Kaisa Vanha-Perttula 

Impact of green bond issuance on stock prices 

Evidence from Nordic markets 

 

 

 

 

 

 
  

Vaasa 2025 

School of Accounting and Finance 
Master’s thesis in Finance 

Master’s Programme in Finance 



2 

UNIVERSITY OF VAASA 
School of Accounting and Finance 

Author: Kaisa Vanha-Perttula 
Title of the Thesis: Impact of green bond issuance on stock prices: Evidence from 

Nordic markets 
Degree: Master of Science in Economics and Business Administration 
Program: Master’s Degree Program in Finance 
Supervisor: Timo Rothovius 
Year of graduation: 2025 Pages: 84 

ABSTRACT: 
Green bonds have become a central instrument in sustainable finance, offering companies a 
way to raise capital for projects with environmental benefits while signaling long-term respon-
sibility to investors. The Nordic region has established itself as a frontrunner in this field, yet 
limited evidence exists on whether green bond issuance generates measurable shareholder 
value. This thesis examines how stock prices react to corporate green bond announcements in 
Nordic markets and whether firm- and bond-specific factors influence these reactions. 
 
The study applies an event study methodology combined with regression analysis to a dataset 
of 101 green bond announcements by 47 listed non-financial companies from Finland, Sweden, 
Norway, and Denmark between 2017 and 2025. Daily stock returns and country-specific bench-
mark indices are used to calculate abnormal and cumulative abnormal returns (CARs) over mul-
tiple event windows. The event study results indicate no statistically significant abnormal re-
turns in the overall sample, suggesting that investors in Nordic markets do not systematically 
adjust valuations following green bond announcements. However, the analysis reveals a mar-
ginally negative reaction to first-time issuances in the longer event window, while repeated is-
suances are treated as routine financing decisions. 
 
Regression analysis provides further insights into the determinants of short-term reactions. Firm 
size, leverage, maturity, and issuance history are found to explain variation in CARs within the [-
1,1] window. Larger companies and more leveraged firms experience more favorable immediate 
responses, while longer bond maturities are associated with weaker reactions. First-time issu-
ances also have a significant positive short-term effect, contrasting with their marginally nega-
tive longer-horizon impact. Supplementary regressions with country and industry dummies 
show no systematic differences across geographical or sectoral dimensions, indicating that Nor-
dic markets treat green bond announcements as relatively uniform events across countries and 
industries. 
 
The findings contribute to the literature on sustainable finance by providing evidence from a 
mature market with strong ESG integration. For investors and corporate managers, the results 
highlight that the financial implications of green bond issuance in the Nordics are limited overall, 
but short-term market responses depend on firm characteristics and bond design. This thesis 
therefore adds a Nordic perspective to the ongoing debate on whether green bonds deliver 
value beyond their environmental benefits. 
 

KEYWORDS: green bonds, sustainable finance, market reaction, event study, Nordic markets 
  



3 

VAASAN YLIOPISTO 
School of Accounting and Finance 

Tekijä: Kaisa Vanha-Perttula 
Tutkielman nimi: Impact of green bond issuance on stock prices: Evidence from 

Nordic markets 
Tutkinto: Kauppatieteiden maisteri 
Oppiaine: Rahoituksen maisteriohjelma 
Työn ohjaaja: Timo Rothovius 
Valmistumisvuosi: 2025 Sivumäärä: 84 

TIIVISTELMÄ: 
Vihreistä joukkovelkakirjoista on tullut olennainen osa kestävää rahoitusta. Ne tarjoavat yrityk-
sille tavan rahoittaa ympäristöä hyödyttäviä hankkeita ja samalla viestiä sijoittajille vastuullisuu-
desta. Vaikka Pohjoismaat toimivat kestävän rahoituksen edelläkävijöinä, tutkimus vihreiden 
joukkovelkakirjojen vaikutuksista osakkeenomistajien näkökulmasta on yhä vähäistä. Tämä tut-
kielma tarkastelee, miten osakemarkkinat reagoivat vihreiden joukkovelkakirjojen liikkeeseen-
laskuihin Pohjoismaissa ja mitkä tekijät selittävät mahdollisia reaktioita. 
 
Tutkimuksen aineisto koostuu 101 liikkeeseenlaskusta 47 pörssilistatulta, ei-rahoitusalan yhti-
öltä vuosina 2017–2025. Tapahtumatutkimuksen ja regressioanalyysin avulla mitataan epänor-
maalit ja kumulatiiviset epänormaalit tuotot eri tapahtumaikkunoissa. Tulosten mukaan vihrei-
den joukkovelkakirjojen julkistukset eivät keskimäärin aiheuta tilastollisesti merkitseviä markki-
nareaktioita. Ensimmäiset liikkeeseenlaskut kuitenkin herättävät pidemmässä tarkastelussa lie-
västi negatiivisen reaktion, kun taas myöhemmät nähdään rutiininomaisina rahoituspäätöksinä. 
 
Regressioanalyysi tuo esiin lyhyen aikavälin selittäviä tekijöitä. Yrityksen koko, velkaantuneisuus, 
maturiteetti ja liikkeeseenlaskuhistoria vaikuttavat tuottoihin erityisesti lyhyen aikavälin [-1,1] 
ikkunassa. Suuret ja velkaisemmat yhtiöt saavat myönteisemmän lyhyen aikavälin markkinare-
aktion, kun taas pitkät maturiteetit aiheuttavat heikompia reaktioita. Ensimmäinen liikkeeseen-
lasku erottuu lisäksi positiivisella lyhyen aikavälin vaikutuksellaan, vaikka pidemmän tarkastelun 
tulos on lievästi negatiivinen. Lisäksi maita ja toimialoja koskevat lisäregressiot osoittavat, ettei 
epänormaaleissa tuotoissa ole systemaattisia eroja maiden tai toimialojen välillä, mikä vahvistaa 
tulkintaa, että markkinareaktiot ovat Pohjoismaissa suhteellisen yhtenäisiä. 
 
Tutkimus täydentää kestävän rahoituksen tutkimusta pohjoismaisella näkökulmalla, jossa ESG 
on jo vahvasti integroitunut markkinoihin. Sijoittajien ja yritysjohdon kannalta tulokset osoitta-
vat, että vihreiden joukkovelkakirjalainojen vaikutus osakkeenomistajien näkökulmasta on ko-
konaisuudessaan rajallinen. Lyhyen aikavälin reaktiot riippuvat kuitenkin selvästi yrityksen omi-
naisuuksista ja liikkeeseen lasketun lainan rakenteesta. Näin ollen tutkielma tarjoaa lisäymmär-
rystä siitä, miten vihreät joukkovelkakirjat nähdään Pohjoismaiden kypsillä markkinoilla osana 
yritysten vastuullista rahoitusta. 
 

AVAINSANAT: vihreät joukkovelkakirjalainat, vastuullinen rahoitus, markkinareaktiot, tapah-
tumatutkimus, pohjoismaiset markkinat 

  



4 

Contents 

1 Introduction 7 

1.1 Background and motivation 8 

1.2 Purpose of thesis 8 

1.3 Research hypothesis 9 

1.4 Contribution of thesis 11 

1.5 Structure of thesis 12 

2 Overview of bonds 13 

2.1 Key characteristics of bonds 13 

2.2 Risk profiles of bonds 14 

2.3 Bond pricing principles 14 

2.4 The role of credit rating agencies 16 

3 Green bonds 18 

3.1 Principles of green bond 18 

3.2 Development of the green bond market 20 

3.3 Green bonds in the Nordic market 21 

3.4 Greenwashing and market credibility 23 

4 Stock valuation and market reactions 25 

4.1 Measuring stock returns 25 

4.2 Stock valuation methods 27 

4.3 Modern portfolio theory 28 

4.4 Capital asset pricing model 30 

4.5 Arbitrage pricing theory 31 

4.6 Market efficiency and information asymmetry 32 

5 Literature review 34 

5.1 Academic perspectives on green bonds 34 

5.2 Environmental responsibility and shareholder value 36 

5.3 Stock market reactions to green bond announcements 37 

6 Data 40 



5 

6.1 The green bond data 40 

6.2 Data limitations 41 

6.3 Final dataset 43 

6.4 Variables in the regression model 45 

6.4.1 Dependent variables 45 

6.4.2 Explanatory variables 47 

6.5 Descriptive statistics for model variables 51 

7 Methodology 54 

7.1 Event study 54 

7.1.1 Estimation of expected returns 55 

7.1.2 Cumulative abnormal returns 56 

7.2 Significance tests 58 

7.3 Regression model 58 

8 Results 61 

8.1 Abnormal returns 61 

8.2 Regression results 66 

8.3 Country and industry effects 70 

8.4 Summary of results 73 

9 Conclusions 76 

References 78 

Appendices 83 

Appendix 1. List of issuers in the dataset 83 

  



6 

Figures 
 
Figure 1. Global green bond issuance by year. ............................................................... 20 

Figure 2. Nordic green bond issuance by year. ............................................................... 22 

 
 

Tables 
 
Table 1. Credit ratings for bonds from major rating agencies. ....................................... 16 

Table 2. Number and value of green bond issuances by industry. ................................. 43 

Table 3. Number and value of green bond issuances by country. ................................. 44 

Table 4. Annual distribution of green bond issuances. .................................................. 45 

Table 5. Descriptive statistics of cumulative abnormal returns (CARs). ......................... 47 

Table 6. Explanatory variables used in the regression model. ....................................... 47 

Table 7. Descriptive statistics of green bond characteristics.......................................... 52 

Table 8. Descriptive statistics of company characteristics. ............................................ 53 

Table 9. Stock market reaction to green bond issuance in the [-10,10] event window. 62 

Table 10. Stock market reaction to green bond issuance in the [-5,5] event window. .. 63 

Table 11. Stock market reaction to green bond issuance in the [-1,1] event window. .. 63 

Table 12. Stock market reaction to green bond issuance in the [0,1] event window. ... 64 

Table 13. CARs for first-time and subsequent green bond announcements. ................ 65 

Table 14. Regression results for CARs in the [-10,10] event window. ............................ 67 

Table 15. Regression results for CARs in the [-1,1] event window. ................................ 68 

Table 16. Regression results for CARs in the [-10,10] event window with country and 

industry dummies. .......................................................................................................... 71 

Table 17. Regression results for CARs in the [-1,1] event window with country and 

industry dummies. .......................................................................................................... 72 

 
 



7 

1 Introduction 

Climate change has become one of the most pressing global challenges, urging compa-

nies and governments to adopt sustainable practices and reduce their carbon footprint 

(Bracking, 2021). The financial sector plays a pivotal role in supporting this transition, 

with green finance emerging as a key mechanism to channel capital into environmentally 

friendly projects. Among these instruments, green bonds enable issuers to raise funds 

earmarked for sustainability-oriented purposes, such as renewable energy and energy-

efficient infrastructure (ICMA, 2021). Since the first issuance by the European Invest-

ment Bank in 2007, the market has expanded rapidly. The European Investment Bank 

(2024) has emphasized the growing importance of climate-related financing, while the 

Climate Bonds Initiative (2024) shows that green bonds have become firmly established 

as a mainstream financing tool within global capital markets. 

