ownership structure and its impact on corporate risk taking
TRANSCRIPT
Ownership structure and its impact on corporate risk taking
Emil Larma s102080
Department of Finance and Statistics
Hanken School of Economics
Helsinki
2016
HANKEN SCHOOL OF ECONOMICS
Department of:
Finance and Statistics
Type of work: Thesis
Author and Student number:
Emil Larma s102080
Date:
27.09.2016
Title of thesis:
Ownership structure and its impact on corporate risk taking
Abstract:
The purpose of the study is to test whether the ownership structure in Finnish firms
can explain their risk taking. The study analyses both the ownership concentration and
the type of the largest investor and their impact on corporate risk taking.
I use two different samples, one consisting of the 500 largest firms in Finland for years
2009-2014 and another sample with Finnish listed firms for years 2008-2014. The
descriptive statistics show that the largest owner in listed Finnish companies own on
average around 31% of the voting rights, whereas a majority owner (>50% of voting
rights) exists, on average, in 25% of the listed companies. Further, both samples show
that the largest owner in Finnish companies is most commonly a family.
The regressions indicate that the ownership concentration, meaning the level of
ownership of the largest owner, does not affect risk taking, instead it is the owner type,
which has a correlation with firm risk taking. The results imply that municipalities,
venture capital firms, foreign investors, families, foundations and corporations have a
positive impact on risk taking, meaning that they are present as investors in
companies with higher risk. On the other hand, companies with dual class shares tend
to have considerably lower risk taking compared to other firms.
Keywords: risk taking, ownership structure, ownership concentration, ownership
types, panel data, dual class shares, family ownership, foreign investors, publicly listed
companies, non-listed companies
CONTENTS
1 INTRODUCTION....................................................................................... 1
1.1 Aim of the study .................................................................................................. 2
1.2 Limits to the study ............................................................................................... 2
1.3 Contribution ........................................................................................................ 3
1.4 Structure of the study .......................................................................................... 4
2 AGENCY THEORY ................................................................................... 5
2.1 Agency problems ................................................................................................. 6
2.1.1 Compensation structures ........................................................................ 6
2.1.2 Free-rider problem .................................................................................. 7
2.1.3 Moral hazard theory ............................................................................... 8
2.1.3.1 Moral hazard theory and ownership concentration ................ 9
3 THEORY ON RISK ................................................................................. 10
3.1 Risk and return .................................................................................................. 10
3.2 Risk and utility functions .................................................................................. 11
3.3 Firm specific risk factors ................................................................................... 11
3.3.1 Capital structure ................................................................................... 11
3.3.2 Investments ........................................................................................... 13
3.4 Measuring risk ................................................................................................... 14
4 OWNERSHIP STRUCTURE AND INVESTOR TYPES .......................... 15
4.1 Ownership structure.......................................................................................... 15
4.1.1 Dual class shares ................................................................................... 15
4.1.2 Pyramid structures ................................................................................ 16
4.1.3 Cross-ownership ................................................................................... 17
4.2 Investor types .................................................................................................... 17
4.2.1 Insiders ................................................................................................ 18
4.2.2 Foreign ownership ................................................................................ 18
4.2.3 Family ownership .................................................................................. 19
4.2.4 Institutional investors ........................................................................... 21
4.2.5 The state as investor.............................................................................. 22
4.2.6 Private equity investors ......................................................................... 23
4.2.7 Summary on investor types ..................................................................24
5 PREVIOUS STUDIES ............................................................................. 25
5.1 Wright, Ferris, Sarin & Awasthi (1996) ............................................................. 25
5.1.1 Data ................................................................................................ 25
5.1.2 Method ................................................................................................26
5.1.3 Results ................................................................................................26
5.2 Coles, Daniel & Naveen (2006) .........................................................................26
5.2.1 Data ................................................................................................ 27
5.2.2 Method ................................................................................................ 27
5.2.3 Results ............................................................................................... 28
5.3 Anderson, Mansi & Reeb (2003)...................................................................... 28
5.3.1 Data ............................................................................................... 28
5.3.2 Method ................................................................................................29
5.3.3 Results ................................................................................................29
5.4 Summary of previous studies ........................................................................... 30
6 DATA ........................................................................................................31
6.1 Talouselämä 500 ............................................................................................... 31
6.1.1 Risk variable .......................................................................................... 31
6.1.2 Ownership variable ............................................................................... 32
6.1.3 Control variables ................................................................................... 33
6.1.3.1 Size ......................................................................................... 33
6.1.3.2 Growth ................................................................................... 33
6.1.3.3 Profitability ............................................................................ 33
6.1.3.4 Industry dummy .................................................................... 34
6.1.4 Summary on variable definitions and sources ...................................... 34
6.1.5 Descriptive data .................................................................................... 35
6.2 Data on listed companies ................................................................................. 38
6.2.1 Risk variables ....................................................................................... 38
6.2.1.1 Ln Volatility .......................................................................... 38
6.2.1.2 Ln Gearing ............................................................................. 39
6.2.1.3 Ln Equity ratio ....................................................................... 39
6.2.1.4 Standard deviation on ROA ................................................... 39
6.2.2 Ownership variables ............................................................................ 40
6.2.2.1 Ownership concentration ...................................................... 41
6.2.2.2 Investor type .......................................................................... 41
6.2.3 Control variables ...................................................................................42
6.2.3.1 CEO ownership ......................................................................42
6.2.3.2 Majority ownership ................................................................ 43
6.2.3.3 Dual class share ..................................................................... 43
6.2.3.4 Foreign investor ..................................................................... 43
6.2.3.5 Size ......................................................................................... 43
6.2.3.6 Growth .................................................................................. 44
6.2.3.7 Profitability ........................................................................... 44
6.2.4 Summary on variable sources and definitions ..................................... 44
6.2.5 Descriptive data ................................................................................... 46
6.3 Data discussion ................................................................................................ 50
6.3.1 Survivorship bias ................................................................................. 50
6.3.2 Endogeneity .......................................................................................... 51
6.3.3 Extreme values ...................................................................................... 52
7 METHODOLOGY ................................................................................... 53
7.1 Models ............................................................................................................... 53
7.1.1 Talouselämä models ............................................................................. 53
7.1.2 Listed sample models ............................................................................ 54
7.2 Estimation methods .......................................................................................... 55
7.2.1 Hausman-test ........................................................................................ 56
7.2.2 Fixed-effects model ............................................................................... 56
7.2.3 Random-effects model .......................................................................... 56
7.2.4 Robust standard errors ......................................................................... 57
7.3 Hypothesis and expected signs ......................................................................... 57
8 REULTS .................................................................................................. 60
8.1 Talouselämä 500 .............................................................................................. 60
8.2 Listed sample ....................................................................................................62
8.2.1 Results for the basic sample ..................................................................62
8.2.2 Results on regressions with lagged ownership .................................... 66
8.2.3 Results on regressions without the main ownership variables ........... 69
8.3 Comparing results with hypothesis and previous research .............................. 70
8.4 Model diagnostics .............................................................................................. 71
8.4.1 Normality .............................................................................................. 72
8.4.2 Multicollinearity.................................................................................... 72
8.4.3 Autocorrelation ..................................................................................... 73
8.4.4 Heteroscedasticity ................................................................................. 73
9 CONCLUSIONS ...................................................................................... 75
9.1 Discussion ......................................................................................................... 75
9.2 Critical discussion on the study ........................................................................ 77
9.3 Suggestions for further research ....................................................................... 78
SVENSK SAMMANFATTNING ................................................................. 79
REFERENCES ............................................................................................ 90
APPENDICES
Appendix 1 Companies in the listed sample ............................................................ 93
Appendix 2 Companies excluded from the listed sample ....................................... 95
Appendix 3 Multicollinearity table listed sample ................................................... 96
Appendix 4 Multicollinearity table Talouselämä sample ......................................... 97
TABLES
Table 1 The relationship between families and minority owners as described by Holan & Sanz 2006 ........................................................................................ 20
Table 2 List of ownership dummies used in the Talouselämä 500 data set ............... 32
Table 3 Variable sources for Talouselämä 500 data ................................................... 34
Table 4 Definitions on variables used in Talouselämä 500 data ................................ 35
Table 5 Descriptive statistics on data used in the Talouselämä 500 sample .............. 36
Table 6 Investor types in the listed sample .................................................................42
Table 7 Summary on primary and secondary sources for variables .......................... 44
Table 8 Summary on variable descriptions ................................................................. 45
Table 9 Descriptive data on listed firms ..................................................................... 46
Table 10 Hypothesis for the regressions, where hypothesis 1 applies for both data sets, whereas hypothesis 2-6 only apply for the data on listed companies ....58
Table 11 Expected signs for Talouselämä 500 variables ..............................................58
Table 12 Expected signs for variables in listed sample data ......................................... 59
Table 13 Talouselämä 500 results................................................................................ 60
Table 14 Results for regressions where the main variable for ownership has been removed .......................................................................................................... 70
Table 15 The results impact on the hypothesis ............................................................. 71
FIGURES
Figure 1 Distribution of ownership dummies .............................................................. 37
Figure 2 Data distribution based on industry type ....................................................... 37
Figure 3 Distribution of the largest owner by type ...................................................... 48
Figure 4 Distribution of the second largest owner by type .......................................... 49
Figure 5 Distribution of the third largest owner by type ............................................. 49
Figure 6 Distribution according to industry type ........................................................ 50
1
1 INTRODUCTION
A modern company is a complex organization with many internal and external
stakeholders who have to interact with each other to make the company work
efficiently. Research in corporate governance tries to find solutions on how
stakeholders should interact with each other as to minimize frictions between parties.
The decisions made within the firm will not always be aligned with those of the owners,
assuming that individuals will secure their own utility before that of the firm. Conflicts
can arise between executives and financiers of the firm, managers and their
subordinates or between owners and executives. In this thesis, I will concentrate on the
conflicts that can arise between owners and managers’ decision-making, with emphasis
on risk taking.
Recent publications within finance, business and corporate governance have studied
whether ownership structure has an impact on various firm characteristics, in
particular, studies on ownership concentration, meaning the largest shareholders, and
their possibility to impact firm decisions. Several studies have proven a relationship
between ownership concentration and firm variables such as firm performance (Morck
et al. 1988), firm value (Slovin and Sushka 1993), competitiveness (Gadhoum 1999),
managerial ownership (Denis et al. 1997), CEO pay-performance sensitivity (Coles et al.
2006) and legal protection of investors (John et al. 2008). There are however, only a
few studies that have touched upon how ownership concentration affects corporate risk
taking.
The agency theory considers that the ownership structure affects the ability of owners
to influence corporate risk taking (Jensen and Meckling 1976). Consequently, large
shareholders have powerful incentives to collect information and monitor managers in
order to maximize their profits (Shleifer and Vishny 1986). As ownership increases,
ceteris paribus, owners have higher incentives to take on risky projects, which could
substantially raise firm profits. However, concentrating much wealth into one firm may
force large shareholders to take a more risk averse position than if they had diversified
portfolios (John et al. 2008). By taking a more risk averse position on investments,
investors can secure their wealth by approving long term projects with lower profit
margins. The net effect of ownership structure on risk taking will therefore become
more difficult to interpret, and will depend on the largest shareholders’ trade-off
between risk taking and securing future wealth.
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Furthermore, this topic is interesting as corporate risk taking is essential for long-term
economic growth. Therefore, it is of special value to understand the determinants of
corporate risk-taking as it helps to identify which policy changes can improve it and
thereby the economic welfare.
This topic is also interesting, as a similar study has not been performed on the Finnish
market, where family ownership is relatively common also in listed firms. Further, the
thesis is done in collaboration with Boardman Oy, which is a company focusing on
Finnish ownership and it is in their interest to find various relationships on how
ownership structure of a firm can affect firm decision making. I have therefore decided
to have a thorough look at the ownership structure of Finnish firms and studied if a
relationship can be found between firm risk taking and ownership structure.
1.1 Aim of the study
The purpose of the study is to test whether the ownership structure of Finnish firms can
explain their risk taking.
1.2 Limits to the study
The geographical limit for data is constricted to only include Finnish firms. This has
some positive and negative effects, firstly it enables me to analyse the whole spectrum
of Finnish listed firms, which consists of a large percentage of investments that the
average Finnish individual invests in. It has been shown in previous research that the
majority of investments are done in an individual’s home country. Thus, the data
should consist of a diversified set of investors and investor types, which is essential for
my study. I have however, not only included listed companies, instead the data also
includes a data set on unlisted firms.
The study is limited to a period of 7 years. The main reason for including 7 years is that
the period should be long enough to gain an insight in how ownership and risk has
changed through time and how they affect each other. On the other hand, some of the
data, especially considering ownership, were gathered manually, which makes it very
time consuming to include more years.
I have excluded all financial firms from the sample, firstly, as their capital structure is
considerably different compared with non-financial firms and secondly, as there is a
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wide range of previous research that has studied the relationship between firm risk
taking and ownership structure for financial firms. Therefore, financial firms will not be
included in the same model.
1.3 Contribution
The relationship between insiders and risk taking has been studied previously,
however, other ownership forms haven’t received as much attention. Therefore this
study takes a much closer look at ownership and how ownership can be defined
compared with previous research. Insider ownership is regardless a very important
form of ownership as the CEO has a big impact on risk taking. This thesis is somewhat
unique as it has detailed information on Finnish CEO ownership.
This study also contributes to previous research as it takes into account privately held
firms, which is not very common in previous research. The main reason for normally
excluding privately held firms is data availability, which is commonly very poor. This is
therefore a relatively unique set of data as it includes financial numbers and ownership
structures of privately held firms.
Further, this paper aims at finding the ultimate owners of companies. I have used the
ownership structure given from databases, and after that manually gone through the
data and looked up the ultimate owners of holding companies as well as pooled
together voting rights from same family members, as one can assume that they vote
similarly on the annual general meeting. By pooling together these voting rights, I have
discovered that the majority of firms actually have families as one of the three largest
investors. Gathering and sorting the data has been very time consuming and hence, I
believe that this is the part of the study with the largest contribution as the data is
rather unique.
As a last point, I will also contribute to previous research as I will use lagged ownership
variables to see if this can explain firm risk taking. This has been performed in only a
few studies, which usually have found a relationship here as well, as one can assume
that leverage is decided for the long term.
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1.4 Structure of the study
The structure of the study is as follows; the first three chapters describe the theory
relating to the paper. The first chapter describes the agency theory, followed by a
section with the definition of risk and finally the theory parts ends with a discussion on
the various ownership structures and the most common investor types. Chapter 5
discusses three articles that on the topic, or which have elements that are important for
this study. Chapter 6 contains data description on the two data sets that are used in the
study. Chapter 7 will describe the methodology used as well as the various regression
models. In chapter 8 I will present the results and key findings in the study, whereas
chapter 9 will make ending conclusions as well as a critical discussion on the study.
5
2 AGENCY THEORY
Studies within business and economics have for a long time documented the issues of
various agency conflicts within modern companies. These conflicts arise from the
classical agency relationship, which is especially visible in publicly held firms. The
underlying problem in the agency relationship is the separation between ownership and
control, meaning that owners do not have access to the managers’ decision-making
(Berle & Means 1932). In this relationship both parties will strive to maximize their
own utility, assuming that both parties behave rationally, which will lead to a situation
where the agent (manager) will not act in the principals (owners) best interest (Jensen
& Meckling 1976).
The principal will not be able to align the agents’ interests with his own without
introducing suitable incentives for the agent. Hence, it is generally impossible for either
the principal or agent to secure that the agent will make decisions, which are aligned
with the principals, without bearing any costs. Most agency relationships will include
decisions made by the agent, which are not going to maximize the principals’ wealth
and the loss in the principals’ wealth between a good and bad decision made by the
agent is defined as agency costs. (Jensen & Meckling 1976)
The relationship can be described by a simple situation. Assuming that a company only
has one owner, who also is the executive, he will always make decision that maximizes
his utility. The decisions can include monetary as well as non-monetary decisions,
however, the company’s actions will always be aligned with the owners’ interests. When
the executive (also only owner) decides to sell a part of his shares to another individual,
a new agency relationship will occur, as the original and “external” owners’ interests
aren’t aligned. The original owner and executive will now be forced to share the cash
flow gains originating from the company with the new owner, however it also means
that all costs occurring from the firm will be shared. Hence, the original owner will gain
less from dividend distributions as his ownership has been diluted. Instead, he will
maximize his own utility by using corporate expenditure on personal gains, as he now
bears these costs together with the external owner. The minority owner, on the other
hand, will recognize that this problem might arise and he will therefore be prepared to
use resources to monitor the agents’ behaviour. This relationship can basically be
applied to corporations with hundreds of owners as there always will be conflicts of
6
interests between both majority- and minority owners as well as between owners and
the managers.
Another type of agency cost arises due to difference in the willingness to take on risky
projects. Investors will commonly think about the systematic risk in the industry in
question or the economy in general, as they usually have a diversified portfolio and can
therefore minimize the company specific risk by diversification. Executives, on the
other hand, usually have a large portion of their wealth tied to the company, both in
form of basic salary, as well as potential compensation programs tied to the firm’s
shares. Therefore, the executives might lose a substantial part of their wealth if they
would obtain very risky investments, which eventually could lead to great personal
losses if projects are unsuccessful. Executives will be more risk averse, compared to the
owners, as they will be able to keep the company risk levels at levels they are
comfortable with. This will lead to a situation where executives will usually prefer
projects with lower risk and longer investment horizons, whereas the owners often have
a rather opposite view on risk appetite. (Easterbrook 1984)
2.1 Agency problems
The term “agency problem” has a wide definition and exists in many types of situations
in companies, as well as on various levels of decision making within the organization.
The most common forms of agency problems are related to the conflicts of interest
between owners and executives. Hereafter I will discuss the most relevant agency costs.
2.1.1 Compensation structures
Owners of listed companies create different compensation instruments in order to align
executives’ interest with theirs. The most common are bonuses, stocks and options.
Bonuses work as short term incentive instruments. They encourage the executives to
increase firm performance for the coming year, as the bonus usually is tied to the
company’s’ end of year profit. Bonuses can however also be value destroying, as
managers might maximize firm profits for the coming year, ignoring long-term profits
(Cai et al. 2010). Options on the other hand, are long-term compensation instruments,
encouraging managers to improve long-term firm performance. A manager taking part
in an option program has the opportunity to buy shares in the company at a pre-
determined price at a future date. Assuming that the manager will maximize his own
utility, taking part of the option program will align his interests together with those of
7
investors. Another long-term compensation instrument is company stocks, which also
encourage maximizing long-term firm performance. Stocks increase manager’s wealth,
just like options, when firm performance increases, however, it also has a negative
effect on the executive’s wealth if the manager makes bad decisions in the firm. Hence,
stocks are seen as the instrument, which most effectively aligns shareholder and
manger interests.
Compensating executives with bonuses, options and stock, is however not free of
charge, hence the owners will need to bear the costs of compensation (agency cost) in
order to align executives interest with theirs. Bonuses can be seen as a relatively cheap
incentive instrument, whereas stock can be rather expensive. If the company in
question is valued at say 1 billion euro, paying a million in bonus, will not be a huge
cost for shareholders, whereas making the executive a major shareholder can become
much costlier due to the dilution of old shareholders.
2.1.2 Free-rider problem
The free-rider problem arises as s conflict of interest between majority- and minority
owners. Assume a situation where the ownership structure is very dispersed, meaning
that there is no controlling shareholder/shareholders, and where managers don’t
behave according to shareholders’ best interest. None of the individual shareholders
will have enough resources to manage the executives, and we will have a classical
agency conflict, which can partly be solved with a proper compensation structure. On
the other hand, if we have a situation with one or several large owners, they will be
willing to protect their wealth by monitoring executives, as their stake in the company
is of considerable size. However, this means that investors with less ownership will not
have any interest in monitoring the company, as they know that another investor has a
considerably larger stake in the company. This is where small investors will freeride,
and not share the monitoring costs associated with monitoring executives and the
company together with other investors. Although the large shareholders will be forced
to bear all the monitoring costs, they will commonly have access to management
information that is not available for other investors and therefore they will have a
better view of the company and its future. (Grossman & Hart 1980) In other words, a
large shareholder might still get more out of the situation, in terms of semi disclosed
information, compared with the monitoring costs they have to bear.
8
2.1.3 Moral hazard theory
A moral hazard situation is generally defined as a situation where one person has the
power to take a decision regarding risk whereas another person bears the costs of those
risks. Economist Paul Krugman described moral hazard as "any situation in which one
person makes the decision about how much risk to take, while someone else bears the
cost if things go badly" (Krugman 2009). The moral hazard theory is therefore closely
linked with the agency theory, or can be described as part of it, as it concentrates on the
question of managerial risk taking.
Executives usually have an urge to quickly grow company size as this will give them
certain benefits, such as higher bonuses and corporate benefits. The effect is called
empire building, where the manager strives to grow firm turnover as quickly as
possible, whereas profitability might deteriorate (Jensen 1986). Growth can be
achieved by taking on risky projects with high possible outfalls. However, this type of
strategy does not satisfy certain large stockholders, as the risk taking is too high for
their preference. At this stage, the shareholders could encourage executives to lower
their risk taking by introducing new compensation structures. Options, will naturally
not be the preferred instrument as it usually significantly increases executive risk
taking (DeFusco et al. 1990, Sanders 2001) whereas stocks have proven to decrease
executive risk taking (assuming that risk taking has been too high in the past) and
should be preferred (Coles et al. 2001).
