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IMPACT OF THE FUKUSHIMA NUCLEAR MELTDOWN ON UNITED STATES
ELECTRIC UTILITY FIRMS
John Adam Shartle III
A Thesis Submitted to the
University of North Carolina Wilmington in Partial Fulfillment
of the Requirements for the Degree of
Master of Business Administration
Cameron School of Business
University of North Carolina at Wilmington
2011
Approved by
Advisory Committee
Nivine Richie Tom Simpson
Cetin Ciner
Chair
Accepted by
______________________________
Dean, Graduate School
ii
TABLES OF CONTENTS
ABSTRACT ................................................................................................................................... iii
AKNOWLEDGEMENTS AND DEDICATIONS ........................................................................ iv
LIST OF TABLES .......................................................................................................................... v
LIST OF FIGURES ....................................................................................................................... vi
CHAPTER 1: INTRODUCTION ................................................................................................... 1
Hypotheses .................................................................................................................................. 4
CHAPTER 2: LITERATURE OVERVIEW .................................................................................. 5
Three Mile Island ........................................................................................................................ 5
Chernobyl .................................................................................................................................... 6
Other Relevant Literature ........................................................................................................... 8
CHAPTER 3: DATA COLLECTION .......................................................................................... 10
CHAPTER 4: METHODOLOGY ................................................................................................ 13
CHAPTER 5: DISCUSSION AND RESULTS ............................................................................ 18
TABLES ....................................................................................................................................... 22
FIGURES ...................................................................................................................................... 30
BIBLIOGRAPHY ......................................................................................................................... 31
APPENDIX ................................................................................................................................... 33
iii
ABSTRACT
This study examines the impact of the meltdown at TEPCO’s Fukushima nuclear power
plant in March of 2011 on U.S. electric utility firms. More specifically, the effect on total risk,
systematic risk, and abnormal returns is examined. A two sample F test identifies changes in pre
and post event total risk. Gujarati’s (1970) model examines post event shifts in Alpha and Beta
estimates. Binder’s (1985) event parameter model isolates returns caused by the meltdown from
confounding events. F test results suggest a highly significant relationship in post event variance
for both the NYSE Index and non-nuclear firms. No significant relationship is identified between
the meltdown and changes in post event systematic risk estimates. In the post event period,
nuclear utility firms show evidence of significant returns pertaining to the event and the release
of information regarding its severity.
iv
AKNOWLEDGEMENTS AND DEDICATIONS
I would like to thank Dr. Luther Lawson for encouraging me in my undergraduate years
to pursue an education abroad in Germany, had I not met you I most certainly would not be on
my current path. I would also like to make aware my appreciation of Dr. Robert Burrus for his
extensive patience with me during my time as both an undergraduate and graduate student.
To Angela Dunkhorst, Mr. Frank, and all others involved with the International MBA
program at the Hochschule Bremen: I am immensely grateful and thank you for tolerating
countless questions and e-mails during my semester in Germany. Your support and the effort
committed to activities extending beyond the classroom played a large role in my international
experience.
I would also like to thank those involved with the program at the University of North
Carolina Wilmington. Karen Barnhill, Anne Nemmers, and Barbara Hoppe, thank you for
keeping your office doors open. To Anne: your sense of humor and perspective of things always
helped to lighten the mood. I thank Dr. Nivine Richie and Dr. Cetin Ciner for instructing two of
my favorite courses in the program. Lastly, I would like to thank Dr. Peter Schuhmann, who,
although not part of my committee made himself readily available to answer any questions
regarding the statistical portion of this paper.
Jessica & Richard Duvall and my Mother, I cannot begin to express my appreciation for
your support during my time in Wilmington, one could not hope for a better brother-in-law. This
paper is dedicated to my father Adam Shartle who did not live to see its completion; I will
forever carry the memories of you close to my heart.
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LIST OF TABLES
Table Page
1. Order of Events ................................................................................................................. 22
2. Gujarati Model - Event Parameter Shift Results............................................................... 24
3. Two Sample F-Tests ......................................................................................................... 25
4. Binder Model - Portfolio A Results .................................................................................. 26
5. Binder Model - Portfolio B Results .................................................................................. 27
6. Binder Model - Portfolio C Results .................................................................................. 28
7. Cumulative Prediction Errors Sub-Period Test Results .................................................... 29
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LIST OF FIGURES
Figure Page
1. Cumulative Prediction Errors ........................................................................................... 30
CHAPTER 1: INTRODUCTION
The interdependencies of international equity markets are a direct outcome of
globalization. The effects of an event severe enough to impact a national exchange extend
beyond domestic boarders to international markets where investors react accordingly. Natural
disasters and accidents are unforeseeable, making the impact on companies and their value
unpredictable. The purpose of this study is the examination of the earthquake occurring off the
coast of Japan impact on US electric utility firms. More specifically, the effect on ex post returns
as well as changes to systematic and total risk is examined.
In 2011 on Friday, March 11th, the fifth largest earthquake recorded since 1900 occurred
in the Pacific Ocean, 230 miles (370 kilometers) northeast of Tokyo. With a magnitude of 9.0,
the earthquake created a tsunami of approximately 9 meters and in less than an hour the
devastating tsunami swept across Japan’s coastline. In addition to the lives lost, the most severe
outcome of the earthquake was the interruption in the supply of cooling fluid to one of the
reactor’s at the Tokyo Electric Power Company’s (TEPCO) Fukushima Daiichi nuclear power
plant, causing the reactor to overheat and disperse hazardous levels of radiation into the
atmosphere.
There are two events similar to the meltdown at the Fukushima plant: 1) Three Mile
Island (TMI), which occurred on March 28th
, 1979 and 2) Chernobyl, which occurred on April
26th
, 1986, in the Ukraine under rule of the Soviet Union. Although all three meltdowns are
similar in nature, there are a number of distinct differences between the three. These differences
raise a number of questions with regards to the effects of the Fukushima meltdown on U.S.
electric utility firms.
2
When comparing the disasters, the meltdown of Fukushima shares more similarities with
Chernobyl than TMI. Although severe, the meltdown at TMI is classified as a level 5 accident
according to the International Nuclear and Radiological Event Scale (INES). The meltdowns of
Fukushima and Chernobyl are classified as a level 7 accident, the highest level on the INES
scale. A level 7 accident it characterized as the radiation released having a severe impact on both
the people and environment and requires the evacuation of individuals in the surrounding area.
