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Assessment of Renewable Energy as Investment Opportunity: Risk-Return Impact

Dwina Andini Soerono

Thesis of 60 ECTS credits Master of Science (MSc) in Sustainable Energy

Science

June 2018


Thesis of 60 ECTS credits submitted to the School of Science and Engineering

at Reykjavík University in partial fulfillment of the requirements for the degree of

Master of Science (M.Sc.) in Sustainable Energy Science

June 2018

Supervisor:

Dr. Stefan Wendt Assistant Professor, Reykjavík University, Iceland

Examiner:

Dr. Már Wolfgang Mixa Assistant Professor, Reykjavík University, Iceland

Copyright


June 2018



June 2018

Abstract The global aspiration towards a cleaner and more sustainable future has accelerated growth in the renewable energy sector. Nevertheless, the transition from non-renewable or conventional energy sources to renewable or alternative energy sources require substantial capital investment and must be attractive and accessible to future investors. This study evaluates the long-term financial performance of renewable, or alternative energy stocks as an investment opportunity for potential US investors. The conventional energy stocks are used as a comparison to evaluate the alternative energy stock performances within the energy sector. The holding periods that are assessed are 5, 10 and 20 years. The Fama-French Five-Factor Model is used to compute the abnormal returns of these stocks. This model also evaluates the level of sensitivity towards exposure to risks included in the model, namely market, size, value, profitability and investment factors. The results illustrate that both alternative and conventional energy stocks were underperforming through 1997 to 2017 compared to broadly diversified global stocks established in the Fama-French factors. Nonetheless, the alternative energy stocks show better performance than conventional stocks during the 5 and 20-year horizons shown by the statistically significant positive differences in the mean and median values. This is likely to be the result of the Dot-Com bubble in 1998 to 2000, and the increased investment towards the renewable energy sector in 2015. When the results are further broken down into 5-year intervals, alternative energy stocks were underperforming in 2002 to 2012, which could be an indication that the alternative energy stocks were not able to recover as fast as conventional energy stocks after the financial crisis in 2008. Through the variable factors, the results suggest that the alternative energy stocks load more heavily on size and investment factors in comparison to the conventional energy stocks. Keywords: Alternative energy stocks, conventional energy stocks, Fama-French Five-Factor Model, abnormal returns, financial risks

Mat á endurnýjanlegum orkugjöfum sem fjárfestingarmöguleikum: út frá áhættu og væntri ávöxtun


Júní 2018

Útdráttur Aukinn áhugi á heimvísu á endurnýjanlegum orkugjöfum hefur ýtt undir þróun í sjálfbæra orkugeiranum. Hinsvegar hefur umbreytingin á markaðinum í átt að sjálfbærum orkugjöfum frá ósjálfbærum orkugjöfum reynst dýr fjárfesting. Sú fjárfesting þarf því að vera meira aðlaðandi fyrir fjárfesta framtíðarinnar. Þetta verkefni metur langtíma fjárfestingar á hlutabréfum í sjálfbærum orkugjöfum eða öðrum staðgenglum fyrir framtíðar fjárfesta. Hefðbundin orkutengd hlutabréf eru notuð til samanburðar til að meta hlutabréf annara staðgengla innan orkugeirans. Eignartímabilin greind í þessu verkefni eru 5, 10 og 20 ár. Fama-French Five módelið er notað til að reikna óeðlilegan arð þessara hlutabréfa. Þetta módel metur einnig hversu næm hlutafbréfin eru fyrir ákveðnum áhættuþáttum þ.m.t. stærð, verðgildi, gróða og fjárfestingarþáttum. Niðurstöður þessa verkefnis sýna að bæði sjálfbærir orkugjafar sem og aðrir staðgenglar hafa ekki staðið undir væntingum á árabilinu 1997 til 2017 þegar þeir eru bornir saman við vísitölur á alþjóðavísu sem má finna innan Farma French stuðlunum. Þrátt fyrir það standa hlutabréf staðgenglana sig betur en hefðbundin hlutabréf á 5 og 20 ára eignarbili. Þetta sést á jákvæðum og tölfræðilegum mismun fengnum frá meðal- og miðgildum. Þessa niðurstöðu má rekja til aukinnar fjárfestingar í sjálfbæra orkugeiranum á árinu 2015. Þegar niðurstöðunum er skipt niður á 5 ára millibil eru staðgenglarnir ekki að standa undir væntingum á árunum 2002 til 2012. Það gæti bent til þess að þeir hafi ekki náð jafnvægi jafn fljótt og hefðbundnir orkugjafar eftir efnahagskreppuna 2008. Eftir að hafa greint marga mismunandi þætti má áætla að hlutabréf staðgengla á orkumarkaðinum eru háðari stærð og fjárfestingu innan geirans heldur en hlutabréf hefðbundna orkugjafa. Leitarorð: Önnur orkuframleiðsla, hefðbundin orkubirgðir, Fama franska fimm þættir, óeðlileg ávöxtun, fjárhagsleg áhætta



Thesis of 60 ECTS credits submitted to the School of Science and Engineering at Reykjavík University in partial fulfillment of

the requirements for the degree of Master of Science (M.Sc.) in Sustainable Energy Science

June 2018

Student:


Supervisors:

Dr. Stefan Wendt

Examiner:

Dr. Már Wolfgang Mixa

The undersigned hereby grants permission to the Reykjavík University Library to reproduce single copies of this Thesis entitled Assessment of Renewable Energy as Investment Opportunity: Risk-Return Impact and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the Thesis, and except as herein before provided, neither the Thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author’s prior written permission.

date

Dwina Andini Soerono MSc. Sustainable Energy Science

I dedicate this thesis to my grandmothers, Toeti Soerono (Saysay) and Nuraeni Dewi Somala (Bottie), who are dearly loved and missed, and my family who are my everyday inspiration.

Acknowledgements

I would like to extend my sincerest gratitude to Dr. Stefan Wendt, my supervisor, who has been guiding and sharing his wealth of knowledge for this thesis. His thorough inputs and comments have challenged my critical thinking and have helped me along my journey in the world of finance. My utmost love and appreciation to my parents (Iwan and Ita), and my sisters (Kara and Mtari), for their continuous and unconditional love and support to complete this Masters Degree. They are my inspiration to strive forward everyday. Lastly, I would like to acknowledge my classmates in Iceland School of Energy, may we all contribute to a more sustainable future.

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Contents

Acknowledgements ......................................................................................................... xvii!

Contents ............................................................................................................................ xix!

List of Figures ................................................................................................................... xxi!

List of Tables .................................................................................................................. xxiii!

List of Abbreviations ...................................................................................................... xxv!

1 Introduction ...................................................................................................................... 1!

2 Literature Review ............................................................................................................ 4!2.1! Classification of energy resources and energy stocks, and trends and performances of energy investments and energy stocks ................................................. 4!

2.1.1! Energy sector investment landscape, trends and performance .................. 6!2.1.2! Energy sector stock performances and trends ......................................... 10!

2.2! Financial performance analysis: risk-return impact ................................ 11!2.3! Previous studies on alternative and conventional energy stocks ............. 14!

3 Methodology and Data .................................................................................................. 16!3.1! Alternative and conventional energy indexes .......................................... 16!3.2! Calculating daily stock returns ................................................................ 19!3.3! Fama-French Multifactor Models ............................................................ 21!

4 Results ............................................................................................................................. 25!4.1! Results for alphas in 5, 10, and 20-year horizons .................................... 25!4.2! Results for betas in 5, 10 and 20-year horizons ....................................... 33!

5 Discussion ........................................................................................................................ 36!

6 Conclusion ...................................................................................................................... 39!

Bibliography ...................................................................................................................... 41!

Appendix ............................................................................................................................ 45!

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List of Figures

Figure 1: Net electricity generation from selected fuels ......................................................... 6!Figure 2: Unsubsidized levelized cost of energy comparison (Lazard, 2017, p. 2) ................ 7!Figure 3: Global new investment in renewable energy by asset class, 2004-2016, $BN ....... 8!Figure 4: Global investment in energy supply over time ........................................................ 9!Figure 5: Histograms of alphas for alternative and conventional energy stocks between 2012 to 2017 (5-year horizon) ....................................................................................................... 27!Figure 6: Histograms of alphas for alternative and conventional energy stocks between 2007 to 2017 (10-year horizon) ..................................................................................................... 29!Figure 7: Histograms of alphas for alternative and conventional energy stocks between 1997 to 2017 (20-year horizon) ..................................................................................................... 31!

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List of Tables

Table 1: Classification based on conventionality and renewability (Chauchan & Srivastava, 2000) ......................................................................................................................................... 5!Table 2: Description of the alternative energy indexes .......................................................... 17!Table 3: Description of the conventional energy indexes ....................................................... 18!Table 4: Summary of sample size ........................................................................................... 19!Table 5: Summary statistics of alphas for alternative and conventional energy stocks in 5, 10 and 20-year horizons ............................................................................................................... 25!Table 6: Summary statistics of alphas for alternative and conventional energy stocks for 5-year horizon ............................................................................................................................ 27!Table 7: Summary statistics of alphas for alternative and conventional energy stocks for 10-year horizon ............................................................................................................................ 30!Table 8: Summary statistics of alphas for alternative and conventional energy stocks in 20-year horizon ............................................................................................................................ 32!Table 9: Summary statistics of betas of alternative and conventional energy stocks in 5, 10 and 20-year horizons ............................................................................................................... 35!

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List of Abbreviations

AMEX American Stock Exchange CAPM Capital Asset Pricing Model CMA Conservative-Minus-Aggressive FIT Feed-in-Tariff GDP Gross Domestic Product GHG Greenhouse Gas GW Gigawatt HML High-Minus-Low IPP Independent Power Producer Mkt-Rf Market Returns minus Risk-free Rate MSCI Morgan Stanley Capital International NASDAQ National Association of Securities Dealers Automated Quotations NEX WilderHill New Energy Global Innovation NYSE New York Stock Exchange PPA Purchasing Power Agreement R&D Research and Development RMW Robust-Minus-Weak ROA Return on Assets ROE Return on Equity ROI Return on Investment S&P Standard and Poor’s SDG Sustainable Development Goals SMB Small-Minus-Big UNFCCC United Nations Framework Convention on Climate Change

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Chapter 1

1Introduction

The accelerated growth of renewable energy development was induced by global aspiration towards a cleaner and sustainable future. The goal of limiting the increase of global average temperature between 1.5 to 2 °C is supported by the United Nations Framework Convention on Climate Change (UNFCCC) members which includes world leaders, policymakers, market players across all sectors and industries, as well as the general public (UNFCCC, 2015). The UNFCCC establishes six focus industries that are essential in ensuring the success of this goal, and these are renewable energy, energy efficiency, urban environment infrastructure, managing carbon and other harmful gases through capture, use and storage, as well as land management (UNFCCC, 2015). The target of capping the increase of global average temperature is pushing the shift from greenhouse gas (GHG) intensive energy resources, such as fossil fuels, to cleaner and more sustainable energy resources such as renewable energy resources. The transition from non-renewable energy sources, such as fossil fuels, to renewable energy sources, such as geothermal, hydro, solar, biomass and wind, require effective policy support, research and development for new and improved technologies as well as significant capital investment (Cottrell, Fortier, & Schlegelmilch, 2015). The aim for this study is to better understand the risk and return factors within the renewable energy sector as an investment opportunity for US institutional investors, hence the focus on the evaluation of financial performance in the renewable energy related stocks in comparison to non-renewable energy related stocks that are traded in the stock exchange. The stocks will be evaluated in three different periods of 1997 to 2017, 2007 to 2017 and 2012 to 2017 to assess the trends of stock performances in different holding periods. The Fama-French Five-Factor Asset Pricing model is used to compute the abnormal returns of these stocks (represented by the alpha), and pinpointing sensitivity to the risk factors that may contribute to the overall financial performance of the stocks (shown by the betas of the variable factors) (Fama & French, 2015). Although past performances cannot be held accountable for the future predictions of the stock performances, the trends can help scholars and institutional investors to understand historical stock performances through different economic cycles, market changes and external events that may affect the energy sector. The relevance of this study is to illustrate long-term stock performances within the alternative and conventional energy sectors, which have not yet been thoroughly evaluated. This is imperative as alternative and conventional energy firms’ characteristics differ, thus generating different returns on investment over long and short-term periods. Additionally, the factors influencing risks and returns in the energy investment

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landscape will be explored. As the purpose of this study is to contribute to the sustainable growth in the renewable energy sector, it is important to consider the effect of the renewable energy sector in contribution to the overall economic growth and to fulfill the increasing energy demand world wide. The sustainable growth in the energy sector is imperative to fulfill the overall increase in demand for energy, and it plays a key role in putting pressure of adopting clean energy. There are a few studies evaluating the relationship between energy consumption and the growth of a nation (Campo & Sarmiento, 2013; Kahia & Aissa, 2014; Tsani, 2010) that illustrate positive relationship between energy consumption and a country’s economic development. Campo and Sarmiento’s (2013) study shows that there is a long-run correlation between energy consumption and economic growth in Latin American countries, where 1% increase in energy consumption is associated with 0.59% increase in the real GDP (Campo & Sarmiento, 2013). Other literature illustrate the uni-directional causal relationship between energy consumption and economic growth in Greece for the period 1960-2006 (Tsani, 2010) and a bidirectional causality relationship between non-renewable and renewable energy consumption with economic growth for the Middle East and North Africa (MENA) region in the year 1980 to 2012 (Kahia & Aissa, 2014). This emphasizes the necessity of energy usage to support economic growth throughout many regions. In parallel to the Sustainable Development Goal (SDG) number 7 of Affordable and Clean Energy (UN, 2017), renewable energy is recognized as one of the criteria in order to achieve a comprehensive improved quality of life in global population. Furthermore, energy is also needed to support the upcoming fourth industrial revolution whereby development of infrastructures, technologies and digital network, to name the least, rely on energy and electricity to be created, implemented and utilized (PWC, 2018). Thus, the leverage in the renewable energy sector is imperative to sustain economic growth and development whilst minimizing environmental, social and political costs on a national and global scale. Similar to the non-renewable energy sector, there are numerous risks associated with the renewable energy sector such as environmental, financial, market, operational and political risks (Watts, 2011). Financial risk however, is perceived to be a detrimental risk as “renewable energy projects are often capital-intensive, and are typically highly leveraged, with up to 70-80% of the project total being financed through debt” (Watts, 2011, p. 10). This a large deterrent for investors as the associated financial risk highly influences the returns of their investments. Ensuring a steady stream of capital investment is necessary for technology advancement that could bring renewable energy resources to be more cost competitive with non-renewable energy. The renewable energy sector has been flourishing in the past few decades, growing competitively with the non-renewable energy sector (Lazard, 2017). There has been an increasing number of literature focusing on the renewable energy sector financial performances through evaluating changes renewable energy stock prices (Bohl, Kaufmann, & Siklos, 2015; Henriques & Sadorsky, 2008; Kumar, Managi, & Matsuda, 2012; Managi & Okimoto, 2013), and evaluating the returns of these stocks (Bohl, Kaufmann, & Stephan,

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2013; Inchauspe, Ripple, & Trück, 2015). Studies on the risk and return of oil and gas companies are also available to provide better insight on the energy sector as a whole (Oberndorfer, 2009; Ramos & Veiga, 2011; Sadorsky, 2001). As the goal of this study is to evaluate the financial performance of the renewable energy sector, the following research questions are used to guide this study. [QL] What is the role of renewable energy as investment opportunity in the energy sector?

[Q1] How does the financial performance of renewable energy stocks compare to the non-renewable energy stocks over time?

[Q2] Which factors influence risk and return of renewable energy investments?

To address the following guiding questions, this thesis is divided into different sections. Section 2 uncovers previous literature discussing classification within the energy sectors, possible models to measure financial risk and return, and explore studies evaluating financial performances amongst energy-related companies. Section 3 will discuss data collection and the Fama-French Factor models as the model to compute abnormal returns and the load factors on the risks. The results will be examined in Section 4, followed by discussions in Section 5. Lastly, Section 6 consists of the conclusion of this study.

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Chapter 2

2Literature Review

This section is divided into 3 subsections. First, subsection 2.1 discusses the general classification of energy resources and energy stocks classification. The trends of renewable and non-renewable investments and stocks are also explored to compare the general performance of the categories. There are two key fundamental parameters that affect the performance of investments and stocks, which are risks and returns. Subsection 2.2 explores the existing financial performance models that have been used to evaluate risks and returns of stocks traded in the market exchange. Lastly, subsection 2.3 accumulates previous literature specifically on financial performances of alternative and conventional energy stocks.

2.1!Classification of energy resources and energy stocks, and trends and performances of energy investments and energy stocks

Energy sources can be classified and overlap into multiple categories. Each category could be defined differently depending on the management, technology used, and the scale of the project for extracting and producing the final output of energy. Consequently, it is important to establish a robust classification of types of energy to avoid misinterpretation of information when discussing the differences between renewable and non-renewable energy. Renewable energy is defined as replenishable resources that are accessible in human timescale as they are derived from natural processes (Skinner & Murck, 2011). Geothermal, hydro, tidal, biomass, solar and wind are amongst those that are categorized as renewable energy sources as the resource availability is infinite if sustainable practices is being implemented and managed. Each renewable resource is derived from different energy sources, and there are consequently different parameters of renewable energy in relation to its availability and replenishment time (Skinner & Murck, 2011). On the other hand, non-renewable energy is characterized by energy resources that deplete over time, and can no longer be re-used once it is processed or utilized (Chauchan & Srivastava, 2000). Table 1 illustrates the classification of energy resources determined by Chauchan and Srivastava (2000). Fossil fuels such as oil, natural gas and coal, as well as nuclear are classified as non-renewable energy resources due to the nature of depleting quantity after they are extracted.

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With the exception of hydropower, renewable energy resources (i.e. solar, biomass, wind, hydrogen, geothermal, tidal and wave, and geothermal) are classified as non-conventional or alternative energy. As such, classification of renewability and conventionality can be used interchangeably when discussing the different energy resources where renewable energy are also called alternative energy, and non-renewable energy is often associated with conventional energy.

Table 1: Classification based on conventionality and renewability (Chauchan & Srivastava, 2000)

Conventionality Renewability Renewable Non-renewable

Conventional Hydropower

Petroleum Natural gas Coal Nuclear

Fuelwood Agricultural waste Animal residues Industrial residues

Non-conventional/Alternative

Solar Biomass Wind Hydrogen Geothermal Tidal and wave Ocean thermal

Energy resources can also be categorized as clean or brown energy depending on the byproduct produced after manufacturing these resources into electricity. The byproducts that are often associated with brown energy are those that produce CO2, or other harmful substances and waste (Tester, Drake, & Driscoll, 2012). Therefore, renewable energy is often associated with clean energy whereas non-renewable energy is considered as brown energy. The goal to mitigate harmful GHG from electricity production is one of the driving forces for the transition from non-renewable to renewable energy (UNFCCC, 2015). The energy sector included in the stock exchanges is comprised of a category of stocks listed from companies that are directly or indirectly associated with the production or distribution, or have diversified their products and services in the energy sector. For this study, this information is derived from the Thomson Reuters Database that includes company profiles of the listed stocks. This includes both alternative or renewable energy stock, and conventional or non-renewable energy stocks. S&P 500 for example, created indexes called S&P Global Clean Energy and S&P 500 Energy for alternative and conventional energy stocks1. Each index lists constituents that represent companies within each category. Further description of energy stock indexes is discussed in Methodology and Data section.

1 This information is derived from S&P Dow Jones Indices, https://eu.spindices.com/index-finder/

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2.1.1! Energy sector investment landscape, trends and performance

As the energy sector is closely associated with economic growth and development, the investment of the sector is closely analyzed to ensure steady improvement in global prosperity, and to ensure that the demand for energy is fulfilled. Specifically for electricity demand, Figure 1 shows that renewable energy resource is projected to surpass electricity generation from coal, nuclear and petroleum in the year 2035 (EIA, 2018). The EIA report uses two difference scenarios based on its own reference case and reference with the Clean Power Plan, which is a US-based policy aiming to reduce CO2 emission from power generation (Ramseur & McCarthy, 2016). Regardless of the scenarios, the projected power generation from different energy sources follow similar trends. The projected total increase of renewable energy resources for power generation is 139%, which is significantly higher than coal, nuclear and petroleum power generation in 2050 (EIA, 2018).

Figure 1: Net electricity generation from selected fuels (EIA, 2018, p. 89)

The increasing maturity of the renewable energy, or the alternative energy technologies allows the sector to be more cost-competitive to the conventional energy sector (IRENA, 2014; Lazard, 2017). Figure 2 represents a comparison of levelized costs of alternative energy generation technologies and conventional energy generation technologies (Lazard, 2017). The alternative energy technologies, namely solar photovoltaic for utility scale, microturbine, geothermal, biomass direct and wind, are cost competitive to conventional energy technologies such as natural gas, coal and gas combined cycle. The increased in the market competitiveness of the alternative energy sector is a result of improved technologies, effective policy and regulations, and overall input cost reductions (IRENA, 2014).

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Figure 2: Unsubsidized levelized cost of energy comparison (Lazard, 2017, p. 2)

Studies have shown that there is a positive correlation between the investment for research and development (R&D) and the development in the energy sector as capital investment allows technology to flourish and become more efficient in generating power production thus increasing cost-competitiveness (Neij, 1997; Watanabe, 1995). Watanabe (1995) evaluates a mitigation approach to overcoming energy and environmental constraints in Japan through technology advancement. The study finds that the “stagnation of the technology stock of energy R&D therefore results in a slower rate of increase in the total technology stock, which in turn results in not only a decrease in production but also in the marginal productivity of technology stock to production” (Watanabe, 1995, p. 456). This emphasizes the importance of sustainable stream of capital investment in advancing technology to compete against the existing technologies used in non-renewable energy production. Neij (1997) also highlights the importance of improved technology as the basis of cost reduction through production changes, product changes and changes in input prices. Thus, technology advancement is a key factor to increase cost competitiveness in the renewable energy production through increase in efficiency and reduction of input costs. To incentivize capital investment into technology development, appropriate policies must be established to create effective cooperation between the public and private participants (IRENA, 2014). Effective regulations and policies are important foundations to strengthen the growth in the energy sector. In United States, “electricity deregulation in the 1990s created greater opportunity for independent power producers (IPPs) to get involved in the generation businesses - not just from renewables, but from all fuel sources” (Barradale, 2010, p. 7702). This policy reduced market barriers allowing IPPs to participate in the otherwise highly competitive and capital intensive industry. Changes in regulations and policies can be used to catalyze the growth of the energy sector. Nonetheless, regulation and policy framework must be stable as abrupt changes could lead to volatility within the market which could deter new players to enter the market, thus leading to a shortage in energy sector investment (Keppler & Schülke, 2009).

