69,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Wiley Finance Editions
- Sprache: Englisch
An essential framework for wealth management using behavioral finance
Behavioral Finance for Private Banking provides a complete framework for wealth management tailored to the unique needs of each client. Merging behavioral finance with private banking, this framework helps you gain a greater understanding of your client’s wants, needs, and perspectives to streamline the decision making process. Beginning with the theoretical foundations of investment decision making and behavioral biases, the discussion delves into cultural differences in global business and asset allocation over the life cycle of the investment to help you construct a wealth management strategy catered to each individual’s needs. This new second edition has been updated to include coverage of fintech and neurofinance, an extension of behavioral finance that is beginning to gain traction in the private banking space.
Working closely with clients entails deep interpersonal give and take. To be successful, private banking professionals must be as well-versed in behavioral psychology as they are in finance; this intersection is the heart of behavioral finance, and this book provides essential knowledge that can help you better serve your clients’ needs.
- Understand the internal dialogue at work when investment decisions are made
- Overcome the most common behavioral biases—and watch for your own
- Learn how fintech and neurofinance impact all aspects of private banking
- Set up a structured wealth management process that places the client’s needs front and center
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 391
Veröffentlichungsjahr: 2018
Ähnliche
Table of Contents
Cover
CHAPTER 1: Introduction
NOTES
CHAPTER 2: Behavioral Biases
2.1 INFORMATION SELECTION BIASES
2.2 INFORMATION PROCESSING BIASES
2.3 BIASES AFTER RECEIVING FEEDBACK
2.4 ARE MORE HEADS SMARTER THAN ONE?
2.5 SUMMARY OF BIASES
2.6 CONCLUSION
NOTES
CHAPTER 3: Cultural Differences in Investors' Behavior
3.1 WHAT IS FINANCIAL CULTURE?
3.2 THE INTRA STUDY
3.3 CONCLUSION
NOTES
CHAPTER 4: Neurological Foundations and Biases' Moderation
4.1 THE HUMAN BRAIN
4.2 INSIGHTS FOR BEHAVIORAL FINANCE
4.3 MODERATION OF BIASES
4.4 CONCLUSION
NOTE
CHAPTER 5: Diagnostic Tests for Investment Personality
5.1 A CASE STUDY
5.2 DESIGN OF DIAGNOSTIC QUESTIONNAIRES
5.3 KNOWLEDGE AND INVESTMENT EXPERIENCE
5.4 PSYCHOLOGY AND EMOTIONS
5.5 CLIENT'S DIAGNOSTIC PROFILE
NOTES
CHAPTER 6: Decision Theory
6.1 INTRODUCTION
6.2 A (VERY) SHORT HISTORY OF DECISION THEORY
6.3 EXPECTED UTILITY
6.4 MEAN‐VARIANCE ANALYSIS
6.5 PROSPECT THEORY
6.6 RATIONALITY OF MEAN‐VARIANCE AND PROSPECT THEORY
6.7 THE OPTIMAL ASSET ALLOCATION
6.8 COMPARING THE DECISION THEORIES
6.9 CONCLUSION
NOTES
CHAPTER 7: Product Design
7.1 INTRODUCTION
7.2 CASE STUDY
7.3 THEORY OF PRODUCT DESIGN
7.4 STRUCTURED PRODUCTS DESIGNED BY CUSTOMERS
7.5 CONCLUSION
NOTES
CHAPTER 8: Dynamic Asset Allocation
8.1 TIME DIVERSIFICATION
8.2 REBALANCING
8.3 CONCLUSION
NOTES
CHAPTER 9: Life‐Cycle Planning
9.1 CASE STUDY
9.2 CASE STUDY WERNER BRUNI
9.3 CONSUMPTION SMOOTHING
9.4 THE LIFE‐CYCLE HYPOTHESIS
9.5 THE BEHAVIORAL LIFE‐CYCLE HYPOTHESIS
9.6 CONCLUSION
NOTES
CHAPTER 10: Risk Profiling
10.1 RISK‐PROFILING METHODOLOGIES
10.2 COMPARING RISK‐PROFILING METHODOLOGIES
10.3 A CASE STUDY
10.4 THE RISK DIMENSIONS
10.5 BEHAVIORAL RISK PROFILER
10.6 RISK PROFILING AND ITS REGULATION
10.7 CONCLUSION
NOTES
CHAPTER 11: Structured Wealth Management Process
11.1 BENEFITS
11.2 IMPLEMENTATION
11.3 REGULATORY REQUIREMENTS
11.4 STRUCTURING THE WEALTH MANAGEMENT PROCESS
11.5 RELEVANCE OF DIFFERENT THEORIES
11.6 COMPLYING WITH THE REGULATORY REQUIREMENTS
11.7 INFORMATION TECHNOLOGY IN CLIENT ADVISORY SERVICES
NOTES
CHAPTER 12: Fintech
