What Influences May Robo-Advisors Have on the Service of Professional Financial Consultants and the Financial Industry?

Master's Thesis, 2017

133 Pages, Grade: 2,0


Table of Contents

List of figures

List of tables

List of abbreviations

1. Introduction
1.1 Context and purpose of the study
1.2 Significance of the study
1.3 Research question and objectives
1.4 Structure of the research project

2. Critical review of literature
2.1 Introduction and general comments
2.2 Definition of the term Fintech and Robo-Advisor
2.3 Target audience of Robo-Advisors
2.4 Investing with Robo-Advisors
2.4.1 Questionnaire
2.4.2 The common investment model via ETFs
2.4.3 Diversification
2.4.4 Rebalancing
2.4.5 Cost of Robo-Advisor
2.4.6 Service of Robo-Advisors
2.5 Investment behaviour
2.5.1 Typical mistakes in investing
2.5.2 Investment behaviour during stock crisis
2.6 Professional consultants and their services
2.6.1 Abilities and services of human advisors
2.6.2 Disadvantages of human advisors
2.7 Conclusion of the literature review

3. Methodology
3.1 Research Paradigm and Methodology
3.2 Research design and method
3.3 Expert interviews
3.3.1 Selection of candidates
3.3.2 Type of interview and data collection
3.4 Online questionnaire about Robo-Advisor
3.4.1 Reasons for the questionnaire
3.4.2 General facts
3.4.3 Structure and content
3.5 Ethical considerations

4. Findings
4.1 Outcomes through the expert interviews
4.2 Outcomes through the online questionnaire

5. Analysis and discussion
5.1 Limitations of the primary data collection methods
5.2 Chances in the future
5.2.1 Market potential
5.2.2 Technical development potential
5.2.3 Cost and its impact
5.3 Possible risks
5.3.1 From start-ups to global players
5.3.2 Competition and profits
5.4 Robo-Advisors in crisis
5.4.1 What Behavioural Economics tells us
5.4.2 The fight against customer loss
5.4.3 Risk or opportunity
5.5 Robo-Advisors and professional consultants

6. Conclusions and recommendations
6.1 Conclusion of the main findings
6.2 Limitations of the research project
6.3 Recommendations and future research

7. Reflection on learning
7.1 Concrete experience - What was learned
7.2 Reflective Observation - How learning took place
7. 3 Abstract learning concept and generalization
7.4 Testing the implications in the workplace

8. References

Appendix A: Keywords for the CLR

Appendix B: Expert Interview Questions

Appendix C: Numbers of Questionnaire Answers

Appendix D: Questionnaire in English

Appendix E: Ethical information sheet and consent form

Appendix F: Transcript of the Interviews

Appendix G: Quantitative analysis of the interviews

Appendix H: Outcomes of the online questionnaire

Appendix I. Current and future Robo-Advice capabilities


Figure 1 - Expected growth of AuM in trillion dollars of Robo-Advisors in the US .

Figure 2 - Possible services of RA

Figure 3 - Important factors for investing

Figure 4 - Growth of ETFs in numbers and AuM

Figure 5 - The effect of rebalancing

Figure 6 - Research Onion

Figure 7 - Stages to make a questionnaire reliable

Figure 8 - Impact of cost on the investment

Figure 9 - Global map of RA

Figure 10 - Dimensional fund flows

Figure 11 - Balance of risk or opportunity

Figure 12 - Kolb’s experimental learning cycle

Figure 13 - Learning Style Inventory


Table 1 - Costs of Robo-Advisors

Table 2 - Comparison of market, fund and investor returns for the US and UK market

Table 3 - Participants of the expert interviews


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1.1 Context and purpose of the study

Six years ago the large and multifaceted market of financial advisory services was based on the services provided by humans, experts and specialised in specific fields such as real estate, insurance, taxes, pensions and wealth management (Deloitte, 2015a).

Financial advisors have not always had a good reputation but they were necessary to oversee the large, globalized and complicated financial market (Schuster, 2013). This research project is going to examine and evaluate a new phenomenon of the financial advisory services market, the so called RoboAdvisors (RA). They represent the next stage of the evolution in the financial advisory and wealth management industry.

RA are a new type of financial adviser or portfolio manager, which work solely online and with minimal human intervention and they stepped right into competition with human wealth managers. In a generation that grew up with the internet, computers and mobile devices, RA have the potential to start their triumph procession making many jobs of professional human financial consultants obsolete (Deloitte, 2015a).

"There was the idea that we must be bad for human advisors," said Jon Stein, CEO of Betterment (O’Brien, 2015) when finding one of the first RA, that are able to make investment and financial advice available for the mass market (EY, 2015).

The main objective of this research project is to sketch an overview of possibilities and disadvantages of RA in the future and give an outline to the academic research that will be needed in the future.

