Annals of the University of North Carolina Wilmington ...PERFORMANCE OF SOCIALLY RESPONSIBLE INDEXES...

Annals of the

University of North Carolina Wilmington

International Masters of Business Administration

http://csb.uncw.edu/imba/

http://www.csb.uncw.edu/imba/

PERFORMANCE OF SOCIALLY RESPONSIBLE INDEXES

OVER DIFFERENT MARKET REGIMES

Maggie A. Land

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 Wilmington

2010

Approved by

Advisory Committee

Cetin Ciner Ravija Badarinathi

William Sackley Chair

Accepted by

_____________________________ Dean, Graduate School

ii

TABLE OF CONTENTS

ABSTRACT ............................................................................................................................. iii

ACKNOWLEDGMENTS..........................................................................................................iv

DEDICATION............................................................................................................................v

LIST OF TABLES.....................................................................................................................vi

LIST OF FIGURES ................................................................................................................ viii

CHAPTER 1: INTRODUCTION ................................................................................................1

CHAPTER 2: LITERATURE REVIEW .....................................................................................3

CHAPTER 3: METHODOLOGY AND RESULTS ....................................................................6

CHAPTER 4: CONCLUSIONS ................................................................................................14

BIBLIOGRAPHY.....................................................................................................................43

APPENDIX ..............................................................................................................................45

iii

ABSTRACT

This thesis extends an investigation by Schröder (2007) into the performance of a

group of socially responsible indexes (SRI). Indexes were examined from the time period of

inception of each index to August 2010 or the end of an index’s life. Index performance was

riskadjusted using the Sharpe ratio and compared to the Sharpe ratio of the SRI’s

conventional benchmark. In addition, the excess returns were measured using Jensen’s alpha.

The contribution of this study is the analysis over a longer period of time and the examination

of differences in return patterns during different market regimes. Results from the study

indicate that SRIs perform comparably to their benchmarks and have less risk than indicated

in prior studies.

Audience: Investors, shareholders and academics.

Keywords: socially responsible investing, equity indexes, performance, risk

iv

ACKNOWLEDGMENTS

My thanks go to my committee chair, Dr. William Sackley, whose hard work and

guidance as my thesis chair has helped me persevere through the completion of my thesis. I

would also like to acknowledge my committee members, Dr. Cetin Ciner and Dr. Ravija

Badarinathi who each provided their expertise and input in my thesis.

My study could not have been performed without the help and support of the Cameron

School of Business, so I thank the school for providing me with invaluable resources and

motivation. The days in the Edward Jones Room staring at the Bloomberg terminals will

always be remembered.

Special thanks go to my fellow International MBA students; I know it was with their

support and endless encouragement that has kept me motivated. The 2010 UNCW Spring

IMBA class has been a great group of students to study with and each student has taught me

more than I ever would imagine. I would especially like to thank Javier Molto and Ramon

Ausina for their assistance in my data collection.

Finally, I would like to thank my family for their constant support and faith in me

throughout the past year.

v

DEDICATION

This thesis is dedicated to my mother and father, Lourdes and David Land, whose

support and encouragement have meant the world to me.

vi

LIST OF TABLES

Table Page

1. SRI Indexes and their conventional benchmarks .................................................. 6

2. Index Performance versus Benchmark Performance – Mean, Standard Deviation, and Sharpe Ratio ................................................................................ 9

3. Index Performance versus Benchmark Performance – Jensen’s alpha and Beta tests.................................................................................................... 12

4. Descriptive Statistics ......................................................................................... 23

5. H0: α = 0 ,H0: β = 1 for INSYV vs. FSX5ES...................................................... 24

6. H0: α = 0 ,H0: β = 1 for CALVIN vs. RIY .......................................................... 25

7. H0: α = 0 ,H0: β = 1 for 4EU5X vs. FTREEURB................................................ 26

8. H0: α = 0 ,H0: β = 1 for 4GEU vs. FTREEURB.................................................. 27

9. H0: α = 0 ,H0: β = 1 for 4GL1 vs. FTAD01 ........................................................ 28

10. H0: α = 0 ,H0: β = 1 for 4GGL vs. FTAD01 ....................................................... 29

11. H0: α = 0 ,H0: β = 1 for 4UK5X vs. ASX ........................................................... 30

12. H0: α = 0 ,H0: β = 1 for 4GUK vs. ASX ............................................................. 31

13. H0: α = 0 ,H0: β = 1 for 4US1 vs. LOUSAX....................................................... 32

14. H0: α = 0 ,H0: β = 1 for 4GUS1 vs. LOUSAX.................................................... 33

15. H0: α = 0 ,H0: β = 1 for HMNIEU vs. MXWO................................................... 34

16. H0: α = 0 ,H0: β = 1 for HMNIXUS vs. SPX ...................................................... 35

17. H0: α = 0 ,H0: β = 1 for HMNIXGL vs. MXWO ................................................ 36

18. H0: α = 0 ,H0: β = 1 for HMNIXSW vs. MXSE.................................................. 37

19. H0: α = 0 ,H0: β = 1 for JSI vs. SPTSX60........................................................... 38

20. H0: α = 0 ,H0: β = 1 for KEMSSRITEUR vs. JSSCEURO.................................. 39

21. H0: α = 0 ,H0: β = 1 for KLD vs. SPX................................................................ 40

22. H0: α = 0 ,H0: β = 1 for NATUR vs. MXWO ..................................................... 41

vii

23. H0: α = 0 ,H0: β = 1 for ETHIEGL vs. SPGLOB ................................................ 42

24. H0: α = 0 ,H0: β = 1 for ETHIEEU vs. SXXP ..................................................... 43

viii

LIST OF FIGURES

Figure Page

1. S&P 500 Monthly Performance ........................................................................... 7

2. Sharpe ratio ASPI Index vs. DJ EUROSTOXX Benchmark............................... 16

3. Sharpe ratio CALVIN Index vs. RIY Benchmark .............................................. 16

4. Sharpe ratio 4EU5X Index vs. FTREEURB Benchmark .................................... 16

5. Sharpe ratio 4GEU Index vs. FTREEURB Benchmark ...................................... 17

6. Sharpe ratio 4GL1 Index vs. FTAD01 Benchmark............................................. 17

7. Sharpe ratio 4GGL Index vs. FTADO1 Benchmark ........................................... 17

8. Sharpe ratio 4UK5X Index vs. ASX Benchmark................................................ 18

9. Sharpe ratio 4GUK Index vs. ASX Benchmark.................................................. 18

10. Sharpe ratio 4US1K Index vs. LOUSAX Benchmark ........................................ 18

11. Sharpe ratio 4GUS Index vs. ASX Benchmark .................................................. 19

12. Sharpe ratio HMNIEU Index vs. MXWO Benchmark........................................ 19

13. Sharpe ratio HMNIXUS Index vs. SPX Benchmark........................................... 19

14. Sharpe ratio HMNIXGL Index vs. MXWO Benchmark..................................... 20

15. Sharpe ratio HMNISW Index vs. MXSE Benchmark......................................... 20

16. Sharpe ratio JSI Index vs. SPTSX60 Benchmark ............................................... 20

17. Sharpe ratio KEMSSRITEUR Index vs. JSSCEURO Benchmark ...................... 21

18. Sharpe ratio KLD Index vs. SPX Benchmark..................................................... 21

19. Sharpe ratio NATUR Index vs. MXWO Benchmark.......................................... 21

20. Sharpe ratio ETHIEGL Index vs. SPGLOB Benchmark..................................... 22

21. Sharpe ratio ETHIEEU Index vs. SXXP Benchmark.......................................... 22

CHAPTER 1: INTRODUCTION

Socially responsible investing (SRI) results when the societal impact of an investment

is included in the investor’s assetselection process. (Socially Responsible Investing Basic for

