Nothing like Home
MASTER THESIS WITHIN Business Administration, Finance NUMBER OF CREDITS: 30 ECTS
PROGRAM OF STUDY:Civilekonomprogrammet
AUTHOR: Amanda Bogren and Clara Ståhl JÖNKÖPING May 2021
Master Thesis in Business Administration; Finance
Title: Nothing like home – An examination of Home Bias among Swedish private investors Authors: Amanda Bogren and Clara Ståhl
Tutor: Fredrik Hansen and Toni Duras Date: 2021-05-24
Key Terms: Behavioral finance, Home bias, Familiarity, Demographical factors, Diversification ______________________________________________________________________________
Abstract
Background: The increased globalization has had a significant impact on the world since it has
increased access to other countries, cultures, and companies. According to traditional finance theory, due to the easy access of information, an investor has the possibility to hold a “perfect” diversified portfolio. However, researchers argue that investors do not behave in line with
traditional finance theory in real life, one must take cognitive psychology into account, leading to the emergence of behavioral finance. Among investors, certain preferences and beliefs for
familiarity and domestic assets exists, one of those is home bias.
Purpose: This paper aims to investigate whether there exists a home biased behavior among
Swedish private investors, concerning the following seven demographical factors; gender, age, marital status, education, occupation, experience, and competence. Further, it investigates how large fraction of the portfolio consists of domestic stocks compared to foreign and what factors could be the motives for not considering the benefits of diversification. Also, if Covid-19 has affected the trading behavior of Swedish private investors.
Method: This study uses a strategy that takes both a deductive and explorative approach with a
position in the positivism and empirical realism philosophy. The data was gathered through a quantitative web survey with 247 participants. Seven hypotheses were established based on previous research and significantly tested through the statistical program SPSS using a binary logistics model.
Conclusion: Findings from this research present that home bias among Swedish private
investors exists. The demographical factors gender, age, and competence confirmed statistically significant results. Indicating that women are more home biased than men, older people are less likely to be home biased, and that people who perceive themselves as less competent are more home bias. In addition, the greatest impact on investors’ decision-making when allocating assets are found to be cultural and institutional factors as known currency and clear tax rules. Further, it is found that the pandemic has not affected investors’ trading behavior to a large extent.
Acknowledgement
We would like to express our gratitude and to thank everyone who has contributed and supported us in the creation of this research. Firstly, we would like to thank our tutor, Fredrik Hansen, for
excellent guidance and support in all parts of the investigation. Additionally, we would like to thank Toni Duras for all the support and knowledge in the statistical area.
Secondly, we would like to thank all the fellow seminar students who contributed with brilliant inputs and feedback, as well as interesting discussions and commitment during all seminars. It
helped to develop the thesis and strengthening the content.
Finally, we would like to thank all the people who participated in our online web survey. Having investors who voluntarily participated in the research was an essential prerequisite when
conducting this thesis. In other words, it would not have been possible without you.
Table of Contents
1 Introduction ... 1
1.1 Background ... 1
1.2 Problem discussion ... 3
1.3 Purpose and research questions ... 4
1.4 Delimitations ... 5
2 Literature review ... 6
2.1 Theoretical approaches to finance ... 6
2.1.1 Neoclassical Finance ... 6 2.1.2 Behavioral Finance ... 7 2.2 Home Bias ... 8 2.2.1 Familiarity ... 10 2.2.2 Cultural factor ... 12 2.2.3 Institutional factor ... 13 2.2.4 Geographical factor ... 13 2.3 Demographical factors ... 15 2.3.1 Age ... 15
2.3.2 Gender, marital status, and overconfidence ... 15
2.3.3 Occupation ... 16
2.3.4 Competence and education ... 16
2.3.5 Experience ... 17
2.4 Covid-19 ... 17
2.5 Summary of literature review and hypothesis ... 19
2.5.1 Hypothesis ... 21 3 Methodology ... 22 3.1 Research philosophy ... 22 3.2 Research approach ... 23 3.3 Research strategy ... 23 3.4 Data Collection ... 24 3.4.1 Survey ... 24
3.4.2 Population and sample selection ... 26
3.5 Empirical method ... 27
3.5.1 Binary logistic model ... 27
3.5.2 Variables ... 28
3.5.3 Motives, preferences, and Covid-19 ... 32
3.6 Data analysis ... 33
3.7 Strengths and limitations ... 34
4 Empirical findings ... 37
4.2 Results ... 38 4.2.1 Home bias ... 40 4.2.2 Gender ... 40 4.2.3 Age ... 41 4.2.4 Marital Status ... 41 4.2.5 Education ... 41 4.2.6 Occupation ... 42 4.2.7 Competence ... 42 4.2.8 Experience ... 42 4.2.9 Country distribution ... 43
4.2.10 Home biased factors ... 44
4.2.11 Covid-19 ... 45
5 Analysis ... 47
5.1 Presence of home bias ... 47
5.2 Gender ... 48 5.3 Age ... 49 5.4 Marital status ... 50 5.5 Education ... 50 5.6 Occupation ... 51 5.7 Competence ... 51 5.8 Experience ... 52 5.9 Country distribution ... 52
5.10 Home bias factors ... 54
5.11 Covid-19 ... 56 6 Conclusion ... 58 7 Discussion ... 61 7.1 Conceptual aspect ... 61 7.2 Societal aspect ... 63 7.3 Future research ... 64 8 References ... 65 9 Appendices ... 68 9.1 Appendix 1: Survey ... 68
Table of Figures
Figure 1 Allocation of Motivation among respondents of the survey 36
Figure 2 Descriptive statistics for age and experience 37
Figure 3 Descriptive statistics of the demographical factors 38
Figure 4 Summary of the results from regressions 38
Figure 5 Pearson's correlation matrix 39
Figure 6 Cramer's V for age and experience 39
Figure 7 Summary of home bias among participants in the survey 40
Figure 8 Summary of distribution of country selection 43
Figure 9 Summary of distribution of current home bias factors 44 Figure 10 Summary of distribution of future home bias factors 45
Figure 11 Summary of changed buying behavior 46
Figure 12 Summary of changed selling behavior 46
Figure 13 Summary of hypothesis 47
List of Tables
Table 1 Summary of previous research ... 19 Table 2 A suggestion of broaden the concept of Home Bias ... 62
1
1 Introduction
This chapter introduces the background of behavioral finance and the emergence of home bias. Subsequently, the problem is explained and presented, as well as the purpose of the paper. Finally, the research questions and the delimitations are addressed.
