Robo-Advisory

Investing in the Digital Age

Peter Scholz

• Publisher: Palgrave Macmillan
• Year: 2020
• Print ISBN: 9783030408176
• eBook ISBN: 9783030408183
• Topic: Business
• Subtopic: Financial Services
• Index: Business

Robo-Advisory is a field that has gained momentum over recent years, propelled by the increasing digitalization and automation of global financial markets. More and more money has been flowing into automated advisory, raising essential questions regarding the foundations, mechanics, and performance of such solutions. However, a comprehensive summary taking stock of this new solution at the intersection of finance and technology with consideration for both aspects of theory and implementation has so far been wanting. This book offers such a summary, providing unique insights into the state of Robo-Advisory.

Drawing on a pool of expert authors from within the field, this edited collection aims at being the vital go-to resource for academics, students, policy-makers, and practitioners alike wishing to engage with the topic. Split into four parts, the book begins with a survey of academic literature and its key insights paired with an analysis of market developments in Robo-Advisory thus far. The second part tackles specific questions of implementation, which are complemented by practical case studies in Part III. Finally, the fourth part looks ahead to the future, addressing questions of key importance such as artificial intelligence, big data, and social networks. Thereby, this timely book conveys both a comprehensive grasp of the status-quo as well as a guiding outlook onto future trends and developments within the field.

Part IStatus Quo of Robo-Advisory

© The Author(s) 2021

P. Scholz (ed.)Robo-AdvisoryPalgrave Studies in Financial Services Technologyhttps://doi.org/10.1007/978-3-030-40818-3_1

Begin Abstract

1. Robo-Advisory: The Rise of the Investment Machines

Peter Scholz¹ and Michael Tertilt²

(1)

Professor for Banking and Financial Markets, Hamburg School of Business Administration, Hamburg, Germany

(2)

Research Affiliate, Hamburg School of Business Administration, Hamburg, Germany

Peter Scholz (Corresponding author)

Email: [email protected]

Michael Tertilt

Email: [email protected]

1.1 Introduction

1.2 Robo-Advisory as Part of the Wealth Management Process

1.3 A Brief History of Robo-Advisory

1.4 Clients and Market for Robo-Advice

1.5 Performance of Robo-Advisors

1.6 Robo-Advice in a Nutshell

References

End Abstract

1.1 Introduction

The Rise of the Machines—these words sound more like the title of a movie or a video game rather than being related to finance. But more than 30 years ago, the story was on the cover of TIME magazine:¹ For the first time, the idea that robots and digital solutions may take over the economy was discussed in the public domain. Meanwhile, the digital revolution has disrupted many sectors like the music and publishing industry, imaging technology, retail business, and so on. And since the financial crisis of 2008, the financial sector has got increasingly into the crosshairs of disruptors.

It is noteworthy, however, that the use of technology is nothing unusual for the financial sector. Capital has always been creative through the application of innovations to capture individual advantage. For example, one of the first applications of the telegraph was the long-distance transmission of news which was relevant for trading in distant marketplaces. The invention of the smartphone has been a comparable technical revolution that has had a huge impact on consumer behavior and on the expectations toward banking services. Therefore, it should not come as a surprise that FinTechs came up with the idea to digitalize the advisory process in asset management.

1.2 Robo-Advisory as Part of the Wealth Management Process

The term “robo-advisor” is a blend of the two words “robot” and “advisor”. It basically describes that the client-interface of the advisory process is not a human anymore but a machine. Based on the wealth management process , in our version adapted from Evensky et al. (2011) (see Fig. 1.1), the question is, which exact process steps are covered by the robo-advisory? In contrast to common perception, robo-advice does not necessarily mean that all these process steps are fully automatized. In fact, the advisory contribution of the robot may be quite diverse. A very common feature of the robo-advisory is the onboarding process of new customers and the determination of the client’s risk profile . The management of the sample portfolios, however, is not necessarily a part of robo-advice.

Fig. 1.1

Wealth management process adapted from Evensky et al. (2011)

If we take the largest robo-advisor, Vanguard Personal Advisor Services, as an example, we find a description explaining that the “proprietary algorithm uses [the clients’ investment profile] data to recommend a particular investing track” (Vanguard Advisers, Inc. 2019, p.8). So, there seems to be a machine that matches customer data to a specific portfolio. However, there is always a human advisor as well if the client feels the need for any discussion (Vanguard Advisers, Inc. 2019). The dominant robo-advisor, therefore, relies more on a hybrid form of advisory. If we dive deeper into the wealth management process, we find that model portfolios are not generated by a robot but an investment committee: “We may propose the addition, removal, or adjustment of sub-asset class exposures based on continuing portfolio construction research performed by Vanguard Investment Strategy Group” (Vanguard Advisers, Inc. 2019, p. 8). On the other hand, there seems to be an algorithm managing risk: “When recommending, setting, and adjusting your asset allocation, we weigh shortfall risk—the possibility that a financial plan or Portfolio will fail to meet longer-term financial goals—against market risk” (Vanguard Advisers, Inc. 2019, p.8). But there is no active market timing for investments: “The algorithms don’t consider prevailing market conditions when making recommendations to you” (Vanguard Advisers, Inc. 2019, p. 10).

