The Impact of Warning Signals on Individual Investors' Trading Behavior Empirical Evidence: a Randomized Experiment

THE IMPACT OF WARNING SIGNALS ON INDIVIDUAL INVESTORS' TRADING BEHAVIOR EMPIRICAL EVIDENCE: A RANDOMIZED EXPERIMENT

Aantal woorden: 18.770 words

Ewout De Brauwer Sarah Goemaere

Stamnummer: 01502737 Stamnummer: 01407713

Promotor: Prof. Dr. Koen Inghelbrecht Commissaris: Dhr. Nicolas Dierick

Masterproef voorgedragen tot het bekomen van de graad van: Master in de handelswetenschappen: finance en risicomanagement

Academiejaar: 2018-2019

Deze pagina is niet beschikbaar omdat ze persoonsgegevens bevat. Universiteitsbibliotheek Gent, 2021.

This page is not available because it contains personal information. Ghent University, Library, 2021.

Summary in Dutch

Deze masterthesis heeft getracht te onderzoeken wat de impact is van waarschuwingssignalen op het handelsgedrag van individuele investeerders. Naar aanleiding van de financiële crisis in 2007-2009, werd de MiFID-wetgeving aanzienlijk verstrengd ter bescherming van de particuliere, vaak onwetende, investeerder. Beursvennootschappen of andere financiële tussenpersonen die beleggingsdiensten, zijnde vermogensbeheer en beleggingsadvies, aanbieden aan ‘retail investors’ zijn rechtswege verplicht een waarschuwingssignaal in de vorm van een negatief, neutraal of positief advies te verstrekken bij elke aankoop- en verkooptransactie die de cliënt wenst te verrichten (European Parliament; The Council, 2014).

Hiervoor werd een experiment uiteengezet die een virtuele, financiële omgeving creëerde waar participanten investeringsbeslissingen konden nemen doorheen verschillende handelsperiodes. Participanten werden willekeurig opgedeeld in een controle-of behandelingsgroep om zo de impact van waarschuwingssignalen te kunnen meten. Handelsgedrag werd geregistreerd door middel van het aantal transacties, de handelsfrequentie, de proportie aangehouden in risicovolle en risicoloze activa (en het resterende gedeelte aangehouden in cash). Daarnaast werd ook gekeken naar de impact van waarschuwingssignalen op ‘overconfident’ (of overmoedig) handelsgedrag.

De eerste testresultaten (preliminary tests) tonen aan dat waarschuwingssignalen een significante impact hebben op het handelsgedrag van individuele investeerders. De empirische analyses bevestigen deze resultaten en voegen eraan toe dat de impact voornamelijk positief van aard is. Zo bleken waarschuwingssignalen een positieve impact te hebben op het aantal transacties, de handelsfrequentie en op de ‘capital allocation decision’ of kapitaaltoewijzingsbeslissing (proportie geïnvesteerd in risicovolle en risicoloze activa). Verder, zouden ze ‘overconfident’ (of overmoedig) gedrag kunnen inperken. Men zou dus kunnen stellen dat waarschuwingssignalen de ‘biases’ (of vertekeningen) en heuristieken van individuele investeerders kunnen reduceren.

Preface

With this master’s dissertation, there comes an end to four years of education in Business Administration at Ghent University. Four fascinating years of hard work, dedication and perseverance which will be remembered as a unique part of our lives. This master’s dissertation would not have been possible without the help of so many people in so many ways, and we would like to thank them accordingly.

At first, we would like to thank prof. Mr. Inghelbrecht for giving us the opportunity to take an in-depth look in this interesting subject. Thanks also to our commissioner Mr. Dierick for the encouraging feedback and useful insights he provided us during the year.

Moreover, special thanks to the participants of our experiment for their time and effort to bring our experiment to a good end. Further, we would like to express our gratitude towards Dirk Desmet (retired teacher) and Franck Awouters (Senior FA Oversight Controller at RBC I&TS Belgium) for teaching us the basics of VBA which was a crucial part to construct our experiment.

The past four years, we did not only develop ourselves academically, but also personally. Therefore, we would like to thank our friends and our brothers Michiel (brother of Ewout) and Jühl &Thomas (brothers of Sarah) for the unbelievable time we had together.

Our deepest gratitude goes to our parents Ann & Peter (parents of Ewout) and An &Hendrik (parents of Sarah) who were of great importance during these four years. Our graduations are also their accomplishment, as they have made us who we are today. We would like to thank them for the unconditional support and for providing us an excellent study environment.

