicals
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31. RESULTS
Table 4
Daily & Cumulative Abnormal
Returns for Diversified
Chemicals
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32. RESULTS
Table 5
Daily & Cumulative Abnormal
Returns for Specialty Chemicals
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33. REFERENCES
• Bolak, M. & Süe, Ö., 2008. The Effect of Marmara Earthquake on Financial Institutions. Dogus
University Journal, 9(2), pp. 135-145
• Armitage, S., 1995. Event Study Methods and Evidence on their Performance. Journal of Economic
Surveys, 9(1), pp. 25-52.
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the Informational Value of Bets. IDEAS Working Paper Series from RePEc, Issue 817.
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Markets Research. Journal of Accounting Research, 22(1), pp. 28-84.
• Ferstl, R., Utz, S. & Wimmer, M., 2012. The Effect of the Japan 2011 Disaster on Nuclear and
Alternative Energy Stocks on Nuclear and Alternative Energy Stocks. German Academic Association
for Business Research, 5(1), pp. 25-41.
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34. REFERENCES
• Geller, R. J., 2011. Shake-up time for Japanese seismology. Nature, Volume 472, pp. 407-409.
• JMA, 2011. Japan Meteorological Agency: Information on the 2011 Great East Japan
Earthquake.[Online] Available at:http://www.jma.go.jp[Accessed 18 March 2016].
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firms. Journal of Financial Economics, 11(1- 4), pp. 85-119.
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Finance: Empirical Corporate Finance. s.l.:Elsevier/North-Holland.
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Industry: Finding the Right path, Japan: KPMG.
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Volume XXXV, pp. 13-39.
• Metro, A., 2011. 2011 Japanese Earthquake: An Event Study Approach, Edinburgh: University of
Edinburgh Business School.
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35. REFERENCES
• Nobuyoshi , Y., 2002. Do Japanese Insurers Benefit from A Catastrophic Event? Market Reactions to
the 1995 Hanshin–Awaji Earthquake. Journal of the Japanese and International Economies, Volume
16, p. 92–108.
• Patell, J. M., 1976. Corporate Forecasts of Earnings per Share and Stock Price Behavior: Empirical
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an empirical application using intervention analysis. Applied Economics, 36(19), pp. 2177-2186
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