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Robo-Advisory

Investing in the Digital Age

  • 2021
  • Buch
Herausgegeben von
Prof. Dr. Peter Scholz
Buchreihe
Palgrave Studies in Financial Services Technology
Verlag
Springer International Publishing
Enthalten in
Springer Professional "Wirtschaft+Technik"
Springer Professional "Technik"
Springer Professional "Wirtschaft"
insite
SUCHEN

Über dieses Buch

Robo-Advisory ist ein Bereich, der in den letzten Jahren an Dynamik gewonnen hat, angetrieben durch die zunehmende Digitalisierung und Automatisierung der globalen Finanzmärkte. Immer mehr Geld fließt in die automatisierte Beratung und wirft wesentliche Fragen hinsichtlich der Grundlagen, der Mechanik und der Leistungsfähigkeit solcher Lösungen auf. Eine umfassende Bestandsaufnahme dieser neuen Lösung an der Schnittstelle von Finanzen und Technologie unter Berücksichtigung sowohl der theoretischen als auch der praktischen Aspekte fehlte bisher. Dieses Buch bietet eine solche Zusammenfassung und einzigartige Einblicke in den Zustand von Robo-Advisory. Auf der Grundlage eines Pools von Fachautoren aus diesem Bereich zielt diese herausgegebene Sammlung darauf ab, für Wissenschaftler, Studenten, politische Entscheidungsträger und Praktiker gleichermaßen, die sich mit dem Thema auseinandersetzen möchten, eine wichtige Anlaufstelle zu sein. Das in vier Teile gegliederte Buch beginnt mit einem Überblick über die akademische Literatur und ihre wichtigsten Erkenntnisse gepaart mit einer Analyse der bisherigen Marktentwicklungen im Robo-Advisory. Der zweite Teil behandelt spezifische Fragen der Umsetzung, die in Teil III durch praktische Fallstudien ergänzt werden. Schließlich blickt der vierte Teil in die Zukunft und behandelt Fragen von zentraler Bedeutung wie künstliche Intelligenz, Big Data und soziale Netzwerke. Dabei vermittelt dieses zeitgemäße Buch sowohl ein umfassendes Verständnis des Status quo als auch einen leitenden Ausblick auf zukünftige Trends und Entwicklungen auf diesem Gebiet.
Mit KI übersetzt

Inhaltsverzeichnis

Frontmatter

Status Quo of Robo-Advisory

Frontmatter
Chapter 1. Robo-Advisory: The Rise of the Investment Machines
Abstract
Robo-advisors are an innovation in the financial market to provide automatized investment advice. We start this introductory chapter with a brief overview about the history of robo-advice. It started about ten years ago in the US and has spread over the globe since. The market volume has risen significantly, and the rise of both assets-under-management and number of clients is predicted to grow even further. We also throw a glance at the performance, which robo-advisors are generating, and find that robo-advice is by and large neither out- nor underperforming. In the next future, we assume that robo-advice may be combined with human elements, resulting in hybrid models which combine the best of two worlds.
Peter Scholz, Michael Tertilt
Chapter 2. Situating Robo-Advisory
Abstract
Academic research into robo-advisory remains in its early stages. The debate has so far focused on (1) definitorial questions regarding the nature of robo-advisory, (2) an evaluation of competitive differentiation based on strengths and weaknesses, (3) strategic options for both legacy service providers and robo-advisors to compete, as well as (4) design and calibration of robo-advisors to provide maximum value to customers. Key insights generated so far support four conclusions. Robo-advisory is so far understood as a phenomenon whose competitive position will be defined by the automizability of processes. Therefore, robo-advisors are expected to focus on standardized low-complexity services targeting the lower- and mid-income ranges, while traditional advisory is expected to focus on high-complexity tasks provided to affluent customers. Traditional advisory is seen to have multiple strategic options to face attacks from challengers, primarily either taking a wait-and-do-nothing approach or engaging in strategic acquisitions to complement service offerings. Factors driving future success are expected to be the ease of interaction, work efficiency, information processing, and, most importantly, transparency, especially regarding pricing of advisory offerings.
Sinan Krueckeberg