Table of Contents

Summary in Dutch ...... I

Preface ...... II

Table of Contents ...... III

List of Used Abbreviations ...... VI

List of Tables and Figures ...... VII

1 Introduction ...... 10

2 Literature review ...... 13 2.1 Traditional finance ...... 13 2.2 Behavioural finance ...... 14 2.2.1 Definitions ...... 15 2.2.2 Building blocks ...... 16 2.2.3 Development ...... 16 2.2.4 Theories ...... 17 2.2.4.1 Cognitive dissonance theory ...... 17 2.2.4.2 Theory of regret ...... 17 2.2.4.3 Prospect theory versus the Expected Utility theory...... 17 2.2.5 Heuristics and biases ...... 18 2.2.5.1 Heuristics ...... 19 2.2.5.2 Biases ...... 23 2.2.6 Limitations arbitrage ...... 26 2.2.7 Explanatory factors investor behaviour ...... 27 2.2.7.1 Risk tolerance ...... 27 2.2.7.2 Wealth ...... 27 2.2.7.3 Personal traits ...... 28 2.2.7.4 Genes...... 29 2.2.7.5 Sex (or gender) ...... 29 2.2.8 Individual investors ...... 30 2.2.9 Financial education to help investors make decisions...... 30 2.3 Warning signals ...... 31

3 Methodology ...... 34

III

3.1 Set-up investment laboratory experiment (experimental design) ...... 34 3.1.1 Sample of participants ...... 36 3.1.2 Pricing process of assets ...... 36 3.1.2.1 Risky assets ...... 36 3.1.2.2 Risk-free asset ...... 40 3.1.3 Course of the experiment ...... 41 3.1.3.1 Introduction ...... 41 3.1.3.2 Financial suitability test and trading periods ...... 41 3.1.3.3 Final results ...... 44 3.1.4 Financial ratios ...... 44 3.1.4.1 Yield portfolio ...... 44 3.1.4.2 Yield per stock (in portfolio) ...... 45 3.1.4.3 Average stock price ...... 46 3.1.4.4 Evolution stock price relative to the last trading period ...... 46 3.1.5 Microsoft Excel VBA and macros ...... 47 3.1.5.1 Message boxes ...... 47 3.1.5.2 Macros ...... 50 3.1.6 Limitations investment laboratory experiment ...... 51 3.2 Data ...... 52 3.2.1 Description variables ...... 52 3.2.1.1 Individual investor’s trading behaviour ...... 52 3.2.1.2 Type of group (Group_dummy) ...... 55 3.2.1.3 Investor knowledge and experience (IKE) ...... 55 3.2.1.4 Level of risk aversion (A) ...... 55 3.2.1.5 Age and gender ...... 56 3.2.2 Extraction empirical data ...... 56 3.2.3 Descriptive statistics ...... 56 3.2.3.1 Total sample ...... 57 3.2.3.2 Two independent samples ...... 59 3.3 Econometric model ...... 61 3.3.1 Nonparametric tests for independent samples ...... 61 3.3.2 Regression models ...... 62 3.4 Empirical results ...... 63 3.4.1 Testing randomization (Group_dummy) ...... 63 3.4.2 Preliminary testing results ...... 64 3.4.2.1 Total Transactions (TT) ...... 64

3.4.2.2 Turnover rate Total Transaction Volume (TTTV) ...... 64

3.4.2.3 Proportion per asset class (RR, RRF & RC) ...... 65 3.4.2.4 Overconfidence ...... 66 3.4.3 Multiple regression analyses ...... 67 3.4.3.1 Total Transactions ...... 67 3.4.3.2 Turn-over rate Total Transaction Volume ...... 69 3.4.3.3 Proportion per asset class ...... 70 3.4.3.4 Overconfidence ...... 74

4 Discussion ...... 78

5 References ...... X

6 Appendices ...... XVI 6.1 Financial suitability test ...... XVI 6.2 Snapshots experiment ...... XXII 6.2.1 Introduction ...... XXII 6.2.2 Financial suitability test ...... XXIII 6.2.3 Trading period 1 ...... XXIII 6.2.4 Trading period 2-5 ...... XXIV 6.2.5 Final results ...... XXIV 6.3 Spearman’s correlation matrix (all variables) ...... XXV 6.4 Syntax VBA ...... XXV 6.4.1 Warning signal & Locking last trading period ...... XXV 6.4.2 Message box 1 & Unhiding next trading period ...... XXVI 6.4.3 Message box 2,3 and 4 & Unhiding next trading period (e.g. Trading Period 2) ...... XXVI 6.5 Syntax SPSS (Normality and non-parametric tests) ...... XXVII