This thesis is one of the first academic papers in English language, examining RA and their possible impact on the industry. As a field study, it delivers a broad overview of a large amount of topics describing the various characteristics of a fully automatic asset management system, and the competitive situation with human consultants or asset managers.

1.2 Significance of the study

Within a relatively short period of time, RA became a global phenomenon in the financial advisory and management industry. The market share and Assets under Management (AuM) of RA are increasingly growing and the question comes up, if or in which way they might change the financial advisory landscape (Weissbluth, 2014).

The dramatic changes in the near history in the music and print industry could be comparable to future developments of the financial advisory industry, which had a global market size of about $70bn (Statista, 2017) in 2016. RA could have a global impact and change our mind-set and the way investment management or financial consulting is seen today (Dapp, 2014; O’Brien, 2015). Deloitte (2015b; 2016) calculated a growth rate of 65.2% for the eleven leading RA in the USA just in 2014.

The future growth rates depend strongly on the market but it is without saying, that there are not many industries looking at such possible increasing rates like the new RA industry. O’Keefe, Warmund and Lewis (2016) from KPMG and Regan (2015) from Bloomberg estimate a growth from $0.3 to $2.2 trillion, which equates a compound annual growth rate (CAGR) of 68% for the US market until 2020 (see Figure 1).

Figure 1 - Expected growth of AuM in trillion dollars of Robo-Advisors in the US

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From a global perspective, Kocianski (2016) from the Business Insider estimates an even stronger growth of 152% p.a. from 2016 to 2020. In general terms in can be concluded, that nobody seriously assumes that RA are going to be a shortterm event (Tracy et al., 2015).

1.3 Research question and objectives

The following research question builds the fundamentals of the research (Saunders, Lewis and Thornhill, 2009; Robson, 2011). What influences may Robo-Advisors have on the service of professional financial consultants and the financial industry?

The aim of this research project is therefore to evaluate the RA potential and the impact on human financial consultants and wealth managers.

This study is based on four Research Objectives.

I. Examining the capabilities and development potentials of Robo-Advisors in the wealth management sector.
II. Showing the differences between Robo-Advisors and professional consultants focusing on service, investment approach and psychology.
III. Exploring the functionality of Robo-Advisers in a period of economic depression.
IV. Evaluating the opportunities and threats of Robo-Advisors from both: the clients' and the financial advisors' perspective.

1.4 Structure of the research project

This research project is divided into seven main chapters. Regarding the research questions, the current knowledge of RA, human advisors or consultants (HA) and investment behaviour is analysed and critically reviewed in Chapter 2. This chapter also represents the theoretical basis for the chapters to follow and to answer the research objectives.

In Chapter 3, the two applied research methods as well as the methodologies are explained and justified.

Chapter 4 shows the key findings of the primary research. In the next Chapter these findings are triangulated with the findings of the secondary data during the critical literature review in a broad and detailed discussion.

Chapter 6 concludes the thesis and provides recommendations for further research. The thesis is completed with the author’s learning outcomes and analysed via a self-reflection in Chapter 7.


2.1 Introduction and general comments

The phenomenon of Robo-Advisors, as we know them today is known to the public for less than a decade (Savitz, 2012; Stein; 2016). Therefore, it is not surprising that the amount of academic literature is very limited. RA are not an indepth researched topic yet.

On the one hand, in the times of rising RA there are almost no financial papers published nowadays that do not mention RA. On the other hand it seems like new inventions get analysed by companies first with the intention to evaluate their possible future and profitability; just much later by universities. Any researches of big banks, consultant companies, RA themselves and wealth management companies need to be questioned critically, as they could lose objectivity easily. In addition, Franke et al. write (2011) that there are also very few academic papers or data regarding HA, in particular fee-only consultants. To get a sufficient academic basic for the research project, the literature review was not bounded to specific topics or even single literature sources but spreads through all possible sources like academic articles, journals, newspaper, books, online articles and even videos. Yet it becomes clear quickly, that the topic of RA and the answers on the research questions are more complex and extensive than it seemed at first sight. In this chapter is examined what a RA are and it is also defined what this term stands for in detail in the course of this paper. The focus is put on their capabilities, their functionality and costs. For a better understanding of RA and their functionality we have a look on ETFs (Exchange Traded Funds) and other investment vehicles next to investment behaviour and critical investment mistakes in order to evaluate future collaboration between investors and RA in a potential financial crisis. Finally, there is a description of the work of human advisors and their comparability to RA.

This chapter is a main point to evaluate the possible conflict of the work of RA and consultants and to answer the research question. Key words for each chapter can be found in Appendix A.

During this research project there are little aspects regarding juristic limitations and future laws related to RA, as future restrictions cannot be foreseen yet. Nevertheless the call for more control gets louder (Woolard, 2016; Novick et al., 2016; Bundesbank, 2017). In addition, restrictions are likely to increase for HA as well with the time and therefore the regulatory aspect is taken as less relevant to answer the research question.