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Although there is a general upward global trend in renewable energy investment, there is apparent fluctuations on a yearly basis. As shown in Figure 3 (McCrone, Moslener, D’Estais, & Grünig, 2017), renewable energy was at all-time high in 2015 since the 2008 crisis, however the growth in the investment decreases by 23% in 2016. The fall in the investment was predominantly due to the economic downturn in two key markets, China and Japan (McCrone et al., 2017). Nonetheless, the new installed capacity derived from renewable sources increased to 138.5GW in 2016 in comparison to 127.5GW in the previous year (McCrone et al., 2017). Improved technology that leads to reduction in capital costs for solar photovoltaics, onshore and offshore wind was the key driver for this achievement.

Figure 3: Global new investment in renewable energy by asset class, 2004-2016, $BN

(McCrone, Moslener, D’Estais, & Grünig, 2017, p. 12). Renewable energy investment has been growing in parallel with urgency to assist the transition from non-renewable energy to renewable energy usage worldwide. The global investment in renewable power capacity more than doubled the new fossil fuel power in 2015, accumulating to 265.8 billion USD and 130 billion USD respectively (McCrone et al., 2017). The trend continued in the following year where investment in the renewables approximately doubled in 2016 relative to the fossil fuel generation (UNEP & BNEF, 2016) highlighting the shift in investment allocation emphasizes the attractiveness of renewable energy projects to a broader number of investors. Figure 4 illustrates the trend of global investment in energy supply which includes both alternative and conventional energy resources, and the total of energy investment was 1,600 billion USD in 2015 (IEA, 2016). There is a consistent general upward trend in the investment for fossil fuel power generation, and the supply of oil, gas and coal until 2015. A total of 900 billion USD of investment was channeled for the conventional energy resources in 2015 (EIA, 2018), highlighting the fact that this conventional energy sector is still the biggest recipient of investment in comparison to alternative energy sector in respect to global energy investment. Consequently, although the growth rate of renewable energy investment is steady increasing, the share of global energy investment is still heavily skewed towards the

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conventional energy sector.

Figure 4: Global investment in energy supply over time (IEA, 2016, p. 23)

The long-term and short-term return on investments (ROIs) are different in the alternative and conventional energy firms due to the different company characteristics, and different financial and market support mechanisms. The alternative energy firms are likely to generate higher ROI in the long-run as many of these firms mitigate their risks through Purchasing Power Agreements (PPAs) (McCrone et al., 2017). PPAs allow for joint agreements between related parties, such as buyers and sellers of electricity, to establish price and quantity of products prior to the commission of the energy projects. This eliminates the possibility of electricity price fluctuations, and market inefficiency in the demand and supply of the energy sector that may negatively affect the ROIs (McCrone et al., 2017). The alternative energy firms’ PPAs tend to expand over long period of time compared to the conventional energy firms’ PPAs allowing the alternative energy assets to offer ROI in the long-term horizon (KPMG, 2018). Conventional energy firms tend to have shorter-term PPAs as the price of fossil fuels is volatile, hence establishing the long-term price and quantity of conventional energy production may result in loses in future revenues. There are different market and financial mechanisms to support the development in the alternative energy sector, such as feed-in-tariffs (FITs), tax incentives, and tradeable green certificates (Abolhosseini & Heshmati, 2014). These mechanisms are used to encourage technological innovations, and strengthen cost-competitiveness within the alternative energy sector in comparison to the conventional energy sector. The short-term ROI for alternative energy firms may be less competitive than the conventional energy firms as the alternative energy firms are still considerably less mature than its counterpart (McCrone et al., 2017). The alternative energy firms need to ensure steady stream of investment for persistent augmentation in technological advancement. Conversely, the conventional energy sector is more mature, hence there is less growth and innovations necessary to maintain its market

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positioning within the energy sector. Paun’s (2017) study evaluates the financial performance of conventional and alternative energy firms in the Romanian energy sector (Paun, 2017). Through comparing the companies' net profit, return on equity (ROE) and return on assets (ROA) in the year 2012 – 2015, the result shows that conventional energy firms tend to have better financial performance than alternative energy firms. This adds to the pre-existing perception that alternative energy companies are a riskier investment opportunity than its counter-part (Watts, 2011). To improve the overall financial performance, alternative energy firms must continue to innovate and improve technology efficiency and efficacy, and government support is also needed to implement effective policies and regulations to push for the sector’s growth (Paun, 2017).

2.1.2! Energy sector stock performances and trends

The impact of changes of oil prices to stocks returns have been evaluated in multiple studies (Driesprong, Jacobsen, & Maat, 2008; Elyasiani, Mansur, & Odusami, 2011; Narayan & Sharma, 2011). Narayan and Sharma’s (2011) study highlights that there is a positive correlation between the increase in oil prices and the returns of firms within the energy and transportation sector. Nevertheless, there is a statistically significant negative correlation between the increase in oil prices and other sectors such as banking, real estate, food and medical (Narayan & Sharma, 2011). The finding is similar to Driesprong et al. (2008) study where they also find negative excess returns on market stocks when oil price is rising. Elyasiani et al. (2011) examines oil price shock to industry stock returns, which include four major types of industries such as oil-substitute, oil-related, oil-user and financial sectors. The result shows that oil price fluctuations affects excess returns of the industry stocks as changes in oil price constitutes as a systematic asset price risk (Elyasiani et al., 2011). Long-run performances of alternative and conventional energy stocks have yet to be explored in many scientific literature or reports. Nevertheless, it is important to highlight the Dot-Com bubble in 1998 to 2000, as an important event affecting the stock prices (Ofek & Richrdson, 2003). The stock returns within the internet index was significantly higher than the S&P 500 and NASDAQ composite index, and this was caused by the increased in investment speculation from the growth in technology and internet usage (Ofek & Richrdson, 2003). As such, the internet sector gained over “1000 percent return on its public equity” during 1998 to 2000 (Ofek & Richrdson, 2003, p. 1113). Few financial analysts have been evaluating the trend of the energy sector within the stock market. McCullum (2017) evaluates ten different S&P 500 market indexes, and compares the performances between sectors from 2007 to 2017. He concludes that the financial sector and the energy sector are the worst performing sector in the past 10 years with annualized total returns of 0.85% and 1.19% respectively (McCullum, 2017). The two periods that have caused the energy sector to perform much poorly than other market sectors within the S&P 500, was the financial crisis in 2008 that dampened oil prices and the crash in oil prices in 2014 (McCullum, 2017). He argues that there is a positive correlation between the energy stocks and changes in crude oil prices.

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The trends of alternative energy stocks have been discussed in relevant literature covering different time horizons and through different indexes (Bohl et al., 2015, 2013; Inchauspe et al., 2015). Inchauspe et al. (2015) investigates the dynamics of excess returns for one of the alternative energy indexes, the WilderHill New Energy Global Innovation (NEX), with respect to the alternative energy sector. Through a multi-factor asset pricing model, they concluded that the MSCI World Index and technology stocks are the key determinants of abnormal return for alternative energy stocks. Furthermore, the results showed that the NEX had positive abnormal returns during 2003 to 2007, however the index was underperforming from 2009 to 2013 as NEX was not able to recover post financial crisis that occurred in 2008. Similarly to the results concluded by Inchauspe et al. (2015), Bohl, Kaufmann and Siklos’ (2015) study evaluates the alternative energy stock prices based of five alternative energy indexes. There was a sharp turn from positive abnormal returns prior to the financial crisis in 2008 to negative abnormal returns in the year 2008 to 2013. Nonetheless, when considering the whole sample period from 2004 to 2013, all the indexes were underperforming as indicated by the negative alphas. Using a multi-factor performance model, with the additional energy proxy based of futures contracts of fossil fuels as one of the factors, they conclude that the price crash in 2008 was caused by increased sensitivity to market fluctuations instead of a burst of a financial bubble (Bohl et al., 2015). Furthermore, country-specific studies regarding financial performance of alternative energy stocks is conducted for the German market during 2004 to 2011. Bohl, Kaufmann and Stephan (2013) perform a multi-factor performance model on two alternative energy indexes, the ÖkoDAX and the DAXsubsector Renewable Energies, and the results illustrate that both indexes have positive alphas during the period of 2004 to 2007. The positive abnormal returns did not hold during 2008 to 2011, shown by negative alphas for both indexes. The shift in performance was due to an “intense sector competition and the economic downturn following the global financial crisis erased profit margins to a large extent” coupled with the new regulation of nuclear phase-out by 2022 (Bohl et al., 2013, p. 40).

2.2!Financial performance analysis: risk-return impact The goal of this study is to examine the role of renewable energy as investment opportunity in the energy sector. The focus is on examining alternative and conventional energy firms that are publicly listed in market exchanges, and evaluating their stock performances over different periods. There are many financial asset pricing models that have been commonly used as theory and in real life practices, and Capital Asset Pricing Model (CAPM), One Factor Model, Fama-French Three-Factor Model, and Fama-French Five-Factor Model (Fama & Macbeth, 1973; Fama & French, 1993, 2004, 2015) are few of the examples. CAPM is an ex-post approach to predict the expected return of a stock based on the idea that the expected return is measured through time-value and market risk factor, and is limited to one period calculation (Reddy & Thomson, 2011). Nevertheless, through extensive studies, this model is described to be insufficient to describe the asset pricing in real life practices as “the relationship between beta and average return is flatter than predicted by the Sharpe-Lintner version of the CAPM” such that there is no positive, linear relationship between expected return and the beta (Fama & French, 2004, p. 43). Based on the CAPM, asset pricing model

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called the One Factor model or the Market Model was created to illustrate time-series regression for assets or stocks (Fama & Macbeth, 1973). This equation is shown in Equation 2.1. The data was based of monthly percentage returns of all common stocks from January 1926 to June 1968 derived from the New York Stock Exchange (NYSE) (Fama & Macbeth, 1973).

!"# − %!&# = % (" + *" !+# − !&# + ,"#%% %% %(2.1)% The dependent variable in this model is the expected return measured by the return on a security or stock (Rit) minus the risk-free rate (Rft). The risk-free rate is based one-month treasury bill issued by the U.S government daily yields (Fama & French, 1992), and this is the benchmark used in the model. Rmt – Rft indicates the market return based of return of market portfolio minus the risk-free rate (Fama & French, 1992). Alpha (!) indicates the abnormal return of the stock, whereas beta (") is indicative of the sensitivity of the stock or portfolio for the specific risk, and lastly, an error term (#) was added to express the margin of error, or the residual of the model (Fama & French, 1992). Thus, the One Factor Model illustrates the risk and return relationship of a given stock or asset. Since the development of One Factor Model, many studies have added more explanatory variables into the regression such as the Fama-French Three-Factor Model and the Fama-French Five-Factor Model. This is further explained in Methodology and Data section. Alpha as a measure for abnormal returns For the purpose of this study, it is important to explore the role of alpha (!) as an indicator for the performance of alternative and conventional energy stocks. Alpha is the intercept of the linear regression line based of the factor model. When discussing about performance model, alpha represents the excess return of the stock or security, given the stock’s exposure to different risk factors (Gorman, Poly, Obispo, & Weigand, 2007). In an efficient market, as predicted by CAPM, alpha is expected to be 0, and this reflects the idea that the securities or stocks should be priced accordingly at the given level of risks (Fama & French, 1993). Similarly, well-specified asset pricing model, such as the One Factor Model, also tend to generate alphas that are indistinguishable from 0 (Fama & French, 1993). The closer alpha, or the intercept, is to 0 represents “a simple return metric and a formal test of how well different combinations of the common factors capture the cross-section of average returns” (Fama & French, 1993, p. 5). Thus, any deviation from alpha equivalent to 0 illustrates an abnormal performance of the security. Positive alpha indicates that the security is outperforming the expected return, taking into account of the related risk exposure included in the model (Gorman et al., 2007). On the contrary, negative value of alpha indicates that the security or stock is underperforming compared to it’s expected return. The performance of alpha is closely related to the variable or the risk factors within the model. The concept of risk is universally understood as the uncertain determinant that could negatively affect an outcome of a scenario. It is argued that risk is “one of the most important characteristics considered by people when evaluating alternative courses of action such as adapting new technologies, choosing a career, or making final decisions” (Ganzach, 2000, p. 353). In terms of investment landscape, final investment decisions are made based of the

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assessment of risk of alternative assets available to investors, thus risk is a key influence in the decision-making factor (Ganzach, 2000). In the energy sector, alternative energy and conventional energy are perceived to bear different degree of risk. According to Schmitz (2009), the alternative energy sector is 69% more risky than the broad market as the sector is highly sensitive to the broad market returns, hence was highly influenced by the volatility in the financial markets. As discussed by Watts (2011), the asymmetry in information has hindered effective pricing of alternative energy stocks, and thus risks are not outsourced and instead are borne by the investors. Stock prices and dividends fluctuate daily caused by internal and external risks that are associated with the firm. When evaluating the stock performance of a company, it is important to understand the discrepancy between systematic and unsystematic risks. Unsystematic risk, also known as firm-specific, idiosyncratic, or diversifiable risk, is the risk associated internally, and is related to firm-specific related news (Berk & DeMarzo, 2014). The risk borne by a specific firm will not affect other firms within the same sector. Examples of unsystematic risk would be increasing market share within the industry, political changes within the firm, or acquiring patent or new technology. The stated incidents affect the prices and dividend of that specific stock but would not necessarily affect other stocks in the same manner (Berk, DeMarzo, & Harford, 2015). The specific news may be perceived as beneficial or disadvantageous to different stocks, or it could also not affect other stocks. Unsystematic risks however can be easily mitigated through diversification of investment allowing investors to reduce the risks by acquiring different types of stocks. On the other hand, systematic risk is not as easily reduced as it is the common risk that is borne is all stocks. This is derived from market-wide news which is an event that affects the economy as a whole (Berk & DeMarzo, 2014). Changes in interest rates, economic crises and natural disasters are considered as systematic or market risks as the events will affect the returns and dividend of all stocks regardless of the types of firm or stock. Measuring this type of risk is detrimental to calculating the stock return to evaluate the additional of compensation required when acquiring a stock. Measuring risks in individual stocks through real-firm beta Each stock has different sensitivity to risk factors, thus measuring the sensitivity gives a more accurate depiction of risk borne in the individual stock. The sensitivity of a stock to the exposure to risk variables can be measured by beta ("), which is the load factor of the variable factors included in the model (Fama & French, 1992). By evaluating the betas of alternative and conventional energy companies, this study can compare the level of sensitivity to risk of each stock. All in all, real-firm beta is a better indicator to measure risks for individual stock as it is indicative to the sensitivity to systematic risk. Investors can mitigate unsystematic risks through diversification of stocks. The systematic risks in stocks however, are more challenging to eliminate. Subsequently, investors require a risk premium, the additional return, as a compensation for bearing this risk (Berk & DeMarzo, 2014). Consequently, investors require a risk premium as an added incentive to hold a risky stock instead of acquiring less risky alternative such as government bonds.

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2.3!Previous studies on alternative and conventional energy stocks

As previously mentioned in section 2.1, the fluctuations of fossil fuel prices affect the performance of both alternative and conventional energy stocks nationally and globally. The studies have shown that there is a positive correlation in the increase in fossil fuel prices, namely oil prices, to the returns of conventional energy stocks (Oberndorfer, 2009; Ramos & Veiga, 2011; Sadorsky, 2001). On the contrary, there is an inverse relationship between increasing fossil fuel prices to the returns of alternative energy stocks (Henriques & Sadorsky, 2008). Through a multifactor market model, Sadorsky’s (2001) results show that there were three main risk factors that had significant impact on the Canadian oil and gas stock returns, and they were the exchange rates, crude oil prices and the interest rates. The result shows that there is a positive correlation between the increase in crude oil prices to the Canadian conventional energy stocks. On the other hand, interest rate is also a key risk factor as oil and gas companies are capital extensive hence they require large capital investment to upgrade and develop their technologies. Hence an increase in interest rate could lead to a decrease in returns for conventional energy stocks. With regards to international relevance, Ramos and Veiga (2011) evaluate the risk and return factors in oil and gas industry where they separated thirty-four different countries into two categories of developed and emerging markets. They concluded that although price of oil is “a globally priced factor for the oil industry” (Ramos & Veiga, 2011, p. 521), there is a stronger response to oil price in developed market than emerging markets. This is because the emerging market tend to be more affected by factors based locally, than globally as they are not fully integrated in the global market. Furthermore, the results indicate that the impact is greater from oil price drop than the rise in oil price which suggests that conventional stock returns are asymmetrical to change in oil prices (Ramos & Veiga, 2011). Specifically in the energy stock returns in the Eurozone, oil price is the main determinant for the changes in energy stock returns where the European utilities stock returns are negatively impacted by the increase in oil prices and coal prices, although oil prices create more significance impact on the utilities stock returns (Oberndorfer, 2009). On the contrary, the increase in oil prices leads to the increase in oil and gas stocks. The study reveals that gas market is not a significant impact in the movement for Eurozone energy stocks (Oberndorfer, 2009). Similarly, evaluating the relationship between fossil fuel prices and its impact on the alternative energy stocks is valuable information “since renewable energies are perceived as a long-term substitute for conventional energy sources, it is likely that prices of alternative energy stocks have been initially driven by soaring oil prices” (Bohl et al., 2015, p. 196). Understanding the dynamics between the two sectors may explain their overall financial performance in the energy market. Henriques and Sadorsky’s (2008) study evaluates the relationship of alternative energy stock prices to technology stock prices, oil prices and interest rates from 2001 to 2007. The study concludes that technology stock prices has a stronger impact to the alternative energy stock prices than oil prices, and the reason being is that the profile of alternative energy companies is viewed similarly to high technology companies in the eyes of the investors (Henriques & Sadorsky, 2008). Thus, positive relationship of oil price and alternative energy stocks is present though not as prominent.

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Technology advancement is a key factor that ties into the development and attractiveness level of the alternative energy industry, thus technology advancement is one of the key determinants to the performance of these stocks (Bohl et al., 2015, 2013; Inchauspe et al., 2015; Kumar et al., 2012; Managi & Okimoto, 2013; Ortas & Moneva, 2013; Rezec & Scholtens, 2017). Technology stocks was another factor that highly influenced the abnormal returns of the index, thus suggesting that the highly-correlated returns of alternative energy stocks and technology stocks. The alternative energy stocks may be more attractive to the investors if they provide similar risk and return tradeoff as technology stocks (Inchauspe et al., 2015). As previously mentioned, Henriques and Sardosky’s (2008) study emphasizes that technology stock prices generate more significant impact on alternative energy stock prices than the changes in oil prices (Henriques & Sadorsky, 2008). The performances of alternative energy related companies are highly related to the success of their technologies. Alternative energy companies and technology companies share the trait of technology-dependence more closely to each other in comparison to conventional energy companies. Other literature (Kumar et al., 2012; Managi & Okimoto, 2013) also share similar conclusions to Henriques and Sadorsky. There is a positive relationship between oil prices and alternative energy stocks, and that alternative energy stocks are perceived similarly to technology stocks in the eyes of investors. A study by Rezec and Scholtens (2017) compare of the role of renewable energy equity indices such as Ardour Global Alternative Energy and S&P Global Alternative energy to conventional benchmark indices such as MSCI World, S&P Global 1200 and NASDAQ. The result shows that “the renewable energy indices’ risk-adjusted return is very poor and suggests renewables is not financially attractive portfolio investment yet” (Rezec & Scholtens, 2017, p. 367).

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Chapter 3

3Methodology and Data

This study is using the Fama-French Five-Factor Model to compute abnormal returns (alpha), and to measure sensitivity to variable factors (betas) of alternative and conventional energy stocks. The results are used to evaluate the performance of the stocks within the alternative and conventional energy sectors, as well as comparing the performance amongst each other. Summary on the execution of the study is described below:

1.! Finalize list of companies for alternative and conventional energy sector by referring to constituents in the existing global energy indexes.

2.! Convert all the stock prices to USD using the daily USD exchange rates to ensure consistency in generating data, and calculate the daily returns of each stock.

3.! Run time-series regressions using the Fama-French Five-Factor Model by using the existing Fama-French global benchmarks for the variable factors used, and run significant tests (t-test and non-parametric tests) for the coefficients of the results.

The study is based on evaluating the pure excess returns of the stocks in 5, 10 and 20-year holding periods. The periods are further broken down into 5 and 10-year intervals for more thorough analysis on time specification. The study will also evaluate alternative and conventional energy stocks that are only listed during a 10-year period (starting from 2007) and a 5-year period (starting from 2012). Breaking the periods in smaller intervals helps to analyze the performance based of different market cycles, or whether there are events that could affect the performance of these stocks.

3.1!Alternative and conventional energy indexes The constituents listed in multiple global energy indexes in the year 2017, are the sources of reference to gather the sample data of alternative and conventional companies. The chosen companies offer a variety of products and services for different stages of energy production. These companies include energy storage and energy resource producers and manufacturers, utility companies, and information technology providers. The historical daily stock prices of the chosen companies are extracted from Thomson Reuters Datastream. This study refers to multiple global alternative energy indexes to gather alternative energy stocks. These indexes are S&P Global Clean Energy, NASDAQ Clean Edge Green Energy, WilderHill Clean Energy, Ardour Global Alternative Energy and Thomson Reuters Global Renewable Energy. The description of each index can be found in Table 2. The S&P Global Clean Energy, WilderHill Clean Energy and Ardour Global Alternative Energy indexes are

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among the few indexes used in the study to evaluate the explosive price behavior in the alternative energy stocks in the mid 2000s (Bohl et al., 2015). Furthermore, NASDAQ Clean Edge Green Energy, S&P Global Clean Energy and WilderHill Clean Energy Indexes are used analyze financial performance of the Clean Tech equity indexes in the primary energy markets (Ortas & Moneva, 2013). Thomson Reuters Global Renewable Energy index is added to the list of alternative energy indexes to provide a broader landscape of global alternative energy companies.