Implementation of Robo-Advisory

Frontmatter
Chapter 3. Risk Preferences of Investors
Abstract
An investor’s risk preferences are captured in their “risk profile” characterized by the balanced state of different dimensions of risk. A person’s financial risk tolerance is the foundation of their risk profile. Risk profiling is a regulatory requirement for both humans and robo-advisors. But there is no agreement on exactly what should be considered in a risk profile, how measurements should be taken, or how data should be weighted in making recommendations. The core questions to be resolved are the following: What methodology will produce valid and reliable risk-tolerance assessments? How is that methodology to be effectively deployed in the advice process? Robo-advisors approach risk profiling at a disadvantage due to their limited data points compared to a human who can observe and question. Compared to a human-based system, a robo-advisor faces extra challenges in developing understanding, writing comprehensive algorithms, and applying professional judgment.
Monika Mueller, Paul Resnik, Craig Saunders
Chapter 4. Robo Economicus? The Impact of Behavioral Biases on Robo-Advisory
Abstract
Human investors are supposed to be rather emotional and prone to biases in their financial decision-making. By contrast, robots and algorithms have the reputation to be fully rational and therefore are very often considered as ideal investors. But since they are programmed by humans, the question arises how unbiased algorithms and robots really are. We analyze robo-advisors with respect to home bias, mental accounting, and overconfidence and find that the recommendation from robo-advice is not free from behavioral biases. After all, it seems that robo economicus is not as close to the model of Homo economicus as supposed.
Peter Scholz, David Grossmann, Joachim Goldberg
Chapter 5. Quant Models for Robo-Advisors
Abstract
Robo-advice uses modern computational capabilities. So do quant models in portfolio management. Since both concepts profit from the same source of technology, it is tempting to merge them into one holistic approach. In this chapter, we show which quantitative models are a particularly good fit with a robo-advisor platform and which quantitative techniques will become available for a broader segment of retail investors thanks to modern technological advances. Our focus is set on risk-based strategies. Excess Return Forecasts can help to improve performance but introduce another source of potential pitfalls. The Black & Litterman approach can help to handle these. Apart from that private investors often come with unrealistic return to risk expectations. Therefore, this relationship should be made as clear as possible before any investment is made to avoid disappointment. In this context, portfolio insurance strategies to meet investors’ risk budgets are considered at the end.
Thorsten Ruehl
Chapter 6. Analysis of the Use of Robo-Advisors as a Replacement for Personal Selling
Abstract
With the development of digital sales tools, for example robo-advisors, and its adoption by consumers, sales management is changing rapidly. This development may lead into disintermediation of salespeople as technologies emancipate consumers to inform themselves about offerings. Consequently, consumers may not view the buying process necessarily driven by humans. Whereas research has already surveyed the perspective of salesperson technology adoption, little is known about the consumer perspective when it comes to customer–salesperson interaction technologies. Thus, our main contribution is to compare different levels of customer–salesperson interaction technologies and its impact on behavioral constructs. Using experiments, we contribute to the literature by investigating how different forms of customer–salesperson interaction technologies impact customer perception.
Goetz Greve, Frederike Meyer
Chapter 7. Regulation of Robo-Advisers in the United States
Abstract
Robo-advisers are regulated in the United States under the existing regime governing investment advisers. This chapter describes how that regime applies to robo-advisers, focusing on regulatory guidance and addressing such matters as the suitability of robo-adviser recommendations, disclosures, compliance, and investor concerns. As discussed herein, US regulators have identified issues unique to robo-advisers but, generally, have taken the position that the existing framework is adequate to regulate their activities and protect investors.
Melanie L. Fein
Chapter 8. Regulation of Robo-Advisory in Europe and Germany
Abstract
In order to present the civil law and regulatory issues in a manageable format, we have tried, on the following pages, to give you a structured overview of the current legal framework based on national and European provisions, consisting, in particular, of the MiFID II Directive and the related German implementation laws: the Securities Trading Act (WpHG), the Banking Act (KWG) and the Anti-Money Laundering Act (AMLA/GWG). Given the broad range of requirements and the high market access barriers, every company has to think carefully about the services it wishes to offer, the licenses it will need and the regulatory requirements placed on its activities. The areas of asset management and investment advice are governed by a complicated set of laws, rules and regulations. It is therefore advisable to focus both on the services offered and on the product range. In response to the licensing requirements, a company can act as an investment intermediary under the German Trade Regulation Act or as a financial services institution with a license under the German Banking Act. The latter offers a further alternative in the form of the liability umbrella. In addition to the regulatory requirements for licensing and activities, onboarding also poses considerable practical problems. Identification obligations under the GWG and the digital conclusion of contracts must be well-thought-out and solved in technical terms. In addition, the strict requirements of the General Data Protection Regulation must be observed.
Christian Hammer

Case Studies of Robo-Advisory

Frontmatter
Chapter 9. (Re-)Launching a Robo-Advisor as a Bank
Abstract
For banks, the securities business for retail clients is increasingly important as an income or revenue source. In recent years, innovative and agile fintechs have challenged the once bank-dominated securities market with robo-advisory solutions. Robo-advisors offer customers intuitive and easy-to-understand digital asset allocation and management. Subsequently banks have recognised the fundamental necessity of digitalising the securities business and are starting this endeavour by introducing their own robo-advisors. In doing so many banks cooperate with fintechs as envisaged solutions can be implemented much faster. However, offering a robo-advisor itself is no sure formula for success. Banks must deal with various challenges and create a strategic and organisational framework for this digitalisation process.
Theodor Schabicki, Yvonne Quint, Soeren Schroeder
Chapter 10. How Can Robo-Advisory be Implemented and Integrated into Existing Banks?
Abstract
The following case study deals with the question of how robo-advisory can be implemented and integrated into the existing bank model, using the example of Hauck & Aufhaeuser Privatbankiers (H&A).
Ana-Maria Climescu, Christian von Keitz, Jan Rocholl, Madeleine Sander

The Future of Robo-Advisory

Frontmatter
Chapter 11. The Role of Artificial Intelligence in Robo-Advisory
Abstract
Predicting consumer behavior and financial markets are two of the most exciting fields in the financial business for applying artificial intelligence. Both of these exciting topics can be found simultaneously in robo-advisory. On the one hand, it is important for a robo-advisory service to understand consumers and provide them with individually optimized data-driven interactions. On the other hand, the information available in financial markets needs to be processed and squeezed with cutting-edge technology to provide best-in-class asset-management services. Both these aspects are examined with practical examples of artificial intelligence, and the chances as well as limitations of applying this technology are discussed.
Alexander D. Beck
Chapter 12. What Role Do Social Media Play for Robo-Advisors?
Abstract
Looking at the current number of users, it becomes clear that social media today play an important role both privately and professionally.
Ana-Maria Climescu
Chapter 13. Success Factors for Robo-Advisory: Now and Then
Abstract
After reading all the implementation details, facts, and best practice in the field of robo-advisory—what are the key takeaways to make robos a success story?
Madeleine Sander
Backmatter
Titel
Robo-Advisory
Herausgegeben von
Prof. Dr. Peter Scholz
Copyright-Jahr
2021
Electronic ISBN
978-3-030-40818-3
Print ISBN
978-3-030-40817-6
DOI
https://doi.org/10.1007/978-3-030-40818-3

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