, born in Germany 1961, is SFI Professor of Financial Economics at the University of Zurich's Swiss Banking Institute, a Fellow of CEPR and Adjunct Professor of Finance at the Norwegian Business School in Bergen. He studied at Bonn and Paris and held professorships in Stanford, Bielefeld and Zurich. Since 2007 he is the Director of the Swiss Banking Institute and since 2003 the scientific coordinator of NCCR-Finrisk. His research areas are -- among others -- behavioural and evolutionary finance. Thorsten Hens is ranked among the top 10 economics professors in the German spoken area (Germany, Switzerland and Austria). In researching how investors make their decisions, Professor Hens draws on work in psychology and applies insights from biology in order to understand the dynamics of financial markets. His consulting experience includes application of behavioural finance for private banking and evolutionary finance for asset management.
Kremena Bachmann, born in Bulgaria in 1976, currently holds a postdoctoral position at the University of Zurich's Swiss Banking Institute. She received an MS in Finance from the University of St. Gallen (HSG) and a PhD in Finance from the University of Zurich, where she held a research position at the Institute for Empirical Research in Economics. Her research interests are behavioural finance and investment management. Mrs. Bachmann worked on different projects for Credit Suisse Asset Management and Bank Wegelin. Her teaching experience includes lectures on behavioural finance and wealth management at the University of Zurich and the Swiss Training Centre for Investment Professionals (AZEK).
1.1 The Private Banking Business.
1.2 Current Challenges in Private Banking.
1.3 Improving Service Quality with Behavioural Finance.
2. DECISION THEORY.
2.2 Mean-Variance Analysis.
2.3 Expected Utility Theory.
2.4 Prospect Theory.
2.5 Prospect Theory and the Optimal Asset Allocation.
2.6 A Critical View on Mean-Variance Theory.
2.7 A Critical View on Expected Utility Axioms.
2.8 Comparison of Expected Utility, Prospect Theory, and Mean Variance Analysis.
3. BEHAVIOURAL BIASES.
3.1 Information Selection Biases.
3.2 Information-Processing Biases.
3.3 Decision Biases.
3.4 Decision Evaluation Biases.
3.5 Biases in Inter-Temporal Decisions.
3.6 Behavioural Biases and Speculative Bubbles.
3.7 Cultural Differences in the Behavioural Biases.
4. RISK PROFILING.
4.1 Dealing with Behavioural Biases.
4.2 The Risk Profiler and its Benefits.
4.3 Designing a Risk Profiler: Some General Considerations.
4.4 Implemented Risk Profilers: Case Study former Bank Leu.
4.5 A Risk Profiler Based on the Mean-Variance Analysis.
4.6 Integrating Behavioural Finance in the Risk Profiler.
4.7 Case Study: Comparing Risk Profiles.
5. PRODUCT DESIGN.
5.1 Case Study ‘Ladder Pop’.
5.2 Case Study ‘DAX Sparbuch’.
5.3 Optimal Product Design.
6. DYNAMIC ASSET ALLOCATION.
6.1 The Optimal Tactical Asset Allocation.
6.2 The Optimal Strategic Asset Allocation.
7. LIFE CYCLE PLANNING.
7.1 Case Study: Widow Kassel.
7.2 Main Decisions over Time.
7.3 Consumption Smoothing.
7.4 The Life Cycle Hypothesis.
7.5 The Behavioural Life Cycle Hypothesis.
7.6 The Life Cycle Asset Allocation Problem.
7.7 The Life Cycle Asset Allocation of an Expected Utility Maximizer.
7.8 The Life Cycle Asset Allocation of a Behavioural Investor.
7.9 Life Cycle Funds.
7.10 Summary 207.
8. STRUCTURED WEALTH MANAGEMENT PROCESS.
8.1 The Benefits of a Structured Wealth Management Process.
8.2 Problems Implementing a Structured Wealth Management Process.
8.3 Impact of the New Process on Conflicts of Interests.
8.4 Learning by ‘Cycling’ Through the Process.
8.5 Case Study: Credit Suisse.
8.6 Mental Accounting in the Wealth Management Process.
9. CONCLUSION AND OUTLOOK.
9.1 Recapitulation of the main achievements.
9.2 Outlook of further developments.
List of Notation.
List of Figures.
List of Tables.