Social Trading, a new attractive opportunity or an unforeseeable risk? A qualitative analysis of the market and a quantitative evaluation of the performance

Master Thesis

Spring Term 2017

Submitted by

Michael Haber

Supervisor: Lars Norup

Social trading, a new attractive investment opportunity or an unforeseeable risk? A qualitative analysis of the social trading market and a quantitative evaluation of the performance

Master Thesis

Supervisor:

Lars Norup

Submitted by

Michael Haber Donnersbergstraße 4 67117 Limburgerhof, Germany CPR-Number: M.Sc. in and Strategic Management

Number of pages: 80 Number of characters: 181,971

Copenhagen Business School (Copenhagen, Denmark), 15th of May 2017 II

III

Abstract

Social trading is currently growing in popularity in the FinTech sector. The idea of collaboratively made investment decisions is not new. Changed customer preferences and technological developments offer new opportunities. This thesis analyzes the concept of social trading from a qualitative and quantitative perspective with the goal to evaluate and critically reflect on risk and success potentials. The work is structured in three parts: a qualitative analysis of the social trading market, a theoretical representation of the concept of social trading, and a quantitative evaluation of the performance of social trading portfolios. On the market for social trading, a variety of companies that so far have no dominating business model can be identified. It should be emphasized that the current way of operationalizing social trading can create high risks for . The theoretical analysis highlights the potential of social trading to increase the transparency of the investment process and to reduce informational asymmetries between investors and portfolio managers. At the same time, the utilization of collective intelligence offers the chance to create informational advantages for investors. The quantitative analysis of a sample of social trading portfolios retrieved from wikifolio suggests that social trading has the potential to generate abnormal returns.

Key words: social trading, influencing factors, business models, performance, investment alternative, risks, trading opportunities

IV

Table of contents

List of figures ...... VI

List of tables...... VII

List of abbreviations ...... VIII

1. Introduction ...... 9

1.1. Background and relevance ...... 9

1.2. Research focus ...... 10

2. Social Trading ...... 13

2.1. Classification and definition ...... 13

2.1.1. Social trading as part of FinTech ...... 13

2.1.2. Emergence and definition ...... 15

2.2. Social trading platforms ...... 18

2.2.1. Financial instruments ...... 19

2.2.1.1. Contracts for difference ...... 19

2.2.1.2. Index certificates ...... 21

2.2.2. Regulation today and in the future ...... 23

2.3. Available business models ...... 28

2.3.1. Fully integrated platforms ...... 30

2.3.2. Information exchange and trading with partner companies ...... 33

2.3.3. Information exchange ...... 37

2.4. Social trading market and future outlook ...... 38

3. Literature ...... 42

3.1. Positioning social trading into financial theory ...... 43

3.1.1. Market efficiency and active vs. passive investment strategies ...... 43

3.1.2. Asymmetric information and agency costs ...... 46

V

3.1.3. Incentive structure in delegated portfolio management ...... 48

3.2. Special characteristics of social trading ...... 51

3.2.1. Collective intelligence ...... 51

3.2.2. Decision-making and social influence ...... 55

3.2.3. Platform characteristics ...... 57

3.2.4. Remuneration ...... 59

4. Methodology ...... 63

4.1. Social trading with wikifolio ...... 63

4.2. Dataset of the analysis ...... 64

4.3. Methodological framework ...... 67

5. Results ...... 71

5.1. Performance of social trading portfolios ...... 71

5.2. Regression analysis ...... 73

6. Discussion ...... 78

6.1. Limitations of the quantitative methodology ...... 78

6.2. Critical reflection of qualitative and quantitative research findings ...... 79

6.3. Potential future developments of social trading ...... 82

7. Summary and concluding remarks ...... 84

References ...... 87

Appendix ...... 102

VI

List of figures

Figure 1: Research focuses and structure ...... 12

Figure 2: Segments of the FinTech market ...... 15

Figure 3: Summary financial instruments and regulation ...... 28

Figure 4: Theoretical model ...... 43

Figure 5: Hypotheses ...... 62

Figure 6: wikifolio labels ...... 65

Figure 7: Number of portfolios issued over time and relative distribution of labels in the category “traded instruments” ...... 66

Figure 8: Number of assigned labels ...... 66

Figure 9: Number of portfolios per and ...... 67

Figure 10: Variables regression analysis ...... 70

Figure 11: Return distribution and correlation ...... 71

Figure 12: Average monthly returns and alphas ...... 72

Figure 13: Regression results, entire sample ...... 74

Figure 14: Regression results geographical focus Germany ...... 75

VII

List of tables

Table 1: Categories of social trading platforms ...... 29

Table 2: Fully integrated social trading platforms ...... 31

Table 3: Social trading platforms with information exchange and trading with partners .. 34

Table 4: SWOT analysis of social trading ...... 83

Table 5: Success factors and risks ...... 85

VIII

List of abbreviations

ATM Automated Teller Machine BaFin “Bundesanstalt für Finanzdienstleistungsaufsicht“ CFD Contract for difference CySec Cyprus Securities and Exchange Commission ETF Exchange-traded fund EUR Euro FCA Financial Conduct Authority GBP Pound Sterling GmbH “Gesellschaft mit beschränkter Haftung“ L&S Lang & Schwarz MiFID Markets in Financial Instruments Directive MiFIR Markets in Financial Instruments Regulation OTC Over the counter USD U.S. Dollar WpHG “Wertpapierhandelsgesetz”

1. Introduction 9

1. Introduction

1.1. Background and relevance

Consultancy of investors is a big business that is recently growing in importance. At the same time, the skepticism of investors is high. According to a European survey, investors have concerns especially regarding the fees charged by advisors. In addition, about 64% of investors fundamentally question whether the advice they receive is actually beneficial (CFA Institute, 2009). It is therefore not surprising that the participation of private investors in financial markets seems to be limited (Campbell, 2006; Guiso, Haliassos, & Jappelli, 2002; Mußler, 2015). Especially among the generation of “millennials,” in financial markets seem to be a burden rather than an opportunity. Even investments in and other financial instruments are historically more common in the United States than in Europe, an increasing antipathy towards financial markets can be noticed independent of geographical region (Egan, 2015; Littmann, 2014). The financial crises of 2000 and 2007/08 and a lack of knowledge about financial markets are possible reasons for the low interest in financial markets. Technological developments over the past 15 years – especially in the area of communication – have created new possibilities for the asset management industry. Social trading as part of the current FinTech movement combines aspects of finance, asset management, and social networks with new technological possibilities. The fundamental idea of social trading is to enable private investors to make investment decisions easily and independently. Social trading platforms promote themselves as facilitators of a new, easy and successful way to invest. High transparency, increased flexibility, and good return opportunities should offer private investors an alternative to and other asset management companies. Since the financial crisis of 2007/08, a variety of new companies emerged with the goal to establish social trading as a new mode of asset management. The business models have differences in execution, but all aim to lower the entry barriers to financial markets and make investing more successful for private investors. Social trading platforms create the infrastructure to enable private investors to follow trading strategies that are shared in the community. The offerings and the use of social trading platforms have been increasing since 2010. For the financial industry, social trading represents an innovative concept, which is different from currently dominating business models. The majority of private investors simply consume financial products with the help of banks or other 1. Introduction 10 institutions. Social trading, in contrast, offers the possibility to actively participate in the investment process, and it could therefore potentially influence the asset management industry. Besides its many opportunities, social trading is also associated with risks, which have to be taken into account to comprehensively assess the collaborative investment approach. In addition to raising concerns about a lack of data or reduced customer services – which are common for digital business models – social trading has a variety of other risk sources. Next to the fundamental investment risk of the and the operational risk regarding the execution of the investments, it can generally be questioned whether social trading platforms are trustworthy institutions. The reliable and transparent execution of investments and a safe and instant interaction between users are key if social trading is to become a serious alternative to banks and other financial-service companies. In addition, it must still be independently proven that social trading can generate positive or abnormal returns. Furthermore, many unknown and potentially risky factors exist due to the relatively short time social trading has been available: for example, the different business models offered on the market, the differing methods of execution, and the fundamental idea of the underlying concept. This work therefore takes a twofold perspective as a starting point – with success potentials on the one hand and uncertainty about future development on the other hand – to analyze and evaluate the concept of social trading.

1.2. Research focus

The overall goal of the analysis is to evaluate social trading from various perspectives. To consider multiple factors, a qualitative and a quantitative research approach is chosen. The research focus can be divided into three parts. First of all, social trading is defined, relevant environmental factors are described and the market is categorized according to the available business models (see Chapter 2). Second, the underlying concept of social trading is analyzed. Key influencing variables are identified and outlined based on a theoretically derived model (see Chapter 3). Third, the performance of social trading is assessed based on a sample of social trading portfolios retrieved from wikifolio (see hapters 4 and 5). In the first part of the analysis, the goal is to identify origins and forces that have influenced the development of social trading in recent years. Based on a definition of social trading, key aspects of social trading platforms are outlined, and the market is categorized according to differences in the available business models. Company homepages and current media articles

1. Introduction 11 serve as primary sources of information to create case studies about selected representatives of the social trading market. Analyses of the different business models work out characteristics of the social trading market and help to assess the trustworthiness of social trading platforms. In the second part of the analysis, the theoretical concept underlying social trading is broken down into key influencing variables. Based on the theoretical model, hypotheses are developed concerning how the performance of social trading portfolios might be influenced. On the one hand, the theoretical model helps to develop hypotheses for the quantitative analysis of the third research focus. On the other hand, the model highlights areas of interest to assess social trading and create an objective evaluation of the collaborative investment concept. The theoretical model is based on scientific research papers in research fields related to social trading: for example, finance theory, network theory, and decision-making theory. In the third research focus, the performance of social trading portfolios is quantitatively evaluated. The analysis is divided into two steps. First, the performance of social trading portfolios is analyzed independently and in comparison, to a benchmark. Second, with the help of a regression analysis factors are identified, which can explain the performance of social trading portfolios. The goal is to evaluate the performance of social trading and to assess whether social trading represents an attractive investment opportunity. Afterwards, the results of the three different research focuses are discussed. In the discussion, the aim is to take findings of all research focuses into account and to develop an assessment of the success potential and risks associated with social trading. Additionally, potential future developments are outlined and reflected. The research goal of the work can therefore be formulated in three key research questions: What is the current situation on the market for social trading? What are the success factors and risks of social trading? How might social trading influence the financial industry?

1. Introduction 12

Social trading

Theoretical Social trading market Quantitative analysis model Environmental factors Concept of social Performance and and categorization of trading and key regression analysis business models variables Discussion

Final assessment of social trading What is the current What are the success How might social situation on the market factors and risks of trading influence the for social trading? social trading? financial industry?

Figure 1: Research focuses and structure Source: Own illustration The structure of this thesis follows the three research focuses. Chapter 2 defines and categorizes the concept of social trading. After identifying relevant and influencing factors of the social trading market the results are shortly discussed and evaluated. The theoretical model is developed in Chapter 3. Chapters 4 and 5 present the method and results of the quantitative analysis. Chapter 6 discusses the findings, and Chapter 7 summarizes the results.

2. Social Trading 13

2. Social Trading

The development and increasing popularity of innovative business models with reduced operating costs, improved retention by customers, and additional revenue sources create the legitimate impression that the financial industry is currently undergoing a phase of structural change (Mead, 2016). Across all product categories, established players are increasingly set under pressure by new market actors summarized under the broad category of FinTechs. Established institutions are faced with the challenge to find ways to compete or cooperate with new market players. Consumers are offered innovative products and services and continuously adapt to the increasingly digitized environment. Nevertheless, out of the large variety of ideas and newly developed business models, only a minority might become a success story. Social trading, as part of the FinTech movement, has the potential to contribute to the structural change of the financial industry. At the same time the future development is attached with a lot of uncertainty. For evaluating the true success potential, different perspectives have to be taken into account. Besides considering operations and the legal framework, a clear classification of the industry context and an understanding of the conceptual origins are needed to formulate a reasonable assessment of newly developed business models. In the following, social trading is defined and the influence of the regulatory framework and the utilized financial instruments is analyzed. Afterwards, the social trading market is categorized and described. Based on the insights generated, the success potential and risks for social trading are discussed.

2.1. Classification and definition

2.1.1. Social trading as part of FinTech

In general, FinTech can be understood as the development of new products and business models that combine financial services and technology (Kawai, 2016). The use of technology in the financial industry has a long tradition. The introduction of the ATM, electronic trading, and online stock-brokerage services are some examples that highlight the importance of technology in recent development. The speed of development has constantly increased over the last decade, and the expression FinTech became popular for describing start-ups or innovative companies in the financial industry (Desai, 2015). In most cases, the term FinTech is associated with the development of innovation facilitated by technology (Kawai, 2016). Improved risk management, accelerated trade processing or innovative data-analysis applications are

2. Social Trading 14 examples of areas in which FinTechs have developed innovative solutions. In recent years, significant amounts of funding and financing have been invested into the FinTech market. In 2015, global FinTech investments were about 18 billion USD (excluding four billion USD invested in ) (Mead, 2016). Split by product, the highest amounts are invested into lending (1.8 billion USD in 2015) and online-payment solutions (1.7 billion USD in 2015) (Money of the future, 2015). The United States is the major market for FinTechs with investments of more than nine billion USD in 2014 (“The FinTech Reveolution,” 2015). Great Britain and Germany are the major European FinTech markets. They accounted for 84.6% of the total European investments in 2015. The European FinTech market invested 1.9 billion EUR in 2015, which is considerably less than in the U.S. market (Murray, 2016). Nevertheless, the importance of FinTech companies is constantly increasing. The total value of investments is expected to increase up to 46 billion EUR in 2020 (Money of the future, 2015). The FinTech market comprises a wide variety of companies that serve almost all categories of financial services. The market can be divided according to the innovativeness of the offered products and according to the type of product. Referring to the innovativeness of FinTechs, it can be differentiated between facilitators and disruptors. Facilitators are usually large and established technology companies that use the existing infrastructure to offer supportive activities for the financial industry. Disruptors are FinTechs that focus on the creation of innovative products and business models that directly compete with established players. The highly innovative disruptors either utilize the existing infrastructure or aim to replace it completely. In contrast to facilitators, the business models of disruptors rely on multiple additional revenue sources, such as subscriptions, advertisements or monetization of data. Facilitators are based mainly on traditional revenue models, such as cost per transaction, percentage of assets or license fees (EY, 2014a). The majority of the new products and services created by facilitators and disruptors use the Internet as a basic technology. The new market players aim to increase customer value with an easy-to-use interface that is efficient and transparent, and by offering a high degree of automation (European Banking Federation, 2015; Mackenzie, 2015). At the same time, the business models of FinTechs often operate in a market with low profit margins and reduced regulatory requirements and can be characterized as companies with low fixed costs and high scalability (Lee & Teo, 2015). The product-range of FinTechs constantly increases and covers different banking, ,

2. Social Trading 15 and other financial services. From a product perspective, four fundamental FinTech market segments can be identified: financing, asset management, payment transaction services, and other specialized services (Dorfleitner & Hornuf, 2016). Social trading can be classified in the FinTech market as a new mode of asset management. Additionally, social trading platforms can be described as disruptors, as their overall goal is to replace the portfolio managers of banks or other financial-service companies. At the same time, the existing infrastructure is used to execute trades.

FinTechs

Asset Payment Other Financing management service FinTechs

Alternative Robo advisory Insurances Crowd- / Payment methods funding Factoring Blockchain & Search engine & Social trading Cryptocurrencies comparison portal Donation based Personal financial Technique, IT, management infrastructure Reward based Banking services Crowdinvesting

Crowdlending

Figure 2: Segments of the FinTech market Source: Adapted from Dorfleitner and Hornuf, 2016

2.1.2. Emergence and definition

The prerequisites for the emergence of social trading were set in the development and dispersion of new technological possibilities. Easy and cheap communication tools that make it possible to interact with a large number of people at low cost have formed the foundation for collaborative investment decisions. The Internet – with its e-mail, forums and other platforms – has created possibilities for connecting a decentral community. The fundamental concept of social trading has already existed for about ten years. Nevertheless, its utilization by an increasing number of investors did not start immediately. The constantly increasing importance of interconnectedness and the growing use of technology in daily life are factors that have fostered the acceptance of digital business models and therefore also of social trading. The utilization of new communication technologies among a large number of people has created a sufficiently large customer base. Besides these technological developments, the increasing

2. Social Trading 16 popularity of social trading is strengthened in two fundamental ways: first, by the attitude of private investors towards financial institutions; second, by the utilization of selected financial instruments (for financial instruments see Chapter 2.2.1).

The financial crisis of 2007/08 had significant impacts on the public perception of banks and financial institutions. The trust of private investors was especially reduced, as more and more information about business practices applied before the financial crisis became public (Sapienza & Zingales, 2012). Trust between the involved parties is a fundamental element of every transaction and is essential for trade and investments (Arrow, 1972). Trust is a key element especially for financial markets, as investors spend money on investments with the promise of a future return (Sapienza & Zingales, 2012). The moment of spending money and the moment of receiving the outcome of an investment are usually different from each other. Investments are not made if investors cannot trust the financial institution that the promised return can be realized. The results of an American survey show that trust in financial markets, banks, and people working for banks declined since the outbreak of the financial crisis (Frank & Lades, 2015; Sapienza & Zingales, 2012). Even more than eight years after the start of the financial crisis, trust in banks and other financial institutions continues to be low. According to a German survey, only 33% of people interviewed trust the financial industry, which is the lowest value across all industries analyzed (Gfk Verein, 2016). On the one hand, this low level of trust can be attributed to greed and to the excessive risk taking of financial institutions, which became public after 2007/08. On the other hand, continued bad experiences – for example bad investment recommendations or sales of disappointing products – have continuously reduced trust in the financial industry (Carlin & Manso, 2011; Inderst & Ottaviani, 2012; Stoughton, Wu, & Zechner, 2011; Strategy&, 2016). The low reputation and bad perception of established market players create chances for new market entrants. Customers might be more open minded towards alternatives and might be easily convinced of new offerings if a more trustful relationship can be expected.

Social trading can be defined from a conceptual and an operational perspective. The conceptual definition outlines the fundamental idea of social trading, whereas the operational definition describes the function. From a conceptual perspective, social trading and associated platforms can be understood as a place to and exchange knowledge about investment opportunities and trading strategies. The idea to collaboratively made investment decisions addresses the high skepticism of private investors towards the financial industry, as it offers the possibility to

2. Social Trading 17 become more independent of financial institutions. Even people with limited knowledge about stock markets should be able to manage their portfolios independently. Trading decisions are based on recommendations of other traders and on the popularity of these recommendations in the community. The collective of a social trading platform replaces advisors and portfolio managers. According to the concept of collective intelligence,1 a group of people has a certain degree of intelligence to solve complex tasks (Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). Social experiments have shown that the decision-making of a crowd can be superior to the decisions of individuals (Surowiecki, 2005). The concept of social trading builds on the idea of using the wisdom of the crowd by connecting many individuals to make collective investment decisions (For a more in-depth analysis of collective intelligence, see Chapter 3.2.1). Besides utilizing collective intelligence, social trading can be defined by three other fundamental pillars: social networks, information, and sharing. Potentially interested people have to register on social trading platforms to become part of a community. As with established social networks, like Facebook or Twitter, only registered users can participate and communicate with other members. On the platform, explicit and implicit information is available. Product evaluations, market assessments, and chats with other members are examples of the explicit information that is shared in the community. Explicit information is not necessarily provided only by members of the community. Platform providers can add additional information sources to provide the collective with the insights it needs to make reasonable and comprehensible investment decisions. Implicit information includes the popularity of a trading strategy or the individual perceptions of members – for example, the aggregated impression of many experienced users. The outcome and goal of participating in social trading is to share (developing or following) information and trading strategies. A conceptual definition of social trading is therefore as follows:

Social trading is a community-based exchange and interpretation of information about financial markets, with the goal to develop, share, and apply trading strategies.

From a more operational perspective, it can be distinguished between three involved parties: social trading platforms, followers, and signal providers. Social trading platforms create and

1 The expression is colloquially also called “wisdom of the crowds,” which is also used in the following.

2. Social Trading 18 maintain the infrastructure needed to execute social trading over online platforms. Users of social trading can act as followers and signal providers. Followers are potential investors who are seeking suitable investment opportunities. Followers can be differentiated according to individual investment preferences, such as amount to be invested, expected return, length of investment period, and investment focus. Signal providers develop and create investment strategies that can be applied by followers. Signal providers can therefore be described as portfolio managers who pick attractive investment opportunities and develop trading strategies. Trading strategies can be focused on specific investments (e.g., geographic or industry focus) or specialized on selected financial instruments.