12.1 HISTORY OF FINTECH
12.2 CURRENT STATE OF FINTECH
12.3 ASSESSMENT OF FINTECH SOLUTIONS
NOTE
CHAPTER 13: Case Studies
13.1 CASE STUDY 1: STRUCTURED WEALTH MANAGEMENT
13.2 CASE STUDY 2: EXPERIENCE SAMPLING
13.3 CASE STUDY 3: GOAL‐BASED APPROACH
NOTE
CHAPTER 14: Conclusions
CHAPTER 15: Appendix: Mathematical Arguments
15.1 PROOF THAT EXPECTED UTILITY SATISFIES THE AXIOMS OF RATIONAL CHOICE
15.2 DERIVATION OF THE FOURFOLD PATTERN OF RISK TAKING
15.3 MEAN‐VARIANCE AS A SPECIAL CASE OF PROSPECT THEORY
15.4 PROSPECT THEORY OPTIMAL ASSET ALLOCATION
15.5 NO TIME DIVERSIFICATION THEOREM
NOTES
References
Index
End User License Agreement
List of Tables
Chapter 2
TABLE 1 Behavioral biases, consequences for investors, and suggested interventions
Chapter 3
TABLE 2 Countries with extreme values of Hofstede's cultural dimension scales
Chapter 4
TABLE 3 Cognitive and emotional biases
Chapter 6
TABLE 4 Fourfold pattern of risk taking behavior
TABLE 5 The asset allocation puzzle
TABLE 6 Expected returns
TABLE 7 Distribution of assets' returns
TABLE 8 Comparison of expected utility, mean‐variance, and prospect theory
Chapter 7
TABLE 9 Willingness to pay of an investor with a constant relative risk aversion
TABLE 10 Willingness to pay of a prospect theory investor
Chapter 8
TABLE 11 Optimal exposure to the risky asset over time with an investment goal
TABLE 12 Optimal exposure to the risky asset over time with a liability constraint
Chapter 10
TABLE 13 Risk dimensions under behavioral and classical finance paradigms
TABLE 14 Key figures for the proposed asset allocation
TABLE 15 Key figures of the overruled asset allocation and of the original asset allocation
Chapter 11
TABLE 16 Investment styles
TABLE 17 The structured wealth management process and the regulatory requirements for private clients
Chapter 13
TABLE 18 Summary of Mike and Julie's goals
TABLE 19 Input risk profiler for investment amount, reserve, and investment horizon
TABLE 20 Input risk profiler for loss aversion
TABLE 21 Optimal asset allocation for each goal
List of Illustrations
Chapter 2
FIGURE 2.01 Market dynamics and decision behavior of a typical investor
FIGURE 2.02 Predicted and true probabilities for runs with the same outcomes
FIGURE 2.03 Average return per information level
Chapter 3
FIGURE 3.04 Loss aversion
FIGURE 3.05 Patience
FIGURE 3.06 Probability weighting with large unlikely gains
FIGURE 3.07 Probability weighting with large unlikely losses
Chapter 4
FIGURE 4.08 The human brain
Chapter 5
FIGURE 5.09 Assessing the long‐term returns potential of asset classes
FIGURE 5.10 The long‐term return potential of asset classes as seen by Fritz Müller
FIGURE 5.11 Assessing the short‐term risk potential of asset classes
FIGURE 5.12 The short‐term risk potential as seen by Fritz Müller
FIGURE 5.13 Assessing the client's view on the performance contributors
FIGURE 5.14 The performance contributors as seen by Fritz Müller
FIGURE 5.15 Assessing the client's view on good diversification
FIGURE 5.16 Good diversification from Fritz Müller's point of view
FIGURE 5.17 Assessment of client's focus on payoffs and probabilities
FIGURE 5.18 Fritz Müller's preference for payoffs with high probability
FIGURE 5.19 Assessing client's view on the importance of different information sources
FIGURE 5.20 The importance of different information sources for Fritz Müller
FIGURE 5.21 Assessing client's behavior on random movements of the market
FIGURE 5.22 The investment behavior of Fritz Müller on random market movements
FIGURE 5.23 Assessing the drivers of client's selling decisions
FIGURE 5.24 The driver of Fritz Müller's selling decisions
FIGURE 5.25 Diagnostic profile of Fritz Müller
FIGURE 5.26 Diagnostic profile of a strategic investor
FIGURE 5.27 Diagnostic profile of an exploring investor
FIGURE 5.28 Diagnostic profile of a realistic investor