Individuals) The growth trend in socially responsible investments and funds has been

significant, with SRI assets rising more than 324 percent from $639 billion in 1995 to $2.71

trillion in 2001. During the same period, the broader universe of assets under professional

management increased less than 260 percent from $7 trillion to $25.1 trillion (2007 Report of

Socially Responsible Investing Trends in the United States, 2008). In the UK, the value of

assets under management by ethical funds has increased from £400 million in May 1992 to

£4,200 million in July 2004 (Collison, Cobb, Power, & Stevenson, 2008). SRI investing has

truly become an integral force in new investment strategies. SRI investments have also been

referred to as ethical funds, or green funds.

There are many factors that have led to the growth in SRIs, for example, the rising

concern for global climate change has promoted the creation of unique SRI funds that either

promote green energy and/or exclude any companies involved in nonenvironmentally

friendly actions. SRI funds and indexes also look at new and old political issues including the

human rights crisis in Sudan and the exclusion of companies that do business with some

countries in Africa that have unethical practices. There are many different exclusionary

screens one can place on their investment choices, such as, excluding alcohol, tobacco,

firearms, gambling, or other criteria that at least in the minds’ of many investors, do not

benefit society. In addition to exclusionary criteria, some permit only investments that

promote green energy. According to the EIRIS (Ethical Investment Research Services) the

following are categories of what some SRIs take into account when created:

Animals: animal testing, fur, intensive farming and meat sale

2

Corporate ethics: bribery and corruption, social, environmental, and ethical risk

management, and women on the board of directors

Developing countries: commodity extraction, debt, breast milk substitutes, access to

medicines, and tobacco marketing

Environment: environmental management, policy, reporting and performance,

chemicals of concern, climate change and greenhouse gases, mining and quarrying, nuclear

power, sustainable timber, pollution, water pollution, biodiversity, and genetic engineering

Human rights

Military issues

People and stakeholder issues: equal opportunities, community involvement, health

and safety, relationship with customers and suppliers, supply chains, trade unions, and

training and development.

Positive products and services

Other ethical issues: alcohol, contraception and abortion, gambling, pornography and

adult entertainment services, and tobacco

The composition of funds and indexes has guidelines that can significantly change the

composition of the individual SRIs.

One of the major questions that is asked when SRIs are analyzed is: “If you invest in a

socially responsible way, will you receive the same returns as if you were to invest

traditionally?” The purpose of this study will be to see if prior research findings still stand.

With the fluctuations in the market over the past three to four years, there may be a variation

in the performance of SRIs, which this study will investigate by looking at SRI indexes and

comparing their performance to the conventional benchmarks. There will be an expansion

and replication of prior research to compare with earlier studies of SRIs.

CHAPTER 2: LITERATURE REVIEW

Given society’s changing preferences, SRIs are of great interest to investors and are

attracting much research attention. One of the original scholars of SRI research is Milton

Moskowitz who looked at the theory of a corporation’s performance in response to what

customers and investors perceive about their social responsibility (Moskowitz, 1972). To

demonstrate the value of Moskowitz’s contributions, an annual award is offered to recognize

outstanding quantitative work in socially responsible investing (Moskowitz Prize for Socially

Responsible Investing).

An extensive array of studies has investigated the many different factors of socially

responsible investing from analyzing reasons behind the phenomena, to composition of SRIs,

to analyzing their performance in comparison to benchmarks. Some of the studies have

analyzed the SRI mutual funds, (Bauer, Koedijk, & Otten, 2002)(Hamilton, Jo, & Statman,

1993)(Kreander, Gray, Power, & Sinclair, 2005), some have evaluated individual companies,

and others have compared a mixture of socially responsible investments. Prior studies have

used time frames from the 1980s to the 2000s and current published research does not extend

beyond 2005. A majority of the research found that on average, SRIs performed in line with

regular investments and there was no statistically significant difference.

This thesis will focus on the recent research that investigates the performance of

socially responsible indexes in comparison to their conventional benchmarks. The following

articles will be discussed further to develop the hypothesis for the present research.

1. Is there a Difference? The Performance Characteristics of SRI Equity Indices

(Schröder, 2007).

This study looks at 29 international SRI equity indexes and examines whether there is

a difference in the performance of the SRI in comparison to their conventional benchmark.

4

Schröder examines the time frame from fund inception to December 2003. The results

indicate that SRI equity indexes do not display a significant difference in performance but

they do carry higher risk in comparison to their benchmarks. A key factor in this study is that

Schröder finds that the SRI equity indexes and the benchmarks have comparable Sharpe

ratios. This research also explains why indexes are an effective way to measure the socially

responsible aspect of investing in comparison to the appropriate benchmark. Schröder’s

research provides is a foundation for the selection of SRI indexes used in this thesis.

2. Socially Responsible Indexes: Composition, performance and tracking error

(Statman, 2005).

Statman analyzes the different social screens that encourage the creation of SRIs, as

well as the performance of these investments against the S&P 500. The four indexes that

were observed were the Domini 400 index, Calvert index, Citizens index, and the U.S.

portion of the Dow Jones Sustainability Index. The social scores of the indexes focus on

different social characteristics and it is observed that each SRI index has different purposes in

their investment policy. Statman found that SRIs performed better during market booms, but

lagged during market busts.

3. The Financial Performance of the FTSE4Good Indices (Collison, Cobb, Power, &

Stevenson, 2008).

This study centralized the research on the financial performance of the FTSE4Good

indexes over the time period of 1996 to 2005 in which it was discovered that the FTSE4Good

indexes outperformed their benchmarks. There are four main FTSE4Good indexes and they

include an index in the United Kingdom, a European index, a United States index, as well as

a Global index. All of the indexes are based on a larger FTSE index, specifically the FTSE

AllShare Index, FTSE All World Europe Index, FTSE US Index, and FTSE AllWorld

Developed Index, respectively. The difference in performance was attributed to differences

5

in risk and the results indicate that investors do not suffer when they follow a socially

responsible strategy.

