optimal portfolio selection and risk-adjusted performance...

Optimal portfolio selection and risk-adjusted

performance of 51 equity funds available in the Swedish

premium pension

Master thesis in Business Administration

Programme of study: Civilekonom

Number of credits: 30 ECTS

Authors: Emilia Svensson 950416

Ninos Khouchaba 951017

Supervisors: Andreas Stephan

Aleksandar Petreski

Jönköping May 2018

Master Thesis in Business Administration

i

Acknowledgements

Foremost, we would like to express our gratitude to our supervisors, Andreas Stephan and

Aleksandar Petreski for their continuous support, dedication, and guidance throughout the

process of writing this master thesis.

The authors would also like to express gratitude to our associated students for their

encouragement and valuable inputs provided during the seminars.

________________________ ________________________

Emilia Svensson Ninos Khouchaba

Jönköping International Business School, May 2018


ii


Title: Optimal portfolio selection and risk-adjusted performance of 51 equity funds

available in the Swedish premium pension.

Authors: Khouchaba, Ninos

Svensson, Emilia

Date: 2018-05

Key terms: Swedish premium pension system, national public retirement pensions, pension

fund performance, risk-adjusted performance measures.

Abstract

Background: In order to assure a livelihood for the working population after retirement, the

national retirement pension was developed. The system is based on 18.5% of each tax-paying

worker’s annual salary. The national retirement pension system in Sweden consist of two parts.

The first and largest part contributing with 16 percentage points, of the 18.5%, is a defined

benefit plan, named the income pension. The second part contributing with 2.5 percentage

points, of the 18.5%, is the premium pension, which is a defined contribution plan. The

premium pension is the sole part of the national retirement pension controlled by the individual

employee, with the opportunity to actively invest in a broad selection of domestic and

international funds. Investors not making a choice will be transferred into the governments

default fund, named the seventh AP fund. By investing in funds, the premium pension is partly

based on each worker’s annual salary but also on the development of the financial market.

Purpose: This thesis has two purposes, the first is to investigate if the default alternative, the

seventh AP fund has had a superior risk-adjusted return compared to fifty of the most commonly

selected equity funds available in the premium pension selection. The second purpose is to

construct portfolios for active investors with different risk-tolerance in order to compare the

risk-adjusted return between an investor that has made an active investment in comparison to

an investor that has not made an active choice.

Conclusion: To conclude, this thesis shows that there are superior funds to select, with regard

to risk-adjusted return and risk-exposure, as an alternative to the seventh AP fund. In addition

to this, the portfolio construction included in this thesis has proven that active participants can

achieve results that are more compatible with their risk preferences in comparison to remaining

in the default fund option. However, it is important for investors to remain active and alter their

fund selections throughout the years, in order to attain the preferable outcome.


iii

Table of Contents

1. Introduction 1

1.1 Background 1

1.2 Problem description 3

1.3 Purpose 5

1.4 Delimitations 6

1.5 Definitions 7

2. Theoretical Framework 9

2.1 Premium Pension Funds in Sweden 9

2.2 Risk-Return Trade-Off 10

2.3 Financial Theory 11

2.3.1 Single Index Model 11

2.3.2 Modern portfolio theory 12

2.3.3 Post-Modern portfolio theory 14

2.3.4 Capital Asset Pricing Model 15

2.3.5 Efficient Portfolio Construction 17

2.4 Previous Research 19

3. Method 22

3.1 Choice of Method 22

3.2 Collection of Data 22

3.3 Research Design 24

3.3.1 Sampling 24

3.3.2 Calculations, Assumptions and Benchmarks 25

3.4 Hypothesis Testing 26

3.5 Critical assessment 27

4. Empirical Results 29

4.1 Risk Exposure 29

4.2 Risk-Adjusted Performance 32

4.3 Portfolio Optimization 33

4.3.1 Minimum Variance Portfolio 34

4.3.2 Optimal Tangency Portfolio 36


iv

5. Analysis 40

5.1 Risk Exposure 40

5.2 Risk-Adjusted Performance 43

5.3 Portfolio Optimization 46

5.3.1 Minimum Variance Portfolio 46

5.3.2 Optimal Tangency Portfolio 47

6. Conclusion 50

7. Contributions to the Research and Suggested Further Studies 52

8. References 54

8.1 References to theory sources 54

8.2 References to data sources 60

9. Appendix 63

9.1 The 51 equity funds included in the study 63

9.2 Start-of-month net asset values for AP7 64

9.3 Risk-free rate 65

9.4 Yearly average rate of return 66

9.5 Yearly risk measurements 67

9.6 Yearly risk measurement ranking 73

9.7 Yearly risk-adjusted return measurements 74

9.8 Yearly risk-adjusted return measurements ranking 78


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Table of Figures

Figure 1 - The distribution between “AP7 equity fund” and “AP7 interest-bearing fund”

during a life-cycle ....................................................................................................................... 7

Table 1 – “AP7 Aktiefond” Yearly Risk Measurements (authors’ calculations) .................... 29

Table 2 - "AP7 Aktiefond" Yearly Risk-Adjusted Return Measurements (authors’

calculations) ............................................................................................................................. 32

Table 3 - Minimum Variance Portfolio 2007-2010 (authors’ calculations) ............................ 34







Table 10 - Minimum Variance Portfolio 2014-2017 (authors’ calculations) .......................... 36

Table 11 - Optimal Tangency Portfolio 2007-2010 (authors’ calculations) ........................... 37








Table 19 - Low St.dev funds yearly average rate of return (authors’ calculations) ................ 42

Table 20 – Standard deviation, MVP in comparison to AP7 (authors’ calculations) ............. 46

Table 21 - Return, MVP in comparison to AP7 (authors’ calculations) ................................. 47

Table 22 - Sharpe Ratio, MVP in comparison to AP7 (authors´ calculations) ....................... 47

Table 23 - Sharpe Ratio, OTP in comparison to AP7 (authors’ calculations) ......................... 48

Table 24 - Return, OTP in comparison to AP7 (authors’ calculations) .................................. 48

Table 25 – Standard deviation, OTP in comparison to AP7 (authors’ calculations) ............... 48

Introduction

1

1. Introduction

1.1 Background

Growing old is a natural course of life and there is no way to avoid it. As years move on,

retirement will one day catch up and when that day comes every retiree will need a pension

income. Previous statistics concerning the national retirement pension has shown that

retirement savers who followed a passive investment strategy concerning their premium

pension, thus automatically being selected into the governments default fund, have performed

better than pension savers that have retained services from financial advisors (AP7, 2017). On

the contrary, the governments default fund presently has a risk level, for participants up to 55

years of age, that is 1.24 times higher than an ordinary global equity fund

(Pensionsmyndigheten, 2018c). The high degree of risk can be explained by the fact that the

seventh AP fund uses leveraging in order to increase the probability of higher returns and thus

higher pension for investors. The fund acquires derivative contracts, which can be comparable

to the fund borrowing to invest and place them in global equities. As a result, the fluctuations

of the seventh AP equity fund are greater than the average global equity fund (AP7, 2018a).

Based on knowledge and preferences, people choose where to invest their money differently

and the choices they make can yield a substantially different outcome (Engström and

Westerberg, 2003).

The previous national retirement pension, known as the ATP system, received criticism for not

being connected to the social economic development and that the system did not adjust to the

increasing life-expectancy of the population (Sundén, 2006). With the increase in life-

expectancy and the fact that economic growth slowed down in the late 20th century, the ATP

system was condemned as not financially stable and a study was initiated to revise it in order

to create a new sustainable pension system (Pensionsmyndigheten, 2018f). As a result, pressure

from the Swedish population led to the creation of a new pension system that was announced

in 1999 (Engström and Westerberg, 2003; Pensionsmyndigheten, 2018e). The reform had the

intention to address three clear shortcomings from the previous national retirement system.

Firstly, a clear connection concerning the contribution and benefits with regards to equality

across generations. Secondly, financial sustainability should be realised by tying the pension

system to financial growth. Thirdly, individuals should be granted more involvement by giving

Introduction

2

them the choice of investing part of their pension saving, namely the premium pension (Barr,

2013).

The new national retirement pension gives every tax-paying worker in Sweden the right to a

pension (Pensionsmyndigheten, 2018a). The system is built with an earnings-related structure

and the rate of contribution is 18.5% of each workers’ annual salary (Sundén, 2006). From these

18.5%, 16% percentage points will contribute to the main part of the national retirement pension,

namely the income pension. The income pension is a notional defined benefit plan, which gives

each worker the right to a predetermined pension based solely on their annual earnings (Hagen,

2017). The income pension works as a pay-as-you-go system, where each worker has an

individual notional account with a defined contribution. The amount of the individual accounts

is based on a defined contribution rate that is applied to the individuals’ earnings from work.

The value of the account symbolises an entitlement on a future pension income (Palmer, 2000).

The remaining 2.5% percentage points contributes to the premium pension which is a defined

contribution system (Hagen, 2017). Previously, all parts in the pension system were based on a

pay-as-you-go system, but this changed with the new national pension system. The changes

were implemented in order to ensure financial stability and to provide adequate pension income

for all inhabitants. Today, the pension system is based on a notional defined contribution and

defined contribution plan with a pay-as-you-go structure along with funded individual accounts

(Sundén, 2006).

The premium pension is not solely based on each workers’ annual income but also from the

development of the financial market (Engström and Westerberg, 2003). Although the premium

pension only has a minor contribution to the national retirement pension, this is the only part

that is controlled by the individual (Pensionsmyndigheten, 2018h). The participants can

actively determine how to invest their premium pension based on a broad selection of domestic

and international funds (Palme, Sundén and Söderlind, 2007). The selectable funds include

equity funds, interest-bearing funds, mixed funds, and generational funds (Czech, 2016). From

the available options, up to five funds can be selected to be a part of the participants’ retirement

savings (Hedesström, Svedsäter and Gärling, 2007) and the pension saver has the possibility to

change funds an unlimited number of times, free of charge (Czech, 2016). However, if the

participant does not make an active fund selection, the government has established a default

fund named the seventh AP fund, also referred to as AP7 Såfa (Palme, Sundén and Söderlind,

Introduction

3

2007). By allowing individuals to select where their premium pension should be invested, a

greater responsibility is put on the individual to plan for their retirement (Sundén, 2006).

With regard to passive and active investment strategies, which can occur in the premium

pension system, Sharpe (1991) defines these strategies by stating that the return on the average

active investment will equal the return on the average passive investment, prior to any costs.

Additionally, the return on the average active investment will be lower than the return on the

average passive investment, after costs. According to Sharpe (1991), the stated definitions will

hold for any time period. Sharpe (1991) continues by defining a passive investor as one that

holds every stock in the market, with each represented in a similar way as the market. In other

words, a passive investor will hold the equivalent proportion of the total outstanding volume of

each security in the market. On the other hand, an active investor is defined as one who is not

passive. Sharpe (1991) continues by stating that the portfolio of an active and passive investor

is different since active investors act on insights of mispricing, and since such insights regularly

change, active investors are inclined to trade more often.

Supplemental to the national retirement pension there is the occupational pension and the

private pension scheme (Pensionsmyndigheten, 2018i). The majority of the Swedish working

population has an occupational pension, where the employer sets aside retirement savings for

the employee. The occupational pension is determined upon settlements between the worker

and the employer or by a collective agreement (Pensionsmyndigheten, 2018d). A worker in

Sweden that receive both national retirement pension and occupational pension will

approximately receive 60-75% of their taxable income in pension depending on the number of

years they have worked, consequently, it can be favourable to also possess a private pension

scheme (Pensionsmyndigheten, 2018e).

1.2 Problem description

Under the new premium pension scheme responsibility is shifted more towards individuals by

encouraging them to take an active interest in where to invest their premium pension (Engström

and Westerberg, 2003), meaning that individuals actively can select in which funds to allocate

their premium pension (Pensionsmyndigheten, 2018h). When creating the premium pension

system, the government wanted to offer the participants a broad number of funds, so all fund

companies that have a licence to operate in Sweden can participate in the system (Sundén, 2006).

Introduction

4

As of today, there are approximately 850 premium pension funds to select from when making

an active investment (Morningstar, 2018). Subsequently, the participants are given the

opportunity to achieve a higher return and to tailor parts of their pension according to their risk

preference (Palme, Sundén and Söderlind, 2007).

However, many people lack both the knowledge and interest to make own investment decisions,

which is why there is an alternative investment route to pursue for these people (Engström and

Westerberg, 2003). The Seventh AP Fund has a generational design and is made up of two

separate funds, “AP7 Aktiefond and AP7 Räntefond” (AP7, 2018b). The two AP7 funds can be

chosen separately for participants that do not wish to finance their premium pension in a

generational fund. “AP7 Aktiefond” is an equity fund with a higher level of risk and “AP7

Räntefond” is an interest-bearing fund for participants with a lower risk preference (Weaver

and Willén, 2014). By investing in the Seventh AP Fund, the distribution between the equity

fund and the interest-bearing fund is adjusted according to the age of the saver. Initially, the

saver is exposed to more risk with higher potential return. The risk decreases over time as the

distribution changes between the funds, meaning that lower-risk investments are made as the

saver approaches retirement (AP7, 2018a). This adjustment of allocation between the funds,

aims to solve the issue of pension savers that retire at the same time as the stock-market drops

(Cobley, 2009).

In recent years, fewer people have actively selected and changed their premium pension funds,

meaning that more savers remain in the predetermined fund, AP7 Såfa (Pensionsmyndigheten,

2015). In 2000 when pension savers were first offered to make an active investment in the

premium pension, 67% of the participants made an active choice and only 33% were transferred

into the default fund. Participants making an active choice has fallen steeply since then and the

reason behind the distinct fall in involvement is according to Czech (2016) the rising number

of fund alternatives. He argues that 850 funds to choose from is more discouraging than

stimulating. Another argument is that the massive media campaign that introduced the premium

pension, has faded away and consequently, so has people’s awareness (Czech, 2016). As for

AP7 Såfa, at the end of 2017, the fund had been selected by approximately 3.7 million people,

out of this only 400 000 had actively selected the fund. In other words, 3.3 million people

passively ended up with holdings in the fund (Pensionsmyndigheten, 2018b). For additional

number of fund selections see appendix 9.1.

Introduction

5

According to Madrian and Shea (2001), people make passive investment decision as a result of

uncertainty and lack of knowledge. In addition, Madrian and Shea (2001) state that individuals

perceive the predetermined alternative, in this case AP7 Såfa, as an investment advice and

therefore tend to pursue that advice by remaining in the specified fund. In some situations,

people tend to follow the path of least resistance which means that they select the easiest thing

to do, which in this case is to not make a choice at all. Additionally, people lean towards making

passive investment decisions due to the reason that the benefits from an active decision are

offset by the unintended transaction costs of collecting and assessing information on the

available funds, therefore it is easier to not make an active choice (Madrian and Shea, 2001).

1.3 Purpose

The primary purpose of this report is to investigate whether the default fund alternative, i.e.

AP7 Såfa, has had the superior risk-adjusted performance from 2007-2017 in comparison to

fifty of the most selected equity funds available in the premium pension selection. The authors

of this report will use risk-exposure and risk-adjusted measurements to compare the funds that

are included in this study in order to draw conclusions. AP7’s equity fund is of interest to study,

partly due to it being the default alternative, but also because the fund recently had a risk level

that is 1.24 times higher than in an ordinary global equity fund.

Furthermore, this thesis will attempt to find up to five funds that should have been included in

an active participants’ portfolio, using a rolling-window approach with eight formation periods

between the years of 2007 and 2017. The authors of this report will construct an optimal

tangency portfolio and a minimum variance portfolio in order to compare the risk-exposure and

risk-adjusted return between a passive and active investor with different levels of risk-tolerance.

The research questions are stated as follows:

• Has AP7’s equity fund had the superior risk-adjusted performance compared to fifty of

the most selected equity funds in the premium pension selection?

• How will an active investor with a constructed portfolio perform in comparison to a

passive investor with holdings in the AP7 equity fund?

Introduction

6

1.4 Delimitations

Limitations have been made to clearly focus on the stated research questions. Primarily, the

authors have limited the scope of this study to the Swedish premium pension system.

Consequently, no statements or conclusions will be made regarding the pension system in other

countries. Furthermore, no statements or conclusions will be made regarding the entire Swedish

national retirement system. This is also applied for the remaining parts of the Swedish pension

system, no research will be made regarding the occupational pension of Swedish workers or

with regards to private pension savings. These limitations do not indicate that the purpose of

this study cannot be applied to other parts of the pension system, but simply that it is not the

objective of the authors.

Additionally, the authors are aware of the changes that AP7 underwent in 2010. Previously,

there were two separate funds named “Sparfonden” and “Valfonden” that had a different

arrangement than the funds that are available today. The authors of this thesis have obtained

the net asset values for the years 2007 to 2010 through contact with an employee at AP7 Såfa.

These net asset values have been used as supplemental to the existing AP7 Equity fund’s net

asset values. Although the fund has undergone changes, this was a requirement in order to

analyse the years 2007 to 2010, which are of interest partly due to the financial crisis.

Furthermore, the authors of this study have calculated the implicit returns for every fund

included in this report. The reasoning behind the choice to calculate implicit returns instead of

the explicit returns is due to the fact that every fund included in the study charge different fees

that have been altered throughout the years. Consequently, there were difficulties for the authors

to find these fees dating back to 2007. For this reason, implicit returns have been calculated

which consequently will make the outcome of this study more accurate given that the authors

could not access the necessary data.

Moreover, a limitation has been made regarding the age of the participants in the premium

pension selection. The authors have limited the scope of participants to those between the age

Introduction

7

of 20 to 55. By including this limitation, the authors can narrow the scope of AP7 Såfa merely

to AP7’s equity fund, since 100% is invested in the equity fund until the participants reach 55

years of age (Pensionsmyndigheten, 2018c). Consequently, the scope of this study will be

limited to only equity funds and no consideration or conclusions will be made in regard to

interest-bearing funds, mixed funds, or generational funds.

The last limitation of this study has been made concerning the number of equity funds to

investigate in addition to the AP7 equity fund. The authors have restricted the number of equity

funds available in the premium pension selection to the fifty most actively selected funds that

have been available prior to 2007.

1.5 Definitions

• Swedish pension authority - Svenska Pensionsmyndigheten

The pension authority is a Swedish government that has the full responsibility over the

national retirement pension.

• National retirement pension - Allmän pension

Every Swedish Citizen that works or lives in Sweden has the right to a national retirement

Pension. The pension is grounded on all tax-based income.

• Premium pension- Premiepension

The premium pension is a part of the national retirement pension. 2.5 % of the tax-based

income is each year set aside to each citizen’s premium pension. Each citizen has then the

right to place the money in 1 to 5 fund selections or leave them in the pre-determined fund

selection, AP7 Såfa.

• Occupational pension - Tjänstepension

The occupational pension originates from working. The occupational pension is determined

upon agreements, either between the worker and employer or by a collective agreement

Figure 1 - The distribution between “AP7 equity fund” and “AP7 interest-bearing fund” during a life-cycle

Introduction

8

• Private pension scheme - Privat pension

A private pension scheme means that each citizen sets aside money for their own retirement.

This pension is a tool for citizens who prefer a higher pension income and it is not

mandatory.

• Equity fund – Aktiefond

An equity fund contains of equities, meaning that the fund holds shares for several

companies. The value of the fund is based on the development of each company.

Subsequently, the fluctuations of the stock market often imply a higher risk, but with the

possibility of a higher return.

• Interest-bearing fund – Räntefond

An interest-bearing fund contains of interest-bearing securities often issued by the

government or by a municipality in order to borrow money. Interest-bearing funds

generally has a stable value change, consequently, the fund have a low risk and usually

implies a lower return.

• Generational fund – Generationsfond

A generational fund contains both equities and interest-bearing securities. The distribution

between the securities is adjusted according to each citizen’s age in order to take on a lower

risk closer to retirement.

Theoretical Framework

9

2. Theoretical Framework

2.1 Premium Pension Funds in Sweden

A fund is a collection of financial securities with ownership to the investor who possesses

holdings in the fund. The purpose of having fund savings is to get an easy access to a portfolio

of financial securities that possibly can implicate an increase in value. The funds available in

the premium pension selection have requirements regarding the distribution of the funds risk

through diversification, by containing a number of different financial securities. The holdings

of the fund are determined by the fund’s manager, but it has to be within the specified

requirements (Pensionsmyndigheten, 2018g). In order for a fund to be accessible in the

premium pension selection, the fund must be approved by the Swedish pension authority and

the fund’s manager must have a certification of operating in fund trading activities. The fund

manager also needs to have a cooperation agreement with the Swedish pension authority and

has to release specified information at requests. Furthermore, managers also need to undertake

the requirements of not charging any withdrawal fees or other fees that has not been permitted

by the agency (Riksdagen, 2018a).

The premium pension selection has different sorts of funds available, namely equity funds,

interest-bearing funds, mixed funds, and generational funds. The motivation for using diverse

funds is to make it easier for the investors to separate them and to be able to make comparisons.

What kind of category the fund belongs to depends on what combination of financial securities

the fund invests in (Pensionsmyndigheten, 2018g). There are two main types of funds, equity

funds and interest-bearing funds. Interest-bearing funds only hold assets in debt securities and

generally have a lower risk than equity funds. Equity funds, that is the most common fund

alternative among Swedish investors and that will be the focus of this report, holds assets that

are traded on the stock market (Avanza, 2018). Consequently, the fund will result in ownership

of several companies. An equity fund contains ownership of at least 16 different companies,

usually more, meaning that the risk will be diversified and not dependent on a single company

(Pensionsmyndigheten, 2018g). An equity fund is therefore determined by the fluctuations on

the stock market. Factors that may affect the degree of risk that the fund is exposed to are

dependent on whether the fund manager invests in equities in Sweden or abroad, and in a stable

or unstable market. An equity fund that is invested in markets and industries that has a high


10

degree of fluctuations due to the world economy and currency movements, will ultimately result

in a higher degree of risk (Avanza, 2018).

AP7’s equity fund functions as a normal equity fund but has particular directions from the

Swedish pension authority. The fund cannot acquire more shares in a single company to the

extent that it exceeds five percent of the total voting rights for all shares. The assets managed

by the Seventh AP equity fund shall be divided into fund units and all fund units must be equal

within the fund. The fund manager must also calculate the fund value every day and inform the

pension authority about the value (Riksdagen, 2018b).

2.2 Risk-Return Trade-Off

In financial markets, the performance of monetary properties are measured by well-known

mathematical models of risk and return. These statistical measurements are central to the

finance community due to the reason that it enables the comparison of performance of various

asset classes, thereby simplifying the selection of investment strategies (Higgins, 2015).

The financial concept, known as the risk-return trade-off, states that the return an investment

will yield should increase as the risk associated with that investment increases. The risk-return

trade-off is an important factor that affects the decision taken by the investor (Aslanidis,

Christiansen and Savva, 2016). Investors have different levels of risk tolerance, meaning that

some investors are willing to take on a low risk investment that can yield a potentially lower

return whereas others are willing to take on higher risk that can yield a greater potential return.

Subsequently, some investors are risk-averse whereas some are risk-takers. The level of risk

tolerance can be affected by years to retirement, gender, and education (Bollen and Posavac,

2018). Furthermore, it is important to recognize that higher risk does not necessary imply a

higher potential return. The risk-return trade-off only indicates that there is a relationship

between a higher risk level and the likelihood of greater returns, however, higher risk can also

cause problematic situations associated with major losses on an investment due to changes in

the economic conditions (Aslanidis, Christiansen and Savva, 2016).

The risk-return trade-off is central to the field of finance, thereby making it an important tool

for investors to use when assessing what level of risk they are willing to retain when making an


11

investment. Moreover, it can be useful for investors to understand how risk and return are

correlated in order to make a good investment decision (Lundblad, 2007).

2.3 Financial Theory

2.3.1 Single Index Model

The single-index model was introduced to the field of finance by William Sharpe (1963) and is

an asset pricing model that measures both the risk and the return of a stock. According to the

single-index model, the systematic risk that affects the returns of the stocks is caused by only

one macroeconomic factor and that this factor can be characterized by the rate of return on an

index, for example the S&P 500. This assumption has been made in order to make the analysing

process easier (Sharpe, 1963). Furthermore, the model is based on the following assumptions

(Sharpe, 2000):

• Nearly all stocks have a positive covariance, due to their similar response to macroeconomic

factors.

• There are firms that are extra sensitive to macroeconomic factors and this firm-specific

difference is usually symbolised by its beta.

• The covariance between stocks arise from different responses to macroeconomic factors.

As a result, the covariance of the individual stock can be calculated by multiplying the beta

of the stock with the market variance.

The single-index model equation, i.e. the mathematical expression, is stated as:

𝑟𝑠 − 𝑟𝑓 = 𝛼 + 𝛽 (𝑟𝑚 − 𝑟𝑓) + 휀

Where, 𝑟𝑠 represents the return of the stock and 𝑟𝑓 is the risk-free rate, 𝛼 symbolises the stocks

abnormal return, 𝛽 represents the stocks sensitivity to the market return, 𝑟𝑚 is the market

portfolio return and 휀 represents the residual returns of the stock which is caused by firm-

specific factors. The single-index model was developed to simplify the portfolio analysis and

to make the process of analysing the relationship amongst securities easier, today the model is

widely used in the finance industry.