2.2. Social trading platforms

Social trading platforms are two-sided marketplaces with the goal to match signal providers and followers. In general, it can be differentiated between two ways to connect followers and signal providers: copy trading and mirror trading. In a copy trade, the follower decides to place the same trade(s) as a signal provider. The follower can still influence which trades are conducted and how the portfolio is composed. Mirror trading is the automated execution of every trade, which is carried out by a signal provider. The portfolio of the follower is directly connected to the portfolio of the signal provider. Besides these two basic operations, the intermediary function between followers and signal providers can be interpreted and executed differently. For example, the degree and mode of guidance and assistance during the investment process, the possibility to interact with other users or the operational execution can differ. In addition to the detailed functioning of the business models (further elaborated in Chapter 2.3), some social trading platforms rely on specific financial instruments and are increasingly subject to regulation. The utilized financial instruments must enable followers to copy or mirror the trading strategies of signal providers in real time and at reasonable costs. At the same time, the investment universe must be sufficient to enable signal providers to develop interesting and attractive trading strategies. From an perspective, it is important to consider both the return potential and the risk associated with the financial instruments. At the same time, rules and regulations by official institutions represent an area of increased importance for FinTechs. The high innovativeness and rapid development of FinTechs cannot be considered instantaneously by regulators. The result is a high uncertainty, as FinTechs might operate in legally grey areas (Athwal, 2015). Adjustments to regulatory requirements can have

2. Social Trading 19 substantial effects on business models. In the assessment of social trading, the potential impact of new rules and regulations and the clarified application of existing judicial frameworks is therefore fundamentally important.

2.2.1. Financial instruments

Social trading platforms, which offer followers the possibility to invest in the trading strategies of signal providers, use some type of to replicate the investment strategy. Either followers use the same instrument as the signal provider or a financial instrument is used to replicate the investment strategy. “Contracts for difference” (CFDs) are the most frequently used instruments on social trading platforms. Nevertheless, it is possible to identify differences between the platform providers, for example, regarding management fees or spreads. Besides many platforms, which utilize CFDs, wikifolio in contrast uses open-end index certificates (also called participation certificates) to replicate trading strategies of signal providers.

2.2.1.1. Contracts for difference

In recent years, the worldwide trading activity of CFDs has increased constantly. Because most CFD trading takes place over the counter (OTC), the exact volume of the activity is difficult to define. The UK and Germany are estimated to be the largest global retail CFD markets with a total transaction volume of 1.45 billion EUR in 2011 (METIS - Management Consulting, 2012). In the United States, CFDs are not traded due to regulations by the Dodd Franck Act (Securities and Exchange Commission, 2012). The currently discussed deregulation may cause changes to the regulatory framework and may in the future also make CFD trading possible in the United States. CFDs are future-like financial instruments2 that do not have a nominal value. Common underlying values for CFDs are stocks, indices, commodities, currencies and bonds3.

2 “A derivative can be defined as a financial instrument whose value depends on (or derives from) the values of other, more basic, underlying variables. Very often the variables underlying derivatives are the prices of traded assets. A stock , for example, is a derivative whose value is dependent on the price of a stock. However, derivatives can be dependent on almost any variable, from the price of hogs to the amount of snow falling at a certain ski resort.” (Hull, 2015, p. 1) 3 Besides OTC traded CFDs, listed CFDs are available in many countries. Listed CFDs have a publicly available bid offer spread, and, in contrast to OTC traded CFDs, usually have an expiry date. Even more standardized are exchange traded CFDs, which are available on the Australian Stock Exchange, for

2. Social Trading 20

With the acquisition of a CFD, the trader opens a theoretical position in the underlying. With the sale of the CFD, the position is closed. The trader receives or has to pay the difference between the opening and the closing position. A trader can either take a long or a short position. Besides a margin payment based on the price development of the underlying, no further capital is required. Additionally, investors may leverage investments by providing only a portion of the required margin and lend the remaining. Dividends of stocks are partially paid out to CFD owners (added for a long position, deducted for a short position). The percentage of the paid- out dividend depends on the broker. The low margin requirements make CFDs a cost-effective possibility to get exposure to the underlying and hedge risk of investments. In the late 1990s, CFDs were also offered to private investors (Lee & Choy, 2014). CFDs have a variety of advantages, such as benefits in the UK4, easy and cost-effective possibilities for leverage, and availability for a large variety of underlying values. It is also easy to take short positions, as the borrowing of a stock is not necessary (Norman, 2009). Additionally, the concept is easy to understand, the costs are manageable, and the CFDs can be used in many different contexts. Nevertheless, CFDs are controversially discussed, especially because unsophisticated investors can be easily exposed to high risks (Brown, Dark, & Davis, 2010). In addition to the market risk of the underlying, investors are exposed to liquidation and counterparty risk. If the market moves against the investors’ position, additional margin payments might be required; otherwise the position is closed. The automated closing of positions usually means substantial losses for investors. The risk is also increased when high leverages are applied. Counterparty risk concerns the possible risk associated with the broker who sells the contract. A CFD is not a traded instrument that can be sold to a third party. In case the owner intends to close a position, the CFD is sold back to the issuer. Potential problems can arise in case of the insolvency of the counterparty or in disputes about transactions. For example, it is possible to imagine a scenario in which a customer deposit cannot be paid back due to the insolvency of the issuer of a CFD contract. The counterparty risk is substantially reduced when deposits made to CFD brokers are

example. The exchange acts as a central clearing counterparty for the transaction, similar to stock and futures trading markets (Norman, 2009). 4 CFDs are excluded from the British stamp duty, which makes CFD profits basically tax-free.

2. Social Trading 21 covered by the national statutory deposit guarantees5. If the CFD broker does not possess a banking license, the deposits have to be transferred and kept by an institution with a banking license. In this case, the statutory deposit fund of the country where the bank is located becomes relevant to the deposits. Nevertheless, open trading positions are not covered by the statutory deposit guarantee and become part of the insolvency assets in case of bankruptcy. The majority of the brokers are connected to a central compensation institution, which pays for 90% of the outstanding accounts but limits the guarantee to a maximum of 20.000 EUR in case of insolvency. Additionally, it must be considered that significant differences between the European countries can occur. Besides the height of the protected deposits, the general security of the statutory deposit funds can differentiate between European countries (Brokervergleich.de, n.d.). For example, deposits in Cyprus might be less secure than in Germany, as the entire financial system in Cyprus is less stable. The geographical location of the broker therefore has a significant impact on the counterparty credit risk. The counterparty risk can be further reduced by providing protection – for example, with additional deposit insurances (Norman, 2009).

2.2.1.2. Index certificates

Index certificates are grouped under the category of structured products, which combine derivative financial instruments with classical financial investments. Structured products can be understood as a future promise to pay by the issuer (bearer ), which depends on the performance of one or more underlying values. An index certificate therefore basically composes the elements of a zero coupon bond combined with an option on an underlying value (Rieger, 2009). The market of structured products comprises a wide variety of available products, which can be either classified according to the underlying (equity, indices, commodities, funds, exchange rates, and interest rates) or according to payment methods. Besides national differences and unique specifications, every issuer can name new structured products individually. The high number of available products and the different names make the market for structured products

5 Statutory deposit guarantees are national funds into which all banks have to pay. In case of the bankruptcy of an institute, the deposits of private customers are protected up to a specified amount: for example, in Germany 100.000 EUR and in Great Britain 50.000 EUR.

2. Social Trading 22 very non-transparent and difficult to oversee. In 2007, the market volume for structured products in Germany reached an all-time high of 139 billion EUR. Since then, the volume decreased continuously. In 2016, it reached a volume of 68.4 billion. Approximately 6% of the market volume is invested in index or participation certificates (Deutscher Derivate Verband, 2016). The certificates are either publicly traded or exchanged over the counter. Approximately 50-60% of the market volume in Germany is publicly traded (Deutscher Derivate Verband, 2016). The trading places of the certificates are, for example, the Stock Exchange in Stuttgart (EUWAX) or Frankfurt (Scoach). Index and participation certificates represent with approximately 9% of the trades one of the most frequently traded instruments. Index- and participation certificates enable investors to participate in the performance of a market or a market segment. The underlying index can be flexibly designed and can refer to a portfolio of stocks or other financial products. The payoff of the certificates is similar to a real equity investment with an unlimited profit and a limited loss potential. Usually index certificates are not standardized, and every issuer publishes guidelines that specify the details around the certificate: for example, the exact underlying, the solvency of the issuer and what happens in case of insolvency. Index certificates can be issued with maturity or be open-ended. Similar to a bond the buyer of a certificate receives a pecuniary claim for the time when the certificate ends or is sold. Investors are therefore always faced with credit risk by the issuer. In most cases, the certificates are issued by solvent institutions, which reduce the risk of bankruptcy of the issuer. Nevertheless, the financial crisis and the bankruptcy of Lehman Brothers showed that issuer risk should not be underestimated. Ratings of Standard & Poor’s, Moody’s or Fitch can be indicators of potential credit risk. Nevertheless, short-term developments and risks are often not represented by the ratings. National institutions such as the German “Derivate Verband” have introduced certificate ratings, which take the tradability, the credit risk of the issuer and the availability of information about the composition of the certificates into account and may provide a more comprehensive evaluation (Mildner & Fuchs, 2010). As a consequence of the financial crisis of 2007/08 and the bankruptcy of Lehman Brothers, the transparency of financial certificates has been frequently discussed. Credit risk and transparency regarding costs and the functioning of certificates have become major issues. Especially the transparency of the products, the suitability to investor needs, and the complexity of certificates are aspects that should be taken into account by the issuer (Gauer, 2010).

2. Social Trading 23

Concerning the costs of open-end index certificates, one can distinguish between the cost of the initial purchase and yearly fees. Similar to management fees, yearly fees are usually calculated as a percentage of the invested amount. Additionally, when the certificate is purchased, the investor has to bear the spread between bid and ask price. Other costs that might occur are sales provisions.

2.2.2. Regulation today and in the future

In the late 1990s, the European Commission started to harmonize the European market for financial services by developing the European Financial Services Action Plan (FSAC). The European Markets in Financial Instruments Directive (MiFID) is an important part of the FSAC and sets the basis for the creation of alternative trading platforms. MiFID defines guidelines and regulations, especially for financial-service companies that operate in the member states. MiFID has been applicable since 2007, and is modified and further developed by MiFID II and MiFIR (Markets in Financial Instruments Regulation)6. The new guidelines and regulations are expected to be applicable from January 2018 onwards. The overall goal of the MiFID regulations are to establish equal standards in all European countries and to create a large and competitive market for financial services (Jacobs & Beker, 2014)7. The new rules and regulations of MiFID II and MiFIR are supposed to take into account current developments of the trading environment and should implement lessons learned from the financial crisis of 2007/08. The overall aim is to make financial markets more efficient, resilient, and transparent (“MIFID (II) AND MIFIR,” n.d.). The increased need for investor protection and market-related issues are the primary forces that drive the implementation. For increasing investor protection, the regulators identified five different areas in which investor protection should be raised. The areas comprise product-related and service-related aspects. Overall, a major impact for financial-service companies can be expected, as all electronic communication with clients must be recorded and saved. At the same time, increased

6 MiFID II is a directive that can be adapted by the member countries, whereas MiFIR is a regulation that must be implemented by the member countries 7 Other relevant European regulation initiatives are the European Market Infrastructure Regulation (EMIR), the Packaged Retail and Insurance Based Investment Products (PRIIP), and the Market Abuse Directive (MAD). These initiatives are not detailed further, as the most significant impacts in the area of social trading can be expected by MiFID II and MiFIR.

2. Social Trading 24 requirements for product governance must be fulfilled: for example, new products must be tested under various market conditions before they can be sold to customers. Particularly for independent investment advisors, the rules for advising customers become stricter regarding the payment of fees, commissions, and benefits. Besides several other more specific rules and regulations, MiFID II offers regulators the possibility to prohibit or limit the marketing, distribution, and sale of financial instruments (Jacobs & Beker, 2014). Additionally, the new rules and regulations target certain market-related topics. MiFID I had a focus on increasing the market transparency for trading stocks. The renewal of the rules and regulations should also increase pre- and post-trade transparency8 for bonds, structured products, and other derivatives (Jacobs & Beker, 2014). Other market-related topics include an increased regulation of high- frequency trading and raised requirements for central clearing counterparties, trading venues, and benchmarks.

The new rules and regulation are supposed to have a substantial impact on asset management firms in particular (Sims & Brandt, 2016). The number of offered products might be reduced, and an increased emphasis is set on aligning product characteristics and customer profiles (EY, 2014b). Increased obligations and specifications will reduce the margins on selected products. In combination with an increased focus on transparency and higher standards for customer communications, the pricing and cost structure of financial-service companies is likely to change. The required categorization of customers regarding investment goals, risk aversion, and loss-bearing ability, and the documentation of customer communications require comprehensive administrative systems. Automated platforms and an increasing application of technology will dominate the product distribution and are required to stay competitive and to comply with the new rules and regulations. The requirements will have more significant impact on the operations of financial institutions and therefore have the potential to change the competitive situation (Wenzel & Coridaß, 2015). It can be questioned how established players of the financial industry are going to handle the new requirements, especially with regards to the profitability and quality of the offered services. It may become increasingly difficult to offer a broad product spectrum to multiple customer

8 Pre-trade transparency (e.g., publication of prices and volumes; post-trade transparency e.g., publication of orders executed) (Jacobs & Beker, 2014).

2. Social Trading 25 groups. A functioning and up-to-date technical infrastructure can help firms comply with the new standards. FinTechs with up-to-date technology and infrastructure might have fewer problems with the implementation and adherence of the new rules and regulations. As with the developments of dark pools9 after the introduction of MiFID I in 2007, new market players or products can be expected. New rules and regulation might therefore also represent a chance for FinTechs and new market players. On social-trading platforms, customer interaction is already almost completely digitized, which makes adherence to the requirements regarding the storing of customer communication possible at low costs.

At the same time, the implementation and application of MiFID II and MiFIR is still unclear at many points. The question and the risk for social trading platforms is first how regulators will classify the trading service and second how the new rules and regulations imposed by MiFID II and MiFIR can be met. Especially with regards to the service of mirroring the portfolio of a signal provider, regulators raise the question whether the automated execution of trades represents a way of portfolio management (Carlo, 2015; Esteves, 2016; Golovtchenko, 2015). In 2015, the Financial Conduct Authority (FCA) released a notification letter to industry insiders in which the authorization of a platform with an automated execution of trades, without further intervention of the follower, was classified as portfolio management. The classification as portfolio manager would require a corresponding license. In case of no automated copying, the service of social trading platforms can be understood as an investment advisory (Golovtchenko, 2015). In order to receive a permission to offer portfolio management, social trading platforms are required to follow more restrictive rules and regulations. For example, the platforms are required to inform investors in detail about the associated investment risks and have to make sure that the type of investments suits the financial and personal circumstances of a customer. In an interview with a spokesperson of a social trading platform (eToro) in 2014, the representative claimed that the offered service is neither portfolio management nor investment advice. Instead, it is only sharing of information between individuals (Hale, 2016). With respect to the example of German regulation, the consequences of the difference between

9 These are private, unregulated and non-transparent trading places, which are, for example, operated by large banks (Iskandar, 2015).

2. Social Trading 26 investment advisory and can be further elaborated. According to the German securities trade act (“Wertpapierhandelsgesetz,” WpHG), an investment management company requires approval by the BaFin to offer services to customers. The accountability of an investment manager is more long-term and more profound than that of an investment advisor. For example, an investment manager is required to determine and document the knowledge and investment target of every investor (Section 31 ff. WpHG). In case of a social trading platform that is categorized as an investment manager, this would require the completion of a form about the stock-market knowledge and the financial situation of every investor. For investors with insufficient characteristics, the offered product range might be limited. In addition to the operational aspects, classification as investment manager causes high certification costs, requires time, and results in increased supervision by the regulators. Operating under the umbrella of an officially regulated and certified company offers a way to bypass an extensive certification process10. Direct monitoring by the supervisory authority is in such cases not required (Berger, n.d.). From an investor perspective, direct certification by a supervisory authority might signal trust and could help convince interested investors.

The various interpretations and understandings of social trading services show the current ambiguities and demonstrate the uncertain future of the market actors. With the application of MiFID II and MiFIR at the beginning of 2018, further clarification and the need of adjustments by the social trading platforms can be expected. One potential danger of increased regulation could be, besides the additional costs, that the entry barriers to participate on a social trading platforms are increased. Currently, every investor can easily register on a social trading platform and start to invest. Increased requirements to assess the knowledge of investors and restrict the access in case of insufficient knowledge could frighten interested people and diminish the usability of social trading, which is currently a differentiating factor to classical investment platforms.

10 For example, banking law in Germany, Section 2, Paragraph 10, Sentence 1 (“Kreditwesengesetz”) enables investment advisory services under the responsibility of a regulated deposit bank or a securities trading company (“vertraglich gebundener Vermittler”). The BaFin publishes a register with all companies operating under the responsibility of a regulated institution: https://portal.mvp.bafin.de/database/VGVInfo/. Similar possibilities are available in the UK to operate under the umbrella of an FCA regulated company.

2. Social Trading 27

Besides increased regulatory requirements concerning the operators of social trading platforms, additional challenges may arise from new regulations concerning the use of financial instruments. MiFID II offers every regulating authority the possibility to ban or restrict the use of financial instruments. For example, the use of CFDs could become increasingly subject to regulation. In Great Britain, the FCA sees an increasing risk that people with insufficient knowledge about financial markets are being offered products that they do not completely understand. In combination with high leverages and low initial margins of CFDs, the associated risk might be underestimated. The regulating authority sees particular need for action, as the number of CFD retail users has doubled since 2010. The FCA particularly proposes regulations and limitations concerning enhanced disclosure requirements, leverage limits, prohibition of bonus promotions or other incentives, and restrictions on financial promotions for incoming firms from other European countries (Mayer Brown, 2017). The Belgian regulating authority recently completely banned OTC-traded CFDs. The drastic decision was justified by the aggressive marketing methods of the financial products in combination with the high and often unforeseeable risks (Mizrahi, 2016a). At the beginning of December of 2016, the German regulating authority announced plans to limit the marketing, distribution, and sale of CFDs (“BaFin plans to limit CFD trading,” 2016). In addition, contracts that encounter additional payment obligations could be banned completely. According to a BaFin representative, the risk for the issuer of the contract is incalculable and can have negative impacts on the entire financial system. Social trading platforms, which rely on CFDs, would be drastically impacted if the use is prohibited.

2. Social Trading 28

Financial instruments + - - Easy understanding - Easily exposed to high investment risks CFD - Easy way for leverage and short positions - Liquidation & counterparty credit risk - Cost effective instrument - National differences - Statutory deposit guarantee + - - High flexibility in the design of the - Many certificates with different Index underlying specialties makes market non transparent certificates - Publicly traded, which ensures price - Counterparty credit risk transparency - No statutory deposit guarantee

Regulation

- Higher market transparency - Classification of social - Further restrictions or MiFID II & - Consequences on cost trading: portfolio limitations for the sale or MiFIR and structure due to categorization management or usage of financial other of customers & storage of investment advisory? instruments regulatory customer communications - Regulation as signal of - Possible reduction of the influence - Changes competitive situation trust for investors easiness of use

Figure 3: Summary financial instruments and regulation Source: Own illustration

2.3. Available business models

In general, it can be said that most of the actors in the social trading market formulate the vision to democratize the market for private investments. Democratization in this context can be understood as the goal to lower the entry barriers for people with no or little stock-market experience. In contrast to the similar business vision, the market itself is very fragmented and nontransparent with many market players and, so far, no dominating business model. The market structure makes it difficult to estimate the precise volume of social trading in Europe. Investment volumes quoted by platform providers frequently state high invested amounts and many users. Due to utilized leverage and a potential conflict of interest the statistics should be reflected critically. Reliable estimations of market size are currently available only on a national level. In 2016, 14 active social trading platforms could be identified in Germany. At the year- end of 2015, the platforms managed a volume of about 190 million EUR, which represents, in comparison to the managed funds in 2014, a growth rate of 63% (Dorfleitner & Hornuf, 2016). Nevertheless, in contrast to other FinTech sectors (for example, banking services with about 1 billion EUR assets under management), social trading still plays a minor role (Dorfleitner & Hornuf, 2016).

2. Social Trading 29

The available business models on the global social trading market can be split into three categories: platforms that offer investors the ability to directly execute trades (1), platforms that execute trades in cooperation with partner companies (2), and platforms that exclude trading from the business model and set the focus on sharing information (3).

Table 1: Categories of social trading platforms Source: Case studies in the Appendix The following analysis gives an overview of the key aspects of every business model category and highlights differences and specifics. The categorization of the business models should help to uncover the success factors and risks that can arise from the mode of operationalizing social trading. Descriptive case studies about some selected business models create the fundamental basis for the categorization of the social trading market. Following the scientific research method of case studies, company homepages and media articles are used as primary information sources. Case studies represent a suitable method of analysis because, on the one hand, commonalities can be highlighted, but on the other hand, the uniqueness of every business model is taken into account (Stake, 1995). Additionally, the method and the data sources ensure an up-to-date representation of the social trading market. The companies selected for the case

2. Social Trading 30 studies have been chosen due to their dominant or important market position and because the companies are good representatives of the particular category. Nevertheless, the method of analysis underlies limitations that may restrict the explanatory power. The selection of case companies and the way of analyzing the companies may be influenced by biases such as the personal preferences of the author or the way a company is represented in the media. Additionally, the speed of development may cause uncertainties due to differing opinions or changed conditions. In the case of social trading, the rapidly changing market environment make it necessary to analyze all available information with caution as rapid changes might occur. In particular, the recently increasing influence of regulators may result in new surrounding conditions or short-term changes of the business models. The analysis of the business model categories is structured into three parts. First, the type of companies operating in the market segment are described and analyzed. Second, the operations are detailed. Third, the fee structures of the business models are elaborated.

