DEGREE PROJECT IN INDUSTRIAL ENGINEERING AND MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2019

A Three-Pronged Sustainability-Oriented Markowitz Model Disruption in the fund selection process?

SIMON LOUIVION

EDWARD SIKORSKI

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT Abstract

Since the term ESG was coined in 2005, the growth of sustainable investments has outpaced the overall asset management industry. A lot of research has been done with regards to the link between sustainability and financial performance, despite the fact that there is a lack of transparency in sustainability of listed companies. This thesis breaks down the word sustainability into two di↵erent categories, and in turn eleven di↵erent parameters. The result is the term Q score which represents a company’s sustainability. The purpose is to increase transparency in the fund selection process for asset managers. Further, a multi- objective optimization problem is solved to analyze the relationships between return, risk and sustainability. The main subject is that accommodating sus- tainability as a third parameter in addition to return and risk modifies the fund selection process. The result indicates that the relationships between sustain- ability, return and risk follow the ecient market hypothesis, implying that an investor would have to sacrifice risk and return in order to achieve higher sus- tainability. With that said, the results indicated that the sacrifice is relatively small, and that there are a number of sustainable portfolios that perform well. Moving on, the reporting of ESG company data is still lacking. For this reason, this master thesis acts as a precursor for any future development within the field.

Key words: Portfolio optimization, ecient frontier, multi-objective optimiza- tion problem, sustainability, ESG, ecient market hypothesis, behavioural fi- nance En tre-dimensionell h˚allbarhetsorienterad Markowitz modell

Sammanfattning

Sedan termen ESG utvecklades ˚ar2005, har tillv¨axten av h˚allbara investeringar vuxit snabbare ¨anden generella f¨orvaltningsindustrin. Mycket forskning har gjorts kring h˚allbarhet kopplat till finansiell avkastning, men trots detta saknas det fortfarande en transparens r˚adande h˚allbarhet av noterade bolag. Detta examensarbete bryter ned termen h˚allbarhet till tv˚akategorier, vilket i sin tur bryts ner till elva kvantifierbara parametrar. Resultatet blir ett s˚akallat Q score, som ¨arett v¨arde p˚aett f¨oretags h˚allbarhet. Syftet med arbetet ¨aratt ¨oka transparensen av fonders h˚allbarhetsarbete. Vidare l¨oses ett optimeringsprob- lem med tre parametrar f¨oratt unders¨oka f¨orh˚allandena mellan avkastning, risk och h˚allbarhet. Resultatet indikerar att dessa f¨orh˚allanden f¨oljer hypotesen om e↵ektiva marknader, vilket inneb¨aratt en investerare m˚aste o↵ra avkastning och risk f¨oratt uppn˚aen mer h˚allbar portf¨olj. Med det sagt, indikererar resul- tatet att en investerare inte beh¨over o↵ra mycket inom avkastning f¨oratt uppn˚a en h˚allbar portf¨olj. Vidare kvarst˚ardet mycket arbete inom rapporteringen av ESG data p˚af¨oretagsniv˚a.Av detta sk¨alanses detta examensarbete vara en f¨oreg˚angare innan datan utvecklas vidare.

Nyckelord: Portf¨oljoptimering, ecient frontier, portf¨oljteori, h˚allbarhet, ESG, hypotesen om e↵ektiva marknader, beteendeekonomi Preface

This Master’s thesis was written in the spring of 2019 by Simon Louivion and Edward Sikorski, during their five-year master’s degree program within Indus- trial Engineering and Management at KTH Royal Institute of Technology.

We would like to take this opportunity to thank our supervisors Sofia W¨arml¨of Helmrich and Christian Thomann at SEB and KTH respectively. We are also thankful to Fredrik Armerin for his guidance with the optimization problem. Lastly, a final thank you to our parents for their never-withering belief in us. We would not be here without them. ”Earth provides enough to satisfy every man’s needs, but not every man’s greed.”

MAHATMA GANDHI

4 Contents

1 Background 1 1.1 Aim ...... 2 1.2 Scope ...... 2 1.3 Research question and problem statement ...... 2 1.4 Expectedcontribution ...... 3

2 Literature Review and Theory 5 2.1 Definitions...... 5 2.1.1 ESG...... 5 2.1.2 Impact ...... 6 2.1.3 Screeningmethods ...... 6 2.2 Modern portfolio theory ...... 6 2.2.1 Multi- optimization problem ...... 6 2.2.2 Markowitz’s mean-variance model ...... 7 2.2.3 Solution to the mean-variance problem ...... 7 2.2.4 The ecientfrontier ...... 8 2.2.5 Trade-o↵problem ...... 9 2.3 Ecientmarkethypothesis ...... 9 2.4 Behavioural finance ...... 10 2.4.1 Herding ...... 10 2.4.2 Availability ...... 11 2.4.3 Home bias ...... 11 2.4.4 Investing in sustainability ...... 11 2.4.5 Overconfidence ...... 11 2.4.6 Present bias or short-termism ...... 12 2.4.7 OthercriticismofEMH ...... 12 2.5 Empiricalresearch ...... 13 2.5.1 Sustainability and performance ...... 13 2.5.2 Sustainability and risk ...... 14 2.5.3 Theory versus practice ...... 14 2.5.4 Real-world impact ...... 15 2.5.5 Three-criterion Markowitz models ...... 16 2.5.6 Morningstar and Sustainalytics’ ESG criteria ...... 16

3 Method 19 3.1 Literaturestudy ...... 19 3.2 Data collection ...... 19 3.2.1 Static company data ...... 19 3.2.2 Financial company data ...... 20 3.2.3 Sustainability company data ...... 20 3.2.4 Investmentfundsdata ...... 21 3.3 IMTdevelopment...... 21 3.3.1 Data transformation ...... 21 3.3.2 ESG parameters ...... 22 3.3.3 EquityESGscore ...... 22 3.3.4 Impact parameters ...... 23 3.3.5 EquityQscore ...... 23 3.3.6 Fund Q score ...... 23 3.3.7 Fund ratings ...... 24 3.4 Optimization problem ...... 24 3.4.1 Calculating returns and covariance from historical data . 25 3.4.2 A sustainability-oriented Markowitz model ...... 25

4 Results 27 4.1 ESGfactors...... 27 4.1.1 Carbon intensity ...... 27 4.1.2 Wasteintensity...... 28 4.1.3 Waterstressintensity ...... 28 4.1.4 Diversity ...... 30 4.1.5 Labour conditions ...... 31 4.1.6 Corporate governance ...... 31 4.2 Impact factors ...... 32 4.2.1 Job creation ...... 32 4.2.2 Other impact indicators ...... 33 4.3 IMTQscores...... 35 4.3.1 ESG scores ...... 35 4.4 Countryscores ...... 36 4.5 Industryscores ...... 37 4.6 Sectorscores ...... 38 4.7 Fundanalysis...... 39 4.7.1 Di↵erence between long-only and long-short funds . . . . 40 4.7.2 Non-sustainable versus sustainable funds ...... 40 4.7.3 Positive versus negative screening ...... 40 4.8 Optimization solution ...... 41

5 Discussion 46 5.1 IMTmodel ...... 46 5.1.1 Model validation ...... 47 5.2 Sustainability-oriented Markowitz model ...... 48 5.3 Generaldiscussion ...... 50

6 Conclusion 53 6.1 Answering the research questions ...... 53 6.2 Implications...... 54 6.3 Furtherresearch ...... 55

References 56

7 Appendices 61 7.1 Graphs and tables ...... 61 List of Abbreviations and Glossary

Alpha - A term used in investing describing a strategy’s ability to outperform the market.

CSR - Corporate social responsibility

EBIT - Earnings before interest and taxes IMT - Impact metric tool

Maximum drawdown - maximum loss from a peak to a trough of a portfo- lio

MPT - Modern portfolio theory

NAV - Net asset value

SDG - Sustainable development goals, set by the United Nations

SRI - Socially responsible investing

UN - United Nations

7 1 Background

The world is changing, for the better and for the worse. Within the asset man- agement industry, sustainable investing has become a growing thematic. The term ESG (Environmental, Social and Governance) has grown significantly since it was first coined in 2005 (Hagart and Knoepfel 2005). In the past decade, the growth of sustainable investments has significantly outpaced the overall asset management industry. Investors have been pressured to adapt to society’s es- calated demand for sustainable investing. More than one in four dollars under professional management in the US was invested in Socially Responsible In- vesting (”SRI”) strategies by the end of 2018 (The Forum for Sustainable Re- sponsible Investment 2018). In Europe, 50% of assets were invested in Socially Responsible Investing (”SRI”) strategies at the end of 2015. (Global Sustain- able Investment Alliance 2016)

The salience of sustainable investing is partly due to pressure from asset own- ers. It is quite common that asset managers have to meet a certain ”ESG-level”, defined by the asset management firm and its clients. A commonly used mea- surement of ESG, is Morningstar’s sustainability ratings. In March of 2016, they first published their ESG ratings of more than 20 000 mutual funds. This was certainly groundbreaking, as investors had previously no simple way of mea- suring the sustainability of mutual funds. A study published in 2018, examined that there was an outflow of $12 billion from low sustainability funds, while high sustainability categorized funds had net inflows of $24 billion since the introduction of Morningstar’s ESG fund ratings. The multi-billion dollar move- ment of funds occurred during an eleven month period after the publishing of Morningstar’s sustainability ratings, proving through causality the tremendous interest for sustainable investing. This does, however, beg the question whether the financial markets have all the information available with regards to sustain- ability. A $24 billion shift as a result of one company’s actions proves that there was a lack of knowledge, and that perhaps that the absence still remains. (Hartzmark and Sussman 2018)

The purpose of this master thesis is to develop a universal framework that can be used to measure the overall sustainability of any portfolio of listed com- panies. This will aid in providing investors with additional information in the fund selection process, whilst also introducing new dimensions when construct- ing portfolios. Lastly, as more and more asset managers are required to allocate a certain amount of funds into sustainable investment as a fiduciary duty; it is of interest to see how they allocate those funds and how an increase in sustain- ability will a↵ect the risk and return of their portfolios.

The relationship between risk and return has been studied extensively since Markowitz laid out the foundation of Modern Portfolio Theory in 1952. During the past 67 years since then, there has been a tremendous change in the finan- cial industry. Technology, risk appetite and other factors of importance have

1 altered investor preferences. Markowitz’s MPT is a theory on how investors can construct portfolios to maximize their expected return, given a specific level of risk. (Markowitz 1952) As a result of the salience in demand for sustainability, we intend to extend Markowitz’s optimization problem with a third criterion. The third criterion is a sustainability measure developed throughout this thesis, which is used and tested in the optimization problem. Additionally, previous re- search has commonly performed similar optimizations with long-only portfolios, which we refer to in section 2. We intend to add to research by also examining long-short funds and how they meet their ESG criterion. Previous research has also performed the analysis using di↵erent forms of utility functions, which will also be covered in this thesis. There has also been done extensive research using regression tools, which is an excellent tool to examine relationships between pa- rameters. However, by using an optimization problem instead, the relationships are easier to apprehend while contributing to this avenue of research.

1.1 Aim The purpose of this master thesis is to develop a universal, factor agnostic frame- work for SEB. The framework can be modified to incorporate any investor’s intentions. The aim can be broken down into three parts. Provide investors with additional information in the fund selection process • Introduce new dimensions when constructing portfolios • Evaluate investment policies in relation to sustainability goals • 1.2 Scope The developed framework must be universal and include every listed company worldwide. The framework has to be able to analyze the sustainability of any given fund. Our data set covers both long and long-short equity funds as well as the equities listed on OMXS30. We will also use SEB’s previously de- fined parameters in the framework. Moreover, we will build a three-parameter Markowitz model in order to calculate how funds and equities perform with respect to risk and return, in relation to their sustainability level. This devel- oped framework will hopefully aid SEB in their client meetings, giving investors further transparency.

1.3 Research question and problem statement With the thematic of sustainable investing undergoing rapid growth, investors are becoming pressed by asset owners to meet certain criteria of sustainability. They are also pressed into knowing specifically what their investments are im- pacting. However, this information in today’s financial industry is either scarce or expensive. General ESG scores and CSR indices are too general to analyze, as they do not break down specifically what a company is exactly doing to provide

2 real-world impact. What does a good ESG rating actually signify? What is the company good at? These are questions that remain unanswered by a general ESG score. A framework, that is fully transparent in its parameters, will be developed in this master thesis.

It is dicult to measure sustainability as there are many angles to a company’s sustainability. A company that is manufacturing solar panels may have a pos- itive impact. However, the company may also be linked to treacherous labour conditions and poor corporate governance. In order for financial institutions to be able to present investors a transparent holistic perspective of any fund, the sustainability factor of a fund has to be broken down to di↵erent parameters. These parameters have to be quantifiable and normalized for comparison. Given these parameters, will that alter the fund selection process? Previous research has stated that investors are more likely to avoid ”sinful stocks” than to invest in virtuous stocks (Johansson 2019).