2.2 Definition of the term Fintech and Robo-Advisor

RA provide a cheap service of investment advice and management. Based on algorithms, they are fully autonomic with limited human intervention is needed and avoids conflicts of interest (Lieber, 2014; Fein, 2015; Sinha, 2016; Scherer, 2016).

They are one part of the so called Fintechs, a word combination of financial and technic (Dapp, 2014; Dorfleitner & Hornuf, 2016; Dhar & Stein, 2016b). After the outbreak of the real estate crisis in the USA in 2008-2009 and the bankruptcy of the investment bank Lehman Brothers, the stock markets crashed and the main stock indices of the world dropped sharply (Naudé, 2009; Erkens et al., 2012). The bad performance of actively managed funds supported the increase of awareness for passive and cheaper investment funds, publicly known as ETFs (Gastineau, 2001; Deville, 2008; Gastineau, 2008; Malkiel, 2010; Malkiel, 2016). The majority of RA uses ETFs to build their worldwide diversified portfolios. ETFs will be defined and described in Chapter 2.4.2.

The possibilities, promises and services offered by RA are already various as shown in Figure 2.

Figure 2 - Possible services of RA

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Even if the term “Robo-Adviser” appeared the first time in 2002, Richard J. Koreto (2002) was more looking into the future of RA than giving any facts. The first RA were Wealthfront and Betterment, both founded in the USA during the financial crisis in 2008 (Savitz, 2012; Stein; 2016b).

As RA has become a widely used term, with hundreds of different suppliers and services, already differing between normal RA (Sinha, 2016) and hybrid models, the term needs to be more specifically defined for the research project:

A Robo-Advisor is a company which provides digitalized and fully automatic asset management via ETF portfolios. The face to face contact to a human advisor is not planned and maximum possible via an extra fee. The automatized management of assets is the priority, financial advice is a minor matter.

The focus of this thesis is on fully automatic asset management, which works as defined by Sinha (2016). This works with algorithm based adjustments and rebalancing methods in line with a pre-defined and fixed strategy. As 80% of RA in the USA, EU and UK are working with this system, this kind of RA is the most significant group (Deloitte, 2016; Deloitte, 2017)

2.3 Target audience of Robo-Advisors

The declared aim of RA is to provide wealth management services to people who do not have enough assets available for a professional asset management via fee-only consultants or private banks (Lieber, 2014). These are based on Directto-Consumer platforms (James, 2016). More about their clients and target group will be described in Chapter 2.6.

As the CEO of Betterment, John Stein, said, there is often a “natural resistance to change” (O’Brien, 2015) as a hint towards their competitive situation to classical advisors and asset managers. When founding the first RA the assumption was that the target group will consist out of young people, the so called millennials, earning the first money but not wealthy enough yet to pay an independent financial advisor (Lieber, 2014; Deloitte, 2015b; Ma, Nahal and Tran, 2015). This is supported by the argument made by Betterment not asking for a minimum deposit so everybody can start saving (Betterment, 2017a). Therefore, it was surprising to see a relatively high average age of investors in RA. The first analysis of the customer base showed Betterment that even 20% of the assets were from clients over the age of 50 (Lieber, 2014). On the other hand, at Wealthfront, 90% of the accounts above $21,000 come from clients younger than 50 years and 60% of this client group was even younger than 35 years (O’Brien, 2015). Another dimension illustrates that the average RA investor is much wealthier than it could be expected. According to EY (2015), the main customer base are millennials with an average account size between $20,000 and $100,000. O’Brien (2015) states that the average customer earns $150,000 a year.

2.4 Investing with Robo-Advisors

Most of the RA commonly invest via ETFs, globally diversified in thousands of different companies and rebalance the portfolio on regular basis. This functionality is described in more detail in the following chapters. Some RA have different structures such as Liqid, a German RA which applies a hybrid strategy. Originated out of a family office, they offer not just ETFs but also several other asset strategies accompanied by professional consultants. Others specialise in retirement services, such as Wealth Wizards in the UK or bloom in the US.

As shown above in Figure 2, the possibilities of RA are already very multifaceted. For RA as well as for financial consultants, the most important information to know about their clients is the time horizon the money should be invested, the volatility and risk clients are willing to accept, the assets available as well as the financial situation of the client and the desired return (Fiegenbaum and Thomas, 1988; Malkiel, 2016; Wealthfront, 2017b) (see Figure 3).

Figure 3 - Important factors for investing

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2.4.1 Questionnaire

To gather this information, RA use questionnaires in the beginning of the account opening process. Questions are regarding

- the reason for investing, - amount of investment, - goals of investing, - age,
- personal income and assets available,
- the time the money should stay invested,
- the volatility and maximum loss an investor is ready to accept, - personal investment experience and
- personal reaction if the share prices decrease significantly.1

This process is the same for all RA offering asset management, the questions differ slightly and depend on the exact investment approach of each system. These questions can be seen critically as they do not elicit complete information about the client and their financial situation (Fein, 2015).