2.3.1. Fully integrated platforms

Fully integrated social trading platforms provide all services around the investment process. Information about different signal providers is presented and can be analyzed by interested investors. Signal providers develop trading strategies based on the available investment universe and share these strategies with followers. Trades of signal providers and followers are directly executed over the social trading platform and the connected brokerage platform. Every follower who wants to mirror trading strategies must open an account at the brokerage platform. Money to be invested in social trading strategies must be transferred to the account and can afterwards be distributed among different trading strategies. The main revenue source of fully integrated platforms is spreads on the execution of trades. The use of the platform itself is usually costless and free of management fees.

2. Social Trading 31

Table 2: Fully integrated social trading platforms Source: Case studies in the Appendix Type of companies The distinctive characteristic of companies defined as fully integrated platforms is that trades are executed over a brokerage platform owned by the operator of the social trading platform. In general, the hardest competition on the social trading market can currently be found among fully integrated platforms. In particular, large providers like eToro, ayondo and tradeo are competing for new customers by offering discounts and incentives. EToro, for example, attracts new investors by topping up the investable amount if a new user makes a deposit for the first time (e.g., additional 1,000 USD for a deposit between 5,000 - 19,999 USD). Currently, fully integrated platforms seem to be the most frequently used social trading platforms concerning number of market players, users, and investment opportunities. Nevertheless, exact statistics from an independent party are not available. According to information available on the homepage of eToro, the social trading platform claims to have 4.5 million users, making it the largest market player. In particular, the high number of users should

2. Social Trading 32 be reflected critically, as it is not confirmable, and a high number of registered users does not indicate many actively participating signal providers or followers. Similarly, as with the apparent high popularity of fully integrated platforms, the largest amounts of financing can also be identified in the first category of social trading platforms. For example, eToro raised about 72.9 million EUR from 16 different investors (see Appendix B: Fully integrated platform – eToro). For some fully integrated platforms, internationalization is already an integral part of the business model. For example, ayondo already has offices in Frankfurt, London, Madrid and Singapore (see Appendix A: Fully integrated platform – ayondo). The internationalization can be interpreted in two ways. On the one hand, it could be an indication that the business model is already at a stage where internationalization is the next logical step. On the other hand, it could be an indication that a constantly growing number of new customers is required. A further critical topic of fully integrated social trading platforms is regulation by official authorities. Because fully integrated platforms offer brokerage activities, all platform providers must be regulated by a supervisory authority. It is conspicuous that, out of the three companies considered in the table above, two are regulated by the supervisory review in Cyprus.

Operations For business models in the first category, the operationalization of social trading is very similar, as the trades are executed over a directly connected brokerage platform. All fully integrated platforms considered in this work utilize CFDs and foreign-exchange products to facilitate investments into trading strategies. Nevertheless, some differences can be noticed. The investment universe depends on the instruments available on the brokerage platform. For example, on ayondo, about 260 CFDs with different underlying values can be used (indices, currencies, stocks, ETFs) (see Appendix A: Fully integrated platform – ayondo). The minimum amount to be invested differentiates between the platforms. For example, on ayondo, at least 1,000 EUR have to be deposited, whereas on eToro, the minimum amount to be invested is 200 USD (see Appendix A: Fully integrated platform – ayondo and Appendix B: Fully integrated platform – eToro). Most of the platform providers additionally offer to apply high leverages, which can substantially increase the invested amount (e.g., eToro offers a leverage up to 400, and ayondo offers a leverage up to 200). A further important aspect in the operationalization of fully integrated social trading platforms is to give investors guidance about the investment strategies of signal providers. The social

2. Social Trading 33 trading platforms assign signal providers with different career levels, which depend on the generated returns, the risk taken, and the number of people already following the investment strategy. The exact configuration of the classification differentiates between the social trading platforms but follows the same purpose. Followers should be informed about the risk-and-return potentials of an investment strategy, and signal providers should be honored for a particular good performance. On some social trading platforms, signal providers are honored for a higher career level with higher compensation. The compensation modalities again differentiate between the platform providers but can, for example, compose a share of the CFD spread (ayondo) or a fixed payment depending on the number of followers (eToro). Differences between the platforms may also occur concerning the communication between signal providers and followers. On ayondo, no communication is possible, whereas communication is an integral part of the business model on eToro. Signal providers on eToro must regularly inform the followers about the current developments and intentions of the trading strategy.

Revenue sources For all fully integrated platforms, the main revenue source is spreads on the brokerage platform. The spreads can be slightly higher in comparison to other brokerage platforms. The use of the social trading platform is generally free. Besides spreads, additional fees occur due to rollover costs or financing costs when open positions are held overnight or weekends. Additionally, transaction costs might occur when money is withdrawn from the account (e.g., eToro).

2.3.2. Information exchange and trading with partner companies

In contrast to fully integrated platforms, the business models of the second category of social trading platforms do not directly profit from spreads or transaction fees. Nevertheless, the basis of the business models is also an online platform that presents and explains trading strategies and investment opportunities. Signal providers create portfolios, and followers can replicate the development of these portfolios. In contrast to other offerings, the execution of the trades is not an integral part of the business models. The trades are executed over partner brokers or other financial-service companies. The higher focus on information exchange has the advantage that funds of customers are not transferred to the platform provider.

2. Social Trading 34

Table 3: Social trading platforms with information exchange and trading with partners Source: Case studies in the Appendix Type of companies The companies classified into the second category of business models are more diverse than the social-trading platforms in the first category. Differences between the platforms can be found in the operationalization of social trading and in the setup of the social trading platforms. In general, cooperating companies, such as brokers, banks or other institutions, have a high relevance for the success of the business models in the second category. For example, wikifolio cooperates closely with the bank Lang and Schwarz (L&S), which issues the open-end index

2. Social Trading 35 certificates to facilitate investments into trading strategies. L&S is even invested in wikifolio. Similarly, ZuluTrade has a partner broker with AAAFX that is closely connected to the social trading platform to ensure a smooth execution of transactions. In contrast to the social trading platforms of the first category, it is noticeable that social trading is promoted more prominently, whereas brokerage activities are obviously less important. The idea to benefit from the effective utilization of the collective intelligence is frequently highlighted by platform providers. At the same time, the operationalization of social trading is communicated more transparently.

Operations Even trade execution is not a core focus of the companies the operationalization to invest into trading strategies is an important feature. Similarly, to fully integrated platforms, some social trading providers in the second category use CFDs to mirror the trading strategy of signal providers (e.g., ZuluTrade). Potential investors who want to be active on the social trading platform have to open an account at one of the cooperating brokers. Beside the fact that the brokerage activities are not executed via a broker owned by the social trading platform, additional operational differences can be identified for some market players. United Signals has introduced mandatory rules for investors who want to act as signal providers. Before signal providers can publish trading strategies, an extensive certification process must be successfully completed. The background of the user, the intended trading strategy, and additional information provided by the user are considered and evaluated during the certification process. Additionally, a signal provider is required to invest private money into the trading strategy (see Appendix E: Information exchange and trading with partners - United Signals). In comparison to other social trading platforms, the goal is to reduce potential risks for followers due to signal providers with insufficient financial knowledge. Similarly, to United Signals, wikifolio has chosen a way of operating that makes it possible to control the quality of the offered investment strategies. Investments are executed via open-end index certificates, which are publicly traded. The underlying of the index certificate is a portfolio11 of a signal provider. Changes to the investment strategy or market movements are automatically reflected in the price of the certificate (see Appendix D: Information exchange

11 On www.wikifolio.com, the portfolios are called wikifolios. To avoid misunderstandings with the company name, wikifolios are here named portfolios.

2. Social Trading 36 and trading with partners – wikifolio). Before a certificate is issued, the signal provider has to run through a certification process and at least ten investors must be willing to invest a minimum of 2,500 EUR. Additionally, the approach of wikifolio has the advantage that, in contrast to many CFD brokerage platforms, the investment universe composes a significantly higher number of investable products (see Appendix D: Information exchange and trading with partners – wikifolio). At the same time, adjustments to the trading strategy are free of transaction costs, which facilitates dynamic trading strategies. As with the business models of the first category, followers receive some guidance during the investment process. Whereas on United Signals the investors receive information only about the performance and background of the investment strategy, wikifolio additionally assigns labels to portfolios. The labels classify the trading strategy according to an investment focus and point out high-performing signal providers (see Appendix H: Explanation wikifolio labels and Appendix D: Information exchange and trading with partners – wikifolio). The business models of the second category may have an advantage over the social trading platforms of the first category, especially concerning reliability. On the one hand, companies of the second category may be more trustworthy due to an increased focus on the quality of the offered investment strategies (e.g., like United Signals and wikifolio). On the other hand, increased trustworthiness may be justified because user money is not transferred directly to the social trading platforms. Situations of contradicting interests may therefore be reduced or even eliminated. Social trading platforms can focus on providing a suitable infrastructure and help investors find promising investment opportunities. With regards to regulation, differences from the first category and among the business models of the second category can be noticed. Wikifolio is not directly part of the investment process and is therefore not regulated by a supervisory authority. L&S as the issuer of the certificates, holds a German banking license and is regulated by the BaFin. United Signals is not directly regulated, but it is under the legal umbrella of a BaFin regulated company. ZuluTrade is regulated by the Greek supervisory authority. In the second category of business model, the unclear regulatory classification of social trading is particularly noticeable. Changes in the regulatory framework may also lead to adjustments of the business models or to regulation by the supervisory authority.

Revenue sources The use of the platforms is basically free, as in the first category of business models. The fee

2. Social Trading 37 structure for investors depends on how the investment process is operationalized by the platform providers. For platforms like ZuluTrade, which utilize CFDs similar to the first category of business models, only the transaction costs for the trades have to be paid. ZuluTrade receives a share of the spread from the broker. For platforms like wikifolio and United Signals, which try to ensure quality standards for the offered investment strategies, management fees accrue. Additional performance fees have to be paid on both platforms, which are shared between platform provider and signal provider. Furthermore, on wikifolio, the followers have to pay transactions costs for acquiring the certificate.

2.3.3. Information exchange

The idea to exchange information about stock markets on online platforms composes the fundamental basis for social trading. Most of the offerings have extended the business models beyond the sharing and discussing of information. In the third category, the platforms focus on the essential element of social trading and limit the offered service to providing a platform for information exchange between investors and people interested in stock markets. The business vision is to use collective intelligence to provide investment recommendations and price predictions. Trading of stocks or other financial instruments is not part of the business model. Investors can collect and share information but must execute the trades privately, as market players of the third category are focused on information exchange only; it does not necessarily represent a new way of investing. Nevertheless, the business models can be attributed to the concept of social trading, as the function of the community is an essential part of the business models. Out of the three categories of business models, the number of players is the lowest in the third category. In addition, as trading is not part of the business model, a direct comparison to the previous categories is difficult. The analysis of the third category of business models is therefore of reduced scope.

Type of companies The focus on generating and sharing information makes it difficult to differentiate the business models from news homepages. The most significant difference is in the high importance of the community and the interaction among the users. On platforms like Sharewise or Trading View, users can share information, discuss current topics and recommend investments.

Operations In contrast to the previous two categories, the operations of the business models set an increased

2. Social Trading 38 focus on exchanging information between users. As with other social trading platforms, users can develop trading strategies and share them with the community. On Sharewise, the users are also ranked according to the success of the investment strategies and according to the accuracy of recommendations made on the platform (see Appendix G: Information exchange – Sharewise). Additionally, users can interact and exchange information. From an investor perspective, the information can be used to privately execute the trading strategies. In comparison to the two other categories of social trading platforms, the third category has the advantage that the investment universe is not limited to certain products. Additionally, interested investors can discuss individual trading ideas and portfolio compositions.

Revenue sources Because use of the platforms is free of costs and the execution of the investment strategies is not part of the business model, the revenue sources are rather limited. In addition to a regularly published market letter, which is based on the information of the platform users, Sharewise also sells the underlying software as white-label products. Other revenue sources were tested in the past, but a fund based on the investment recommendation of the community was not successful.

2.4. Social trading market and future outlook

The analysis classified the business models of social trading platforms into the FinTech sector, which is currently disrupting the financial industry, at least in parts. At the same time, the analysis shows that it is difficult to estimate how long-lasting and influential the effect on the financial industry will be. Amongst other factors, success especially depends on the reaction of the established market players, the impact of regulation, and the future development of customer preferences. Nevertheless, in some areas, for example in “personal financial management,” FinTechs are currently setting consumer trends and driving the developments. Due to the speed of development, established market players can hardly react to the new offerings. Especially regarding cost structure, easiness of use, and individualization, established banks and other financial institutions might have or already have a hard time competing against FinTechs (Drummer, Jerenez, Siebelt, & Thaten, 2016). Nevertheless, FinTechs still have to prove whether a sustainable success can be realized in other product areas. Social trading is in an early stage of development and plays a minor role in comparison to traditional asset managers. Considering the different business models underlying social trading platforms, the potential

2. Social Trading 39 influence on the asset-management industry differentiates. Platforms specialized on information exchange (third category) may only influence the public interest and have the potential to increase the education level about financial markets, whereas a direct effect on asset management or investment behavior may not be expected. Business models, which also offer the possibility to make investments can be evaluated differently. Fully integrated platforms or platforms with information exchange and trading with partners theoretically have the potential to substitute other asset management services. The fact that social trading platforms also sell platform software solutions as white-label products may indicate that the concept of social trading is not only interesting and suitable for FinTech companies.

In spite of the currently relatively low importance of social trading, the potential influence on the asset-management industry should not be underestimated. The concept to make community- based investment decisions on online platforms corresponds to the current trends of collaborative business models and technological opportunities. At the same time, a collaborative investment approach makes it possible to consider the current needs and requirements of financial service providers and customers. The digital operationalization and flexible application opportunities of social trading facilitate a competitive cost structure and make it possible to offer asset-management services to a wide variety of potential customers. Social trading may have the potential to reach out to customers who are currently not attractive enough from an economic perspective or to attract customers who are not reached through existing retail channels. Besides factors relevant to financial service providers, which may result in an increased influence of the concept of social trading on the asset-management industry, some influencing factors for the customers should also be considered. Social trading offers a high degree of flexibility and many possibilities to individualize asset management. Personal preferences can easily be taken into account, and the dependence on banks or asset managers can be reduced. The popularity can be positively influenced by the high skepticism towards financial institutions or by people who work for financial-service companies. The community-based concept of social trading may therefore have the potential to convince people who are currently skeptical of financial markets.

Nevertheless, the analysis of the social trading market also reveals many unsecure factors and weaknesses. Future changes to the regulatory framework create many uncertainties that have the potential to strongly and negatively influence social trading platforms. Additionally, the

2. Social Trading 40 utilized financial instruments comprise risks that may reduce the attractiveness of social trading for many retail investors. Under MiFID I, FinTechs benefited from a particularly low degree of regulation. With the implementation of MiFID II and MiFIR, the surrounding conditions may change and become stricter. Higher regulatory requirements can cause higher costs, which could substantially influence the economic basis of social trading business models. The vague classification of social trading services makes the evaluation of the future development even more difficult. A classification as portfolio management or further restrictions of CFDs/certificates could cause existential difficulties for some business models. Additionally, the operationalization of social trading still has significant differences in comparison to . The financial instruments utilized to invest in trading strategies of signal providers mirror the performance but also have additional risks. In particular, the risk created by the utilization of high leverages may be underestimated by retail investors. In contrast to investments into stocks, a loss of the entire investment is easily possible. Financial instruments like CFDs or index certificates are speculative financial instruments. It is highly questionable whether these types of instruments are suitable for the portfolios of private investors. Additionally, the question must be asked whether current social trading platforms sufficiently inform investors about the associated risks. In the same way, it can be doubted whether social trading platforms represent reliable and trustful institutions, for example, with regards to the security of customer deposits. Though national and private guarantees exist, a substantial risk remains. Besides the risk concerning customer deposits, investors largely rely on information that is published solely on social trading platforms. Especially on fully integrated platforms, investment and trading decisions are based completely on the infrastructure offered by the platform provider. Order execution and other operational aspects are executed completely over the social trading platform. In situations with operational difficulties, significant losses of wealth are imaginable for signal providers and followers. In addition, due to the differing geographical locations of some social trading platforms, differences in national guarantees can occur. The height of the statutory deposit guarantee and the stability of the financial systems can make deposits less secure on some social trading platforms. From an investor perspective, these differences are difficult to identify. The trustworthiness of social trading platforms must therefore in some cases be critically examined. For investors who are looking for a long-term investment opportunity, it is currently questionable whether social trading platforms represent a serious alternative. In

2. Social Trading 41 particular, the concept and the available trading strategies on fully integrated social trading platforms appear to have a short-term and speculative investment focus. Investors who are looking for a sustainable investment alternative may easily encounter large losses. Similarly, it can be generally questioned whether the mode of operationalization is suitable for investments of larger volumes. Furthermore, it is difficult from an investor perspective to evaluate whether the performance of signal providers can be attributed to luck or superior investment skills. Some platforms review the trading strategies of signal providers to ensure the quality of the trading strategies. Followers again have to trust platform providers that the trading strategies are picked according to criteria that are beneficial to investors. In line with the selection criteria of signal providers, it must be ensured that the labels assigned to the trading strategies are meaningful and represent the truth.

Overall, it can be concluded that social trading principally has the potential to be a serious alternative to other asset-management offerings and therefore has the potential to significantly influence the asset-management industry. Nevertheless, the market has many uncertainties that may hinder a positive development. Besides the surrounding conditions of the social-trading market (e.g., regulation), the trust into social trading platforms may limit the number and size of investments. It could be that a reliable partner, for example, a large financial institution, could create additional opportunities. From a customer and market perspective, the idea of collaboratively made investment decisions has potential; nevertheless, the current business models also come with considerable risks.

3. Literature 42

3. Literature

The categorization of the social trading market shows the success potential of social trading from a practical perspective. Social trading represents a new and innovative way for private investors to manage their portfolios. On the one hand, social trading has many touch points with the classical finance literature. On the other hand, the concept of social trading is fundamentally different from existing offerings and has, due to the community-based approach, many connections with other theoretical concepts. The following paragraphs outline the intersections with classical finance literature and the most relevant connections to other research fields. The idea in the first step is to describe the theoretical concepts of financial literature that form the basis for the functioning and the potential success of social trading. Three relevant research fields can be identified: market efficiency, asymmetric information and incentive structures. The underlying theory is shortly described and afterwards broken down into key factors that might influence or can be used by social trading. The guiding question is to identify how social trading might have the potential to improve asset management. In the second part of the literature review, the theoretical concepts related to the idea of social trading are described and analyzed more deeply. The idea to utilize collective intelligence to make investment decisions and the operationalization via an online platform can influence the behavior and decision-making of users. The guiding question is therefore to identify factors in the concept of social trading that have the potential to influence investment decisions.

3. Literature 43

Finance Theory

...... Asymmetric Incentive systems Market efficiency information

Basis for the functioning of social trading

Factors with major influence Social on social trading Trading

Collective Platform Social influence Remuneration intelligence characteristics

Influencing variables

Figure 4: Theoretical model Source: Own illustration Based on analyses of the theoretical concepts, hypotheses are derived for the research areas of “social influence,” “platform characteristics,” and “remuneration.” The hypotheses are examined further in the quantitative analysis in chapters 4 and 5. Due to the difficulty of quantitatively analyzing the phenomena of collective intelligence, no hypotheses are defined. For the research field of collective intelligence, only influencing factors are identified, which are qualitatively discussed in Chapter 6. Nevertheless, the utilization of the collective intelligence is central part of social trading. A comprehensive understanding and the identification of influencing variables is crucial for a critical reflection on social trading.

3.1. Positioning social trading into financial theory

3.1.1. Market efficiency and active vs. passive investment strategies

A frequently discussed topic in financial research is the comparison of active versus passive investment approaches. It seems to be a matter of faith whether investors believe in the superior performance of an active or a passive investment strategy. On the one hand, supporters of actively managed investment strategies argue that sophisticated techniques make it possible to identify promising return opportunities on the market. On the other hand, supporters of passively managed investment strategies disagree and believe that no technique or skill can outperform the market. Even science disagrees over whether financial markets are efficient or

3. Literature 44 whether they are influenced by the behavior of irrational actors. If stock prices move irrationally, this could be identified and used by investors. Eugene Fama, one of the most famous representatives of efficient markets,12 distinguishes between three forms of market efficiency: weak, semi-strong, and strong. Theoretically, fundamental analyses13 of publicly available data offer the possibility to generate excess returns only in the weak form of market efficiency (Fama, 1970). In the semi-strong form, all publicly available information is priced into the market, and neither technical14 nor fundamental analyses offer the possibility to realize excess returns or to identify alpha.15 Only non-publicly available information such as insider information offers a chance to beat the market. Under the strong form of market efficiency, no abnormal returns can be realized. Public and private information is reflected in market prices. In a fully efficient market, the development of stock prices is therefore not foreseeable and can be described by a random walk. The theory of efficient markets – according to which information is automatically priced into the market – is the essential element of neoclassical finance theory. Observed market anomalies such as financial crises call the theory of fully efficient markets into question. Other definitions of market efficiency stem from Grossman and Stiglitz (1980), who define markets as efficient when the additional costs for actively managing a portfolio are compensated by management fees. The costs for searching for additional information, which are not priced into the market, equals the additional compensation by performance and management fees. The idea of not fully efficient markets was further developed in neo- institutional financing theory, which considers actors as not fully rational. The theory of bounded rationality and prospect theory describe the limited cognitive abilities of individuals, which result in irrational actions and behavior (Kahneman & Tversky, 1979; Simon, 1955). Markets can therefore be described as not completely efficient due to informational

12 Eugene Fama (1970) defines markets as efficient when market prices fully reflect available information. 13 A is an attempt to determine the intrinsic value of an equity investment. The analysis is based on determinants like corporate profits, dividends, and interest rates (Arnswald, 2001). 14 aims to identify repetitive trends in market prices solely based on past prices and trade volumes. Due to the repetitive character of the trends, a prediction of the recurrence can be made (Arnswald, 2001). 15 See Chapter 4.3 for a more detailed description of alpha.