1.1 Background
During the last decades, globalization has had a significant impact on the world, as it increases, so does access to other countries, cultures, and companies (Levis et al., 2016). In financial trade, the possibility for an investor to have a diversified cross-border portfolio has never been better. Theoretically, it is possible to hold a “perfect” diversified portfolio and investors can spread risk across markets, industries, and countries. Referring to the traditional finance approach, an investor is assumed to act in a neoclassical economical rational behavior (hereafter referred to “rationality” in this paper) and make decisions based on given information and maximized utility (Barberis & Thaler, 2003). Although this seems to be a simple task to execute, especially with today’s access of rapid information, studies argue that investors do not behave in line with traditional finance theory in real life. Instead, the rationality aspect is questioned as investors have other beliefs and preferences than assumed when making decisions, leading to the emergence of behavioral finance theory (Subrahmanyam, 2008). Since behavioral finance theories and results take cognitive psychology into account, one has been able to identify and explain certain preferences and biases among investors. For instance, in real life many investors prefer familiarity and invest to a large extent in domestic assets, hence a home biased behavior has been identified. Within finance, Levis et al., (2016) argue that home bias was originally used to describe the issue when the benefits of a diversified portfolio are ignored, and international diversification is not executed.
According to recently shared data, approximately 12 percent of the Swedish inhabitants are holding stocks in the Swedish stock market, further, the interest for savings and investments have increased (Statistiska Centralbyrån, 2021a). Additionally, in 2019 one could see an increase in foreign asset ownership (Statistiska Centralbyrån, 2020). Due to this, it is arguable that the need
2 to identify behaviors and biases, such as home bias, among investors is of importance. Subsequently based on previous research, one can question how large fraction of Swedish investors who work actively to improve and to remain a diversified portfolio. The discussion about a diversified portfolio has been ongoing for decades with Markowitz’s (1952) work as a benchmark, with the idea that it is the contribution of a security to the portfolio that matters and not a security’s own risk. In other words, the decision to hold any security should depend on what other securities the investor wants to hold. As Rubinstien (2002) states, Markowitz’s approach has been generalized and developed in innumerable ways and is now commonly used among institutional portfolio managers in their work to structure and measure the performance of their portfolios. Even though it has been almost 70 years since Harry Markowitz’ published his breaking paper “Portfolio selection”, which was a vital part when rewarding him with the Nobel prize in 1990, no clear definition about how a diversified portfolio should be allocated has been established. Hence, it is a struggle for investors in real life to know what to improve or remain in their portfolio when no practically workable explanation exists.
Since theory suggest that a diversified portfolio is the most beneficial, one can wonder what are the motives behind home biased behavior? In addition, another negative consequence of home bias that could be considered is the fact that in times of financial crises, if working and investing domestically, one might suffer to a larger extent in the case of poor performance of the domestic market. Meanwhile, when holding an internationally diversified portfolio, the risk is not as significant. On the other hand, as familiarity is a key aspect when investing, why should investors invest in unfamiliar markets, industries, and countries? Obviously, the balance between diversification and risk of entering unfamiliar markets is complex and more research is needed. Therefore, this paper will focus on the issue of individuals ignoring information and opportunities abroad and having overconfidence in the domestic market.
3 1.2 Problem discussion
According to the review essay by Ardalan (2019), home bias can be arguable to be a combination of both institutional and behavioral factors; hence it is a most complex task to build and establish an empirical model that successfully captures the actual portfolio choice among private investors. In fact, it is argued by Ardalan (2019) that no single explanation exists for describing the home bias phenomenon. In the previous literature, the concept of home bias has been discussed and reviewed from a wide perspective (Ardalan, 2019; Balta & Delgado, 2009; Levis et al., 2016). However, when addressing the individual financial decision-maker, one can argue that the amount of research is limited. Hence, what is the specific characteristics of a home biased individual when allocating their assets? Further, it seems like most of the research is conducted before and around 2010 and it is therefore arguable that the concept of home bias in terms of investor behavior has limited recent research, and the reason for these years of gap is not completely clear. It seems like other aspects of behavioral finance, such as overconfidence and loss-aversion, have gained greater attention in the research field. The lack of recent research does not implicitly imply a decrease in home bias behavior, therefore a re-visit in this area is essential.
Considering Sweden, one of the few previous articles about individuals and their tendency for home bias has been examined by Karlsson and Nordén (2007). Subsequently, the investigation employed a dataset from Swedish households to estimate the probability of home bias in investment decisions within the new defined-contribution pension plan. The investigation was published 14 years ago and the data is gathered from statistic of the Swedish pension authority in the year 2000. It is reasonable to argue that dataset of asset choices may be insufficient when investigating the psychological aspects that affect home bias among Swedish private investors, such as, underlying motives and attitudes during decision-making. Moreover, globalization has continued to increase, hence, affected the investors’ preferences and attitudes. According to the Statistics Sweden (Statistiska Centralbyrån, SCB), one could identify an increase in holdings of foreign assets of five percent among Swedish private investors at the end of 2019 compared to the beginning of the year. As a consequence of the outbreak of the Covid-19, during 2020 one can question if this behavior has remained since several countries have suffered from lockdowns meanwhile Sweden has not. Another consequence of the pandemic is that one can see an increase
4 in interest for investments. Statistics Sweden posted in March (2021a), a report showing that the number of shareholders in Sweden has increased by 80 000 in 2020, to 1 223 000 people. Hence, one wants to investigate if the increased interest can be seen across borders or if the increase is mainly in domestic assets. On those grounds, this thesis will contribute to an extended investigation of how home bias has emerged and to what extent it is present among Swedish private investors.
1.3 Purpose and research questions
This paper aims to investigate how home bias among Swedish private investors differs and varies concerning demographical factors when making investment decisions. In other words, investigates how Swedish investors allocate their investments and how large part of their portfolio consists of domestic stocks compared to foreign stocks. Further, what factors could be the motives for not taking the benefits of diversification into account. Subsequently, demographical factors include age, gender, marital status, education, competence, experience, and occupation. Another factor to consider is the geographical aspects, hence, investigate whether Swedish private investors have a preference for a certain region or continent. One could then establish whether physical distance has a significant impact on investment decisions or whether aspects such as culture or institution have greater influence. Besides, the aspect of Covid-19 is considered by investigating if Swedish private investors have changed their portfolio allocation habits because of uncertainty and crises. Based on the previous problem discussion the following research questions will be considered:
• Are Swedish private investors home biased?
• Does demographical factors such as gender, age, marital status, education, occupation, experience and competence, influence home bias behavior?