Chapter 6
FIGURE 6.29 History of decision theory
FIGURE 6.30 Risk aversion and risk premium
FIGURE 6.31 Mean‐variance diagram with efficient
frontier and risk preference
s
FIGURE 6.32 Historical performance displayed with different time frames
FIGURE 6.33 The value function of Tversky and Kahneman (1992)
FIGURE 6.34 Probability weighting
function
FIGURE 6.35 Probability distribution with annual S&P 500 returns from 1871 to 2010, weighted probabilities and normally distributed returns
FIGURE 6.36 Two different products with the same mean and variance
FIGURE 6.37 The two‐fund separation theorem
FIGURE 6.38 Behavioral efficient frontier (BEF)
FIGURE 6.39 Optimal asset allocation with mean‐variance
FIGURE 6.40 Optimal asset allocation with expected utility
FIGURE 6.41 Optimal asset allocation based on prospect theory
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
FIGURE 7.43 Optimal traditional portfolio from the perspective of a structured product
FIGURE 7.44 Possible components of a payoff diagram for a structured product
FIGURE 7.45 Payoff of Ladder Pop at maturity
FIGURE 7.46 SMI and Ladder Pop prices (indexed) from January 15, 2002, to December 12, 2006
FIGURE 7.47 State price density and subjective density for the DAX in April 2000
FIGURE 7.48 Optimal structured product for a CRRA investor with α = 0.8
FIGURE 7.49 Optimal structured product of a quadratic value function
FIGURE 7.50 Optimal structured product for a prospect theory investor with
FIGURE 7.51 Optimal structured product for an investor with probability weighting
FIGURE 7.52 Payoff of a barrier reverse convertible
FIGURE 7.53 The payoff of worst‐of‐basket certificates in dependence of the price of the assets in the underlying basket
FIGURE 7.54 A screen‐shot of the interactive multitouch table
FIGURE 7.55 Structured products chosen by participants
FIGURE 7.56 The average structured product as designed by the participants in the experiment (line) with 95% confidence intervals (circles)
Chapter 8
FIGURE 8.57 Relative frequency of bonds outperforming stocks on various investment horizons
FIGURE 8.58 Attractiveness of investment strategies in dependence of investor's preferences and investment horizon
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
FIGURE 9.60 Performance German stocks (DAX) versus bonds (REX)
FIGURE 9.61 Dividends from DAX versus interest from REX
FIGURE 9.62 Consumption smoothing
FIGURE 9.63 Mental accounts
FIGURE 9.64 Hyperbolic versus exponential discounting
Chapter 10
FIGURE 10.65 Prospect theory and mean‐variance value functions on final wealth to characterize risk preferences
FIGURE 10.66 Question on the investment goals
FIGURE 10.67 Question on the investment amount and the investment horizon
FIGURE 10.68 Question on expectations
FIGURE 10.69 Question on investment experience
FIGURE 10.70 Question on the attitude toward losses
FIGURE 10.71 Question on the attitude to uncertainty
FIGURE 10.72 Question on the investment temperament
FIGURE 10.73 Risk profile
FIGURE 10.74 Asset allocation on the behavioral efficient frontier (BEF) and its characteristics
FIGURE 10.75 Overruled asset allocation versus the original asset allocation
FIGURE 10.76 Investment performance under different scenarios
FIGURE 10.77 Various financial services and their regulation in Switzerland
Chapter 11
FIGURE 11.78 Exploiting the comparative advantage of the relationship manager (RM)
FIGURE 11.79 Recommended wealth management process
FIGURE 11.80 Example of a personal balanced sheet
FIGURE 11.81 Assets split
FIGURE 11.82 The asset split protects the client from a collapse of diversification
FIGURE 11.83 Experience sampling combined with a stack of goals
FIGURE 11.84 Loss due to discontinuation of investment strategy
FIGURE 11.85 Illustrating the pros and cons of various risk profiles on the roller coaster.