3. Literature 45 incompleteness and inconsistent behavior of market actors (Kahneman & Tversky, 1979; Shiller, 2003). Supply and demand mechanisms differentiate market prices from their fundamental values. Markets could be defined as efficiently inefficient (Pedersen, 2015). The markets would therefore be inefficient enough to compensate active investors for taking the risk of an active investment strategy. At the same time, the markets would be efficient enough to not attract too many active investors by limiting the success potential of active investing.

It is frequently discussed whether actively managed portfolios can generate excess returns over a longer period of time. The results show that especially high performance and management fees for actively managed portfolios have a significant impact on the overall performance. Research shows that the investment decisions of actively managed portfolios are able to generate excess returns (Cremers & Petajisto, 2009; Grinblatt & Titman, 1992). Nevertheless, it has also been determined that, due to high costs of actively managed investment strategies, the overall performance was below the market benchmark (Wermers, 2000). High performance and management fees can therefore eliminate the chance for investors to receive a return superior to passively managed funds, which operate at much lower costs. The question remains whether a superior performance of an actively managed investment approach can be attributed to luck or to unique investment skills. Scientific research can so far hardly identify indicators, which could explain superior performance due to the stock-picking skills of actively managed portfolios (Jensen, 1986; Grinblatt & Titman, 1992; Gruber, 1996). Nevertheless, scientific research has identified evidence for performance persistence for at least limited time periods (Brown & Goetzmann, 1995; Malkiel, 1995; Brown, Harlow, & Strakes, 1996). The large number of studies conducted and the partly contradictory opinions that have resulted from them highlight the difficulty of assessing the success potential of active investment strategies. The reoccurring success of some actively managed trading strategies allows the theory of efficiently inefficient markets to be imaginable. Actively managed trading strategies – for example, those created by signal providers on a social trading platform – could therefore, at least in some cases, profit from temporary market inefficiencies. Nevertheless, research also shows that the influence of management fees on overall performance should not be underestimated.

3. Literature 46

3.1.2. Asymmetric information and agency costs

Information forms the basis of every investment decision in financial markets. Following the idea of neo-institutional financing theory, information is a central element in efficiently inefficient markets. Besides access to information, the ability to analyze and understand the implications of new information is essential. The value of information can be assessed based on the exclusivity and explanatory power of information (Hirshleifer, 1971). Due to the differing availability of information and means to process it, informational asymmetries can occur. Actors in financial markets are at risk to be negatively affected due to asymmetrically distributed information. Better-informed investors benefit at the costs of less-informed investors. Under the assumption of a semi-strong market efficiency, the use of insider information to achieve higher returns is a classic example. The availability of superior information is in most cases restricted to a few actors (Shapiro & Varian, 1998). The risk that better-informed investors use their informational advantages is therefore omnipresent for many market actors. The problem of asymmetric information on financial markets can theoretically be solved by making additional information public. Informational asymmetries might be reduced or even eliminated by making private information publicly available and accessible to other market actors (Hirshleifer, 1971). The difficulty is that investors who dispose of valuable information have no incentive to share it. Informational asymmetries can therefore only be reduced or eliminated when systems provide reasonable arguments and incentives to share information. Systems or platforms (e.g., social trading platforms) on which valuable information is shared with other people could therefore constitute an opportunity to reduce informational asymmetries in financial markets for participating individuals.

In anticipation of asymmetric information, investors frequently delegate their investment decisions to professional portfolio managers. In the context of the employment of portfolio managers, the problems of agency theory and asymmetric information arise. In every contractual relationship, asymmetric information between the principal and the agent occur and cause agency costs16 (Jensen & Meckling, 1976). Based on the assumption of personal utility

16 According to Jensen and Meckling (1976), agency costs compose monitoring expenditures by the principle, bonding expenditures by the agent and residual losses.

3. Literature 47 maximization, individuals act opportunistically. After completing a contract, informational asymmetries can cause moral hazard17 The agent can act in his or her own interest and against the interest of the principal, as no complete monitoring can be realized. Additionally, hidden information18 before entering into a contract can cause the problem of adverse selection19. In case an investor does not make investment decisions independently, the selection of investment opportunities is delegated to an investment or portfolio manager. In this scenario, the investor acts as principal and the portfolio manager as agent. The delegation of investment decisions is mainly justified by the expectation of specific knowledge, better infrastructure, and more contacts, which overall should lead to informational advantages of portfolio managers and in the end to higher returns (Öynhausen, 2015). Investors who delegate their investment decisions assume that professional portfolio managers have superior abilities to receive and process information. The relationship between investor and portfolio manager is a classical principal-agent relationship, which can cause agency costs. The agent disposes of unobservable information in the pre-contracting and post-contracting stages (Bhattacharya & Pfleiderer, 1985). In the pre-contracting stage, asymmetric information exists concerning the assessment of the abilities of the portfolio manager to make beneficial investment decisions. The portfolio manager might refer to past experience and associated abilities. Nevertheless, the principal can hardly prove the truth of these claims or might lack the knowledge required to correctly assess the performance. Following the concept of adverse selection, the quality of portfolio-managing services could be questioned in general, as truly good portfolio managers are pushed out of the market due to high costs (Akerlof, 1970). In the post-contracting stage, the principal is confronted with the risk that the decisions of the agent can only partly be observed and monitored. The consequence is that disadvantageous decisions are not noticed or are noticed too late. The portfolio manager

17 Moral hazard defines the problem of hidden action by the agent that is disadvantageous for the principal (Arrow, 1968; Pauly, 1968). 18 Hidden information is information that is known only by the agent and cannot be observed by the principal (Arrow, 1984). 19 Following Akerlof (1970), buyers cannot distinguish between good and bad offerings due to asymmetric information concerning the quality of the offered products. Sellers of good products can therefore not achieve a legitimate price for their offerings. The result is that sellers of good products will not sell and only bad products remain in the market.

3. Literature 48 could behave opportunistically and maximize the personal welfare instead of making decisions that are favorable to the principal. Additionally, the non-transparent relationship between principal and agent makes it difficult to determine whether the trader is actually pursuing a strategy or is for example following a noise-trading strategy20 (Dow & Gorton, 1997). An intermediary between principal and agent could help to reduce informational asymmetries and increase the monitoring abilities of the principal. Social trading platforms as an independent authority could help to increase transparency by providing accurate and comprehensible information to the principal about the activities of the agent. Social trading could therefore make it possible to reduce agency costs by reducing asymmetric information.

3.1.3. Incentive structure in delegated portfolio management

Investors who delegate portfolio-management activities are faced with the challenge to find ways to prevent moral hazard and adverse selection. The interest of the principal who has the responsibility for the pursued actions might differ from the interest of the agent. These differences can result in differing motivations. Modern incentive theory addresses how the interests of principal and agent can be aligned (Sappington, 1991). The difficulty is to find ways how the agent can be motivated to pursue actions that are in the interest of the principal even the actions of the agent cannot be observed by the principal. The agent makes decisions to pursue actions based on observations that are not available to the principal. In many cases, the principal can only observe the outcome of the actions, which makes traceability difficult (Arrow, 1984). Possibly conflicting objectives and decentralized information compose the basic issues of incentive theory (Laffont & Martimort, 2009). The general idea of incentive theory is to make the agent a residual claimant for the actions to be executed. The agent should benefit directly from the pursued actions, which should in theory align the interests of principal and agent (Sappington, 1991). The high popularity of delegated portfolio management makes the principal/agent relationship between investor and portfolio managers an important topic with potential impact on the economic and financial system. In the context of portfolio management, three main purposes of an incentive system can be

20 Noise trading describes the difficulty of distinguishing whether the agent is “actively doing nothing” or whether the agent is “simply doing nothing,” which often results in trading behavior even though no suitable investment opportunities are available (Dow & Gorton, 1997).

3. Literature 49 identified (Stracca, 2006). First, risk will be shared between the principal and the agent. Second, the effort of the agent to search for information with which to make promising investments will be influenced. Third, incentive structures will influence the attraction of agents who might be interested in managing the portfolio. An optimal compensation contract would provide suitable solutions for all three aspects. For a theoretical situation in which the effort of the agent is completely observable, a contract with linear risk sharing between the principal and the agent is sufficient to align the interests of both parties (Stoughton, 1993). The agent receives a fixed payment and a share of the ending portfolio value. In this scenario, no asymmetric information exists, and the invested effort of the agent is observable and is sufficiently honored by the principal. Nevertheless, this idealized situation rarely holds in reality, which again highlights the fact that increased transparency between investor and portfolio manager can have positive effects on delegated portfolio- management situations. For the more realistic case in which the effort of the portfolio manager is not completely observable, the conditions change. The agent confronts the principal with the potential danger of hidden action, because the agent may not be incentivized to invest sufficient effort in the search for information with which to make attractive investments. In a classical principal/agent setting, the agent is also motivated by increasing the share of the agent’s output. It can be questioned whether a similar solution would work in a delegated portfolio- management setting. Stoughton (1993) and Admati and Pfleiderer (1997) describe the “irrelevance result,” in which an increased share of the portfolio return has no positive influence on the information search of a portfolio manager. The problem is that the incentive is set after the portfolio manager has invested effort. The portfolio manager therefore always expends the same amount of effort for information search, independent of the compensation. The agent has the ability to undo the incentive effect after the contract is closed, which makes it impossible to solve the effort disincentive problem (Stoughton, 1993). Other researchers contradict the findings of the “irrelevance result” and suggest that portfolio managers can be sufficiently motivated by linear contracts. Ozetruk (2004) proposes that an increased share of the portfolio return can motivate the agent, whereas Hui Ou-Yang (2003) sees a possible solution in providing sufficient incentives with a symmetric contract in which, besides a fixed compensation, a bonus or a penalty is paid to the agent. In line with Hui Ou-

3. Literature 50

Yang (2003), other researchers suggest that compensation contracts should be symmetric,21 because otherwise the respective attitudes towards risk held by principal and agent are not aligned (Starks, 1987). Contradicting to linear contracts are call-option type incentive systems, which are popular in the hedge-fund industry. In particular, the option-like (non-linear) characteristic of, for example, a high watermark fee, has the potential to foster excessive risk taking by the agent and might lead to a short-term orientation, which is usually not in the interest of the principal (Goetzmann, Ingersoll, & Ross, 2003). A further issue that should be considered for designing incentive structures are the possible effects of limited liability. According to Grinblatt and Titman (1989) a limited liability of the agent can create incentives to make riskier investments, which would result in an option-like incentive structure. In a multi-period setting, the impact of reputation can create additional implicit incentives. The aim to convince new investors or to realize career enhancements can influence the motivation similar to an explicit performance-based compensation. Stracca (2006) summarizes existing research with the conclusion that, so far, more negative rather than constructive results have been generated regarding the identification of an optimal compensation contract. Because the agent controls effort and risk taking at the same time, it is especially difficult for the principal to write incentive-compatible contracts (Stracca, 2006). Creating suitable incentive structures in the context of portfolio management is a complicated affair that relies on many different variables. Incentive systems may reduce agency costs in the relationship between investor and portfolio manager. At the same time, investors are confronted with the risk that incentive systems increase agency costs by fostering moral hazard and adverse selection. The risks and possibilities should be considered in every delegated portfolio- management situation. The remuneration of signal providers on social trading platforms therefore represents an area that may impact the entire concept of social trading.

21 Symmetric compensation considers positive and negative performance in the incentive contract.

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3.2. Special characteristics of social trading

3.2.1. Collective intelligence

The phenomenon of collective intelligence is closely related to the concept of swarm intelligence, which is most commonly known from animals (Bonabeau, 2009). Swarm intelligence describes a process of self-organization and information collection with the goal to make collective decisions and achieve a common goal without an assigned leader22. The cognitive ability to analyze situations of complexity and contribute assessments or ideas are limited for animals. The advanced ability of humans to analyze surrounding conditions and to take personal perceptions into account differentiates the swarm intelligence of animals from the collective intelligence of humans. Due to the ability to analyze and evaluate situations of complexity, and due to the higher independence of the actors, the collective intelligence of humans can be understood as a more advanced version of swarm intelligence (Öynhausen, 2015). For humans, collective intelligence represents a concept that can assist in the decision-making process. In contrast to swarm intelligence, every actor keeps its independence and can decide whether a reaction to the actions of the collective is executed. Based on the importance of the interaction between the individuals, and based on the intelligence of human actors, collective intelligence can be defined as,

The ability of a group to achieve superior solutions to problems due to the ability to combine the decentralized intelligence of many individuals.

Available information represents a crucial factor for almost all decision-making situations. The use of multiple sources of information makes it possible to diversify risk associated with the natural inaccuracy of information. Collective intelligence builds on the idea to connect multiple actors and by this gain access to more and better information. The higher information accuracy can reduce informational asymmetries and facilitate better decision-making. Collective intelligence therefore creates situations in which information generated by the collective is

22 A classic example of swarm intelligence is the behavior of bee colonies in which hundreds of bees interact and make decisions without a formal leader and solely based on observable behavior (Bonabeau, 2009). An example of the use of swarm intelligence in a human context is high-frequency trading. Investment decisions are based solely on observable prices, and trades are executed if predefined limits are reached. Traders follow market trends created by other traders.

3. Literature 52 superior to information generated by an individual. The higher value of information generated by a collective can be attributed to several factors. For example, different information sources and different interpretations of information result in a diversification effect and can thereby reduce the likelihood of faulty information (Page, 2008). Another factor that positively influences the information quality is that information shared in a collective is already pre- selected by the community (Öynhausen, 2015). Overall, collective intelligence can significantly lower transaction costs for information searches due to economies of scale. Following Surowiecki (2005), collective intelligence can help to solve cognition, coordination, and cooperation problems. Cognition problems are difficulties for which a definitive solution is searched: e.g., how will the stock price be next month? Coordination problems concern potential difficulties in cooperation between individuals: e.g., how do buyers and sellers find each other? Cooperation problems concerns the challenge to convince independent people to interact and cooperate with each other: e.g., how can people be motivated to share information even their personal benefit is limited?

A possible application of collective intelligence to financial markets is in a group of investors who make investments based on collective intelligence. The collective can generate and evaluate information and by this make investment decisions that achieve superior returns. The collective engages as a provider of information and, due to the ability to generate information of high quality, informational asymmetries can be reduced. In several analyses, scientific research highlights the capabilities of collective intelligence in the context of financial markets. For example, a group of fund investors collectively predicted the financial crisis and had the ability to predict monthly stock returns (Chalmers, Kaul, & Phillips, 2013; Kelley & Tetlock, 2013). The findings of another analysis show that the investment recommendations of a group of investors generated outperformance in comparison to a benchmark (Nofer, 2015). Similarly, portfolios based on collective investment decisions have significantly outperformed passively managed index funds (Gottschlich & Hinz, 2014). The concept of collective intelligence applied to a group of investment experts has outperformed the S&P 500 (Hill & Ready- Campbell, 2011). The high complexity of financial markets, the large amount of information available, and the high likelihood to make mistakes might overburden individual investors (Kaplan, 2001). A collective of investors on a social trading platform therefore has the potential to realize superior performance by jointly solving coordination, cooperation, and cognition problems. To ensure

3. Literature 53 an effective development and utilization of collective intelligence, several variables have to be taken into account. Following the areas of cooperation, coordination, and cognition, in which collective intelligence is able to solve problems, important influencing variables can be identified concerning the collective, the structure, and the decision-making process.

Collective Every individual of the community represents one part of the collective. The knowledge and background of the individuals constitute the fundament of the collective intelligence. Diversity among the individuals can be identified as one factor that promotes the development and performance of collective intelligence (Hong & Page, 2004; Surowiecki, 2005). A diverse network increases the chance to receive new and valuable information. Diversity therefore increases the value of a network. The value of diversity can even be superior to the expert knowledge of individuals, as the missing ability of non-experts is more than offset by the additional value of problem-solving diversity (Hong & Page, 2004). Additionally, the decentralized organization structure of the collective can be identified as a success factor (Surowiecki, 2005). A decentralized structure ensures the independent opinions of the individuals and makes it possible to consider different perspectives in the community. At the same time, the independence of the individuals reduces the risk of manipulation. A social trading platform with a diverse network may have the potential to dispose of information sources that together make it possible to have informational advantages over other market actors. To collect information, the collective requires a sufficient size (Nofer, 2015).

Structure The underlying structure of a collective must ensure the effective coordination of individuals (Surowiecki, 2005). The way to coordinate individuals depends, amongst other factors, on the overall purpose of the collective. The underlying structure must provide a suitable platform for interaction: for example, by defining a common goal, ensuring technical prerequisites, and providing a juridical framework. Additionally, the structure of a collective must develop and provide sufficient incentives to motivate individuals to participate. The contribution by individuals can only be expected as long as an atmosphere for trustful reporting is created (Krause, Ruxton, & Krause, 2010). The incentive structure must therefore be targeted towards the goal of the collective and must compensate individuals for the costs of sharing information. Individuals must be sufficiently

3. Literature 54 motivated to provide and share valuable information. The total transaction costs23 for the users must be below alternative options. For example, the transaction costs for participating on a social trading platform must be below the alternative to screen and analyze information from news homepages and to make investment decisions based on private assessments. The costs for information search are relatively high (Williamson, 1979). The advantage of having access to more and diverse information may automatically incentivize individuals to share information. Nevertheless, experts who possess rare and therefore especially valuable information might have to be additionally incentivized (Hirshleifer, 1971). At the same time, individuals must trust the underlying structure, because otherwise the information provided and the quality of the outcomes are limited.

Decision-making The decision-making process of the collective must enable every individual to make decisions independently. If the independence of actors is not ensured, the risk arises that decisions are not utilizing collective intelligence. Individuals would rather follow the behavior of other individuals, which represents swarm intelligence. Hill and Ready-Campbell (2011) show that a higher weighting of expert recommendations has the potential to increase the performance of investment recommendations. This indicates that some kind of categorization among the members of a collective can positively influence the recommendation quality. On a social trading platform, a differentiation according to the level of knowledge or according to the activity might be imaginable (Öynhausen, 2015). The categorization according to the skills of the actors would consider informational asymmetries in the decision-making process. For example, it would be possible to place greater weight on the decisions of better informed individuals. At the same time, a transparent display of the knowledge and skills of every individual could reduce informational asymmetries. Nevertheless, the danger of a categorization or ranking is that users conclude that past performance leads to future performance, which is not necessarily the case. Additionally, categorization could lead to the exclusion of individuals. Under the assumption that market

23 Referring to Arrow (1969), transaction costs can be defined as the costs for running an economic system. The idea of an economic system can be applied to a group of people who make collective investment decisions. Every individual has to decide whether it is, from a transaction-cost perspective, more beneficial to join the collective or whether it is more beneficial to make the investment decisions independently.

3. Literature 55 prices reflect public and private information, the exclusion of an individual could lead to the exclusion of information, even it is only minor. The decision-making process therefore must consider the marginal utility of the information contributed by every individual (Öynhausen, 2015).

3.2.2. Decision-making and social influence

The decision-making of individuals in the context of financial markets has been frequently analyzed. The foundations of its analysis are set in the neo-classical models of portfolio selection: for example, in the capital asset pricing model (CAPM) and in arbitrage-pricing theory (APT) (Lintner, 1969; Mossin, 1966; Sharpe, 1964). Multifactor models have been developed based on the CAPM and the APT that allow for more accurate price predictions due to the larger number of variables considered (Fama & French, 1992). Nevertheless, restricting assumptions24 and empirical weaknesses limit the applicability of neoclassical models (Fama & French, 2004; Lewellen & Nagel, 2006). In reality, the information processing and decision- making of investors is influenced by a wide variety of variables, which are not or are rarely taken into account in neo-classical finance models. Especially since the recent financial crisis, sufficient reasons exist to question the neoclassical models and to weight behavioral variables more heavily. The increasing use of online-based interaction systems and platforms create the opportunity to analyze the influence of social interaction on financial markets (Lazer et al., 2009). Individuals underlie limitations in the collection and processing of information, which can negatively influence or hinder decision-making. Besides the cognitive abilities of an investor, the social and cultural environment influences the decision-making process. The easy availability and variety of information on financial markets make observations of decision- making reasonable from a behavioral perspective.