CHAPTER 3: METHODOLOGY AND RESULTS

This study looks at 20 investible, international SRI equity indexes and their

conventional benchmarks. The use of benchmarks to compare performance has become

somewhat the research standard and is referred to as the matching approach. Selected

benchmarks are either the official benchmark chosen by the SRI index or the index used in

prior analysis based on a correlation of performance to the SRI fund in question. The SRI

equity indexes are composed of global, European, American, Canadian, British, and Swedish

indexes and are listed in Table 1 with their corresponding ticker. The returns for all indexes

were obtained from the Bloomberg database in a monthly time series.

Table 1. SRI Indexes and their conventional benchmarks

Index Name Ticker Benchmark Name BM Ticker Region

ASPI INSYV DJ EUROSTOXX FSX5ES Eurozone

Calvert Social Index CALVIN Russell 1000 RIY US

Ethical Index Euro ECAPEG13 STOXX Europe 600 SXXP Eurozone

Ethical Index Global ECAPGCMB MSCI World MXWO Eurozone

FTSE4Good Europe 50 4EU5X FSTE AW Europe FTREEURB Europe

FTSE4Good Europe 4GEU FSTE AW Europe FTREEURB Europe

FTSE4Good Global 100 4GL1 FTSE Developed World FTAD01 World

FTSE4Good Global 4GGL FTSE Developed World FTAD01 World

FTSE4Good UK 50 4UK5X FT All Share ASX UK

FTSE4Good UK 4GUK FT All Share ASX UK

FTSE4Good US 100 4US1 FTSE Local USA LOUSAX US

FTSE4Good US 4GUS FTSE Local USA LOUSAX US

Humanix 175 Europe HMNIEU DJ STOXX DJW1 Europe

Humanix 175 US HMNIXUS S&P 500 SPX US

Humanix 200 Global HMNIXGL MSCI World MXWO World

Humanix 50 Sweden HMNIXSW MSCI Sweden MXSE Sweden

Jantzi Social Index JSI S&P/TSE60 SPTSX60 Canada

Kempen SNS Smaller KEMSSRIT HSBC Smaller European JCSCEURO Europe

KLD Domini 400 Social KLDI400P S&P 500 SPX US

Naturaktienindex NX25 MSCI World MXWO World

7

The index returns are all converted into USD to allow for comparisons of returns, and the

time frame for analysis includes all the data available for the fund, from inception until the

end of August 2010. Some SRI equity indexes were only available for a brief period of time;

for example, the Humanix Social indexes were only in use from June 2001 to May 2004.

There are several reasons as to why indexes are being analyzed instead of SRI

equities or SRI mutual funds as analyzed in past studies. The advantages of using SRI

indexes are that the SRI screen can be measured directly and the performance of a portfolio

does not interfere with the SRI screens. In addition, the market timing and transaction costs

of investment funds does not need to be considered in analyzing indexes as they would with

individual SRI funds (Schröder, 2007). The performances of these indexes have been risk

adjusted using the Sharpe ratio and also over specified time periods to distinguish trends

during different market regimes. The time periods that are analyzed are differentiated by

whether the market was increasing or decreasing in relation to the S&P 500, shown in Figure

1.

Figure 1. S&P 500 Monthly Performance

8

In past studies, SRI indexes have been analyzed over a period of time where the

market may have trended in one predominant direction. Thus, one innovation in this study is

the segmentation of study period by market regime. In addition, the time period for data

analysis has been extended to include all returns available to August 2010. The largest time

frames examined are for KLD Domini 400, one of the oldest SRI equities, which was

examined over 10 years. The shortest time frames studied would be with the Humanix

indexes, at approximately three years each. The time periods that are observed are: the whole

period in which an index is available (from inception to its close if applicable), the time

frame of the 1990s to August 2000 in which the market was expanding, September 2000 to

February 2003, March 2003 to October 2007, November 2007 to June 2009, and the final

period of July 2009 to August 2010. Statman analyzed the KLD against the S&P 500 and

found that there were performance differences in comparison to how the market was

performing (Statman, 2006). His analysis motivated the idea of looking at the performance

of the SRI indexes over the ups and downs of market performance over the past 10 years.

Although prior studies frequently use multifactor models, their use should not be

necessary in this study for three reasons, according to Schröder:

1. SRI indexes do not follow a specific investment style.

2. Indexes are adjusted infrequently.

3. SRI indexes are closely related to their conventional benchmark index.

This study will follow similar methodology to Schröder in the use of the Sharpe ratio, and

will use monthly data for all the indexes, as well as, the onemonth federal funds rate as a

proxy for the riskfree interest rate.

9

The Sharpe ratio measues the return above the riskfree interest rate divided by the total

risk of the investment.

(1)

μ = annualized mean of the index’s returns

rf = riskfree interest rate (US onemonth Federal Funds Rate)

σ = Volatility –as measured by the standard deviation of the index’s returns

The purpose of calculating the Sharpe ratio for the SRI indexes and their benchmarks is to

allow for comparison of performance. As seen in Table 2, eight out of the 20 indexes

outperform their conventional benchmark.

Table 2. Index Performance versus Benchmark Performance – Mean, Standard Deviation, and Sharpe Ratio

SRI Indexes Mean Mean BM

Standard Deviation

Standard Deviation

BM

Sharpe Ratio

Sharpe Ratio BM

ASPI 0.0306 ‐0.0119 0.2224 0.2322 0.0160 ‐0.1597 CALVIN ‐0.0335 ‐0.0241 0.1785 0.1630 ‐0.3771 ‐0.1597 4EU5X ‐0.0173 0.0395 0.1993 0.2329 ‐0.2070 0.0506 4GEU 0.0006 0.0395 0.2027 0.2329 ‐0.1080 0.0506 4GL1 ‐0.0269 0.0042 0.1733 0.1672 ‐0.3054 ‐0.1102 4GGL ‐0.0215 ‐0.0016 0.1721 0.1754 ‐0.2661 ‐0.1357 4UK5X ‐0.0043 0.0098 0.1771 0.1801 ‐0.1519 ‐0.0728 4GUK ‐0.0051 0.0098 0.1767 0.1801 ‐0.1564 ‐0.0728 4US1 ‐0.0542 ‐0.0663 0.1990 0.1991 ‐0.4076 ‐0.4824 4GUS ‐0.0526 ‐0.0663 0.2004 0.1991 ‐0.3948 ‐0.4824 HMNIEU ‐0.0276 ‐0.0130 0.2257 0.1568 ‐0.1992 ‐0.1893 HMNIXUS ‐0.0511 ‐0.0282 0.0991 0.1619 ‐0.7117 ‐0.2819 HMNIXGL ‐0.0661 ‐0.0130 0.0954 0.1568 ‐0.9177 ‐0.1893 HMNIXSW 0.0596 0.0628 0.2634 0.2901 0.1510 0.1470 JSI 0.1138 0.1216 0.2178 0.2177 0.2651 0.2838 KEMSSRITEUR 0.1199 0.0598 0.2345 0.2211 0.2057 0.0759 KLD 0.1256 0.0937 0.1544 0.1504 0.1629 0.0974 Natur 0.3370 0.0201 0.2251 0.1579 0.7654 ‐0.0542 ETHIEGL 0.1231 0.1396 0.2287 0.2300 0.3922 0.4359 ETHIEEU 0.1890 0.2068 0.1314 0.1308 0.8146 0.8924

i

f i

r SR

σ µ −

= i

10

In this table we can see that eight of the 20 indexes outperform for the entire time frame,

which extends from index creation to either when it ended or the end of August 2010

