DOES IT PAY to BE ESG? an Empirical Analysis of Sustainability in the Nordic Countries from a Risk and Valuation Perspective

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DOES IT PAY TO BE ESG? An empirical analysis of sustainability in the Nordic countries from a risk and valuation perspective

Corentin Arnou, Marcus Hammarstedt

Department of Business Administration Civilekonomprogrammet Degree Project, 30 Credits, Spring 2021 Supervisor: Lars Lindbergh

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Abstract

In the field of sustainable finance, Environmental-, Social- and Governance-ratings (ESG) have become an acknowledged measurement of a firm's sustainability performance. The increased awareness of sustainability issues in today's society is undeniable. However, based upon contradicting results from previous research, it was uncertain if investors were rewarding a firm’s sustainability efforts in the form of a lower cost of equity. The purpose of this thesis has therefore been to examine the relationship between sustainability, risk and valuation as well as stock-price behavior in times of crisis regarding large firms publicly listed in the Nordic countries. In order to fulfil the purpose, various multiple regression models have been conducted on quarterly data from the period between 2011 to 2020. The approach chosen to examine if ESG has a relation to the cost of equity has been to calculate the implied cost of equity inferred from consensus forecasts of future financial development and stock price at each point in time, also known as the ex-ante cost of equity. Since the independent variable ESG-score was not likely to be the sole variable to affect the independent variables in our multivariate regression models, we have followed previous studies in the choice of control variables. The empirical results of this study showed a significantly negative relationship between a firm’s ESG-score and the cost of equity. In addition, our results showed a significantly positive relationship between a firm’s ESG-score and both the price-to-earnings ratio as well as the price-to-book ratio while no significant relationship between a firm’s ESG- score and the enterprise value to earnings before interest and taxes ratio could be established. Finally, the results of this thesis showed that firms with a greater ESG-score generated excess- returns during the latest market turmoil of 2020 caused by the Covid-19 outbreak.

This thesis challenges the value-destruction view of ESG-efforts since our results indicate that investors are pricing sustainability risk with a negative risk premium in line with the value- creation approach. No causality test has been performed during this study, however several possible mechanisms by which ESG impacts the valuation and crisis resistance have been discussed based upon previous research and the theoretical framework. We argue for the reduced cost of equity to reflect diminished information asymmetry, a larger investor base, improved growth and cash-flow opportunities as well as reduced risk for litigations as a consequence of a more sustainable business conduct.

To the best of our knowledge, no previous study on the topic has been conducted on the Nordic markets. This study fills thus a research gap on the relation between sustainability, risk and equity market valuation and we sincerely hope to have contributed to academia with new approaches.

Keywords ESG, Cost of equity, Sustainable finance, Price-to-earnings ratio, Price-to-book ratio, Enterprise value to earnings before interest and taxes ratio, Covid-19, Shareholder’s theory, Stakeholder’s theory, Information asymmetry.

Acknowledgments

We would like to thank our supervisor Lars Lindberg for his academic guidance and important feedback during the course of this study. We are thankful that Lars Lindberg gave us his insights, which definitely have helped us to develop this thesis.

We furthermore would like to thank our family, close friends, and each other for the support during this tough semester.

Umeå 10th of May 2021,

Corentin Arnou and Marcus Hammarstedt.

Table of contents

1. Introduction ------1 1.1 Problem Background ------1 1.2 Purpose and Research Questions ------4 1.3 Choice of the subject and preconceptions ------5 1.4 Delimitations ------5 1.5 Target audience ------6 2. Theoretical point of reference ------7 2.1 Theoretical Framework ------7 2.1.1 Defining sustainability ------7 2.1.2 ESG Investments - Preferences, risk and return in equilibrium ------8 2.1.3 Shareholder’s theory ------11 2.1.4 Stakeholder’s theory ------11 2.1.5 Legitimacy theory ------12 2.1.6 Information asymmetry ------13 2.1.7 Signaling theory ------14 2.1.8 The cost of equity capital ------14 2.1.8.1 Ex-Post Cost of Equity models ------15 2.1.8.2 Ex-Ante Cost of Equity models ------16 2.1.9 Price-based multiples ------17 2.1.10 Market crashes ------18 2.2 Previous research ------19 2.2.1 The impact of ESG on firm’s operational and financial performances ----- 19 Line of reflections ------20 2.2.2 The impact of ESG on firm’s cost of equity capital ------20 Line of reflections ------22 2.2.3 The impact of ESG on firm’s market valuation, stock return and volatility 22 Line of reflections ------24 2.2.4 General conclusions of the previous research ------26 2.3 Positioning of the thesis within the theoretical framework ------27 2.4 Theoretical hypothesis ------28 3 Scientific methodology ------30 3.1 Ontological assumptions ------30 3.2 Epistemological assumptions ------31 3.3 Research design and methodological choice ------31

3.4 Research approach ------32 3.5 Literature search and evaluation of sources ------33 3.6 Ethical considerations ------33 4 Research Method ------35 4.1 Population and Sample ------35 4.2 Statistical hypothesis ------37 4.2.1 Hypothesis connected to research question one ------37 4.2.2 Hypothesis connected to research question two ------37 4.2.3 Hypothesis connected to research question three ------38 4.3 Regression analysis ------38 4.3.1 OLS - Ordinary Least Squares ------38 4.3.2 GLS - Generalized least square regression ------39 4.4 Regression models ------39 4.4.1 Dependent variable ------40 4.4.2 Independent variable ------42 4.4.3 Control variables ------42 4.4.4 Problem with chosen variables ------45 5 Data ------46 5.1 Descriptive statistics ------46 5.2 Model diagnostics ------48 5.2.1 Linearity of the regression model ------49 5.2.2 Distribution and mean value of the error term ------50 5.2.3 All explanatory variables are uncorrelated with the error term. ------51 5.2.4 Observations of the error term are uncorrelated with each other. ------51 5.2.5 No heteroskedasticity ------52 5.2.6 No perfect multicollinearity ------53 5.3 Critics against the model ------55 5.4 Type 1 & Type 2 errors ------55 6 Empirical results ------57 6.1 Results of hypotheses testing ------57 6.2 ESG score and the cost of equity capital ------57 6.3 ESG score and the P/E ratio ------58 6.4 ESG score and the P/B ratio ------59 6.5 ESG score and the EV/EBIT ratio ------60 6.6 ESG score and the CRISIS-variable ------61

7 Analysis ------62 8 Conclusions and recommendations ------65 8.1 Theoretical and practical contributions ------65 8.2 Societal and ethical implications ------67 8.3 Truth criteria ------67 8.3.1 Validity ------67 8.3.2 Reliability ------68 8.3.3 Generalizability ------69 8.4 Limitations and future research ------69 Reference list ------72 Appendix ------81

List of appendices

Appendix 1 - List of companies within sample (184 units) ...... 81 Appendix 2 - Correlation matrices connected to research question 2 ...... 82 Appendix 3 - Correlation matrix connected to research question 3 ...... 83 Appendix 4 - Scatterplot of Residuals vs. Fitted values of P/Ew ...... 83 Appendix 5 - Scatterplot of Residuals vs. Fitted values of P/Bw ...... 84 Appendix 6 - Scatterplot of Residuals vs. Fitted values of EV/EBITw ...... 84 Appendix 7 - Scatterplot of Residuals vs. Fitted values of CRISIS ...... 85 Appendix 8 - Distribution of the error term regarding estimations of P/E ratio ...... 85 Appendix 9 - Distribution of the error term regarding estimations of P/B ratio ...... 86 Appendix 10 - Distribution of the error term regarding estimations of EV/EBIT ratio ...... 86 Appendix 11 - Distribution of the error term connected to estimations of CRISIS ...... 87 Appendix 12 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of P/E ratio ...... 87 Appendix 13 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of P/B ratio ...... 87 Appendix 14 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of EV/EBIT ratio ...... 88 Appendix 15 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of CRISIS ...... 88 Appendix 16 - Breusch- Godfrey LM test for autocorrelation related to research question one ...... 88 Appendix 17 - Breusch and Pagan Lagrangian multiplier test for random effects ...... 88 Appendix 18 - Cluster-Robust Hausman test for fixed effect or random effect model ...... 89 Appendix 19 - Breusch-Pagan / Cook-Weisberg test for heteroskedasticity related to research question one ...... 89 Appendix 20 - Correlation coefficients between independent variables regarding research question two ...... 89 Appendix 21 - Correlation coefficients between independent variables regarding research question three ...... 89 Appendix 22 - VIF-test regarding research question two ...... 90

List of tables

Table 1 - Overview of the previous research ...... 26 Table 2 - Keywords used in the literature search ...... 33 Table 3 - Un-winsorized descriptive statistics of all variables included in the regression models ...... 46 Table 4 - Correlation matrix between variables connected to the first research question ...... 48 Table 5 - Partial and semi partial correlations of residuals with explanatory variables ...... 51 Table 6 - Correlation matrix between independent variables ...... 54 Table 7 - Variance inflation factor (VIF) test ...... 55 Table 8 - Presentation of results from hypothesis tests ...... 57 Table 9 - GLS Robust Random Effects regression model – ESG and Cost of equity ...... 58 Table 10 - OLS regression model – ESG and P/E ratio ...... 59 Table 11 - OLS regression model – ESG and P/B ratio ...... 59 Table 12 - OLS regression model – ESG and EV/EBIT ratio ...... 60 Table 13 - OLS regression model – ESG and the CRISIS-variable ...... 61

List of figures

Figure 1 - Exemplification of several sustainability issues categorized in E-, S- and G-factors. Source: PRI, n.d.b...... 8 Figure 2 - Market stages of adoption to socially responsible investments. Source: Hofmann et al. (2009, p. 105) ...... 9 Figure 3 - Trade-off between financial return and externalities. Source: Renneboog (2008, p. 1734) .. 9 Figure 4 - Positioning of the thesis within the theoretical framework ...... 27 Figure 5 - Process of the scientific assumptions and approaches...... 30 Figure 6 - The process of deduction (Bryman & Bell, 2011, p. 11) ...... 32 Figure 7 - Nasdaq OMX Nordic Large Cap GI EUR, 2020-01-01 to 2020-12-31 where market top of 2020-02-19 is set at value 100. Source: Nasdaq (n.d.b), calculations are author's own...... 36 Figure 8 - Scatterplot of Residuals vs. Fitted values of CoE ...... 49 Figure 9 - Scatterplot of Residuals vs. Fitted values of CoEw ...... 49 Figure 10 - Distribution of the error term ...... 50 Figure 11 - Residuals from CoEw regression model plotted against the independent variable ESG ... 53

List of equations Equation 1 - Firm value in equilibrium. Source: Cornell, 2021, p. 13...... 10 Equation 2 - Expected return in equilibrium. Source: Cornell, 2021, p. 14...... 10 Equation 3 - Capital Asset Pricing Model ...... 15 Equation 4 - Arbitrage Pricing Theory ...... 15 Equation 5 - Fama & French three-factor model ...... 16 Equation 6 - Ohlson-Jeuttner model ...... 17 Equation 7 - Multivariate regression model connected to the main research question ...... 40 Equation 8 - Multivariate regression models connected to the research question two ...... 40 Equation 9 - Multivariate regression model connected to the research question three ...... 40 Equation 10 - Price to earnings ratio ...... 41 Equation 11 - Enterprise value to earnings before interest and taxes ratio ...... 41 Equation 12 - Price to book value ratio ...... 41 Equation 13 - Calculation of the crisis variable ...... 41

Definitions

Company Market Cap - The Company Market Capitalization represents the sum of market value for all outstanding share types. The market value is calculated by multiplying outstanding shares by the latest close price.

Cost of equity – The cost of equity represents the rate of return a shareholder requires in exchange for owning the asset and bearing the risk of ownership.

CSR – Corporate Social Responsibility.

EIKON – Thomson Reuters database.

ESG Score - Refinitiv ESG Combined Score is an overall company score based on the reported information in the Environmental, Social, and corporate Governance pillars (ESG Score) with an ESG Controversies overlay.

EV/EBIT ratio - This ratio measures how much a company is valued per each unit of EBIT. EBIT is the earnings before interest and taxes. Enterprise Value (EV) represents the sum of Market Capitalization, Total Debt, Preferred Stock and Minority Interest minus Cash and Short-Term Investments for the most recent fiscal period. EV to EBIT ratio is not calculated when EBIT is less than or equal to zero.

Market risk premium - The difference between the average required return and the risk-free rate.

Nordic Countries - The definition of the Nordic countries throughout this thesis refers to Sweden, Norway, Finland, and Denmark.

P/B ratio - A valuation ratio of a company's current share price relative to its book value per share.

P/E ratio - A valuation ratio of a company's current share price relative to its earnings per share from continuing operations. P/E is not calculated when EPS is less than or equal to Zero.

PRI – Principal for Responsible Investments.

SRI – Socially Responsible Investments.

Total Assets - Represents the total assets of a company as reported.

Total Debt - Represents total debt outstanding, which includes: Notes Payable/Short-Term Debt, Current Portion of Long-Term Debt/Capital Leases and Total Long-Term Debt.

1. Introduction 1.1 Problem Background

Sustainability is not a new phenomenon, neither in society nor within the field of finance. It has however recently evolved from a small niche in the financial markets to become central in many investors investment process. The idea that companies need to consider the whole society in its conduct of business is not new, Freeman’s (1984) well-known Stakeholder theory was developed several decades ago. Even though the stakeholder theory has been of large influence on academia since its development, it has only been of secondary interest for investors for a long time and it is only in the recent years that financial markets participants have become more and more concerned with sustainability issues (Eccles & Klimenko, 2019). There could be several reasons for the increased interest in sustainability issues, one is the concentration of firms in the industry of asset management, where a vast amount of assets is managed by a few asset management firms. When these firms decide to integrate sustainability into the investment decision process, it becomes the new normal. “Firms that have trillions of dollars under management have no hedge against the global economy; they have become too big to let the planet fail” (Eccles & Klimenko, 2019).

A common way to describe sustainability within the field of finance is ESG, which stands for Environmental, Social and Governance. The consumption of day-to-day products or uses of chemicals in production processes will result in carbon pollution, waste, and resource depletion. This is captured by the environmental part of ESG which concerns our ecological footprint. The social aspect is how companies affect the society as a whole, working conditions, minimum wages and labor rights. The governance pillar of ESG concerns issues such as how well the company is managed and how well the company's internal control system works. If the company has a deficient control system, the lack of governance increases risk for bribery and corruption will be higher.

The Paris climate agreement aims at holding the increase of global warming well below 2 degrees Celsius above pre-industrial level and to pursue efforts to limit the increase to 1,5 degrees Celsius above pre-industrial level (UNFCCC, n.d.). In order for the European Union to meet the EU climate and energy targets for 2030, the European Commission has developed an EU taxonomy for sustainable activities (European Commission, n.d.), which is a classification system that provides investors, companies and policymakers with definitions on which economic activities can be classified as environmentally sustainable. EU taxonomy is expected to protect investors from greenwashing and help companies in the transition towards sustainability.

The United Nation Global Compact (UNGC) which was founded in 2000, is the world’s largest corporate sustainability initiative. The UNGC is a sustainability framework based upon ten principles regarding human rights, labor rights, environmental and lastly anti-corruption and is implemented by 12 452 companies within 160 different countries (UNGC, n.d.).

While UNGC focuses on corporate actions, the United Nations also has another initiative regarding sustainability, called Principles for Responsible Investments (PRI) which focuses on encouraging investors to incorporate sustainability into their investment process. PRI describes themselves as the world’s leading proponent of responsible investment, with a total of 3038

signatories and assets of 103,4 trillion dollars under management as of 2020 (PRI, n.d.a). PRI believes that an ”economically efficient, sustainable global financial system is a necessity for long-term value creation. Such a system will reward long-term, responsible investment and benefit the environment and society as a whole” (PRI, n.d.a).

Investment strategies taking sustainability into consideration have significantly increased during the last decade. In the US, the current level of ESG investing represents 33% of all professionally managed assets (USSIF, 2020, p. 1). Modern sustainable investing is now focused both on return potential and on beneficial environmental and social transformation. This has not escaped firms' notice, since 90% of the S&P500 corporations had a sustainability report in 2019, which can be compared to the level in 2011 that only was 20% (GA-Institute, 2020). These phenomena are probably the results of increased pressure from customers and regulators, a raised awareness and reporting of environmental issues from journalists and pressure from governments.

As described above, the increasing awareness of sustainability in society also affects consumers, regulators, companies, and investors. If sustainability is seen as an important aspect in society, it could also be reflected in shareholders preference. Blackrocks CEO Larry D. Fink described this in his 2021 letter to CEOs. “from automobiles to banks to oil and gas companies – we are seeing another divergence: companies with better ESG profiles are performing better than their peers, enjoying a “sustainability premium.” He further writes that “The more your company can show its purpose in delivering value to its customers, its employees, and its communities, the better able you will be to compete and deliver long-term, durable profits for shareholders” (Laurence, n.d.). The importance and popularity of investing in sustainable companies and products has not gone unnoticed. According to Morningstar, the European sustainable fund market was up 10% in the second quarter of 2020 while overall fund assets only grew at a pace of 1,6% (Bioy, 2020). Among individual investors in the US, 85% are interested in sustainability investing. This number is even higher among millennials since 95% of millennials are interested in sustainability investing (Morgan Stanley, 2019, p. 4).

In the past years it has been discussed if this large interest for sustainability investing only has positive effects or if there could be negative side effects as well. This is unquestionably a very important subject where companies and investors have an important role to play, and capital allocation is crucial to make the transition towards a more sustainable society possible. When more money flows into passive index funds with niches like sustainability, this could make valuations very high and derive from fundamentals, creating a price-bubble. There have been several inputs in this debate. Chris Dyer, director of global equity at Eaton Vance, discusses the risks that popular ESG exchange traded funds have become overvalued. He continues declaring that both active and passive investors are chasing themes and driving valuations to uncomfortable levels, he ends with saying that this type of naive investing tends to end badly (Galouchko & De Paoli, 2021). In another debate article published on Bloomberg by Dillian (2020), he states that “ESG has become something of a self-fulfilling prophecy, driven entirely by liquidity and flows” he continued with “What’s interesting is that ESG investing is having its intended effect — it is raising the cost of capital for “bad” companies and lowering it for good ones”. He also sees problems with the ESG investing stating that it's probably in an early stage of a bubble. One that thinks the risk of bubbles in ESG investing is overstated is Larry Fink in an interview he concludes that there will always be winners and losers in new trends, it could be like the technology companies 20 years ago eventually they will grow into their valuations (Stevens, 2021).

At the same time as ESG investing has gained in popularity over the last years for good reasons, there are voices being raised that investors are not doing enough to tackle sustainability issues, that they pretend to be better than they are because it is expected from them. Sasja Beslik has had a big influence on sustainability investing in Sweden, he is the former head of sustainability at Nordea. He expressed his opinion this way “Everybody is doing ESG [environmental, social and corporate governance], everybody is doing integration, everybody is doing sustainability. But only about 5% or 10% of global capital is tilted towards sustainability, so the net effect is zero” (Balch, 2018). This is very interesting because companys’ sustainability initiatives are in the eyes of the beholder. The companies can disclose what they are doing but it will be the different stakeholders such as customers, suppliers and investors that make the judgement based on the information disclosed by the company. Unfortunately, there is as of today, only a few universal set of reporting standards in the sustainability area and the few of them that exist are very vague. This makes it difficult for stakeholders to know how good a company's ESG profile really is and especially compare them with each other. One example of this is the Global reporting initiative (GRI) which provides guidelines on what companies can report and how they should disclose these but are not mandatory (Frostenson et al., 2015, p. 14). This is also problematic because they are voluntary, and companies can change these measures but still say they are using the GRI standards. In a study made by Boiral (2013) he concluded that only 10% of significant news regarding sustainability were reported clearly and explicitly in the sustainability reports, meaning that 90% of the significant negative events were not reported (Boiral, 2013, p. 1051). The reports were not transparent and clear. This makes it more difficult for stakeholders to understand the company's operation and its sustainability impact. If stakeholders are not able to understand how companies affect and report regarding these issues, how can they make rational decisions?

Different investors can also have different views on sustainability regardless of the quality of the disclosed information and how it should be measured. If we look at the Swedish/Norwegian oil company Lundin energy, some might say that Lundin Energy deserves to be a highly rated ESG company because when looking at its peers, Lundin Energy declares having one of the lowest carbon footprints in the industry (Lundin Energy, n.d.). While others would protest and say that an oil company could never be sustainable especially when looking at the E in ESG.

If a transition to sustainability is to be done, it is clear that capital markets will have a tremendous role. Both the fixed-income market, which can be categorized in the money market and bond market, and the equity market need to be functioning as efficiently as possible to enable sustainable transition at the lowest cost possible for the best possible outcome. In 2015, Nasdaq launched in Stockholm the first sustainable bond market in the world, which has more than doubled in size every year since then (Nasdaq, 2019). Lauri Rosendahl, former CEO of Nasdaq Stockholm, stated in an interview with Dagens Industri, that there will be a day when all investors are looking for sustainable investments (Höiseth, 2018, p. 15). On the other hand, when he was asked if Nasdaq could refuse listings of non-sustainable firms, he answered that it is not the role of a marketplace to judge if such firms should be given listing approval or not, as long as the business is not illegal (Höisteth, 2020, p. 15). Every market participant has its own role in the transition to sustainability, the role of exchanges might not be to refuse listings of unsustainable firms but to rather promote and highlight sustainable firms.

