Cover
CHAPTER 1: Introduction
1. NOTES
CHAPTER 2: Behavioral Biases
1. 2.1 INFORMATION SELECTION BIASES
2. 2.2 INFORMATION PROCESSING BIASES
3. 2.3 BIASES AFTER RECEIVING FEEDBACK
4. 2.4 ARE MORE HEADS SMARTER THAN ONE?
5. 2.5 SUMMARY OF BIASES
6. 2.6 CONCLUSION
7. NOTES
CHAPTER 3: Cultural Differences in Investors' Behavior
1. 3.1 WHAT IS FINANCIAL CULTURE?
2. 3.2 THE INTRA STUDY
3. 3.3 CONCLUSION
4. NOTES
CHAPTER 4: Neurological Foundations and Biases' Moderation
1. 4.1 THE HUMAN BRAIN
2. 4.2 INSIGHTS FOR BEHAVIORAL FINANCE
3. 4.3 MODERATION OF BIASES
4. 4.4 CONCLUSION
5. NOTE
CHAPTER 5: Diagnostic Tests for Investment Personality
1. 5.1 A CASE STUDY
2. 5.2 DESIGN OF DIAGNOSTIC QUESTIONNAIRES
3. 5.3 KNOWLEDGE AND INVESTMENT EXPERIENCE
4. 5.4 PSYCHOLOGY AND EMOTIONS
5. 5.5 CLIENT'S DIAGNOSTIC PROFILE
6. NOTES
CHAPTER 6: Decision Theory
1. 6.1 INTRODUCTION
2. 6.2 A (VERY) SHORT HISTORY OF DECISION THEORY
3. 6.3 EXPECTED UTILITY
4. 6.4 MEAN‐VARIANCE ANALYSIS
5. 6.5 PROSPECT THEORY
6. 6.6 RATIONALITY OF MEAN‐VARIANCE AND PROSPECT THEORY
7. 6.7 THE OPTIMAL ASSET ALLOCATION
8. 6.8 COMPARING THE DECISION THEORIES
9. 6.9 CONCLUSION
10. NOTES
CHAPTER 7: Product Design
1. 7.1 INTRODUCTION
2. 7.2 CASE STUDY
3. 7.3 THEORY OF PRODUCT DESIGN
4. 7.4 STRUCTURED PRODUCTS DESIGNED BY CUSTOMERS
5. 7.5 CONCLUSION
6. NOTES
CHAPTER 8: Dynamic Asset Allocation
1. 8.1 TIME DIVERSIFICATION
2. 8.2 REBALANCING
3. 8.3 CONCLUSION
4. NOTES
CHAPTER 9: Life‐Cycle Planning
1. 9.1 CASE STUDY
2. 9.2 CASE STUDY WERNER BRUNI
3. 9.3 CONSUMPTION SMOOTHING
4. 9.4 THE LIFE‐CYCLE HYPOTHESIS
5. 9.5 THE BEHAVIORAL LIFE‐CYCLE HYPOTHESIS
6. 9.6 CONCLUSION
7. NOTES
CHAPTER 10: Risk Profiling
1. 10.1 RISK‐PROFILING METHODOLOGIES
2. 10.2 COMPARING RISK‐PROFILING METHODOLOGIES
3. 10.3 A CASE STUDY
4. 10.4 THE RISK DIMENSIONS
5. 10.5 BEHAVIORAL RISK PROFILER
6. 10.6 RISK PROFILING AND ITS REGULATION
7. 10.7 CONCLUSION
8. NOTES
CHAPTER 11: Structured Wealth Management Process
1. 11.1 BENEFITS
2. 11.2 IMPLEMENTATION
3. 11.3 REGULATORY REQUIREMENTS
4. 11.4 STRUCTURING THE WEALTH MANAGEMENT PROCESS
5. 11.5 RELEVANCE OF DIFFERENT THEORIES
6. 11.6 COMPLYING WITH THE REGULATORY REQUIREMENTS
7. 11.7 INFORMATION TECHNOLOGY IN CLIENT ADVISORY SERVICES
8. NOTES
CHAPTER 12: Fintech
1. 12.1 HISTORY OF FINTECH
2. 12.2 CURRENT STATE OF FINTECH
3. 12.3 ASSESSMENT OF FINTECH SOLUTIONS
4. NOTE
CHAPTER 13: Case Studies
1. 13.1 CASE STUDY 1: STRUCTURED WEALTH MANAGEMENT
2. 13.2 CASE STUDY 2: EXPERIENCE SAMPLING
3. 13.3 CASE STUDY 3: GOAL‐BASED APPROACH
4. NOTE
CHAPTER 14: Conclusions
CHAPTER 15: Appendix: Mathematical Arguments
1. 15.1 PROOF THAT EXPECTED UTILITY SATISFIES THE AXIOMS OF RATIONAL CHOICE
2. 15.2 DERIVATION OF THE FOURFOLD PATTERN OF RISK TAKING
3. 15.3 MEAN‐VARIANCE AS A SPECIAL CASE OF PROSPECT THEORY
4. 15.4 PROSPECT THEORY OPTIMAL ASSET ALLOCATION
5. 15.5 NO TIME DIVERSIFICATION THEOREM
6. NOTES
References
Index
End User License Agreement