The research question culminates in the following two problem statements:

Can a framework quantify the sustainability of funds? • How will the implementation of a diverse sustainability measure impact • portfolios in relation to risk and return, and how may it a↵ect investors in their decision making?

The developed framework will be called the Impact Metric Tool (IMT) and will be used by SEB to assess the sustainability of investment funds.

1.4 Expected contribution Previous research in sustainability-oriented Markowitz models has increased during the last five years. As mentioned previously, we aim to add to this field of research by expanding the third criterion. The third criterion is of im- portance, as Utz et al. (2015) concluded that there is a need for increased service in relation to investing in sustainable funds. We believe that the quantification of sustainable factors will increase transparency for investors, allowing them to precisely pinpoint what their investments are contributing to. The framework should not be a single measurement, but rather show what each parameter has contributed to the overall rating of each equity/fund.

Previous research examining the relationship between stock price performance and sustainability has been somewhat inconclusive, which we delve into in sec- tion 2.5.1. Our thesis will contribute to the examining of how long-short funds meet their sustainability targets. Their ability to short an equity, gives a fur- ther dimension to the existing research. If a fund goes short a company with a negative sustainability score, this will result in a positive contribution to the portfolio score. Thus, our optimization problem intends to examine the rela- tionship between sustainability, risk and return given the ability to be able to

3 go short an equity. In addition, research on financial performance and sustain- ability has been extensively conducted using regression tools. As mentioned, this thesis will instead use a Markowitz model to analyze the relationships.

4 2 Literature Review and Theory

In this section, definitions will first be set to mitigate any potential confusion. Subsequently, relevant literature and theory will be presented within all fields of the master thesis. Modern portfolio theory, the ecient market hypothesis and behavioural finance are the three main pillars of this chapter.

2.1 Definitions Parallel to the emergence of sustainability-oriented funds and investment strate- gies, there has been a plethora of terms used to describe sustainability. We intend to distinguish the di↵erence between di↵erent terms in order to leave no room for confusion. These definitions have been set in collaboration with SEB, as well as from the white paper written in preparation for the thesis. (Johansson 2019)

Sustainability is the most used word in the space of Socially Responsible In- vesting strategies for obvious reasons. The word does, however, lack a certain dimension to it. For example, the exclusion of controversial stocks such as oil stocks is defined as sustainable investing. However, does the exclusion of the world’s largest oil producer ExxonMobil really contribute to making the world a better place? In addition, software companies receive in general positive ESG scores, due to their relatively low carbon emissions. However, one might question if these companies are producing positive real-world impact. For this reason, this thesis divides the term sustainability into two subcategories, namely ESG and Impact.

2.1.1 ESG The term ESG is today widely used in the financial industry to describe the environmental, social and governance aspects of a company. The abbreviation is successful in describing what is of importance for investors, as the three pil- lars are exhaustive in sustainability terms. In this thesis, the term will denote a company’s operational eciency in relation to externalities. For example, the terms ROI or operating margin can be used as a metric for evaluating the fi- nancial eciency of a company. In the case of ESG, the waste intensity metric quantifies environmental eciency, in a similar way.

The concept of ESG intends to entail how a company’s business is conducted. Demonstrating favourable ESG characteristics does not always signify the cre- ation of impact. To exemplify this, a company might be highly ecient in terms of having a low carbon footprint. However, if the company does not try to further improve itself; there is no impact being created. The ESG factors measure the eciency of a company, but they do not include the improvement of operations.

5 2.1.2 Impact The shortcomings of the term ESG, are aimed to be covered by introducing the term impact. Using the Sustainable Development Goals (SDGs) developed by the United Nations, we intend to define the term impact. We use these goals to measure whether a company is having a real-world impact. There are a plethora of ways to create an impact in the world. Most importantly, companies that engage in the production of goods or services that are aligned with any of the SDGs have an impact solely based on their output. Other ways a company can have an impact is through their operations. If a firm has employees in devel- oping countries, this could align with the SDG ”No poverty” as job creation counteracts poverty. In essence, a company can both directly provide impact with its products, but also indirectly through how the company conducts its business. (United Nations 2015)

It is of importance now to distinguish the di↵erence between impact and ESG, as impact refers to what a company does, as opposed to ESG capturing how a company does it.

2.1.3 Screening methods Positive screening implies a screening method with the inclusion of sustain- able companies. Examples of industries that these companies would operate in are waste management, public transport, education, environmental technology and renewable energy.

Negative screening revolves around the exclusion of ”bad” companies. This methodology can either be done by excluding entire sectors. These industries include but are not limited to oil & gas, tobacco, mining and pornography. An- other common approach is by setting a minimum ESG score for a security in the screening process. If a company has an ESG score below a certain level, it would be excluded from the screening process. This approach can be argued as non-sustainable, as the fund does not necessarily invest in companies working towards a more sustainable society. However, in this thesis, funds are considered sustainable as long as they apply a screening method, regardless if positive or negative.

2.2 Modern portfolio theory This chapter will include the mathematical theory used in the thesis. Markowitz (1952), Armerin (2004), Zopounidis et al. (2015) and Hult et al. (2012) were used as sources of literature.

2.2.1 Multi- optimization problem Multi-objective optimization is a commonly used methodology to solve portfolio selection problems. It is a method for dealing with a mathematical problem

6 where than objective function has to be optimized over more than one constraint. In some cases, these objective functions are conflicting, which allows for multiple solutions. These problems are defined as follows:

optimize F (x)=[f1(x),...,fk(x)] (1) subject to x X 2 x is a vector containing decision variables while X is a set of feasible solutions. F (x) is regarded as the objective function, while x X is regarded as the constraints. The objective function can either be minimized,2 maximized or both. The purpose of multi-objective optimization is to find all optimal solutions. (Zopounidis et al. 2015)

2.2.2 Markowitz’s mean-variance model Modern Portfolio Theory (MPT), first introduced by Markowitz in 1952, is based on a portfolio optimization problem with two criteria: expected return and risk. In the model, risk is measured by using variance to quantify the variability of the return. Expected return may be calculated using random variables, but it can also be proxied by using historical returns. (Markowitz 1952) The latter will be the case in this thesis.

MPT states that an investor can construct a portfolio of multiple assets for a certain level of risk. A key assumption to MPT is that investors are risk- averse, i.e. they prefer to invest in a less risky portfolio than a riskier portfolio for a certain level of return. In turn, this implies that investors are willing to take on more risk, in order to achieve higher returns. (Markowitz 1952)

The mean-variance model can be formulated as an optimization problem, with the objective function to minimize the portfolio’s expected risk, with the con- straint of attaining a certain level of return. 1 minimize wT ⌃w 2 subject to µT w µ (2) min wT 1 =1 w is a vector with the weight of each asset. ⌃is the covariance matrix of the assets and µ is a vector with the expected return of each asset. µmin is the minimum level of return the investor wishes to obtain. (Armerin 2004)

2.2.3 Solution to the mean-variance problem The mean-variance problem introduced in the previous section can be solved by using the Lagrange multiplier method (Markowitz 1952). In this problem, the Lagrangian function is written as 1 (w, , )= wT ⌃w + (µ wT µ)+ (1 wT 1) (3) L 1 2 2 1 min 2

7 Following this, the Lagrange multiplier method states:

L = 0, L = 0, L =0 w 1 2 From equation 3, we can derive the portfolio’s optimal weights. They are:

1 w =⌃ (1µ + 21) (4)

We can now derive the Lagrange multipliers 1 and 2. Multiplying equation 4withw and 1, while applying the constraints of the mean-variance model in section 2.2.2 results in:

T T 1 w µ = µmin = w ⌃ (1w + 21), T T 1 (w 1 =1=1 ⌃ (1w + 21). or in matrix form:

T 1 T 1 w ⌃ ww⌃ 1 1 µmin T 1 T 1 = w ⌃ 11⌃ 1 ⇥ 2 1    To simply matters, we define the matrix A, the vector and variables a, b, c and d. They are defined as follows:

T 1 T 1 w ⌃ ww⌃ 1 ab 2 1 T 1 T 1 = ,d= ac b ,= w ⌃ 11⌃ 1 bc 2   

Proposition 1 The optimal weights of the mean-variance problem are

1 1 w = ⌃ ((cµ b)w +(a bµ )1) (5) mv d min min While the variance of the portfolios can be calculated as

cµ2 2bµ + a 2 (µ )= min min (6) mv min d

2.2.4 The ecient frontier The solution to the Markowitz mean-variance problem presents an interesting dilemma for the asset manager. Given that an asset manager’s risk preference varies, there are a plethora of possible solutions. In other words, there is a set of optimal portfolios that o↵er the highest expected return, given di↵erent levels of risk. These points (p,µp) are referred to as the ecient frontier. The portfolios that lie on the frontier are the optimal portfolios, while the portfolios that lie to the left of the frontier are sub-optimal as they do not o↵er enough return for the given level of risk. (Markowitz 1952)

8 In order to calculate the ecient frontier, we use the formula for portfolio vari- ance (equation 6, while rewriting the term d.Thisresultsin:

2 2 2 cw 2bwp + a cw 2bwp + d/c + b /c 2 (w )= p = p mv p d d Now, we multiply each side with c and simply further.

2 2 2 2 2 2 w 2bwp/c + d/c + b /c (w b/c) c2 (w )=c2 p = c2 p + 1 (7) mv p d d Moving the first part of the r.h.s. over to the l.h.s. results in

(w2 b/c)2 c2 c2 p =1 mv d This can be regarded as the equation for the hyperbola (i.e. the ecient frontier) b with a center lying in point (0, c ).

2.2.5 Trade-o↵problem The premise of the trade-o↵problem is that future expected return is positively correlated with risk. Thus, an investor will give up potential returns in order to lower the risk of the portfolio. In MPT, there is a trade-o↵between risk and return, and the following optimization problem depicts this. 1 c maximize wT µ wT ⌃w 2 2V0 (8) subject to wT 1 =1 c is a dimensionless positive constant, that is used to adjust for the investor’s risk preference. There is no straightforward methodology to choose the parameter. An investor would have to decide between two di↵erent investment opportunities using the same value of c, which would result in greater comparability. (Hult et al. 2012)

2.3 Ecient market hypothesis Eugene Fama developed the ecient market hypothesis (EMH) in 1965 and won the Nobel Prize in Economics for his work in 2013. The main premise of the hypothesis is that equities always trade at their fair price, essentially making stock-picking a game of luck. The ecient market paradigm states that in- vestors are rational, and the mistakes that are made are uncorrelated. Further, if mistakes are correlated, arbitrageurs assure that the prices reflect the funda- mental values. There are three forms of the hypothesis, which will be defined. (Fama 1965)

Weak-form eciency implies that future stock movements cannot be pre- dicted by looking at past performance. Prices do reflect all past information,

9 however, future returns are identically and independently distributed. In the long run, excess returns from investment strategies cannot be gained. In 1991, Fama updated his categorization for weak form. He attempted to predict future returns using past performance. These tests supported the weak-form EMH, as abnormal returns were often small when present. Furthermore, transaction costs were larger than these returns to a certain extent. (Fama 1991)

Semi-strong-form eciency indicates that prices reflect all publicly avail- able information. In other words, investors cannot profit from trading around specific events, due to the fact that the share price would adjust rapidly to the new piece of information. Moreover, there have been several studies that have examined the semi-strong form. The summary of these studies is that they supported the semi-strong form of the EMH. Most event studies found that the market reacted correctly to news, with no systematic over- or under-reaction. (Fama 1991; Jensen 1967; Carhart 1997)

Strong-form eciency is built on that share prices reflect all information: both public and private. Strong-form eciency is near impossible, as there are legal barriers that keep private information from the public. (Burton and Shah 2013)

2.4 Behavioural finance Since Fama developed the cornerstone of modern financial theory, the ecient market hypothesis, there has been a lot of criticism towards it. Believers of the ecient market argue that humans’ irrational decisions cancel each other out. Behavioural economists argue that the financial markets are imperfect because people often stray from rational decisions. Moreover, they state that behaviour is correlated, and as such, mistakes are also correlated. Behavioural economists state that the imperfections of the financial markets are a result of cognitive bi- ases. These cognitive biases are human errors in information processing, but also reasoning. In the following subsections, we have defined various relevant cogni- tive biases. (Shiller 1999) This section is of great importance as it investigates how investors act and behave given the results of this thesis. There are various reasons for the salience of sustainable funds, and this section sheds light upon why investors invest in socially responsible funds. It also contains explanatory information regarding the financial performance of sustainable funds.

