Robo-Advisors – Business Models and Strategies
Online asset management has been experiencing a rapid rise in Germany for several years. Since 2017, the number of users has grown by a factor of 7 from around 291,000 in 2017 to around 2.01 million in 2020 (cf. o.V. 2020), while the investment volume has increased more than tenfold from around 756 million euros to 8.068 billion euros (cf. o.V. 2020). Two factors in particular are key to this trend: firstly, the loss of trust in personal banking advisory services caused by the financial crisis in 2007, and secondly, the increasing demand for digital offerings by digital natives. The new generation of customers who have grown up with smartphones and tablets, also known as “Generation Y,” is much more attuned to electronic communication, which means that personal contact such as with customer advisors at banks is losing relevance (cf. Alt/Puschmann 2016, 29). In the course of the shift from a personal, individual customer experience at a bank to the desire for standardized and digitized processes, “robo-advisors”, which replace personal, human advice with the offer of algorithm-based investment proposals, are becoming increasingly important (cf. Dapp 2016, 1).
For this reason, in this series of articles, I would like to provide an overview of what a robo-advisor is, what business models and strategies robo-advisors are pursuing, and how the traditional customer advisory process is changing through the use of robo-advisors. The articles are based on my bachelor thesis “An Analysis of the Impact of Robo-Advisors on the Customer Advisory Process in the Investment Sector”, which I wrote at the Information Systems Institute of the Faculty of Economics at Leipzig University.
Last week’s post was about how robo advisory services are defined and how they came into being, whether they are serious competition for banks, and what stage of development robo-advisors are at now. Today I present the prevailing business models and strategies of robo-advisors.
As mentioned in the last article, it is not possible to define exactly what a robo-advisor is, as the individual providers offer a range of services of varying breadth. In fact, robo-advisors have long since ceased to offer mere recommendations or advice, and most providers are steadily expanding their services into a fully integrated solution. Accordingly, people now associate a robo-advisor with a platform that can also be used to make an investment directly (see [Bloch/Vins 2017, 114]). However, this service, for example, is linked to certain regulatory requirements, which are presented below. It should be noted here that this is the regulatory framework in Germany. In terms of regulation, four business models can be distinguished in the area of robo advisory services:
- investment brokerage (german: Anlagenvermittlung),
- investment advice (Anlagenberatung),
- acquisition brokerage (Abschlussvermittlung), as well as
- financial portfolio management (Finanzportfolioverwaltung), also known as asset management.
Basically, the breadth of the service offering of each individual robo-advisor is determined by the respective permission, which is mandatory for one of the four business models mentioned. The main difference between the business models lies in who makes the investment decision, i.e. decides on the purchase of the securities: the customer or the provider. In all business models except asset management, this is the customer; in asset management, this is part of the provider’s service, which enjoys a certain degree of discretion. Robo-advisors providing only investment brokerage or advice are considered so-called “financial investment intermediaries” (Finanzanlagenvermittler) and are not classified as financial services institutions, unlike “acquisition brokers” and “financial portfolio managers.” Financial investment intermediaries in general specifically advertise certain investment products, a personal-individual consultation is only partially carried out here. In contrast to the investment broker, the investment advisor asks for data on the investor’s personal situation, such as income and assets, as well as investment goals, which further narrow down the selection of the right investment product for the customer, allowing a more individualized investment proposal to be presented. After the customer advisory process, for both the investment broker and the investment advisor, the customer contact ends with their onward referral to the product provider. A acquisition broker goes one step further and makes the desired investment on behalf of the customer. Finally, the financial portfolio manager has the most comprehensive offering and accompanies the customer beyond the customer advisory process. In compliance with defined rules, the latter can manage the customer’s portfolio independently and adjust it as required. Robo advisory services in the true sense of the term can only be found with providers offering investment advice or financial portfolio management, as here the customer receives an investment proposal that is as individual as possible and takes into account their asset situation and goals. The recommendation is made on the basis of the questions previously answered (see [Oppenheim/Lange-Hausstein 2016, 1966ff]).
Since the ultimate goal of robo-advisors is to replace the advice provided by a human investment advisor and a trend toward increasingly comprehensive solutions can be observed, I will assume in the further course that a robo-advisor has a license to manage assets and can thus be classified as a financial services institution.