Table 2: Description of the alternative energy indexes

Index Number of constituents

Sectors Scope

S&P Global Clean Energy

29 Alternative energy, alternative fuels, con. electricity, renewable energy equipment, waste disposal services

Global

NASDAQ Clean Edge Green Energy

39

Aerospace, alternative electricity, alternative fuels, automobiles, building materials and fix., computer services, con. electricity, electrical equipment, electronic equipment, mortgage REITs, renewable energy equipment, semiconductors, specialty finances

US

WilderHill Clean Energy

38

Aerospace, alternative electricity, alternative fuels, auto parts, automobiles, commodity chemicals, electrical equipment, electronic equipment, heavy constructions, renewable energy equipment, semiconductors, specialty chemicals, unquoted equities, waste disposal services

North America

Ardour Global Alternative Energy

97 Alternative electricity, alternative fuels, auto parts, automobiles, building materials and fix., commodity chemicals, divers. industrials, electrical equipment, electronic equipment, food products, heavy constructions, industrial machinery, renewable energy equipment, semiconductors, specialty chemicals, specialty retailers, waste disposal services, water

Global

Thomson Reuters Global Renewable Energy

37 Alternative electricity, alternative fuels, building materials and fix., heavy constructions, renewable energy equipment, semiconductors, specialty chemicals, transport services, waste disposal services

Global

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On the other hand, the list of companies for conventional energy stocks are derived from the S&P 500 Energy and Thomson Reuters Global Energy Index to mirror the indexes for the alternative energy stocks. Table 3 illustrates the number of constituents, sectors and the scope of each index.

Table 3: Description of the conventional energy indexes

Index Number of constituents

Sectors Scope

S&P 500 Energy 32 Exploration and production, oil equipment and services, integrated oil and gas, pipelines, and gas distribution

US

Thomson Reuters Global Energy Index

514 Alternative fuels, asset managers, brewers, building materials, business support services, clothing and accessory, coal, commercial vehicles and trucks, commodity chemicals, conductivity electricity, consumer electronics, exploration and production, food products, gas distribution, general mining, heavy constructions, industrial machinery, industrial suppliers, integrated oil and gas, iron and steel, marine transportation, nonferrous metals, oil equipment and services, pipelines, renewable energy equipment, semiconductors, specialty chemicals, specialty retailers, transportation services, and waste disposal services

Global

All the companies listed in S&P 500 Energy are related to conventional energy, thus all the companies are suitable to be included in the conventional energy category. On the other hand, Thomson Reuters Global Energy Index includes both conventional and alternative energy related companies. As such, the companies within the renewable energy equipment and alternative fuels sectors are discarded to ensure the consistency for the conventional company profile. Companies such as Canadian Solar, Ballard Power Systems and Vestas Windsystems are listed within the renewable energy equipment or alternative fuels sectors as they produce renewable energy such as solar, wind and fuel cells. They are also constituents of the alternative energy indexes, hence they are eliminated from the conventional company profile. On the other hand, sectors such as brewery, food products, asset managers, and clothing and accessory are included within the conventional energy company profile. These companies are aggregate conglomerates with diversified products which incorporate oil and gas related products as a major part of their companies’ segments. For example, San Miguel Corporation is well-known for their beverage product, nonetheless the company´s segment also includes food, packaging, energy, fuel and oil as well as infrastructure. Asset managers sector is included within the scope of conventional energy company as the listed companies provide financial services in the oil and gas industry. Similarly, companies within the food products,

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and clothing and accessories also diversified their products in the conventional energy-related products and services, hence they are still qualified within the category. In total, the sample sizes for alternative and conventional energy companies in different time horizons can be found in Table 4. Finalized sample sizes for each time horizon in alternative and conventional energy do not reflect the total constituents listed in the indexes. Duplicates of constituents are removed to avoid an overlap in data within the category. A few alternative energy stocks are listed in the Thomson Reuters Global Energy Index, which are extracted out of the list of sample data as they do not represent the characteristics of conventional energy-related stocks. Moreover, some stocks were eliminated due to missing time-series data on stock pricing.

Table 4: Summary of sample size

Sample size Holding period Alternative energy

stocks Conventional energy

stocks 5 years 133 409 10 years 95 334 20 years 44 173

Throughout the years, the constituents within the indexes change as companies get delisted from the indexes, or due to the event of new comers of energy related companies listed in the stock exchanges. The growing number of stocks is especially evident in the alternative energy sector in the past decade in parallel to the growth of the sector. It is necessary to assess the stock prices over a longer period of time as the prices of common stocks fluctuate on a daily basis, hence the trend loses its significance over a short period of time (Brealey, Myers, & Allen, 2017). Dividing of the holding periods into 5 and 10-year intervals enable the study to analyze the data set with more depth. This could help to explore external factors and events that led to the fluctuations in the stock returns such as changes in economic and market conditions and policies, or natural disasters which is often highly linked to the availability of natural resources to produce energy. Additionally, the different time periods allow the study to see pattern in time series, and evaluate if the horizon of time will affect the returns of the companies.

3.2!Calculating daily stock returns As a starting point, numerous energy indexes are evaluated to gather sample data for alternative and conventional energy companies. The indexes encompass global companies, thus daily stock prices vary in different currencies. The daily stock prices are converted to USD by matching the daily exchange rate of the respective currencies. The sample data of companies are taken from global indexes resulting in a diversity of company origin ranging from different regions. The companies’ daily historical stock prices thus are listed in different currencies. To ensure homogeneity, stock prices are converted to USD prior to further analysis. Daily historical exchange rates were derived from Thomson Reuters Datastream.

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The daily returns are the dependent variable to measure a company’s financial performance, however this data is not readily available on Thomson Reuters Datastream. Consequently, the stock returns are then calculated from the daily stock prices. The daily stock prices listed in the Thomson Reuters Datastream is adjusted for capital measures, hence dividend payment and reinvestment in earnings are already accounted when listing the stock prices. The first step is to convert all the daily stock prices to USD to ensure consistency in the currency used in this study. The global Fama-French Five-Factor benchmarks, namely the variable factors and the risk-free rate, are based of the average portfolio returns and the risk-free rate expressed in USD. Since exchange rate fluctuates each day, daily historical exchange rate is used to convert the stock prices. Thomson Reuters Datastream generally provides three exchange rates which are bid, middle and offer rate. Middle exchange rate is chosen as it provides the most complete data available across all currencies. This approach is taken to ensure consistency for exchange rate conversions. Datastream provides an exchange rate per 1 USD, thus the equation for exchange rate conversion can be found in Equation 3.1.

-#,/01 =23,45667849:;3,45667849:

% % % %%%% % (3.1)%

Pt,currencyA is the daily stock price, and Et,currencyA is the exchange rate of the currency per 1 USD on a given day. Should there be missing data on a stock price or the exchange rate, the study uses the previous day data under the assumption that the price or exchange rate remain unchanged from the previous trading day. The daily stock return (Rit) is calculated by taking the natural logarithm (ln) of stock price in that a given day (Pt,usd) divided by the stock price of the previous day (Pt-1,usd) as shown in Equation 3.2. The stock returns are then calculated according within the time horizons. The logarithmic return depicts a more accurate measure of performance of stock returns as it considered the continuously compounded returns over the course of the time periods (Aas, 2004). Time consistency is especially important as the study evaluates three different periods which are 5, 10 and 20-year analysis.

!"# = <=% 23,5>?23@A,5>?

% % % % % (3.2)% The daily stock return (Rit) is then multiplied by 100 to get the percentage value as shown in Equation 3.3. This step is imperative prior to subtracting risk-free rate which is expressed in percentage term.

!"# = <= 23,5>?23@A,5>?

∗ %100% % % % (3.3)

%

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3.3!Fama-French Multifactor Models The Fama-French multifactor models have been widely used as a tool to measure the performance of stocks in different industries. This model has also been used in previous studies mentioned in the Literature Review when evaluating the energy stock performances (Bohl et al., 2015; Elyasiani et al., 2011). The Fama-French Five-Factor Model is specifically chosen as the methodology to measure the performance of alternative and conventional energy stocks as the model allows for long-term evaluation of performances using historical stock returns. The independent variables to calculate the alphas and variable coefficients in the Fama-French Five-Factor model was derived from the Kenneth French Data Library2 which are available in the time horizon from 2nd July 1990 to 31st June 2017. Furthermore, the Fama-French Five-Factor Model includes two additional factors, namely profitability and investment, from the Fama-French Three-Factor Model. Due to the increase in variable factors, the Fama-French Five-Factor Model is more effective in capturing the average stock returns than the Fama-French Three-Factor Model (Fama & French, 2015). The Fama-French Three-Factor model was created in 1992 as an expansion of financial asset model from the CAPM (Fama & French, 1992). This model includes two additional factors, size and value of a stock or a portfolio, which are believed to be crucial factors in calculating more accurate stock returns. Fama and French were assessing numerous stocks from the New York Stock Exchange (NYSE), American Stock Exchange (AMEX) and National Association of Securities Dealers Automated Quotations (NASDAQ) from 1963 to 1990 to create portfolios categorized by market-capitalization (size) and book-to-market ratio (value). The dependent variable in this model is the expected return measured by the return on a security or portfolio (Rit) minus the risk-free return (Rft). The risk-free rate is based of monthly treasury bill rate issued by the United States government. Although this study evaluates the long-term performances of the energy stocks in 5, 10 and 20-year horizons, monthly risk-free rate as listed in the Kenneth French Data Library is chosen for this study to ensure the consistency of the independent factors incorporated in the Fama-French factor model. As previously mentioned in the literature review section, ! and " are the coefficients of the model, and # is the error term. The error term (#) indicates the unsystematic risk of each security or portfolio, and can be mitigated through diversification of the portfolio. Since the focus of this study is to evaluate the long-term performance and sensitivity to systematic risks of individual alternative and conventional energy stock, indicated by ! and ", the error term is not computed. It is imperative however, to understand the variable factors, or the systematic risks, that are used to explain the risk-return relationship of an individual stock. 2 This database was derived from Kenneth French Data Library that provides historical benchmarks for each factor listed in the Fama-French multifactor model, http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html#HistBenchmarks

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Excess Market Return (RMt – Rft) The market factor, or the excess market return, is calculated by taking the difference between market returns (RMf) and the risk-free rate (Rft). The market return is based on the value-weighted returns of all the firms included the NYSE, AMEX and NASDAQ, and the risk-free rate is the US one-month Treasury bill rate (Fama & French, 1993). The market factor is used as the benchmark of the global market return as it includes a diversified sample of firms in different sectors. Small-Minus-Big (SMB) The Small-Minus-Big (SMB) factor reflects the market capitalization, or the size of the company. Firstly, the stocks are ranked based on size, by calculating the current market size times the number of outstanding shares in the market. These stocks are then categorized as ‘small’ or ‘big’ with respect to the NYSE median market capitalization as the size breakpoint (Fama & French, 2015). Small stocks are the stocks with market capitalization below the market median, and big stocks are those above the market median. The SMB is then calculating by taking the difference of the average small portfolios and the average of big portfolios from a total 25 portfolios (Fama & French, 1993). High-Minus-low (HML) The High-Minus-Low (HML) indicates whether the stock is considered as a value or growth stock. Value stocks lie on the 70th percentile of book-to-market (B/M) equity, in other words the stock has a high book-to-market ratio. On the other hand, the growth stocks are on the 30th percentile of the B/M equity, which indicates a low book-to-market ratio. Book-to-market ratio is used to measure the company’s book value in comparison to its market value. The model computes HML by taking the difference of the average value stocks and the average of growth stocks (Fama & French, 1993). The Fama-French Three-Factor Model is shown in Equation 3.4 (Fama & French, 2015).

!"# − %!&# = (" + E"F !F# − !&# + E"0GH*# + E"IJHK# + ,"#% (3.4)% The Fama-French Three-Factor Model concludes that returns tend to be the highest amongst stocks that have small market-cap and high book-to-market ratios (Fama & French, 1992). Furthermore, it is argued that this model is more accurate as it explains 95% of the variability of the return in comparison to the 70% explanation of variability explained by the CAPM (Antonacci, 2015). Albeit of the increased accuracy in measuring returns, Fama and French expands this model by adding two more additional factors for the Fama-French Five-Factor Model. Fama and French developed the Five-Factor Model by including profitability and investment as the two additional factors. This model is predicted to explain 71% to 94% of the variability of the returns when considering market, size, value, profitability and investment as risk factors (Fama & French, 2015). Market portfolio, SMB, and HML, as well as the coefficients (alpha and betas) and the residual errors, are consistent with the previous model. Description below can be found to further explain the profitability and investment factors.

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Robust-Minus-Weak (RMW) Robust-Minus-Weak (RMW) illustrates the difference between robust and weak operating profitability of diversified portfolios of stocks (Fama & French, 2015). Operating profitability is based on the accounting data of the firm, and it is measured by taking the difference of revenues and the cost of 1) goods sold, 2) administrative expenses, and 3) interest expenses (Fama & French, 2015). Fama and French created a table categorizing portfolios that are perceived as robust or weak based on the firm’s operating profitability. The breakpoints for robust profitability stocks lie at or above the 70th percentile of the stocks listed in the NYSE, whereas the weak profitability stocks lie at or below the 30th percentile of the same index (Fama & French, 2015). The RMW factor is measured by the average returns on two robust profitability portfolios minus the average returns on two weak profitability portfolios. Fama and French concludes that small-cap and high operating profitability tend to generate higher average returns. Conservative-Minus-Aggressive (CMA) The last factor added in the Fama-French Five-Factor Model is the Conservative-Minus-Aggressive (CMA), and it is used as an indication whether the stock or portfolio is from conservative or aggressive investment firms. Investment is calculated based on the change in total asset from current and previous year divided by the total asset from the previous year (Fama & French, 2015). Stocks within the NYSE are used as the investment breakpoint when creating categories of conservative and aggressive investment firms. Conservative investment firms lie at or below the 30th percentile in the NYSE, and aggressive investment firms are the the firms positioned at or above the 70th percentile of the NYSE. The Fama-French CMA benchmark is computed by taking the difference of the average return on two conservative investment portfolios and the average return from two aggressive investment portfolios (Fama & French, 2015). Fama and French concludes that average returns tend to be higher amongst small-cap and low investment portfolios. With the additional profitability and investment factors, the equation for Fama-French Five-Factor Model is shown in Equation 3.5.

!"# − %!&# = % (" %+ %E"F !F# − !&# + %E"0GH* +%E"IJHK +%E"L!HM +%E"NOHP +%,"# % %%(3.5)%

Fama and French tested the five-factor asset-pricing model to investigate the average stock returns in international markets such as North America, Europe, and Asia Pacific. They conclude that small stocks’ average returns in these regions have increased with high book-to-market ratio and high profitability, however it has negative relationship in relations to investment (Fama & French, 2017). Value and high profitability stocks earn higher average stock returns whereas high investment firms earn lower average stock returns. Further study on the five-factor model was implemented in the Brazilian stock market, and there is a slight difference in the results. It concludes that book-to-market ratio and profitability amongst other variables such as momentum and liquidity, are significant factors to predict stock returns (Machado, Faff, & Silva, 2017). On the other hand, the investment variable is considered insignificant to predict stock returns; the authors argue that the result may be

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specific to the Brazilian market as “the market reacts positively to investment in assets”, and this is due to the heavy reliance to the banking system to finance asset growth and the result of subsidies provided by the government for lending at a low cost which does not yield added risks (Machado et al., 2017). After computing the alphas and betas through the Fama-French Five-Factor Model for the alternative and conventional energy stocks, this study computes the differences in means and medians to compare the performances of the two stock categories. The computation is done by subtracting the means and medians of the alphas and betas of the conventional stocks from the alternative energy stocks in the respective horizons. Statistical significant testing is then performed to measure the significance of the results. T-statistics test is used to calculate the significance of the means, whereas non-parametric test is performed to calculate the significance of the medians. The tests are measuring the significance level at 10%, 5% and 1% level.

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Chapter 4

4Results

The results section is divided into 2 subsections where subsection 4.1 provides the summary statistics of the alphas for alternative and conventional energy stocks for 5, 10, and 20-year horizon, and then further broken down to 10 and 5-year intervals. This section also includes comparisons of alternative and conventional energy stocks performances based on the differences in means and medians values. Subsection 4.2 illustrates the beta coefficients for the variable factors according to the Fama-French Five-Factor Model, which can be further used to explain the characteristics and risks associated with the two categories of stocks.

4.1!Results for alphas in 5, 10, and 20-year horizons Table 5: Summary statistics of alphas for alternative and conventional energy stocks in 5, 10 and 20-year horizons

Alternative Conventional Difference Horizon 5 Years 10 Years 20 Years 5 Years 10 Years 20 Years 5 Years 10 Years 20 Years

Time period 2012-2017 2007-2017 1997-2017 2012-2017 2007-2017 1997-2017 2012-2017 2007-2017 1997-2017

Mean -0.032*** -0.037*** -0.013* -0.065*** -0.031*** -0.031*** 0.033*** -0.005 0.017** Standard Deviation 0.082 0.063 0.048 0.091 0.048 0.038 Kurtosis 1.443 1.699 0.865 3.380 2.252 0.832 Skewness -0.866 -1.150 0.169 -0.815 -0.557 -0.383 Median -0.019*** -0.027*** -0.015** -0.057*** -0.028*** -0.032*** 0.038*** 0.001 0.017** Minimum -0.291 -0.263 -0.130 -0.521 -0.237 -0.166 Maximum 0.161 0.084 0.100 0.231 0.114 0.076 N 133 95 44 409 333 173

This table illustrates the summary statistics of alphas or abnormal returns of alternative and conventional energy stocks for 5, 10 and 20-year horizons. One sample T-test is performed to evaluate the statistical significance of the means, and independent sample T-test is calculated to measure the significance of differences between alternative and conventional energy stock alphas where *, ** and *** represent statistical significance of 10%, 5% and 1% level. Non-parametric test of one-sample Wilcoxon test is performed to measure significance of medians of the alphas, and Mann-Whitney U test is performed to find statistical significance for the median differences where *, ** and *** denote statistical significance of 10%, 5% and 1% level.

5-year horizon: As shown in Table 5, the mean alphas for alternative energy stocks is -0.032 whereas the mean alphas for conventional energy stocks is -0.065 for the 5-year horizon between 2012 to 2017. The focus of the 5-year horizon is to evaluate the performance of the stocks in the last 5 years, regardless if these stocks have been listed in the stock exchange for 10 or 20 years. Both categories of stocks are underperforming compared to the diversified global stocks included in the Fama-French factors, as shown by the negative values. Nevertheless, the alternative energy stocks are performing better than conventional energy stocks with statistically significant at 1% level. This is also shown by the higher alpha median of alternative compared to the conventional energy stocks at -0.019 and -0.057 respectively, which are statistically significant at 1% level. The difference of mean alphas for alternative

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and conventional energy stocks is 0.033 and is statistically significant at 1% level. This suggests that the performance of alternative energy stocks is significantly different to the conventional energy stocks. This is further strengthened by the statistically significant difference of median alphas at 0.038. Previous studies have yet to uncover the performance of alternative energy stocks within this specific period, nonetheless there is a general upward trend of renewable energy sector investment between the year 2013 to 2015, prior to the plummet in the overall investment in the energy sector in 2016 (IEA, 2016; McCrone et al., 2017). This is potentially the reason behind the better performance in alternative energy stocks compared to the conventional energy stocks. The standard deviation for alphas in alternative energy stocks is 0.082 in comparison to 0.091 for conventional energy stocks. The dispersion of data is higher among the alphas of conventional energy stocks relative to the alternative energy stocks. The level of dispersion illustrates that the stocks are deviating farther from the mean alpha. This indicates that conventional energy stocks are slightly more risky than alternative energy stocks during the 5-year horizon, as there is a larger spread of alphas from the means within each category. It is also important to refer to the skewness and the kurtosis of the alphas as the distribution of the data may not be normally distributed. The distributions of both categories’ alphas are negatively skewed, however the kurtosis of mean alpha for conventional energy stocks is significantly higher than the alternative energy stocks, as shown in Table 5. The standard normal distribution of kurtosis is 3, and values above 3 indicates heavy-tailed distribution (Westfall, 2014). The kurtosis for conventional energy stocks is 3.380 in comparison to 1.443 for alternative energy stocks kurtosis. This can be graphically seen from Figure 5, where the histogram from alphas for the conventional energy stocks has longer tails than the histogram of alphas for the alternative energy stocks. Higher kurtosis indicates that there are more outliers in the data set hence there are more extreme occasions of abnormal returns in the conventional energy stocks. There are two reasons that might lead to the higher distribution in the conventional energy stocks. Firstly, the higher dispersion of alphas can also be caused by the larger data sample for conventional energy stocks than the alternative energy stocks sample data. Secondly, the conventional energy stocks are derived from companies with diversified products such as food products, brewery or retail where other market related changes can influence the performance of these stocks.

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Consequently, there is a wider range between the maximum and the minimum alphas in the conventional energy stocks. The minimum alpha is at -0.521, and the maximum alpha is at 0.231. The alternative energy stocks’ minimum and maximum alphas are at -0.291 and 0.161 respectively. Nonetheless, the range of alphas may not truly reflect the stock return behavior of each category as the data sample of conventional energy stocks is much larger than the alternative energy stocks. There are 409 conventional energy stocks being analyzed in comparison to the 133 alternative energy stocks for the 5-year horizon.

Table 6: Summary statistics of alphas for alternative and conventional energy stocks for 5-year horizon

Alternative Conventional Difference Horizon 5 Years 5 Years Time period 2012 - 2017 2012 - 2017

Panel A: 5 Year Total Mean -0.032*** -0.065*** 0.033*** Standard Deviation 0.082 0.091 Kurtosis 1.443 3.380 Skewness -0.866 -0.815 Median -0.019*** -0.057*** 0.038*** Minimum -0.291 -0.521 Maximum 0.161 0.231 N 133 409

Panel B: 5 Year Mean -0.050*** -0.089*** 0.039** Standard Deviation 0.090 0.089 Kurtosis 0.604 3.283 Skewness -0.441 -1.448 Median -0.031*** -0.083*** 0.051** Minimum -0.269 -0.425 Maximum 0.160 0.056 N 38 76 This table represents alphas of alternative and conventional energy stocks for 5-year horizon. Panel A shows mean alphas of all the stocks available in the last 5 years, and this includes the stocks are also listed in the 10 and 20-year horizon. Panel B only includes stocks that are present in 2012 – 2017, hence the smaller sample size. One sample T-test is performed to evaluate the statistical significance of the means, and independent sample T-test is calculated to measure the significance of differences between alternative and conventional energy stock alphas where *, ** and *** represent statistical significance of 10%, 5% and 1% level. Non-parametric test of one-sample Wilcoxon test is performed to measure significance of medians of the alphas, and Mann-Whitney U test is performed to find statistical significance for the median differences where *, ** and *** denote statistical significance of 10%, 5% and 1% level.