 

In recent years, the Nordic countries have emerged as leaders in sustainable finance. The 

Nordic Council (2024) highlights how sustainability has become deeply embedded in cor-

porate and policy agendas across the region. This development has been reinforced by 

record issuance from Nordic corporates and municipalities, as documented by Nordic 

Trustee (2024), which demonstrates the region’s central role in the global green bond 

market. Despite this strong momentum, academic research on the financial implications 

of green bonds remains limited. Much of the existing literature has focused on environ-

mental outcomes, while less attention has been given to whether such instruments in-

fluence stock price performance (Tang & Zhang, 2020). 

 

This thesis addresses this gap by investigating the stock price effects of green bond issu-

ance within the Nordic context. By focusing on this specific market, the study contributes 

to the literature on sustainable finance and provides practical insights for investors and 

policymakers. The findings are intended to support informed decision-making regarding 

sustainable investment strategies in the Nordic region. 



8 

1.1 Background and motivation 

The rapid development of the green bond market reflects the growing demand for fi-

nancing tools that combine economic growth with environmental responsibility. In the 

Nordic countries, sustainability is strongly embedded in corporate and governmental 

strategies, positioning the region as a pioneer in green finance (Nordic Council, 2024). 

Although the market has expanded rapidly, academic research has only partly addressed 

the financial implications of green bond issuance. Earlier studies have highlighted envi-

ronmental and reputational benefits, but evidence on stock market impacts remains lim-

ited. Since share prices reflect investor sentiment and expectations, examining whether 

green bond announcements influence stock performance is essential for understanding 

the financial relevance of sustainable finance (Tang & Zhang, 2020). 

 

This thesis addresses this gap by analyzing stock price reactions to green bond announce-

ments in the Nordic market. The study contributes to the limited body of research on 

the financial effects of green bonds in developed economies and aims to provide insights 

relevant for both investors and corporate managers. 

 

 

1.2 Purpose of thesis 

The purpose of this thesis is to investigate the financial impact of green bond issuance 

on stock prices in the Nordic market. As green bonds have gained prominence in sustain-

able finance, understanding their influence on shareholder value is increasingly im-

portant for investors, policymakers, and corporate decision-makers (Bracking, 2021). 

Green bond issuance is often viewed as a signal of a company’s commitment to environ-

mental responsibility, potentially enhancing its reputation and attracting ESG-focused 

investors (Tang & Zhang, 2020). However, while existing research highlights the environ-

mental and reputational benefits of green bonds, few studies have empirically examined 

their effects on stock price performance, particularly in the Nordic region where ESG 

standards are highly regarded (Nordic Council, 2024). 



9 

To address this gap, this study applies the event study methodology to analyze stock 

market reactions to green bond announcements, specifically examining abnormal and 

cumulative abnormal returns (AR, CAR) around issuance dates. The event study method 

is widely used in finance research to evaluate the impact of specific events on stock 

prices, allowing for a precise measurement of investor response to green bond issuances 

(MacKinlay, 1997). By focusing on both initial and subsequent issuances, this thesis also 

explores whether the frequency or novelty of green bond issuance has differential ef-

fects on stock performance. 

 

This research is limited to publicly listed Nordic companies that have issued green bonds 

in recent years. Previous studies on green bonds have primarily focused on global mar-

kets or emerging economies (World Bank, 2024), leaving a gap in understanding the fi-

nancial implications within mature markets like the Nordics. The findings aim to contrib-

ute to the growing body of literature on sustainable finance by offering insights into how 

green bond issuance impacts corporate value in a region renowned for its sustainability 

leadership. This study will inform investors, corporate managers, and policymakers about 

the potential financial outcomes of green finance strategies in the Nordic market. 

 

 

1.3 Research hypothesis 

The purpose of this thesis is to examine how the issuance of green bonds influences the 

stock prices of publicly traded companies in the Nordic market. Previous research on 

green bonds has predominantly focused on global markets, European markets, or 

emerging economies, with limited studies addressing mature markets like the Nordics, 

where ESG standards are highly integrated into corporate strategies (Nordic Council, 

2024). Moreover, findings from prior studies have been inconsistent: some studies re-

port a positive market reaction to green bond issuances, while others find a negative or 

neutral response (Tang & Zhang, 2020; Flammer, 2021). This thesis seeks to contribute 

to the existing literature by clarifying these mixed findings within the Nordic context and 



10 

exploring the potential impact of green bond issuance on shareholder value. To achieve 

this, the thesis tests the following research hypotheses: 

 

H₀: The value of the share does not react to the announcement of the green bond issu-

ance in the Nordic market. 

 

If the null hypothesis holds, the issuance of a green bond would have no observable 

effect on the stock price of the issuing firm. However, based on previous studies that 

suggest a connection between green bond issuance and stock performance, it is hypoth-

esized that green bond announcements do impact share value. Thus, the main research 

hypothesis is: 

 

H₁: The value of the share reacts to the announcement of the green bond issuance in the 

Nordic market. 

 

To further investigate H₁, potential market reactions are divided into the following hy-

potheses: 

 

H₂a: The value of the share reacts positively to the announcement of the green bond 

issuance in the Nordic market. 

H₂b: The value of the share reacts negatively to the announcement of the green bond 

issuance in the Nordic market. 

 

H₂a explores whether the announcement of a green bond positively affects shareholder 

value, measured through cumulative abnormal returns (CARs). This hypothesis is based 

on the assumption that green bond issuances can enhance corporate reputation and 

attract ESG-focused investors, potentially driving stock prices higher (Tang & Zhang, 

2020). In contrast, H₂b considers the possibility of a negative reaction, indicating that 

investors might view green bonds as adding financial constraints or operational commit-

ments, which could lead to a decrease in stock valuations. 



11 

In addition to examining the overall reaction, this thesis follows research by Lebelle et al. 

(2020) and Flammer (2021) in exploring whether market responses differ between first-

time green bond issuances and subsequent issuances. This leads to the third hypothesis: 

 

H₃: The reaction differs between the first-time green bond issuance announcement and 

subsequent announcements. 

 

Based on studies by Lebelle et al. (2020) and Flammer (2021), the assumption is that 

first-time green bond issuances may generate more significant positive or negative reac-

tions compared to subsequent issuances. First-time issuances may signal a substantial 

strategic shift toward sustainability, potentially attracting heightened investor attention. 

 

 

1.4 Contribution of thesis 

This thesis provides a focused analysis of the financial impact of green bond issuance on 

stock prices within the Nordic market, a region recognized for its commitment to sus-

tainable finance and ESG (Environmental, Social, and Governance) practices. While ex-

isting literature has explored green bond impacts on stock prices in emerging and global 

markets, studies specific to the Nordic market remain limited. This thesis addresses this 

gap by investigating how green bond announcements influence shareholder value in a 

market with a mature ESG framework (Tang & Zhang, 2020). 

 

Using event study methodology, this research provides a detailed analysis of stock mar-

ket reactions to green bond issuances. By examining both first-time and repeat issuances, 

it investigates whether investor reactions differ based on a company’s history with sus-

tainable finance. Unlike studies that aggregate data from diverse regions or focus on 

markets outside the Nordics, this thesis concentrates on the unique characteristics of 

Nordic investors, who tend to prioritize environmental and social factors in their financial 

decisions (Flammer, 2021). 

 



12 

The findings from this study are intended to contribute to both academic literature and 

practical applications. Academically, this research enriches the field of sustainable fi-

nance by providing empirical evidence on the relationship between green bond issuance 

and stock performance in the Nordic market. Practically, it offers insights for investors, 

corporate managers, and policymakers regarding the potential benefits and limitations 

of green bonds as a financing tool in sustainability-focused markets. By examining a spe-

cific, ESG-forward region, this study complements existing global and emerging market 

research, providing a valuable perspective on how green financing mechanisms impact 

corporate value in established sustainable economies. 

 

 

1.5 Structure of thesis 

The structure of the thesis consists of nine chapters. After the introduction, the second 

chapter presents the theoretical framework of conventional bonds, providing an over-

view of bond features, risks, pricing, and the role of credit rating agencies. The third 

chapter focuses exclusively on green bonds, discussing their principles, market develop-

ment, and specific characteristics within the Nordic market, as well as addressing issues 

such as greenwashing. 

 

The fourth chapter delves into stock valuation theory, covering key models and concepts 

relevant to analyzing stock price reactions. Chapter five reviews the existing literature, 

summarizing previous studies on green bonds, stock valuation, and ecological impacts 

on stock performance. Chapter six presents the data used in this study, while chapter 

seven describes the methodology, including the event study approach and regression 

analysis. Chapter eight contains the main findings, presenting and discussing the results 

of the analysis. Finally, chapter nine concludes the thesis, summarizing the findings and 

their implications, and suggesting directions for future research. 



13 

2 Overview of bonds 

Bonds are fixed-income securities that enable governments, municipalities, and corpo-

rations to raise capital from investors. They provide regular interest payments, known as 

coupons, and repay the principal at maturity, offering issuers a reliable funding source 

and investors predictable returns (Fabozzi, 2012). Despite their stability, bonds carry risks 

such as interest rate fluctuations, creditworthiness of issuers, and inflation. Additionally, 

bonds come in diverse forms, including treasury, municipal, and corporate bonds, to 

meet varying financial needs, each offering different risk and return profiles (Hull, 2018). 

This chapter examines the fundamental features of bonds, their risks, pricing mecha-

nisms, and the role of credit rating agencies in evaluating issuers' creditworthiness. 

 

 

2.1 Key characteristics of bonds 

Bonds are financial instruments with specific features that define their structure, valua-

tion, and investor appeal. These features outline the rights and obligations of issuers and 

bondholders and shape the bond’s risk–return profile (Fabozzi, 2012). The face or par 

value represents the amount repaid at maturity and serves as the basis for calculating 

interest. The coupon rate specifies the annual interest as a percentage of face value and 

may be fixed, offering stability, or floating, adjusting periodically to a benchmark such as 

LIBOR or SOFR (Hull, 2018). The maturity date indicates when the principal is repaid, 

ranging from short-term (under one year) to long-term (over 10 years). Longer maturities 

typically involve higher risks, such as interest rate and inflation risk, but also offer higher 

yields (Bodie et al., 2018). 

 

Additional features include embedded options, such as callable or puttable bonds, which 

allow early redemption under specified conditions. Callable bonds benefit issuers when 

interest rates fall, while puttable bonds protect investors by permitting resale to the is-

suer at a predetermined price (Fabozzi, 2012). Convertible bonds provide bondholders 

the option to convert holdings into shares, combining fixed-income characteristics with 



14 

equity upside. The diversity of bond features caters to different investor preferences and 

financing needs, making bonds a flexible capital market instrument. These characteris-

tics are central to bond pricing, risk, and marketability, which are discussed further in 

subsequent sections. 