List of Tables

Chapter 2
1. TABLE 1 Behavioral biases, consequences for investors, and suggested interventions
Chapter 3
1. TABLE 2 Countries with extreme values of Hofstede's cultural dimension scales
Chapter 4
1. TABLE 3 Cognitive and emotional biases
Chapter 6
1. TABLE 4 Fourfold pattern of risk taking behavior
2. TABLE 5 The asset allocation puzzle
3. TABLE 6 Expected returns
4. TABLE 7 Distribution of assets' returns
5. TABLE 8 Comparison of expected utility, mean‐variance, and prospect theory
Chapter 7
1. TABLE 9 Willingness to pay of an investor with a constant relative risk aversion
2. TABLE 10 Willingness to pay of a prospect theory investor
Chapter 8
1. TABLE 11 Optimal exposure to the risky asset over time with an investment goal
2. TABLE 12 Optimal exposure to the risky asset over time with a liability constraint
Chapter 10
1. TABLE 13 Risk dimensions under behavioral and classical finance paradigms
2. TABLE 14 Key figures for the proposed asset allocation
3. TABLE 15 Key figures of the overruled asset allocation and of the original asset allocation
Chapter 11
1. TABLE 16 Investment styles
2. TABLE 17 The structured wealth management process and the regulatory requirements for private clients
Chapter 13
1. TABLE 18 Summary of Mike and Julie's goals
2. TABLE 19 Input risk profiler for investment amount, reserve, and investment horizon
3. TABLE 20 Input risk profiler for loss aversion
4. TABLE 21 Optimal asset allocation for each goal