12

2.3.2 Modern portfolio theory

The modern portfolio theory was introduced by Harry Markowitz (1952) and is a commonly

used financial theory. Markowitz highlighted the importance of utilizing diversification and its

effects on the portfolio to increase risk-adjusted returns. Markowitz emphasized that investors

could through diversification reduce the risk, by allocating the holdings in different asset types,

such as in different companies and industries. Risk is measured as the standard deviation and

through risk spreading, investors can exploit the relationship between risk and return thereby

improving their investment selections to maximize returns given a certain level of risk. Stated

differently, it is significant to analyse how the selected assets in a portfolio relate to each other

in terms of risk and return.

Markowitz theory is essentially built around the basis of two assumptions. Firstly, all investors

are rational in their investment decision making. Secondly, all investors want to achieve the

greatest return as possible given the lowest risk. In other words, investors are risk-averse, i.e.

they want to avoid risk. Given two investment opportunities that yield the same return, the

investor would choose the alternative with the lowest risk as there is no reason for the investor

to choose the high-risk option given these assumptions.

The Modern Portfolio Theory contains several risk-measurements and risk-adjusted

performance measurements, such as variance and standard deviation as well as Treynor ratio

and Sharpe ratio.

2.3.2.1 Variance

The variance is a frequently used measure of dispersion, known as the squared expected

deviation from the mean. The variance formula is stated as:

𝑉𝑎𝑟(𝑅) = 1

𝑇 − 1∑ (𝑅𝑡 − �̅�)2

𝑇

𝑡=1

Where 𝑅𝑡 expresses the rate of return, �̅� is the average rate of return and 𝑇 denotes the number

of assets (Berk and DeMarzo, 2014). In general, when calculating the variance, the mean rate

of return is needed. However, the mean rate of return typically is an unknown factor and as a

result of this the average realized rate of return can be used instead.


13

2.3.2.2 Standard Deviation

The standard deviation, frequently referred to as the volatility, is calculated as the square root

of the variance. The standard deviation is used to calculate the degree of dispersion when using

a set of data. A low standard deviation specify that the data points are near the mean whereas a

higher standard deviation express that the data points are further from the mean, resulting in a

higher deviation. The standard deviation formula is expressed as (Berk and DeMarzo, 2014):

𝑆𝐷 (𝑅) = √𝑉𝑎𝑟(𝑅)

2.3.2.3 Treynor Ratio and Beta

The Treynor ratio, developed by Jack Treynor (1965), is a measure that exploits the correlation

amongst annualized risk-adjusted return and risk in order to measure efficiency. In other words,

the ratio tries to quantify to what extent an investment has compensated the investors given the

degree of risk. The Treynor ratio is dependent on beta, which is a measurement of a stock's

sensitivity to market fluctuations. The principle idea of the Treynor ratio is that systematic risk,

i.e. risk that is central to the whole market, must be penalized due to the fact that it cannot be

reduced through diversification.

The Beta formula is expressed as: 𝛽𝑖 = 𝑐𝑜𝑣 (𝑅𝑖,𝑅𝑚)

𝜎2(𝑅𝑚), where 𝑅𝑖 denotes the return of the asset

and 𝑅𝑚 the return of the market (Hübner, 2005). A Beta equal to one indicates that the price of

an asset moves with the market, whereas a Beta lower than one signifies that the asset

theoretically is less volatile than the market and a Beta greater than one means that the asset

theoretically is more volatile in comparison to the market. The Treynor ratio can be calculated

as: 𝑇 = 𝑟𝑖− 𝑟𝑓

𝛽𝑖, where 𝑟𝑖 represents the return of portfolio i, 𝑟𝑓 is the risk-free rate and 𝛽𝑖 denotes

the portfolio beta (Hodges, Taylor and Yoder, 2003).

2.3.2.4 Sharpe Ratio

The Sharpe ratio, developed by William Sharpe (1966), measures the excess rate of return of a

given asset and adjusts for risk. There are similarities between the Sharpe ratio and the Treynor

ratio, but instead of using the beta factor, Sharpe ratio uses standard deviation. A higher value

of the Sharpe ratio indicates that the security has superior performance, concluding in a greater


14

risk-adjusted portfolio with a higher rate of return for every unit of risk (Sharpe, 1966). The

Sharpe ratio is defined as (Berk and DeMarzo, 2014):

𝑆ℎ𝑎𝑟𝑝𝑒 𝑟𝑎𝑡𝑖𝑜 = 𝐸[𝑅𝑝] − 𝑟𝑓

𝑆𝐷(𝑅𝑝)

Where the 𝐸[𝑅𝑝] is the expected portfolio return, 𝑟𝑓 denotes the risk-free rate and 𝑆𝐷(𝑅𝑝)

symbolises the standard deviation of the portfolio.

2.3.3 Post-Modern portfolio theory

The Post-modern Portfolio Theory is a further development of the well-known Modern

Portfolio Theory. The basis for both theories is to explain how investors ought to use

diversification to enhance their portfolios and how to price risky assets. In the early days, the

Modern Portfolio Theory reformed the decision-making process of making investments within

the field of finance by describing risk associated to investments and thus introducing a risk-

return framework. The limitations of the Modern Portfolio Theory, such as assuming that

variance is the correct risk measure and referring to all returns as normally distributed, steered

the model to addressing all uncertainties the same. As a response to the inadequacies of the

Modern Portfolio Theory, and the fact that the model penalizes both upside and downside

deviation similarly, the creation of a new investment decision-making framework that would

overcome the limitations of the Modern Portfolio Theory started. Today, this model is known

as, The Post-modern Portfolio Theory (Rom and Ferguson, 1993).

2.3.3.1 Sortino Ratio

The Sortino ratio differentiates itself from the Sharpe ratio by measuring the risk-adjusted return

of an asset, or portfolio, using the target rate of return. Although both ratios quantity an assets

risk-adjusted return, the way of calculating is significantly different. The Sortino ratio use the

so-called downside risk in the denominator, as a substitute for the standard deviation, meaning

that the ratio only penalizes the return below the target rate of return (Sortino, 2010).

The Sortino Ratio is expressed as (Sortino and Satchell, 2001):


15

𝑆𝑜𝑟𝑡 = 𝑅𝑖 − 𝑅𝑚𝑎𝑟

δ

Where 𝑅𝑖 signifies the rate of return, 𝑅𝑚𝑎𝑟 denotes the minimal acceptable rate of return and 𝛿

symbolizes the downside deviation.

2.3.4 Capital Asset Pricing Model

A central question in the field of finance is how the risk affects the expected return of an asset.

The first consistent framework that answered this question, was the Capital Asset Pricing Model

(CAPM). The model was presented in the early 1960s by William Sharpe (1964), John Lintner

(1965a and 1965b) and Jan Mossin (1966). The CAPM is built upon the notion that not all risks

have an impact on asset prices. Particularly, a risk that is diversified away when united in a

portfolio with other investments is, roughly considered, not a risk of any kind. The CAPM is

an advancement of Markowitz’s (1952) portfolio theory which is founded by the concept that

specific risk can be disregarded with diversification, on the other hand, systematic risk can only

be reduced and not removed. Furthermore, the CAPM provides us with understandings about

what type of risks that is correlated to return.

The Capital Asset Pricing Model is based on four assumptions:

• Investors are risk averse and assess their investment portfolios only in the matter of

expected return and standard deviation of return.

• Capital markets are perfect: all assets are endlessly divisible; transactions costs, short

selling restrictions and taxes are non-existent; information is free and accessible by

everyone; and investors can borrow and lend at the risk-free rate.

• All investors have the equivalent investment opportunities.

• Investors make similar estimations of expected returns, standard deviations of return

and correlation concerning individual assets.

The assumptions above characterise a remarkably simple and faultless world, however, they

are needed for the CAPM to be functional. Under the stated assumptions, every investor will

determine a matching portfolio of risky assets with the highest Sharpe Ratio. Given their degree

of risk tolerance, every investor will assign a part of their wealth to this collection of assets,


16

known as the optimal portfolio, and the rest to risk-free borrowing or lending. In order to

guarantee that the market is in equilibrium, the price, i.e. expected return, of every individual

asset must be such that investors cooperatively agree to hold precisely the supply of the asset.

If all investors hold the equivalent amount of risky assets, that amount must be equal to the

amount of risky assets that are held in the market portfolio, which is the portfolio containing all

existing shares of each risky asset. When in equilibrium, consequently, the portfolio of risky

assets that have the highest Sharpe Ratio has to be the market portfolio.

The risk premium of every asset has the obligation to satisfy the condition of, 𝐸 (𝑅𝑖) − 𝑅𝑓 =

𝛽𝑖 (𝐸(𝑅𝑚) − 𝑅𝑓), where 𝐸 (𝑅𝑖) is the expected return of the asset, 𝑅𝑓 is the risk-free rate and

(𝐸(𝑅𝑚) characterise the expected excess return of the market portfolio and when deducting the

risk-free rate from this, the equity risk premium is derived. The variable 𝛽𝑖 stand for the Beta

of the asset which represents the volatility of the given asset. The expected return of an asset is

derived by 𝐸 (𝑅𝑖) = 𝑅𝑓 + 𝛽𝑖 (𝐸(𝑅𝑚) − 𝑅𝑓).

2.3.4.1 Alpha

Alpha, also known as Jensen’s Performance Index or Jensen’s alpha, was developed by Michael

Jensen (1968), and is used to determine the excess return of an individual security, or a portfolio

of securities, in comparison to the returns proposed by the Capital Asset Pricing Model (CAPM).

The securities can be any assets, for instance stocks, bonds, or derivatives. When measuring the

value of the excess return, the outcome can be positive, negative, or zero. When a security is

fairly priced, the actual return of that security will be equal to what the CAPM suggests, i.e. the

alpha will be zero. The reasoning behind this is that the CAPM considers the risk of the security

and measures the risk-adjusted returns and if the security does not earn more than the risk-

adjusted returns the outcome of the two measures will be equal. In contrast, the security, or

portfolio of securities, will have a positive alpha if it receives more than the risk-adjusted return.

A positive, or higher, alpha is continuously looked-for by managers since a negative alpha

implies that the return of the portfolio is less than the required return. Jensen’s alpha is

symbolised by α𝐽 and is mathematically expressed as:

α𝐽 = 𝑅𝑝 − (𝑅𝑓 + 𝛽 (𝑅𝑚 − 𝑅𝑓))


17

Where, 𝑅𝑝 is the return of the portfolio, 𝑅𝑓 signifies the risk-free rate, 𝛽 denotes the stock’s

beta and 𝑅𝑚 is the market return. Jensen’s alpha is stated in percentage form, signifying the

fraction by which the collection of securities, or a single security, has either underperformed or

over-performed in contrast to the market.

2.3.5 Efficient Portfolio Construction

The Modern Portfolio Theory, presented by Harry Markowitz (1952), introduced the concept

of having an efficient portfolio, which is a process that allows the investor to find the portfolio

that yields the highest expected return for a certain level of risk, or the minimum risk for a given

return. The portfolios that satisfy the requirements of an efficient portfolio are located on what

is referred to as the efficient frontier, which is the assembly point of optimal portfolios that

offers the maximum expected return for a given level of variance (or volatility) or the lowest

risk for a defined level of return. According to Markowitz (1952), the portfolios that are not on

the efficient frontier are insufficient, as they do not offer the required return for the given level

of risk, or they yield a higher risk for the given level of return

2.3.5.1 Global Minimum Variance Portfolio

The global minimum variance portfolio (GMV) is a low-risk investment strategy. The portfolio

lies on the efficient frontier, below the capital market line (Bodnar, Mazur and Podgoriski,

2016). Among the optimal mean-variance portfolios, GMV is the portfolio that has the lowest

variance. The GMV portfolio has the solution to the following optimization problem (Bodnar,

Parolya and Schmid, 2018):

𝑤′Σ𝑛𝑤 → 𝑚𝑖𝑛, 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑤′1 = 1

Where w = (w1…., wp)’ stands for the vector of portfolio weights, 1 is an appropriate vector of

ones, and Σ𝑛denotes the covariance matrix of the asset returns. The solution of the optimization

formula is given by (Bodnar, Parolya and Schmid 2018):

𝑊𝐺𝑀𝑉 = Σ𝑛

−11

1′Σ𝑛−11


18

2.3.5.2 Optimal Tangency Portfolio

The optimal tangency portfolio is the portfolio that has the highest Sharpe ratio. This portfolio

can be found on the efficient frontier, where the capital market line and the efficient frontier

intercepts and offers the highest return per unit of risk. Stated differently, it is the portfolio that

gives the investor the optimal risk-return trade-off. The optimal tangency portfolio does not

only consist of the securities with the highest expected return or the ones with the lowest risk.

Instead, this portfolio intends to balance securities with the highest possible returns with an

appropriate level of risk or securities with the lowest level of risk for a certain level of potential

return (Berk and DeMarzo, 2014).

In order to find the optimal portfolio, the expected return and the variance of the portfolio must

be calculated. The portfolio expected return is calculated as (Berk and DeMarzo, 2014):

𝐸 (𝑅𝑃) = 𝑤1𝐸 (𝑅1) + 𝑤2𝐸(𝑅2)+ . . . + 𝑤𝑛𝐸(𝑅𝑛)

Where, 𝑤1𝐸 (𝑅1) symbolizes the weight for security 1 multiplied with the expected return of

that security and 𝑤2𝐸(𝑅2) represent the weight for security 2 multiplied with the expected

return for that portfolio. Furthermore, the portfolio variance is calculated as (Berk and DeMarzo,

2014):

𝜎𝑝2 = ∑ 𝑤𝑖

2𝜎𝑖2 +

𝑛

𝑖=1

∑ ∑ 𝑤𝑖𝑤𝑗𝑐𝑜𝑣𝑖𝑗

𝑛

𝑗=1𝑖≠𝑗

𝑛

𝑖=1

Where, 𝑤𝑖2𝜎𝑖

2 represents the squared weight of security i multiplied with the variance of that

security, and 𝑤𝑖𝑤𝑗𝑐𝑜𝑣𝑖𝑗 is the covariance between the securities included in the portfolio.

Moreover, the portfolio standard deviation is needed which can be found by taking the square

root of the portfolio variance. Additionally, the Sharpe ratio is required to find the optimal

tangency portfolio. The portfolio Sharpe ratio formula is stated as (Berk and DeMarzo, 2014):

𝑆ℎ𝑎𝑟𝑝𝑒 𝑟𝑎𝑡𝑖𝑜𝑃 =𝐸(𝑅𝑃)−𝑅𝑓

𝜎𝑃 , where, 𝐸(𝑅𝑃) is the expected portfolio return, 𝑅𝑓 signifies the

risk-free rate and 𝜎𝑃 represents the portfolio standard deviation.


19

2.4 Previous Research

When searching for previous research within the field that this thesis focuses on, the authors of

this study cannot find another study with a similar purpose as this thesis. However, there are

several studies focusing on fund and stock performance, pensions systems and investor

behaviour regarding the pension system. The studies discuss why the involvement rate is

decreasing regarding how many savers that participate in making an active choice and the

performance of AP7 Såfa in comparison with other funds.

In a study made by Dahlquist, Martinez and Söderlind (2016) evidence is found that active

investors make higher risk-adjusted returns and returns compared to inactive investors who

remains in the predetermined fund, AP7. The findings are analysed from a sample of 100 000

premium pension savers daily portfolio changes over a 10-year period from 2000-2010. The

results revealed that inactive investors, who contributed with 93.5% of the sample, earned

average returns of 3.82% per year. Active investors that had made one up to nine portfolio

changes during 2000-2010, equivalent to 5.8% of the sample, earned average returns of 6.68%

per year. The investors with the highest activity, 0.6% of the sample, received an average return

of 12.57% per year. The study produced similar results for the risk-adjusted returns,

consequently showing that active investors did not take on a higher amount of risk. The authors

found that a significant part of the highly active investors’ success was related to their

propensity to invest in premium funds with recent good past performances, also known as the

momentum effect.

In a report by Czech (2016), evidence is found that that the falling rate of involvement in the

Swedish premium pension fund selection is a response to the growing number of funds

available to the population. Czech (2016) states that the increasing number of investment funds

has discouraged investors to actively make an investment decision, when there were only a few

available funds to select from during the year of 2000, the percentage of people that made an

active choice was as high as 67%. Later, due to the increasing number of funds, only 1.5% of

newcomers made an active fund selection in 2011. Furthermore, the negative rate of return on

capital has had an impact on the falling percentage of active choice makers. According to Czech

(2016), 100 SEK invested in the system in 2000 was worth less than 60 SEK after two years.

Although the market improved during the subsequent years, it fell again in 2008, due to the

financial crisis. The results of these circumstances exposed that individual choice did not matter


20

compared to irresistible market forces, consequently people started to ask themselves why they

should make a time-consuming investment decision if the result was negative anyway. Czech

(2016) states that most pension savers are not interested in managing their accounts. During the

years of 2000-2011, only 7% of pension savers made at least one change per year in their

portfolio. The percentage of savers that did not bother to select a fund and thus automatically

were transferred into AP7 Såfa, amounted to 51%.

Czech (2016) continues to argue that since only a small fraction of the people makes an active

choice, Sweden must have a reliable and well-performing default alternative. According to

Czech (2016), AP7 Såfa, had an average yield of 6.2% while an average pension fund had 4.8%,

during the years of 1995-2013. Moreover, in 2013, these numbers increased significantly, AP7

Såfa’s average yield was 26.6% whereas the yield of an average pension fund was 16.7%. As

stated by Czech (2016), the reason behind why the default alternative had such a positive

outcome in comparison to other funds can be described by the fact that most of the fund’s

holdings are made up of stocks, not only domestically but also globally. As a result, the default

alternative can be more profitable but at the same time it is much riskier.

In another study made by Dahlquist and Martinez (2015), evidence is found that inattention to

pension funds past performance among Swedish investors can be inefficient and costly. The

findings were based on 263 equity funds covering a time series between October 2000 and July

2008. The authors created a portfolio containing funds with good performance in the past and

received an average abnormal return of 6% per year. Contrary, the authors created a portfolio

with past bad performance funds with an abnormal return of -2%. A significant difference that

can cause a huge reduction in retirement capital. The authors reasoned that the main reason for

investors in the premium pension selection to be so inattentive is due to the mandatory pension

system. Since the system includes individuals which may have no interest in saving or investing

for retirement.

Moreover, Madrian and Shea (2001) presents evidence that uncertainty has an impact on the

investment decisions made by individuals. They claim that people tend to notice the pre-

determined alternative as investment advice. Moreover, Choi et al. (2002) conclude that

individuals often take the easy route, known as the path of least resistance, meaning that the

easiest thing to do is nothing, which can be an explanation to why people tend to lean towards

investing in AP7 Såfa instead of other alternatives. Stated differently, there is an explanation to


21

why people tend to make passive investment decisions and it could be that it is time-consuming

to form an understanding and to gather knowledge about how to invest your money.

Methodology

22

3. Method

3.1 Choice of Method

This thesis undertakes a positivistic approach by assessing research findings that are observable

and quantifiable. A positivistic approach will allow the authors of this thesis to analyse the

findings in an objective manner, meaning that the authors are independent, and the results are

based on facts. Moreover, the use of a positivistic research approach will address the purpose

of this study in a manner that fits the vision of the authors, with hypothesis testing and the

ambition to explain the findings of this thesis (Esterby-Smith, Thorpe & Jackson, 2015).

Moreover, this thesis will apply a formal study using a quantitative research approach to

measure if AP7’s equity fund in the premium pension selection has the greatest combination of

risk and return. A quantitative methodology is commonly used when conducting a business

research to assess information about consumer behaviour, experience and attitude (Cooper and

Schindler, 2011). The quantitative research will be performed using numerical data, which

essentially is deductive. With a deductive theory the research question will be tested using

statistical measurements, the analysis will be applied, and conclusions will be drawn in order

to answer the stated research question (Watson, 2015).

Additionally, a monitoring approach has been used when collecting data, meaning that the

researchers have retrieved information about the chosen topic without attempting to obtain

opinions from anyone. This type of data collection has been made with a longitudinal time

dimension, since the authors of this report have tracked alterations of data over time.

Subsequently, the researchers have no control over the data that has been used and therefore no

manipulation has been made, which is known as an ex post facto design. In other words, the

authors have only used statistical manipulation of what has happened with the data over the

chosen time-horizon (Cooper and Schindler, 2011). The authors of this thesis have faith in that

the use of a quantitative research method will give this study a more precise result, with an

accurate study of the chosen subject and its problems.

3.2 Collection of Data

This report is based on secondary data, the gathered information is evaluated and analysed in

order to draw significant conclusions that will be used in this thesis. Secondary data is retrieved

Methodology

23

from relevant literature, peer-reviewed articles, and journals, but also from appropriate and

trustworthy websites, such as the Swedish Pension Authority. In order to find high-quality

articles and books, databases such as Primo and Google Scholar have been used. When

conducting this thesis, the authors recognized several advantages of using secondary data. The

use of secondary sources makes it possible to gather high-quality data over a longitudinal time-

horizon during a short period of time (Bryman and Bell, 2011). Even though the use of

secondary sources has limitations, such as biasness, lack-of-control and familiarity of the data,

the sources have been used with confidence (Cooper and Schindler, 2011). The authors of this

report have faith in that the sources used in this thesis have a high level of trustworthiness, since

many reports and statistics that have been used are written in agreement with Swedish law.

Even though this report is based on secondary sources, knowledge of the potential

disadvantages has been deliberated. Consequently, every source is evaluated thoroughly by

considering for what purpose the source is written, when the source was written and by who it

was published (Cooper and Schindler, 2011).

Thomson Reuters DataStream is used as a source in this report to retrieve the majority of the

financial information, such as monthly net asset values of the various equity funds that are

included in this thesis. DataStream is a well-recognized provider of financial information and

investment research around the world. Thus, the authors of this report believe that DataStream

is a reliable source to use, as it fulfils the data-quality requirements such as data objectiveness,

data representativeness, data completeness and data accuracy (Jesilevska, 2017). Since

DataStream lacked information of each year’s monthly net asset values for some funds,

additional sources have been used in order to retrieve the required data. The supplementary

sources have been retrieved from banks and fund managers’ websites, namely AMF, Swedbank

Robur, SEB and Lannebo Fonder. In order to retrieve the complementary returns of the

predetermined fund, AP7, we had to request additional data from an employee at the seventh

AP Fund who transferred the necessary data over email. The start-of-month net asset values for

the AP7 fund is available in appendix 9.2.

When using sources from market participants such as AMF, Swedbank Robur, SEB and

Lannebo Fonder, there may be a problem because secondary sources may be biased. But, the

authors have used sources from market participants with carefulness and only after discussion

and evaluation if the source is appropriate for this thesis. When retrieving information about

the history of the Swedish pension system and the background, information published by, most

Methodology

24

importantly, the Swedish Pension Authority has been essential. The information retrieved from

the Swedish Pension Authority is believed to be relevant, trustworthy, and useful for this report.

The authors of this study have confidence in that the use of different sources will help them

grasp the chosen topic from different perspectives. In addition to improving the overall

assessment of this thesis, the use of different sources ensures that this study is well-written and

non-biased.

3.3 Research Design

3.3.1 Sampling

The funds in the Swedish pension premium system is used as the population of this research.

Due to time constraint, a sample of 51 equity funds have been selected. The sample have been

selected based on highest amount of fund selections and the time of operation. The funds

included in the sample are the funds that have the highest selection rate and have been active

for at least eleven years. The authors believe that the sample will give a good reflection of the

Swedish premium pensions equity funds due to the high level of market share that the selected

funds possess. The authors are aware that smaller funds may outperform AP7’s equity fund,

but with regard to the time constraint of this thesis, a sample had to be selected since all funds

available in the premium pension cannot be included. See appendix 9.1 for the funds included

in this study.

The sample period includes monthly net asset values from 2007-01-01 until 2017-12-01,

representing a period of eleven years. In order to use monthly net asset values, calculations of

the funds return and fluctuations during the eleven-year period can be made. The underlying

reason behind why a time-horizon of eleven years was chosen is due to time constraints and

inaccessibility of data for several equity funds. Subsequently, the chosen time-period made it

possible to include the majority of the most selected equity funds available in the selection.

Another interesting aspect of the sample period is that it included a time of a financial crisis,

giving the opportunity to see how the analysed funds performs in a time of financial distress.