2.4.1 Herding Herding is a hardwired human tendency to mimic the actions of a larger group. Herd behaviour is detrimental to asset pricing. This behaviour can be spread through word-of-mouth, social pressure or authorities. (Burton and Shah 2013) Characterization of herds is explained below. Mass behaviour: individuals choose to ignore their own information and • act irrationally.

10 Fragile equilibrium: the actions of a few people can possibly a↵ect millions • of people. Discrete switches: Herd behaviour can quickly reverse once started. • 2.4.2 Availability Humans’ cognition can sometimes rely on the first thing that comes to mind regarding specific topics or decisions. The heuristic operates on the notion that if something is remembered, it must be of some importance. However, the remembered detail does not necessarily have to be correct as some things have to be analyzed properly rather than instinctively thought out. An example could be trying to answer the question: Is being a tree-logger more dangerous than being a policeman? Naturally, we recall news regarding police shootings, which leads us to believe that being a police ocer is more dangerous than being a tree-logger. Although, the more dangerous profession is, in fact, being a logger. (Kahneman and Tversky 1974)

2.4.3 Home bias The home bias is based on the notion that investors tend to include equities that are local to them. Investors consistently favour domestic securities, and is an intriguing puzzle in international finance due to the under-diversification it entails. Geographic proximity plays a definite role in the choice of an in- vestor’s portfolio. (Coval and Moskowitz 1999) This bias could be connected to the availability bias, as domestic stocks are more visible in day-to-day life. Moreover, this also has a link with herding, as domestic stocks could grow in popularity through contagion e↵ects.

2.4.4 Investing in sustainability Research in 2017 argued that two of the reasons for owning SRI funds was so- cial signalling and intrinsic social preferences. The piece of research conducted a broad survey among Dutch investors and concluded that investors were willing to invest in sustainable funds despite the fact that they do not generate alpha, have higher management fees and have lower Sharpe ratios. Social signalling was a lesser reason for owning SRI funds, while the main reason was investors’ intrinsic social preferences. Social signalling is defined by owning a particular asset for the sole purpose of being able to signal the fact to peers or the pub- lic. The inclusion of virtuous stocks could be regarded in social terms as an indication of good ethics and values. Nevertheless, one could argue that social signalling remains a factor. (Smeets and Riedl 2017)

2.4.5 Overconfidence Overconfidence is common today and can be identified in many human be- haviours. It can lead to confirmation bias, cognitive dissonance and the so-

11 called Ostrich e↵ect. The latter is an e↵ect identified when investors choose to verify news of their portfolio returns selectively. The term was coined in 2006 by Galai and Sade (2006), and defined as ”the avoidance of apparently risky financial situations by pretending they do not exist”. Overconfidence is partic- ularly common in situations where investors aren’t fully educated and in noisy environments. (Burton and Shah 2013)

2.4.6 Present bias or short-termism Present bias is a general term that refers to the human tendency to give stronger weights to payo↵s that are closer to the present time, rather than a payo↵further down the line. It is a self-control bias, where lack of discipline influences humans to act irrationally. (O’Donoghue and Rabin 1999) An example of present bias would be that most individuals would prefer $100 today over $200 in one years time. However, if choosing between $100 in six years and $200 in seven years the individual would choose $200 (Fidelity 2015).

At the point of a new year, many investors speculate on their prospects for the upcoming 12 months. However, this is usually the moment when the investors speculate the furthest. Short term thinking is something that has emerged with time, today people discuss weekly and monthly returns and the impact of quarterly earnings on the stock markets are relentless. This behaviour is con- tradictory in relation to long-term portfolio building. Experiments have shown that investors value short-term return, creating dopamine e↵ects associated to the same ones obtained from cigarettes and alcohol. This is paradoxical as the trade-o↵s in both cases are identical. Due to the short term focus of investors and sell-side analysts, the market is very ecient in pricing short-term earnings. Although, the market is less e↵ective when pricing in a long-term perspective and this is an opportunity for the investors, especially when investing in firms with strong structural growth. In essence, short-termism is a term that de- scribes the financial markets overweighted focus on short-term earnings, rather than the long-term financial performance of a company. (Fidelity 2015)

2.4.7 Other criticism of EMH Other critics of the EMH have argued that stock prices are too volatile to be consistent with the hypothesis. They also argue that there are predictable patterns in historical data, which can be used to forecast future returns. Despite this, money managers do not necessarily generate alpha which behaviouralists suggest they should. The one consensus that remains is that the strong form of the EMH is unlikely. Luckily for believers in the EMH, the weak and semi- strong form can still be true regardless of the strong form’s validity. (Burton and Shah 2013)

12 2.5 Empirical research 2.5.1 Sustainability and performance As mentioned earlier, Hartzmark and Sussman (2018) presented evidence of a $24 billion net inflow to sustainable funds after Morningstar’s introduction of ESG fund ratings. Proof of this rare quasi-exogenous shock in the financial markets proves that there is a real demand for sustainable investments. How- ever, this proof did not find evidence supporting the hypothesis that sustainable funds that conduct positive screens generate alpha. Instead, it found evidence supporting the contrary. (Hartzmark and Sussman 2018) This is also in line with research by Dobrovolskien´eand Tamoˇsi¯unien˙e(2016). In the face of this information, observations still indicate that investors have a tendency to believe that higher sustainability-measures can deliver higher returns. On the other hand, this might not be the case at all. Some asset managers could perhaps have a fiduciary duty to include sustainable investments, which begs the ques- tion of whether and why investors are willing to give up higher returns in order to meet these goals.

There are studies that have proved that there is a positive relationship between ESG scores and financial performance. A study done by Friede et al. (2015) conducted an empirical analysis of over 2 000 previous studies of ESG factors and financial performance. The conclusion was that, on average, there is a positive relationship between financial performance and sustainability. These studies, however, had a time span from 1970 to 2015. We believe that despite the positive link, it is hard to provide conclusive evidence with such an outdated dataset. The term ESG was not coined until 2005, and the studies in the data set defined the term ESG in many di↵erent ways.

Research by Utz et al. (2014) showed by using a tri-criterion portfolio opti- mization model that there was no significant di↵erence in expected return and ESG scores between conventional and socially responsible funds. Further, Utz et al. (2015) computed certain ecient portfolios in their tri-criterion model that included risk, return and sustainability. By comparing ecient portfolios with real portfolios of mutual funds, they found that sustainable mutual funds could improve their sustainability profile without sacrificing financial performance. In essence, sustainable mutual funds have room for improvement with regards to sustainability. A master thesis at KTH has also investigated links between pa- rameters E, S and G in relation to financial performance. They could not find a significant relationship between the three parameters and financial performance, both market and accounting based. The only parameter that showed a signifi- cant relationship with market-based performance was the environmental factor, and it was only through the log(Tobin’s q) variable. (Ahlklo and Lind 2019)

This discouraging ambiguity in the link between market-based financial per- formance and sustainability might be a result of the aspect of time. An article

13 published in 2018 showed both positive and negative relationships between fi- nancial performance and ESG scores, depending on time span. If time is broken down into cycles, i.e. economic booms and recessions, the results are of inter- est. The authors showed that sustainable firms have higher alphas than non- sustainable firms during economic booms. However, the opposite would ensue during recessions. In essence, their results suggest that a sustainable portfolio’s ability to generate alpha is time-varying. (Bansal et al. 2018)

ESG was first coined in 2005, and company-specific data with regards to sus- tainability must have initially been limited. Given the alpha’s time-varying characteristic, in combination with the fact that the financial markets have performed exceptionally since 2008, it is quite dicult to prove that there is a positive relationship between sustainability scores and financial performance. Sustainability data has been limited, rendering any conclusion inconclusive. De- spite this, there is a legitimate need for increased transparency in sustainable investing. Most studies conducted in the field could be regarded as precursors. As sustainability data becomes more widely reported, this will result in more accurate results and in turn more accurate conclusions.

2.5.2 Sustainability and risk Verheyden et al. (2016) examined the link between ESG, return, risk and di- versification. They found that screening of sustainable stocks added on average 0.16% to annual returns. They also noticed that volatility was lower, as well as conditional value-at-risk and maximum drawdown. This is also supported by a study by Clark et al. (2015) that compiled over 200 previous studies about ESG score and financial performance. They presented the result that higher sustainability is correlated with lower risk. Since there are studies that prove a positive correlation, there remains ambiguity similarly to the correlation be- tween sustainability and returns.

Utz et al. (2014) found that socially responsible mutual funds had a slightly lower volatility than conventional funds, by using a tri-criterion portfolio opti- mization methodology.

2.5.3 Theory versus practice In sections 2.3 and 2.4, the two sides to the coin of the financial markets were described. The ecient market hypothesis states that a security is priced given all public information. Meanwhile, behavioural economists believe that the fi- nancial markets are imperfect and that the imperfections are a result of human’s cognitive biases.

In our empirical research, we examined the correlation between sustainability, risk and return. According to the EMH, sustainable funds should on average un- derperform the market. This is as the universe of equities an investor can invest

14 in is smaller. As a result of the lower diversification, in theory, sustainable funds should also have lower expected returns and higher risk. However, there were studies that did prove a positive correlation between return and sustainability. Behavioural economists would perhaps point to the fact that a cognitive bias such as herding, which is also evident in the study by Hartzmark and Sussman (2018) A paper by Anderson and Robinson (2019) connected extreme weather events to financial decisions made in Swedish households. They found that ex- treme weather conditions that have been occurring since 2014, caused investors to allocate a higher percentage of savings into mutual funds that were labelled sustainable.

With that said, the summary of our empirical research is that there still remains an inconclusively in the field. This is partly due to the lack of sustainability data, as well as the short time span it has existed. Moreover, not all studies could prove a positive correlation between return and sustainability.

2.5.4 Real-world impact There is a distinct di↵erence between positive and negative screening with re- gards to delivering tangible impact on society. In particular, negative screening can be regarded as an indirect way of investing in a more sustainable world, as capital is not being allocated to companies that have a negative e↵ect on the planet.

There is however a combined approach to both positive and negative screen- ing. A study by Statman and Glushkov (2009) suggested that a combination of the two does not lead to higher returns. Despite this, more and more investment funds are choosing to combine the screening methods. Norway’s largest pension fund, Kommunal Landspenjonskasse, decided to divest all of its investments in coal companies. Instead, the fund has chosen to invest all those funds in renewable-energy production companies in emerging economies (Arabella Ad- visors 2015). Another example is the global insurance firm Allianz SE. They have divested funds from any company that bases more than 30% of its energy production on coal, or that generates more than 30% of revenue through mining coal. Moreover, Allianz plans to double the size of its investments in renewable energy sources. (Associated Press 2015)

Active investors may as mentioned have an impact on corporate-level as there worldwide has become a trend to work towards a sustainable world. Further, the start-up environment is also becoming more oriented with disruptive sus- tainability trends. Orange Fiber is an Italian company, founded in 2014 that received funding by H&M Foundation in collaboration with Accenture and KTH Royal Institute of Technology. The company identified that over 700 000 tonnes of citrus fruits by-products were wasted over year. Through their innovation, they can now manufacture fabric using orange peels. They now have a collabo- ration with Italian brand Salvatore Ferragamo, with a market capitalization of

15 $3 billion. The case of Orange Fiber is just an indication of the disruption that can occur in many industries, and specifically in the historically tainted fashion industry. It is of interest to note that large corporations such as H&M and Sal- vatore Ferragamo are attempting to work towards more sustainable products, due to the pressure both from investors and consumers. (Orange Fiber 2019) (Foundation 2018)

2.5.5 Three-criterion Markowitz models Since Markowitz developed this mean-variance model, there has been consid- erable research developing tri-criterion Markowitz models, e.g. Chow (1995), Anagnostopoulos and Mamanis (2010), Gasser et al. (2016), Qi et al. (2017), Dobrovolskien´eand Tamoˇsi¯unien˙e (2016), and Utz et al. (2015). The latter two included a sustainability-parameter as their additional criteria. They suc- cessfully developed Markowitz-style optimizations allowing them to analyze the relationships between sustainability, risk and return. The results were unani- mous in that a sustainability parameter lowers the expected return and increases risk. Moving on, Dobrovolskien´eand Tamoˇsi¯unien˙e(2016) focused on examining construction companies. Meanwhile, Utz et al. (2014) examined 4 999 mutual funds for a total of 55 654 portfolios. Their ”ESG-criterion” was based on four scores (Environmental, Social, Governance and Composite ESG). They conclude that there is ”considerable slippage in the mutual fund industry” in relation to the needs of service for sustainable investors. Gasser et al. (2016) use a tri- criterion Markowitz model to analyze the relationship between sustainability, risk and return. The authors use data on 6 281 equities including sustainability score from Thomson Reuters. The conclusion of the analysis was that while it was possible to find an optimal portfolio, the expected return decreased with an optimal portfolio of a higher sustainability score. Also, with a higher sus- tainability score for the optimal portfolio, the risk of that portfolio decreased. Although, the Sharpe ratio of Sustainability/Risk optimal portfolio was much lower than the Return/Risk optimized portfolios. It was further assessed that it was possible to find optimal portfolios with an adequate level of sustainability with a slight decrease in the portfolios Sharpe ratio. This could actually be im- plemented by positive screening by removing assets with negative sustainability scores when optimizing for Return/Risk.