Additionally, Chernobyl, just as Fukushima, occurred outside the boarders of the U.S. As a
result, any effects of the disaster on domestic firms are not directly related to the U.S. utility
industry and furthermore, the location of the plants themselves subjects them to different
regulations and safety standards than U.S. requirements.
At the time of the Chernobyl meltdown, Ukraine was under the rule of the Soviet Union.
This resulted in the release of an inconsistent and unreliable supply of information regarding the
severity of the meltdown (Fields and Janjigian 1989), thus impacting U.S. investors’ reactions to
the disaster. This sporadic and unreliable provision of information complicates the analysis of
Chernobyl’s effects on daily returns following the event since international markets were unable
to react accordingly. Lastly, TMI and Chernobyl were operation/manufacturing related incidents,
whereas the Fukushima meltdown and resultant categorization as a level 7 disaster was caused
by a natural disaster.
Examining the time period between the past disasters and Fukushima alters the
expectations of the meltdowns impact on utility firms as well. If nuclear energy is no longer
deemed a safe means of energy production, an alternative means of energy production will need
to replace the capacity previously filled by nuclear power. Traditional fossil fuels such as oil and
coal based electricity generation were utilized in both the past and present, however, a shift from
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nuclear generated power to coal or oil is unlikely due to the current strain on the market, coupled
with paramount cost increases due to progressive environmental regulations regarding the
extraction of these natural resources. Renewable energy sources are more popular and readily
available than in the past, which may cause a decline in demand from nuclear based power and a
shift towards less harmful power generation sources. However, renewable energy sources, with
the exception of solar energy, such as wind, geothermal, hydroelectric, and tidal power are
dependent upon location. As a result of the aforementioned, generalizations may be drawn
regarding the effects of the Fukushima crisis on U.S. electric utility firms but it is reasonable to
assume the equity value of nuclear based firms will not decline severely as no alternative energy
source is readily available or cost effective at this time.
The purpose of this study is the examination of the Fukushima meltdown’s effect on
systematic risk and share prices of U.S. electric utility firms. Fifty-five publicly traded energy
companies are studied in the 250 days prior to the incident and 30 days following the meltdown.
Three portfolios are created to examine the events impact relative to a firm’s nuclear generating
capacity as a percentage of total output. The three portfolios are as follows: 1.) All companies
observed, 2.) Companies with no nuclear generating capacity, and 3.) Companies with nuclear
power responsible for 13-percent or greater of total capacity.
4
Hypotheses
My expectations are based on the assumption of Fukushima’s meltdown resembling the
effects identified in similar studies focusing on the meltdown at Chernobyl. I expect the
Fukushima meltdown will cause negative returns across the U.S. utility industry. However, I
believe firms with greater commitment to nuclear energy will experience elevated negative
returns relative to non-nuclear firms. I also expect the systematic risk and total risk of all three
portfolios to change as a result of the meltdown. My hypotheses for this study are as follows:
: The daily returns of a portfolio are unaffected by the Fukushima incident.
: Pre and post event systematic risk of a portfolio are unaffected by the meltdown.
: Pre and post event total risk are unaffected by the meltdown.
CHAPTER 2: LITERATURE OVERVIEW
Numerous studies examine the effects of negative and positive information releases and
their impact to intra-industry equity. More apposite to this paper are those studies which examine
the impact of the nuclear meltdowns at the TMI nuclear power plant in March of 1979 and the
Chernobyl power plant crisis in April of 1986 on U.S. electric utility firms. The review of these
prior studies is pertinent if market reactions to the recent meltdown at the TEPCO Fukushima
Daiichi nuclear power plant are examined and the information extracted is proven to be relevant
to the findings of TMI and Chernobyl studies.
Three Mile Island
Following the nuclear meltdown at TMI a number of studies examined the intra-industry
impact on the U.S. electric utility industry. Specifically, three studies examine the impact the
meltdown at TMI had on U.S. based electric utility share prices. A significant relationship exists
amongst electric utility firms and their equity values according to Bowen, Castanias, and Daley
(1983). They provide evidence of a correlation between the levels of share price decline relative
to the firm’s commitment to nuclear power generation, revealing that firms with greater nuclear
generating capacity experience a greater fall in share price. Furthermore, the study identifies a
significant relationship between a firm’s nuclear capacity and the post event shift of the firm’s
beta.
Independent researchers Hill and Schneeweis (1983) utilize a two index market model to
examine the impact of the accident on non-nuclear utility firms. The study reveals negative
abnormal returns in the two months following the nuclear meltdown at TMI for both nuclear and
non-nuclear electric utility firms. Furthermore, in the months extending beyond the two months
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of negative abnormal returns, share prices of the observed firms failed to return to levels prior to
the meltdown. They are unable to provide a statistically significant explanation for the decreased
share prices but it may be reasonably assumed that implementation of additional safety
regulations and more stringent operating procedures result in higher operating costs, thus
decreasing future cash flows causing lower profit margins.
The work of Fraser, Kolari, and Uselton (1988), on the other hand, focus more
specifically on the meltdowns effect on total risk, systematic risk, and firm-specific risk. Their
study compares the changes of risk in nuclear and non-nuclear firms with the changes
experienced by airline and petroleum firms during the same time period. The study reveals
statistically insignificant increases in total risk for both nuclear and non-nuclear firms following
the meltdown at TMI. However, although changes in total risk are insignificant, the study does
reveal, similarly to the study of Bowen et. al., (1983), firm-specific risk increases for all utility
firms examined.
Chernobyl
Chernobyl is the second major nuclear power plant meltdown to occur. Similarly to TMI,
a number of publications investigating the meltdowns impact on the U.S. electric utility industry
ensued. The studies examining the meltdown’s impact on the U.S. electric utility industry use the
foregoing TMI studies as a basis for their research. Although similar in nature, a number of
distinct differences must be accounted for. The Chernobyl meltdown was not directly associated
with the United States electric utility industry (Karla et al., 1993). As a result, any impact on
U.S. electric utility firms is strictly due to intra-industry association. The findings of the
Chernobyl studies are consistent with those of TMI studies -- negative abnormal returns occur in
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the period following the meltdown. Fields and Janjigian (1989) were the first to publish a study
examining the effects of Chernobyl on abnormal returns and risk. The results of the study reveal
firms with greater commitment to nuclear power generation experienced elevated levels of
negative returns relative to firms with less commitment to nuclear power generation. Electric
utility firms independent of nuclear power generation experienced half the level of negative
returns relative to nuclear committed firms, but unlike the findings of Bowen, Castanias, and
Daley (1983), the research indicates the Chernobyl meltdown resulted in no significant changes
to beta, implying the event did not alter investors’ risk perceptions. This is consistent with the
study of Boer and Catsburg (1988) which, through public survey, examines the impact nuclear
accidents have on the public’s perception of nuclear energy. Their study reveals that for a short
period of time following the event the public’s negative perceptions of nuclear power increases
but dissipates quickly in succeeding months.