 

 

2.2 Risk profiles of bonds 

While bonds are often regarded as stable investments, they are subject to several risks 

that affect their value and appeal. Interest rate risk is particularly significant: as rates rise, 

bond prices typically fall, with long-maturity bonds most sensitive to these changes (Hull, 

2018). Credit risk is another central concern, especially for corporate bonds that depend 

on issuer stability. Unlike government bonds, corporate bonds carry higher default risk, 

which investors assess using ratings from agencies such as Moody’s and Fitch (Fabozzi, 

2012). 

 

Inflation also erodes the purchasing power of fixed coupon payments, a problem espe-

cially pronounced for long-term bonds (Tuckman & Serrat, 2011). Inflation-linked bonds 

such as TIPS mitigate this by adjusting payments in line with inflation. Liquidity risk can 

emerge in less active markets, forcing investors to accept lower prices if bonds must be 

sold quickly—a challenge often faced with smaller issuers (Hull, 2018). Finally, callable 

bonds introduce reinvestment risk, as issuers may redeem debt early when rates fall, 

leaving investors to reinvest at less favorable levels (Fabozzi, 2012). 

 

 

2.3 Bond pricing principles 

The pricing of bonds is based on the principle of discounted cash flows, which values a 

bond by summing the present value of its expected future payments. These payments 

include periodic coupon payments and the repayment of the principal at maturity. The 



15 

discount rate applied reflects the bond’s yield to maturity (YTM), accounting for the 

bond's risk, time to maturity, and prevailing market conditions (Bodie et al., 2018). 

 

The formula for bond pricing is expressed as follows in Formula 1 below: 

 

𝑃0 = ∑
𝐶𝑡

(1+𝑟)𝑡
+

𝐹

(1+𝑟)𝑇
𝑇
𝑡=1  ,     (1) 

where: 

𝑃0 = Present value 

𝑇 = Time to maturity (in years) 

𝑡 = Number of periods 

𝐶𝑡 = Annual coupon rates 

𝑟 = Required rate of return 

𝐹 = Face value of the bond 

 

Formula 1 highlights the inverse relationship between the discount rate and the price of 

a bond. As the discount rate increases, the present value of future cash flows decreases, 

leading to a lower bond price (Hull, 2018). The discount rate, or yield to maturity (r), 

represents the compensation investors require for bearing risks associated with the 

bond. Credit risk is a key factor influencing bond pricing. Bonds with lower credit ratings, 

reflecting a higher probability of default, offer higher yields to compensate investors for 

the increased risk. Conversely, highly rated bonds offer lower yields due to their per-

ceived safety (Fabozzi, 2012). 

 

Additionally, bond pricing is sensitive to interest rate changes, as captured by the con-

cept of duration. Longer-duration bonds exhibit greater price volatility, underscoring 

their higher sensitivity to market fluctuations (Tuckman & Serrat, 2011). Specialized 

bonds, such as callable and convertible bonds, further complicate pricing. Callable bonds 

allow issuers to repay early, introducing reinvestment risk for investors, while convertible 

bonds include an option to convert into equity, requiring more sophisticated valuation 

models (Choudhry, 2019). 



16 

2.4 The role of credit rating agencies 

Credit rating agencies (CRAs) play a central role in assessing issuer creditworthiness and 

supporting transparency in financial markets. The major CRAs include Moody’s, Standard 

& Poor’s (S&P), Fitch Ratings, and Kroll, which dominate the industry with globally rec-

ognized rating systems (Fabozzi, 2012). Their evaluations help investors gauge default 

risk and make informed decisions. Bonds are typically classified as investment grade or 

speculative grade. Investment-grade bonds, rated BBB- or higher by S&P and Fitch and 

Baa3 or higher by Moody’s, are considered lower-risk and attract more risk-averse inves-

tors. Speculative-grade, or junk bonds, carry higher risks but compensate with poten-

tially higher returns (Bodie et al., 2018). 

 

Table 1. Credit ratings for bonds from major rating agencies (Jiang, 2021). 

Moody’s Standard & 
Poor’s 

Fitch Ratings Kroll Category 

Aaa AAA AAA AAA Best quality 

Aa1 
Aa2 
Aa3 

AA+ 
AA 
AA- 

AA+ 
AA 
AA- 

AA+ 
AA 
AA- 

High quality 

A1 
A2 
A3 

A+ 
A 
A- 

A+ 
A 
A- 

A+ 
A 
A- 

Upper medium 
Grade 

Baa1 
Baa2 
Baa3 

BBB+ 
BBB 
BBB- 

BBB+ 
BBB 
BBB- 

BBB+ 
BBB 
BBB- 

Investment 
Grade 

Ba 
B 
Caa 
Ca 
C 

BB 
B 
CCC 
CC 
C 

BB 
B 
CCC 
CC 
C 

BB 
B 
CCC 
CC 
C 
D (default) 

Speculative 

 

Above Table 1 (Jiang, 2021) shows the credit rating scales used by major agencies. The 

rating system divides bonds into categories that reflect their credit quality. Bonds rated 

AAA represent the strongest creditworthiness and the lowest risk of default. In contrast, 

bonds rated Ba3/BB- or below are considered speculative grade and carry a higher risk 

of default. 



17 

CRAs use both quantitative and qualitative criteria to assign ratings. Quantitative metrics 

such as leverage ratios, cash flow stability, and interest coverage offer objective financial 

insights, while qualitative factors like management quality, market position, and macro-

economic conditions provide a broader perspective on creditworthiness (Choudhry, 

2019; Fabozzi, 2012). The credibility of CRAs has been questioned, particularly due to 

the issuer-pays model, which critics argue creates conflicts of interest. The 2008 financial 

crisis highlighted this problem, as several highly rated securities defaulted. Since then, 

regulatory reforms have aimed to improve transparency and rating quality. Cheng and 

Neamtiu (2009) find that CRAs responded by refining methodologies and increasing ac-

countability, enhancing the reliability of their evaluations. 

 



18 

3 Green bonds 

Green bonds have become a central instrument in the field of sustainable finance, offer-

ing a way to finance projects that deliver clear environmental benefits, such as renewa-

ble energy, energy efficiency, and biodiversity conservation (Tang & Zhang, 2020). While 

structurally similar to conventional bonds, green bonds differ in that their proceeds must 

be allocated exclusively to environmentally beneficial projects. 

 

The green bond market has grown rapidly since the first green bond was issued by the 

European Investment Bank in 2007. This development reflects rising interest from both 

institutional and retail investors in financial instruments aligned with environmental, so-

cial, and governance (ESG) criteria. In addition, frameworks such as the Green Bond Prin-

ciples (GBP), developed by the International Capital Market Association (ICMA), have 

contributed to greater transparency and standardization, reinforcing the credibility of 

the market (ICMA, 2021). 

 

 

3.1 Principles of green bond 

The Green Bond Principles (GBP) are voluntary guidelines developed by the International 

Capital Market Association (ICMA) to promote integrity, transparency, and consistency 

in the green bond market. They provide a globally recognized framework to ensure that 

proceeds are allocated exclusively to projects with environmental benefits (ICMA, 2021). 

The GBP strengthens investor confidence by helping issuers define, evaluate, and report 

on green investments, thereby supporting the growth of sustainable finance. 

 

The GBP are built on four core components: 

 

Use of proceeds. Proceeds must be directed to projects that advance environmental ob-

jectives, such as renewable energy, clean transportation, sustainable water and 

wastewater management, biodiversity conservation, and climate change adaptation 



19 

(ICMA, 2021). Clear disclosure of how funds are used is essential to avoid greenwashing 

(Tang & Zhang, 2020). 

 

Process for project evaluation and selection. Issuers should define transparent proce-

dures for selecting projects, often by aligning with international targets such as the UN 

Sustainable Development Goals (SDGs) or the Paris Agreement (Flammer, 2021). They 

are encouraged to disclose risk assessments and show how projects support broader 

sustainability strategies. 

 

Management of proceeds. Issuers must establish formal processes to track allocation of 

proceeds, typically through dedicated accounts or sub-portfolios. External audits and 

third-party verification are commonly applied to ensure compliance with the GBP 

(Choudhry, 2019). 

 

Reporting. Issuers are expected to provide annual updates on allocation and environ-

mental impacts. Reports often include quantitative indicators, such as reductions in 

greenhouse gas emissions or energy efficiency improvements, which enhance transpar-

ency and credibility (ICMA, 2021). 

 

The adoption of the GBP has been central to the rapid expansion of the green bond mar-

ket. By providing a common language for issuers and investors, they reduce information 

asymmetry and make issuances easier to compare. This standardization has encouraged 

institutional participation and improved market integrity (Tang & Zhang, 2020). Adher-

ence to the GBP can also enhance issuers’ reputations by signaling a commitment to 

sustainability (Flammer, 2021). Despite these benefits, the voluntary nature of the GBP 

creates challenges. Enforcement varies, leading to inconsistencies in reporting and pro-

ject quality (Choudhry, 2019). To strengthen credibility, additional certification schemes 

such as the Climate Bonds Standard have emerged, offering independent verification of 

a bond’s environmental credentials and reinforcing trust in green bond issuances 

(Demski et al., 2025). 



20 

3.2 Development of the green bond market 

The global green bond market has expanded rapidly since the launch of the first Climate 

Awareness Bond by the European Investment Bank (EIB) in 2007. This inaugural bond 

linked proceeds to climate-related disbursements and introduced a financing model that 

supported the transition toward low-carbon development (European Investment Bank, 

2024). A year later, the World Bank issued its first green bond, further contributing to 

the institutional foundation of sustainable capital markets (World Bank, 2019). 

 

According to the Climate Bonds Initiative (2024), the issuance of sustainable debt instru-

ments reached USD 1.67 trillion in 2024. Green bonds formed the largest category, with 

annual issuance totaling USD 639.6 billion. Notably, 64 percent of the 2024 volume was 

aligned with recognized taxonomies, reflecting increasing standardization and market 

maturity. Regionally, Europe accounted for 55 percent of taxonomy-aligned issuance, fol-

lowed by Asia-Pacific with 27 percent and North America with 14 percent. The remaining 

4 percent came from other regions, including emerging markets, reflecting differences 

in regulation, investor awareness, and market readiness. 

 

 

Figure 1. Global green bond issuance by year (Climate Bonds Initiative, 2024). 