Methodology

25

3.3.2 Calculations, Assumptions and Benchmarks

The measurements of risk-exposure, presented in sections 2.3.2.1 to 2.3.4.1 and the risk-

adjusted performance measures presented in sections 2.3.2.3, 2.3.2.4 and 2.3.3.1 have been

fundamental to this report. The foundation of this report is built upon the presented models in

these sections and the analysis is based on the equations derived from these models. The

Modern Portfolio Theory, Post-Modern Portfolio Theory, the Single Index Model, and the

Capital Asset Pricing Model have been central in order to analyze the retrieved data, particularly

the risk-exposure measurements, such as Standard deviation and Beta, and risk-adjusted

measurements, mainly Sharpe Ratio, Sortino Ratio, and Treynor Ratio. The analysis is built on

numerous measurements that quantify the intended portfolio characteristic, such as risk, giving

the authors a broad understanding of how to analyze the outcome of the calculations.

Additionally, a rolling-window approach has been used in this thesis when solving for the

minimum variance portfolio and the optimal tangency. The rolling-window contains a total of

eight formation periods, where each period includes four years. The first formation period

covers the years 2007-2010 and by moving one-year ahead during the calculations, the

subsequent holding period cover the years 2008-2011, 2009-2012, 2010-2013, 2011-2014,

2012-2015, 2013-2016 and 2014-2017. The authors of this thesis are confident that the use of

a rolling-window will be a beneficial approach that levels out short-term variations and make

long-lasting trends more apparent.

As previously mentioned, Thomson Reuters DataStream has been used as the main source of

financial information, in the form of monthly net asset values. For several funds

complementation’s has been made from banks and fund managers websites. The supplementary

sources have been stated in daily net asset values and not in monthly terms. These net asset

values have been manually transformed into start-of-the-month net asset values, in order to

match the data retrieved from DataStream. This transformation has been made in order to have

the same amount of observations across the sample when performing the assessments and

regression analysis. The sample of monthly net asset values has thereafter been converted into

index values in order to compute the rate of return. In order to calculate the monthly rate of

return, continuous compounding has been used.

Methodology

26

As a benchmark, the MSCI World Index has been selected. The MSCI World Index is an

extensive worldwide equity benchmark that denotes large and mid-cap equity performance

across 23 established market countries (MSCI, 2018). According to the authors, this index will

perform as a suitable benchmark for the purpose of this thesis, since the majority of the funds

possess holding in international equities. Arguments for this selection is mainly the fact that

MSCI World Index is a representation of a fluctuating equity market and that this study focuses

on assets traded on the stock market. Subsequently, the performance of the equity funds studied

in this thesis is determined by the fluctuations on the stock market. For this reason, the MSCI

World Index will be the benchmark the risk-adjusted performance of the Swedish equity funds

in the premium pension are compared to.

The risk-free rate used in the calculations is a 10-year Swedish government bond note. The

authors consider this to be a suitable benchmark since government bond yields frequently are

used as a representation for risk-free rates. The risk-free rates for the period 2007-2017 are

available in appendix 9.3 (Riksdagen, 2018c). The minimal acceptable rate of return, 𝑅𝑚𝑎𝑟 ,

used in the calculations of the Sortino ratio is the annual Swedish inflation target of 2.00%

(Riksdagen, 2018d). The argument for this benchmark is that the investors within the Swedish

premium pension system at least want to be reimbursed with the money they invest. Setting the

minimum acceptable rate of return to the Swedish inflation target, fulfills the assumption of not

losing the investment put in due to the macroeconomic environment.

3.4 Hypothesis Testing

Hypothesis testing is a method resulting from statistical inference, with the purpose of drawing

conclusions using a set of rules. Hypothesis testing is used to decide whether the probability of

a given statement, or hypothesis, is true by creating a constant decision-making principle. The

method of hypothesis testing involves a number of phases. First, the null hypothesis must be

formulated, then the test statistics and significance level need to be selected and to conclude,

the computed value must be compared with the designated significance level (Pereira and Leslie,

2009).

This report will test whether the corresponding yearly alpha for each fund, from 2007 to 2017,

is significant using a two-tailed hypothesis test. Contrary to a one-tailed test, which only allows

Methodology

27

the alternative hypothesis to be either greater or lower than that of the null hypothesis, a two-

tailed test allows the value of the alternative hypothesis to be both (Pereira and Leslie, 2009).

The two-tailed hypothesis test, in this report, will be based on a t-test of which the test statistic

is calculated as (Anderson, Sweeney, Williams, Freeman, and Shoesmith, 2010):

𝑡 = �̅� − 𝜇0

𝑠/√𝑛

Where, 𝑥 ̅is the sample mean, 𝜇0 represents the mean of the hypothesized population and

𝑠/√𝑛 signifies the standard error. The hypothesis testing in this report will use a significance

level of 1.96 with an alpha equal to 5% (0.05) and the decision to reject or not reject the null

hypothesis will be tested using the t-stat corresponding to each fund for every year. The

hypotheses that will be tested are stated as:

𝐻0 = −1.96 > 𝛼 > 1.96

𝐻1 : − 1.96 < 𝛼 < 1.96

3.5 Critical assessment

To begin with, the authors would like to draw attention to the inadequacies of Microsoft Excel.

Throughout this thesis, Excel and the Solver add-in contained in the program, has been used

extensively. The authors have experienced difficulties when using the mentioned software,

particularly the Solver function in order to find the efficient portfolios. As a result of the

inability to create a constraint that would not allow the program to select more than five funds

when using Solver, complications occurred, and new means of calculating had to be found.

Instead of calculating the intended portfolios using a constraint that automatically selected up

to five funds that should be included, the authors created an approximate solution by selecting

up to five funds with the highest weights derived by Solver and re-scaling the weights of these

funds so that the sum of the weights amounted to 100%. The authors have faith in that the

approximate solution is nearly as good as the solution that would have been generated using a

superior software than Excel. Regardless of the experienced shortcomings, the authors have

used Excel with confidence and are certain that it has been beneficial to the calculations made

in this report.

Methodology

28

Regarding the financial theories used in this thesis and although the Capital Asset Pricing

Model has been criticized for the models’ simplicity due to its unrealistic assumption that all

markets are perfect, the authors have faith in that the model will be useful for the calculations

that will be made in this thesis. The CAPM is a widely used and straightforward model that

considers systematic risk which is disregarded by several other return models, therefore it

should be included in this thesis. Furthermore, the Sharpe ratio has been criticized due to the

reason that standard deviation is perceived as not a good enough measure to quantify risk.

Sortino and Satchell (2001) states that standard deviation does not capture the entire risk and

that measurements, such as downside risk, using semi-variance and minimum acceptable return

(MAR) delivers a more precise measurement of risk. Sortino and Satchell (2001) carry on by

emphasizing the problem with possibly misrepresentative results when using standard deviation

as a measurement of risk. This is caused by both non-normally distributed returns and

irregularity of risk from the investors’ standpoint, since the investors do not actually identify

upside volatility as a risk. On the contrary, Sharpe ratio is one of the most commonly used risk-

return ratio, and the authors have faith in that the advantages of this ratio outweigh the

disadvantages, for that reason it has been used with confidence in this thesis.

With reference to the Treynor ratio, the retrospective feature of the measurement is a weakness

that the authors have knowledge about. Investments are subject to the fluctuations of the stock

market and therefore past results will change in the future. A stock that has generated a 5%

return last year cannot be expected to produce the same result next year. However, in this thesis,

the authors rely on past returns during the previous eleven years and therefore consider the

Treynor ratio an appropriate and advantageous measurement.

Empirical Results

29

4. Empirical Results

4.1 Risk Exposure

The risk-exposure measurements that have been calculated to clarify whether AP7’s equity fund

in fact has had a superior performance than the other funds included in this study are Beta,

Standard Deviation, Downside Deviation and Alpha. The annual calculations of the selected

risk-exposure ratios, has allowed this thesis to straightforwardly clarify how the funds have

performed each year, using Microsoft Excel’s (=rank) function, during the selected timeframe

and the risk-exposure development during these years. The outcome of the calculations

mentioned in this section are available in appendix 9.5, except for AP7 that is available in table

1 below.

Table 1 – “AP7 Aktiefond” Yearly Risk Measurements (authors’ calculations)

Initially, beta has been calculated to understand which of the funds that has experienced the

utmost impact as a response to market fluctuations. In other words, which of the funds that has

been affected the most, by the movements of the MSCI World Index. The beta value for each

fund has been ranked to distinguish the funds that are theoretically less volatile than the market

(𝛽 < 1) and the funds that have more systematic risk than the market (𝛽 > 1). From the

calculations, one can straightforwardly determine that the beta of AP7’s equity fund has

increased overall during the studied years, with a percentage increase of around 80%. From

St.Dev Beta Alpha Alpha Significance Downside.Dev

2007 9.11% 0.7072 -0.0003 -0.05 9.74%

2008 20.87% 0.7651 0.0028 3.15 28.58%

2009 14.93% 0.3985 0.0124 1.54 9.03%

2010 15.62% 0.6658 0.0040 0.57 14.32%

2011 20.96% 0.9968 -0.0045 -0.67 20.41%

2012 13.66% 0.7936 0.0079 2.49 11.11%

2013 10.66% 0.9030 0.0079 1.23 6.77%

2014 11.34% 0.9149 0.0149 2.93 8.52%

2015 22.13% 1.3951 0.0141 1.80 17.98%

2016 13.78% 1.0500 0.0080 0.97 12.76%

2017 9.62% 1.2723 -0.0063 -0.54 6.80%

Average 14.79% 0.8966 0.0055 1.22 13.28%

AP7 Aktiefond

Empirical Results

30

2007 to 2014, the beta of AP7’s equity fund remained at a level below one, indicating that the

fund is less volatile than the selected benchmark. However, the beta has remained at a level that

is higher than one during the years 2015-2017.

The mean beta value of AP7’s equity fund, during the selected time-period, is equal to 0.8966.

In comparison to all the studied funds, this mean value is equivalent to the fourth highest.

Swedbank Robur Medica, CWorldWide Medical, SEB Läkemedelsfond, SPP Aktiefond USA

and SPP Aktiefond Japan has the lowest mean beta value of the 51 funds during the years 2007

to 2017, of which SEB Läkemedelsfond’s beta value is the lowest, equal to 0,2996. The

calculations show that AP7's equity fund is more sensitive to market movements, compared to

most of the studied funds between 2007-2017. The years when AP7's equity fund has a beta

value below one, most of the other funds has an even lower value. Consequently, the equity

fund of AP7 does not distinguish itself as being the optimal choice relating to beta value, any

single year.

An alternative measurement of risk, of which the calculations points towards AP7’s equity fund

having a higher risk exposure compared to most of the other funds, is the standard deviation.

AP7’s equity fund’s lowest standard deviation was in 2007, when it was equal to 9.11%.

Conversely, in 2015 the fund reached its highest standard deviation value of the studied years,

equal to 22.13%. To further develop, the mean standard deviation of AP7’s equity fund during

the selected time-period is equal to 14.79%. When ranking these values, the equity fund of AP7,

places itself in the middle (26th and 31st)1 of the 51 examined funds. On the contrary, the five

funds with the lowest mean standard deviation during the years 2007-2017, are Swedbank

Robur Medica, Swedbank Robur Globalfond Mega, SEB Läkemedelsfond, Öhman Hjärt-

Lungfond and Handelsbanken Global Tema. The lowest value achieved is the one of Öhman

Hjärt-Lungfond, which equals 11.28%. Again, in terms of risk exposure, the equity fund of AP7

does not prove to be a good choice in comparison to many of the other funds that, according to

the calculations made, have a lower standard deviation.

The equity fund of AP7 has a mean downside deviation during the studied years equal to 13.28%

which ranks at 32nd place out of 512. Once again, many other fund alternatives have a lower

1 The fund that ends up in 51st place has the lowest standard deviation. 2 The fund that ends up in 51st place has the lowest downside deviation.

Empirical Results

31

risk exposure ratio in comparison to AP7’s equity fund. The five funds with the lowest mean

downside deviation during the investigated years are SEB Läkemedelsfond, SPP Aktiefond

USA, Öhman Hjärt-Lungfond, Swedbank Robur Globalfond Mega and Swedbank Robur

Medica, of which SEB Läkemedelsfond has the lowest mean value equal to 10.93%. From the

calculations, it becomes evident that the equity fund of AP7 is exposed to more target downside

deviation than many of the other funds. AP7’s equity fund had its lowest downside deviation

in 2013, equal to 6.77%3. This can be compared to Skagen Global (2.31%), Swedbank Robur

Småbolagsfond Europa (3.31%), Skandia Time Global (3.76%), Öhman Global Growth (4.24%)

and Swedbank Robur Småbolagsfond Sverige (4.74%) during the same year.

Furthermore, alpha has been measured for each included fund. Regarding alpha, AP7’s equity

fund only has a superior value than most of the other funds during the year of 2008. During that

year, AP7’s equity fund has the 5th highest alpha value equal to 0.28%. Hence, building a

portfolio of five funds for the year of 2008, regarding only the value of alpha, AP7’s equity

fund would have been included. However, when measuring the mean value for 2007 to 2017,

AP7’s equity fund ends up at 17th place with a mean alpha value of 0.55%. The five funds with

highest mean value of alpha during the examined time-period are Skandia Time Global (1.08%),

SEB Teknologifond (0.85%), Swedbank Robur Technology (0.83%), UBS Equity fund –

BioTech (0.82%) and Lannebo Vision (0.77%). This indicates that it would not be efficient to

keep AP7’s equity fund in a portfolio of five funds, only regarding alpha, during the time-period

2007 to 2017 since other funds has a higher excess return that is not a consequence of overall

movements in the market, in other words, a higher alpha value.

In addition to calculating the yearly alpha value for each fund, a hypothesis testing was

conducted through a t-test in order to see whether the annual alpha value corresponding to each

fund was significant or not. From the hypothesis testing, regarding the equity fund of AP7, the

authors of this thesis concluded that alpha was only significant for the years 2008, 2012, and

2014, where the corresponding alpha value for each year was 0.28%, 0.79% and 1.49%.

Furthermore, the calculations have proved that not a single fund had an alpha that was lower

than the significance level of -1.96, this indicates that neither fund performed significantly

worse than the index. Moreover, during the years of 2014 and 2015, many of the funds had a

significantly higher alpha than the index.

3 Ranking at 29𝑡ℎplace for that year.

Empirical Results

32

4.2 Risk-Adjusted Performance

For each individual premium pension fund, the risk-adjusted performance has been calculated

in order to analyse whether investors are being properly compensated for the risk taken on. The

calculations presented in table 2 below and in section 9.7 in the appendix will clarify whether

the AP7 equity fund has outperformed the additional studied funds in terms of risk-adjusted

performance. The risk-adjusted performance measurements presented in this section is the

Sharpe ratio, Treynor ratio and Sortino ratio. The annual calculations of the selected ratios will

clarify how the funds have performed each year using Microsoft Excel’s (=rank) function,

during the selected timeframe and the risk-adjusted return development during these years.

Table 2 - "AP7 Aktiefond" Yearly Risk-Adjusted Return Measurements (authors’ calculations)

To begin with, the Sharpe ratio has been calculated both annually and as a mean value for the

entire time-period for each fund. When observing the results regarding the equity fund of AP7

and the corresponding rank for each Sharpe ratio, it is evident that the fund does not have the

superior Sharpe ratio in comparison to most of the other included fund on a year-to-year basis,

with the exception for the year of 2012. For the selected time-period, which stretches from 2007

to 2017, the fund does not place itself at top five once. The highest achieved rank, conversely

also the highest attained Sharpe ratio for AP7’s equity fund, was in 2012 when the Sharpe ratio

was equal to 0.9892 which ranked at 8th place in comparison to the other funds. When looking

at the mean Sharpe ratio, the fund ranks at 9th place overall with a Sharpe ratio equal to 0.5572.

The five funds with the highest mean Sharpe ratio are Skandia Time Global (0.9920), Lannebo

Sharpe Ratio Treynor Ratio Sortino Ratio

2007 0.1183 0.0152 0.1106

2008 -2.4514 -0.6653 -1.7903

2009 1.6405 0.6376 2.7118

2010 0.2014 0.0665 0.2198

2011 -0.5855 -0.1073 -0.6014

2012 0.9892 0.2027 1.2158

2013 2.2778 0.2915 3.5843

2014 1.7345 0.2417 2.3075

2015 0.5054 0.1049 0.6221

2016 0.3883 0.0856 0.4191

2017 1.3110 0.1267 1.8546

Average 0.5572 0.0909 0.9685

AP7 Aktiefond

Empirical Results

33

Vision (0.7950), Swedbank Robur Technology (0.7626), SEB Teknologifond (0.6667) and

Skagen Global (0.6546).

In addition to the Sharpe ratio, the Treynor ratio has been calculated both as a mean value for

the entire time-period from 2007 to 2017 and annually. The calculations conclude that AP7’s

equity fund positions itself in the middle of the ranking in comparison to the additional funds

investigated on a year-to-year basis. During the years of 2007 to 2017 the fund positions itself

in top five once, in 2008, when it is ranked as the 5th superior fund with a Treynor ratio of -

0.6653. When observing the mean Treynor ratio for the entire time period the fund ranks at 23rd

place with a mean Treynor ratio equal to 0.0909. The five funds that achieved the highest mean

Treynor ratio are Swedbank Robur Technology (1.2482), Swedbank Robur Rysslandsfond

(0.7678), SEB Läkemedelsfond (0.4984), Nordea Småbolagsfond Norden (0.4671) and

CWorldWide Medical (0.4356).

Finally, the Sortino ratio’s results has been calculated both yearly and as a mean value for the

entire time-period. When observing the result, it is evident that the equity fund of AP7 did not

have the superior risk-adjusted return in terms of Sortino ratio in comparison to the majority of

the investigated funds. The fund was best ranked in 6th place in the year 2012 with a ratio of

1.2158. When observing the mean Sortino ratio for the whole time period, the AP7 equity fund

is ranked at 18th place with a mean ratio of 0.9685. The fund ranked in 1st place is Skandia Time

Global with a Sortino ratio of 1.8324. The remaining four funds with the highest mean Sortino

ratio are Lannebo Vision (1.6064), Skagen Global (1.3998), Lannebo Småbolag (1.3351) and

Swedbank Robur Technology (1.2828).

4.3 Portfolio Optimization

In order to attain a comprehensive understanding of the studied funds’ performance and to

realise which funds that should be included in a portfolio when individuals make their premium

pension selection, the authors of this thesis have constructed two different portfolios, one that

maximizes Sharpe ratio and one that minimizes the variance. By creating portfolios that can

hold up to five funds, conclusions can be drawn regarding AP7's performance in comparison to

the other funds and whether or not the fund is included in either portfolio gives the authors an

indication of whether AP7’s equity fund actually is the superior selection, in contrast to the

Empirical Results

34

other alternatives. As mentioned in section 3.3.2, a rolling-window approach was used with

eight different formation periods.

4.3.1 Minimum Variance Portfolio

To begin with, the constructed minimum variance portfolio for the formation period 2007-2010

can be seen in table 3 below. For this portfolio, the standard deviation obtained equalled 9.81%

and the portfolio return corresponded to -2.22%.

Table 3 - Minimum Variance Portfolio 2007-2010 (authors’ calculations)

The minimum variance portfolio for the time-period 2008-2011 can be observed in table 4

below. For this portfolio, the standard deviation attained corresponded to 9.60% and the

portfolio return equalled -1.79%.


Moreover, the minimum variance portfolio for the time-period 2009-2012 can be accessible in

table 5 below. This portfolio had a standard deviation of 6.63% and the portfolio return equalled

8.38%.


2007-2010 SEB Läkemedelsfond Öhman Hjär t-Lungfond SPP Aktiefond Japan Blackrock Global Funds Nordea Småbolagsfond Norden

Weights 56.20% 17.43% 15.30% 7.45% 3.62%

St.Dev 9.81%

Return -2.22%

2008-2011 SEB Läkemedelsfond Öhman Hjär t-Lungfond SPP Aktiefond Japan Blackrock Global Funds Nordea Småbolagsfond Norden

Weights 61.28% 19.81% 9.70% 6.76% 2.45%

St.Dev 9.60%

Return -1.79%

2009-2012 SEB Läkemedelsfond Skandia Time Global KPA Etisk Aktiefond Lannebo Vision Blackrock Global Funds

Weights 59.78% 16.41% 12.58% 6.02% 5.21%

St.Dev 6.63%

Return 8.38%

Empirical Results

35

In table 6 below, the minimum variance portfolio for the time-period 2010-2013 is available.

The portfolio had a standard deviation equal to 6.69% and the portfolio return equalled 6.97%.


Furthermore, the minimum variance portfolio for the time-period 2011-2014, had a standard

deviation equal to 6.94% and the portfolio return equalled 16.32%. This portfolio can be

observed in table 7 below.


For the years of 2012-2015, the minimum variance portfolio, that is available in table 8

underneath, had a standard deviation of 9.71% and the portfolio return totalled 18.68%.


The constructed minimum variance portfolio for the time-period 2013-2016, available in table

9 below, had a standard deviation that equalled 9.80% and the portfolio return corresponded to

10.14%.

2010-2013 SEB Läkemedelsfond Lannebo Vision Swedbank Robur M edica SPP Aktiefond Japan Blackrock Global Funds

Weights 44.76% 21.07% 12.86% 10.77% 10.54%

St.Dev 6.69%

Return 6.97%

2011-2014 SEB Läkemedelsfond Skandia Time Global Swedbank Robur M edica SEB Teknologifond SPP Aktiefond Japan

Weights 33.39% 26.38% 16.93% 16.92% 6.38%

St.Dev 6.94%

Return 16.32%

2012-2015 Nordea Småbolagsfond Norden SEB Läkemedelsfond C Wor ldwide Global Equities Lannebo Vision SPP Aktiefond Japan

Weights 28.82% 24.55% 20.00% 17.20% 9.43%

St.Dev 9.71%

Return 18.68%

Empirical Results

36


Lastly, the minimum variance portfolio for the time-period 2014-2017 has been calculated and

can be seen in table 10 below. For this portfolio, the standard deviation was equal to 9.26% and

the portfolio return corresponded to 10.45%.


By observing the tables available in this section, it is evident that at least one of SEB

Läkemedelsfond, Öhman Hjärt-Lungfond, SPP Aktiefond Japan and CWorldWide Global

Equities has been included in most of the constructed minimum variance portfolios.

Furthermore, for the years of 2009-2012, the lowest standard deviation was attained,

corresponding to 6.63%. This can be seen in table 5. On the contrary, for the years of 2007-

2010, the highest standard deviation was attained, when it equalled 9.81%. In addition, this

portfolio also had the lowest return, equal to -2.22%, which can be observed in table 3. To

develop, the years of 2007-2010 included the financial crisis, which can be a reason to why the

standard deviation was highest during that formation period. The portfolio with the highest

return is available in table 8, when it was equal to 18.68%. The standard deviation for that

portfolio is equal to 9.71%.

4.3.2 Optimal Tangency Portfolio

To begin with, the optimal tangency portfolio for the time-period 2007-2010, presented in table

11 below, had a Sharpe ratio equal to 0.3594 and the portfolio return corresponded to 11.97%.

2013-2016 C Wor ldwide Global Equities Länsförsäkr ingar Europa Aktiv Swedbank Robur Småbolagsfond Norden SPP Aktiefond Japan Skagen Global

Weights 38.23% 23.95% 22.12% 12.12% 3.58%

St.Dev 9.80%

Return 10.14%

2014-2017 C Wor ldwide Global Equities SPP Aktiefond USA Swedbank Robur Småbolagsfond Norden Länsförsäkr ingar Europa Aktiv Blackrock Global Funds

Weights 40.37% 21.75% 18.27% 10.76% 8.85%

St.Dev 9.26%

Return 10.45%

Empirical Results

37

Table 11 - Optimal Tangency Portfolio 2007-2010 (authors’ calculations)

The optimal tangency portfolio for the time-period 2008-2011 is available in table 12 below.

This portfolio had a Sharpe ratio equal to 0.0551 and the portfolio return corresponded to 4.94%.


For the time-period 2009-2012, the optimal tangency portfolio had a Sharpe ratio that equalled

1.2059 and the portfolio return was 12.62%. This can be observed in table 13.


Moreover, the optimal tangency portfolio for the time-period 2010-2013, obtainable in table 14

below, had a Sharpe ratio equal to 1.4926 and the portfolio return totalled 14.32%.


2007-2010 SKAGEN Kon-Tiki Blackrock Global Funds

Weights 92.98% 7.02% - - -

Sharpe Ratio 0.3594

Return 11.97%

2008-2011 Blackrock Global Funds UBS Equity Fund - BioTech

Weights 73.20% 26.80% - - -

Sharpe Ratio 0.0551

Return 4.94%

2009-2012 SEB Läkemedelsfond Skandia Time Global Länsförsäkringar fastighetsfond Blackrock Global Funds Didner & Gerge Aktiefond

Weights 35.93% 35.13% 16.51% 8.09% 4.34%

Sharpe Ratio 1.2059

Return 12.62%

2010-2013 SEB Läkemedelsfond CWorldWide M edical Skandia Time Global Länsförsäkringar fastighetsfond UBS Equity Fund - BioTech

Weights 39.85% 31.19% 17.34% 5.99% 5.63%

Sharpe Ratio 1.4926

Return 14.32%

Empirical Results

38

The optimal tangency portfolio, for the time-period 2011-2014, can be observed in table 15

underneath. For this portfolio, the Sharpe ratio equalled 2.3402 and the portfolio return was

equal to 18.94%.