In order for firms to finance the transition into sustainability, the financial system provides a variety of debt financing instruments. While green bonds remain the most mature and developed green debt-finance instrument, a wide range of innovative financial instruments are available for investors seeking to invest in sustainability through the debt capital market. Green

bonds are one way firms could finance the transition to sustainability, which offer several benefits for the issuers and investors. The proceeds from a green bond issuance are earmarked for funding climate and environmental-friendly projects exclusively (ICMA, 2018a). Green bonds enable firms to issue debt aligned to long-term projects at a premium, reducing cost of debt combined with lower environmental footprint and greater financial performance (Flammer, 2018). Unlike green bonds who are exclusively for green projects, sustainability bonds are bonds where proceeds are earmarked for funding green and social projects (ICMA, 2018b). Other popular sustainable debt-finance instruments are, among other, social bonds, green loans or sustainability-linked loans. However, the main focus of this thesis is not on the debt financing side of the balance sheet. 1.2 Purpose and Research Questions

As described above, sustainability is a subject which is heavily debated in society. The practical relevance of working with sustainability is rather clear. Companies can work with sustainability in many forms, tackling issues regarding environmental, social, or governance impact. Apart from the obvious moral reasons, a firm’s operational and financial performance can be impacted when ESG is integrated in the business model. The purpose of our thesis is to provide academia with findings related to risk and valuation in regards to sustainability, in order to understand capital market behavior and optimal resource allocation of capital.

Long term forecasting is the Achilles heel of financial markets. Wrong forecasting of long- term growth rates poses constant challenges in investment analysis and financial management. All fundamentally based valuation models rely upon two variables, the growth rate of future cash-flows in the form of dividends or free cash-flows and a discount rate at which these cash- flows are discounted. Many financial analysts publish equity research with a long-term growth forecast, believing they are able to predict the long-term development of a firm’s financial performance. Although, research has shown that analyst’s target prices are excessively optimistic (Bradshaw et al., 2012, p. 953) and inaccurate (Kerl, 2011, p. 93). This is also highlighted by Chan et al. (2003) in their research about the level and persistence of growth rates, where it was showed that “over long horizons, however, there is little forecastability in earnings, and analysts’ estimates tend to be overly optimistic” (Chan et al. 2003, p. 683). Investors may themself therefore suffer from such judgmental biases, facing difficulties to forecast long-term financial development and risk. Since sustainability risk can be classified as a long-term risk, this study will examine if financial markets are rewarding firms with lower sustainability risk or not. However, this thesis will mainly not focus on the eventual impact of ESG on the cash-flow streams but rather on the discount rate at which these cash-flows are discounted and if sustainability impacts firm valuation.

Crises disturbs established rules and behaviors taken for granted and sends financial markets into a bearish trend. In these stormy times when headlines are predicting economic downturns and portfolios are bleeding money, it could be questionable if long-term risks and development gets as much focus as in good times or if investors get short-termism. Based on this reasoning and in order to test the robustness of any possible findings regarding the impact of sustainability on the cost of capital and valuation, the purpose of this thesis is also to provide academia with findings related to how sustainable firms preform in time of crises.

Based on the background described above, this thesis will strive to answer the following research question: ● Are equity market participants pricing sustainability risk?

Side-questions: ● Are investors willing to pay a price premium for sustainability? ● Is there a pattern between a firm’s ESG-impact and their stock returns during times of crises? 1.3 Choice of the subject and preconceptions

This thesis was written during the last semester of our higher education at Umeå School of Business, Economics and Statistics (USBE). We are both studying our first-year master within the course of our degree of Master of Science in business and economics. One of us has a major in finance, while the other has a major within the field of accounting. During the time of our studies, both of us have had a number of courses in the field of finance and accounting. Apart from finance and accounting, we have also studied courses within marketing, organizational theory, statistics and economics i.a., which have been helpful for understanding the larger picture of business administration.

When studying finance and accounting, we both have obtained a good understanding of how shareholders can impact the decision-making in corporations, but we are also conscious about the problematization of separation between agents and principals. We have learnt how perfectly efficient markets should behave, however we also have developed a critical eye towards the efficient market hypothesis.

The choice of the subject has been relatively simple since we both are interested in sustainability and finance. We think that Umeå School of Business, Economics and Statistics (USBE) 's large focus on sustainability has made us acquire better knowledge and become more curious within this field. We think that the connection between sustainability and finance is very interesting and natural. During our education we have learned about different valuation techniques, especially discounted free cash flows. These models are very sensitive to the discount rate, that's the reason we have chosen to highlight if there is a connection between firms' ESG efforts and the cost of equity.

We both follow the news on a daily basis, and it has not gone unnoticed for us that all segments of the society from regulators to consumers, and everything in between, is highlighting the importance of sustainability and puts pressures on corporations to intensify their transformation to conducting business in a sustainable way. 1.4 Delimitations

In order to conduct the study, several delimitations have been made. Firstly, only large cap firms are included in the study since most smaller firms do not have analyst coverage nor an ESG-score. To include all-cap firms would therefore have been problematic since we would have had a systematic reduction of the sample since earnings estimates and ESG-score are required variables in our models.

Secondly, the Nordic countries have been defined as Sweden, Finland, Denmark, and Norway which means that the data only include large firms listed on Nasdaq OMX Stockholm, Nasdaq OMX Helsinki, Nasdaq OMX Copenhagen, and Oslo Børs. In fact, Iceland is part of the Nordic countries but is excluded from this study since Iceland’s stock market is significantly smaller than the other markets of this thesis, indeed only 18 firms are listed on Iceland’s stock market (Nasdaq, n.d.a).

Over the last decades there have been several crises with different magnitudes such as the dotcom bubble, the financial crises, the European sovereign debt crisis, and the covid-19 outbreak. Because of the limited time of this thesis, we have chosen to only focus on the most recent crisis caused by the covid-19 outbreak. Under the most turbulent days between the top on 2020-02-19 and bottom 2020-03-23, the OMX Nordic Large Cap fell 34%. This is an interesting time-period to investigate because of the extreme volatility in a short period of time. This specific time-period will from here on be called crisis. Furthermore, the sanitary crisis is still ongoing when this thesis is written, however financial markets have already recovered to pre-crisis level and seems to be pricing a bright economic outlook.

Lastly, financial institution has been excluded from this study since financial institutions possess unique characteristics that would distorts our findings. Financial institutions are, as a consequence of their unique business model, highly leveraged. Debt can be seen as bank’s equivalent to raw materials and are therefore unique in that sense. 1.5 Target audience

Our hope with this thesis is to provide important insights within the area of sustainability and finance. We trust our research will help scholars, regulators, investors and managers in the Nordics to better understand to what extent the equity market prices sustainability risk. We hope to contribute within this area because of its importance in today's society and we believe that investors and companies have a huge role in making the society more sustainable.

2. Theoretical point of reference

In this part of the thesis, the theoretical foundation for this thesis will be explained. There are several theories that are of importance to understanding our research and how we position our thesis towards this. We will also further explain the concepts of ESG and CSR which are of great importance, as well as highlight previous research of high relevance for this thesis. Finally, we will present the theoretical hypothesis that will later be tested. 2.1 Theoretical Framework

Several factors will affect a company's perceived risk, finance theory teaches us that this risk should be reflected in the cost of equity capital. Furthermore, a lot of factors or risks are susceptible to influence investors' decisions, the most important ones will be discussed below. In this theoretical framework traditional risks and models for the cost of equity will be explained but in addition to these traditional views, previous research will be closely examined to see how sustainability performance affects the perceived risk from equity investors.

Over the last 80 years, several theories on how companies should act and behave in society have evolved. Some of these theories are specifically addressing the problems with sustainability while others are more far-reaching. Two famous far-reaching theories that have been used in previous research to discuss the impact of ESG and firm performance are the stakeholder and shareholder theory. These two theories have often been seen as directly opposed to each other. In today's society, other forms of investing have appeared than the classic investment theory which depends on strict financial criteria. Socially Responsible Investments (SRI) refers to the process where sustainability is an integrated part of the investment decision and is used to exclude firms from the investment process (Renneboog et al., 2008, p. 1723; Sahut & Pasquini-Descomps, 2015, p. 41). Others have claimed that regardless of investment strategy, investors can never ignore the ESG risks when investing. This is not only because of moral reasons. In today's globalized world with social media companies, firms that diverge from established values relating to sustainability will face a large risk of being exposed (Staub-Bisang, 2012, p. 72). Reducing ESG risks will reduce reputational risks further increasing the financial performance (Staub-Bisang, 2012, p. 72).

2.1.1 Defining sustainability Due to the complexity of measuring sustainability, many concepts and theories have been evolved. Within the field of finance, sustainable investments can be seen as a term for investments that pursue to yield a long-term financial return, in harmony with positive effects on society and environment. Even though we do not attempt to assess the sustainability of firms or investment portfolios in this thesis, it is of great interest to present the different approaches to sustainability in literature and previous research. As previously mentioned in the problem background, Socially Responsible Investment (SRI) have grown significantly during the two last decades and is now an integrated step in the investment process of an increasing number of professional investors (Renneboog et al., 2008, p. 1726). Despite the SRI industry’s growth and academia’s great interest in the subject there is, as of today, no consensus on the definition of SRI (Hofmann et al., 2009, p. 103). PRI’s definition of responsible investments and one of the most commonly used concepts is the ESG-score where E, S and G stand for Environmental, Social and Governance (PRI, n.d.b). Unfortunately, there is no standardized procedure and methods to calculate an ESG-score, nor what each letter (E, S & G) is composed of. Since each provider of market data and research uses their own methods for calculating ESG-scores, ESG-

ratings are subjective. However, PRI (n.d. b) provides example of ESG-issues in each category as follow:

Figure 1 - Exemplification of several sustainability issues categorized in E-, S- and G- factors. Source: PRI, n.d.b.

Another aspect of sustainable finance is Corporate Social Responsibility (CSR), which is defined by the European Commission (2011) as “the responsibility of enterprises for their impacts on society” (European Commission, 2011, p. 6). While ESG is more of a measuring tool of sustainability, CSR could be seen more as an organizational policy that should be aligned to the firm’s business model. The demarcation between CSR, SRI and ESG is not straightforward, and we have in this thesis therefore chosen to interpret CSR as a proxy for ESG, and SRI to be the umbrella term referring to an investment process which takes ESG- criteria into account. In the research method part of this thesis, the different ESG rating systems will be compared, and we will provide an extensive explanation of the chosen rating system.

2.1.2 ESG Investments - Preferences, risk and return in equilibrium Hofmann et al. (2009) developed a model where markets can be classified into three stages for sustainable investment. In the early stage, exclusion of non-ESG is the method of screening and relatively little awareness is given to ESG. The screeners consist of negative criteria, i.a. exclusion of firms involved in production of weapons, tobacco, etc, and most focus is on environmental issues. In the second and intermedial stage, exclusion and inclusion methodologies are combined. Inclusion consists of positive screening, i.a. investment in firms involved in development of renewable energy, satisfying diversity, etc, and the focus is now on environmental as well as social issues. Finally, in the third and final stage, investors influence firms also through shareholder activism by participating in the shareholder's annual general meeting and putting pressure on management (Hofmann et al. 2009, p. 104). Other researchers such as Renneboog et al. (2008) do not go as far as Hofmann et al. (2009) regarding if shareholder activism is to be included in the concept of SRI. Renneboog et al. (2008, p. 1723) rather describes that SRI is applying several screeners of ESG-character throughout the investment-process in order to select or exclude securities.

Figure 2 - Market stages of adoption to socially responsible investments. Source: Hofmann et al. (2009, p. 105) Few investors actively refuse to invest in high ESG firms, but ESG-neutral investors probably simply do not include ESG-screeners in the investment process. In contrast, high ESG-firms will attract SRI-investors as shown by Renneboog (2008) and Hofmann et al. (2009). Consequently, this must imply that high ESG-firms have a broader investor base, ceteris paribus. The capital market equilibrium model with incomplete information of Merton (1987, p. 502) provides algebraic proof that an increase in the relative size of a firm’s investor base will result in lower cost of capital and a higher market value for the firm. In the same spirit, the capital market equilibrium model of Heinkel et al. (2001, p. 439) shows that fewer ESG-neutral investors lead to a lower firm value and a higher cost of capital for non-sustainable firms.

In parallel with investor’s choice of including (or not) ESG-aspects in the investment process, executives of firms need to choose to include (or not) ESG-aspects in the conduct of their business and its eventual impact on firm value. If an investment generates positive net present value and positive externalities to other stakeholders, the firm is likely to increase its value. The debate is materialized when there is a trade-off between financial return and externalities (ESG), where positive NPV together with negative externalities will attract only conventional investors and in contrast, negative NPV together with positive CSR only will attract SRI- investors (Renneboog, 2008, p. 1734).

Investment Positive NPV Negative NPV

Positive CSR Both SRI & conventional investors Only SRI

Only conventional investors Neither SRI nor conventional Negative CSR investors Figure 3 - Trade-off between financial return and externalities. Source: Renneboog (2008, p. 1734) The use of non-financial screeners in the investment process might indicate a lower interest of SRI-investors in financial performance than non-SRI investors, ceteris paribus. This is also the conclusion of Bollen (2007, p. 706), who investigated the dynamics of SRI-fund flows against fund flows of conventional mutual funds. Bollen further shows empirical evidence of significantly lower volatility of SRI-fund flows than conventional mutual funds (Bollen, 2007, p. 703) and that this is must be due to non-financial preferences of socially responsible investors, which can be “represented by a conditional multiattribute utility function in the sense that they appear to derive utility from being exposed to socially responsible attribute, especially when socially responsible funds deliver positive returns (Bollen, 2007, p. 706).”

Cornell (2021) studied the relation between risk, returns and ESG-characteristics and stated that “finance theory teaches that premiums above risk-free rate have its roots in three general sources: (1) Rewards for bearing risk, (2) behaviour bias, (3) market impediments, such as limited liquidity” (Cornell, 2021, p. 12). Cornell conducted the research exclusively on actively

traded securities on developed markets in order to focus on risk-premiums arising from the first and second sources. Additionally, investors’ preference for ESG-investments is the only behavioral bias considered in the study (Cornell, 2021, p. 13).

Even if the purpose of Cornell’s study is not to study the process of ESG-rating, he highlights the fact that a colossal number of organizations are providing ESG-rating and research, which creates complications when classifying a firm as highly sustainable or not (Cornell, 2021, p. 13). Cornell exemplified this ambiguity by discussing if Tesla’s Chinese manufacturing operations should be classified as high-ESG or not (Cornell, 2021, p. 13). Some ESG-rating agencies would argue that Tesla’s cars are zero-emissions and Tesla should therefore be rewarded with a high ESG-rating. Other agencies would argue that since 60% of Chinese electricity is generated by coal-plants, manufacturing and driving a Tesla generates more carbon-related emission than regular gas-powered cars and therefore deserves a low ESG- rating. Unfortunately, Cornell does not express his opinion on this issue in his study.

To start off, Cornell distinguished between equilibrium returns and actual historical returns. Actual historical returns are observed during a transition period when preferences for ESG- stocks are changing. In an efficient market, the value of company is equal to the present value of expected cash flows as given by equation 1 where !"! is the cash-flow to equity in year #, $ is the cost of equity-capital at which the cash-flow is discounted and % is the expectations operator (Cornell, 2021, p. 13).

%(!" ) %(!" ) %(!" ) & = # + $ + ⋯ + ! + ⋯ " (1 + $) (1 + $)$ (1 + $)!

Equation 1 - Firm value in equilibrium. Source: Cornell, 2021, p. 13. From equation 1, Cornell shows that the expected return of holding equity must therefore be as given in equation 2 and that the valuation given by equation 1 requisites that the expected return, in equilibrium, equals the cost of equity capital at which cash-flows are discounted independently of the cash-flow forecast (Cornell, 2021, p. 14).

%(/ ) − / + %(!" ) %(.) = # " # = $ /"

Equation 2 - Expected return in equilibrium. Source: Cornell, 2021, p. 14. The conclusion is that though the market is pricing ESG-risk, that does not imply that tilting into ESG securities is a superior investment strategy (Cornell, 2021, p. 18). By doing so, high ESG-stocks would generate good holding period returns only in the case where the premium associated with holding ESG-stocks is higher than the risk-adjusted return. Although this phenomenon has nothing to do with the fact that the stock is ESG-friendly, the excess return must be generated by the fact that the market adapts to the new equilibrium at a lower cost of equity-capital, ceteris paribus. Finally, Cornell concludes that given the considerable focus on ESG in the field of finance, mispricing of ESG-risk is unlikely and that investing in ESG-stocks comes with a cost in form of lower expected returns (Cornell, 2021, p. 18).

The recent outperformance of ESG Indices compared to traditional indices (Giese et al., 2020) could however indicate that equity markets are in the last stage of adoption to SRI (Hofmann

et al. 2009) and still in a transition period adapting to the new equilibrium and hence providing generous returns.

2.1.3 Shareholder’s theory The famous shareholder theory was established by Friedman (1962). The foundation of the shareholder approach is that the only objective of a company is to maximize profits and the shareholder value for its owners. In a free society, companies have only one objective ''there is one and only one social responsibility of business - to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition without deception or fraud.'' (Friedman, 1962, p. 133)

He later evolved his thoughts in an article in the New York times (1970). Friedman claims that it's unreasonable to assume that companies have some form of social responsibilities, responsibilities can only belong to individuals (Friedman, 1970, p. 33). He sees this as very problematic if corporate leaders would start to make their own decisions that are not in line with principals’ goals, this would jeopardize the entire concept of capitalism (Friedman, 1970, p. 33). If corporate leaders want to perform social responsibilities, he or she should do so on their own time, with their own money and energy. The primary goal of all corporations is profit maximation (Friedman, 1970, p. 33).

He argues that this may of course differ if the principal and agent are the same person, then companies could have explicit responsibility through the owner. In Friedman's view, these two tasks can never go hand in hand in a for-profit organization because the principal will always spend someone's money, either Shareholders or the customers (Friedman, 1970, p. 33). He further argues that this would be a threat for society because the principal will decide what the social interests are. In some organizations such as public hospitals or schools, a broad stakeholder view may work. But in these types of organizations the goal is different than in for-profit organization (Friedman, 1970, p. 33).

However, Brook & Oikonomou (2018) who conducted a literature review on the effect of ESG on firm value found that, even though many studies in the field would argue that no consensus has been reached between CSR and financial performance, “the burden of evidence lies heavily on the side of a modest positive link between CSR and financial performance at the firm level” (Brooks & Oikonomou, 2018, p. 8). Furthermore, Brook & Oikonomou stated “given that a case for value-destroying arising from high CSR receives minimal support, it is even easier to state that, at the very least, the CSR-Financial performance association is a non-negative one” (Brooks & Oikonomou, 2018, p. 8). In other words, the legitimacy of the stakeholder theory can be questioned.

2.1.4 Stakeholder’s theory The stakeholder theory takes a much broader approach to the social responsibility of a company. The stakeholder approach has a completely different view on companies' social responsibility. It agrees with the shareholder approach that owners are crucial for the company, but it needs to satisfy a broader group of people (Freeman, 1984, p. 52).

The main point of the stakeholder theory is that a firm is a part of society and that all interactions are two sided, if the society flourish, the firm will also flourish. This means that companies' responsibilities are not only to the shareholders, but their business should also be

beneficial for all stakeholders in the society (Freeman, 1984, p. 25). If this is not the case, the company will not be able to deliver value to its shareholders either. In order for companies to flourish they need to have a broader approach than just the owners, if suppliers, banks, customers and employees are unwilling to work with the company it will cease to exist (Freeman, 1984, p. 25). The shareholder approach argues that all investments that managers invest in should at least cover the cost of capital, while in the stakeholder approach managers must equipoise this so it will be beneficial for all stakeholders (Renneboog, 2008, p. 1730).

These two different approaches to corporate responsibility and why it exists has been well debated. While Friedman sees one goal, and one goal only for organizations, Freeman has a broader view but does not exclude the owners from this picture. Freeman says that when shareholder and stakeholders' interests are conflicting, managers need to find a way to make them go hand in hand, it needs to be beneficial for all stakeholders. According to this argument these two theories would rather be seen as complements than in conflict with each other (Agle et al., 2008, p. 166). In contrast, Buallay (2019, p. 491-492) found these two theories contradictory and therefore used them to explain that if market valuation and financial performance differ, this must mean that the market does not only look at financial data when evaluating company performance.

There is criticism against the shareholder model especially since ethical considerations take a larger part of the debate and new regulations make this area highly relevant, that's how the stakeholder model was born. But all models have their weaknesses, Friedman has not been the only voice in the debate. One critic against the stakeholder model is that implementing it is an excuse for executives to benefit their own self-interests, because it's impossible to hold anyone accountable for their stewardship of the company's resources (Jensen, 2001, p. 14). This will increase agency costs within the firm and for the society as a whole (Jensen, 2001, p. 14). If companies engage in CSR activities just because it is expected from them this might result in higher costs, lower revenues and investments in low-yielding projects that would otherwise have been rejected (Hendersson, 2001, p. 108). If this is true, the perceived risk should be higher in firms with high sustainable performance. Others have gone further in their critics of the stakeholder theory. Antonacopoulou & Méric (2015, p. 24) criticize the entire scientific foundation of the theory and conclude that stakeholder theory is an ideological creation.

2.1.5 Legitimacy theory There are several different definitions of the legitimacy theory, but we have chosen to follow Shuman’s (1995) broad definition of legitimacy stating that “legitimacy is a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions” (Shuman, 1995, p. 574). It is of high importance for companies to achieve legitimacy due to the fact that, that all organizations are somehow dependent on different stakeholders. Legitimacy will not only affect the image of the company but will also directly influence the unsystematic risk. Firms with lower corporate environmental legitimacy will have a larger unsystematic risk, furthermore companies could benefit from improving their environmental impact even though the cost of doing so may be high (Bansal & Clelland, 2004, p. 100).

Lindblom (1994, referred to in Clarke & Gibson-Sweet 1999, p. 6) provides four broad legitimacy strategies that companies use to eliminate legitimacy threats. These are (1) Informing stakeholders about intended improvements in performance (2) Seeking to change stakeholders’ perception of the event (3) Distracting attention away from the issue (4)

Changing external expectation about its performance. CSR initiative can be used as a legitimacy tool by companies and can often be explained by one or more of these four strategies (Gray et al., 1996, p. 47).