List of Illustrations

Chapter 2
1. FIGURE 2.01 Market dynamics and decision behavior of a typical investor
2. FIGURE 2.02 Predicted and true probabilities for runs with the same outcomes
3. FIGURE 2.03 Average return per information level
Chapter 3
1. FIGURE 3.04 Loss aversion
2. FIGURE 3.05 Patience
3. FIGURE 3.06 Probability weighting with large unlikely gains
4. FIGURE 3.07 Probability weighting with large unlikely losses
Chapter 4
1. FIGURE 4.08 The human brain
Chapter 5
1. FIGURE 5.09 Assessing the long‐term returns potential of asset classes
2. FIGURE 5.10 The long‐term return potential of asset classes as seen by Fritz Müller
3. FIGURE 5.11 Assessing the short‐term risk potential of asset classes
4. FIGURE 5.12 The short‐term risk potential as seen by Fritz Müller
5. FIGURE 5.13 Assessing the client's view on the performance contributors
6. FIGURE 5.14 The performance contributors as seen by Fritz Müller
7. FIGURE 5.15 Assessing the client's view on good diversification
8. FIGURE 5.16 Good diversification from Fritz Müller's point of view
9. FIGURE 5.17 Assessment of client's focus on payoffs and probabilities
10. FIGURE 5.18 Fritz Müller's preference for payoffs with high probability
11. FIGURE 5.19 Assessing client's view on the importance of different information sources
12. FIGURE 5.20 The importance of different information sources for Fritz Müller
13. FIGURE 5.21 Assessing client's behavior on random movements of the market
14. FIGURE 5.22 The investment behavior of Fritz Müller on random market movements
15. FIGURE 5.23 Assessing the drivers of client's selling decisions
16. FIGURE 5.24 The driver of Fritz Müller's selling decisions
17. FIGURE 5.25 Diagnostic profile of Fritz Müller
18. FIGURE 5.26 Diagnostic profile of a strategic investor
19. FIGURE 5.27 Diagnostic profile of an exploring investor
20. FIGURE 5.28 Diagnostic profile of a realistic investor
Chapter 6
1. FIGURE 6.29 History of decision theory
2. FIGURE 6.30 Risk aversion and risk premium
3. FIGURE 6.31 Mean‐variance diagram with efficient frontier and risk preferences
4. FIGURE 6.32 Historical performance displayed with different time frames
5. FIGURE 6.33 The value function of Tversky and Kahneman (1992)
6. FIGURE 6.34 Probability weighting function
7. FIGURE 6.35 Probability distribution with annual S&P 500 returns from 1871 to 2010, weighted probabilities and normally distributed returns
8. FIGURE 6.36 Two different products with the same mean and variance
9. FIGURE 6.37 The two‐fund separation theorem
10. FIGURE 6.38 Behavioral efficient frontier (BEF)
11. FIGURE 6.39 Optimal asset allocation with mean‐variance
12. FIGURE 6.40 Optimal asset allocation with expected utility
13. FIGURE 6.41 Optimal asset allocation based on prospect theory
14. FIGURE 6.42 Optimal allocation to asset 1 in Example 6.5 for prospect theory (PT), expected utility theory (EUT), and mean‐variance investors (MV)
Chapter 7
1. FIGURE 7.43 Optimal traditional portfolio from the perspective of a structured product
2. FIGURE 7.44 Possible components of a payoff diagram for a structured product
3. FIGURE 7.45 Payoff of Ladder Pop at maturity
4. FIGURE 7.46 SMI and Ladder Pop prices (indexed) from January 15, 2002, to December 12, 2006
5. FIGURE 7.47 State price density and subjective density for the DAX in April 2000
6. FIGURE 7.48 Optimal structured product for a CRRA investor with α = 0.8
7. FIGURE 7.49 Optimal structured product of a quadratic value function
8. FIGURE 7.50 Optimal structured product for a prospect theory investor with
9. FIGURE 7.51 Optimal structured product for an investor with probability weighting
10. FIGURE 7.52 Payoff of a barrier reverse convertible
11. FIGURE 7.53 The payoff of worst‐of‐basket certificates in dependence of the price of the assets in the underlying basket
12. FIGURE 7.54 A screen‐shot of the interactive multitouch table
13. FIGURE 7.55 Structured products chosen by participants
14. FIGURE 7.56 The average structured product as designed by the participants in the experiment (line) with 95% confidence intervals (circles)
Chapter 8
1. FIGURE 8.57 Relative frequency of bonds outperforming stocks on various investment horizons
2. FIGURE 8.58 Attractiveness of investment strategies in dependence of investor's preferences and investment horizon
3. FIGURE 8.59 The asset allocation without rebalancing when the market goes up (point B) or down (point C) compared to the optimal asset allocation with rebalancing (point A)
Chapter 9
1. FIGURE 9.60 Performance German stocks (DAX) versus bonds (REX)
2. FIGURE 9.61 Dividends from DAX versus interest from REX
3. FIGURE 9.62 Consumption smoothing
4. FIGURE 9.63 Mental accounts
5. FIGURE 9.64 Hyperbolic versus exponential discounting
Chapter 10
1. FIGURE 10.65 Prospect theory and mean‐variance value functions on final wealth to characterize risk preferences
2. FIGURE 10.66 Question on the investment goals
3. FIGURE 10.67 Question on the investment amount and the investment horizon
4. FIGURE 10.68 Question on expectations
5. FIGURE 10.69 Question on investment experience
6. FIGURE 10.70 Question on the attitude toward losses
7. FIGURE 10.71 Question on the attitude to uncertainty
8. FIGURE 10.72 Question on the investment temperament
9. FIGURE 10.73 Risk profile
10. FIGURE 10.74 Asset allocation on the behavioral efficient frontier (BEF) and its characteristics
11. FIGURE 10.75 Overruled asset allocation versus the original asset allocation
12. FIGURE 10.76 Investment performance under different scenarios
13. FIGURE 10.77 Various financial services and their regulation in Switzerland
Chapter 11
1. FIGURE 11.78 Exploiting the comparative advantage of the relationship manager (RM)
2. FIGURE 11.79 Recommended wealth management process
3. FIGURE 11.80 Example of a personal balanced sheet
4. FIGURE 11.81 Assets split
5. FIGURE 11.82 The asset split protects the client from a collapse of diversification
6. FIGURE 11.83 Experience sampling combined with a stack of goals
7. FIGURE 11.84 Loss due to discontinuation of investment strategy
8. FIGURE 11.85 Illustrating the pros and cons of various risk profiles on the roller coaster.
9. FIGURE 11.86 Historic performance of stocks and bonds in various periods
10. FIGURE 11.87 Commitment to strategy illustrated by exit periods
11. FIGURE 11.88 Explaining the reward–risk trade‐off of various asset classes and investment strategies
12. FIGURE 11.89 Constructing the optimal asset allocation
13. FIGURE 11.90 The monitoring process
14. FIGURE 11.91 Integrated goal‐based wealth management process
15. FIGURE 11.92 Advisory process in a digital world
Chapter 13
1. FIGURE 13.93 Daniel's answers to the risk‐profiling questions
2. FIGURE 13.94 Daniel's risk profile
3. FIGURE 13.95a Description of Daniel's risk profile – part I
4. FIGURE 13.95b Description of Daniel's risk profile – part II
5. FIGURE 13.96 Suggested asset allocation
6. FIGURE 13.97 Key figures of the suggested asset allocation
7. FIGURE 13.98 Description of the key figures
8. FIGURE 13.99 Scenario analysis
9. FIGURE 13.100 Average recommendations
10. FIGURE 13.101 Experience sampling
11. FIGURE 13.102 Overall asset allocation of Mike and Julie McGeorge
12. FIGURE 13.103 Simulation of the portfolio distribution
Chapter 15
1. FIGURE 15.104 Risk taking in dependence on the probability for gains and losses