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Figure 5: Histograms of alphas for alternative and conventional energy stocks between 2012 to 2017 (5-year horizon)

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Panel B in Table 6 illustrates the energy stocks that are only listed starting from 2012, and the difference between the alternative and conventional energy stocks alphas is statistically significant at 0.039. This shows that the alternative energy stocks that are listed starting in 2012, is also performing better than the conventional energy stocks. Looking at the difference in mean alphas for the total sample used for the 5-year horizon (panel A) in comparison the stocks that are only listed in 2012 (panel B), the values are marginally different at 0.033 and 0.039. Nevertheless, the result highlights that the difference of median alphas of newer stocks (panel B) is higher than the median alphas for all the stocks (panel A) at 0.051 and 0.038 respectively. Thus, if the result is focusing on the evaluation on median alphas, it is apparent that there is a higher difference in performance of the new listed stocks (panel B) than the total sample stocks (panel A). 10-year horizon: As seen in Table 5, the difference between the mean alphas of alternative and conventional energy stocks in the 10-year horizon is small compared to 5 and 20-year horizons. The mean and median differences are statistically insignificant, at -0.005 and 0.001 respectively. This illustrates that the performance of alternative and conventional energy stocks is not significantly different in the period 2007 to 2017. The overall energy sector underperformance could be derived from the financial crisis in 2008, and the crash in oil prices in 2014 (McCullum, 2017). Both mean alphas are negative indicating an overall underperformance in both stock categories, however this is the only horizon that the alternative energy stocks are performing worse than the conventional energy stocks compared to 5 and 20-year horizons. The mean alphas of the alternative energy stocks are lower, at -0.037, than the mean alphas for the conventional energy stocks which is -0.031. This is parallel to the findings by previous studies where the results show that the WilderHill New Energy Global Innovation (NEX) is underperforming in the year 2009 to 2013, and the ÖkoDAX and DAXsubsector Renewable Energies negative abnormal returns in the year 2008 to 2011 (Bohl et al., 2013; Inchauspe et al., 2015). The Fukushima incident occurred in March 2011, which could have also negatively impacted the performance of the alternative energy stocks as investors may perceive the alternative energy sector as less appealing. The standard deviation of alphas is higher in alternative energy stocks (0.063) than the conventional energy stocks (0.048) signifying that the alternative energy stocks have a larger spread of alphas to its mean. At -1.150, the alphas of alternative energy stocks are more negatively skewed than the conventional energy stock category which is skewed at -0.557. This highlights that the abnormal returns of alternative energy stocks are generally worse than the conventional energy stocks between the year 2007 to 2017. The conventional energy stock alphas kurtosis is higher than alternative energy stock alphas with 2.252 and 1.699 respectively. Nonetheless, both kurtosis fall under the benchmark of 3 for normal distribution kurtosis. This suggests that the alphas for both alternative and conventional energy stocks have less extreme fluctuations, and in this case alternative energy alphas have less fluctuations than the

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conventional energy stocks alphas. This can be graphically seen in Figure 6 where the histogram for conventional energy stock alphas has longer tails than the histogram for alternative energy stock alphas. The result suggests that although alternative energy stocks were generating larger negative abnormal returns than the conventional energy stocks, lower kurtosis for alternative energy stocks suggests that there are less fluctuations in the returns for alternative energy stocks. This could however, be the result of smaller sample size in alternative energy stocks than the conventional energy stocks.

The minimum and maximum alphas range similarly between the two categories. The minimum alpha in alternative energy stocks is -0.263 in comparison to -0.237 for alpha among the conventional energy stocks. On the other hand, the maximum for alternative energy stock alpha is 0.084 which is lower than 0.114 for the conventional energy stock category. Similar to the 5-year horizon, the sample size for conventional energy stocks is larger than the alternative energy stocks, thus the range may not be an effective indication of the mean alpha. To further evaluate the performance of alternative and conventional energy stocks, panel A in Table 7 breaks down all the stock in the 10-year horizon into two 5-year intervals which are 2007 to 2012 and 2012 to 2017. The mean and median differences of alternative and conventional energy stock alphas are not statistically significant in the 10-year horizon, however the differences are statistically significant at 1% level in 5-year intervals. In 2007 to 2012, the alternative energy stocks were performing worse than the year 2012 to 2017 in comparison to the conventional energy stocks. This is shown by the negative values of the mean differences in the first 5-year intervals, and positive values of the mean in differences in the last 5 years, at -0.038 and 0.034 respectively. Similar patterns are found through the median differences of alphas, where alternative energy stocks are performing worse at -0.028 in 2007 to 2012, however better in 2012 to 2017 with 0.040 with respect to the conventional energy stocks. Nonetheless, panel B shows that the pattern does not hold when the focus is on the alternative and conventional energy stocks alphas that are only listed from 2007 to 2017, meaning they are the newer stocks within the data sample used in panel A. The results from the differences

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in mean and median of alternative and conventional energy stocks alphas show that the alternative energy stocks are performing worse than its counterpart. The mean differences are statistically significant where the alternative energy stocks were underperforming by -0.06 in 2007 to 2012, and -0.042 in 2012 to 2017. The median differences also highlight the same condition where the median difference in 2012 to 2017 is slightly lower at -0.046.

Table 7: Summary statistics of alphas for alternative and conventional energy stocks for 10-year horizon

Alternative Conventional Difference!Horizon 10 Years 5 Years 10 Years 5 Years 10 Years 5 Years

Time period 2007 - 2017 2007 - 2012 2012 - 2017 2007 - 2017 2007 - 2012 2012 - 2017 2007 - 2017 2007 - 2012 2012 - 2017 Panel A: 10 Year Total!Mean -0.037*** -0.048*** -0.025*** -0.031*** -0.010*** -0.059*** -0.005 -0.038*** 0.034*** Standard Dev. 0.063 0.097 0.078 0.048 0.063 0.091 Kurtosis 1.699 0.473 2.354 2.252 3.160 3.490 Skewness -1.150 -0.810 -1.090 -0.557 -0.802 -0.716 Median -0.027*** -0.034*** -0.012*** -0.028*** -0.007** -0.052*** 0.001 -0.028*** 0.040*** Minimum -0.263 -0.357 -0.289 -0.237 -0.306 -0.522 Maximum 0.084 0.124 0.161 0.114 0.170 0.233 N 95 95 95 333 333 333

Panel B: 10 Year Mean -0.044*** -0.071*** -0.018*** -0.032*** -0.011* -0.060*** -0.012 -0.060*** -0.042*** Standard Dev. 0.066 0.102 0.076 0.046 0.069 0.088 Kurtosis 1.766 -0.844 3.133 2.823 2.304 4.822 Skewness -1.191 -0.328 -0.937 -0.736 -0.720 -1.089 Median -0.031*** -0.068 -0.010*** -0.029*** -0.008 -0.056*** -0.002 -0.060*** -0.046*** Minimum -0.263 -0.271 -0.289 -0.237 -0.306 -0.522 Maximum 0.055 0.088 0.161 0.100 0.170 0.184 N 51 51 51 160 160 160 ! ! !This table shows the alphas for alternative and conventional energy stocks in 10-year horizon. Panel A includes all stocks that exist in the 10 years holding period including stocks that also exist in the 20 years period. This is then further broken down into 5-year intervals of 2007 - 2012, and 2012 - 2017. Panel B only includes stocks that are listed during the year 2007 - 2017, then divided into 5-year intervals. One sample T-test is performed to evaluate the statistical significance of the means, and independent sample T-test is calculated to measure the significance of differences between alternative and conventional energy stock alphas where *, ** and *** represent statistical significance of 10%, 5% and 1% level. Non-parametric test of one-sample Wilcoxon test is performed to measure significance of medians of the alphas, and Mann-Whitney U test is performed to find statistical significance for the median differences where *, ** and *** denote statistical significance of 10%, 5% and 1% level.!

In terms of the distribution of the data, panel B in Table 7 shows that the alphas for alternative and conventional energy stocks are negatively skewed with higher skewness in 2012 to 2017 than 2007 to 2012. This suggests that the occurrence of negative abnormal returns is more frequent during the latter half of the 10-year horizon. The kurtosis in 2012 to 2017 for both categories of stocks are also larger than the kurtosis in 2007 to 2012. This highlights that the kurtosis of alphas of alternative and conventional energy stocks have thicker tails, which suggests that the distribution of the abnormal returns is more extreme in this period. 20-year horizon: The results in Table 5 show that the mean alpha for alternative energy stocks is -0.013, and -0.031 for conventional energy stocks for the 20-year horizon. The negative mean alphas indicate that both categories are underperforming, nonetheless the conventional energy stocks are performing worse than the alternative energy stocks. This is highlighted by the statistically significant difference of mean alphas between alternative and conventional energy stock at 0.017. The difference in median alphas is also statistically significant, where the result shows that alternative energy stocks are performing better in comparison to the

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conventional energy stocks with medians of -0.015 and -0.032 respectively. Within the 20-year horizon, the result of the mean and median differences illustrates that the alternative energy stocks were performing better in comparison to the conventional energy stocks. The standard deviation for alternative energy stock alpha is 0.048 in comparison to 0.038 for the conventional energy stock alphas. This signifies the presence of a larger spread of alphas for alternative energy stock than in conventional energy stocks. There is larger occurrence of abnormal returns deviating from the mean alphas among the alternative energy stocks. The alternative and conventional energy stock alphas are skewed in the opposite directions. Alternative energy stock alphas are positively skewed at 0.169 whereas the conventional energy stock alphas are negative skewed at -0.383. As seen from Figure 7, the distribution of alphas for the alternative energy stocks generate a longer tail on the right side of the histogram. Conversely, it is the opposite of the conventional energy stock alphas. This suggests that the alternative energy stocks negative abnormal returns is less frequent than the negative abnormal returns for the conventional energy stocks. As previously mentioned, this can be caused by the dissimilar sample size as conventional energy category includes more stocks at 173 in comparison for the 44 stocks in the alternative energy category. The kurtosis for alternative and conventional energy stock alphas are similar at 0.865 and 0.832 respectively. This suggests that the extreme abnormal returns however, are similar in both categories.

The range of minimum alphas and maximum alphas are similar for both categories. The minimum alpha for the alternative energy stocks is -0.130, and -0.166 for the conventional energy stocks. Maximum alpha for the alternative energy stocks is 0.100, and 0.076 for the conventional energy stocks. The result shows that the alphas for alternative and conventional energy stocks are ranged similarly. Referring to panel C in Table 8, the differences in mean and median of alternative and conventional energy stock alphas are positive, indicating that alternative energy stocks are performing better than the conventional energy stock. However, the result is only statistically significant in the first 10-year interval. When the stocks are evaluated by 5-year intervals,


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alternative energy stocks were performing better from 1997 to 2002, and 2012 to 2017. The statistical significant differences in the means and medians is 0.078 and 0.023 in 1997 to 2002, and 0.025 and 0.029 in 2012 to 2017. Conversely, alternative energy stocks were performing worse than the conventional energy stocks, as shown by the negative values of the mean and median differences, during 2002 to 2007 and 2007 to 2012. The results, however, are not statistically significant. This is similar to the findings by Rezec et. Al (2017), where the authors compare the performance of 14 different alternative energy indexes to global benchmarks such as MSCI World, NASDAQ Composite and the S&P Global 1200, and the results highlight that 10 of the alternative energy indexes were underperforming from the year 2000 to 2013 (Rezec & Scholtens, 2017). Though the authors did not specify the reasons behind the underperformance of the alternative energy indexes, they conclude that the overall underperformance of these indexes makes the alternative energy sector seem less attractive in the financial perspective (Rezec & Scholtens, 2017).

Table 8: Summary statistics of alphas for alternative and conventional energy stocks in 20-year horizon

Horizon 20 Years 10 Years 5 Years Time period 1997 - 2017 1997 - 2007 2007 - 2017 1997 - 2002 2002 - 2007 2007 - 2012 2012 - 2017

Panel A: Alternative Energy Stocks Mean -0.013* 0.014 -0.028*** 0.022 0.008 -0.020 -0.034*** Standard Deviation 0.048 0.070 0.059 0.123 0.084 0.083 0.080 Kurtosis 0.865 1.555 1.694 -0.540 1.588 5.666 1.859 Skewness 0.169 0.239 -1.097 0.559 -0.778 -1.640 -1.270 Median -0.015** 0.021 -0.018*** -0.021 0.005 -0.005 -0.022**

Minimum -0.130 -0.182 -0.213 -0.173 -0.259 -0.356 -0.285 Maximum 0.100 0.212 0.083 0.298 0.171 0.126 0.082 N 44 44 44 44 44 44 44

Panel B: Conventional Energy Stocks Mean -0.031*** -0.023*** -0.031*** -0.057*** 0.016** -0.009** -0.058*** Standard Deviation 0.038 0.060 0.050 0.073 0.095 0.056 0.094 Kurtosis 0.832 4.343 1.823 1.495 8.574 4.343 2.585 Skewness -0.383 -0.834 -0.417 -0.727 -1.613 -0.895 -0.434 Median -0.032*** -0.022*** -0.027*** -0.044*** 0.016*** -0.007** -0.050*** Minimum -0.166 -0.294 -0.216 -0.323 -0.463 -0.289 -0.437 Maximum 0.076 0.167 0.114 0.158 0.330 0.136 0.233 N 173 173 173 173 173 173 173 Panel C: Difference Mean 0.017** 0.037*** 0.003 0.078*** -0.008 -0.011 0.025* Median 0.017** 0.043*** 0.008 0.023*** -0.011 0.001 0.029** This table shows summary statistics of alphas for alternative and conventional energy stocks that are listed through the 20 years holding period. Panel A and panel B show the summary statistics of alternative and conventional energy stocks respectively, while panel C illustrates the differences of means and medians in the different horizons. These alphas are divided into 10-year intervals from 1997 - 2007 and 2007 - 2017. The results are further broken down into 5-year intervals to evaluate the stock performance in 1997 - 2002, 2002 - 2007, 2007 - 2012, and 2012 - 2017. One sample T-test is performed to evaluate the statistical significance of the means, and independent sample T-test is calculated to measure the significance of differences between alternative and conventional energy stock alphas where *, ** and *** represent statistical significance of 10%, 5% and 1% level. Non-parametric test of one-sample Wilcoxon test is performed to measure significance of medians of the alphas, and Mann-Whitney U test is performed to find statistical significance for the median differences where *, ** and *** denote statistical significance of 10%, 5% and 1% level.

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4.2!Results for betas in 5, 10 and 20-year horizons As discussed in the methodology section, the regression of Fama-French Five-Factor model is used to calculate the coefficients, or betas, for both alternative and conventional energy stocks. In this model, there are five factors that are included to establish the risks related to these stocks. These variable factors are the market excess return (Mkt-Rf), size (Small-Minus-Big), value (High-Minus-Low), operating profitability (Robust-Minus-Weak), and investment (Conservative-Minus-Aggressive) (Fama & French, 2015). The beta coefficient is an indicator to the sensitivity of the stocks to these variables (Fama & French, 1992). Table 9 illustrates the summary statistics of the betas of these variable factors for alternative and conventional energy stocks in 5, 10 and 20-year horizon. Panel A in Table 9 provides summary statistics of the market excess return betas for the alternative and conventional energy stocks, and the results illustrate that the mean and medians of betas for the alternative energy stocks are higher than its counterpart for all the horizons. All the mean and median betas for the alternative energy stocks are above the benchmark of 1 indicating that these stocks move more than the market, hence more volatile in comparison to the common stocks listed in the market portfolio benchmark. High-technology stocks generally generate market excess return beta higher than 1. This can be an indication that alternative energy stocks are perceived to have similar characteristics to the high-technology stocks, as discussed in previous literature (Kumar et al., 2012; Managi & Okimoto, 2013). On the other hand, the means and medians of betas for the conventional energy stocks are below 1 which indicates that the stocks are moving less than the market. The differences in means and medians are positive, and are statistically significant highlighting the discrepancy of volatility in the stock categories. The coefficient of the size variable factor is illustrated in panel B. The result shows that the alternative energy stocks load more heavily on the SMB factor than the conventional energy stocks in the 5-year horizon, and is statistically significant. The significant differences in mean and median, 0.199 and 0.228 respectively, emphasize positive SMB loading in the alternative energy stocks than the conventional energy stocks. The 10-year horizon generates contradicting results on the loaded SMB factor for the alternative and conventional energy stocks. From the median values, this factor is heavily loaded in alternative energy stocks shown by higher median than the conventional energy stocks. Conversely, the mean indicates that the conventional energy stocks load more heavily to the SMB factor than the alternative energy stocks. Consequently, the difference in median results in positive SMB coefficient, at 0.1, and the difference in mean generate negative SMB coefficient, at -0.023. The 20-year horizon results however, are consistently show that the conventional energy stocks’ load heavily on the SMB factor in comparison to the alternative energy stocks. Nonetheless, the differences in means and medians for 10 and 20-year horizons are shown to be statistically insignificant. The values for both alternative and conventional energy stock betas are positive across all periods. βSMB value bigger than 0 is often used as an indication that the stocks are predominantly small-cap stocks (Fama & French, 1993), however the idea was later contradicted with a study illustrating that the large-cap stocks can also can generate positive SMB coefficients (Chen & Bassett, 2014). Deriving stocks sizes used in the study is not

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within the scope of this research, hence this study cannot conclude whether the sample of alternative or conventional energy stocks are more tilted towards small-cap or big-cap stocks. Nonetheless, the results indicate that the alternative energy stocks have higher risk exposure to size factor than the conventional energy stocks. Panel C illustrates the coefficient of the value factor (βHML), where the results show that the majority of the mean betas of alternative energy stocks are negative (excluding the median beta of 20-year horizon), which is the opposite of the positive value betas of the conventional energy stocks. This is an indication that alternative energy stocks are predominantly behaving like growth stocks, whereas the conventional energy stocks are predominantly behaving like value stocks. The mean and median differences are negative, and are statistically significant at 1% level. There is a significant difference between the sensitivity of the alternative and conventional energy stocks in relations the value factor, where the conventional energy stocks are loaded more heavily to the value risk factor than the alternative energy stocks. The betas for operating profitability (βRMW) factor for the alternative and conventional energy stocks are shown in panel D. The means and medians of betas for alternative and conventional energy stocks illustrate dissimilarities between the two categories. The values of betas are all negative for the alternative energy stocks, and for the 5-year horizon of conventional energy stocks. This illustrates that these stocks are behaving similarly to low operating profitability firms, hence weak stocks. On the contrary, the conventional energy stocks in 10 and 20-year horizon generate positive beta which insinuate that these stocks are behaving like robust stocks. The difference in means and medians are significant throughout all periods suggesting that the conventional energy stocks load more heavily on the profitability risk factor than the alternative energy stocks. The last factor is the investment risk factor which is depicted by the βCMA in panel E. The negative value of betas across all stocks and horizons indicate that both alternative and conventional energy stocks are behaving similarly to aggressive stocks. The βCMA means and medians of the conventional energy stocks however, are smaller than the alternative energy stocks across all horizons. The differences in means and medians are significantly positive indicating that the alternative energy stocks are loaded heavily to the investment risk factor than the conventional energy stocks.

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Table 9: Summary statistics of betas of alternative and conventional energy stocks in 5, 10 and 20-year horizons

Alternative Conventional Difference Horizon 5 Years 10 Years 20 Years 5 Years 10 Years 20 Years 5 Years 10 Years 20 Years

Time period 2012-2017 2007-2017 1997-2017 2012-2017 2007-2017 1997-2017 2012-2017 2007-2017 1997-2017

Panel A: βMkt-Rf Mean 1.117 1.126 1.163 0.970 0.953 0.997 0.147*** 0.173*** 0.166** Standard Deviation 0.442 0.435 0.366 0.451 0.437 0.453 Kurtosis 0.094 -0.788 -0.435 0.326 -0.278 -0.816

Skewness 0.518 0.075 -0.041 0.331 0.230 -0.041 Median 1.078 1.149 1.175 0.934 0.945 0.969 0.143*** 0.204*** 0.207** Minimum 0.319 0.270 0.424 -0.315 0.005 0.028 Maximum 2.388 2.103 1.903 2.615 2.209 2.007

Panel B: βSMB Mean 0.540 0.535 0.486 0.341 0.557 0.609 0.199*** -0.023 -0.123 Standard Deviation 0.564 0.601 0.528 0.606 0.688 0.485 Kurtosis -0.163 -0.240 -0.732 0.698 0.422 0.352 Skewness 0.345 0.172 0.261 0.545 0.553 0.470 Median 0.512 0.574 0.400 0.283 0.474 0.563 0.228*** 0.100 -0.163 Minimum -0.541 -0.771 -0.466 -1.103 -0.893 -0.620 Maximum 2.181 2.033 1.508 2.405 3.030 1.993

Panel C: βHML Mean -0.087 -0.141 -0.226 1.341 0.619 0.729 -1.428*** -0.760*** -0.955*** Standard Deviation 0.679 0.543 0.797 1.196 0.534 0.490 Kurtosis 0.710 -0.552 -0.406 0.336 0.574 -0.399 Skewness 0.003 -0.193 -0.894 0.773 0.468 0.370 Median -0.128 -0.027 0.153 1.142 0.568 0.709 -1.270*** -0.595*** -0.556*** Minimum -2.232 -1.415 -2.020 -1.174 -1.070 -0.351 Maximum 1.623 1.258 0.740 5.793 2.706 2.239

Panel D: βRMW Mean -0.841 -0.690 -0.784 -0.088 0.255 0.372 -0.753*** -0.944*** -1.156*** Standard Deviation 1.218 1.109 1.234 0.838 0.605 0.371 Kurtosis -0.631 -0.928 -0.563 -0.013 1.090 1.530 Skewness -0.105 -0.194 -0.777 -0.329 -0.496 0.190 Median -0.781 -0.477 -0.455 -0.020 0.265 0.362 -0.761*** -0.742*** -0.817*** Minimum -3.799 -3.231 -3.513 -2.738 -1.794 -0.849 Maximum 2.032 1.754 0.798 1.887 2.167 1.807

Panel E: βCMA Mean -0.357 -0.750 -0.150 -0.878 -1.081 -0.465 0.521*** 0.331*** 0.315*** Standard Deviation 0.963 0.817 0.543 0.820 0.807 0.439 Kurtosis 1.317 1.384 0.781 0.269 -0.104 0.117 Skewness 0.067 -0.703 -0.289 -0.317 -0.271 -0.192 Median -0.394 -0.759 -0.161 -0.800 -1.077 -0.444 0.406*** 0.318*** 0.282*** Minimum -3.337 -3.518 -1.753 -3.574 -3.375 -1.831 Maximum 3.402 1.154 0.908 1.453 0.964 0.799 N 133 95 44 409 333 173 This table illustrates the betas of the factors from the Fama-French Five-Factor Model. Mkt-Rf is the market risk factor minus the risk-free rate, whereas SMB, HML, RMW and CMA indicate the size, value, profitability and investment of the stocks respectively. The alternative and conventional energy stocks are categorized in 5, 10 and 20-year horizons. The differences are taken from each holding period between alternative and conventional energy stocks. Independent sample T-test is calculated to measure the significance of differences between alternative and conventional energy stock betas where *, ** and *** represent statistical significance of 10%, 5% and 1% level. Non-parametric test of Mann-Whitney U test is performed to find statistical significance for the median differences where *, ** and *** denote statistical significance of 10%, 5% and 1% level.