This means that companies use CSR actions to achieve legitimacy in the society because it is expected from them. This is natural, as the society evolves so will companies. However, if companies adapt strategy two and three, this can be very problematic for the whole society. It makes it harder for stakeholders to evaluate the companies, it can also make ESG-scores hard to measure and this would be troublesome for our study.

Legitimacy theory is therefore very important for our study. It connects with the stakeholder model; a company will be judged by all its stakeholder not only shareholders. Companies need to behave in a way that is socially accepted, entities do not operate in a vacuum, they need to adjust to survive. You could argue that there is a social contract between the company and the society that stipulates the social responsibility. Some of these legitimacy strategies can also relate to ESG efforts, where companies use these strategies to create legitimacy. In a research paper written by Boiral (2007) he examined companies' adoption to ISO14001, which is a standard regarding effective environmental management system (ISO, n.d.). He concluded that the number of certified companies has increased significantly but at the other end, the impact of ISO14001 on environmental performance remains uncertain (Boiral, 2007, p. 142). Consequently, this could imply that companies are looking for certification rather in order to eliminate legitimacy threats than for actually improving sustainability impact and that companies are trying to embellish their sustainability efforts, which is called greenwashing. If companies do not achieve legitimacy, we expect the perceived risk for the company to increase.

2.1.6 Information asymmetry Information asymmetry occurs in a business transaction when one party has more or better information than his counterparty. In a paper written by Akerlof (1970) he uses the used automobile market as an example to illustrate how information asymmetry affects the efficiency of the market. When the buyers are looking for used cars, they do not know if the car is good or a “lemon” (which is a car in poor condition). The sellers will have better information about the car's condition than the buyer will. In extreme case, bad cars will drive good cars out of the market because there will not be any differences in the price and therefore impossible for the buyer to know if he or she is buying a good car or a lemon. Furthermore, seller of good car will not be willing to sell their car at the “too low” market price (Akerlof, 1970, p. 489-490).

This analogy can also be used in the capital markets. Because of information asymmetry where the different parties in the transaction have different amounts of information, insiders probably have better information than buyers. This will lead to underpricing for good companies and overpricing of bad companies (Greenwald et al., 1984, p. 195). In case of information asymmetry, good companies can be pushed away from the market and not able to raise money at the “right” price while bad companies that are perceived as good by the market will be able to obtain funds at a lower level because of the lemon problem.

Information asymmetry and the lemon problem is an interesting aspect in the capital markets and especially related to sustainability. As highlighted in the problem background, 90% of the significant negative sustainability events were not reported in Boiral’s study (2013, p. 1051), which suggests that companies thus not fully disclose their sustainability impact in the

sustainability reports. This implies that it is likely to be information asymmetry to some extent regarding sustainability efforts in the companies where insiders will have better information than the market participants. A big concern for investors related to information asymmetry in its decision to consider ESG effects when investing is therefore greenwashing. If companies make claims about their CSR and ESG commitment that are not true or exaggerated, can asset prices ever be correctly priced? As long as information asymmetry occurs in capital market, there will always be is a risk that companies with good sustainability focus will get less access to capital than they should, and companies making false claims receive more capital than they should.

2.1.7 Signaling theory The signaling theory is built upon the concept of information asymmetry and an important theory in understanding the behavior when two parties have different knowledge. This will lead to different decisions when communicating, the sender must decide how to send the signal and the receiver must select how to interpret the signal that the receiver is sending out (Connelly et al., 2011, p. 39). This theory was originally published by Spence (1973) who exemplified this problem by looking at the job market. When an employer hires, he is incapable of knowing the capacity of the employee which is a form of informational asymmetry (Spence, 1973, p. 356). The employer will not be able to evaluate the employee for a long time after employment. Further Spence argues that the employee can send signals to distinguish himself by showing he has a higher ability level, an example of this is education credentials. This makes it possible for the employer to distinguish different kinds of workers (Spence, 1973, p. 358). The work of Spencer and the signaling theory has later also been used to explain other areas within the field of economics and business administration such as IPO’s and dividend policies (Alkebäck, 1997; Miller & Rock, 1985; Park et al., 2016).

Uyar et al. (2020) examined if CSR reporting is used by companies as a signaling tool or for greenwashing purposes. They found that companies with higher CSR performance also are more likely to publish CSR reports (Uyar et al., 2020, p. 8). They conclude that companies use CSR reports as a signaling tool to communicate with stakeholders of the company, not for greenwashing purposes (Uyar et al., 2020, p. 8). The signaling theory is thus important since management can reduce information asymmetry by communicating and disclosing voluntary information with the different stakeholders of the company.

2.1.8 The cost of equity capital Investors tend to be risk-averse and therefore require higher returns when taking higher risk (Fama, 1970, p. 411), which implies that stock prices should reflect investors’ expectations about a firm's future profit and rentability. According to the Efficient Market Hypothesis (EMH), the price of a security should reflect the information and the fair value of a firm (Fama, 1970, p. 414). A stock-investor’s profit sources are both a received income in form of dividends and capital gains through rising share price. However, the uncertainty of these cash-flows is the risk an equity investor is bearing. The cost of equity is therefore the minimum return a company requires for an investment and represents the marginal compensation that the market requires for holding that company’s common shares.

The cost of equity can be calculated in several ways, which all likely leads to different estimations of the cost of equity. Academia has provided various ways of estimating the cost of equity, some are ex-post while other models are ex-ante calculations.

2.1.8.1 Ex-Post Cost of Equity models Ex-Post models are based on historical returns, assuming it is the best-known approach of forecasting future returns. A well-known and one of the most used models for determining a company's cost of equity is the Capital Asset Pricing Model (CAPM) developed by Sharpe (1964) and Lintner (1965).

Equation 3 - Capital Asset Pricing Model

%(.%) = .& + 1% × 3.' − .&4 Where, .& = 567$ 8599 5:;9 1% = <9;: .' − .& = =:5$9; 59;>5# − 567$ 8599 5:;9 = =:5$9; 567$ ?59=6>=

And,

!@A[=, 6] 1% = $ E'

Where, E$ = A:56:#F9 @8 ;ℎ9 59;>5#7 !@A[=, 6] = !@A:56:#F9 <9;H99# ;ℎ9 =:5$9; (=) :#I 6#A97;=9#; (6)

The risk-free rate used in the CAPM is often a long-term government bond in a relevant market (Penman, 2013, p. 96). There are many ways to estimate the market risk premium. Some would argue to simply reduce the risk-free rate from the observed historical return over a period (Mehra, 2008, p. 6) while other researchers have provided academia with advanced statistical models (Mayfield, 2004, p. 469). Finally, the market risk premium could also be estimated through the survey approach (Fernandez, 2020), where questionnaires are sent to market participants or the implied approach where forward-looking estimates are used to calculate the implied premium (Damodaran, 2009, p. 337-353). The beta calculation is based upon actual returns of the individual stock against a relevant market during a defined time-period. The CAPM’s underlying idea is hence that the required return for a risky investment is a function of the market risk calculated with a beta, which when multiplied by a risk-premium and added to the risk-free rate, gives the cost of equity.

However, CAPM is not the only model available to calculate the ex-post cost of equity. Other models such as the Arbitrage Pricing Theory (APT), multi-factor models and proxy models have also been developed to complement the CAPM. The APT was developed by Ross (1976, p. 355), as a substitute to the CAPM, where risk premium for individual market risk factors are estimated.

Equation 4 - Arbitrage Pricing Theory

()*

%(.%) = .& + J 1( 3.67$ ?59=6>=(4 ()#

The downside of the APT is that the market risk factors are not specified so the estimated sources of systematic risk can vary from one investor to another. Multi-factor models are hence models following APT, with specified sources of systematic risks. One of the most famous multi-factor models is the Fama & French three-factor model. The model includes the factors of size (SMB) and value (HML) to derive the required return (Fama & French, 1995, p. 131).

Equation 5 - Fama & French three-factor model

%(.%) = .& + 1#3.' − .&4 + 1$(KLM) + 1+(NLO) + P

The SMB factor is a size factor based on the market capitalization of a firm. The SMB-factor reveals that small-cap seems to generate higher returns than larger mature firms (Fama & French, 1995, p. 154). The value factor (HML) is based on the book-to-market value of equity. The key rationale behind HML is that value stocks (high B/M) generate higher returns than growth stocks (low B/M) on the longer run (Fama & French, 1995, p. 154).

Lioui (2018) proposes an ESG-factor as the continuation of Fama’s research about the relation between stock return and size, average return and book equity to market equity ratio. Lioui finds that his suggested ESG-factor is priced by the market and that the estimated market price of ESG-risk is negative, which means investors are ready to give up on expected return to protect themself against sustainability risk (Lioui, 2018, p. 18-20). To the contrary, bankruptcy risk is a risk with a positive risk premium where investors require higher return to compensate for the risk of losing whole or part of their investments in accordance with the second proposition of the Miller & Modigliani theorem (1958, p. 271).

2.1.8.2 Ex-Ante Cost of Equity models While ex-post models rely on historical returns, ex-ante models are on the contrary estimating the cost of equity inferred from consensus forecasts of future financial development and asset price at a certain point in time. The rationale behind ex-ante models is to insert current stock price and analyst forecasts into a fundamental valuation model and to extract out the cost of equity as the Internal Rate of Return (IRR) that results into the current stock price. Ex-ante models are not always correct since it is always harder to predict future development and account for all variables susceptible to impact anticipated financial performance than observing historical data. However, there is evidence that historical returns are poor proxies for estimating the cost of equity. As exemplified by Elton (1999, p. 1199), there are periods longer than 10 years during which the stock market, in this case the US stock market, realized returns on average lower than the risk-free rate. Consequently, several ex-ante models have been developed (Claus & Thomas, 2001; Easton, 2004; Gebhardt et al., 2001; Ohlson & Jeuttner- Nauroth, 2005) which thereafter have been used in a significant number of studies (Boubakri et al., 2012; El Ghoul et al., 2011; Hail & Leutz, 2006).

The Ohlson-Juettner model (OJ-model): The OJ-model (Ohlson & Jeuttner-Nauroth, 2005) parameterize the behavior over time of the expected future development of earnings per share, "%/K,-!, adjusted for dividends per share, Q/K,-#. The concept behind the parameterization is to model a smooth decay in the growth of "%/K,-! , adjusted for Q/K,-# as a function of the future date. The short-term growth R$ is assumed to decay asymptotically to S, which is set to be equal to the real long-term economic growth rate (Ohlson & Jeuttner-Nauroth, 2005, p. 359).

Equation 6 - Ohlson-Jeuttner model

"%/K $ ,-# 5 = T + UT + ∗ (R$ − (S − 1)) /,

Where:

T = 0,5 ∗ YS − 1 + ./0!"#Z and S is a constant that is equal to 1 + the long-term growth rate, 1! where the long-term growth rate (S − 1) is fixed at the country’s inflation premium, estimated as the country’s 10-year government bond yield minus inflation rate.

2314!"$ 6 2314!"# And R$ = 2314!"#

The OJ-model has two favorable features. Firstly, the OJ-model makes directly uses of earnings instead of dividends and neither book value of equity nor return on equity needs to be forecasted. The only assumption about dividend one needs to make is about Q/K,-#. Moreover, the OJ-model keeps things simple, since S determines both the perpetual growth rate as well as the decay rate of short-term growth at the same time. The OJ-model has been used in many relevant studies for estimating the ex-ante cost of equity (Boubakri et al., 2012, p. 557; El Ghoul et al., 2011, p. 2402; Hail & Leuz, 2006, p. 491) and is the chosen model to calculate the cost of equity in this thesis.

Other ex-ante cost of equity models: The OJ-model is far from the only ex-ante cost of equity estimation model available at this moment. Other models such as the Claus and Thomas model (Claus & Thomas, 2001) and the Gebhardt et al.-model (Gebhardt et al., 2001) infers the cost of equity based upon forecasted residual earnings and book values. While the Claus and Thomas model use forecasted eps, the Gebhardt et al. model uses analyst’s forecasted return on equity. Finally, the Easton-model (Easton, 2004) is a continuation of the PEG-model (Price to earnings growth) and is based upon the OJ-model. The ex-ante models named above, except the OJ-model, all have in common an explicit forecast horizon set to two or more years which thereafter grow at another, perpetual growth rate. Since several years are discounted in these models, the calculation needs to manually be estimated through either trial and error, straight-line estimations or using goal- seek function in Excel.

2.1.9 Price-based multiples A common way to put a value on a firm besides discounted cash flows are relative valuation. It's a common valuation technique used in equity research reports and acquisition valuations (Barker, 1999, p. 393; Damodaran, 2012, p. 453). The use of relative valuation instead of other valuation techniques can be explained by several factors, it's easier to use and fewer assumptions are needed. It might also be easier to explain the conclusions to clients rather than explaining an DCF-model (Damodaran, 2012, p. 453). This is also the big drawback of the relative valuation approach, it's easy with a lot of assumptions. It takes for granted that the company you're comparing with has the right value on the market (Damodaran, 2012, p. 454). This becomes contradictory with the EMH, that one company has a fair value but the other does not.

In relative valuation there are several different kinds of multiples that can be used. Some multiples derive from earnings, such as the Price-to-Earnings (P/E) ratio. The P/E ratio is one of the most used valuation models (Barker,1999, p. 393). The P/E ratio tells investors how many times the market is paying for its earnings. The P/E ratio is commonly used because of its simplicity and low time cost. Other factors than net incomes can be used such as EBITDA or EBIT. The EBIT has several drawbacks as it is earnings before interest and taxes but despite this, it is a commonly used measurement in finance. Instead of using the market capitalization (P), other ratios focus on the enterprise value (EV) which also include eventual debt and cash on the company’s balance sheet. By calculating price ratios upon enterprise value makes it possible for investors to judge and compare companies regardless of each firm’s capital structure.

Book value multiples are also commonly used in relative valuations. Book value multiples such as the price-to-book (P/B) ratio compares the market value of a company and its book value. A firm's market capitalization usually differs from a firm's net assets book value for plenty of reasons. A firm’s book assets are based on the management valuation of the firm’s assets, which in many circumstances can be subjective assumptions that differ from the market participant’s valuation of the assets. Another reason why book and market value of equity can differ is because many intangible assets, such as trademarks or synergies, can not be recognized unless they occur as a result of an acquisition, in that case these assets can be recognized as goodwill (IAS 38). Consequently, many internally generated intangible assets are not visible on a firm's balance sheet and the market capitalization is therefore in many cases not equal to the book value of equity. Finally, another reason why market value of equity and book value of equity can differ is because investors are discounting a firm’s future cash flow to its present value. Investors are buying a stock because of their expectation of future development. Per definition, a growing firm will therefore have a higher market value than a firm that has a negative growth-rate, ceteris paribus. Easily put, a company which has neutral accounting and is generating abnormal earnings will trade at a P/B>1. If companies have intangible assets or other assets missing from the balance sheet this will result in a higher price-to book, ceteris paribus (Penman, 2013, p. 164).

2.1.10 Market crashes Research shows that equity risk premiums increase during periods of business cycle downturn and crises (Fama & French, 1989, p. 48), however there is no reason why ESG-stocks should resist crises better due to a change in market risk premium since the change in market risk premium is per definition the same for ESG stocks and non-ESG stocks. Nevertheless, Dajcman (2012) showed empirical evidence that investors are allocating capital to quality in periods of market turmoil, causing an equity sell-off and routing capital flows towards the sovereign bond market (Dajcman, 2012, p. 1661). Since sovereign bonds are considered as safe investments, the flight-to-quality must imply that investors are willing to reduce risk in their portfolios during times of crises. If this risk-off allocation is true, low-beta stocks should suffer from a lower sell-off than high-beta stocks due to their lower exposure towards systematic risk in the form of market risk premium, ceteris paribus.

2.2 Previous research

In this part of the thesis, a highlight of previous research which is of relevance for this thesis is presented. Some studies contradict each other, while other studies have similar conclusions. The methodology and variables used in studies presented below will be of high influence on the way the model of this thesis later will be designed.

2.2.1 The impact of ESG on firm’s operational and financial performances In a study by Buallay (2019) she investigates the relationship between sustainability disclosures and companies financial, operational and market performance. Market performance is measured as firm value through Tobin's Q, operational performance through Return on Assets (ROA) and financial performance as Return on Equity (ROE). To determine when sustainability reporting adds value and when it is value destructive, there are two different approaches to sustainability in previous literature. The value creation perspective and the cost of capital reduction theory. The cost of capital reduction theory sees ESG efforts as an increased cost and therefore it should result in lower market value for the firm (Buallay, 2019, p. 484). The value creation perspective is of the opposite opinion. That firm's ESG investments should be seen as a competitive advantage which should lead to increased financial performance.

This research paper is very interesting because it finds conflicting results that support the different theories in previous literature. Buallay (2019, p. 491) finds that there is a positive relationship between ESG disclosures and market performance, which supports the value creation theory. At the same time, a negative significant relationship between ESG disclosures and financial and operating performance was found, which supports the cost of capital reduction theory (Buallay, 2019, p. 492). This means that companies will have higher market values even though their operating performance is not better compared with firms with worse ESG disclosures. This is a conflicting result. Consequently, companies that have good ESG disclosures will enjoy a higher market value.

Several other studies have also attempted to identify and assess the impact of ESG on financial performance. Albuquerque et al. (2012, p. 19) argued that CSR activities could generate opportunities for corporations to increase image or sales. In the research conducted by Albuquerque et al., the authors present a capital asset pricing model in industry equilibrium in order to analyze how corporation’s choices of CSR affect their systemic risk (Albuquerque et al, 2012, p. 26). Furthermore, they also show that systematic risk is reduced by CSR, which implies that profitability is less correlated with the business cycle for CSR firms than non-CSR firms (Albuquerque et al, 2012, p. 28). In addition, Albuquerque et al. (2012) shows that in the case of industry adoption of CSR, non-CSR firms of that same industry are exposed to a greater extent against systematic risk (Albuquerque et al, 2012, p. 33). One interesting finding of this study is that Albuquerque et al.’s theory shows that customers are of higher importance than investors as agents in impacting firms on their CSR policies and risk profile since corporate social responsibility generates a more loyal customer base and hence follows the profit maximizing view of CSR (Albuquerque et al, 2012, p. 33).

Apart from that CSR activities result in greater profits arising from greater customer loyalty (Albuquerque et al, 2012), CSR activities can also help firms to attract and motivate employees. Balakrishnan et al. conducted a laboratory experiment to investigate whether a firm's giving to charity motivates employees. The main finding of the study is that employee productivity significantly increases as the level of corporate giving to charity increases (Balakrishnan et al.,

2011, p. 1905). These findings are hand in hand with Kramer and Porter (2011) who argued that CSR could be a competitive advantage of a firm if it is addressed in a strategic way. The connection between competitive advantage and social issues could be addressed in many ways to yield both societal and productivity benefits. As exemplified, “by reducing its packaging and cutting 100 miles from the delivery routes of its trucks, Wal-Mart lowered carbon emissions and saved $200 million in costs” (Kramer & Porter, 2011, p. 8).

Based upon the research presented above (Albuquerque et al, 2012; Balakrishnan et al., 2011; Kramer & Porter, 2011), it is reasonable to assume that sustainable firms generate greater accounting performance than non-ESG firms. These assumptions are substantiated with the study from Alareeni and Hamdan (2020) who investigates the ESG impact on performance of US S&P 500-listed firms. Their research investigates whether there is a relationship between ESG and firm’s ROA, ROE and Tobin’s Q as well as if these relationships are positives, negatives or neutral. The result shows that ESG has a significant positive impact on the firm’s ROA and ROE as well as the firm’s Tobin’s Q at less than 5% confidence level (Alareeni and Hamdan, 2020, p. 1420). These findings are in line with previous research suggesting that ESG is positively related to a firm's performance (Fatemi et al., 2018; Sahut & Pasquini-Descomps, 2015). On the other hand, these findings contradict other research showing that there is no empirical evidence proving that ESG and firm’s financial performance in terms of ROA and ROE is positively related (Buallay, 2019; Orlitzky, 2013).

Line of reflections As described above, Buallay (2019) shows that ESG has a negative impact on firm’s operating and financial performance (measured as ROA and ROE). On the other hand, Albuquerque et al. (2012), Balakrishnan et al. (2011), Kramer & Porter (2011), as well as Alareeni & Hamdan (2020) shows empirical evidence of the value creation perspective, stating that firm's ESG investments should be considered as a competitive advantage which should lead to increased financial performance. In accordance, they affirm that CSR should result in increasing a firm’s image and sales as well as reducing the fluctuation of profitability caused by business cycles and employee productivity in addition to better ROA and ROE. Overall, even if there are conflicting results, the majority of the research tends to lean towards the opinion that ESG and CSR efforts lead to greater operational and financial performance.

2.2.2 The impact of ESG on firm’s cost of equity capital In a study conducted by Chen et al. (2009), they investigated whether the quality of corporate governance can reduce the cost of equity capital in emerging markets where the legal protection of investors is relatively poor (Chen et al., 2009, p. 273).

According to the agency theory of Jensen & Meckling (1976, p. 5), there is good reason to believe that conflicts of interest occur between principals (shareholders) and agents (managers). One of the main purposes of corporate governance is therefore to protect shareholders from expropriation by agents, i.a. managers or controlling shareholders. This mechanism diminishes agency costs and consequently, firms with good corporate management should in theory result in higher valuations (Chen et al., 2009, p. 273).

The mechanisms by which corporate governance or the country’s legal protection of investors impacts the market value of firms are yet uncertain (Hail & Leuz, 2006, p. 487). Hail & Leuz (2006, p. 486) argued for the possibility that the impact on valuation relates from the levels of expropriation and/or different growth opportunities (which would impact the cash-flow effect,

the numerators of the equation 1 referred to in the review of Cornell’s study above). Successful corporate governance or legal protection of investors could also reduce the risk premium demanded by investors and, consequently, reduce the cost of equity capital at which future cash flows are discounted (Hail & Leuz, 2006, p. 487). However, the cost of capital reduction depends on “the extent to which differences in corporate governance or legal protection led to measurable differences in nondiversifiable risk across firms or countries” (Chen et al., 2009, p. 274).

