Behavioral Finance E-Book

Rolf J. Daxhammer

• Herausgeber: UVK Verlag
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2023

Beschreibung

Over the last 50 years, neoclassical financial theory has been dominating our perception of what is happening in financial markets. It has spurred numerous valuable theories and concepts all based on the concept of Homo Economicus, the strictly rational economic man. However, humans do not always act in a strictly rational manner. For students and practitioners alike, our book aims at opening the door to another perspective on financial markets: a behavioral perspective based on a Homo Oeconomicus Humanus. This agent acts with limited rationality when making decisions. He/she uses heuristics and shortcuts and is prone to the influence of emotions. This sounds familiar in real life and can be transferred to what happens in financial markets, too.

Leseprobe

Dr. Rolf J. Daxhammer is professor for Financial Markets at ESB Business School, Reutlingen University. His teaching and research interests are International Financial Markets, Investment Banking and Behavioral Finance. In his consulting work he is engaged in projects in Private Wealth Management und Financial Nudging, amongst others.

Máté Facsar is Vice President Sales for Management Consulting & Professional Services Firms at FactSet, a global provider of integrated financial information and analytical applications. His close cooperation with Leaders in Asset and Wealth Management over the last decade enables him to monitor the application of Behavioral Finance and to address the challenges Portfolio Managers and Wealth Advisors face.

Zsolt Papp, Managing Director, is a senior investment specialist in Global Fixed Income, Currency and Commodities group of J.P. Morgan Asset Management, a global leader in asset management services. He has 30 years’ experience in the financial industry in the UK and Switzerland on the sell-side and buy-side, with a special focus on emerging markets.

Rolf J. DaxhammerMáté FacsarZsolt Papp

Behavioral Finance

Limited Rationality in Financial Markets

3rd edition

Umschlagmotiv: © deli - Fotolia.com

Bibliografische Information der Deutschen Nationalbibliothek

Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.dnb.de abrufbar.

3rd Edition 2023

DOI: https://doi.org/10.24053/9783739881195

© UVK Verlag 2023

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ISBN 978-3-7398-3119-0 (Print)

ISBN 978-3-7398-8119-5 (ePDF)

ISBN 978-3-7398-0586-3 (ePub)

Preface 3rd Edition

The most obvious adjustment in the 3rd edition of “Behavioral Finance” is its language. This is the first time, that it is available in English, too. And with the additional language comes another author: Zsolt Papp. He brings the weight of thirty years in the financial industry to the team, adding even more “reality check” to the book’s blend of theoretical rigour and practical perspective.

As for all good economists the prime motivation for offering a product variation is demand. Over the last ten years we have learnt that our approach to insights into Behavioral Finance is not only appreciated by a German speaking audience, but by an international one, too. So, the basic concept of the book remains unchanged. In addition, we have added some up-to-date information especially in chapter 5 on historic and recent speculative bubbles and in chapter 13 on latest developments.

With all that we hope that the reader will enjoy this 3rd edition as much as the previous ones.

December 2022: Rolf Daxhammer, Máté Facsar and Zsolt Papp

Online Knowledge Check available:https://files.narr.digital/9783739831190/Check.zip

Dedication

To Gela Daxhammeras well to Josef Daxhammerand Katharina Daxhammer

To Fanny Facsarand Gábor Facsar

To Mária Erzsébet and Sándor Pappand Marcsi

Table of Content

Preface 3rd Edition

Dedication

Introduction

Section I − The Homo Economicus in the center of Traditional Finance

1How Neoclassical Theory shaped rational economic behavior

1.1From Traditional Finance to Emotional Finance

1.2Classical theories of Traditional Finance

1.2.1The rational economic market participant according to Smith

1.2.2Random Walk Theory according to Bachelier

1.2.3Expected Utility Theory according to von Neumann & Morgenstern

1.2.4Information processing according to Bayes

1.2.5Efficient Market Hypothesis according to Fama

Summary Chapter 1

2Limitations of Traditional Finance

2.1Models of Neoclassical Capital Market Theory

2.1.1Portfolio Selection Theory

2.1.2Capital Asset Pricing Model (CAPM)

2.1.3Arbitrage Pricing Theory as an alternative to CAPM

2.2Valuation methods as a basis for financial decisions

2.2.1Fundamental Analysis

2.2.2Technical Analysis

2.3Old vs. new reality – the Black Swan

Summary Chapter 2

Concluding remarks Section I

Section II – Recurring speculative bubbles – triggered by the Homo Economicus Humanus

3Investor behavior from the perspective of Behavioral Finance

3.1Starting point and objective of Behavioral Finance

3.1.1Evolving concept of rationality

3.1.2Departure from the Expected Utility Theory – Bounded Rationality

3.2Change of perspective within the framework of Behavioral Finance

3.2.1Comparison of neoclassical and behavioral capital market theory

3.2.2Research methods of Behavioral Finance

3.2.3The investor in the course of time

Summary Chapter 3

4Speculative bubbles as a sign of market anomalies

4.1Causes of speculative bubbles and their intensification

4.1.1Herding

4.1.2Limits of arbitrage

4.2Anatomy of speculative bubbles according to Kindleberger & Minsky

4.3Detailed review of bubbles and market anomalies

4.3.1Significance of speculative bubbles for economies

4.3.2Types of speculative bubbles

4.3.3Types of capital market anomalies

Summary Chapter 4

5Speculative bubbles from the 17th to 21st century

5.1Benoit Mandelbrot’s market characteristics

5.2Examples of significant speculative bubbles

5.2.1The Tulip Mania of 1636

5.2.2The Mississippi bubble of 1716

5.2.3The stock market boom and crash of 1929

5.2.4The dot-com speculative bubble of the late 1990s

5.2.5The U.S. real-estate credit bubble between 2001 and 2006

5.2.6Speculative bubbles after the U.S. mortgage crisis

5.3Indications of speculative bubbles in Private Equity

Summary Chapter 5

Concluding remarks Section II

Section III – The Homo Economicus Humanus within the information and decision-making process