Users of social trading platforms are exposed to a variety of information when deciding for an investment strategy or for a signal provider. Following the already analyzed concept of collective intelligence, the decision-making abilities of individuals can be increased by utilizing the aggregated decision-making abilities of a collective. Private investors might therefore be able to successfully analyze complex situations of financial markets with help from the

24 E.g., perfect information and completely rational behavior by the investors.

3. Literature 56 collective of a social trading platform. Decisions of individuals in a group context are based on both the individual and collective formation of an opinion (Kimmerle, Cress, & Held, 2010). Users of social trading platforms make investment decisions first by collecting and analyzing information and contributing it to the platform (the individual forming of an opinion) and second based on the aggregated information available on the social trading platform (the collective forming of an opinion). The collective forming of an opinion can help to prevent irrational behavior, as a group is able to suppress private signals more effectively than it is possible with a decision-making process that is based solely on one individual opinion. The collective influences the individual by the alternating interaction, differing expectations and interdependencies (Fahr & Irlenbusch, 2011). Therefore, decision-making in a group on the one hand increases the cognitive abilities but on the other hand ties the members to the collective and creates dependences. Findings how the decision-making of traders are socially influenced can help to increase the understanding of financial markets and may lead to explanations for market irregularities (Pan, Altshuler, & Pentland, 2012). At the same time, potential influences on investment decisions in social trading platforms can be identified. Pan et al. (2012) identify evidence for preference-attachment behavior on social trading platforms. The decision to invest on a social trading platform is heavily influenced by the number of followers an investment strategy already has. Followers might decide to invest; even more accurate information is available. In combination with the finding that the number of followers of a trading strategy does not in all cases correlate with performance, potential misinterpretations by the users can occur. The return of followers may be negatively influenced, as the investment decision is influenced by the irrational behavior of other users. Pan et al. (2012) also show that individuals are prone due to preference-attachment behavior to use riskier investment strategies and tend to overreact when the community overreacts. Users of social trading platforms are therefore confronted with the risk that the information collected during cooperation with other individuals outweighs individual rational thinking. This is especially dangerous, as the advantage of collective intelligence is rooted in the diversity of the members and in their ability to represent contradicting opinions. Preference-attachment behavior could therefore reduce the success potential of the wisdom of the crowd. Decisions based on a collective could be suboptimal for individuals. The danger of informational cascades occurs in the context of preference-attachment behavior (Bikhchandani, Hirshleifer, & Welch, 1992). Informational cascades arise if it is optimal for

3. Literature 57 individuals to follow the decisions of the environment without considering private information. Investors in social trading platforms might therefore be faced with the danger of ignoring individual information and may make decisions based solely on the fact that others did the same. The individual forming of an opinion might therefore be neglected. For a social trading platform, this could cause a loss of critical thinking and could increase the risk for investors, as personal preferences and characteristics are not considered in the investment decision.

In the ideal situation of collective decision-making the social impact, for example, through preference-attachment behavior and informational cascades should be reduced to a minimum. In the context of social trading platforms, it can be doubted whether the influence of other investors on individual investment decisions can be kept at a low level. The fact that social trading platforms want to motivate interested people to invest might even result in the deliberate use of social influence. The ranking of signal providers based on the number of followers could be a reference for this. Some social trading platforms even provide expert information and mark experienced users. Overall, a high focus is set on promoting the popularity of trading strategies to convince followers to invest. It is therefore questionable whether social influence can be prevented in the decision-making process. The following hypothesis about decision-making under the influence of other investors is therefore formulated:

Hypothesis 1: Tools and mechanisms that foster underlying phenomena such as preference- attachment behavior or informational cascades can negatively influence the ability to select successful investment opportunities.

3.2.3. Platform characteristics

The development of the Internet has created a variety of new industries and business models such as eBay, Amazon, and Uber. The specialty of these newly developed business models is that the Internet is used as a communication medium to connect buyers and sellers. Platform providers are intermediate actors that facilitate the connection between two distinct parties (Rochet & Tirole, 2003). Social trading platforms are also two-sided marketplaces that bring together signal providers and followers. The offered service composes the identification of potentially suitable trading partners, the appropriation of information to select partners, and the execution of trades (Rochet & Tirole, 2003). In contrast to a traditional business model, platform providers have costs and create revenue on two sides of the value chain (Eisenmann, Parker, & Alstyne, 2006).

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For the success of a two-sided platform, the size of the community is a crucial factor. Only with a sufficient size network effects can be realized (Liebowitz & Margolis, 1994). Buyers and sellers can only be convinced to participate on a platform when enough opposite parties are available. For sellers, it is necessary to reach out to a sufficient number of buyers, whereas for buyers it is necessary to be able to select from a reasonable product range. At the same time, if sufficient number of buyers and sellers are available, the platform attracts additional users (Tucker & Zhang, 2010). A loss of one party can destruct the entire value of a platform. The need to have a sufficient number of users to create network effects can create high competition between platform providers. For social trading platforms, it is necessary to ensure a sufficient number of signal providers and a sufficient number of followers. A limited number of followers would reduce the chance of signal providers to convince investors. The revenue potential would therefore be lower, which could cause a lack of motivation or prohibit the attraction of skillful portfolio managers. Similarly, a limited number of signal providers could reduce the chance that followers find suitable investment opportunities. Following research about two-sided platforms, one side of the business model is usually subsidized, as an increased number of users generates significant network effects and increases the value of a platform (Eisenmann et al., 2006). Similar practices can be noticed for social trading platforms. In most cases, signal providers are attracted by receiving compensation or discounts for good performances. In the next step, followers join the social trading platform as skillful portfolio managers offer promising investment strategies. The subsidization of signal providers is therefore justifiable, as it promotes the side of the platform, on which the quality is key to the success of the business model. From a platform perspective, it might therefore not be preferable to increase the number of signal providers without limit. Some selected skillful managers might be more valuable to the success of a social trading platform. A low quality of investment strategies can have negative network effects and can reduce the size of the network. The quality of signal providers can therefore be identified as a crucial part for the success of a social trading platform. Under consideration of platform theory, it might be reasonable to distinguish between high- quality and low-quality signal providers. The success potential and the value for followers might be increased by attracting a higher number of high-quality signal providers. An increased quality of investment recommendations would make the platform more attractive to followers, which again would increase the value of the entire platform. Besides subsidizing signal

3. Literature 59 providers, the analysis of the social trading market shows that some platforms also subsidize followers – for example, by granting discounts for the initial registration. The strategy to attract new users (especially in the case of followers) can be attributed to the high competition between the different social trading platforms. In this context, research shows that mature platform markets are usually dominated by one or two market players25. Structural changes on the social trading market are therefore imaginable in the future.

As already mentioned, social trading platforms function as a communication tool between signal providers and followers. According to the concepts of asymmetric information and agency cost (Chapter 3.1.2), the platform must provide sufficient ways to correctly assess the abilities of a signal provider. The challenge is how social trading platforms can create trust26 between signal providers and followers even if the parties do not know each other. In a two- sided platform market for electronic products, repetitive feedback mechanisms help to build trust and create a foundation for making transactions (Ba & Pavlou, 2002). Similarly, evaluations of followers of the performance and trading behavior of signal providers could be a way to create trust. At the same time, a system that facilitates the evaluation of followers could control the behavior of signal providers and create reputation. Reduced asymmetric information due to a platform that facilitates feedback and offers the chance to create a reputation should facilitates better functioning of a two-sided platform. Following hypothesis can therefore be formulated:

Hypothesis 2: The utilization of tools and mechanisms for communication between signal providers and followers can positively influence the ability to select successful investment opportunities.

3.2.4. Remuneration

The remuneration of signal providers builds directly on the discussed theory of incentive structures in delegated portfolio management. Various remuneration systems can be identified

25 According to Eisenmann (2006), three factors can be identified that drive the dominance by one market player: Costs for being active on multiple platforms are high at least for one side of the platform, network effects are at least significant for the side with high switching costs and no prevailing special preferences for one side of the platform users. 26 Trust can be understood as the subjective evaluation of a follower that a signal provider is able to perform the task as expected (Bhattacharya, Devinney, & Pillutla, 1998).

3. Literature 60 on the available social trading platforms. All platforms apply multiple remuneration elements to attract and incentivize signal providers. The aim of all incentive structures is to align the interest between signal provider and follower. On social trading platforms, it can be differentiated between three fundamental remuneration elements: follower-based, volume- based, and performance-based systems. Follower-based remuneration systems compensate signal providers for an increased number of followers. For example, the signal provider receives a share of the transaction fee of every trade executed by followers. Regularly receiving a share of the total assets under management can be defined as a volume-based compensation. A performance-based model incentivizes followers with portions of the realized profits. For example, in a high-watermark concept, signal providers receive a fixed percentage of every new year high. Potential difficulties can be identified by analyzing the remuneration elements with regards to the issues of incentive structures in delegated portfolio management. In case of follower-based remuneration systems, a particular danger exists for hidden action by the agent (signal provider). The agent may be incentivized to increase the number of trades to maximize the personal profit although the trades are not necessary for the principal’s wealth (follower). Even if the informational transparency about investment decisions is relatively high on social trading platforms, the agent still has the ability to act in secret. For the principal, it can be difficult or almost impossible to assess and critically reflect on a large number of trades. In a purely follower-based remuneration system, the interest of principal and agent are therefore only partly aligned. Similarly, the interests are imperfectly aligned in the case of a volume-based remuneration system. The agent may not be sufficiently incentivized to take risks in the investment strategy, as the primary purpose is to increase the assets under management. The return of the investment strategy might therefore not be optimal for the principal. From an investor perspective, the aim is to generate returns whereas the portfolio manager is incentivized to attract more investors. A remuneration system based purely on volume might therefore be suboptimal due to differing pre-contractual beliefs. On all named social trading platforms, follower-based or volume-based remuneration systems are combined with some type of performance-based compensation. Signal providers either participate directly in the performance or are able to become more advanced users, which is associated with additional benefits (e.g., discounts on spreads). Performance-based compensation therefore creates a central element in remuneration systems on social trading

3. Literature 61 platforms. The effectiveness and suitability of performance-based compensation can be questioned in light of the research findings of the previous chapter. Some type of performance- based compensation with asymmetric payoff structures might have the potential to cause excessive risk taking by the portfolio manager. Research generates varying results about the effectiveness of asymmetric incentive structures. In the context of hedge funds Aragon and Nanda (2012) find that a high watermark fee lowers the risk shifting after periods of poor performance. Additionally, this research paper shows that portfolio managers who have invested a significant amount of personal capital are less prone to excessive risk taking. The combination of a high watermark with the downside risk of a significant personal investment may be a suitable remuneration element (Aragon & Nanda, 2012; Hodder & Jackwerth, 2007). In the context of social trading platforms, portfolios that are marked as real-money portfolios could therefore indicate a better alignment of interests between signal providers and followers. Other research results reflect high watermarks more critically. Portfolio managers who are likely to fail the high watermark could be prone to take especially high risks (Hodder & Jackwerth, 2007; Sirri & Tufano, 1998). In case of a multi-period setting, the situation might be different. Taking high risks in one period to achieve the high watermark may cause even lower results in the following period and is therefore not beneficial to the portfolio manager (Panageas & Westerfield, 2009). On social trading platforms with a multi- period setting, the incentive to take high risks might therefore be reduced. Additionally, the motivation to take risks might be compensated in some cases by the aim to prove a solid track record over a multiple period setting to achieve reputation. Nevertheless, in the context of social trading platforms, it should be considered that it is easy to newly register and create a new trading strategy. Portfolio managers may take excessive risks before closing an account and changing to a new one (Basak, Pavlova, & Shapiro, 2007; Hodder & Jackwerth, 2007). The low entry barriers and low costs for registering on social trading platforms create possibilities for such a behavior. Especially during economic downturns, signal providers may be motivated to close portfolios as performance fees based on a high watermark principle may be out of range for a longer period of time. On the one hand, asymmetric remuneration contracts with a high watermark fee in combination with a significant equity investment can therefore constitute a reasonable remuneration system. On the other hand, the future will show how signal providers react during disadvantageous conditions that last over a longer period of time.

3. Literature 62

In summary, it can be said that, in contrast to a classical situation with delegated portfolio management, the transparency on social trading platforms is increased, which may reduce asymmetric information between signal providers and followers. Social trading platforms provide extensive data to enable investors to follow the investment strategies of signal providers, which makes it easier to sufficiently honor the performance of portfolio managers. The three identified elements of remuneration systems have advantages and disadvantages that may also outweigh each other. Besides the interest of signal providers and followers, the interest of the platform providers may also complicate the situation. Research offers multiple interpretations of the advantages and disadvantages of incentive systems, which make it difficult to reach a clear conclusion. From the perspective of incentive theory, it can be emphasized that investments of private money by signal providers may create a reasonable basis to find a suitable remuneration system. Both parties benefit in the same way during periods of success, and the risk is shared between signal provider and follower. Nevertheless, it can be expected that the way of incentivizing signal providers influences investment performance. The following hypothesis can therefore be formulated:

Hypothesis 3: The remuneration of signal providers influences the performance of social trading portfolios.

Collective Intelligence Hypothesis 1: Tools and mechanisms that foster underlying Social Factors phenomena such as preference-attachment behavior influence or informational cascades can negatively influence the Collective Diversity ability to select successful investment opportunities. Decentralized organization Hypothesis 2: The utilization of tools and mechanisms for Structure Suitable Platform communication between signal providers and platform characteristics followers can positively influence the ability to select Incentives to successful investment opportunities. motivate

Decision Independence Hypothesis 3: Remuneration The remuneration of signal providers influences the making Categorization performance of social trading portfolios.

Figure 5: Hypotheses Source: Own illustration

4. Methodology 63

4. Methodology

This methodical section outlines and justifies the approach used in the quantitative analysis of social trading portfolios. In the first part of the chapter, wikifolio is justified as a primary data source, and background information about the functioning of the social trading platform is provided. In the second part, the dataset of the analysis is described and key facts are highlighted. Additionally, the utilized variables are further detailed. The third part of Chapter 4 defines and details the applied methodological framework.

4.1. Social trading with wikifolio

The data for the quantitative analysis is based on social trading portfolios that have been retrieved manually from wikifolio. On the homepage, historical values and descriptions of the trading strategies are available for all portfolios. The social trading platform wikifolio is among the most popular in Europe and offers, in comparison to competitors, an extensive trading universe. Furthermore, a unique aspect of wikifolio is that transparency is a key component of the business model. A clear and comprehensible cost structure, and detailed explanations of the functioning of the social trading platform should help investors to identify suitable investment opportunities. The high importance of transparency makes it possible to evaluate wikifolio as a good and reliable source of information. In contrast to other social trading platforms, wikifolio is therefore a good representative for this analysis of social trading. Under consideration of limitations regarding the generalizability of the results, the sample of social trading portfolios derived from wikifolio can be used as a proxy to analyze the concept of social trading in general. (For a more detailed discussion of the limited generalizability, see Chapter 6.1.)

In February of 2016, 12,365 portfolios were available on wikifolio, of which approximately 4,000 portfolios were investable (Dorfleitner et al., 2016). The platform offers basically everybody the opportunity to create or invest in social trading portfolios. The trading strategies are mirrored with open-end index certificates, which are issued by the bank Lang & Schwarz and publicly traded at stock exchanges. The underlying value of every certificate is a portfolio managed by a signal provider. Before a certificate is issued, the portfolio manager and the trading strategy must run through a certification process. During the certification process, the identity of the trader and the practicability of the intended trading strategy are evaluated. Investors have to pay a certificate fee, which is 0.95% p.a. of the invested amount. Furthermore, a performance fee in the range between 5-30% has to be paid. The fees are automatically priced

4. Methodology 64 into the quoted certificate price (details of wikifolio see Appendix D: Information exchange and trading with partners – wikifolio). To assist investors during the investment process, wikifolio has defined 20 different labels that are assigned to selected portfolios. The labels are given out only when the trading strategies comply with specified rules and critical values. Followers can categorize and select portfolios according to the labels, whereas signal providers can use them to describe the trading strategy or to prove the success of the portfolio.

4.2. Dataset of the analysis

Before the data is analyzed, the sample is adjusted for extreme values. The portfolios with the 5% highest and the lowest 5% standard deviation are excluded from the analysis. The average returns and alphas would otherwise be blurred by extreme values, which would distort the outcomes of the analysis. In total, the sample of the quantitative analysis comprises 1,261 portfolios created and issued as open-end index certificates between 1 January of 2012 and 31 December of 2014. The sample therefore contains all investable portfolios created on wikifolio until the end of 2014. Besides not considering non-investable portfolios and leveraged portfolios in the analysis, no further restrictions are made. Leveraged portfolios are not considered, as a potential influence on the trading strategy due to the leverage should be eliminated. The labels wikifolio assigns to every portfolio are used to differentiate and classify the trading strategies of portfolio managers. The labels are based on predefined rules that take different aspects into account. There are four categories: “traded instruments,” “trading style,” “quality characteristics” and “risk/return” (for a detailed description of the labels see Appendix H: Explanation wikifolio labels).

4. Methodology 65

Traded Focus Germany Focus USA instruments Focus Europe Dividend strategy

Trading Actively diversified Middle to long term style Heavy trader

Rising Star Top ten trader Regular Activity Loyal investors Quality Outperformer Good communicator Bestseller Frequently bought

High performance Continuous growth Risk/ return Good money manager

Figure 6: wikifolio labels Source: “Knowledge for investors – wikifolio labels,” n.d. The categories “traded instruments” and “trading style” describe the trading strategy applied by the portfolio manager, whereas the categories “quality characteristics” and “risk/return” evaluate the performance of a trading strategy. The labels provide a reliable source of information about the applied trading strategy. Portfolio managers can influence the categorization only by adjusting the portfolio composition or the investment strategy. For the purpose of identifying indicators that influence the performance of social trading portfolios, the labels therefore represent suitable indicators. In addition to the labels assigned to every portfolio, the data set includes the performance fee and whether the signal provider invested private money (real-money portfolios). The sample size shows a relatively steady growth in the number of portfolios over time, which avoids a possible weighting of historical events. With about 32% of the portfolios, a clear focus of the investment strategies is set on the German . The number of portfolios with a focus on the European and U.S. stock market is approximately equal.

4. Methodology 66

Without focus on traded instruments 1400

1200 Geo. focus Germany 32,36% 1000 34.18%34,18% 32.36%

800

600 6,34% 400 6.34% 13,402%13.40% Focus on 13,72%13.72% dividends Number portfolios of 200 Geo. focus EU

0 Geo. focus US 03.01.12 03.01.13 03.01.14 03.01.15 03.01.16 Time N = 1261

Figure 7: Number of portfolios issued over time and relative distribution of labels in the category “traded instruments” Source: Own illustration, based on the data sample derived from www.wikifolio.com Besides a focus on financial instruments with high dividends and without assigned geographical focuses (“focus on dividends”), some portfolios with a geographical focus are also assigned the label of a dividend focus. For example, approximately 24% of the portfolios with a focus on Germany also have a focus on financial instruments with high dividends. In addition to the labels of the category “traded instruments,” the trading strategy is furthermore characterized by the category “trading style.” In the sample, approximately 45% of the portfolios are classified as being actively diversified or as following a middle- to long-term trading strategy. Even 33% of the portfolios are assigned both labels. Most of the labels in the categories “quality characteristics” and “risk/return” are assigned less frequently. Nevertheless, for some categories, a sufficient number of labels are awarded to consider the labels in the analysis. The labels “rising star,” “outperformer,” “top ten trader,” “bestseller,” “high performance,” and “continuous growth” are excluded from the analysis, as the number of portfolios assigned with these labels is too small. An analysis would therefore lead to no or misleading results.

Quality characteristics Risk/ Return Good Frequently High Good money Continous Rising Star Out-performer Top ten trader Regular activity Bestseller Loyal investors communicator bought performance manager growth Sum Entire sample 0 0 3 38 520 8 38 97 6 114 6 830 Geo. focus Germany 0 0 3 20 201 8 22 57 3 54 5 373 Traded Geo. focus EU 0 0 0 8 60 0 1 2 0 7 0 78 instruments Geo. focus US 0 0 0 8 60 0 1 2 0 7 0 78 Focus on dividends 0 0 0 12 111 4 11 21 0 25 0 184 Sum 0 0 3 48 432 12 35 82 3 93 5

Figure 8: Number of assigned labels Source: Own illustration, based on the data sample derived from www.wikifolio.com In addition to the automatically assigned labels, the trader has some ability to affect the perception on wikifolio. First, it is possible to manage multiple portfolios with one account.

4. Methodology 67

Second, the portfolio manager determines the level of the performance fee. Third, the portfolio manager can invest private money in the trading strategy. Of the 1,261 portfolios, 139 portfolio managers have invested at least 5,000 EUR and their portfolios are therefore marked as real- money portfolios. The majority of the signal providers manage only one portfolio, whereas some traders manage two or three portfolios. About 66% of the portfolio managers have set a performance fee in the lower fifth (5-10%) of the possible range (5-30%).