The other side of the moral hazard theory is that we can experience executives who only
invest in low risk projects. These types of managers are especially dominant in
companies with high industry leverage, such as banks (Demsetz et al. 1997). The higher
level of leverage means that mangers feel that they are in a riskier situation and will
therefore be risk averse in order to secure their wealth (salary). Many studies (e.g.
Demsetz et al. 1997, Low 2009) show that agency conflicts arise as managers are not
willing to bear as much risk as the owners would like to undertake. Here again, the
situation can be corrected for by introducing appropriate compensation instruments.
Stocks and options will hopefully increase managers risk-taking as they will notice the
upside potential that riskier projects entails to.
9
2.1.3.1 Moral hazard theory and ownership concentration
As noticed from the moral hazard theory, large shareholders have an incentive to align
firm risk taking with their own. This in turn speaks for a relation between large
blockholder ownership and firm risk taking, which is the purpose of this study. A large
concentration of large owners, as well as one large shareholder should therefore be able
to explain the risk taking in a firm. If we take this one step further, it also means that if
one of the largest owners also happens to be an insider in the company, one could
assume that a relationship should also be found between ownership concentration and
firm risk taking.
10
3 THEORY ON RISK
What is risk and how can you define it? One way to look at it is volatility of an uncertain
outcome such as the value of an asset. There are three basic types of risks that a
company is exposed to; business, strategic and financial risk. Business risk consists of
all the risk that the managers deliberately take on the firm, in order to grow firm value,
strategic risk stem from the general movement in the economy, whereas financial risks
are related to the risks on the financial market (Jorion 1997). This chapter will start
with a definition of risk and return as well a short introduction to utility functions,
followed by discussion on firm specific risk factors. The chapter ends with a short
description on how risk can be measured statistically.
3.1 Risk and return
Risk and return are one of the most important terms when it comes to investing. A
general rule within investing is that less risk commonly yields lower returns, whereas
higher risk can yield higher returns, but also great losses. In other words, an investor
cannot yield high returns without taking on more risk. Risk does not only imply the
potential yield (low/high) on the investment but also on the potential loss of the
principal amount invested. The definition of risk and return refers to the relationship
between risk and expected return, not between risk and realized returns. Generally,
people perceive expected return as an outcome that will most likely happen, whereas it
actually refers to an average value. Therefore, the outcome might be significantly higher
or lower than the expected return on a specific investment. (Hull, 2010)
An individual investor can relatively easily perceive what risk is and how risky
investments are for them. A company on the other hand, has a more complex
relationship between risk and return. Theory says that a company should always choose
projects that have a positive net present value, meaning that the investment yields
more than the expected return set by the firm. The company needs to put more
attention to the individual projects risks, whereas investors are more diversified
towards the individual project as they usually have a diversified investment portfolio.
We can hence conclude that there is a positive correlation between risk and return and
the only way to increase expected returns is to bear more risk.
11
3.2 Risk and utility functions
We can generally divide investors in three groups according to their attitude towards
risk; risk-averse, risk-neutral and risk seeking. Investors are assumed to behave
rationally, which means that they will be risk averse. This means that given a situation
with many projects with the same expected return, the investor will always choose the
project with least risk.
Humans have varying attitudes towards risk, although the average person is
considered risk averse. Hence, people have different preferences when it comes to risk,
some are comfortable with low returns and low yields, whereas others enjoy bearing
risk with potential high returns. According to this logic, we can assume that all
individuals have a utility function that varies according to personal risk preferences
(Pratt 1964). Individuals will try to maximize their own utility functions, however, the
individual utility functions will vary depending on the individuals’ risk taking
preferences.
The individual utility functions are therefore the underlying factor that differentiates
one investor from another when it comes to risk taking. This thesis aims at looking
whether a group of investors with same characteristics (e.g. family, state or investment
advisors) have a similar appetite for risk, or in other words similar utility functions.
3.3 Firm specific risk factors
Next, I will describe the background theory on how firms choose their capital structure
followed by a discussion around investments and how they are related to firm risk
taking.
3.3.1 Capital structure
The basic theory on capital structure was laid out by Modigliani & Miller in 1958 and is
still used today. The proposition says that firm value is independent from the capital
structure, hence, the name irrelevance theory. They argue that the market value of the
firm stems from its earnings and from the risk of its underlying assets and that the
value is hence independent from the way the assets are financed. The following
statements are assumed in a perfect market; no taxes, no transaction costs, no
bankruptcy costs, all market players have the same lending costs and that the firm
value is not predictable based on financial decisions.
12
The M&M proposition has some drawbacks when applying it to reality, as perfect
markets are not as perfect as in theory. The foremost variable that differs between
theory and current markets is the existence of bankruptcy costs, as they have a
significant effect on modern corporations. Kraus & Litzenberg (1973) took into account
the effect of bankruptcy costs and developed their trade-off theory. According to their
model, optimal capital structure in a firm is chosen based on a trade-off between
benefits received from the tax shield and the disadvantages from bankruptcy costs.
Hence, a modern company has three alternatives on how to finance their need of
capital; 1) retained earnings, 2) debt and 3) share emission. There are a few theories
that try to explain how a company makes decisions between financing alternatives and
the most famous once, the pecking-order theory, signalling theory and market-timing
theory will be discussed next.
The pecking-order theory assumes that there is a certain ranking that specifies in
which order a company should seek for capital. The theory is based on the information
asymmetries that exist between parties. According to the theory a firm prefers to
finance itself by retained earnings, followed by external capital, such as bank loans or
bonds. As a last resource of financing, a firm goes to the market for capital by issuing
new shares. (Myers & Majluf 1984) By using this order of financing, the company will
minimize the need to disclose firm specific information to outsiders.
According to the signalling theory the firm signals the market about their financial and
future state, through their choice of financing. If a firm chooses to use debt as financing
instrument, it signals that the company is in good financial state as it shows investors
that the firm has enough capital to pay back its debt and does not have to take into
account potential bankruptcy costs. (Ross 1977)
The market timing theory assumes that there is no difference between financing the
company with equity or debt. Instead, the company choses between financing
instruments depending on the company’s market value. Hence, if mangers consider
that the firm is overvalued, they will issue new shares, instead of using any of the other
instruments. (Baker & Wurgler 2002)
The choice of capital structure in a firm is based on many factors, such as those
described above. The capital structure is not chosen by random, instead it is a
conscious choice made by the management of the firm. Accordingly, the capital
13
structure is affected by the company’s financial health, as well as by the state of the
general market. Leverage has a significant effect on a company’s risk taking, hence,
managers can alter firm leverage and choose the preferred level of risk taking that the
company bears.
3.3.2 Investments
An investment can be defined as a transaction where an investor pays a principal at the
current date in order to possibly receive remuneration in the future. An investment is
considered irreversible, meaning that after the initial payment, one cannot undo the
investment. Further, it is commonly not possible to predict the future return, it can
either be considerably better than expected or it can yield nothing, thus loosing also the
principal.
A company’s investments can be defined as how the managers choose to allocate capital
between various projects. The managers also need to decide on the type of investment
that capital will be allocated to. A company’s investments can be divided into two main
categories; Capital Expenditure (CapEx) and Research and Development (R&D).
Capital expenditure is considered as a newly purchased capital asset or an investment
that improves the usefulness of an existing asset. Purchase of new assets can include an
investment into new headquarters or acquisition of new machinery for factories. Capex
also includes larger improvements in existing assets, such as machinery, which
considerably lengthens the usefulness of the machine. Although capex is commonly
associated only with investments in fixed assets, investments in immaterial assets, such
as acquisition of patents, can be included in capex. Capital expenditures vary from
normal costs in the company, as they tend to be of considerable size, and therefore they
are accounted for in a different fashion compared to other costs. Therefore, capital
expenditures are usually depreciated over time or amortized (if we speak about
immaterial assets).
Capital expenditure varies significantly between industries, as the need for material
assets differs considerably between industries. Industries, such as airlines, have
significant assets, and will require considerable capital expenditures annually, whereas
other industries, such as service businesses, have a light need of material assets, and
will therefore have low annual capital expenditures. The risk that is associated with
14
capex, is that one can never know how profitable an investment will turn out in the
future, therefore large capital expenditures can usually be associated with more risk.
Research and development is the second type of investment, which is used for
innovations in new products and the development of existing products. R&D
investments are considered significantly riskier than capital expenditures, partly as the
future outcome is more unclear and partly as there is no underlying asset that could be
divested in order to get some of the original principal back. R&D investments are
commonly characterized as a longer investment compared with Capex, which also
increases risk. As an example; a technology developed today, might be out of date when
it is launched after several years of development. Hence, investments in R&D are
usually considerably smaller, compared to capex, as the risk is perceived as much
higher.
A manager can therefore adjust the risk taking in the firm depending on what type of
investments that are made. If the manager is risk-averse, he will prefer capital
expenditures that only just keep the machines and other fixed assets in working
condition. Alternatively, the manager can make larger capital expenditures or/and
increase R&D expenditure to increase the risk in the company, but at the same time
increase the expected returns on these investments.
3.4 Measuring risk
As earlier mentioned in the chapter, risk is commonly measured as volatility. This is not
only restricted to firm value, instead it is used for various variables, such as price
volatility on commodities. Volatility (σ) is defined as the standard deviation on a
variables return for a time period, where the return is continuously compounded (Hull
2010). Volatility is given through time and is defined as the standard deviation of:
Where St is the price at time T and S0 is the price at the beginning of the period. The
function equals the total cumulated continuously compounded return for time T, not
the return per time unit. If volatility is expressed per day or year, then, T also needs to
be accounted in days or years. The variance is another variable for measuring risk and
is defined as the volatility squared.
15
4 OWNERSHIP STRUCTURE AND INVESTOR TYPES
This part of the theory will discuss the various controlling investor types, such as
families, insiders, institutions and the state. I will analyse their investor rationale as
well as their impact on company risk taking. Before moving on to the investor types
themselves, I will first describe the various defensive actions that owners can use to
secure their influence in companies.
4.1 Ownership structure
As a company gets new owners, e.g. through an initial public offering (IPO), the
founders’ ownership will be diluted. There are a couple of actions that the founders can
do in order to keep high voting rights in the company, and therefore control. The most
common measures include pyramid structures, dual share classes and cross-holdings.
The purpose of these actions is to separate voting rights from cash flow rights, by
transferring significant voting rights but only a fraction of the cash flow rights.
Regardless of what type of structure is used, the ownership can be described as a
controlling-minority structure (CMS).
The separation of voting and cash flow rights will also create some conflicts of interest
between parties. As an investor has more voting rights than equity rights, he can
influence the company in ways that maximizes his own utility, e.g. the investor could
raise or lower risk taking according to his own preferences. By doing this the investor is
expropriating minority shareholders in the firm. There is, however, little that can be
done to prevent the controlling-minority structures described below to expropriate
other shareholders.
4.1.1 Dual class shares
Perhaps the easiest form of CMS is the creation of a dual class share structure, where a
company has two or more stock types. Each stock class will have a different amount of
voting and equity rights, where one of the stock classes will have e.g. 10-times the
voting rights compared with the other stock class, whereas the equity rights are equal.
Hereby, a founder can keep a significant part of the voting rights to himself while the
firm raises new capital by a share issue of the other stock class. This structure gives the
controlling shareholder a strong foothold in the company, making it difficult for other
shareholders to have their voices heard in the company. The controlling shareholder
16
can therefore use his voting rights and effectively influence manager’s decisions. This
form of CMS is the only one that does not require the creation of multiple firms in order
to secure large voting rights. (Bebchuk et al. 2000)
Although the structure is simple, dual class stock is not the most common CMS
structure. One reason might be that it is relatively difficult for a company to introduce
dual classes at a later date. It is obviously easier for large founding owners to introduce
dual stocks before an IPO, as they have a large influence in the whole IPO process.
However, if we assume a situation where an investor has increased ownership gradually
to say, 30%, it will be difficult for this investor to introduce dual class stocks to the firm.
It will be easier for the investor to use other CMS structures that are discussed later.
Another reason why dual class stocks are not that common is due to the restrictions
that corporate law puts on the allocation between voting and equity rights. Laws usually
restrict the ratio of voting rights per equity right per share, which makes this type of
CMS relatively inefficient (Bebchuk et al. 2000). Legal jurisdictions cannot wholly
explain the relatively low popularity of dual class shares. La Porta et al. (1999) show in
their study that even in countries with dual share classes, CMS companies usually do
not reduce the control rights to the legal minimum.
Dual class stocks are particularly popular in Sweden and South Africa. In Sweden, the
best example is the Wallenberg companies. The Wallenberg Group owns a significant
share of large, international companies such as ABB, Electrolux and SEB through its
Family Trust and principal holding company Investor. The family holds some 40% of
the listed shares on the Stockholm Stock Exchange. The family’s voting rights in
holdings through Investor consists of 50%, whereas the equity rights consist of 23%
(Wallenberg.com). Dual class stocks are used by some 9% of companies on the Helsinki
Stock Exchange (Euroclear). Probably the best example of this CMS structure in
Finland is the Herlin family, which holds more than 50% of voting rights in KONE,
through its ownership of class-A shares, which were originated prior to the listing of
KONE.
4.1.2 Pyramid structures
An investor can create a CMS structure with a single stock class, by building a pyramid
structure of multiple corporations. In a pyramid structure of two companies, the
investor has a controlling minority ownership in an investment company, which in turn
17
is the controlling minority shareholder in an operating company. The same applies for
a structure with two or more holding companies. It is much less costly for the investor
to become a controlling shareholder in the operating company through the ownership
in the holding company, compared to a direct holding in the operating company. This
structure also implies that the investor has higher voting rights compared with equity
rights. (Bebchuk et al. 2000).
La Porta et al. (1999) find in their study that pyramid structures are the most common
mechanisms for concentrating control in CMS structures. There are probably two main
reason for the use of pyramid structures; firstly, as mentioned earlier it is far less costly
to buy a controlling minority ownership through a pyramid structure compared to a
direct holding; secondly, a pyramid structure can make the investor more anonymous
for the general public. A pyramid structure consisting of one holding company is also
popular in Finland due to taxation reasons as an investor can raise part of the income
from the holding company as tax-free.
The use of pyramid structures is common both in Asian countries (Claessens et al.
1999) and some European countries (Bianchi et al. 1997 and Holmen & Hogfeldt 1999).
Hong Kong is especially known for families with large pyramid structures.
4.1.3 Cross-ownership
Cross ownership refers to a situation where companies, doing business together, own
part of the other company. In other words, companies with business relationships are
linked horizontally through cross-holdings that reinforce the decision-making power in
both companies. Cross-ownership differs from pyramid structures as the voting rights
for controlling the companies remain across the range of companies, instead of in the
hands of a single shareholder or company. (Bebchuk et al. 2000). Cross-ownership is
mainly found in Asia, and less common in the rest of the world.
4.2 Investor types
In this chapter, I have already discussed the various ways in which shareholders can
create ownership structures, where they keep significant voting rights in the company,
while equity rights remain relatively low. In this part of the chapter, I will have a look at
how one can assume that different investor types use this situation to their advantage.
18
4.2.1 Insiders
An insider can be described as a person who is either a CEO, member of the executive
board or the board of directors and holds an ownership stake in the company. By
having both control rights, through the position as executive, and equity rights, through
shareholdings, a mangers interest should be more in line with those of shareholders. As
discussed previously, the moral hazard theory gives a good theoretical reference on the
theory on risk taking behaviour of insiders.
The existing literature does argue for a positive relationship between firm risk taking
and equity ownership, where an increase in insider ownership aligns manager’s interest
with those of shareholders (e.g. Chen & Steiner 1999, Shleifer & Vishy 1986 and
Gadhoum & Ayadi 2003). On the other hand, Wright et al. (1996) show in their study
that insider ownership does increase risk taking, however, the relationship may become
negative at high levels of insider ownership. In other words, low level of insider
ownership increases firm risk taking, whereas high levels of insider ownership is
associated with lower risk taking. This would imply that insiders with large holdings
perceive that a too large part of their wealth is tied to the company and their willingness
to invest in riskier projects decreases.
4.2.2 Foreign ownership
The basic definition of a foreign investor is a person who invests in assets that exists
outside of the person’s country of residence. Foreign investors are commonly not
looking for a controlling ownership as they are often looking for geographical
diversification. However, at some instances foreign ownership can lobby for foreign
persons to the corporate board. This is perceived to have a positive effect on the
company as an outsider might bring new thinking and ideas into the corporation.
Otherwise, foreign owners usually have little effect on the companies themselves. The
implications are, though, very different if a Finnish company/subsidiary is acquired by
a foreign company. Finnish takeover targets usually go through a considerable change
in company structure, as the company’s functions are streamlined according with its
acquirer. Further, it has been proven that international acquisitions of Finnish firms
have a positive effect on target companies (Ylä-Anttila et al. 2004).
Finnish law had strict regulations in the past regarding the level of foreign ownership in
Finnish companies, which resulted in low foreign ownership (3-9%). However, these
19
regulations changed in 1992, when the market was opened freely for foreign ownership
and as a consequence foreign ownership increased from 9% in 1992 to 65% in 1999
(Ylä-Anttila et al. 2004). Some studies have also argued that the Helsinki stock
exchange is among the most international, due to high percentage of foreign investors.
This effect is largely due to the Nokia effect, as Nokia consisted at its height of some
70% of the market capitalization on the exchange, whereof 90% were foreign investors.
Therefore, we had a considerable bias that was not taken into account.
The relationship between foreign investors and risk taking is quite unclear. Some
foreign investors seem to have interest in affecting the people chosen to the board,
whereas most investors seem to be inactive. Ylä-Anttila et al. 2004 made a study on
foreign investors on the Finnish market and they found a positive effect between
foreign investors and firm performance. I however, think that the data might have a
bias, as it only includes the years from 1992-2002, right after the regulation change.
Foreign investors could therefore choose to invest only in the most promising
companies, such as Nokia, which probably skewed the data.
4.2.3 Family ownership
Family ownership is very prominent in many Finnish firms, both listed and privately
held. Examples include listed firms such as KONE, Lemminkäinen and Ahlstrom, and
privately held firms such as Fazer, Paulig and Etola. Family ownership can be seen as a
special case of insider ownership, as the family commonly has a representative either as
the CEO or in the Board of Directors.
Looking at the situation from a shareholder protection point of view, Burkat et al. 2003
reported a correlation between minority shareholder protection and ownership
concentration. The study shows that agency costs increase as the juridical protection
decreases, which in turns gives a controlling family ownership even higher ownership
concentration and control. On the other hand, it has been shown that families usually
act as a substitute of sorts for juridical protection, as the family wants to protect its
wealth tied to the firm (Holan & Sanz 2006). The relationship between family dynamics
and juridical protection can be illustrated according to table 1.
20
Strong juridical protection
of minority shareholders
Weak juridical protection of
minority shareholders
Good family
dynamics
The minority owners interests
are taken into account, easy to
keep existing and find new
minority owners
The family works as a substitute
for the juridical protection, but
can be weakened by bad family
relations
Dysfunctional
family
Current minority owners follow
the company, difficult to find
new owners
Expropriation and
misunderstandings with
minority shareholders
Table 1 The relationship between families and minority owners as described by Holan & Sanz 2006
The table speaks for itself, good juridical protection and functioning family dynamics, is
represented by minimum agency costs between owners and managers. In the opposite
corner, where both the juridical protection and family relationships are weak, the
relationship is characterized by a situation with increasing risk for major agency costs
between parties.
It is somewhat unclear whether family ownership can create value in a company, in
other words, do they create value by taking on risk or do they destroy value by being too
conservative as owners. Families have large incentives to monitor the management,
when they are controlling shareholders, which should decrease the classical agency
costs. Therefore, the agency theory predicts a positive correlation between family
ownership and value creation in the company. On the contrary, they have a large risk in
the company and might stay as conservative and risk averse investors. The current
literature is somewhat contradictory as some studies find that companies with founders
within the management are more profitable, whereas other studies show that the
company value decreases as family members are nominated to the company
management.
Palia & Ravid (2002) find that companies lead by the founders tend to be more
profitable compared to other firms. Adams et al. (2009) also find a positive correlation
between founders as CEOs and firm performance, and show that the founders usually
step down from the position as CEO only when firm performance is high. A further
21
study by Anderson & Reeb (2003) prove that firm performance is higher for companies
with families as controlling shareholders compared with other form of controlling
shareholder types. Additionally, moral hazard problems appear to decrease in firms
with families as controlling shareholders.
On the other hand, Smith & Amokau-Adu (1999) show that firm value decreases when a
family member is nominated as executive, whereas there is no reaction to firm value as
outsiders are nominated as executives. The reason for the negative reaction appeared to
be the newly appointed executive’s low age, which reflects to low experience.
Bennedsen et al. (2006) shows that the short-term (two days) stock return decreases by
1% when a family member is nominated, whereas it increases by 2%, when an external
person is nominated. Further, Anderson et al. (2003) showed that families tend to
avoid risk, as they want to secure the value of the firm for the next generation.