In order to run a regression to investigate the effect of firm-level corporate governance on the cost of capital, the control variable apart from corporate governance consisted of market beta, size of the firm, book-to-market equity, inflation rate, price momentum, analysts forecast error, liquidity, ownership concentration, and fixed effect of country, industry and year Chen et al., 2009, p. 282). In the regression model, the coefficient of corporate governance is -0,080, which is significant at the 1% level (Chen et al., 2009, p. 282). This finding is in accordance with Chen et al.’s first hypothesis that firms with greater corporate governance can enjoy a lower cost of equity capital (Chen et al., 2009, p. 275). Furthermore, Chen et al. found that institutional investors across the world are willing to pay a price-premium up to 20% for shares in companies with good corporate governance and that the price premium is even greater in regions with weak legal protection of shareholders (Chen et al., 2009, p. 286).

Ng & Rezaee (2015) examined how different dimensions of sustainability performance, measured as the ESG-score and economic sustainability disclosures, affects the cost of equity. They investigate whether cost of equity is associated with financial numbers or non-financial ESG performance or both of them to see if the market prices sustainability risks (Ng & Rezaee, 2015, p. 129). Furthermore, different components of non-financial ESG value creation and the effect on the cost of equity capital is examined (Ng & Rezaee, 2015, p. 129). The conclusion from this article is that firms with higher ESG performance are significantly negatively associated with cost of equity. To examine this further, the authors break down ESG into Environmental, Social and Governance. They find that environmental and governance performance lowers the cost of equity. While they find no significant relationship between social performance and cost of equity (Ng & Rezaee, 2015, p. 146).

The conclusion that ESG factors will influence the cost of equity capital and capital constraints is also supported by other research papers. In an experimental case study conducted by Crifo et al. (2015) with professional private equity investors, they conclude that ESG disclosure will affect the equity valuation and investment decision (Crifo et al., 2015, p. 176). Another interesting finding is that it seems that bad ESG practice matters more than good (Crifo et al., 2015, p. 177). In other words, the findings implies that sustainability risk is asymmetric, where it matters more for the private equity investors that the companies do not have a bad ESG practice than being the best.

In another study conducted by El Ghoul et al. (2011), the relation between corporate social responsibility score and the cost of capital on US firms is investigated. This study contributes to existing research because there is a large interest in CSR in the society but still little is known how this reflects on the cost of capital (El Ghoul et al., 2011, p. 2389). To quantify this, the authors compare the ex-ante cost of equity of companies with higher and lower than average CSR score. The use of ex ante cost of capital has several positive effects instead of using the popular Tobin's-Q to measure firm value, it gives the authors the possibility to control the differences in growth rates and future cash flows when determining the cost of equity. The results from this study shows that companies with higher CSR scores have a lower cost of

equity. This is also true when excluding governance, which in previous studies also has been proven to lower cost of equity (El Ghoul et al., 2011, p. 2394). Firms with low CRS score are perceived by the investor as riskier and have a higher cost of equity vice versa (El Ghoul et al., 2011, p. 2400). Other interesting results is that the component in the CSR affects the cost of equity differently. CSR initiatives in employee relations and environmental policies substantially reduces the cost of capital while initiatives on community relations, human rights and diversity do not lower the cost of equity (El Ghoul et al., 2011, p. 2395). CSR investments can create value for investors through reduction of cost of equity. Specific industries can also have a big impact on the cost of equity, this study found that tobacco and nuclear power has a higher cost of equity than others.

These findings are also supported by Feng et al., (2015) who gather further contributions in the area by using data from 25 different countries located in North America, Europe and Asia (Feng et al., 2011, p. 249). What we find extra interesting in this article is that the results differ depending on regions. They find support for the theory that firms who have better CSR performance will have a lower cost of equity in North America in line with El Ghoul et al., (2011) as well as for Europe (Feng et al., 2011, p. 275). In contrast, for Asia, the study found a positive relationship between cost of equity and CSR performance, which is contradictory to what was found in the western world (Feng et al., 2011, p. 275). The results regarding the western world are although contradicted by the findings of Nguyen, P. & Nguyen, A. (2015), who have found a positive relationship between CSR and firm risk when conducting a study on the S&P 500 firms between 1991 to 2003 (Nguyen, P. & Nguyen, A., 2015, p. 336). The relationship derives from that CSR concerns such as diversity, employee relation and corporate governance increases a firms’ fixed cost which will transfer risk to shareholders (Nguyen, P. & Nguyen, A., 2015, p. 336). The regional divergence of findings in Feng et al.’ study could be explained by different local and country influences (Feng et al., 2011, p. 275). The rationale behind is based upon previous theories such as stakeholder versus shareholder model. Some investors will see CSR investments as value destructive while others will use it as an important indicator for firm performance. This could also be explained by different ethical considerations in different parts of the world and not only be related to financial performance.

Line of reflections Within the area of CSR, ESG and cost of equity capital a lot of work has been done. In this part of the literature review we have covered what we think is the most important part of previous literature related to these issues. What we find extra interesting is that to the best of our knowledge, no research has specifically examined this relationship in the Nordic countries. The vast majority of research has found a negative relationship between cost of equity capital and ESG, CSR performance. This is also true when excluding governance that has been found to lower the cost of equity capital (Hail & Leuz, 2006). Although, there are still conflicts within the area where several researchers have found the opposite relationship, that increased CSR and ESG efforts results in a higher cost of equity in different parts of the world (Feng et al., 2011; Nguyen, P. & Nguyen, A., 2015).

2.2.3 The impact of ESG on firm’s market valuation, stock return and volatility While the previous research presented above is rather focusing on the implications of ESG on operational and financial performance as well as the cost of equity capital, the purpose of this section is to highlight previous research with a focus on the impact of ESG on market performance and unsurprisingly, there are contradicting results within this area too. Fatemi et al. (2018) investigated the effects of ESG performance and firm value on publicly traded US

firms. What we find extra interesting in this article connected to our research problem is that they use both ESG disclosures and ESG performance to measure firm value through Tobin's- Q (Fatemi et al., 2018, p. 46). They discuss the effects of greenwashing and adverse selection, which is an important problem in sustainability investing since investors need to assess if the information given to them is correct in order to make the right investment decision (Fatemi et al., 2018, p. 48).

First, they conclude that ESG strength increases firm value and that ESG weakness decreases firm value (Fatemi et al., 2018, p. 58) which is in line with previous research (Alareeni & Hamdan, 2020; Buallay, 2019). But what we find even more interesting is that when they isolate the factor “ESG disclosure” from other factors in the regression model, they found a negative relationship between ESG disclosure and firm value. When the authors combine ESG strengths vs weakness and disclosure a more nuanced picture emerges (Fatemi et al., 2018, p. 58). When firms with high ESG performance increase ESG disclosures, this will lower the positive valuation effect first seen when only examining ESG strength. The authors explain this finding that the market may be scared that the companies are over investing in sustainability thus having problems to rely on the information (Fatemi et al., 2018, p. 58).

Previous research within this field also goes beyond examining ESG’s impact on market valuation of firms. Several research papers search for the answer if CSR and ESG performance are linked to lower risk, shouldn't this also be reflected in the stock volatility? Kim et al. (2014, p. 11) found that CRS performance is significantly negatively related to one-year-ahead stock price crash risk. One important finding of this study is that this relationship was only significant when the firms had less effective governance and lower level of long-term institutional ownership. This implies that the role of CSR is very important to mitigate the risk for stock price crashes when governance is poor (Kim et al., 2014, p. 11).

Harjoto et al. (2017, p. 95) have found a nonlinear relationship between CSR and stock return volatility, where increased CSR leads to lower stock market volatility but at a decreasing rate through its effect on institutional ownership, until the point that institutions see it as optimal (Harjoto et al., 2017, p. 105). Institutional ownership is significantly negative related to firm risk, but the concave relationship casts doubt about whether institutional investors invest in CSR because of ethical considerations (Harjoto et al., 2017, p. 95).

Furthermore, Ashwin Kumar et al. (2016) investigated corporations listed on the Dow Jones Sustainability Index and observed that ESG-firms showed lower volatility, lower risk, and higher risk-adjusted returns (Ashwin Kumar et al., 2016, p. 298). This hypothesis is also substantiated by Nandita et al. (2018), who finds that during the financial crisis of 2008, mutual funds with higher ESG-score generated lower losses compared to lower rated ESG funds (Nandita et al., 2018, p. 67). In contrast, Folger-Laronde et al. (2020) who examined the resilience of ESG exchange traded funds (ETF) against the market turmoil caused by the Covid- 19 outbreak, found that ESG did not immunized exchange traded funds (ETF) against the market crash (Folger-Laronde et al., 2020, p. 5).

Although, the risk-reduction perspective of ESG is in contradiction with the findings in a research conducted by Orlitzky (2013), who challenges the conventional perception regarding the net benefits of CSR. Orlitzky also deviates from the assumption of semi-strong market efficiency and shows “how a particular set of social forces and dynamics may create noise and disrupt the market efficiency” (Orlitzky, 2013, p. 239). Hence, the two major assumptions in the study are that definitions of CSR are highly inconstant and subjective and that financial

markets are inefficient (Orlitzky, 2013, p. 239). Orlitzky provides a model of the unintended consequences of CSR for financial markets and states that CSR creates and increases stock volatility (Orlitzky, 2013, p. 245). Orlitzky also declares that the current environment, which is promoting ESG, is leading to excess market valuations and stock price bubbles since stock price increases are not fundamentally justified (Olitzky, 2013, p. 247). According to Orlitzky, the increased volatility derives from the “disagreement among investors about the nature, extent, or implications of a specific organization’s CSR” (Orlitzky, 2013, p. 244). In other words, Orlitzky highlights that several groups of investors have opposite judgments of a firm’s CSR activities. One group of investors assumes an inverse relationship between CSR and firm’s corporate financial performance and therefore are likely to short-sell stocks of corporations that have recently reported a growth of their CSR activities (Orlitzky, 2013, p. 245). On the other hand, the second category of investors, categorized as social conscious investors, are likely to go long stocks of CSR firms since these investors assume CSR activities increase faith between the organization and its shareholders, improve corporate reputation and reduce the business risk as well as agency costs (Orlitzky, 2013, p. 245). The third and last group of investors are believers of stakeholder theory, which are expected to buy stocks of firms that are considered to be highly ranked in CSR (Orlitzky, 2013, p. 245). The main conclusion drawn by Orlitzky is that the investor’s disagreement named above are likely to have a different view on the impact of CSR on firm’s operating, financial and market performance, leading to an increased trading volume and excess volatility of the stock prices of CSR firms (Orlitzky, 2013, p. 245).

Nevertheless, according to Sahut & Pasquini-Descomps, an increased ESG performance should in theory result in reduced risk and thus the market value is expected to increase (Sahut & Pasquini-Descomps, 2015, p. 59). But this is not the result found in this study, the authors found a neutral or slightly negative relationship between the overall ESG rating and monthly stock excess return for the UK but no relationship for US or Switzerland (Sahut & Pasquini- Descomps, 2015, p. 59). Interesting to note is that investors do not seem to use ESG as a perception of risk except for when the market is sensitive to specific conditions (Sahut & Pasquini-Descomps, 2015, p. 59-60) such as after an industry scandal caused by i.a. oil spills, nuclear leaks or money laundering.

Line of reflections Within the sphere of ESG and market performance, more have been done in the field of operational and financial performance as well as the cost of equity and valuation than examining the impact of ESG on return, risk and volatility. Furthermore, we have not found any research covering the Nordic countries. Fatemi et al. (2018), Buallay (2019) as well as Alareeni & Hamdan (2020) have shown that the market value of firms, all measured as Tobins’Q, is positively correlated with firm’s ESG and CSR efforts. This is partially in line with Orlitzky’s (2013) point, since Orlitzky describes that stock price of ESG firms increases, but according to Orlitzky, the increase of market value has the characteristics of a price bubble and is not sustainable (Orlitzky, 2013, p. 247). Furthermore, Orlitzky shows that CSR leads to increased stock price volatility (Orlitzky, 2013, p. 245), but this finding is in contradiction with the studies of both Kim et al. (2014) and Cho et al. (2014), who provided evidence of the opposite relationship between CSR and stock return volatility. Finally, Sahut & Pasquini- Descomps tried to find evidence of a relationship between ESG and stock returns (2015) but only managed to prove a neutral or slightly negative relationship for the UK and no relationship for US or Switzerland (Sahut & Pasquini-Descomps, 2015).

The research papers mentioned above has provided us with important information and insight that we will use in our thesis. Several ways of measuring ESG, from a news-based approach to databases with ESG information such as Eikon and Bloomberg have been used. We have also found several control variables that will be useful in our statistical test, which will be discussed in the research method chapter.

To the best of our knowledge, little has been done so far to examine if ESG provided a safeguard during the Covid-19 crisis. As named above, one research done regarding Covid-19 and ESG have found that ESG did not immunized exchange traded funds (ETF) against the covid-19 market crash (Folger-Laronde et al., 2020, p. 5). But since Folder-Laronde et al. (2020) focuses on ETFs, we still believe there is a research gap that needs to be filled with focus on common stocks in the Nordics.

2.2.4 General conclusions of the previous research Table 1 - Overview of the previous research

In this literature review we have found a lot of research in the area and we have categorized this research in three different categories. Whereof ESG is first compared to operational and financial performance, secondly compared to the cost of equity capital and finally to market valuation, volatility, and return. Our literature review contains the most important research in the field that is of importance for our thesis. We have found research that points in different directions which makes this a very interesting subject for our thesis. To the best of our knowledge, we have not found any research on cost of equity and the connection with ESG performance in the Nordic countries, which makes the Nordic countries a very interesting geographical area to investigate. Furthermore, we have not found any research connecting ESG performance with multiples-based

valuation which we think would been very interesting. Some of the previous research has focused on CSR and risk for one-year-ahead stock price crash (Kim et al., 2014) and ESG ETF resilience to the Covid-19 market crash (Folger-Laronde, 2020) but since the previous research is limited regarding the risk for stock price crashes and ESG performance, we think that this is a subject worth investigating further in connection to the corona-virus market crash in 2020.

2.3 Positioning of the thesis within the theoretical framework

Figure 4 - Positioning of the thesis within the theoretical framework

Figure 4 describes the thesis’ positioning to the theoretical framework. The figure highlights relevant theories that show how ESG performance could affect the cost of equity, price multiples and stock price fall. It goes however without saying that a vast number of financial theories have been excluded from the theoretical framework. Two main theories which also would have made sense to include are the pecking-order theory of capital structure and a framework for behavioral finance.

The pecking-order theory of capital structure provides an explanation about how firms prefer to finance themselves in a specific pecking-order. According to Myers (1984), as a consequence of adverse selection, there is evidence proving that firms prefer internal to external finance (Myers, 1984, p. 582). Accumulated profits (internal equity) are the preferred way of financing the business, followed by the issuance of new debt and at least, the issuance of new equity as external finance. The rationale behind the theory is that when external funds are necessary, management prefer debt to equity because of the signals that new equity issuance is sending. The pecking-order theory has been excluded since this thesis mainly focuses on the cost of equity and not the underlying capital structure.

While the pecking-order theory focuses on preferences in corporate funding, another theory that also could had been introduced is behavioral finance. Behavioral finance is an area of study which shows how psychological influence impacts asset prices (Subrahmanyam, 2007, p. 16). We are aware that traditional models have a limited role explaining market movements and believe that behavioral finance will be of growing influence in the coming years. However, this thesis only takes a traditional approach in the measurement of risk and valuation and excluded the investigation of any impact psychological influences on risk and valuation in regard to sustainability. 2.4 Theoretical hypothesis

The main purpose of this thesis is to investigate if sustainable firms can enjoy a lower cost of equity capital. Therefore, the main hypothesis (H1) of this thesis is formulated as below:

H1: High ESG score is negatively correlated to the cost of equity

We have described several potential impacts of ESG on a firm’s cost of equity in the theoretical point of departure, showing opposing forces that could explain both an increase and a decrease of the cost of equity of a sustainable firm. Previous research has found mixed results how the perceived risk is influenced by sustainability. We expect that firms with higher sustainability will have a lower cost of equity.

In order to increase the robustness of the findings regarding H1, we have developed a secondary hypothesis. The secondary hypothesis will show if investors are willing to pay a premium for sustainability and is formulated as below:

H2: High ESG score is positively correlated to price-based multiples

Finally, in order to establish firmly any eventual findings from H1 and H2, we have developed a third and final hypothesis to investigate the behavior of ESG-stocks during a crisis and

particularly during the market turmoil caused by the Covid-19 outbreak. Therefore, our third hypothesis is formulated as below:

H3: Stock prices of high ESG firms resist better to crises and market crashes

In order to provide statistically significant answers to the hypothesis disclosed above, statistical hypotheses will be developed in the research method section of the thesis.

3 Scientific methodology

In this section, methodological assumptions and approaches are described. Furthermore, a reasoning is given of the study’s literature search and evaluation is given. The chapter concludes with a table, highlighting main keywords which have been used in the literature search.

When constructing research, several approaches can be used. In the scientific community, there are traditions about how research should be performed, and these have evolved over time. A common way to achieve a distinct and understandable scientific method in a thesis according to O’Gorman & MacIntosh (2015, p. 50-52) is to begin with research philosophy. Research philosophy consists of two main assumptions, ontological and epistemological assumptions. These assumptions work as a framework for how research should be implemented to answer the research question and should be based on our view of the reality and the nature of knowledge. Following, methodology (quantitative or qualitative) is to be chosen and lastly, the research approach (inductive or deductive) should be disclosed.

Figure 5 - Process of the scientific assumptions and approaches. 3.1 Ontological assumptions

While conducting research, one should reflect on the nature and development of knowledge. Ontological assumptions are concerning the nature of reality, where one assumption is the belief that social reality is objective and external to the researcher. Don-Solomon & Eke (2018, p. 2) states that it's important for researchers to reflect upon their ontological assumptions before starting your research because it will influence the research approach, methods of data collections and data analysis. According to objectivists, there is therefore only one reality, and everyone has the same sense of reality. This objective approach sees the world consisting of solid objects that can be measured and tested. There can only be one reality and this social reality is objective and external to the researcher. On the other hand, the opposite assumption is the belief that reality is subjective since reality is socially constructed (Collis & Hussey, 2013, p. 47). The opposite assumption is the belief that each single person therefore has his or her own perception of reality which leads to an assumption that there are multiple realities. Researchers who are of that belief see the world as subjective and assume reality can never be separated because it's made up by perceptions and interactions between living subjects. This means that reality can never be separated from the eyes of the beholder and thus leading to several realities (O’Gorman & MacIntosh, 2015, p. 56).

Since this study aims to investigate if there is a relationship between a firm's ESG-rating and it’s cost of equity, price multiples and resistibility to market turmoil, our statistical tests, analysis and findings will rely upon secondary data. A subjective view of reality would require an understanding of the equity market investor's view of reality and motivations of investing. A subjective point of view is thus not appropriate, and the results would in that case not be considered as reliable. Thereby, we consider the equity market as one single entity, representing

an overall consensus taking into account each individual investor's perceptions. Furthermore, we assume that there is only one observable reality, making an objective point of departure more suitable in the context of our research. We opted for taking the view that objective aspects are of higher relevance for our research purpose than the way investors and traders themselves perceive a phenomenon such as sustainability. 3.2 Epistemological assumptions

While ontology concerns the nature of reality, epistemology concerns what is to be considered as valid knowledge (Collis & Hussey, 2013, p. 47). Epistemological assumptions involve scrutiny of the relationship between the researcher and what is being researched. Epistemological assumptions are important for the research design because it concerns how we obtain valid knowledge and what is seen as valid knowledge. The two main and opposite paradigms which guide how research should be conducted are positivism and interpretivism (Collis & Hussey, 2013, p. 43). At the positivism end of the continuum, knowledge is viewed as something that is observable, independent, objective and measurable. Knowledge is also viewed to be valid for everyone, regardless of background (Bell & Bryman, 2011, p. 15). At the other extreme view of knowledge there is interpretivism. Interpretivism sees knowledge as subjective and unique in every situation which makes results ungeneralizable, but in contrast with positivism, it is not of relevance if the result is not generalizable on a larger population. Studies conducted with the interpretivist view of knowledge want to focus on the meaning and really understand why it is happening with small samples but with a great depth (Collis & Hussey, 2013, p. 54). This is important to understand because it will influence the research method where following the positivism paradigm the researcher keeps his distance towards the data while in the interpretivist view the researcher wants to be close to the respondents to evolve the research and the understanding of the issue. This also means that when an author sees knowledge as subjective, they know that their own opinions may affect the research. But this doesn't matter because the results are not meant to be generalizable, and the knowledge must be understood in certain contexts.

Since interpretivism typically aims to develop new theories and ideas rather than testing theories, interpretivism is naturally connected to a qualitative methodology (O’Gorman & MacIntosh, 2015, p. 60). For this reason, we believe interpretivism does not suit this thesis. Furthermore, since positivism assumes social phenomenon can be measured, positivism is usually associated with quantitative methods of analysis (Collis & Hussey, 2013, p. 44) and therefore fit this thesis well. 3.3 Research design and methodological choice

Research design describes which steps that will be taken to ally the research questions of this thesis to the data collection and analysis in a consistent way. According to Collis & Hussey (2013, p. 4), research can be classified in four different types: exploratory, descriptive, analytical and predictive. Exploratory research is typically performed when there is very few or no earlier research to which the researcher can refer to for information about the research problem (Collis & Hussey, 2013, p. 4). The aim of exploratory studies is therefore to look for patterns and ideas and develop rather than test an already established hypothesis. Descriptive studies are done to describe phenomena as they exist. Descriptive research is commonly used to identify and acquire information on the attributes of a particular issue or problem (Collis & Hussey, 2013, p. 4) and tries to answer “what” or “how'' to a specific research question (Collis & Hussey, 2013, p. 4). Finally, analytical and predictive studies are continuations of

exploratory studies, going even further by testing causality in case of analytical studies and by forecasting the likelihood of a similar situation occurring elsewhere in case of predictive studies (Collis & Hussey, 2013, p. 5).