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Chapter 5

5Discussion

Using time-series regressions based of the Fama-French Five-Factor Model, the financial performances of alternative and conventional energy stocks are evaluated in 5, 10, and 20-year horizons. The performances of the stock category are analyzed individually, and are compared to one another to discuss the discrepancy in performance between the alternative and conventional energy stocks. The analysis of stock performance is mainly based on the evaluation of the alphas, or the abnormal returns, of the stocks. The alpha is also known as the constant, or the intercept of the time-series regression. Statistically significant non-zero intercept indicates that there are unexplained events outside the variable factors used in the model (Fama & French, 2017). Thus, the results of the alphas can help scholars and US institutional investors to comprehend trends in performance of the alternative and conventional energy stocks, however do not guarantee the future performance of these stocks. This study is based on the US investors’ perspectives as the Fama-French Five-Factor model does not account for the potential risk from currency swings that could be generated when converting the stock prices to USD. The results show that in comparison to the broadly diversified global stock indexes, both alternative and conventional energy stocks are underperforming throughout the 20-year horizon, as indicated by the negative values of alphas. However, the differences of means and medians of these alphas highlight that the alternative energy stocks are performing better than the conventional energy stocks during 1997 to 2002, and 2012 to 2017. The better performance of the alternative energy stocks could be affected by the Dot-Com bubble in 1998 to 2000 where the returns of stocks, namely the internet sector stocks, increased over 1000 percent returns on its public equity (Ofek & Richrdson, 2003). The explanation behind better performance in the last five years could be derived from the increase in investment within the alternative energy sector (McCrone et al., 2017). Furthermore, there is a significant increase in the number of alternative energy stocks that are listed in the stock exchange. This emphasizes the opportunity for increased participation from institutional investors to indirectly partake in the renewable energy investment through the secondary market which makes investment more commercial instead of locally based (Rezec & Scholtens, 2017). This is an added incentive for investors to have an exit strategy instead of opting for conventional project financing investment which usually require a large capital investment (Rezec & Scholtens, 2017). There is also an increased advocacy in responsible investment practices such as the “Principles for Responsible Investment” by the United Nations in 2016 that advocates for investment transparency and investment policy to include environmental, social and governance issues for institutional investors (Rezec & Scholtens, 2017).

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It is worth noting that a small sample of the alternative energy stocks are perform worse than the conventional energy stocks in 2012 to 2017. These stocks are the 51 stocks that are listed in the stock exchange from 2007 for the 10-year horizon. The statistically significant differences in means and medians of the alternative and conventional energy alphas in this sub-sample do not follow the trends of differences in all other horizons. These 51 alternative energy stocks were performing worse than the conventional energy stocks within the last 5 years. Further study needs to be conducted to explain this result. In 2002 to 2007, and 2007 to 2012, the alternative energy stocks are performing worse than to the conventional energy stocks. The results of mean and median differences are negative, however is not statistically significant. The underperformance trend is parallel to the studies previously conducted that resulted in negative abnormal performance during the year 2008 to 2013 for alternative energy indexes such as the WilderHill New Energy Global Innovation Index (NEX), ÖkoDAX and the DAXsubsector Renewable Energies (Bohl et al., 2015, 2013; Inchauspe et al., 2015). The alternative energy sector was not able to quickly recover from the financial crash that occurred in 2008 (Inchauspe et al., 2015). Additionally, the negative abnormal returns can also be derived from the sensitivity to the market fluctuations as discussed in Bohl et. al, (2015). Referring to the market excess return factor, alternative energy stocks generate beta higher than 1 throughout all horizons. This an indication that the alternative energy stocks are more sensitive to market changes in comparison to the conventional energy stocks which generate beta less than 1. All in all, during 2002 to 2012, alternative energy stocks were performing poorly in comparison to the conventional energy stocks. The difference in abnormal returns of the alternative and conventional energy stocks is an indication that the Fama-French Five-Factor model was not able to capture all the risks associated with the returns of these stocks. External factors, namely policies and regulations in the energy sector, could be the cause for the differences in performance. Alternative energy policies differ to policies for the conventional energy sectors, hence the better performance for the alternative energy stocks could be derived from more effective policies created within the alternative energy sector. It would be interesting to evaluate different energy policies within each sector to uncover the reasoning behind the difference in the abnormal returns for alternative and conventional energy stocks. Energy policies also differ in different countries leading to the different responses to stock trends in different regions. Another interesting approach of evaluating alternative and conventional energy stocks can be evaluated on regional basis, as a method to evaluate the effects of regional policies. The trends of the abnormal returns in alternative and conventional energy stocks can be further evaluated by using different factor models, such as the One-Factor model or the Fama-French Three-Factor model. By using other time series asset pricing models, the results can be compared to see if the better performance for the alternative energy stocks hold by using other models.

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The results generated from the variable factors of SMB, HML, RMW and CMA show that the alternative energy stocks load heavily on the SMB and the CMA factors in comparison to the conventional energy stocks. The results are statistically significant for differences in mean and median for SMB for 5-year horizon, and throughout all periods for CMA. From this study, the results cannot conclude the characteristics of the alternative energy stocks, however the coefficient of the SMB and HML factors suggest that these stocks behave similarly to small-cap, growth stocks. Furthermore, alternative energy stocks behave more similarly to weak stocks, as oppose to conventional energy stocks that behave more similarly to robust stocks. Lastly, both alternative and conventional energy stocks have the tendency to behave like aggressive stocks indicated by the negative values of betas for the means and medians throughout all periods. Further studies can be executed to test the results of the said stock characteristics from this study.

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Chapter 6

6Conclusion

The aim of the study is to contribute to the growing literature to assess the long-term financial performance of alternative energy stocks as an investment opportunity, specifically in the financial secondary market, or the stock exchange. The sample data is divided into categories of alternative and conventional energy stocks, where global energy indexes’ constituents are used as the references for the listed stocks. To uncover the financial performance of these stocks, the Fama-French Five-Factor Model and the Fama-French benchmarks are used to generate the abnormal returns, also known as alpha (!), in 5, 10 and 20-year horizons. This model also includes five variable factors, which the study can derive different levels of exposure to the risks for alternative and conventional energy stocks. This is shown by the load factor, or beta ("), of market excess return (Mkt-Rf), size (SMB), value (HML), profitability (RMW) and investment (CMA) factors. All in all, the results illustrate that the abnormal return, shown by alpha, are statistically significant and negative, suggesting that alternative and conventional energy stocks are underperforming compared to the diversified stocks included in the Fama-French factors throughout 1997 to 2017. As the kurtosis and skewness of these stocks indicate that the distributions of the results are not normally distributed, it is also important to evaluate the medians of the abnormal returns. The statistically significant differences in means and median suggest that the alternative energy stocks are performing better than the conventional energy stocks during the 5 and 20-year horizons (between 2012 to 2017, and throughout 1997 to 2017). The sharp increase in total investment in the renewable energy sector in 2015, coupled with the decrease in investment rate for conventional energy sector may have contributed to the better performance of alternative energy stocks in comparison to the conventional energy stocks. Nonetheless, when the 20-year horizon are divided into 5-year intervals, the alternative energy stocks are performing worse than the conventional energy stocks during the period 2002 to 2007, and 2007 to 2012. This could be an indication that the alternative energy stocks were not able to recover as fast as the conventional energy stocks after the financial crisis in 2008. Annual intervals research to assess performance could be beneficial to further investigate the causes of these trends. Through the beta coefficients, the results suggest that the alternative energy stocks are more sensitive to the market factor than the conventional energy stocks as shown by the higher betas of market excess returns (Mkt-Rf). Furthermore, the alternative energy stocks load more heavily to the SMB and CMA factors than the conventional energy sector. On the contrary, the conventional energy sector load more heavily to the HML and the RMW factors than the alternative energy stocks. The alternative energy stocks behave similarly to small-cap, growth

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stocks, and the results indicates that they are also behaving similarly to weak and aggressive stocks. Further studies are required to test these characteristics of the alternative energy stocks, as the scope of this study focuses on the abnormal returns and the sensitivity to the variable factors in the Fama-French Five-Factor Model for the alternative and conventional energy stocks. The results of this study show that the long-term performances of alternative energy stocks are better than the conventional energy stocks. These realizations could be valuable to related stakeholders such as institutional investors, energy-related firms and policymakers in the energy sector. In this context, institutional investors may choose to diversify their energy stock portfolio towards the alternative energy stocks as an approach to increase their portfolio returns, or minimize financial losses in their portfolio returns. Conventional energy-related firms can also expand their product portfolio towards alternative energy-related goods and services. Firms could benefit from the increasing trend of investment stream towards alternative energy sector, as well as from engaging with a larger audience of institutional investors who are attracted to invest in alternative energy stocks due to their better performance. Policymakers in the energy sector can use this study as a reference in evaluating the comparison performance of alternative and conventional energy stocks, and determine appropriate regulations and policies to incentivize the growth in the alternative energy sector to sustain the growth within the sector. Further research is required to assess other variable factors that are not included in the Fama-French Five-Factor Model, so that relevant stakeholders can mitigate these risks appropriately.

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Appendix

List of stocks and its characteristics included in the alternative energy category

Number Ticker Company Index Sector (INDM) Country City Start End 1 C:VNP 5N PLUS Ardour Global Alternative Energy Commodity Chemicals CA SAINT-LAURENT 2007-12-20 2018-01-01 2 U:AYI ACUITY BRANDS NASDAQ Clean Edge Energy Building Mat.& Fix. US ATLANTA 2001-12-03 2018-01-01 3 @ADES ADVANCED EMISSIONS SLTN. Ardour Global Alternative Energy Waste, Disposal Svs. US HIGHLANDS RANCH 2003-10-22 2018-01-01 4 @AEIS ADVANCED ENERGY INDS. NASDAQ Clean Edge Energy Semiconductors US FORT COLLINS 1995-11-17 2018-01-01 5 AFC AFC ENERGY Ardour Global Alternative Energy Alternative Fuels GB Cranleigh Surrey 2007-04-23 2018-01-01 6 U:APD AIR PRDS.& CHEMS. Wilderhill Clean Energy Commodity Chemicals US ALLENTOWN 1973-01-02 2018-01-01 7 C:AXY ALTERRA POWER Ardour Global Alternative Energy Alt. Electricity CA VANCOUVER 2009-07-07 2018-01-01 8 U:AMRC AMERESCO CLASS A Wilderhill Clean Energy Heavy Construction US FRAMINGHAM 2010-07-22 2018-01-01 9 @AMSC AMERICAN SUPERCONDUCTOR Wilderhill Clean Energy Electrical Equipment US DEVENS 1991-12-13 2018-01-01

10 @ASYS AMTECH SYS. Ardour Global Alternative Energy Semiconductors US TEMPE 1983-08-02 2018-01-01 11 @AMRS AMYRIS Ardour Global Alternative Energy Alternative Fuels US EMERYVILLE 2010-09-28 2018-01-01 12 U:AVX AVX NASDAQ Clean Edge Energy Electrical Equipment US FOUNTAIN INN 1995-08-15 2018-01-01 13 U:BMI BADGER METER Ardour Global Alternative Energy Electronic Equipment US MILWAUKEE 1973-01-02 2018-01-01

14 C:BLDP BALLARD POWER SYSTEMS Thomson Reuters Global Renewable Energy Alternative Fuels CA BURNABY 1993-08-12 2018-01-01

15 @BLDP BALLARD PWR.SYS. (NAS) NASDAQ Clean Edge Energy Alternative Fuels CA BURNABY 1995-11-08 2018-01-01 16 C:BLX BORALEX 'A' S&P Global Clean Energy Alt. Electricity CA KINGSEY FALLS 1988-08-31 2018-01-01 17 U:CCC CALGON CARBON Ardour Global Alternative Energy Specialty Chemicals US MOON TOWNSHIP 1987-06-02 2018-01-01 18 @CSIQ CANADIAN SOLAR S&P Global Clean Energy Renewable Energy Eq. CA GUELPH 2006-11-09 2018-01-01 19 C:CMH CARMANAH TECHS. Ardour Global Alternative Energy Renewable Energy Eq. CA VICTORIA 1997-01-08 2018-01-01 20 @CECE CECO ENV. Ardour Global Alternative Energy Industrial Machinery US DALLAS 2002-05-28 2018-01-01 21 CWR CERES POWER HOLDINGS Ardour Global Alternative Energy Alternative Fuels GB HORSHAM 2004-11-24 2018-01-01 22 K:CLYU CHIN.LONGYUAN PWR.GP.'H' S&P Global Clean Energy Renewable Energy Eq. CN BEIJING 2009-12-10 2018-01-01

23 K:CHEI CHINA EVERBRIGHT INTERNATIONAL S&P Global Clean Energy Waste, Disposal Svs. HK NA 1988-06-07 2018-01-01

24 T:BIOT CHINA EVERBRIGHT WATER Ardour Global Alternative Energy Water BM SHENZHEN 2004-02-16 2018-01-01

25 K:CSTH CHINA SYE.SLR.TECHS.HDG. Thomson Reuters Global Renewable Energy Renewable Energy Eq. BM NA 2009-01-13 2018-01-01

26 U:CIG CIA.ENGT.DE MINASGR.ADR 1:1 S&P Global Clean Energy Alt. Electricity US BELO HORIZONTE 1995-01-12 2018-01-01

46

27 @CLNE CLEAN ENERGY FUELS Ardour Global Alternative Energy Specialty Retailers US NEWPORT BEACH 2007-05-25 2018-01-01

28 U:ELP CMPH.PARNS.DE ENGA.SPN. ADR 1:1 S&P Global Clean Energy Alt. Electricity US CURITIBA 1997-04-17 2018-01-01

29 @CDXS CODEXIS Ardour Global Alternative Energy Specialty Chemicals US REDWOOD CITY 2010-04-22 2018-01-01 30 K:COMT COMTEC SOLAR SYS.GROUP Ardour Global Alternative Energy Renewable Energy Eq. KY SHANGHAI 2009-10-30 2018-01-01 31 K:NPH CONCORD NEW ENERGY GROUP Ardour Global Alternative Energy Alt. Electricity BM NA 1991-11-27 2018-01-01 32 @CWCO CONSOLIDATED WT. Ardour Global Alternative Energy Water KY GRAND CAYMAN 1995-05-17 2018-01-01 33 Z:CENZ CONTACT ENERGY S&P Global Clean Energy Alt. Electricity NZ WELLINGTON 1999-05-11 2018-01-01 34 U:CZZ COSAN 'A' Ardour Global Alternative Energy Food Products BM SAO PAULO 2007-08-16 2018-01-01 35 K:COSL COSLIGHT TECH.INTL.GP. Ardour Global Alternative Energy Nondur.Household Prod BM NA 1999-11-17 2018-01-01 36 U:CVA COVANTA HOLDING S&P Global Clean Energy Waste, Disposal Svs. US MORRISTOWN 1990-12-06 2018-01-01 37 @CREE CREE NASDAQ Clean Edge Energy Semiconductors US DURHAM 1993-02-09 2018-01-01 38 D:CE2 CROPENERGIES Ardour Global Alternative Energy Alternative Fuels DE MANNHEIM 2006-09-28 2018-01-01 39 TW:DAN DANEN TECHNOLOGY Ardour Global Alternative Energy Renewable Energy Eq. TW TAOYUAN 2010-07-20 2018-01-01 40 U:DQ DAQO NEW ENERGY ADR 1:25 Wilderhill Clean Energy Specialty Chemicals US CHONGQING 2010-10-07 2018-01-01 41 K:DONG DONGFANG ELECTRIC 'H' Ardour Global Alternative Energy Industrial Machinery CN CHENGDU 1994-06-06 2018-01-01

42 KO:DNK DONGKUK STUTR.& CON. Thomson Reuters Global Renewable Energy Heavy Construction KR POHANG 2009-08-31 2018-01-01

43 U:ETN EATON Ardour Global Alternative Energy Divers. Industrials IE DUBLIN 1973-01-02 2018-01-01

44 A:EDEX EDEN INNOVATIONS Thomson Reuters Global Renewable Energy Alternative Fuels AU PERTH 2006-06-06 2018-01-01

45 P:EDPR EDP RENOVAVEIS S&P Global Clean Energy Alt. Electricity ES MADRID 2008-06-03 2018-01-01 46 C:EFL ELECTROVAYA Ardour Global Alternative Energy Electrical Equipment CA MISSISSAUGA 2000-11-16 2018-01-01 47 U:ENIA ENEL AMERICAS ADR 1:50 S&P Global Clean Energy Con. Electricity US SANTIAGO 1993-10-20 2018-01-01 48 D:EKT ENERGIEKONTOR Ardour Global Alternative Energy Alt. Electricity DE BREMEN 2000-05-24 2018-01-01 49 PH:PED ENERGY DEVELOPMENT Ardour Global Alternative Energy Alt. Electricity PH PASIG 2006-12-13 2018-01-01 50 @EFOI ENERGY FOCUS Ardour Global Alternative Energy Electrical Equipment US SOLON 1994-08-18 2018-01-01 51 @ERII ENERGY RECOVERY Ardour Global Alternative Energy Industrial Machinery US SAN LEANDRO 2008-07-02 2018-01-01 52 U:ENS ENERSYS NASDAQ Clean Edge Energy Electrical Equipment US READING 2004-07-30 2018-01-01 53 @ENPH ENPHASE ENERGY Ardour Global Alternative Energy Renewable Energy Eq. US PETALUMA 2012-03-30 2018-01-01 54 U:ESE ESCO TECHS. Ardour Global Alternative Energy Electronic Equipment US ST LOUIS 2000-01-18 2018-01-01 55 TW:ETS E-TON SOLAR TECH. Ardour Global Alternative Energy Renewable Energy Eq. TW TAINAN 2005-03-03 2018-01-01 56 TW:ELE EVERLIGHT ELECTRONICS Ardour Global Alternative Energy Electrical Equipment TW NEW TAIPEI 1997-11-14 2018-01-01

57 D:AA4 FALK RENEWABLES (FRA) Ardour Global Alternative Energy Alt. Electricity IT SESTO SAN GIOVANNI 2005-05-12 2018-01-01

58 @FSLR FIRST SOLAR S&P Global Clean Energy Renewable Energy Eq. US TEMPE 2006-11-17 2018-01-01 59 @FELE FRANKLIN ELECTRIC Ardour Global Alternative Energy Industrial Machinery US FORT WAYNE 1973-01-02 2018-01-01 60 @FCEL FUELCELL ENERGY Wilderhill Clean Energy Renewable Energy Eq. US DANBURY 1992-06-25 2018-01-01 61 F:FTRN FUTUREN Ardour Global Alternative Energy Alt. Electricity FR PARIS 2002-07-17 2018-01-01 62 K:GCLP GCL-POLY ENERGY HOLDINGS S&P Global Clean Energy Renewable Energy Eq. KY NA 2007-11-13 2018-01-01 63 U:BGC GENERAL CABLE Wilderhill Clean Energy Electrical Equipment US HIGHLAND HEIGHTS 1997-05-16 2018-01-01