To summarize, the relationship between family ownership and risk and return is rather
unclear based on previous research. Given that shareholder protection is stable and
fixed during the time period of the sample in Finland, we can assume that the amount
of agency conflicts in a company is given by the dynamics within the family, as can be
seen from table 1, instead of fluctuating and unclear shareholder protection.
4.2.4 Institutional investors
The group of institutional investors consists mainly of various funds, such as pension,
mutual and insurance funds. These investors are a significant group of investors, as
they possess a large amount of the capital under management. This means that they
have a large amount of capital that needs to be invested. Institutional investors usually
have regulations that limit the managers acting and require the manager to have
sufficient information about companies that they invest in. This implies that they
usually have a close relationship with companies that they invest in, in order to be
updated on company issues, which in turn suggests that they possess inside
information that can be used as a valuation tool. Getting hold of information from the
company obviously takes time and is somewhat costly for the institutional investor,
however, it is usually very fruitful as the potential upside from a close relationship with
managers can be very high. (Schnatterly et al. 2008)
Institutional investors commonly own large blocks of shares, which can be both a
positive or negative aspect considering how they can affect manager’s behaviour.
22
Edmans (2009) argue that large block holders with long investment horizons, such as
institutional investors, have a strong incentive to manage the firm’s fundamental value.
The investors can then trade on inside information, causing the share price to reflect
fundamental values rather than current earnings. This does not satisfy managers, as
share prices will seem to be off, and will therefore encourage mangers to invest in long-
term growth projects, instead of short-term profits.
Institutional investors are thus mostly governed by their internal regulations, which
largely affect their ability to impact company management. On the other hand,
institutional investors usually have a close relationship with companies they invest in
and therefore have a lot of information regarding e.g. risk taking and that they
therefore can choose to invest only in companies which risk profile suit them.
Therefore, it is difficult to assess how an institutional investor will affect company risk
taking.
4.2.5 The state as investor
The state is a major player in some Finnish listed companies. The state has a majority
ownership in three listed companies (Fortum, Neste and Finnair), as well as a minority
ownership in some 10 companies. There are also numerous large privately held
companies where the state has significant ownership, and can hence influence decision
making. In state-owned non-listed companies, the state tends to drive for decisions
with political value, instead of maximizing the value of the company itself.
Listed companies with high state ownership can’t be controlled in the same way as
private companies, as companies on the stock exchange have stricter regulations.
Further, the ownership policy must be consistent and equal throughout all companies,
as investors will get rid of their shares, if large changes are made by influence of the
state, for projects that maximize the value for the state instead of the value of the
company.
Pedersen & Thomsen (2003) show that the state puts considerable thought on its own
political goals, and hereby drives matters such as pricing of products and employment,
in ways that maximize their political agenda, instead of firm value. These decisions
decrease the efficiency of the company and will often be reflected in negative firm
performance and value.
23
Foreign investors, as well as some domestic investors, usually see state ownership as a
negative factor, which is usually associated with risk. Therefore, these companies will
be perceived as riskier and investors will require a higher rate of return on these shares.
La Porta et al. (2002) studied how state ownership affects banks and their
performance. They found that the state drives their political agenda through control in
banks, which in turn leads to inefficiencies and slow economic and financial
development in both wealthy and poor countries.
As a conclusion, one can say that large state ownership tends to have a slightly negative
impact on at least firm performance, but there has been no clear research on how risk is
affected. However, given the lower firm performance, it could be argued based on the
theory between risk and return, that firm risk taking is also lower when the state has
larger shareholdings.
4.2.6 Private equity investors
Generally, Private Equity (PE) investors invest in both listed and unlisted firms. In
Finland though, most investments are done in unlisted companies, due to the small
number of firms on the Helsinki stock exchange, and hence the possibilities to find new
opportunities from listed firms are slim. Private equity firms make acquisitions,
commonly called buyouts, in order to gain a majority stake in the target company. The
investment period tends to be between 3 and 10 years, where the old/new management
is usually required to buy a small amount of shares that incentivize the management to
perform well. The investments are made through limited partnerships, where the
investors have very limited liabilities, whereas the PE-firm takes on the administrative
tasks of the investments. The PE-firm usually only has a minor equity stake in the
limited partnership, whereas the limited partners own the majority equity stake. The
voting rights, are however, distributed fully to the PE-firm, leaving the limited partners
without any decisions making power.
The companies owned by PE firms tend to take on a substantial amount of debt, as this
increases the return that the PE firm and its limited partners get on the investment.
The Finnish private equity market is rather underdeveloped as only 0,4% of GDP was
invested in risk capital investments during 2011-2012, whereas the same number was
0,8% in Sweden, where the private equity market is much more developed.
24
There are a rather limited number of studies that show what private equity backed
companies risk and return characteristics look like. This is due to the limited
availability on data as PE firms disclose very seldom any data on their investments.
Ljungqvist and Richardson (2003), argue that although private equity backed firms
might have higher risk, they still have a better risk-adjusted return as the general
return on PE-investments are high and diversified. Emery (2003) showed that private
equity firms have a low correlation with stock exchanges, making it an attractive
investment class. However, the general thought is that private equity backed firms tend
to take on more risk, as they look for considerable growth.
4.2.7 Summary on investor types
The issues between minority and majority owners will always exist in companies, where
the majority owner will control the situation, irrelevant form the existence of
controlling-minority structures. Depending on who the controlling owner is, they will
have different preferences regarding how the firm should be lead and what level of risk
that should be chosen.
The relationship between insider ownership and risk can be described by a curve where
zero ownership may reflect in low risk taking, a modest ownership (compared with the
insider’s total wealth) increases risk taking, whereas too high ownership can again
lower risk taking. Families are usually perceived to hold a low or medium level of risk,
and can at times work as a substitute for juridical protection for minority shareholders.
Institutional investors on the other hand, are characterized as investors with significant
capital holdings and block holders. They also tend to have more detailed information
on the firms as they commonly have close relationships with managers. The state is
perceived as an investor that invest in strategically important companies, however the
state tends to drive a political agenda, instead of maximizing firm value for
shareholders. This tends to lead to less efficiency in firms with the state as a controlling
owner. Lastly, Private Equity firms are generally assumed to have higher risk taking
profiles in their portfolio companies compared with other firms.
25
5 PREVIOUS STUDIES
In this chapter, I will describe three research papers that are relevant to this thesis.
None of the papers can be directly comparable to my study, but they have elements that
are closely linked with this paper. The first paper has the closest similarity to my study
as it takes into account both the ownership structure and various owner types and how
they affect corporate risk taking. The second article studies the relationship between
managerial wealth and its impact on risk taking. The various measures of risk used in
this study are of high relevance for my study. Finally, the third paper describes
founding family ownership and its relation to risk, calculated through publicly traded
debt. This paper is of relevance as many Finnish firms have considerable founding
family ownership.
5.1 Wright, Ferris, Sarin & Awasthi (1996)
Wright et al. write in their article “Impact of corporate insider, blockholder and
institutional equity ownership on firm risk taking” about ownership concentration and
its impact on risk taking. The study puts considerable weight on the relationship
between insiders and risk taking. The authors find that low to medium managerial
ownership increases firm risk taking, whereas high managerial ownership decreases
firm risk taking, as managers’ portfolios become less diversified. The study also looks at
the relationship between blockholders and institutional ownership on firm risk taking.
This paper is of special interest as it puts considerable weight on insider ownership and
its effect on risk taking at different levels.
5.1.1 Data
The study uses data on listed companies from 1986 and 1992, to test whether their
hypothesis is stable through time. The data on a company had to meet three restrictions
in order to be taken into account in the study; 1) insider equity ownership was
available, 2) financial analysts’ forecasts on earnings per share (EPS) had to be
available and 3) financial data had to be available in COMPUSTAT so that Tobin’s Q
could be calculated. A last requirement stated that companies needed to have fiscal
years from January-December, so that statistical estimation would not have a bias. The
final data that filled all requirements included 358 firms from 1986 and 514 firms for
1992. The data was later extended to include financial data from 1988 and 1994, as
26
ownership structure in a given year may affect corporate risk taking in following years.
Hence, ownership data from 1986 was used to evaluate firm risk raking in 1988 and the
influence of firm ownership in 1992 on risk taking in 1994.
5.1.2 Method
The method included a moderated cross-sectional regression analysis, in which risk
taking, defined as the standard deviation of the spread between financial analysts’
forecasts on EPS, was regressed against separate variables of insider ownership. The
study used Tobins Q as a moderator variable to test whether or not a company had
growth potential, where after the data was divided in two groups. The authors believe
that a nonlinear relationship exists between insider ownership, therefore ownership
was divided into groups where managers had low ownership (0-7,5%) and high
ownership (7.5%<). Control variables were used to control for size (assets) and industry
effects (two-digit SIC code dummies).
5.1.3 Results
The results prove that there is a nonlinear relationship between insider equity
ownership and corporate risk taking. Low insider equity ownership correlates positively
with corporate risk taking, whereas high insider ownership tends to reduce firm risk
taking. The relationship between institutional equity ownership and risk taking is
positive, suggesting that institutional investors do use their position as controlling
owners on firm risk taking. Further, the results show that blockholders have no
statistically significant effect on risk taking, meaning that a blockholder, on average,
does not seem to influence corporate risk taking, although blockholders would have the
possibility to do so.
5.2 Coles, Daniel & Naveen (2006)
Coles et al. article “Managerial incentives and risk-taking” discusses the relationship
between managerial compensation and investment policy, debt policy and firm risk.
The authors use managerial ownership to test whether it incentivizes the manager to
take on more risk in the company. The primary variable used for managerial risk is the
sensitivity of CEO wealth to stock return volatility (vega). The study finds a positive
correlation between CEO compensation and firm risk, measured with a number of
variables. Further, riskier policy decisions commonly lead to compensation structures
27
with higher vega. This paper is of specific interest as it includes relevant measures of
risk.
5.2.1 Data
The study uses data from 1992 to 2002 for companies on the following indices; S&P
500, S&P 400 Midcap and S&P Smallcap 600 (finance and utilities firms are
eliminated). The compensation data is taken from Standars & Poor’s Execucomp
database and contains the following variables both for the CEO and for the top five
managers; salary, bonus and total compensation. The total data sample consists of
10,687 observations after data requirements have been met.
The authors define delta as the change in dollar value of the executive’s wealth for a one
percentage point change in stock price. Vega is defined as the change in dollar value of
the executive’s wealth for a 0,01 change in the annualized standard deviation of stock
return. The following variables are used as variables measuring investments and
financial policy; (1) R&D, defined as research and development expenditures related to
assets, (2) CAPEX, defined as capital expenditures (capital expenditures less sales of
property, plant and equipment) related to assets, (3) Segments defined as the number
of different businesses in which the firm operates, (4) Herfindahl Index captures
income concentration between segments and is defined as the sum of the square of
segment sales divided by the square of firm sales and (5) Book leverage defined as total
book debt scaled by book value of assets. The authors assume that the variables
presented above should be captured in stock return volatility (Firm Risk), defined as
the logarithm of the variance of daily stock returns. Following variables where used as
control variables; Logarithm of sales, Market-to-Book, Surplus Cash, Sales Growth,
Stock Return, ROA, Dividend Cut, CEO Turnover, Net PPE, Z-Score (proxy for the
probability of bankruptcy), CEO Tenure and CEO Cash Compensation.
5.2.2 Method
The authors use panel data with the fixed-effects model. The study regress each of the
five risk variables presented above, against vega, delta and the other control variables.
Further, the regressions are made separately for both CEOs and managers. All
regressions are also run through robustness checks, using 3SLS regressions. This leads
to a wide range of regressions, making the results even more thorough and robust.
28
5.2.3 Results
The authors find a strong causal relationship between managerial compensation, and
investment and debt policy. The results also show that higher vega implies riskier
policy choices, which can be noticed from more investments in R&D and less
investments in capital expenditures, higher focus on chosen business lines and higher
firm leverage. These results are in line with hypotheses that higher sensitivity on share
price volatility in the managerial compensation structure incentivizes managers to
invest heavier on risky investments and takes on more debt. The authors also find that
stock price volatility has a statistically positive relationship with R&D as well as
company focus and leverage, and a negative correlation with capital expenditures.
5.3 Anderson, Mansi & Reeb (2003)
Anderson et al. article “Founding family ownership and the agency cost of debt”
discusses the relationship between family ownership and firm risk. The study looks
specifically on how debt financing costs differ between companies where founding
families have large ownership stakes and other companies. The authors expect that
companies with founder family ownership will have lower costs of debt as they differ
from other companies in three distinct ways; 1) families have commonly undiversified
portfolios 2) family firms want to ensure a successful transfer of the firm to the next
generation as a large part of the families’ wealth is tied to the company and 3) the
family has a great pride in the company and will therefore ensure firm survival instead
of concentrating on value maximization. Due to these differences, the authors argue
that family firms will be able to create incentive instruments that better align interest in
the firm, thus lowering agency costs. This article is relevant for my study as it shows the
importance of family ownership, which is considered substantial in Finland.
5.3.1 Data
The study contains data on companies that could be found both in the Lehman
Brothers Bond Database (LBBD) and the S&P 500 Industrial Index between years 1993
and 1998. The LBBD provides monthly observations on outstanding publicly traded
debt. The ownership data is collected by hand from annual reports, and the authors
aimed at finding all the founding families’ ownership, although it might be widely
spread. Any missing data was filled in from the Compustat Industrial Files. The final
data sample consists of 1,052 firm-year observations on 252 firms.
29
The authors argue that there is no generally accepted variable to measure ownership
and therefore they decided to use a binary variable for firms with family ownership.
Other variables such as size of family ownership compared with blockholders, and
family ownership as a fraction of outstanding shares, were used to increase robustness.
The cost of debt on the other hand is measured by the yield spread. This is calculated as
the “difference between the weighted-average yield to maturity on the firm’s
outstanding traded debt and yield to maturity on a Treasury security with
corresponding duration”.
The control variables consisted of Performance (Cash flow/Assets), Risk (the standard
deviation of the firm’s cash flows scaled by long-term debt for the previous 5 years),
Leverage (Debt/Assets), Size (logarithm of assets), Duration as well as a conversion
number for Credit Ratings.
5.3.2 Method
The method used in the study is relatively straight forward; the authors use cross-
sectional regressions to test the relationship between the various family ownership
variables and the yield spread on bonds. The authors also test for the effect of outliers
on the result by eliminating observations based on the R-Student and DFFITS scores.
The authors find that there is no substantial difference in the results after removing
outliers.
5.3.3 Results
The authors find a statistically negative correlation between founding family ownership
and bond yields, confirming that founding family ownership decreases the cost of debt.
The study finds that firms with founding family ownership receive financing on the
market at a 0,32% lower cost compared with other firms. The firm receives the largest
gains when family ownership is beneath 12% of the firm’s shares. However, at
ownership levels above 12%, the cost of debt increases but it is still lower than for other
companies. Finally, the study finds that debt costs rise as a founding family member is
appointed as CEO, but costs still stay lower compared with other firms.
30
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31
6 DATA
This chapter will present the data used in this study. The chapter will be divided into
two separate main chapters as I use two sets of data in the study. The first chapter will
introduce the Talouselämä 500 data, whereas the second part will present the data on
listed companies. Both chapters include descriptive data.
6.1 Talouselämä 500
This set of data is based on the ETLA 500 list of the 500 largest companies in Finland
and includes both listed and privately held companies. The list is published annually. I
have used Talouselämäs modified version of the list, which includes ownership
dummies for companies. The data was received from Talouselämä, however it required
quite a bit of modifications, which will be discussed below. The data includes six years
and spans from 2009 to 2014, as Talouselämä started using ownership dummies from
year 2009 onwards. I have excluded all financial companies, as their capital structure is
very different compared with other firms, an also excluded companies with no values
for the risk variables.
6.1.1 Risk variable
Due to the restrictions in data availability, I will use two measures of risk; Gearing and
Equity ratio. These two variables measure the capital structure of the company. A
company is said to be riskier when the equity proportion is lower, meaning that debt as
a proportion of total assets increases. Gearing is defined as follows:
where net debt consists of long term interest-bearing debt less cash and cash
equivalents (ETLA). Thus, risk increases as gearing increases. Equity ratio is defined as
follows:
where total equity includes the equity value, provisions, minority interest, and concern
reserve. Total Equity and Liabilities includes the amount stated on the balance sheet
32
adding the pension-fund liability deficit (if not already included in the balance sheet)
and removing advance payments (ETLA). This variable works in the opposite way
compared with gearing as risk increases when the equity ratio decreases.
6.1.2 Ownership variable
Talouselämä started adding certain ownership dummies for companies in 2009 (family
and foreign) and extended the amount of dummies in 2011 (municipality, shares
publicly traded, cooperative, venture capital and state). The data did not include
dummies for all companies for all years, and hence I had to manually go through most
of the companies to confirm that the dummies where correct. The ownership data was
gathered from firm annual reports, webpages, Kauppalehti and Solidium.
The purpose of the ownership dummy is to give an indication on the characteristics of
the largest owner. I want to test whether a certain group of investors, whether it be
family, municipality or venture capital, tend to make similar decisions in the company
when it comes to risk taking. As many firms also can have two dominant ownership
characteristics, it is possible for firms to have multiple ownership dummies. For
example, Ahstrom is a listed company, which is controlled by a family, therefore
Ahstrom has an indicator variable for family ownership and one to note that its shares
are listed. Talouselämä did not have sufficient dummies to correctly categorize all
companies, therefore I added the following dummies; 1) Employees for companies that
are owned by its employees such as KPMG and PwC and 2) Subsidiary for companies
that are owned 50/50 by two listed companies such as Steveco. Below is a list of all the
ownership dummies used for this data set.
Table 2 List of ownership dummies used in the Talouselämä 500 data set
Cooperative/association
Employees
Family ownership
Foreign ownership
Municipality
Shares publicly traded
State
Subsidiary
Venture capital ownership
Ownership Dummies
33
6.1.3 Control variables
6.1.3.1 Size
A variable to control for size is needed as companies with different size characteristics
can be assumed to have different risk preferences. A small and growing company might
be more prone to take on debt, as they don’t have enough retained earnings to finance
their operations. Larger firms, on the other hand, commonly strive for less growth and
want to attain a more conservative capital structure, as well as have more retained
earnings. I will use the logarithm of sales as a proxy for size for two reasons, firstly,
previous research (Coles et al 2006) have also used this variable and secondly, the data
given from Talouselämä does not have any other variables that could be used as a proxy
for size. As earlier said, smaller companies often want to take on more risk as they
strive for greater growth compared with larger companies, therefore I assume a
negative relationship between size and risk.
6.1.3.2 Growth
A control variable for growth is useful as companies that strive for growth commonly
take on more risk, in order to facilitate high growth. As classical risk theory suggests,
higher returns and growth can usually only be implemented by taking on significant
risk. I will use the logarithm on sales growth as a proxy for firm growth. This is also in
line with previous research (John et al. 2008). I assume that growth has a positive
correlation with risk, meaning that firms with more growth also take on more risk.
6.1.3.3 Profitability
The relationship between profitability and risk is somewhat unclear, as discussed in the
theory chapter (3.1). The classical pecking order theory suggests that a company with
higher profitability, which in turn increases retained earnings, will have less debt and
therefore less risk. The trade-off theory, on the other hand, suggests that a company
should maximize its tax shield by taking on more debt, which would be reflected in
higher risk. It is therefore difficult to predict which sign the variable should have. I have
used return on investments (ROI) as a proxy for profitability, as it was the most
suitable variable for this based on the data received from Talouselämä.
34
6.1.3.4 Industry dummy
Companies in different industries have different characteristics that might make a bias
in the regressions if they are not taken into account. One example is the financial
leverage of a firm, which can vary vastly between industries. I have therefore included
industry dummies based on Kauppalehti’s industry classifications.
6.1.4 Summary on variable definitions and sources
Below is a table that describes both the primary and secondary sources for information
on the Talouselämä 500 data. The ownership variable required the most attention in
the data gathering.
Table 3 Variable sources for Talouselämä 500 data
Ownership Talouselämä Annual reports, Kauppalehti, Solidium
Gearing Talouselämä -
Equity ratio Talouselämä -
Net sales Talouselämä -
Sales growth Talouselämä -
ROI Talouselämä -
Industry dummy Talouselämä Kauppalehti
Secondary sourceVariable Primary source
35
The variables used in the Talouselämä 500 data are defined as follows.
Table 4 Definitions on variables used in Talouselämä 500 data
6.1.5 Descriptive data
The original data included 3 001 firm-year observations. The one extra company stems
from an additional company that was added to the year of 2010 by Talouselämä in year
2011. All financial firms were removed from the data (170), as these firms have a very
different capital structure compared with other firms, which would distort the results. I
also removed observations that did not have any values for gearing or equity ratio (1
005). It is mostly non-listed companies whose equity and gearing values are missing
and it is unfortunate that this removes a third of all the observations from the sample.
The final set of data consists of 1 826 firm year observations.
The following variables were winsorized at the 1% percentile at both the higher and
lower end of the scale: Gearing, Equity ratio and ROI. This should improve the
statistical significance of the results as extreme values have now been removed from the
sample. The gearing ratio had values below -1 and hence I have used the formula
ln(2+gearing), in order to use all off the values and not having to omit any variables in
the regressions. The table below shows the descriptive statistics on the data set that is
used in the regressions.