2.5.6 Morningstar and Sustainalytics’ ESG criteria As mentioned in section 2.5.1, Morningstar has ESG ratings on most mutual funds worldwide. They have acquired the relevant data by using Sustainalytics ESG scores. Sustainanalytics is a firm that services institutional clients with ESG ratings of over 10 000 listed companies. (Hale 2016)

Sustainanalytics measures sustainability for a company by analyzing the most relevant ESG matters on an industry level. Each of the three components E, S and G, are evaluated on three dimensions: preparedness, disclosure and perfor-

16 mance. Preparedness is a measurement representing a company’s engagement in policies or programs to restrain ESG risk. Disclosure measures the level of transparency in ESG related issues. Further, the quality and amount of a com- pany’s ESG reporting is also included in the measure. Lastly, the performance of a company’s ESG is set by a number of indicators, both quantitative and qualitative. Sustainanalytics has created around 70 indicators for this purpose. Finally, the scores for each three components are once again weighted in relation to their importance to the industry. (Hale 2016)

Morningstar uses the ESG score for companies set by Sustainanalytics to de- velop their portfolio sustainability score. Their portfolio sustainability score is calculated as the following:

Step 1 Sustainanalytics evaluates companies on ESG in relation to firms in the same industry, also called peer groups. Di↵erent combinations of indicators are used in di↵erent industries. An ESG rating for a company in one industry could outperform all companies in another peer group whilst underperform in its own peer group. In order to make scores normalized and applicable in a diversified portfolio, indi↵erent of there peer group, a z-score transformation is used. (Hale 2016)

peer ESGx µpeer Zx = (9) peer

Where: ESGX is Sustainanalytics company ESG score, µpeer is the peer group mean ESG score and peer is the peer groups standard deviation of ESG scores. In order to normalize ESG scores over a scale of 0 100 the following equation is used: (Hale 2016) ESGNormalized = 50 + (Zpeer 10) (10) x x ⇤ Step 2 A portfolios ESG score is then set by multiplying each assets weight in the portfolio with its normalized ESG score, demonstrated in the following equation. (Hale 2016) n Portfolio ESG Score = ESGNormalized w (11) x · x x=1 X where wx is the weight of the x:th equity.

Step 3 The sustainability score is then calculated as the following: Portfolio Sustainability = Portfolio ESG Score - Portfolio Controversy (12) Step 4 Portfolio controversy deduction is a measure created by Sustainanalytics to

17 track and characterize ESG related incidents. These events are characterized by their e↵ect on ESG related risk to the company. An example of controversy is Volkswagen’s carbon emission manipulation. In order to calculate the portfolio controversy, the weight of each asset in the portfolio is multiplied by the asset’s controversy score. The score is then scaled between 0 20 which means that controversies can at a maximum deduct 20 units from the portfolio sustainabil- ity score. (Hale 2016)

Step 5 Further, the Morningstar portfolio sustainability score includes a historical as- pect. To set a historical portfolio sustainability score an average of 12 months trailing scores are used, however the more recent a score is the greater will its a↵ect be on the overall score. The formula is as follows:

11 i=0(12 i) Portfolio Sustainability Hist. Portfolio Sus. = · 11 (13) P i=0(i + 1) Step 6 P Finally a Morningstar sustainability rating is set as funds are ranked within Morningstar Global Categories based on the historical portfolio sustainability score. In essence, the normally distributed historical score is ranked relative to the fund’s global category. The rating distribution can be seen in the following table:

Figure 1: Morningstar’s fund ratings (Morningstar 2018)

18 3 Method

In this section, the methodology of the study is presented. It will cover the literature review, data collection, development of the IMT and lastly the selected Markowitz model.

3.1 Literature study The literature study consisted of three parts. Initially, SEB provided their white paper ”Impact Metric Tool” written by Edvard Johansson in January 2019. It served as the foundation for this thesis. The report provided us with inspiration for what later became this master thesis. Secondly, our comprehensive exam- ination of the literature was conducted. A deeper understanding of our topic was required, and it was attained by conducting a systematic literature review. As our topic is two-fold with one side in sustainable investments and the other in modern portfolio theory, two di↵erent searches were appropriate. We used key-words ”three-parameter”, ”multi-parameter”, ”Markowitz model”, ”ecient surface”, ”sustainability-oriented”, ”socially responsible investing, ”sustainabil- ity”, ”asset management” and ”rating*”. We noticed that previous literature within three-parameter Markowitz models was somewhat unexplored, with re- occurring authors and fewer citations. Research within sustainable investing was more researched, especially during the latest decade. We reviewed articles with more citations and that were included in familiar journals.

The last part of our literature study was a constant. As our thesis developed, new areas were explored and thus our literature study was extended. We were also given stimulating and topical articles by our supervisor Christian Thomann, which provided ongoing inspiration and support.

3.2 Data collection The eleven di↵erent sustainability measures are created from quality and cov- erage data. This information has been received from several di↵erent providers which resulted in 49 335 listed equities. Around 700 data types of sustainability were used for analysis and construction of the 11 factors. Further, narrowed in- formation of asset’s investments and revenue streams in regard to sector, region, etc. was acquired to understand the attributions of the companies.

3.2.1 Static company data Static data divides companies into di↵erent classifications, showing which sector, currency, stock exchange they are active in. This data ranges from NAICS & GICS sector classifications to the country of incorporation. The data is provided by Standard and Poor’s (S&P).

19 3.2.2 Financial company data Financial data is found from S&P and consists of data extracted from income statements, balance sheets and cash flow statements. In addition, historical prices and market data is also provided from S&P. Lastly, the equities in the Swedish index OMXS30 were accessed through The Swedish House of Finance’s equity database.

The equity prices accessed from the Swedish House of Finance ranged from 2017-01-05 to 2018-12-28, and consisted of weekly closing prices. Companies that were not listed on the OMXS30 prior to 2017-01-05 were excluded from the data set.

3.2.3 Sustainability company data Data on the sustainability is provided by Thomson Reuters, and was collected in March of 2019. The data for the companies is based on their reporting on the 2019-03-01. The eleven parameters are divided into ESG and impact, and are illustrated below.

Figure 2: Factor definitions

20 Variable Number µ Median Min Max Carbon intensity 49334 290 1720 44 0 184340 Waste intensity 49335 377 19870 6 0 3600531 Water stress intensity 49335 620 12001 0 0 1355318 Diversity 49335 41 12 50 0 50 Labour conditions 49335 69 515 35 0.01 52279 Corporate governance 49316 45 22 50 10 90 Job creation 39299 16 35 0 0 100 Pos environmental impact 32657 0.02 0.14 0 0 1 Neg environmental impact 32657 0.08 0.26 0 0 1 Pos social impact 32657 0.13 0.33 0 0 1 Neg social impact 32657 0.01 0.11 0 0 1

Table 1: Statistics regarding the eleven parameters pre-transformation

3.2.4 Investment funds data The holdings of 22 funds were given by SEB, based on their holdings on 2019- 03-12. For confidential purposes, the names of the funds are undisclosed in this thesis. Moreover, we attained changes in the funds’ returns through Bloomberg. Twelve of the 22 funds are defined as sustainable funds. Moreover, five of the funds are long/short funds.

The time span of each fund’s daily NAVs ranged from 2018-03-01 to 2019-03-01, and this was used to calculate the daily returns as well as the covariance matrix of the funds. The Q score of each fund is calculated according to chapter 3.3.6.

3.3 IMT development The Q score in the IMT is developed on two di↵erent levels. Firstly, a Q score for each individual stock will be calculated. Followingly, we will calculate a fund’s Q score by multiplying the weight of each position by the equity’s Q score. If data were missing for any particular equity, the available data was used.

3.3.1 Data transformation The data containing eleven sustainability parameters was in most cases in need of transformation. A common characteristic of many of the parameters is skew- ness. High levels of skewness in data make it hard to find any clear patterns and is dicult to interpret. In order to handle this issue and make the data more symmetric and applicable, transformations are used. For example, looking at figure 3, the data is extremely skewed for the parameter labour conditions. The parameter is calculated by dividing the company’s CEO’s salary by the average salary in the company. The data seems to be exponentially distributed and contains outliers. In other words, the data requires transformation. In these types of cases, we examine the behaviour of the data and apply an appropriate

21 transformation. The data for the parameter labour conditions was exponen- tially distributed, and therefore a logarithmic transformation was performed. This resulted in a slightly more normally distributed data set, which is the tar- get distribution for the parameters. The goal is not to achieve a perfect normal distribution with the data set. Companies are truly very di↵erent in their sus- tainability work, and reality is far from such a distribution. Rather, the aim of the transformations is to reduce the distance between outliers and the mean. The remaining ten parameters’ charts have been omitted to the appendix.

Figure 3: Pre-transformation Figure 4: Post-transformation

Labour conditions

3.3.2 ESG parameters An assumption made in this master thesis is that the data of the six ESG parameters had to be normalized, and ranged from -10 to 10. -10 implies that the company is the worst in comparison to all the other listed companies in the world, and vice versa. The normalization is done through the following formula, by using the function normalize in R. 20 (x min(x)) p = ⇤ i 10 (14) i max(x) min(x) 3.3.3 Equity ESG score The calculation of the equity Q score is divided in two parts. Firstly, we sum the six ESG parameters for each stock.

6 QESG = pi (15) i=1 X The sum is then adjusted with regards to which industry the equity is labelled as. This is similar to how Morningstar’s methodology, seen in equation 9. However,

22 the di↵erence is that the mean in our case is 0, and not Morningstar’s 50. This is done in order to simplify the calculations of going short an equity, and its contribution to the fund Q score. Moreover, it is also more intuitive for investors that a negative Q score implies a sub-par company in sustainability terms.

QESG µpeer ZESG = (16) peer

The ESG score ZESG is finally normalized between -30 to 30, using a similar pro- cedure to equation 14. This range allows each parameter to have a contribution of -5 to 5.

3.3.4 Impact parameters The remaining five parameters are the so-called impact parameters. We do not include the impact scores to equation 15 as the impact scores are sector depen- dent, and for that reason cannot be adjusted with regards to their peer group. If an equity operates in a positive-impact sector, it should be rewarded regardless of its peers. This is similarly done to Morningstar’s methodology of subtracting the controversy score from each equity.

The impact parameters are also transformed if necessary, and then normal- ized. Positive impact parameters are normalized between 0 to 5, while negative impact parameters are normalized from -5 to 0, using a similar approach to equation 14. These ranges were calculated by examining the contribution of each ESG parameter to the ESG score.

3.3.5 Equity Q score Given that the parameters are normalized and the ESG parameters sector ad- justed, the next step is to add the impact parameters to the ESG score for each equity. 11 Q = ZESG + pi (17) i=7 X 3.3.6 Fund Q score Using the weight of a stock in the fund, we multiply it with its Q score giving us each stocks contribution to the fund. We divide this with the sum of all equity weights in the fund, as some funds had cash reserves, derivatives or other non-equity holdings. n w Qe Qf = i=1 i ⇤ i (18) n w P i=1 i f e where Q is the Q score of each fund. QPi is the Q score of the i:th equity in the fund. n is the total number of stocks in the fund portfolio. wi is the weight for

23 the i:th equity in the fund. Note that weights can be negative in certain invest- ment funds, implying a short position in the equity. In such cases, the negative weight multiplied with a positive Q score will result in a negative contribution to the fund’s Q score. This allows the model to incorporate the dynamics of shorting an equity.

Note that the holdings of each fund can in reality vary during a year. How- ever, in this thesis we assume that the holdings are constant during the selected time span.

3.3.7 Fund ratings A system based on the 49 335 equities in the data set is created in order to rate the funds. The equity Q scores are then divided into di↵erent quantiles, giving us the ranges for the five possible ratings. This is similarly done to Morningstar’s methodology, however, they construct their system based on all global funds, rather than equities. Since we have a smaller set of funds, such a system would not be satisfactory. Therefore, the system is based on equity scores.