Since this thesis will test several hypotheses on established theories and will strive to answer if ESG affects the cost of equity, price multiples and relative performance during stock crashes, descriptive research is suitable for this thesis. Furthermore, causality of any possible findings will not be tested since it is not required at our level of studies, which naturally is leading to the fact that an analytical design is not suitable for this thesis.

After covering the different research philosophies and design as done above, the next step according to O’Gorman & MacIntosh (2015, p. 51) is the data gathering methodology. Data can be categorized either as qualitative data or quantitative data. Qualitative data is data in nominal forms and quantitative data are data in a numerical form (Collis & Hussey, 2013, p. 52). The data collection methods used for this thesis relies exclusively on numerical data retrieved from Thomson Reuters Eikon database that will further be statistically tested and analyzed. This implies that the methodological choice is a quantitative research design. It would also have been possible to construct this study in order to fit a qualitative design by conducting in-depth dialogue or interviews with a few Nordic investors in order to gain some insight into how these investors reflect upon e.g., ESG ratings and their required returns. Since the aim of this thesis is to be able to draw general conclusions if any findings are to be found, a quantitative approach with a large sample is more appropriate. 3.4 Research approach

The research approach describes the relationship between research and theory. There are two main approaches to this relationship, the deductive and the inductive approach. Although, the deductive approach represents the most common view of the nature of the relationship between theory and research according to Bryman & Bell (2011, p. 11). The process of deduction starts with theory. From established theories, the researcher deduces hypotheses prior to collecting data which finally is tested on the empirical environment. Lastly, the hypotheses are confirmed or rejected and the theory might or might not be revised or modified (Bryman & Bell, 2011, p. 11).

Figure 6 - The process of deduction (Bryman & Bell, 2011, p. 11)

In contrast, the process of induction starts in an empirical environment and strives to generate a new theory in order to explain the empirical observations. In the corresponding way that deductive approach is associated with quantitative research, an inductive approach of linking data and theory is usually associated with a qualitative study (Bryman & Bell, 2011, p. 13). Since the aim of this thesis is not to develop a new model for cost of equity including an ESG- factor but rather test if ESG impacts the cost of equity, a deductive approach is the right approach for this thesis.

3.5 Literature search and evaluation of sources

Due to the fact that this thesis is following a deductive approach, a major part of a deductive approach relies on a well-performed collection of theories and publications which is the starting point for formulating hypotheses. In order to conduct a literature review and develop a theoretical framework, a number of databases have been used. The main databases used are the Umeå University Library database, Google Scholar and DiVa Database. The choice of databases has been natural since these are the most relevant databases which we both have access to during the course of our studies at Umeå University. A vast majority of the literature used is peer-reviewed, which increases the validity of this thesis. Furthermore, the quality of the literature has been considered, where poor-quality papers have been filtered-out. Since sustainability is a relatively new topic, a restricted amount of literature comes from articles published in business magazines and blogs. Moreover, we have strived to use the most recent research and work back in time in order to use relevant literature to the society and market characteristics of our time. Since this thesis is written in English, we have mainly used literature written in English and only few sources are in Swedish. Below is a table with keywords which was used to find literature.

Table 2 - Keywords used in the literature search

English Swedish Environmental, Social, Governance (ESG) Hållbarhet Corporate Social Responsibility (CSR) Capital Asset Pricing Model (CAPM) Financial performance Stakeholder’s theory Shareholder’s theory Green finance Sustainable finance Efficient Market Hypothesis (EMH) Equity Capital Market (ECM) Cost of Equity Ex-ante Cost of Equity

Regarding sources of data collection, all data have been retrieved from the Thomson Reuters Eikon database. We believe that Thomson Reuters is a credible data source, as it is broadly used in previous research and one of the largest firms worldwide in the industry of media and information. 3.6 Ethical considerations

In this part of this thesis, societal, social and ethical considerations of the study will be discussed. One might think we do not have to take into consideration the ethical impacts of this thesis since no individual will be involved in the study, so the integrity of others does not have to be considered as a concern. However, since the research topic is based on societal and ethical

concerns, it is relevant to discuss “how different stakeholders are affected by, or look upon the phenomenon, how short-term profit maximization balances with long-term environmental sustainability, or how the rights and responsibilities of different actors converge or diverge” (USBE, 2018, p. 26).

The purpose of the study is to analyze the impact of ESG on the cost of equity, price multiples as well as relative stock return in times of crisis. A possible outcome of this thesis could be empirical results showing ESG to be a value-destructive variable, leading to a higher cost of equity, lower price-multiples and negative impact on relative return compared with non-ESG firms. Such results would imply that investors are not rewarding ESG which could lead shareholders to advocate for giving sustainability issues a lower priority. Such results do not necessarily need to be seen as unethical in itself, although it would imply that markets are incapable of pricing sustainability risk. One solution to tackle this issue could be that sustainability risk should be priced throughout policymaking from politicians. However, we want to emphasize that such conclusions are not preferable, as such results would contradict our own ethical standpoints. We believe sustainability is of high relevance in order to reduce humanity’s impact on climate change and it should therefore be high on the agenda of corporations, consumers and policy makers, regardless of the strictly financial rationale behind such consideration.

To further discuss the ethical considerations of this thesis, the Good research practice (Swedish research council, 2017) has been examined. It is important to evaluate how the integrity of the participants in the study are looked after (Sweden research council, 2017, p. 41). In this thesis quantitative data available in EKON and other open sources will be used because of this we do not see any integrity problems in this study. Even though this thesis is conducted with qualitative data the integrity and quality of the research will depend on the professional ethics from the researchers, professional ethics concerns with the researcher's behavior in various roles. This is of great importance in any study because all steps in the research are based on decisions from the authors. It is not only pure research misconduct such as altering the data or using someone else's work that should be seen as unethical. If the research question is unprecise, the research design is not appropriate to answer the research question (Sweden research council, 2017, p. 16). Researchers should always strive to be as transparent as possible so that the research and the different choses can be verified and understood. The goal of this study is to contribute with important knowledge to academia, stakeholders, and legislators. The question of research ethics is therefore of great importance because if these questions are neglected, our research could do more harm than good.

Many studies are performed in collaboration with different organizations and the proportion of research conducted with funding from external organizations is growing (Sweden research council, 2017, p. 46). This creates problems related to the research ethics, can different organizations impact the outcome of the research? What are the intentions of the external organizations? This thesis has however not been financed or influenced by any external organization, which contributes to reliability of this thesis.

4 Research Method

In this chapter, a presentation of the population and sample as well as the several statistical hypotheses will be presented. Thereafter, the several regression models together with independent, dependent and control variables will be disclosed and discussed.

To answer the research question by using statistical tests, data must be collected and organized. The research design could be described as a guideline for collecting and analyzing data (Bryman & Bell, 2011, p. 45). There are several research methods that could be implemented depending on the research strategy, were longitudinal and cross-sectional design with panel data are two examples (Bryman & Bell, 2011, p. 41). In a cross-sectional design, data is collected for a specific date from several entities. This makes it possible for the researcher to acquire large amounts of quantitative data that can be tested to identify possible relationships between variables (Bryman & Bell, 2011, s. 53). In contrast, a longitudinal study builds upon the cross-sectional design, but data is collected from several time-periods in order to see changes over time (Bryman & Bell, 2011, p. 57). The longitudinal study design is therefore not relevant for this thesis since the aim of this study is not to see the evolution over time of any possible relations. To answer the different research questions in this thesis, a cross-sectional approach is therefore taken where data has been collected both from Thomson Reuters Eikon and from the webpage of the central banks of the Nordic countries. Some of the data collected has the characteristics of time-series data and some as cross-sectional data. To be able to perform a statistical analysis in STATA with this material, it was structured as panel data and manually reorganized in Excel.

The data collection process could be divided into five steps in a positivistic study (Collis & Hussey, 2013, p. 197). The starting point is to choose a sampling method, followed by identifying variables, choosing data collection methods, conducting a pilot study and finally collect the research data (Collis & Hussey, 2013, p. 197). In quantitative study under a positivistic paradigm, it is important to reflect upon the sampling method. In these types of studies, it is important to be able to generalize the result to the entire population in a trustworthy way. To be able to generalize the results on the entire population, it is important that the sample correctly reflects the population and is unbiased (Collis & Hussey, 2013, p. 197). This could be done in several different ways such as conducting a random sampling process where all the large cap companies in the Nordic market have the same chance of being chosen (Collis & Hussey, 2013, p. 197). Because the population of this study is relatively small with 277 firms all firms are included in the sample. This increases the generalizability of the results, and the study will not have to deal with any uncertainty related to the chosen sampling method (Collis & Hussey, 2013, p. 198). Another important step is to identify what variables should be included in the tests. Under a positivistic and deductive approach, the idea is to test existing theories with empirical data (Collis & Hussey, 2013, p. 201). Therefore, it is important to base all variables on the theories and previous research discussed in the theoretical framework. This step is explained extensively in section 4.4 related to all variables included in the hypothesis tests. Lastly, no pilot study has been conducted due to the limited time available for this degree project. However, we feel comfortable in the choice of empirical methodology and research design. 4.1 Population and Sample

Only large cap firms listed on the Nordic countries’ stock exchange are included in the data collection. Therefore, data for all firms included in OMX Stockholm Large Cap Index, OMX

Helsinki Large Cap Index, OMX Copenhagen Large Cap Index and Oslo Børs Benchmark Index have been retrieved, which sums up to a total of 277 firms. As earlier described, financial institutions have been removed due to their unique characteristics. Furthermore, investment holding companies are also excluded since many of these firm’s holdings already are independently a part of the sample. To sum up, after removing financial institutions and investment holding companies (33 firms), the population of the study is composed of 244 firms. The same population is used for all research questions and a list of the 244 firms is provided in appendix 1.

The study’s sample consists of all firms within the population with complete data. Since the data is retrieved over a time-period, beside from missing data points such as ESG-scores, earnings estimates or calculations related to the cost of equity, the number of firms also varies over time due to IPOs as well as mergers and acquisitions. For the main research question, the data consists of 46 unique firms as of Q1 2011, 162 unique firms as of Q4 2020 and a total of 178 unique firms in between these two periods. Regarding the second research question, the data consists of 47 unique firms as of Q1 2011, 148 unique firms as of Q4 2020 and a total of 171 unique firms in between these two periods.

The timeframe that will be used for the primary and secondary research question in this thesis consist of quarterly data from 2011-01-01 to 2020-12-31 (40 quarters). The reasoning behind the covered time span is because ten years should be significant enough to capture the long- term effects of ESG. The sample used to answer the first and second hypothesis is the same.

Figure 7 - Nasdaq OMX Nordic Large Cap GI EUR, 2020-01-01 to 2020-12-31 where market top of 2020-02-19 is set at value 100. Source: Nasdaq (n.d.b), calculations are author's own.

In order to answer the side-hypothesis regarding market crashes, we have defined the market top as of 2020-02-19 and bottom as of 2020-03-23. Daily close prices and ESG scores have been retrieved during that time span, leading to a sample of 212 firms.

The corona crisis is still ongoing as this thesis is written, but 21 trading days was selected to be defined as the timeframe of the market turmoil caused by the Covid-19 crisis. The timeframe, from the market top the 19th of February 2020 to the bottom the 23rd of March 2020 was, as it can be observed from the chart above a period of turbulence in the financial market. By the time investors started to realize the impact of restrictions adopted to reduce the spread of the disease around the world, stock market felt sharply. Nasdaq OMX Nordic Large Cap GI EUR recorded a loss of approximately -34% of its value before the trend reversed. It can also be observed that the market return to pre-crisis level relatively rapidly, pricing in a bright economic outlook. 4.2 Statistical hypothesis

Prior to performing any regression analysis, the first step in hypothesis testing is to clarify the hypothesis that is to be tested. Otherwise, if the hypothesis is developed after the regression analysis is estimated, the estimation suffers the risk of being justifications of particular results rather than tests of validity of these results (Studentmund, 2014, p. 128). In addition, when conducting hypothesis testing, a null hypothesis and an alternative hypothesis should be stated. The null hypothesis states the outcome which the study does not expect, and the alternative hypothesis states the outcome which the study expects to be found (Studentmund, 2014, p. 129). Because previous studies have found both a significant positive and negative relationship between the cost of equity and sustainability performance a two-sided test will be conducted (Verbeek, 2008, p. 25).

4.2.1 Hypothesis connected to research question one Since the first research question is “Are equity market participants pricing sustainability risk?”, the connected hypothesis is:

!!":There is no significant relationship between ESG score and cost of equity !#":There is a significant relationship between ESG score and cost of equity

4.2.2 Hypothesis connected to research question two Since the second research question is “Are investors willing to pay a price premium for sustainability?” the connected hypothesis is:

!!$.":There is no significant relationship between ESG score and P/E !#$.":There is a significant relationship between ESG score and P/E

!!$.$:There is no significant relationship between ESG score and P/B !#$.$:There is a significant relationship between ESG score and P/B

!!$.&:There is no significant relationship between ESG score and EV/EBIT !#$.&:There is a significant relationship between ESG score and EV/EBIT

4.2.3 Hypothesis connected to research question three The third and final research question of this thesis is “Is there a pattern between a firm’s ESG- impact and their stock returns during times of crises?”, which results in the following hypothesis:

!!&:There is no significant relationship between ESG score and index-relative price decline during the market turmoil of March 2020 caused by the Covid-19 outbreak. !#&:There is a significant relationship between ESG score and index-relative price decline during the market turmoil of March 2020 caused by the Covid-19 outbreak. 4.3 Regression analysis

In this thesis we have chosen regression analysis to answer our three different research questions. Regression analysis is a statistical tool that tries to explain the change in one variable with other variables, these are often called dependent and independent variables (Studenmund, 2014, p. 5). Regression analysis is commonly used when it comes to hypothesis testing (Studenmund, 2014, p. 3) and gives us the opportunity to study previous theories and test these on empirical data. This approach is in line with the chosen scientific methodology described above. Previous studies that have examined the relationship between ESG performance and cost of equity have also used regression analysis, we have thus concluded that this is a good fit for our study. One of the most influential and most cited papers in the field is El Ghoul et al. (2011) who conducted a multivariate regression analysis where they regress company's cost of equity and CSR proxies and control variables (El Ghoul et al., 2011, p. 2394). The majority of previous research that we have discussed and used as foundation for our thesis uses regression analysis.

A regression model can never prove any causality (cause-and-effect relationship), but a regression model can test the strength and the direction of the quantitative relationship involved between an independent and dependent variable (Studenmund, 2014, p. 6). Consequently, if any relationship between ESG and the cost of equity is to be found when the regression model are performed, the conclusion that ESG provides a lower cost of equity will not be possible to be drawn regardless of significance or degree of explanation in our model. The only conclusion that can be drawn from the regression analysis is if there is a relationship between ESG performance and the capital cost of equity. This should however not be seen as a delimitation for this study since the conducted research is a descriptive research, which implies no test of causality.

4.3.1 OLS - Ordinary Least Squares Ordinary least squares are the most used regression analysis tool. The ordinary least square technique has several benefits which contributes to its popularity. The model is easy to use (Studenmund, 2014, p. 37) which is beneficial because of the limited time frame for this thesis. Linear regression models are also useful to find economical relationships to draw conclusions about what happens if other variable changes (Verbeek, 2008, p. 13).

The OLS-model will minimize the summed squared residuals to calculate the regression coefficients. By minimizing the squared residuals to zero the estimated regression model will come as close as possible to the actual data (Studenmund, 2014, p. 39). Through the regression coefficients in the regression equation, we will be able to see what happens with the dependent

variable if there is a one-unit change in an independent variable when holding the other variables constant (Studenmund, 2014, p. 42). An OLS-model built to explain if there is a relationship between ESG performance and the cost of equity capital will therefore be used as a regression estimation technique in this thesis.

In order to evaluate if our OLS model is reliable in estimating the relationship between the dependent and the independent variable, seven classical assumptions have to be fulfilled. The seven classical assumptions that must be met (Studenmund, 2014, p. 98) are:

I. The regression model is linear, is correctly specified, and has an additive error term. II. The error term has a zero population mean. III. All explanatory variables are uncorrelated with the error term. IV. Observations of the error term are uncorrelated with each other (no serial correlation). V. The error term has a constant variance (no heteroskedasticity). VI. No explanatory variable is a perfect linear function of any other explanatory variable(s) (no perfect multicollinearity). VII. The error term is normally distributed (this assumption is optional but usually is invoked).

If one or more of these assumptions are not fulfilled, another statistical model should be used or the model should be revised. Because these assumptions are crucial for the OLS-model and the conclusion that can be drawn from it, an extensive description on how each of the assumptions have been tackled will be provided in the next chapter.

4.3.2 GLS - Generalized least square regression As previously stated, an OLS-model needs to fulfill the seven classical assumptions in order for the OLS-model to be reliable. If an OLS-model fails to meet one or several of these assumptions, there are several methods that can be used in order to overcome these problems and still get statistically reliable estimates of the coefficients. If there is evidence that an OLS- model fails to meet the assumption IV, a generalized least squares model (GLS-model) can be used instead of an OLS-model. This is because the GLS regression equation solves the problem with first order serial correlations and restores the minimum variance attribute to its estimation, making the estimated coefficients reliable (Studenmund, 2014, p. 338). If this is done, it will result in the regression model fulfilling the assumption of no serial correlation.

It is important to remember that there could still be problems in the model even if GLS estimates are used instead of OLS estimates. Even though the model will not have any problem regarding the autocorrelation and will estimate reliable coefficients, the OLS and GLS estimates will differ which is problematic because the expected value will be the same. Furthermore, a GLS does not solve possible violations of the six remaining classical assumptions. 4.4 Regression models

In order to test this thesis’s different hypotheses, several multivariate regression models have been developed. We think that the selected previous research has provided us with a good theoretical background which makes us comfortable with the choice of variables. These studies previously mentioned have used several different techniques to calculate the cost of equity and slightly differs in their choice of control variables. A discussion regarding the

choice and calculation of variables will therefore be given succeeding the presentation of the regression models below.

Equation 7 - Multivariate regression model connected to the main research question

!"#! = & + ("#)*! + (#+#,-! + ($)./#! + (%+,0! + (&1+.-)! + ('020! + ((-3-! + ()4#5! + 6!

Equation 8 - Multivariate regression models connected to the research question two

#/%' = & + ("%)*' + ($,-%' + (&./0' + (()12%' + ()030' + 4' #/.' = & + ("%)*' + ($,-%' + (&)12%' + ((030' + 4' %5/%.1/' = & + ("%)*' + ($,-%' + (&./0' + (()12%' + ()030' + 4'

Equation 9 - Multivariate regression model connected to the research question three

,61)1)' = & + ("%)*' + ($.%/7' + 4'

Where: ,-% = ,-89 -: %;<=9> #/% = #?=@A 9- AB?C=CD8 ?B9=- #/. = #?=@A 9- E--F GBH ?B9=- %5/%.1/ = %C9A?I?=8A GBH 89-@F IA?:-?MBC@A J 9- MB?FA9 GBH R.17) = R-?A@B89 E=B8 030 = 0-MAC989 @-GA?BDA S%5 = SAGA?BDA T = ,-C89BC9 4' = 6BCJ-M A??-? 9A?M

4.4.1 Dependent variable Dependent variable connected to the first research question In order to answer if a higher ESG score provides a lower cost of equity or not, we have estimated the cost of equity of the firms within our sample. As mentioned in the theoretical framework, the cost of equity can be estimated in a variety of approaches. Some researchers have used the CAPM model (Dahiya & Singh, 2020, p. 8), while other researchers have calculated the implied cost of equity based on forward-looking valuation models (ex-ante cost of equity). Due to the critics against the capital asset pricing model in previous research (Elton, 1999, p. 1199; Rossi, 2016, p. 614), we have chosen to perform our calculation based upon an ex-ante calculation of the cost of equity. We follow recent research in finance and accounting (Chen et al., 2009; El Ghoul et al., 2011; Hail & Leuz, 2006) to estimate the ex-ante cost of equity implied in current stock prices and analyst forecasts. The implied cost of equity calculations are based upon analyst forecasts of dividends, cash-flows and profits, perpetuity growth rate relies on the mean reversion assumption and is based on industry mean growth-

rate. More precisely, the cost of equity is an average of four different models: The Claus & Thomas model (Claus & Thomas, 2001), the Gebhardt et al. model (Gebhardt et al., 2001), the Easton model (Easton, 2004) and the OJ-model (Ohlson & Jeuttner-Nauroth, 2005). All models apart from the OJ-model require to manually search for the cost of capital using the goal-seek function in excel (or manually estimate the unknown variable with a straight-line equation) since those models are discounting several years. Due to the limited time available for us to write this thesis, only the OJ-model will be used to calculate the cost of equity. The fact that previous research has used ex ante models makes us confident with the choice of model. The OJ-model is calculated according to equation 3, presented in the theoretical point of reference.

Dependent variable connected to the second research question In the analysis of ESG performance connected to multiple valuation, we have chosen three different commonly used multiple within valuation. Because these measurements are commonly used by professionals to make investment decisions, we think this will be interesting to investigate. Furthermore, these measurements have also been included in previous research which makes them highly relevant for our study (Barker, 1999; Schueler, 2019).