Furthermore, the optimal tangency portfolio for the time-period 2012-2015, which can be seen

in table 16, had a Sharpe ratio equal to 2.1672 and the portfolio return totalled 23.57%.


For the time-period 2013-2016, the optimal tangency portfolio had a Sharpe ratio equal to

1.8406 and the portfolio return equalled 21.94%. This portfolio is available in table 17 below.


Lastly, the optimal tangency portfolio for the time-period 2014-2017 has been constructed. For

this portfolio, the Sharpe ratio equalled 1.6688 and the portfolio return totalled 19.59%. This

portfolio is accessible in table 18 below.

2011-2014 SEB Läkemedelsfond Skandia Time Global CWor ldWide M edical UBS Equity Fund - BioTech

Weights 62.38% 26.08% 11.04% 0.50% -

Sharpe Ratio 2.3402

Return 18.94%

2012-2015 Lannebo Vision SEB Läkemedelsfond Swedbank Robur Småbolagsfond Sver ige Länsförsäkr ingar fastighetsfond

Weights 39.64% 34.74% 20.74% 4.87%

Sharpe Ratio 2.1672

Return 23.57%

2013-2016 Lannebo Vision Lannebo Småbolag SPP Aktiefond Japan Länsförsäkr ingar fastighetsfond SPP Aktiefond USA

Weights 47.28% 35.29% 6.21% 5.80% 5.43%

Sharpe Ratio 1.8406

Return 21.94%

Empirical Results

39


The tables available in this section show that at least one of Lannebo Vision, Länsförsäkringar

Fastighetsfond, SEB Läkemedelsfond and UBS Equity Fund - BioTech has been included in

most of the constructed optimal tangency portfolios. The highest Sharpe ratio was obtained in

2011-2014, when it was equal to 2.3402. This portfolio can be seen in table 15. In contrast, the

lowest Sharpe ratio was attained in 2008-2011, which can be seen in table 12. The Sharpe ratio

for that formation period equalled 0.0551. This particular portfolio also had the lowest return,

equal to 4.94%. Again, the financial crisis of 2008 may have had an impact on the funds

included for that formation period. The portfolio with the highest return, equal to 23.57%, is

available in table 16 and corresponded to the years of 2012-2015.

2014-2017 Lannebo Vision Nordea Småbolagsfond Norden Länsförsäkr ingar fastighetsfond Blackrock Global Funds

Weights 63.23% 17.95% 13.29% 5.53%

Sharpe Ratio 1.6688

Return 19.59%

Analysis

40

5. Analysis

5.1 Risk Exposure

As previously mentioned in this thesis, the preferred level of risk that each individual investor

is willing to take on differs depending on each individuals’ preference. There are risk-averse,

risk-neutral, and risk-seeking investors which essentially states that an investment opportunity

that is attractive to one investor may be unattractive to another. Primarily, it has to do with

different investors having dissimilar utility functions. Taking this into account, it can be

problematic to point out a single fund and defining it as the optimal choice, due to the

differences in risk-preference between individual investors. However, this analysis will

investigate the difference between the equity fund of AP7 in contrast to the other included funds

and select the optimal fund(s) to pursue depending on the results of the calculated ratios and

the return given the risk level of the fund.

The calculated risk-exposure ratios all indicate that the equity fund of AP7 is not the optimal

choice in the premium pension fund selection. To begin with, even though the mean beta

coefficient of AP7’s equity fund for the studied years is equal to 0.8966, which indicates that

the fund is less volatile than the market, there are superior funds to select. Adding to that, the

last three years the beta coefficient of AP7’s equity fund has had an average value of 1.2391.

Given the circumstance that each individual can select up to five funds in their premium pension

selection, the proper funds to choose with regard to beta should be SEB Läkemedelsfond, SPP

Aktiefond Japan, Swedbank Robur Medica, CWorldWide Medical and SPP Aktiefond USA.

The mentioned funds have a mean beta coefficient ranging from 0.2996 to 0.5110. The fund

that has the lowest mean beta value is SEB Läkemedelsfond, over the studied time-period the

beta coefficient of that fund has only been above one a single time and corresponded to 1.0082

in 2015 (see appendix 9.5). The reasoning behind why AP7’s equity fund has a higher beta

value than the majority of the other funds can be explained by the fact that the fund is a high-

risk fund. AP7 continually state, when describing their equity fund, that it is impossible to

combine low risk with high returns (AP7, 2018).

Although historical price movements are poor predictors of the future, since the market can

change rapidly, and the fact that beta only reflects the future to some degree, this thesis is

Analysis

41

focusing on the past. On the contrary, beta suggests a clear, assessable measure that

is straightforward to use in these circumstances. There are, unquestionably, disparities on beta

contingent on effects such as the index used, and the years measured but generally speaking,

the concept of beta is a fairly useful measure that in this case point out that AP7’s equity fund

is inferior to other alternatives available.

To further develop, SEB Läkemedelsfond has the third lowest mean standard deviation for the

selected time-period. The other funds with the lowest standard deviation are Öhman Hjärt-

Lungfond, Swedbank Robur Medica, Swedbank Robur Globalfond Mega and Handelsbanken

Global Tema. Yet again, the result indicates that the equity fund of AP7 has higher risk than

most of the other funds. As stated in section 4.1, the mean standard deviation of AP7’s equity

fund for the entire time-period is equal to 14.79%, representing 26th place in comparison to all

studied funds. In accordance with what the result has shown regarding standard deviation, AP7

is not a better alternative than many of the other funds. However, when observing the yearly

average returns, it is evident that AP7’s equity fund has had greater return than the funds

mentioned previously that had the lowest standard deviation. This is an indication of the fact

that higher risk tends to imply higher returns, confirming the ideas presented in the risk-return

trade-off. Regardless of the fact that some individuals are risk-averse, they are required to take

on this risk when making a passive choice since AP7 is the default alternative and refereeing to

earlier statements in this thesis around 98.4% have passively taken on this risk since 2007.

When comparing AP7’s equity fund with SEB Läkemedelsfond, which is in the lower-risk

category regarding standard deviation, it is obvious that the latter is the better alternative. As

described above, the mean standard deviation for AP7’s equity fund equalled 14.79% whereas

that of SEB Läkemedelsfond amounted to 11.42%, signifying a difference of 3.37 percentage

points (see appendix 9.5). With that in mind, the yearly average return for AP7’s equity fund

for the years from 2007 to 2017 corresponded to 8.88% whereas the one for SEB

Läkemedelsfond totalled 8.31%, indicating a difference of 0.57 percentage points (see table 19

below). Although a minor difference in return can yield a significantly greater outcome, the

difference in return put into relation of the risk between these funds cannot justify the choice

of AP7’s equity fund instead of SEB Läkemedelsfond. However, when comparing AP7’s equity

fund to Handelsbanken Global Tema, which was another fund in the lower-risk category

regarding standard deviation, the preferred choice may differ. Handelsbanken Global Tema had

a mean standard deviation of 11.75%, ranking the fund as the fifth best fund in that category.

Analysis

42

However, the fund had an average yearly return of 5.11% during the years 2007 to 2017.

Referring back to AP7’s average yearly rate of return for the same time-period, it was equal to

8.88%, whereas the mean standard deviation totalled 14.79%. The mean standard deviation is,

unquestionably, higher than that of Handelsbanken Global Tema, but the difference in yearly

average return between the two funds, for a risk-seeking investor, may be significant enough to

pursue.

Table 19 - Low St.dev funds yearly average rate of return (authors’ calculations)

Yet, the result leads investors towards pursuing a different investment route than that of AP7’s

equity fund. The risk measurements provide sufficient arguments for that. The fund that has the

lowest mean downside deviation is SEB Läkemedelsfond with 10.93% compared to AP7’s

equity fund that was equal to 13.28%. As mentioned in section 4.1, the other funds with the

lowest downside deviation are SPP Aktiefond USA, Öhman Hjärt-Lungfond, Swedbank Robur

Globalfond Mega and Swedbank Robur Medica. By separating the negative part of the volatility

and considering returns that fall below the minimum acceptable return, it is clear that when in

times of financial distress, AP7’s equity fund is exposed to more risk than many of the other

funds. For instance, when the financial crisis of 2008 uncovered the weaknesses of the financial

field, the downside deviation of AP7’ equity fund increased from 9.82% in 2007 to 35.41% in

2008. In comparison to other funds that has proven to be less hazardous, that is SEB

Läkemedelsfond and Swedbank Robur Medica, their downside deviation in 2008 equalled

16.57% and 18.29%, this is a good indication of how AP7’s equity fund performs in distressed

financial times, which can be explained by the fact that AP7’s equity fund has proven to be a

high-risk fund. This is more obvious when considering the yearly average returns for 2008. The

average return for AP7’s equity fund declined by 65.85% that year in relation to 2007, in

comparison to SEB Läkemedelsfond and Swedbank Robur Medica that only declined by 13.35%

and 19.98%, which is a substantial difference.

Yearly average returns 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Average

AP7 Aktiefond 5,24% -47,00% 28,66% 7,31% -8,11% 17,67% 28,45% 23,83% 15,35% 9,51% 16,77% 8,88%

Swedbank Robur M edica -0,90% -19,98% 6,90% -4,86% 4,86% 12,74% 28,06% 29,47% 16,74% -3,78% -0,73% 6,23%

Swedbank Robur Globalfond M ega4,11% -41,49% 15,70% 2,39% -6,60% 9,97% 19,93% 17,79% 17,30% 2,09% 16,73% 5,27%

SEB Läkemedelsfond 0,77% -13,35% 1,75% 0,86% 5,35% 15,65% 26,09% 33,36% 18,60% -6,46% 8,79% 8,31%

Öhman Hjärt-Lungfond -8,24% -29,23% 24,29% 6,04% -6,53% 12,98% 25,34% 21,39% 15,47% -1,59% 12,21% 6,56%

Handelsbanken Global Tema 1,56% -37,94% 14,12% 7,46% -10,91% 8,46% 16,88% 16,87% 17,89% 3,29% 18,58% 5,11%

Analysis

43

With regard to alpha, the result has proven that there are funds that outperform the market better

than AP7’s equity fund. As stated previously in section 4.1, the five funds that have the highest

mean alpha are Skandia Time Global, SEB Teknologifond, Swedbank Robur Technology, UBS

Equity fund – BioTech, and Lannebo Vision. Again, AP7’s equity fund does not appear as the

superior alternative in comparison to other funds. Taking into consideration that AP7’s equity

fund is a high-risk fund and still has a lower excess return in comparison to many funds, is an

indication that risk-averse, nor risk-neutral, investors should not select the equity fund of AP7.

The above analysis has proven that the return given as a result of investing in AP7 clearly does

not cover the risk taken on.

5.2 Risk-Adjusted Performance

When analysing the results regarding the risk-adjusted performance measurements, it is clear

that there are superior funds to select than AP7’s equity fund. To begin with, AP7’s equity fund

has the 9th highest mean Sharpe ratio of the included funds. However, even though the fund

possesses a seemingly good position when ranking the funds, it is important to consider the risk

taken and the measurements calculated regarding risk-exposure.

When holding a risky asset, such as the equity fund of AP7, the holder of that asset must be

appropriately compensated for the additional risk taken on. The risk-adjusted performance

measurements, especially the Sharpe ratio, have proven that this does not apply to investments

made in AP7’s equity fund. By analysing the result of the calculations made, investors in the

equity fund of AP7 are not receiving enough excess return for the additional risk endured by

holding that asset. For example, both Swedbank Robur Technology and SEB Läkemedelsfond

both are considered lower-risk funds than that of AP7 in the previous section of this analysis.

When calculating the Sharpe ratio of these funds, they both have a higher mean Sharpe ratio

than AP7’s equity fund. To further develop, Skagen Global and Skandia Time Global also have

a higher mean Sharpe ratio and a lower risk-exposure rank than the equity fund of AP7.

To further explain, when analysing the calculations on a year-to-year basis instead of the mean

value for the entire time-period, the result is almost similar. When comparing AP7’s equity

fund with Swedbank Robur Technology, it is clear that AP7’s equity fund only has a higher

Sharpe ratio for three of the eleven years that this thesis has analysed, this was in 2012, 2013

Analysis

44

and 2016. The same is applicable when comparing the equity fund of AP7 with Skandia Time

Global. The comparison between the two funds imply that AP7’s equity fund only has a higher

Sharpe ratio than Skandia Time Global two times during the entire time-period.

Given these circumstances, it is evident that investors are not being compensated for the

additional risk taken on as a consequence of investing in the equity fund of AP7. This becomes

even more apparent when regarding the fact that this thesis has proven that less risky funds

have a higher Sharpe ratio.

To clarify that there are funds with preferable risk-adjusted returns, and less risk-exposure, than

that of AP7’s equity fund, the result has proven that Swedbank Robur Technology, SEB

Läkemedelsfond, Skagen Global, Swedbank Robur Medica and SPP Aktiefond USA all are

exposed to less risk by measurements of standard deviation and downside deviation and have a

superior mean Treynor ratio. By analysing the Treynor ratio, it becomes obvious which funds

that most effectively provide investors compensation, considering the risk level of that fund.

When evaluating the risk-exposure ratios and the Treynor ratio together, and compiling the

results from these measurements, it is obvious that AP7's equity fund is not good enough

compared to other funds mentioned in this paragraph. All these funds have generated higher

returns per unit of risk taken on by investing in these funds in comparison to the equity fund of

AP7.

Considering the fact that AP7’s equity fund is a risky asset, and that Treynor ratio is reliant on

beta, it can be reasonable that the equity fund of AP7 has a lower Treynor ratio than many of

the other funds due to the fund being exposed to more risk. The problem arises when

considering whether investors are being properly compensated for the risk they are being

exposed to. Keeping in mind that there are risk-taking investors that invest in high-risk assets,

they should be properly compensated for the additional risk and since AP7’s equity fund is the

default option, this fund should appropriately reward holders of that asset. As before, this thesis

has proven that this is not the case. Therefore, it is clearly not worth taking on the added risk

that arises from investing in AP7's equity fund, but instead selecting another option. However,

it is important to bear in mind that in order to optimize the portfolio of funds regarding the

premium pension selection, an active investor must continue on a similar path throughout the

following years. In other words, when making an active fund selection, the investors must

actively optimize their portfolios every year by altering their holdings. Subsequently, it is not

Analysis

45

preferable for an investor to make an active selection for one year and then not adjust the

selection the remaining years until retirement. This is clearly demonstrated when observing

SEB Läkemedelsfond, although the fund has performed well over the examined time-period,

there is an apparent downward trend in recent years with higher standard deviation and

declining returns. Considering the fact that many of the calculated measurements have a

backward-looking nature, which means that the measurements do not reflect future outcomes.

Given the risky environment of the financial market, funds can perform substantially different

on a year-to-year basis. Therefore, as previously stated, it is important for investors to

continuously optimize their portfolio by following the funds’ performance each year in order

to attain the optimal end result.

A similar analysis is applicable when regarding the Sortino ratio. The result has proven that

AP7's equity fund is not the optimal fund to select, given the risk level of the fund, since there

are superior alternatives to choose instead. Again, Swedbank Robur Technology, Skagen

Global, Skandia Time Global, SEB Läkemedelsfond and many other funds have a higher

Sortino ratio than AP7’s equity fund and a lower risk with regard to the measured risk-exposure

ratios. However, the Sortino ratio, Treynor ratio and the Sharpe ratio, all indicate rankings that

are substantially similar to each other. Hence, which can be observed in section 4.2, the ratios

will depict a similar outcome regarding the optimal funds to select. Although the outcome of

these ratios is similar, they do not refer to how the ratios are rewarding or penalizing the studied

funds.

Moreover, it is important to understand the nature of these ratios and not only consider the

outcome. For example, the Sortino ratio is the sole risk-adjusted performance measurement that

allows for alteration of the rate of return with an input other than the risk-free rate, in

comparison to the other risk-performance ratios used in this thesis. Considering the fact that

each investor can adjust the rate of return by using an individual variable in the calculations

increases the opportunity to find the preferable fund to invest in. Therefore, it can be unclear to

state which fund that has the optimal performance due to the fact that the individual preference

in the calculation can alter the results. In this thesis, in order to have equal calculations based

on fairly similar assumptions, the same variable has been used for each fund when calculating

the Sortino ratio, which is the target inflation level in Sweden of 2% and the result has shown

that the AP7's equity fund is not preferred.

Analysis

46

5.3 Portfolio Optimization

When analysing the portfolios in section 4.3, it is clear that the AP7 equity fund was not

included in the minimum variance portfolio nor the optimal tangency portfolio during any of

the eight time-periods. As a demonstration to how active investor better can tailor their

investment strategy after their risk preference, the portfolio result presented in section 4.3 has

been analysed in comparison to passive investors with holdings in AP7.

5.3.1 Minimum Variance Portfolio

To best capture the utility function of a risk-averse investor, the minimum variance portfolio of

an active investor has been compared to a passive investor with holdings in the AP7 equity fund.

Examining table 20 below it is evident that a risk-averse investor can attain an extensively lower

standard deviation by making an active investment. The passive investment of AP7 has been at

least 2.45 times riskier than the active investment in a minimum variance portfolio during each

of the time periods from 2007 to 2017.

Table 20 – Standard deviation, MVP in comparison to AP7 (authors’ calculations)

Consequently, due to the high standard deviation, it is of interest to examine the return

generated from AP7 in contrast to the minimum variance portfolio. This is particularly

interesting since AP7 states that it is impossible to combine low risk with high returns, when

they describe their fund (AP7, 2018). Observing table 21 below we can indeed see that the AP7

equity fund has had the superior return during each time-period except the period of 2011-2014.

However, the difference in percentage is greater in terms of risk in comparison to the return.

Which consequently raises the question if the passive investors are properly compensated for

the risk they are exposed to.

Standard Deviation 2007-2010 2008-2011 2009-2012 2010-2013 2011-2014 2012-2015 2013-2016 2014-2017

AP7 Equityfund 15.13% 18.10% 16.29% 15.23% 14.15% 14.45% 14.48% 14.22%

MVP Portfolio 9.81% 9.60% 6.63% 6.69% 6.94% 9.71% 9.80% 9.26%

Analysis

47

Table 21 - Return, MVP in comparison to AP7 (authors’ calculations)

Examining the risk-adjusted return measurement in table 22 below it is apparent that neither

investment alternative has had the highest Sharpe ratio a consecutive amount of times.

Implicating that in terms of risk-adjusted return, neither an active or passive investor would

have received a superior outcome during the investigated years.

Table 22 - Sharpe Ratio, MVP in comparison to AP7 (authors´ calculations)

Analysing the portfolios evaluated in section 4.3.1 and the analysis of the risk exposure

presented in section 5.1 it is apparent that the majority of funds presented as low risk funds in

section 5.1 is indeed included in the minimum variance portfolios in section 4.3.1. For example,

SEB Läkemedelsfond, SPP Aktiefond Japan, Lannebo Vision and CWorldWide global equities

are funds that has been included numerous times in the minimum variance portfolios.

Examining the yearly risk measurement ranking presented in appendix 9.6, the equity fund

Blackrock global fund had the highest rank in terms of standard deviation and downside

deviation, meaning that it is a high-risk fund, and is still included in five of the eight minimum

variance portfolios. Subsequently, this must entail that the Blackrock global fund consists of

other securities in comparison to the remaining funds in the portfolio. Subsequently, the

portfolio acquires a varying degree of risk through diversification that will minimize the overall

risk in times of market fluctuations.

5.3.2 Optimal Tangency Portfolio

In order to analyse the utility function of a risk-neutral or risk-seeking investor, a passive

investor with holdings in AP7 equity fund has been compared to an optimal tangency portfolio

of an active. Analysing table 23, it is evident that an active investor can achieve a more desirable

Return 2007-2010 2008-2011 2009-2012 2010-2013 2011-2014 2012-2015 2013-2016 2014-2017

AP7 Equityfund -1.45% -4.78% 11.48% 11.33% 15.46% 21.33% 19.29% 16.37%

MVP Portfolio -2.22% -1.79% 8.38% 6.97% 16.32% 18.68% 10.14% 10.45%

Sharpe Ratio 2007-2010 2008-2011 2009-2012 2010-2013 2011-2014 2012-2015 2013-2016 2014-2017

AP7 Equityfund -0.1228 -0.2987 0.5614 0.7207 1.1040 1.3767 1.2265 0.9848

MVP Portfolio -0.5881 -0.5158 0.8747 0.6980 2.0609 1.7662 0.9059 1.0302

Analysis

48

risk-adjusted return compared to a passive investor with holdings in AP7. The active investment

has exceeded the holdings in AP7 each time-period from 2007 to 2017.

Table 23 - Sharpe Ratio, OTP in comparison to AP7 (authors’ calculations)

Investigating the returns in table 24 from an active investment in an optimal tangency portfolio

to a passive investment in AP7, it is clear that the active portfolio has exceeded the passive

investment in terms of average yearly return each year as well as the risk-adjusted return.

Table 24 - Return, OTP in comparison to AP7 (authors’ calculations)

Given that the returns of the optimal tangency portfolio exceeded the passive investment each

time-period we can observe table 25 below to see if the greater return entailed a higher risk.

From viewing the table, it is apparent that the active investment had a significantly lower

standard deviation in comparison to AP7 in six of the eight time periods. For the portfolio

covering the years of 2007-2010, the standard deviation was equal to 23.43%, compared to

AP7’s equity fund that had a standard deviation equal to 15.13. Subsequently, the constructed

portfolio for the years of 2008-2011 had a standard deviation of 32.28% compared to 18.10%

for a passive investor. However, during these formation periods the constructed optimal

tangency portfolio entailed an average yearly return of 11.97 % and 4.94% in relationship to a

passive investment with a yearly average return of -1.45 % and -4.78% respectively.

Table 25 – Standard deviation, OTP in comparison to AP7 (authors’ calculations)

Sharpe Ratio 2007-2010 2008-2011 2009-2012 2010-2013 2011-2014 2012-2015 2013-2016 2014-2017

AP7 Equityfund -0.1228 -0.2987 0.5614 0.7207 1.1040 1.3767 1.2265 0.9848

OTP Portfolio 0.3594 0.0551 1.2059 1.4926 2.3402 2.1672 1.8406 1.6688

Return 2007-2010 2008-2011 2009-2012 2010-2013 2011-2014 2012-2015 2013-2016 2014-2017

AP7 Equityfund -1.45% -4.78% 11.48% 11.33% 15.46% 21.33% 19.29% 16.37%

OTP Portfolio 11.97% 4.94% 12.62% 14.32% 18.94% 23.57% 21.94% 19.59%

Standard Deviation 2007-2010 2008-2011 2009-2012 2010-2013 2011-2014 2012-2015 2013-2016 2014-2017

AP7 Equityfund 15.13% 18.10% 16.29% 15.23% 14.15% 14.45% 14.48% 14.22%

OTP Portfolio 23.43% 32.28% 8.32% 8.05% 7.24% 10.16% 11.23% 11.20%

Analysis

49

Examining the optimal tangency portfolios in section 4.3.2 in relation to the yearly average

risk-adjusted return measurement presented in section 9.7 in the appendix, it is apparent that

the funds included in the portfolio varies to a greater extend depending on which fund that

achieved a high risk-adjusted return ratio in comparison to the minimum variance portfolio.

Consequently, taking return into account leads to a greater degree of fluctuations and by

examining the portfolios it is evident that alterations has to be made regularly in order to attain

a portfolio with a high risk-adjusted return outcome. This conclusion is made with regards to

the minimum variance portfolio that ensured a greater degree of comfort in an active decision

in terms of fewer alterations. However, analysing table 23, 24 and 25 above it is evident that a

clear enhancement can be achieved between a passive and an active investment in terms of

lower risk and a higher return.

Conclusion

50

6. Conclusion

Throughout this thesis, it has become evident that investors that hold assets in the equity fund

of AP7 are exposed to high levels of risk without being properly compensated for the additional

risk taken on. According to the risk-exposure measurements included in this thesis, AP7’s

equity fund is exposed to higher levels of risk in comparison to many of the other included

funds. Furthermore, the research of this thesis has proven that there are many funds that have

preferred levels of risk-adjusted return than AP7’s equity fund and lower risk-exposure,

signifying that these funds are superior alternatives to select rather than the default option,

namely AP7.

In addition to this, the equity fund of AP7 has performed worse in periods of financial distress

than many of the included funds, and the authors believe that a default alternative should ensure

a more stable environment for investors with holdings in the default fund. As a result of the

high levels of risk taken on by investors that hold assets in AP7’s equity fund, and the fact that

they are not being properly compensated for that additional risk, the authors of this thesis

conclude that AP7’s equity fund does not prove to have a superior risk-adjusted performance

compared to many of the other funds included in this study. The intention of this conclusion is

not to criticise the entire premium pension system, but to shed light on the insufficiencies of

AP7’s equity fund and the high level of risk-exposure that investors are exposed to.