Distribution Q Score range Score (1-5) Descriptive rank 90% - 100% 7.44 to 32.44 5 Excellent 67.5% - 90% 0.52 to 7.44 4 Above Average 32.5% - 67.5% -6.88 to 0.52 3 Average 10% - 32.5% -12.02 to -6.88 2 Below Average 0% - 10% -31.55 to 12.02 1 Worst

Table 2: Fund sustainability ratings

The funds’ Q scores are then matched to the Q score distribution of the equities and scored in a range between one to five. In addition to the diculty of using the same approach as Morningstar’s fund rating system; It is plausible to use equities as a benchmark when rating the funds due to a few reasons. Primarily, funds consist mainly of equities. Secondly, fund managers actively decide which equities to include in the funds. A fund might be very competitive in its sustainability score in relation to other funds with the same investment strategy. However, it might be rated ”Average” which means that the equities chosen are mainly ”Average”, therefore this would also be its fair rating.

3.4 Optimization problem In section 2, we assessed various multi-objective optimization problems. In our own methodology, we intend to solve the multiple optimization problem below and conduct analysis on the solution. We use the software R to solve them, using package quadprog. If data was missing for any particular equity, the available data was used.

24 3.4.1 Calculating returns and covariance from historical data The financial data of each fund ranged from 2018-03-01 to 2019-03-01. Berk and DeMarzo (2014) recommend using at least two years of weekly data, however, it should be mentioned that there is no correct way of choosing historical data. Historical data can also be a misrepresentation of funds, due to changed asset allocation. In this thesis, we have used one year of daily data to calculate our returns and covariance. Investment funds alter their asset allocation more often than once a year, and as the Q score of each fund would change per altercation, we seek to have a shorter time span without jeopardizing the results. In order to calculate the returns of each fund, the funds’ returns were logarithmized and summarized for n 1days. n 1 r = log(P ) log(P ) (19) i i+1 i i=1 X Risk is the second parameter, and for that reason we are interested in calculating the funds’ covariance matrix. This allows us to know which degree the funds share risk and how their returns correlate. Using historical data, we use the formula below on all combinations i, j of securities. 1 Cov(r ,r )= (r r )(r r ) (20) i j T 1 i,t i j,t j t X The covariance matrix is positive definite, which we check using the R package matrixcalc.

3.4.2 A sustainability-oriented Markowitz model The introduction of a sustainability-oriented parameter adds a third dimension to the optimization problem. The sustainability-oriented parameter corresponds to the developed Q score. An assumption is that an investor would prefer to invest in a more sustainable portfolio than a less sustainable portfolio, given equal risk and return. The relationships we intend to analyze with the help of our optimization problem are: Return, risk and sustainability • Risk and sustainability • For this reason, the optimization problem is formulated as follows: 1 minimize wT ⌃w 2 subject to µT w µ min wT Q Q (21) min wT 1 =1 w 0 i

25 Q is the vector containing the Q score of each asset. Firstly, the aim is to minimize the variance of the portfolio. The first and second constraints set the minimum expected return and Q score respectively. The third constraint ensures that the weights of the portfolio add up to the entire portfolio. (Do- brovolskien´eand Tamoˇsi¯unien˙e2016) Note that the last constraint is only used when performing the optimization on fund-level. On equity level, the constraint is removed. The last constraint is an addition to previous research. It is impossi- ble to short a fund, and therefore only positive weights are allowed on fund-level.

Two di↵erent data sets were used for this optimization problem. Primarily, an optimization was performed based on the 30 equities in the main Swedish index OMXS30. The results of this optimization problem are presented in sec- tion Results. Secondly, the investment fund data was used, and the results can be seen in the appendix.

26 4 Results

In this section, the reader will first be guided through the 11 Q score factors. Subsequently, the calculation of Q score on both equity and fund level will be motivated. Following, the scores will be presented on a country, industry and sector level. Lastly, the results of the sustainability-oriented Markowitz model will be presented.

4.1 ESG factors Developing a refined method of measuring sustainability is near-impossible, es- pecially since most companies do not report sustainability data. The Impact Metric Tool will use eleven parameters which can aid us in triangulating the sustainability factor of an equity. These factors can be divided into two parts. The first part, consisting of the first six factors, are related to environmental, social and governance factors.

The measurements for these eleven parameters are not entirely precise, as it is dicult to create a universal measurement for a parameter such as Job Cre- ation. The aim is that the eleven measurements together can create a holistic picture of a company’s contribution to ESG and impact.

4.1.1 Carbon intensity Carbon intensity or carbon footprint from a company perspective is a term used to define the firm’s impact on global warming in relation to its size. In this re- port, the impact is divided by company revenue rather than the total assets due to the fact that it more eciently represents a company’s size.

World Resource Institute (2018) defined carbon emission in three di↵erent classes.

1. Direct emissions from owned or controlled sources. 2. Indirect emissions from generation of purchased energy.

3. All indirect emissions that occur in the value chain of the reporting com- pany, where both upstream and downstream emissions are included.

Due to the diculty in estimating the third class, only the first and second clas- sification will be apprehended in this report where the measure carbon intensity is defined as: Tons CO emitted Carbon intensity = 2 mUSD revenue After normalizing the Q scores for each company, the histogram for carbon intensity is visualized below. The carbon intensity score is shifted to the left,

27 indicating that a majority of companies have poor carbon intensity scores. The red line in the figure implies the mean value of the parameter.

Figure 5: Histogram of equities’ carbon intensity scores

4.1.2 Waste intensity Waste generated in this case is defined as both dangerous and non-dangerous waste. In some sectors, however only sector specific waste is available such as coal and nuclear waste. Company reporting with respect to waste reporting is currently limited, which makes data on waste less available. Tons waste generated Waste intensity = mUSD revenue After normalizing the Q scores for each company, the histogram for waste in- tensity is visualized below. The red line in the figure implies the mean value of the parameter.

Figure 6: Histogram of equities’ waste intensity scores

4.1.3 Water stress intensity The data for this parameter was received from Aquastat (Food Agriculture Organization of the United Nations 2016). Water stress intensity is a measure

28 that defines the amount of water a company uses in areas where the supply is low (”water stressed”) per unit of revenue. Since water from non-stressed areas does not have a severe environmental impact and maintains the option of being recycled, the measurement di↵erentiates between areas with a high and low supply of water. Further, even if it would be more ecient to di↵erentiate between di↵erent sources or types water; this will not be implemented in this thesis due to the limitation of data.

The input data for the measure is the amount of water in litres a company consumes in relation to revenue, and the location of where the water is coming from. The reason for approximating water withdrawal by a geographical break- down of revenue is because a company rarely reports geographical breakdowns of costs. We deem it plausible to multiply the proportion of a firm’s revenue in each country by one or zero (if experiencing water stress or not, respectively). w s r Water stress intensity = n ⇤ i ⇤ i i=1 Total revenue P w is the water withdrawn by the company. si is an indicator that decides if a country i’s water supply is in distress.

0, if qi < 30% si = 1, if q 30% ( i Where Total water withdrawal q = i Total renewable water resources The histogram below shows that a majority of the companies have good water stress intensity scores. This is reasonable due to that most companies do not have operations in water distressed regions, and even fewer use a significant amount of water in those regions. The red line in the figure implies the mean value of the parameter.

Figure 7: Histogram of equities’ water intensity scores

29 4.1.4 Diversity The diversity measure signifies the gender equality within a firm. Gender equal- ity has grown in importance during the last decade, and has become highly prioritized in most firms (Krivkovich et al. 2018). The measure calculates the deviation from an equal gender distribution and constitutes of firm data re- ported on either executive management, all level of management or board of directors. Further, gender equality is a very discussed subject in the SDGs (United Nations 2015).

In order to calculate the gender equality measure, the following model is used:

Gender inequality = w 50 | | where w is defined as, # women w = 100* #women+#men By utilizing this measure, it can be observed how much a company deviates from equality. The measure does not take into consideration the direction of whether it deviates more towards women or men in a company, it is equally significant. Although in most realistic situations, inequality is present due to a higher number of men, especially at the higher ranks. (Women on Boards 2015)

After normalizing the Q scores for each company, the histogram for gender equality is visualized below. Evidently, a clear majority of companies have an extreme presence of males in executive positions. The red line in the figure implies the mean value of the parameter.

Figure 8: Histogram of equities’ gender equality scores

30 4.1.5 Labour conditions During the last decade, society and media have debated about the implausible salaries that CEOs earn even in situations when a company is underperforming. It has been labelled as unsustainable. This measure shows how many times more the CEO of a firm earns in relation to the average salary paid out to employees. The labour conditions measure is calculated as: CEO salary Labour conditions = Average salary After normalizing the Q scores for each company, the histogram for labour conditions is visualized below. The figure shows that the labour conditions score is approximately normally distributed with a mean close to zero. The red line in the figure implies the mean value of the parameter.

Figure 9: Histogram of equities’ labour conditions scores

4.1.6 Corporate governance Corporate governance is a measurement created to calculate board indepen- dence.

Di↵erent useful aspects in determining board independence from its members are:

1. Employment by the company 2. Employment by representation of majority shareholders 3. Time of service on board 4. Cross-board relationships; 5. Family ties to the corporation 6. Compensation arrangements.

31 In this report only the number of independent board members was used which was extracted from financial statements. Due to data limitations and problems when estimating other factors, this was the singular perspective used. It was calculates as the following: # of independent board members Corporate governance = # of board members There exists a link between corporate governance and the SDGs related to strong institutions (United Nations 2015). Even if this mainly is related to governmen- tal organizations, many companies maintain resembling systematic importance which could allow them to be a part of the definition.

After normalizing the Q scores for each company, the histogram for corporate governance is visualized below. It is worth mentioning that, on average, 50% of a company’s board members are independent. The red line in the figure implies the mean value of the parameter.

Figure 10: Histogram of equities’ corporate governance scores

4.2 Impact factors The other five measurements are impact factors. The notion ”impact” is defined as whether a company aids in the overcoming of global challenges. These global challenges are in accordance with the Sustainable Development Goals set by the United Nations. (United Nations 2015) In total, these eleven parameters intend to depict the sustainability profile of each company, both through ESG and impact aspects. The SDGs can be found in Appendix, in table 9.

4.2.1 Job creation One essential parameter of the impact factors is job creation. Jobs create eco- nomic growth and better living standards. In order to measure the impact of job creation in developing countries from a company perspective, revenue is broken

32 down into di↵erent geographical locations and it is further assumed that those locations correspond to where the labour has been issued. It would most likely be more ecient with geographical data on costs, factories and employees but these are rarely reported, making the revenue approach the best option.

There is no systematic approach when companies report on the geographical distribution of their revenues. Some report on a regional level and other on a country level. When looking at a region the best way is to use the revenues for the countries in that region and then weight them through each countries GDP.

Further, when classifying if a country is a developing country or not; the human development index is used (United Nations 2016). Countries labelled as both ”Low Human Development” or ”Medium Human Development” are classified as ”developing” when calculating the job creation impact factor. The measure- ment for job creation is calculated as the proportion of a company’s revenue earned in developing countries divided by its total revenue. Revenue in developing countries Job creation = (22) Total revenue

After normalizing the Q scores for each company, the histogram for job cre- ation is visualized below. The figure shows that most listed companies have revenue sources from developed countries. The red line in the figure implies the mean value of the parameter.

Figure 11: Histogram of equities’ job creation scores

4.2.2 Other impact indicators Four distinct business model indicators were developed by SEB in collaboration with the hedge fund Auriel. These can be positive, neutral or negative and are developed by mapping the lowest level of subsector classified by NAICS to UN’s SDGs (North American Industry Classification System, 2018).

33 The four business model indicators are:

Positive environmental impact, which is calculated as: Revenue in environmentally aligned industries (23) Total revenue Negative environmental impact, which is calculated as: Revenue in environmentally misaligned industries (24) Total revenue Positive social impact, which is calculated as: Revenue in socially aligned industries (25) Total revenue Negative environmental impact, which is calculated as: Revenue in socially misaligned industries (26) Total revenue It can be problematic to categorize if products or services are aligned or mis- aligned with the SDGs. One example is if a company uses windmills to generate green electricity which is positive. However, this company cuts down trees and distorts the existing ecosystem, which in turn is a negative impact. When there are conflicting circumstances like this; the indicators are neutral. Due to this reason, only around 10% of the sectors have been given a non-neutral score. Some examples of aligned and misaligned situations that are non-neutral can be seen below.

After normalizing the Q scores for each company, the histogram for the four remaining impact scores are visualized in figures 12, 13, 14 and 15. As ex- pected, all four indicators have a mean close to zero. Only around 10% of sectors are given a non-neutral score, and this is evident in the figures below. The red line in the figure implies the mean value of the parameter.

Figure 12: Pos. social impact Figure 13: Neg. social impact

34 Figure 14: Pos. environ. impact Figure 15: Neg. environ. impact

4.3 IMT Q scores Following the methodology of section 3.3.5, we calculate the individual Q scores of each equity. The mean of the Q scores is -2.85, with a standard deviation of 7.62. The ”best” equity received a Q score of 32.45, while the ”worst” received a score of -31.55. The theoretical maximum and minimum value is 45 and -40 respectively.