The dependent variables are calculated as follow:

Equation 10 - Price to earnings ratio #?=@A IA? 8ℎB?A #/% = %B?C=CD8 IA? 8ℎB?A

Equation 11 - Enterprise value to earnings before interest and taxes ratio 0B?FA9 @BI=9BH=UB9=-C + PA9 JAE9 + 0=C-?=9> =C9A?A89 %5/%.1/ = %B?C=CD8 EA:-?A =C9A?A89 BCJ 9BKA8

Equation 12 - Price to book value ratio #?=@A IA? 8ℎB?A #/. = .--F GBH

Dependent variable connected to the third research question Since the third research question strives to answer if ESG is correlated with the relative return performance in time of market crashes, the relative performance is calculated as the daily return of the stock minus the daily return of the market index, as shown in the equation below.

Equation 13 - Calculation of the crisis variable )9-@F #?=@A 1CJAK #?=@A ,61)1) = V + − 1X − V + − 1X )9-@F #?=@A+," 1CJAK #?=@A+,"

A higher value of the CRISIS variable is positive and implies that the stock generated a greater daily return than the market. The index used in the data is as earlier mentioned, the Nasdaq Nordic Large Cap index.

4.4.2 Independent variable Since the purpose of this thesis is to investigate the impact of ESG on cost of equity, valuation, and attributes in time of crisis, an ESG score will be used in all statistical models. The ESG- score is retrieved from Thomson Reuters Eikon Database and contains values from 0 to 100. There are many different databases that classify companies' ESG performance into ESG or CSR scores. We have chosen Eikons ESG score to describe ESG performance in this thesis. Because ESG-scores are crucial for our thesis and our conclusion we will describe why we have used Eikon and how these are calculated. Eikon is one of the largest ESG databases available and it covers 70% of the global market cap (Refinitiv, 2021, p. 3). These scores are also highly relevant as they are updated weekly. This database has also been used in previous research (Feng et al., 2015) which makes us comfortable to use Eikons ESG score as a measurement of ESG performance.

Eikons ESG scores are calculated based on three pillars. These are Environmental, Social, Governance and with an ESG controversies overlay. The Environmental, Social and Governance are then further broken down to its components. To capture these pillars Eikon uses over 500 company level ESG measurements that reflect the ten components of ESG (Refinitiv, 2021, p. 4). We have chosen to include the controversies overlay in our analysis. We think that the ESG controversies overlay contributes to the accuracy of how well the ESG- score captures the actual ESG performance. This will capture aspects that companies do not report in their annual reports and sustainability reports.

Eikon categorizes companies as ESG leaders or ESG laggers. Because different sectors have different challenges it's reasonable to compare companies within the same sector and weigh the importance of these differences (Refinitiv, 2021, p. 6). This makes the score Environmental and Social based on relative performance within its sector. When calculating the governance score these are based on relative performance dependent on which country it's incorporated in, this is a percentile ranking methodology (Refinitiv, 2021, p. 9). We see all of these factors as a strength in the Eikon ESG rating methodology which should make our analysis more reliable.

All forms of ESG scores regardless of source will have drawbacks. If we would have used Bloomberg or any other source that provides ESG ratings, we would likely have different scores in our dataset. This is important to remember because we use ESG score as a proxy for ESG performance. Eikon collects a lot of its ESG data from annual reports, company website, stock exchange filings and CSR reports. As previously discussed, this is very problematic because this is a source that the companies themself often control. We are aware of these problems but there will always be concerns regarding the data collection. Eikon is still one of the best databases available and it's used by investors and researchers. This makes us comfortable to use Eikon ESG scores as a proxy for companies ESG performance.

4.4.3 Control variables Since the independent variable ESG is not likely to be the only variable to affect neither the cost of equity nor the price-multiples or the crisis variable in our multivariate regression models, we follow previous studies such as (Boubakri et al., 2012; El Ghoul et al., 2011; Hail & Leuz, 2006) in the choice of control variables. A total of seven control variables are included in our first research hypothesis, such as beta, size, book to market, forecast bias, momentum, leverage and analyst coverage. Three of them (book to market, size, and momentum) as well as the cost of equity are included as control variables in the regression models connected to the

second research question. Finally, only beta is included as a control variable in the regression model connected to the third research question. Other variables that previous studies have included are industry dummy, age of firm, volatility, liquidity, and more. However, it is not possible to include all variables suspected to impact the cost of equity. We believe that the chosen variable reflects academia’s consensus on the most important control variable for measuring the cost of equity since those variables are the most commonly used in previous relevant research. Furthermore, by only conducting our study on large cap firms, we assume that firms in our sample are mature firms with high liquidity for which investors do not require an age or liquidity premium.

Beta The betas are calculated according to equation 3, based on the daily closing price of each individual stock against the market index. The market index of reference chosen is Nasdaq OMX Nordic Large Cap calculated in SEK, NOK, EUR and DKK. The beta used in the model is thereafter the one year rolling average for each single quarter. We have support in several studies such as (Boubakri et al., 2012; El Ghoul et al., 2011; Hail & Leuz, 2006) for including beta and since beta reflects the amount of systematic risk, we expect a positive coefficient with the cost of equity in line with previous research (Boubakri et al., 2012; El Ghoul et al., 2011; Hail & Leuz, 2006). In line with Dajcman (2012, p. 1661), we expect investors to reduce risk in their portfolios in times of crisis. We thus anticipate low-beta stocks to suffer from a lower sell-off than high-beta stocks due to their lower exposure towards systematic risk. This implies that we expect a negative coefficient with the crisis variable.

SIZE The size factor is calculated as the natural logarithm of total assets. Size is a commonly used control variable used as a proxy for risk. We have support in several studies such as (Boubakri et al., 2012; El Ghoul et al., 2011; Hail & Leuz, 2006) for including size as a control variable. In line with previous research, we expect firm size to be negatively associated with the implied cost of equity thus larger companies should have a lower cost of equity capital. Furthermore, the size factor is expected to show a negative relationship with the price multiples. The relationship is expected to be negative with the P/E and EV/EBIT ratio since larger firms should have a lower growth rate, ceteris paribus. Furthermore, the relationship with P/B is also expected to be negative since larger firms are expected to generate lower residual profits, ceteris paribus.

BTM According to previous studies within the field, it is reasonable to include the book-to-market ratio as a control variable in implied cost of equity regressions (Boubakri et al., 2012; El Ghoul et al., 2011; Hail & Leuz, 2006). This is also in line with the conclusion from the Fama & French three factor model, that stocks with high B/M ratios generate higher returns than stocks with low B/M (Fama & French, 1995, p. 131). The book-to-market ratio is calculated as book value of shareholders equity to the market value of equity. The BTM variable is supposed to control for different growth opportunities among our set of companies as well as differences in accounting rules (Hail & Leuz, 2006, p. 497). The BTM variable is more controversial than our other control variables. It has been debated if this variable should be included and how it should be related to the implied cost of equity (Hail & Leuz, 2006, p. 497). In line with previous research, we expect the BTM variable to be positively related with the cost of equity, implying that companies with higher BTM ratio have a higher cost of equity. The relation of the BTM- ratio against the P/E and EV/EBIT ratios is therefore also expected to be negative. Finally, the

BTM-ratio is excluded from the model concerning the P/B ratio since the BTM-ratio is an inverted P/B ratio and therefore does not make sense to be included in that regression.

FBIAS In our calculation of the implied cost of equity analyst forecasts will be used. As previously discussed in this thesis, forecasts are often overly optimistic, which can be problematic for our calculations because the implied cost of equity may be biased upwards because of the noise in analyst forecasts. In line with previous research (Boubakri et al., 2012, p. 552; El Ghoul et al., 2011, p. 2396; Hail & Leuz, 2006, p. 498), we include FBIAS as a variable to control for this. The FBIAS variable is calculated as the spread between one year ahead forecasted earnings and the actual earnings divided with the forecast period stock price. We expect the FBIAS variable to be positively associated with the cost of equity.

MOM Momentum is also included as a control variable in the regression model. The momentum variable measures the price momentum and is calculated as the compounded stock return of the last twelve months. MOM is included in the regression for the same reason as FBIAS. FBIAS and MOM helps to mitigate concerns that noise in analyst forecasts is driving our conclusion and therefore tackle the problem with forecast bias in our calculation of the implied cost of equity. This will therefore mitigate the risk of forecast errors (Chen et al., 2009 p. 279; El Ghoul et al., 2011, p. 2397). In line with previous research, we expect MOM to be negative related to the implied cost of equity. Since MOM is expected to be negatively related to the cost of equity, firms with higher momentum are expected to have larger market values, ceteris paribus. The coefficient of the MOM variable against all three price multiple tested is therefore expected to be positive.

LEV Leverage is a very important control variable in our regression equation. Finance theory indicates that companies with higher leverage have higher risk thus having a higher cost of equity to compensate investors for this risk. The leverage variable is calculated as a ratio between debt and shareholders’ equity. In previous research the leverage variable has been calculated in different ways, while some focus on the market value of equity others uses book value of assets. We have chosen to use debt to equity in our analysis because this focuses on the accounting values rather than market values. In line with previous research within the field (Boubakri et al. 2012 p. 550; El Ghoul et al., 2011, p. 2395), we expect LEV to be positively related to the implied cost of equity.

ANA In the regression equation, analyst coverage was also included. The rationale behind this choice is that companies with analyst coverage should have reduced information asymmetry as they have more media and analyst coverage and thus lowering the risk and the cost of equity capital (El Ghoul et al., 2011, p. 2392). The control variable was calculated as the natural logarithm of one plus the number analysts following the company. In line with previous research (El Ghoul et al., 2011, p. 2405), we expect ANA to be negatively associated with the cost of equity.

CoE In the regression models connected to the second research question, the cost of equity calculated according to the Ohlson-Juettner model is used as a control variable. As shown by Cornell (2021), a higher cost of equity leads to a lower valuation, ceteris paribus. We therefore

want to control for this phenomenon and have chosen to include the cost of equity as a control variable in our regression models connected to the second research question. The coefficient is expected to have a negative slope with all three price multiples tested.

4.4.4 Problem with chosen variables Another important issue regarding the regression model that we would like to highlight is the fact that there might be other important response variables that could explain the cost of equity but has not been included in our study. This problem is called an omitted variable bias, stating the regression equation without an important variable leads to bias in the estimations of the coefficients (Studenmund, 2014, p. 178). If this variable or variables would have been included in the statistical test, the result would probably differ.

This issue is something that is common in this field of research because the cost of equity is very complex. Prior research suggests that there are several other important variables that are correlated with the cost of equity capital such as capital constraints, ownership concentration and firm-level corporate governance (Chen et al., 2009, p. 282; El Ghoul et al., 2011, p. 2397). Furthermore, they discuss if the choice to engage in ESG activities really are independent from the cost of equity capital, this could cause reverse causality.

To handle these problems, we have followed previous studies and carefully selected relevant variables. Because of the limited time of this thesis, we have not included variables regarding capital constraints, ownership concentration or firm-level corporate governance, which could be a weakness in our model. In previous research, these robustness checks have however not been materially different from the original model (El Ghoul et al., 2011, p. 2396), which makes us comfortable with the variables included in our regression equation.

Furthermore, there is also a risk that scientists include too many variables in the statistical test, this is called irrelevant variables. In contrast to the omitted variable this does not cause bias in the estimations, but it will lead to increased variance of the estimated coefficients (Studenmund, 2014, p. 186). We have chosen our variables carefully and have support from previous research which makes this problem less significant for our study. Previous research about the cost of equity and sustainability is substantive which makes us comfortable in our choices. The variables have been chosen carefully and we have good arguments why certain variables are included.

5 Data

In this chapter, descriptive statistics of the data will be provided as well as the tests performed in order to establish whether or not the seven classical assumptions of the OLS regression model are met. The main focus will be given the regression model concerning the first research question, however all tests for research question two and three can be found in the appendix. 5.1 Descriptive statistics

In this section we will discuss the descriptive statistics from our dependent, independent and control variables that will be included in our regression models. As it can be observed in table 3 below, the data consists of 4 807 observations of the independent variable ESG-score which is measured from zero to one and has a minimum value of 0,074 and a maximum value of 0,934 with a mean of 0,55. It can be seen that there are less observations for cost of equity and ESG score than for other variables. The reason behind this is that when calculating the implied cost of equity with the Ohlson & Juettner-Nauroth model, it is sensitive towards forecasted earnings growth. Furthermore, many companies have not received ESG ratings from EIKON the entire time frame from 2011 to 2020 even though they have been listed and balance sheet information is available from EIKON.

Table 3 - Un-winsorized descriptive statistics of all variables included in the regression models

In the table above, it can be seen that there are 4 376 observations on the cost of equity capital included in the sample. The cost of equity ranges from -0,367% to 197,5% with a mean of 13,50%. It can clearly be seen that these min and max values are strange and may be seen as statistical outliers. It's clear that these values of the cost of equity derived from the Ohlson & Juettner-Nauroth model is unreasonable. No company will have a cost of equity of -0,367% or 197,4%. To overcome this problem the data was winsorized according to previous research. El Ghoul et al. (2011, p. 2392) restricted the cost of equity to be between 0 and 100% while Chen et al. (2009, p. 279) argues that the cost of equity should be winsorized between 0-60%. We have chosen to follow Chen et al. (2009) and have winsorized the cost of equity to be between

0 and 60%. Winsorizing is always sensitive because it alters the data, but with our knowledge within the field and previous research we feel confident with our winsorizing process.

The minimum and maximum values of the price-multiples P/E, P/B and EV/EBIT are obviously outliers. In order to get rid of extreme values and improve the model, the price- multiples variables are winsorized at the 5th and 95th percentile.

The LEV-variable is included in the analysis to capture the capital structure and its influence on the cost of equity capital. LEV ranges from minimum value of -4 630,03% to maximum value of 3 293,23% in our sample with a mean value of 81% with 7374 observations. The reason for the negative ratio is that a few companies in our sample have negative shareholders equity, as for example Swedish match. The extreme values in this case also belong to Swedish match because at some point in time, they had an extremely low level of shareholders equity which makes the leverage variable very high. Despite this, we have chosen to keep Swedish match in the sample and have not winsorized the LEV variable since we could not find any support for winsorization of the leverage variable in previous research. Furthermore, BTM is included as a ratio between book equity and the market value of equity and thus faces the same problem as the LEV because some companies will have negative or very low book equity. The BTM variable ranges from -0,084 to a maximum value of 35,67 with a mean of 0,419. These variables are in line with what previous research has found in other markets (El Ghoul et al., 2011, p. 2394).

Beta is an important control variable that accounts for the quantification of the systematic risk in our sample. In this sample, it can be seen that beta ranges from -0,52 to a maximum value of 2,77. Furthermore the mean value is 0,83 with 6 950 observations. In line with Chen et al. (2009, p. 279), beta is winsorized between 0-4.

As previously mentioned, size is measured as the natural logarithm of total assets. The firms in our sample are traded on Large cap indices on the Nordic market and because no small or mid cap companies are included in the sample, the difference in size should be quite small. The smallest company observation is 15,15 and ranges to a maximum value of 27,77, the mean value is 23,60 with 7 828 observations. The smallest size observation within the sample belongs to SBB a Swedish real estate company and the largest observation belongs to the Norwegian oil company Equinor. Finally, FBIAS, MOM and ANA are left un-winsorized.

Table 4 - Correlation matrix between variables connected to the first research question

In table 4, a correlation matrix with all variables regarding the first research question is included (correlation matrix regarding research question two and three can be found in appendix 2 & 3). It can be seen that there is a large span in how the variables are correlated from 0,67 to -0,14. The highest correlation is between size and number of analysts. This is very intuitive that large firms have more analytical coverage than smaller firms. It's also interesting to note the correlation between size and ESG score. ESG score is most correlated with size, this indicates that larger companies have higher ESG scores, when companies grow, they enhance their sustainability performance. This could be because larger companies have the resources to invest in green projects or have better ability than smaller companies to brand themself as high ESG performers.

It can also be seen that the correlation between cost of equity and the BTM ratio is quite high. Indicating that companies with higher BTM ratios also have higher cost of equity in line with the conclusion from Fama & French. This is also in line with correlations tests from previous research (Boubakri et al. 2012 p. 548; Chen et al., 2009 p. 280). As previously stated, size is commonly used as a proxy for risk. In this sample the correlation between size and cost of equity is low, this is contradicting previous research that found higher correlations between these two variables. Previous research such as El Ghoul et al. (2011) included a larger sample with a broader spectrum of companies. Since our sample only contains large cap companies in the Nordic markets, it could be one explanation of the low correlation between size and cost of equity.

These are the correlations that we see most interesting when doing a first evaluation of the data, although we want to highlight that we otherwise have small correlations between our other variables. It's important to remember that these thoughts are just an initial analysis, it does not include any significance test and therefore no conclusions can be drawn. 5.2 Model diagnostics

As discussed above, the fulfillment of the seven classical assumptions is of high importance for the OLS regression model to be reliable. In this part of the chapter, the tests performed to establish a reliable regression model will therefore be presented.

5.2.1 Linearity of the regression model As described above, according to the first assumption, the regression model needs to be linear in order for the coefficient estimations of an OLS to be reliable. For multivariate regression models, the validity of the assumption can be performed by examining the scatterplot of the residuals versus fitted values (Chatterjee & Hadi, 2012, p. 103). If the residuals show a clear linear pattern, the assumption is violated. As one can observe from figure 8 below, the residuals clearly show a linear relation to the fitted values.

Figure 8 - Scatterplot of Residuals vs. Fitted values of CoE In order to overcome the problem of non-linearity of the model, one can winsorized the data. As described above, the cost of equity capital has been winsorized between 0 and 60%. As it can be observed in figure 9, the residuals do not show any clear pattern anymore. With a bit of imagination, one could argue a negative linear relation of the residuals against fitted values and that the assumption is not fulfilled. However, we believe that the first assumption of linearity is fulfilled.

Figure 9 - Scatterplot of Residuals vs. Fitted values of CoEw

Scatterplot of residuals vs fitted values of the regression models connected to research questions two and three are presented in appendix 4 to 7.

5.2.2 Distribution and mean value of the error term The second classic assumption of the OLS model states that the error term should have a zero population mean. If the sample is small, it's likely that this assumption will not be fulfilled but as the sample goes to infinity the mean will of the sample approaches zero (Studenmund, 2014, p. 100). To mitigate the risk that the error term does not have a zero population mean, a constant term is added in the regression model which will solve the problem (Studenmund, 2014, p. 100). Furthermore, the last classical assumption states that the error term should be normally distributed. The VII assumption is optional when conducting an OLS estimation, however the assumption is important in hypothesis testing because hypothesis testing looks at the estimated regression coefficient (Studenmund, 2014, p. 104). If the error term is not normally distributed, it could lead to biased hypothesis testing as the t-statistic and the f-statistic becomes unreliable (Studenmund, 2014, p. 105).

Figure 10 - Distribution of the error term To examine the mean value and distribution of the error term, a histogram of the residual was plotted to see if the error term is bell shaped as presented in figure 10. Histograms of error term regarding the regression models connected to research questions two and three are presented in appendix 8 to 11. In the histogram above, it can be seen that the population mean of the error term does not equal zero. This is solved by including a constant term in the regression model. Regarding the distribution of the error term, it can be observed that the error term looks approximately normally distributed. It looks like the residuals show some skewness to the left and some kurtosis as the distribution is larger than what we would expect. This bell-shaped distribution is skinny which indicates different variance of the residuals (Studenmund, 2014, p. 105). The fact that the histogram looks approximately normally distributed, and we have a large sample makes us comfortable with moving forward with our model. We therefore conclude that the second and seventh assumptions are fulfilled.

5.2.3 All explanatory variables are uncorrelated with the error term. The third assumption states that the observed values of the explanatory variables are independent from the error term. If some explanatory variable and the error term would be correlated in our regression model, this will affect the estimated regression coefficient and the coefficient becomes biased which would make our model unreliable (Studenmund, 2014, p. 101). For example, if one explanatory variable is positively correlated with the error term, it is likely that the OLS model will attribute some variance in the cost of equity to X even though it actually comes from the error term (Studenmund, 2014, p. 101).

To ensure that our model does not suffer from this, a correlation matrix between the error term and the explanatory variables will be studied. In table 5 below, it can be seen that no explanatory variables are correlated with the error term. Correlation matrices between the error term and the explanatory variables of the regression models connected to research questions two and three are presented in appendix 12 to 15.

Table 5 - Partial and semi partial correlations of residuals with explanatory variables

Furthermore, the most common violation to the third assumption is when an important explanatory variable is omitted from the regression equation. This is impossible to see from a correlation matrix because an important variable may not be included. This is important because even if the variable is not included in our regression equation the error term will change when the omitted variable changes (Studenmund, 2014, p. 101).

To make sure that our model does not violate the third assumption, it is therefore of importance to reflect over if any important variables have been left out from the regression model. As previously discussed, we feel confident in the variables that have been included and not included in the model. We have strictly followed previous research to conclude what variables affect the cost of equity capital. Even though we feel confident in our model it's important to remember that there are many different factors that affect companies' cost of equity capital and we are aware of the risk with omitted variable bias.

5.2.4 Observations of the error term are uncorrelated with each other. This assumption states that there is no serial correlation (autocorrelation) in the OLS model. For the model to not violate this assumption the error term should be uncorrelated and independent from each other. Serial correlation occurs in the equation when the error terms are somehow correlated with each other (Studenmund, 2014, p. 101). If there is a positive

correlation between the error term from one observation with positive value will increase the possibility that the next observation of the error term is also positive. The problem with systematic correlation between the error terms is that it will cause the OLS model to struggle in estimating the standard error of the coefficients (Studenmund, 2014, p. 101). This assumption is often violated because in reality there is a big chance that a random shock will last for several time periods not only in one time period even though the event has already occurred (Studenmund, 2014, p. 102). This assumption is highly relevant in economic models because the problem with autocorrelations is often seen in time series data where the order of the data is important (Studenmund, 2014, p. 322). Because we have panel data across times this is something that we expect our model to suffer from.