(Outperformance is indicated by the highlighted Sharpe ratio.). The indexes that

outperformed were: ASPI, the US FTSE4Good indexes, Humanix 50 Sweden, Kempen SNS

Smaller European SRI Index, KLD Domini 400 Social index, and Naturaktienindex. While

seven of the 20 indexes outperformed, the comparison shows that six of the 20 indexes have a

higher mean than their benchmark. This accounts for about 35% of the indexes while in prior

research from Schröder, 17 out of 29 (approximately 59%) indexes had a mean excess return

that was higher for the SRI indexes. A higher mean return might only be the result of a

higher risk exposure; the riskadjusted returns should also be compared using the Sharpe ratio.

Eight of the SRI indexes show that the Sharpe ratio is higher than the relevant benchmark,

and for six of these, the SRI indexes outperform the relevant benchmark both in terms of

mean excess returns and the Sharpe ratio. In comparison to Schröder’s work, the results have

improved for the benchmark indexes and have worsened for the SRI indexes, where in the

earlier 18 indexes out of 29 indexes, the Sharpe ratio was higher and in 15 indexes, the mean

excess returns and Sharpe ratio were higher.

By calculating the Sharpe ratios for each period and graphing the results, we visually

see when particular indexes outperform or underperform. The FTSE4Good indexes in

Figures 4 – 11 have similar market movements. The FTSE4Good UK indexes had very high

returns in comparison to the ASX index’s life, but their performance resembles the

benchmark over time. The Humanix indexes in Figures 12 – 15 show that the Humanix SRI

equity indexes underperformed in comparison to their benchmark which may explain why the

indexes ended in May 2004. While the figures show that at times the SRIs may under/out

perform their benchmark, other tests must be calculated to verify whether there is a

significant difference in performance.

11

The next part of the study compares the relevant risk of each index to the differences

in characteristics between SRI equity indexes and their conventional benchmarks through the

use of regressionbased tests. The first test looks at the SRI equity index against one

benchmark and will test for out/underperformance. The second test will test for

significance in the differences in risk exposure using equation (2).

Therefore, the basic equation used to measure the relative performance of the SRI

indexes is the linear regression of the excess returns of the SRI index (r ) on the excess

returns of the benchmark index (r ):

(2) = + +

The performance of the SRI indexes is estimated by Jensen’s alpha ( ), i.e., the

excess return that is not explained by the systematic risk exposure with respect to the

benchmark index. As in the Capital Asset Pricing Model (CAPM), a β > 1 indicates that the

systematic risk of the SRI index is higher compared to the benchmark. For β < 1 the SRI

index has a lower systematic risk compared to the benchmark.

12

Table 3: Index Performance versus Benchmark Performance – Jensen’s Alpha and Beta tests

Ticker Start Date End date Adj. R 2 Alpha Beta

H0: α = 0 H0: β = 1 INSYV Jan‐04 Aug‐10 0.9785 0.0014 *** 0.9397 *** CALVIN Apr‐00 Aug‐10 0.9685 ‐0.0004 not rejected 1.0748 *** 4EU5X Jul‐01 Aug‐10 0.9403 ‐0.0017 *** 0.8226 *** 4GEU Jul‐01 Aug‐10 0.9667 ‐0.0010 ** 0.8512 *** 4GL1 Jul‐01 Aug‐10 0.9590 ‐0.0013 *** 1.0077 not rejected 4GGL Nov‐01 Aug‐10 0.9877 ‐0.0005 ** 1.0500 *** 4UK5X Jul‐01 Aug‐10 0.9823 ‐0.0005 * 0.9690 ** 4GUK Jul‐01 Aug‐10 0.9922 ‐0.0005 *** 0.9739 *** 4US1 Aug‐06 Aug‐10 0.9509 0.0005 not rejected 0.9691 not rejected 4GUS Aug‐06 Aug‐10 0.9567 0.0006 not rejected 0.9791 not rejected HMNIEU Jun‐01 May‐04 0.8847 ‐0.0004 not rejected 1.3416 *** HMNIXUS Jun‐01 May‐04 0.9614 ‐0.0008 not rejected 1.1683 *** HMNIXGL Jun‐01 May‐04 0.9365 ‐0.0022 ** 1.1368 ** HMNIXSW Jun‐01 May‐04 0.9394 0.0002 not rejected 0.8765 *** JSI May‐01 Aug‐10 0.9723 ‐0.0001 not rejected 0.9819 not rejected KEMSSRITEUR Jan‐99 Aug‐10 0.8860 0.0011 not rejected 0.9789 not rejected KLD May‐90 Aug‐10 0.9262 0.0004 not rejected 0.9887 not rejected Natur May‐03 Jun‐09 0.8270 0.0058 *** 1.2953 *** ETHIEGL Dec‐02 Jun‐08 0.9567 ‐0.0002 not rejected 0.9637 not rejected ETHIEEU Dec‐02 Jun‐08 0.9758 ‐0.0003 not rejected 0.9894 not rejected

* denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

In Table 3, Column 4 contains the estimated values for the parameter α. The rejection

of the null hypothesis H0: α = 0 is indicated by asterisks. The results show that Jensen’s alpha,

i.e., the relative riskadjusted performance, is in almost all cases not significantly different

from zero. This provides clear indication that the performance of the SRI equity indexes does

not deviate systematically from their benchmarks. There are nine exceptions compared with

the two exceptions we find in previous work (Schröder, 2007).

The relatively high values for the adjusted R 2 (column 4 of Table 3), which are for

most SRI indexes above 80%, show that the majority of SRI indexes can be well

approximated by the benchmark indexes. Therefore, the results of the SRI stock indexes

using equation (2) show that most of the indexes exhibit no out or under performance

compared to their benchmark and combined with the high estimates for the adjusted R 2 , it can

13

be concluded that the majority of the SRI stock indexes deviate only slightly from their

individual benchmark. If such a deviation is significant, it is in most cases due to a β

coefficient different from one.

Column 5 shows the results for the βcoefficients and for the test of H0: β = 1. The

estimated values can be interpreted as a measure of risk relative to the benchmark index. For

some SRI indexes the estimated β is higher than 1; 8 out of the 20 SRI indexes were not

rejected and therefore the null hypothesis could not be rejected while 12 out of the 20 SRI

indexes can be characterized by a statistically significant higher or lower systematic risk.