6Information and Decision-Making Process

6.1Phases of the information and decision-making process

6.1.1Information perception

6.1.2Information Processing/Evaluation

6.1.3Investment Decision

6.2Basis of decision-making from the perspective of Behavioral Finance

6.2.1Decision-making based on Prospect Theory

6.2.2Features of the valuation functions

6.2.3Valuation of securities based on the Prospect Theory

Summary Chapter 6

7Limited rationality during information perception

7.1Heuristics of cognitive origin

7.1.1Misperception of probabilities

7.1.2Misinterpretation of information

7.2Heuristics of emotional origin

7.3Assessment of the risk/return-harmfulness of reviewed heuristics

Summary Chapter 7

8Limited rationality during information processing

8.1Heuristics of cognitive origin

8.1.1Misperception of probabilities

8.1.2Misperception of information

8.1.3Misperception of objective reality

8.1.4Misperception of one’s own abilities

8.2Heuristics of emotional origin

8.3Assessment of the risk/return-harmfulness of the heuristics considered

Summary Chapter 8

9Limited rationality during decision-making

9.1Heuristics of cognitive origin

9.1.1Misperception of objective reality

9.1.2Misperception of own abilities

9.2Heuristics of emotional origin

9.2.1Misperception of objective reality

9.2.2Misperception of one’s own abilities

9.3Assessment of the risk-/return-harmfulness of the considered heuristics

9.4Overview of the heuristics considered in the information and decision-making process

Summary Chapter 9

Concluding remarks Section III

Section IV – Applications of Behavioral Finance and Recent Developments

10Applications of Behavioral Finance in Wealth Management

10.1Overview of limited rational behavior in investment advice

10.2Dealing with heuristics in investment advice

10.2.1Applied heuristics during information perception

10.2.2Applied heuristics during information processing

10.2.3Applied heuristics during decision-making

Summary Chapter 10

11Application of Behavioral Finance in corporate governance

11.1Overconfidence in entrepreneurial investment decisions

11.2Dividend policy from the perspective of Behavioral Finance

11.3Initial Public Offerings from the perspective of Behavioral Finance

11.4Corporate Governance from the perspective of Behavioral Finance

11.5Equity Premium Puzzle

Summary Chapter 11

12Financial Nudging – behavioral approaches for better financial decisions

12.1Libertarian Paternalism

12.1.1Choice architecture

12.1.2Freedom of choice and paternalism

12.1.3Types and characteristics of nudging

12.1.4Criticism of libertarian paternalism

12.2Financial nudging approaches

12.2.1Behavioral science foundations of financial nudging

12.2.2Personal Loans

12.2.3Credit Cards

12.2.4Mortgages

12.2.5Pension provisions

12.2.6Shares and bonds

Summary Chapter 12

13Further development of Behavioral Finance – a look into the future

13.1Limits of Behavioral Finance

13.2Emergence of Neurofinance/Neuroeconomics

13.2.1Research on the human brain

13.2.2Decision processes from the perspective of Neurofinance

13.3Origin of Emotional Finance

13.3.1Emotions as a basis for investment decisions

13.3.2Interpretation of market movements from an Emotional Finance perspective

Summary Chapter 13

Concluding remarks Section IV

Glossary

Literature

Books

Journals and Essays

Websites

Biographies

Index

Introduction

Thousands of business school students around the world are learning to assess the risks of investments and calculate expected returns using Harry Markowitz’ portfolio theory or William Sharpe’s capital asset pricing model. The Swedish Nobel Committee has awarded many prizes for the underlying scientific achievements and the concepts and models of neoclassical capital market theory are widely used in the practice of portfolio managers and CFOs. What are these models based on? To what extent are they able to reflect reality? Can market participants (primarily buyers and sellers in the financial markets) really be expected to follow the concepts and models and to include them in their financial decisions?

The concepts and models of traditional economics illustrate what the majority of economists still assume: the existence of fundamentally efficient markets. According to this assumption, manias, panics, or crashes on the capital market cannot occur, at least not systematically, because markets react to new information efficiently and, thus, result in the best, pareto-efficient allocation of resources.

This view is increasingly questioned with the analysis of speculative bubbles in the second section of this book. The cryptocurrency hype in 2020/2021 as the latest major example of speculative market developments is an example of the existence of fundamental limits to rational markets. Thus, over the course of the centuries, time and again speculative bubbles developed, because the market participants fell into “irrational exuberance” and bought, for example, even when they could already guess that the speculative objects were clearly overvalued.

Over the past 40 years, Behavioral Finance research has produced numerous results according to which, when making financial decisions, we are guided by our emotions or simple rules of thumb rather than by strictly rational motives. Daniel Kahneman, one of the best-known researchers in the field of Behavioral Finance, received the Nobel Prize in 2002 for his insights into decisions under uncertainty. With the help of magnetic resonance tomographs, it was shown that the cerebellum is often the most active part of the brain when it comes to financial decisions ‒ it is linked to emotions and connects us evolutionarily, for example, with reptiles. It is therefore not surprising that our brain occasionally takes shortcuts in order to be able to make decisions more quickly more easily.

Behavioral Finance is based on the insight that market participants are only capable of limited rational behavior due to psychological, mental, and neural limitations. This goes against the assumption of rationality in the theory of expected utility. The concept of widespread limited rationality is a central component and starting point of Behavioral Finance research. It also contradicts the assumption that even if there was limited rational behavior of a few individuals it would be neutralised due to the heterogeneity of market participants. Therefore, limited rationality should not be reflected in the market outcome. Rather, the proponents of Behavioral Finance expect a paradigm extension, which supplements the economic concepts and principles of neoclassical capital market theory with psychological, sociological, and neurological aspects.

The first section of this book is devoted to the behaviors expected from market participants within the framework of neoclassical theory. The study of the assumptions on which the individual concepts and models of neoclassical capital market theory are based is crucial in order to be able to apply them in the subsequent sections to classify and interpret the actual behavior of market participants.

The second section provides an overview of the development of Behavioral Finance as a new field of research in order to be able to interpret and explain the behavior of market participants (primarily investors on the financial markets). In the sense of the paradigm expansion mentioned above, doubts are growing as to whether the behavior of market participants can be explained using the traditional theory alone.

In the third section it is to be clarified how the market participant, in the person of the investor from the viewpoint of asset management, simplifies decisions by using heuristics. In addition, it will be discussed which influences leading to suboptimal decisions can have an impact on the investor in the decision-making process. In this context, the limited rational behavior of market participants is examined from the perspective of wealth management (financial advice for highnet-worth individuals) and, where appropriate, from the perspective of the private equity investment process. The focus is on the phases of decision-making. It is shown which heuristics are used by investors and investment advisors in the different phases of the decision-making process. The aim of the explanations given is to point out limited rational behavior by findings which, according to the current state of research, are responsible for the observable behavior of market participants. It should be expressly pointed out that Behavioral Finance research in this area in particular is subject to ongoing development.