Researchers Karla, Henderson, and Raines (1993) investigate the meltdown’s impact on
electric utility firms by constructing three portfolios comprised of sixty-nine domestic utility
firms. The capacity of nuclear power generated by a firm is used as a basis for determining
which portfolio each firm is assigned. The portfolios include nuclear, mixed, and conventional
utilities. The results reveal negative returns across all portfolios but more interestingly, the mixed
utility portfolio exhibited greater losses than the nuclear based utility portfolio. These results are
consistent with the research of Aktar (2005). Aktar compares the U.S. stock market reactions to
the Chernobyl and TMI accidents. The study reveals the utility industry as a whole experiences a
loss following the event but more severe losses are not restricted to nuclear oriented utility firms.
The research of Karla, Henderson, and Raines (1993) and Aktar (2005) provide evidence that
higher levels of negative returns are dependent upon 2 factors: 1) the number of problems a firm
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has experienced in the past with its’ nuclear facilities and 2) a firm’s level of commitment to the
expansion of its’ nuclear generating capacity.
Other Relevant Literature
The research of Blacconiere and Patten (1994) examine the intra-industry impact of the
Bhopal chemical leak on other chemical manufacturers. Analogous to the effects of prior nuclear
crises, the study reveals a negative reaction to firms involved in chemical manufacturing.
Disparate to the negative intra-industry impacts addressed thus far, the study of Dowdell,
Govindaraj, and Perm (1992) examines the positive outcomes of a negative event. Their
hypothesis suggests, and is later proven; that a negative event specific to a particular industry
may in fact be beneficial to competitors. The focus of their study is the Johnson & Johnson (J &
J) Tylenol crisis of 1982. The researchers argue the event may have in fact been beneficial to the
competitors of J & J. The supporting research reveals substantial negative returns for J & J
following the event while the remainder of the industry was relatively unaffected and a few firms
exhibited an increase in share price. This suggests that a company specific negative event, not
expected to alter future regulations of an industry and decrease expected future cash flows, are
advantageous to industry competitors.
In the study of Sweeney and Bremer (1991), daily returns in the days following an
extreme negative return of 10-percent or greater in a single day are examined. The study
concludes that a firm’s lowest share price occurs the day the relevant event is announced,
alluding to an initial overreaction by investors. The day following a 10-percent fall, firms, on
average, exhibited a positive return of 1.773-percent and a cumulative return of 2.215-percent by
the second day. This supports the notion of markets reacting quickly to new information and
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furthermore, negative returns caused by the announcement occur within a short interval, thus
limiting the post event time period observed to ensure the daily returns examined are not caused
by confounding events but restricted to the target event.
CHAPTER 3: DATA COLLECTION
In order to identify publicly traded electric utility companies, equity screeners were
created using Bloomberg and Compustat. The screeners identified U.S. electric utility companies
with SIC codes 4911/4910 (electric services) and 4931 (electric & other services). The
Bloomberg screener identified 66 companies. Information for Alaska Power and Telephone Co.,
China National Appliance OF, Crownbutte Wind Power Inc., First National Energy Corp., Out-
Takes Inc., and York Research Corp. lacked necessary information for the period examined and
are therefore excluded. Further review of the companies revealed the primary business
operations of Centerpoint Energy Inc., CH Energy, Entergy’s Energy Group Inc., Nstar, and
Unitil Corporation is the transmission and distribution of electricity, no electricity is actually
generated, eliminating them from the study as well. NACEL Corporation is a penny stock with
limited trade volume and is therefore excluded. As a result, the initial list of 66 companies is
reduced to 54.
The Compustat screener identified the same companies as Bloomberg but managed to
identify an additional company which is included in the study, resulting in a total of 55
companies.
The Nuclear Energy Institute’s (NEI) U.S. Reactor Ownership and Management report in
addition to annual reports released by the examined firms are used to determine the total nuclear
generating capacity of each firm.
Total nuclear output in terms of megawatts (MW) is used as the basis in determining
which portfolio a firm is assigned. Firms generating 600 MW or greater of nuclear energy are
assigned to the nuclear portfolio. This is based on the idea of 600 MW nuclear generating
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capacity being large enough to cause more pronounced effects on the given firm. Portfolio A
contains all 55 firms observed in the study. Portfolio B contains firms with no nuclear capacity, a
total of 35 companies (Central Vermont Public Services only produced 20 MW of nuclear energy
and is therefore part of the non-nuclear portfolio). The remaining 20 nuclear firms comprise
Portfolio C. Refer to Appendix A, Appendix B, and Appendix C for each portfolio and its’
respective firms.
An additional 3 portfolios are created to compare the results of Portfolios A – C. The
same firms are used but size of the firm is used as the basis for dividing the companies between
Portfolios D – F. Market and debt values obtained from Compustat are used to determine a
firm’s overall size, refer to Appendix D, Appendix E, and
12
Appendix F.
Daily closing share prices for the period examined are obtained from Compustat.
Compustat’s share prices are adjusted for dividends and stock splits, ensuring consistency across
the data. The period examined in the study begins 250 trading days prior to the meltdown and 30
days following the meltdown.
CHAPTER 4: METHODOLOGY
The event study methodology of Fama et al. (1969) is used to examine share price
reaction to information of the Fukushima accident. The adjusted daily stock prices are used to
calculate the daily returns for the companies and the NYSE Composite Index.
The following formula is used to calculate daily returns:
(
)
Where:
= Return on security j for day t;
= Adjusted closing price on day t; and
= Adjusted closing price on day t – 1.