FIGURE 11.86 Historic performance of stocks and bonds in various periods
FIGURE 11.87 Commitment to strategy illustrated by exit periods
FIGURE 11.88 Explaining the reward–risk trade‐off of various asset classes and investment strategies
FIGURE 11.89 Constructing the optimal asset allocation
FIGURE 11.90 The monitoring process
FIGURE 11.91 Integrated goal‐based wealth management process
FIGURE 11.92 Advisory process in a digital world
Chapter 13
FIGURE 13.93 Daniel's answers to the risk‐profiling questions
FIGURE 13.94 Daniel's risk profile
FIGURE 13.95a Description of Daniel's risk profile – part I
FIGURE 13.95b Description of Daniel's risk profile – part II
FIGURE 13.96 Suggested asset allocation
FIGURE 13.97 Key figures of the suggested asset allocation
FIGURE 13.98 Description of the key figures
FIGURE 13.99 Scenario analysis
FIGURE 13.100 Average recommendations
FIGURE 13.101 Experience sampling
FIGURE 13.102 Overall asset allocation of Mike and Julie McGeorge
FIGURE 13.103 Simulation of the portfolio distribution
Chapter 15
FIGURE 15.104 Risk taking in dependence on the probability for gains and losses
Guide
Cover
Table of Contents
Begin Reading
Pages
C1
ii
iii
iv
1
2
3
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
125
126
127
128
129
130
131
132
133
134
135
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
235
236
237
238
239
240
241
242
243
244
E1
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers' professional and personal knowledge and understanding.
The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e‐commerce, risk management, financial engineering, valuation and financial instrument analysis, as well as much more.
For a list of available titles, visit our website at www.WileyFinance.com.
Behavioral Finance for Private Banking
From the Art of Advice to the Science of Advice
Second Edition
KREMENA BACHMANNENRICO G. DE GIORGITHORSTEN HENS
Copyright © 2018 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc, Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 646‐8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc, 111 River Street, Hoboken NJ07030, (201) 748‐6011, fax (201) 748‐6008, or online at www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762‐2974; outside the United States at (317) 572‐3993, or fax (317) 572‐4002.
Wiley publishes in a variety of print and electronic formats and by print‐on‐demand. Some material included with standard print versions of this book may not be included in e‐books or in print‐on‐demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.
Library of Congress Cataloging‐in‐Publication Data is Available
ISBN 9781119453703 (cloth)
ISBN 9781119453734 (ePDF)
ISBN 9781119453710 (ePub)
Cover Design: Wiley
Cover Image: © Nomad_Soul/Shutterstock; © Menna/Shutterstock; © crystal51/Shutterstock; © Peshkova/Shutterstock
CHAPTER 1Introduction
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
.
CHAPTER 2Behavioral Biases
Behavioral finance research is driven by observations suggesting that individuals' decisions can be irrational and different from what previous theories assume. In this chapter, we will see that individuals' decisions can be systematically wrong because people's decisions are driven by emotions or misunderstandings or because people use inappropriate rules of thumb, also called heuristics, to handle information and make decisions. Certainly, financial markets are very complex so that optimization can lead to fragile results and good heuristics are preferable.1 But what is typically observed is that people apply successful heuristics from other domains without properly assessing their effect in the investment domain. One example for the latter is adaptive learning, which is very successful in many day‐to‐day situations like choosing food: One tries out a new wine. If one likes it, one buys it again. However, in finance it leads to buying assets when they are expensive and selling them when they are cheap, as the roller coaster in Figure 2.01 illustrates.