After answering the questions, the intended client receives a proposal for a portfolio consisting of several ETFs, investing in both shares and bonds.

2.4.2 The common investment model via ETFs

The concept of ETFs was founded 1970 by John Bogle, founder of Vanguard, the biggest supplier of funds and the second biggest asset manager in the world following Blackrock (Kremer, 2017).

ETFs, often called index funds, are listed on stock exchanges. They can be traded intraday, are available for several asset classes and stand for a low cost but widely diversified portfolio (Gastineau, 2001; Kostovetsky, 2003; Gastineau, 2008; Kosev & Williams, 2011; Schuster, 2013). The idea of ETFs is to deliver investors the return of a benchmark, e.g. an equity index (Kosev & Williams, 2011). The reason behind this approach is the insight that “no one person or institution consistently knows more than the market” (Malkiel, 2016; p. 105). This is a very controversial statement as the majority of the financial advisory industry believes that they perform better than the market, otherwise they would lose their right to exist (Lance, Purves and Teahan, 2017).

Another very controversial discussed topic is the Efficient Market Theory (EMT) which states that markets are unpredictable and not able to outperform in the long term (Fama, 1998; Fama, 1991; Fama & French, 2002; Bogle, 2008; Fama and French, 2010; Malkiel & Ellis, 2010; Nobelprice.org, 2013; Malkiel, 2016). Others criticise the EMT and mention investment behaviour and imperfections in every market (Shiller, 2003) and argue that it is possible to beat the market, e.g. through trend following models (Covel, 2009). The presumably never ending discussion is probably impossible to answer (Mishkin and Eakins, 2012; Lance, Purves and Teahan, 2017). This thesis does not claim to answer these questions neither but focuses on ETFs as the main investment vehicle for RA. These are one of the biggest inventions in finance in the last century and are becoming increasingly more important as shown in Figure 4.

Figure 4 - Growth of ETFs in numbers and AuM

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ETFs can be differentiated between physical and synthetic replicated. Physical ones hold the assets as underlying, while synthetic ones replicate the index via products called SWAPs (Kosev & Williams, 2011). Thanks to their simple structure, they are much less expensive than normal funds managed by a company and a fund manager (Malkiel & Ellis, 2010; Malkiel, 2016). Comparing ETFs with actively managed funds, we find higher trading costs and mismatches in timing However, these tracking errors are impossible to avoid (Kostovetsky, 2003). EY (2015) add a critical comment, which leads to Research Objective III and the fact that the robustness of the investment model in combination with RA has never been tested in a significant market downturn.

2.4.3 Diversification

Diversification is a major part of investing and often misinterpreted within private investors (Jordan and Miller, 2009). Their way of diversification is explained in more detail in Chapter 2.5.1. Broad diversification is essential to diversify risk in order to protect the investment from heavy losses (Malkiel and Ellis, 2010; Jordan and Miller, 2009). In line with the Capital Asset Pricing Model, diversification aims to neutralize the diversifiable risk and the non-diversifiable risk gets paid through the market return (Nawrocki, 1997). The major aim is to build an efficient portfolio that generates returns, which are in proportion to the risks (Banks, 2007). Good diversification includes at least:

- Securities,
- asset classes and
- geography (Wealthfront, 2017b).

The exact asset allocation of each RA cannot be evaluated due to different strategies and slightly different investment approaches. All RA invest worldwide to diversify the risk, avoid home-bias and to capture growth from all over the world (Scalable, 2016; Betterment, 2017b). The primary pattern follows the approach of ETFs, i.e. replicating the index regarding the market capitalization. The global market capitalization of the equity and bond market, can be seen in the Dimensional Matrix Book (Dimensional, 2016). But as the exact market capitalization differs depending on several factors, the fact sheet of the MSCI All- County Word Index (MSCI ACWI, 2017) shows a slightly different allocation which e.g. Betterment uses (Betterment, 2017b). As RA are transparent in their investment decisions (EY, 2015), each website will show in which markets the investments go to, sometimes there are even the ISINs mentioned (quirion, 2017b; fintego, 2017).

2.4.4 Rebalancing

Every RA found during the research were all rebalancing. Rebalancing is the mechanism to restore the original share/bond ratio after significant value changes. Rebalancing is not a tool to increase profits but to reduce risks in the first place (Jaconetti et al., 2010; Barber and Odean, 2000; Charles Schwab, 2017a; Jordan and Miller, 2009). Private investors usually increase risk over time and get more adventurous with growing wealth, which is shown in an evaluation done in Sweden (Calvet et al., 2009). Even if it is a relatively small market, it gets supported by several authors who mention that investors are actively mismanaging their portfolio over time (Shefrin and Statman, 1985; Kahnemann and Tversky, 1979; Shefrin, 2001; Schuster, 2013; Malkiel, 2016). An example how rebalancing works is shown in Figure 5, where the shares increase in value until they have 60% of the value of the portfolio. Shares are known for having a higher volatility and risk than bonds. The investor has now a higher risk in his original risk profile. Therefore, parts of the shares will be sold and invested into bonds to rebalance the investment again.