The 12 that were characterized as statistically significant had 6 that attributed to higher

systematic risk and 6 to lower systematic risk.

These results differ slightly from previous works. Schröeder (2007) obtained the

following results: 19 out of the 29 SRI indexes were significant, and only 5 out of 29 had a β

lower than 1, which exhibits how SRI indexes, over time, are becoming less risky compared

with their benchmark. Although it is outside the scope of this study to ascertain the changing

risk exposure of the SRI indexes over time, it may reflect the availability of additional

investment alternatives within the socially responsible realm. The two indexes that

significantly outperform are ASPI and Naturaktienindex shown in Figures 2 & 19,

respectively.

CHAPTER 4: CONCLUSIONS

The study exhibits that investors can invest in a socially responsible way without the

risk of receiving lower returns, at least on a riskadjusted basis. The SRIs have less excess

returns than in prior studies and their risk is lower. Over time, SRIs have under/out

performed their benchmarks, but for the most part they have moved within their benchmarks’

performance. There was no significant pattern to SRI performance over various time periods

overall, but some groups of SRIs, such as FTSE4Good Indexes and Humanix did behave

similarly within their SRI indexes.

One explanation as to why SRIs have less excess returns could be that social corporate

responsibility in the past represented an uninteresting cost for a company that was perceived

to have few benefits. The popularity and in some cases the obligation of acting socially

responsible has impacted the changes in performance. For example, the United Nations

Global Compact initiative 1 requires all companies that wish to work for the United Nations to

sign and demonstrate that they follow SRI practices that are based on the 10 principles from

the policy. If companies wish to work for international or national public organizations, they

have to implement social responsibility in their organization. It would be interesting to study

whether other factors influence the behavior of SRIs, such as new legislation or societal

changes.

At least one limitation to this study deserves mentioning in hopes of adding value to

future research extensions. Most SRI indexes are owned by proprietary companies and the

data was not accessible. Therefore, opportunities to expand the database would add

credibility to the results. Overall, however, the available data provided a basis for estimating

the regressions which led to the calculated Sharpe ratios and the conclusions which were

drawn.

1 www.unglobalcompact.org – The United Nations Global Compact is a strategic policy initiative for businesses that are committed to aligning their operations and strategies with 10 universally accepted principles in the areas of human rights, labor, environment and anticorruption.

15

Figures 2 – 21 have an xaxis of the time periods and a yaxis of the indexes’ Sharpe ratio Blue lines represent the SRI index and Red lines represent the corresponding benchmark index