FIGURE 2.01 Market dynamics and decision behavior of a typical investor
To more deeply understand why we may observe such behavior, we consider a typical decision‐making process and discuss how each stage of the process can be biased. First, decision makers select the information that appears to be relevant for their decisions. Then, they process the selected information to form beliefs and to compare alternatives. After deciding, individuals receive new information as a feedback. This feedback influences, in return, the way the decision makers search for more data, that is, the loop is closed.
The chapter provides evidence that certain mistakes can occur in each of these steps. It discusses the relevance of these mistakes for investors and suggests strategies to avoid the mistakes.
2.1 INFORMATION SELECTION BIASES
When confronted with information, individuals need to judge how relevant it is for the task they need to handle. Thereby individuals seem to consider only particular information while disregarding other that might be relevant as well. For investment decisions, such information filtering can be dangerous since there is uncertainty about the relative importance of economic factors for the future—investment rules that have worked in the past do not always work in the future. So, are there any patterns in the way people select relevant information, and why should we expect that their impact is systematic?
2.1.1 Attention Bias
The first observation on individuals' selection of information is that it can be biased due to a specific task. People gather information that they think is relevant for dealing with the problem and disregard others, which they would otherwise notice. This is demonstrated in an experiment, where participants have been asked to watch a video with two basketball teams: one team wearing black shirts and another one wearing white shirts (Simons & Chabris, 1999). The task was to count the passes of the white team. Afterward, participants have been asked whether they have observed something unusual. Some participants spotted that there was a second ball. But only a few noticed a big black gorilla walking slowly through the picture, stopping in the middle, winking, and passing slowly away. The reason for not seeing the gorilla is the attention bias. Due to the limited attention that people have, they can get only the information they consider important for solving a specific task. All other information remains disregarded, independent of how extreme it is. Hence, when people focus too much on one task, something unexpected can happen that they might not notice. Moreover, related experiments show that even when people know that something unexpected might happen (e.g., that a gorilla would appear), this doesn't help them notice otherunexpected things.
Relevance for Investors and Moderation The attention bias is relevant for investors because all investors use media to inform themselves. But the media process follows certain patterns. Some media set the agenda, other media follow, and for some time all media report the same story. In these times, other investment relevant information is not seen—like the gorilla in the experiment just mentioned. For example, in summer 2011 we observed a global stock market downturn: From the end of July to the end of August, the DJIA fell from 12,700 to 10,700, the Euro Stoxxs 50 fell from 2,800 to 2,200, and the Nikkei from 10,000 to 8,750 (i.e., stock markets plunged by 16%, 21% and 12.5%, respectively). Looking at the words Internet users searched in Google2 during summer 2011, we see that the public attention mainly focused on the US debt ceiling debate that was positively resolved by August 1st. So why did stocks decline after the showdown in the US Congress was resolved? One explanation is that the gorilla “US recession” was not seen in July, so the attention for a possible recession in the United States was hidden behind the budget ceiling debate while after that debate was over the recession attracted the attention of the public. Indeed, the search for the words “US debt” peaked in July 2011 while the words “US recession” peaked in August 2011. And indeed, the US business cycle slowed down considerably during the summer of 2011.
The best moderation of the attention bias is to agree on certain key information (e.g., macroeconomics, politics, valuation levels, sentiment of the market) that one always discusses with the investors irrespectively of whether it is topical or not.
2.1.2 Selective Perception Based on Experience
Perception of information is, by its nature, always selective. But in many situations people might not be able to see things just because they do not expect them to occur given their experience. This has been demonstrated in an experiment with playing cards (Bruner & Postman, 1949). Participants were shown five playing cards and asked what they have seen. What researchers were testing is whether the participants would recognize doctored cards (e.g., a black three of a heart). They found out that, on average, participants needed four times longer to recognize a doctored card than a normal card. Most of the people were very sure that the doctored card was a normal card. Even when participants recognized that something was wrong, they sometimes misperceived the incongruity (e.g., people who were shown a black four of hearts declared that the spades were “turned the wrong way”). This experiment shows that experience can influence the way people look at new evidence. When people have enough experience with a specific situation, they often see what they expect to see based on their experience. Hence, in some cases, experience may lower performance.