The natural log is used to calculate the continuously compounded daily returns for
individual securities. The continuously compounded formula, opposed to the standard approach
of (( - / ), allows for the calculation of more accurate returns when
examining longer time frames since the results are time consistent. The average of the returns on
a given day represents the portfolio’s realized return. The return of the portfolio is used in the
event parameter model (EPM) to avoid the problem of cross correlation, as is common when an
event impacts multiple companies simultaneously, between the firms. Binder’s (1985) EPM,
shown below, uses daily portfolio returns, thus eliminating the cross correlation problem.
Binder’s (1985) EPM is an extension of the standard market model as it includes an
additional dummy variable in the regression. The additional variable specifies if the returns
14
observed in the period following the event are the effect of the event or the result of other
exogenous events. Binder’s EPM model is as follows:
∑
Where:
= Realized return on portfolio i on day t;
= OLS estimate of intercept for portfolio i;
= OLS estimate of systematic risk for portfolio i;
= Realized return of a diverse market index on day t, in this study - the NYSE
Composite Index;
= Announcements regarding the event;
= Any day an announcement occurs equals one and zero otherwise;
= Estimated coefficient on dummy variable or excess return on portfolio i on
observation n;
= Dummy variable, which is equal to 1 on observation n and zero other-wise; and
= A stochastic disturbance term.
The model’s dummy variables generate coefficients based on the difference between the
actual portfolio return on day t and what the model would have estimated the return to be on day
t based upon the 250 observations prior to the event. Thirty dummy variables will be used in
total, beginning on March 11th
and ending on April 21st. Japan announced a state of emergency at
the Fukushima power plant on March 11th
at 8:45 PM, 9:15 AM EST, refer to 1 for information
releases regarding the incident. March 11th
is used as the beginning of the event interval based on
15
the notion that reactions to the announcement in the U.S. market occurred on this date, justifying
the decision to begin the event interval on this date and not the following Monday.
The following formula, developed by Gujarati (1970), measures the equality between the
coefficients in the regression. This test the effect of the event on the regression parameter
estimates, estimating pre and post event shifts in alpha and beta estimates:
Where:
= Realized return on portfolio i on day t;
= Intercept term for portfolio i;
= Shift in the intercept term for portfolio i during the event period;
= A dummy variable that is equal to 1 during the event interval and 0 otherwise;
= Systematic risk of portfolio i;
= Return on the market on day t, realized return of the NYSE Composite Index;
= Shift in the systematic risk of portfolio i during the event period; and
= A random disturbance term.
In the regression, a portfolio’s daily returns (independent variable) are run against the
dependent variables outlined in the equation above. The use of dummy variables during the event
interval ensures the regression results restrict any shifts in parameter estimates to the Fukushima
meltdown, thus ensuring confounding events do not affect regression results.
The use of dummy variables in Binder’s (1985) model eliminates the need for a separate
formula to calculate prediction errors since it is the equivalent of the prediction error
methodology. Our prediction errors are the coefficients of the dummy variables in Binder’s
model.
16
Therefore:
Standard prediction errors ( ) are the t-stat of the coefficient in the regression output
on day t. Maintaining is normally distributed in a linear regression, the sum of the cumulative
prediction errors (CPE) (calculated as + = CPE on day t) equals zero, indicating no
abnormal returns and leading us to accept the null hypothesis.
The following model, introduced by Karafiath (1988), provides a t-test across sub-periods of
the 30 day event interval to determine if coefficients sum to zero:
∑
A total of 5 sub-periods, each spanning a period of 6 days, is examined for Portfolio’s A, B, and
C. The results of the model support Binder’s EPM estimates and offer additional incite.
A two sample F test is used to measure the equality of variance between two samples of
data, identifying any changes to total risk in the pre and post event periods, the formula is as
follows:
Where:
= Pre event variance of portfolio daily returns; and
= Post event variance of portfolio daily returns during event interval.
17
The first variance ( ) is calculated using a sample of daily portfolio returns 250 days
prior to the event. This variance is compared to the second variance ( ) that uses a sample of
the event day plus 29 succeeding days. In the event F equals 1, the null hypothesis may not be
rejected.
CHAPTER 5: DISCUSSION AND RESULTS
The results of Binder’s EPM are summarized for portfolios A, B, and C in 4, 5 and 6.
The variables listed under the event date column correspond to the observed 30 day event
interval, where D0 through D29 correspond to March 11th
through April 21st respectively. The R-
Square and Adjusted R-Square for all three data sets are strong. The prediction errors for
Portfolios A and B are primarily negative in the days immediately following the Fukushima
incident; however none are significant above the 10% interval. A reasonable assumption for the
negative returns is a wide spread sell off across international markets as investors anticipate a
decline in global economic progress due to the extensive damage inflicted upon Japan’s
economy. The results for the nuclear portfolio in 6 suggest the event is responsible for negative
returns on D1, D2, and D4, all of which are significant at the 1% interval. The significance of
these returns is further supported with an F value of 6.59 during the D0 to D5 event period, refer
to 7. All three portfolios experience negative returns on D21, April 11th
, significant at the 10%
interval. This is likely attributable to Japan’s release of information on April 11th
regarding the
severity of the meltdown. It is interesting the nuclear portfolio experienced a day of positive
returns significant at the 10% interval, on April 15th
, D25, a date which corresponds to the
release of information regarding progress in attempts to control the damage at the power plant.
Section I of 2 summarizes the Alpha and Beta estimates of the Gujarati Model for the
robustness check and Portfolios A, B, and C. The R-Square for the robustness check is a
considerably low .37 and with the exception of the Beta estimate, the results yield no significant
impact to post event Beta and Alpha estimates. The R-Square for Portfolios A, B, and C indicate
a strong correlation between the variables. The Alphas for Portfolios A and B are substantially
low and Portfolio C’s Alpha is not statistically different from zero. Amongst the three portfolios,
19
the Beta estimate of Portfolio B, .77, suggest non-nuclear firms carry the greatest systematic risk
while nuclear firms, .60, carry the least. All three Beta estimates are significant at the 1%
interval. In the post event period Portfolio C experienced the largest shifts in Alpha and Beta,
however none of these estimates are significant.
Section II of 2 provides the estimates for Portfolios D, E, and F. R-Square is strong for
each portfolio and pre event Alphas are low. Pre event Beta estimates are all significant at the
1% interval. As could be expected, the portfolio comprised of the largest firms carries the least
amount of systematic risk, .61, while the portfolio of the smallest firms carries the greatest, .77.