The fourth and last section focuses on the application of the findings from Behavioral Finance in selected subject areas. The focus here is on investment advice in wealth management, the strategic decisions of corporate leaders and financial nudging. In addition, the fourth section will provide an outlook on future research directions and introduce new areas such as Neurofinance and Emotional Finance. These two research areas have already contributed to the investigation of the causes of limited rational behavior. They investigate amongst others processes that have so far been running unconsciously, such as emotions, fantasies, and fears. And they put them into the centre of financial market decisions.

The book is divided into a total of thirteen chapters. The following information provides a first overview of the topics covered and the contents conveyed.

In the first chapter, the decision theories and concepts of rational decision-making are at the forefront. After working through the chapter, you will learn about the development of economic perspectives, starting from classical economics to emotional finance. In the first subchapter you will be able to follow the ever-changing integration of psychology into economics. In addition to looking at the individual perspectives, you will learn about the fundamental decision theories and concepts of neoclassical capital market theory. Here, the focus is on the concept of homo economicus as well as on the behavioral patterns postulated based on neoclassical capital market theory. When studying the decision theories and concepts, you will recognize clear deviations from the actual behavior of market participants, which can increasingly be viewed as a motivation for a paradigm expansion through Behavioral Finance.

In the second chapter you will learn about the models of neoclassical capital market theory that are used to determine the expected return and the risk of securities. In addition, you will learn about the valuation approaches used in financial decisions based on fundamental and technical analysis. After working through this chapter, you will understand the increasing criticism of the listed models and you will also gain an insight into real market conditions that are difficult to reconcile with neoclassical capital market theory.

The third chapter is devoted to the Homo Economicus Humanus ‒ the market participant who symbolizes the paradigm shift towards Behavioral Finance. As you work through this chapter, you will learn about the objectives and development of Behavioral Finance. On the other hand, you will get to know the market participant as an investor acting rationally only to a limited extent.

The fourth chapter focuses on speculative bubbles as signs of recurring and persistent market anomalies. In addition to the origin and causes of the formation of speculative bubbles, you will learn about the different phases and types of speculative bubbles. Furthermore, you will be able to classify the role of the herd instinct as the driving force of speculative bubbles in the structure of recurring market anomalies. Finally, you will encounter other significant capital market anomalies, some of which are short-lived, while others are medium- to long-term capital market anomalies.

The fifth chapter is devoted to historical speculative bubbles. After working through this chapter, you will know the most important speculative bubbles in the history of the financial markets and you will understand typical characteristics of the capital markets that can lead to turbulence. You will also be able to explain the development of historical bubbles based on the Kindleberger/Minsky five-phase model and you will apply it to current and future bubbles.

In the sixth chapter, you will learn the basis of the information and decision-making process and you will understand which perceptual disturbances can prevent market participants from absorbing and processing information. You will also learn the basis of decision-making from the perspective of Behavioral Finance: The Prospect Theory as the alternative to traditional expected utility theory. You will understand how, on the one hand, the S-shaped value function is used to describe the market participant’s attitude to risk and, on the other hand, how the weighting function is used to transform objective probabilities into subjective ones. These two approaches will illustrate the valuation of securities based on Prospect Theory and show the cognitive limitations of market participants.

The seventh chapter focuses on the behavior of market participants during information perception, the first phase within the information and decision-making process. You will learn about the cognitive and emotional heuristics that facilitate the perception of information, but make it difficult for market participants to gain an objective view of the capital market. In this and the following chapters 8 and 9 you will also be able to recognise the effects of the heuristics on the behavior of the market participant and you will classify the risk-/return damaging effect of each individual heuristic.

The eighth chapter deals with the second process stage in the information and decision-making process: information processing. In this phase, too, market participants use certain heuristics which can lead to limited rational behaviour. In this chapter you will learn about the most important heuristics that facilitate but also distort information processing and evaluation for the Homo Economicus Humanus.

In the ninth chapter you will explore the third and final stage of the information and decision-making process. You will learn about the essential heuristics used during decision-making and you will be able to understand the limited rational behavior of the Homo Economicus Humanus.

In the tenth chapter, you will recognize the intensity to which both financial advisors and their clients can be influenced in their decision-making by the application of heuristics. You will identify possibilities to limit risk-/return damaging behavior depending on the wealth of the investor and the origin of the heuristics. In addition, this chapter will present measures for each individual heuristic that aim to increase the quality of advice (in the sense of a customer-oriented presentation of returns and risks).

The eleventh chapter focuses on limited rational behavior in the context of corporate decision-making. You will get to know the drivers of limited rational behavior, such as overestimating the self-confidence of corporate leaders, and you will be able to classify their effects on the development of the overall profitability of corporates. In addition, you will look at certain entrepreneurial activities from the perspective of Behavioral Finance and thus recognize how strongly psychological influences can influence corporate decisions. In addition to dividend policy and the initial issue of shares, the impact of different remuneration concepts within the framework of corporate governance will also be considered. The chapter is rounded off with a discussion of the “Equity Premium Puzzle” from a Behavioral Finance perspective.

In the twelfth chapter, a rather new application of the Behavioral Finance findings is presented. This involves identifying and presenting approaches to how, from an economic policy perspective, people can be persuaded to make better decisions about financial products and services. To this end, so-called nudges on loans, credit cards, mortgages, retirement provisions and shares/bonds are explained. “Libertarian paternalism” forms the theoretical framework for this and is therefore discussed in detail in the chapter.

In the thirteenth and closing chapter, an outlook on new research directions within Behavioral Financial market research will be given. New ideas and correspondingly new research results have already shifted the existing boundaries of Behavioral Finance. In this sense, the chapter leads to the mentioned boundaries and then presents two new research directions within Behavioral Finance research. The focus is on Neurofinance, which aims to investigate the causes of limited rational behavior on the basis of brain research. In addition, Emotional Finance is presented as a new research area, in which unconscious mental processes can be explored.

Note: Central core statements are framed in grey.

Section I − The Homo Economicus in the center of Traditional Finance

1How Neoclassical Theory shaped rational economic behavior

The first chapter explores the development of changing perspectives on market participants from the Traditional Finance to Behavioral Finance. It presents an overview on the fluctuating influence of psychology in economics on the one hand, and the normative decision theories and concepts of the Neoclassical Theory on the other hand. The focus of this first chapter lies on the concept of the Rational Economic Market Participant also called the Homo Economicus. When exploring the decision theories and concepts, you will recognize significant deviations from the expected behavior of market participants, which can increasingly be interpreted as an impulse for a paradigm shift through the rapidly expanding field of Behavioral Finance.