Figure 5 - The effect of rebalancing

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2.4.5 Cost of Robo-Advisor

One of the main and most obvious unique selling points (USP) of RA is cost. It goes without saying that the cost of investing with RA is very low (EY, 2015; Novick et al., 2016; Sinha, 2016).

The following Table 1 - Costs of Robo-Advisors shows the cost of some of the biggest RA in the US, Germany and Great Britain when investing up to 100,000 in the associated currency2. It is important to mention, that the services are evolving and RA are continuing to adjust their strategies, the following tabular is a snapshot of some major RA regarding AuM in early 2017.

Table 1 - Costs of Robo-Advisors

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In general, the cost consist of standard fees with some additional expenses for the trading and the ETF. The additional costs are about 0.15 basis points (bps) in the US and due to higher ETF cost, up to 0.3 bps in Europe (Cremers et al., 2013; Wealthfront, 2017c; Scalable, 2017c). Important for RA is the transparency of the fees and the absolute prevention of hidden cost. The RA in the US are significantly less costly than in Europe. One reason behind is their slightly different business model. One example is Schwab Intelligent Portfolios where they offer the RA service for free and charge just the transactional and product costs, which is possible due to their revenue of the remaining business with their own ETFs (Schwab Intelligent Portfolios, 2017c). Some businesses in the US have a much stronger financial backing than the counterparts in Europe (Statista, 2017b), whereby some RA have special offers such as Wealthfront that manages the first $10,000 for free. Others charge a flat rate with no trading or product costs (Wealthfront, 2017c).

Besides the cost, it can be seen that RA try to diversify their services within the industry. As already mentioned, Betterment is using the USP in the US by requesting no minimum deposit (Betterment, 2017a).

2.4.6 Service of Robo-Advisors

In the chapters above, it became clear what RA are about. They invest and manage the assets of their clients through worldwide diversification, rebalancing and tax harvesting methods etc. (Sinha, 2016). They send out newsletters and invite their clients to join webinars to learn more about the financial markets and the investment methodology. There are two types of RA, the classic model or the hybrid RA, where human advisors are available via phone, videoconference or even in person. Vanguard or Liqid are to mention here as examples for hybrid RA (Deloitte, 2015b). But due to an increasing competition, standard RA look for additional value for their clients and offer consulting services via phone or video as well. As a result, the differences between normal and hybrid RA are becoming more and more indistinct.

Nevertheless, the service is the main weakness of RA in comparison to HA. Fein (2015) mentioned that there is no human interaction or emotional connection between a RA and a client. Regarding the irrational behaviour of investors and the panic that can occur during stock crashes (Cohen, 1997), the service of RA may play a significant role in their future and thus will be evaluated and compared with HA throughout the thesis.

2.5 Investment behaviour

Behavioural finance or investment behaviour is a science, which does not describe how traders and investors should invest, but describes the way they actually do (Shiller, 2003; Welfens, 2007). Campbell (2006) found another term to describe this phenomenon of private investors and called it “Household Finance”. The science clearly shows that the long term return of an average investor is significantly lower than the market performance (Malkiel, 1995; Barber & Odean, 2000; Anderson, 2007; Jordan and Miller, 2009; Barber et al., 2009; Schuster, 2013; Dalbar, 2016). The outcomes are shown by Schuster (2013) and Anderson (2017).

Even if Anderson’s (2007) timeline goes for just four years and the timeline of Schuster’s (2013) seven years, the results are very robust and not questioned. The same effect is analysed for a longer time frame by Bogle (2005) for the US market as the biggest fund market in the world and by Schneider (2007) for the UK market as the third biggest fund industry market in the world (see Table 2).

Table 2 - Comparison of market, fund and investor returns for the US and UK market Referring to Malkiel and Ellis (2010), even mutual funds managed by professionals are not able to beat the market in the long term.

The reasons for the underperformance are explained by investment behaviour and occasionally by several mistakes during investing. The most typical mistakes during investing are described in the following chapter. The questions might raise in what extent private investors are able to participate in the advantages of the global financial markets, and in particular, if RA or HA are able to increase the long-term return of investment for their clients.