 

0

100

200

300

400

500

600

700

800

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

U
SD

 b
ill

io
n

Amount of Green Bonds Issued Globally by Year



21 

Figure 1 illustrates the annual growth of global green bond issuance. The market, which 

began with only a few issuances after 2007, has since grown sharply and reached record 

levels in recent years. This trend underscores the statistics above and shows how green 

bonds have evolved from a niche instrument into a significant segment of sustainable 

debt markets. Institutional issuers have played a central role in this expansion. The EIB, 

for example, remains one of the most active issuers globally. By the end of 2023, it had 

issued EUR 93 billion in CABs and Sustainability Awareness Bonds (SABs), with EUR 81 

billion still outstanding. These instruments are aligned with the EU Taxonomy and con-

tribute to objectives such as climate mitigation and water resource protection. Issuers 

like the EIB often provide external verification and detailed impact reporting, which 

strengthens transparency and investor trust (European Investment Bank, 2024). 

 

Despite this growth, several challenges remain. A key issue is the risk of greenwashing, 

particularly in jurisdictions with limited regulatory oversight. Without consistent stand-

ards, investors may find it difficult to verify the environmental quality of projects fi-

nanced through green bonds. The absence of harmonized reporting frameworks also 

complicates comparisons across issuers and regions. As Choudhry (2019) notes, improv-

ing disclosure and regulatory alignment is essential to safeguard the credibility of the 

market and ensure that green bonds deliver measurable environmental benefits. 

 

 

3.3 Green bonds in the Nordic market 

The Nordic countries are recognized for their leadership in sustainable finance and pro-

gressive climate policies. The region has been central to the development of the global 

green bond market, both through innovation and issuance volume relative to population 

size. Sweden holds a key position: in 2008, Skandinaviska Enskilda Banken (SEB) collabo-

rated with the World Bank to issue its first green bond, helping to establish a globally 

recognized framework for green finance (World Bank, 2019). Between 2020 and 2024, 

the outstanding volume of Nordic corporate green bonds more than doubled to EUR 

33.1 billion, representing 27 percent of the total corporate bond market (Nordic Trustee, 



22 

2024). In 2024 alone, issuance reached EUR 12.5 billion, up from EUR 5.6 billion in 2023, 

setting a new record. Sweden accounted for 54 percent of issuance, followed by Norway 

with 34 percent. Figure 2 shows the total outstanding volume of Nordic corporate green 

bonds by country, highlighting Sweden’s leading role and Norway’s growing contribution. 

 

 

Figure 2. Nordic green bond issuance by year (Nordic Trustee, 2024). 

 

Frequent issuers include municipalities, financial institutions, and companies in the en-

ergy and real estate sectors. Issuances are typically aligned with the Green Bond Princi-

ples (ICMA, 2021) and the EU Taxonomy, supporting transparency and investor confi-

dence. Real estate led the market in 2024 with 42 percent of outstanding volume, while 

utilities and industrials also expanded. In the high-yield segment, industrial and real es-

tate issuers together accounted for 92 percent of volumes (Nordic Trustee, 2024). 

 

Innovation has been a hallmark of the Nordic market. Frameworks have been developed 

to make issuance accessible to smaller municipalities and mid-sized firms, while institu-

tional investors such as pension funds are required to integrate sustainability considera-

tions into their portfolios. Proceeds primarily finance renewable energy, green buildings, 

and sustainable transport, with newer themes such as the circular economy, biodiversity, 

and climate adaptation gaining ground. Despite its strengths, the Nordic market faces 

0

5

10

15

20

25

30

35

2020 2021 2022 2023 2024

EU
R

 b
ill

io
n

Amount of Green Bonds Issued in the Nordics by Year

Norway Sweden Finland Denmark



23 

challenges. The relatively small size can limit liquidity, and concerns about greenwashing 

persist, although strong regulation and transparency help mitigate these risks. Ongoing 

initiatives, including the EU Green Bond Standard and broader ESG reporting, aim to en-

hance credibility and support further growth (European Union, 2022). Looking ahead, 

the Nordic region is well positioned to remain a global leader, offering a model for scaling 

sustainable finance worldwide. 

 

 

3.4 Greenwashing and market credibility 

As the green bond market expands, concerns about greenwashing have become increas-

ingly prominent in academic and policy discussions. Greenwashing refers to situations 

where issuers overstate or misrepresent the environmental benefits of projects financed 

through green bonds. It may involve weak project selection criteria, vague reporting, or 

limited evidence of actual environmental impact. These risks threaten the credibility of 

the market and can reduce investor trust in sustainable finance as a whole (Choudhry, 

2019; Healy & Palepu, 2001). Although voluntary frameworks such as the Green Bond 

Principles (GBP) encourage transparency and accountability, the absence of binding rules 

allows variation in how issuers apply them. As noted by Choudhry (2019), issuers may 

selectively disclose information or adopt broad eligibility definitions without clear meas-

urement of impact. Demski et al. (2025) also highlight that some bonds are marketed 

under sustainability labels without rigorous validation, increasing the risk of misleading 

investors. These issues are particularly concerning in jurisdictions where regulatory over-

sight is still developing. 

 

The problem of greenwashing is closely tied to information asymmetry between issuers 

and investors. When investors cannot verify how proceeds are used or what outcomes 

are achieved, they face higher uncertainty and perceive greater reputational or financial 

risk. This may lead to pricing inefficiencies or reduced participation, especially among 

institutional investors with strict ESG mandates (Healy & Palepu, 2001). To mitigate these 

risks, the market has developed voluntary and regulatory mechanisms aimed at 



24 

improving credibility. Certification schemes such as the Climate Bonds Standard provide 

third-party verification of frameworks, while ongoing use-of-proceeds reporting 

strengthens post-issuance accountability. On the regulatory side, the EU Green Bond 

Standard introduces stricter criteria and alignment with the EU Taxonomy, aiming to cre-

ate a more consistent definition of environmentally sustainable investments (European 

Union, 2022). These tools reduce information gaps, discourage opportunistic behavior, 

and support a more trustworthy green bond market. 

 

In the Nordic region, issuers commonly align with international verification schemes and 

employ external reviewers to reinforce environmental integrity. They also complement 

frameworks with detailed impact reporting and alignment with national and EU-level 

policy goals, further reducing the risk of greenwashing (European Investment Bank, 2024; 

Climate Bonds Initiative, 2024). These practices are essential to maintaining the credibil-

ity of green finance and ensuring that environmental goals linked to green bond issuance 

are genuinely achieved. 



25 

4 Stock valuation and market reactions 

This chapter presents the key theoretical frameworks related to stock valuation. The aim 

is to provide a foundation for understanding how stock prices are formed and how dif-

ferent risk and return components influence valuation. The discussion begins with the 

basic concepts of stock returns and pricing mechanisms. It then introduces four central 

models in financial theory: Modern Portfolio Theory (Markowitz, 1952), the Capital Asset 

Pricing Model (Sharpe, 1964; Bodie et al., 2018), the Arbitrage Pricing Theory (Ross, 1976; 

Nikkinen et al., 2002), and the Efficient Market Hypothesis (Fama, 1970). Together, these 

models form the theoretical background for analyzing market behavior in the context of 

green bond announcements. 

 

A common approach to stock valuation is based on dividend discount models, where the 

value of a stock is defined as the present value of expected future dividends (Nikkinen 

et al., 2002, pp. 149–150). However, this process is rarely straightforward. As noted by 

Knüpfer and Puttonen (2018, p. 93), forecasting future dividend payments is challenging 

due to uncertainty in corporate earnings and the absence of legal obligations to distrib-

ute dividends. As a result, stock pricing often involves significant assumptions about a 

firm's profitability, future cash flows, and market expectations. 

 

 

4.1 Measuring stock returns 

Return on stock refers to the income an investor earns from holding a stock over a period, 

which typically includes both capital gains and dividends. It is a fundamental concept in 

finance and a key metric for evaluating investment performance (Bodie et al., 2018, p. 

400). The total return is used to assess whether a stock has created value and how it has 

responded to market events (Nikkinen et al., pp. 150–151). In the context of this thesis, 

return measurement is essential for identifying market reactions to green bond an-

nouncements, where short-term abnormal returns are observed using event study 

methodology (Campbell et al., 1997, p. 10). 



26 

The total return of a stock over a single period is typically expressed as: 

 

𝑅𝑡 =
𝑃𝑡−𝑃𝑡−1+𝐷𝑡

𝑃𝑡−1
,       (2) 

where, 

𝑅𝑡 = Total return at time 𝑡 

𝑃𝑡 = Stock price at the end of the period 

𝑃𝑡−1 = Stock price at the beginning of the period 

𝐷𝑡 = Dividend paid during the period (Bodie et al., 2018, p. 401). 

 

This formula 2 captures the combined effect of capital appreciation and income from 

dividends. Dividend income is especially relevant when analyzing companies with stable 

payout policies, and the dividend yield can be used to compare the income return across 

different stocks (Brealey et al., 2020, p. 88). 

 

In financial econometrics and academic studies, particularly those involving short-term 

price movements, logarithmic returns are often preferred due to their statistical proper-

ties. The continuously compounded return is defined as: 

 

𝑟𝑡 = ln⁡(
𝑃𝑡

𝑃𝑡−1
) ,      (3) 

where, 

𝑟𝑡 = Logarithmic return 

𝑃𝑡 = Stock price at the end of the period 

𝑃𝑡−1 = Stock price at the beginning of the period (Brooks, 2019, p. 67). 

 

Log returns are time-additive and normally distributed under standard market assump-

tions, which makes them particularly suitable for modeling purposes. According to 

Strong (2003, p. 4), using log returns also facilitates more accurate aggregation over mul-

tiple periods, which is often needed in event studies. 

 



27 

This thesis applies daily log returns when analyzing short-term market responses to 

green bond issuance announcements. Using daily data allows for capturing immediate 

market reactions and detecting abnormal returns around event dates (MacKinlay, 1997, 

p. 13). This is consistent with the efficient market hypothesis, which assumes that stock 

prices adjust quickly to new information (Fama, 1970). 

 

 

4.2 Stock valuation methods 

The price of a stock reflects the present value of its expected future cash flows, most 

commonly in the form of dividends. Stock valuation models provide a theoretical frame-

work for determining whether a stock is fairly priced in relation to its expected profita-

bility and risk. In fundamental analysis, the most widely used valuation approach is the 

dividend discount model, which assumes that investors base their valuation on antici-

pated future dividend payments (Nikkinen et al., 2002, p. 150). 

 

The most common framework for valuing dividend-paying stocks is the Dividend Dis-

count Model (DDM). It assumes that the current stock price equals the sum of all future 

dividends, discounted at the investor’s required rate of return. The general form of the 

DDM is (Nikkinen et al., 2002, p. 150): 

 

𝑃0 = ∑
𝐷𝑡

(1+𝑟)𝑡
∞
𝑡=1 ,       (4) 

where, 

𝑃0 = Current stock price 

𝑟 = Required rate of return 

𝐷𝑡 = Future dividends 

𝑡 = Time in years (Nikkinen et al., 2002, p. 150). 

