Robo-Advisory: Investing in the Digital Age (Palgrave Studies in Financial Services Technology)

Foreword
Preface
	Reference
Acknowledgements
Contents
Notes of Contributors
Acronyms
List of Figures
List of Tables
Part I Status Quo of Robo-Advisory
	1 Robo-Advisory: The Rise of the Investment Machines
		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
	2 Situating Robo-Advisory
		2.1 Introduction
		2.2 What Are Fintech and Robo-Advisory, Really?
		2.3 Differentiation: Strengths and Weaknesses
		2.4 Impact on Incumbents
		2.5 The Path Ahead
		References
Part II Implementation of Robo-Advisory
	3 Risk Preferences of Investors
		3.1 Introduction
		3.2 Risk Profiling Explained
			3.2.1 Financial Risk Tolerance
			3.2.2 Risk Capacity
			3.2.3 Risk Required
			3.2.4 Risk Profiling Is Part of Knowing Your Client
		3.3 Choosing a Valid and Reliable Risk-Tolerance Test
			3.3.1 Psychometrics Versus Gambles-Based Testing
			3.3.2 The Science of Prospect Theory
			3.3.3 The Science of Psychometrics
		3.4 Risk Profiling Is Harder for Robo-Advisors
		3.5 Mapping Risk Profiles to Investments
		3.6 Conclusion
		References
	4 Robo Economicus? The Impact of Behavioral Biases on Robo-Advisory
		4.1 Introduction
		4.2 Home Bias
			4.2.1 Does Robo-Advisory Fall for Home Bias?
		4.3 Mental Accounting
			4.3.1 Does Robo-Advisory Care About Mental Accounts?
		4.4 Overconfidence
			4.4.1 How Confident Are Robo-Advisors?
		4.5 Summary
		References
	5 Quant Models for Robo-Advisors
		5.1 Introduction
		5.2 What Strategies Are Suitable for Robo-Advisory?
		5.3 What Quantitative Approaches Does the Robo-Advisory Model Offer?
			5.3.1 Maximization of the Diversification Effect
			5.3.2 Equal Distribution of Risks to the Investment Instruments Contained in the Portfolio
			5.3.3 Risk Minimization
			5.3.4 Methods Based on Return Forecasts
		5.4 Dealing with Risk Targets
			5.4.1 Adherence to Lower Value Limits
			5.4.2 Specification of a Risk Preference by Choosing a Target Investment Period
		5.5 Return Targets and Risk-Bearing Capacity: Need for Information
		5.6 Requirements for the Investment Universe and Instruments
		5.7 Customization by Investors
		5.8 Summary
		References
	6 Analysis of the Use of Robo-Advisors as a Replacement for Personal Selling
		6.1 Introduction
		6.2 Robo-Advisors as Customer–Salesperson Interaction Technologies
		6.3 Theoretical Background and Research Propositions
			6.3.1 Trust
			6.3.2 Perceived Risk
			6.3.3 Psychological Reactance
			6.3.4 Perceived Use
		6.4 Experimental Design and Data Collection
		6.5 Results
			6.5.1 Data Preparation and Manipulation Checks
			6.5.2 Hypotheses Test
		6.6 Managerial Implications and Limitations
		References
	7 Regulation of Robo-Advisers in the United States
		7.1 Introduction
		7.2 Investment Advisers Act of 1940
			7.2.1 Registration of Robo-Advisers
			7.2.2 Anti-fraud Provisions
			7.2.3 Fiduciary Duty
			7.2.4 SEC Staff Guidance
				7.2.4.1 Disclosures
				7.2.4.2 Suitability of Recommendations
				7.2.4.3 Compliance
			7.2.5 SEC Investor Alert
			7.2.6 Supervision
		7.3 Securities Exchange Act of 1934
		7.4 Investment Company Act of 1940
		7.5 Employee Retirement Income Security Act of 1974
		7.6 State Regulation
		7.7 Conclusion
		References
	8 Regulation of Robo-Advisory in Europe and Germany
		8.1 What Are the Activities for Which a Provider Needs a License?
			8.1.1 Product-Related
			8.1.2 Service-Related
				8.1.2.1 Investment Advice
				8.1.2.2 Investment Broking
				8.1.2.3 Contract Broking
				8.1.2.4 Portfolio Management
			8.1.3 Liability Umbrella as an Alternative to Holding a License
		8.2 Regulatory Requirements for Fintech Firms and Robo-Advisors: Practical Issues
			8.2.1 Information Obligations