Figure 2: Sharpe ratio ASPI Index vs. DJ EUROSTOXX Benchmark

Figure 3: Sharpe ratio CALVIN Index vs. RIY Benchmark

Figure 4: Sharpe ratio 4EU5X Index vs. FTREEURB Benchmark

‐1.0

‐0.5

0.0

0.5

1.0

1.5

90‐00 00‐03 03‐07 07‐09 09‐10

ASPI

DJ EUROSTOXX

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

90‐00 00‐03 03‐07 07‐09 09‐10

CALVIN

RIY

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

90‐00 00‐03 03‐07 07‐09 09‐10

4EU5X

FTREEURB

16

Figure 5: Sharpe ratio 4GEU Index vs. FTREEURB Benchmark

Figure 6: Sharpe ratio 4GL1 Index vs. FTADO1 Benchmark

Figure 7: Sharpe ratio 4GGL Index vs. FTADO1 Benchmark

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

90‐00 00‐03 03‐07 07‐09 09‐10

4GEU FTREEURB

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

90‐00 00‐03 03‐07 07‐09 09‐10

4GL1

FTAD01

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

90‐00 00‐03 03‐07 07‐09 09‐10

4GGL

FTAD01

17

Figure 8: Sharpe ratio 4UK5X Index vs. ASX Benchmark

Figure 9: Sharpe ratio 4GUK Index vs. ASX Benchmark

Figure 10: Sharpe ratio 4US1K Index vs. LOUSAX Benchmark

‐4.0

‐3.0

‐2.0

‐1.0

0.0

1.0

2.0

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

4UK5X

ASX

‐4.0

‐3.0

‐2.0

‐1.0

0.0

1.0

2.0

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

4GUK

ASX

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

90‐00 00‐03 03‐07 07‐09 09‐10

4US1 LOUSAX

18

Figure 11: Sharpe ratio 4GUS Index vs. ASX Benchmark

Figure 12: Sharpe ratio HMNIEU Index vs. MXWO Benchmark

Figure 13: Sharpe ratio HMNIXUS Index vs. SPX Benchmark

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

90‐00 00‐03 03‐07 07‐09 09‐10

4GUS LOUSAX

‐2.0

‐1.0

0.0

1.0

2.0

3.0

4.0

90‐00 00‐03 03‐07 07‐09 09‐10

HMNIEU MXWO

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

HMNIXUS

SPX

19

Figure 14: Sharpe ratio HMNIXGL Index vs. MXWO Benchmark

Figure 15: Sharpe ratio HMNISW Index vs. MXSE Benchmark

Figure 16: Sharpe ratio JSI Index vs. SPTSX60 Benchmark

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

90‐00 00‐03 03‐07 07‐09 09‐10

HMNIXGL

MXWO

‐1.0 ‐0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

90‐00 00‐03 03‐07 07‐09 09‐10

HMNIXSW MXSE

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

90‐00 00‐03 03‐07 07‐09 09‐10

JSI

SPTSX60

20

Figure 17: Sharpe ratio KEMSSRITEUR Index vs. JSSCEURO Benchmark

Figure 18: Sharpe ratio KLD Index vs. SPX Benchmark

Figure 19: Sharpe ratio NATUR Index vs. MXWO Benchmark

Figure 20: Sharpe ratio ETHIEGL Index vs. SPGLOB Benchmark

‐2.0

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

KEMSSRITEUR

JSSCEURO

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

90‐00 00‐03 03‐07 07‐09 09‐10

KLD SPX

‐1.5

‐1.0

‐0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

Natur

MXWO

21

Figure 21: Sharpe ratio ETHIEEU Index vs. SXXP Benchmark

‐5.0

‐4.0

‐3.0

‐2.0

‐1.0

0.0

1.0

2.0

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

ETHIEGL

SPGLOB

‐5.0

‐4.0

‐3.0

‐2.0

‐1.0

0.0

1.0

2.0

3.0

90‐00 00‐03 03‐07 07‐09 09‐10

ETHIEEU

SXXP

22

Table 4. Descriptive Statistics Mean

Standard Error

Median Standard Deviation

Sample Variance

Range Min Max Sum Count

FSX5ES ‐0.0005 0.0034 0.0029 0.0305 0.0009 0.1909 ‐0.1227 0.0682 ‐0.0361 79

INSYV 0.0010 0.0033 0.0043 0.0290 0.0008 0.1715 ‐0.1091 0.0625 0.0807 79

RIY ‐0.0010 0.0019 0.0039 0.0210 0.0004 0.1248 ‐0.0836 0.0412 ‐0.1257 124

CALVIN ‐0.0015 0.0021 0.0016 0.0229 0.0005 0.1292 ‐0.0806 0.0486 ‐0.1862 124

FTREEURB 0.0012 0.0029 0.0047 0.0302 0.0009 0.1862 ‐0.1196 0.0665 0.1343 109

4EU5X ‐0.0007 0.0025 0.0012 0.0256 0.0007 0.1426 ‐0.0878 0.0549 ‐0.0752 109

FTREEURB 0.0012 0.0029 0.0047 0.0302 0.0009 0.1862 ‐0.1196 0.0665 0.1343 109

4GEU 0.0000 0.0025 0.0025 0.0262 0.0007 0.1549 ‐0.0995 0.0554 0.0025 109

FTAD01 0.0002 0.0021 0.0036 0.0217 0.0005 0.1372 ‐0.0918 0.0454 0.0164 109

4GL1 ‐0.0011 0.0021 0.0017 0.0223 0.0005 0.1269 ‐0.0778 0.0491 ‐0.1228 109

FTAD01 ‐0.0001 0.0022 0.0026 0.0227 0.0005 0.1385 ‐0.0888 0.0497 ‐0.0062 105

4GGL ‐0.0009 0.0022 0.0017 0.0222 0.0005 0.1269 ‐0.0778 0.0491 ‐0.0917 105

ASX 0.0003 0.0022 0.0017 0.0232 0.0005 0.1526 ‐0.0982 0.0544 0.0374 109

4UK5X ‐0.0002 0.0022 0.0003 0.0227 0.0005 0.1422 ‐0.0883 0.0539 ‐0.0174 109

ASX 0.0003 0.0022 0.0017 0.0232 0.0005 0.1526 ‐0.0982 0.0544 0.0374 109

4GUK ‐0.0002 0.0022 0.0015 0.0227 0.0005 0.1439 ‐0.0920 0.0520 ‐0.0204 109

LOUSAX ‐0.0029 0.0038 0.0044 0.0262 0.0007 0.1335 ‐0.0858 0.0477 ‐0.1371 48

4US1 ‐0.0023 0.0038 0.0050 0.0261 0.0007 0.1200 ‐0.0757 0.0443 ‐0.1087 48

LOUSAX ‐0.0029 0.0038 0.0044 0.0262 0.0007 0.1335 ‐0.0858 0.0477 ‐0.1371 48

4GUS ‐0.0022 0.0038 0.0054 0.0263 0.0007 0.1219 ‐0.0772 0.0447 ‐0.1050 48

MXWO ‐0.0005 0.0035 0.0021 0.0204 0.0004 0.0872 ‐0.0512 0.0360 ‐0.0172 35

HMNIEU ‐0.0011 0.0049 ‐0.0062 0.0291 0.0008 0.1424 ‐0.0751 0.0674 ‐0.0375 35

SPX ‐0.0011 0.0036 0.0031 0.0211 0.0004 0.0866 ‐0.0506 0.0360 ‐0.0384 35

HMNIXUS ‐0.0021 0.0042 0.0007 0.0251 0.0006 0.1115 ‐0.0636 0.0479 ‐0.0723 35

MXWO ‐0.0005 0.0035 0.0021 0.0204 0.0004 0.0872 ‐0.0512 0.0360 ‐0.0172 35

HMNIXGL ‐0.0027 0.0041 ‐0.0011 0.0240 0.0006 0.1046 ‐0.0576 0.0470 ‐0.0959 35

MXSE 0.0021 0.0064 0.0049 0.0377 0.0014 0.1528 ‐0.0804 0.0724 0.0749 35

HMNIXSW 0.0020 0.0058 0.0023 0.0341 0.0012 0.1408 ‐0.0719 0.0689 0.0714 35

SPTSX60 0.0031 0.0027 0.0056 0.0281 0.0008 0.2203 ‐0.1319 0.0884 0.3459 110

JSI 0.0030 0.0027 0.0056 0.0280 0.0008 0.2051 ‐0.1218 0.0834 0.3351 110

JSSCEURO 0.0017 0.0024 0.0026 0.0288 0.0008 0.2287 ‐0.1423 0.0864 0.2300 139

KEMSSRITEUR 0.0027 0.0025 0.0064 0.0299 0.0009 0.2171 ‐0.1240 0.0931 0.3800 139

SPX 0.0019 0.0012 0.0044 0.0191 0.0004 0.1266 ‐0.0806 0.0459 0.4631 243

KLD 0.0023 0.0013 0.0025 0.0196 0.0004 0.1169 ‐0.0739 0.0430 0.5508 243

MXWO 0.0007 0.0024 0.0048 0.0208 0.0004 0.1367 ‐0.0918 0.0449 0.0508 73

Natur 0.0067 0.0035 0.0112 0.0295 0.0009 0.2105 ‐0.1307 0.0797 0.4883 73

SPGLOB 0.0038 0.0016 0.0065 0.0133 0.0002 0.0740 ‐0.0380 0.0359 0.2503 66

ETHIEGL 0.0034 0.0016 0.0047 0.0131 0.0002 0.0786 ‐0.0365 0.0421 0.2272 66

SXXP 0.0051 0.0020 0.0054 0.0165 0.0003 0.0995 ‐0.0459 0.0535 0.3334 66

ETHIEEU 0.0047 0.0020 0.0046 0.0166 0.0003 0.1050 ‐0.0463 0.0588 0.3128 66

Benchmark

Table 5: H0: α = 0 ,H0: β = 1 for INSYV vs. FSX5ES SUMMARY OUTPUT y = INSYV Index

x = FSX5ES Index Regression Statistics

Multiple R 0.989314 R Square 0.978742 Adjusted R Square 0.978466 Standard Error 0.004257 Observations 79

ANOVA Df SS MS F Significance F

Regression 1 0.064261 0.064261 3545.214 0.000000 Residual 77 0.001396 0.000018 Total 78 0.065657

Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept 0.001450 0.000479 3.026292 0.003365 0.000496 0.002404 0.000184 0.002715 FSX5ES Index 0.939727 0.015783 59.541702 0.000000 0.908299 0.971154 0.898042 0.981412

t‐statistic

H0: β = 1 tstat = ‐3.82

Degree of Freedom =

78

10% 5% 2.5% 1% /t/ > 1.6646 1.9908 2.2855 2.6403

Reject H0: β = 1 with 99% confidence

24

Table 6: H0: α = 0 ,H0: β = 1 for CALVIN vs. RIY SUMMARY OUTPUT y = CALVIN Index

x = RIY Index Regression Statistics

Multiple R 0.984278 R Square 0.968803 Adjusted R Square 0.968547 Standard Error 0.004069 Observations 124 ANOVA