To see if the model suffers from autocorrelation an Breusch-Godfrey LM test for autocorrelation was conducted (see appendix 16). The null hypothesis of the test states no serial correlation. We can reject the null hypothesis on a 99% significance level. This means that our model suffers from autocorrelation.

As previously discussed, if the model would suffer from serial correlation(autocorrelations) that would be a violation against the IV assumption and the OLS model will not be appropriate. Furthermore, this implies that an GLS model should be used instead of an OLS model to solve the problem and thus leading to a model where the error term does not suffer from autocorrelation and restoring the minimum variance (Studenmund, 2014, p. 338; Westhoff, 2013, p. 561).

To further examine if the GLS model is a better statistical model to test the hypothesis a Breusch-Pagan Lagrange multiplier test for random effects was conducted (see appendix 17). In the Breusch-Pagan Lagrange multiplier test the null hypothesis states that there are no random effects. We can reject the null hypothesis on a 99% significance level. There are random effects in the model and the generalized least square regression should be used instead of the ordinary least square regression. Both the OLS and the GLS model will provide unbiased estimates of the coefficient but the GLS model solves the problem with biased estimates of the variance (Westhoff, 2013, p. 573). These two tests make us comfortable to switch to a GLS model to estimate our coefficients in the regression.

Furthermore, when conducting the regression analysis fixed effects or random effects can be used. The decision between a fixed or random effect model can make a large difference especially when T is small (Verbeek, 2008, p. 367). To test this a Hausman test can be performed (see appendix 18). The null hypothesis of the test states that the differences in coefficients are not systematic (Verbeek, 2008, p. 368). We cannot reject the null hypothesis on a 95% significance level thus concluding that the differences in coefficients are not systematic and the individual effects should be considered random, and a random effect model should be used.

5.2.5 No heteroskedasticity This assumption requires the variance of the error-term to be constant in order to be fulfilled. If the variance of the distribution from which the observations of the error term are drawn increases for larger values of Z, the model suffers from heteroskedasticity (Studenmund, 2014, p. 102). In case of heteroskedasticity, the OLS generates inaccurate estimations of the standard error and is therefore not an appropriate model.

Heteroskedasticity can be tested in several different ways, both graphically by observing the variance of the error terms in relation to some other variable in the model and/or through statistical tests. As it can be observed in figure 11, the distribution of the error term against the ESG-score is not entirely normally distributed. The pattern observed is that the variance of the residuals seems to be larger for larger values of ESG.

Figure 11 - Residuals from CoEw regression model plotted against the independent variable ESG However, the pattern is difficult to visually confirm, and we have therefore decided to also test if the model contains heteroskedasticity by testing it statistically. In order to reject the null hypothesis of no heteroskedasticity, a Breusch-Pagan / Cook-Weisberg test for heteroskedasticity has been performed. The result shows that we can reject the null hypothesis and that the model consequently suffers from heteroskedasticity (see appendix 19). To overcome the effects of heteroskedasticity, the standard errors of the GLS-model are therefore made robust (Westhoff, 2013, p. 538).

5.2.6 No perfect multicollinearity Perfect multicollinearity occurs in the OLS model if two or more independent variables explain the same phenomenon. This could be a problem in our model if two independent variables measure the same factor and we are unaware of this problem. If two or more independent variables are the same, this will lead to a perfect linear relationship and this will violate the Vl assumption and prevent the OLS model to be the best estimation technique. If this happens in the model the OLS estimation will not be able to separate different variables from each other. This can be solved by dropping one of the variables in the equation that is suffering from collinearity. However, multicollinearity problems will not result in any bias in the estimates. The main problem of multicollinearity will be that the variance and standard errors of the estimations will increase (Studenmund, 2014, p. 266).

There are no generally accepted methods to establish if the sample suffers from multicollinearity but according to (Studenmund, 2014, p. 272-273; Verbeek, 2008, p. 43) one can examine simple correlations coefficients and VIF tests. According to (El Ghoul et al., 2011,

p. 2394) a good way to examine if the regression suffers from multicollinearity is to examine the correlations coefficients between the explanatory variables which makes us comfortable with this method even though there's no generally accepted method.

To test the model for multicollinearity two different tests are going to be used. Firstly, the correlation matrix is going to be analyzed. Secondly a VIF (variance inflation factor) test will be performed.

Table 6 - Correlation matrix between independent variables

In this correlation matrix, the simple correlation coefficients between all independent variables can be seen. Generally, a correlation that exceeds 0,8 is seen as high, thus indicating that we have multicollinearity (Studenmund, 2014, p. 272). The largest correlation coefficient in the sample is between SIZE and ANA which states 0,67. This indicates that SIZE and ANA nearly measure the same thing, but we do not see this as any problem when comparing it with the critical level of 0,8. Otherwise the independent variables have very small correlation coefficients. Correlation coefficients between all independent variables regarding research question two and three can be found in appendix 20 - 21.

The problem with only studying a correlation matrix is that the independent variables could be working together and causing multicollinearity. The VIF test focuses on how multicollinearity affects the variance of the estimated coefficients (Studenmund, 2014, p. 273). The VIF test goes further than the previous test because it takes into account how all explanatory variables affect each other not only pairwise. According to (Studenmund, 2014, p. 274) to evaluate the result from the VIF test a common rule is that a VIF value higher than five indicates that the model is suffering from multicollinearity.

Table 7 - Variance inflation factor (VIF) test

The results from the correlations coefficients and the low VIF value makes us comfortable with the classic assumption VI and our regression model does not suffer from multicollinearity. VIF test for research question two can be found in appendix 22. 5.3 Critics against the model

As previously discussed, one problem with regression analysis is that cannot provide any answers regarding the causality of the relationship. This is something that we are aware of and why we have included previous research and relevant economics theories that can help the interpretation of our statistical tests. The data that has been used in this thesis is collected from the Eikon database, Eikon is one of the best databases available which makes us comfortable in the data collection process but it's important to remember that we could have missing data and because of the amount of observation this is impossible to control for. Another important aspect in our model is that the cost of equity has been calculated from one model while in previous research four different models have been used. This is a weakness in our thesis but because of the limited time frame we could not manually search for the cost of equity in the other models.

We have strived to find the best regression model to estimate the coefficient and see if there are any significant relationships between our dependent and independent variables. To do this we aimed to use an OLS model but because of violation against some of the classical assumptions, we changed to an GLS model with robust standard errors. 5.4 Type 1 & Type 2 errors

There are two types of errors that can occur in hypothesis testing that are important to recognize these are type I and type II errors (Studenmund, 2014, p. 130). A type I error occurs if the null hypothesis is rejected even though the null hypothesis is true. The type II error happens if the null hypothesis is accepted even though it's false (Studenmund, 2014, p. 130).

To know if the null hypothesis should be rejected or accepted critical t- values and z-scores are used. These critical values are based on the significance level chosen. Setting a low significance level will increase the risk to make a type II error (Studenmund, 2014, p. 139). If the significance level is higher, the risk of making a type I error increases (Verbeek, 2008, p. 31).

How researchers handle type I errors are controlled through the significance level. If a significance level of 5% is used the probability of rejecting the null hypothesis even though it is true are 5% (Verbeek, 2008, p. 31). To decrease the risk for type II errors, researchers should include larger samples in the test because larger samples will provide smaller standard errors (Verbeek, 2008, p. 32).

6 Empirical results

In this part of the thesis, the empirical results from the study will be presented. Firstly, the hypothesis testing will be described and secondly, the results from the different regression models will thereafter be presented. 6.1 Results of hypotheses testing

In the table below, an overview of the results and significance levels from the statistical hypotheses testing is presented. The table describes which hypotheses have been rejected, and to what level the results are significant. After conducting the statistical tests, four out of five null hypotheses are rejected.

Table 8 - Presentation of results from hypothesis tests

Hypothesis Result Z-Score T-Score

!!":There is no significant relationship between Reject -1,85* N/A ESG score and the cost of equity

!!$.":There is no significant relationship Reject N/A 5,77*** between ESG score and P/E

!!$.$:There is no significant relationship Reject N/A 8,01*** between ESG score and P/B

!!$.&:There is no significant relationship Accept N/A 0,57 between ESG score and EV/EBIT

!!&:There is no significant relationship between Reject N/A 5,34*** ESG score and index-relative price decline during the market turmoil of March 2020 caused by the Covid-19 outbreak.

*** Statistically significant at the 1 % level or lower.

** Statistically significant at the 5 % level or lower.

* Statistically significant at the 10 % level or lower. 6.2 ESG score and the cost of equity capital

In table 9 below, the results from the generalized least squares robust random effects multiple regression model with the winsorized dependent variable cost of equity is presented. The purpose of the model is to test the null hypothesis !!"that there is no significant relationship between ESG score and the cost of equity. We can see that, at a 10% significance level, when the ESG-score increases by one percentage point, the cost of equity decreases by 0,014 percentage points. Thereby, the null hypothesis is rejected since there is a significant relationship between the ESG-score and the cost of equity. As it also can be observed from table 9, on a 95% confidence level, the true value of the coefficient is somewhere between the interval of -0,0294 and 0.0008.

Table 9 - GLS Robust Random Effects regression model – ESG and Cost of equity

As we have described earlier, we expected beta, book-to-market ratio, leverage and forecast bias to have a positive coefficient against the cost of equity. In contrast, we expected size, analyst coverage and momentum to be negatively associated with the cost of equity. As shown in table 9 above, all control variables except forecast bias are significant at 1% significance level. There is no statistical evidence at either 1, 5 or 10% significance level that the forecast bias variable is significant. In contradiction with previous research, analyst coverage seems to be positively associated with the cost of equity.

The overall r-square value of the model is 29,71%, which is in-line with previous research who obtained r-square values between 33,1% (El Ghoul et al., 2011, p. 2397) and 32,2% (Boubakari et al., 2012, p. 549) or 43,3% (Chen et al., 2009, p. 282). The interpretation of the r-square value is that 29,71% of the variation in the cost of equity can be explained by the regression model. 6.3 ESG score and the P/E ratio

In table 10 below, the results from the OLS regression model with the winsorized dependent variable P/E ratio is presented. The purpose of that model is to test the null hypothesis !!$."that there is no significant relationship between the ESG score and the P/E ratio. The coefficient of the ESG-score is positive, with a value of 10,3367 and a t-statistic of 5,77. The result are therefore statistically significant at the 1% level or better and the null hypothesis can be rejected. An increase of the ESG-score with one percentage point increases the P/E ratio with 0,1034 units.

Table 10 - OLS regression model – ESG and P/E ratio

The control variables cost of equity and book-to-market ratio and size were expected to have a negative relationship with the P/E ratio, which is in line with the results presented in table 10 above. Furthermore, momentum was also expected to a positive relationship with the P/E ratio, which is contradicted by the model above. Although, the coefficient of the momentum variable is not statistically significant and the true value of the coefficient with a 95% confidence interval is somewhere between -0,401 and 0,308. As it can be observed from table 10, an increase of the cost of equity with one percentage point decreases the P/E ratio with 0,36 units. 6.4 ESG score and the P/B ratio

The P/B ratio is as the P/E ratio also positively impacted by the ESG-score as shown in the table 11 below. The null hypothesis !!$.$that there is no significant relationship between ESG- score and the P/B ratio can be rejected on a 1% level. The coefficient of the ESG-score is positive with a value of 2,209, implying that an increase of one percentage point of the ESG score increases the P/B ratio with 0,02209 units of the P/B ratio.

Table 11 - OLS regression model – ESG and P/B ratio

The control variables cost of equity and size were expected to have a negative relationship with the P/B ratio, which is in line with the results presented in table 11 above. In contrast, we expected momentum to be positively associated with the P/B ratio, which also is the case in the OLS-model presented above. All control variables are statistically significant at the 1% level.

The adjusted r-square value of the model is 28,51%. Due to the fact that, in the best of our knowledge, there is no previous research examining the relation between ESG and the price to book ratio, it is difficult to compare the adjusted r-square value of the model against previous research. However, we feel comfortable with an adjusted r-square of 28,51%. 6.5 ESG score and the EV/EBIT ratio

Regarding the third model connected to the second research question, the relation between ESG and EV/EBIT ratio is examined. The null hypothesis !!$.&that there is no significant relationship between the ESG-score and the EV/EBIT ratio cannot be rejected neither at a 1%, 5% or 10% level. The coefficient of the ESG-score is however positive with a value of 0,6476, but since the t-score is 0,57, the result is not statistically significant.

Table 12 - OLS regression model – ESG and EV/EBIT ratio

As for the previous statistical tests, the control variables cost of equity, book to equity and size was expected to have a negative relationship with EV/EBIT. As it can be seen in table 12 above, the BTM variable is surprisingly positively related with the EV/EBIT ratio, indicating that firms with low growth opportunities have a higher multiple. As for other statistical tests performed, we expected firms with higher discount rates to have lower multiples which is also the case for the EV/EBIT multiple. The same goes for size where our expectation was that smaller firms could have higher multiples due to growth opportunities. The coefficients of all control variables are statistically significant at the 1% level and have a slope in accordance with our expectations except the BTM-variable.

Due to the fact that, in the best of our knowledge, there is no previous research examining the relation between ESG and the EV/EBIT ratio, it is difficult to compare the adjusted r-square value of the model against previous research. However, we feel comfortable with an adjusted r-square of 11,97%.

6.6 ESG score and the CRISIS-variable

Regarding the final model connected to the third and last research question, the relation between ESG and the CRISIS variable is examined. The null hypothesis !!&that there is no significant relationship between the ESG-score and the CRISIS variable can be rejected at a 1% level. The coefficient of the ESG-score is positive with a value of 0,0217, implying that when the ESG-score changes by one unit, the daily return relative to the market index increases with 0,0217 percentage points.

Table 13 - OLS regression model – ESG and the CRISIS-variable

The coefficient of the control variable BETA is statistically significant at the 1% level and has a slope in accordance with our expectations. The intuition is that a higher beta should imply larger percentage falls than what the market falls within time of crisis. Since the change in market value in times of crisis is not expected to only be explained by ESG and BETA, the adjusted r-square of 1,16% is low but not unexpected.

7 Analysis

In this chapter, the empirical results of this study will be connected to the theoretical framework as well as previous research discussed earlier in this thesis. The three research questions will thereafter be answered, followed by an analysis of the results.

The primary purpose of this thesis is to answer if equity market participants are pricing sustainability risk, which has been proven to be the case. In order to answer the main research question, this study examines whether ESG-score affects firms’ ex ante cost of equity implied in stock prices and analyst forecasts. We expected that, everything else equal, high ESG firms should have a lower cost of equity than low ESG firms due to several factors such as a lower perceived risk in the investment as well as an increased investment base or reduced agency costs. This study uses a sample of 2720 firm-quarter observations from 2011 to 2020 and controls for other firm-specific variables. We have been able to find that high ESG-firms enjoy a statistically significant lower cost of equity than low ESG-firms. According to this study, an increase of the ESG-score by one percentage point decreases the cost of equity by 0,014 percentage points. This finding is in line with most but not all previous research conducted. Crifo et al. (2015) and El Ghoul et al. (2011) also found results in line with this thesis. Our results however contradict Nguyen P & Nguyen A (2015) who found a positive relationship between ESG and the cost of equity or Feng et al. (2015) who found contrasting results in different parts of the world.

One of the most fundamental purposes of financial markets is to price risks. Based on previous research presented earlier in this thesis, it was however uncertain if the ESG-score would be priced by market participants due to two main factors; the long-term characteristics of ESG- investments and the complexity to assess how ESG-friendly a firm really is. In this transformation toward a more sustainable future, our findings implies that financial markets play their roles since we found a statistically significant negative relationship between ESG and the cost of equity. Our findings provide complementing insights into the debate on whether ESG-efforts are value-increasing, value-destructive or value-neutral by showing that ESG- efforts can enhance firm value through a lower cost of equity.

The shareholder’s theory and stakeholder theory are often opposed to each other as if one theory’s existence requires the rejection of the other theory. However, we interpret the findings that high ESG firms enjoy lower cost of equity to speak in favor of both theories. A lower cost of equity capital could indicate that ESG-investments might generate higher profits and a lower volatility of earnings, which in the point of view of both an equity investor and other stakeholder is desirable. The shareholder’s theory stating that the only purpose of executives is to maximize firm value is still valid with our findings, since a lower cost of equity generates a higher valuation, ceteris paribus.

The legitimacy theory, which is the assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions (Shuman, 1995, p. 574) could be one of the explanations why the cost of equity has a negative relationship against ESG. If firms would find themselves in a vacuum without being dependent upon a socially constructed system of norms or values, the ESG-score would not show any significant relation with the cost of equity. Since a relationship was found, we can argue that firms need to consider norms or values in order to maintain their level of competitiveness in the longer run. Our findings are in line with Deephouse et al. (2017, p. 34)

who states that legitimacy will not only affect the image of the company but also directly influence the financial performance of the company.

In previous research different results regarding sustainability performance and the cost of equity capital have been found depending on where the research has been conducted. Our thesis has focused only on the Nordic markets where we find a negative relationship. There could be several reasons why the results differ between regions. Feng et al. (2015) found that there is a positive relationship between sustainability and the cost of equity in Asia. This suggests that market participants in Asia see sustainability efforts as a risk and ESG-efforts to be value destructive. It's important to remember that results from these types of studies may differ substantially and depends on regional norms that also applies on the financial markets.

Furthermore, since a significant relationship between the ESG-score and the cost of equity has been found, the conclusion drawn is that investors analyze firms’ ESG-efforts and that it is a part of their investment decision process. In contrast, if no relationship would have been found, it would indicate that investors do not consider sustainability in the investment decision process. The findings of this thesis not only tell us that the signaling theory is valid, but furthermore that greenwashing is effective since the firm's own report influences the ESG- score used in our data. Greenwashing is however a dangerous game to play since it is likely to be discovered by stakeholders at one point in time, causing more damages to the firm than if the disclosures would have been unbiased and accurate in the first place.

The mechanisms by which ESG impacts the cost of equity is however still uncertain. Several theories which complement each other could explain our finding that high ESG firms have a lower cost of equity. Firstly, it is possible that the reduced cost of equity reflects diminished information asymmetry as a consequence of greater governance which should reduce agency costs (Akerlof, 1978, p. 500; Chen et al., 2009, p. 286). Another possible mechanism by which ESG impacts the cost of equity is the fact that few investors actively refuse to invest in high ESG firms. In contrast, high ESG firms attract SRI-investors (Hofmann et al. 2009; Renneboog, 2008), which must imply that high ESG-firms have a broader investor base, ceteris paribus. According to capital market models of Merton (1987) and Heinkel et al. (2001), a broader investment base drives up the demand for the firm’s stock, increases firm value and lowers the cost of capital. Thirdly, a plausible explanation of our findings could be that the lower cost of equity may also reflect the effects of ESG-efforts on the firm's growth and cash-flow opportunities. This conclusion is shared by both Hail & Leuz (2006, p. 27) and Chen et al. (2009, p. 286). ESG-efforts could generate less volatile cash-flows or greater growth opportunities than traditional investments, which would thus be perceived as less risky and discounted at a lower discount rate from investors. Furthermore, another explanation to our findings could be that non-ESG firms are facing risks of environmental, social and governmental liabilities in the form of litigation, which is according to Ng & Rezaee (2015, p. 146) one of the main explanations to a lower cost of equity for ESG-firms. Scandals such as the Volkswagen diesel gate which have cost the firm 31,3b€ so far (Taylor & Martin, 2020) or the Wirecard scandal where the firm went for tech star to bankruptcy due to reported assets of $2 billion who didn’t exist (Davies, 2020) are recent examples of ESG-liabilities that low ESG- firms could be facing and therefore have a higher cost of equity.

Considering that a lower cost of equity generates a higher valuation, everything else equal, we wanted to investigate the relation between price-based multiples and the ESG-factor. Given that a significant negative relation between ESG and the cost of equity is found, the second research question is even more interesting. If significant negative relationships between the

price-multiples and the ESG-factor would have been found, it would imply that ESG reduces risk at the expense of operating profitability. The second research question regarding if investors are willing to pay a price premium for sustainability or not has been tackled throughout examining the relationship between the ESG-score and three several price-based multiples. The results are somewhat contradictory since a significant positive relationship between ESG and the P/E ratio as well as the P/B ratio was found but no significant relationship between the ESG-score and the EV/EBIT ratio could be statistically confirmed. However, the coefficient of the ESG-score is still positive against all three price-multiples but statistically significant against only two out of three multiples.

Finally, the third and last purpose of this thesis has been to investigate if ESG-firms better resists tail events and crises than non-ESG-firms. We expected ESG-firms to generate excess- returns in times of crisis based upon Kim et al. (2014) who conducted a global study that found that ESG decreases crash risk as well as Harjoto et al. (2017) and Ashwin Kumar et al. (2016) who found that ESG decreases stock volatility. Our empirical results show a statistically significant positive relationship between ESG-score and the stock’s excess-return during the market crash caused by the Covid-19 outbreak early 2020, which is in line with both previous research and our findings regarding the first research question. However, it would have been preferable to conduct this study including other crises such as the dot-com bubble, the financial crisis of 2008, and the European sovereign debt crises, but due to the limited available time we had for this thesis, it was not possible. Since a reduced cost of equity is equivalent with reduced perceived risk, the rationale behind our findings could imply that in times of crises, investors are flying to safety. The “fly to safety” implies that investors sell ESG-stocks to a lesser extent than non ESG-stocks, which consequently generates excess-returns compared with the overall market. Our contradicts the findings from Folger-Laronde et al. (2020, p. 5) who investigated the resilience of ESG ETFs against the Covid-19 crash and found no resilience against the Covid-19 market crash.

Except from the flight-to-safety theory, another explanation why ESG-stocks generated excess- returns during the market crash caused by the Covid-19 outbreak early 2020 could also derive from eventual resilience in earnings and cash-flows. Even if this thesis does not examine the numerator in equation 2 (expected dividends, cash-flows or residual profits depending on the chosen valuation model), it is important to highlight that the excess-return does not necessarily exclusively come from the cost of capital reduction.