476 700 500 624 450 600 400 366 500 350 300 400 250 300 200 160 149 150 200 145 100 75 35 100 58 50 9 22 Number traders of 2 0 4 Number portfolios of 0 0 8 7 6 5 4 3 2 1 5% 6-10% 11-15% 16-20% 21-25% 26-30% Number of portfolios N = 1261 Clustered performance fee N = 1261

Figure 9: Number of portfolios per trader and performance fee Source: Own illustration, based on the data sample derived from www.wikifolio.com 4.3. Methodological framework

The sample of social trading portfolios is analyzed in a two-step approach. First, the performance of all portfolios is determined and evaluated relative to a benchmark. Second, a regression analysis aims to explain excess returns with the labels assigned by wikifolio. The quantitative analysis is based on daily portfolio values retrieved from wikifolio. Daily and monthly returns are computed for every portfolio. The average monthly returns provide a first indicator of the performance of social trading portfolios. In the next step, the average monthly returns are used to compute monthly alphas for every portfolio. Alpha is the expected return of an investment (security or portfolio) after adjusting for market returns. Jensen’s alpha is closely related to the standard alpha but is defined as the ex-post abnormal return of an investment in comparison to a specified benchmark (Jensen, 1968). As the market can hardly be replicated, indices are usually used as representative of the market, which represents by definition the calculation of Jensen’s alpha. For simplicity, alpha is in the following used in place of Jensen’s alpha. The performance indicator alpha represents a simple but effective way to analyze the performance of investments. A positive alpha indicates an excess return over the respective benchmark, whereas a negative alpha indicates underperformance in comparison to the benchmark.

4. Methodology 68

The computation of monthly portfolio alphas is based on a one-factor model and a three-factor model. William Sharpe (1964) and John Lintner (1969) were among the first to develop financial models to predict future stock prices. In a one-factor model as developed by William Sharpe (1964), the future stock price is based on the responsiveness to market returns, the abnormal returns, and residual (random) returns of a stock. Later research showed that predictive power can be increased by adding additional factors to the model. Eugene Fama and Kenneth French created a three-factor model by adding the ratios “small (market capitalization) minus big” (SMB) and “high (book to market ratio) minus low” (HML) (Fama & French, 1992). Factors multiplied with the corresponding ratios adjust the predicted returns of the model. The ratio SMB takes performance differences between large and small firms into account, whereas HML considers performance differences between companies with a low and a high price-to- book ratio. The ratios are computed based on historical values of selected companies.

Following the single-factor model of Sharpe (1964), the monthly alphas are defined as follows:

!" = %"& − %(& − )*"& ∗ (%-& − %(&), where Rit is the average monthly return of every portfolio i at point t and Rft represents the risk- free rate, which is based on the most recent ten-year German-government bond for every observation point t. The ten-year German-government bond is used as risk-free rate, as it has a particular low probability of default. At the same time, the bond is traded frequently and at high volumes, which prevents extreme price movements due to trades of individual investors. Rmt is the average monthly market return at point t. In the analysis, different market returns are used. For calculations with the entire sample, the returns of the MSCI world index represent a global benchmark. In the analysis of the entire sample, no distinction according to a geographical focus is made to ensure the comparability of the results. For calculations with portfolios that have a focus on Europe, the Stoxx Europe 600 is used as a benchmark. For the subsample of portfolios with a geographical focus on the United States, the S&P 500 index is applied as a benchmark. Finally, for portfolios with a focus on Germany, the DAX index represents the benchmark. These indices are chosen because the composition represents a good performance indicator for the economic development. Nevertheless, the benchmarks do not have a claim to be complete but are a good representative for the market. The beta factor )*"& defines how the investment moves in comparison to the market. The variable is calculated by dividing the monthly

4. Methodology 69 covariance between the returns of the portfolios and the returns of the benchmark through the monthly variance of the benchmark at point t.

Following the three-factor model of Fama and French (1992), the monthly alphas are defined as follows:

!" = %"& − %(& − )*"& ∗ %-& − %(& − )/"& ∗ 012& − )3"& ∗ 415&.

In comparison to the one-factor model, the three-factor model adds the ratios SMB and HML, for which historical values for different geographical regions can be retrieved online.27 The factors )/"& and )3"& are calculated with a linear regression between the ratios SMB and HML and the portfolio returns.

The first part of the analysis therefore yields average monthly returns and alpha values (one- factor and three-factor models). It is from an investor perspective, and with regards to the research goal, particularly interesting to know whether the excess returns of portfolio managers can be explained by the selected variables. The second part of the quantitative analysis therefore aims to explain the portfolio performance. Independent variables that at least partially explain excess returns may indicate that a superior performance by signal providers can be attributed to investment skills and is not based on luck. For identifying factors that explain the performance of social trading portfolios, the average monthly three-factor alphas are used to run several regression analyses. The seven selected wikifolio labels, the performance fee for every portfolio, the total number of labels, and information about equity investments by signal providers are used as explanatory variables. The average monthly portfolio alphas represent the dependent variables. Except for the “number of labels” and the “performance fee,” all labels utilized in the regression analyses are dummy variables. The labels are suitable indicators to systematically analyze the trading strategies, as the underlying criteria of the labels are objectively assigned and make it possible to distinguish between different trading approaches: e.g., short-term vs. long-term or different geographical focuses. The regression analysis therefore has the potential to identify factors of trading strategies that distinguish a good portfolio manager from a bad one. From an investor

27 On the homepage of Kenneth R. French, daily values of the SMB and HML ratio can be downloaded for different geographical regions (US, Europe) and for a global scope (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html#HistBenchmarks).

4. Methodology 70 perspective, factors that significantly explain alpha values have the potential to reduce investment risks. First, the regression analysis is run for every variable individually. Afterwards, a regression analysis is executed in a model with all variables together. The individual regression analysis provides results about the individual influences of every label, whereas the complete model takes interdependencies into account.

Dependent variable Independent variable

α wikifolio labels

- Maximum of 1,261 average monthly three-factor Regular Activity Loyal investors portfolio alphas - Categorization according to geographical focus and trading style Frequently bought Good money manager

Focus Germany Focus USA 1. Other variables Focus Europe Dividend strategy Real money Performance fee Actively diversified Middle to long term 2. Number of labels Heavy trader

Figure 10: Variables regression analysis Source: Own illustration Overall, the methodological framework is chosen because it provides a comprehensible and effective way to analyze the performance of social trading. The chosen framework represents a suitable option for adding a third perspective of analysis to the theoretical model considered in Chapter 3 and to the social trading market considered in Chapter 2. Other research frameworks might have the potential to identify additional results, but under consideration of the data basis, the twofold approach of a one-factor/three-factor model combined with a regression analysis is suitable to answer the research goal.

5. Results 71

5. Results

In line with the methodological approach, the presentation of the results is structured in two parts. First, the performance of the social trading portfolios is presented and interpreted. Second, the outcomes of the regression analyses are described and analyzed with the help of the hypotheses of Chapter 3. The distinction suits the aim to answer the underlying research question. On the one hand, the success potential of social trading and the potential influence on the financial industry can be evaluated based on the performance of social trading portfolios. On the other hand, based on the regression analyses, variables that explain the performance can be used to identify success and risk factors.

5.1. Performance of social trading portfolios

The distribution of returns has a slightly non-normal skewness of -1.46, which indicates a higher number of portfolios on the right side of the mean than a normal distribution would exhibit. The kurtosis of the return distribution is 5.46. The higher kurtosis in comparison to a normal distribution indicates that the distribution contains a higher number of peak values. The Kolmogorov-Smirnov-test shows that the returns are not normally distributed. Test results under the assumption of a normal distribution must therefore be interpreted with caution.

Scatterplot avg. monthly portfolio returns (y) and monthly returns Stoxx Europe 600 (x) 0,30%0.30%

0,20%0.20%

200 0,10%0.10%

180 0,00%0.00% --0,60%0.60% -0,40%0.40% --0,20%0.20% 0,00%0.00% 0,20%0.20% 0,40%0.40% 160 --0,10%0.10%

140 --0,20%0.20%

120 --0,30%0.30%

100 --0,40%0.40% 80 Scatterplot average monthly portfolio returns (y) and monthly returns S&P 500 (x) 60 0.30% 40 0.20% 20 0.10% Number portfolios of 0 0.00% -0,40%0.40% -0,30%0.30% -0,20%0.20% --0,10%0.10% 0,00%0.00% 0,10%0.10% 0,20%0.20% 0,30%0.30% 0,40%0.40% -0.10%

Average monthly portfolio return -0.20%

-0.30% -0.40% Figure 11: Return distribution and correlation Source: Own illustration, based on the data sample derived from www.wikifolio.com The monthly returns of the portfolios show varying correlations with the different benchmarks introduced in the previous chapter. The correlation with the Stoxx Europe 600 index is, with a correlation coefficient of 0.5929, the highest of the four indices. The correlation coefficient

5. Results 72 with the S&P 500 index is, at 0.4733, the lowest. For the DAX index, the correlation coefficient is 0.5610, and it is 0.5445 for the MSCI world index.

The average monthly return of the entire dataset is a positive value: 0.359%. Nevertheless, a t- test against an expected value of zero does not generate a significant result. The median of the return distribution is an average monthly portfolio return of 0.459%. Breaking the sample down into subsamples according to geographical focus, real-money portfolios or investment strategies makes it possible to identify different results.

Average Average Average montly Monthly Alpha t-test inter- 1 1 Std. Dev. 2,3 Std. Dev. 2,3 Std. Dev. N Criteria 1 Criteria 2 monthly return t-test alpha t-test (3-Factor pretation Model) Total Dataset 0.359% 0.767% 0.028% 0.980% 0.148% 1.028% 1261 Geo. Focus Germany 0.385% 0.843% 0.154% 1.112% 0.954% 1.603% 408 Geo. Focus Germany Actively Diversified 0.700% 0.490% * 0.552% 0.660% 1.581% 0.896% ** 165 Geo. Focus Europe 0.347% 0.580% 0.160% 0.750% 1.217% 1.012% 169 Geo. Focus Geo. Focus Europe Actively Diversified 0.522% 0.435% 0.270% 0.618% 1.514% 0.772% ** 81 Geo. Focus US 0.591% 0.753% 0.231% 0.934% -0.138% 1.423% 173 Geo. Focus US Actively Diversified 0.771% 0.492% * 0.431% 0.674% -0.053% 1.304% 84 Dividends 0.615% 0.516% 0.200% 0.7514% 0.334% 0.829% 278 Dividends Actively Diversified 0.697% 0.410% ** 0.264% 0.6998% 0.407% 0.772% 189 Investment Dividends Middle to Long-Term 0.682% 0.450% * 0.266% 0.7471% 0.398% 0.796% 168 Focus Actively Diversified 0.603% 0.511% 0.280% 0.7647% 0.403% 0.846% 577 Actively Diversified Middle to Long-Term 0.667% 0.438% * 0.329% 0.7107% 0.452% 0.789% 333 Equity Real money portfolios only 0.814% 0.543% * 0.514% 0.790% 0.558% 0.888% 139 No Label -0.056% 0.935% -0.246% 1.0127% -0.151% 1.039% 132

1 : Categorization of the portfolios according to the labels assigned on wikifolio 2 : T-test of mean values against an expected value of zero 3 : *** statistical siginificane at the 1% level, ** statistical siginificane at the 5% level, statistical siginificane at the 10% level Notes: Alphas in the one- and three-factor model are dependening on the geographical focus calculated against a global or a regional benchmark Figure 12: Average monthly returns and alphas Source: Own illustration, based on the data sample derived from www.wikifolio.com Portfolios with a geographical focus on Germany and an actively diversified investment strategy exhibit, on average, positive monthly returns of 0.700%. In contrast to the entire data set, the average monthly return of the subsample is statistically significantly different from zero at the 10% significance level. Similarly, portfolios with a geographical focus on the United States and an actively diversified investment strategy show positive returns that are statistically significant different from zero at the 10% significance level. A subsample of portfolios focused on investments with high dividends in combination with an actively diversified investment approach also generates positive returns that are significantly different from zero at the 5% significance level. Nevertheless, in the interpretation of the results, it must be considered that some of the dividend portfolios have a geographical focus. Besides the categorization of the portfolios according to the “traded instruments” labels, it is notable that real-money portfolios generate, on average, positive monthly returns of 0.814%, which are significantly different from zero at the 10% significance level. Portfolios without assigned labels generate, on average, a negative monthly return of -0.056%, which are statistically not significantly different from zero.

5. Results 73

The computation of monthly alphas with the one-factor model generates average monthly alpha values that range from -10.00% to 2.86%. On average, a slightly positive monthly alpha value of 0.028% can be observed. Similar to the average monthly returns, the subsamples, according to the label “traded instruments,” exhibit positive average monthly alpha values. The exceptions are again portfolios with no assigned labels, which generate on average a negative monthly alpha of -0.246%. Nevertheless, it has to be noted that none of the monthly alpha values in the one-factor model are statistically significantly different from zero. The average monthly alphas of the portfolios computed with the three-factor model range from -7.62% to 3.92%. In contrast to the values of the one-factor model, the monthly alphas of the three-factor model for the subsamples with a geographical focus on Germany and Europe and with an actively diversified investment strategy show alpha values that are significantly different from zero at the 5% level.

Based on the results, it can be concluded that some portfolios in the sample generate positive returns and positive alpha values. Nevertheless, from an investor perspective, it has to be considered that a high number of portfolios still exhibit a low performance or even generate negative returns. At the same time, some selected portfolio managers seem to have the ability to generate abnormal positive returns that are significantly different from zero. The fact that investment strategies with a focus (geographical or dividend) exhibit a good average performance could be explained by reduced asymmetric information. The portfolio managers may select a focus in which they dispose over specific knowledge or extensive experience. The analysis therefore shows that applied investment strategies and social trading in general could be a promising investment opportunity. The difficulty for investors remains that successful trading strategies have to be identified. For example, though investors with a focus on Germany create positive returns, the subsample still contains portfolios that perform poorly.

5.2. Regression analysis

The results of the regression analysis in figures 13 and 14 are based on the three-factor monthly alpha as dependent variable. The residuals of the regression analysis are checked for homoscedasticity, independence, multicollinearity, and distribution. The Breusch-Pagan test for homoscedasticity, the Durbin-Watson test for independence, and the correlation coefficients of the independent variables do not generate critical results. Nevertheless, the Kolmogorov- Smirnov test shows that the residuals are not normally distributed.

5. Results 74

Except for the explanatory variable “performance fee,” the regression results indicate a positive relationship between the independent variables and the performance of the portfolios. The variable “real money” shows the highest positive impact on the performance. Because the label “real money” is a dummy variable, the result suggest that the performance of a real-money portfolio has a 0.46% higher alpha than a portfolio that is not characterized as a real-money portfolio. The “number of labels” and the label “good communicator” have the second and third largest regression coefficient – even the variables are considerably smaller. The level of the performance fee shows a significant negative influence on portfolio performance. Even the regression coefficients are small or very small. All variables, with the exception of the label “regular activity” show a significant influence at the 1% significance level. The R2 values of the individual regression analyses range from 0.09% for the label “regular activity” to 6.25% for the variable “number of labels.” The entire model (regression analysis including all variables) has an adjusted R2 value of 9.07%, which indicates that about 9% of the variance of the dependent variable (three-factor alpha values) can be explained by the independent variables (labels included in the regression analysis). In the combined model, the variables “real money,” “number of labels” and “good money manager” remain positive and significant. The label “loyal investors” is negative and insignificant. The label “good communicator” is insignificant but stays positive. The negative influence of the “performance fee” remains negative and significant.

Regression results entire sample - dependent variable: Three factor monthly alpha - N=1261 Constant 0.0013 *** 0.0012 *** 0.0013 *** 0.0010 *** 0.0009 *** 0.0034 *** 0.0010 *** -0.0029 *** -0.0006 Good communicator 0.0005 *** 0.0001 Regular activity 0.0000 -0.0002 *** Loyal investors 0.0004 *** -0.0001 Frequently bought 0.0004 *** 0.0002 ** Good money manager 0.0003 *** 0.0002 *** Performance Fee -0.0168 *** -0.0123 *** Real money 0.0046 *** 0.0022 ** Number of lables 0.0018 *** 0.0015 *** R2 0.0096 0.0009 0.0140 0.0258 0.0328 0.0125 0.0197 0.0625 0.0907 1 Note: *** statistical siginificane at the 1% level, ** statistical siginificane at the 5% level, statistical siginificane at the 10% level The table provides the results of a regression analysis run between the three factor monthly alpha and the labels assigned by wikifolio; In the last column the results of the full regression model are given, whereas as in the the previous columns the individual regression coefficients are presented 1 2 : Adjusted R Figure 13: Regression results, entire sample Source: Own illustration, based on the data sample derived from www.wikifolio.com The regression analysis run for the sub-sample with a geographical focus on Germany generates similar results. Except for the label “regular activity,” all variables show a significant influence either at the 1%, 5% or 10% significance level. The regression coefficient is slightly larger for some variables in comparison to the analysis of the entire sample, but it remains at a low level. The labels “real money” and “number of labels” have the largest influence. The labels “good communicator,” “loyal investors” and “frequently bought” apparently also have a positive

5. Results 75 influence, though the level of influence is very small. The label “good money manager” has a significant positive influence as well, but it shows the smallest regression coefficient among the variables. The R2 values for the individual regression analyses range from 0.62% to 10.52% for the variable “number of labels.” For the model including all variables the adjusted R2 is with 10.98% slightly increased in comparison to the analysis with the entire sample. The variable “performance fee” stays negative and significant at the 5% level. The variable “number of labels” remains positive and significant at the 1% level. In addition, the variable “regular activity” becomes significant at the 10% level and has a small negative influence.

Regression results geographical focus Germany - dependent variable: 3 factor monthly alpha - N=408 Constant 0.0095 *** 0.0091 *** 0.0083 *** 0.0090 *** 0.0084 *** 0.0137 *** 0.0089 *** -0.0006 0.0022 Good communicator 0.0007 * 0.0000 Regular activity 0.0002 -0.0002 * Loyal investors 0.0006 * -0.0003 Frequently bought 0.0005 *** 0.0001 Good money manager 0.0004 *** 0.0001 Performance Fee -0.0353 ** -0.0238 ** Real money 0.0046 ** -0.0001 Number of lables 0.0034 *** 0.0038 *** R2 0.0167 0.0062 0.0182 0.0290 0.0290 0.0103 0.0103 0.1052 0.1098 1 Note: *** statistical siginificane at the 1% level, ** statistical siginificane at the 5% level, statistical siginificane at the 10% level The table provides the results of a regression analysis run between the three factor monthly alpha for portfolios with an investment focus in Germany and the labels assigned by wikifolio; In the last column the results of the full regression model are given, whereas as in the the previous columns the individual regression coefficients are presented 1 : Adjusted R2 Figure 14: Regression results geographical focus Germany Source: Own illustration, based on the data sample derived from www.wikifolio.com All in all, the results of the regression analysis provide limited explanatory power due to the small regression coefficients and small R2 values. The outcomes therefore have to be interpreted with caution. Nevertheless, the results may serve as guiding indicators for the relatively young phenomena of social trading. In general, it can be said that, among the various results of the regression analyses, the variables “performance fee,” “real money,” and “number of labels” have the highest explanatory power of the three-factor monthly alpha values. The variables “good communicator,” “loyal investors,” “frequently bought,” and “good money manager” also have a significant influence, but at a considerably lower level.

Given the simplifying assumptions made in the analysis, and with regard to the limited explanatory power of the results, the labels can be operationalized to assess the hypotheses developed in Chapter 3.

5. Results 76

Hypothesis 1: Tools and mechanisms that foster underlying phenomena such as preference- attachment behavior or informational cascades can negatively influence the ability to select successful investment opportunities.

Hypothesis 1 concerns the potential impact of preference-attachment behavior or informational cascades. The theoretical analysis in Chapter 3.2.2 shows that it is possible that interested investors buy a portfolio because other investors often buy it. Personal investment goals or other influencing factors would not be considered in the investment decision. The labels “loyal investors” and “frequently bought” could convince investors to buy a portfolio based on the confidence that other followers have also invested in the portfolio. The results of the regression analysis do not show a clear negative relationship between the labels “loyal investors” and “frequently bought” and the portfolio performance. Overall, this does not indicate that investing on social trading platforms is free of social influences such as preference-attachment behavior or informational cascades. But at least the portfolios in the sample do not indicate that just buying portfolios because they are bought by other investors causes a negative influence on the performance of investors. The hypothesis must therefore be rejected.

Hypothesis 2: The utilization of tools and mechanisms for communication between signal providers and followers can positively influence the ability to select successful investment opportunities.

Hypothesis 2 concerns potential influences that can result from the manner of operationalizing the concept of social trading via online platforms. The theoretical justification and explanation of the hypothesis in Chapter 3.2.3 shows that the creation of trust between signal providers and investors reduces informational asymmetries. Tools and mechanisms on social trading platforms therefore have the potential to increase the chance that followers select a successful signal provider. The “number of labels” and the label “good communicator” can be operationalized to examine the influence of communication tools on social trading platforms. The regression results show a positive and significant relationship with the portfolio performance for both variables. In particular, the variable “number of labels” is among the most influential of the factors in the regression analysis. The hypothesis therefore cannot be rejected. The results show that tools and mechanisms such as labels or regular communication can potentially help followers identify signal providers with promising investment strategies.