Ln Equity ratio Natural logarithm of: equity / total assets
Ln Gearing Natural logarithm of: (interest bearing debt - cash and equiv alents) /equity
Municipality Receiv es v alue of 1 if a municipality is a major owner
Publicly traded shares Receiv es v alue of 1 if the shares are publicly traded
Cooperative Receiv es v alue of 1 if a cooperativ e is a major owner
Venture capital Receiv es v alue of 1 if a v enture capital firm is a major owner
Family Receiv es v alue of 1 if a family is a major owner
Foreign Receiv es v alue of 1 if a foreign inv estor is a major owner
State Receiv es v alue of 1 if the state is a major owner
Employee Receiv es v alue of 1 if it is owned by its employ ees
Subsidiary Receiv es v alue of 1 if it is a subsidiary owned 50/50 by two firms
Ln Sales Natural logarithm on firm sales
Ln Sales Growth Natural logarithm on firms sales growth
ROI (Total profit + Interest expense) / Inv ested capital
Variable Definition
36
Table 5 Descriptive statistics on data used in the Talouselämä 500 sample
The table above gives arise to some interesting facts about the data. The descriptive
statistics show that 8 out of the 13 variables consist of dummies that are given to
companies according to their ownership structure. Family ownership as well as foreign
ownership are the largest ownership types in the sample. At the other end, employee,
venture capital and municipality owned companies are least represented in the sample.
Gearing has a relatively high spread between the minimum and maximum values,
although the variable has been winsorized. This implies that there are very large
differences in capital structures between companies. The same logic also applies for the
equity ratio where the lowest value is close to zero, whereas the highest value is close to
100%.
The sales figures show that the 500 largest companies in Finland in general are
relatively small as the median revenue is only EUR 227mm and lowest value is only
EUR 78mm. The large spread can also be seen from the rather large standard deviation.
There is also a rather large gap in sales growth, as the mean is 5%, whereas standard
deviation is 18%. Further, return on investments (ROI) also has high extreme values,
although winsorized, as the largest ROI is 105%.
Gearing, Equity ratio, Sales, Sales growth and ROI variables are taken as the logarithm
for the regressions, which should make the data more normally distributed.
Gearing -1 ,36 6,53 0,59 0,29 1 ,23
Equity ratio 0,06 0,89 0,46 0,44 0,20
Municipalty 0 1 0,06 0 0,24
Sharespublic 0 1 0,22 0 0,41
Cooperative 0 1 0,1 2 0 0,32
Venturecapital 0 1 0,04 0 0,21
Family Ownership 0 1 0,31 0 0,46
Foreign Ownership 0 1 0,29 0 0,45
State 0 1 0,09 0 0,29
Employee 0 1 0,01 0 0,09
Sales (EURmm) 7 7 ,65 50 7 1 0,00 857 ,7 4 227 ,90 2 7 1 4,00
Sales Growth -41 % 7 8 % 5 % 4 % 1 8 %
ROI -27 % 1 05 % 1 5 % 1 0 % 1 7 %
Std. Dev.Variable Min Max Mean Median
37
27 %
26 %19 %
10 %
8 %
5 %4 %1 %
Family Ownership
Foreign Ownership
Sharespublic
Cooperative
State
Municipalty
Venturecapital
Employee
Figure 1 Distribution of ownership dummies
The data consists of a total of 2 096 dummies, meaning that each company has on
average 1.15 dummies. The graph above clarifies the distribution of ownership
dummies that were assigned to the companies in the sample. The largest owner type is
families (private individuals. The second largest group consists of companies or
subsidiaries of large international corporations. The graph also shows that only 19% of
the largest companies in Finland are listed on the stock exchange, which is somewhat
surprising. The listed companies get a lot of attention in the media, most likely because
their information is public, however more attention could be given to privately held
companies as they represent over 80% of the largest companies in Finland.
Cooperatives are also an important form of owners in Finland, as some 10% of
companies in the sample have this ownership structure. The rest of the companies are
owned by the state (8%), municipalities (5%), venture capital (4%) and employees (1%).
Figure 2 Data distribution based on industry type
36 %
19 %
18 %
12 %
8 %4 %
1 % 1 % 1 %Industrial Products & Services
Industry
Consumer Service
Consumer Goods
Energy
Technology
Telecommunication
Oil & Gas
Healthcare
38
The figure above gives some light on the various industries that the companies are
present in. The largest segment consists of Industrial products & services followed by
general Industry and Consumer service. The smallest segments consist of Oil & gas and
Healthcare firms.
6.2 Data on listed companies
This data set is based on listed Finnish firms. Listed companies have considerably
better reporting regarding ownership structure compared with privately held
companies, which make these companies easier to analyse. The data is restricted to
eight years and consists of years 2008-2014. The restriction is made, as ownership data
before 2008 is more difficult to access from databases due to regulatory reasons. Before
2008 Finnish companies did not have the same obligations to report ownership
structures and hence the data starts from year 2008.
Some Finnish investors have foreign investment companies that invest in Finnish
firms. I have taken this into account when going through the material and changed the
nationality of foreign investment firms held by Finnish investors. Thus, companies with
a foreign ownership indicator variable, does not include these Finnish holding
companies.
The sample only consists of companies that have been listed during the whole time
period, as this makes data handling considerably easier. Financial firms have been
excluded from the sample. Appendix 1 shows the entire list of 92 companies that were
used in the regressions, whereas the table in appendix 2 shows companies that have
been excluded as well as the reason for their exclusion from the data set.
6.2.1 Risk variables
As earlier mentioned, there is no right way to measure risk and it is difficult to define
an exact definition for risk. I have therefore used multiple variables for risk as I hope
that this will yield more robust results.
6.2.1.1 Ln Volatility
This variable measures the volatility of the stock price of a company and is defined as
the logarithm of volatility on weekly returns. The volatility is calculated from prices
39
spanning for one year. Previous studies (Anderson & Reeb 2008) have used this
measure to describe risk however, previous research has commonly used daily data to
calculate the returns. This is not wise on Finnish data, as the Helsinki Stock Exchange
includes many small cap companies that have very thin trading. Therefore, I regard
weekly returns as a more appropriate interval for calculating returns.
Higher volatility on stock returns implies that there is more uncertainty regarding the
firm’s future success. This can in other words be regarded as a definition of risk. One
can expect that a company with stable share price development has less uncertainty
regarding its future development.
When talking about the share price, one has to bear in mind that it does not only take
into account the company’s performance and prediction on its future outcome. Instead,
it also consists of the general state of the economy and its movements. As the data at
hand includes years 2008-2014, it is obvious that general movements in the economy
has had a large effect on stock price volatility and should be kept in mind when
analysing results from this variable. The logarithm of weekly returns would probably be
better suited for time periods with more stable movements in the economy, in order to
capture the firm specific effects. Therefore, I do not expect significant results for this
variable.
6.2.1.2 Ln Gearing
The gearing variable was already discussed in the previous chapter and will be included
for the same reasons.
6.2.1.3 Ln Equity ratio
The gearing variable was already discussed in the previous chapter and will be included
for the same reasons.
6.2.1.4 Standard deviation on ROA
The standard deviation on ROA measures the volatility on returns that a share has in
relationship to its average assets. It therefore describes how effectively a company can
produce returns in relationship to its assets. This measure of risk has been used by
previous research (John et al. 2008) and can also be seen as a measure of profitability.
40
This makes ROA especially good for comparing firms within the same industry. I have
included industry dummies to take into account the difference of capital structures of
firms and the difference in capital structure between industries should be taken into
account in the results.
The standard deviation on ROA can also be seen as a measure of how stable the returns
are. This can be analysed both as a risk measure, but I see it also as an indicator on how
stable returns the firm can achieve. It however, does not give an indication on the level
of returns that the company is producing.
Return on assets can be calculated for various periods of time, such as quarterly, half-
year or yearly basis. I have decided to use it yearly data, due to data restriction for
Finnish firms. The variable is counted as the logarithm on standard deviation on ROA
for three years. The standard deviation has been calculated for three years; for the year
preceding the observation (t-1), the year in question (t) and the year following the
observation (t+1). This is done for all years, except for 2014, which only includes two
years, as financial data was not available for firms for year 2015 at the time of data
collection.
6.2.2 Ownership variables
Gathering the ownership material has been done thoroughly for all companies. The raw
data on ownership structure has been taken from Thomson One Banker, which then
has been gone through manually. The 20 largest owners for each company were
analysed closer, in order to find the ultimate owners of these companies. Ownership
was gathered manually for all firms with dual class shares, as this information was not
available from Thomson One Banker. I added together all the voting rights of members
of the same family, as this gives a better view of the family’s voting rights in total (one
can assume that members of the same family vote together), once the ultimate owners
were identified. Families became the largest owners in many cases once all the shares
were calculated together. Especially families such as Ehrnrooth, Herlin and Erkko
commonly have shareholdings through multiple holding companies, which eventually
made them the largest owners, although the individual family members did not have a
controlling share ownership.
The consolidated ownership data included the three largest owners, which enables me
to have a look at the characteristics of the largest owners as well as the sum of the three
41
largest owners. I have used multiple variables to describe ownership and they will be
discussed below.
6.2.2.1 Ownership concentration
The ownership concentration in a firm is a proxy for how much control an investor has
in a specific company. When ownership concentration is higher, it means that the
investor has more voting power in the company and thus has more possibilities to
impact corporate risk taking. I have used five different proxies for ownership control;
(1) the voting percentage of the largest owner (2) the voting rights of the three largest
owners (3) an indicator that receives the number one if the largest owner has voting
rights in excess of 10%, (4) an indicator that receives the number one if the largest
owner has voting rights in excess of 15% and (5) an indicator that receives the number
one if the largest owner has voting rights in excess of 20%.
The use of dummies to represent different thresholds of ownership is used in most
previous research as it gives a good representation on the different levels of ownership
and how they affect risk taking. Further, the five variables described above are mutually
exclusive, meaning that they will not be used in the same regressions, as they aim at
describing the same phenomena, and have high collinearity.
6.2.2.2 Investor type
Another way of representing the ownership structure is by using dummies for different
ownership types. The owners have been divided into groups according to investor
types. The variable has been determined by the information received from Thomson
One banker however, it has been slightly modified, in order to decrease the number of
ownership types, as some investor types had limited number of observations. Table 7
shows on the left hand side the original investor types that where received from
Thomson One Banker, whereas the right hand side shows the investor type groups used
in the regressions.
42
Table 6 Investor types in the listed sample
6.2.3 Control variables
Next I will present all the control variables that I used in the data set for listed firms as
well as how they have been collected.
6.2.3.1 CEO ownership
CEO ownership is a logical variable to describe ownership as a CEO has considerable
authority to impact the risk taking of a firm. When the CEO buys shares in the
company, he automatically becomes an insider, who hopefully will think about risk
taking from a shareholders’ perspective. Thus, this could alter the risk taking of the
firm. CEO ownership has been collected manually from company annual reports, which
has been very time consuming. Insider ownership is also commonly used in previous
research.
The variable is presented both in total market value and as a percentage of total
outstanding shares. The variable can have a significant impact on risk taking as high
managerial ownership might reduce the mangers risk taking as more of his total wealth
43
is tied to the firm. On the other hand, smaller ownership values might increase risk
taking. Due to this trade-off it is difficult to predict the sign for this variable.
6.2.3.2 Majority ownership
Majority ownership is an indicator variable that takes the value of one when the largest
owner controls more than 50% of the shares in a company. The estimated sign for
majority ownership is difficult to assess, but assuming that the investor owning more
than 50% of a company might not have a diversified portfolio, I therefore predict a
negative relationship between majority ownership and risk taking.
6.2.3.3 Dual class share
Dual class share is an indicator variable that takes the value of one when the company
has two series of shares. This variable is included as the voting rights of the largest
owners seem to be considerably larger compared with companies with only one series
of shares. Regarding the estimated sign, I predict a negative relationship between dual
class shares and risk taking, based on the same argument as for majority ownership;
one can assume that the person also has a large portion of his wealth tied to the
company and therefore also an undiversified portfolio.
6.2.3.4 Foreign investor
Foreign investor is an indicator variable that takes the value of one when the company
has a foreign investor as the largest investor, or among the three largest investors when
using the ownership concentration variable describing the three largest owners.
Foreign investors usually have a diversified portfolio, otherwise they would not invest
abroad, and therefore I predict that the relationship between foreign investors and risk
taking is positive.
6.2.3.5 Size
As discussed in the data chapter on the Talouselämä data, the regression needs a proxy
variable for size, as companies of different sizes have different risk preferences. I have
used the logarithm on annual sales as the proxy for a firm’s size, which is in line with
some previous research. As described earlier, I predict a negative relationship between
the size of a company and its risk taking.
44
6.2.3.6 Growth
This variable was also discussed previously for the Talouselämä data. Companies with
high growth can commonly be characterized by higher risk and therefore this control
variable is relevant for this study. I use annual sales growth as a proxy variable for
growth. As described earlier, I predict a positive relationship between risk and growth.
6.2.3.7 Profitability
Profitability was as well discussed for the Talouselämä. A firm does not commonly
receive high profitability figures, without taking on considerable risk. This is
particularly the case for smaller, less developed companies. I have used return on
average assets as a proxy variable for risk. As described earlier, it is challenging to
estimate the relationship between risk and profitability.
6.2.4 Summary on variable sources and definitions
This chapter illustrates the sources for data on variables as well as the exact definitions
of these variables. The first table is simplified and only includes the main category for
all variables, instead of all the individual variables. Most of the data was extracted from
data bases, however, the ownership data needed a large amount of manual work.
Table 7 Summary on primary and secondary sources for variables
The table below lists all the variables used in the data set as well as the definition for
each variable. All variables were not used in the same regression and this will be
explained in further detail in the methodology chapter.
Risk variables Factset -
All ownership variables Thomson One Annual reports
Control variables Factset -
Industry dummies Factset US Department of Labour
Variable Primary source Secondary source
45
Table 8 Summary on variable descriptions
46
6.2.5 Descriptive data
Table 9 Descriptive data on listed firms
The table above gives a good overlook of the variables used in the listed sample. The
following variables have been winsorized at 1% in the table: Volatility, Gearing, Equity
ratio, Standard deviation on ROA, Sales, Sales growth and ROA.
Risk variables
Volatility 1 0,38 1 ,7 2 4,95 4,64 1 ,83
Gearing 5,21 -0,67 0,52 0,40 0,87
Equity Ratio 0,7 9 0,1 0 0,44 0,42 0,37
Stand ROA 1 7 ,87 0,24 3,52 2,45 3,48
Largest owner
Voting % 93 % 2 % 31 % 24 % 23 %
Ov er 1 0% 1 0 0,83 1 0,38
Ov er 1 5% 1 0 0,66 1 0,47
Ov er 20% 1 0 0,55 1 0,50
Corporation 1 0 0,1 8 0 0,38
Fam ily 1 0 0,57 1 0,49
Foundation 1 0 0,02 0 0,1 5
Inv estm ent Adv isor 1 0 0,06 0 0,24
Pension and Insurance Fund 1 0 0,07 0 0,25
Risk Capital 1 0 0,02 0 0,1 5
State 1 0 0,07 0 0,26
Three largest owners
Voting % 94 % 4 % 45 % 41 % 24 %
Corporation 1 0 0,34 0 0,47
Fam ily 1 0 0,68 1 0,47
Foundation 1 0 0,09 0 0,29
Inv estm ent Adv isor 1 0 0,33 0 0,47
Pension and Insurance Fund 1 0 0,45 0 0,50
Risk Capital 1 0 0,03 0 0,1 8
State 1 0 0,1 3 0 0,34
Control variables
CEO Ownership 0,58 0,00 0,02 0,00 0,08
Majority 1 0 0,25 0 0,44
Dual Class 1 0 0,24 0 0,43
Foreign 1 0 0,1 3 0 0,34
Sales 1 61 40,51 4,96 1 527 ,53 31 2,53 2896,38
Sales growth 7 3 % -51 % 2 % 2 % 1 9 %
ROA 22 % -38 % 2 % 3 % 9 %
Mining 1 0 0,01 0 0,1 0
Construction 1 0 0,03 0 0,1 8
Manufacturing 1 0 0,54 1 0,50
Transportation 1 0 0,1 0 0 0,30
Wholesale Trade 1 0 0,06 0 0,23
Retail Trade 1 0 0,02 0 0,1 5
Serv ice 1 0 0,22 0 0,41
Public Adm inistration 1 0 0,02 0 0,1 4
Max Min Mean Median Std.Dev
47
Looking at the volatility on share prices, it is apparent that there is a large difference
between the maximum and minimum values. Especially the maximum value of 10,4
implies that there are a number of volatile companies, presumably smaller listed
companies. The gearing ratio also has a relatively large gap between the lowest and
highest values. Especially the maximum value of 5,21 implies that there are companies
that have very high net debt compared with equity, and these companies could be
manufacturing companies with large fixed assets. The negative gearing values indicate
that there are companies with very little debt and high cash reserves, thus making it
possible to have negative gearing values.
The equity ratio, is perhaps as expected almost from 0% to 100%, as companies’ capital
structures vary among the companies in the sample. The mean (44%) and median
(42%) implies that companies on average have slightly below 50% debt and slightly
above 50% equity on their balance sheet. The standard deviation of ROA shows how
volatile the ROA is for a specific company and based on the descriptive statistics, the
gap between highest and lowest values are rather large. One has to take into account
that the financial crisis has probably affected the maximum value, as a standard
deviation of 18% is rather a lot.
Moving on to the ownership variables, the voting percentages of the largest owner
already show that there is a big difference between the voting right between companies.
The mean of 31% is rather large and perhaps more than what could be anticipated,
given the fact that all companies are listed. This shows that the investor with largest
voting rights has, on average, roughly a third of all the votes. The other dummies also
reflect the same effect, as 83%, 66% and 55% of companies have an investor with at
least 10% 15% and 20% respectively voting rights.
The largest owner type is clearly families and private individuals as these are the largest
owners in 57% of the companies. The second largest group is corporations followed by
pension and insurance funds and the state. The mean for these variables add up to 100,
as every observation only gets one indicator. The dummies for the three largest owners
however, get a much higher total number as it is the combined number of dummies
that the three largest owners are characterized by. We can though see that families (and
private individuals) are still by far the largest owner, followed by pension and insurance
funds and corporations.
48
The descriptive data also shows that the three largest owners hold on average almost
half of the voting rights, which is a considerable amount. However, the gap between the
largest owner (31%) and the top three owners (45%) is not that large, implying that the
largest holder has considerably more voting rights compared to the average second and
third largest owners.
Regarding the rest of the variables, the CEO ownership variable shows that some CEO’s
hold significant ownership in the companies they work for, and this is certainly because
they are founders or heirs. On average CEO’s hold 2% of the companies’ stocks. Some
25% of the largest owners have a majority holding (more than 50% of voting rights) in
their companies’. Further, dual class shares are present in 25% of the companies.
Figure 3 Distribution of the largest owner by type
Figure 3 displays the distribution of the largest owners for the listed companies.
Families (and private individuals) are rather surprisingly clearly the largest ownership
type. This is followed by corporations, the state and pension and insurance funds.
Another perhaps interesting fact is that investment advisors are a rather small group of
investors. As expected, risk capital investors are a smaller group compared with the
Taloueslämä data.
57 %
1 8 %
7 %
7 %
6 %3 %2 % Family
Corporation
State
Pension and InsuranceFundInvestment Advisor
Risk Capital
Foundation
49
Figure 4 Distribution of the second largest owner by type
Figure 5 Distribution of the third largest owner by type
Figure 4, on the other hand, shows ownership types for the second largest owners by
type, whereas figure 5 shows the same data for the third largest owner. This gives a
more realistic picture of the overall distribution of owners. It is firstly evident that
families are not an equally large ownership type among the three largest owners,
suggesting that a private individual or family prefers to be the largest owner, instead of
being the second or third largest owners. This is an interesting finding as it strongly
suggests that families want to have a strong foothold in one company, being the largest
owner, and hence use their voting rights to impact firm decisions. The large decrease in
family ownership between the graphs also suggests that families are either the largest
owner or alternatively prefer to diversify their holdings.
Corporations seem to have an almost equal share of holdings. Pension and insurance
funds, on the other hand, are more present as second and third largest owners. This
suggests that these funds have a large interest in the companies, but they do not have
37 %
21 %
1 8 %
1 6 %
5 % 3 % 0 %Family
Pension and Insurance Fund
Corporation
Investment Advisor
State
Foundation
Risk Capital
31 %
28 %
1 9 %
1 4 %
4 % 3 % 1 %Family
Pension and Insurance Fund
Investment Advisor
Corporation
State
Foundation
Risk Capital
50
the need to be the largest owners, instead they tend to prefer a slightly smaller stake in
the companies. The same applies for investment advisors, as these investors do not
appear to see the benefit or need of being the largest investors. The rest of the investor
types appear to have roughly the same distribution.
Figure 6 Distribution according to industry type
Figure 6 shows the industry distribution of the companies used in the sample. It
becomes clear that most companies that are included in the data are categorized as
manufacturing companies, followed by service, transportation and wholesale. This
classification differs from the one used in the Talouselämä 500 data, making it slightly
difficult to make any comparisons between the two samples.
6.3 Data discussion
In this chapter I will briefly discuss some of the issues that might arise with the data
used in the study. The main issues are related to survivorship bias, endogeneity and
extreme values.