 

In cases where dividends are assumed to remain constant, the model simplifies into a 

zero-growth dividend model (Knüpfer & Puttonen, 2018, pp. 96–97): 



28 

𝑃0 =
𝐷1

𝑟
.      (5) 

 

If dividends are expected to grow at a constant rate, the model further simplifies into 

the Gordon Growth Model (GGM): 

 

𝑃0 =
𝐷1

𝑟−𝑔
,      (6) 

where, 

𝑔 = Constant dividend growth rate (Knüpfer & Puttonen, 2018, pp. 96–97). 

 

Dividend-based models are particularly useful for valuing stable, dividend-paying firms. 

However, they are less effective when applied to companies that do not distribute divi-

dends or have irregular payout policies. In such cases, models based on free cash flows, 

such as the Discounted Cash Flow (DCF) approach, are more appropriate (Brealey et al., 

2020, pp. 104–106). Stock prices are sensitive to changes in growth expectations, inter-

est rates, and perceived risk. As such, even small changes in projected cash flows or re-

quired returns can significantly impact the theoretical valuation. This is particularly im-

portant in event studies, where investor reactions to new information such as a green 

bond issuance may quickly alter market expectations (Campbell et al., 1997, p. 12). 

 

 

4.3 Modern portfolio theory 

Modern Portfolio Theory (MPT), developed by Harry Markowitz in 1952, is one of the 

cornerstones of modern financial thought. The theory introduced a systematic approach 

to portfolio construction based on the idea that risk can be reduced through diversifica-

tion. According to Markowitz (1952), investors can improve their risk-return trade-off by 

combining assets that do not move perfectly in tandem. This insight challenged the ear-

lier focus on selecting individual securities in isolation and shifted attention toward op-

timizing the performance of the portfolio as a whole. 



29 

The central objective of MPT is to help investors construct portfolios that maximize ex-

pected return for a given level of risk or, alternatively, minimize risk for a given level of 

return. This trade-off is visually represented by the efficient frontier, which defines the 

set of portfolios that offer the highest possible return at each risk level. Portfolios lying 

below the efficient frontier are considered suboptimal because they deliver lower re-

turns for the same amount of risk. The final portfolio choice depends on the investor’s 

risk tolerance, which is often represented using utility functions or indifference curves 

(Bodie et al., 2018, pp. 208–210). 

 

A key contribution of MPT is the understanding that portfolio risk is not simply the sum 

of individual asset volatilities. Instead, it depends on how asset returns move in relation 

to each other. If the returns of different assets are imperfectly correlated, the overall 

portfolio volatility can be reduced, even if the individual assets are risky. This principle 

forms the basis of diversification and remains highly relevant in both academic research 

and practical investment strategies (Elton et al., 2014, pp. 136–137).  

 

While MPT has been highly influential, it also has its limitations. The model assumes that 

investors behave rationally, asset returns are normally distributed, and parameters such 

as expected returns, variances, and covariances are known and stable. These assump-

tions may not hold in real-world markets, where investor behavior can be affected by 

emotions, and returns can exhibit skewness or fat tails (Elton et al., 2014, pp. 145–146). 

In addition, estimating future return distributions with accuracy is inherently difficult, 

which can affect the practical applicability of the model. 

 

Despite these challenges, MPT continues to provide a fundamental framework for port-

folio management. It also serves as the theoretical foundation for later models, most 

notably the Capital Asset Pricing Model (CAPM), which builds on MPT’s risk-return logic 

but incorporates market-wide factors and equilibrium pricing. As such, MPT remains an 

essential part of financial theory and a relevant reference point for understanding inves-

tor behavior and asset allocation. 



30 

4.4 Capital asset pricing model 

The Capital Asset Pricing Model (CAPM) is a widely used model for estimating the ex-

pected return of a financial asset based on its exposure to market risk. The model was 

developed by Sharpe (1964), Lintner (1965), and Mossin (1966), and it builds on Modern 

Portfolio Theory by formalizing the relationship between systematic risk and expected 

return. CAPM assumes that investors are rational, risk-averse, and operate in a friction-

less market with homogeneous expectations. According to the model, the return on an 

asset depends on the time value of money and the compensation for taking on addi-

tional risk. This relationship is illustrated by the Security Market Line (SML), which de-

scribes the expected return of a security as a function of its sensitivity to market move-

ments, measured by beta (Bodie et al., p. 280–282). 

 

Beta (β) represents the responsiveness of an individual asset’s returns to changes in the 

market portfolio. A beta greater than one indicates higher volatility than the market, 

while a beta less than one implies lower sensitivity. This measure is central to CAPM’s 

role in asset pricing, as it quantifies systematic risk that cannot be eliminated through 

diversification (Elton et al., 2014, p. 287). 

 

The expected return of a security in the CAPM framework is given by: 

 

𝐸(𝑅𝑖) = 𝑅𝑓 + 𝛽𝑖[𝐸(𝑅𝑚) − 𝑅𝑓] ,     (7) 

where, 

𝐸(𝑅𝑖) = Expected return of the asset 

𝑅𝑓 = Risk-free rate 

𝛽𝑖 = Beta of the asset 

𝐸(𝑅𝑚) = Expected return of the market portfolio (Bodie et al., 2018, p. 282). 

 

While CAPM has become a standard tool for estimating the cost of equity and evaluating 

risk-adjusted returns, empirical studies have raised concerns about its assumptions and 

explanatory power. Fama and French (2004) argue that the model’s reliance on a single 



31 

market factor is insufficient to capture the full variation in asset returns. They propose 

additional factors, such as company size and book-to-market value, to improve return 

predictions. Despite its limitations, CAPM continues to serve as a useful benchmark in 

financial analysis, capital budgeting, and event studies. It offers a straightforward way to 

quantify the trade-off between risk and return using a market-based approach. 

 

 

4.5 Arbitrage pricing theory 

The Arbitrage Pricing Theory (APT) was introduced by Stephen Ross in 1976 as a more 

flexible alternative to the Capital Asset Pricing Model (CAPM). While CAPM assumes that 

a single market risk factor explains the variation in asset returns, APT allows for multiple 

sources of systematic risk to influence expected returns (Ross, 1976). This multifactor 

structure makes the model especially useful in explaining asset pricing in complex or 

volatile environments.  

 

APT assumes that the return on a financial asset can be modeled as a linear function of 

several macroeconomic or firm-specific risk factors. These can include variables such as 

interest rate movements, inflation, GDP growth, exchange rates, or industrial production. 

Each factor contributes to the expected return through a sensitivity coefficient known as 

a factor loading, and each has its own risk premium (Elton et al., 2014, pp. 311–313). 

The idea is that investors should be compensated for exposure to each of these system-

atic risks, depending on how sensitive the asset is to changes in those factors. 

 

The core logic of APT relies on the absence of arbitrage in efficient markets. If two assets 

or portfolios are expected to deliver the same future payoffs but are priced differently, 

arbitrageurs would exploit this price difference by buying the underpriced asset and sell-

ing the overpriced one. Their actions would continue until the price discrepancy disap-

pears, thereby restoring market equilibrium. This principle allows APT to generate asset 

pricing predictions without relying on the assumption of a well-defined market portfolio, 

which is a key limitation of CAPM in real-world applications (Bodie et al., 2018, p. 308). 



32 

One of the main strengths of APT is its flexibility. Since it does not require a specific 

benchmark portfolio, the model can be adapted to different investment contexts by se-

lecting appropriate risk factors. This flexibility has made APT particularly useful in empir-

ical finance, where researchers often tailor the factor set based on the market being 

studied or the assets being analyzed. In practice, APT has influenced the development 

of widely used multifactor models, including the Fama–French three- and five-factor 

models, which aim to improve the explanatory power of traditional asset pricing tools. 

 

Despite these strengths, APT also has its limitations. The theory does not specify which 

risk factors should be included in the model or how many are relevant. This creates chal-

lenges in empirical testing, as different studies may use different factor sets and produce 

inconsistent results (Strong, 2003, p. 358). Additionally, identifying and measuring the 

correct factors often depends on data availability and economic assumptions, which can 

limit the model’s practical reliability across time and market environments. 

 

In the context of this thesis, APT is relevant because it provides a framework for under-

standing how multiple sources of risk, including environmental and regulatory factors, 

may influence stock returns. While not often applied directly in green bond event studies, 

the theory supports the idea that markets may react to complex information beyond just 

market-wide risk, such as a firm’s sustainability profile or its exposure to green finance 

trends. 

 

 

4.6 Market efficiency and information asymmetry 

Market efficiency describes how well asset prices reflect available information. Accord-

ing to the Efficient Market Hypothesis (EMH), introduced by Fama (1970), markets are 

efficient when securities trade at prices that fully incorporate known information. EMH 

is commonly divided into three forms: weak, semi-strong, and strong. The weak form 

suggests that past price movements cannot be used to predict future performance. The 

semi-strong form assumes that all publicly available information is already reflected in 



33 

stock prices, while the strong form states that prices incorporate all information, includ-

ing both public and private (Bodie et al., 2018, pp. 360–362). 

 

In an efficient market, investors should not earn consistently abnormal returns using 

publicly available information. This principle is especially relevant in event studies, which 

seek to isolate how new information affects stock prices. If markets are semi-strong form 

efficient, the stock price should adjust quickly to announcements such as the issuance 

of a green bond (Campbell et al., 1997, p. 10). Information asymmetry arises when one 

party has access to more or better information than the other. In capital markets, this 

usually refers to the advantage that corporate insiders may hold over external investors. 

When information is unevenly distributed, it can lead to suboptimal pricing, volatility, or 

misallocation of capital. In the case of green bonds, asymmetry may occur if issuers fail 

to disclose sufficient details about how the proceeds will be used, raising concerns about 

greenwashing and reducing investor trust (Healy & Palepu, 2001). 

 

To address this issue, companies often adopt transparency measures such as third-party 

verification, impact reporting, and adherence to frameworks like the Green Bond Princi-

ples. These actions help reduce uncertainty, improve information flow, and increase the 

credibility of the bond offering. In event studies, minimizing information asymmetry is 

important to ensure that observed price movements reflect genuine investor reactions 

rather than speculation or confusion (Elton et al., 2014, pp. 476–477). 

 



34 

5 Literature review 

This chapter reviews existing academic literature on green bonds and their relationship 

with firm value and investor behavior. The aim is to establish a foundation for under-

standing how financial markets interpret environmental financing instruments and how 

such interpretations may influence stock prices. The review is organized into three 

themes: general research on green bonds and their market role, the connection between 

corporate environmental performance and firm value, and finally, empirical studies fo-

cused specifically on stock price reactions to green bond announcements. 

 

 

5.1 Academic perspectives on green bonds 

Green bonds have attracted growing academic attention as financial instruments that 

aim to connect capital markets with environmental objectives. The literature has evolved 

rapidly, examining green bonds from multiple angles, including market development, 

pricing behavior, investor preferences, and associated risks such as greenwashing. These 

perspectives offer a valuable foundation for analyzing how green bonds are perceived 

by financial markets and how they may affect firm value. 