• What aspect of home bias has the greatest impact on decision-making? Culture, institution, or physical distance?
• Have the investment allocation habits changed among Swedish private investors due to Covid-19-pandemic?
5 When answering these questions, this study seeks to contribute to the awareness of home bias among private investors. As stated before, individuals being home bias might forego the benefits of a diversified portfolio, such as minimizing risks by not putting all eggs in the same basket. Therefore, this study investigates the motives and attitudes among Swedish private investors when making foreign investments.
1.4 Delimitations
In the case of behavioral finance, there are several factors and biases that results in a less efficient portfolio and deselected returns, however, this paper will only consider home bias. Further, this research will only consider whether the respondents are home biased or not, and what demographical factors are characterizing this tendency. The aspect of financial benefits, such as yields and returns, are excluded, due to the fact that this information is not accessible.
In order to gather data, an online survey was conducted. The survey will only investigate Swedish private investors and the survey will only be in Swedish, leading to only Swedish-speaking people will be able to participate.
6
2 Literature review
This chapter describes two theoretical approaches within finance, neoclassical and behavioral finance. Further, previous research about the concept of home bias has been discussed and reviewed from the following point of view; familiarity, culture, institution, and geography. Subsequently, different demographical factors and investor characteristics are deliberated. To conclude, the hypotheses that will be tested are defined.
2.1 Theoretical approaches to finance
In traditional finance theory, Barberis and Thaler (2003) argue the investors have been described as rational to the degree that decisions are based on maximized expected utility. However, when reviewing data, it is observable that the picture is more complex, therefore, the theory of behavioral finance has emerged.
2.1.1 Neoclassical Finance
According to Szyszka (2013), the approach of neoclassical finance assumes that all agents in the market act on neoclassical rationality when making decisions. This implies that all agents are capable to process and interpret their beliefs in an accurate way when new information is given to them, as well as decoding this information when predictions for probabilities of future events are made. Furthermore, it is assumed that in a large economic setting, like the financial market, biases, and errors of naïve- and noise agents are exploited quickly by more informed and sophisticated agents. Leading to the outcome that markets will behave as if all agents acted rationally. Hence, it can be carefully assumed that the actions of perfectly rational and knowledgeable market agents are the drivers of the financial market.
Throughout the years’ many normative models have been developed in concurrence with the neoclassical approach of finance. Models that all have their founding in the neoclassical approach are for example, Fama’s efficient market hypothesis (1970), Markowitz’s portfolio theory (1952) as well as Sharpe’s (1964) and Litner’s (1965) development of capital asset pricing model. Further, according to Szyszka (2013) there is no doubt that the neoclassical framework has contributed a lot to economic development and the critic against it is mainly about the failure of the doctrine to explain the existence of market anomalies. In line with this, Barberis and Thaler (2003) argue that
7 when one wants to understand the basic facts about the aggregate stock market, the cross-section of average returns, and individual trading behavior, the neoclassical framework is not the optimal choice to use, and the framework of behavior finance is rather preferred.
2.1.2 Behavioral Finance
Behavioral finance is a phenomenon that has emerged in the theory of finance. In contradiction to neoclassical finance theory, behavioral finance does not assume that the markets and agents are rational (Barberis & Thaler, 2003). Nor assume that all agents are unbiased and act in line with their self-interest (Baker & Nofsinger, 2010). In real life, the maximized utility theory or the fully rational reasoning might not be the most accurate or valid assumptions, therefore, one might argue that the theorem of behavioral finance was established due to the absence of evidence that could explain the decision-making process in finance (DeBondt et al., 2010). Behavioral finance is described to combine cognitive psychological theory and traditional finance when defining how decisions are made. In other words, it explains the inconsistent behavior of an individual as well as groups (Baker & Nofsinger, 2010). As described by Barberis and Thaler (2003) it defines the behavior of different groups of investors and how they decide to allocate their investments, hence, which portfolios they consider and how their trading changes over time.
According to Barberis and Thaler (2003), in the case of asset pricing, the price is considered to attain the correct value, which is the discounted sum of expected future cash flows. Further, one assumes that the investors fully understand and process the available information. This approach is also discussed by Hirshleifer (2015), who additionally argues that one assumes the investors to be rational when processing information. This theory, which assumes that prices replicate the available information, is referred to as the Efficient Market Hypothesis (EHM).
Barberis and Thaler (2003) discuss that there are two aspects of behavioral finance, limits to
arbitrage and investor psychology, whereas this paper will focus on the latter. In the case of investor psychology, different biases that occur due to beliefs and preferences are considered.
Regarding beliefs or expectations, biases such as overconfidence and optimism may appear and affect the behavior of the investors. Meanwhile, in the case of preferences, one wants to understand how risk-evaluation is made by the investors. Further argued by Barberis and Thaler (2003), the
8 theory of investor behavior has great significance, since discussing and establishing how investors behave and allocate their assets, both laypeople and professionals. Further, two main reasons are identified; the cost of entering the stock market has decreased, hence, the interest in equity investment has improved. Also, people are considered to manage their retirement and saving plan to a larger extent than several years ago. Therefore, one wants to investigate how well this task is managed.
There has been an ongoing discussion about how neoclassical finance theory and behavioral finance theory should be interpreted and integrated. In an interview series by John Cassidy for The
New Yorker, both Fama (2010a) and Thaler (2010b), express trust for neoclassical finance and
behavioral finance to be complements to each other (though not perfect complements), as they target different fragments of financial reality. Since in general, neoclassical finance focuses more on the performance of the stock market (i.e., aggregate level), hence emphasizes asset-pricing models. Behavioral finance, on the other hand, shows a larger interest in explaining the financial decision-making among individuals. Further argued, the effects of behavioral finance do not automatically affect in what way neoclassical finance works on aggregate levels, and vice versa.
2.2 Home Bias
The benefits of holding an international diversified portfolio have been known for decades and if capital is completely mobile across borders, modern portfolio theory would expect all investors to hold an international diversified portfolio. Starting with Markowitz (1952), who advocates the concept of diversification and promotes that investors should diversify across a large number of stocks instead of putting a large holding into just a few numbers of stocks. One of the benefits of holding a diversified portfolio is the spread and minimization of risks, hence, not putting all eggs (stocks) into one basket (market/industry), as one is not only reliant on the performance of the domestic market. In real life, individual investors seem to hold a disproportionally large share of domestic assets in their portfolio and not being diversified. French and Poterba (1991) reported that a very small fraction of US equity is invested abroad. There are many different explanations to be found why this is the case, and why investors choose to not hold a diversified portfolio, even if the theory argues it to be the most beneficial. Barberis and Thaler (2003) discuss in their survey of behavioral finance that one explanation may be the behavior among investors called home bias.