2.5.1 Typical mistakes in investing

To analyse individual investor behaviour and question the assumption that people are not acting rational in the financial market, from a micro perspective, researchers analysed several behavioural biases. Some well-known examples, which contradict rational investors are, the portfolio under-diversification (Benartzi & Thaler, 2001; Welfens, 2007; Goetzmann & Kumar, 2008; Jordan and Miller, 2009), the gender bias (Barber & Odean, 2001; Jordan and Miller, 2009), the home bias (Coval & Moskovitz, 1999; Schuster, 2013), excessive trading (Odean, 1999; Barber & Odean, 2000; Welfens, 2007; Jordan and Miller, 2009; Malkiel, 2016), overconfidence (Barber & Odean, 1999; Ritter, 2003; Shiller, 2003; Jordan and Miller, 2009; Malkiel, 2016), the disposition effect (Shefrin & Statman, 1985; Ritter, 2003; Campbell, 2006; Welfens, 2007; Stuber, 2011) and the prospect theory by Kahneman and Tversky (1997). These typical investment mistakes are broadly analysed and their existence and significance are no longer questioned throughout in the academic world. During this research, no author stated that private investors are acting rational. These investment mistakes are so evident and strong that investors do not usually learn from their mistakes when they gain experience (Koestner, Meyer and Hackethal, 2012).

Under-diversification - Next to Polkonichenko (2005) and Goethmann and Kumar (2008) for the US households, Welfens (2007) analysed the diversification behaviour of private investors. Even if their observation period is only between 6 to 15 years, a single paper could not be found describing private investors having a well-diversified portfolio. The main reason for under-diversification is the home- bias.

Home-bias - This term describes the behaviour of investors to overweight shares or companies from the market in which they live. A study by French and Poterba (1991) reveals that US investors allocate almost 94 percent of their assets to domestic securities. Coval and Moskowitz (1999) mention different reasons, for instance that investors simply feel more comfortable about local companies and which they feel, gives them an advantage in information.

Overconfidence - This plays a major role regarding all the other investment mistakes. According to Jordan and Miller (2009), the strong belief in their personal abilities leads to excessive trading, home-bias and under diversification. Malkiel (2016) shows that overconfidence is not just a phenomenon in investing but it is much stronger among men.

Gender-bias - Is the opinion that investing is generally a ‘male thing’. Barber and Odean (2001) show that men are more self-confident, trade 45% more than woman but have lower returns.

Excessive trading - Strong correlation between risk-orientated traders, the amount of trades and a declining performance over time (Odean, 1999; Barber & Odean, 2000; Schuster, 2013). Malkiel (2016) as well as Jordan and Miller (2009) both argue that the more an investor trades, the worse the results get. This is completely irrational, as an increasing risk should be paid by the market in terms of a higher return.

Disposition effect - According to Welfens (2007), the disposition effect was mentioned the first time in a famous paper by Shefrin and Statman (1985) and describes that investors “sell winners too early and ride losers too long” (p. 777). This behaviour is commonly explained by mental accounting (Thaler, 1985) in combination with the prospect theory of Kahnemann and Tversky (1979). This is supported by Stuber (2011) who stated, that the prospect theory by Kahnemann and Tversky is probably the most accepted theory in behavioural finance.

It is an expensive bias (Odean, 1998a) and one of the most robust findings in behavioural finance (Welfens, 2007).

2.5.2 Investment behaviour during stock crisis

It is not possible to describe investment behaviour during stock crises easily and with just a few words, as humans are very diverse and react very differently.

Nevertheless, it is important to have a look at this topic as RA have never seen a significant stock crisis (Fein, 2015), which may have the capability to stop the new development. Regarding the financial crisis in 2008, Teixeira (2011) describes “significant events of financial instability, which included a loss of confidence in the soundness of European banks, bank-runs and the prospect of failure of cross-border and domestic financial institutions” (page 9).

Regarding single investors, Cohen (1997) describes that even rational investors became irrational, latest with the panic in the markets. She mentions the large crisis between May 1972 and January 1975 when the share prices in the US fell by 60-75%. In 1929, during the black Thursday, we saw that enormous orders were to sell but no buyers existed (ibid, 1997). In the bear market during 2008- 2009, the S&P 500 lost approximately 50% of its value.

According to Shiller (2003), investors become more irrational and try to predict the future market even more by using, e.g. historical movements, if there is panic in the market.

Cohen (1997) describes a survey made by Robert Shiller in 1987, where he sent a questionnaire to 2000 private and 1000 institutional investors. The responses show that 20.3% of private investors and 43.1% of institutional investors suffered panic symptoms like sweaty palms, irrationality, poor concentration, chest pain and a high pulse.

Li and Yu (2012) argue that the actions of investors are increasingly more motivated by psychological factors around the peaks and bottoms of an index. Hoffmann, Post and Pennings (2013) paint a different picture of financial behaviour. They describe that the observed investors do not sell in panic but even use the lower share prices to increase their position.

The interpretation can be different but it shows that the questions about investors’ behaviour is not easy to answer and that the reaction might depend on several factors which need to be taken into consideration.