5. Results 77

Hypothesis 3: The remuneration of signal providers influences the performance of social trading portfolios.

Hypothesis 3 is concerned with the influence of the remuneration of signal providers on social trading platforms. The unclear and controversial topic of incentive structures and remuneration in delegated portfolio management creates the expectation that the remuneration of signal providers also has an influence on the performance of social trading portfolios. The results of the regression analysis make it impossible to reject the hypothesis. The analysis shows a significant negative relationship between the performance of social trading portfolios and the remuneration of signal providers. In addition, the variable “real money” shows in part a positive influence on the performance of social trading portfolios. The identified positive effect of a personal investment is consistent with other results of delegated portfolio management and may be attributed to reduced risk taking, as the interest of signal providers and followers are aligned. On the other hand, the variable “performance fee” has a significantly negative influence. From an investor perspective, an increased performance fee can therefore not be justified.

6. Discussion 78

6. Discussion

Overall, it can be concluded that the quantitative analysis generates results that are at least partly consistent with the theoretically derived hypotheses. The quantitative evaluation of social trading therefore provides insights that add value to the analysis of the social trading market in Chapter 2 and to the underlying theory considered in Chapter 3. In the following, the three applied research focuses are connected and critically reflected. In addition, the first section of Chapter 6 outlines limitations of the applied methodology.

6.1. Limitations of the quantitative methodology

Although the quantitative analysis of social trading portfolios generates interesting and beneficial results, the outcomes have to be interpreted with caution and under consideration of different limitations. First, limitations in the applied methodology have to be taken into account. Besides limiting factors due to simplifying assumptions (such as normally distributed returns or the use of indices as market benchmarks), the applied one- and three-factor models also contain restrictions. The most influential restrictions are the assumption that investors aim to invest only in a “mean-variance efficient portfolio” and that risk-free borrowing is possible (Fama & French, 2004). Additionally, the suitability and explanatory power of the factors (HML and SMB) used in the three-factor model is critically discussed and must therefore be highlighted as a limitation (Kothari, Shanken, & Sloan, 1995). Furthermore, the results of the regression analysis have to be analyzed under the limitation that the residuals are not normally distributed. Second, limitations with regards to the database must be considered. The results of the analyses are based solely on data derived from wikifolio. Social trading portfolios from other platforms might generate different results. Nevertheless, many social trading platforms exhibit differences in the operationalization of social trading services, a sample with portfolios from different social trading platforms might cause difficulties with regards to comparability. Under considerations of the limited generalizability, the results of the analysis can be used as an indication for other social trading platforms and for the concept of social trading in general. Furthermore, the dataset is confronted with a survivorship bias (Carhart, Carpenter, Lynch, & Musto, 2002). The sample contains only portfolios that were still traded when the data was collected. The performance of certificates of which the underlying portfolio suffered a complete loss are not included in the dataset. Furthermore, the analysis has, in some subsamples, a limited

6. Discussion 79 number of observations, which might compromise the results. A quantitative analysis of a larger scope might help to identify additional influences or relevant factors. In the same way, an analysis over a longer period of time could generate additional and more detailed results. Finally, the data sample is directly derived from the homepage of wikifolio, which requires consideration of the reliability of the data as a potentially limiting factor. Concerning the portfolio values, the risk of errors or faulty data is minor, as the certificates are publicly traded, which ensures transparency. Nevertheless, the labels assigned to the portfolios are accessible only over the homepage of wikifolio. The accuracy of the labels is therefore difficult to prove.

6.2. Critical reflection of qualitative and quantitative research findings

The results of the quantitative analysis show that some portfolio managers are able to generate abnormal returns. Following the theory of efficiently inefficient financial markets, selected portfolio managers seem to have the ability to successfully apply actively managed trading strategies. These findings are in line with other research papers that prove superior portfolio management skills for some private investors (Coval, Hirshleifer, & Shumway, 2005; Mizrach & Weerts, 2009). In contrast to already existing research in the area of social trading, the analysis only considers investable portfolios. Unlike the results of other research papers that also consider non-investable social trading portfolios, the average and median of the entire sample in this work is positive (Oehler, Horn, & Wendt, 2016). In this aspect, the quantitative analysis adds value to the current research and shows that social trading can constitute an interesting alternative from an investor point of view. The results indicate that social trading platforms can offer a suitable structure to form a collective and to offer investors the possibility to make decisions that benefit from the utilization of collective intelligence.

The question remains in which way social trading can be successfully operationalized to become a serious alternative to existing asset-management solutions. The analysis of social trading platforms in Chapter 2 has already uncovered a number of potential risks and success factors. It can be emphasized that the current competition, especially among fully integrated platforms, might be a risk factor. Platforms seem to have a high focus on increasing the number of signal providers and followers and therefore may follow a strategy of quantity rather than of quality. This might be a wrong focus, especially for the offered trading strategies. Risky or low- quality investment strategies may disappoint interested investors and raise the question whether social trading platforms can be a serious investment alternative. In the same way, the business

6. Discussion 80 models of some platforms are based primarily on the number of transactions. Similar, the incentives for signal providers are often influenced by the number of executed trades. Though most of the platform providers create incentive structures for signal providers that combine fixed and variable bonuses (e.g., risk taken or asset under management), it can be asked whether the interests of signal providers and followers are aligned. In combination with the utilized speculative financial instruments, it can be asked whether the current design of social trading is suited for investors who are looking for a sustainable medium- to long-term investment opportunity. The perceived short-term orientation in the investment strategies can be attributed primarily to the way social trading is operationalized and to the conception of the underlying business models. The relatively high investment risk can therefore be prevented only in part by the theoretically derived success factors of a suitable online platform or by the effective use of collective intelligence.

The results of the regression analysis contribute to the discussion about the risks and success potentials of social trading from a different perspective. Besides proving that social trading can generate excess returns, the analysis has identified the positive influence of some assigned labels and equity investments by signal providers. Nevertheless, it must be considered that, in addition to many well-performing portfolios, a high number of portfolios with poor performance are part of the sample. For investors, it is therefore crucial to dispose over tools to oversee the wide variety of investment opportunities. The labels assigned to the portfolios provide, at least in part, guidance for investors, but the quantitative analysis shows that the explanatory power differs. For investors who want to choose an attractive investment strategy, it might be difficult to identify truly helpful labels given the large number of available indicators. In the current concept, the labels therefore help only in part to reduce asymmetric information between signal providers and followers. In addition to the identification of promising investment strategies, it might be difficult for inexperienced investors to identify trading strategies that are suited to the individual risk preferences. This interpretation is in line with results from other research papers, which identify that, on the one hand, social trading platforms can reduce asymmetric information but, on the other hand, that trading strategies bear substantial risk and generate hedge-fund-like returns (Doering, Neumann, & Paul, 2015). The significantly positive relationship of excess returns and the variable “number of labels” can be interpreted as an indication that clearly defined investment strategies have a higher success potential. The relationship can be theoretically justified by reference to the superior knowledge

6. Discussion 81 of signal providers about specific segments or markets. Following the theory of efficiently inefficient financial markets, the signal provider is able to generate excess returns due to specific knowledge about certain markets. The utilization of specific knowledge does not indicate that signal providers dispose over insider information from unknown sources. In fact, the performance can be attributed to the effective utilization of collective intelligence. Experts share specific knowledge about financial markets via trading strategies with the community. The social-trading platform provides the necessary infrastructure to bundle and combine the experience of many signal providers to make promising investment decisions. The remuneration of signal providers on social trading platforms seems, like other delegated portfolio-management constellations, to be a critical topic. The regression analysis shows a negative relationship between the height of the performance fee and the excess returns. It has to be considered that the performance fee is automatically priced into the certificate price, which could explain the negative relationship. It therefore cannot be concluded that investors who charge a higher performance fee are generally performing worse. Nevertheless, from an investor perspective, a higher performance fee cannot be justified. In contrast to the remuneration, a significant positive influence on the performance can be identified for the variable “equity investment.” A private investment of signal providers could therefore signal trust by followers that the trading strategy avoids excessive risk taking. In contrast to a signal provider who provides investment strategies without personal commitment, the interests between portfolio managers and followers seem to be more closely aligned for real-money portfolios.

Though the quantitative analysis provides no indications that the influence of other users has a negative impact on the performance of social trading portfolios, behavioral aspects in the context of social trading should not be underestimated. Other research papers show that social interaction influences investments in a digital context (Pan et al., 2012). The research field definitely represents an area for further research and may have the potential to identify ways to increase the utilization of collective intelligence. It could be especially interesting, for example, to conduct a more detailed analysis about how signal providers are influenced by the decisions of other signal providers. As portfolio managers are competing against each other for investors, a potential source of influence between the investment decisions is imaginable. Furthermore, an analysis of social trading during periods of stressed financial markets could add value to the question of how persistent signal providers generate excess returns. In line with additional

6. Discussion 82 research about the influence of environmental factors on social trading portfolios, could be a more detailed analysis of the composition of the underlying portfolios. A comprehensive understanding of trading strategies could help to identify more ways in which signal providers are able to generate excess returns. In addition, a quantitative analysis that compares the different categories of social trading business models could add value to the question concerning what mode of operationalization is most suitable.

6.3. Potential future developments of social trading

Overall, the qualitative and quantitative analysis confirmed a basic success potential for social trading. In particular, due to the ability to reduce informational asymmetries between signal providers and followers, and due to the chance to increase transparency during the investment process, investment strategies on social trading platforms might become more comprehensible for investors. From the perspective of asymmetric information, social trading therefore has the potential to become a serious alternative to current methods of asset management. Based on the insights generated in the analysis, several future developments of social trading are imaginable. In general, the combination of social trading with other FinTech solutions might be a way to further increase usability for retail investors. Like robo-advisors,28 investors could receive automatic recommendations for suitable investment opportunities based on background information about the intended investment goals. Social trading platforms would then be able to further reduce informational asymmetries between portfolio managers and investors. In such a constellation, the importance of social trading platforms as mediating parties would be increased, and the fit between signal providers and followers could be reinforced, which could create a higher degree of satisfaction for the three involved parties. In the context of further reducing informational asymmetries, the fundamental problem to evaluate the skills and abilities of signal providers can also be raised. The evaluation of signal providers that is based primarily on historical performance involves a high degree of uncertainty. A positive performance can still be attributed to luck even if a signal provider disposes over a positive track record. Proof of knowledge about financial markets may have the

28 Robo-advisory can be defined as “investment advice and discretionary investment management services without the intervention of a human adviser, using algorithms and asset allocation models that are advertised as being tailored to each individual’s investment needs” (Fein, 2015, p. 2).

6. Discussion 83 potential to reduce the probability that a successful trading strategy is grounded in the luck of the portfolio manager. Like the business model of United Signals (see Appendix E: Information exchange and trading with partners - United Signals), in which signal providers are extensively screened, a quality check for signal providers might have the potential to increase the quality of social trading portfolios and reduce disappointments for followers. Furthermore, potential future development might be expected concerning the institutions that operate social trading platforms. The frequently discussed adjustments to the regulatory framework and the limited trustworthiness of social trading platforms may create the chance for established financial institutions to enter the social trading market. The infrastructural possibilities for banks, might enable a different operationalization of social trading, which may not require the mirroring of investment strategies by utilizing speculative financial instruments. In the mid- to long-run, it is therefore questionable whether social trading platforms are going to stay independent or disappear. Nevertheless, the concept of social trading has a variety of success potentials and may therefore have a significant impact on the asset-management industry.

Table 4: SWOT analysis of social trading Source: Derived from the analyses in the previous chapters

7. Summary and concluding remarks 84

7. Summary and concluding remarks

The analysis shows that the idea of social trading has considerable success potential but also comes with a variety of risks. The market for social trading services comprises different business models that operationalize social trading with varying approaches. Although the fundamental concept of social trading has existed for more than ten years, the differing business models can be interpreted as an indication that the social trading market is still in an early stage of development. Additionally, the market size in comparison to other asset management solutions seems to be limited; even exact statistics from independent sources are rare. The existing social trading platforms can be divided into three categories: fully integrated platforms, platforms that offer information exchange, and trading with partners and platforms, which focus on information exchange. Only the first and second business model categories offer users the possibility to make investments. For both categories, the current mode of operation is substantially different from stocks, bonds or funds. Besides operational differences, the utilization of speculative financial instruments to facilitate investments in trading strategies can create significant risks for investors. Nevertheless, the easy usability, low entry barriers, and a possible independence from financial institutions are identified as primary success factors and may motivate and convince retail investors to participate. All in all, the market for social trading comprises a wide variety of companies and so far has no dominating business model, which might in the future cause increased competition and can potentially push players out of the market. Furthermore, the analysis highlights the influence of regulators, which will most likely increase in coming years and may significantly change the business models. Adjustments to the regulatory framework can therefore be identified as a major risk for providers of social trading platforms.

From an investor perspective, the basic concept of social trading has the advantage that the sharing of investment strategies has the potential to significantly reduce informational asymmetries between portfolio managers and investors. In contrast to other delegated portfolio- management constellations, social trading platforms can increase the transparency during the investment process. The quantitative analysis generates results, which confirm that a social trading platform can provide tools and mechanisms to reduce asymmetric information. In addition, social trading platforms offer personalized investment opportunities at low cost and therefore might serve new or additional customer segments. The quantitative analysis also shows that social trading portfolios have the potential to generate excess returns. Amongst other

7. Summary and concluding remarks 85 factors, the superior performance can be attributed to the effective utilization of the theoretically derived concept of collective intelligence. A diverse community of a social trading platform can create informational advantages in comparison to a single investor. To ensure an effective utilization of the collective intelligence on a social trading platform, the theoretical analysis identified important factors concerning the composition of the collective, the underlying structure of the community, and the decision-making process. Furthermore, the creation of trust can be identified as a crucial success factor. To convince investors to make investments, trust is required in both the social trading platform and the signal providers who develop the trading strategies. Besides tools and mechanisms that explain the trading strategies and reduce asymmetric information between signal providers and followers, a further alignment of interest can be achieved by an increased personal commitment by the signal providers. The quantitative analysis confirms a positive influence on the performance of social trading portfolios if signal providers have invested private wealth. Nevertheless, the way to design the remuneration differs per platform provider and can also create the wrong incentives.

Table 5: Success factors and risks Source: Derived from the analyses in the previous chapters

7. Summary and concluding remarks 86

In summary, it can therefore be emphasized that the most significant risks both for investors and for the future of social trading platforms derive from the manner of operationalizing the collective investment process. The utilized financial instruments, in combination with high leverages as they are frequently applied on many social trading platforms, create high risks for investors and in consequence also for the platform providers. The basic idea of social trading – to share trading strategies among a community to help retail investors to easily make successful investments – is therefore an idealized description of the current offerings on the social trading market. In the prevailing way of operation (fully integrated platforms), social trading does not represent a new way of asset management for inexperienced investors; instead, it can be described as a platform for private speculators. In addition, the manner of conceptualization raises the concern that the interests of platform providers, signal providers, and followers are not aligned. On some platforms, the goal seems to be to process as many trades as possible rather than to offer attractive trading strategies. In the same way, the reliability and trustworthiness of social trading platforms is questionable. The potential disappointment and substantial loss of wealth for private investors who have not been sufficiently informed about the associated risks may prohibit a positive future for social trading. Nevertheless, the basic success potential of the concept of social trading must be highlighted. If a suitable manner of operation can be identified, social trading can become an attractive investment alternative.

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Appendix 102

Appendix

Appendix A: Fully integrated platform – ayondo ...... 104

Appendix B: Fully integrated platform – eToro ...... 106

Appendix C: Fully integrated platform – tradeo ...... 107

Appendix D: Information exchange and trading with partners – wikifolio ...... 111

Appendix E: Information exchange and trading with partners - United Signals ...... 112

Appendix F: Information exchange and trading with partners – ZuluTrade ...... 114

Appendix G: Information exchange – Sharewise ...... 115

Appendix H: Explanation wikifolio labels ...... 117

Appendix 103

Ayondo

Background: Thomas Winkler and Robert Lempka founded ayondo in 2008 in Frankfurt. In five funding rounds, ayondo received more than 10 million USD from five different investors (“ayondo | crunchbase,” n.d.). The business model of ayondo comprises a social trading platform and a brokerage platform. Ayondo GmbH is responsible for all services around the social trading platform, whereas ayondo markets Ltd. runs the brokerage platform. Ayondo markets is located in London and is therefore regulated by the Financial Conduct Authority (FCA). Ayondo GmbH is based in Frankfurt and is therefore regulated by the German exchange supervisory BaFin. The internationalization with offices in Frankfurt, London, Madrid and Singapore is an integral part of the company strategy. High growth potential is expected, especially in the Asian market. In 2016, ayondo went public on the Singapore stock exchange with a reverse takeover transaction (Muhn, 2016). According to ayondo, the capital raised should be spent primarily to quickly expand operations in Asia. Part of the growth strategy was the acquisition of the Singapore investor education app TradeHero at the end of 2016 (Heong Tung, 2016).

Platform operations: Investments on ayondo are limited to CFDs, which are directly traded over the connected brokerage platform ayondo markets. Currently, more than 260 different contracts are available in the categories: indices, currencies, stocks, ETFs, commodities, interest rates, and bonds. Signal providers are not required to invest private money into their trading strategy. Nevertheless, signal providers who have invested in their investment strategies are marked as real-money portfolios. On ayondo, followers can pick a maximum of five signal providers and have to invest at least 1,000 EUR. The invested amount can be increased by using leverage up to 200 times. Payments can be made via credit card or transferal. In case an investor loses money and has a negative account balance, ayondo does not require additional payments. Like other financial institutions, ayondo provides insurance for customer deposits up to 500,000 GBP. Signal providers are categorized according to their performance, number of followers, career level, and risk score. Career level is based on positive performance, good risk management, and the time registered on the trading platform. Ayondo differentiates between

Appendix 104 five career steps: street trader, advanced, professional, risk-adjusted, and institutional. Signal providers must achieve the requirements for the next career step in a specified time period (30, 60, 90, 180, 365 days). During this period, a specified number of trades must be executed (5, 5, 10, 15, 30). The maximum drawdown is not allowed to exceed 25%, and the trader must achieve an increasing positive performance (0.5%, 1%, 1.5%, 3%, 6%). If the requirements are not achieved, the career level has to be repeated. In addition to the career level, traders are assigned a risk score. The risk score (between 1 to 10) evaluates the sensitivity of a trading strategy by considering the current and historic leverage of the portfolio. The goal is to identify and inform followers of short-term and therefore highly risky trading strategies. In contrast to other social trading platforms, ayondo does not offer any possibilities for communication between signal providers and followers. Signal providers are incentivized to share their trading strategies by receiving a share of the spread for every trade executed by a follower. The share depends on the career level of the trader and whether the portfolio is a real- or virtual-money portfolio. For real-money traders, the share is between 2% to 12% of the spread, whereas the range for virtual traders is between 1% to 6%. The income for the signal providers therefore depends primarily on the trading activity. A higher number of trades executed by a signal provider results in more executed trades by the followers, which increases the revenue.

Revenue sources: The main revenue source of ayondo is spreads on the CFD brokerage platform. Additional revenue is generated through financing fees or rollover costs. Financing costs occur if an open position is held overnight or over weekends. The fixed is 2.5% p.a., and the costs are calculated on a daily basis. For rolling over a position into another contract, only half of the spread is charged. In addition to revenue generated by users of the platform, ayondo sells the platform software as white label solutions to other financial institutions.

Appendix A: Fully integrated platform – ayondo Source: www.ayondo.com

Appendix 105

eToro

Background: eToro was founded in 2007 by Yoni Assia and is headquartered in London and Cyprus. In seven funding rounds, eToro raised 72.9 million EUR capital from 16 investors. The most recent funding round in April of 2015 yielded about 12 million EUR. It was financed by CommerzVentrues, which is the corporate venture capital fund of the Commerzbank Group (“eToro | crunchbase,” n.d.). EToro claims to be the largest social trading platform, with about 4.5 million users worldwide. In contrast to other social trading platforms, eToro places a major focus on interaction between the users. EToro defines itself as a social investment network. Communication between signal providers and followers is an integral part of eToro’s business idea. As with Facebook, a newsfeed shows current topics and informs the investor instantaneously about market news or market evaluations of other traders. Investments at eToro are made via contracts for difference (CFD). The trades are executed over eToro’s own brokerage platform. The social trading platform eToro (UK) Ltd. is based in London, and the brokerage platform eToro (Europe) Ltd. is headquartered in Cyprus. The brokerage service is regulated by the Cyprus Securities and Exchange Commission (CySEC), whereas eToro (UK) Ltd. is regulated by the Financial Conduct Authority (FCA).