6.3.1 Survivorship bias
Within finance, survivorship bias reflects a situation where only companies that have
survived a given time period are included in the sample. In other words, companies
that have been removed or added to the peer group are excluded from the sample. This
leads usually to a positive skewness, as only companies that have performed well are
included in the sample. The effect is common in portfolio management.
54 %
22 %
10 %
6 %3 %
2 % 2 % 1 %
Manufacturing
Service
Transportation
Wholesale Trade
Construction
Retail Trade
Public Administration
Mining
51
For my study this means that any firm that has been removed (due to e.g. bankruptcy,
de-listing) or added (due to e.g. listing, spin-off) to the Helsinki stock exchange are not
included in my sample. This creates a certain bias, as the whole spectrum of firms on
the exchange is not included and only companies that have been active during the
whole period are measured. We might therefore see that these companies have a
slightly lower risk taking level, compared to the average.
6.3.2 Endogeneity
There are three different sources of endogeneity; omitted variable, self-selection and
simultaneity. The omitted variable source of endogeneity implies a situation where the
x-variable in the regression model correlates with the error term, which means that the
error term contains some of the explanatory power that is missing from the model. The
effect occurs as one or more variables have not been included in the data, leading to
problems with endogeneity.
Self-selection bias reflects a situation where the endogeneity problem arises from the
choice of the data sample, where the risk is that the sample doesn’t include the whole
population due to e.g. an external factor. There is therefore a risk that the sample
includes observations, which affect the x-variable either positively or negatively, due to
an external, unknown factor.
Endogeneity that stems from simultaneity reflects a situation where we can observe a
causality relationship between the x-variable and y-variable. The source of endogeneity
is in essence that the x-variable correlates with the error term. This can also imply that
the x-variable explains the y-variable, but the situation can also be the opposite, that
the y-variable explains the x-variable. Hence, it is difficult to assess with variable drives
the relationship. (Verbeek, 2012)
In my study, the largest problem probably relates to the simultaneity endogeneity, as it
is somewhat unclear whether an investor invests in a company and then affects the risk
taking once he becomes an owner or if the investor does a conscious decision only to
invest in companies with a certain risk profile. Therefore, it is difficult to assess the
causality of investor’s investment decisions when it comes to risk taking.
52
6.3.3 Extreme values
The problem of extreme values stems from a situation where a few outliers (extreme
values) lie far away from the rest of the data. This can affect the results as these few
data points can skew the aggregated data. I have corrected for this effect by winsorizing
some of the variables, in order to remove outliers. There are only a few variables that
were winsorized, as most of the data consists of dummies.
53
7 METHODOLOGY
This chapter will present the individual regressions used in the study as well as the
methods used and motivations for why they are used. The chapter ends with a
presentation of the hypothesis and expected signs for the variables.
7.1 Models
This chapter describes the different methods used in the study. First I will present the
models for the Talouselämä data, whereas the second part will describe the models
used in the sample with listed firms.
7.1.1 Talouselämä models
In this data set, I have done two regressions, where both have the same control
variables, whereas the dependent variable, measuring risk, is different. The models are
presented below.
Where,
α = is the intercept
β1-9 = Ownership variables; municipality, publicly traded shares, cooperative, venture
capital, family, foreign, state, employee and subsidiary.
β10-12 = Control variables; sales (ln), sales growth (ln) and ROI.
β13-21 = Industry dummies; technology, telecommunication, oil, industry, industrial
products and services, consumer goods, healthcare, consumer service and energy.
ε = the error term
54
it = the time and firm specific terms
7.1.2 Listed sample models
The listed sample consists of four risk variables and five different mutually exclusive
variables to describe ownership. Therefore, the combination of variables is considerably
larger compared with the privately held data set. Below is a description of the models
used for listed companies:
Where,
α = is the intercept
β1-4 = Ownership variables, that are mutually exclusive; voting percentage of largest
owner, cumulative voting rights of the three largest owners, dummy that receives the
number 1 when the largest owner controls more than 10% of voting rights, dummy that
receives the number 1 when the largest owner controls more than 15% of voting rights,
dummy that receives the number 1 when the largest owner controls more than 20% of
voting rights.
55
Β5-12 = Ownership characteristics variables; bank, corporation, family, foundation,
investment advisor, pension and insurance fund, risk capital and state.
β13-19 = Control variables; CEO ownership, majority ownership, dual class shares,
foreign investor, sales (ln), sales growth and ROA.
Β20-27 = Industry dummies; mining, construction, manufacturing, transportation,
wholesale trade, service and public administration.
ε = the error term
it = the time and firm specific terms
I have also used lagged variables for ownership to test if ownership affects risk taking
on a longer time horizon. One can assume that decisions regarding leverage are long
term discussions and therefore the effect of a decision on leverage has a long term effect
on the leverage of a firm. Therefore, I have also included a one-year lag on ownership to
see the effect of ownership on the company’s long term leverage effect. As an example, I
have used the ownership from 2011 to describe the risk taking on firm leverage in year
2012. This doubled the amount of regressions made.
7.2 Estimation methods
Generally, data can be categorized in three different groups; time-series, cross sectional
and panel data, where the characteristics of the data determine the group. The data in
this study consists of company specific data points that vary between years, such as
ownership concentration, which changes yearly but is company specific. We can
therefore categorize the data in this study as panel data, which is a combination
between cross sectional and time-series data. Econometrically the general formula for
panel data is the following:
Where is the dependent variable, α is the intercept, β is a k × 1 vector of parameters
to be estimated on the explanatory variables and is a 1 × k vector of observations on
the explanatory variables, t = 1,…,T; i =1,…,N (Brooks, 2008).
56
The individual regressions have to be tested by using the Hausman test, whether a
fixed- or random-effects method should be used, before running the regressions
themselves in STATA. This study will use random effects models, which enables me to
analyse firm specific variables through time.
7.2.1 Hausman-test
The Hausman-test in STATA determines whether a fixed- or random-effects model
should be used for the data at hand for the specific regressions. The test compares the
two models for the specific data in order to determine whether the fixed- or random-
effects model should be used. The results from the Hausman tests for all the models
show that random effects should be used. The probability value in the Hausman test for
all models was above 5%, suggesting that random effects should be chosen.
7.2.2 Fixed-effects model
There are two requirements for running a fixed-effects model, firstly, all the company
specific variables must vary through time, and secondly, all the variables observations
must exceed N=1. The definition of the simplest fixed-effects model is that the model
allows the intercept in the regression model to differ cross-sectionally but not over
time, while all of the slope estimates are fixed both cross-sectionally and through time.
This can be explained statistically by decomposing the disturbance term, , from the
formula in chapter 7.2, into an individual specific effect, and the “remainder
disturbance” which varies cross-sectionally and through time. We thereby get the
following formula;
Where contains all of the variables that affect cross-sectionally but do not vary
through time and can be thought of as an independent indicator for all variables.
(Brooks, 2008)
7.2.3 Random-effects model
The alternative method for fixed-effects in a panel data regression is the random effects
model. The random effects model is similar to the fixed effects, as it also assumes
different intercept terms for each firm specific variable and these intercepts are again
57
constant over time, and the relationships between the explanatory and explained
variables are proposed to be the same both cross-sectionally and temporally (Brooks,
2008). The main difference between the models is that in the random effects model, the
intercept for all cross-sectional units is assumed to come from a common intercept α
and a further random variable that varies cross-sectionally but not through time.
Econometrically the formula for random effects panel data can be written as follows;
The random effects model does not include dummy variables to capture the variation in
the cross-sectional dimension. This happens instead through the random variable .
The parameters in the regression with random effects will be estimated inconsistently
by OLS, and therefore GLS should be preferred. (Brooks, 2008)
7.2.4 Robust standard errors
The data will need to be tested for autocorrelation and heteroscedasticity after that the
Hausman test has determined whether a fixed or random effects model should be used.
Autocorrelation will be tested with the Wooldridge test in Stata, whereas
heteroscedasticity will be tested with the Walds test in Stata. Stata offers a tool for
correcting models with heteroscedasticity through the use of robust standard errors, in
other words lagged time variables, which can be used with random effects models. The
results from the Wooldridge and Walds test can be found in the model diagnostics
chapter (8.2).
7.3 Hypothesis and expected signs
The main idea with this thesis is firstly, to test whether ownership concentration affects
firm risk and secondly if the largest ownership types affect corporate risk taking.
Therefore, these two questions are included as hypothesis in the study. Further, a
couple of other ownership related characteristics are tested for in the hypothesis and
these include the effect of CEO ownership, majority ownership and existence of dual
class shares and their effect on risk taking. The hypothesis can be found in the table
below.
58
Table 10 Hypothesis for the regressions, where hypothesis 1 applies for both data sets, whereas hypothesis 2-6 only apply for the data on listed companies
The two tables below show the expected signs for the variables for the Talouselämä 500
and listed company data sets. Most of the expected signs stem from the theory chapter
on the different ownership types (4.2). Generally, I have assumed that the following
ownership characteristics decrease risk taking: municipality, state and majority
ownership. On the other had the following ownership types are assumed to increase
risk taking: venture capital, foreign investors and CEO ownership. Larger sales imply a
more mature company, with less risk taking preferences, whereas high sales growth
implies more risk.
Table 11 Expected signs for Talouselämä 500 variables
Hypothesis 1The largest inv estor ty pe doesn't
impact risk taking
The largest inv estor ty pe does
impact risk taking
Hypothesis 2Ownership consentration doesn't
impact risk taking
Ownership consentration does
impact risk taking
Hypothesis 3CEO ownership doesn't impact risk
taking
CEO ownership does impact risk
taking
Hypothesis 4Majority ownership doesn't impact
risk taking
Majority ownership does impact risk
taking
Hypothesis 5The existance of dual class shares
doesn't impact risk taking
The existance of dual class shares
does impact risk taking
Hypothesis 6Lagged ownership doesn't affect risk
taking
Lagged ownership does affect risk
taking
Variable H0 H1
Municipality + -
Publicly traded shares
Cooperative
Venture capital - +
Family -/+ -/+
Foreign - +
State + -
Employee
Subsidiary
Sales + -
Sales Growth - +
ROI - +
Variable Equity Ratio Gearing
59
It is difficult to assess how high ownership concentration and family ownership will
affect corporate risk taking. This has been discussed previously in the theory chapter
and I have decided to show them as +/- as there is no clear consensus on these
variables.
The rest of the variables are hard to predict or that they are not assumed to have an
effect on corporate risk taking and therefore they have been left as blank.
Table 12 Expected signs for variables in listed sample data
Voting % (largest owner) +/- +/- +/- +/-
Voting % (3 largest owner) +/- +/- +/- +/-
Over 10% +/- +/- +/- +/-
Over 15% +/- +/- +/- +/-
Over 20% +/- +/- +/- +/-
Bank
Corporation
Family +/- +/- +/- +/-
Foundation
Investment Advisor
Pension & Insurance Fund
Risk Capital + + - +
State - - + -
CEO Ownership + + - +
Majority - - + -
Dual Class
Foreign + + - +
Sales - - + -
Sales growth + + - +
ROA + + - +
Variable Volatility GearingEquity
RatioStdv ROA
60
8 REULTS
8.1 Talouselämä 500
The Talouselämä 500 sample uses two risk variables; equity ratio and gearing. Hence, a
total of 2 regressions were performed for this data. When interpreting the results, one
has to bear in mind that when the equity ratio increases, it means that risk decreases,
whereas the opposite is true for the gearing ratio.
Table 13 Talouselämä 500 results
Municipality -0.508 0.21 2
(-2.01 )** (1 .1 3)
Publicly traded shares 0.053 -0.084
(1 .00) (-1 .1 5)
Cooperative 0.002 -0.046
(0.02) (-0.63)
Venture Capital -0.335 0.1 1 9
(-2.7 2)*** (1 .51 )
Family -0.029 -0.036
(-0.34) (-0.7 2)
Foreign -0.37 0 0.099
(-3 .53)*** (1 .63)
State -0.003 -0.07 2
(-0.04) (-1 .32)
Employee -0.062 -0.506
(-0.27 ) (-3 .7 8)***
Subsidiary -0.41 2 0.086
(-1 .64) (0.7 6)
Ln Sales -0.07 4 0.028
(-2.46)** (1 .7 8)*
Ln Sales Growth -0.21 6 0.07 2
(-5.1 3)*** (2.03)**
ROI 0.505 -0.564
(5.41 )*** (-7 .33)***
Constant -0.042 0.7 7 8
(-0.1 5) (4.49)***
Observations 1 826 1 826
R sqd 1 6,9 % 1 9,7 %
Industry dummy Yes Yes
*** 1 % significance **5% significance *1 0% significance
Ln Equity Ratio Ln Gearing
61
The result above shows that municipality ownership has a statistically significant
negative relationship with equity ratio. This suggests that companies owned by
municipalities have higher risk compared with other companies. This can partly be
explained by the fact that most municipality-owned companies are energy and utility
companies, which are characterized by having large tangible assets, such as
manufacturing plants, which enables these companies to have significantly higher
leverage ratios compared to other companies. Further, these companies are usually
backed by the municipalities, which increases their ability to take on more debt. This
should be taken into account in the industry indicator variable, but seems to be strong
enough to affect the results. This is not in line with my estimated sign for
municipalities, as these companies are commonly characterized by slow and stable
growth. Previous research and theory also suggests that municipality/state owned
companies should have a lower level of risk taking, as they use the companies often to
drive a political agenda, instead of maximizing financial utility.
Secondly, venture capital backed companies appear to have a positive impact on risk,
meaning that the presence of a venture capital backed firm takes has higher firm risk
compared with other companies. This is in line with my hypothesis as venture capital
firms seek to grow both sales and profitability, through adding leverage in the
company. Previous research also suggests that venture capital backed companies
should have increased risk taking.
Thirdly, the results suggest that foreign ownership has a positive impact on risk,
suggesting that companies owned by foreign investor have higher risk taking compared
with other firms in the sample. This is also in line with my hypothesis, as foreign
investors usually have a more diversified investment portfolio and are therefore more
prone to higher risk taking. However, one has to bear in mind that many of the
companies characterized by foreign ownership are subsidiaries of large, multinational
companies. These companies can therefore use higher leverage as the parent company
can at any given time inject more capital to the subsidiary if it becomes necessary. The
interpretation of the result on foreign ownership is therefore somewhat unclear.
The results also show that employee owned companies appear to have a statistically
significant negative relationship with gearing, implying that firms owned by its own
employees have a tendency to take on less risk compared with other firms. This can
62
partly be explained by the nature of the companies that are employee owned, as they
tend to be large accounting firms, which need less risk to operate and are also restricted
not to use excessive risk taking.
Sales appear to have a statistically significant relationship with both risk variables and
this implies that larger firms take on more risk compared with smaller firms. The same
applies for sales growth. This means that firms that grow faster have more risk and
larger firms take on more risk. The result for sales variable is not according to
expectations, as I expected larger sales to have the opposite effect on risk taking. Sales
growth is though showing results in line with expectations.
Return on investments shows a strong statistically significant relationship between risk
taking and profitability. The results show that higher profitability is associated with
decreased risk taking, which is not what I expected. Reasons for this could be that a
firm that has a strong profitability doesn’t need as much debt financing compared with
other firms.
8.2 Listed sample
The listed sample has considerably more variables compared with the Talouselämä
data, which results in more variable combinations. The results are separated in three
chapters, making it easier to follow the difference between the variables used in the
regressions. A total of 44 regressions where performed for the listed sample.
8.2.1 Results for the basic sample
In the first stage of results, I have included 20 regressions as there are four risk
variables and five mutually exclusive ownership variables. All of the 20 regressions are
included in the table on the next page. Before going in to the results in detail, I will
highlight the most significant results. To start, it is quite clear that ownership
concentration in itself does not seem to affect the risk taking, but instead it is the owner
type that influences risk taking. A company with dual class shares has a significantly
lower risk taking level compared to other firms, whereas firms owned by families and
corporations have higher risk taking preferences.
Now, moving on to the more detailed analysis of each variable. To start, it is apparent
that ownership concentration only has minor effect on firm risk taking. The form of
63
ownership seems to have a more dominant effect on risk taking, instead of the
ownership concentration itself. Ownership concentration only has a statistically
significant effect on the standard deviation for ROA, which decreases as ownership
concentration increases, meaning that risk decreases. Thus, it can be assumed that as
the ownership concentration of the largest and three largest owners increase, it
decreases the variation in ROA for the company. The result implies that the return on
assets become more stable through time when the company has more concentrated
ownership. This does not reflect the level of returns, but rather that this variable
becomes more stable.
The results show that increased CEO ownership has a statistically significant negative
impact (although only at a 10% significance interval) on the standard deviation of ROA,
suggesting that increased CEO ownership will decrease the volatility on returns in
relation to assets. This is true for the regressions where I use a dummy to describe
ownership concentration. The result is in line with previous theory as well as previous
research (Wright et al 1996), however, the results are not as significant as I would have
anticipated. None of the other risk variables was statistically significant and I can’t
therefore reject the null hypothesis (hypothesis 3).
Risk appears to decrease when the owner has a majority of voting rights in the
company. This suggests that an investor with more that 50% of the voting rights will
decrease risk in the company as the investor has a large exposure in the company at
hand. The result is in line with the theory in the subject, which suggests that a high
ownership concentration tends to lead to an undiversified portfolio and thus lowering
risk taking.
64
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8
R s
qd
12
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23
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25
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22
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22
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Ind
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ign
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10
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ce
Ln Equity Ratio
StdvROA
Ln Volatility
Ln Gearing
Ln Equity Ratio
StdvROA
Ln Volatility
Ln Gearing
StdvROA
Ln Volatility
Ln Gearing
Ln Equity Ratio
StdvROA
Ln Volatility
Ln Gearing
Ln Equity Ratio
StdvROA
Ln Volatility
Ln Gearing
Ln Equity Ratio
65
What is common for all regressions, irrelevant of the ownership variable, is that the
existence of dual class shares has a statistically positive significance on the equity ratio.
This suggests that firms with dual class shares take statistically less risk compared to
other firms. This was somewhat expected as the person who owns a large portion of
voting rights in a firm through dual class share structure, usually also has significant
cash flow rights in the company. Therefore, the person does not want to take large
risks, and instead wants to preserve the company’s future outlook instead of
maximizing future profits.
Foreign investors did not show any statistically significant relationships with any of the
risk variables, which is somewhat surprising. It was expected that foreign investors
would increase firm risk taking, as a foreign investor most likely already has a
diversified portfolio and is ready to take on more risk.
Family and corporation owned companies have a statistically significant negative
relationship with equity ratio. This suggests that firm risk taking increases when a
family or corporation is among the largest owners. This is also true for all the different
measures of ownership concentration. In addition, corporations appear to have a
statistically significant negative relationship with the standard deviation of ROA,
suggesting that higher corporate ownership decreases the volatility on ROA. This is true
for the regressions where dummies have been used to describe the ownership
concentration. The results for family ownership is interesting as previous research
hasn’t been consistent regarding the risk taking of families, however this result suggests
that families do increase risk taking.
Foundations as an owner, also has a statistically significant positive impact on risk
taking in most of the regressions as well as a statistically negative impact on standard
deviation on ROA. This implies that foundations tend to increase firm risk taking, while
at the same time decreasing the volatility on ROA. These results mean that risk
variables in relation to capital structure have a positive impact on risk taking, whereas
risk measured by realized ROA gives opposite results, which is somewhat contradictory.
One explanation for these results is that the industry dummies do not remove industry
related effects as hoped from the data. The strong correlation between foundation
ownership and risk taking is somewhat surprising. The result suggests that foundations
are among the most risk taking type of owner in this sample.
66
Pension and insurance funds only showed one statistically significant relationship on
risk taking (equity ratio), and only on a 10% level of significance. It implies that the risk
taking increases when pension and insurance funds are the largest owners. However,
the weak significance of the results does not make this finding very robust.
Investment advisors and risk capital as owners did not appear to have any statistically
significant relationship with risk taking. The results regarding risk capital is somewhat
unexpected as theory suggests that firms with risk capital owners tend to have
increased risk taking compared with other firms.
The logarithm of sales has a statistically significant negative relationship with both the
equity ratio and standard deviation on ROA. This suggests that companies with higher
sales will tend to have less risk, which is in line with expectations, as larger companies
are associated with less risk. Sales growth, on the other hand tends to increase the
volatility of the share, although only at a 10% level of significance.
Increased ROA appears to decrease risk taking, which is consistent with three of the
risk variables. This is somewhat unexpected as it shows that firms with high
profitability, in terms of ROA, take on less risk.
8.2.2 Results on regressions with lagged ownership
I have also done the same 20 regressions as in the previous chapter but with lagged
ownership, using ownership data from the previous year (t - 1) to describe a certain
year’s risk taking (t). I have used lagged variables of ownership to see the long-term
effect between ownership and risk taking. This is done as it can be assumed that an
owner will have a larger impact on long-term risk taking, instead of the short term risk
taking.
Before going in to the results in detail, I will highlight the most significant results. In
general, lagged ownership does not seem to give equally much significant results
compared to the regressions in the previous chapters. This suggests that owners
manage to influence the firm risk taking in a short period time and that risk taking
appears to be changing rather rapidly.