47

64 @THRM GENTHERM Wilderhill Clean Energy Auto Parts US NORTHVILLE 1993-06-10 2018-01-01 65 TW:GNE GINTECH ENERGY Ardour Global Alternative Energy Renewable Energy Eq. TW JHUNAN 2007-11-02 2018-01-01 66 TW:GNY GREEN ENERGY TECHNOLOGY Ardour Global Alternative Energy Renewable Energy Eq. TW TAIPEI 2008-01-25 2018-01-01 67 @GPRE GREEN PLAINS NASDAQ Clean Edge Energy Alternative Fuels US OMAHA 2006-03-15 2018-01-01 68 S:GUR GURIT HOLDING Ardour Global Alternative Energy Specialty Chemicals CH WATTWIL 1986-01-03 2018-01-01 69 @HQCL HANWHA Q CELLS ADR 1:50 Wilderhill Clean Energy Renewable Energy Eq. US SEOUL 2006-12-20 2018-01-01 70 U:HXL HEXCEL NASDAQ Clean Edge Energy Aerospace US STAMFORD 1973-01-02 2018-01-01 71 K:HRCL HUANENG RENEWS. 'H' S&P Global Clean Energy Renewable Energy Eq. CN BEIJING 2011-06-10 2018-01-01 72 C:HYG HYDROGENICS Ardour Global Alternative Energy Renewable Energy Eq. CA MISSISSAUGA 2000-10-27 2018-01-01 73 @HYGS HYDROGENICS (NAS) Wilderhill Clean Energy Renewable Energy Eq. CA MISSISSAUGA 2000-10-27 2018-01-01 74 C:INE INNERGEX RENEWABLE EN. Ardour Global Alternative Energy Alt. Electricity CA LONGUEUIL 2007-12-06 2018-01-01 75 @IDTI INTEGRATED DEVICE TECH. NASDAQ Clean Edge Energy Semiconductors US SAN JOSE 1984-02-17 2018-01-01 76 @ITRI ITRON NASDAQ Clean Edge Energy Electronic Equipment US LIBERTY LAKE 1993-11-05 2018-01-01 77 @IXYS IXYS DEAD - DELIST.18/01/18 Ardour Global Alternative Energy Semiconductors US MILPITAS 1998-09-24 2018-01-01 78 @JASO JA SOLAR HDG.ADR 1:5 NASDAQ Clean Edge Energy Renewable Energy Eq. US BEIJING 2007-02-07 2018-01-01 79 U:JKS JINKOSOLAR HOLDING ADR 1:4 S&P Global Clean Energy Renewable Energy Eq. US SHANGRAO 2010-05-14 2018-01-01 80 J:GT@N KURITA WATER IND. Ardour Global Alternative Energy Industrial Machinery JP NAKANO-KU 1973-01-01 2018-01-01 81 @LYTS LSI INDUSTRIES Wilderhill Clean Energy Electrical Equipment US CINCINNATI 1985-06-17 2018-01-01 82 D:M5Z MANZ Ardour Global Alternative Energy Renewable Energy Eq. DE REUTLINGEN 2006-09-21 2018-01-01 83 @MXWL MAXWELL TECHNOLOGIES NASDAQ Clean Edge Energy Electrical Equipment US SAN DIEGO 1983-03-18 2018-01-01 84 @MSCC MICROSEMI NASDAQ Clean Edge Energy Semiconductors US ALISO VIEJO 1979-07-31 2018-01-01 85 TW:MII MOTECH INDUSTRIES Ardour Global Alternative Energy Renewable Energy Eq. TW NEW TAIPEI 2003-05-15 2018-01-01 86 @MYRG MYR GROUP Wilderhill Clean Energy Heavy Construction US ROLLING MEADOWS 2008-08-12 2018-01-01

87 N:NEL0 NEL Thomson Reuters Global Renewable Energy Alternative Fuels NO OSLO 2000-06-19 2018-01-01

88 TW:NSP NEO SOLAR POWER Ardour Global Alternative Energy Renewable Energy Eq. TW HSINCHU 2009-01-12 2018-01-01 89 U:NEE NEXTERA ENERGY NASDAQ Clean Edge Energy Con. Electricity US JUNO BEACH 1973-01-02 2018-01-01 90 W:NIBE NIBE INDUSTRIER 'B' Ardour Global Alternative Energy Building Mat.& Fix. SE MARKARYD 1997-06-17 2018-01-01 91 D:NDX1 NORDEX S&P Global Clean Energy Renewable Energy Eq. DE HAMBURG 2001-03-30 2018-01-01 92 @ON ON SEMICONDUCTOR NASDAQ Clean Edge Energy Semiconductors US PHOENIX 2000-04-28 2018-01-01 93 @OESX ORION ENERGY SYSTEMS Ardour Global Alternative Energy Electrical Equipment US MANITOWOC 2007-12-19 2018-01-01 94 U:ORA ORMAT TECHNOLOGIES NASDAQ Clean Edge Energy Alt. Electricity US RENO 2004-11-11 2018-01-01 95 @PEIX PACIFIC ETHANOL NASDAQ Clean Edge Energy Alternative Fuels US SACRAMENTO 1996-12-16 2018-01-01

96 K:GAYG PANDA GREEN ENERGY GP. Thomson Reuters Global Renewable Energy Renewable Energy Eq. BM NA 2000-04-13 2018-01-01

97 @PLUG PLUG POWER NASDAQ Clean Edge Energy Renewable Energy Eq. US LATHAM 1999-10-29 2018-01-01 98 D:PNE3 PNE WIND Ardour Global Alternative Energy Alt. Electricity DE CUXHAVEN 1998-12-14 2018-01-01 99 C:PIF POLARIS INFRASTRUCTURE Ardour Global Alternative Energy Alt. Electricity CA TORONTO 2004-08-31 2018-01-01

100 @POWI POWER INTEGRATIONS NASDAQ Clean Edge Energy Semiconductors US SAN JOSE 1997-12-12 2018-01-01 101 @PCYO PURE CYCLE Ardour Global Alternative Energy Water US DENVER 1989-05-18 2018-01-01

48

102 U:PWR QUANTA SERVICES Wilderhill Clean Energy Heavy Construction US HOUSTON 1998-02-13 2018-01-01 103 N:REC REC SILICON S&P Global Clean Energy Renewable Energy Eq. NO LYSAKER 2006-05-09 2018-01-01 104 U:SOL RENESOLA ADR 1:10 Ardour Global Alternative Energy Renewable Energy Eq. US SHANGHAI 2008-01-29 2018-01-01 105 @REGI RENEWABLE ENERGY GROUP S&P Global Clean Energy Alternative Fuels US AMES 2012-01-19 2018-01-01

106 U:REX REX AMERICAN RESOURCES Thomson Reuters Global Renewable Energy Alternative Fuels US DAYTON 1984-07-17 2018-01-01

107 KO:SSU SHINSUNG E&G Thomson Reuters Global Renewable Energy Semiconductors KR SEONGNAM 1996-07-31 2018-01-01

108 K:SPIL SHUNFENG INTL.CLEAN EN. Thomson Reuters Global Renewable Energy Renewable Energy Eq. KY WUXI 2011-07-13 2018-01-01

109 E:GAM SIEMENS GAMESA RENEWABLE ENERGY S&P Global Clean Energy Renewable Energy Eq. ES ZAMUDIO 2000-10-30 2018-01-01

110 TW:SIT SIMPLO TECHNOLOGY Ardour Global Alternative Energy Electrical Equipment TW HUKOU 2001-11-27 2018-01-01 111 D:S92 SMA SOLAR TECHNOLOGY S&P Global Clean Energy Renewable Energy Eq. DE NIESTETAL 2008-06-26 2018-01-01 112 K:SOLR SOLARGIGA ENERGY HDG. Ardour Global Alternative Energy Renewable Energy Eq. KY NA 2008-03-31 2018-01-01

113 E:SEM SOLARIA ENERGIA Y MEDIO AMBIENTE Ardour Global Alternative Energy Renewable Energy Eq. ES MADRID 2007-06-19 2018-01-01

114 TW:SRP SOLARTECH ENERGY Ardour Global Alternative Energy Renewable Energy Eq. TW HUKOU 2008-12-05 2018-01-01 115 Q:SOLM SOLARTRON Ardour Global Alternative Energy Renewable Energy Eq. TH BANGKOK 2005-03-30 2018-01-01 116 D:ST5 STEICO Ardour Global Alternative Energy Building Mat.& Fix. DE FELDKIRCHEN 2007-06-22 2018-01-01

117 STOB STOBART GROUP ORD. Thomson Reuters Global Renewable Energy Transport Services GB ST MARTIN 2004-09-28 2018-01-01

118 @SPWR SUNPOWER S&P Global Clean Energy Renewable Energy Eq. US SAN JOSE 2005-11-17 2018-01-01

119 IN:SZE SUZLON ENERGY Thomson Reuters Global Renewable Energy Renewable Energy Eq. IN PUNE 2005-10-18 2018-01-01

120 G:TEN TERNA ENERGY Ardour Global Alternative Energy Alt. Electricity GR ATHINA 2007-11-14 2018-01-01 121 @TSLA TESLA NASDAQ Clean Edge Energy Automobiles US PALO ALTO 2010-06-29 2018-01-01 122 K:TPIL TIANNENG POWER INTL. Ardour Global Alternative Energy Auto Parts KY HUZHOU 2007-06-11 2018-01-01

123 KO:UNS UNISON Thomson Reuters Global Renewable Energy Building Mat.& Fix. KR SACHEON 1996-07-03 2018-01-01

124 @OLED UNIVERSAL DISPLAY NASDAQ Clean Edge Energy Electrical Equipment US TRENTON 1992-05-14 2018-01-01 125 U:HTM US GEOTHERMAL (ASE) Wilderhill Clean Energy Alt. Electricity US BOISE 2004-01-28 2018-01-01 126 @VECO VEECO INSTRUMENTS NASDAQ Clean Edge Energy Electronic Equipment US PLAINVIEW 1994-11-29 2018-01-01

127 D:VBK VERBIO VER.BIOENERGIE Thomson Reuters Global Renewable Energy Alternative Fuels DE ZOERBIG 2006-10-13 2018-01-01

128 O:VERB VERBUND Ardour Global Alternative Energy Alt. Electricity AT WIEN 1988-12-05 2018-01-01 129 DK:VEW VESTAS WINDSYSTEMS S&P Global Clean Energy Renewable Energy Eq. DK AARHUS 1998-04-30 2018-01-01 130 @VICR VICOR Ardour Global Alternative Energy Electrical Equipment US ANDOVER 1990-04-03 2018-01-01

131 KO:WJE WOONGJIN ENERGY Thomson Reuters Global Renewable Energy Renewable Energy Eq. KR DAEJEON 2010-06-30 2018-01-01

132 K:XGST XJG.GOLDWIND SCTC. 'H' Thomson Reuters Global Renewable Energy Renewable Energy Eq. CN BEIJING 2010-10-08 2018-01-01

133 U:YGE YINGLI GREEN EN.HOLDING ADR 1:1 Ardour Global Alternative Energy Renewable Energy Eq. US BAODING 2007-06-08 2018-01-01

49

List of stocks and its characteristics included in the conventional energy category

Number Ticker Company Index Sector Country City Start End 1 IN:ALC ABAN OFFSHORE Thomson Reuters Global Energy Oil Equip. & Services IN CHENNAI 1990-01-01 2018-01-01 2 @AXAS ABRAXAS PETROLEUM Thomson Reuters Global Energy Exploration & Prod. US SAN ANTONIO 1991-09-20 2018-01-01 3 ID:ADT ADARO ENERGY TBK Thomson Reuters Global Energy Coal ID JAKARTA SELATAN 2008-07-16 2018-01-01 4 C:AAV ADVANTAGE OIL & GAS Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1988-09-15 2018-01-01 5 IN:AEG AEGIS LOGISTICS Thomson Reuters Global Energy Transport Services IN MUMBAI 1990-01-01 2018-01-01 6 C:AOI AFRICA OIL Thomson Reuters Global Energy Exploration & Prod. CA VANCOUVER 1999-09-08 2018-01-01 7 K:KHIN AGRITRADE RESOURCES Thomson Reuters Global Energy Clothing & Accessory BM NA 1997-03-19 2018-01-01 8 N:AKA AKASTOR Thomson Reuters Global Energy Oil Equip. & Services NO LYSAKER 2004-04-02 2018-01-01 9 N:AKEP AKER BP Thomson Reuters Global Energy Exploration & Prod. NO LYSAKER 2007-12-17 2018-01-01

10 ID:AKR AKR CORPORINDO Thomson Reuters Global Energy Industrial Suppliers ID JAKARTA BARAT 1994-10-03 2018-01-01 11 EG:AMN ALEXANDRIA MRL.OILS Thomson Reuters Global Energy Exploration & Prod. EG ALEXANDRIA 2005-09-29 2018-01-01 12 N:AMSC AMERICAN SHIPPING CO. Thomson Reuters Global Energy Marine Transportation NO LYSAKER 2005-07-11 2018-01-01 13 U:APC ANADARKO PETROLEUM S&P 500 Energy Exploration & Prod. US THE WOODLANDS 1986-09-08 2018-01-01 14 U:ANDV ANDEAVOR S&P 500 Energy Exploration & Prod. US SAN ANTONIO 1973-01-02 2018-01-01 15 CL:ATA ANTAR CHILE Thomson Reuters Global Energy Specialty Finance CL LAS CONDES 1996-07-05 2018-01-01 16 K:ANTO ANTON OILFIELD SVS.GROUP Thomson Reuters Global Energy Oil Equip. & Services KY BEIJING 2007-12-14 2018-01-01 17 U:APA APACHE S&P 500 Energy Exploration & Prod. US HOUSTON 1973-01-02 2018-01-01 18 C:ARX ARC RESOURCES Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1996-07-11 2018-01-01 19 N:ARCH ARCHER Thomson Reuters Global Energy Oil Equip. & Services BM HAMILTON 2010-11-26 2018-01-01 20 U:AROC ARCHROCK Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 2000-05-24 2018-01-01 21 IS:ARK ARKO HOLDINGS Thomson Reuters Global Energy Specialty Finance IL HERZLIYA 1992-06-01 2018-01-01 22 C:ATH ATHABASCA OIL Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2010-04-08 2018-01-01 23 PK:APL ATTOCK PETROLEUM Thomson Reuters Global Energy Integrated Oil & Gas PK RAWALPINDI 2005-03-04 2018-01-01 24 PK:ATR ATTOCK REFINERY Thomson Reuters Global Energy Exploration & Prod. PK RAWALPINDI 1992-07-29 2018-01-01 25 A:AWEX AWE Thomson Reuters Global Energy Exploration & Prod. AU NORTH SYDNEY 1997-07-02 2018-01-01 26 TK:AYG AYGAZ Thomson Reuters Global Energy Gas Distribution TR ISTANBUL 1988-01-13 2018-01-01 27 U:BHGE BAKER HUGHES A S&P 500 Energy Oil Equip. & Services US HOUSTON 1987-04-06 2018-01-01 28 Q:BCPL BANGCHAK CORPORATION ORS Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 1994-08-02 2018-01-01 29 Q:BPCT BANPU Thomson Reuters Global Energy Coal TH BANGKOK 1989-06-16 2018-01-01 30 C:BTE BAYTEX ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2003-09-08 2018-01-01 31 A:BPTX BEACH ENERGY Thomson Reuters Global Energy Exploration & Prod. AU ADELAIDE 1973-01-01 2018-01-01 32 ID:BPM BENAKAT INTEGRA Thomson Reuters Global Energy Exploration & Prod. ID JAKARTA SELATAN 2010-02-11 2018-01-01 33 A:BKYX BERKELEY ENERGIA Thomson Reuters Global Energy General Mining AU LONDON 2003-09-16 2018-01-01 34 IN:BHP BHARAT PETROLEUM Thomson Reuters Global Energy Integrated Oil & Gas IN NAGPUR 1992-09-16 2018-01-01 35 C:BIR BIRCHCLIFF ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2001-02-22 2018-01-01

50

36 PO:LWB BOGDANKA Thomson Reuters Global Energy Coal PL LECZNA 2009-07-22 2018-01-01 37 U:BCEI BONANZA CREEK ENERGY Thomson Reuters Global Energy Exploration & Prod. US DENVER 2011-12-15 2018-01-01 38 C:BNP BONAVISTA ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1991-05-01 2018-01-01 39 N:BON BONHEUR Thomson Reuters Global Energy Oil Equip. & Services NO OSLO 1980-01-02 2018-01-01 40 C:BNE BONTERRA ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1983-06-16 2018-01-01 41 F:GBB BOURBON CORP. Thomson Reuters Global Energy Oil Equip. & Services FR PARIS 1998-10-19 2018-01-01 42 BP. BP Thomson Reuters Global Energy Integrated Oil & Gas GB LONDON 1964-12-30 2018-01-01 43 J:TROL BP CASTROL KK Thomson Reuters Global Energy Specialty Chemicals JP SHINAGAWA-KU 1995-03-07 2018-01-01 44 L:ARMO BUMI ARMADA Thomson Reuters Global Energy Oil Equip. & Services MY KUALA LUMPUR 2011-07-20 2018-01-01 45 ID:BMH BUMI RESOURCES Thomson Reuters Global Energy Coal ID JAKARTA SELATAN 1990-07-31 2018-01-01 46 N:BWO BW OFFSHORE Thomson Reuters Global Energy Oil Equip. & Services BM HAMILTON 2006-05-31 2018-01-01 47 U:COG CABOT OIL & GAS 'A' S&P 500 Energy Exploration & Prod. US HOUSTON 1990-02-08 2018-01-01 48 CNE CAIRN ENERGY Thomson Reuters Global Energy Exploration & Prod. GB EDINBURGH 1988-12-21 2018-01-01 49 C:CFW CALFRAC WELL SERVICES Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 1996-01-02 2018-01-01 50 U:CPE CALLON PTL.DEL. Thomson Reuters Global Energy Exploration & Prod. US NATCHEZ 1994-09-19 2018-01-01 51 A:CTXX CALTEX AUSTRALIA Thomson Reuters Global Energy Exploration & Prod. AU SYDNEY 1983-01-04 2018-01-01 52 C:CCO CAMECO Thomson Reuters Global Energy Nonferrous Metals CA SASKATOON 1991-07-17 2018-01-01 53 C:CNE CANACOL ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1991-04-24 2018-01-01 54 C:CNQ CANADIAN NATURAL RES. Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1976-05-17 2018-01-01 55 @CRZO CARRIZO O&G. Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 1997-08-06 2018-01-01 56 IN:CAS CASTROL INDIA Thomson Reuters Global Energy Specialty Chemicals IN MUMBAI 1990-01-01 2018-01-01 57 C:CVE CENOVUS ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2009-11-02 2018-01-01 58 C:CEU CES ENERGY SOLUTIONS Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 2006-03-02 2018-01-01 59 U:LNG CHENIERE EN. Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 1999-07-08 2018-01-01 60 IN:MAD CHENNAI PETROLEUM Thomson Reuters Global Energy Exploration & Prod. IN CHENNAI 1992-10-30 2018-01-01 61 U:CHK CHESAPEAKE ENERGY S&P 500 Energy Exploration & Prod. US OKLAHOMA CITY 1993-02-05 2018-01-01 62 U:CVX CHEVRON S&P 500 Energy Integrated Oil & Gas US SAN RAMON 1973-01-02 2018-01-01 63 T:CHAV CHINA AVTN.OIL (SING.) Thomson Reuters Global Energy Exploration & Prod. SG NA 2001-12-06 2018-01-01 64 K:CCEC CHINA COAL ENERGY 'H' Thomson Reuters Global Energy Coal CN BEIJING 2006-12-19 2018-01-01 65 K:CHOL CHINA OILFIELD SVS.'H' Thomson Reuters Global Energy Oil Equip. & Services CN BEIJING 2002-11-20 2018-01-01 66 K:CHPE CHINA PTL.& CHM. 'H' Thomson Reuters Global Energy Integrated Oil & Gas CN BEIJING 2000-10-19 2018-01-01 67 K:CSHE CHINA SHENHUA EN.CO.'H' Thomson Reuters Global Energy Coal CN BEIJING 2005-06-15 2018-01-01 68 K:CTL CHINA TIAN LUN GAS HDG. Thomson Reuters Global Energy Gas Distribution KY ZHENGZHOU 2010-11-10 2018-01-01 69 CIMAREX EN. 70 K:ENRC CIMC ENRIC HOLDINGS Thomson Reuters Global Energy Industrial Machinery KY SHENZHEN 2005-10-18 2018-01-01 71 U:CIR CIRCOR INTL. Thomson Reuters Global Energy Industrial Machinery US BURLINGTON 1999-10-14 2018-01-01 72 K:SEAW CITIC RESOURCES HOLDINGS Thomson Reuters Global Energy Industrial Suppliers BM NA 1997-09-08 2018-01-01 73 @CLNE CLEAN ENERGY FUELS Thomson Reuters Global Energy Specialty Retailers US NEWPORT BEACH 2007-05-25 2018-01-01 74 K:HFG CMBC CAPITAL HOLDINGS Thomson Reuters Global Energy Industrial Suppliers BM NA 1998-03-12 2018-01-01

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75 K:CNOO CNOOC Thomson Reuters Global Energy Exploration & Prod. HK BEIJING 2001-02-28 2018-01-01 76 U:CNX CNX RESOURCES Thomson Reuters Global Energy Exploration & Prod. US CANONSBURG 1999-04-30 2018-01-01 77 U:CXO CONCHO RESOURCES S&P 500 Energy Exploration & Prod. US MIDLAND 2007-08-03 2018-01-01 78 U:COP CONOCOPHILLIPS S&P 500 Energy Integrated Oil & Gas US HOUSTON 1973-01-02 2018-01-01 79 U:CLR CONTINENTAL RESOURCES Thomson Reuters Global Energy Exploration & Prod. US OKLAHOMA CITY 2007-05-15 2018-01-01 80 A:COEX COOPER ENERGY Thomson Reuters Global Energy Exploration & Prod. AU ADELAIDE 2002-03-13 2018-01-01 81 U:CLB CORE LABORATORIES Thomson Reuters Global Energy Oil Equip. & Services NL AMSTERDAM 1995-09-21 2018-01-01 82 U:CZZ COSAN 'A' Thomson Reuters Global Energy Food Products BM SAO PAULO 2007-08-16 2018-01-01 83 BR:COS COSAN INDUSTRIA E COMERCIO ON Thomson Reuters Global Energy Food Products BR SAO PAULO 2005-11-18 2018-01-01 84 K:SHIN COSCO SHIP.INTL. (HONG KONG) Thomson Reuters Global Energy Specialty Chemicals BM NA 1992-02-11 2018-01-01 85 C:CPG CRESCENT POINT ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2003-09-10 2018-01-01 86 C:CR CREW ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2003-09-08 2018-01-01 87 U:CVI CVR ENERGY Thomson Reuters Global Energy Exploration & Prod. US SUGAR LAND 2007-10-23 2018-01-01 88 AD:DAG DANA GAS Thomson Reuters Global Energy Gas Distribution AE SHARJAH 2005-12-06 2018-01-01 89 DCC DCC Thomson Reuters Global Energy Industrial Suppliers IE DUBLIN 1994-05-18 2018-01-01 90 IS:DLE DELEK GROUP Thomson Reuters Global Energy Specialty Finance IL HERZLIYA 1986-01-03 2018-01-01 91 U:DK DELEK US HOLDINGS Thomson Reuters Global Energy Exploration & Prod. US BRENTWOOD 2006-05-04 2018-01-01 92 C:DEE DELPHI EN. Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1997-11-24 2018-01-01 93 ID:DOR DELTA DUNIA MAKMUR Thomson Reuters Global Energy Coal ID JAKARTA SELATAN 2001-06-15 2018-01-01 94 U:DNR DENBURY RES. Thomson Reuters Global Energy Exploration & Prod. US PLANO 1995-08-25 2018-01-01 95 C:DML DENISON MINES Thomson Reuters Global Energy Nonferrous Metals CA TORONTO 1997-05-27 2018-01-01 96 U:DVN DEVON ENERGY S&P 500 Energy Exploration & Prod. US OKLAHOMA CITY 1985-07-22 2018-01-01 97 U:DHT DHT HOLDINGS Thomson Reuters Global Energy Marine Transportation MH HAMILTON 2005-10-13 2018-01-01 98 L:DIAL DIALOG GROUP Thomson Reuters Global Energy Oil Equip. & Services MY PETALING JAYA 1996-05-06 2018-01-01 99 U:DO DIAMOND OFFS.DRL. Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1995-10-11 2018-01-01