Platform operations: The minimum deposit (geographical differences can appear) to open an account in Germany is 200 USD. The amount invested can be leveraged up to 400 times. Besides a normal account, eToro offers the possibility to subscribe to a premium account. Benefits of a premium account include a credit card, a personal account manager or weekly market analysis videos. A premium account can be unlocked by having more than 20,000 USD in the eToro account. Money can be transferred to eToro via multiple options: e.g., by credit card or Paypal. Signal providers are incentivized to share their trading strategies by receiving a compensation that depends on the number of followers. Four different levels of signal providers can be differentiated. Signal providers receive compensation from the second career level on. The requirement to reach the second career level is to attract 50 followers. The signal provider receives a fixed monthly compensation of 500 USD. In the highest rank, the fixed compensation

Appendix 106 is raised to 1,000 USD and 2% of the asset under management. Higher ranked investors are required to increase the deposit, for example, at the second level by 5,000 USD (3rd level 5,000 USD, 4th level 20,000 USD). Furthermore, investors with many followers receive discounts on CFD spreads on the brokerage platform (20% for the first level up to a 100% rebate for the top level). Traders who reach a high level receive more advanced and sophisticated tools to manage their investments. For example, eToro provides additional information about the profitability of the trades for the followers. At the same time, eToro expects that popular investors communicate frequently with their followers. Posts on twitter or Facebook about the current market conditions or about investment decisions should help followers to understand trading decisions and convince new followers. Recently, eToro introduced obligate guidelines for responsible trading, which should help to prevent risky trading strategies. For example, a popular investor is not allowed to invest more than 20% of the total invested amount into one trade. This should help to prevent extremely volatile trading strategies. Besides the guidelines on eToro every trade is assigned a risk score. Followers should be aware that some trading strategies are short-term oriented and can easily cause high losses. Popular investors with a particular high-risk score due to a very high volatility, for example, cannot be copied by other investors.

Revenue sources: The main revenue source of eToro is spreads on the brokerage platform. In addition to the spreads, the followers have to pay a variety of fees. Users have to pay fees for CFDs on stocks and for all open positions held overnight. Furthermore, a transaction fee is charged for withdrawing money from eToro accounts. All trades on eToro are made in U.S. Dollar. The currency conversion to Euros creates additional fees for investors.

Appendix B: Fully integrated platform – eToro Source: www..com

tradeo

Background: Tradeo is among the younger social trading platforms and was founded in 2011 by Jonathan Adest. In 2016, tradeo was able to collect 10.5 million USD from venture capital companies and angel investors (Mizrahi, 2016b). The social trading platform originally started as a

Appendix 107 software developer but extended the business model and started to offer social trading services. It founded its own brokerage platform to offer social trading. The company has offices in Sofia, Tel Aviv, and Cyprus, and the brokerage platform disposes over an official license from the Cyprus Securities and Exchange Commission (CySEC). The business vision is to offer private traders a platform on which to share trading strategies and interact with other interested people.

Platform operations: The underlying software of tradeo sets a focus on communication between users and aims to ensure that the platform is easy to use. According to tradeo, a direct connection to its brokerage platform facilitates easy trade execution, which makes it possible to realize operations that are superior in comparison to social trading platforms with partner brokers. The trading universe of tradeo composes a selection of CFDs and forex products. Users have to invest at least 100 USD, but can by using a leverage of up to 200 substantially increase the invested amount. As in other social trading platforms, signal providers at tradeo are ranked and classified. Variables include, for example, past performance, maximum drawdown or the average of the applied leverages. Additionally, every trader is assigned a risk score that takes into account the standard deviation of the daily returns. A higher standard deviation results in a higher risk score. Tradeo also depicts how many users are interested in the trading strategy and how many are actually invested into the trading strategy. From an outside perspective, it is difficult to evaluate whether signal providers are additionally incentivized with bonuses. The homepage describes an affiliate program but does not provide any indication of how signal providers may participate from successfully shared trading strategies.

Fees: The use of tradeo is free of charge. After registration, users can operate with a demo account. For copying investment strategies, deposits have to be made at the brokerage platform of tradeo. The spreads charged on the brokerage platform might be slightly higher than those of other brokers. Transaction fees can occur if investors want to withdraw money from the broker account (0.15% fee for withdrawals above 10,000 EUR). Additionally, fees can occur due to currency conversions, and users who have been inactive for more than 90 days have to reactivate the account by paying a fee of 20 EUR.

Appendix C: Fully integrated platform – tradeo Source: www.tradeo.com

Appendix 108

wikifolio

Background: Wikifolio is based in Vienna and was founded in 2008 by Andreas Kern. In addition to undisclosed amounts of seed financing, wikifolio received about 8 million EUR funding during the series A funding round. Besides different angel investors and venture capitalists the private bank Lang and Schwarz (L&S) and the venture-capital department of the publishing group Handelsblatt invested in wikifolio (“Crunchbase - Wikifolio,” 2017). Wikifolio is also cooperating with several online brokers: e.g., Comdirect, Consors Bank, and the Frankfurter Sparkasse. Besides L&S and cooperating brokers, wikifolio has several other partners that mainly provide current market information (e.g., Onvista, Finanztreff, Finanzen 100). The German business journal Handelsblatt is not only a shareholder but is also an important marketing and advertisement partner. Handelsblatt frequently publishes advertisements, and journalists manage portfolios on the platform and write about it in the newspaper. Especially during the start of wikifolio, the cooperation with Handelsblatt helped to quickly gain attention and prove reliability . Furthermore, and in contrast to many other social trading platforms, wikifolio communicates transparently about the functioning of the business model. Transparency is an integral part of the business strategy.

Platform operations: The use of the platform is basically free. Interested people can register and screen the available investment opportunities. Wikifolio has no restrictions for followers or signal providers. If a user wants to invest in trading strategies, the transaction is not executed over wikifolio. For every investable portfolio, the partner bank L&S issues open-end index certificates. Since the end of March 2017, wikifolio and L&S are also providing security for the issued certificates. To ensure that it is possible to pay back the value of the certificates even during periods of trouble, L&S makes deposits at other, independent banks (“Alle wikifolio-Zertifikate sind besichert,” n.d.). The deposits should reduce the counterparty credit risk, which is naturally encountered with index certificates. Before L&S issues certificates, the portfolios are run through a predefined certification process. For non-investable portfolios, no certificates are created, and investors have no possibility to invest money into them. The performance of the

Appendix 109 certificate precisely mirrors the performance of the portfolio on wikifolio. Trades of signal providers are automatically reflected in the value of the certificate. The signal provider who is managing the portfolio has no transaction costs for trades to change the composition of the portfolio, which makes dynamic portfolio management possible. Followers buy the certificate over a broker or a bank and thereby add the trading strategy of a signal provider to the private portfolio. In general, investors have no influence on the portfolio composition and can only buy or sell the certificate. Wikifolio provides a variety of performance indicators to evaluate the portfolios and to facilitate suitable investment decisions (e.g., performance over a variety of time horizons, maximum draw down, sharp ratio). Explanations of the performance indicators and a detailed description of the trading strategy and the ability to comment on the trading strategy should ensure a high transparency and make it possible to invest even with limited background knowledge. Every interested investor can participate either as follower or as signal provider. In the end, the crowd decides which portfolios become popular and investable. Wikifolio tries to ensure a high portfolio quality with sophisticated underlying trading strategies by reviewing every portfolio before it is published. When a portfolio is created, the investor has to choose whether an investment focus (instruments, regions) should be set or whether the entire investment universe should be used. The investment universe of wikifolio comprises 2,500 stocks, more than 1,000 exchange traded products (exchange traded funds, exchange traded commodities, and exchange traded notes) and about 95,000 structured products.29 After deciding on a trading approach, the user can start trading and apply for publication of the portfolio. Employees of wikifolio review whether the trading strategy and the executed transactions are consistent. During the first 21 days after the portfolio is published, at least ten investors with a total investment volume of 2,500 EUR have to express their interest. If not enough people signal their willingness to invest, the signal provider can apply again with a new strategy. If enough people have expressed their willingness to invest in the portfolio, L&S issues a certificate. Before the certificate is issued, the signal provider must be officially registered, checked (ID checked), and approved by wikifolio. The portfolios are classified according to several labels. In total, four different categories of labels exist: traded instruments, trading style, quality characteristics, and risk/return. The labels

29 The investment universe composes products of L&S, HSBC, Sociéte General and UBS.

Appendix 110 about traded instruments highlight portfolios with a geographical focus30 or a trading strategy that targets stocks with high dividends.31 Signal providers who invest at least 5,000 EUR into a portfolio are marked as “real-money portfolios”. A special type of portfolio is the so-called “Dachwikifolio.” In these portfolios, signal providers can combine different certificates of portfolios. The “Dachwikifolio” offers investors the opportunity to diversify their risk by combining different trading strategies. If the signal provider decides to close a portfolio, all investments are sold at the current market price. Afterwards, the followers have the opportunity to sell the certificate for the last quoted price. At the year-end, the certificate is closed and all open positions are liquidated. Followers automatically receive the money on their wikifolio account, if the position is not already sold. Another exceptional case are dividends, stock splits, spin-offs, and de-listings. For European standard stocks, usually 100% of the qualified dividends are transferred as virtual cash to a portfolio. For other stocks, up to 25% of the dividends might be deducted for various tax reasons. For U.S. stocks, no dividends are transferred due to an unclear jurisdictional situation.32 Stock splits or re-splits are automatically considered in the portfolio. In the case of spin-offs, knock-outs or de-listings, the value is booked as cash into the portfolio.

Fees: The business model of wikifolio has two revenue sources: fees for traded certificates and performance fees. The certificate fee is 0.95% p.a. and is calculated on a daily basis. The trader sets the performance fee in a range of 5 to 30%. The performance fee is based on a high- watermark principle and is applied when the portfolio reaches a new year high. The performance fee is split between the signal provider and wikifolio. The share of the performance fee for the signal provider depends on the asset under management: 10,000 EUR equals 30%, 50,000 EUR equals 40%, and 125,000 EUR equals 50%. For “Dachwikifolios” the performance fee is calculated based on the performance fees generate by the certificates composed in the portfolio. At the end of the year, the high-watermark is reset. The fees are automatically priced

30 For example, Germany: Over the last 12 weeks 60% of the portfolio value was invested in Germany. 31 Over the last 12 months at least 3% of the portfolio value resulted from dividends. 32 Since January 1st of 2017, the U.S. withholding tax also accounts for dividends on derivative financial products. So far no procedure to guarantee an accurate process can be worked out (“Steuerrechtliche Änderungen in den USA,” n.d.).

Appendix 111 into the portfolio values on a daily basis. When the trader sells the certificate, transaction fees are applied that depend on the conditions of the broker. Additionally, differences between the bid and ask price have to be taken into account.

Appendix D: Information exchange and trading with partners – wikifolio Source: www.wikifolio.com

United Signals

Background: United Signals is a Frankfurt based social trading platform that was founded in 2011. In contrast to other social trading platforms the signal providers at United Signals are selected and screened. The business vision is to become a trustworthy platform for investors by sharing the trading strategies of professional traders. Similar to wikifolio, United Signals offers users the possibility to invest into “strategy baskets” that combine multiple trading strategies. The platform of United Signals provides the service of an automated risk-management system, which should protect investors against extreme losses. The trades for signal providers and followers are executed over cooperating brokerage firms (Forex Capital Markets, Saxo Bank). United Signals is not directly regulated by the BaFin but operates under the juridical umbrella of HPM Hanseatische Portfoliomanagement GmbH as a BaFin regulated securities-trading company (Wertpapierhandelsunternehmen according to Section 31 WpHG).

Platform operations: A central aspect of the business idea is that signal providers have to run through a certification process before trading strategies can be shared. After fulfilling the prerequisites, the certification process starts with the first trade and lasts over 100 days. Traders who want to become signal providers have to invest private money in their trading strategy (I), have to be registered with their ID-card (II), and have to provide extensive information about the intended trading strategy (III). During the evaluation process, the trading approach, the execution of the trading strategy, the investor profile, additional information provided by the trader, and past performances are taken into account. Additionally, every position must have a stop-loss order. Concentration into a specific investment is limited and the maximum leverage is 25. If the trader fulfills the requirements and the executed trades fit to the intended trading strategy, the executive committee of United Signals certifies the portfolio. If problems or ambiguities occur, the 100-day certification period can be extended. After the certification process, the integrated

Appendix 112 software continuously monitors the portfolios to ensure that specified rules – for example, the maximum leverage – are not exceeded. Additionally, once a month, a strategy is analyzed more in depth to ensure a responsible trading approach. If United Signals has doubts about a portfolio, the certification can be detracted. The minimum amount to be invested by followers depends on the trading strategy and is calculated for every user individually. The trades are executed over the partner brokers, which means that the money of the investors stays in the private portfolio and is not transferred to United Signals. The use of the platform is generally free; only the transaction costs for the trades have to be paid. For investing into ETFs, a service fee of between 0.29-0.49% accrues, depending on the amount invested.

Fees: The main revenue of United Signals is based on returns generated by the users. The performance fee for every portfolio is 20%, of which the signal provider receives 15% and United Signals 5%. The fee is automatically deducted from the deposit of the follower. The performance fee is based on a high-water-mark principle and is charged only when the portfolio reaches a new high. Additionally, United Signals receives a share of the spread from the partner brokers. In contrast to other social trading platforms the spread on the brokerage platform is not increased and is comparable to a regular transaction.

Appendix E: Information exchange and trading with partners - United Signals Source: www.united-signals.com

ZuluTrade

Background: ZuluTrade was founded in 2006 and is one of the first companies to offer social trading services. The company is based in Greece and has a global customer base. The operations are supervised and regulated by the Greek supervisory review. Because ZuluTrade executes trades over partner brokers, customer deposits are not transferred to ZuluTrade. Currently, 14 brokers are quoted as partners on the homepage of ZuluTrade. The brokers must comply with rules and regulations in the countries of origin.

Appendix 113

Platform operations: The investment universe on ZuluTrade depends on the instruments available at the partner brokers. Before being able to copy investment strategies, investors must create an account or use an existing account at one of the cooperating brokers. AAAFx is the most important partner and is closely connected to ZuluTrade. The social trading accounts on ZuluTrade are directly linked to the partner brokers. Signal providers manage their trading strategies over the homepage of ZuluTrade. The software automatically captures executed trades and transmits the signals to the connected brokerage accounts of the followers. ZuluTrade sets a high focus on the development of a well-functioning software, which makes it easy to embed partner brokers. The social trading platform also enables users to interact easily with signal providers and other followers. The interaction and the discussion with other users is an important aspect on ZuluTrade. The platform provides tools and mechanisms to aggregate the current opinions and to make trading decisions based on assessments of the community (e.g., social charts, which show trading decisions of the community and help to identify trends). Additionally, signal providers are continually ranked according to a variety of criteria, such as amount of trading activity, drawdown of each trade, activity of the trader, time of open trading positions, and age of the trader. Followers on ZuluTrade can define security rules for every signal provider in which they are invested. For example, in case of a predefined maximum loss, the signals of a portfolio manager are no longer mirrored. Similarly, the maximum number of open positions can be restricted and in general is not allowed to exceed more than 30. The rules should limit the risk of losses for followers. Signal providers in contrast often use demo accounts without investing private money into the trading strategy and therefore do not encounter any losses. Signal providers are incentivized by receiving a share of the spread of every trade executed by the followers. The incentive structure basically honors the number of trades executed by receiving a share of the trading volume or a fixed payment, which again depends on the trading volume. In addition, ZuluTrade also has an affiliate program that honors people who successfully promote the homepage.

Appendix 114

Fees: The business model of ZuluTrade is based solely on extended spreads for the executed trades. In comparison to exchange markets, the spreads are slightly larger. From an investor perspective, no additional costs accrue for using the social trading platform.

Appendix F: Information exchange and trading with partners – ZuluTrade Source: www.zulutrade.com

Sharewise

Background: Sharewise was founded in 2007 in Munich and was acquired by the Japanese company Minkabu in 2012 (Hüsing, 2013). The business activities of Minkabu are focused on social media services around stock markets and other financial information. Similar to news homepages, Sharewise provides a variety of information about markets, stocks, and current trends. The difference is that market assessments, buy recommendations, and price targets are based on the opinions of the users.

Platform operations: Registered users can express expectations about the future development of share prices, indices or currencies. Based on the assessments of every user, Sharewise creates a ranking about the accuracy of the forecasts. After having given a minimum of five recommendations, users can create a theoretical portfolio. Sharewise creates a ranking according to the portfolio performance of the users. The index Stoxx Europe 600 is the benchmark for every user portfolio. Users who beat the benchmark have a skill level above zero and are therefore more highly ranked. Besides the performance, the social aspect on Sharewise can also influence the ranking of users. Similar to classical social media homepages, users can write posts or share content. For example, if a highly-ranked user recommends a trade, it can be justified with a personal assessment. Other users can comment or share the information. People who are popular in the community due to their recommendations and market assessments are honored with a higher ranking. Additionally, a forum offers the possibility to discuss certain topics in more detail. Sharewise uses these multiple sources of information to provide interested people with assessments of the current market situation. Based on the assessment of the community

Appendix 115 sell, hold or buy recommendations are given and target share prices are calculated. Sharewise tries to motivate highly ranked users by providing them with benefits such as free market letters or participation in raffles.

Fees: In contrast to other social trading platforms, Sharewise does not receive any revenue from trades or management fees. Until 2015, the information generated on Sharewise was used to create a fund based on the investment recommendations of the community. The low performance and conservative demand caused the termination of the fund. The primary revenue source of Sharewise is from white-label sales of the community software developed and used on the online platform. Other financial institutions buy the software to support and improve their own online activities. In addition, Sharewise regularly publishes and sells market letters based on the market evaluation of the most successful users.

Appendix G: Information exchange – Sharewise Source: www.sharewise.com

Category of Labels Labels Description

In the last twelve weeks, on average at least 60 percent of the total portfolio value was invested in securities Focus from Germany. Days on which only virtual cash was Germany held were not considered. The number of days on which investments were made must be at least 15.

During the last twelve weeks, at least 60 percent of the portfolio’s total value was invested in securities from Focus Europe Europe on average. Days on which only virtual cash was held were not considered. The number of days on which Traded Instruments investments were made must be at least 15.

During the last twelve weeks, at least 60 percent of the portfolio total value was invested in securities from the Focus USA US on average. Days on which only virtual cash was held were not considered. The number of days on which investments were made must be at least 15.

At least three percent of the total portfolio value has Dividend been generated from dividend payments (at least five) strategy within the last twelve months.

Appendix 116

Trades the All securities available on wikifolio.com can be traded entire universe in this portfolio.

Trades Leverage products can be traded in this portfolio. leverage products

In the last six weeks, at least ten different securities were Actively always contained in the portfolio. None of these ten diversified securities exceeded 20 percent of the total portfolio value in this period.

In the last seven weeks, at least seven times the total Heavy trader value of the current portfolio was traded in the portfolio (buys and sells).

Trading style During the last 24 months, less than the total value of the current portfolio was traded in the portfolio, as counted per six-month intervals. The initial building up of the portfolio is excluded. The minimum period of time for Middle to long the calculation is 4 months. Example: The portfolio has term been in existence for nine months. In this period, the portfolio traders would have been allowed to trade 2.5 times the current portfolio volume (1.5 for the 9 months plus 1 for the initial building up of the portfolio).

Only young portfolio whose first issuance dates back to less than five months ago get this designation. Within Rising Star these five months, at least 500 portfolio certificates were bought as part of at least ten orders. The performance since creation must be above 25%.

Within the last six months, the “performance 1 month Outperformer has always been among the top 50% of all portfolio that are visible on the platform. Quality Within the last six months, the portfolio was among the characteristics Top ten trader top ten of the top portfolio ranking for at least 100 days.

Good In the last three weeks, at least five comments were communicator published in the portfolio.

Regular In nine of the last twelve weeks, the portfolio trader has activity logged in at least four times a week on wikifolio.com.

Bestseller Within the last two weeks, the portfolio certificate belonging to the portfolio was among the 25 best-selling

Appendix 117

portfolio certificates (number of buy orders). In addition, the portfolio certificate was bought more often than sold.

Within the last 24 months, at least 15 buy orders were executed for the portfolio certificate belonging to the Loyal investors portfolio. The number of sales did not exceed 35 percent in this period – considering all transactions in this portfolio certificate.

Since the first issuance, there is currently a positive Frequently balance of at least 25 plus buy orders between buys and bought sells in the portfolio certificate belonging to the portfolio.

Within the past 12 months, the performance of the portfolio has been higher than 40 percent. The average monthly performance of the portfolio has been higher High than 4 percent in the last six months, and higher than 8 performance percent in the last two months. To receive this label, a portfolio needs to be in status "Published" or "Investable" for at least two months.

No open or closed trade in the portfolio has so far caused a loss greater than 8 percent based on the Good money portfolio size. During the past 6–24 months, the average Risk /Return manager monthly performance has been above 0.3 percent, and at least 35 sell trades were carried out in the portfolio. In addition, the maximum loss must not exceed 20 percent.

In 60 percent of the months since the creation of the portfolio, the portfolio has achieved a positive performance. The overall performance has averaged at Continuous least 1 percent per month. The maximum loss has (so growth far) never been higher than 12 percent over the entire period under review. The minimum period of time for the calculation is 4 months. Appendix H: Explanation wikifolio labels Source: “Knowledge for investors – wikifolio labels,” n.d.