Let us imagine a theatre stage play for investment decisions in the financial markets. First, we see the proponents of traditional finance ‒ a group of rationally acting protagonists also referred to as Homo Economicus; the emotional market participant (also called Homo Economicus Humanus) does not appear in the stage play.

Rather, the protagonists in this play make perfectly rational decisions, apply unlimited analytical capacities to any available information and align their preferences according to the →Expected Utility Theory.

As such this play is likely to be met with a good dose of disbelief by the audience who might be looking for a script with more credible protagonists. Here, the proponents of Behavioral Finance enter, are replacing the Homo Economicus with a market participant who is more in line with reality, with observed decision-making, and who occasionally succumbs to speculative fever. In short, the proponents of Behavioral Finance are intending to put a more realistic play on stage. This involves characters who seemingly are prone to repeat past errors. For instance, some would compare the incredible rally in cryptocurrencies in 2020/21 to the infamous tulip mania in the 17th century in the Netherlands, when investors supposedly were willing to bet entire farms on rising tulip bulb prices (newest insights in chapter 5 will help to reflect on a more realistic view of the tulip mania). Cryptocurrencies will be reviewed in chapter 5 as well.

Richard Thaler, one of the central protagonists of Behavioral Finance1, recorded the smouldering conflict about the real market participant at a conference of the National Bureau of Economic Research (NBER) with Robert Barro, advocate of the traditional view, as follows:

„The difference between us is that you assume people are as smart as you are, while I assume people are as dumb as I am.“ (Thaler, quoted after Robert Bloomfield, 2010, p. 23)

Following the above quote, the aim of the first two chapters is to guide you through the debate on the fundamental assumptions regarding the behavior of market participants and at the same time to suggest possible starting points for adjustments to the traditional framework of →Neoclassical Economics.

1.1From Traditional Finance to Emotional Finance

The development of economic sciences and its fundamental assumptions about human behavior has been shaped by the views of leading scientists. Depending on the prevailing opinion, psychological influences on the decision-making of market participants were followed with varying intensity. They played an important role in the age of Classical Economics but were subsequently largely suppressed until the emergence of Behavioral Finance. It is therefore not surprising that the theoretical framework of rational behavior developed in the era of Neoclassical Economics is still reflected in the concepts and models applied today.

Development of Economic Sciences

Fig. 1: Development of Economic Sciences

18th - 19th Century – The Age of Classical Economics

In the middle of the 18th century, economists began to analyze human influences on decision-making. These beginnings formed the basis for the emergence of behavioral research in financial markets. One tried to combine the economic benefits of consumption with psychological approaches. Adam Smith2 was instrumental in the development of Classical Economics. In his much-acclaimed essay “The Theory of Moral Sentiments” in 1759, he used social psychology to describe the foundation of human morality, with the goal to moderate one’s behavior and preserve harmony.

His book “An Inquiry into the Nature and Causes of the Wealth of Nations” in 1776 is perceived as one of the most influential books ever written and equated with the beginning of Classical Economics. It laid the intellectual foundation of the great 19th century era of free trade and economic expansion. Consequently, the common sense of free trade is accepted worldwide even though this could be questioned today considering the various global trade disputes.

National wealth was defined in Smith’s days in terms of a country’s reserve of gold and silver that shall not be reduced through importing goods from other countries. Protectionism through taxes on imports and protection of domestic industries were common practice. Smith argued that markets were best kept free from governmental influence and are guided by an invisible hand. The self-regulation of market forces should almost automatically lead to equilibrium and full employment. The basis of this way of thinking was human action, which was based solely on economic motives and rational considerations. In addition, Smith was of the opinion that a nation’s wealth is not the quantity of precious metals but the total amount of its production and commerce – a term we call today GDP or Gross Domestic Product (see Adam Smith Institute, 2021).

Psychology experienced its upswing in the 19th century, when science started to be applied to it. Hermann Ebbinghaus3 pioneered in the development of experimental methods and made outstanding contributions to the research of learning and memory. He showed that memories have different life cycles. Some are short-lived, others last for days or even weeks and remain stored in the long-term memory. His research showed that scientific methods could be applied to the study of the higher thought process (see Britannica, 2021).

In the middle of the 19th century, the widespread observation of animal behavior followed, based on Charles Darwin’s4 assumption that mental characteristics of mammals are similar to each other.

From the 20th Century on – The Age of Neoclassical Economics

In the course of the 20th century, Classical Economics was replaced by Neoclassical Economics. The central assumption of neoclassical economics was the model of the Rational Economic Market Participant or better known as the Homo Economicus, which presents market participants as rational, benefit oriented and fully informed individuals (see chapter 1.2.1). As a result, the attempt to explain the investment behavior of market participants through psychology was largely suppressed.

Initially, however, investment decisions were not considered from a scientific point of view, but rather as art. Even John M. Keynes5 saw investing in companies primarily as speculation and compared the stock market with a beauty contest.

„It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.“ (Keynes, quoted after Montier, 2007, p. 91)

The beginning of the development of the Neoclassical Economics is associated with the doctoral thesis “The Theory of Speculation” by Louis Bachelier6 in 1900. Bachelier is credited to be the first person to model the stochastic process under which equity prices evolve. His finding, that price movements follow a random process was the basis for the Random Walk Theory (see chapter 1.2.2) ‒ the theory according to which, stock prices move upwards or downwards without “memory", i.e., independently of historic prices (see Gehrig/Zimmermann, 1999. p. 5 and Mandelbrot/Hudson, 2004. p. 87).

Most of the decision-making theories and concepts used as a basis for rational behavior had been developed during the Great Depression of 1929 followed by the burst of the speculative bubble of the golden twenties (see chapter 5.2.4).

For example, the theory of efficient capital markets developed when Alfred Cowles7 first systematically analyzed the predictability of security prices in the 1930s. The hypothesis that security prices are not predictable according to the random walk theory was finally operationalized and empirically tested by Holbrook Working8 in the 1940s. Today, with the vast amount of data and the surge of artificial intelligence and machine learning, even the weakest signals are explored to predict future price developments.

In 1936, another attempt to incorporate psychological influences into the decision-making of market participants became apparent. In his work “General Theory of Employment, Interest and Money", John M. Keynes9 argued that the economy is not dominated by rational market participants alone, who pursue economic advantages as if guided by an invisible hand. While acknowledging that economic action is largely determined by economic motives, he countered this by saying that it is often also influenced by instincts. These instincts, which he referred to as animal spirits, were an important cause of economic fluctuations and involuntary unemployment. Keynes was convinced that economies, which are left to their own, were prone to excesses. Manias occur, which in turn lead to outbreaks of panic. He believed that the state should play an appropriate role in regulating the markets and counteract excesses caused by animal spirits.