9 The concept of home bias is typically described as the issue when investors ignore the benefits of a diversified portfolio and hold a larger amount of domestic assets compared to foreign. There have been limited studies where home bias has been investigated from an individual’s point of view. In Sweden, Karlsson and Nordén (2007) could confirm that home bias is present among Swedish individuals, at least in their defined-contribution pension plan portfolio. Further, Abreu et al., (2011) confirm that home bias is present among Portuguese investors as well. Using panel-data from a Portuguese bank, they investigated how experience in the domestic market affects the decision to make investments abroad.
As mentioned, when choosing to only invest largely in domestic assets, the investor might accept a less beneficial combination of portfolio risk and return. Some explanations of the domestic preference among individual investors could be the barriers that may occur during foreign investments. Coval and Moskowitz (1999) used a domestic setting with panel-data from the US monetary management industry, and found that governmental restrictions, such as transaction cost and taxes may be a cause of home bias. Additionally, Levis et al., (2016) used panel-data from the OECD database to investigate home bias in foreign direct investment flows across a large number of countries using a bilateral country framework. Findings suggest that formal and informal barriers can arise within international trade and at national borders, such as culture and language - but if that is the case, home bias would not occur on a national level. However, Wolf (2000) argues that home bias occur on a subnational level in his empirical study of home bias within the states in U.S. based on data from the 1993 U.S. Commodity Flow Survey (CFS).
Even though several obstacles and governmental restrictions have been reduced to increase foreign investments, it is clear that the preference for domestic investments remains significant (Coval & Moskowitz, 1999). It has been observed by Ardalan (2019) that the volume of international financial asset trade has significantly increased as a result of financial globalization, where factors such as a reduction in transaction costs, liberalization of the capital account, growth of electronic trading, and boosted speed of information exchange have had a major role. Yet, Ardalan (2019) confirms in his review essay that home bias is still predominant in most countries, although gradually falling, and tends to be at higher rates in emerging markets.
10 Further aspects of home bias can be found when considering behavioral finance. Among other things, in behavioral finance, it is argued that investors’ decisions are based on subjective probability distributions rather than to evaluate investment risk constructed on objective probabilities of returns (Kilka & Weber, 2000). Due to the differences in utility functions, there are differences across investors from different countries as well. Ardalan (2019) further confirms that these differences between investors are another factor of home bias being present in each country. One might believe that home bias would have diminished because of the increased globalization that has emerged throughout the years. However, one can observe that factors such as culture, institution, and geography remain significant in investment decision-making. In other words, the “global village” that one would imagine the world to be might not be as established as expected (Levis et al., 2016). Stated differently, as home bias is considered a combination of both institutional and behavioral factors, it is a very complex task to build and establish an empirical model that correctly describes the actual portfolio choice among private investors (Ardalan, 2019). Nevertheless, as no single explanation or framework exists for describing the phenomenon of home bias, it is hard to get the whole picture of what home bias really is and what it consists of. Therefore, when trying to measure home bias, selectiveness might be needed and this thesis hereafter focuses on the familiarity, cultural, institutional, and geographical aspects of home bias and how they are connected to each other.
2.2.1 Familiarity
The familiarity explanation of home bias is grounded on the statement that people, in general, prefer to invest in the familiar. Ardalan (2019) argues that in similarity to that people root their home team, people tend to invest in stocks of companies that are observable and near to them, and that they feel comfortable about. As investors tend to invest in what is familiar, a consequence might be that investors are less familiar with foreign markets. Chan et al., (2005) argue that through less familiarity, a greater information cost might be realized hence discouraging investors from investing abroad.
The relation between familiarity and ambiguity is discussed by Barberis and Thaler (2003), who argue that people to a larger extent prefer familiar situations over ambiguous ones. In familiar situations, people feel that they are in a superior state compared to others in evaluating a gamble.
11 But when people are in an ambiguous setting, evidence display that people feel unable to evaluate the gamble’s probability distribution due to them feeling less competent. The authors argue that investors might evaluate the national stock market, local firms, and employer’s stock to be of more familiar characteristics. Meanwhile, foreign stock indices and a firm geographically located far away might be considered more ambiguous and challenging to estimate (Barberis & Thaler, 2003). That investors are affected by the familiarity during risk-taking is further confirmed by Massa and Simonov (2006), who used Swedish data from different sources to investigate if investors use financial assets to hedge the household’s labor income risk. Further, they found that investors tend to tilt their portfolio towards assets that are geographically and professionally closer to the investor, rather than towards the market portfolio. Empirical evidence from their study display that the portfolio tends to be skewed to familiar stocks, hence not being diversified.
However, the bias towards familiar assets is not only present in international portfolios among small private investors. As stated by Coval and Moskovitz (1999), the familiarity phenomenon can be found among 1189 U.S. investment managers who as well demonstrate a bias for geographically close firms located to the managers. People tend to invest more in attractive assets, as familiar assets are seen as attractive, more investments will be made in those. Subsequently, if one only includes familiar assets into the portfolio, this might lead to an insufficiently diversified portfolio, since one ignores the possible unfamiliar opportunities.
The connection that home bias is associated with familiarity has been found in previous research (Huberman, 2001; Kang & Stulz, 1997). Hence, Huberman (2001) proposes home bias to be a cause of misperception of risk as well as perceived confidence in native assets leading to an underestimation of those assets and an overestimation of foreign assets. The definition of someone who overestimates the precisions of one’s estimate is commonly referred to as an overconfident investor. Thus, Karlsson and Nordén (2007) investigated home bias based on data from Swedish individual’s pension plan portfolios and discovered that overconfident investors have a perceived information advantage concerning the investments he or she is feeling familiar with. Consequently, familiarity and eventually overconfidence can be linked with a perceived information advantage.
12
2.2.2 Cultural factor
As discussed in the previous section, familiarity has many different aspects and affects home bias in several ways. In the case of investor preference, familiarity attributes of the firm, such as language, culture, and distance, are significant aspects when determining the preference for a company (Grinblatt & Keloharju, 2001). Moreover, Chan et al., (2005) reviewed data that was obtained from Thomson Financial Securities and showed how 26 developed and developing countries allocated the investments of their mutual funds across 48 countries during a two-year period, and further distinguished between the case of domestic bias and foreign bias. The findings suggest that home bias was significantly affected by the degree of familiarity.