Referring to Allen (2001), there has not been much attention by academic or financial literature about advisory institutions and their behaviour during financial crisis, which is surprising as it is a billion dollar market (Statista, 2017) and used by a significant percentage of the population in the Western world (Schuster, 2013). Furthermore, Allen (2001) states that financial investors seem to have a significant impact on the decision making of their clients.

The question, which needs to be discussed is, how investors of RA will react in a crisis, especially since there are no professional contact persons available.

2.6 Professional consultants and their services

Human financial advisors are advising their clients regarding financial situations, e.g. pension, capital market investments, insurances etc. In this thesis, the attention is on professionals advising related to asset management and stock market investments and not on other financial products.

A target group for professional consultants cannot be defined easily. Theoretically everybody could be somebody with the need for financial advice, regardless of age, gender, profession etc. There are differentiations invented by the consulting industry itself regarding the amount of assets invested. Some smaller assets are managed by banks, big funds and consultants paid through commissions. Wealthier people have the additional possibility of private banks, asset management companies. The last group are the so called ‘UHNWI’ (Ultra-High- Net-Worth-Individuals) (Henley, 2014; EY, 2015; Sinha, 2016). This group has all possibilities from private consultants to family offices. Taking the last group out of consideration, as they are an insignificant small group of people and not investing major parts of their assets via RA yet, the focus is on consultants for assets in a range from 5,000€ to several million €.

Generally, the academic knowledge about financial advisors, their work and their benefits for clients is very limited (Stuber, 2011; Franke et al., 2011)

2.6.1 Abilities and services of human advisors

It is without question, that due to the currently limited amount of technical possibilities, RA cannot be described as a perfect substitute for human advisors yet. Fein (2015) mentions the possible emotional connection between the advisor and the client. Ludden, Thompson and Mohsin (2015) believe that the personal connection between client and advisor is essential and will remain essential. In their research, 81% of the clients said that face-to-face interaction is important to them. Fein (2015) mentions the advisors ability to evaluate the financial and personal situation of a client in much more detail. The result could be an investment approach, which takes the complete financial situation of the client into consideration and is tailored to the personal life situation. Therefore, the investment can be adjusted if the life situation changes. In addition he could give life investment guidance and encourage investors to safe more. Regarding the described investment mistakes in Chapter 2.5.1, Stuber (2011) shows that financial advice can mitigate several mistakes done by private investors, the disposition in the first place with room for improvement regarding other investment mistakes.

There are several studies, which discuss an increasing supply of investment products to clients (Horn, Meyer and Hackethal, 2009; Finke, Huston and Waller, 2009), a better diversification (Bluethgen, Gintschel, Hackethal and Müller, 2008) and a lower risk ratio (Gerhardt and Hackethal, 2009) of the investments thanks to consultants.

Financial advice can enhance the overall performance of investors, while a difference in the form of financial advice cannot be measured significantly (Gerhardt and Hackethal, 2009; Stuber, 2011).

Schuster (2013) comes to the slightly different conclusion and describes that positive effects on the performance occurs just if the interests of the consultant are covered (Stuber, 2011). This finding reveals that clients cannot be certain about getting better results if being advised. Regardless the performance of the investment there are several clear disadvantages of the industry of financial consulting shown in the literature.

2.6.2 Disadvantages of human advisors

Lieber (2014), Malkiel, (2016) and many other authors differentiate mainly between two types of financial advisors. Those, who are paid through commissions, are often not advising in the interest of the client, as the money comes from investments or insurance companies, while fee-only consultants earn a fixed-fee paid by the client.

This differentiation is definitely necessary as Beyer, de Meza and Reyniers (2013) show. They argue that the amount of paid commission strongly influences the choice of product. They argue that the needs of the clients are secondary. Bolton, Freixas and Shapiro (2007) even compare the market of commission paid financial advisors with the drug market, where “sellers have better information than buyers regarding the matching between the buyer’s needs and the good’s actual characteristics” (p.297). The assumption that consultants paid through commissions have a strong incentive to mislead the client towards too expensive products is shared and proven by many authors (Ottaviani, 2009; Inderst and Ottaviani, 2009; Beyer, de Meza and Reyniers, 2013; Schuster, 2013; Lieber, 2014; EY, 2015). The only ones to deny this are often these consultants themselves. The serious problem is that especially people with smaller assets choose advisors paid through commissions (Franke et al., 2011). One possible choice for a fair consulting in the interest of the client can be the fee-only consultant (Schuster, 2013; Lieber, 2014; Malkiel, 2016). However, this consulting possibility does not come without problems either and the amount of fee only consultants in western countries is still limited (Franke et al., 2011). Another problem is the inequality of chances, as clients with smaller assets tend to choose commission paid consultants as they do not have to pay a bill for the service (ibid, 2011).

In addition there are several cases of miscounselling the client, starting in smaller offices and banks and go up to massive fraud scandals, e.g. the Bernie Madoff ponzi scheme (Arvedlund, 2009).