100 N:DNO DNO Thomson Reuters Global Energy Exploration & Prod. NO OSLO 1982-01-04 2018-01-01 101 N:DOF DOF Thomson Reuters Global Energy Oil Equip. & Services NO STOREBO 2000-10-04 2018-01-01 102 KO:DNK DONGKUK STUTR.& CON. Thomson Reuters Global Energy Heavy Construction KR POHANG 2009-08-31 2018-01-01 103 U:DRQ DRIL-QUIP Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1997-10-23 2018-01-01 104 KO:LGG E1 Thomson Reuters Global Energy Gas Distribution KR SEOUL 1997-08-27 2018-01-01 105 EG:EKH EGYPTIAN KUWAITI HOLDING Thomson Reuters Global Energy Specialty Finance EG GIZA 2002-07-02 2018-01-01 106 CL:COE EMPRESAS COPEC Thomson Reuters Global Energy Exploration & Prod. CL LAS CONDES 1989-07-03 2018-01-01 107 E:ENAG ENAGAS Thomson Reuters Global Energy Gas Distribution ES MADRID 2002-06-26 2018-01-01 108 C:ENB ENBRIDGE Thomson Reuters Global Energy Pipelines CA CALGARY 1973-01-01 2018-01-01 109 U:EEQ ENBRIDGE EN.MAN. Thomson Reuters Global Energy Pipelines US HOUSTON 2002-10-11 2018-01-01 110 C:ENF ENBRIDGE INCOME FD.HDG. UT. Thomson Reuters Global Energy Pipelines CA CALGARY 2003-06-30 2018-01-01 111 C:ECA ENCANA (TSX) Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1973-01-02 2018-01-01 112 C:EFX ENERFLEX WNI. Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 2011-06-03 2018-01-01 113 U:EGN ENERGEN Thomson Reuters Global Energy Exploration & Prod. US BIRMINGHAM 1973-01-02 2018-01-01

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114 C:ERF ENERPLUS Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1990-01-19 2018-01-01 115 I:ENI ENI Thomson Reuters Global Energy Integrated Oil & Gas IT ROMA 1995-11-28 2018-01-01 116 ENQ ENQUEST Thomson Reuters Global Energy Exploration & Prod. GB LONDON 2010-04-05 2018-01-01 117 U:ESV ENSCO CLASS A Thomson Reuters Global Energy Oil Equip. & Services GB LONDON 1980-02-28 2018-01-01 118 C:ESI ENSIGN EN.SVS. Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 1990-05-01 2018-01-01 119 U:EOG EOG RES. S&P 500 Energy Exploration & Prod. US HOUSTON 1989-10-04 2018-01-01 120 K:WALL EPI HOLDINGS Thomson Reuters Global Energy Consumer Electronics BM NA 1991-04-15 2018-01-01 121 U:EQT EQT S&P 500 Energy Exploration & Prod. US PITTSBURGH 1973-01-02 2018-01-01 122 F:ESSO ESSO Thomson Reuters Global Energy Integrated Oil & Gas FR PARIS 1973-01-01 2018-01-01 123 Q:ESOT ESSO THAILAND Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 2008-05-06 2018-01-01 124 B:EURN EURONAV Thomson Reuters Global Energy Marine Transportation BE ANTWERPEN 2004-12-01 2018-01-01 125 B:EXM EXMAR Thomson Reuters Global Energy Marine Transportation BE ANTWERPEN 2003-06-23 2018-01-01 126 R:EXXJ EXXARO RESOURCES Thomson Reuters Global Energy Coal ZA PRETORIA 2001-11-26 2018-01-01 127 U:XOM EXXON MOBIL S&P 500 Energy Integrated Oil & Gas US IRVING 1973-01-02 2018-01-01 128 A:FARX FAR Thomson Reuters Global Energy Exploration & Prod. AU MELBOURNE 1996-01-02 2018-01-01 129 U:FTK FLOTEK INDUSTRIES Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1999-07-22 2018-01-01 130 TW:FPC FORMOSA PETROCHEMICAL Thomson Reuters Global Energy Exploration & Prod. TW TAIPEI 2003-12-26 2018-01-01 131 U:FET FORUM ENERGY TECHS. Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 2012-04-12 2018-01-01 132 C:FRU FREEHOLD ROYALTIES Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1997-02-20 2018-01-01 133 N:FRO FRONTLINE Thomson Reuters Global Energy Marine Transportation BM HAMILTON 1998-05-12 2018-01-01 134 H:FUR FUGRO Thomson Reuters Global Energy Oil Equip. & Services NL LEIDSCHENDAM 1992-03-31 2018-01-01 135 J:AH@N FUJI OIL Thomson Reuters Global Energy Exploration & Prod. JP SHINAGAWA-KU 1973-01-01 2018-01-01 136 P:GES GALP ENERGIA SGPS Thomson Reuters Global Energy Integrated Oil & Gas PT LISBOA 2006-10-24 2018-01-01 137 U:GLOG GASLOG Thomson Reuters Global Energy Marine Transportation BM MONACO 2012-03-30 2018-01-01 138 RS:GAZ GAZPROM Thomson Reuters Global Energy Integrated Oil & Gas RU MOSCOW 2000-02-10 2018-01-01 139 C:GEI GIBSON ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2011-06-08 2018-01-01 140 GLEN GLENCORE Thomson Reuters Global Energy General Mining JE BAAR 2011-05-18 2018-01-01 141 IN:GES GREAT EASTERN SHIPPING Thomson Reuters Global Energy Marine Transportation IN MUMBAI 1990-01-01 2018-01-01 142 PO:LTS GRUPA LOTOS Thomson Reuters Global Energy Integrated Oil & Gas PL GDANSK 2005-06-09 2018-01-01 143 KO:GSG GS HOLDINGS Thomson Reuters Global Energy Integrated Oil & Gas KR SEOUL 2004-08-05 2018-01-01 144 IN:GMD GUJARAT MRL.DEV.CORP. Thomson Reuters Global Energy General Mining IN AHMEDABAD 1998-01-05 2018-01-01 145 QA:GIS GULF INTERNATIONAL SVS. Thomson Reuters Global Energy Oil Equip. & Services QA DOHA 2008-09-23 2018-01-01 146 @GPOR GULFPORT ENERGY Thomson Reuters Global Energy Exploration & Prod. US OKLAHOMA CITY 1998-01-14 2018-01-01 147 U:HK HALCON RESOURCES Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 2004-05-25 2018-01-01 148 U:HAL HALLIBURTON S&P 500 Energy Oil Equip. & Services US HOUSTON 1973-01-02 2018-01-01 149 KO:HSO HANKOOK SHELL OIL Thomson Reuters Global Energy Exploration & Prod. KR BUSAN 1988-08-10 2018-01-01 150 ID:HET HARUM ENERGY Thomson Reuters Global Energy Coal ID JAKARTA PUSAT 2010-10-06 2018-01-01 151 U:HLX HELIX ENERGY SLTN.GP. Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1997-07-01 2018-01-01 152 G:HPI HELLENIC PETROLEUM Thomson Reuters Global Energy Integrated Oil & Gas GR ATHINA 1998-06-30 2018-01-01

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153 U:HP HELMERICH & PAYNE S&P 500 Energy Oil Equip. & Services US TULSA 1973-01-02 2018-01-01 154 L:SHEL HENGYUAN REFINING COMPANY Thomson Reuters Global Energy Exploration & Prod. MY PORT DICKSON 1986-01-02 2018-01-01 155 U:HES HESS S&P 500 Energy Integrated Oil & Gas US NEW YORK 1973-01-02 2018-01-01 156 L:HIBI HIBISCUS PETROLEUM Thomson Reuters Global Energy Exploration & Prod. MY KUALA LUMPUR 2011-07-22 2018-01-01 157 C:HWO HIGH ARCTIC ENERGY SVS. Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 2005-07-21 2018-01-01 158 K:HHLI HILONG HOLDING Thomson Reuters Global Energy Oil Equip. & Services KY SHANGHAI 2011-04-21 2018-01-01 159 IN:HNC HINDUSTAN OIL EXP. Thomson Reuters Global Energy Exploration & Prod. IN CHENNAI 1990-04-30 2018-01-01 160 IN:HPT HINDUSTAN PETROLEUM Thomson Reuters Global Energy Integrated Oil & Gas IN MUMBAI 1992-09-11 2018-01-01 161 N:HLNG HOEGH LONG HOLDINGS Thomson Reuters Global Energy Marine Transportation BM HAMILTON 2011-07-05 2018-01-01 162 U:HFC HOLLYFRONTIER Thomson Reuters Global Energy Exploration & Prod. US DALLAS 1973-01-02 2018-01-01 163 K:HGHG HONGHUA GROUP Thomson Reuters Global Energy Oil Equip. & Services KY CHENGDU 2008-03-07 2018-01-01 164 HTG HUNTING Thomson Reuters Global Energy Oil Equip. & Services GB LONDON 1970-07-29 2018-01-01 165 C:HSE HUSKY EN. Thomson Reuters Global Energy Integrated Oil & Gas CA CALGARY 2000-08-28 2018-01-01 166 J:IDKO IDEMITSU KOSAN Thomson Reuters Global Energy Exploration & Prod. JP CHIYODA-KU 2006-10-24 2018-01-01 167 C:IMO IMPERIAL OIL Thomson Reuters Global Energy Integrated Oil & Gas CA CALGARY 1976-01-02 2018-01-01 168 IN:IO INDIAN OIL Thomson Reuters Global Energy Exploration & Prod. IN NEW DELHI 1995-08-09 2018-01-01 169 ID:IKX INDIKA ENERGY Thomson Reuters Global Energy Coal ID JAKARTA SELATAN 2008-06-11 2018-01-01 170 ID:INM INDO TAMBANGRAYA MEGAH Thomson Reuters Global Energy Coal ID JAKARTA SELATAN 2007-12-18 2018-01-01 171 J:INPX INPEX Thomson Reuters Global Energy Exploration & Prod. JP MINATO-KU 2004-11-17 2018-01-01 172 C:IPL INTER PIPELINE FUND Thomson Reuters Global Energy Pipelines CA CALGARY 1998-12-01 2018-01-01 173 TK:IPM IPEK MATBAACILIK SANVETC. Thomson Reuters Global Energy Business Support Svs. TR ANKARA 2000-06-16 2018-01-01 174 Q:TPI IRPC Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 1995-03-17 2018-01-01 175 J:WA@N ITOCHU ENEX Thomson Reuters Global Energy Specialty Retailers JP MINATO-KU 1979-08-13 2018-01-01 176 J:JE@N IWATANI Thomson Reuters Global Energy Gas Distribution JP OSAKA-SHI 1973-01-01 2018-01-01 177 J:JDCL JAPAN DRILLING Thomson Reuters Global Energy Oil Equip. & Services JP CHUO-KU 2009-12-17 2018-01-01 178 J:JPEC JAPAN PETROLEUM EXP. Thomson Reuters Global Energy Integrated Oil & Gas JP CHIYODA-KU 2003-12-10 2018-01-01 179 IS:JOE JOEL Thomson Reuters Global Energy Exploration & Prod. IL PETAH TIKVA 1986-01-31 2018-01-01 180 PO:JSW JSW Thomson Reuters Global Energy Coal PL JASTRZEBIE ZDROJ 2011-07-06 2018-01-01 181 K:JUTA JUTAL OFFSHORE OIL SVS. Thomson Reuters Global Energy Oil Equip. & Services KY SHENZHEN 2006-09-21 2018-01-01 182 J:JXHO JXTG HOLDINGS Thomson Reuters Global Energy Integrated Oil & Gas JP CHIYODA-KU 2010-04-01 2018-01-01 183 J:KMIC KAMEI Thomson Reuters Global Energy Specialty Retailers JP SENDAI-SHI 1988-04-15 2018-01-01 184 A:KARX KAROON GAS AUSTRALIA Thomson Reuters Global Energy Exploration & Prod. AU MELBOURNE 2004-06-08 2018-01-01 185 C:KEY KEYERA Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2003-05-30 2018-01-01 186 U:KMI KINDER MORGAN S&P 500 Energy Pipelines US HOUSTON 2011-02-11 2018-01-01 187 U:KOS KOSMOS ENERGY Thomson Reuters Global Energy Exploration & Prod. BM HAMILTON 2011-05-11 2018-01-01 188 K:PARG KUNLUN ENERGY Thomson Reuters Global Energy Exploration & Prod. BM NA 1988-06-01 2018-01-01 189 N:KVAE KVAERNER Thomson Reuters Global Energy Oil Equip. & Services NO LYSAKER 2011-07-08 2018-01-01 190 U:LPI LAREDO PETROLEUM Thomson Reuters Global Energy Exploration & Prod. US TULSA 2011-12-15 2018-01-01 191 A:LNGX LIQUEFIED NATURAL GAS Thomson Reuters Global Energy Exploration & Prod. AU PERTH 2004-09-14 2018-01-01

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192 W:LUPE LUNDIN PETROLEUM Thomson Reuters Global Energy Exploration & Prod. SE STOCKHOLM 2001-09-06 2018-01-01 193 U:MRO MARATHON OIL S&P 500 Energy Exploration & Prod. US HOUSTON 1991-04-16 2018-01-01 194 U:MPC MARATHON PETROLEUM S&P 500 Energy Integrated Oil & Gas US FINDLAY 2011-06-24 2018-01-01 195 PK:MAR MARI GAS Thomson Reuters Global Energy Exploration & Prod. PK ISLAMABAD 1995-06-19 2018-01-01 196 U:MTDR MATADOR RESOURCES Thomson Reuters Global Energy Exploration & Prod. US DALLAS 2012-02-02 2018-01-01 197 U:MDR MCDERMOTT INTL. Thomson Reuters Global Energy Oil Equip. & Services PA HOUSTON 1982-12-22 2018-01-01 198 ID:MEC MEDCO ENERGI INTL. Thomson Reuters Global Energy Exploration & Prod. ID JAKARTA SELATAN 1994-10-12 2018-01-01 199 C:MEG MEG ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2010-07-29 2018-01-01 200 IN:MCJ MERCATOR LINES Thomson Reuters Global Energy Marine Transportation IN MUMBAI 1994-02-14 2018-01-01 201 A:MMIX METRO MINING Thomson Reuters Global Energy Coal AU BRISBANE 2009-12-04 2018-01-01 202 K:MIEH MIE HOLDINGS Thomson Reuters Global Energy Exploration & Prod. KY BEIJING 2010-12-14 2018-01-01 203 J:MITM MITSUI MATSUSHIMA Thomson Reuters Global Energy Specialty Retailers JP FUKUOKA-SHI 1973-01-01 2018-01-01 204 J:MODE MODEC Thomson Reuters Global Energy Oil Equip. & Services JP CHUO-KU 2003-07-03 2018-01-01 205 HN:MMG MOL MAGYAR OLAJ-ES GAZIPARI Thomson Reuters Global Energy Integrated Oil & Gas HU BUDAPEST 1995-11-28 2018-01-01 206 K:MOM MOL MAGYAR OLAJ-ES GAZIPARI Thomson Reuters Global Energy Coal KY GRAND CAYMAN 2010-10-13 2018-01-01 207 J:MORC MORESCO Thomson Reuters Global Energy Specialty Chemicals JP KOBE-SHI 2003-11-13 2018-01-01 208 G:MOH MOTOR OIL Thomson Reuters Global Energy Exploration & Prod. GR ATHINA 2001-08-03 2018-01-01 209 C:MTL MULLEN GROUP Thomson Reuters Global Energy Transport Services CA OKOTOKS 1993-12-14 2018-01-01 210 U:MUR MURPHY OIL Thomson Reuters Global Energy Exploration & Prod. US EL DORADO 1973-01-02 2018-01-01 211 U:NBR NABORS INDUSTRIES Thomson Reuters Global Energy Oil Equip. & Services BM HAMILTON 1973-02-01 2018-01-01 212 IS:NFT NAPHTHA Thomson Reuters Global Energy Exploration & Prod. IL PETAH TIKVA 1986-01-03 2018-01-01 213 U:NOV NATIONAL OILWELL VARCO S&P 500 Energy Oil Equip. & Services US HOUSTON 1996-10-29 2018-01-01 214 KU:NPS NATIONAL PTL.SVS. Thomson Reuters Global Energy Oil Equip. & Services KW AL AHMADI 2005-10-03 2018-01-01 215 PK:NAR NATIONAL REFINERY Thomson Reuters Global Energy Exploration & Prod. PK KARACHI 1992-07-16 2018-01-01 216 U:NVGS NAVIGATOR HOLDINGS Thomson Reuters Global Energy Marine Transportation MH LONDON 2007-01-09 2018-01-01 217 M:NEST NESTE Thomson Reuters Global Energy Integrated Oil & Gas FI ESPOO 2005-04-18 2018-01-01 218 A:NHCX NEW HOPE CORP. Thomson Reuters Global Energy Coal AU IPSWICH 2003-09-16 2018-01-01 219 Z:NZRZ NEW ZEALAND REFINING Thomson Reuters Global Energy Exploration & Prod. NZ RUAKAKA 1988-01-04 2018-01-01 220 U:NFX NEWFIELD EXPLORATION S&P 500 Energy Exploration & Prod. US THE WOODLANDS 1993-11-12 2018-01-01 221 K:KOSO NEWOCEAN ENERGY HDG. Thomson Reuters Global Energy Specialty Retailers BM NA 1993-03-03 2018-01-01 222 U:NR NEWPARK RESOURCES Thomson Reuters Global Energy Oil Equip. & Services US THE WOODLANDS 1990-09-06 2018-01-01 223 J:MMCL NIPPON COKE & ENGR. Thomson Reuters Global Energy Coal JP KOTO-KU 1973-01-01 2018-01-01 224 U:NE NOBLE CORPORATION Thomson Reuters Global Energy Oil Equip. & Services GB LONDON 1985-09-23 2018-01-01 225 U:NBL NOBLE ENERGY S&P 500 Energy Exploration & Prod. US HOUSTON 1973-01-02 2018-01-01 226 U:NAT NORDIC AMER.TANKERS Thomson Reuters Global Energy Marine Transportation BM HAMILTON 1997-09-30 2018-01-01 227 N:NOE NORWEGIAN ENERGY CO. Thomson Reuters Global Energy Exploration & Prod. NO OSLO 2007-11-09 2018-01-01 228 NOG NOSTRUM OIL & GAS Thomson Reuters Global Energy Exploration & Prod. GB AMSTERDAM 2008-03-27 2018-01-01 229 RS:NTV NOVATEK Thomson Reuters Global Energy Exploration & Prod. RU TARKO-SALE 2004-12-30 2018-01-01 230 C:NVA NUVISTA EN. Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2003-07-07 2018-01-01

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231 U:OAS OASIS PETROLEUM Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 2010-06-17 2018-01-01 232 C:OBE OBSIDIAN ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1980-08-01 2018-01-01 233 RS:RSF OC ROSNEFT Thomson Reuters Global Energy Integrated Oil & Gas RU MOSCOW 2006-07-19 2018-01-01 234 U:OXY OCCIDENTAL PTL. S&P 500 Energy Exploration & Prod. US HOUSTON 1973-01-02 2018-01-01 235 U:OII OCEANEERING Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1975-11-03 2018-01-01 236 PK:OGD OIL & GAS DEVELOPMENT Thomson Reuters Global Energy Exploration & Prod. PK ISLAMABAD 2004-02-06 2018-01-01 237 IN:ONG OIL & NATURAL GAS Thomson Reuters Global Energy Exploration & Prod. IN DEHARADUN 1995-08-01 2018-01-01 238 RS:LKO OIL COMPANY LUKOIL Thomson Reuters Global Energy Integrated Oil & Gas RU MOSCOW 1998-01-27 2018-01-01 239 IN:OII OIL INDIA Thomson Reuters Global Energy Exploration & Prod. IN NOIDA 2009-09-30 2018-01-01 240 IS:OIR OIL REFINERIES Thomson Reuters Global Energy Exploration & Prod. IL HAIFA 2007-02-21 2018-01-01 241 A:OSHX OIL SEARCH Thomson Reuters Global Energy Exploration & Prod. PG PORT MORESBY 1973-01-01 2018-01-01 242 U:OIS OIL STS.INTL. Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 2001-02-09 2018-01-01 243 O:OMV OMV Thomson Reuters Global Energy Integrated Oil & Gas AT WIEN 1987-12-02 2018-01-01 244 U:OKE ONEOK S&P 500 Energy Gas Distribution US TULSA 1973-01-02 2018-01-01 245 OPHR OPHIR ENERGY Thomson Reuters Global Energy Exploration & Prod. GB LONDON 2011-07-07 2018-01-01 246 KU:OUL OULA FUEL MARKETING Thomson Reuters Global Energy Specialty Retailers KW KUWAIT CITY 2006-12-18 2018-01-01 247 C:PONY PAINTED PONY EN. Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2007-05-23 2018-01-01 248 PK:POF PAKISTAN OILFIELDS Thomson Reuters Global Energy Exploration & Prod. PK RAWALPINDI 1992-07-16 2018-01-01 249 PK:PPL PAKISTAN PETROLEUM Thomson Reuters Global Energy Exploration & Prod. PK KARACHI 2004-09-28 2018-01-01 250 PK:PSO PAKISTAN STATE OIL Thomson Reuters Global Energy Integrated Oil & Gas PK KARACHI 1992-07-16 2018-01-01 251 C:POU PARAMOUNT RESOURCES 'A' Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1984-11-30 2018-01-01 252 C:PXT PAREX RESOURCES Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2009-11-12 2018-01-01 253 C:PKI PARKLAND FUEL Thomson Reuters Global Energy Specialty Retailers CA RED DEER 1984-01-09 2018-01-01 254 C:PSI PASON SYS. Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 1997-12-30 2018-01-01 255 @PTEN PATTERSON UTI ENERGY Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1993-11-02 2018-01-01 256 IS:PZO PAZ OIL Thomson Reuters Global Energy Exploration & Prod. IL YAKUM 2006-12-07 2018-01-01 257 @PDCE PDC ENERGY Thomson Reuters Global Energy Exploration & Prod. US DENVER 1976-08-31 2018-01-01 258 C:PPL PEMBINA PIPELINE Thomson Reuters Global Energy Gas Distribution CA CALGARY 1998-10-26 2018-01-01 259 C:PGF PENGROWTH ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2004-07-23 2018-01-01 260 K:PECH PETROCHINA 'H' Thomson Reuters Global Energy Integrated Oil & Gas CN BEIJING 2000-04-07 2018-01-01 261 PFC PETROFAC Thomson Reuters Global Energy Oil Equip. & Services GB SAINT HELIER 2005-10-03 2018-01-01 262 BR:PET PETROLEO BRASILEIRO ON Thomson Reuters Global Energy Integrated Oil & Gas BR Rio de Janeiro 1990-01-02 2018-01-01 263 BR:POB PETROLEO BRASILEIRO PN Thomson Reuters Global Energy Integrated Oil & Gas BR Rio de Janeiro 1990-01-03 2018-01-01 264 N:PGS PETROLEUM GEO SERVICES Thomson Reuters Global Energy Oil Equip. & Services NO OSLO 1992-08-26 2018-01-01 265 PH:PTC PETRON Thomson Reuters Global Energy Integrated Oil & Gas PH MANDALUYONG 1994-09-07 2018-01-01 266 L:PMRM PETRON MAL.REFN.& MKTG. Thomson Reuters Global Energy Exploration & Prod. MY PORT DICKSON 1986-01-02 2018-01-01 267 L:PETS PETRONAS DAGANGAN Thomson Reuters Global Energy Specialty Retailers MY KUALA LUMPUR 1994-03-08 2018-01-01 268 L:PETT PETRONAS GAS Thomson Reuters Global Energy Gas Distribution MY KUALA LUMPUR 1995-09-04 2018-01-01 269 IN:NET PETRONET L N G Thomson Reuters Global Energy Gas Distribution IN NEW DELHI 2004-03-26 2018-01-01