Similarly to the results of Section I, the meltdown resulted in no significant shifts in post event
Beta and Alpha estimates. The purpose of using the different portfolios in Section II is to obtain
a set of results in which to compare those estimated in Section I. Comparing the results of the
two sections determines if Beta and Alpha estimates are statistically different if the portfolios are
assembled based on alternative criteria.
3 includes the results of the two sample F test. The results indicate the event caused no
significant changes to total risk of the nuclear portfolio. The F and P values of the NYSE Index,
Portfolio A, and Portfolio B are highly significant. The event caused total risk to change in the
post event period, allowing for the rejection of the null hypothesis. Portfolio A experiences less
post event variance than Portfolio B while the NYSE Index exhibits the largest post event
variance among the three. A reasonable explanation for the NYSE Index’s elevated variance is
investors speculating as to Japan’s ability to recover and progress economically with the damage
inflicted to the county’s infrastructure.
CHAPTER 6: CONCLUSIONS
The majority of significant abnormal returns occur primarily in the nuclear portfolio.
Major findings of negative returns take place within a 4 day period following the event. A single
day in the post event period, corresponding to the release of information regarding the severity of
the event, results in a day of significant negative returns across all portfolios. As a result, we fail
to accept the null hypothesis which states: The daily returns of the portfolio are unaffected by
the Fukushima incident.
No clear relationship between the meltdown and changes to systematic risk is identified.
In addition, using the same model to test a second set of portfolios created relative to a firm’s
size as opposed to nuclear capacity suggests the failure to identify significant shifts in systematic
risk is not the result of our initial portfolio standards. We are unable to reject which states:
Pre and post event systematic risk of a portfolio are identical.
Variance in the post event period is highly significant for the NYSE Index, Portfolio A, and
Portfolio B. Amongst the three, post event variance is the most pronounced for the NYSE Index
while Portfolio A exhibits less variance than Portfolio B. We fail to accept which states: Pre
event total risk is identical to post event total risk.
In closing, the results are as expected and present no real surprises. Markets reacted
quickly to the news of the disaster and effects on U.S. utility firms are relatively small and short-
lived. The outcome is drastically similar to those occurring nearly 25 years ago with the
meltdown at Chernobyl; negative returns occur across the industry -- more so with nuclear
committed firms, total risk is affected but no significant changes to post event systematic risk
occur. As a result, it is reasonable to assume nuclear disasters extending beyond domestic
21
boarders will result in significant negative returns for nuclear committed firms but any changes
in systematic risk in the post event period is not attributable to the disaster itself.
22
TABLES
1. Order of Events
Event Date Description
Friday, March 11th
At 0546 GMT (1446 in Japan) a massive earthquake, 8.9 on the Richter
scale, unleashes a tsunami which crashes through Japan's eastern
coastline, sweeping buildings, boats, cars and people miles inland.
8:15 PM (U.S. Eastern Standard Time: 9:15 AM) A "state of emergency"
is declared at the Fukushima nuclear power plant, around 30 miles inland
from the north east coast, after one of the reactors suffers a cooling
system failure. Around 3,000 people are evacuated from a 6.2-mile
exclusion zone.
Saturday, March 12th
There is an explosion at the Fukushima Dai-ichi nuclear plant.
Operators at the plant's Unit 1 detect eight times the normal radiation
levels outside and 1,000 times normal inside Unit 1's control room.
Japan's government spokesman says the explosion that tore through the
nuclear plant did not affect the reactor.
Sunday, March 13th
Japan's nuclear safety agency says the cooling system of a third nuclear
reactor at Fukushima has failed - experts constantly monitor levels of
radioactivity in the quarantined area.
Approx. 170,000 people have been evacuated from a 12-mile radius
around the Fukushima number one nuclear plant.
A government spokesman says the blast destroyed a building which
housed a nuclear reactor, but the reactor escaped unscathed.
Nuclear plant operators try to keep temperatures down in a series of
reactors.
Chief cabinet secretary Yukio Edano warns a hydrogen explosion could
occur at Unit 3 of the Fukushima Dai-ichi nuclear complex - the latest
reactor to face a possible meltdown.
Mr. Edano declares the radiation released into the environment so far is
so small it does not pose any health threats.
Japan's nuclear agency says up to 160 people were taken to hospital after
possibly being exposed to radiation while waiting to be evacuated.
23
Monday, March 14th
A second hydrogen explosion is reported, this time at the unit 3 reactor at
the Fukushima plant. Six people are injured.
Japanese stocks nosedive as the huge cost of the disaster fuels fears about
the country's economy.
The Bank of Japan moves to stabilize markets by injecting a record 15
trillion yen (£114.4 billion) into money markets.
Fears of a major slowdown in the world's third-largest economy spark a
huge slump in Japanese shares, with Tokyo's Nikkei 225 index closing
more than 6% lower and some of the world's biggest firms, such as
Toshiba, Toyota and Honda, sustaining heavy share price losses.
Environmental campaigners call for a rethink of plans for new nuclear
power stations in the UK.
Tuesday, March 15th
Dangerous levels of radiation leak from the Fukushima plant after a third
explosion, believed to be in the number 2 reactor, and a fire, rock the
complex.
In a televised statement after the blast, prime minister Kan urges those
within 19 miles of the area to stay indoors.
Monday, April 11th Japan escalates Fukushima to a level 7 (INES) disaster
Friday, April 15th
The IAEA provided the following information on the current status of
nuclear safety in Japan: Overall, the situation at the Fukushima Daiichi
nuclear power plant remains very serious but there are early signs of
recovery in some functions, such as electrical power and instrumentation.