Variable Number µMedian Min Max Q Score 49335 -2.85 7.63 -3.63 -31.55 32.45

Table 3: Q score summary

Figure 16: Histogram of equities’ Q scores worldwide

4.3.1 ESG scores In equation 16, we normalize the six ESG parameters for each equity. We later add the impact scores, to calculate the Q scores. The normalized ESG scores are illustrated below, where we see that the mean value is -3.90. This is roughly 1 unit lower compared to the Q score’s mean, indicating that the impact scores have a slight positive e↵ect on the Q score. Furthermore, the data is skewed

35 slightly to the left, which could be a result of the carbon intensity score which is heavily skewed to the left (see figure 5).

Figure 17: Histogram of equities’ ESG scores worldwide

4.4 Country scores The Q scores of each country are calculated by taking the mean value of all equities in the country. It is important to note that some countries have lacking sustainability data on an equity level. In the figure below, the number next to each column represents the number of listed equities with sustainability data in each country.

An examination of the country scores tells us that the countries with the poor- est scores are Qatar, Kuwait and Panama. Naturally, these are heavily oil- dependent countries and assumably a majority of their equities are operating in this field. Countries with positive scores are Bahamas, Cura¸cao, Congo, Finland and Vietnam. However, the top three best ranked countries have a combined four equities. The more interesting conclusion is that there are a lot of so-called developing countries with high Q scores. This is as a result of parameters such as Job creation, which rewards countries with revenue streams in developing countries. Moreover, the country scores provide a plausible model validation of the Q scores.

As the sample size for developing countries is small as well as that the posi- tive and negative Q scores are dominated by sector-dependent and developing countries, it would be interesting to examine how OECD countries rank. In fig- ure 31 found in the appendix, the scores for each OECD country are visualized.

36 Figure 18: Ranking of country Q scores. The numbers next to columns repre- sents the number of equities in the country.

4.5 Industry scores Communication services and health care can be regarded through an ESG stand- point as ”clean” sectors. The energy, utilities and consumer staples sectors are among the worst sectors.

Industry Number of equities Average Q score Communication Services 2333 -0.927 Health Care 3930 -1.198 Information Technology 5719 -2.507 Materials 6433 -2.874 Financials 5975 -2.910 Consumer Discretionary 6714 -2.953 Industrials 8463 -2.994 Real Estate 3083 -3.183 Consumer Staples 3267 -3.397 Utilities 1207 -3.502 Energy 2211 -6.014

Table 4: Q score per industry

37 4.6 Sector scores As the aforementioned industries are quite broad, it was decided to break them down even further, into sectors. Water utilities was the most sustainable sec- tor, which contains water purification or recycling companies, with high impact scores. Multiline retails such as Sachs Fifth Avenue score well. The reason behind it is that these multiline retails do not produce anything themselves but rather sell retail from other brands. They are for that reason not associated with the negative carbon footprint, which companies such as H&M and Inditex are (see sector ”textiles, apparel & luxury goods” which scores poorly). The bottom of the table consists of sectors in industries such as Energy or Industrials.

Figure 19: Sector Q scores

38 4.7 Fund analysis In figure 29, the Q scores of each fund are visualized. Accompanying the Q scores are the 11 normalized parameters for each fund. Four of the five best funds with respect to Q score are sustainable funds, while none of the top five worst funds with respect to Q score are sustainable funds. It is also of interest to note that two hedge funds are among the worst with respect to Q score. This is similar to the result in subsection 4.4, where Sweden outperformed most OECD countries.

Moving on, table 5 shows the percentage of equities in each fund that have a Q score higher than the universal equity median. The result shows that Swedish funds have a high percentage of equities above the median, despite being cat- egorized as global funds. The few international asset management companies’ funds have somewhat lower percentages.

Fund Equities above median Fund 1 81.12% Fund 2 80.93% Fund 3 58.18% Fund 22 92.00% Fund 4 88.24% Fund 19 91.11% Fund 5 93.00% Fund 6 83.50% Fund 20 76.47% Fund 7 71.43% MSCI ACWI 63.67% Fund 8 76.32% OMXS30 100.00% Fund 9 72.09% Fund 10 74.41% Fund 11 72.31% Fund 12 84.25% Fund 21 93.75% Fund 13 83.56% Fund 14 80.54% Fund 15 81.25% Fund 16 88.24% Fund 17 93.48% Fund 18 89.36%

Table 5: Percentage of equities above universal median Q score per fund

39 4.7.1 Di↵erence between long-only and long-short funds Five of the funds in the data set were long-short funds, allowing for another dynamic in the equity selection process. Upon examining the Q score of solely the short positions in these funds, only hedge fund 2 had a higher Q score in its short positions than long positions. This is in no terms a convincing conclusion, however, the five funds go short on certain equities as they believe that they will underperform the market, rather than for sustainability reasons.

4.7.2 Non-sustainable versus sustainable funds Of the twenty funds analyzed, twelve of these had a sustainability profile and eight were regarded as normal or non-sustainable funds. The mean Q-score for the sustainable funds is 7.34, whilst the normal funds have an average score of 5.35. Figure 4 illustrates that the top five most sustainable industries are communication services, health care, information technology, materials and fi- nancials. The sustainable funds have higher equity allocations in four of the industries and significantly higher in both health care and information tech- nology. In the bottom six industries (consumer discretionary, industrials, real estate, consumer staples, utilities and energy), the normal funds have higher equity allocations in five of those. Even though both sorts of funds have low levels of allocation to energy, the normal funds’ allocation is three times higher.

Industry Non-sustainable Sustainable Communication Services 11.83% 8.05% Consumer Discretionary 17.41% 11.22% Consumer Staples 8.46% 5.97% Energy 3.36% 1.13% Financials 13.13% 15.54% Health Care 6.63% 11.16% Industrials 12.13% 15.02% Information Technology 16.07% 21.26% Materials 5.78% 6.03% Real Estate 2.59% 2.08% Utilities 2.61% 2.53%

4.7.3 Positive versus negative screening Five of the twelve sustainable funds are considered to use negative screening in their investment process. The asset managers had guidelines set by the asset owners that were to exclude certain ”unethical” industries. The remaining sus- tainable funds use positive screening. Table 6 highlights that sustainable funds with negative screening allocate more of their funds in industries such as Fi- nancials and Information Technology. Meanwhile, funds with positive screening have a slightly more diversified equity allocation.

40 Industry Negative screening Positive screening Communication Services 6.43% 9.27% Consumer Discretionary 10.83% 11.52% Consumer Staples 5.61% 6.24% Energy 1.49% 0.86% Financials 17.93% 13.74% Health Care 9.33% 12.54% Industrials 14.64% 15.31% Information Technology 25.89% 17.77% Materials 5.62% 6.34% Real Estate 1.18% 2.77% Utilities 1.05% 3.64%

Table 6: Funds’ industry breakdown according to neg. vs pos. screening

4.8 Optimization solution This subsection will present the solution of the sustainability-oriented Markowitz model presented in 3.4.2. As the proposed problem was three-dimensional, the relationships between risk, return and sustainability will also be presented. A presentation of the equity data is presented in table 11 in the appendix. It is important to note that the standard deviations of each equity were not solely used. As mentioned in the Methodology section, the utility function aims to minimize risk utilizing the covariance matrix.

The results of the optimization model are illustrated in figure 20 and 21. The three-dimensional plot shows the ecient surface for the parameters risk, re- turn and sustainability. The ecient surface shows that to maximize return, an investor would have to attain higher risk and lower sustainability in the portfolio. This can be seen as the surface bends for higher Q score and risk, indicating lower returns. The ecient surface can be dicult to interpret, and for that reason, the three following graphs are included. In figure 21(b), we see the traditional mean-variance ecient frontier. Whereas in figure 21(c), we see a similar relationship between the Q score and the standard deviation. This indicates that Q score has a similar relationship with return, in the sense that a higher Q score leads to higher risk. As the objective function in the optimiza- tion problem is to minimize risk, figure 21(a) shows no relationship. However, it does indicate that there are various ways to achieve high returns with high sustainability scores.

41 Figure 20: E[Rp]vsStDev(Rp)vsQ

(a) Q vs E[Rp] (b) E[Rp]vsStDev(Rp) (c) Q vs StDev(Rp)

Figure 21: Ecient frontier from three angles

42 The charts illustrate the relationships between the parameters. In the optimiza- tion model, the objective function is to minimize risk subject to return and Q score constraints set by the investor. A brute force approach was used where all di↵erent combinations of constraints on Q score and expected return were tried in order to find the most optimal risk-adjusted portfolios for every combination and is illustrated in figure 20. When examining the relationship between return and risk in figure 21(b), in addition to the fact that all di↵erent combinations of return and Q score were tried in the model, a conclusion can be formed. The coecient of the slope illustrates that per increased basis point in volatility, the return increases by 4.15 basis points, regardless of the Q score constraint. In figure 21(c), a similar result appears. The Q score increases on average 2.18 units per basis point of volatility. Moving on, figure 21(a) does not really show any significant relationship between return and Q score. Due to the nature of the optimization problem where both Q score and return are in the constraints the relationship observed in 21(a) defines the feasible region for the constraints that leads to feasible solutions in the objective function. The feasible region barely changes with movements on either axis, indicating on a very weak rela- tionship. Further, looking at the same figure 33(a) from the results when using the funds data, a non-linear relationship could be observed. One could argue that this di↵erence is highly a↵ected by the fact that the funds are not allowed to take short positions in the model whilst equities can. However, it would still not be possible to draw any sucient conclusions on the relationship between sustainability and return.

Moving on, for the sake of simplicity, we have composed three di↵erent in- vestor profiles according to di↵erent investor preferences, where these scenarios were illustrated below. Due to the small Q score interval in the investment funds, we chose the OMXS30 data set. The preferences can be seen in table 7. Each hypothetical investor has a fixed preference regarding one of the three parameters, as well as the aspiration to minimize the risk. The third parameter is then redundant, as the investor has already specified two criteria which will determine the fund selection. The third criterion that is redundant is expressed as ”-”.

2 # Description E[Rp] Q 1 High E[Rp] 80% Min - 2 High Q - Min 80% 3MediumE[Rp] 30% Min -

Table 7: Investor profiles and preferences

The results of the investor profiles’ fund selections are illustrated in table 8. The result indicates primarily that sustainable equities performed well during the selected period. Profile 1, which seeks to maximize return, accumulated a return of 21.70% with a Q score of 11.19. Profile 2’s portfolio had a Q score of 14.09, but only achieved a return of 4.40%. In this case, the di↵erence in

43 Q score is relatively small compared to the loss in financial performance. Ex- amining profile 3’s portfolio shows that the portfolio achieved a poorer Q score compared to profile 1.

The result suggests that an investor would have to sacrifice a substantial amount of financial return in order to achieve the most sustainable portfolio in the mar- ket. With that said, there is still room for healthy financial gains whilst still holding a sustainable portfolio.

Profile 1 2 3

E[Rp] 21.70% 4.40% 10.96% 2 18.46% 17.96% 17.08% Q 11.19 14.09 9.77 ABB ALFA ASSA B 1.96% AZN 8.22% 0.23% ATCO A ATCO B ALIV SDB 21.70% 7.14% 9.62% BOL 0.05% 4.98% ELUX B ERIC B 2.55% GETI B HM B 6.88% 0.91% 4.44% HEXA B 4.40% 2.43% INVE B KINV B NDA SE 4.12% 9.50% SAND 5.13% SECU SEB A 2.21% SKA B 5.88% 1.13% 19.17% SKF B SSAB A 5.28% 4.39% SCA B SHB A SWED A 11.72% 21.94% 15.03% SWMA 2.92% 4.20% TEL2 B 19.65% 15.97% 10.73% TELIA 23.57% 23.96% 11.12% VOLV B 6.88% # stocks included 9 12 14

Table 8: Fund selection for investor profiles

44 The results from the optimization problem on fund-level have been omitted to the appendix. The results from the funds are similar to the equities, with one in- teresting exception. Looking at figure 33(a), one can see a line forming between sustainability and return. It depicts a mirrored ecient frontier, indicating a link between the two parameters. Q score decreases given a higher Q score.

Furthermore, note that there were 22 funds listed in section Portfolio Scores. Four funds were excluded due to the fact that they either reported their NAV values on a weekly or monthly basis. Our model, as mentioned before, uses daily returns and for that reason these funds were excluded from our optimiza- tion problem. See table 10 in the appendix for more data regarding the funds. The results of the fund optimization problem can also be found in the appendix.