In the case of international portfolio decisions, a key aspect to consider is cultural distance. This aspect is significant in information asymmetry due to lack of translation and during adjustment to cultural conduct (Ardalan, 2019). Moreover, cultural proximity is a significant factor during investment decision-making, since a competitive advantage can be identified between countries with a shared language, history, and similar organizational cultures (Levis et al., 2016). This can further be confirmed by Grinblatt and Keloharju (2001) who state that investors prefer to invest in firms with shared language and culture. For instance, when Finnish firms published their annual reports in both Swedish and Finnish, a larger number of Swedish-speaking investors held assets of that firm.
Previous research has reported that culture and patriotism have a significant impact on home bias, meaning that an investor has a preference to invest in home stocks (Ardalan, 2019). Morse and Shive (2011) measured patriotism using World Values Survey, which consists of a questionnaire with 250 questions and a face-to-face interview about politics, religion, family, and other issues. Further, they argue that one can identify that investors from a patriotic country tend to prefer domestic equities to a larger extent. One could even diversify between different US regions and discover that investors in regions who are considered more patriotic hold more domestic equities. These findings were established after controlling for information asymmetry, risk, barriers, and familiarity.
13
2.2.3 Institutional factor
French and Poterba (1991) discussed that one explanation for overinvestments in domestic equity markets might be due to institutional factors, hence, factors that limit the possibility to hold foreign bonds or factors that reduce the returns of foreign investments. Institutional factors are discussed to be difficult to identify, although factors such as increased tax burden and high transaction cost are established when investing abroad. Referring to more recent studies, Levis et al., (2016) argue that in the case of institutional distance, familiarity in the business climate is significant, thus countries with similar legal and economic systems are preferred by the investors.
Although European countries have geographical proximity, one can observe a demand for domestic assets and products. In order to diminish home bias within Europe, several policies have been introduced. The core economic policies of the European Union (EU) are implemented to reduce obstacles and ease access to goods and capital across countries. In the 80s, the “Single Market Programme” (SMP) was implemented in the EU, to increase competitiveness and growth. When reviewing the situation two decades after the implementation, the demand for domestic assets and products remains, despite increased globalization. In other words, Europeans prefer to invest and purchase in their own country, thus home bias remains present. Nevertheless, the level of home bias varies from country to country, and across markets. In the case of goods and services, the degree of home bias remains at the same level as in 2000, meanwhile, the degree of home bias has decreased in the equity markets (Balta & Delgado, 2009). An additional reason for the increased integration in the European financial market might be due to the launch of the Eurocurrency in 1999. The launch resulted in a reduction of home bias across Europe, but as a consequence, it also led to the emergence of Euro bias. Further described as the situation where European investors tend to hold a large proportion of assets issued within the European region. Moreover, two significant factors explaining the emerge of Euro bias are the decrease in transaction costs and default risk (Balli et al., 2010). It is observable that institutional factors might act as barriers to enter foreign markets, and policies that reduce these barriers are significant.
2.2.4 Geographical factor
Considering the increased globalization, one might question whether the influence of physical or geographical distance still matters in terms of investment decisions. In contradiction to the
14 exchange of products and services, the physical distance would not be an issue in the case of financial trade and investments since transportation cost does not occur. According to Levis et al., (2016) that investigated the foreign direct investment flows from the OECD database over a 30 year period, found that physical proximity remains a significant aspect.
Accessibility of information could perhaps be an explanation of the geographical aspect, since information is more available from local firms (Levis et al., 2016). Hence, the information advantage, from personal contact and local media, might encourage investors to hold local firms (Coval & Moskowitz, 1999). Besides, Ardalan (2019) discusses that in some cases the investor might prefer to invest in their employers’ company due to loyalty and information advantage, this could be a further example of information asymmetry. While information nowadays is easier accessible, asymmetric information still utilizes an influence due to investors’ decision to concentrate their portfolio in domestic assets and hence remains uninformed about foreign assets. Further, Riff and Yagil (2016) has observed that many investors still choose to invest and specialize in local assets, hence amplifying their initial small information asymmetry, even though foreign information, in most cases, are not difficult to obtain or understand nowadays.
Differences in beliefs are another factor of investors exhibiting home bias. Although investors receive the identical information set, the beliefs about the information signals will differ amongst the investors. Ardalan (2019) argue that local investors value information sets in local signals to be more trustworthy, meanwhile information signals of foreign characteristics tend to be weighted as less reliable by local investors. Home bias is a consequence of asymmetric beliefs and arises if non-residential investors find themselves less knowledgeable about a country and its companies. Dahlqvist and Robertsson (2001) argues that these asymmetries may include the differences in processing information between foreign and domestic investors as a reason for intellectual or emotional biases. In the research, a detailed dataset of equity ownership including 352 listed Swedish firms, was used to identify which firm attributes that are common to foreign ownership of Swedish firms. Their findings also suggests that investors overall are more optimistic when it comes to expected returns in the domestic market than in foreign markets and that this pattern of forming expectations may as well be an implication of home bias.
15 2.3 Demographical factors
2.3.1 Age
Considering the demographical factors, age is an important aspect. The variable age is directly related to the investment horizon, hence may affect the decision-making among individuals in their asset allocation. In previous research by Graham et al., (2009) as well as Karlsson and Nordén (2007), it is found that older investors are less likely to own foreign assets. On the other hand, research conducted by Bailey et al., (2008) and Kyrychenko and Shum (2009) find the opposite and concludes that younger investors show lower tendencies to hold foreign assets. Due to the inconsistent result, one can argue this variable should be additionally tested.
2.3.2 Gender, marital status, and overconfidence
Karlsson and Nordén (2007) concluded that men normally tend to be more home biased than women. This statement is aligned with Barber and Odean (2001) who concluded that men usually are more overconfident compared to women, and a more overconfident investor is generally more home biased. In addition, they argued that between single men and single women, the difference in frequency of trading was greatest, single men trade more than single women. However, the marital status of individuals may reduce the gender effect on overconfidence. Significant results presented by Karlsson and Nordén (2007) concludes that an unmarried man showed higher tendencies for home bias than a married man. Moreover, the typical home biased candidate would be an unmarried man employed in the public sector, with a low level of education, and who is slightly overconfident (Karlsson & Nordén, 2007). Graham et al., (2009) concluded the opposite and found that “male investors, and investors with larger portfolios or more education, are more likely to perceive themselves as competent than female investors, and investors with smaller portfolios or less education” (p.1095). In other words, investors who believe they are more skillful, knowledgeable, and competent about foreign stock express weaker tendencies of home bias. Based on the inconsistency in their findings and the absence of control for psychological motives, further investigation is required.