Now, moving on to the more detailed analysis of the results. The regression on lagged
ownership shows that higher ownership concentration has a statistically significant
negative relationship with the standard deviation of ROA. This suggests that owners
67
Vo
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8
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Ln Equity
Ratio
Ln Volatility
Ln Gearing
Ln Equity
Ratio
StdvROA
Ln Volatility
Ln Gearing
StdvROA
Ln Volatility
Ln Gearing
Ln Equity
Ratio
StdvROA
Ln Volatility
Ln Gearing
Ln Equity
Ratio
StdvROA
Ln Volatility
Ln Gearing
Ln Equity
Ratio
StdvROA
68
with higher ownership percentage can affect the volatility on ROA in the long term. The
result is quite interesting as it shows that long-term ownership increases the stability of
risk taking in a firm.
CEO ownership appears to have a statistically significant negative (only at 10% level of
significance) relationship with the standard deviation on ROA, which suggests that
CEO ownership decreases the volatility on ROA. This is in line with the results found in
the previous regressions.
One variable that is especially in common with previous regressions is that dual class
shares have a statistically significant positive effect on the equity ratio. This suggests
that firms with dual class shares take on less risk compared with other firms in the long
term. The rest of the ownership variables only have some inconsistent statistical
significance but nothing that I would draw any conclusions from.
The logarithm of sales has a statistically significant negative relationship with both the
equity ratio and standard deviation on ROA in most regressions. This suggests that
companies with higher sales will tend to have less risk, which is in line with
expectations, as larger companies are associated with less risk. Increased ROA appears
to decrease risk taking, which is consistent with three of the risk variables. This is
somewhat unexpected as it shows that firms with high profitability, in terms of ROA,
take on less risk.
Overall, one has to keep in mind that regressions with lagged ownership variables can
also be impacted by endogeneity, as discussed in chapter 6.3.2. Lagged ownership is
generally less sensitive to endogeneity stemming from simultaneity where it is unclear
whether it is the x or the y-variable that drives the relationship. By using the ownership
structure from the previous year, one could assume that these owners had a better
chance to influence risk taking compared with owners in the current period, therefore
the risk for endogenity is smaller.
69
8.2.3 Results on regressions without the main ownership variables
I also performed regressions where I removed the main variables for ownership, hence
only including control variable and the results can be found in table 14.
The results from these regressions convey the same message from earlier regressions
regarding the dual class shares. Thus, this finding gets even more robust. As a further
note, the regression also confirms earlier findings regarding corporations and families
as owners, as it shows that corporations and families increase risk taking in firms
measured by the equity ratio.
The results also show that corporations, families and foundations as owners
significantly decrease the volatility on ROA, suggesting that returns are more stable in
the long term.
The results will be further discussed and analysed in the conclusion chapter. In the next
chapter, I will present the most relevant model diagnostics, related to the data material
and regressions used in the study.
70
Table 14 Results for regressions where the main variable for ownership has been removed
8.3 Comparing results with hypothesis and previous research
In this chapter I will briefly compare the results with my own hypothesis. The
hypothesises are made based on theory on the subject and therefore this chapter is a
reflection on the theory for this subject. Table 15 shows which of the hypothesis could
CEO Own ersh ip 0.5 1 5 0.9 7 4 -0.03 7 -4 .4 00
(1 .2 6 ) (1 .2 2 ) (-0.2 8 ) (-1 .6 3 )
Ma jorit y own ersh ip -0 .03 9 0.02 5 -0.03 0 0.05 0
(-0.6 9 ) (0.3 3 ) (-1 .9 5 )* (0.07 )
Du a l Cla ss Sh a re 0.03 3 -0.1 6 9 0.04 9 -0.6 6 7
(0.4 7 ) (-1 .4 7 ) (2 .01 )** (-1 .07 )
Foreign In v est or 0.04 8 -0.003 0.01 7 0.6 04
(0.5 3 ) (-0.03 ) (0.8 1 ) (0.7 4 )
Corpora t ion -0 .1 3 1 0.02 5 6 -0.04 3 -1 .09 3
(-1 .4 1 ) (0.1 6 ) (-2 .06 )** (-2 .1 1 )**
Fa m ily -0 .08 6 0.08 4 -0.05 9 -1 .1 04
(-1 .02 ) (0.5 3 ) (-2 .5 0)** (-1 .6 9 )*
Fou n da t ion -0 .09 8 0.1 8 4 -0.04 4 -1 .2 7 3
(-0.6 4 ) (1 .2 5 ) (-2 .05 )** (-2 .3 8 )**
In v est m en t a dv isor -0 .05 9 -0.08 0 -0.01 5 -0.07 8
(-0.4 4 ) (-0.5 0) (-0.6 7 ) (-0.09 )
Pen sion a n d In su ra n ce Fu n d -01 .2 0 -0.09 0 -0.03 4 -0.3 7 2
(-1 .4 3 ) (-0.4 3 ) (-1 .6 0) (-0.7 6 )
Risk Ca pit a l -0 .003 -0.2 05 -0.2 09 -1 .08 6
(-0.02 ) (-0.6 2 ) (-1 .5 2 ) (-0.9 5 )
St a t e - - - -
- - - -
Ln Sa les 0.02 3 0.06 7 -0.2 0 -0.7 02
(1 .2 7 ) (1 .1 5 ) (-2 .4 1 )** (-4 .3 3 )***
Sa les Growt h 0.06 5 0.06 0 -0.001 0.5 9 4
(1 .6 9 )* (1 .1 9 ) (-0.07 ) (1 .05 )
ROA -0 .7 2 6 -1 .6 8 1 0.3 5 8 -4 .9 9 3
(-2 .3 3 )** (-7 .7 5 )*** (9 .4 7 )*** (-1 .6 6 )*
Con st a n t 2 .6 00 -0.6 3 3 0.5 3 2 9 .3 6 6
(8 .8 2 )*** (-1 .1 5 ) (5 .3 1 )*** (7 .5 7 )***
Observ a t ion s 6 2 8 6 2 8 6 2 8 6 2 8
R sqd 1 3 ,0 % 2 2 ,8 % 2 5 ,5 % 2 6 ,2 %
In du st ry du m m ies Yes Yes Yes Yes
*** 1 % significance **5% significance *1 0% significance
Ln
Vo
lati
lity
Ln
Ge
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ing
Ln
Eq
uit
y R
ati
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Std
vR
OA
71
and could not be rejected. Firstly, the results show that the largest, or three largest
ownership types, does have a statistically significant impact on risk taking of a firm.
Some of the ownership types have a positive impact on risk taking and others a negative
effect. The results also imply that the ownership concentration in itself, meaning the
percentage of shares owned, does not affect risk taking. In other words, the
concentration of ownership is not a factor that affects risk taking.
Table 15 The results impact on the hypothesis
CEO ownership has a very weak statistical impact on risk taking and therefore I don’t
consider that this variable affects corporate risk taking. Majority ownership on the
other hand shows a statistically significant relationship with risk taking and it turns out
that this variable does decrease risk taking, meaning that a company with an owner
who has more than 50% of the voting rights in the company has a lower risk taking. The
same effect also applies for companies with dual class shares. Lagged ownership does
also affect risk taking, but its effect is considerably weaker compared to non-lagged
data. To conclude, it appears that most of the hypothesis could be rejected.
8.4 Model diagnostics
In this part of the chapter I have a closer look at the possible problems that arises with
model diagnostics, such as normality, multicollinearity, autocorrelation and
heteroscedasticity. The general assumption for least square estimations and their
effectivity is that observations need to be independent and identically distributed,
otherwise the results will not be as reliable. However, most data have some flaws, such
as panel data that commonly suffers from autocorrelation.
Hypothesis 1The largest inv estor ty pe doesn't
impact risk taking
The largest inv estor ty pe does
impact risk takingH1
Hypothesis 2Ownership consentration doesn't
impact risk taking
Ownership consentration does
impact risk takingH0
Hypothesis 3CEO ownership doesn't impact risk
taking
CEO ownership does impact risk
takingH0
Hypothesis 4Majority ownership doesn't impact
risk taking
Majority ownership does impact risk
takingH1
Hypothesis 5The existance of dual class shares
doesn't impact risk taking
The existance of dual class shares
does impact risk takingH1
Hypothesis 6Lagged ownership doesn't affect risk
taking
Lagged ownership does affect risk
takingH1
Variable H0 H1 Result
72
8.4.1 Normality
Normality refers to the normal distribution of data and is crucial for getting reliable
results. A set of data is said to be normally distributed when it follows one of the
normal distributions, which commonly means that the values are close to the mean and
has a low standard deviation. The central limit theorem, however argues that the
arithmetic mean of a large enough number of independent random values will roughly
represent a normal distribution, irrespective of the underlying distribution. Therefore,
a larger sample will generally be more normally distributed compared to a smaller one.
The hypothesis for normality is as follows:
There are various tests for normality, however I use the Jarque-Bera test for all
variables to determine which of them are normally distributed. After running the
normality check for all variables in all of the regressions it becomes apparent that all of
the variables have non-normality in the residuals (Ho can be rejected). This means that
the data is not normally distributed, which will mean that the estimates are less
accurate. However, many of the variables are taken as the logarithm in order to
improve normality figures.
8.4.2 Multicollinearity
Multicollinearity refers to a situation where two variables correlate with each other, in
other words, both of the variables explain partially the same effect in the dependent
variable. This will lead to a situation where small changes in the two variables can lead
to large changes in their predictive power, thus giving slightly misleading results.
However, multicollinearity does not affect the predictive power or reliability of the
model as a whole. The effects of multicollinearity can be tested for by using a
correlation matrix. Two variables are said to have multicollinearity when the
correlation coefficient in the matrix is close to +1 or -1. Multicollinearity will commonly
be present when there are an excess number of variables in the model. There are two
ways to eliminate multicollinearity, firstly, remove the variable all together from the
sample, and secondly, not to use the same variables in the individual regressions.
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Multicollinearity can be tested for using a table to test for the collinearity between
variables. The tables can be found from Appendix 3 and 4. In the listed sample, there
are a few of the variables that have relatively high correlations, however none of these
variables are used in the same regressions. I can therefore conclude that there is not a
problem with regards to multicollinearity in the data for the listed sample.
The Talouselämä sample, however has two variables with high correlation, municipality
ownership and the energy sector. This implies that both variables try to explain the
same effect in the risk variable. This suggests a minor problem with multicollinearity
with this data sample.
8.4.3 Autocorrelation
Autocorrelation occurs when a lagged y-observation has high predictability powers of
future y-observations, which means that the error terms of the observations are
correlated. If a correlation can be found between current values and the lagged
residuals, it weakens the models overall significance, as lagged variables are proxies for
current values. This situation is especially common for panel data, as it consists of both
firm and time specific data. Some financial variables, especially accounting ratios
relating to capital structure, tend to suffer from autocorrelation as these values
generally change gradually through time and seldom have large changes. The
hypothesis for autocorrelation is as follows:
A Wooldrigde-test in Stata is used to test for the existence of autocorrelation in the
data. The tests for all the variables in all the regressions show that the data is
autocorrelated (Ho can be rejected). However, robust standard errors are used in the
regressions in order to decrease the biases created by autocorrelated variables.
8.4.4 Heteroscedasticity
One of the requirements of the data in a regression model is that the variance of the
error terms is constant, in other words homoscedastic. In situations where the error
terms don’t have a constant variance, we have a heteroscedastic set of data, which will
have a considerable effect on the result. Using heteroscedastic data means that the
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variables themselves will still be unbiased estimates, however, the model is no longer
BLUE, which implies that the unbiased estimators no longer have minimum variance.
(Brooks. 2008)
Using OLS in the presence of heteroscedasticity will commonly lead to results that are
inaccurate and misleading. There are however ways to deal with heteroscedastic data,
the first being the use of GLS. By using GLS the model will correct for the existence of
heteroscedasticity. Another way to treat the data is to use robust standard errors in
Stata. The hypothesis for heteroscedasticity is as follows:
The models are tested for heteroscedasticity with the Breusch-Pagan test. The tests for
all the variables in all the regressions show the presence of heteroscedasticity (Ho can
be rejected). However, I have used GLS models and the model is corrected for by using
robust standard errors in the regressions in order to decrease the biases created by
heteroscedastic variables.
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9 CONCLUSIONS
This chapter will start with a discussion on the main results from the descriptive data
and regressions, followed by a critical discussion on the reliability of the study as a
whole. The chapter ends with suggestions for further research.
9.1 Discussion
This paper aims at analysing the relationship between both ownership concentration
and owner type and its impact on corporate risk taking. The data includes two data sets,
one on the 500 biggest companies in Finland and another on listed firms in Finland. I
have used considerable time for finding the ultimate owners of the listed firms, in order
to increase the accuracy of the data. This is something that has not to my knowledge
been done in the past on Finnish data, however the extensive time put on data
gathering has paid off.
The descriptive data gives light on some interesting findings regarding the ownership
structure and ownership concentration. It becomes apparent that the largest owner in
Finnish companies own on average around 31% of the voting right in a company and
that the ownership level is higher than 20% in 55% of the companies. Further, a
majority owner (>50% of voting rights) exists on average in 25% of the listed
companies. This implies that ownership is rather concentrated in Finnish firms. The
results also show that families are the largest types of owners in companies (either
direct ownership or through holding companies). In the listed sample families are the
largest owners in 57% of the companies, which is perhaps more than what was
expected, whereas the same figure is 27% for the Talouselämä 500 sample.
The descriptive statistics also show that families are less present as second largest
(37%) or third largest (31%) owners. This implies that families prefer to be the largest
owner of a company and therefore have control of the company, instead of being the
second or third largest owner. This is already an interesting finding, which shows that
family ownership plays a large role in Finnish listed companies.
Further, the descriptive data shows that pension and insurance funds are much more
commonly the second (21%) or third largest (28%) owner, as opposed to the largest
owner (7%), which suggests that these companies do not have the need to act as the
largest investor in a company. Instead, they like to have a more active investor as the
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largest investor. An alternative theory is that pension and insurance funds simply
cannot become the largest investor due to anchor investors.
The results from the Talouselämä 500 sample implies that municipality owned
companies increase risk taking, although one has to bear in mind that many of these
companies within the sample are in the energy and utility sector. Secondly, venture
capital backed firms tend to increase risk taking, which is expected as these firms aim
for high growth and leverage. The results also suggest that foreign ownership increases
risk taking, which supports the theory that foreign investors already have a diversified
portfolio and therefore look for more risk.
The listed sample gives some further interesting results. The most prominent finding is
that the existence of dual class shares in a company significantly decreases risk taking.
Companies with dual class shares are usually controlled by one or a few owners, who
usually have a significant stake in the company, and hence they have an undiversified
portfolio and thus wants to decrease risk taking in the company.
The regressions also found that families, foundations and corporations as owners
increase the risk taking in companies. As noticed from the descriptive statistics,
families are a large owner and the regressions confirm that they like to use their voting
rights in order to increase risk taking in firms that they control.
Something perhaps surprising is that foreign investors and CEO ownership did not
show a statistically significant relationship with risk taking in this sample, which
suggests that these investors do not use their votes to impact risk taking in listed
companies.
I also used lagged ownership to test for its impact on the relationship between
ownership concentration and risk taking. The results show that increased ownership
concentration decreases the standard deviation on ROA. This implies that ownership
concentration can affect the stability of ROA in the long term. Lagged ownership also
confirmed that the existence of dual class shares decreases the risk taking in a firm. In
general, however, lagged ownership showed less significant results compared with non-
lagged ownership.
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9.2 Critical discussion on the study
This part of the chapter will critically discuss some aspect of the thesis, which should be
taken into account when assessing the reliability of the results. The following items
should be looked at critically and can lead to problems in the interpretation of the
results:
- Manual data gathering
- Survivorship bias
- Assumptions regarding family voting behaviour
- Definition of risk taking
- Talouselämä 500 missing values
Firstly, one has to remember that some of the ownership variables have been gathered
or modified manually, which might lead to inaccurate data. This is true for both data
samples and could thereby imply a bias in the data. Fortunately, most of the data has
only been modified based on data that was extracted from databases, which mitigates
the risks of human errors.
Secondly, the data sample for listed firms only includes firms that have been present on
the OMXH during the whole period, implying that there might be a slight bias in the
data. This has been further elaborated in the “Data discussion” chapter.
Thirdly, I have made the assumption that members of the same family (having the
same surname) will vote in the same way during the AGM and that their preferences on
risk are the same. This might not be accurate for all families, which could mean that
some ownership percentages are not calculated in the correct manner. However, I have
assumed that the majority of families vote in the same way, making it more sensible to
pile together voting rights for families, instead of leaving the raw data untouched.
Fourthly, the definition of risk is somewhat unclear, meaning that not even previous
research has been able to decide what variable is the most accurate proxy for risk
taking. I have decided to include four commonly used variables for risk, as I hope that
some of these should be able to capture risk taking. Hence, if only one of the variables
show a statistically significant relationship, it does not necessarily mean that this gives
an accurate view of the company’s risk taking.
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Fifthly, the Talouselämä 500 data has limited data points, as most of the companies are
privately held and only a limited number of them report all financials. Hence, only half
of the firm year data points could be included from the original data. Therefore, we
might have a bias, as not all of the companies could be included.
9.3 Suggestions for further research
The back-up data for this thesis is rather extensive and especially the ownership data
could be developed into many research topics for future reference. There are also a
number of ways how this thesis could be extended in order to get more extensive
results.
Firstly, it would be interesting to have a closer look at the largest private
individuals’/families investments; does investors have large stakes in only one
company or is the ownership spread into a number of companies? Depending on the
answers, one could construct a dummy variable that explains if the investor has an
undiversified portfolio of listed companies (only has one large stake in one company) or
if the investor has a diversified portfolio (has a large stake in more than one company).
This could yield some interesting facts on investor risk preferences.
It would be interesting to get the needed data for all the companies in the Talouselämä
500 sample. If one would have the time and resources to go through annual reports
(bought from e.g. Asiakastieto) it would be possible to get a more comprehensive
picture of how ownership types affect risk taking as a larger amount of companies
would be included.
It could also be interesting to analyse more in depth the various owner types and what
kind of companies that they invest in. One could guess that families invest in different
companies compared to e.g. the state. A lot of this information could already be found
by just increasing the descriptive data chapter and making it more comprehensive. The
more you have a look at the data available in this paper, the more possibilities you
could come up with.
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SVENSK SAMMANFATTNING
Introduktion till ämnet
Företag är komplexa organisationer med flera interna och externa intressenter som
måste samspela med varandra för att kunna fungera effektivt. Forskning inom
bolagsstyrning (corporate governance) studerar hur de olika relationerna mellan
intressenter i företagen påverkar varandra. Flera studier tittar på aktieägarna och deras
inverkan på företagets beteende och beslutsfattande. Denna studie kommer att
koncentrera sig på samspelet mellan aktieägare och huruvida de har en möjlighet att
inverka på risktagningen i företagen som de har rösträtter i. Studien ser på hur själva
ägarkoncentrationen i företagen samt ägartypen inverkar på risktagningen.
Motivering av studien och syftesformulering
Syftet med studien är att undersöka ifall ägarskapsstrukturen kan förklara
risktagningen i finländska företag.
Geografiskt är studien begränsad till endast finländska företag. Studien inkluderar två
olika datamaterial, den ena med börslistade företag och ett annat som innehåller de
500 största företagen i Finland. Därmed kommer jag inte endast att undersöka listade
bolag, utan även privata bolag.
Studien är avgränsad tidsmässigt till 7 år. Orsaken till att denna tidsperiod har valts är
att den borde ge tillräckligt breda och omfattande data för att få tillförlitliga resultat i
regressionerna. Utöver detta så har ägarskapsdata samlats in för hand, vilket gör denna
process väldigt tidskrävande och därför har inte ytterligare år inkluderats.
Alla finansiella företag har tagits bort från data eftersom dessa företag har en väldigt
annorlunda kapitalstruktur jämfört med andra företag. På grund av detta går det inte
att jämföra finansiella företag med andra företag och därför måste dessa lämnas bort.
Kontribution
Tidigare forskning har aktivt sett på hur insiderägarskap påverkar företagets
risktagning, medan andra ägarskapsparametrar fått mindre uppmärksamhet. Denna
studie kontribuerar till tidigare forskning genom detaljerade data över
ägarskapsstrukturen i företag, något som inte tidigare har använts i publicerade
artiklar.
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Denna studie är unik eftersom den även tar i beaktande privata företag, vilket är
ovanligt i forskning. Tack vare Talouselämä 500-data har denna studie insyn även i
privata företag och därmed bidrar studien även på detta område.
Data i studien var väldigt tidskrävande att samla och modifiera. Den främsta orsaken är
att studien har gått in på att hitta de ultimata ägarna, det vill säga inte endast lita på
data från databaserna, utan även manuellt gå igenom alla de tre största ägarna för alla
företag för varje år och kategorisera dessa rätt. Utöver detta så har jag lagt ihop
rösterna för personer som hör till samma familj, eftersom man kan anta att dessa röstar
på samma sätt på bolagsstämman. Detta borde ge en mera realistisk bild över hur
koncentrerat ägarskapen är i företagen. Denna del av studien bidrar antagligen mest,
eftersom data har samlats in och modifierats för hand.
Presentation av tidigare forskning
Till näst kommer jag att gå igenom resultat från tidigare forskning i ämnet, med
tyngdpunkt på de olika ägartyperna samt hur man kan anta att dessa ägare inverkar på
företagens risktagning.