24
2. Gujarati Model - Event Parameter Shift Results
Section I
All Firms Non-Nuclear Nuclear Robustness Check
R Square 0.7415 0.7425 0.6492 0.3731
Adjusted R Square 0.7387 0.7397 0.6454 0.3663
Standard Error 0.0050 0.0054 0.0053 0.0063
F Value 263.8686 265.2919 170.2378 54.7577
Alpha 0.0001 0.0002 0.0000 0.0005
t-Stat 0.4085 0.6042 -0.0214 1.1990
P-Value 0.6832 0.5462 0.9830 0.2316
Beta 0.7124 0.7765 0.6002 0.4026
t-Stat 27.2108 27.3654 21.6981 12.1339
P-Value 0.0000 0.0000 0.0000 0.0000
Alpha Shift -0.0003 -0.0002 0.0010 -0.0010
t-Stat -0.3202 -0.1709 -0.5266 -0.8140
P-Value 0.7491 0.8644 0.5989 0.4164
Beta Shift 0.0595 0.0246 0.1174 0.1597
t-Stat 0.5356 0.2041 1.0277 1.1340
P-Value 0.5926 0.8385 0.3050 0.2578
25
Section II
Largest Firms Mid-Sized Firms Smallest Firms
R Square 0.6657 0.6658 0.7262
Adjusted R Square 0.6621 0.6622 0.7233
Standard Error 0.0052 0.0063 0.0056
F Value 183.2413 183.3081 244.0507
Alpha 0.0001 0.0001 0.0001
t-Stat 0.3335 0.3303 0.4094
P-Value 0.7390 0.7414 0.6826
Beta 0.6121 0.7502 0.7716
t-Stat 22.4526 22.7220 26.2931
P-Value 0.0000 0.0000 0.0000
Alpha Shift -0.0010 -0.0002 0.0002
t-Stat -0.9378 -0.1626 0.1717
P-Value 0.3492 0.8710 0.8638
Beta Shift 0.1467 0.0384 -0.0030
t-Stat 1.2677 0.2736 -0.0241
P-Value 0.2060 0.7846 0.9808
3. Two Sample F-Tests
NYSE Index All Firms Non-Nuclear Nuclear
F 1.98244102 1.66748032 1.88133318 1.24143539
Pre Event Variance 0.00014603 0.00009994 0.00011866 0.00008037
Post Event Variance 0.00007366 0.00005934 0.00006307 0.00006474
P(F<=f) one-tail 0.01478567 0.04683561 0.02181299 0.24767482
26
4. Binder Model - Portfolio A Results
Alpha 0.00012906
Beta 0.7124
R-Square 0.7583
Adj. R-Square 0.7281
F Value 25.1
P Value <.0001
Event Day PE t Value P Value CPE
D0 -0.00049689 -0.1 0.9225 -0.00049689
D1 -0.00297 -0.58 0.5607 -0.00346689
D2 -0.00471 -0.92 0.3576 -0.00817689
D3 0.00211 0.41 0.6813 -0.00606689
D4 -0.00824 -1.61 0.109 -0.01430689
D5 -0.00098622 -0.19 0.847 -0.01529311
D6 0.00276 0.54 0.5911 -0.01253311
D7 0.00243 0.48 0.6338 -0.01010311
D8 -0.00217 -0.43 0.6707 -0.01227311
D9 -0.00117 -0.23 0.8184 -0.01344311
D10 0.00023659 0.05 0.9631 -0.01320652
D11 -0.002 -0.39 0.6956 -0.01520652
D12 0.00536 1.05 0.2948 -0.00984652
D13 0.00469 0.92 0.3591 -0.00515652
D14 -0.00054553 -0.11 0.9149 -0.00570205
D15 0.00349 0.68 0.4955 -0.00221205
D16 -0.00197 -0.39 0.7004 -0.00418205
D17 -0.00329 -0.64 0.5199 -0.00747205
D18 0.0052 1.02 0.309 -0.00227205
D19 -0.00251 -0.49 0.623 -0.00478205
D20 -0.0017 -0.33 0.7386 -0.00648205
D21 -0.00958 -1.88 0.0618 -0.01606205
D22 0.0001701 0.03 0.9735 -0.01589195
D23 0.00103 0.2 0.8402 -0.01486195
D24 0.00407 0.8 0.4262 -0.01079195
D25 0.00731 1.43 0.1531 -0.00348195
D26 -0.00071694 -0.14 0.8887 -0.00419889
D27 -0.00632 -1.24 0.2167 -0.01051889
D28 0.00338 0.66 0.51 -0.00713889
D29 -0.00002687 -0.01 0.9958 -0.00716576
27
5. Binder Model - Portfolio B Results
Alpha 0.000177
Beta 0.77977
R-Square 0.7567
Adj. R-Square 0.7263
F Value 24.88
P Value <.0001
Event Day PE t Value P Value CPE
D0 -0.00184 -0.33 0.7424 -0.00184
D1 0.00137 0.25 0.8065 -0.00047
D2 -0.00033878 -0.06 0.9518 -0.00080878
D3 0.00606 1.08 0.2816 0.00525122
D4 -0.0065 -1.16 0.2474 -0.00124878
D5 -0.00102 -0.18 0.8551 -0.00226878
D6 0.00358 0.64 0.5231 0.00131122
D7 0.00186 0.33 0.7388 0.00317122
D8 -0.00313 -0.56 0.5757 4.122E-05
D9 -0.00132 -0.24 0.8135 -0.00127878
D10 0.00112 0.2 0.8416 -0.00015878
D11 -0.00167 -0.3 0.7655 -0.00182878
D12 0.00595 1.07 0.2876 0.00412122
D13 0.00384 0.69 0.4924 0.00796122
D14 -0.000081 -0.01 0.9884 0.00788022
D15 0.00413 0.74 0.4606 0.01201022
D16 -0.00262 -0.47 0.6396 0.00939022
D17 -0.00292 -0.52 0.6014 0.00647022
D18 0.004 0.72 0.4738 0.01047022
D19 -0.00237 -0.42 0.6712 0.00810022
D20 -0.00143 -0.26 0.7981 0.00667022
D21 -0.00954 -1.71 0.0888 -0.00286978
D22 -0.00289 -0.52 0.606 -0.00575978
D23 -0.00075718 -0.14 0.8922 -0.00651696
D24 0.00335 0.6 0.5491 -0.00316696
D25 0.00638 1.14 0.2545 0.00321304
D26 -0.00088896 -0.16 0.874 0.00232408
D27 -0.00782 -1.4 0.1628 -0.00549592
D28 0.00472 0.84 0.3995 -0.00077592
D29 0.00164 0.29 0.7695 0.00086408