Consequently, AP7’s equity fund is not the preferable fund that individuals should select with

regard to risk-exposure and risk-adjusted return.

Regarding the portfolio construction of the minimum-variance portfolio and the optimal

tangency portfolio, the authors of this thesis can conclude that neither portfolio throughout the

rolling-window estimation of eight formation periods included the equity fund of AP7.

Considering the fact that AP7’s equity fund was not included in any portfolio; the authors of

this report have highlighted the importance that investors should remain well-informed about

the financial market and the funds available to select from. Furthermore, when comparing the

portfolios constructed to the equity fund of AP7, the authors of this thesis found that a lower

variance can be achieved by the minimum-variance portfolio however, the return was lower

than that of AP7’s equity fund all formation periods, except for one. Considering the optimal

Conclusion

51

tangency portfolio, the authors can conclude that it was superior to that of AP7’s equity fund

regarding both Sharpe ratio and return for all examined formation periods.

Moreover, the findings of this thesis coincide with the findings made by Dahlquist, Martinez

and Söderlind (2016) that active investor receive higher risk-adjusted returns compared to

inactive investors that remain in the default fund. Furthermore, the authors of this thesis agree

with the statements made by Czech (2016) regarding that the declining participation rate in the

Swedish premium pension fund selection is a result of too many fund alternatives, which is

discouraging for investors. This thesis has shown that investors can benefit from making an

active fund selection, however, the vast amount of funds to select from can be an explanation

to why many investors end up with holdings in the default alternative.

To conclude, this thesis has proven that there are superior funds to select rather than the equity

fund of AP7 with regard to risk-adjusted return and risk-exposure. Furthermore, the minimum-

variance portfolio constructed outperformed the equity fund of AP7 in terms of lower risk-

exposure for risk-averse investors. Subsequently, optimal tangency portfolio proved to be

superior when comparing it to AP7’s equity fund as well, with regard to both risk-adjusted

return and risk-exposure. Consequently, active participants can obtain improved results of their

investments according to their risk preferences when remaining active in their fund selection.

However, it is of great importance to bear in mind that an active investor must regularly make

alterations to their portfolio in order to achieve the desired outcome.

Contributions to the Research and Suggested Further Studies

52

7. Contributions to the Research and Suggested Further Studies

This thesis has proven that the equity fund of AP7 expose holders of that asset to high levels of

risk with a lack of ability to properly compensate the investors for that additional risk.

Furthermore, this thesis has identified several funds available in the premium pension fund

selection that has a superior risk-adjusted performance including a lower risk-exposure than the

equity fund of AP7. The authors have faith in that the returns of AP7’s equity fund does not

justify the risk-exposure of that fund, consequently the system must prioritize a stable

environment regarding investors that do not make an active investment decision since a pre-

default option should entail more stability than in does today. The research of this thesis has

acknowledged both advantages and disadvantages of the included risk-exposure and risk-

adjusted measurements and specified how these ratios have been used throughout this thesis.

The authors have used these ratios when selecting the preferable funds available to investors in

order to conclude which of the funds that have the best risk-adjusted return or lowest risk-

exposure. Furthermore, the authors trust that this thesis has highlighted the importance of

knowledge and consistency when making an active fund selection, meaning that an investor

must remain active throughout the years to retirement in order to obtain the optimal end result.

Moreover, this thesis has emphasised that fund performance is related to the fluctuations of the

financial market, meaning that unexpected market fluctuations can alter the performance of

funds. Hence, the authors have encouraged investors to pursue the path of being an active

participant in order to attain the desired objective. The authors have confidence in that this

thesis can contribute to the existing research concerning the Swedish pension system as a result

of the observations made with regard to the issues of the pre-default alternative and the risk-

level that investors in the default fund are exposed to. The authors wish that this thesis will

broaden the knowledge of the readers and encourage them to be active participant in the

premium pension fund selection given the benefits of being an active participant. This thesis is

not suggesting that the Swedish pension system should be restructured, instead it is highlighting

the importance of the potential shortcomings derived by high levels of risk-exposure to passive

investors and wish that the analysis of this thesis will persuade the Swedish population to be

well-informed resulting in a higher ratio of active participants.

Contributions to the Research and Suggested Further Studies

53

The authors of this thesis propose that future research may include a study that compares the

Swedish premium pension system to the pension system in another country, for example a

neighbouring country such as Finland or Denmark. The ability to ensure pension savings for

the working population after retirement is a globally important concern. Consequently, benefits

and shortcomings in the pension systems can be examined in order to contribute with possible

enhancements beyond the borders of Sweden. Considering the fact that assurance of a source

of revenue after retirement is an international subject that must be addressed properly, the

authors hope that this thesis encourages the readers to keep examining and criticizing the

structure of the current system including the inadequacies and advantages in order for the

system to progress for the benefit of retirees. By having a well-educated population that

understands the insufficiencies of the pre-default option, and by proposing improvements

through prospective studies, a more stable environment for passive investors that do not have

the required knowledge to make an active selection can be accomplished.

Forthcoming researches can also examine the share of the premium pension system in relation

to the national public pension system and examine whether the 2.5% that is controlled by the

individual today is appropriate or if it should be increased or decreased. Although, the

remaining parts of the Swedish pension system can be taken into attention as well. The authors

believe that it would be of interest to perform a study with regard to the fund selection in the

occupational pension instead of the premium pension. Each employee with an agreement for

an occupational pension have the right to invest 50% of their pension into a fund insurance with

higher risk than in a traditional insurance, but with the possibility to obtain a higher return. The

employee has the ability to invest capital in one or several funds in order to obtain their risk-

preference, similarly to the premium pension selection.

Lastly, the authors of this thesis would like to suggest future researches to make a deeper

analysis of fund performance in times of financial distress. It is important to understand that

fund performance can change rapidly due to the fluctuations of the market, and since the

pension is an important part of each individual life after retirement it is important to highlight

the issues that can arise from unstable financial market.

Appendix

54

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Appendix

63

9. Appendix

9.1 The 51 equity funds included in the study

Information about the included equity funds (Pensionsmyndigheten, 2018b)

Number of fund selections Market value (million SEK) Country of origin

AP7 Aktiefond 3 819 029 420 406 Sweden

Swedbank Robur Technology 289 755 22 119 Sweden

Didner & Gerge Aktiefond 236 051 25 229 Sweden

AMF Sverige 226 338 20 222 Sweden

AMF Världen 205 091 17 226 Sweden

Swedbank Robur Aktiefond Pension 160 338 15 227 Sweden

Swedbank Robur Medica 114 841 5 403 Sweden

Swedbank Robur Sverigefond Mega 100 809 7 253 Sweden

SPP Aktiefond Sverige 88 610 6 441 Sweden

Avanza Zero 88 421 6 647 Sweden

Swedbank Robur Globalfond Mega 85 016 6 431 Sweden

CWorldWide Medical 81 796 4 062 Luxembourg

Länsförsäkringar fastighetsfond 77 868 5 091 Sweden

SEB Läkemedelsfond 71 920 4 448 Sweden

Skagen Global 66 393 4 068 Norway

SPP Aktiefond Europa 63 355 2 632 Sweden

SPP Aktiefond USA 61 265 4 083 Sweden

SKAGEN Kon-Tiki 59 361 3 101 Norway

Swedbank Robur Småbolagsfond Europa 58 981 3 506 Sweden

Swedbank Robur Europafond Mega 58 850 2 667 Sweden

C Worldwide Global Equities 57 345 3 616 Luxembourg

Skandia Time Global 53 394 2 896 Sweden

Swedbank Robur Östeuropafond 51 407 1 916 Sweden

Lannebo Småbolag 49 883 4 825 Sweden

Swedbank Robur Småbolagsfond Sverige 49 639 4 486 Sweden

Handelsbanken Svenska Småbolagsfond 49 555 5 201 Sweden

Nordea Småbolagsfond Norden 46 318 3 475 Finland

AMF Aktiefond Europa 45 539 2 291 Sweden

Öhman Hjärt-Lungfond 43 872 2 474 Sweden

AMF Aktiefond Småbolag 43 454 3 811 Sweden

Carnegie Rysslandsfond 40 977 2 566 Sweden

Swedbank Robur Rysslandsfond 39 858 1 807 Sweden

UBS Equity Fund - BioTech 38 737 2 731 Luxembourg

SEB Teknologifond 35 976 2 507 Sweden

Handelsbanken Tillväxtmarknad Tema 35 249 2 070 Sweden

Swedbank Robur Nordenfond 34 314 1 889 Sweden

Baring Hong Kong China Fund 33 144 2 334 Ireland

East Capital Ryssland 32 542 1 485 Sweden

Swedbank Robur Småbolagsfond Norden 32 452 2 157 Sweden

Öhman Global Growth 31 691 1 856 Sweden

Skandia USA 31 574 1 802 Sweden

Skandia Världen 31 379 1 624 Sweden

Länsförsäkringar Europa Aktiv 30 880 1 347 Sweden

Blackrock Global Funds 29 390 1 993 Luxembourg

Appendix

64

9.2 Start-of-month net asset values for AP7

The start-of-month net asset values that was retrieved through e-mail

2007-01-01 109.24

2007-02-01 111.84

2007-03-01 109.94

2007-04-01 113.16

2007-05-01 116.04

2007-06-01 121.43

2007-07-01 119.28

2007-08-01 115.86

2007-09-01 117.57

2007-10-01 117.98

2007-11-01 119.30

2007-12-01 115.12

2008-01-01 114.35

2008-02-01 105.67

2008-03-01 102.18

2008-04-01 101.96

2008-05-01 105.71

2008-06-01 107.41

2008-07-01 97.44

2008-08-01 96.95

2008-09-01 99.03

2008-10-01 90.80

2008-11-01 76.96

2008-12-01 71.95

2009-01-01 72.99

2009-02-01 69.75

2009-03-01 67.51

2009-04-01 70.67

2009-05-01 79.05

2009-06-01 83.88

2009-07-01 83.11

2009-08-01 88.20

2009-09-01 90.48

2009-10-01 92.18

2009-11-01 92.65

2009-12-01 95.83

2010-01-01 98.64

2010-02-01 96.83

2010-03-01 97.44

2010-04-01 104.13

2010-05-01 104.60

Appendix

65

9.3 Risk-free rate

The risk-free rate - 10-year Swedish government bond (Riksdagen, 2018c)

Monthly Monthly Monthly Yearly

2007-01-01 0.33% 2010-09-01 0.21% 2014-05-01 0.16% 2007 4.17%

2007-02-01 0.33% 2010-10-01 0.22% 2014-06-01 0.15% 2008 3.90%

2007-03-01 0.32% 2010-11-01 0.24% 2014-07-01 0.13% 2009 3.25%

2007-04-01 0.34% 2010-12-01 0.27% 2014-08-01 0.13% 2010 2.88%

2007-05-01 0.35% 2011-01-01 0.27% 2014-09-01 0.13% 2011 2.59%

2007-06-01 0.37% 2011-02-01 0.28% 2014-10-01 0.11% 2012 1.59%

2007-07-01 0.37% 2011-03-01 0.28% 2014-11-01 0.09% 2013 2.12%

2007-08-01 0.35% 2011-04-01 0.28% 2014-12-01 0.08% 2014 1.72%

2007-09-01 0.35% 2011-05-01 0.25% 2015-01-01 0.07% 2015 0.72%

2007-10-01 0.36% 2011-06-01 0.24% 2015-02-01 0.05% 2016 0.53%

2007-11-01 0.35% 2011-07-01 0.23% 2015-03-01 0.05% 2017 0.65%

2007-12-01 0.36% 2011-08-01 0.18% 2015-04-01 0.03%

2008-01-01 0.34% 2011-09-01 0.15% 2015-05-01 0.06%

2008-02-01 0.33% 2011-10-01 0.16% 2015-06-01 0.08%

2008-03-01 0.33% 2011-11-01 0.14% 2015-07-01 0.07%

2008-04-01 0.34% 2011-12-01 0.14% 2015-08-01 0.06%

2008-05-01 0.35% 2012-01-01 0.14% 2015-09-01 0.06%

2008-06-01 0.37% 2012-02-01 0.16% 2015-10-01 0.06%

2008-07-01 0.36% 2012-03-01 0.16% 2015-11-01 0.07%

2008-08-01 0.34% 2012-04-01 0.15% 2015-12-01 0.08%

2008-09-01 0.33% 2012-05-01 0.13% 2016-01-01 0.09%

2008-10-01 0.30% 2012-06-01 0.12% 2016-02-01 0.06%

2008-11-01 0.28% 2012-07-01 0.11% 2016-03-01 0.07%

2008-12-01 0.22% 2012-08-01 0.12% 2016-04-01 0.07%

2009-01-01 0.23% 2012-09-01 0.13% 2016-05-01 0.06%

2009-02-01 0.24% 2012-10-01 0.13% 2016-06-01 0.04%

2009-03-01 0.24% 2012-11-01 0.12% 2016-07-01 0.01%

2009-04-01 0.26% 2012-12-01 0.13% 2016-08-01 0.01%

2009-05-01 0.30% 2013-01-01 0.15% 2016-09-01 0.02%

2009-06-01 0.30% 2013-02-01 0.17% 2016-10-01 0.02%

2009-07-01 0.28% 2013-03-01 0.16% 2016-11-01 0.04%

2009-08-01 0.29% 2013-04-01 0.14% 2016-12-01 0.05%

2009-09-01 0.28% 2013-05-01 0.15% 2017-01-01 0.05%

2009-10-01 0.27% 2013-06-01 0.17% 2017-02-01 0.06%

2009-11-01 0.27% 2013-07-01 0.18% 2017-03-01 0.06%

2009-12-01 0.27% 2013-08-01 0.20% 2017-04-01 0.05%

2010-01-01 0.28% 2013-09-01 0.22% 2017-05-01 0.05%

2010-02-01 0.27% 2013-10-01 0.20% 2017-06-01 0.04%

2010-03-01 0.27% 2013-11-01 0.19% 2017-07-01 0.06%

2010-04-01 0.26% 2013-12-01 0.20% 2017-08-01 0.05%

2010-05-01 0.23% 2014-01-01 0.20% 2017-09-01 0.05%

2010-06-01 0.22% 2014-02-01 0.19% 2017-10-01 0.07%

2010-07-01 0.23% 2014-03-01 0.18% 2017-11-01 0.06%

2010-08-01 0.20% 2014-04-01 0.17% 2017-12-01 0.06%

Appendix

66

9.4 Yearly average rate of return

Yearly average rate of returns 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

M SCI World 7.81% -65.85% 34.46% 3.75% -2.68% 10.40% 20.99% 6.48% -1.15% -0.09% 19.15%

AP7 Aktiefond 5.24% -47.00% 28.66% 7.31% -8.11% 17.67% 28.45% 23.83% 15.35% 9.51% 16.77%

Swedbank Robur Technology 6.94% -35.19% 21.02% 8.98% 1.23% 5.90% 16.53% 32.13% 21.00% 8.25% 27.34%

Didner & Gerge Aktiefond -1.34% -62.15% 54.64% 22.18% -16.71% 21.07% 25.12% 15.63% 16.41% 5.92% 13.61%

AM F Sverige -4.07% -64.81% 44.03% 17.59% -16.11% 9.46% 22.88% 14.48% 14.83% 0.02% 12.53%

AM F Världen 0.10% -53.77% 30.89% 11.11% -15.02% 9.81% 21.82% 16.09% 16.08% 3.87% 11.43%

Swedbank Robur Aktiefond Pension 2.32% -52.07% 28.27% 11.24% -10.00% 6.98% 17.85% 16.29% 13.10% 3.43% 15.85%

Swedbank Robur M edica -0.90% -19.98% 6.90% -4.86% 4.86% 12.74% 28.06% 29.47% 16.74% -3.78% -0.73%

Swedbank Robur Sverigefond M ega -1.27% -68.73% 49.83% 18.21% -20.76% 8.93% 23.18% 14.55% 14.16% 4.09% 16.60%

SPP Aktiefond Sverige -4.80% -61.59% 41.65% 17.18% -15.62% 10.79% 22.20% 14.92% 13.19% 0.48% 13.92%

Avanza Zero -0.49% -61.99% 44.44% 17.62% -15.27% 11.08% 21.97% 14.29% 8.79% 0.73% 10.98%

Swedbank Robur Globalfond M ega 4.11% -41.49% 15.70% 2.39% -6.60% 9.97% 19.93% 17.79% 17.30% 2.09% 16.73%

CWorldWide M edical -1.49% -30.92% 3.33% 15.74% 1.11% 22.17% 25.64% 30.32% 13.97% -17.36% 2.63%

Länsförsäkringar fastighetsfond -12.86% -72.42% 48.99% 30.46% -8.39% 14.45% 18.62% 28.49% 28.41% 10.28% 9.37%

SEB Läkemedelsfond 0.77% -13.35% 1.75% 0.86% 5.35% 15.65% 26.09% 33.36% 18.60% -6.46% 8.79%

Skagen Global 10.09% -51.48% 28.79% 20.38% -6.65% 7.51% 29.42% 9.81% 19.04% -4.16% 19.73%

SPP Aktiefond Europa 6.90% -48.54% 17.20% -6.42% -11.00% 1.62% 19.78% 11.93% 11.13% -2.81% 15.96%

SPP Aktiefond USA -4.04% -29.48% 7.10% 8.89% -0.64% 11.09% 24.56% 25.65% 19.43% 12.41% 9.48%

SKAGEN Kon-Tiki 20.96% -57.11% 54.02% 28.63% -16.22% 2.22% 20.08% 6.25% 6.02% -2.41% 25.41%

Swedbank Robur Småbolagsfond Europa 2.03% -72.18% 30.03% 11.61% -21.25% 18.03% 32.86% 8.52% 23.07% -5.26% 21.05%

Swedbank Robur Europafond M ega 9.68% -52.38% 19.70% -8.02% -12.57% 10.02% 18.93% 11.84% 12.80% -2.97% 18.34%

C Worldwide Global Equities 24.47% -63.00% 26.61% 3.85% -1.68% 15.32% 13.47% 11.67% 4.93% -10.13% 25.61%

Skandia Time Global 0.30% -43.79% 29.87% 13.77% 1.43% 7.62% 32.42% 20.95% 28.48% 17.52% 23.12%

Swedbank Robur Östeuropafond 15.57% -97.95% 50.56% 14.65% -16.04% 7.66% 2.27% -12.73% -4.89% 15.27% 14.09%

Lannebo Småbolag -7.16% -49.59% 47.11% 19.29% -13.41% 7.32% 31.70% 18.65% 31.19% 4.71% 15.77%

Swedbank Robur Småbolagsfond Sverige -5.28% -76.21% 52.44% 16.04% -11.54% 9.12% 33.09% 13.10% 36.08% 1.38% 13.24%

Handelsbanken Svenska Småbolagsfond -6.55% -68.61% 50.23% 19.87% -8.04% 9.41% 32.67% 19.49% 33.04% 5.37% 12.77%

Nordea Småbolagsfond Norden -2.26% -41.18% 41.54% 9.73% -4.71% 10.85% 25.58% 9.93% 25.40% 13.00% 19.34%

AM F Aktiefond Europa 10.95% -52.43% 19.08% -12.52% -14.58% 11.19% 22.05% 12.73% 13.50% -2.48% 16.52%

Öhman Hjärt-Lungfond -8.24% -29.23% 24.29% 6.04% -6.53% 12.98% 25.34% 21.39% 15.47% -1.59% 12.21%

AM F Aktiefond Småbolag -7.45% -74.96% 55.08% 19.87% -9.79% 5.21% 33.07% 11.51% 32.31% 7.76% 11.72%

Carnegie Rysslandsfond 9.84% -101.74% 75.02% 14.03% -11.73% -4.06% 6.91% -19.45% 10.41% 22.87% 2.73%

Swedbank Robur Rysslandsfond 21.27% -120.67% 73.66% 16.91% -13.25% -8.41% 7.89% -30.69% 7.60% 28.21% 7.96%

UBS Equity Fund - BioTech 8.60% -32.02% 31.37% 11.77% 4.02% 32.42% 42.46% 32.25% 4.58% -19.47% 20.06%

SEB Teknologifond 6.97% -37.14% 29.21% 8.57% 0.25% 7.58% 15.92% 24.39% 21.96% 8.94% 18.20%

Handelsbanken Tillväxtmarknad Tema 23.01% -63.52% 43.63% 16.31% -18.95% 5.88% 6.22% 14.98% -0.18% 10.86% 22.18%

Swedbank Robur Nordenfond 10.69% -70.57% 38.28% 7.41% -15.48% 9.58% 13.64% 11.27% 19.61% -5.33% 12.62%

Baring Hong Kong China Fund 61.23% -89.12% 55.76% 4.61% -18.62% -0.08% 16.15% -4.46% 0.24% -1.37% 31.25%

East Capital Ryssland 17.20% -105.44% 64.91% 25.07% -16.52% -5.91% 2.66% -39.98% 2.28% 32.62% 9.34%

Swedbank Robur Småbolagsfond Norden -1.80% -74.34% 49.96% 13.36% -18.69% 12.36% 19.58% 4.69% 27.49% 4.45% 13.95%

Öhman Global Growth 3.24% -43.71% 27.13% 2.40% 0.18% 5.05% 20.79% 25.71% 32.35% 5.85% 23.50%

Skandia USA -6.85% -31.86% 7.48% 5.94% 0.38% 11.54% 26.56% 27.34% 18.29% 10.52% 10.25%

Skandia Världen -1.73% -43.26% 18.10% 5.15% -7.71% 5.93% 17.78% 14.76% 12.57% 3.95% 14.33%

Länsförsäkringar Europa Aktiv 1.66% -52.81% 15.52% -7.84% -11.96% 5.86% 16.90% 14.86% 11.74% -0.18% 13.61%

Blackrock Global Funds 28.51% -68.39% 76.64% 15.32% -2.14% -17.77% -67.58% -5.85% -26.01% 33.01% 6.92%

Länsförsäkringar Sverige Aktiv -4.62% -56.81% 36.82% 20.01% -14.97% 11.48% 16.75% 12.88% 22.13% 1.15% 16.94%

Aktiespararna Topp Sverige -5.15% -61.82% 40.13% 16.16% -16.16% 10.31% 21.69% 13.97% 8.52% 0.55% 10.76%

Lannebo Vision -2.89% -50.67% 29.80% 7.45% -2.42% 10.68% 21.34% 30.44% 34.32% 6.31% 16.85%

Handelsbanken Global Tema 1.56% -37.94% 14.12% 7.46% -10.91% 8.46% 16.88% 16.87% 17.89% 3.29% 18.58%

KPA Etisk Aktiefond -3.31% -50.29% 31.42% 6.62% -10.03% 6.04% 22.97% 17.44% 13.89% 1.73% 12.88%

Länsförsäkringar Global Hållbar 2.21% -43.40% 21.57% 1.56% -12.23% 9.34% 16.64% 15.72% 11.83% 6.86% 11.13%

SPP Aktiefond Japan -11.19% -26.53% -6.57% 5.01% -17.03% -4.09% 25.09% 12.75% 22.71% 7.09% 11.36%