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2.3.3 Occupation
Occupation might be a factor affecting individuals’ characteristics, hence influencing their level of home bias. According to Karlsson and Nordén (2007), people with low job security - employed in the private sector or self-employed – should to a larger extent diversify their holdings internationally. The main reason is that an economic downturn in the domestic market could lead to unemployment which only has a limited effect on international investments, hence smoothing the individual’s consumption. Contrarily, people with higher job security – employed in the public sector – are less concerned about job loss and more anxious for hedging domestic purchasing power leading to less interest in international diversification when considering investments. Therefore, people employed in the public sector show higher tendencies to act on home biased behavior than those who are not. Since limited research had been found that investigates the relation between home bias and an investor’s occupation, this is of further interest.
2.3.4 Competence and education
The level of competence an investor feels about investing domestically can affect home bias. In general, investors consider themselves to be more competent in domestically investing than foreign, and therefore tend to treat foreign stocks differently for the fear of exposing incompetence (Ardalan, 2019). The theory of competence affecting investors’ behavior has been investigated by Heath and Tversky (1991), who argued it to be an explanation for investors to sacrifice the advantage of a diversified portfolio. Instead, investors show a tendency to concentrate their investments in a small number of firms for which they are presumably competent and knowledgeable. Furthermore, the concept of investors’ competence was explored by Graham et al., (2009) who considered it from an international investing perspective, embracing home bias. Results showed that investors who have a degree from a college or higher, tend to feel more competent about investments. As a consequence of feeling more competent, investors displayed behavior of more frequent trading as well as holding a more internationally diversified portfolio. Karlsson and Nordén (2007) further confirm that lower tendencies of home bias can be identified among investors with a higher level of education. Referring to previous research, perceived competence and education are significant aspects of investment decisions. Therefore, one wants to include these factors when investigating the motives behind home bias.
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2.3.5 Experience
Ardalan (2019) argues that experience can be associated with the portfolio composition of private investors and connected to the home bias they execute. Discussed by both Grinblatt and Keloharju (2001) and Karlsson and Nordén (2007), investors that are less experienced tend to be more home biased. Kyrychenko and Shum (2009) broaden this spectrum and investigated different demographical, financial, and behavioral characteristics among U.S individual investors and how these factors are associated with investors’ holdings of foreign stocks. Findings displayed that the likelihood of ownership in foreign securities and the proportional amount in the portfolios was negatively affected by the lack of financial experience together with the lack of awareness of the benefits of diversification. Another interesting aspect was investigated by Abreu, et al., (2011) whose findings suggest that investors experience in the domestic financial market play a substantial role in when they will invest in foreign markets, hence greater experience will reduce the likelihood of home bias. In addition, these findings support Graham et al.,’s (2009) idea that investors will only invest in foreign markets after they feel familiar and informed by the benefits and risks associated to the investment. Further, their research argues that investors who are more frequent traders in the domestic market will invest in foreign markets earlier, than those with less trading frequency. One can argue that experience is a key factor in investment decision-making and have a relation to home bias, hence, should be included in the research.
2.4 Covid-19
In 2020, the outbreak of Covid-19 occurred, and as a consequence, the world became aware that natural disasters might impact the economy. Ortmann et al., (2020), collected data from a UK broker and used transactions to investigated how trading behavior responds to Covid-19. By using a dataset of 456,365 investors who executed 45,003,637 transactions during a period of August 1, 2019, to April 17, 2020, they found that investors increase their trading frequency in line with the emergence of Covid-19. Moreover, the number of new investors has increased and established investors have increased their trading activity. Ortmann et al., (2020) also found that investors do not move towards safer investments, such as gold, but neither risky investments, such as cryptocurrencies. Besides, they could establish that male investors and older investors increased their trading activity to the largest extent.
18 Referring to Sweden, Hagen et al., (2021) used fund-level data that was collected from the Seventh AP Fund and investigated how investors in the Swedish Premium Pension System (PPS) changed their trading activity in March 2020 compared to March 2019. However, this is a working paper and not yet peer-reviewed, which could probably be due to Covid-19 articles still being under production. Moreover, by reviewing the development of trading activity among investors before and after the outbreak of Covid-19, they found that the number of trading investors doubled and most of them moved their investments from capital to low-risk interest funds. However, the trading frequency in PPS remained low, which could be due to investors having a long-term attitude towards PPS investments. Further, this is in line with Statistics Sweden (2021a) who also found that one can observe an increase in new investors and in trading frequency. Moreover, Hoffmann et al., (2013) investigated the trading behavior of investors during the financial crises of 2008-2009, by combining monthly brokerage records of 1510 clients during the period of April 2008 and March 2009 in the Netherlands. They found that the perception of the investors does to a large extent affect the behavior of the investors, however, overall the trading frequency of investors did not change and nor did the risk-taking. But they found that new investor considers the low asset prices as an entry to the stock market.
19 2.5 Summary of literature review and hypothesis
Table 1 Summary of previous research
Article by Country and Data Method Results and Findings
Abreu, Mendes and Santos (2011)
Portugal, 3 252 investors
Duration analysis, event of interest is investor’s first investment in a foreign security.
Investors with experience in the domestic are more likely to invest abroad.
Anderson, Fedenia, Hirschey and Skiba (2011) 25,000 intuitional portfolios from 60 countries Survey-based country-specific variables.
Investors have a preference for culturally close markets over distant ones.
Bailey, Kumar and Ng (2008)
United States 77,995 investors
Database with tens of thousands of brokerage records of individual investors during the period 1991-1996.
More experienced investors are better at utilizing international diversification.
Barber and Odean (2001)
Unites States 35,000 households
Households’ account data. Measuring trading frequency and performance, to identify
behavioral biases.
Men, particularly single men, have a higher trading frequency and are more overconfident than women.
Chan, Covrig and Ng (2005)
Data obtained from Tomson Financial Securities.
2 year-data on the equity holdings on the mutual funds from 26 developed and developing countries
Investigate the allocation across 48 countries, distinguish between domestic bias and foreign bias.
Strong indication of home bias could be found, further, stock market development and familiarity are significant aspects of home bias.
Coval and Moskowitz (1999)
United States 1189 managers
Nelson’s 1996 Directory of investment managers, with data on the largest US money managers
US investment managers prefer firms with local headquarter.