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Published simultaneously in Canada.

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Cover Design: Wiley

CHAPTER 1
Introduction

Behavioral finance is an interdisciplinary research area that combines insights from psychology with finance to better understand investors' behavior and asset prices. It has managed to bridge the gap between theory and practice. Moreover, the psychological research that behavioral finance is based on recently got a foundation in biological differences found in the brain.

Traditional finance has focused on the ideal scenario of thoroughly rational investors in efficient markets. According to this standard paradigm in finance, individuals rationally search for information and know all available actions that serve their preferences. The latter are stable over time and robust to the occurrence of unanticipated events. As a result, rational investors searching for superior returns detect and eliminate any predictability in the asset prices—the market is efficient. According to traditional finance, the market remains efficient even if some investors behave irrationally. Indeed, rational investors will detect any mispricing generated by irrational investors and exploit it with the use of arbitrage strategies, which are assumed to be unlimited.¹ Consequently, any mispricing will very quickly be corrected, irrational investors will be driven out of the market, and the market will again quickly become efficient. A statistical consequence of prices being unpredictable is that returns are (log)‐normally distributed—which is the content of the central limit theorem—a cornerstone of statistics. Consequently, optimal decisions can be taken based on the two parameters of a normal distribution: the mean and the variance. Thus, the mean‐variance optimization and the efficient markets hypothesis are logical consequences of the rationality assumption.

In practice, however, we observe that even professional investors behave irrationally. Moreover, there is empirical evidence that the use of arbitrage strategies to exploit observed mispricing is limited (e.g., implementing an arbitrage strategy could be expensive and typically not at zero risk). The consequence of irrational investors and limited arbitrage is inefficient markets. As we will discuss in detail, investors are not always able to make rational decisions so that market prices show anomalies. For example, investors tend to adopt the behavior of other investors, and this herding behavior causes short‐term predictability that leads ultimately to market crashes. Consequently, asset returns are no longer normally distributed. For example, they have fat tails (i.e., too many very bad returns)—which Taleb (2007) called black swans. Moreover, in inefficient markets, the mean‐variance optimization is no longer rational. Thus, ignoring the insights from behavioral finance can be costly for investors adhering to traditional finance.