45 5 Discussion

In this section, the results presented in the previous section will be discussed in relation to theory and previous research. Reliability, validity and if the results can be generalized will be discussed as well.

5.1 IMT model Depending on an investor’s interests and priorities within the field of sustain- ability, di↵erent parameters would likely be more important to them than to others. Further, some parameters would perhaps not even be relevant for an in- vestor. In the program created, it is flexible to choose the desired weight of each of the eleven parameters. This means that some parameters could contribute and be more important to the IMT model, given an investor’s preferences. The function of flexibility was highly essential when creating the program as it was created for SEB and their clients. In this master thesis, all the parameters were equally weighted. It was the most justifiable and general outset.

Once again, the calculation of the eleven parameters may not have been the most suitable but the best ones available. For example, the parameter job cre- ation looks at the amount of revenue that is created in developing countries. What actually would be more relevant is to observe a companies labour costs in the developing countries, as labour costs are more significant to the eciency of the jobs created. However, due to the fact that companies are not required and in some circumstances unwilling to report this; data was limited. From this point of view, it is believed that the formulas used when calculating the eleven parameters were plausible but not perfect. Another issue associated with data limitation is that there are many companies who have not disclosed some pa- rameters in the data set. This obviously a↵ects the result as some firms receive a higher or lower sustainability score than they would have if all parameters were reported. Only the parameters with reported values are included in the calculations whilst the rest are ignored. As the IMT data set consisted of more than 50 000 equities, ignoring the empty parameters for each equity was re- garded as the most sucient option. Perhaps if the data set was much smaller it would have been possible to calculate the missing values manually but this was certainly not the case.

Most of the ESG parameters were somehow transformed. However, the goal was to interfere as little as possible due to the nature of Q score, i.e. being the representation of a company’s sustainability profile. It is possible to achieve a perfect normal distribution, but that would distort the nature of the data set which is not the purpose of the IMT. However, in some cases, it was needed to use transformations. For example, the parameter carbon emission had a few extreme values that were several thousand times greater than the average. By only scaling this parameter without transformation, a hand full of observations

46 would be at the bottom of the scale whilst all the other values would receive the highest score. In order to apprehend issues like this, transformations were used which enhanced the patterns of the data and made it easier to analyze. The data also became more normally distributed which was hoped to be somewhat achieved by the transformation. Further, the impact scores were not trans- formed. The explanation behind that was that only ten per cent of the firms had impact scores so a clear pattern was present. The reason of why only ten per cent of the equities is that most firms maintain positive and negative oper- ations and thereby receive a neutral score.

When analyzing the funds’ Q scores, it was observed that more often than not, sustainable funds ranked higher than non-sustainable funds. However, the di↵erences in Q score were relatively little, which begs the question if the IMT incorporates sustainability well or if funds are just not that di↵erent in terms of sustainability. If it is the former, one could argue that the sector-adjusted ESG scores have an impact. In essence, it allows di↵erent sectors to be di- rectly comparable to a certain extent. The best company in materials receives a higher sector-adjusted ESG score than for instance the worst health care com- pany, which could be regarded as questionable. Despite this, we believe that the sector adjustment is valid for three reasons. Firstly, these sectors are very wide, and within health care lies health-care equipment etc which could be tied with carbon intensive production, which perhaps a pharmaceutical company is tied with. Secondly, one could argue that a health care company with poor gen- der equality and labour conditions should score worse than a mining company that has great scores within these corporate and social parameters. Lastly, the impact scores that are added onto the ESG scores are not sector-adjusted as they are already sector dependent. In turn, they reward companies that have positive impact and deter companies with negative real-world impact.

5.1.1 Model validation In order to investigate the credibility and goodness of the Q score model, the mean Q Score was measured from a country, industry and sector perspective. From a country perspective, the results demonstrated that the countries such as New Zealand and Finland were at the top. This was followed by a few developing countries in Africa and Asia who received strong scores on parameters associ- ated with real-world impact such as job creation and positive social impact. In addition, it was discussed with SEB that perhaps the impact parameters were having too much of an impact on the Q Score. In turn, this yet again strikes up the discussion on how an investor values each di↵erent parameter. Some might value environmental factors over social factors. Lastly, inarguably if a large investor would invest large amount of funds in a Ugandan company, this would inevitably boost the Ugandan economy. For this reason, it is fully plausible that these countries receive such high scores.

When analyzing countries with worst Q scores, oil-rich countries in the Mid-

47 dle East can be observed and this is something that was essential for the model to demonstrate in order to be considered credible. It is generally known that most Middle Eastern listed companies operating, are to a great extent involved in the oil industry, which in most cases a↵ect the environment very negatively. In addition, Venezuela scores poorly due to its oil dependency.

It is further essential to mention that some parts of the results are suggested to be inconsistent with reality. For example, Mexico and Brazil score higher than Japan and Germany which from a general point of view could be questionable. There are many possible explanations behind this. Primarily, the number of listed companies in Japan and Germany are several times higher than in Mex- ico and Brazil. Secondarily, one could argue that the reporting of the eleven parameters is much more consistent in Germany and Japan. Due to the fact that countries di↵er a lot in their reporting and our model only considers the parameters that are reported for each equity, non-logical occurrences like the one above can appear. This is also assumed to a↵ect the industry and sector ranking in a similar way. In addition, the use of only eleven parameters might come short when trying to develop an optimal model, as other parameters may be needed to complete the model. In relationship to the eleven parameters, Sustainalytics uses 70 indicators which put things into perspective.

Communication services, health-care and IT were at the top from an indus- try point-of-view. This is in line with reality as these industries do not have any significant relation to pollution as well as have a positive real-world impact. The three worst industries were energy, utilities and consumer staples which was somewhat expected and somewhat discussed from a country perspective.

On sector level, the results were similar to what the industry point of view demonstrated. As there exist more categories on sector-level, there were some unexpected but reasonable results. For example, multi-line retail scored well whilst the retail industry in general scored much lower. The explanation be- hind this is that multi-line retail stores do not produce products themselves but rather sell clothing from other brands. In turn, retail brands that produce their own clothes are unanimously associated with poor labour conditions and high carbon emission. The best sector consisted of water utilities companies, that worked with water purification. These companies scored well on carbon emission, water intensity and positive environmental impact. When analyzing the equities of the categories that stood out, it was concluded that the results were reasonable.

5.2 Sustainability-oriented Markowitz model Our results in section 4.8 showed two clear relationships between risk/sustainability and risk/return. The third relationship that we intended to identify between sustainability and return did not show a clear link. From the optimization re- sults of the OMX data where short positions were allowed no relationship could

48 be concluded and from the funds data did not allow short positions a non-linear relationship was present. However, looking at figure 33(a), there was a large number of portfolios that contain both high returns and Q scores. Despite the rapid decline in Q score when seeking the highest possible returns, one could ar- gue that if an investor was willing to give up some return, he/she could achieve respectable returns. Given this slight sacrifice, asset managers could gain in- tangible success. From an ethical standpoint this would allow investors to work in line with their intrinsic social preferences, which was also proven to be a significant reason why investors allocate funds to sustainable funds (Smeets and Riedl 2017). In addition, the social aspect of sustainable investing was only one side benefit of sacrificing some return for higher sustainability. As mentioned, asset managers are already pressured to meet certain ESG quotas through their fiduciary duty. Sustainable funds could be regarded as marketable to investors, and many funds have already reaped the benefits of this, which was proven by Hartzmark and Sussman (2018).

Regarding the chosen three-parameter optimization problem, there were var- ious of other utility functions that could have been selected. Utz et al. (2015) constructed a utility function aiming to maximize return and sustainability, while minimizing risk. Dobrovolskien´eand Tamoˇsi¯unien˙e(2016) constructed a trade-o↵problem with parameters ↵ and . Both sets of results showed links in line with our results and the ecient market hypothesis. The particular model in this thesis was selected for multiple reasons. After discussions with SEB, sev- eral criteria for the optimization problem were made. The model was deemed to require as little user-input as possible, whilst still analyzing the relationships be- tween the three parameters. For that reason, a trade-o↵problem similar to the one done by Dobrovolskien´eand Tamoˇsi¯unien˙e(2016) was excluded. In general, a trade-o↵problem is dependent on the selection of constant c, seen in equation 8, which impairs comparability. Due to the aim of the thesis and the absence of relentlessness in previous studies, a minimum-variance model was suggested to be the superior method of choice. In addition, SEB sought after a program that could set up optimal portfolios, while also investigating the relationships between the three parameters. The division at SEB who requested this pro- gram, SEB Solutions, develop and launch di↵erent funds with sustainability as an important criterion. The Impact Metric Tool will be used as a complemen- tary tool in order to construct these funds. Using the Markowitz model, this enabled the possibility of extracting the weights of the securities in the optimal portfolios, making it useful in real life situations with investors. Moving on, the selected model allows the user to analyze all three of the parameter relationships at once. Due to the third parameter, an ecient surface is generated rather than an ecient frontier.

The white paper done prior to this master thesis discussed how the knowledge of portfolio sustainability translates to achieving actual impact. It was found that providing market participants with additional information has positive e↵ects on market eciency, leading to two indirect e↵ects of which one is relevant.

49 Excluding companies with negative externalities increases their cost of capital, and thus lowers their valuations. The reverse argument hold as well, as invest- ing in companies with positive externalities lowers their cost of capital, and in turn, increases their valuation. Though, this would have to imply that investors consider sustainability as an important factor. In connection with our results, a weakly negative relationship between sustainability and financial performance was identified on fund-level. In theory, a sustainability-parameter should lower the expected return, which was the case on fund-level. However, there was no strong link between the two parameters, which could indicate that the market values positive sustainability-related externalities.

The reader might note that only 29 equities were used from the OMXS30, which consists of 30 equities. Essity AB was removed from the index. Essity is a spin-o↵of Svenska Cellulosa AB (SCA), and was listed on the Stockholm Stock Exchange 2017-05-17, and provided the problem of missing data. There are various methods of dealing with missing data, for example, to simulate the covariance vector for the vector. However, we chose to exclude the equity for equitable reasons.

Moreover, the IMT database consisting of sustainability data for each equity worldwide is point-in-time. This signifies that the data represents each com- pany’s sustainability profile at the start of 2019. Sustainalytics, who have an inarguably more in-depth scoring system, also take into consideration the per- spective of time. This is to incorporate how a funds’ holdings di↵er from one point-in-time to another. Our thesis does not incorporate this, as we assume that the funds’ holdings have been constant over the entire year. This poten- tial source of error can be mitigated in any future development of the model. Though, one could also argue that an investor invests in the fund today, and not in previous holdings. Therefore, the fund Q score does not necessarily have to include past holdings.

The fund NAVs ranged from 2018-03-01 to 2019-03-01. Berk and DeMarzo (2014) recommended to either use two years of weekly data or five years of monthly data. Though, they also mentioned that there is no universally correct way of choosing time span. As the funds are both long-term and short-term funds, it was assumed that they change their allocations on a regular basis. As the funds’ holdings would vary from year to year, a time span for the historical NAVs could not be longer than a year. A solution to this could have been to access the holdings of each fund last year, and calculate a time-adjusted average Q score. This was unfortunately not possible to attain.

5.3 General discussion Utz et al. (2014) found in their study that there was no significant di↵erence in asset allocation in socially responsible and conventional mutual funds, after the screening process. They were unable to identify any evidence of social respon-

50 sibility taken into account in the asset allocation stage. Our results, despite being inconclusive, showed slight di↵erences in asset allocation. Sustainable funds were more inclined to have larger allocations in IT and health care. With regards to positive versus negative screening, our results showed little di↵er- ences. The main di↵erence was that funds with a negative screening process allocated more to IT and financials. However, the sample size is too small to render any conclusive result.

The funds were randomly selected and contained both sustainable funds as well as non-sustainable funds. The funds held assets globally, however, a majority of the fund managers were in fact Swedish. Despite the fact that the funds invested in equities worldwide, one might argue that a Swedish fund manager and a US-based fund manager would have di↵erent biases. We noted that the global funds with Swedish managers contained a high percentage of Swedish equities. The location bias makes some of the global funds perhaps less com- parable. However, in the grand scheme of things, this should only have a slight impact.

In order to investigate the relationship between sustainability, risk and return we both used the equities from OMXS30 and mostly Swedish-based funds. This makes the results somewhat country biased. It is important to acknowledge that Sweden as a country is at the forefront of sustainability. By examining the main Swedish index, OMXS30, we see that its equities receive a Q score well above the world median. This perhaps a↵ects how the results can be disregarded as entirely general. If the same study would have been conducted based on another country’s equities, for instance, Qatar (who scored poorly), the results would most likely be di↵erent. In relation to solely using Swedish securities, which demonstrated that investors did not have to give up any major amounts of in- verted risk and return to achieve a decent level of sustainability, a study only based on, for example, Qatari securities would with a high probability manifest that in order to achieve high levels of sustainability a significant amount of re- turn and inverted risk would need to be relinquished. Therefore the conclusion is that although the relationships between risk, return and sustainability would be the same universally, the levels of change in risk and return per unit of Q score would be country specific.