En insider kan beskrivas som en person som är verkställande direktör, ingår i
ledningsgruppen eller styrelsen och som även är ägare i företaget. Personen har i så fall
möjlighet att fatta beslut kring risktagning, samtidigt som han har en allt större del av
egendomen bunden till företaget. Därmed kunde man förvänta sig att personen
kommer att ha intressen som är mera i linje med de andra aktieägarna. Den befintliga
litteraturen argumenterar för ett positivt förhållande mellan företagets risktagning och
insiderägarskap (t.ex. Chen & Steiner 1999, Shleifer & Vishy 1986 och Gadhoum &
Ayadi 2003). Däremot visar studien av Wright et al. (1996) att insiderägarskap ökar på
risktagningen till en början, men att förhållandet blir negativt vid högre nivåer av
ägarskap. Med andra ord betyder det att en låg nivå av ägarskap ökar på risktagningen,
medan ett alltför högt ägarskap tenderar att minska på risktagningen i företaget.
Utländskt ägarskap kan definieras av en person som investerar i tillgångar utanför sitt
hemland. Generellt sett försöker inte utländska ägare få en kontrollerande position i
företag, istället söker investeraren efter geografisk spridning på sina tillgångar. Det har
dock visats att utländska investerare ofta försöker uppmuntra och rösta för utländska
personer till ledningen och styrelsen. Utöver detta så har inte tidigare forskning
kommit fram till signifikanta samband mellan utländska ägare och risktagning.
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Familjeägarskap är mycket framträdande i många finländska företag, både i listade och
privata företag. Teorin kring familjen som ägare och dess inverkan på risktagning är
aningen tudelad. Då en familj är den största ägaren så vill den såklart minska på de
agentkonflikter som finns mellan ledningen och ägarna, vilket skulle tyda på att
företagets värde ökar. Däremot har familjen en stor finansiell risk i företaget, vilket kan
leda till en lägre riskpreferens eftersom familjen vill säkra den långsiktiga framgången i
företaget istället för att maximera den kortsiktiga vinsten i företaget.
Studien av Palia & Ravid (2002) visar att företag som leds av grundaren tenderar att
vara lönsammare jämfört med andra företag. Adams et al. (2009) hittar även ett
positivt samband mellan grundare som verkställande direktör och lönsamhet, samt att
grundaren tenderar att lämna posten som vd endast då det går finansiellt väl för
företaget. Anderson & Reeb (2003) visar att lönsamheten är högst för företag där
familjen är den största ägaren, jämfört med andra typer av ägare.
Dmith & Amokau-Adu (1999) visar dock att företagets värde sjunker då en
familjemedlem blir vald till ledningen av ett företag, medan det inte finns en signifikant
reaktion i företagsvärdet då en utomstående blir vald till ledningen. En orsak till denna
reaktion är att familjemedlemmen i samplet tenderar att vara yngre jämfört med andra
personer i ledningen, vilket tyder på mindre erfarenhet. Bennedsen et al. (2006) visar
att den kortsiktiga avkastningen (två dagar) minskar med 1 % då en familjemedlem blir
vald till ledningen, medan avkastningen ökar med 2 % då en utomstående blir vald.
Anderson et al. (2003) visar även att familjer tenderarar att undvika risk eftersom de
vill säkerställa företagets värde för kommande generationer. Sammanfattningsvis så
har tidigare forskning kommit fram till varierande resultat gällande förhållandet
mellan risk och familjeägarskap.
Institutionella investerare innefattar generellt olika fond-, pensionsförsäkrings- och
försäkringsbolag. Denna grupp av investerare har en stor mängd tillgångar som skall
investeras, vilket ofta leder till att dessa investerare har en nära relation med företag
som de investerar i. Schnatterly et al. (2008) visar att institutionella investerare sätter
mycket tid och resurser på att ta reda på företagets ställning och kan således antas ha
mera information om företag jämfört med andra investerare. Edmans (2009)
argumenterar för att stora blockägare med lång investeringshorisont har stort
incitament att titta på det fundamentala värdet på företaget i det långa loppet.
Investerarna kan sedan använda den information de fått från företaget för att handla
aktier på basis av det långsiktiga värdet, vilket kan få aktiepriset att reflektera det
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långsiktiga aktiepriset istället för den kortsiktiga värdemaximeringen. Därmed kan
ledningen även ha en tendens att investera i långsiktiga projekt istället för kortsiktiga
projekt då institutionella investerare är bland de största ägarna. Det är dock aningen
oklart hur mycket institutionella investerare påverkar själva risktagningen i företag.
Staten är ännu en stor investerare i finländska företag. Staten är majoritetsägare i tre
finska publika företag och minoritetsägare i ytterligare 10 publika företag. Staten är
även ägare i ett flertal privata företag, där staten tenderar att driva en politisk agenda
istället för att maximera vinsten. Pedersen & Thomsen (2003) visar att staten använder
mycket tid på att utveckla den politiska agendan i företag som de äger, istället för att
tänka på hur företagsvärdet kunde maximeras. Speciellt utländska investerare ser
statligt ägande som en negativ aspekt och kommer att kräva en högre avkastning på
dessa aktier. La Porta et al. (2002) visade i sin undersökning att staten ofta driver en
politisk agenda via banker, och därmed sänker deras värde. Därmed kan man
sammanfatta att företagets värde tenderar att vara lägre då staten är den största
ägaren, medan det inte finns bevis på hur det påverkar risken.
Riskkapitalinvesterare investerar huvudsakligen i privata företag, men även i en del
listade företag. Forskning kring riskkapitalinvesterare är väldigt knapp eftersom de ofta
bestämmer att inte offentliggöra finansiella siffror för företagen de äger. Det är dock
allmänt förstått att riskkapitalinvesterare vill ta an mycket risk i de företag de
investerar i, det vill säga att de har mera lån än det genomsnittliga företaget. Ljungqvist
och Richardson (2003) argumenterar för att även om företag ägda av
riskkapitalinvesterare har en högre risk, så har riskkapitalfonder en bättre riskjusterad
avkastning än andra företag, eftersom fonden har diversifierat ägarskap.
Data
Detta kapitel kommer att presentera de två olika datasampel som jag använt i studien.
Tabellerna som jag hänvisar till finns i själva avhandlingen. Jag börjar med att
presentera data för Talouselämä 500, på följt av samplet av listade företag.
Talouselämä 500-data baserar sig på ETLA 500-listan över de 500 största företagen i
Finland och denna lista publiceras årligen. Detta sampel innehåller både listade och
privata företag. Talouselämä har använt sig av Etlas lista och modifierat den genom att
tillägga ägarskapsdummyn åt företagen i samplet från år 2009 till 2014.
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Som riskvariabler använder jag mig av Gearing (nettoskuld/eget kapital) och Equity
ratio (eget kapital/tillgångar). Talouselämädata innehöll inga andra variabler för att
mäta risk och därför används dessa två. Båda måtten mäter risk utifrån bokföringstal.
Ägarskapsvariablerna var aningen svårare att få fram, Talouselämä hade givetvis märkt
ut en del av dem, men för vissa år fattades variablerna totalt och vissa företag hade helt
enkelt inga indikatorvariabler. Därför måste jag gå igenom data manuellt för att justera
variablerna samt ta fram de rätta ägarskapsvariablerna. Ägarskapsvariabeln berättar
vilken karaktär som den största ägaren har, är det en familj, staten, riskkapital och så
vidare. Det är möjligt för företag att ha mera än en ägarskapsvariabel, exempelvis har
Ahlstrom både en dummy som visar att företaget är listat samt en som visar att
företagets största ägare är en familj. Tabell 2 visar vilka ägarskapsvariabler som har
använts.
Som kontrollvariabler har jag använt omsättning för att kontrollera för storleken av
företaget, ökning i omsättningen för att kontrollera hur snabbt företaget växer samt
ROI (return on investments) för att kontrollera företagens lönsamhet. Tabell 4 ger en
bättre översikt av variablerna som har använts, samt deras definition.
Till näst kommer jag i korthet gå igenom deskriptiv statistik. Rådata innehöll 3 001
företags årsobservationer. Från data har alla finansiella företag tagits bort (170) samt
alla observationer som inte hade equity ratio- eller gearing-värden (1 005). Största
delen av observationerna som inte hade värden tillhörde privata företag och det är
beklagligt att så många av dessa företags observationer fallit bort. Det slutliga samplet
innehåller 1 826 observationer. Följande variabler winsoriserades på en 1 % nivå, för
att minska på extremvärdens inverkan på resultaten: gearing, equity ratio och ROI.
Vidare har följande variabler tagits som naturliga logaritmer i regressionerna för att
öka på normaldistributionen: gearing, equity ratio, omsättningstillväxt och ROI.
I tabell 5 kan man läsa noggrannare siffror för data. Hela 8 av 13 variabler är
indikatorvariabler, vilka beskriver ägarskapsstrukturen. Familjeägarskap och utländskt
ägande är klart de två största formerna av ägarskap i samplet. Tabellen visar även att
både gearing och equity ratio har relativt hög spridning, även om variablerna är
winsoriserade. Figur 1 visar fördelningen av dummyn för observationerna. Data
inkluderade 2 096 dummyn, vilket betyder att ett företag hade i genomsnitt 1,15
ägarskapsdummyn. Av figuren kan man avläsa att endast 19 % av de 500 största
företagen i Finland är börslistade företag, vilket är något som kanske inte kommer så
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starkt fram i allmänheten, då börslistade företag får i förhållande mycket mera
uppmärksamhet jämfört med privata bolag.
Resten av detta kapitel kommer att koncentrera sig på att gå igenom data för det andra
samplet, nämligen listade företag. Data för listade företag är mycket bredare, vilket gör
det möjligt att analysera dessa företag noggrannare än Talouselämä 500-data. Data
innehåller till en början åren 2008-2014, medan ägarskapsdata för år 2007 tilläggs
senare för att se hur ägarkarakteristika påverkar risktagningen i det långa loppet. I
samplet har jag endast inkluderat företag som varit listade under hela tidsperioden och
alla finansiella företag har exkluderats. Appendix 1 visar en lista med alla 92 företag
som inkluderades i samplet, medan appendix 2 visar alla företag som tagits bort, samt
orsaken till varför de inte är med.
I detta sampel används fyra olika riskvariabler, nämligen equity ratio, gearing,
aktiekurs volatilitet och standardavvikelsen av ROA (return on assets).
Aktiekursvolatiliteten räknas som volatiliteten av aktiekursen på veckobasis och räknas
för 52 veckor. Ju högre volatiliteten är, desto mera riskfylld är företaget. Equity ratio
och gearing förklarades redan under Talouselämä 500-data. Standardavvikelsen av
ROA beräknas som standardavvikelsen av vinsten som företaget gör i jämförelse med
totala tillgångar. En högre fluktuation av ROA tyder på högre risk. I samplet räknas
standardavvikelsen av ROA för tre år; året före observationen, observationsåret samt
året efter observationen.
Ägarskapsvariablerna innehåller liknande dummyn som Talouselämä 500-data, men
innehåller även mycket andra variabler. Samlandet av data har varit väldigt tidsdrygt
eftersom ägarskapsdata har hämtats från en databas men efter det har de tre största
ägarna för alla företag och år gåtts igenom och justerats manuellt. Bland annat har jag
grupperat ihop samma familjers ägarskap i företag, eftersom man kan anta att dessa
röstar på samma sätt under bolagsstämman, vilket har lett till en hel del manuellt
arbete. Genom detta kan man få en mera realistisk bild av hur ägarskapsfördelningen
ser ut. Jag har använt mig av fem olika variabler för att beskriva ägarkoncentrationen i
företag; (1) största ägarens rösträtter, (2) de tre största ägarnas rösträtter, (3) dummy
då största ägaren har mera än 10 % av rösterna, (4) dummy då största ägaren har mera
än 15 % av rösterna, (4) dummy då största ägaren har mera än 20 % av rösterna. Dessa
dummyn beskriver i princip samma sak och har en hög kollinearitet och därför kommer
de att användas i skilda regressioner. Utöver dessa ägarskapsvariabler så har jag använt
mig av indikatorvariabler för att beskriva ägartypen. Vidare så använder jag mig av
85
följande variabler för att beskriva ägarskapet i företagen: vd:ns ägarskap, en dummy
som får värdet 1 då största ägaren äger mera än 50 % av rösterna i företaget, dummy för
dubbla aktieserier och dummy för utländskt ägande. Som kontrollvariabler har jag
använt omsättningen, omsättningstillväxt, ROA och industridummyn. Alla variabler
samt deras förklaringar hittas i tabell 8.
I tabell 9 kan man läsa detaljerad deskriptiv statistik över samplet. I genomsnitt
innehar de tre största ägarna hela 45 % av rösträtterna i företagen, vilket är en
betydande andel. I 25 % av företagen har den största ägaren över hälften av alla
rösträtter. Vidare så ser vi att verkställande direktören i genomsnitt har 2 % av
rösträtterna. Dessutom ser vi att dubbla aktieserier existerar i 25 % av företagen i
samplet.
I figurerna 3, 4 och 5 ser man tydligt att familjer och privatpersoner är klart den största
ägartypen. Vi märker dock att andelen familjer som ägare sjunker då vi går till den
andra och tredje största ägaren i företag, samtidigt som pensions- och
försäkringsföretag samt investeringsrådgivare blir allt mer betydande som ägare. Detta
tyder på att familjer tenderar att vara den största ägaren i företag, medan de är mindre
vanligt för familjer att vara andra eller tredje största ägaren. Å andra sidan så visar
figurerna även att pensions- och försäkringsföretag prefererar att inte vara den största
ägaren, men gärna andra eller tredje största ägaren.
Metod
Detta kapitel kommer ytligt att presentera metoden som har hanvänts i studien. En
betydligt djupare redovisning och motivering till modellerna hittas i själva
avhandlingen.
Talouselämä 500-samplet har två stycken variabler för risk och därför kommer jag att
använda mig av två olika modeller. De finns presenterade under kapitel 7.1.1. Totalt
körs två stycken regressioner för Talouselämä 500-data. Samplet med listade företag
har fyra stycken variabler för risk och därför används fyra olika modeller som finns
presenterade under kapitel 7.1.2. Dessutom har jag använt mig av fem olika
ägarskapsvariabler och därför körs varje modell 5 gånger, vilket betyder att 20
regressioner körs. Senare används även laggat ägarskap, vilket ökar antalet
regressioner med 20 och till sist lämnas ägarskapsvariablerna totalt bort, vilket
86
resulterar i ytterligare fyra regressioner. Därmed körs totalt 44 regressioner för det
listade samplet.
Data har både företags- och årsspecifika observationer och därför har jag använt mig av
paneldata, eftersom denna metod tar i beaktande både företags- och tidsspecifika
effekter. Då paneldata används kan man använda antingen fasteffekt eller
slumpmässiga effekter. Genom att göra ett Hausman-test, kan man avgöra vilkendera
av modellerna som lämpar sig bättre för data. Hausman-testet för data i samplet visade
att den slumpmässiga modellen lämpar sig bättre för data.
Parametrarna i regressionen med slumpmässiga effekter kommer att estimeras
inkonsekvent av OLS och således prefererar jag att använda GLS-modellen. Data testas
för heteroskedasticitet och autokorrelation och det visar sig att båda existerar i data.
Stata erbjuder ett verktyg för att korrigera estimaten för heteroskedasticitet genom
användning av robusta standardfel.
Huvudhypoteserna lyder enligt följande; (H1) största ägartypen inverkar inte på
risktagning, (H2) ägarkoncentrationen inverkar inte på risktagningen, (H3) vd:ns
ägarskap inverkar inte på risktagningen, (H4) majoritetsägande inverkar inte på
risktagningen, (H5) dubbla aktieserier påverkar inte risktagningen och (H6) laggat
ägarskap påverkar inte risktagning. Förväntade förtecken för alla variabler finns i tabell
11 för Talouselämä 500-data och tabell 12 för det listade samplet.
Resultatredovisning
Till en början går jag igenom de mest signifikanta resultaten från Talouselämä 500-
data och sedan för det listade samplet.
För Talouselämä 500-samplet gjordes som sagt två stycken regressioner. Resultaten
visar att företag ägda av kommunen tenderar att ha högre risktagning, jämfört med
andra företag. Största delen av dessa företag är dock inom energibranschen, där företag
ofta har stora tillgångar och kan således ha högre skuldnivåer jämfört med andra
företag. Vidare tenderar dessa företag ha starkt stöd av kommunerna, vilket ökar på
deras möjlighet att vara mera riskfyllda. Resultatet är inte i linje med mina
förväntningar eftersom företag ägda av kommunen eller staten brukar karaktäriseras av
långsam tillväxt samt att dessa företag drivs mera av en politisk agenda istället för att
maximera den finansiella avkastningen. Vidare så visar resultaten att företag ägda av
riskkapitalinvesterare tenderar att ha högre risk jämfört med andra företag. Detta
87
resultat är i linje med mina förväntningar eftersom riskkapitalägare tenderar att öka på
skuldsättningen och därigenom risken i företag som de äger.
Regressionerna visar att utländskt ägande inverkar positivt på riskvariabeln, vilket
antyder att företag som karaktäriseras av utländska ägare har en högre risk jämfört
med andra företag. Detta är i linje med mina förväntningar eftersom utländska
investerare tenderar att söka efter mera risk. Vi måste dock ta i hänsyn att merparten
av företag som karaktäriseras av utländska ägare ofta är dotterbolag till större
internationella företag. Dessa företag har finansiellt stöd från moderbolaget och har
därför möjligheten att ta mera skulder. Därmed är det aningen oklart hur resultatet
kring utländskt ägande egentligen skall tolkas. Resultaten visar även att företag ägda av
arbetstagarna är mindre riskfyllda jämfört med andra företag, vilket är aningen
överraskande. Vidare karaktäriseras större företag och företag med högre tillväxt av
mera risktagande, medan företag med högre ROI karaktäriseras av mindre risktagande.
Resultaten från regressionerna finns i tabell 13.
Till näst kommer jag att redovisa för resultaten från det listade samplet. Jag har gjort
tre olika typer av regressioner, beroende på vilka data som har använts i
regressionerna. I det första samplet har jag använt mig av grundläggande data, i det
andra har jag använt laggade data och i det sista samplet har jag lämnat bort alla
variabler för att beskriva ägarkoncentrationen.
Regressionerna med grundläggande data kommer fram med många intressanta
resultat. För det första så visar resultaten att själva ägarkoncentrationen, det vill säga
procenten av aktier som ägs av den största ägaren (eller kumulativa andelen av de tre
största ägarna), inte har en inverkan på risktagningen. Istället antyder resultaten att
ägartypen eller karakteristikan för den största ägaren har en avgörande roll för hurdan
risktagningsprofil som företaget har. Variabeln som visar sig ha största anknytningen
till risktagning är dubbla aktieserier. Det visar sig att företag som har dubbla aktieserier
har en statistiskt signifikant negativ inverkan på risktagningen, det vill säga att dessa
företag har en lägre risktagningsnivå jämfört med andra företag. En möjlig förklaring
till detta är att företag som har dubbla aktieserier normalt karaktäriseras av en eller
några ägare som har stort aktieinnehav i företaget och därmed en stor del av deras
förmögenhet bunden till företaget. Personerna har således en hög rösträtt i företaget
och tenderar att preferera en lägre risktagningsnivå.
88
Företag ägda av familjer och företag visar sig att ha en statistiskt signifikant negativ
inverkan på equity ratio, vilket tyder på att företag som är ägda av antingen familjer
eller företag tenderar att ha högre risktagning jämfört med andra företag. Företag som
ägare visar även ett statistiskt signifikant negativt förhållande på standardavvikelsen av
ROA, vilket tyder på att företag med andra företag som ägare har en större fluktuation
av ROA, det vill säga högre risk. Resultaten visar alltså att familjer, privatpersoner och
företag är färdiga att ta an mera risk än andra typer av investerare.
Av kontrollvariablerna så visar sig en högre omsättning att minska på risktagningen
både mätt på bokföringsmässiga relationstalen samt på standardavvikelsen av ROA.
Det vill säga att större företag mätt enligt omsättningen har lägre risk än andra företag.
En ökning av ROA visar sig även minska på risktagningen, vilket betyder att ju högre
lönsamheten är, mätt enligt ROA, desto lägre är risktagningen.
Jag gjorde även regressioner med laggat ägarskap, det vill säga hur risktagningen för
ett visst år (t) påverkas av ägarskapet från året före det (t-1). På detta sätt får man reda
på huruvida ägarskapen kan påverka risktagningen i det långa loppet. Generellt så visar
resultaten mindre statistiskt signifikanta förhållanden jämfört med det tidigare
samplet. Detta tyder på att ägarna inte inverkar lika mycket på den långsiktiga som på
den kortsiktiga risktagningen.
I regressionerna visar sig igen dubbla aktieserier ha en negativ inverkan på
risktagningen, vilket ytterligare förstärker resultaten för denna variabel. Utöver detta
så visar sig vd:ns aktieinnehav ha en svag negativ statistisk inverkan på
standardavvikelsen av ROA. Detta tyder på att vd:n tenderar att minska risken i
företagen i det långa loppet, speciellt med tanke på att hålla lönsamheten av företagen
på en jämnare nivå.