Behavioral finance emerged when Nobel laureate Daniel Kahneman and his colleague Amos Tversky conducted psychological research to question the assumptions of rationality—a cornerstone of the classical decision theory. Kahneman & Tversky (1979) developed a new theory, which they called prospect theory. Prospect theory has two phases: an editing phase and an evaluation phase. In the first phase, Kahneman and Tversky show how choice alternatives are mentally coded and transformed to be evaluated in the second phase. The editing phase has developed into a rich knowledge of behavioral biases—the topic of the next section. In the evaluation phase, Kahneman and Tversky develop a new decision model, which is the main content of our section on decision theory. The knowledge of behavioral biases is very valuable for a better understanding of clients in wealth management. Prospect theory also offers a risk measure that is consistent with the client's experience. With this measure one can construct asset allocations that better suit the clients than the asset allocations based on the volatility used in traditional finance. Prospect theory states that investors dislike losses more than volatility. In fact, investors react more to losses than they react to gains. Unlike volatility, the psychological risk measure is not the same for all investors, but is a characteristic of the individual. For this reason and others, the advantages of having a quality risk profiling procedure are numerous.

In this book, we apply these insights from behavioral finance to truly identify the client's situation from a holistic standpoint. With discoveries in the way people deal with information and respond to it in investment risk taking, it is reasonable to say that behavioral finance gives more attention to the investor's behavior. A more realistic investor, as described in behavioral finance, has a different perception and a different understanding of risk than the theoretical investor in the traditional decision theory. Consequently, this investor will need to invest differently than the theoretical investor in the traditional decision theory.

The book combines new research results with practical applications. It draws on the rich research body of behavioral finance and on profound experience in the practice of wealth management. The book starts with the behavioral biases—the mistakes that people make when dealing with information and making financial decisions. The chapter describes the biases, discusses their implications for financial decisions, and suggests strategies with a proven success in moderating the biases. The following four chapters discuss the cultural dimensions of the biases and their biological foundation as well as their moderation and suggest how advisors could proceed in assessing the biases of their clients.

Thereafter, we explain decision theory (rational and behavioral) as a foundation of finance and show how it can be used in the construction of clients' portfolios and for the design of structured products. The question of how optimal portfolios should be adjusted over time is discussed in the following two chapters. The last chapters show how the new insights that behavioral finance has generated can be applied to client advisory, to designing behaviorally founded risk profiles, and to structuring the wealth management process. Thus, our books give a scientific foundation to financial advice given in private banking, which in practice is seen more as an art than a science. We believe that practitioners find some useful foundation for their work and that the transition from the art of advice to the science of advice is not disruptive but smooth.

This book is the second edition of the book Behavioral Finance for Private Banking that was published in the middle of the financial crisis. Many banks and financial advisors used the existing body of knowledge to improve their products and advisory services. A tool that we have developed demonstrates how this can be done.² In addition, this book benefits from insights of new areas of research such as cultural finance, neurofinance, and fintech. Finally, it compares the insights behavioral finance has gained with the new regulatory requirements in Europe (MiFID II) and in Switzerland (FIDLEG).

We are grateful to Mei Wang and Marc Oliver Rieger for their collaboration in the assessment of the cultural dimensions of investors' behavior. Moreover, this work greatly benefited from BhFS Behavioral Finance Solutions, a spinoff firm of the University of Zurich and the University of St. Gallen, which allowed us to present their tools. Last but not least we are grateful to the Wiley team and to Marie Hardelauf for their patience and help in editing our book.

NOTES

1 An arbitrage strategy is a strategy that generates positive returns at no risk. The assumption that arbitrage is unlimited means that arbitrage strategies can be implemented in the real‐world and their costs is low.

2 Access to a demo version of the tool can be requested from info@bhfs.ch.

From the Art of Advice to the Science of Advice

NOTES