			8.2.2 Investment Broking and Execution Only Transactions
			8.2.3 Duty of Care Obligations in Investment Advice and Portfolio Management
			8.2.4 Practical Importance and Border Cases in the Robo-Advisory Universe
				8.2.4.1 Onboarding Using Online Communication Channels
				8.2.4.2 Hotline and Chat
				8.2.4.3 Order Placement
			8.2.5 Reallocation
		8.3 Onboarding Obligations Under GWG
			8.3.1 Organizational Requirements
			8.3.2 General Due Diligence Requirements
		8.4 Conclusion of Contract
		8.5 Data Management
			8.5.1 Profiling
			8.5.2 Use of Cloud Services
		8.6 Conclusion
		References
Part III Case Studies of Robo-Advisory
	9 (Re-)Launching a Robo-Advisor as a Bank
		9.1 Introduction
		9.2 Background
		9.3 Key Challenges and Considerations
		9.4 Implementation of a Robo-Advisor
			9.4.1 Strategic Framework and Parameters
			9.4.2 Robo-Advisory Specification
			9.4.3 Evaluation Criteria for Provider Selection
				9.4.3.1 Functional Specification
				9.4.3.2 Technical Specification
				9.4.3.3 Profitability
				9.4.3.4 Provider-Related Specification
			9.4.4 Implementation of the Robo-Advisor
		9.5 Evaluation of Existing Robo-Advisors
			9.5.1 Evaluation Criteria
				9.5.1.1 Target Vision
				9.5.1.2 Financial Figures
				9.5.1.3 Sales Control
				9.5.1.4 Customer Usage and Satisfaction
				9.5.1.5 Product Design
				9.5.1.6 Technical Integration
				9.5.1.7 Providers
			9.5.2 Options for Action
		9.6 Future Trends
			9.6.1 Digitalisation Requires Individualisation
			9.6.2 Marketplaces and Ecosystems
			9.6.3 Start Small to Go Big
			9.6.4 Once a Competitor, Now an Ecosystem Partner
	10 How Can Robo-Advisory be Implemented and Integrated into Existing Banks?
		10.1 Background
			10.1.1 Introduction
			10.1.2 Client Needs
			10.1.3 Market Analysis
		10.2 The Implementation and Integration Process
			10.2.1 Strategy
			10.2.2 Product
			10.2.3 Marketing and Sales
			10.2.4 Regulatory Requirements
			10.2.5 Technical Platform
		10.3 The Real Question Is: ``How Can the Bank Use the Latest Technology to Expand its Range of Services in the Interest of the Client?''
		References
Part IV The Future of Robo-Advisory
	11 The Role of Artificial Intelligence in Robo-Advisory
		11.1 Introduction to Artificial Intelligence
			11.1.1 Historical Development of AI in the Investment Management Industry
			11.1.2 What Can be Expected from AI Today?
		11.2 Value Potential for AI Along the Core Processes of Robo-Advisory
			11.2.1 Customer Data and Collection Strategies
			11.2.2 Marketing, Sales, and Service
				11.2.2.1 Chatbots
				11.2.2.2 Product Recommendations
			11.2.3 Matching Customers to Portfolios
		11.3 AI Supporting Portfolio Management
		11.4 The Way Forward
		References
	12 What Role Do Social Media Play for Robo-Advisors?
		12.1 Introduction
		12.2 Social Media vs. Robo-Advisor
		12.3 Robo-Advisor Feat. Social Media
		12.4 Social Advisory
			12.4.1 Social Media as Marketing and Sales Support
			12.4.2 Social Media as an Alternative to Robo-Advisory
			12.4.3 Social Media as Potential Cooperation Partner for Robo-Advisors
		References
	13 Success Factors for Robo-Advisory: Now and Then
		13.1 Introduction
		13.2 Growth Factors Identified
			13.2.1 No. 1: Overall Market Growth
			13.2.2 No. 2: Replacing Existing Products
			13.2.3 No. 3: Attracting Previously ``Uninvested'' People to the Financial Markets
		13.3 Success Factors for Achieving the Identified Potentials and Increasing the Market Penetration of Robo-Advisors
			13.3.1 Extending the Reach
			13.3.2 Improving Financial Literacy
			13.3.3 Greater Transparency
			13.3.4 Targeting Women
			13.3.5 Hybrid Access
			13.3.6 Expanding and Improving the Product Range
		13.4 Summary
		References
Glossary
Index