List of Used Abbreviations

VBA Visual Basic for Applications HPR Holding Period Return PV Present Value FV Face Value YTM Yield-To-Maturity APP Average Purchase Price TT Total Transactions TTTV Turnover Rate Total Transaction Volume

PR Proportion in Risky Assets

PRF Proportion in Risk-free Assets

PC Proportion in Cash SPSS Statistical Package for Social Sciences CLRM Classical Linear Regression Model OLS Ordinary Least Squares GLS Generalised Least Squares CV Coefficient of Variation

List of Tables and Figures

Figure 1: Behavioural finance as an interdisciplinary science (Ricciardi & Simon, 2000) ...... 15 Table 1: Investment goals per age stage ...... 27 Table 2: Number of participants per group ...... 36 Table 3: Input parameters to simulate random price paths (own content) ...... 37 Figure 2: Simulation random price paths stocks (Hull, 2015) ...... 37 Figure 3: Equation simulation stock price of Bonous at year T (own content) ...... 38 Table 4: Simulation random price paths (own content) ...... 39 Figure 4: Bond pricing process (Bodie, Kane, & Marcus, 2017)...... 40 Table 5: Pricing zero-coupon bonds (own content) ...... 40 Figure 5: Experiment explained: what-why-how (own content) ...... 41 Figure 6: Warning signal in Microsoft Excel (own content) ...... 42 Table 6: Investment decision form (own content) ...... 42 Table 7: Overview portfolio template trading period 1 (own content)...... 43 Table 8: Overview portfolio value template trading period 1 (own content) ...... 43 Figure 7: Questions to observe overconfidence (own content) ...... 43 Figure 8: Yield portfolio (own content) ...... 44 Figure 9: Average share price (own content) ...... 45 Figure 10: Yield per stock in portfolio (own content) ...... 45 Figure 11: Equation yield per stock of Bonous at trading period 2 (own content) ...... 46 Figure 12: Average stock price (own content) ...... 46 Figure 13: Evolution stock price relative to the last trading period (own content) ...... 46 Figure 14: Message box financial suitability test (own content) ...... 47 Figure 15: Message box warning signal (own content) ...... 48 Figure 16: Message box insufficient funds (own content) ...... 48 Figure 17: Message box sales exceeding purchases (own content) ...... 49 Figure 18: IF function in Excel comparing sales with current portfolio and purchases ...... 49 Figure 19: Message box completing the first trading period (own content) ...... 50 Figure 20: IF function in Excel checking for blank cells (own content)...... 50 Table 9: Median yield through different trading periods among different groups ...... 54

VII

Figure 20: level of risk aversion (Bodie, Kane, & Marcus, 2017) ...... 55 Table 10: Descriptive statistics total sample ...... 57 Table 11: Frequency table better-than-average effect (total sample) ...... 58 Table 12: Frequency table miscalibration effect (total sample) ...... 58 Table 13: Frequency table gender (total sample) ...... 58 Table 14: Descriptive statistics control and treatment group ...... 60 Table 15: Frequency table gender (control and treatment group) ...... 60 Table 16: Frequency table better-than-average effect (control and treatment group) ...... 60 Table 17: Frequency table miscalibration effect (control and treatment group) ...... 61 Figure 21: Hypothesis testing quantitative variables ...... 61 Figure 22: Hypothesis testing qualitative variables ...... 61 Figure 23: Hypothesis testing regression models ...... 62 Table 18: Logit model testing randomization (N=81) ...... 63 Table 19: Wilcoxon rank-sum test total transactions (N=81) ...... 64 Table 20: Wilcoxon rank-sum test turnover rate total transaction volume (N=81) ...... 64 Table 21: Wilcoxon rank-sum test proportion risky assets (N=81) ...... 65 Table 22: Wilcoxon rank-sum test proportion risk-free assets (N=81) ...... 65 Table 23: Wilcoxon rank-sum test proportion cash (N=81) ...... 66 Table 24: Contingency table better-than-average effect ...... 66 Table 25: Contingency table miscalibration effect ...... 67 Table 26: Spearman’s correlation matrix total transactions, warning signals and controls ... 67 Table 27: CLRM model total transactions (N=79) ...... 68 Table 28: Number of participants per group after removing outliers (TT) ...... 69 Table 29: Spearman’s correlation matrix turnover rate total transaction volume, warning signals and controls ...... 69 Table 30: CLRM model turnover rate total transaction volume (N=78) ...... 69 Table 31: Number of participants per group after removing outliers (TTTV) ...... 70 Table 32: Spearman’s correlation matrix proportion risky assets, warning signals and controls ...... 70 Table 33: CLRM model proportion risky assets (N=81) ...... 71 Table 34: Spearman’ correlation matrix risk-free assets, warning signals and controls ...... 72 Table 35: CLRM model proportion risk-free assets (N=81) ...... 72