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270 VT:PVD PETROVIETNAM DRILLING Thomson Reuters Global Energy Oil Equip. & Services VN HO CHI MINH 2007-06-15 2018-01-01 271 VT:GAS PETROVIETNAM GAS Thomson Reuters Global Energy Exploration & Prod. VN HO CHI MINH 2012-05-21 2018-01-01 272 C:PEY PEYTO EXP.&.DEV. Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1997-12-08 2018-01-01 273 U:PSX PHILLIPS 66 S&P 500 Energy Integrated Oil & Gas US HOUSTON 2012-04-12 2018-01-01 274 PH:PNX PHOENIX PETROLEUM Thomson Reuters Global Energy Specialty Retailers PH DAVAO 2007-07-11 2018-01-01 275 U:PXD PIONEER NTRL.RES. S&P 500 Energy Exploration & Prod. US IRVING 1997-08-08 2018-01-01 276 PO:PLK PLKNC.NAFTOWY ORLEN Thomson Reuters Global Energy Integrated Oil & Gas PL PLOCK 1999-11-26 2018-01-01 277 PO:PGN POLISH OIL AND GAS Thomson Reuters Global Energy Integrated Oil & Gas PL WARSZAWA 2005-10-20 2018-01-01 278 C:PD PRECISION DRILLING Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 1989-04-19 2018-01-01 279 PMO PREMIER OIL Thomson Reuters Global Energy Exploration & Prod. GB LONDON 1973-02-21 2018-01-01 280 N:PRS PROSAFE Thomson Reuters Global Energy Oil Equip. & Services CY LARNACA 1997-04-23 2018-01-01 281 PH:PPC PRYCE Thomson Reuters Global Energy Commodity Chemicals PH CAGAYAN 1991-10-29 2018-01-01 282 Q:PTTB PTT Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 2001-12-06 2018-01-01 283 Q:PTTE PTT EXPLORATION & PRDN. Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 1993-06-10 2018-01-01 284 QA:QFL QATAR FUEL COMPANY Thomson Reuters Global Energy Specialty Retailers QA DOHA 2003-12-31 2018-01-01 285 QA:QGT QATAR GS.TRAN.NAKILAT Thomson Reuters Global Energy Marine Transportation QA DOHA 2005-10-03 2018-01-01 286 U:QEP QEP RESOURCES Thomson Reuters Global Energy Exploration & Prod. US DENVER 2010-06-16 2018-01-01 287 BR:QGE QGEP PARTICIPACOES ON Thomson Reuters Global Energy Exploration & Prod. BR RIO DE JANEIRO 2011-02-09 2018-01-01 288 C:RRX RAGING RIVER EXPLORATION Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2012-03-16 2018-01-01 289 U:RRC RANGE RES. S&P 500 Energy Exploration & Prod. US FORT WORTH 1990-04-16 2018-01-01 290 RS:RAS RASPADSKAYA Thomson Reuters Global Energy Coal RU MEZHDURECHENSK 2006-11-14 2018-01-01 291 IN:REL RELIANCE INDUSTRIES Thomson Reuters Global Energy Exploration & Prod. IN MUMBAI 1990-01-01 2018-01-01 292 E:REP REPSOL YPF Thomson Reuters Global Energy Integrated Oil & Gas ES MADRID 1989-05-10 2018-01-01 293 U:REN RESOLUTE ENERGY Thomson Reuters Global Energy Exploration & Prod. US DENVER 2007-10-08 2018-01-01 294 U:RDC ROWAN COMPANIES CL.A Thomson Reuters Global Energy Oil Equip. & Services GB LONDON 1973-01-02 2018-01-01 295 RDSA ROYAL DUTCH SHELL A(LON) Thomson Reuters Global Energy Integrated Oil & Gas GB S-GRAVENHAGE 2005-07-20 2018-01-01 296 RDSB ROYAL DUTCH SHELL B Thomson Reuters Global Energy Integrated Oil & Gas GB S-GRAVENHAGE 1964-12-30 2018-01-01 297 U:RES RPC Thomson Reuters Global Energy Oil Equip. & Services US ATLANTA 1984-06-11 2018-01-01 298 F:RUI RUBIS Thomson Reuters Global Energy Gas Distribution FR PARIS 1989-04-18 2018-01-01

299 I:SPM SAIPEM Thomson Reuters Global Energy Oil Equip. & Services IT SAN DONATO MILANESE 1984-11-20 2018-01-01

300 J:SALC SALA Thomson Reuters Global Energy Gas Distribution JP TOYOHASHI-SHI 2002-04-30 2018-01-01 301 PH:SMA SAN MIGUEL Thomson Reuters Global Energy Brewers PH MANDALUYONG 1987-09-09 2018-01-01 302 J:SAIO SAN-AI OIL Thomson Reuters Global Energy Exploration & Prod. JP SHINAGAWA-KU 1973-01-01 2018-01-01 303 U:SN SANCHEZ ENERGY Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 2011-12-14 2018-01-01 304 A:STOX SANTOS Thomson Reuters Global Energy Exploration & Prod. AU ADELAIDE 1973-01-01 2018-01-01 305 L:SAKE SAPURA ENERGY Thomson Reuters Global Energy Oil Equip. & Services MY SERI KEMBANGAN 2012-05-16 2018-01-01 306 I:SARA SARAS Thomson Reuters Global Energy Exploration & Prod. IT SARROCH 2006-05-18 2018-01-01 307 R:SOLJ SASOL Thomson Reuters Global Energy Specialty Chemicals ZA SANDTON 1979-10-01 2018-01-01

57

308 H:SBMO SBM OFFSHORE Thomson Reuters Global Energy Oil Equip. & Services NL SCHIPHOL 1973-01-01 2018-01-01 309 U:SLB SCHLUMBERGER S&P 500 Energy Oil Equip. & Services AN HOUSTON 1973-01-02 2018-01-01 310 O:SCBL SCHOELLER-BLECKMANN Thomson Reuters Global Energy Oil Equip. & Services AT TERNITZ 2003-03-27 2018-01-01 311 U:STNG SCORPIO TANKERS Thomson Reuters Global Energy Marine Transportation MH MONACO 2010-03-31 2018-01-01 312 U:CKH SEACOR HDG. Thomson Reuters Global Energy Oil Equip. & Services US FORT LAUDERDALE 1992-12-17 2018-01-01 313 C:SES SECURE ENERGY SERVICES Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 2010-03-30 2018-01-01 314 U:SEMG SEMGROUP 'A' Thomson Reuters Global Energy Pipelines US TULSA 2010-03-12 2018-01-01 315 PH:SCC SEMIRARA MINING &.PWR. Thomson Reuters Global Energy Coal PH MAKATI 2005-02-04 2018-01-01 316 A:SXYX SENEX ENERGY Thomson Reuters Global Energy Exploration & Prod. AU BRISBANE 1989-06-07 2018-01-01 317 KO:STT SEOBU T&D Thomson Reuters Global Energy Specialty Retailers KR SEOUL 1996-07-01 2018-01-01

318 K:SHDO SHANGHAI DASHENG AGRIC. FIN.TECH.'H' Thomson Reuters Global Energy Building Mat.& Fix. CN SHANGHAI 2005-07-13 2018-01-01

319 C:SCL SHAWCOR Thomson Reuters Global Energy Oil Equip. & Services CA ETOBICOKE 1976-01-05 2018-01-01 320 PK:PBS SHELL PAKISTAN Thomson Reuters Global Energy Integrated Oil & Gas PK KARACHI 1993-04-01 2018-01-01 321 K:ONGP SHENGLI OIL & GAS PIPE HDG. Thomson Reuters Global Energy Heavy Construction KY ZIBO 2009-12-18 2018-01-01 322 J:SOGG SHINKO PLANTECH Thomson Reuters Global Energy Industrial Machinery JP YOKOHAMA-SHI 1988-04-15 2018-01-01 323 KO:SSU SHINSUNG E&G Thomson Reuters Global Energy Semiconductors KR SEONGNAM 1996-07-31 2018-01-01 324 U:SFL SHIP FINANCE INTL. Thomson Reuters Global Energy Marine Transportation BM HAMILTON 2004-06-14 2018-01-01 325 J:SHSS SHOWA SHELL SEKIYU Thomson Reuters Global Energy Integrated Oil & Gas JP MINATO-KU 1973-01-01 2018-01-01 326 Q:SGPC SIAMGAS AND PETROCHEM. Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 2008-06-03 2018-01-01 327 J:SGAW SINANEN HOLDINGS Thomson Reuters Global Energy Exploration & Prod. JP MINATO-KU 1987-08-03 2018-01-01 328 K:PNF SINO OIL & GAS HDG. Thomson Reuters Global Energy Exploration & Prod. BM NA 2000-02-09 2018-01-01 329 K:SIKA SINOPEC KANTONS HOLDINGS Thomson Reuters Global Energy Oil Equip. & Services BM NA 1999-06-25 2018-01-01 330 K:YICF SINOPEC OILFIELD SERVICE 'H' Thomson Reuters Global Energy Oil Equip. & Services CN BEIJING 1994-03-29 2018-01-01

331 K:SHPT SINOPEC SHANGHAI PETROCHEMICAL 'H' Thomson Reuters Global Energy Commodity Chemicals CN SHANGHAI 1993-07-26 2018-01-01

332 KO:YGA SK GAS Thomson Reuters Global Energy Gas Distribution KR SEONGNAM 1997-08-27 2018-01-01 333 KO:SBG SK INNOVATION Thomson Reuters Global Energy Exploration & Prod. KR SEOUL 2007-07-25 2018-01-01 334 KO:SGF SK NETWORKS Thomson Reuters Global Energy Industrial Suppliers KR SUWON 1984-07-02 2018-01-01 335 U:SM SM ENERGY Thomson Reuters Global Energy Exploration & Prod. US DENVER 1992-12-16 2018-01-01 336 ID:SMR SMR UTAMA Thomson Reuters Global Energy General Mining ID JAKARTA BARAT 2011-10-10 2018-01-01

337 I:SRG SNAM Thomson Reuters Global Energy Gas Distribution IT SAN DONATO MILANESE 2001-12-05 2018-01-01

338 KO:SSO S-OIL Thomson Reuters Global Energy Exploration & Prod. KR SEOUL 1987-05-27 2018-01-01 339 KO:SSF S-OIL PF. Thomson Reuters Global Energy Exploration & Prod. KR SEOUL 1994-09-26 2018-01-01 340 N:SOFF SOLSTAD FARSTAD Thomson Reuters Global Energy Oil Equip. & Services NO SKUDENESHAVN 1997-10-27 2018-01-01 341 KU:SFM SOOR FUEL MARKETING Thomson Reuters Global Energy Specialty Retailers KW KUWAIT CITY 2008-06-30 2018-01-01 342 U:SWN SOUTHWESTERN ENERGY Thomson Reuters Global Energy Exploration & Prod. US SPRING 1973-01-02 2018-01-01 343 C:SPE SPARTAN ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1990-05-02 2018-01-01 344 U:SRCI SRC ENERGY Thomson Reuters Global Energy Exploration & Prod. US DENVER 2008-02-27 2018-01-01

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345 N:STL STATOIL Thomson Reuters Global Energy Integrated Oil & Gas NO STAVANGER 2001-06-18 2018-01-01 346 STOB STOBART GROUP ORD. Thomson Reuters Global Energy Transport Services GB ST MARTIN 2004-09-28 2018-01-01 347 U:SGY STONE ENERGY Thomson Reuters Global Energy Exploration & Prod. US LAFAYETTE 1993-07-09 2018-01-01 348 K:STPC STRONG PETROCH.HOLDINGS Thomson Reuters Global Energy Exploration & Prod. KY NA 2009-01-12 2018-01-01 349 N:SUBC SUBSEA 7 Thomson Reuters Global Energy Oil Equip. & Services LU LONDON 1997-06-05 2018-01-01 350 C:SU SUNCOR ENERGY Thomson Reuters Global Energy Integrated Oil & Gas CA CALGARY 1993-03-18 2018-01-01 351 K:SS0L SUNSHINE OILSANDS Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2012-03-01 2018-01-01 352 U:SPN SUPERIOR ENERGY SVS. Thomson Reuters Global Energy Oil Equip. & Services US HOUSTON 1992-07-07 2018-01-01 353 C:SPB SUPERIOR PLUS Thomson Reuters Global Energy Specialty Retailers CA TORONTO 1996-10-09 2018-01-01 354 C:SGY SURGE ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1998-09-09 2018-01-01 355 RS:SNG SURGUTNEFTEGAS Thomson Reuters Global Energy Integrated Oil & Gas RU SURGUT 1998-01-27 2018-01-01 356 RS:SNP SURGUTNEFTEGAZ PREF. Thomson Reuters Global Energy Integrated Oil & Gas RU SURGUT 1998-04-17 2018-01-01 357 C:TVE TAMARACK VALLEY ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2002-06-14 2018-01-01 358 ID:TBB TAMBANG BTBR.BUKIT ASAM Thomson Reuters Global Energy Coal ID JAKARTA 2002-12-23 2018-01-01 359 U:TRGP TARGA RESOURCES Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 2010-12-07 2018-01-01 360 RS:TAT TATNEFT Thomson Reuters Global Energy Integrated Oil & Gas RU ALMETYEVSK 2001-12-10 2018-01-01 361 E:TECN TECNICAS REUNIDAS Thomson Reuters Global Energy Oil Equip. & Services ES MADRID 2006-06-21 2018-01-01 362 U:TK TEEKAY Thomson Reuters Global Energy Marine Transportation MH PEMBROKE 1995-07-20 2018-01-01 363 @TELL TELLURIAN Thomson Reuters Global Energy Exploration & Prod. US HOUSTON 1973-01-02 2018-01-01 364 I:TEN TENARIS Thomson Reuters Global Energy Iron & Steel LU LUXEMBOURG 2002-12-17 2018-01-01 365 W:TETY TETHYS OIL Thomson Reuters Global Energy Exploration & Prod. SE STOCKHOLM 2004-04-06 2018-01-01 366 U:TPL TEXAS PACIFIC LAND TRUST Thomson Reuters Global Energy Exploration & Prod. US DALLAS 1973-01-02 2018-01-01 367 N:TGS TGS-NOPEC GEOPHS. Thomson Reuters Global Energy Oil Equip. & Services NO ASKER 1997-10-30 2018-01-01 368 Q:THOI THAI OIL Thomson Reuters Global Energy Exploration & Prod. TH BANGKOK 2004-10-26 2018-01-01 369 IN:TWD TIDE WATER OIL INDIA Thomson Reuters Global Energy Exploration & Prod. IN KOLKATA 2002-08-07 2018-01-01 370 K:GEMZ TITAN PETROCHEM. GLD. Thomson Reuters Global Energy Oil Equip. & Services BM NA 1998-06-17 2018-01-01 371 J:TOKH TOKAI HOLDINGS Thomson Reuters Global Energy Gas Distribution JP SHIZUOKA-SHI 2011-04-01 2018-01-01 372 C:TOG TORC OIL & GAS Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2005-11-07 2018-01-01 373 F:TAL TOTAL Thomson Reuters Global Energy Integrated Oil & Gas FR COURBEVOIE 1973-01-01 2018-01-01 374 C:TOT TOTAL ENERGY SERVICES Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 1997-03-10 2018-01-01 375 C:TOU TOURMALINE OIL Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2010-11-23 2018-01-01 376 K:PANV TOWNGAS CHINA Thomson Reuters Global Energy Gas Distribution KY NA 2001-04-20 2018-01-01 377 C:TRP TRANSCANADA Thomson Reuters Global Energy Pipelines CA CALGARY 1985-06-03 2018-01-01 378 RS:TRP TRANSNEFT PREF. Thomson Reuters Global Energy Pipelines RU MOSCOW 2002-05-22 2018-01-01 379 S:TRAN TRANSOCEAN (SWX) Thomson Reuters Global Energy Oil Equip. & Services CH STEINHAUSEN 2010-04-20 2018-01-01 380 C:TCW TRICAN WELL SER. Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 1996-12-11 2018-01-01 381 C:TDG TRINIDAD DRILLING Thomson Reuters Global Energy Oil Equip. & Services CA CALGARY 2000-10-11 2018-01-01 382 TLW TULLOW OIL Thomson Reuters Global Energy Exploration & Prod. GB LONDON 1989-10-04 2018-01-01 383 TK:TUP TUPRAS TKI.PEL.RFNE. Thomson Reuters Global Energy Exploration & Prod. TR KOCAELI 1991-05-30 2018-01-01

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384 BR:U3G ULTRAPAR PARTICIPOES ON Thomson Reuters Global Energy Gas Distribution BR SAO PAULO 2002-12-17 2018-01-01 385 CZ:UNP UNIPETROL Thomson Reuters Global Energy Integrated Oil & Gas CZ PRAHA 1997-09-01 2018-01-01 386 KO:UNS UNISON Thomson Reuters Global Energy Building Mat.& Fix. KR SACHEON 1996-07-03 2018-01-01 387 U:UNT UNIT Thomson Reuters Global Energy Oil Equip. & Services US TULSA 1979-04-06 2018-01-01 388 K:TOPS UNITED ENERGY GROUP Thomson Reuters Global Energy Exploration & Prod. BM NA 1992-04-08 2018-01-01

389 ID:UTR UNITED TRACTORS Thomson Reuters Global Energy Comm. Vehicles,Trucks ID JAKARTA TIMUR 1990-04-02 2018-01-01

390 C:U URANIUM PARTICIPATION Thomson Reuters Global Energy Specialty Finance CA TORONTO 2005-05-10 2018-01-01 391 U:VLO VALERO ENERGY S&P 500 Energy Exploration & Prod. US SAN ANTONIO 1979-06-15 2018-01-01 392 F:VLR VALLOUREC Thomson Reuters Global Energy Industrial Machinery FR BOULOGNE-BILLANCOURT 1973-01-01 2018-01-01 393 C:VET VERMILION ENERGY Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 1996-06-27 2018-01-01 394 L:PDIH WAH SEONG Thomson Reuters Global Energy Oil Equip. & Services MY KUALA LUMPUR 1991-09-19 2018-01-01 395 A:SOLX WASH.H SOUL PATSN.& CO. Thomson Reuters Global Energy Coal AU SYDNEY 1973-01-01 2018-01-01 396 U:WFT WEATHERFORD INTL. Thomson Reuters Global Energy Oil Equip. & Services IE DUBLIN 1973-01-02 2018-01-01 397 C:WCP WHITECAP RESOURCES Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2002-01-16 2018-01-01 398 A:WHCX WHITEHAVEN COAL Thomson Reuters Global Energy Coal AU SYDNEY 2007-06-01 2018-01-01 399 U:WLL WHITING PETROLEUM Thomson Reuters Global Energy Exploration & Prod. US DENVER 2003-11-20 2018-01-01 400 U:WMB WILLIAMS S&P 500 Energy Pipelines US TULSA 1973-01-02 2018-01-01 401 WG. WOOD GROUP (JOHN) Thomson Reuters Global Energy Oil Equip. & Services GB ABERDEEN 2002-05-28 2018-01-01 402 A:WPLX WOODSIDE PETROLEUM Thomson Reuters Global Energy Exploration & Prod. AU PERTH 1973-01-01 2018-01-01 403 U:INT WORLD FUEL SVS. Thomson Reuters Global Energy Transport Services US DORAL 1986-08-28 2018-01-01 404 A:WORX WORLEYPARSONS Thomson Reuters Global Energy Oil Equip. & Services AU NORTH SYDNEY 2002-11-28 2018-01-01 405 U:WPX WPX ENERGY Thomson Reuters Global Energy Exploration & Prod. US TULSA 2011-12-12 2018-01-01 406 C:YGR YANGARRA RESOURCES Thomson Reuters Global Energy Exploration & Prod. CA CALGARY 2002-12-19 2018-01-01 407 K:YNCL YANZHOU COAL MINING 'H' Thomson Reuters Global Energy Coal CN JINING 1998-04-01 2018-01-01 408 K:NGAI YUAN HENG GAS HDG. Thomson Reuters Global Energy Consumer Electronics BM NA 1992-09-25 2018-01-01 409 K:SHAC YUHUA ENERGY HOLDINGS N1 Thomson Reuters Global Energy Consumer Electronics KY NA 2005-07-14 2018-01-01

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