Appendix

67

9.5 Yearly risk measurements


2007 12.31% 0.4609 0.0030 0.28 10.99%

2008 22.70% 0.5807 0.0025 0.13 26.20%

2009 8.65% 0.0147 0.0171 2.14 5.94%

2010 16.63% 0.3740 0.0063 0.49 14.50%

2011 8.37% 0.1722 0.0014 0.21 10.04%

2012 11.05% 0.5187 0.0004 0.07 11.07%

2013 8.70% 0.2629 0.0092 1.05 7.06%

2014 7.65% 0.2760 0.0253 4.03 4.53%

2015 22.39% 1.3543 0.0188 2.00 15.80%

2016 17.91% 0.8935 0.0069 0.50 14.45%

2017 12.90% 1.4209 0.0001 0.01 7.60%

Average 13.57% 0.5753 0.0083 0.99 11.65%

Swedbank Robur Technology


2007 14.11% 0.8280 -0.0071 -0.66 13.74%

2008 25.30% 0.6894 -0.0140 -0.68 34.52%

2009 26.41% 0.5749 0.0290 1.59 12.79%

2010 17.79% 0.8129 0.0159 2.64 12.77%

2011 20.83% 0.8855 -0.0119 -1.18 21.98%

2012 17.70% 0.9081 0.0097 1.18 13.79%

2013 11.43% 0.6076 0.0103 1.00 7.18%

2014 10.90% 0.8131 0.0086 1.48 8.89%

2015 16.53% 0.8116 0.0145 1.42 13.32%

2016 14.45% 1.0430 0.0050 0.55 13.75%

2017 10.86% 0.9759 -0.0042 -0.29 8.87%

Average 16.94% 0.8136 0.0051 0.64 14.69%

Didner & Gerge Aktiefond


2007 16.94% 1.1860 -0.0121 -1.07 16.38%

2008 30.08% 0.9025 -0.0045 -0.21 38.43%

2009 27.79% 0.5866 0.0198 1.01 16.22%

2010 16.24% 0.7253 0.0124 2.01 12.27%

2011 20.50% 0.8070 -0.0116 -1.01 21.82%

2012 14.98% 0.6764 0.0020 0.23 13.14%

2013 10.14% 0.8479 0.0042 0.67 7.39%

2014 7.93% 0.5709 0.0090 2.00 7.37%

2015 17.09% 0.9129 0.0132 1.40 13.98%

2016 12.27% 1.0473 0.0001 0.01 13.49%

2017 10.59% 0.8318 -0.0028 -0.19 9.65%

Average 16.78% 0.8268 0.0027 0.44 15.47%

AM F Sver ige


2007 14.54% 1.0202 -0.0072 -0.74 14.23%

2008 24.86% 0.7627 -0.0030 -0.17 32.49%

2009 20.60% 0.4376 0.0132 0.91 12.84%

2010 14.76% 0.6540 0.0072 1.24 12.74%

2011 17.29% 0.7003 -0.0109 -1.19 19.37%

2012 11.45% 0.5290 0.0036 0.55 10.37%

2013 8.04% 0.6925 0.0061 1.29 6.26%

2014 7.19% 0.5498 0.0104 2.83 6.59%

2015 17.51% 0.9849 0.0143 1.62 14.38%

2016 11.28% 0.9117 0.0033 0.53 12.04%

2017 8.94% 0.9619 -0.0058 -0.50 8.57%

Average 14.22% 0.7459 0.0028 0.58 13.62%

AM F Vär lden


2007 12.43% 0.7475 -0.0032 -0.34 12.08%

2008 22.68% 0.7801 -0.0006 -0.05 30.62%

2009 19.09% 0.4675 0.0101 0.86 11.77%

2010 12.55% 0.5203 0.0077 1.29 10.70%

2011 12.76% 0.5091 -0.0072 -1.03 15.49%

2012 11.52% 0.5939 0.0007 0.13 11.17%

2013 7.15% 0.6258 0.0039 0.98 6.09%

2014 7.65% 0.5855 0.0104 2.66 6.97%

2015 18.70% 1.0606 0.0119 1.28 15.72%

2016 12.87% 1.0035 0.0029 0.39 13.22%

2017 8.75% 0.9322 -0.0017 -0.15 6.97%

Average 13.29% 0.7115 0.0032 0.55 12.80%

Swedbank Robur Aktiefond Pension


2007 10.15% 0.2586 -0.0027 -0.29 11.81%

2008 15.57% 0.2609 -0.0023 -0.15 18.29%

2009 12.27% 0.0482 0.0044 0.39 11.60%

2010 8.90% -0.0933 -0.0038 -0.49 11.83%

2011 8.48% -0.0488 0.0039 0.53 9.41%

2012 7.04% 0.0806 0.0099 1.61 6.76%

2013 9.85% 0.3930 0.0165 1.74 6.47%

2014 7.54% 0.1207 0.0239 3.62 4.60%

2015 21.08% 1.0469 0.0150 1.17 17.17%

2016 13.84% 0.6785 -0.0031 -0.29 14.67%

2017 10.87% 0.7237 -0.0122 -0.79 11.17%

Average 11.42% 0.3154 0.0045 0.64 11.25%

Swedbank Robur M edica


2007 14.19% 1.0486 -0.0086 -0.97 14.04%

2008 29.09% 1.0403 -0.0002 -0.01 38.58%

2009 27.77% 0.6672 0.0224 1.27 14.99%

2010 16.69% 0.7219 0.0129 1.81 12.29%

2011 18.81% 0.7428 -0.0156 -1.49 21.72%

2012 17.56% 0.8218 0.0003 0.03 16.03%

2013 9.96% 0.8150 0.0051 0.79 6.89%

2014 8.32% 0.6315 0.0087 2.02 7.55%

2015 17.24% 0.9563 0.0127 1.42 14.15%

2016 13.91% 1.1778 0.0035 0.49 14.03%

2017 11.27% 0.9970 -0.0021 -0.14 9.19%

Average 16.80% 0.8746 0.0036 0.47 15.40%

Swedbank Robur Sverigefond M ega


2007 15.61% 0.8856 -0.0107 -0.88 15.62%

2008 28.93% 0.9421 0.0004 0.02 37.04%

2009 24.41% 0.4251 0.0225 1.18 14.75%

2010 14.98% 0.6216 0.0124 1.73 11.55%

2011 18.21% 0.8001 -0.0112 -1.37 20.37%

2012 15.24% 0.7536 0.0025 0.32 13.51%

2013 10.14% 0.8368 0.0039 0.60 7.35%

2014 7.28% 0.5014 0.0097 2.22 6.78%

2015 17.26% 0.9223 0.0119 1.24 14.22%

2016 12.70% 1.0804 0.0005 0.07 13.72%

2017 10.30% 0.9236 -0.0031 -0.22 9.16%

Average 15.91% 0.7902 0.0035 0.45 14.92%

SPP Aktiefond Sver ige

Appendix

68


2007 14.58% 1.0501 -0.0079 -0.84 14.32%

2008 26.03% 0.8451 -0.0053 -0.32 35.14%

2009 22.63% 0.5972 0.0199 1.59 12.87%

2010 15.53% 0.6422 0.0127 1.69 11.72%

2011 16.21% 0.6097 -0.0114 -1.18 18.87%

2012 15.09% 0.7527 0.0027 0.36 13.14%

2013 9.86% 0.8705 0.0031 0.57 6.83%

2014 8.51% 0.6346 0.0085 1.86 7.85%

2015 17.82% 0.9251 0.0082 0.80 15.32%

2016 12.21% 0.9327 0.0007 0.09 13.27%

2017 10.45% 0.7242 -0.0024 -0.16 9.82%

Average 15.36% 0.7804 0.0026 0.41 14.47%

Avanza Zero


2007 10.96% 0.5376 -0.0001 -0.01 10.73%

2008 19.72% 0.6215 -0.0005 -0.04 26.18%

2009 12.63% 0.2873 0.0048 0.57 9.50%

2010 10.80% 0.2983 0.0011 0.14 11.19%

2011 10.18% 0.3432 -0.0047 -0.71 13.09%

2012 6.52% 0.3454 0.0053 1.93 7.14%

2013 7.60% 0.4597 0.0086 1.33 6.16%

2014 8.77% 0.6594 0.0113 2.43 7.38%

2015 18.87% 1.0960 0.0155 1.74 14.98%

2016 11.43% 0.6873 0.0018 0.22 12.19%

2017 10.37% 1.2743 -0.0064 -0.49 7.51%

Average 11.62% 0.6009 0.0033 0.65 11.46%

Swedbank Robur Globalfond M ega


2007 13.14% 0.8255 -0.0072 -0.75 13.51%

2008 17.57% 0.3884 -0.0045 -0.27 22.54%

2009 16.99% 0.2703 -0.0050 -0.36 16.78%

2010 11.37% 0.1277 0.0127 1.31 9.64%

2011 8.99% 0.2463 0.0015 0.22 10.58%

2012 10.24% 0.2894 0.0160 1.99 7.33%

2013 10.34% 0.3935 0.0145 1.44 6.48%

2014 10.27% 0.0831 0.0248 2.73 6.12%

2015 23.32% 1.0641 0.0127 0.83 20.27%

2016 17.93% 1.2233 -0.0144 -1.21 20.39%

2017 13.16% 0.2612 -0.0020 -0.10 12.45%

Average 13.94% 0.4702 0.0045 0.53 13.28%

CWor ldWide M edical


2007 19.31% 0.7115 -0.0167 -0.99 20.74%

2008 26.60% 0.8646 -0.0129 -0.76 37.70%

2009 28.68% 0.2196 0.0345 1.34 12.69%

2010 19.52% 0.6815 0.0233 1.92 10.75%

2011 18.65% 0.7502 -0.0053 -0.53 17.65%

2012 13.88% 0.4820 0.0079 0.78 10.95%

2013 12.33% 0.6987 0.0033 0.31 9.91%

2014 11.61% 0.4440 0.0213 2.27 6.99%

2015 21.33% 0.9241 0.0246 1.69 12.83%

2016 16.44% 1.1213 0.0086 0.80 14.05%

2017 12.16% 1.8316 -0.0214 -1.56 10.84%

Average 18.23% 0.7936 0.0061 0.48 15.01%

Länsförsäkr ingar Fastighetsfond


2007 10.58% 0.2592 -0.0011 -0.12 11.56%

2008 15.26% 0.1136 -0.0049 -0.29 16.57%

2009 12.01% -0.0398 0.0026 0.24 12.36%

2010 8.55% -0.1177 0.0011 0.15 10.41%

2011 8.17% -0.1063 0.0042 0.61 9.27%

2012 7.01% 0.0483 0.0126 2.04 6.40%

2013 9.91% 0.3992 0.0148 1.55 6.75%

2014 6.65% 0.1009 0.0273 4.68 3.54%

2015 21.01% 1.0082 0.0165 1.25 17.71%

2016 15.09% 0.8269 -0.0053 -0.47 15.92%

2017 11.39% 0.8029 -0.0055 -0.34 9.73%

Average 11.42% 0.2996 0.0057 0.84 10.93%

SEB Läkemedelsfond


2007 11.31% 0.6902 0.0043 0.51 9.43%

2008 18.16% 0.6870 -0.0052 -0.86 27.83%

2009 24.53% 0.7805 0.0016 0.27 16.97%

2010 11.96% 0.4219 0.0157 2.13 8.98%

2011 14.67% 0.6154 -0.0042 -0.57 15.61%

2012 12.19% 0.6405 0.0007 0.13 11.88%

2013 4.61% 0.1257 0.0223 4.81 2.31%

2014 8.67% 0.5886 0.0050 0.94 8.70%

2015 18.54% 0.9630 0.0168 1.57 13.99%

2016 13.81% 0.9214 -0.0034 -0.37 15.03%

2017 10.31% 0.5142 0.0082 0.56 8.42%

Average 13.52% 0.6317 0.0056 0.83 12.65%

Skagen Global


2007 11.54% 0.5444 0.0024 0.25 10.72%

2008 19.49% 0.6300 -0.0059 -0.47 27.99%

2009 16.63% 0.4001 0.0028 0.27 12.70%

2010 15.05% 0.5410 -0.0070 -0.78 15.36%

2011 15.68% 0.7396 -0.0075 -1.41 17.85%

2012 13.06% 0.5171 -0.0031 -0.36 13.55%

2013 8.99% 0.6553 0.0050 0.75 7.20%

2014 7.83% 0.6165 0.0066 1.76 7.86%

2015 19.57% 1.1170 0.0103 1.08 16.52%

2016 11.44% 0.7516 -0.0023 -0.29 13.16%

2017 9.74% 1.1257 -0.0047 -0.37 8.45%

Average 13.55% 0.6944 -0.0003 0.04 13.76%

SPP Aktiefond Europa


2007 12.58% 0.5593 -0.0076 -0.72 13.53%

2008 19.03% 0.4726 0.0014 0.08 23.00%

2009 12.67% 0.0362 0.0049 0.42 10.95%

2010 14.13% 0.2309 0.0067 0.57 12.64%

2011 10.19% 0.3095 0.0002 0.02 11.60%

2012 4.94% 0.1124 0.0083 2.03 5.79%

2013 10.27% 0.4177 0.0132 1.33 7.37%

2014 7.25% 0.3849 0.0193 3.64 4.57%

2015 18.87% 1.0676 0.0172 1.82 14.45%

2016 13.72% 0.8007 0.0104 1.05 11.66%

2017 11.32% 1.2291 -0.0117 -0.79 9.57%

Average 12.27% 0.5110 0.0056 0.86 11.38%

SPP Aktiefond USA

Appendix

69


2007 14.75% 0.9478 0.0123 1.16 9.00%

2008 25.21% 0.8972 0.0016 0.13 33.29%

2009 25.08% 0.7563 0.0233 2.50 11.62%

2010 13.09% 0.4714 0.0224 2.84 8.71%

2011 19.17% 0.7435 -0.0119 -1.08 20.34%

2012 13.63% 0.6557 -0.0038 -0.53 13.20%

2013 6.95% 0.1708 0.0137 1.95 5.33%

2014 13.74% 0.7040 0.0014 0.14 12.62%

2015 17.38% 0.9218 0.0059 0.61 15.83%

2016 15.85% 1.2270 -0.0019 -0.21 16.07%

2017 10.34% 0.6955 0.0101 0.69 6.22%

Average 15.93% 0.7446 0.0067 0.75 13.84%

Skagen Kon-Tiki


2007 19.23% 0.8558 -0.0042 -0.26 17.28%

2008 28.21% 0.9937 -0.0056 -0.39 38.74%

2009 15.54% 0.2979 0.0165 1.41 8.97%

2010 19.03% 0.7953 0.0072 0.80 15.13%

2011 18.52% 0.7958 -0.0159 -1.81 21.77%

2012 14.61% 0.7472 0.0085 1.25 11.67%

2013 7.28% 0.4583 0.0194 3.20 3.31%

2014 11.24% 0.8335 0.0026 0.43 10.82%

2015 15.65% 0.8500 0.0200 2.37 12.30%

2016 18.08% 1.2552 -0.0043 -0.36 18.35%

2017 10.10% 1.2875 -0.0030 -0.24 7.37%

Average 16.14% 0.8336 0.0037 0.58 15.06%

Swedbank Robur Småbolagsfond Europa


2007 10.43% 0.6907 0.0039 0.53 9.54%

2008 19.73% 0.6687 -0.0070 -0.61 28.77%

2009 18.35% 0.5007 0.0020 0.21 13.70%

2010 14.49% 0.5532 -0.0084 -1.04 15.09%

2011 13.81% 0.5609 -0.0092 -1.25 16.87%

2012 12.24% 0.6854 0.0024 0.60 11.53%

2013 9.34% 0.6323 0.0047 0.64 7.47%

2014 7.96% 0.6518 0.0063 1.85 7.94%

2015 18.15% 1.0769 0.0117 1.44 15.44%

2016 10.90% 0.6084 -0.0024 -0.30 12.73%

2017 9.36% 1.0187 -0.0010 -0.08 7.74%

Average 13.16% 0.6952 0.0003 0.18 13.35%

Swedbank Robur Europafond M ega


2007 11.78% 0.9719 0.0153 2.52 7.84%

2008 23.21% 0.8865 -0.0039 -0.55 33.52%

2009 23.28% 0.7423 0.0009 0.16 17.82%

2010 18.79% 0.8668 0.0005 0.08 16.42%

2011 21.56% 1.0002 0.0008 0.11 18.80%

2012 15.57% 0.8842 0.0051 1.12 13.47%

2013 8.74% 0.8418 -0.0035 -1.03 8.23%

2014 9.19% 0.7800 0.0055 1.61 8.80%

2015 14.58% 0.9088 0.0050 0.91 13.60%

2016 11.32% 0.9809 -0.0084 -1.52 14.37%

2017 4.36% 0.5688 0.0123 2.29 2.55%

Average 14.76% 0.8575 0.0027 0.52 14.13%

CWorldWide Global Equities


2007 15.28% 0.7207 -0.0048 -0.38 15.08%

2008 28.35% 0.8224 0.0086 0.40 32.33%

2009 12.07% -0.0146 0.0253 2.27 5.63%

2010 15.18% 0.4153 0.0102 0.92 11.67%

2011 10.53% 0.3698 0.0020 0.30 11.13%

2012 9.50% 0.4078 0.0028 0.48 9.52%

2013 8.60% 0.1611 0.0242 2.74 3.76%

2014 5.00% 0.3767 0.0154 5.86 4.31%

2015 23.46% 1.4005 0.0251 2.44 15.32%

2016 17.60% 1.0518 0.0147 1.16 12.62%

2017 12.21% 1.4909 -0.0045 -0.30 8.12%

Average 14.34% 0.6548 0.0108 1.44 11.77%

Skandia Time Global


2007 14.32% 0.4148 0.0112 0.87 9.76%

2008 40.38% 1.4888 0.0001 0.00 51.92%

2009 34.27% 0.7911 0.0194 0.86 19.67%

2010 19.11% 0.6387 0.0102 0.83 14.03%

2011 23.25% 0.9056 -0.0113 -0.86 24.18%

2012 25.81% 1.2885 -0.0048 -0.37 19.99%

2013 12.63% 0.9383 -0.0145 -1.58 12.09%

2014 24.81% 0.5505 -0.0136 -0.63 21.80%

2015 20.53% 1.0535 -0.0031 -0.26 18.81%

2016 15.84% 0.9269 0.0128 1.12 14.11%

2017 8.78% 0.6639 0.0011 0.09 7.52%

Average 21.79% 0.8782 0.0007 0.01 19.44%

Swedbank Robur Östeuropafond


2007 13.93% 0.8668 -0.0127 -1.24 15.21%

2008 24.76% 0.8402 0.0048 0.33 31.49%

2009 22.08% 0.3664 0.0287 1.63 6.80%

2010 15.96% 0.7554 0.0137 3.29 11.21%

2011 20.90% 0.8769 -0.0092 -0.88 21.13%

2012 13.61% 0.6469 0.0005 0.07 12.53%

2013 11.69% 0.4629 0.0183 1.62 6.29%

2014 11.00% 0.7868 0.0113 1.80 9.05%

2015 14.55% 0.7495 0.0267 3.16 9.95%

2016 12.51% 1.0077 0.0040 0.58 12.63%

2017 10.60% 1.3729 -0.0088 -0.67 8.77%

Average 15.60% 0.7938 0.0070 0.88 13.19%

Lannebo Småbolag


2007 15.39% 0.9286 -0.0114 -0.99 15.70%

2008 31.33% 1.1155 -0.0023 -0.15 41.59%

2009 26.89% 0.6273 0.0257 1.46 12.76%

2010 16.56% 0.7037 0.0112 1.50 12.09%

2011 20.42% 0.8231 -0.0078 -0.71 20.33%

2012 14.15% 0.5826 0.0025 0.28 12.41%

2013 9.69% 0.5113 0.0186 2.14 4.74%

2014 12.06% 0.8877 0.0061 0.93 10.68%

2015 15.55% 0.7007 0.0307 2.98 9.60%

2016 15.56% 1.0614 0.0012 0.12 15.37%

2017 11.32% 1.5019 -0.0129 -0.94 9.62%

Average 17.17% 0.8585 0.0056 0.60 14.99%

Swedbank Robur Småbolagsfond Sverige

Appendix

70


2007 15.02% 0.9073 -0.0124 -1.10 15.88%

2008 27.26% 0.9256 -0.0064 -0.41 37.28%

2009 25.21% 0.3716 0.0312 1.50 9.63%

2010 18.65% 0.8083 0.0140 1.77 12.95%

2011 22.13% 0.8853 -0.0047 -0.39 20.02%

2012 15.22% 0.6606 0.0021 0.23 13.53%

2013 10.54% 0.6143 0.0165 1.81 5.45%

2014 10.98% 0.7185 0.0124 1.77 8.77%

2015 17.77% 1.0081 0.0285 3.23 11.26%

2016 14.60% 1.2010 0.0046 0.58 14.24%

2017 11.22% 1.5277 -0.0137 -1.02 9.81%

Average 17.15% 0.8753 0.0065 0.72 14.44%

Handelsbanken Svenska Småbolagsfond


2007 12.43% 0.7998 -0.0077 -0.87 13.37%

2008 19.47% 0.6731 0.0026 0.25 26.45%

2009 17.27% 0.0836 0.0322 2.04 8.20%

2010 15.26% 0.6655 0.0060 0.95 13.63%

2011 19.25% 0.8226 -0.0021 -0.23 18.32%

2012 12.34% 0.5771 0.0040 0.58 10.10%

2013 8.03% 0.5546 0.0116 1.85 5.19%

2014 9.97% 0.7071 0.0045 0.77 9.45%

2015 15.27% 0.7834 0.0219 2.46 11.28%

2016 11.84% 0.9558 0.0109 1.66 10.27%

2017 10.13% 1.2465 -0.0038 -0.30 7.96%

Average 13.75% 0.7154 0.0073 0.83 12.20%

Nordea Småbolagsfond Norden


2007 12.65% 0.9045 0.0035 0.43 10.75%

2008 22.85% 0.7043 -0.0050 -0.32 30.93%

2009 25.00% 0.6062 -0.0015 -0.10 19.41%

2010 18.90% 0.7559 -0.0128 -1.31 19.56%