 

One area of focus in the literature is the structure and development of green bond mar-

kets. Li et al. (2022) present a comparative study of the Chinese and U.S. markets, high-

lighting how policy environments shape the growth trajectory of green finance. In the 

United States, private sector initiatives have played a central role, whereas in China, the 

market has expanded through strong government involvement and regulation. The study 

underscores the importance of institutional support, as well as the challenges in ensur-

ing cross-border consistency in standards and definitions. Bhutta et al. (2022) add a 

broader policy perspective by examining how green bonds can finance projects aligned 

with the UN Sustainable Development Goals. They emphasize that successful market de-

velopment depends on both credibility and global coordination. 

 



35 

Another frequently discussed topic is the pricing behavior of green bonds. Many studies 

point to a so-called “greenium,” or green bond premium, meaning that green bonds of-

ten carry lower yields compared to similar conventional bonds. MacAskill et al. (2021), 

in a comprehensive literature review, find that a premium typically exists in both primary 

and secondary markets, although its size varies by issuer and region. The authors identify 

issuer reputation, third-party verification, and environmental reporting transparency as 

key drivers behind this effect. Zerbib (2019) supports these findings with empirical evi-

dence, reporting a small but statistically significant yield advantage for green bonds, 

which may reflect investors' growing interest in sustainability-oriented assets. 

 

In addition to pricing, the literature discusses the signaling function of green bonds. Tang 

and Zhang (2020) investigate market reactions to green bond announcements across 28 

countries. They find positive abnormal stock returns, especially for firms with strong ESG 

performance. The authors interpret green bond issuance as a corporate signal that en-

hances firm reputation and attracts investors with sustainability mandates. Flammer 

(2021) contributes to this discussion by showing that certified green bonds elicit stronger 

market responses than uncertified ones, reinforcing the importance of external valida-

tion in establishing credibility. 

 

Despite these positive trends, the rapid growth of the green bond market has raised 

concerns about greenwashing. Some issuers may exaggerate the environmental benefits 

of their projects to gain investor attention or meet regulatory expectations. Xu et al. 

(2022) examine this issue in the Chinese market and find that investors respond to weak 

or questionable environmental claims by demanding higher yields. However, they also 

show that third-party certification can reduce credit spreads and restore investor confi-

dence, underlining the role of external assurance in mitigating greenwashing risk. 

 

 



36 

5.2 Environmental responsibility and shareholder value 

The relationship between a company's environmental performance and its market valu-

ation has been a subject of extensive academic research. While earlier views often 

framed environmental actions and financial performance as opposing objectives, more 

recent studies argue that corporate responsibility can support shareholder value. This 

shift reflects a growing understanding that environmental factors are embedded in firm-

level risk assessments, operational efficiency, and investor expectations. 

 

One of the earliest empirical contributions in this area comes from Shane and Spicer 

(1983), who analyzed how financial markets reacted to firms' pollution control perfor-

mance. Their findings revealed a negative relationship between stock prices and pollu-

tion intensity, especially for companies with inadequate environmental controls. In con-

trast, firms that had stronger environmental systems experienced less severe valuation 

penalties. These results suggest that investors may interpret poor environmental perfor-

mance as a risk factor that undermines long-term value. Hamilton (1995) further devel-

oped this idea by focusing on how public visibility influences market reactions to envi-

ronmental disclosures. His analysis of stock price movements following the U.S. Environ-

mental Protection Agency's Toxics Release Inventory found that firms suffered significant 

valuation declines when pollution data became public, particularly if the events at-

tracted media coverage. The study highlighted the amplifying effect of transparency and 

public awareness on the financial consequences of environmental misconduct. 

 

Later research emphasized that capital markets respond to both negative and positive 

environmental news. Klassen and McLaughlin (1996) examined how firms were affected 

by environmental awards and crises. Their study showed that firms announcing environ-

mental achievements experienced positive abnormal stock returns, while those associ-

ated with environmental accidents saw a decline in market value. This dual effect sug-

gests that markets reward firms that demonstrate credible environmental leadership 

and penalize those that damage their environmental reputation. Flammer (2013) ex-

panded the scope by exploring investor reactions to a wide range of environmentally 



37 

relevant corporate events. She found that financial markets consistently responded pos-

itively to initiatives promoting sustainability, especially when these efforts were made 

public. Harmful environmental behavior, on the other hand, resulted in swift and nega-

tive stock price adjustments. These findings point to a growing integration of environ-

mental concerns in investor decision-making. 

 

Krüger (2015) added nuance by showing that capital markets react more strongly to neg-

ative environmental events than to positive ones. His research demonstrated that envi-

ronmental controversies, particularly those involving regulatory violations or unmet 

stakeholder expectations, led to a more pronounced decline in stock prices. This asym-

metrical pattern implies that environmental irresponsibility poses both reputational and 

financial risk. A broader economic perspective is offered by Weidenbaum et al. (1997), 

who challenged the idea that environmental responsibility is a trade-off with profitability. 

Instead, they argued that ecological behavior can align with financial goals by improving 

internal efficiency, reducing regulatory pressure, and building investor confidence. This 

argument supports the idea that sustainability efforts are not only ethically motivated 

but also strategically valuable. 

 

 

5.3 Stock market reactions to green bond announcements 

In recent years, a growing body of empirical research has examined the relationship be-

tween green bond announcements and stock market performance. These studies have 

primarily used event study methodology to isolate the effect of such announcements on 

firm value, measured through cumulative abnormal returns (CARs). The results generally 

suggest that green bond issuance is positively received by investors, although outcomes 

vary depending on certification status, firm characteristics, and regional factors. 

 

Flammer (2021) presents one of the most comprehensive global studies on this topic. 

Her dataset includes nearly 1,200 green bond issuances by 400 firms worldwide. The 

results show a statistically significant CAR of 0.49 percent in the [-5, +10] day event 



38 

window. Notably, the study finds that certified green bonds lead to stronger positive 

market reactions compared to non-certified ones. According to Flammer (2021), this 

highlights how external validation strengthens the credibility of green bond announce-

ments and signals a firm’s genuine commitment to environmental goals. In addition, she 

observes long-term benefits such as improved environmental performance and a shift in 

equity ownership toward sustainability-focused investors. 

 

Tang and Zhang (2020) also contribute to the global evidence, analyzing 1,510 green 

bond announcements across multiple countries. Using the Capital Asset Pricing Model 

(CAPM), they estimate a significant CAR of 1.4 percent over a [-10, +10] day window. The 

effect is particularly strong for firms with high environmental performance, first-time is-

suers, and companies based in countries with more stringent environmental regulations. 

Their findings support the idea that green bond issuance acts as a strategic signal of en-

vironmental responsibility, especially when backed by regulatory frameworks and credi-

ble corporate behavior. 

 

Region-specific studies offer additional insights into investor reactions. Wang et al. (2020) 

focus on the Chinese market and find significant positive CARs in the [-3, +3] and [-10, 

+10] windows for 159 green bond announcements. The study highlights corporate social 

responsibility (CSR) as a moderating factor, showing that firms with stronger CSR profiles 

enjoy more favorable market responses. This indicates that green bond announcements 

are more effective when they align with an established track record of environmental 

responsibility. Baulkaran (2019) narrows the focus to Europe and reports statistically sig-

nificant abnormal returns following green bond announcements among publicly listed 

companies. The peak CAR of 1.48 percent is observed in the [-10, +10] window. The study 

suggests that investors interpret green bond issuance not only as a financing decision 

but also as a strategic move to reduce climate-related risks and appeal to long-term in-

vestors. 

 



39 

While the majority of studies point to a positive market reaction, some offer a more 

cautious view. Lebelle et al. (2020) analyze 475 international green bond announce-

ments and find negative CARs in narrower windows such as [0, +1] and [-1, +1]. Their 

results suggest that investors in developed markets may react skeptically, particularly 

when the bonds lack transparency or third-party certification. Interestingly, they find 

that green bond announcements in emerging markets lead to more favorable responses, 

possibly due to the stronger signaling effect in less mature regulatory environments. Xi 

and Jing (2022) further explore the Chinese market using both CAPM and the Fama–

French five-factor model. Their findings show that while the short-term reaction to first-

time green bond announcements is modest, subsequent issuances produce more sub-

stantial positive effects. The authors argue that investors gain confidence as firms build 

a consistent record of engagement in green finance, which reinforces the signaling value 

of each new issuance. 



40 

6 Data 

In this chapter, the data used in the study is presented. First, the green bond data collec-

tion process is described, including the filtering criteria and final sample formation. Sec-

ond, the data limitations encountered during the data collection process are discussed. 

Third, the final dataset is summarized with descriptive statistics related to the green 

bond issuances and issuing firms. Then, the variables used in the regression model are 

introduced, and finally, descriptive statistics of the selected variables are presented. 

 

 

6.1 The green bond data 

This section presents the dataset used in the thesis, including origin, scope, and the main 

variables collected. The green bond data used in this study has been collected from the 

Bloomberg database and includes green bonds issued by publicly listed non-financial 

corporate entities incorporated in the Nordic countries: Finland, Sweden, Norway, Den-

mark, and Iceland. The dataset covers the period from November 2017 to June 2025, 

which reflects the active years of corporate green bond issuance in the region. The initial 

dataset includes all instruments classified as green bonds by Bloomberg. To ensure com-

parability, only bonds denominated in Nordic currencies (EUR, SEK, NOK, DKK, and ISK) 

were retained. The sample covers both first-time and subsequent green bond announce-

ments, which allows testing whether the market reaction differs between initial and re-

peated issuances. 

 

From Bloomberg, detailed information was collected on both bonds and their issues. For 

each green bond, the dataset includes the issuer name and ticker, announcement date, 

issue date, amount issued, maturity, coupon rate, callable feature, and whether the bond 

was the firm’s first green bond. Issuer-level information covers country of incorporation, 

industry classification based on Bloomberg Industry Classification Standard (BICS), credit 

ratings from S&P, ESG disclosure scores, and selected accounting variables such as total 

assets, liabilities and return on assets (ROA). Financial variables are drawn from the fiscal 



41 

year preceding the bond issuance to ensure consistency in measurement. In addition to 

bond and firm-level data, stock return data and relevant market indices were obtained 

from Bloomberg. Daily stock price data was retrieved for each issuer to compute abnor-

mal returns around the announcement dates. Country-specific stock indices were used 

as benchmarks to control market-wide movements.  

 

 

6.2 Data limitations 

This study focuses on green bond announcements by publicly listed non-financial com-

panies in the Nordic countries. The aim was to include all five Nordic countries—Finland, 

Sweden, Norway, Denmark and Iceland. However, the final dataset includes companies 

from four countries, as no eligible corporate green bonds issued in Iceland could be iden-

tified within the sample period. This reflects the relatively limited development of Ice-

land’s corporate bond market in comparison to the more active green financing environ-

ments in the other Nordic countries. 

 

The sample includes green bonds announced between November 2017 and June 2025. 