It is very difficult to answer the question of cost for human advisory services. Commissions are often called ‘hidden costs’ and are in generally very non- transparent (Beyer, de Meza and Reyniers, 2013). Whatever consultant to consider, the cost are significantly higher than these of RA in a range between 0.6 and 1.5 percent of the AuM in the USA and Europe (Lieber, 2014; EY, 2015; Sinha, 2016; EY, 2016; Wealthfront, 2017c). In the European market, the fees or commissions are higher and less transparent than in the US market, usually between 2 to 3 percent p.a. (Franke et al., 2011).

2.7 Conclusion of the literature review

The academic analyses about RA is almost non-existent as it is a new phenomenon. The most vital information is provided by consulting firms, banks or by analyses of the websites of RA. In general, there are many internet based sources available but the most of them are in forums or other non-certified webpages. Surprisingly, there is not much information about HA either, although it is a massive industry. In comparison, there is a lot information about investment behaviour but as it is a very complex topic, the opinions are far apart.

Throughout the literature review, it becomes clear how RA work and what they do. Their strengths and weaknesses are discussed, and the basis for a discussion is built. The abilities of HA are critically analysed. Regarding the investment behaviour no clear results can be made as it is a psychological, complex and multi-faceted topic, with infinite possible outcomes. It is still noticed that humans are acting emotionally and irrationally and that HA are able to help the average investor to achieve better investment results under specific circumstances.


3.1 Research Paradigm and Methodology

In order to answer the research question, primary data research is needed to be undertaken. In this behalf the suitable methodology is needed to be identified. According to Saunders, Lewis and Thornhill (2009), methodology can be differentiated in the philosophies, the approaches, the strategies, the choices and methods, the time horizon and proper techniques and procedures. This is shown graphically in the Research Onion (Figure 6), including the choices followed in this thesis, written in orange.

Figure 6 - Research Onion

Abbildung in dieser Leseprobe nicht enthalten


Abbildung in dieser Leseprobe nicht enthalten

The research for this project was structured as an empiric based professional enquiry.

Saunders, Lewis and Thornhill (2009) stress that the research philosophy describes the way, the world and its realities are seen as well as the existing knowledge and the way it is developed.

The philosophical approach was based on the assumption that reality is built by people, even though there is not one simple reality or truth, as the world became too complex. This reflects an interpretivist point of view, aiming to examine existing realities and predict these, which do not exist yet (Saunders, Lewis and Thornhill, 2009; Robson, 2011). The interpretivist point of view in combination with the assumption of a complicated word leaded to a flexible Multi-method qualitative study design and a combination out of deduction and induction. This approach made it possible to adapt to changing research parameters in a complicated word and a complex and wide research topic. Saunders, Lewis and Thornhill (2009) add, the deduction approach suits the topics with a wide range of literature, e.g. about investment behaviour, as the inductive approach is used in fields with little existing literature, e.g. RA, so new data needs to be generated. Either approach on its own would have restricted the research possibilities and the possible findings. The research method is not simple to assign, as it has some characteristics of a survey, an interview and a case study. But as the research project was not written in cooperation with a company, it fits best as a case industry or field study, where not a company specific case is analysed but a whole industry in a large extent (Saunders, Lewis and Thornhill, 2009).

The deductive part is covered via collecting qualitative data through expert interviews, which is compared with qualitative data collected via an online questionnaire to cover the inductive approach. According to the definition of a multi-method approach, both research methods need to use the same data collection method (Tashakkori and Teddlie, 2003). In the course of the research, experts can answer questions about actual capabilities as well about the future of RA, their possibilities and possible threats. Contentious points between a RA manager and a human wealth manager will reveal the possibility of several truths, because none of their points or views will be necessarily wrong. This argument will be underpinned by a survey, revealing many different reasons for the same actions or the other way around.


1 Questions are taken exemplarily from the opening process of Wealthfront (2017a) (USA), Nutmeg (2017)(GB) and quirion (2017a) (Germany)

2 The equivalent source can be found in the references through the name of the RoboAdvisor and the year 2017 including a ‘c’ for cost in brackets, e.g. Wealthfront (2017c). Even if no other sources have been titled with ‘a’ or ‘b’ in advance, this method is assumed to be the clearest and most transparent.

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What Influences May Robo-Advisors Have on the Service of Professional Financial Consultants and the Financial Industry?
Liverpool John Moores University
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Robo-Advisor, Wealth Management, ETF, Passive Investment, Comission, Asset Management, Fund Industry, quirion, Scalable, fintego, Fee-only, economics, behavioral economics, investment mistakes
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Patrick Reverchon (Author), 2017, What Influences May Robo-Advisors Have on the Service of Professional Financial Consultants and the Financial Industry?, Munich, GRIN Verlag, https://www.grin.com/document/378460


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