US investors prefer geographically close investments.
Dahlqvist and Robertsson (2001)
Sweden
352 Swedish firms that was listed
1991- 1997
Data set on equity and firm-specific attributes to characterize foreign ownership in Swedish firms.
Multivariate regressions.
Foreign investors tend to
disproportionally allocate large share of their funds to large firms.
Graham, Harvey and Huang (2009)
United States 1,000 respondents
Interviews where trading
frequency, competence, and home bias along with several
demographical factors were analyzed.
Investors who feel competent have higher trading frequency and more international diversified portfolios
20 Grinblatt and Keloharju (2001) Finland 44,135 firm-municipality combinations
Two years of historical data from Finnish Central Securities Depositary of shareholder-ownership for publicly traded finish companies.
Investors simultaneous display a preference for nearby firms and for same-language and same-cultural firms.
Hagen, Malisa and Post (2021)
(working paper)
Sweden PPS-data
Fund-level data from Seventh AP Fund which shows decision of Swedish Premium Pension System (PPS)
Number of trading investors doubled and investments moved from capital to low-risk interest funds.
Heath and Tversky (1991)
United States Unknown
Series of experiments.
Investigates the relation between judgements of probability and preferences between bets.
Results challenge existing models on beliefs defined in terms of preferences. Hence, help to explain why investors sometimes forego the benefits of diversification and concentrate on a small quantity of firms.
Hoffmann, Post and Pennings (2013)
The Netherlands 1510 clients
Combining brokerage records with monthly questionnaire data during the period April 2008 and March 2009
The investors’ perception affects the behavior of the investor. Overall, the trading frequency or the risk-taking of the investors did not change. New investor considers the low asset prices as an entry to the stock market.
Karlsson and Nordén (2007)
Sweden
13,749 individuals
Dataset of Swedish investors decisions in the new defined contribution plan and how these decisions are linked to
demographical and socioeconomic data.
The most likely home biased individual is found to be a single, older,
uneducated man working in the public sector who only invest a small amount of money and has no experience with risky investments. Kyrychenko and Shum (2009) United States Number of households in each year: 3906 in 1992 4299 in 1995 4309 in 1998 4449 in 2001 4519 in 2004
Survey of Consumer Finances to conduct empirical analysis.
Age is positively related to the likelihood/ extent of home bias among investors.
Average ratio of foreign to total holdings lie within 30% - 50% in U.S. households.
Levis, Muradoǧlu, and Vasileva (2016)
Sample of developing and developed countries. 6263 bilateral country pairs over 30-year period from OECD data base.
Analyze home bias and foreign direct investments (FDI). Home bias framework from finance literature and economic gravity model from economics literature.
Home bias in foreign direct investments does not decrease over time. Further, physical proximity matters after cultural and institutional factors are checked.
21 Ortmann, Pelster and T. Wenerek (2020) United Kingdom 456,365 investors and 45,003,637 transactions
Sample contains all executed trades between August 1, 2019 and April 17, 2020.
Collected from an UK broker.
Investors’ trading activity increase in line with Covid-19, and increased number of new investors.
Wolf (2000) United States, Unknown
1993 U.S. Commodity Flow Survey (CFS) on data for flow of goods and materials by mode of transport.
Proved home bias within the states of U.S. (sub-national level).
2.5.1 Hypothesis
Based on the demographical factors from previous research within the area of home bias, the following alternative hypotheses have been stated:
• Men are more home bias than women. • Older people are more home biased.
• People in a relationship are more home bias.
• People with lower level of education are more home biased. • People with higher job security are more home biased.
• People who perceive themselves as less competent are more home biased. • People with less experience are more home biased.
In addition to the hypotheses, country preference, motives that might affect a home biased behavior and potential change in trading behavior due to Covid-19 will be examined and discussed.
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3 Methodology
This chapter describes the methodology of the paper, such as the research philosophy, approach, and strategy that is used. Furthermore, the primary data collection tool, survey, and sample selection are elaborated. The empirical method is explained, hence the binary logistics model and the variables. Finally, the data analysis, as well as strengths and limitations are discussed.
3.1 Research philosophy
A central part of conducting a research academic study is the research philosophy, as it acts as a ground for how to administer scientific research. According to Bayer (2016), there are three main philosophical positions to take. To start with, positivism originates in natural science and assumes that social study is not affected by the investigating process. Positivism contains both features from a deductive approach and an inductive approach. The elements from deductive principles are signified through hypothesis testing and thereby acknowledge the explanation of laws to be evaluated. Contrary, the inductive approach is represented through information gathering which postulates the foundation for the laws. According to Collis and Hussey (2014) positivism is related to quantitative methods of analysis as it assumes that social phenomenon can be measured.
Further, Bryman (2016) states that another philosophy position is realism. This philosophy shares some elements with positivism and believes that similar approaches to the collection of data and explanation should apply to both natural and social science. Furthermore, this position can be divided into empirical realism and critical realism. Hence, empirical realism manifests that reality can be understood using suitable methods. On the other hand, critical realism asserts that only by the recognition of the structures that creates occasions and homilies, reality can be understood.
The third one, interpretivism, contradicts with positivism and is reinforced with the belief that social reality is highly subjective and not objective since it is shaped by our perceptions. In this philosophy, the researcher interacts with that being investigated as it is impossible to separate what exists in the social world from what is in the mind of the researcher. While the focus in positivism is mainly on measuring social phenomenon, interpretivism emphasizes studying the complexity of social phenomenon with a vision to achieve explanatory understanding. Moreover, Collis and
23 Hussey (2014) states that interpretivism is associated with qualitative research methods since its main focus is to gain an interpretative understanding.
3.2 Research approach
To test and build the relationship between theory and research, Bryman (2016) suggests that two main approaches can be considered, deductive and inductive theory. Furthermore, when considering social research, one needs to decide which one is the most appropriate. To relate theory and research, the most common view is to address a deductive theory, where the researcher derives hypotheses that need to be subjected to empirical observations based on what is already known within the research field and on appropriate theoretical concepts. Hence, this theory is regularly related to quantitative research methods. When applying inductive theory, the researcher’s conclusions are grounded on what has been observed and from that data collection form a theory. Furthermore, an inductive theory is generally associated with qualitative research methods.
In addition, some implications of the explorative approach will be used. By using this approach, Collis and Hussey (2014) claim that this enables the researchers to explore if existing concepts and theories can be applied to the problem or if the development of new ones are vital to explain the research questions. Subsequently, this approach does not provide a definite answer, in its place, new concepts and recommendations for further investigations are presented. Further, Bryman (2016) confirms this and argues that to take on a more exploratory angle is appropriate and appreciated when analyzing quantitative survey data in order to develop new models and concepts.