Finally, it is important to highlight that any involvement of non-financial screeners in the investment process could indicate a lower interest of ESG-investors in strictly financial metrics. This is also the conclusion of Bollen (2007), who investigated the dynamics of sustainable fund flows against fund flows of conventional mutual funds as described in the theoretical framework. We can therefore not exclude that ESG-firms resisted the market crash caused by the Covid-19 outbreak strictly due to ideological convictions from sustainable investors who refused to sell ESG-stocks in order to help these firms to get through the crisis. This reasoning could also be true regarding the first and second research question, where the lower cost of equity or higher price-multiples could exclusively derive from ideological convictions and not from a lower perceived financial risk in sustainable firms.

8 Conclusions and recommendations

To fulfill the purpose of this study the cost of equity capital, multiples and volatility was examined. Data was collected from EIKON between 2011 and 2020, this data was then processed and statistical analysis was made through Stata. This thesis has focused on perceived risk rather than financial performance which is important to remember because the discount rate is not the only component in equity valuation.

The purpose of the thesis is answered through three different research questions where the first is our main focus, because of the limited time of this thesis we were not able to evolve the second and third research question as much as we would have wanted to. The logic behind the decision to include three different research questions was to first test if sustainability performance effects the cost of equity capital, to see the robustness of these findings we also wanted to see if sustainability performance effects the valuations multiples and the if high sustainability performance can be considered a “safe haven” in times of crises.

Firstly, we found a negative relationship between the sustainability performance measured as ESG-score and the ex-ante cost of equity capital. This is a very important finding, suggesting that companies with a high sustainability profile are rewarded through a lower cost of equity, and therefore can finance their business cheaper than non-sustainable firms. This will also lead to a higher valuation of the company as the discount rate is lower, ceteris paribus. Financial market plays their intended role in the opposite way too, making it harder and more expensive for non-sustainable firms to access equity capital. As previously mentioned, a regression model was used to test our hypothesis, it's important to remember that this only tells us if there is a significant relationship, not if there's any causality. Regression models could therefore be problematic in these types of research, since the choice of engaging in sustainability efforts may not be independent from the cost of equity capital (El Ghoul et al., 2011, p. 2397). Corporations with a lower cost of equity capital can acquire cheaper capital and may thus choose to invest more money in sustainability efforts, which would imply reversed causality.

Secondly, the relationship between sustainability and valuation multiples was tested to see if the previous findings hold. Lower cost of equity should lead to higher price multiple, keeping everything else equal, since future cash flows are discounted with a lower discount rate. The findings from these tests were somehow conflicting. Sustainability performance and the price- earnings ratio as well as the price-to-book ratio was statistically significant. Furthermore, the EV/EBIT multiple was not statistically significantly related to the sustainability performance. However, it is fair to say that our results still implies that firms with good sustainability performance enjoys a price premium in terms of a higher P/E and P/B multiple.

Thirdly, the stock return in times of crises was examined. A significant relationship between excess return and sustainability performance was found. This indicates that in times of crises, where investors fly to safety, firms with higher sustainability performance works as a “safe haven”. 8.1 Theoretical and practical contributions

The large interest and importance of sustainability has not gone unnoticed. Everyone has an interest and a responsibility in the transformation towards a sustainable world. This can be especially important for large corporations who have a lot of power and resources to lead by example. Capital market participants have a huge role in the transition to provide capital to

sustainable firms, at the right price. This thesis provides evidence that sustainability risk is priced through the implicit cost of equity with a negative risk premium, generating a lower cost of equity for sustainable firms. Yes, it pays to be ESG.

It is critical that decisions made by companies, governments and regulators are science based. Decisions taken today will impact coming generations and set the foundation for how we tackle sustainability issues and the climate crisis. Furthermore, it's important that these decisions are based on the best information available at any point in time. Our results provide important theoretical and practical contributions within the field of sustainable finance. Our findings suggest that sustainability affects investors' perceived risk of the company, companies with higher sustainable performance are rewarded through a reduced cost of equity capital. As described in the problem background, several inputs have been made in the debate about sustainability and valuation. Some mean that the valuation looks like a price bubble, which according to us never can be seen as sustainable even though it is having a desirable effect. Sustainable firms will have access to a large amount of cheap capital and unsustainable firms will have harder to finance their operations. This mechanism in the financial market is important to understand for companies, regulators and investors. Our thesis contributes with information that is applicable in the Nordic markets. Besides the obvious moral reason for companies to work with environmental, working conditions, human rights and governance issues, our research also finds that it is profitable for companies to engage in these activities. Because companies are able to finance their operations at a cheaper rate, they can invest in projects with a lower internal rate of return. This could further stipulate sustainability efforts, including projects with an even lower profitability but still with a positive net present value.

For investors, our findings suggest that when investing in a sustainable firm they are willing to accept a lower return because of the lower risk. Furthermore, it could be an interesting investing strategy to invest in companies with low sustainability performance (low ESG score) but are actively working to enhance their sustainability performance or encourage management to become more sustainable. Because firms with higher ESG scores are perceived as less risky this will lead to a new equilibrium where investors can benefit from the adjustment in the cost of equity. In contrast, this also means that there is a risk for greenwashing since our results are based on ESG scores as a proxy for sustainability. If companies focus on ESG score rather than the underlying operating sustainability our results are misleading. If this thesis is not interpreted correctly, it could provide evidence that greenwashing pays off in case investors do not notice it.

This study also provides practical contributions towards policymakers since our results shows that capital markets are able to price sustainability risks. We cannot say anything to which amount markets are able to do this, but it seems to be self-regulated and fulfilling its intended role. If capital markets were not able to price some sustainability risk, you could argue that policymakers should look at further regulations to stipulate sustainable investments. In contrast, our findings show the importance of regulation regarding greenwashing and sustainability reporting. Financial reporting is as of today already highly regulated and supervised by authorities in order to ensure the authenticity of financial reports, unfortunately the same cannot be said regarding sustainability reporting. Since our results showed that investors are taking into consideration sustainability, the main focus from a regulatory point of view should rather be on the quality of the sustainability disclosure. Fortunately, the new EU- taxonomy aims to reduce greenwashing and it should help stakeholder to distinguish between successful sustainability efforts and greenwashing practices.

8.2 Societal and ethical implications

The foundation of this thesis is sustainability finance and its implication for society cannot be stressed enough. The purpose of this thesis is to provide knowledge on how firm valuation is affected by sustainability performance. The societal and ethical considerations of this thesis are rather clear, sustainability is the number one problem in the world especially environmental problems. Our view is that it's important to conduct unbiased research and have an open mind. Our job is to provide knowledge within the field regardless of what results. Of course, with our backgrounds and ethical beliefs we hoped to find evidence that investors acknowledge firms that show good sustainability performance especially through our main research question. If we would have found no relationship or a positive relationship between the cost of equity and sustainability this would be very problematic from a societal perspective. Because that would suggest that equity investors do not see sustainability as a risk and because this risk would have been disregarded, companies that actually are high sustainable performers would not be rewarded for this. Since equity markets play a huge role in society to provide capital to good firms and the transformation towards a sustainable world this would have been very problematic.

Our conclusion is in line with our expectations that highly sustainable companies are rewarded for this in terms of lower cost of equity and in some cases higher price multiples. These are important findings with high societal and ethical implications. It implies that companies do not face a hard decision to adopt a sustainability strategy or not, not only do companies have the moral responsibility to do so they can also lower the perceived risk and their financing costs. This contradicts the classical view that the shareholder theory and stakeholder theory are in direct conflict with each other. According to Friedman a firm's only responsibility is towards its shareholders, our findings support that engaging in sustainability increases firm value ceteris paribus and should not be seen as an agency cost. At the same time the stakeholder theory is also valid, companies that work with different stakeholders in terms of environmental, working conditions and governance are also rewarded. It's important to remember that these implications are based on the risk measured as the cost of equity and not how the cash flow is impacted by green investments. 8.3 Truth criteria

In order to motivate the trustworthiness of this thesis, the three main truth criteria validity, reliability and generalizability will be discussed below. The discussion will revolve around actions taken in this thesis in order to improve the trustworthiness of the thesis as well as our own judgement of the thesis’s level of validity, reliability and generalizability.

8.3.1 Validity According to Collis & Hussey (2013, p. 53), validity reflects to what extent the research conducted measures what the researcher wants it to measure, and the results reflect the phenomena under study. To put it in another way, sample size or calculation methods as well as the use of correct models to get accurate measurement of the subject are a matter of measurement validity of study. One common way to assess a studies validity is face validity, this involves testing that the measures and the test conducted actually reflects what it is intended to measure (Bryman & Bell, 2011, p. 160; Collis & Hussey, 2013, p. 53).

To evaluate the face validity criteria, it's important to reason if our test is able to measure if there is a relationship between the sustainability performance and the cost of equity capital. Intuitively we feel confident that our statistical test is able to answer our research question of course there are several factors beyond sustainability that will affect the cost of equity. To handle these problems several control variables have been included in the regression model. Thereby we feel confident that our study has a high level of face validity.

Furthermore, it's important to evaluate the studies construct validity also called measurement validity, the construct validity is important because this study is focusing on a relationship that is not directly observable. Therefore, it's important that the researcher constructs hypotheses deducted from previous research and theories (Bryman & Bell, 2011, p. 160; Collis & Hussey, 2013, p. 53). To evaluate the construct validity of this study it's important to evaluate the choice of variables and how these have been calculated. It's important that the variables included have support from previous research both in the choice of variables as well as how these variables are calculated. All variables used in this study are based on several previous studies. However, our ex-ante cost of equity variable is only calculated based upon one model which we see as a big drawback of our study and hurts the validity. Another important variable is our proxy variable for sustainability, the ESG-score collected from EIKON. We have studied the process of how EIKON calculates these sustainability scores and the fact that previous research has also used the database makes us comfortable to use the ESG-score as a proxy for sustainability performance.

When researchers discuss validity, much focus is put on internal and external validity. Internal validity tackles the question about causality. We can see a relationship between two variables in our statistical test, but this does not necessarily mean that there is causality between the two events (Bryman & Bell, 2011, p. 42). There could be a third factor that affects y. In our case we find a negative relationship between the cost of equity and the sustainability performance, but we cannot conclude that we have a causal relationship. This is something that we are aware of and to overcome this problem economic theories and previous studies have been included to be able to discuss the causal relationship between the independent and dependent variable together with common sense.

External validity concerns how well the conclusions from this research can be generalized beyond our sample in different contexts (Bryman & Bell, 2011, p. 43). This will be discussed below in the generalizability section.

8.3.2 Reliability Reliability of research refers to “the accuracy and precision of the measurement and absence of differences in the results if the research were repeated” (Collis & Hussey, 2013, p. 52). For a research to be reliable, a repeat of the study should therefore generate the same results. Replication is furthermore important in positivist studies (Collis & Hussey, 2013, p. 53), which is the epistemological assumption of this study.

All the data retrieved for this thesis comes from the well-known database Thomson Reuters Eikon as well as the Nordic country’s central bank website. The data used in this study is in our opinion thus reliable in itself. The ESG-score is, in the same manner as financial data, retrieved from Thomson Reuters Eikon. In contrast to the financial data which should not differ if retrieved from another database, the ESG-score is however Eikons own rating. Therefore,

we would like to highlight the risk that results could differ in case the study is replicated with ESG-scores from other rating agencies than Eikon.

Furthermore, the main risk regarding reliability in this study is the occurrence of human errors in the data processing process. In order to overcome that risk, we have both been present during the data retrieving and analysis process. Furthermore, we have methodologically processed and analyzed the data step by step. All assumptions, data processing steps and results have been disclosed in chapters regarding research method, data and empirical results in order to make this study replicable. Overall, we cannot guarantee that no error exists, but steps have been taken to reduce human errors as much as possible and this thesis should therefore be seen as reliable.

8.3.3 Generalizability Generalizability is an important criteria in quantitative research, the researcher is often concerned with being able to generalize the findings beyond the sample included in the test (Bryman & Bell, 2011, p. 163).

Previous research has found a mixed relationship between the ex-ante cost of equity and the sustainability performance. Some of these different findings can be explained by where in the world the research has been conducted. Feng et al., 2015 examined the relationship between the cost of equity capital and sustainability performance in different parts of the world. In the western world they found a negative relationship and in Asia they found a positive relationship. This suggests that investors value ESG differently in different geographical areas which can be reasonable because the investors' perceptions are a reflection of the society as a whole, the risk is perceived differently. In the western world it seems that high sustainability performance indicates a lower risk and in Asia sustainability performance increases the risk. This suggests that in the western world investing in sustainability is lowering the risk through the stakeholder theory where these efforts create value through relationships with important stakeholders of the company. In contrast, in Asia it seems that sustainability efforts are seen as value destructive (increased perceived risk) and investors focus on the shareholder theory rather than the stakeholder theory.

Our data was collected from the Nordic Large cap containing information from companies listed in Sweden, Norway, Finland and Denmark. Some companies were excluded from the statistical test because of missing data related to the calculation for the ex-ante cost of equity. It's important to remember that the results and conclusions from this thesis are only valid in the Nordic market. As previously discussed, previous research has found conflicting results in different parts of the world, we should be very careful to implement our findings on other equity markets around the world. 8.4 Limitations and future research

This thesis has studied the impact of sustainability performance on the cost of equity capital, price multiples and if ESG-firms have generated excess-returns in time of crisis. Since it is, to the best of our knowledge, the first study that has focused on the Nordic markets, our findings provide important knowledge to different stakeholders in the society. Even though this thesis has provided several important conclusions, there is more to be done within the field. We will therefore provide suggestions towards future research below.

● To evolve the research further, future research within the area should use more than one model to calculate the ex-ante cost of equity. As discussed earlier in this thesis, the OJ- model was used to calculate the ex-ante cost of equity. One limitation with the model is that it has significantly reduced our sample since it is not possible to calculate the square root of a negative number. An average of the ex-ante cost of equity from several models as done in El Ghoul et al. (2011) or Boubakri et al. (2012) would provide a better estimation of the variable but also lead to a larger sample. The use of several cost of equity calculation models would significantly increase the validity of the study and it would be interesting to see if this would lead to similar results.

● This thesis has focused on large publicly traded companies on the Nordic market. Even though the majority of the largest companies in the Nordic is listed companies, there are also private firms that have an important role in the transition towards a sustainable world. Therefore, it would be highly relevant to see if the results we have found also are present in an unlisted environment.

● To answer the research questions of this thesis a quantitative research methodology has been used, it would be highly interesting to apply a qualitative approach as well. A qualitative approach would provide deeper insights in how equity investors value sustainability performance and how this reflects on the perceived risk. This could also provide important information about which channel the cost of equity is reduced through, information asymmetry, less risk for litigation or larger investor base to name a few possibilities.

● The multiple regression models used in this thesis does not test any causality between variables. This is not a limitation of our study since we did not aim to test for causality between variables. Instead, in the analysis chapter, the results are connected to several theories and previous research to provide several plausible explanations of our results. However, it would undeniably be of high interest to conduct a causality test in further research.

We also recognize that this thesis is far from perfect. During the process of writing this thesis we have learned a lot both regarding research methodology but also about the subject itself. In the theoretical background, relevant theories that we base our results upon are accounted for. It's important to remember that these theories are the theories we feel are relevant for this study, which might create bias in the research. To overcome this problem as much as possible, we have done a comprehensive analysis of the literature to find the most relevant theories.

We would also like to highlight the fact that we are not statisticians. With our academic background, our strength is rather more in the data collection, data processing and the theoretical background than in the hypothesis design and statistical tests. To overcome this problem, we have carefully followed recommendations found in literature in the field of statistics for business administration regarding the development of multiple regression models and hypothesis testing.

As previously discussed, there are several limitations of this study related to the available timeframe. The principal focus of this thesis is our main research question regarding the cost of equity capital. Because of the limited time, we were not able to evolve the second and third research question to the extent we would have liked. This is especially related to the statistical part where you could question the regression models and the control variables. There is a risk

that these models suffer from omitted variable bias. We have provided information about model diagnostics related to all regression models but only discussed the issues related to the first research question.

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Appendix

Appendix 1 - List of companies within sample (184 units)

Company Company Company AAK AB (publ) Frontline Ltd Orkla ASA AB SKF G4S PLC Orsted A/S Abb Ltd Gaming Innovation Group Inc Outokumpu Oyj Addtech AB Genmab A/S P/F Bakkafrost Adevinta ASA Getinge AB Pandora A/S AF Poyry AB Gjensidige Forsikring ASA Pandox AB Aker ASA GN Store Nord A/S Peab AB Aker BP ASA Golden Ocean Group Ltd PGS ASA Aker Solutions ASA Grieg Seafood ASA Resurs Holding AB (publ) Alfa Laval AB H & M Hennes & Mauritz AB Rockwool International A/S ALK-Abello A/S H Lundbeck A/S Royal Unibrew A/S ALM. Brand A/S Hexagon AB Saab AB Ambu A/S Hexagon Composites ASA Sagax AB AP Moeller - Maersk A/S Hexpol AB SalMar ASA Arjo AB (publ) Holmen AB Samhallsbyggnadsbolaget I Norden AB Assa Abloy AB Hufvudstaden AB Sampo plc AstraZeneca PLC Huhtamaki Oyj Sandvik AB Atea ASA Husqvarna AB Sanoma Oyj Atlas Copco AB ICA Gruppen AB Scandinavian Tobacco Group A/S Atrium Ljungberg AB Indutrade AB Scatec ASA Autoliv Inc Intrum AB Schibsted ASA Avanza Bank Holding AB Iss A/S Schouw & Co A/S Axactor SE JM AB Sectra AB Axfood AB Kemira Oyj Securitas AB B2holding ASA Kesko Oyj Simcorp A/S Beijer Ref AB (publ) Kindred Group PLC Sinch AB (publ) Betsson AB Klovern AB Skanska AB BillerudKorsnas AB (publ) Kojamo Oyj SSAB AB Boliden AB Kone Oyj Stolt-Nielsen Ltd Bonheur ASA Konecranes Abp Stora Enso Oyj Bravida Holding AB Kongsberg Automotive ASA Subsea 7 SA BW LPG Ltd Kongsberg Gruppen ASA Svenska Cellulosa SCA AB BW Offshore Ltd Kungsleden AB Sweco AB (publ) Cargotec Corp Leroy Seafood Group ASA Swedish Match AB Carlsberg A/S Loomis AB Swedish Orphan Biovitrum AB (publ) Castellum AB Lundin Energy AB Tele2 AB Catena AB Lundin Mining Corp Telefonaktiebolaget LM Ericsson Chr Hansen Holding A/S Maersk Drilling A/S Telenor ASA Citycon Oyj Metsa Board Oyj Telia Company AB Coloplast A/S Metso Outotec Corp TGS NOPEC Geophysical Company ASA

Crayon Group Holding ASA Millicom International Cellular SA Thule Group AB Demant A/S Mowi ASA TietoEVRY Corp Dfds AS Mycronic AB (publ) Tomra Systems ASA Dno ASA NCC AB Topdanmark A/S Dometic Group AB (publ) Nel ASA Trelleborg AB DSV Panalpina A/S Neles Oyj Tryg A/S Electrolux AB Neste Oyj UPM-Kymmene Oyj Electrolux Professional publ AB Netcompany Group A/S Valmet Oyj Elekta AB (publ) Nibe Industrier AB Veidekke ASA Elisa Oyj Nobia AB Veoneer Inc Elkem ASA Nokia Oyj Vestas Wind Systems A/S Entra ASA Nokian Tyres plc Vitrolife AB Epiroc AB Nolato AB Volvo AB Equinor ASA Nordic Entertainment Group AB Wallenius Wilhelmsen ASA Essity AB (publ) Nordic Nanovector ASA Wallenstam AB Europris ASA Nordic Semiconductor ASA Wartsila Oyj Abp Evolution Gaming Group AB (publ) Norsk Hydro ASA Wihlborgs Fastigheter AB Fabege AB Norwegian Air Shuttle ASA XXL ASA Fastighets AB Balder Novo Nordisk A/S Yara International ASA Fjordkraft Holding ASA Novozymes A/S Zealand Pharma A/S Flsmidth & Co A/S Nyfosa AB Fortum Oyj Orion Oyj

Appendix 2 - Correlation matrices connected to research question 2

Appendix 3 - Correlation matrix connected to research question 3

Appendix 4 - Scatterplot of Residuals vs. Fitted values of P/Ew

Appendix 5 - Scatterplot of Residuals vs. Fitted values of P/Bw

Appendix 6 - Scatterplot of Residuals vs. Fitted values of EV/EBITw

Appendix 7 - Scatterplot of Residuals vs. Fitted values of CRISIS

Appendix 8 - Distribution of the error term regarding estimations of P/E ratio

Appendix 9 - Distribution of the error term regarding estimations of P/B ratio

Appendix 10 - Distribution of the error term regarding estimations of EV/EBIT ratio

Appendix 11 - Distribution of the error term connected to estimations of CRISIS

Appendix 12 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of P/E ratio

Appendix 13 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of P/B ratio

Appendix 14 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of EV/EBIT ratio

Appendix 15 - Partial and semipartial correlations of residuals with explanatory variables connected to estimations of CRISIS

Appendix 16 - Breusch- Godfrey LM test for autocorrelation related to research question one

Appendix 17 - Breusch and Pagan Lagrangian multiplier test for random effects

Appendix 18 - Cluster-Robust Hausman test for fixed effect or random effect model

Appendix 19 - Breusch-Pagan / Cook-Weisberg test for heteroskedasticity related to research question one

Appendix 20 - Correlation coefficients between independent variables regarding research question two

Appendix 21 - Correlation coefficients between independent variables regarding research question three

Appendix 22 - VIF-test regarding research question two

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