Relevance for Investors and Moderation To give an example of how selective perception can affect investments, recall the stock market crash in the years 2007–2008. From the summer of 2007 to the beginning of 2009, the DJIA fell from 14,100 to 6,525, the Euro Stoxxs 50 from 4,500 to 1,800 and the Nikkei from 18,250 to 7,125—that is, stock markets plunged between 50% and 60% around the world. Unfortunately, none of the standard indicators could predict this decline. The P/E ratios and the Fed measure that could predict for example the crash of the dot‐com bubble signaled no risk during the summer of 2007. Investors who used those risk measures because of the positive experience with them were caught by surprise during the stock market crash of 2007–2008. Indeed, that stock market crash did not come from overvaluation of stocks but from a bubble in the housing market in the United States, the United Kingdom, and Spain. This housing bubble resulted in a financial crisis, which then slowed down the global economy. Thus, experience with some indicators might seduce investors to stop thinking transversally.
The best way to deal with the selective perception bias is to ask yourself: What is my motivation to see things in a certain way? What expectations did I bring into the situation? Why do others not share my view?
2.1.3 Confirmation Bias
Previous experience influences the way we perceive information that we face, but it also affects the way we search for information. People tend to search for information that confirms one's beliefs or hypotheses, while they give disproportionately less consideration to alternative possibilities. This bias in information selection is known as the confirmation bias. It has been first discovered by Wason (1960). In his experiment, participants were asked to identify a rule applied to triples of numbers (e.g., 2, 4, and 6). To discover the rule, participants could decide on their own triples and receive a feedback on whether their numbers conform to the rule or not. While the true rule was “three numbers of increasing order of magnitude,” most participants tested a specific hypothesis as for example “increasing by 2.” However, those who test their rule can never discover that their rule is wrong because all examples that fit their rule fit also the true rule. Thus, to test the rule “increasing by 2,” it is critical to try, for example, 2, 4, and 7.
Although there are circumstances where searching for confirmatory evidence can be useful in testing a particular hypothesis (Klayman & Ha, 1987), it is unlikely that people are aware of them and adjust their test strategy. It is more likely that people use the same test strategy that can be useful in certain circumstances, but which, like any all‐purpose heuristic, can lead to serious mistakes.
In another experiment, individuals were asked to decide whether the costs of alternative treatment methods should be covered by the mandatory insurance or not (Jonas, Schulz‐Hardt, Frey, & Thelen, 2001). They have been offered different expert reports, each of them providing arguments why these costs should be covered by the mandatory insurance and why not as a preparation for a final decision. The participants showed a clear preference for reports that supported their initial opinion. Such biased information search can lead to the maintenance of the initial opinion, even if this position is not justified based on all available information.
Relevance for Investors and Moderation Like the experimental evidence already presented, different investors reading the same article discussing the future development of an asset may come to different conclusions regarding the prospects of the asset, depending on whether they hold the asset. As experiments suggest, it is more difficult to recognize news about a company as negative when holding shares of that company than when holding cash (Kuhnen & Knutson, 2011). Again, confirmation is sought but not information.
As a possible moderation, it is important to seek discussion with people who hold the opposite position. Thereby, one should try to avoid the natural impulse to seek for reasons why the opponent's opinion is wrong. Instead, one should listen to the arguments and evaluate them as rationally as possible. To avoid the tendency to see evidence in support of previous investment decisions, one could ask: How would I decide in the face of the new evidence if I must decide again today?