From the 1960s – The Age of Keynesian Economics

Subsequently, and particularly in the 1960s and 1970s, Keynes’ General Theory was “post-Keynesianized” in that Animal Spirits were almost completely removed. The result was a theory that narrowed the differences between the General Theory and the standard statements of Neoclassical Economics to such an extent that there was hardly any room left for investment behavior based on instincts. The neoclassics of the 1960s believed that instincts should be completely removed from economic theory (see Shiller 2009, pp. 8).

Based on the findings of Louis Bachelier, Eugene F. Fama10 developed the Efficient Market Hypothesis in the 1960s (see chapter 1.2.5). It describes a market as efficient if share prices reflect all available information. Thus, consistent generation of alpha (excess returns) is impossible.

At the same time, however, the rationality of individuals was increasingly questioned by the experiments of Maurice Allais11 in 1953 and Daniel Ellsberg12 in 1961. The initial results of the experiments are as a matter of fact regarded as the basis for Behavioral Finance. The experiments made it clear that individuals violated the axioms developed earlier in the 1940s by John von Neumann13 and Oskar Morgenstern14 to underpin the Bernoulli Principle of rational investors (see chapter 1.2.3).

In the area of collective rational behavior or collective rationality, the contribution of John F. Muth15 are to be highlighted. He developed the concept of rational expectations. Muth states in his article “Rational Expectations and the Theory of Price Movements (1961)” that market participants use all available information to form their expectations and learn from their expectation mistakes. Expectations are created by constantly updating and reinterpreting the available information.

The Portfolio Selection Theory developed in 1952 by Harry M. Markowitz16 was acknowledged as a key milestone for the development of models in Neoclassical Economics (see chapter 2.1.1). The core idea of the theory is the development of efficient portfolios by considering the correlation of individual securities (see Karlen, 2004, p. 13). However, Markowitz’ theory was only the beginning of a development away from a purely descriptive to a normative capital market theory.

Building on Markowitz’s portfolio theory, William F. Sharpe17, John V. Lintner18 and Jan Mossin19 independently developed the well-known Capital Asset Pricing Model (CAPM) in the 1960s (see chapter 2.1.2). The model became a fundamental tool in the modern portfolio theory as it allowed to track the different risks of investments back to an easily understandable, linear relationship (see Garz/Günther/Moriabadi, 2002, pp. 17). It is a mathematical model with the goal to describe how securities prices should be based on their relative riskiness compared to the return on risk-free assets (see Baker/Nofsinger, 2010, p. 136.).

In 1976 the CAPM was challenged by the Arbitrage Pricing Theory (APT) developed by Stephen A. Ross20 (see chapter 2.1.3). In contrast to the CAPM, this theory considers multiple risk factors of systematic nature and therefore is closer to reality. According to its name, price information is derived from arbitrage opportunities (see Bank/Gerke, 2005, pp. 4).

A further milestone was the work of Franco Modigliani21 and Merton H. Miller22 in the field of Corporate Finance Theory in 1958, which showed that, assuming an efficient and perfect capital market, the capital structure from equity and debt capitalization is irrelevant for the level of capital costs. The reason for the irrelevance lies in the constant total capital costs, which do not change regardless of the amount of debt in a perfect and efficient market. With a higher level of indebtedness, the cost of equity capitalization increases, but it only relates to a smaller share of capital. At the same time, the share of debt capitalization increases, and the lower and constant costs of debt financing compared to equity relate to a higher share of capital and thus fully compensate for the higher costs of equity capitalization. The respective costs of equity and debt capitalization as well as their proportions change exactly in such a way that the effects compensate each other and thus have no influence on the level of the total cost of capital in an efficient and perfect market.

Finally, a ground-breaking innovation in the field of derivatives (options) valuation was made by Fischer S. Black23, Myron S. Scholes24 and Robert C. Merton25 in the early 1970s with the development of the option pricing formula. The three scientists based their findings on the research of Markowitz, Modigliani and Miller by constructing a risky portfolio consisting of a loan and the underlying security, mirroring the cash-flows associated with the option and thus ultimately opening the door to valuing derivatives.

From the 1980s to present – The Age of Behavioral Finance/Economics

From around 1980, Behavioral Economics developed as a sub-field of economics. This direction was instrumental in increasingly incorporating sociological and psychological aspects to economic sciences. Behavioral Economics examines behavioral patterns of market participants that are inconsistent with the concept of Homo Economicus ‒ for example, the rejection of utility maximisation.

Although most of the findings from research on the actual observable behavior of market participants did not come to light until after 1980, two new fields of scientific research developed as early as 1950 and are considered the basis of Behavioral Finance. On the one hand, scientists in the field of Cognitive Psychology began to analyze mental processes that seemed to be responsible for human behavior (see chapters 6-9). The central component and starting point of Behavioral Finance is the Theory of Bounded Rationality by Herbert A. Simon26 from the mid-1950s onwards. According to this theory, market participants are only capable of limited rational behavior.

On the other hand, decision-making under uncertainty received considerable attention when Daniel Kahneman27 and Amos N. Tversky28 developed the →Prospect Theory (1979, 1992) which becamethe intellectual foundation for Behavioral Finance (see Pompian, 2006, pp. 20). With their experiments, the two Israeli psychologists attempted to classify the previously unexplainable deviations from the ideal image of the Homo Economicus.

The increased focus on emotional and cognitive driven behavior of the market participants, almost simultaneously gave birth to the emergence of Behavioral Finance as a new specific field of research on the decision-making process of individuals. It attempts to explain what happens on the financial markets by taking human behavior into account. It examines which factors lead to a different evaluation of information and consequently to a deviating decision-making from the assumptions made by traditional finance. Based on these insights, Behavioral Finance attempts, among others, to make forecasts about the future behavior of market participants. Daniel Kahneman and Vernon L. Smith29 both share the Nobel Prize in Economic Sciences in 2002. Amos N. Tversky died in 1996 and could not receive the Nobel Prize posthumously (see Blechschmidt, 2007, pp. 11).

Another important contributor in the field of Behavioral Finance is the American economist and Nobel Prize Winner of 2017 Richard H. Thaler30. His main interest was the investigation of decision anomalies as systematic deviations from rational behavior (see Wahren, 2009, p. 45).