Although Bloomberg records green bond activity from earlier years, the first corporate 

green bond issued by a listed Nordic firm did not appear until late 2017. Therefore, this 

starting point reflects the actual beginning of green bond issuance activity within the 

defined scope. The end of the sample period is determined by the date of data collection. 

Some of the most recent announcements from 2025 were included to provide as com-

prehensive a view as possible of recent market activity. To ensure consistency and com-

parability, only green bonds issued by non-financial corporates were included. Financial 

institutions were excluded because their green bond proceeds are often used to finance 

external lending portfolios rather than internal sustainability investments. This structural 

difference in how proceeds are applied could affect investor responses and reduce the 

interpretability of the results. The exclusion of financials is also supported by previous 

research on corporate green bonds (Fatica et al., 2021; Gilchrist et al., 2021). 

 



42 

Only companies with publicly listed equity were included, as the event study method 

requires access to reliable and complete stock price data. Furthermore, firms were only 

retained in the sample if they had at least 252 trading days of return data prior to the 

green bond announcement. This allows for the use of a 200-day estimation window, 

which is commonly applied in financial event studies (Flammer, 2021). In addition, the 

availability of market index data for the country of incorporation was required for all 

firms to calculate abnormal returns. To minimize potential confounding effects, all green 

bond announcements were manually reviewed for overlapping major corporate events 

around the announcement date. If significant events such as earnings releases, mergers 

and acquisitions, or CEO changes occurred within the [-10, 10] or [-5, 10] event windows, 

the observations were excluded from the final dataset (Tang & Zhang, 2020). 

 

Both first-time and subsequent green bond announcements were retained in the sample 

to allow for comparison between initial issuances and follow-up announcements. This 

design supports the testing of differences in market reactions between the two, as high-

lighted in earlier literature (Flammer, 2021; Lebelle et al., 2020). In cases where multiple 

green bonds were issued by the same firm on the same day, they were treated as one 

announcement. If the same firm issued green bonds on different dates, each was in-

cluded as a separate event. 

 

Some limitations relate to data availability. Although the data was primarily retrieved 

from Bloomberg, firm-level information such as credit ratings and ESG disclosure scores 

was not available for every issuer. Consequently, the number of usable observations in 

the regression analysis is lower for these variables, with 70 observations for credit ratings 

and 97 for ESG scores. This does not, however, affect the event study sample or the de-

scriptive analysis. Private placements were not excluded, since the main interest of this 

study is on the market’s reaction to the announcement of a green bond. As all firms in 

the sample are publicly listed, their announcements are expected to be visible to inves-

tors and thus meaningful from a market signaling perspective.   

 



43 

6.3 Final dataset 

The final dataset includes 101 green bond announcements issued by 47 publicly listed 

non-financial companies in the Nordic region, with a total issuance volume of 26 billion 

euros. The sample covers both first-time and subsequent issuances. No qualifying green 

bond issuance by listed non-financial companies was identified from Iceland within the 

sample period. 

 

Green bond issuance is distributed across multiple industries, with the industrials sector 

being the most active. A total of 52 green bonds (51.5%) were issued by companies in 

this sector, amounting to 9.01 billion euros, or 35.3% of the total volume. This result is 

in line with expectations, as industrial firms typically engage in large-scale projects such 

as infrastructure, logistics, and manufacturing upgrades, many of which are eligible for 

green financing. The materials sector accounts for 15 bonds (14.9%) and 4.13 billion eu-

ros (16.2%), while consumer discretionary follows with 14 bonds (13.9%) and 2.67 billion 

euros (10.5%). Although the energy sector accounts for only 14 bonds (13.9%), it repre-

sents the second-largest issuance amount of 7.72 billion euros, or 30.2% of the total. 

This is consistent with the capital-intensive nature of energy transition projects. Smaller 

volumes and lower activity were observed in the technology, communications, and util-

ities sectors. Table 2 shows the number of green bond issuances and the corresponding 

total issued amount for each industry. 

 

Table 2. Number and value of green bond issuances by industry, including percentage shares. 

Industry Amount of GB Percent Total issued 
amount (bn€) 

Percent 

Industrials 52 51.5% 9.01 35.3% 

Materials 15 14.9% 4.13 16.2% 

Consumer Discretionary 14 13.9% 2.67 10.5% 

Energy 14 13.9% 7.72 30.2% 

Communications 4 4.0% 1.47 5.8% 

Technology 1 1.0% 0.50 2.0% 



44 

Utilities 1 1.0% 0.05 0.2% 

Total 101 100% 25.56 100% 

 

Table 3 presents the distribution of green bond issuances by country, based on the num-

ber of announcements and the total volume issued. From a geographical perspective, 

Sweden leads in terms of the number of green bond announcements, with 45 issuances 

(44.6%), representing 7.31 billion euros or 28.6% of the total volume. Finland, on the 

other hand, has the highest total issued amount, reaching 8.47 billion euros (33.1%) from 

21 bonds (20.8%). This indicates that Finnish firms tend to issue larger-sized green bonds 

compared to their Swedish counterparts. Denmark issued 12 bonds (11.9%) with a total 

volume of 6.55 billion euros (25.6%), while Norway issued 23 bonds (22.8%) totaling 3.23 

billion euros, or 12.6% of the dataset’s volume. These figures highlight that while Swe-

den is the most active in terms of issuance count, Finland dominate in total capital raised 

through green bonds. 

 

Table 3. Number and value of green bond issuances by country, including percentage shares. 

Country Amount of GB Percent Total issued 
amount (bn€) 

Percent 

Sweden 45 44.6% 7.31 28.6% 

Norway 23 22.8% 3.23 12.6% 

Finland 21 20.8% 8.47 33.1% 

Denmark 12 11.9% 6.55 25.6% 

Total 101 100% 25.56 100% 

 

Issuance activity has also increased significantly over time. While only one bond was 

issued in 2017, volumes and announcements have grown steadily since. The most active 

year was 2023, with 40 green bonds issued (27.8%) amounting to 8.95 billion euros 

(28.0%). 2022 followed with 24 issuances and the largest total volume of 6.59 billion 

euros, corresponding to 20.6% of the sample’s issuance amount. This development re-

flects broader regulatory trends, such as the adoption of the EU Taxonomy and Green 



45 

Bond Standards, as well as increased investor demand for ESG-aligned instruments. Ta-

ble 4 provides an overview of the development of green bond issuance by year. 

 

Table 4. Annual distribution of green bond issuances, including number of bonds and total issued 
amount (bn€). 

Year Amount of GB Total issued amount (bn€) 

2017 1 0.75 

2018 0 0.00 

2019 3 0.92 

2020 10 2.98 

2021 13 3.27 

2022 15 4.70 

2023 21 4.65 

2024 24 4.75 

2025 14 3.54 

Total 101 25.56 

 

 

6.4 Variables in the regression model 

This study applies a regression framework to analyze the determinants of stock market 

reactions to green bond announcements. In the model, cumulative abnormal returns 

(CARs) are used as the dependent variable, while the explanatory variables are divided 

into two categories: characteristics of the issued green bonds and company-specific 

characteristics of the issuing firms. This structure allows the analysis to capture both 

bond-level and firm-level factors that may influence investor reactions. 

 

 

6.4.1 Dependent variables 

The dependent variable in this study is the cumulative abnormal return (CAR), which 

measures the stock market reaction to the announcement of a green bond issuance. 



46 

CARs are obtained by aggregating abnormal returns (ARs) across different event win-

dows around the announcement date. The event study methodology follows earlier lit-

erature, where the announcement effect of green bond issuances is captured within 

short-term and medium-term windows (Flammer, 2021; Tang & Zhang, 2020; Wang et 

al., 2020). 

 

To examine the robustness of market reactions, four event windows are applied: [-10,10], 

[-5,5], [-1,1], and [0,1]. These windows are commonly used in prior studies on green 

bond issuances and conventional bond offerings, as they allow detection of both imme-

diate and slightly delayed stock price responses (Flammer, 2021; Tang & Zhang, 2020; 

Lebelle et al., 2020). The [-10,10] and [-5,5] windows capture broader investor reactions 

and potential information leakage prior to the announcement, while the shorter [-1,1] 

and [0,1] windows focus on the immediate market response. 

 

Descriptive statistics of the CARs are presented in Table 5. The mean CARs are close to 

zero for all windows, ranging from –0.6% in the [-10,10] window to +0.1% in the [0,1] 

window. This indicates that, on average, stock price reactions to green bond announce-

ments are modest. The largest variation appears in the [-10,10] window, where CARs 

range from –33.1% to +24.9%, reflecting that individual firms may experience substantial 

differences in market responses. In comparison, the [0,1] window shows a much nar-

rower range of –8.9% to +6.8%, suggesting more concentrated movements around the 

actual announcement date. 

 

The distributional characteristics also differ between windows. For instance, the [-1,1] 

window exhibits a negative skewness of –0.9, implying that negative reactions are more 

common than positive ones in the immediate days surrounding the announcement. Fur-

thermore, the Jarque-Bera test statistics are significant at the 1% level for all windows, 

which indicates that the CAR distributions deviate from normality. This observation is 

consistent with prior event study findings where stock price reactions often display non-

normal characteristics (Campbell et al., 1997). 



47 

Table 5. Descriptive statistics of cumulative abnormal returns (CARs) across four event windows. 

 CAR [-10,10] CAR [-5,5] CAR [-1,1] CAR [0,1] 

Mean -0.006 0.000 -0.002 0.001 

Median -0.005 0.003 0.001 0.005 

Maximum 0.249 0.199 0.073 0.068 

Minimum -0.331 -0.180 -0.119 -0.089 

Std. Dev. 0.081 0.062 0.029 0.022 

Skewness -0.310 0.205 -0.917 -0.635 

Kurtosis 3.060 1.485 3.165 2.424 

     

Jarque-Bera 41.025 9.990 56.304 31.513 

Probability 0.000000 0.006771 0.000000 0.000000 

     

Sum -0.627 -0.019 -0.207 0.061 

Sum Sq. Dev. 0.660 0.388 0.082 0.049 

     

Observations 101 101 101 101 

 

 

6.4.2 Explanatory variables 

The explanatory variables included in the regression model are divided into two catego-

ries: green bond characteristics and company characteristics. This division allows the 

analysis to account for both the features of the issued bonds and the underlying attrib-

utes of the issuing firms. All variables are defined in line with earlier empirical research 

on green bond announcements and corporate bond event studies. The 10 explanatory 

variables used in the regression model are presented in table 6. For each variable, a short 

definition is provided along with the data source. 

 

Table 6. Explanatory variables used in the regression model, including definitions and data 
sources. 

Variable name Definition Source 

Amount_issued Natural logarithm of the green bond issue size (in mil-
lion euros). 

Bloomberg 

Coupon Fixed coupon rate of the green bond. Bloomberg 



48 

D_First_GB Dummy variable which equals to 1 if the bond is the is-
suer’s first green bond, otherwise equal 0. 