2.1.4 Availability Bias
Finally, the perception of information is influenced by its properties. Concrete, imaginable, and exciting information is more easily perceived and stored than abstract or statistical data. Such kind of information is also more “available” and easy to retrieve when one tries to think of an instance. This is the reason, why there is a discrepancy between people's judgment on the likelihood of an event and the statistical data. For example, most Americans think that homicide or car accidents kill more people than diabetes and stomach cancer and that tornados claim more lives than lightning, while the statistical evidence show that it is exactly other way around (Combs & Slovic, 1979). This bias in the perception is called availability bias. Because car accidents, tornadoes and murderers are on the headlines, they are more easily perceived and stored in memory than other information so that when people try to think of an instance this information influences the probability judgments because of its high availability. A close cousin to availability is vividness. It usually refers to how concrete and imaginable or how exciting some information is. Experiments show that decision makers are affected more strongly by vivid information than by abstract information.
Relevance for Investors and Moderation A famous study by Barber & Odean (2008) shows that individual investors are net buyers of attention‐grabbing stocks. For example, they buy into stocks in the news, stocks experiencing high abnormal trading volume, and stocks with extreme one‐day returns. Attention‐driven buying results from the difficulty that investors have searching the thousands of stocks they can potentially buy. Individual investors do not face the same search problem when selling because they tend to sell only stocks they already own. Barber and Odean find that many investors consider purchasing only stocks that have first caught their attention. Thus, preferences determine choices after attention has determined the choice set. However, attention‐driven investments do not generate superior returns.
Overall, the information selection biases make investors use either a subset of evidence or evidence that is inappropriate for the decision problem. This motivates the development of erroneous beliefs and hinders learning. One approach to correct these developments is to compare explicitly over‐ and underestimated dangers with evidence for the opposite view. Advisors should, however, be cautious not to induce the opposite effect—that is, motivate clients who previously overestimated some risks to underestimate them. It is best is to show long‐term empirical evidence for similar cases.
2.2 INFORMATION PROCESSING BIASES
Selected information needs to be evaluated. What does the evidence say about the likelihood of events? Which alternative is now more attractive? Some rules of thumb cause systematic misperceptions.
2.2.1 Representativeness Bias
When making judgments, people often rely on the degree to which their observations represent known characteristics. This rule of thumb is called representativeness bias.
To give an example of tasks where the representativeness bias can affect decisions, suppose that one observes A and needs to judge whether it comes from B or from C, where B and C are samples of observations with different characteristics. For example, A might be a person (e.g., a fund manager) and B might be a group (e.g., fund managers with skills) and C might be another group (e.g., fund managers without any skills). The judgment task is to estimate the probability that the person is a member of the group B (e.g., that the fund manager is skilled). Similarly, A could be an event and B might be a process. For example, B might be the process of flipping a fair coin and A might be the event of getting six tails in a row. The judgment task could then concern the estimation of the likelihood for observing the event with an unbiased coin.
Let us now consider some examples of how the representativeness bias can affect decisions. Suppose that a fund manager is known to beat the market in two of three years. Let B mean that the manager beats the market and F mean that the manager fails to beat the market. Now consider the following protocols of the success of the manager: (a) BFBBB; (b) FBFBBB; and (c) FBBBBB. Which of the three protocols is most likely?
Most of the people answering this question consider protocol (b) as most likely. The reason for their judgment is that if the manager beats the market in two of three years, the probability for success is two‐thirds. Hence, a protocol is considered as most likely if the protocol's realizations match this probability. In protocol (a) the manager beats the market in four of five years, but in protocol (b) the manager beats the market in four of six years. Comparing the success rate in the protocols with the expected probability for success given in the description of the problem, people looking for a closer match judge protocol (b) as more likely. However, protocol (b) is in fact equivalent to protocol (a) but it has the additional condition that in the first year the manager fails to beat the market. By the properties of conjunct probabilities, it is less likely to observe protocol (b) than protocol (a).3
The observation that people fail to apply the conjunction rule correctly is known as the conjunction fallacy. It describes the tendency to overestimate the probability of conjunctive events. For example, suppose that you can build a complex machine consisting of 500 independent parts. Suppose also that each part were 99% reliable when used the very first time. What are the chances that the system would work on its first attempt? The answer is less than 1%, which surprises many people. In the example with the fund managers, the fallacy emerges due to the representativeness bias.