2011 19.09% 0.8266 -0.0103 -1.16 21.08%

2012 15.91% 0.7647 0.0027 0.32 13.85%

2013 9.43% 0.6499 0.0070 0.95 7.04%

2014 7.47% 0.5391 0.0077 1.82 7.30%

2015 19.03% 1.0707 0.0123 1.27 15.97%

2016 11.94% 0.7132 -0.0020 -0.24 13.46%

2017 9.83% 1.2857 -0.0068 -0.56 8.40%

Average 15.65% 0.8019 -0.0005 0.10 15.25%

AM F Aktiefond Europa


2007 11.53% 0.7212 -0.0126 -1.50 14.28%

2008 16.91% 0.3832 -0.0033 -0.22 22.10%

2009 11.43% 0.3006 0.0116 1.83 7.63%

2010 8.27% 0.2916 0.0041 0.81 9.22%

2011 11.48% 0.3209 -0.0047 -0.56 13.99%

2012 7.95% 0.3522 0.0078 1.62 7.70%

2013 7.89% 0.6207 0.0103 1.91 5.27%

2014 6.79% 0.5048 0.0151 4.12 5.25%

2015 19.47% 0.9736 0.0138 1.18 15.97%

2016 12.20% 0.8575 -0.0013 -0.16 13.27%

2017 10.21% 1.1618 -0.0084 -0.64 8.79%

Average 11.28% 0.5898 0.0029 0.76 11.23%

Öhman Hjär t-Lungfond


2007 17.33% 1.1182 -0.0147 -1.19 17.66%

2008 30.84% 1.0335 -0.0058 -0.31 41.02%

2009 28.26% 0.6331 0.0277 1.45 12.09%

2010 19.10% 0.8296 0.0140 1.73 13.15%

2011 22.23% 0.8754 -0.0062 -0.50 20.90%

2012 17.08% 0.6873 -0.0016 -0.14 15.07%

2013 8.87% 0.4344 0.0200 2.44 4.80%

2014 10.71% 0.6848 0.0059 0.85 9.90%

2015 17.93% 1.0343 0.0279 3.24 11.47%

2016 14.47% 1.2204 0.0066 0.88 13.78%

2017 11.26% 1.4061 -0.0127 -0.90 10.00%

Average 18.01% 0.9052 0.0056 0.69 15.44%

AM F Aktiefond Småbolag


2007 10.08% -0.0455 0.0093 0.97 8.48%

2008 43.58% 1.4616 -0.0046 -0.18 54.38%

2009 38.18% 0.4484 0.0496 1.51 18.19%

2010 20.53% 0.5672 0.0099 0.66 14.78%

2011 29.38% 1.1058 -0.0073 -0.42 27.99%

2012 24.32% 1.2491 -0.0142 -1.26 21.85%

2013 10.88% 0.6785 -0.0061 -0.67 10.69%

2014 27.20% 0.5120 -0.0190 -0.80 25.42%

2015 27.93% 1.2574 0.0099 0.53 20.50%

2016 16.70% 0.9875 0.0191 1.59 13.75%

2017 13.00% 0.2825 -0.0022 -0.12 12.93%

Average 23.80% 0.7731 0.0040 0.17 20.81%

Carnegie Rysslandsfond


2007 12.64% 0.0226 0.0192 1.60 7.77%

2008 49.98% 1.7112 -0.0067 -0.24 62.87%

2009 40.50% 0.5191 0.0465 1.35 20.42%

2010 19.86% 0.5621 0.0123 0.86 13.60%

2011 31.39% 1.2302 -0.0083 -0.47 30.27%

2012 27.74% 1.4078 -0.0192 -1.45 24.92%

2013 11.95% 0.8039 -0.0075 -0.79 10.87%

2014 31.73% 0.6565 -0.0291 -1.06 30.40%

2015 31.64% 1.3753 0.0077 0.36 23.49%

2016 20.20% 1.1683 0.0236 1.61 16.08%

2017 13.85% 0.2104 0.0033 0.16 12.02%

Average 26.50% 0.8789 0.0038 0.18 22.97%

Swedbank Robur Rysslandsfond


2007 15.93% 1.2444 -0.0010 -0.11 13.48%

2008 27.18% 0.6656 0.0098 0.41 28.83%

2009 30.14% 0.7022 0.0060 0.30 20.82%

2010 24.48% 1.0587 0.0065 0.62 20.17%

2011 22.38% 0.9288 0.0054 0.47 18.33%

2012 20.86% 0.8181 0.0199 1.42 13.50%

2013 20.16% 1.2963 0.0127 0.77 11.45%

2014 25.68% 0.4359 0.0245 1.09 16.46%

2015 25.33% 1.2800 0.0050 0.33 21.61%

2016 31.74% 2.8299 -0.0160 -1.11 31.24%

2017 17.40% -0.0635 0.0177 0.69 12.87%

Average 23.75% 1.0179 0.0082 0.44 18.98%

UBS Equity Fund - BioTech

Appendix

71


2007 14.90% 0.6531 0.0017 0.13 13.04%

2008 27.29% 0.6806 0.0064 0.27 29.39%

2009 13.21% -0.0534 0.0259 2.14 7.15%

2010 16.65% 0.3221 0.0061 0.46 14.32%

2011 9.14% 0.2118 0.0007 0.10 10.55%

2012 10.23% 0.4550 0.0024 0.39 10.02%

2013 8.74% 0.1874 0.0100 1.12 7.67%

2014 6.72% 0.2616 0.0189 3.48 4.76%

2015 20.34% 1.2672 0.0195 2.55 14.52%

2016 17.25% 0.8238 0.0075 0.56 14.25%

2017 12.91% 1.2941 -0.0055 -0.32 8.80%

Average 14.31% 0.5548 0.0085 0.99 12.22%

SEB Teknologifond


2007 14.97% 0.7788 0.0154 1.28 10.73%

2008 26.01% 0.8330 -0.0072 -0.42 35.12%

2009 16.51% 0.3806 0.0254 2.34 6.87%

2010 12.51% 0.3349 0.0125 1.36 8.84%

2011 19.06% 0.6999 -0.0142 -1.23 21.29%

2012 14.16% 0.4127 0.0013 0.12 11.37%

2013 10.43% 0.5124 -0.0038 -0.39 10.46%

2014 12.50% 0.6615 0.0089 0.97 10.86%

2015 19.94% 0.9949 0.0008 0.07 18.63%

2016 13.94% 0.7227 0.0091 0.86 12.37%

2017 7.46% 0.4801 0.0108 1.02 5.07%

Average 15.23% 0.6192 0.0054 0.54 13.78%

Handelsbanken Tillväxtmarknad Tema


2007 12.82% 1.0114 0.0025 0.35 10.85%

2008 28.73% 1.0299 -0.0023 -0.17 38.72%

2009 22.18% 0.4894 0.0178 1.17 11.83%

2010 15.00% 0.6464 0.0042 0.64 13.59%

2011 17.52% 0.7474 -0.0112 -1.33 19.70%

2012 16.32% 0.9104 0.0001 0.02 14.77%

2013 10.72% 0.7826 -0.0023 -0.29 8.64%

2014 8.13% 0.5901 0.0062 1.36 7.74%

2015 15.52% 0.7992 0.0171 1.90 12.68%

2016 13.97% 1.0774 -0.0044 -0.53 15.31%

2017 9.11% 0.9175 -0.0041 -0.34 8.34%

Average 15.46% 0.8183 0.0021 0.25 14.74%

Swedbank Robur Nordenfond


2007 28.24% 1.0953 0.0479 1.95 17.02%

2008 36.27% 0.7167 -0.0349 -1.00 46.92%

2009 30.88% 0.8413 0.0223 1.39 17.98%

2010 18.79% 0.7977 0.0014 0.16 16.71%

2011 31.51% 1.3343 -0.0125 -0.81 29.71%

2012 18.42% 0.6775 -0.0059 -0.46 17.14%

2013 15.45% 0.9194 -0.0026 -0.20 13.04%

2014 14.29% 0.5116 -0.0065 -0.55 15.24%

2015 28.32% 1.3041 0.0014 0.08 24.37%

2016 19.34% 1.5885 -0.0010 -0.10 18.21%

2017 10.98% -0.0755 0.0272 1.69 7.58%

Average 22.95% 0.8828 0.0033 0.20 20.36%

Baring Hong Kong China Fund


2007 10.80% -0.1406 0.0166 1.64 7.87%

2008 46.80% 1.6082 0.0004 0.01 57.68%

2009 40.75% 0.3916 0.0428 1.19 21.75%

2010 20.85% 0.6545 0.0189 1.33 14.03%

2011 31.53% 1.2642 -0.0109 -0.64 30.53%

2012 31.72% 1.7020 -0.0197 -1.54 28.07%

2013 12.25% 0.9311 -0.0141 -1.62 12.31%

2014 34.16% 0.6041 -0.0366 -1.23 33.84%

2015 34.14% 1.4199 0.0033 0.14 26.34%

2016 20.77% 1.2989 0.0273 1.88 16.76%

2017 14.81% 0.1737 0.0050 0.23 11.80%

Average 27.14% 0.9007 0.0030 0.13 23.73%

East Capital Ryssland


2007 14.73% 0.9365 -0.0083 -0.78 14.80%

2008 30.18% 1.0654 -0.0035 -0.23 40.49%

2009 22.06% 0.4056 0.0300 1.77 9.82%

2010 18.64% 0.8122 0.0086 1.10 16.01%

2011 22.92% 0.9733 -0.0134 -1.20 23.81%

2012 18.10% 0.9474 0.0021 0.26 14.69%

2013 10.54% 0.6252 0.0054 0.60 7.80%

2014 12.52% 0.9575 -0.0013 -0.20 12.17%

2015 13.45% 0.6079 0.0235 2.64 10.21%

2016 13.08% 0.8835 0.0038 0.43 13.09%

2017 10.79% 1.3894 -0.0106 -0.79 9.26%

Average 17.00% 0.8731 0.0033 0.33 15.65%

Swedbank Robur Småbolagsfond Norden


2007 14.98% 0.9880 -0.0041 -0.39 13.80%

2008 21.05% 0.6841 0.0011 0.08 27.65%

2009 17.14% -0.0184 0.0231 1.46 8.31%

2010 15.91% 0.4875 0.0005 0.04 15.83%

2011 13.82% 0.5731 0.0014 0.20 13.72%

2012 10.49% 0.3882 0.0008 0.11 10.69%

2013 5.45% 0.2342 0.0132 2.55 4.24%

2014 12.15% 0.6099 0.0181 1.99 7.20%

2015 18.87% 1.1276 0.0280 3.40 13.67%

2016 14.55% 0.8984 0.0049 0.48 13.54%

2017 11.16% 1.5921 -0.0058 -0.45 7.27%

Average 14.14% 0.6877 0.0074 0.86 12.36%

Öhman Global Growth


2007 12.31% 0.6937 -0.0112 -1.17 14.32%

2008 19.40% 0.5205 0.0020 0.13 23.89%

2009 10.74% 0.0364 0.0052 0.53 10.05%

2010 12.65% 0.2645 0.0041 0.41 12.31%

2011 10.29% 0.2647 0.0009 0.12 11.35%

2012 5.74% 0.1995 0.0079 1.89 6.43%

2013 10.48% 0.4883 0.0136 1.39 7.09%

2014 6.69% 0.3974 0.0206 4.49 3.94%

2015 19.22% 1.0491 0.0162 1.58 14.56%

2016 15.47% 0.9879 0.0088 0.83 13.28%

2017 11.50% 1.1482 -0.0098 -0.64 9.41%

Average 12.23% 0.5500 0.0053 0.87 11.51%

Skandia USA

Appendix

72


2007 11.73% 0.6495 -0.0062 -0.67 12.94%

2008 19.66% 0.6253 -0.0017 -0.13 26.81%

2009 11.92% 0.2246 0.0086 0.96 7.98%

2010 10.75% 0.3591 0.0032 0.46 10.65%

2011 11.89% 0.4782 -0.0054 -0.83 14.16%

2012 9.59% 0.4682 0.0009 0.18 9.81%

2013 6.82% 0.4888 0.0063 1.22 5.70%

2014 9.48% 0.5983 0.0091 1.46 8.29%

2015 17.80% 1.0555 0.0115 1.44 14.86%

2016 12.02% 0.7765 0.0033 0.41 11.74%

2017 10.27% 1.2141 -0.0074 -0.57 7.97%

Average 11.99% 0.6307 0.0020 0.35 11.90%

Skandia Vär ldenSt.Dev Beta Alpha Alpha Significance Downside.Dev

2007 11.09% 0.6391 -0.0030 -0.36 11.59%

2008 17.80% 0.5665 -0.0129 -1.09 27.97%

2009 17.24% 0.2991 0.0043 0.32 13.52%

2010 13.25% 0.5521 -0.0083 -1.31 15.00%

2011 14.64% 0.6388 -0.0085 -1.28 17.00%

2012 11.51% 0.4654 0.0008 0.11 11.72%

2013 8.43% 0.5039 0.0053 0.73 6.91%

2014 8.27% 0.6012 0.0091 1.97 7.61%

2015 18.11% 0.8916 0.0106 0.96 14.93%

2016 8.68% 0.5351 -0.0001 -0.02 10.80%

2017 10.77% 1.2323 -0.0083 -0.60 9.50%

Average 12.71% 0.6296 -0.0010 -0.05 13.32%

Länsförsäkr ingar Europa Aktiv


2007 28.15% 1.6997 0.0139 0.66 11.93%

2008 53.94% 0.8302 -0.0114 -0.21 56.68%

2009 43.67% 0.8060 0.0407 1.22 17.30%

2010 21.79% 0.3093 0.0118 0.65 18.24%

2011 29.50% 0.6519 -0.0003 -0.01 26.79%

2012 29.67% 0.9621 -0.0231 -1.04 26.40%

2013 34.12% 0.6137 -0.0671 -1.91 40.57%

2014 34.80% 0.9089 -0.0098 -0.33 29.45%

2015 27.60% 0.2304 -0.0215 -0.90 28.21%

2016 51.13% 0.5000 0.0275 0.62 32.09%

2017 18.22% -0.3534 0.0114 0.43 14.28%

Average 33.87% 0.6508 -0.0025 -0.08 27.45%

Blackrock Global Funds


2007 14.66% 1.1302 -0.0122 -1.42 15.42%

2008 24.04% 0.7829 -0.0044 -0.29 32.77%

2009 22.14% 0.3732 0.0200 1.14 13.13%

2010 14.88% 0.6555 0.0146 2.45 11.33%

2011 17.94% 0.7552 -0.0108 -1.21 19.77%

2012 16.53% 0.8005 0.0026 0.30 14.58%

2013 9.92% 0.7657 0.0006 0.08 8.10%

2014 8.46% 0.5648 0.0077 1.46 7.93%

2015 16.39% 0.8774 0.0193 2.13 12.15%

2016 12.26% 1.0746 0.0010 0.18 13.29%

2017 10.76% 0.9466 -0.0010 -0.07 8.76%

Average 15.27% 0.7933 0.0034 0.43 14.29%

Länsförsäkr ingar Sverige Aktiv


2007 15.85% 1.1623 -0.0129 -1.29 16.32%

2008 26.06% 0.8428 -0.0053 -0.31 35.12%

2009 23.80% 0.6721 0.0141 1.25 14.39%

2010 15.84% 0.6403 0.0115 1.43 12.16%

2011 16.29% 0.6047 -0.0121 -1.24 19.11%

2012 15.23% 0.7478 0.0021 0.27 13.35%

2013 9.87% 0.8696 0.0029 0.53 6.93%

2014 8.53% 0.6345 0.0082 1.79 7.92%

2015 17.82% 0.9265 0.0080 0.78 15.36%

2016 12.22% 0.9347 0.0005 0.07 13.31%

2017 10.46% 0.7401 -0.0028 -0.19 9.86%

Average 15.63% 0.7978 0.0013 0.28 14.89%

Aktiespararna Topp Sverige


2007 14.74% 0.7554 -0.0080 -0.67 15.20%

2008 30.56% 0.6980 -0.0039 -0.14 35.29%

2009 12.92% 0.1535 0.0204 1.84 4.56%

2010 14.02% 0.4603 0.0048 0.52 13.36%

2011 11.30% 0.3322 -0.0013 -0.16 12.59%

2012 10.29% 0.4269 0.0052 0.78 9.44%

2013 8.27% 0.2516 0.0134 1.62 6.25%

2014 7.01% 0.2739 0.0239 4.22 3.95%

2015 17.50% 1.0082 0.0296 3.51 11.69%

2016 15.10% 0.6875 0.0053 0.45 13.47%

2017 11.98% 1.1866 -0.0049 -0.31 8.35%

Average 13.97% 0.5667 0.0077 1.06 12.20%

Lannebo Vision


2007 11.76% 0.7469 -0.0039 -0.46 11.74%

2008 18.98% 0.5667 -0.0005 -0.04 25.32%

2009 13.29% 0.2551 0.0044 0.44 10.08%

2010 11.11% 0.3517 0.0051 0.68 10.39%

2011 12.83% 0.4788 -0.0080 -1.04 15.48%

2012 8.56% 0.3987 0.0036 0.74 8.26%

2013 7.11% 0.4960 0.0054 0.98 6.20%

2014 6.69% 0.4489 0.0116 2.81 6.04%

2015 18.23% 1.0360 0.0159 1.76 14.90%

2016 11.77% 0.7032 0.0028 0.33 11.61%

2017 8.94% 0.9567 0.0002 0.02 6.22%

Average 11.75% 0.5854 0.0033 0.57 11.48%

Handelsbanken Global Tema


2007 10.63% 0.7944 -0.0086 -1.32 12.60%

2008 20.58% 0.6189 -0.0079 -0.53 28.81%

2009 17.92% 0.4501 0.0133 1.24 11.10%

2010 11.55% 0.4979 0.0040 0.79 10.79%

2011 11.83% 0.4771 -0.0073 -1.15 14.71%

2012 10.09% 0.5366 0.0004 0.09 10.42%

2013 7.49% 0.6509 0.0078 1.81 5.69%

2014 6.81% 0.5242 0.0117 3.40 6.08%

2015 17.25% 0.9666 0.0125 1.42 14.37%

2016 11.73% 0.8770 0.0015 0.21 12.72%

2017 8.76% 1.0197 -0.0055 -0.49 8.06%

Average 12.24% 0.6740 0.0020 0.50 12.31%

KPA Etisk Aktiefond

Appendix

73

9.6 Yearly risk measurement ranking

St.Dev Beta Downside.Dev

AP7 Aktiefond 26 4 32Swedbank Robur Technology 36 43 44Didner & Gerge Aktiefond 13 16 19AM F Sver ige 15 14 9AM F Vär lden 31 25 28Swedbank Robur Aktiefond Pension 39 28 34Swedbank Robur M edica 50 49 49Swedbank Robur Sver igefond M ega 14 9 11SPP Aktiefond Sverige 18 22 16Avanza Zero 23 23 20Swedbank Robur Globalfond M ega 48 40 47CWor ldWide M edical 34 48 31Länsförsäkr ingar fastighetsfond 8 20 14SEB Läkemedelsfond 49 51 51Skagen Global 38 36 35SPP Aktiefond Europa 37 30 27SPP Aktiefond USA 42 47 48SKAGEN Kon-Tiki 17 26 25Swedbank Robur Småbolagsfond Europa 16 13 13Swedbank Robur Europafond M ega 40 29 29C Wor ldwide Global Equities 27 12 24Skandia Time Global 29 34 43Swedbank Robur Östeuropafond 7 7 6Lannebo Småbolag 21 19 33Swedbank Robur Småbolagsfond Sverige 10 11 15Handelsbanken Svenska Småbolagsfond 11 8 21Nordea Småbolagsfond Norden 35 27 39AM F Aktiefond Europa 19 17 12Öhman Hjär t-Lungfond 51 41 50AM F Aktiefond Småbolag 9 2 10Carnegie Rysslandsfond 4 24 4Swedbank Robur Rysslandsfond 3 6 3UBS Equity Fund - BioTech 5 1 7SEB Teknologifond 30 45 38Handelsbanken Tillväxtmarknad Tema 25 39 26Swedbank Robur Nordenfond 22 15 18Bar ing Hong Kong China Fund 6 5 5East Capital Ryssland 2 3 2Swedbank Robur Småbolagsfond Norden 12 10 8Öhman Global Growth 32 31 36Skandia USA 44 46 45Skandia Vär lden 45 37 42Länsförsäkr ingar Europa Aktiv 41 38 30Blackrock Global Funds 1 35 1Länsförsäkr ingar Sver ige Aktiv 24 21 22Aktiespararna Topp Sver ige 20 18 17Lannebo Vision 33 44 40Handelsbanken Global Tema 47 42 46KPA Etisk Aktiefond 43 32 37Länsförsäkr ingar Global Hållbar 46 33 41SPP Aktiefond Japan 28 50 23


2007 10.70% 0.7403 -0.0032 -0.45 11.19%

2008 18.44% 0.5737 -0.0047 -0.37 26.20%

2009 13.23% 0.1818 0.0128 1.15 7.99%

2010 11.91% 0.4833 -0.0002 -0.03 12.67%

2011 12.89% 0.5541 -0.0090 -1.46 15.68%

2012 10.70% 0.5083 0.0034 0.58 10.30%

2013 7.49% 0.6052 0.0033 0.67 6.64%

2014 6.72% 0.5132 0.0103 2.99 6.34%

2015 16.84% 0.9684 0.0108 1.33 14.66%

2016 12.84% 1.0032 0.0058 0.78 12.45%

2017 10.02% 1.2302 -0.0104 -0.82 8.70%

Average 11.98% 0.6692 0.0017 0.40 12.07%

Länsförsäkr ingar Global HållbarSt.Dev Beta Alpha Alpha Significance Downside.Dev