In addition to a low minimum investment amount, ongoing annual costs are also an important component of investment advice. European robo-advisors offer their services for an average of around 0.8 percent of the invested capital, while U.S. robo-advisors charge as little as 0.4 percent. Considering a similar portfolio, competing banks and asset managers charge significantly more at around one percent on average (see [Kaya 2017, 9f]). The reason that robo-advisors can charge a lower service fee is that investments are mostly made in so-called “exchange-traded funds” (ETFs). Around 96 percent of European providers prioritize ETFs as their most important investment instrument, among other things, and 55 percent even use them exclusively (see [Kaya 2017, 4]). Exchange-traded funds are mostly passively managed index funds that are traded on exchanges and thus without issue surcharges (see [Müller/Pester 2019, 233]). The fact that they merely track a market index such as the DAX and the securities and their respective shares in the index are predefined means that a fund manager is no longer necessary (cf. [Müller/Pester 2019, 230]). Thus, ETFs can be offered at a significantly lower cost than actively managed funds, which in turn benefits the customers of robo-advisors through a lower service fee. In addition to the cost factor, ETFs also offer investors a high degree of diversification or risk spreading of their investment: for example, each index fund consists of several shares, bonds or commodity units, depending on which asset classes the ETF relates to (see [Ruffner/Süer 2017, 173]). To further increase diversification, investments are made in three to 15 different ETFs per customer and per portfolio, depending on the respective provider, which are integrated into five to 21 model portfolios. Robo-advisors basically only offer their customers prefabricated portfolios, which only need to be allocated to the customer depending on the respective risk assessment (cf. [Hölscher/Nelde 2018, 69]).
Robo-advisors can follow an active or passive investment approach not only in terms of their product range, but also in the composition of the individual products. In active management, for example, the market is constantly monitored and, on the basis of this, the securities that appear to be most advantageous at a given time are included in the portfolio. This targeted approach is described as so-called “stock picking” (see [Müller/Pester 2019, 229f]). Due to market fluctuations, there are thus regular purchases and sales of securities with the aim of achieving a higher return than the passive market. In the course of this, the percentage distribution of the asset classes in the portfolio can also be continuously adjusted and regular risk assessments carried out. As a result, the portfolio may be subject to constant change. The passive management approach is based on the strategy of maintaining the portfolio created at the beginning, including the asset allocation and the defined securities, unchanged and independent of market fluctuations. If a change in asset allocation should occur due to market fluctuations, the original state can be restored through various adjustment methods, also called “rebalancing”. In contrast to active management, this adjustment is not carried out on an ongoing basis, but at predetermined times or according to specific rules. In so-called “periodic rebalancing”, a restoration of the asset allocation is carried out as needed at the time of a previously defined temporal interval change. Another variant of rebalancing provides for an adjustment only if the portfolio value exceeds or falls below a previously defined mark, the threshold (see [Hölscher/Nelde 2018, 69]).
Next week, between Christmas and New Year, the blog will take a short break. After that, we will continue with the question of how much the customer advisory process of a robo-advisor differs from that of traditional investment advice. For this, I will first introduce both advisory processes in the next part. So stop by again in two weeks or register to make sure you don’t miss any of the posts. And until then: Merry Christmas and a Happy New Year!
|[Alt/Puschmann 2016]||Alt, R., Puschmann, T., Digitalisierung der Finanzindustrie, Springer-Verlag, Berlin, 2016.|
|[Bloch/Vins 2017]||Bloch, T., Vins, O., Private Banking via FinTech: Strategie und Schnittstellen, in: Fleischer, K., Trends im Private Banking, 3. Aufl., Bank-Verlag GmbH, Köln, 2017, S. 111–128.|
|[Dapp 2016]||Dapp, T.-F., Robo Advice, Deutsche Bank Research, 2016.|
|[Hölscher/Nelde 2018]||Hölscher, R., Nelde, M., Darstellung, Funktion und Portfolioaufteilung von Robo-Advisory, in: Zeitschrift für das gesamte Kreditwesen (2018) 2, S. 68–73.|
|[Kaya 2017]||Kaya, O., Robo-advice – a true innovation in asset management, Deutsche Bank Research, Frankfurt am Main, 2017.|
|[Müller/Pester 2019]||Müller, M., Pester, M., Passive Anlagestrategien und Digitalisierung in der Vermögensverwaltung, in: Seidel, M., Banking & Innovation 2018/2019, Springer Fachmedien, Wiesbaden, 2019, S. 227–244.|
|[o.V. 2020]||o.V., Robo-Advisors, Statista, 2020.|
|Oppenheim, R., Lange-Hausstein, C., Robo Advisor: Anforderungen an die digitale Kapitalanlage und Vermögensverwaltung, in: Zeitschrift für Wirtschafts- und Bankrecht (2016) 41, S. 1966–1973.|
|[Ruffner/Süer 2017]||Ruffner, M., Süer, L., Robo Advisor und die Zukunft der Vermögensverwaltung, in: Fleischer, K., Trends im Private Banking, 3. Aufl., Bank-Verlag GmbH, Köln, 2017, S. 169–184.|