Next, operant conditioning, in which the learning process is accomplished by trial and error, resulted from the research findings of Edward L. Thorndike31 and formed a further basis of Behavioral Financial market research. The psychology of learning based on these experiments developed over time into →Behaviorism. This allowed other approaches in the study of memory, as human and animal behavior could be investigated using scientific methods (see Schriek, 2009, pp. 20).

From early 2000 – The Age of Neuroeconomics

Increasing research using non-invasive computer tomography via fMRI – functional Magnetic Resonance Imaging ‒ and the subsequent collaboration between neuroscientists, psychologists and economists has led to the development of a new area in economic science ‒ Neuroeconomics and the specific direction of Neurofinance (see chapter 13.2). Technological developments in brain research are providing opportunities to examine neuronal activities to help explain the actual behavior of market participants. The goal is to determine how choices are reflected biologically and which neuronal processes are activated when decisions under uncertainty are taken. Analyzing decision-making would no longer rely on the traditional axiomatic approach only, but combined with brain imaging to better understand why and how we react to a specific situation (see Elger/Schwarz, 2009, p. 36 and Bossaerts/Murawski, 2010).

From 2009 ‒ The Age of Emotional Finance

First approaches to the exploration of unconscious processes became visible through the description of Keynes’ animal spirits. The research of unconscious processes did not really surface until 2009 with the development of Emotional Finance by Richard Taffler32 and David Tuckett33. Central elements of this specific area of research are the effects of illusion and the desire for wish-fulfillment (see chapter 13.3). The aim is to investigate the consequences of unconscious and highly complex processes that lead market participants to emotionally driven behavior. Consequently, unconscious processes are to be brought into consciousness in order to develop strategies for dealing with reoccurring emotional phenomena (see Baker/Nofsinger, 2010, p. 95).

Traditional Finance presents the market participant as a rational individuum. Behavioral Finance, on the other hand, examines what happens on the financial markets by taking human behavior into account. Finally, Neurofinance uses findings to decipher the neuronal basis of decisions and human behavior based on the processes in the brain.

1.2Classical theories of Traditional Finance

The following chapter dives into the classical theories of traditional finance where normative assumptions play a key role. In other words, this chapter highlights how investors “should” make decisions. While this chapter might be somewhat challenging from a general interest point of view, we aim to give a good overview of the classical theories developed in traditional finance. Doing so, we reflect both on challenges and possibilities they offer to evaluate risk return profiles of investments.

As such, the Neoclassical Capital Market Theory (or simply Neoclassical Theory) developed at the beginning of the 20th century from the old financial market theory, which focused on accounting and →Fundamental Analysis. It evolved around the premises of perfect rationality of market participants as well as perfect financial markets. The resulting equilibrium theories are based on rational and at the same time risk-averse market participants. In this sense, the processing of information according to the Bayes’ Theorem and decision-making within the framework of the Expected Utility Theory represent important core elements of the neoclassical theory. Besides these two theories, the neoclassical theory is decisively influenced by the Efficient Market Hypothesis.

In the following subchapters, we will be focusing on the concept of the Rational Economic Market Participant according to Smith (see chapter 1.2.1), the Random Walk Theory of Bachelier (see chapter 1.2.2), the Expected Utility Theory of Morgenstern and von Neumann (see chapter 1.2.3), information processing according to Bayes (see chapter 1.2.4) and lastly the Efficient Market Hypothesis of Fama (see chapter 1.2.5).

1.2.1The rational economic market participant according to Smith

The concept of the Rational Economic Market Participant34 forms the basis for the neoclassical theory. The origin of this concept dates to the 18th century, the time of classical economics. Scottish economist Adam Smith is regarded as its founder, who, with the following quotation, points out the perfect self-interest as one of three fundamental principles of the Rational Economic Market Participant also referred to as the Homo Economicus:

"It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest." (A. Smith, The Wealth of Nations, 1776)

The term “Homo Economicus" may sound exaggerated, but the individual concepts and models of the classical theories of traditional finance hardly allow for a different view of the expected behavior of market participants. The concepts share the core assumption that market participants are “rational maximizers”. It implies a positive model of behavior with the aim of explaining and predicting economic and social developments and is governed by three fundamental principles in any economic decision:

Perfect Self-Interest, whereby one’s own goals and ideas are at the forefront of one’s actions.

Perfect Rationality, when making decisions: allowing optimal implementation of well-planned actions in which benefit-maximizing behavior with scarce goods is aimed at.

Perfect Information, since neither information asymmetries nor transaction costs exist.

Due to these simplifications and the extremely high abstraction of the models underlying these principles, the neoclassical theory can be represented very elegantly by mathematical equations (see Bank/Gerke, 2005, p. 2).

It is the simplification of economic analysis, which is one of the two advantages why economists like to use the concept of the Homo Economicus. The second advantage is the possibility to quantify the findings (see Pompian, 2006, pp. 15).

The above advantages imply that reality and the complexity of human beings (the way they take decisions, the implication of emotions etc.) is reduced to a minimum. This reduction or simplification causes most criticism. Psychologists argue that human behavior is less the product of perfect rationality but rather of subjective impulses such as greed & fear, love & hate or pleasure & pain, where any of the impulses can cause significant valuation errors in asset prices.

Perfect self-interest stands in absolute contradiction to voluntary activities, to self-lessness and to kindness religions stand for as much as over 2,000 years. Hence, social engagements in our communities show that we sometimes think far less only of ourselves than is assumed by the self-interest-oriented concept of the Homo Economicus. Furthermore, the rapidly expanding areas in investment management (e.g., introduction of Artificial Intelligence (AI) and Machine Learning (ML)) suggests that market participant cannot have perfect information or knowledge on every subject. Despite the simplification of reality, the concept is, in some areas, quite suitable for systematically analyzing reactions to changes in the environment. Notwithstanding far-reaching concerns about the assumptions of this concept, the behavior of market participants is to some extent similar to that of the Homo Economicus, in that they also react systematically to changes in their environment (see Mazanek, 2006, pp. 14).

Later, we will examine the extent to which market participants deviate from the assumptions of the Homo Economicus. In chapter 3.2.3 the focus is explicitly on the observable differences, leading to recurring speculative bubbles (see chapter 5) and to limited rational decisions (see chapters 7 to 9).

The concept of the Homo Economicus is a behavioral assumption with the aim of explaining and predicting economic and social developments.

1.2.2Random Walk Theory according to Bachelier

The Random Walk Theory suggests that changes in security prices are independent of each other. In other words, yesterday’s price change has no effect on today’s price change and today’s price change has no effect on tomorrow’s price change. Based on the name of the theory, it proclaims that security prices follow a random and hence unpredictable path, which is why predicting their price movements is futile in the long run. Fundamental or technical analysis would be of no value, hence passive overactive portfolio management is to be favored.