I de sista regressionerna använde jag mig av grundläggande data, det vill säga utan
laggat ägarskap, men jag lämnade bort alla variabler som beskriver
ägarkoncentrationen. I dessa regressioner fick jag åter samma resultat för dubbla
aktieserier, vilket ytterligare förstärker dess förhållande till risktagning. Utöver detta så
visade inte regressionerna lika mycket signifikanta resultat som för de andra samplen.
Avslutning
Målet med studien är att analysera ifall det finns en relation mellan
ägarkoncentrationen och ägartypen med risktagningen i finska företag. Den deskriptiva
89
statistiken visar att genomsnittliga ägarandelen av den största ägaren i publika företag
är hela 31 % av alla rösträtter, medan familjer är klart den största ägartypen genom att
57 % av företagen har en familj eller privatperson som den största ägaren. Vidare visar
den deskriptiva statistiken att familjer är betydligt oftare den största ägaren i företag,
medan det är mycket osannolikare för dem att vara andra eller tredje största ägaren.
Däremot ökar andelen av diverse fonder och investeringsrådgivare som andra och
tredje största ägare.
Talouselämä 500-regressionerna visar att företag ägda av kommunen,
riskkapitalinvesterare och utlänningar tenderar att ha högre risktagning. Samplet på
publika bolag visar att företag med dubbla aktieserier har klart mindre risktagning.
Vidare så tenderar företag ägda av andra företag och privatpersoner att ha en högre
risktagning. Samtidigt visade sig inte ägarkoncentrationen påverka risktagningen,
istället är det ägartypen som har samband med risktagning.
Det som måste tas i beaktande vid tolkning av resultaten är att endogeniteten kan ha en
inverkan på hur förhållandet ser ut i verkligheten. Man måste alltså fråga sig ifall det är
så att investerarna i praktiken klarar av att inverka på risktagningen eller ifall det är så
att investerarna väljer att investera i företag med risktagning enligt deras preferenser.
Därmed har studien påvisat en korrelation mellan variablerna medan kausaliteten
förblir oklar. Detta är någonting som vore intressant att studera i kommande forskning.
Resultaten av studien ger en djupare insyn i ägartypernas inverkan på risktagning i
Finland och jag anser personligen att det har varit intressant att studera detta.
Samlandet av ägarskapsdata har varit väldigt tidskrävande och formateringen av dem
har lett till unika data som inte mig veterligen tidigare har gjorts på finska data.
90
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93
APPENDIX 1 COMPANIES IN THE LISTED SAMPLE
Companies included in the listed sample and their industry classification
Company Industry
1 Afarak Group Basic Resources
2 Affecto Oyj Technology
3 Ahlstrom Oyj Basic Resources
4 Alma Media Oyj Media5 Amer Sports Oyj Personal & Household Goods6 Apetit Food & Beverage
7 Aspo Oyj Industrial Goods & Services
8 Aspocomp Group Oyj Industrial Goods & Services
9 Atria Oyj A Food & Beverage
10 Basware Oyj Technology
11 Biohit Oyj B Health Care
12 Biotie Therapies Oyj Health Care
13 Cargotec Oyj Industrial Goods & Services
14 Componenta Oyj Industrial Goods & Services
15 Comptel Oyj Technology
16 Cramo Oyj Industrial Goods & Services
17 Digia Oyj Technology
18 Dovre Group Industrial Goods & Services
19 Efore Oyj Industrial Goods & Services
20 Elecster Oyj A Industrial Goods & Services
21 Elektrobit Oyj (Bittium) Technology
22 Elisa Oyj Telecommunications
23 Etteplan Oyj Industrial Goods & Services
24 Exel Composites Oyj Industrial Goods & Services
25 Finnair Oyj Travel & Leisure
26 Finnlines Oyj Industrial Goods & Services
27 Fiskars Oyj Abp Personal & Household Goods
28 Fortum Oyj Utilities
29 F-Secure Oyj Technology
30 Glaston Oyj Abp Construction & Materials
31 HKScan Oyj A Food & Beverage
32 Honkarakenne B Personal & Household Goods
33 Huhtamäki Oyj Industrial Goods & Services
34 Ilkka-Yhtymä 2 Media
35 Incap Oyj Industrial Goods & Services
36 Ixonos Oyj Technology
37 Kemira Oyj Chemicals
38 Keskisuomalainen A Media
39 Kesko Oyj A Retail
40 Kesla A Industrial Goods & Services
41 KONE Oyj Industrial Goods & Services
42 Konecranes Oyj Industrial Goods & Services
43 Lassila & Tikanoja Industrial Goods & Services
44 Lemminkäinen Oyj Construction & Materials
45 Marimekko Oyj Personal & Household Goods
46 Martela A Personal & Household Goods
94
Company Industry
47 Metso Oyj Industrial Goods & Services
48 Metsä Board A Basic Resources
49 Neste Oil Oyj Oil & Gas
50 Nokia Oyj Technology
51 Nokian Renkaat Oyj Automobiles & Parts52 Nurminen Logistics Oyj Industrial Goods & Services
53 Okmetic Oyj Technology
54 Olvi Oyj A Food & Beverage
55 Oriola-KD A Health Care
56 Orion A Health Care
57 Outokumpu Oyj Basic Resources
58 Outotec Oyj Industrial Goods & Services
59 PKC Group Oyj Industrial Goods & Services
60 Pohjois-Karjalan Kirjapaino Media
61 Ponsse 1 Industrial Goods & Services
62 Pöyry Oyj Industrial Goods & Services
63 QPR Software Oyj Technology
64 Raisio Oyj Vaihto-osake Food & Beverage
65 Ramirent Oyj Industrial Goods & Services
66 Rapala VMC Personal & Household Goods
67 Raute Oyj A Industrial Goods & Services
68 Revenio Group Oyj Industrial Goods & Services
69 Saga Furs C Personal & Household Goods
70 Sanoma Oyj Media
71 Solteq Oyj Technology
72 Soprano Oyj Technology
73 SRV Yhtiöt Oyj Construction & Materials
74 SSH Communications Security Technology
75 Stockmann Oyj Abp A Retail
76 Stora Enso A Basic Resources
77 Suominen Personal & Household Goods
78 Takoma Oyj Industrial Goods & Services
79 Talentum Oyj Media
80 Tecnotree Oyj Technology
81 Teleste Oyj Technology
82 Tieto Oyj Technology
83 Trainers´ House Oyj Technology
84 Tulikivi Oyj A Construction & Materials
85 UPM-Kymmene Oyj Basic Resources
86 Uponor Oyj Construction & Materials
87 Vaisala Oyj A Industrial Goods & Services
88 Viking Line Abp Travel & Leisure
89 Wulff-Yhtiöt Oyj Industrial Goods & Services
90 Wärtsilä Oyj Abp Industrial Goods & Services
91 YITOyj Construction & Materials
92 Yleiselektroniikka E Industrial Goods & Services
95
1 Aktia Bank Banks Industry
2 Capman Oy Financial Serv ices Industry
3 Cav erion Industrial Goods & Serv ices Listed in 201 3
4 Cencorp Oy j Industrial Goods & Serv ices Valoe Oy j in 201 5, not enough information
5 City con Real Estate Industry
6 Endomines Basic Resources Listed on the Stockholm stock exchange
7 eQ Financial Serv ices Industry
8 Innofactor Plc Technology Listed in 201 0
9 Munksjö Basic Resources Listed in 201 3
1 0 Neo Industrial Industrial Goods & Serv ices Industry (Inv estments)
1 1 Nordea Banks Industry
1 2 Norv estia Financial Serv ices Industry
1 3 Oral Hammaslääkärit Oy j Health Care Delisted, no information av ailable
1 4 Orav a Asuinkiinteistörahasto Real Estate Listed in 201 3, Industry
1 5 Panostaja Financial Serv ices Industry
1 6 Pohjola Pankki Banks Industry
1 7 Rautaruukki Oy j Basic Resources Sold to SSAB, no information av ailable
1 8 Restamax Trav el & Leisure Listed in 201 3
1 9 Sampo Insurance Industry
20 Scanfil Industrial Goods & Serv ices Listed in 201 2
21 Siev i Capital Financial Serv ices Industry
22 Sotkamo Silv er Basic Resources Listed in 201 2
23 Sponda Oy j Real Estate Industry
24 SSK S.Sääst.Kiint. Real Estate Industry
25 Talv iv aara Basic Resources Listed in 2009
26 Technopolis Real Estate Industry
27 TeliaSonera Telecommunications Listed on the Stockholm stock exchange
28 Tikkurila Construction & Materials Listed in 201 0
29 Turv atiimi Industrial Goods & Serv ices Listed on the Swiss Exchange
30 Vaahto Group Industrial Goods & Serv ices Uutechnic group in 201 5, no information
31 Vacon Industrial Goods & Serv ices Listed on the Swiss Exchange
32 Valmet Industrial Goods & Serv ices Listed in 201 4
33 Ålandsbanken Banks Industry
Company Industry Reason for excludement
APPENDIX 2 COMPANIES EXCLUDED FROM THE LISTED SAMPLE
96
Ln_Volatility
Gearing
Ln_Gearing
Ln_Equity_Ratio
Stand ROA
CEO Ownership
Voting %
Over 5%
Over 10%
Over 15%
Over 20%
Majority
Dual Class
Foreign
Corporation
Family
Foundation
Investment
Advisor
Pension and
Insurance Fund
Risk Capital
State
Voting % L
Bank L
Corporation L
Family L
Foundation L
Investment
Advisor L
Pension and
Insurance Fund L
Risk Capital L
State L
Ln_sales
Sales growth
ROA
Ln
_V
ola
tili
ty1.
00
Gea
rin
g0
.13
1.0
0
Ln
_G
eari
ng
0.0
70
.79
1.0
0
Ln
_E
qu
ity_
Ra
tio
-0.0
9-0
.55
-0.5
91.
00
Sta
nd
RO
A0
.12
0.0
9-0
.01
0.0
41.
00
CE
O O
wn
ersh
ip0
.08
0.0
80
.05
0.0
00
.04
1.0
0
Vo
tin
g %
-0.0
10
.04
0.0
7-0
.10
-0.0
40
.11
1.0
0
Ove
r 5
%-0
.06
-0.1
30
.01
0.0
8-0
.13
0.0
40
.21
1.0
0
Ove
r 10
%0
.02
0.0
40
.10
-0.1
3-0
.04
0.1
00
.47
0.4
01.
00
Ove
r 15
%0
.01
0.0
80
.11
-0.1
30
.03
0.1
40
.66
0.2
50
.64
1.0
0
Ove
r 2
0%
0.0
30
.09
0.0
9-0
.15
0.0
30
.15
0.7
60
.20
0.5
10
.80
1.0
0
Ma
jori
ty-0
.04
0.0
30
.09
-0.1
2-0
.04
-0.0
20
.87
0.1
10
.27
0.4
20
.52
1.0
0
Du
al
Cla
ss-0
.02
-0.1
0-0
.10
0.1
0-0
.16
-0.0
70
.32
0.0
2-0
.08
0.1
30
.20
0.3
21.
00
Fo
reig
n0
.12
-0.0
3-0
.03
0.0
1-0
.01
-0.0
8-0
.13
-0.1
4-0
.14
-0.0
7-0
.04
-0.1
4-0
.14
1.0
0
Co
rpo
rati
on
-0.0
80
.02
0.0
80
.03
-0.1
0-0
.10
-0.0
50
.06
-0.0
20
.02
-0.0
5-0
.08
0.1
2-0
.01
1.0
0
Fa
mil
y0
.01
0.0
90
.04
-0.0
30
.12
0.2
20
.25
0.0
50
.21
0.2
00
.26
0.1
40
.02
-0.2
7-0
.54
1.0
0
Fo
un
da
tio
n-0
.04
0.0
10
.05
0.0
2-0
.05
-0.0
50
.03
0.0
30
.07
0.0
80
.01
0.0
80
.09
-0.0
6-0
.07
-0.1
81.
00
Inve
stm
ent
Ad
viso
r0
.08
-0.1
1-0
.17
0.0
30
.05
-0.0
7-0
.21
-0.2
6-0
.24
-0.2
5-0
.27
-0.1
3-0
.14
0.5
2-0
.12
-0.2
9-0
.04
1.0
0
Pen
sio
n a
nd
In
sura
nce
Fu
nd-0
.01
-0.1
0-0
.14
0.0
8-0
.01
-0.0
7-0
.23
-0.0
2-0
.28
-0.2
3-0
.26
-0.1
6-0
.02
-0.1
1-0
.13
-0.3
1-0
.04
-0.0
71.
00
Ris
k C
ap
ita
l0
.03
-0.0
3-0
.02
-0.0
40
.00
-0.0
4-0
.04
0.0
30
.04
0.0
70
.10
-0.0
90
.08
0.3
7-0
.07
-0.1
8-0
.02
-0.0
4-0
.04
1.0
0S
tate
0.0
50
.00
0.0
7-0
.08
-0.1
1-0
.09
0.0
10
.05
0.0
5-0
.04
0.0
20
.13
-0.1
6-0
.02
-0.1
3-0
.33
-0.0
4-0
.07
-0.0
8-0
.04
1.0
0
Vo
tin
g %
L-0
.03
0.0
60
.09
-0.1
1-0
.06
0.2
00
.93
0.2
60
.55
0.7
40
.79
0.7
40
.34
-0.0
90
.03
0.2
20
.06
-0.2
3-0
.23
0.0
1-0
.07
1.0
0
Ba
nk
L0
.05
0.0
10
.02
0.0
10
.03
-0.0
20
.07
0.0
10
.03
0.0
40
.05
0.1
00
.10
-0.0
20
.05
-0.0
1-0
.01
-0.0
1-0
.02
-0.0
1-0
.02
0.1
01.
00
Co
rpo
rati
on
L-0
.07
-0.0
8-0
.07
0.0
5-0
.03
-0.1
1-0
.09
0.0
5-0
.05
-0.0
6-0
.14
-0.0
70
.10
-0.0
20
.65
-0.3
5-0
.09
-0.0
90
.07
-0.0
9-0
.18
-0.0
30
.02
1.0
0
Fa
mil
y L
-0.0
60
.10
0.0
50
.04
0.1
30
.20
0.1
90
.09
0.1
70
.15
0.2
00
.12
0.0
5-0
.33
-0.3
40
.79
-0.0
4-0
.24
-0.2
6-0
.21
-0.4
00
.19
-0.0
2-0
.24
1.0
0
Fo
un
da
tio
n L
0.0
3-0
.03
-0.0
3-0
.03
-0.0
5-0
.10
0.0
90
.06
0.0
30
.08
0.0
70
.08
0.1
8-0
.07
-0.1
30
.05
0.4
8-0
.01
-0.0
6-0
.01
-0.0
90
.09
-0.0
2-0
.04
0.1
01.
00
Inve
stm
ent
Ad
viso
r L
0.0
3-0
.08
-0.0
70
.02
-0.0
3-0
.05
-0.1
6-0
.10
-0.1
2-0
.22
-0.2
0-0
.02
-0.3
30
.25
-0.0
5-0
.33
0.1
90
.36
0.0
90
.00
0.1
8-0
.20
-0.0
4-0
.11
-0.3
50
.00
1.0
0
Pen
sio
n a
nd
In
sura
nce
Fu
nd
L0
.03
-0.0
20
.01
-0.1
0-0
.05
-0.1
1-0
.09
-0.0
5-0
.03
-0.0
2-0
.02
-0.1
0-0
.18
0.0
9-0
.09
-0.2
0-0
.09
0.0
70
.30
0.0
00
.21
-0.1
5-0
.05
-0.1
5-0
.27
-0.0
90
.01
1.0
0
Ris
k C
ap
ita
l L
0.0
90
.18
0.0
6-0
.10
0.0
2-0
.03
-0.0
7-0
.02
0.0
40
.03
0.0
5-0
.11
0.0
40
.33
-0.0
8-0
.16
-0.0
3-0
.01
-0.0
10
.86
-0.0
5-0
.02
-0.0
1-0
.09
-0.1
7-0
.03
-0.0
10
.00
1.0
0
Sta
te L
0.0
90
.00
0.0
7-0
.11
-0.1
5-0
.12
-0.0
4-0
.06
0.0
3-0
.04
-0.0
50
.06
-0.1
40
.29
-0.1
7-0
.32
-0.0
30
.18
-0.1
10
.15
0.7
2-0
.06
-0.0
2-0
.25
-0.4
2-0
.11
0.1
60
.21
0.1
21.
00
Ln
_sa
les
0.0
4-0
.03
0.1
0-0
.19
-0.3
9-0
.28
-0.1
1-0
.06
-0.1
0-0
.08
-0.1
00
.00
0.1
00
.20
0.0
9-0
.41
0.0
60
.17
0.0
50
.05
0.3
8-0
.15
0.0
00
.02
-0.4
70
.13
0.1
70
.24
0.0
30
.51
1.0
0
Sa
les
gro
wth
0.0
3-0
.09
-0.1
20
.09
0.0
90
.06
-0.0
3-0
.07
-0.0
8-0
.05
-0.0
3-0
.02
-0.0
10
.05
-0.0
3-0
.04
0.0
00
.12
0.0
3-0
.03
-0.0
2-0
.02
0.0
0-0
.03
-0.0
60
.05
0.0
50
.02
-0.0
2-0
.06
-0.0
81.
00
RO
A-0
.27
-0.3
1-0
.30
0.3
5-0
.30
-0.0
8-0
.05
0.0
0-0
.07
-0.1
4-0
.12
0.0
00
.10
-0.0
4-0
.01
-0.0
40
.05
-0.0
30
.08
-0.0
30
.01
-0.0
70
.02
0.0
2-0
.05
0.0
20
.07
0.0
6-0
.06
-0.0
20
.22
0.1
31.
00
Cumulated dummies for three
largest owners
Dummy for largest
ownerAPPENDIX 3 MULTICOLLINEARITY TABLE LISTED SAMPLE
97
Municipality
Shares
publicly
traded
Cooperative
Venture
capital
ownership
Family
ownership
Foreign
ownership
State
Employee
Technology
Telecommuni
cation
Oil
Industry
Industrial
Prod&Serv
Consumer
Goods
Healthcare
Consumer
Service
Energy
Ln_Sales
ROI
Ln_Gearing
Ln_EquityRati
o
Employees
Mu
nic
ipal
ity
1.0
Shar
es
pu
bli
cly
trad
ed
-0.1
1.0
Co
op
era
tive
-0.1
-0.2
1.0
Ve
ntu
re c
apit
al o
wn
ers
hip
-0.1
-0.1
-0.1
1.0
Fam
ily
ow
ne
rsh
ip-0
.20.
0-0
.20.
01.
0
Fore
ign
ow
ne
rsh
ip-0
.2-0
.3-0
.20.
1-0
.41.
0
Stat
e-0
.10.
1-0
.1-0
.1-0
.2-0
.11.
0
Emp
loye
e
0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
1.0
Tech
no
logy
-0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
1.0
Tele
com
mu
nic
atio
n0.
00.
00.
10.
0-0
.10.
00.
10.
00.
01.
0
Oil
0.0
-0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
1.0
Ind
ust
ry-0
.10.
2-0
.20.
00.
10.
00.
10.
0-0
.1-0
.1-0
.11.
0
Ind
ust
rial
Pro
d&
Serv
-0.2
0.0
-0.2
0.0
0.1
0.2
-0.1
0.0
-0.2
-0.1
-0.1
-0.4
1.0
Co
nsu
me
r G
oo
ds
-0.1
0.0
0.0
-0.1
0.2
-0.1
-0.1
0.0
-0.1
0.0
0.0
-0.2
-0.3
1.0
He
alth
care
0.0
0.0
0.0
0.2
-0.1
0.0
0.0
0.4
0.0
0.0
0.0
0.0
-0.1
0.0
1.0
Co
nsu
me
r Se
rvic
e-0
.1-0
.10.
40.
0-0
.1-0
.10.
10.
0-0
.1-0
.1-0
.1-0
.2-0
.4-0
.20.
01.
0
Ene
rgy
0.8
-0.1
-0.1
-0.1
-0.2
-0.1
0.1
0.0
-0.1
0.0
0.0
-0.1
-0.2
-0.1
0.0
-0.1
1.0
Ln_S
ale
s-0
.20.
40.
1-0
.1-0
.1-0
.20.
3-0
.1-0
.10.
00.
10.
1-0
.10.
0-0
.10.
00.
01.
0
RO
I-0
.1-0
.1-0
.10.
0-0
.10.
20.
00.
00.
10.
00.
00.
00.
1-0
.10.
00.
0-0
.1-0
.11.
0
Ln_G
ear
ing
0.1
0.0
0.0
0.1
-0.1
0.1
-0.1
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.1
0.0
-0.3
1.0
Ln_E
qu
ityR
atio
0.0
0.0
0.0
-0.2
0.1
-0.2
0.0
0.0
0.0
0.1
-0.2
0.0
-0.1
0.0
0.0
0.1
0.0
-0.1
0.1
-0.6
1.0
Emp
loye
es
-0.1
0.4
-0.1
0.0
-0.1
-0.1
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
-0.1
-0.1
0.6
0.0
0.0
0.0
1
APPENDIX 4 MULTICOLLINEARITY TABLE TALOUSELÄMÄ SAMPLE