During the pre-study, we intended to perform our analysis on sustainability funds that have the ability to short equities as well. Unfortunately, the hold- ings from such funds proved to be dicult to obtain. However, with both the IMT tool being developed and other companies such as Sustainalytics having sustainability data, this unexplored avenue would be achievable for future re- search. There remains a lot of di↵erent uncharted areas within sustainable investing and behavioural finance. Having mentioned this, there was little dif- ference in results compared to previous research, indicating that the long/short funds in the data set do not have a significant influence.

51 An alternative for companies to mitigate their carbon footprint is to carbon o↵set. None of our eleven parameters takes carbon o↵setting into considera- tion, as it is not directly related to a company’s business. However, it would be of interest to analyze how companies work with carbon o↵setting, and if there is any di↵erence in sustainability ratings if it was taken into consideration.

Looking through a more holistic perspective, the cohesion between sustainabil- ity and capital allocation is highly dependent on short-termism. Whilst clean energy is underfunded and in need of investments over the next 25 years to achieve the goals of the Paris climate treaty; fossil fuel reserves are seemingly abundant and overfunded. To achieve the goals of the treaty, one article ar- gues that a majority of today’s fossil fuel reserves should remain untouched. (McGlade and Ekins 2015)

This begs the question of whether the market has eciently priced in climate mitigation. Research has shown that full extraction of the worlds’ fossil fuels is inconsistent with today’s climate mitigation goals. In turn, this might be the cause of the so-called ”carbon bubble”, where companies that depend on fossil fuel reserves become stranded assets. (McGlade and Ekins 2015) The car- bon bubble is essentially based on the fact that fossil-fuel companies’ forecasted growth is based on the continuation of fossil-fuel extraction. The evidence of this potential bubble is overwhelming. It was explained that to keep the valu- ations at a stable level, big oil companies are in need to continuously explore new reserves. If this was to be stopped, it would demonstrate to investors that their current reserves are not as highly valued as they thought. This pressure from the capital markets contradicts the goals of the climate treaty. (Martin and Kemper 2015)

The dilemma between the capital markets and climate work is dicult to solve. Fossil fuel dependent companies’ financial performances exhibit that reallocat- ing capital away may improve risk-adjusted return. This could be the beginning of the end, as these assets could be mispriced and stranded in the future. A carbon bubble could perhaps exist, as behavioural heuristics and biases that epitomize a bubble prevail. Investors solely focus on the net asset value of each oil rig, rather than looking at the risks and consequences of each rig. If the carbon bubble is real, investing in it would be both unsustainable for the envi- ronmental and financial capital.

Simultaneously, there exists a lot of green energy opportunities for many oil companies. However, these projects are long term and will not pay o↵in the short term. This raises the issue with the major presence of short-termism in the markets, as companies have less incentive to invest in clean energy due to the longer payback period. It prevents sustainability to become a greater part of investors’ capital allocation even though it is highly beneficial for the future. (Martin and Kemper 2015)

52 6 Conclusion

This final section will begin by answering the research questions, by concluding the analysis and discussion of the results. Implications for the field of research, as well as SEB will then be discussed. Following, suggestions for further research in the field will be mentioned.

6.1 Answering the research questions Can a framework quantify the sustainability of funds?

Sustainability is a broad term with various di↵erent aspects. ESG is considered synonymous with the term, as it considers three vital aspects of sustainability. However, these three aspects do not cover the entire scope of sustainability, as in many ways it does not include the impact a company has in the real world. The developed model quantifies the sustainability of each listed equity worldwide, to then quantify the sustainability of a fund. This culminates in the so-called Q score.

The Q score is a triangulated representation of a company’s attribution to sus- tainability, however, it is not perfect. Since companies are not required to report the necessary data, there is a slight source of error. However, given the situa- tion, the alternatives chosen in the model are plausible and the models’ results are more than valid. With that said, the results from the model can be regarded as a precursor within the field, and when the reporting from companies improve, so will the model.

How will the implementation of a diverse sustainability measure impact portfolios in relation to risk and return, and how may it a↵ect investors in their decision making?

There are various components to the choices an investor makes. Preferences, biases and moods are among a few of them. For example, one could argue that depending on the location and upbringing of an investor, he or she would invest di↵erently. As the results from our tri-criterion model shows, an investor would have to be willing to sacrifice some return in order to achieve higher sustainabil- ity. Nevertheless, the results on fund-level showed that an investor would not have to sacrifice tremendous amounts in order to attain a more sustainable port- folio. The sacrifice, as mentioned, does bring about intangible value in the form of marketability, as well as investing in line with intrinsic social preferences. As proven by Hartzmark and Sussman (2018), the introduction of a sustainability measure had a great impact on sustainable funds. There was a $24 billion net inflow to sustainable funds worldwide after Morningstar’s development of ESG fund ratings. This indicates that a sustainability measure does a↵ect investors’ decision making. As mentioned, research has suggested that weather calamities drive investors to trade into sustainable labelled mutual funds (Anderson and

53 Robinson 2019). This, in combination with raised awareness of sustainability measures such as the Q score, could perhaps drive a further influx of invest- ments into sustainable funds. However, it is a truly undesirable situation in which investors have to be driven through behavioural biases caused by catas- trophic climate events to invest in a more sustainable world.

In summary, the results of this master thesis suggest the following: 1. Using 11 parameters that cover environmental, social, governance and im- pact aspects of equities worldwide, a sustainability measure was developed. The measurement showed valid results, suggesting that a framework can quantify sustainability. However, there remains a lot of progress to be done with respect to data collection. 2. The relationship between sustainability and risk follows the hypothesis of ecient markets. An ecient frontier between the two parameters presented itself both on a fund and equity level. This is in line with previous research in the field. 3. The implementation of a sustainability measure showed that, on average, an investor would gain 2.18 units of Q score when increasing volatility by one basis point. An investor would also gain 4.15% in return per unit increase of volatility. This was calculated using a sustainability-oriented Markowitz model. 4. Investing in a sustainable manner is as important as ever. In order to meet the goals of the Paris climate treaty, a further re-allocation of cap- ital into sustainable investments is needed. Research has shown that a Morningstar’s sustainability measure of mutual funds had a massive im- pact, and more work in this field would aid further influx to sustainable funds.

6.2 Implications Previous research had concluded through similar optimization problems that risk and sustainability have a relationship similar to the mean-variance model. The relationship between return and sustainability proved to be more dicult to trace, but on fund-level, there was a hint of a mirrored ecient frontier. This implies that the funds’ sustainability scores are priced ineciently by the mar- kets, and in turn the equities that they hold.

A more tangible implication is the Impact Metric Tool’s value for financial institutions. The aim of this master thesis was to increase the transparency of a fund’s sustainability. The programme is built so that a fund’s scores on each of the 11 parameters are visible, allowing any investor to be able to identify which fund suits his/her preferences. This can be seen in figure 29. Moreover, the developed tool also contains a Q score for every equity listed in the world, which could be useful in other avenues.

54 6.3 Further research The IMT acts as a precursor within the measurement of sustainability, due to the limited data. In turn, the limited data a↵ected the parameters’ calculations. However, with time, the reporting of sustainability data will improve. As such, the model and its calculations can be adjusted accordingly.

Moreover, the IMT can calculate the Q score of any fund, which was one of the important functions. Given this, it was then utilized in order to perform the sustainability-oriented Markowitz model on a fund-level data set. The fund analysis could be further extended by performing a similar optimization on the world’s long/short sustainable funds. Nevertheless, the holdings of such funds are confidential and dicult to obtain.

The results showed that on average, an investor would gain 2.18 units of Q score per basis point of increased volatility. It would perhaps be interesting to conduct a qualitative study with regards to investor preferences, examining how much risk an investor would actually be willing to sacrifice in order to achieve a sustainable portfolio.

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59 60 7 Appendices 7.1 Graphs and tables

Principle Goal Description 1 No poverty Include economic growth to provide sustainable jobs and promote equality 2 Zero hunger Prioritize the food and agriculture sector as these are the main solutions for development, and is central for hunger and poverty prevention 3 Good health & well-being Certify healthy lives and promote for peoples well-being indi↵erent of age 4 Quality education Achieving a quality education is the foundation to improv- ing people’s lives and sustainable development 5 Gender equality A fundamental human right, but also an essential founda- tion for a prosperous and sustainable world 6 Clean water & sanitation Having access to clean water for every individual is essential 7 A↵ordable & clean energy Energy is vital to most major challenges and opportunities 8 Decent work & economic growth Sustainable economic growth need societies to create con- ditions that create the opportunity to obtain quality jobs 9 Industry, innovation & infrastructure Investments in infrastructure are crucial to achieving sus- tainable development 10 Reducesinequalities Toreduceinequalities,policiesshouldbeuniversalinprin- ciple, paying attention to the needs of disadvantaged and marginalized populations 11 Sustainable cities & communities Thereneedstobeafutureinwhichcitiesprovideopportu- nities for all, with access to basic services, energy, housing, transportation and more 12 Responsible production & consumption It is important to not produce and consume in excess for a sustainable future 13 Climate action Cimate change is a great threat against our existence 14 Life below water Careful management of this essential global resource is a key feature of a sustainable future 15 Life on land Sustainably manage forests, combat desertification, halt and reverse land degradation and biodiversity loss 16 Peace, justice & strong institutions Access to justice for all, and building e↵ective, accountable institutions at all levels 17 Partnerships for the goals Revitalize the partnerships for sustainable development 61 Table 9: United Nation’s 17 Sustainable Development Goals Figure 22: Carbon intensity data pre- and post-transformation

Figure 23: Waste intensity data pre- and post-transformation

62 Figure 24: Water stress intensity data pre- and post-transformation

Figure 25: Gender equality data

63 Figure 26: Labour conditions data pre- and post-transformation

Figure 27: Corporate governance data

64 Figure 28: Job creation data pre- and post-transformation

Fund Q score µ (%) 2 (%) Fund 1 7.43 6.23 11.73 Fund 2 7.71 7.38 12.25 Fund 3 5.30 11.53 13.47 Fund 4 7.35 15.03 13.44 Fund 5 8.80 11.07 12.66 Fund 6 6.26 9.85 12.13 Fund 7 7.17 13.79 17.57 Fund 8 8.51 14.67 12.47 Fund 9 5.32 6.58 13.88 Fund 10 6.34 10.61 12.45 Fund 11 5.27 4.37 15.02 Fund 12 7.72 7.13 13.03 Fund 13 6.95 11.51 12.34 Fund 14 7.24 9.51 12.49 Fund 15 6.91 7.31 13.10 Fund 16 7.35 7.20 13.82 Fund 17 8.49 7.16 16.39 Fund 18 7.08 13.90 13.28

Table 10: Fund data 65 Figure 29: Breakdown of funds’ Q scores

66 Figure 30: Breakdown of funds’ short positions’ Q scores

Figure 31: OECD countries’ Q scores

67 µ2 Q score ABB 0.12 21.11 1.30 ALFA 0.21 23.76 0.28 ASSA B 0.49 34.68 -2.54 AZN 0.056 19.80 10.86 ATCO A 0.28 32.39 7.19 ATCO B 0.26 33.64 7.19 ALIV SDB 0.31 22.90 5.20 BOL 0.23 31.74 7.83 ELUX B 0.15 25.94 -3.02 ERIC B 0.39 34.49 7.30 GETI B 0.62 35.88 7.76 HM B 0.19 23.99 3.51 HEXA B 0.67 34.72 16.73 INVE B 0.10 18.79 3.35 KINV B 0.04 20.68 10.96 NDA SE 0.26 20.04 12.11 SAND 0.11 21.68 4.03 SECU 0.20 24.16 13.31 SEB A 0.08 16.88 9.52 SKA B 0.02 17.74 2.96 SKF B 0.22 18.69 5.55 SSAB A 0.41 22.30 10.08 SCA B 0.19 25.23 9.28 SHB A 0.13 29.20 6.52 SWED A 0.06 17.61 12.77 SWMA 0.18 24.95 4.52 TEL 2 B 0.43 25.48 18.36 TELIA 0.12 18.29 17.04 VOLV B 0.08 25.26 16.93

Table 11: OMXS30* data

68 Figure 32: E[Rp]vsStDev(Rp)vsQ

(a) Q vs E[Rp] (b) E[Rp]vsStDev(Rp) (c) Q vs StDev(Rp)

Figure 33: Ecient frontier from three angles (funds)

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