Behavioral Finance for Private Banking

KREMENA BACHMANN is senior research associate at the University of Zurich, senior lecturer at the Zurich University of Applied Sciences (ZHAW) and academic advisor at Moor & Bachmann AG, a Swiss multi-family office. As a behavioral finance researcher publishing in peer-reviewed journals she is also actively engaged in the development of practical solutions that help to improve financial decision making. ENRICO DE GIORGI is professor of mathematics and director of the institute of mathematics and statistics at University of St. Gallen. He is also associate editor of Mathematics and Financial Economics and Decisions in Economics and Finance and founding partner of BhFS Behavioral Finance Solutions. His research has been published in the top peer-reviewed journals in Finance and Management. THORSTEN HENS is a professor of financial economics and a member of the directorate of the department of banking and finance at the University of Zurich. He is a founding partner of BhFS Behavioral Finance Solutions, Swiss Fintech Innovations and the University of Zurich Research Priority Program on financial regulation, Finreg. He is a coauthor of 60 peer-reviewed scientific publications and seven books.

• Chapter 1 Introduction 1Chapter 2 Behavioral Biases 52.1 Information Selection Biases 62.2 Information Processing Biases 112.3 Biases after Receiving Feedback 312.4 Are More Heads Smarter Than One? 332.5 Summary of Biases 352.6 Conclusion 39Chapter 3 Cultural Differences in Investors’ Behavior 413.1 What Is Financial Culture? 413.2 The INTRA Study 433.3 Conclusion 46Chapter 4 Neurological Foundations and Biases’ Moderation 474.1 The Human Brain 474.2 Insights for Behavioral Finance 484.3 Moderation of Biases 494.4 Conclusion 50Chapter 5 Diagnostic Tests for Investment Personality 515.1 A Case Study 515.2 Design of Diagnostic Questionnaires 525.3 Knowledge and Investment Experience 535.4 Psychology and Emotions 595.5 Client’s Diagnostic Profile 65Chapter 6 Decision Theory 696.1 Introduction 696.2 A (Very) Short History of Decision Theory 706.3 Expected Utility 736.4 Mean-Variance Analysis 766.5 Prospect Theory 786.6 Rationality of Mean-Variance and Prospect Theory 876.7 The Optimal Asset Allocation 916.8 Comparing the Decision Theories 1026.9 Conclusion 103Chapter 7 Product Design 1057.1 Introduction 1057.2 Case Study 1077.3 Theory of Product Design 1147.4 Structured Products Designed by Customers 1207.5 Conclusion 123Chapter 8 Dynamic Asset Allocation 1258.1 Time Diversification 1268.2 Rebalancing 1298.3 Conclusion 134Chapter 9 Life-Cycle Planning 1379.1 Case Study 1379.2 Case Study Werner Bruni 1399.3 Consumption Smoothing 1409.4 The Life-Cycle Hypothesis 1419.5 The Behavioral Life-Cycle Hypothesis 1439.6 Conclusion 146Chapter 10 Risk Profiling 14710.1 Risk-Profiling Methodologies 14810.2 Comparing Risk-Profiling Methodologies 15110.3 A Case Study 15210.4 The Risk Dimensions 15310.5 Behavioral Risk Profiler 15510.6 Risk Profiling and Its Regulation 16610.7 Conclusion 167Chapter 11 Structured Wealth Management Process 16911.1 Benefits 17211.2 Implementation 17311.3 Regulatory Requirements 17411.4 Structuring the Wealth Management Process 17711.5 Relevance of Different Theories 19611.6 Complying with the Regulatory Requirements 19711.7 Information Technology in Client Advisory Services 197Chapter 12 Fintech 20112.1 History of Fintech 20112.2 Current State of Fintech 20112.3 Assessment of Fintech Solutions 202Chapter 13 Case Studies 20313.1 Case Study 1: Structured Wealth Management 20413.2 Case Study 2: Experience Sampling 20913.3 Case Study 3: Goal-based Approach 210Chapter 14 Conclusions 219Chapter 15 Appendix: Mathematical Arguments 22115.1 Proof that Expected Utility Satisfies the Axioms of Rational Choice 22115.2 Derivation of the Fourfold Pattern of Risk Taking 22215.3 Mean-Variance as a Special Case of Prospect Theory 22215.4 Prospect Theory Optimal Asset Allocation 22415.5 No Time Diversification Theorem 225References 227Index 235