28
6. Binder Model - Portfolio C Results
Alpha 0.00004565
Beta 0.5945
R-Square 0.6942
Adj. R-Square 0.6559
F Value 18.16
P Value <.0001
Event Day PE t Value P Value CPE
D0 0.00185 0.36 0.7194 0.00185
D1 -0.01058 -2.06 0.0407 -0.00873
D2 -0.01237 -2.4 0.0171 -0.0211
D3 -0.0048 -0.93 0.3541 -0.0259
D4 -0.01128 -2.19 0.0296 -0.03718
D5 -0.00092574 -0.18 0.8572 -0.03810574
D6 0.00131 0.25 0.8 -0.03679574
D7 0.00343 0.67 0.505 -0.03336574
D8 -0.00049917 -0.1 0.9227 -0.03386491
D9 -0.00091829 -0.18 0.8584 -0.0347832
D10 -0.0013 -0.25 0.8 -0.0360832
D11 -0.00258 -0.5 0.6159 -0.0386632
D12 0.00432 0.84 0.401 -0.0343432
D13 0.00618 1.2 0.2306 -0.0281632
D14 -0.00136 -0.26 0.7917 -0.0295232
D15 0.00236 0.46 0.6464 -0.0271632
D16 -0.00082569 -0.16 0.8725 -0.02798889
D17 -0.00393 -0.77 0.4447 -0.03191889
D18 0.0073 1.42 0.1569 -0.02461889
D19 -0.00276 -0.54 0.5921 -0.02737889
D20 -0.00219 -0.43 0.6709 -0.02956889
D21 -0.00965 -1.88 0.0617 -0.03921889
D22 0.00552 1.07 0.2845 -0.03369889
D23 0.00416 0.81 0.4192 -0.02953889
D24 0.00532 1.04 0.3011 -0.02421889
D25 0.00895 1.74 0.0827 -0.01526889
D26 -0.00041589 -0.08 0.9358 -0.01568478
D27 -0.00371 -0.72 0.4717 -0.01939478
D28 0.00102 0.2 0.8437 -0.01837478
D29 -0.00294 -0.57 0.5678 -0.02131478
29
7. Cumulative Prediction Errors Sub-Period Test Results
Portfolio A
Event Period CPE F Value P Value
D0 to D5 -0.04780756 1.47 0.2269
D6 to D11 -0.07676548 0 0.9949
D12 to D17 -0.03457124 0.38 0.5408
D18 to D23 -0.0603521 0.34 0.5589
D24 to D29 -0.04329633 0.37 0.5431
Portfolio B
Event Period CPE F Value P Value
D0 to D5 -0.00138512 0.12 0.7251
D6 to D11 0.00125732 0 0.9708
D12 to D17 0.04783332 0.38 0.5406
D18 to D23 0.01009414 1.07 0.303
D24 to D29 -0.0030376 0.17 0.6848
Portfolio C
Event Period CPE F Value P Value
D0 to D5 -0.12916574 6.59 0.0109
D6 to D11 -0.21355599 0 0.96
D12 to D17 -0.17910058 0.25 0.6185
D18 to D23 -0.18402334 0.12 0.7309
D24 to D29 -0.1142569 0.75 0.3861
30
FIGURES
1. Cumulative Prediction Errors
-0.045
-0.035
-0.025
-0.015
-0.005
0.005
0.015
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
C
P
E
Event Day
Cumulative Prediction Errors
All
Firms
Non-
Nuclear
Nuclear
BIBLIOGRAPHY
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APPENDIX
Appendix A. Portfolio A - All Electric Utility Firms
Portfolio A
Ticker Firm Name Ticker Firm Name AES AES CORP PNW PINNACLE WEST CAPITAL
ALE ALLETE INC PNM PNM RESOURCES INC
LNT ALLIANT ENERGY CORP POR PORTLAND GENERAL ELECTRIC CO
AEE AMEREN CORPORATION PPL PPL CORPORATION
AEP AMERICAN ELECTRIC POWER PGN PROGRESS ENERGY INC
AVA AVISTA CORP PEG PUBLIC SERVICE ENTERPRISE GP
BKH BLACK HILLS CORP SCG SCANA CORP
CPN CALPINE CORP SO SOUTHERN CO
CV CENTRAL VERMONT PUBLIC SERV TE TECO ENERGY INC
CNL CLECO CORPORATION UIL UIL HOLDINGS CORP
CMS CMS ENERGY CORP UNS UNISOURCE ENERGY CORP CO
ED CONSOLIDATED EDISON INC HTM US GEOTHERMAL INC
CEG CONSTELLATION ENERGY GROUP WR WESTAR ENERGY INC
CVA COVANTA HOLDING CORP WEC WISCONSIN ENERGY CORP
D DOMINION RESOURCES INC/VA XEL XCEL ENERGY INC
DPL DPL INC
DTE DTE ENERGY COMPANY
DUK DUKE ENERGY CORP
DYN DYNEGY INC
EIX EDISON INTERNATIONAL
EE EL PASO ELECTRIC CO
EDE EMPIRE DISTRICT ELECTRIC CO
ETR ENTERGY CORP
EXC EXELON CORP
FE FIRSTENERGY CORP
GEN GENON ENERGY INC
GXP GREAT PLAINS ENERGY INC
HE HAWAIIAN ELECTRIC INDS
IDA IDACORP INC
MGEE MGE ENERGY INC
NEE NEXTERA ENERGY INC
NI NISOURCE INC
NU NORTHEAST UTILITIES
NWE NORTHWESTERN CORP
NRG NRG ENERGY INC
NVE NV ENERGY INC
OGE OGE ENERGY CORP
ORA ORMAT TECHNOLOGIES INC
PCG P G & E CORP
POM PEPCO HOLDINGS INC
34
Appendix B. Portfolio B - Non-Nuclear Utility Firms
Portfolio B
Ticker Firm Name
AES AES CORP
ALE ALLETE INC
LNT ALLIANT ENERGY CORP
AVA AVISTA CORP
BKH BLACK HILLS CORP
CPN CALPINE CORP
CV CENTRAL VERMONT PUBLIC SERV
CNL CLECO CORPORATION
CMS CMS ENERGY CORP
ED CONSOLIDATED EDISON INC
CVA COVANTA HOLDING CORP
DPL DPL INC
DYN DYNEGY INC
EE EL PASO ELECTRIC CO
EDE EMPIRE DISTRICT ELECTRIC CO
GEN GENON ENERGY INC
GXP GREAT PLAINS ENERGY INC
HE HAWAIIAN ELECTRIC INDS
IDA IDACORP INC
MGEE MGE ENERGY INC
NI NISOURCE INC