With the continued strong inflows into index-tracking funds (often packaged as Exchange Traded Funds - ETFs), where an index is simply “followed” without an active security selection, one could argue that the Random Walk Theory of security prices is leveraged by financial institutions. According to fund data provider Morningstar, assets in passive U.S. equity funds overtook those in active funds for the first time in Augst 2019 (see Skypala, 2020). In addition, the significant variation on the cost structure of passive versus active products can make ETFs more attractive to the investment community. Having said that, there are many actively managed funds that deliver excess returns (also called “alpha”), at least for a certain time.

A quick glance into the history of this theory: it is based on the dissertation of French mathematician Louis Bachelier entitled “Théorie de la Spéculation” (1900). In his work, Bachelier claimed that futures quotes for government bonds on the Paris securities exchange in the 19th century followed a random pattern and therefore would not allow market participants to generate excess returns (see Schredelseker, 2002, pp. 407). At that time, →Fundamental Analysis and the growing importance of →Chart Analysis played a central role.

The basic idea of the Random Walk Theory evolves around the assumption that security prices always change with the same probability ‒ analogous to the probability of a coin flip ("heads" or “tails") (see Mandelbrot/Hudson, 2004, pp. 9). The magnitude of the price change can be measured. According to the theory, most price changes of securities ‒ 68 percent ‒ are relatively small movements within one →Standard Deviation(σ) from the mean value. The standard deviation illustrates the →Volatility of an investment around its mean value, which is key for assessing the risk of an investment.

Within +/- two standard deviations, 95 percent of all price changes take place, and within +/- three standard deviations, 99 percent of all price changes would be found. A few price changes however ‒ the remaining 1 percent ‒ represent particularly large deviations and are therefore, according to the theory, very unlikely.

If the price movements are connected, a bell curve shaped distribution appears. The large number of small price movements are in the middle, the rare large price movements at the two ends of the bell curve. The distribution of price movements described here corresponds to the widely known normal distribution of Johann Carl Friedrich Gauss35 ‒ also called Gaussian distribution (see Fig. 2).

Price movements according to the normal distribution

Fig. 2: Percentage of security price changes by standard deviation

We have become very aware of the importance of the normal distribution in the wake of the SARS-CoV-2 (Covid-19) Pandemic 2020/21. While in the financial world a flat bell curve is by no means preferred due to the higher volatility in the form of a broader distribution of returns, during the Corona pandemic a flat bell curve was very much desired and intended by social distancing and lock-downs in order to avoid the sudden influx of infected people into hospitals in the case of a tapered curve.

Covid outbreak leading to fastest sell-off in financial history

Fig. 3: Meltdown in days at specific market crash events, FactSet

In the capital markets, however, contrary to the above description, sharp price drops of over 5 percent and more can be observed time and again. Apparently, such severe outliers as can be found at the outer ends of the bell curve occur more often than expected by theory. For example, the fastest price decline in the history of financial markets in the wake of the Corona pandemic from March 2020 is a perfect example of why a risk analysis based on a normal distribution is not able to simulate realistic results when rather a fat-tail approach would indicate the correct portfolio risk. The capital markets lost over 30 percent within a very short period of time (approx. 30 trading days). In comparison, such losses in the past, including the 1987 crash, lasted up to 180 trading days (see Fig. 3).

Despite the evidence of outlier events at the outer ends of the distribution (also called fat-tail distribution) ‒ such as, for example, March 2020 when markets dropped over 30 percent from their respective peaks, faster than in any other stock market crash before in history ‒ the findings of Carl Friedrich Gauss gained great attention in the field of financial markets. As a matter of fact, the former ten deutsche mark banknotes of the Federal Republic of Germany showed the image of Gauss as well as the bell curve (see Fig. 4).

Fig. 4: Ten-DM-banknote with bell-curve and Carl Friedrich Gauss

The Random Walk Theory as a fundamental concept of the neoclassical theory already reveals one of the main problems of this economic approach, namely that the conclusions drawn from empirical tests may not be valid, since the assumptions made can be falsified from the outset (see example 1.1). March 2020 is another example why risk analysis based purely on normal distribution may not capture all potential outcomes. Applying a fat-tail approach could improve exante risk analysis helping to capture the correct outcome.

Example 1.1: Lack of validity of empirical tests

The following chart of the S&P 500 illustrates extreme price developments in the period from 2000 to 2021, which do not correspond to the assumptions of the Random Walk Theory (see chapter 2.3).

Thus, market participants may face considerable losses if they rely on moderate price fluctuations within a standard deviation under the assumption of the random walk theory. Strong price fluctuations occur as a result of unexpected events and lead to higher frequency of price movements found at the outer edges of the density function than would be expected under the assumption of the random walk theory. However, in addition to substantial losses, enormous gains can also be recorded if market participants can time entry/exit properly.

Extreme price movements between 2000 and 2022 – S&P 500

Fig. 5: S&P 500 Price chart (2000-2022); FactSet

Biography of Louis Bachelier

Louis Jean-Baptiste Alphonse Bachelier was born on March 11, 1870 in the French port of Le Havre.

He began studying mathematics as a graduate student at the Sorbonne at the age of 22. His doctoral supervisor was Henri Poincaré, with whom Bachelier obtained his doctorate in 1900 with the thesis “Théorie de la Spéculation", in which he sought a probabilistic approach to securities price movements.

Until the outbreak of the First World War, Bachelier financed his upkeep through scholarships and as a lecturer at the Sorbonne. In 1919, after the end of his army service, Bachelier found a position as an assistant professor in Besançon. Due to a misinterpretation of one of his papers by Paul Lévy in 1926, he was blackballed when he attempted to receive a permanent professorship in Dijon. Lévy, without having read his entire work, accused him of making serious mistakes, which he regretted later. Finally, in 1927 he was awarded a permanent position in Besançon. His work was hardly noticed by the economists of his time. Only after his death was the importance of his theory recognized. Bachelier is regarded as the founder of financial mathematics and one of the pioneers of the theory of stochastic processes in the field of financial markets. He died on 26 April 1946 in St-Servan-sur-Mer, France (see Mandelbrot/Hudson, 2004, pp. 47).

Deep Dive Random-Walk

Formally a random walk can be represented as:

P stands for the price of the security at times t or t+1.

The expression εt represents a random term that determines the form of the random walk based on the assumptions made.