Df SS MS F Significance F

Regression 1 0.062734 0.06273

4 3788.5

95 0.000000

Residual 122 0.002020 0.00001

7 Total 123 0.064754

Coefficients Standard Error t Stat P‐value Lower 95%

Uppe r 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000412 0.000366

‐ 1.12694

0 0.2619

80 ‐0.001137 0.000 312 ‐0.001370 0.000545 ‐0.001019 0.000194

RIY Index 1.074788 0.017462

61.551567

0.000000 1.040221

1.109 355 1.029096 1.120480 1.045846 1.103730

t‐statistic H0: β = 1 tstat = 4.28 10% 5% 2.5% 1%

Degree of Freedom =

123 /t/ > 1.6602 1.984 2.2757 2.6259


25

Table 7: H0: α = 0 ,H0: β = 1 for 4EU5X vs. FTREEURB SUMMARY OUTPUT y = 4EU5X Index

x = FTREEURB Index Regression Statistics



Regression 1 0.066836 0.066836 1,701.52 0.000000 Residual 107 0.004203 0.000039 Total 108 0.071039

Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept ‐0.001703 0.000601 ‐2.835017 0.005480 ‐0.002894 ‐0.000512 ‐0.003279 ‐0.000128

FTREEURB Index 0.822579 0.019942 41.249536 0.000000 0.783047 0.862111 0.770281 0.874877

t‐statistic

H0: β = 1 tstat = ‐8.90 10% 5% 2.5% 1%

Degree of Freedom =

108 /t/ > 1.6602 1.984 2.2757 2.6259


26

Table 8: H0: α = 0 ,H0: β = 1 for 4GEU vs. FTREEURB SUMMARY OUTPUT y = 4GEU Index

x = FTREEURB Index Regression Statistics


ANOVA df SS MS F Significance F

Regression 1 0.071567 0.071567 3133.978 4.41E‐81 Residual 107 0.002443 2.28E‐05 Total 108 0.07401

Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept ‐0.00103 0.000458 ‐2.23907 0.027221 ‐0.00193 ‐0.00012 ‐0.00223 0.000176

FTREEURB Index 0.851191 0.015205 55.98194 4.41E‐81 0.82105 0.881333 0.811316 0.891067

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐9.79 /t/ > 1.6602 1.984 2.2757 2.6259

Degree of Freedom =

108


27

Table 9: H0: α = 0 ,H0: β = 1 for 4GL1 vs. FTAD01 SUMMARY OUTPUT y = 4GL1 Index

x = FTAD01 Index Regression Statistics




Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept ‐0.001278 0.000432 ‐2.955725 0.003838 ‐0.002135 ‐0.000421 ‐0.002412 ‐0.000144

FTAD01 Index 1.007741 0.020038 50.290606 0.000000 0.968018 1.047465 0.955189 1.060293

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = 0.39 /t/ > 1.6602 1.984 2.2757 2.6259 Degree of Freedom =

108

H0: β = 1 NOT REJECTED

28

Table 10: H0: α = 0 ,H0: β = 1 for 4GGL vs. FTAD01 SUMMARY OUTPUT y = 4GGL Index

x = FTAD01 Index Regression Statistics





FTAD01 Index 1.050019 0.011499 91.310429 0.000000 1.027213 1.072826 1.019840 1.080198

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = 4.35 /t/ > 1.6602 1.984 2.2757 2.6259

Degree of Freedom =

104


29

Table 11: H0: α = 0 ,H0: β = 1 for 4UK5X vs. ASX SUMMARY OUTPUT y = 4UK5X Index

x = ASX Index Regression Statistics



Regression 1 0.054666 0.05466

6 5980.4

43 0.000000

Residual 107 0.000978 0.00000

9 Total 108 0.055644


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000492 0.000290

‐ 1.69899

5 0.0922

26 ‐0.001066 0.0000

82 ‐0.001252 0.000267 ‐0.000973 ‐0.000012

ASX Index 0.969037 0.012531

77.333324

0.000000 0.944196

0.993877 0.936174 1.001899 0.948246 0.989828

t‐statistic 10% 5% 2.5% 1% H0: β = 1 tstat = ‐2.47 /t/ > 1.6602 1.984 2.2757 2.6259

Degree of Freedom =

108

Reject H0: β = 1 with 97.5% confidence

30

Table 12: H0: α = 0 ,H0: β = 1 for 4GUK vs. ASX SUMMARY OUTPUT y = 4GUK Index

x = ASX Index Regression Statistics




Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept ‐0.000521 0.000193 ‐2.705261 0.007943 ‐0.000902 ‐0.000139 ‐0.001026 ‐0.000016 ASX Index 0.973903 0.008329 116.923467 0.000000 0.957391 0.990415 0.952059 0.995747

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐3.13 /t/ > 1.6602 1.984 2.2757 2.6259

Degree of Freedom =

108


31

Table 13: H0: α = 0 ,H0: β = 1 for 4US1 vs. LOUSAX SUMMARY OUTPUT y = 4US1 Index

x = LOUSAX Index Regression Statistics



Regression 1 0.030399 0.030399 911.909

4 0.000000 Residual 46 0.001533 0.000033 Total 47 0.031932


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept 0.000502 0.000838 0.599211 0.55197

2 ‐0.001185 0.0021

90 ‐0.001750 0.002755 ‐0.000905 0.001910

LOUSAX Index 0.969118 0.032092

30.19783 7

0.00000 0 0.904520

1.033717 0.882886 1.055351 0.915246 1.022990

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐0.96 /t/ > 1.6779 2.0117 2.3155 2.6846 Degree of Freedom =

47


32

Table 14: H0: α = 0 ,H0: β = 1 for 4GUS1 vs. LOUSAX SUMMARY OUTPUT y = 4GUS Index

x = LOUSAX Index Regression Statistics



Regression 1 0.031028 0.03102

8 1040.2

84 0.000000

Residual 46 0.001372 0.00003

0 Total 47 0.032400


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept 0.000609 0.000793 0.76754

4 0.4466

80 ‐0.000988 0.0022

05 ‐0.001522 0.002740 ‐0.000723 0.001940

LOUSAX Index 0.979096 0.030356

32.253429

0.000000 0.917992

1.040200 0.897528 1.060664 0.928138 1.030054

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐0.69 /t/ > 1.6779 2.0117 2.3155 2.6846 Degree of Freedom =

47


33

Table 15: H0: α = 0 ,H0: β = 1 for HMNIEU vs. MXWO SUMMARY OUTPUT y = HMNIEU Index

x = MXWO Index Regression Statistics



Regression 1 0.025584 0.02558

4 261.768

5 0.000000

Residual 33 0.003225 0.00009

8 Total 34 0.028810


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000413 0.001672

‐ 0.24683

3 0.80656

7 ‐0.003813 0.0029

88 ‐0.004981 0.004156 ‐0.003241 0.002416

MXWO Index 1.341577 0.082920

16.179261

0.00000 0 1.172876

1.510278 1.114935 1.568219 1.201247 1.481906

t‐statistic 10% 5% 2.5% 1% H0: β = 1 tstat = 4.12 /t/ > 1.6909 2.0322 2.3451 2.7284