NU NORTHEAST UTILITIES
NWE NORTHWESTERN CORP
NVE NV ENERGY INC
OGE OGE ENERGY CORP
ORA ORMAT TECHNOLOGIES INC
POM PEPCO HOLDINGS INC
PNM PNM RESOURCES INC
POR PORTLAND GENERAL ELECTRIC CO
TE TECO ENERGY INC
UIL UIL HOLDINGS CORP
UNS UNISOURCE ENERGY CORP CO
HTM US GEOTHERMAL INC
WR WESTAR ENERGY INC
WEC WISCONSIN ENERGY CORP
35
Appendix C. Portfolio C - Nuclear Utility Firms
Portfolio C
Ticker Firm Name MW
EXC EXELON CORP 16,715
ETR ENTERGY CORP 10,129
D DOMINION RESOURCES INC/VA 5,691
NEE NEXTERA ENERGY INC 5,470
DUK DUKE ENERGY CORP 5,173
FE FIRSTENERGY CORP 3,862
PGN PROGRESS ENERGY INC 3,771
SO SOUTHERN CO 3,644
PEG PUBLIC SERVICE ENTERPRISE GP 3,611
PCG P G & E CORP 2,240
EIX EDISON INTERNATIONAL 2,236
PPL PPL CORPORATION 2,093
AEP AMERICAN ELECTRIC POWER 2,069
CEG CONSTELLATION ENERGY GROUP 1,939
XEL XCEL ENERGY INC 1,668
AEE AMEREN CORPORATION 1,190
PNW PINNACLE WEST CAPITAL 1,147
NRG NRG ENERGY INC 1,126
DTE DTE ENERGY COMPANY 1,122
SCG SCANA CORP 644
36
Appendix D. Portfolio D - Largest Utility Firms
Largest Utility Firms
Company Ticker Debt - Total
Qtly [Q4Y10]
Market Value -
Total Qtly
[Q4Y10]
Firm Size
(Market Value +
Total Debt)
SOUTHERN CO SO 20752.00 32240.89 52992.89
NEXTERA ENERGY INC NEE 20822.00 21880.62 42702.62
DOMINION RESOURCES INC D 17641.00 24820.32 42461.32
DUKE ENERGY CORP DUK 18426.00 23669.49 42095.49
EXELON CORP EXC 12828.00 27559.23 40387.23
AMERICAN ELECTRIC POWER CO AEP 18631.00 17299.44 35930.44
PG&E CORP PCG 13395.00 18907.66 32302.66
AES CORP AES 19733.00 9593.05 29326.05
PPL CORP PPL 13357.00 12722.85 26079.85
FIRSTENERGY CORP FE 14765.00 11284.99 26049.99
PROGRESS ENERGY INC PGN 12642.00 12739.64 25381.64
CONSOLIDATED EDISON INC ED 10683.00 14455.41 25138.41
EDISON INTERNATIONAL EIX 12534.00 12576.31 25110.31
PUBLIC SERVICE ENTRP GRP INC PEG 9004.00 16095.07 25099.07
ENTERGY CORP ETR 11816.31 12660.58 24476.88
XCEL ENERGY INC XEL 9784.96 11358.97 21143.92
CALPINE CORP CPN 10256.00 5928.76 16184.76
DTE ENERGY CO DTE 8164.00 7678.48 15842.48
37
Appendix E. Portfolio E - Mid-Sized Utility Firms
Med-Sized Utility Firms
Company Ticker
Debt - Total
Qtly
[Q4Y10]
Market Value
- Total Qtly
[Q4Y10]
Firm Size
(Market Value
+ Total Debt)
NRG ENERGY INC NRG 10511.00 4889.61 15400.61
AMEREN CORP AEE 7737.00 6776.88 14513.88
NISOURCE INC NI 7352.80 4913.43 12266.22
CMS ENERGY CORP CMS 7386.00 4643.08 12029.08
WISCONSIN ENERGY CORP WEC 5063.30 6879.91 11943.21
CONSTELLATION ENERGY GRP INC CEG 4786.50 6119.54 10906.03
NORTHEAST UTILITIES NU 5147.72 5625.16 10772.89
SCANA CORP SCG 4909.00 5156.20 10065.20
GENON ENERGY INC GEN 6081.00 2936.97 9017.97
PEPCO HOLDINGS INC POM 4679.00 4107.75 8786.75
NV ENERGY INC NVE 5280.03 3306.29 8586.32
PINNACLE WEST CAPITAL CORP PNW 3694.27 4508.52 8202.79
TECO ENERGY INC TE 3238.40 3825.22 7063.62
OGE ENERGY CORP OGE 2507.90 4444.70 6952.60
ALLIANT ENERGY CORP LNT 2752.10 4077.57 6829.67
GREAT PLAINS ENERGY INC GXP 3796.40 2631.48 6427.88
WESTAR ENERGY INC WR 3033.46 2821.14 5854.60
DYNEGY INC DYN 4774.00 680.35 5454.35
38
Appendix F. Portfolio F - Smallest Utility Firms
Smallest Utility Firms
Company Ticker
Debt - Total
Qtly
[Q4Y10]
Market Value
- Total Qtly
[Q4Y10]
Firm Size
(Market Value
+ Total Debt)
COVANTA HOLDING CORP CVA 2367.71 2576.63 4944.34
DPL INC DPL 1324.10 3006.14 4330.24
HAWAIIAN ELECTRIC INDS HE 1627.18 2158.01 3785.19
IDACORP INC IDA 1677.76 1827.00 3504.76
PORTLAND GENERAL ELECTRIC CO POR 1827.00 1634.36 3461.36
CLECO CORP CNL 1561.98 1861.78 3423.76
UNISOURCE ENERGY CORP UNS 1899.40 1309.67 3209.06
UIL HOLDINGS CORP UIL 1672.88 1513.13 3186.01
PNM RESOURCES INC PNM 1787.85 1128.48 2916.33
BLACK HILLS CORP BKH 1440.23 1178.07 2618.30
AVISTA CORP AVA 1322.34 1286.34 2608.68
NORTHWESTERN CORP NWE 1103.92 1044.51 2148.43
ORMAT TECHNOLOGIES INC ORA 789.67 1343.85 2133.52
ALLETE INC ALE 786.00 1333.91 2119.91
EL PASO ELECTRIC CO EE 854.45 1171.98 2026.43
EMPIRE DISTRICT ELECTRIC CO EDE 717.95 923.01 1640.96
MGE ENERGY INC MGEE 358.52 988.36 1346.87
CENTRAL VERMONT PUB SERV CV 226.41 291.63 518.04
U S GEOTHERMAL INC HTM 13.59 86.58 100.17