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.

v

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

vi

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

3

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

6

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

7

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

8

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

9

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

11

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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Changes in Firm Value. Journal of Accounting and Economics, (18), pp. 357 - 377.

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De Boer, C. & Catsburg, I., 1988. A Report: The Impact of Nuclear Accidents on Attitudes

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Stock Prices. Journal of Financial and Quantitative Analysis, 27 (2), pp. 283 - 301.

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Information. International Economic Review, 10 (1), pp. 1-21.

Fields, M. A. & Janjigian, V., 1989. The Effect of Chernobyl on Electric-Utility Stock Prices.

Journal of Business Research, (18), pp. 81 - 87.

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Fraser, D. R., Kolari, J. W. & Uselton, G. C., 1988. Intra industry Risk Changes in the Electric

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Coefficients in Linear Regressions: A Generalization. American Statistician, 24 (5), pp. 18 - 22.

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Prices: A Note. The Journal of Finance, (38), pp. 1285 - 1292.

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Available at:

http//www.nei.org/filefolder/US_Nuclear_Power_Plant_Operations_Owners_and_Holding_Com

panies.xls [Accessed July 2011].

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