Degree of Freedom =

34


34

Table 16: H0: α = 0 ,H0: β = 1 for HMNIXUS vs. SPX SUMMARY OUTPUT y = HMNIXUS Index

x = SPX Index Regression Statistics



Regression 1 0.020637 0.020637 847.046

5 0.000000 Residual 33 0.000804 0.000024 Total 34 0.021441


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000782 0.000835 ‐

0.935451 0.35635

1 ‐0.002481 0.00091

8 ‐0.003065 0.001502 ‐0.002196 0.000632

SPX Index 1.168255 0.040141

29.10406 3

0.00000 0 1.086589

1.24992 2 1.058540 1.277971 1.100323 1.236188

t‐statistic 10% 5% 2.5% 1% H0: β = 1 tstat = 4.19 /t/ > 1.6909 2.0322 2.3451 2.7284

Degree of Freedom =

34


35

Table 17: H0: α = 0 ,H0: β = 1 for HMNIXGL vs. MXWO SUMMARY OUTPUT y = HMNIXGL Index






MXWO Index 1.136849 0.050702 22.422171 0.000000 1.033695 1.240003 0.998266 1.275432

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = 2.70 /t/ > 1.6909 2.0322 2.3451 2.7284 Degree of Freedom =

34

Reject H0: β = 1 with 97.5% confidence

36

Table 18: H0: α = 0 ,H0: β = 1 for HMNIXSW vs. MXSE SUMMARY OUTPUT y = HMNIXSW Index

x = MXSE Index Regression Statistics



Regression 1 0.037116 0.037116 527.853

7 0.000000 Residual 33 0.002320 0.000070 Total 34 0.039436


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept 0.000163 0.001420 0.115126 0.90904

3 ‐0.002725 0.003 052 ‐0.003717 0.004044 ‐0.002239 0.002566

X Variable 1 0.876487 0.038149 22.97506

6 0.00000

0 0.798871 0.954 103 0.772214 0.980760 0.811924 0.941050

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐3.24 /t/ > 1.6909

2.032 2 2.3451 2.7284

Degree of Freedom =

34


37

Table 19: H0: α = 0 ,H0: β = 1 for JSI vs. SPTSX60 SUMMARY OUTPUT y = JSI Index

x = SPTSX60 Index Regression Statistics



Regression 1 0.083443 0.083443 3864.57

3 0.000000 Residual 109 0.002353 0.000022 Total 110 0.085796


Uppe r

95% Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000083 0.000444 ‐

0.187861 0.85133

5 ‐0.000962 0.00 0796 ‐0.001246 0.001079 ‐0.000819 0.000652

SPTSX60 Index 0.981937 0.015795

62.16569 0

0.00000 0 0.950631

1.01 3244 0.940527 1.023348 0.955733 1.008141

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐1.14 /t/ > 1.6602

1.98 4 2.2757 2.6259

Degree of Freedom =

110


38

Table 20: H0: α = 0 ,H0: β = 1 for KEMSSRITEUR vs. JSSCEURO SUMMARY OUTPUT y = KEMSSRITEUR Index

x = JSSCEURO Index Regression Statistics



Regression 1 0.109513 0.10951

3 1073.8

26 0.000000

Residual 137 0.013972 0.00010

2 Total 138 0.123485


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept 0.001114 0.000858 1.29861

4 0.1962

58 ‐0.000582 0.0028

11 ‐0.001127 0.003355 ‐0.000307 0.002535 JSSCEURO Index 0.978887 0.029872

32.769277

0.000000 0.919817

1.037957 0.900855 1.056918 0.929417 1.028356

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐0.71 /t/ > 1.6602 1.984 2.2757 2.6259

Degree of Freedom =

138


39

Table 21: H0: α = 0 ,H0: β = 1 for KLD vs. SPX SUMMARY OUTPUT y = KLD Index

x = SPX Index Regression Statistics



Regression 1 0.086219 0.086219 3025.89

7 0.000000 Residual 240 0.006839 0.000028 Total 241 0.093058


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept 0.000368 0.000345 1.067445 0.28684

4 ‐0.000311 0.0010

48 ‐0.000527 0.001264 ‐0.000201 0.000938

SPX Index 0.988656 0.017973

55.00815 1

0.00000 0 0.953251

1.024061 0.941990 1.035322 0.958978 1.018333

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐0.63 /t/ > 1.6602 1.984 2.2757 2.6259

Degree of Freedom =

241


40

Table 22: H0: α = 0 ,H0: β = 1 for NATUR vs. MXWO SUMMARY OUTPUT y = Natur Index





Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept 0.005787 0.001439 4.021720 0.000142 0.002918 0.008656 0.001978 0.009596

MXWO Index 1.295266 0.069709 18.580955 0.000000 1.156270 1.434263 1.110755 1.479778

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = 4.24 /t/ > 1.6663 1.9935 2.2892 2.6458

Degree of Freedom =

72


41

Table 23: H0: α = 0 ,H0: β = 1 for ETHIEGL vs. SPGLOB SUMMARY OUTPUT y = ETHIEGL Index

x = SPGLOB Index Regression Statistics



Regression 1 0.010747 0.010747 1436.12

1 0.000000 Residual 64 0.000479 0.000007 Total 65 0.011226


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000212 0.000350 ‐

0.605390 0.54706

1 ‐0.000912 0.0004

88 ‐0.001142 0.000718 ‐0.000797 0.000373

SPGLOB Index 0.963667 0.025429

37.89618 5

0.00000 0 0.912867

1.014468 0.896157 1.031178 0.921226 1.006109

t‐statistic 10% 5% 2.5% 1% H0: β = 1 tstat = ‐1.43 /t/ > 1.6686 1.9971 2.2945 2.6536

Degree of Freedom =

65


42

Table 24: H0: α = 0 ,H0: β = 1 for ETHIEEU vs. SXXP SUMMARY OUTPUT y = ETHIEEU Index

x = SXXP Index Regression Statistics



Regression 1 0.01738

4 0.017384 2627.12

3 0.000000

Residual 64 0.00042

3 0.000007

Total 65 0.01780

7


Upper 95%

Lower 99.0%

Upper 99.0%

Lower 90.0%

Upper 90.0%

Intercept ‐0.000258 0.00033

1 ‐

0.778643 0.43905

9 ‐0.000920 0.000 404 ‐0.001138 0.000622 ‐0.000811 0.000295

SXXP Index 0.989361

0.01930 3

51.25547 2

0.00000 0 0.950799

1.027 922 0.938115 1.040606 0.957145 1.021577

t‐statistic 10% 5% 2.5% 1%

H0: β = 1 tstat = ‐0.55 /t/ > 1.6686

1.997 1 2.2945 2.6536

Degree of Freedom =

65


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