If we consider the pursuer, Schwab Intelligent Portfolios, they seem to follow a similar process. The asset allocation apparently is not created by an algorithm but an investment committee : “Using asset allocations and ETF selection parameters determined by Schwab, CSIA has created a number of investment strategies for the Programs” (Charles Schwab & Co., Inc. 2019, p. 2).² The robo-advisor algorithm itself is applied to select a portfolio recommendation based on the client’s investment preferences for rebalancing, for tax optimization, and for triggering orders. In contrast to the two market leaders, the first robo-advisors, Betterment and Wealthfront, seem to rely more on rule-based allocations. Both explicitly refer to an asset allocation process that is driven inside the robo-advisor and relies on the Modern Portfolio Theory (Wealthfront 2020a; Grealish 2019). In Europe, Nutmeg and Liqid seem to have a committee approach for asset allocation as well (Port 2013; Nutmeg 2020b), whereas Scalable, comdirect, and Quirion operate an explicit asset allocation process as part of their robo-advisory services. A rather lean approach is the service of WeltInvest (Weltsparen 2020), where only sample portfolios with a fixed asset allocation are presented and the investor must choose between the four ETF portfolios. However, there is no advice on which portfolio to select or how to identify risk tolerance , risk budget , and so on. Hence, the investor is basically left on his own. In return, the service is rather competitive with an approximate 0.5% p.a. fee, including product costs.

In summary, it is a bit difficult to describe what really defines a robo-advisor. From our perspective, the very core is the advisory process—the risk profile of the investor is determined, and a portfolio recommendation is given. Hence, we would not denote the offer from WeltInvest as robo-advisor. Within the group of robo-investors, there could be many subgroups. At least, we would form a group based on those robos with a quantitative, rule-based allocation process, and another group that relies on investment committees or external advisors. In conclusion, based on Peter’s experience as an investment advisor, the essence of robo-advisory is the replacement of the human advisor , or maybe better, the human salesperson, with an online interface by granting benefits like 24/7 availability, potentially lower costs, scaling, and automatization of at least some aspects of the portfolio management like rebalancing. Consequently, a robo-advisor just substitutes and eases certain steps within the asset management process, but it certainly does not create a whole new process on its own.

1.3 A Brief History of Robo-Advisory

The first robo-advisory services, which were designed for retail investors, came into life in the US after the financial crisis of 2008, probably also as an answer to the increasing distrust in the investment advisory business (Becchi et al. 2018). Betterment, founded by a team including Jon Stein, and Wealthfront, launched by Andy Rachleff and Dan Carroll, are considered as the first robo-advisors in the retail market (Fisch et al. 2018). Only a few years later, the first robo-advisory solutions came up in Europe: The first one founded in the UK was Nutmeg in 2011 (Lielacher 2016), and Quirion was established in Germany in 2013 (Kümpel 2016). Interestingly, the roots of robo-advisory can be traced back either to classic start-ups or to asset managers, whereas the banks by and large did not belong to the early adaptors . Especially for asset managers, robo-advisory services offer a huge leveraging potential (Becchi et al. 2018)—they can easily shift parts of their clients’ capital to digital advisory services and lower the entry barrier for new customers. It comes as no surprise that currently the largest robo-advisor, measured in assets-under-management, is owned by Vanguard. The initial idea of robo-advisory has been to disrupt the human advisory services of banks by offering affordable investment advice to widen the customer base by including retail clients and help them make good investment decisions (Nguyen 2018). Today, we can see that robo-advisory is mostly cooperating with financial institutions like banks instead of disrupting them. Based on institutional economics in the FinTech sector (Scholz 2018), it can be shown that mainly due to regulation and trust issues, such as moral hazards , investors are hesitant: What does the robo-advisor do with the money the moment it is invested? Despite the financial crisis, banks still enjoy a kind of bonus trust over start-ups. Moreover, banks are trying to mitigate the distrust issue regarding innovation by applying hybrid models where humans and robo-advisors are combined.

While the early business models have been rather simple—they were concerned with determining the risk preferences of the client and recommending an ETF -based portfolio of stocks and bonds—the advisory tends to become more complex over time, for example, by including more asset classes, active funds, tax optimization, and so on. However, based on our analysis from 2018, we found that the investment process by robo-advisors has been very simple at that time (Tertilt and Scholz 2018). The measurement of risk preferences typically relied only on a few questions, but it does not seem enough to provide proper risk classification and hence to derive adequate investment advice. From that perspective, we assume that the robo-advisory business is still in fledgling stages and further development seems absolutely necessary.

1.4 Clients and Market for Robo-Advice

Since its early be...

Table of contents

• Cover
• Front Matter
• Part I. Status Quo of Robo-Advisory
• Part II. Implementation of Robo-Advisory
• Part III. Case Studies of Robo-Advisory
• Part IV. The Future of Robo-Advisory
• Back Matter