3.3 Research strategy
In this paper, the research strategy adopted will be of the quantitative form, which emphasizes the collection of numerical data with an objective approach and with a position of positivism. To analyze a vast volume of data, several computer software programs can be used, for instance, SPSS. Subsequently, to analyze the tested variables there exist several approaches to adopt, varying on how many clusters the tested variable can be separated into. When analyzing one variable at a time, the univariate analysis is commonly used and is also the simplest form of analyzing data. Approaches that are generally used with this approach are frequency tables and diagrams, where explicit values of each tested variable are displayed. Next, when two variables
24 are tested at a time to uncover if the variables are correlated, a bivariate analysis can be used. Frequent approaches to discover this is regression and correlation statistics (Bryman, 2016). Furthermore, in order to conduct this research, the strategy chosen is to take on both a deductive and explorative approach with a position in positivism and with a touch of empirical realism philosophy, as one wants to understand the reality by using targeted models.
3.4 Data Collection
3.4.1 Survey
To conduct this research, the empirical data was gathered through an online quantitative web survey, when analyzing the data, one needs to differentiate between primary and secondary data. Primary data refer to data that has been gathered by the responsible researcher and the analysis is conducted by this researcher. Meanwhile, secondary data is explained to be data that has been collected earlier and can now be found on different databases and archives. In contradiction to primary data, other researchers analyze the secondary data (Bryman, 2016). Referring to previous literature and researchers, both Anderson et al., (2011) and Graham et al., (2009) conducted surveys when investigating home bias. However, Anderson et al., (2011) considered survey-based country-specific variables when conducting their research about home bias and culture on a country level, using 25000 institutional portfolios. Meanwhile Graham et al., (2009), investigated home bias on an individual level. However, their survey was conducted by telephone interviews with 1000 respondents in the United States and overconfidence among investors was also included to a large extent in the research. Hence, it is observable that both articles had large sample sizes.
Since this paper aims to investigate home bias among Swedish private investors, with respect to demographical factors, one can argue that both secondary data and primary data needs to be included. When collecting primary data, the data might be more targeted to the research question and the research becomes more valid. Subsequently, it will be easier to establish which demographical factors, hence which people, show a tendency for home biased behavior and eventually establish findings that can prevent them from doing so.
In order to collect primary data, an online web survey is conducted. This is due to the fact that there are several advantages with constructing a web survey. For instance, the web survey will
25 reach a lot of individuals since it will be posted online. Further, when the answers are logged, one can easily retrieve the data set, which saves a lot of time, as well as decreases the risk of error. In addition, a web survey is explained to be cost-efficient. However, some disadvantages with an online web survey are that the questions will be prepared in advance and there will not be any interviewer. Therefore, if any of the participants do not understand a question, they will not be able to receive clarification from the interviewer, which may eventually question the accuracy of the data. Further, one cannot ask the participants to elaborate on their answers, which in some cases could be necessary (Bryman, 2016). Another negative aspect to consider when conducting a survey is the hypothetical bias – that the stated behavior of the respondents does not correspond to the true behavior. For instance, the respondent stated willingness to pay exceeds the true willingness to pay (Loomis, 2011). Subsequently, hypothetical bias is to some extent referred to as one of the main weaknesses of a valuation survey (Penn & Hu, 2021). However, no generally accepted explanation is yet constructed to define the respondent behavior nor hypothetical bias (Loomis, 2011). Still, it needs to be considered as the collected data might not be completely correct and accurate, however, some errors in the data collection are unavoidable.
When conducting the online web survey that gathered the primary data, the advantages and disadvantages above were taken into account. In order to prevent any issues, the survey questions were carefully considered and discussed. The main focus and importance were that all questions had a significant underlying purpose and could be motivated. To find advice and inspiration, previous articles and research that used s survey as a data collection tool were reviewed. Further, the survey was tested several times by different people, ranging from age 20 to 70, as well as different sexes, leading to changes in the formulation and style. Eventually, after being tested and improved, the final survey could be established and shared.
The survey was conducted through the database Qualtrics and consisted of fifteen questions. Subsequently, it was completely voluntary to participate, and all respondents were anonymous and no financial compensation was offered. The answering time to complete the survey was estimated to be approximately four minutes. All important information was given before the survey could be started, such as that the survey was anonymous and the completion time. Also, the questions were
26 numbered, to prevent that the respondents would not finish the survey. The complete survey, introduction, and confirmation can be found in Appendix 1.
3.4.2 Population and sample selection
Since this paper aims to investigate home bias with respect to demographical factors among Swedish private investors, it is evident that the population that one wants to target in this study is Swedish private investors. Mostly when conducting a research, it is difficult, if not impossible, to reach out to all individuals that one would like to target. One does not only have to consider access, but it is a question of time and cost. Thus, one has to sample (Bryman, 2016). Since, Sweden has roughly 10 million inhabitants and approximately 1.223 million private investors, it is evident that one cannot target the entire population (Statistiska Centralbyrån, 2021a, 2021b). According to Bryman (2016), in order to create a trustworthy sample, a probability sample is preferred, because the sample is randomly selected and is considered to reflect the population in a good way, hence, one can make general conclusions and assumptions from the sample. However, in the case of the survey of this thesis, the distribution was a non-probability sample, since there is not an equal probability of being selected to participate in the survey. Hence, the sample might not reflect the population in a completely accurate way, and sampling errors might occur.
In order to target Swedish private investors and receive a lot of respondents, the survey was posted on different online investment forums, such as Avanza, Nordnet, and RikaTillsammans. Further, these sites had different discussion and focus forums, for instance; Avanza had funds, general and investment strategy, meanwhile, RikaTillsammans had saving and investing, and Nordnet had funds. Subsequently, to diversify the sample even further, the survey was sent by email to female investment groups and also published in different Facebook groups. Besides, the survey was also posted in the Facebook group for all students that study Civilekonom at Jönköping International Business School and also sent by email to the JSA Investment Club. In addition, Unga Aktiesparare in Jönköping was also contacted and received the survey. The survey was posted online between 2021-03-11 until 2021-03-23, hence 13 days, and received a number of 247 respondents. Moreover, the data collection was conducted in line with the GDPR-rules, and to certify this, all respondents were required to give their consent before completing the survey. Besides, the survey was anonymous and did not collect any personal data, such as names or social security numbers.