The strictest form of random walk would result if the random term εt is subject to a normal distribution, independent of the past and has an expected value of zero (see Mandelbrot/Hudson, 2004, pp. 10).

This would mean as stated in the beginning, that yesterday’s price change has no effect on today’s price change and today’s price change has no effect on tomorrow’s price change.

Normal distribution as the basis of the Random Walk Theory

Due to the central assumption that price changes and thus also the return of securities can be approximately described by means of the normal distribution (see Fig. 6), it is important to consider the characteristics of such a distribution, which can be listed as follows (see Mandelbrot/Hudson, 2004, pp. 35):

The area under the frequency function is always 100 percent.

The height of the bell curve illustrates the most frequently occurring return ‒ this return is also referred to as the mean of the returns or the average return.

The normal distribution is symmetrical, looks the same on the left and right of the mean value.

The probability of higher yields decreases more and more to the right of the mean value, as does the probability of lower yields to the left of the mean value.

The normal distribution is described by the mean value of the return μ and the standard deviation in the form of the volatility σ.

Fig. 6: Exemplary return development based on the normal distribution

Depending on the mean and the standard deviation36, the normal distribution can take on different forms, which simultaneously indicate the expected return and volatility (see Fig. 7).

Fig. 7: Forms of the normal distribution

Three distributions are shown in Fig. 7 (A, B and C). The distributions A and B have the same mean and are located at the same place, measured by the mean. The distribution C has a higher mean and is therefore located further to the right on the x-axis. In terms of volatility, distributions A and C are equally volatile. The distribution B, on the other hand, shows a higher volatility. This can be seen from the fact that the distribution is flatter than the other two. In distributions A and C, far more observations are close to the mean value, while in distribution B, more observations are at more extreme values. So, the flatter a distribution is, the higher is the risk ‒ measured as standard deviation.

1.2.3Expected Utility Theory according to von Neumann & Morgenstern

The neoclassical capital market theory describes market participants as “rational” if they formulate realistic expectations and implement them according to the expected utility theory. In contrast to this view, the behaviorally biased market participant is prone to unrealistic expectations and consequently disregards the expected utility theory explained below.

The Expected Utility Theory has the objective to analyze rational decisions, when the decision-maker is facing risky outcomes or in other words, faces different choices with respective probabilities of outcome (see Bank/Gerke, 2005, pp. 35). Together with the Bayes’ Theorem (see chapter 1.2.4) of information processing, the expected utility theory forms the basis for the Efficient Market Hypothesis. In the case of the expected utility theory, there are two types to differentiate:

Objective Expected Utility Theory by Oscar Morgenstern & John von Neumann (1947) – the distribution function of possible consequences is known.

Subjective Expected Utility Theory by Leonard J. Savage37 (1954) – the distribution function of the consequences is unknown; the decision-maker must determine the probability of the consequences through subjective estimation.

This subchapter focuses on the Objective Expected Utility Theory. For Morgenstern and von Neumann, it was a normative model of how a rational person should make decisions when facing alternative outcomes and not a descriptive model about how decisions are really made. The theory is anchored in certain axioms which, however, are often violated when considering the actual behavior of market participants. These violations and doubts about the validity of the assumptions spurred the emergence of →Behavioral Finance, taking on the challenging task of uncovering why and how market participants choose as they do (see Forbes, 2009, p. 26). To do so, the Prospect Theory (see chapter 6.2) was developed as a descriptive and alternative theory to the Expected Utility Theory. It was developed by the psychologists Daniel Kahneman and Amos Tversky and assumes that market participants assess their investment results relative to a reference point rather than looking at their final assets. Therefore, depending on the reference point, the results can be positive (gains) or negative (losses).

The objective of the expected utility theory is to analyze rational behavior under uncertainty. The central object of the investigation is the making of decisions without their results/consequences being known in advance.

Biographies of Morgenstern and von Neumann

Oskar Morgenstern was born on January 24, 1902 in Görlitz/Germany. In 1925 he received his doctorate in political science from the University of Vienna. Shortly afterwards he received a scholarship from the Rockefeller Foundation. In 1929 he returned to Vienna from the U.S. and accepted a professorship at the University of Vienna. During his time at the university, he belonged to the so-called “Austrian Circle”, a group of Austrian economists. In 1938 he emigrated to the U.S. and became professor at Princeton University, where he developed the game theory together with von Neumann.

In addition to game theory, they also developed the Expected Utility Theory as a method of evaluating decisions under uncertainty.

Morgenstern was appointed Distinguished Professor of Game Theory and Mathematical Economics by the New York University. He died in Princeton on July 26, 1977.

John von Neumann was born in Budapest/Hungary on December 28, 1903. His high intelligence was already evident at the age of six, when he was able to divide eight-digit numbers.

After graduating from high school, he attended various universities in Europe and obtained his diploma in chemical engineering at the ETH Zurich. In addition, he studied mathematics and obtained his doctorate from the University of Budapest in 1926. In 1928, he habilitated at the University of Berlin with his work Allgemeine Eigenwerttheorie symmetrischer Funktionaloperatoren.

In 1933 he became professor of mathematics at the newly founded Institute for Advanced Study in Princeton, New Jersey. In 1933 von Neumann became co-editor of the Annals of Mathematics and in 1935 of Compositio Mathematica. Together with Oskar Morgenstern he wrote The Theory of Games and Economic Behavior in 1944, with which he became the founder of game theory. He also wrote a book on quantum mechanics and participated in the development of axiomatic set theory.

During World War II von Neumann was an advisor to the U.S. Army. From 1943 he worked on the Manhattan Project in Los Alamos on the development of atomic bombs.

Neumann received numerous honors for his scientific achievements, including the Medal of Merit, the Medal for Freedom, and the Albert Einstein Commemorative Award. In addition, the John von Neumann Institute for Computing in Jülich was named after him. He died on 8 February 1957 in Washington D.C.

Deep Dive Expected Utility Theory

Basic idea of the objective Expected Utility Theory

The central element is a utility function u, whose expected value can be used to represent preferences. The determination of the expected value plays a special role in the calculation of the expected benefit EU.

The term u(ai) represents the respective benefit of state i of alternative a. pi is the corresponding probability of the occurrence of this state.

The sum of the probabilities of all states pi is 1.

In consequence, two alternatives a and b emerge. If a has a higher expected utility than b, alternative a is preferred over b, i.e., a > b, if EU(a) > EU(b).

Based on the above, the expected utility of an alternative is the key element for a rational decision, whereby the market participant chooses the alternative that has the highest expected utility (see Kottke, 2005, p. 8).

Axioms for rational behavior