Bloomberg 

Maturity_years Time to maturity of the bond in years. Bloomberg 

D_Callable Dummy variable which equals to 1 if the bond is calla-
ble, otherwise equal 0. 

Bloomberg 

Firm_size Natural logarithm of the total assets of the company (in 
million euros). 

Bloomberg 

Leverage The company’s total debt divided by total assets Bloomberg 

ROA The company’s net income divided by total assets Bloomberg 

S&P_Rating S&P credit bond rating (20 for AAA, 19 for AA+, … 2 for 
CC and 1 for C). 

Bloomberg 

ESG_Score Company’s ESG disclosure score from Bloomberg (scale 
0-100) 

Bloomberg 

 

When selecting the explanatory variables for the regression model, a bottom-up ap-

proach was used. Starting with two variables, explain the characteristics of the green 

bond. The starting variables were: “issued amount” and the “first green bond” to deter-

mine if the two factors have a strong effect pm the cumulative abnormal returns when 

not considering any other variables. In the final regression, all 10 variables are included 

in the model. The following subchapters present the included explanatory variables in 

more detail. The result of the regression model is presented in chapter 8.2. 

 

Amount_issued 

The variable Amount_issued measures the size of the green bond issue, expressed as the 

natural logarithm of the amount in million euros to reduce skewness and account for 

outliers. Larger issues may carry stronger signaling value and visibility. Flammer (2021) 

finds that bond size can influence investor reactions around announcements, while 

Wang et al. (2020) show that issue size enhances credibility in environmental finance 

contexts. 

 



49 

D_First_GB 

The dummy variable D_First_GB equals one if the bond is the first green bond issued by 

a company and zero otherwise. Flammer (2021) shows that first-time issuances can act 

as a strong signal of a firm’s sustainability commitment, which may strengthen investor 

confidence. Accordingly, this variable is expected to be positively related to CARs. 

 

Coupon 

The variable Coupon represents the fixed interest rate attached to the green bond. Cou-

pon levels reflect both the issuer’s risk profile and the market conditions at issuance 

(Fabozzi, 2012). Higher coupon rates generally signal greater perceived credit risk, which 

may lead to more cautious investor reactions (Hull, 2018). 

 

Maturity_years 

The variable Maturity_years measures the length of the bond in years. Longer maturities 

generally imply higher uncertainty and greater risk exposure, while shorter maturities 

are associated with lower risk (Bodie et al., 2018). Prior literature finds mixed evidence 

on how bond maturity influences stock market reactions (Tang & Zhang, 2020). 

 

D_Callable 

The dummy variable D_Callable equals one if the bond is callable and zero otherwise. 

Callable bonds provide issuers with flexibility to redeem debt before maturity but may 

increase reinvestment risk for investors (Fabozzi, 2012). Prior studies suggest that the 

market reaction to callable bonds depends on the broader market environment and in-

vestor expectations (Wang et al., 2020). 

 

Firm_size 

Firm_size is measured as the natural logarithm of the company’s total assets in million 

euros from the fiscal year prior to the bond issuance (Bodie et al., 2018). Larger compa-

nies often benefit from greater visibility and financial stability, which can enhance 



50 

investor confidence (Choudhry, 2019). Previous studies show that firm size is positively 

associated with market reactions to financing decisions (Flammer, 2021; Wang et al., 

2020). 

 

Leverage 

Leverage is calculated as the ratio of total debt to total assets (Bodie et al., 2018). A 

higher leverage ratio implies greater financial risk and higher obligations, which may neg-

atively affect investor perceptions. Prior research shows that leverage can influence 

stock market reactions to bond issuances (Lebelle et al., 2020). 

 

ROA 

ROA (Return on Assets) measures company profitability, defined as net income divided 

by total assets from the year prior to issuance (Bodie et al., 2018). ROA is a widely used 

indicator of performance and efficiency, and prior studies find that profitability is often 

positively related to market valuation effects (Flammer, 2021). 

 

S&P_Rating 

The S&P_Rating variable reflects the long-term issuer credit rating, transformed into a 

numeric scale from 1 (lowest) to 20 (highest). Credit ratings convey information on a 

company’s creditworthiness and capacity to meet financial obligations (Fabozzi, 2012). 

A higher rating is therefore expected to generate a more favorable investor reaction 

(Tang & Zhang, 2020). 

 

ESG_Score 

The ESG_Score is taken from Bloomberg and measures company performance across en-

vironmental, social, and governance dimensions on a scale from 0 to 100. A higher ESG 

score reflects stronger sustainability practices, which can enhance investor trust. Prior 

studies show that firms with higher ESG ratings tend to receive more favorable market 

responses when issuing green bonds (Flammer, 2021). 

 



51 

In addition to these variables, the analysis also considers whether abnormal returns dif-

fer systematically across countries or industries. For this purpose, dummy variables were 

created for each country (Finland, Norway, Denmark) and for each industry sector (Ma-

terials, Consumer Discretionary, Energy, Communications, Technology, and Utilities), 

with Sweden and Industrials serving as the baseline categories. These variables are in-

cluded in supplementary regressions presented in Chapter 8.3. 

 

 

6.5 Descriptive statistics for model variables 

This section provides an overview of the explanatory variables included in the regression 

analysis. The variables are grouped into two categories: green bond characteristics and 

company characteristics. For each variable, descriptive statistics such as the mean, me-

dian, standard deviation, minimum, maximum, skewness, and kurtosis are reported. 

Amount issued is expressed as the natural logarithm of millions of euros, firm size as the 

natural logarithm of the company’s total assets, leverage as the debt-to-assets ratio, ROA 

in percent, issuer rating as a rank from 1 to 20 where AAA corresponds to 20 and C to 1, 

and ESG score as a value between 0 and 100. 

 

Table 7 summarizes the descriptive statistics for the green bond characteristics. The av-

erage issuance size, measured as the natural logarithm of the issue amount, is 5.02, with 

values ranging between 2.94 and 6.80. This reflects that the sample includes both rela-

tively small green bond issues and significantly larger ones. The natural logarithm is ap-

plied to compress outliers and reduce skewness in the variable distribution. The mean 

coupon rate is 4.65 %, but the maximum of 13.0% indicates that some issues were prices 

at relatively high interest levels, reflecting either higher risk or market conditions at the 

time of issuance. Of the total sample, 47 issuances (46.5%) were first-time green bonds, 

highlighting that a substantial portion of the dataset captures firms entering the green 

bond market for the first time. 

 



52 

The maturity of the bonds averages 8.9 years, but the wide standard deviation shows 

that the sample includes both very short- and very long-term issuances, with the longest 

exceeding 61 years. Callable bonds are also common in the sample, with 62 issues (61.4%) 

containing a call option. The Jarque-Bera statistics indicate that several variables depart 

from normality, consistent with skewed distributions typical in financial datasets.  

 

Table 7. Descriptive statistics of green bond characteristics, including number of issuances, bond 
features, and distribution measures. 

 
Amount_ 

issued (m€) Coupon D_First_GB 
Maturity_ 

years D_Callable 

Mean 5.023 4.646 0.465 8.900 0.614 

Median 4.929 4.403 0.000 5.001 1.000 

Maximum 6.802 13.000 1.000 61.25 1.000 

Minimum 2.936 0.125 0.000 2.189 0.000 

Std. Dev. 1.082 2.276 0.501 12.408 0.489 

Skewness -0.038 0.701 0.141 3.350 -0.475 

Kurtosis -1.350 1.417 -2.021 10.044 -1.811 

      

Jarque-Bera 7.695 16.724 17.515 613.461 17.594 

Probability 0.021329 0.000234 0.000157 0.000000 0.000151 

      

Sum 507.308 469.229 47.000 898.880 62.000 

Sum Sq. Dev. 117.069 518.067 25.129 15394.912 23.941 

      

Observations 101 101 101 101 101 

 

Table 8 reports the descriptive statistics for the company-level variables. The average 

firm size, measured as the natural logarithm of total assets, is 8.59, ranging from 3.04 to 

11.22. This indicates that the sample covers both relatively small firms and very large 

Nordic corporations. The leverage ratio has a mean of 26.4%, with values ranging from 

7.7% to 61.1%. The distribution is relatively narrow compared to other variables but still 

displays non-normal characteristics. Profitability, measured as return on assets (ROA), 

averages 4.7%, with a minimum of –20.1% and a maximum of 24.0%, highlighting that 

the dataset includes firms both negative and strongly positive profitability prior to issu-

ance. 

 



53 

The S&P long-term issuer rating variable has a mean of 7.84 on the applied 1-20 scale. 

However, not all companies in the dataset had credit ratings, resulting in 70 observations 

for this variable. The ratings range from B+ (the lowest) to A- (the highest), while the 

average rating corresponds to BBB. Around half of the rated companies fall within the 

medium investment-grade category between BBB+ and BBB-, reflecting a typical level of 

credit quality among Nordic corporate bond issuers. The ESG score has a mean of 54.87 

on a 0-100 scale, with values ranging between 22.46 and 78.31. Not all companies re-

ported ESG scores, which reduces the number of observations to 97. The range of ESG 

scores illustrates notable differences in sustainability practices and reporting across Nor-

dic issues. 

 

Table 8. Descriptive statistics of company characteristics, including firm size, leverage, profitabil-
ity, credit rating, and ESG score. 

 
Firm_size 

(m€) Leverage ROA 
S&P_Rating 

(1-20) 
ESG_Score 

(1-100) 

Mean 8.589 0.264 0.047 7.843 54.870 

Median 8.619 0.248 0.047 8.000 56.020 

Maximum 11.220 0.611 0.240 11.000 78.310 

Minimum 3.035 0.077 -0.201 4.000 22.460 

Std. Dev. 1.519 0.127 0.059 2.424 13.541 

Skewness -0.989 0.669 -0.339 -0.031 -0.393 

Kurtosis 1.486 -0.315 3.495 -1.681 -0.368 

      

Jarque-Bera 25.743 7.959 53.353 8.255 3.049 

Probability 0.000003 0.018696 0.000000 0.016122 0.217686 

      

Sum 867.530 26.630 4.780 549.000 5322.400 

Sum Sq. Dev. 230.742 1.608 0.349 405.271 17602.580 

      

Observations 101 101 101 70 97 



54 

7 Methodology 

This chapter presents the empirical methods used in the analysis. First, an event study 

framework is applied to measure whether green bond announcements generate abnor-

mal stock market reactions. The approach begins with the estimation of expected re-

turns and the calculation of cumulative abnormal returns across different event windows. 

Second, statistical significance tests are conducted to evaluate whether the observed 

market reactions differ from zero. Finally, an ordinary least squares (OLS) regression is 

used to examine how bond- and firm-specific characteristics explain the variation in cu-

mulative abnormal returns. 

 

 

7.1 Event study 

The event study methodology is widely applied in financial research to examine how 

specific events affect the value of a firm. The method is based on the idea that financial 

markets react quickl