The representativeness bias emerges very often when people deal with small samples. People start to believe that a sample randomly drawn from a population should resemble other samples drawn randomly from the same population more closely than statistical sampling theory would predict. However, randomly drawn small samples may look quite different than larger samples drawn from the same population.
To demonstrate this, one could draw random numbers from 0 to 100 and order them in 10 equally large bins. The relative frequency of the numbers in each bin should be 10% as each number is equally likely to be drawn. This is true for a sample with 10,000 observations. However, a smaller sample with 10 observations, for example, may look quite different—that is, some bins may contain more than 10% of the observations; other bins may be empty. The smaller sample should, however, be considered as random as the sample with 10,000 observations, although each distribution looks different.
In some instances, the reliance on stereotypes leads people to ignore the relative frequency with which events occur (base rates). This has been demonstrated in an experiment where participants were told that psychologists have been interviewed and administrated personality tests of engineers and lawyers (Kahneman & Tversky, 1973). Based on this information, the psychologists have written thumbnail descriptions. For example, a description of an engineer was:
Jack is a 45‐year‐old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies, which include home carpentry, sailing, and mathematical puzzles.
One group of participants was told that there were 30 engineers and 70 lawyers. Another group was told that there were 70 engineers and 30 lawyers. When asked to estimate the probability that someone randomly selected from the pool of 100 descriptions would be an engineer, the average estimate in the first group was 30% and in the second group 70%. In other words, participants in both groups used the base rates given in the problem. However, when participants were provided with descriptive information as shown about Jack, they tended to ignore the base rates. The average estimate in both groups was the same. Thereby, it did not matter whether the information was informative or not. Even provided with information that is equally descriptive for an engineer or a lawyer, participants ignored on average the base rates and gave a median probability estimate of 50%. Hence, participants ignored the base rate information and simply judged the description as equally representative of an engineer and a lawyer. This observation remained in the literature as the base rate fallacy.
When do people tend to neglect base rates? People appear to use base rates when they are consistent with their intuitive theories on cause and effect. In one experiment, participants were asked to predict the average grade points of a student based on either causal factors (such as the number of hours in a week spent for preparation) or noncausal information (such as student's income). Participants were told that noncausal factors have the same predictive power as causal factors but on average participants used base rates more often when the information was causal.
Relevance for Investors and Moderation The representativeness bias has important implications for investors looking for price patterns that they could exploit. After a short sequence of positive returns, they might develop the belief that the economics producing them has turned in favor of good returns, even though this might not be true. Indeed, De Bondt & Thaler (1985) showed that portfolios of prior losers (stocks with recent negative performance) outperformed portfolios of past winners (stocks with recent positive performance). That is, representativeness bias led investors to overreact to positive (negative) information relative to prior winners, as these appeared more representative for the recently observed good (bad) returns. The best moderation to address this fallacy is to reveal it by statistical evidence.
2.2.2 Conservatism
There are also circumstances where people overweight the base rates and ignore new information. This is called conservatism.
The famous Monty Hall problem (derived from the TV show Let's Make a Deal) is one example. In this problem, there are three doors; two have a goat and one a car behind it. You are asked to choose one of the doors—not knowing which door hides which object. Then, before you can open it someone who knows what is behind the doors opens one of the doors you have not chosen that hides a goat. The question is whether observing this action you need to swap away from your door to the other door that is still closed. Many people answer “no,” because at that point there are two doors and one car and one goat left. So, they assume that the chance of getting the car when sticking to the originally chosen door is 50%. However, this is wrong, since the action of opening a door that you have not chosen and behind which there is a goat reveals important information. Indeed, comparing the two strategies “sticking to your door” and “swapping with the other door” shows that following the latter you win in two out of three cases, while the former is only successful half that often.
Another example that shows the effect of conservatism has been proposed by Edwards (1968):
There are two urns; each one contains 10 balls. Urn A contains 7 red and 3 blue balls, while urn B contains 3 red and 7 blue balls. One urn is randomly chosen by flipping a fair coin. 12 balls are now drawn from this urn with replacement. The result is the following: 8 red and 4 blue balls were drawn. What is the probability that the randomly drawn urn is urn A when observing this result (8 red and 4 blue balls)?