2007 13.06% -0.2063 -0.0087 -0.71 15.57%

2008 18.32% 0.3070 -0.0053 -0.28 21.56%

2009 14.06% 0.1248 -0.0091 -0.72 15.81%

2010 13.69% -0.1299 0.0046 0.39 12.61%

2011 19.21% 0.1293 -0.0139 -0.83 21.55%

2012 10.55% 0.3211 -0.0062 -0.76 12.91%

2013 14.42% 0.1015 0.0191 1.27 10.61%

2014 10.41% 0.2411 0.0093 1.04 8.51%

2015 18.79% 0.9367 0.0198 1.75 15.10%

2016 16.03% 0.5017 0.0059 0.44 13.64%

2017 11.22% 1.1327 -0.0086 -0.58 8.83%

Average 14.52% 0.3145 0.0006 0.09 14.25%

SPP Aktiefond Japan

Appendix

74

9.7 Yearly risk-adjusted return measurements


2007 0.2256 0.0602 0.2525

2008 -1.7339 -0.6730 -1.5021

2009 1.9476 12.1022 2.8375

2010 0.2897 0.1631 0.3322

2011 -0.3512 -0.0791 -0.2926

2012 0.1574 0.0832 0.1572

2013 1.4209 0.5480 1.7513

2014 3.6558 1.1018 6.1760

2015 0.7519 0.1498 1.0655

2016 0.2280 0.0864 0.2825

2017 1.7967 0.1878 3.0513

Average 0.7626 1.2482 1.2828

Swedbank Robur Technology


2007 -0.3903 -0.0665 -0.4010

2008 -2.6209 -0.9582 -1.9214

2009 1.9116 0.8940 3.9461

2010 1.0125 0.2374 1.4110

2011 -1.0020 -0.2179 -0.9497

2012 0.9550 0.2145 1.2259

2013 1.8334 0.3785 2.9172

2014 1.0511 0.1711 1.2888

2015 0.7404 0.1933 0.9191

2016 0.1217 0.0517 0.1279

2017 0.8693 0.1327 1.0644

Average 0.4074 0.0937 0.8753

Didner & Gerge Aktiefond


2007 -0.4863 -0.0695 -0.5032

2008 -2.2931 -0.7614 -1.7951

2009 1.4346 0.6953 2.4583

2010 0.8266 0.2028 1.0941

2011 -0.9889 -0.2317 -0.9291

2012 0.3533 0.1163 0.4025

2013 1.8451 0.2449 2.5314

2014 1.3010 0.2236 1.3990

2015 0.6240 0.1546 0.7629

2016 -0.3379 -0.0049 -0.3072

2017 0.7900 0.1428 0.8668

Average 0.2790 0.0648 0.5437

AM F Sver ige


2007 -0.2795 -0.0398 -0.2857

2008 -2.3304 -0.7560 -1.7831

2009 1.2973 0.6317 2.0820

2010 0.4703 0.1258 0.5451

2011 -1.1098 -0.2514 -0.9906

2012 0.4928 0.1553 0.5439

2013 2.1944 0.2844 2.8193

2014 1.6579 0.2614 1.8100

2015 0.6804 0.1560 0.8289

2016 -0.0264 0.0366 -0.0247

2017 0.8125 0.1120 0.8476

Average 0.3509 0.0651 0.5812

AM F Vär lden


2007 -0.1484 -0.0247 -0.1526

2008 -2.4794 -0.7175 -1.8367

2009 1.2630 0.5351 2.0472

2010 0.5637 0.1606 0.6611

2011 -1.1099 -0.2473 -0.9143

2012 0.2445 0.0907 0.2522

2013 1.9127 0.2513 2.2470

2014 1.5845 0.2489 1.7388

2015 0.4780 0.1167 0.5684

2016 -0.0573 0.0289 -0.0558

2017 1.3351 0.1630 1.6754

Average 0.3261 0.0551 0.5664

Swedbank Robur Aktiefond Pension


2007 -0.4988 -0.1957 -0.4285

2008 -1.5512 -0.9152 -1.3204

2009 0.2229 0.7576 0.2358

2010 -1.0140 0.8304 -0.7631

2011 0.0819 -0.4657 0.0738

2012 1.2179 1.3838 1.2693

2013 2.4268 0.6601 3.6916

2014 3.3579 2.2993 5.4980

2015 0.5965 0.1530 0.7325

2016 -0.5738 -0.0635 -0.5415

2017 -0.4502 -0.0191 -0.4379

Average 0.3469 0.4023 0.7281

Swedbank Robur M edica


2007 -0.3829 -0.0518 -0.3871

2008 -2.5057 -0.6982 -1.8897

2009 1.6445 0.6981 3.0472

2010 0.8412 0.2123 1.1430

2011 -1.3252 -0.3143 -1.1475

2012 0.2714 0.0893 0.2974

2013 1.9099 0.2584 2.7592

2014 1.2479 0.2032 1.3758

2015 0.5797 0.1405 0.7061

2016 -0.0051 0.0303 -0.0050

2017 1.1034 0.1600 1.3541

Average 0.3072 0.0662 0.6594

Swedbank Robur Sverigefond M ega


2007 -0.5746 -0.1013 -0.5742

2008 -2.2729 -0.6951 -1.7752

2009 1.5357 0.9034 2.5408

2010 0.8687 0.2299 1.1268

2011 -1.0864 -0.2276 -0.9714

2012 0.4345 0.1220 0.4903

2013 1.7784 0.2400 2.4523

2014 1.4773 0.2634 1.5855

2015 0.5230 0.1352 0.6346

2016 -0.2902 -0.0005 -0.2687

2017 0.9470 0.1437 1.0650

Average 0.3037 0.0921 0.5733

SPP Aktiefond Sver ige


2007 -0.3194 -0.0443 -0.3251

2008 -2.5412 -0.7796 -1.8828

2009 1.7796 0.6897 3.1281

2010 0.8663 0.2294 1.1481

2011 -1.1990 -0.2929 -1.0300

2012 0.4580 0.1260 0.5260

2013 1.8068 0.2280 2.6055

2014 1.1899 0.1981 1.2896

2015 0.2597 0.0873 0.3021

2016 -0.2814 0.0021 -0.2589

2017 0.6522 0.1426 0.6937

Average 0.2429 0.0533 0.5633

Avanza Zero


2007 -0.0055 -0.0011 -0.0056

2008 -2.3152 -0.7302 -1.7437

2009 0.9129 0.4332 1.2140

2010 -0.1641 -0.0165 -0.1585

2011 -1.0569 -0.2676 -0.8221

2012 0.8901 0.2425 0.8127

2013 2.0753 0.3874 2.5584

2014 1.5532 0.2438 1.8463

2015 0.6960 0.1513 0.8771

2016 -0.1817 0.0226 -0.1704

2017 1.2112 0.1261 1.6726

Average 0.3287 0.0538 0.5528

Swedbank Robur Globalfond M ega


2007 -0.4300 -0.0684 -0.4184

2008 -1.9972 -0.8965 -1.5568

2009 -0.0491 0.0030 -0.0497

2010 1.0180 1.0068 1.2011

2011 -0.3400 -0.0601 -0.2889

2012 1.7585 0.7113 2.4552

2013 2.0761 0.5978 3.3126

2014 2.5459 3.4437 4.2741

2015 0.4206 0.1245 0.4838

2016 -1.2006 -0.1462 -1.0556

2017 -0.1165 0.0757 -0.1232

Average 0.3351 0.4356 0.7486

CWor ldWide M edical


2007 -0.8819 -0.2393 -0.8210

2008 -2.8795 -0.8828 -2.0315

2009 1.5630 2.0823 3.5328

2010 1.3468 0.4046 2.4446

2011 -0.6729 -0.1463 -0.7112

2012 0.7411 0.2669 0.9397

2013 1.1727 0.2362 1.4587

2014 2.0949 0.6030 3.4822

2015 1.1366 0.2996 1.8906

2016 0.3719 0.0869 0.4351

2017 0.4278 0.0476 0.4801

Average 0.4019 0.2508 1.0091

Länsförsäkr ingar Fastighetsfond

Appendix

75


2007 -0.3206 -0.1308 -0.2932

2008 -1.1482 -1.5192 -1.0574

2009 -0.2013 0.3774 -0.1955

2010 -0.3865 0.1718 -0.3173

2011 0.1454 -0.2600 0.1280

2012 1.6390 2.9131 1.7935

2013 2.2129 0.6003 3.2455

2014 4.3917 3.1360 8.2536

2015 0.6872 0.1774 0.8152

2016 -0.7038 -0.0845 -0.6673

2017 0.4063 0.1014 0.4756

Average 0.6111 0.4984 1.1073

SEB Läkemedelsfond


2007 0.5235 0.0858 0.6283

2008 -3.0641 -0.8061 -1.9993

2009 1.0040 0.3273 1.4511

2010 1.3554 0.4147 1.8048

2011 -0.7374 -0.1502 -0.6933

2012 0.2741 0.0924 0.2811

2013 5.4845 2.1725 10.9187

2014 0.6511 0.1375 0.6486

2015 0.8025 0.1902 1.0631

2016 -0.6031 -0.0510 -0.5542

2017 1.5097 0.3710 1.8488

Average 0.6546 0.2531 1.3998

Skagen Global


2007 0.2374 0.0503 0.2554

2008 -2.7046 -0.8323 -1.8828

2009 0.7836 0.3486 1.0261

2010 -0.7033 -0.1719 -0.6890

2011 -0.9674 -0.1838 -0.8495

2012 -0.1945 0.0006 -0.1875

2013 1.7376 0.2695 2.1696

2014 0.9915 0.1657 0.9885

2015 0.3559 0.0932 0.4217

2016 -0.6100 -0.0445 -0.5304

2017 1.2110 0.1360 1.3958

Average 0.0125 -0.0153 0.1925

SPP Aktiefond Europa


2007 -0.6516 -0.1466 -0.6060

2008 -1.7677 -0.7063 -1.4629

2009 0.2319 1.0639 0.2685

2010 0.3342 0.2599 0.3735

2011 -0.4719 -0.1044 -0.4145

2012 1.4014 0.8450 1.1957

2013 1.9864 0.5371 2.7687

2014 2.9637 0.6217 4.7005

2015 0.8089 0.1753 1.0568

2016 0.6007 0.1483 0.7065

2017 0.4694 0.0718 0.5551

Average 0.5368 0.2514 0.8311

SPP Aktiefond USA


2007 1.1383 0.1772 1.8659

2008 -2.4302 -0.6800 -1.8408

2009 1.9873 0.6712 4.2909

2010 1.8687 0.5460 2.8082

2011 -1.0629 -0.2529 -1.0019

2012 -0.1426 0.0096 -0.1473

2013 2.2891 1.0516 2.9872

2014 0.1514 0.0643 0.1649

2015 0.1069 0.0575 0.1173

2016 -0.4148 -0.0240 -0.4090

2017 2.0547 0.3560 3.4140

Average 0.5042 0.1797 1.1136

Skagen Kon-Tiki


2007 -0.1111 -0.0250 -0.1236

2008 -2.7061 -0.7657 -1.9710

2009 1.6643 0.8989 2.8814

2010 0.3913 0.1097 0.4923

2011 -1.3720 -0.2996 -1.1674

2012 0.9485 0.2199 1.1876

2013 3.9425 0.6708 8.6600

2014 0.3875 0.0816 0.4026

2015 1.2077 0.2630 1.5367

2016 -0.5217 -0.0462 -0.5138

2017 1.6724 0.1584 2.2924

Average 0.5003 0.1151 1.2434

Swedbank Robur Småbolagsfond Europa


2007 0.5283 0.0798 0.5777

2008 -2.8666 -0.8417 -1.9653

2009 0.8462 0.3285 1.1337

2010 -0.8405 -0.1971 -0.8071

2011 -1.2115 -0.2702 -0.9919

2012 0.4780 0.1229 0.5075

2013 1.5812 0.2658 1.9757

2014 0.9636 0.1553 0.9659

2015 0.4758 0.1122 0.5595

2016 -0.6546 -0.0575 -0.5607

2017 1.5143 0.1736 1.8311

Average 0.0740 -0.0117 0.2933

Swedbank Robur Europafond M ega


2007 1.7232 0.2089 2.5889

2008 -2.8931 -0.7546 -2.0036

2009 0.9644 0.3147 1.2599

2010 -0.0168 0.0111 -0.0193

2011 -0.2711 -0.0427 -0.3109

2012 0.7166 0.1553 0.8281

2013 1.0643 0.1348 1.1304

2014 0.8168 0.1276 0.8528

2015 0.0526 0.0463 0.0564

2016 -1.2631 -0.1087 -0.9952

2017 4.9141 0.4388 8.3952

Average 0.5280 0.0483 1.0711

CWor ldWide Global Equities


2007 -0.2531 -0.0537 -0.2566

2008 -1.6912 -0.5798 -1.4832

2009 2.1295 -18.2133 4.5653

2010 0.6327 0.2621 0.8225

2011 -0.2598 -0.0313 -0.2458

2012 0.3638 0.1479 0.3631

2013 3.2845 1.8807 7.5163

2014 3.3574 0.5105 3.8959

2015 1.0363 0.1982 1.5868

2016 0.7591 0.1616 1.0584

2017 1.5527 0.1507 2.3342

Average 0.9920 -1.4151 1.8324

Skandia Time Global


2007 0.7962 0.2748 1.1676

2008 -2.5291 -0.6841 -1.9670

2009 1.3538 0.5980 2.3588

2010 0.5486 0.1842 0.7474

2011 -0.8690 -0.2057 -0.8358

2012 0.1353 0.0471 0.1748

2013 -0.1500 0.0016 -0.1567

2014 -0.6809 -0.2624 -0.7749

2015 -0.4411 -0.0533 -0.4814

2016 0.7011 0.1591 0.7872

2017 1.1298 0.2023 1.3202

Average -0.0005 0.0238 0.2128

Swedbank Robur Östeuropafond


2007 -0.8131 -0.1307 -0.7448

2008 -2.1712 -0.6366 -1.7071

2009 1.9453 1.1970 6.3119

2010 0.9477 0.2172 1.3498

2011 -0.8409 -0.1825 -0.8318

2012 0.2319 0.0886 0.2520

2013 2.3549 0.6390 4.3736

2014 1.3175 0.2153 1.6014

2015 1.8577 0.4065 2.7149

2016 0.0433 0.0414 0.0429

2017 1.0948 0.1101 1.3232

Average 0.5425 0.1787 1.3351

Lannebo Småbolag


2007 -0.6136 -0.1017 -0.6014

2008 -2.5654 -0.7182 -1.9329

2009 1.7952 0.7841 3.7819

2010 0.7174 0.1870 0.9825

2011 -0.7691 -0.1716 -0.7725

2012 0.3498 0.1292 0.3990

2013 2.9861 0.6057 6.1094

2014 0.7415 0.1283 0.8374

2015 2.0518 0.5046 3.3236

2016 -0.1790 0.0080 -0.1811

2017 0.8014 0.0838 0.9436

Average 0.4833 0.1308 1.1718

Swedbank Robur Småbolagsfond Sver ige

Appendix

76


2007 -0.7138 -0.1181 -0.6749

2008 -2.6694 -0.7833 -1.9520

2009 1.8275 1.2642 4.7861

2010 0.8420 0.2102 1.2131

2011 -0.5518 -0.1201 -0.6097

2012 0.3447 0.1184 0.3878

2013 2.7040 0.4972 5.2301

2014 1.3960 0.2474 1.7488

2015 1.6253 0.3206 2.5644

2016 0.0826 0.0403 0.0847

2017 0.7664 0.0793 0.8770

Average 0.5140 0.1596 1.2414

Handelsbanken Svenska Småbolagsfond


2007 -0.5169 -0.0803 -0.4806

2008 -2.3293 -0.6697 -1.7141

2009 2.1637 4.5792 4.5597

2010 0.3648 0.1029 0.4085

2011 -0.4614 -0.0888 -0.4847

2012 0.5416 0.1604 0.6617

2013 2.6649 0.4229 4.1223

2014 0.5782 0.1161 0.6099

2015 1.3911 0.3150 1.8829

2016 0.7463 0.1305 0.8605

2017 1.4985 0.1499 1.9064

Average 0.6038 0.4671 1.1211

Nordea Småbolagsfond Norden


2007 0.5366 0.0751 0.6317

2008 -2.4764 -0.7997 -1.8299

2009 0.5965 0.2611 0.7683

2010 -0.8827 -0.2037 -0.8528

2011 -0.9818 -0.2077 -0.8890

2012 0.4417 0.1255 0.5073

2013 1.8961 0.3067 2.5397

2014 1.1471 0.2043 1.1742

2015 0.4905 0.1194 0.5846

2016 -0.5567 -0.0422 -0.4935

2017 1.2566 0.1234 1.4719

Average 0.1334 -0.0034 0.3284

AM F Aktiefond Europa


2007 -1.0764 -0.1720 -0.8687

2008 -1.9745 -0.8644 -1.5108

2009 1.7607 0.6998 2.6362

2010 0.2261 0.1081 0.2030

2011 -0.9314 -0.2842 -0.7643

2012 1.1090 0.3233 1.1451

2013 2.6848 0.3741 4.0160

2014 2.5350 0.3896 3.2786

2015 0.5809 0.1515 0.7080

2016 -0.4720 -0.0248 -0.4341

2017 0.7874 0.0994 0.9148

Average 0.4754 0.0728 0.8476

Öhman Hjär t-Lungfond


2007 -0.6701 -0.1039 -0.6577

2008 -2.5661 -0.7631 -1.9293

2009 1.8013 0.8187 4.2102

2010 0.8221 0.2047 1.1940

2011 -0.6277 -0.1414 -0.6676

2012 0.0613 0.0527 0.0695

2013 3.2576 0.7124 6.0225

2014 0.6860 0.1431 0.7426

2015 1.5695 0.3054 2.4544

2016 0.2484 0.0592 0.2608

2017 0.6713 0.0787 0.7556

Average 0.4776 0.1242 1.1323

AM F Aktiefond Småbolag


2007 0.5631 -1.2463 0.6695

2008 -2.4303 -0.7227 -1.9475

2009 1.8558 1.6006 3.8944

2010 0.4803 0.1964 0.6674

2011 -0.5411 -0.1295 -0.5680

2012 -0.3381 -0.0452 -0.3762

2013 0.2519 0.0705 0.2564

2014 -0.8682 -0.4135 -0.9292

2015 0.2236 0.0771 0.3047

2016 1.1198 0.2262 1.3598

2017 -0.1102 0.0736 -0.1109

Average 0.0188 -0.0284 0.2927

Carnegie Rysslandsfond


2007 1.3528 7.5561 2.2015

2008 -2.4976 -0.7279 -1.9855

2009 1.7160 1.3564 3.4039

2010 0.6418 0.2495 0.9373

2011 -0.5549 -0.1288 -0.5755

2012 -0.4533 -0.0710 -0.5046

2013 0.3120 0.0718 0.3430

2014 -1.0987 -0.4936 -1.1467

2015 0.1086 0.0500 0.1463

2016 1.1904 0.2369 1.4955

2017 0.2738 0.3470 0.3153

Average 0.0901 0.7678 0.4210

Swedbank Robur Rysslandsfond


2007 0.2781 0.0356 0.3288

2008 -1.3315 -0.5396 -1.2550

2009 0.9025 0.4004 1.3063

2010 0.3109 0.0840 0.3772

2011 -0.0066 0.0154 -0.0080

2012 1.3546 0.3768 2.0929

2013 1.8993 0.3112 3.3440

2014 1.0939 0.7005 1.7058

2015 0.0163 0.0301 0.0192

2016 -0.7444 -0.0707 -0.7564

2017 0.9136 -3.0576 1.2355

Average 0.4261 -0.1558 0.7628

UBS Equityfund - BioTech


2007 0.1881 0.0429 0.2149

2008 -1.5137 -0.6030 -1.4055

2009 1.8955 -4.8580 3.5033

2010 0.2648 0.1766 0.3078

2011 -0.4277 -0.1102 -0.3708

2012 0.3340 0.1317 0.3411

2013 1.3443 0.7361 1.5327

2014 3.0096 0.8666 4.2514

2015 0.8748 0.1676 1.2259

2016 0.2768 0.1021 0.3350

2017 1.0874 0.1356 1.5957

Average 0.6667 -0.2920 1.0483

SEB Teknologifond


2007 1.2587 0.2419 1.7559

2008 -2.6021 -0.8093 -1.9274

2009 2.3911 1.0611 5.7412

2010 0.9708 0.4009 1.3740

2011 -1.2130 -0.3078 -1.0859

2012 0.1210 0.1039 0.1507

2013 0.1967 0.0799 0.1962

2014 0.8657 0.2005 0.9961

2015 -0.2182 -0.0091 -0.2334

2016 0.4802 0.1429 0.5408

2017 2.4153 0.4483 3.5533

Average 0.4242 0.1412 1.0056

Handelsbanken Tillväxtmarknad Tema


2007 0.5086 0.0645 0.6012

2008 -2.6014 -0.7230 -1.9299

2009 1.5379 0.7157 2.8846

2010 0.2162 0.0700 0.2386

2011 -1.1211 -0.2418 -0.9972

2012 0.3320 0.0878 0.3668

2013 0.8839 0.1472 1.0961

2014 0.8749 0.1619 0.9184

2015 0.9955 0.2364 1.2185

2016 -0.6793 -0.0544 -0.6198

2017 0.9283 0.1304 1.0136

Average 0.1705 0.0541 0.4355

Swedbank Robur NordenfondSharpe Ratio Treynor Ratio Sortino Ratio

2007 2.0206 0.5210 3.3536

2008 -2.5715 -1.2978 -1.9880

2009 1.6705 0.6241 2.8694

2010 0.0238 0.0217 0.0268

2011 -0.7233 -0.1590 -0.7670

2012 -0.2307 -0.0247 -0.2479

2013 0.7761 0.1526 0.9196

2014 -0.6035 -0.1207 -0.5659

2015 -0.1387 -0.0037 -0.1612

2016 -0.2860 -0.0119 -0.3037

2017 2.4663 -4.0514 3.5712

Average 0.2185 -0.3954 0.6097

Bar ing Hong-Kong China Fund

Appendix

77


2007 1.2069 -0.9272 1.6554

2008 -2.3421 -0.6799 -1.9001

2009 1.4909 1.5746 2.7930

2010 1.0027 0.3390 1.4902

2011 -0.6561 -0.1512 -0.6776

2012 -0.3175 -0.0440 -0.3588

2013 -0.1229 0.0058 -0.1223

2014 -1.2925 -0.6902 -1.3047

2015 -0.0553 0.0110 -0.0717

2016 1.3696 0.2470 1.6977

2017 0.3490 0.4996 0.4381

Average 0.0575 0.0168 0.3309

East Capital Ryssland


2007 -0.4050 -0.0637 -0.4031

2008 -2.6012 -0.7343 -1.9389

2009 2.0757 1.1517 4.6636

2010 0.4931 0.1290 0.5743

2011 -0.9972 -0.2187 -0.9602

2012 0.4526 0.1136 0.5576

2013 1.4628 0.2793 1.9774

2014 0.0419 0.0310 0.0431

2015 1.7341 0.4403 2.2836

2016 0.0218 0.0444 0.0218

2017 0.9066 0.0957 1.0568

Average 0.2896 0.1153 0.7160

Swedbank Robur Småbolagsfond Norden


2007 -0.0617 -0.0094 -0.0670

2008 -2.2744 -0.6959 -1.7314

2009 1.3394 -12.9705 2.7645

2010 -0.1111 -0.0100 -0.1116

2011 -0.2881 -0.0420 -0.2902

2012 0.0839 0.0890 0.0823

2013 3.0498 0.7972 3.9171

2014 1.7731 0.3935 2.9923

2015 1.4933 0.2805 2.0622

2016 0.1160 0.0592 0.1247

2017 1.7335 0.1435 2.6605

Average 0.6231 -1.0877 1.1276

Öhman Global Growth


2007 -0.8955 -0.1588 -0.7697

2008 -1.8569 -0.6869 -1.5081

2009 0.3087 1.1612 0.3301

2010 0.1400 0.1154 0.1438

2011 -0.3679 -0.0834 -0.3334

2012 1.2844 0.4985 1.1459

2013 2.1369 0.5006 3.1613

2014 3.4611 0.6447 5.8791

2015 0.7349 0.1675 0.9697

2016 0.4110 0.1012 0.4787

2017 0.5294 0.0836 0.6468

Average 0.5351 0.2130 0.9222

Skandia USA


2007 -0.5027 -0.0908 -0.4559

2008 -2.4122 -0.7543 -1.7690

2009 1.1687 0.6611 1.7457

2010 0.0916 0.0631 0.0926

2011 -0.9988 -0.2153 -0.8386

2012 0.1840 0.0927 0.1799

2013 1.9959 0.3204 2.3871

2014 1.1184 0.2181 1.2786

2015 0.4720 0.1122 0.5655

2016 -0.0180 0.0440 -0.0184

2017 0.9897 0.1126 1.2754

Average 0.1899 0.0513 0.4039

Skandia Vär lden


2007 -0.2262 -0.0392 -0.2164

2008 -3.2001 -1.0009 -2.0372

2009 0.6585 0.4102 0.8396

2010 -0.9059 -0.1943 -0.8003

2011 -1.1015 -0.2277 -0.9482

2012 0.1468 0.0916 0.1442

2013 1.5102 0.2934 1.8434

2014 1.2928 0.2186 1.4054

2015 0.4181 0.1235 0.5071

2016 -0.4999 -0.0132 -0.4020

2017 0.8768 0.1051 0.9940

Average -0.0937 -0.0212 0.1209

Länsförsäkr ingar Europa Aktiv


2007 0.8648 0.1432 2.0406

2008 -1.3451 -0.8707 -1.2801

2009 1.6596 0.9106 4.1890

2010 0.5121 0.4022 0.6116

2011 -0.2138 -0.0726 -0.2354

2012 -0.7394 -0.2013 -0.8309

2013 -2.1029 -1.1358 -1.7683

2014 -0.2877 -0.0832 -0.3400

2015 -1.0934 -1.1602 -1.0697

2016 0.5640 0.6495 0.8988

2017 0.1512 -0.1774 0.1931

Average -0.1846 -0.1451 0.2190

Blackrock Global Funds


2007 -0.5994 -0.0777 -0.5698

2008 -2.5368 -0.7755 -1.8609

2009 1.4750 0.8995 2.4871

2010 1.0643 0.2612 1.3980

2011 -1.0670 -0.2326 -0.9683

2012 0.4428 0.1236 0.5021

2013 1.2692 0.1911 1.5532

2014 1.0305 0.1977 1.0998

2015 1.0961 0.2440 1.4788

2016 -0.2456 0.0058 -0.2265

2017 1.1876 0.1721 1.4588

Average 0.2833 0.0917 0.5775

Länsförsäkr ingar Sver ige Aktiv


2007 -0.5876 -0.0801 -0.5707

2008 -2.5320 -0.7798 -1.8789

2009 1.5113 0.5487 2.4991

2010 0.7573 0.2074 0.9869

2011 -1.2479 -0.3101 -1.0638

2012 0.4033 0.1166 0.4603

2013 1.7757 0.2250 2.5295

2014 1.1494 0.1931 1.2383

2015 0.2443 0.0842 0.2834

2016 -0.2958 0.0002 -0.2715

2017 0.6300 0.1365 0.6682

Average 0.1644 0.0311 0.4437

Aktiespararna Topp Sverige


2007 -0.4784 -0.0934 -0.4641

2008 -1.7941 -0.7817 -1.5536

2009 1.9846 1.7301 5.6202

2010 0.2341 0.0991 0.2456

2011 -0.5828 -0.1508 -0.5231

2012 0.6332 0.2130 0.6906

2013 2.0770 0.7640 2.7496

2014 3.7468 1.0488 6.6462

2015 1.7230 0.3332 2.5798

2016 0.1423 0.0841 0.1596

2017 1.0588 0.1365 1.5194

Average 0.7950 0.3075 1.6064

Lannebo Vision


2007 -0.2219 -0.0349 -0.2223

2008 -2.2188 -0.7383 -1.6629

2009 0.7487 0.4260 0.9877

2010 0.2966 0.1301 0.3174

2011 -1.1752 -0.2820 -0.9737

2012 0.5015 0.1723 0.5198

2013 1.7880 0.2976 2.0513

2014 1.9001 0.3375 2.1034

2015 0.7526 0.1657 0.9206

2016 -0.0743 0.0393 -0.0753

2017 1.6125 0.1874 2.3185

Average 0.3554 0.0637 0.5713

Handelsbanken Global Tema


2007 -0.7027 -0.0940 -0.5927

2008 -2.6463 -0.8755 -1.8900

2009 1.5213 0.6259 2.4548

2010 0.2122 0.0749 0.2272

2011 -1.2002 -0.2646 -0.9655

2012 0.1857 0.0829 0.1800

2013 2.5102 0.3204 3.3026

2014 1.9480 0.2999 2.1847

2015 0.5639 0.1363 0.6767

2016 -0.2078 0.0137 -0.1917

2017 0.9946 0.1199 1.0809

Average 0.2890 0.0400 0.5879

KPA Etisk Aktiefond

Appendix

78

9.8 Yearly risk-adjusted return measurements ranking


AP7 Aktiefond 9 23 18

Swedbank Robur Technology 3 1 5

Didner & Gerge Aktiefond 22 20 20

AMF Sverige 35 28 37

AMF Världen 25 27 30

Swedbank Robur Aktiefond Pension 29 30 34

Swedbank Robur Medica 26 6 25

Swedbank Robur Sverigefond Mega 30 26 27

SPP Aktiefond Sverige 31 21 32

Avanza Zero 36 33 35

Swedbank Robur Globalfond Mega 28 32 36

CWorldWide Medical 27 5 24

Länsförsäkringar fastighetsfond 23 10 16

SEB Läkemedelsfond 7 3 13

Skagen Global 5 8 3

SPP Aktiefond Europa 47 43 49

SPP Aktiefond USA 11 9 22

SKAGEN Kon-Tiki 15 12 12

Swedbank Robur Småbolagsfond Europa 16 19 6

Swedbank Robur Europafond Mega 44 42 45

C Worldwide Global Equities 13 35 14

Skandia Time Global 1 51 1

Swedbank Robur Östeuropafond 48 39 48

Lannebo Småbolag 10 13 4

Swedbank Robur Småbolagsfond Sverige 17 16 8

Handelsbanken Svenska Småbolagsfond 14 14 7

Nordea Småbolagsfond Norden 8 4 11

AMF Aktiefond Europa 42 41 44

Öhman Hjärt-Lungfond 19 24 21

AMF Aktiefond Småbolag 18 17 9

Carnegie Rysslandsfond 46 45 46

Swedbank Robur Rysslandsfond 43 2 41

UBS Equity Fund - BioTech 20 47 23

SEB Teknologifond 4 48 15

Handelsbanken Tillväxtmarknad Tema 21 15 17

Swedbank Robur Nordenfond 40 31 39

Baring Hong Kong China Fund 37 49 28

East Capital Ryssland 45 40 43

Swedbank Robur Småbolagsfond Norden 32 18 26

Öhman Global Growth 6 50 10

Skandia USA 12 11 19

Skandia Världen 39 34 42

Länsförsäkringar Europa Aktiv 49 44 50

Blackrock Global Funds 51 46 47

Länsförsäkringar Sverige Aktiv 34 22 31

Aktiespararna Topp Sverige 41 37 38

Lannebo Vision 2 7 2

Handelsbanken Global Tema 24 29 33

KPA Etisk Aktiefond 33 36 29

Länsförsäkringar Global Hållbar 38 25 40

SPP Aktiefond Japan 50 38 51


2007 -0.1830 -0.0264 -0.1751

2008 -2.5797 -0.8246 -1.8156

2009 1.3153 1.0078 2.1795

2010 -0.2184 -0.0273 -0.2053

2011 -1.2718 -0.2675 -1.0455

2012 0.4839 0.1525 0.5026

2013 1.6652 0.2400 1.8799

2014 1.7192 0.2729 1.8232

2015 0.4551 0.1147 0.5227

2016 0.2100 0.0631 0.2168

2017 0.6953 0.0852 0.8008

Average 0.2083 0.0718 0.4258

Länsförsäkr ingar Global Hållbar


2007 -1.1760 0.7444 -0.9863

2008 -1.6755 -0.9910 -1.4239

2009 -0.7637 -0.7875 -0.6791

2010 0.0618 -0.1638 0.0671

2011 -1.1036 -1.5177 -0.9835

2012 -0.7820 -0.1768 -0.6393

2013 1.4513 2.2632 1.9729

2014 0.8242 0.4574 1.0087

2015 0.9870 0.2348 1.2283

2016 0.1823 0.1307 0.2142

2017 0.6415 0.0945 0.8147

Average -0.1230 0.0262 0.0540

SPP Aktiefond Japan

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