VIII

Table 36: Spearman’s correlation matrix proportion cash, warning signals and controls ...... 73 Table 37: CLRM model proportion cash (N=77) ...... 73

Table 38: Number of participants per group after removing outliers (PC) ...... 74 Table 39: Spearman’s correlation matrix better-than-average effect, warning signals and controls ...... 74 Table 40: Logit model better-than-average effect (N=81) ...... 75 Table 41: Spearman’s correlation matrix miscalibration effect, warning signals and controls ...... 76 Table 42: Logit model miscalibration effect ...... 76

1 Introduction

At the end of the financial crisis (2007-2009) or also known as the Great Recession, the interest to understand consumer financial behaviour has substantially risen by financial institutions, researchers and public authorities (Kenton, 2018). The financial crisis taught that the majority of investors made ignorant decisions when buying a financial product, due to the complexity of these products such as the former synthetic CDO’s. From an ethical point of view, there should be supportive guidance in the form of compulsory regulations that assist the (retail) investor to make decisions according their investor profile. Therefore, scientific research could support government policies regarding consumer financial behaviour.

A lot of retail investors have difficulties understanding and choosing the appropriate financial products. At the same time, they experience the need to take care of their retirement by mostly their long-term savings and their own financial future together with its risks. That proves the major importance for financial institutions and policy makers to acquire knowledge and insights into the decision-making process of investors, including how financial educated they are together with their mistakes and how to correct them (Klöhn, 2009).

Regulators are nowadays strongly interested in behavioural finance, which they use as a theory and resource to improve financial regulations (i.e. the MiFID regulatory system). Securities laws entered into force to reduce investor biases and heuristics (Klöhn, 2009). The goal of these laws is to guarantee a more careful approach regarding investment decisions of retail clients (individual investors). However, empirical evidence has shown that the prevention of The Markets in Financial Instruments Directive (MiFID) and its implementations are not enough to deal with market failures caused by investors’ psychological biases such as overconfidence. Investors who are optimistic and confident have the tendency to be predominant in various ways towards the MiFID regulatory system due to the use of execution-only services. They do not obtain investment advice when entering the market because they do not give the investment firm the right information which is needed for the suitability and appropriateness test. As response, MiFID introduced standardised warnings and the investment firm is not obliged to give investment advice, in case of an execution-only

service. This means that if the client demands to an investment firm for an execution-only service, before execution of the client’s order, he/she will get a one-time standardised warning signal that the investment firm does not take care of the suitability of the financial product . As response, MiFID introduced standardised warnings and the investment firm is not obliged to give investment advice, in case of execution-only service. This means that if the client demands to an investment firm for an execution-only service, before execution of the client’s order, he/she will get a one-time standardised warning signal that the investment firm does not take care of the suitability of the financial product . If this transaction contains a complex financial instrument such as a turbo, the investment firm is obliged to evaluate the appropriateness of the transaction. If the transaction is not appropriate, the standardised warning is issued to the client. However, these measures are not good enough to prevent poorly calibrated investors from making wrong and often ignorant investment decisions. Securities firms should warn overconfident clients before invest, informing them about the danger of excessive trading and risk-taking. Unfortunately, the issuing of warning signals under Art. 19 MiFID usually occurs after the client has made his/her investment decision. It can be concluded that these signals have no effect on the trading behaviour and that optimistic or overconfident investors interpret these signals are non-relevant, or issued ‘for others’. They still find reason to trade excessively (Klöhn, 2009, pp. 445-447). Since 3 January 2018, an updated version of MiFID came into force also known as MiFID II. The updated regulation is more strictly, but follows the same principles (Febelfin, 2019). Note that the section regarding warning signals is now included in Article 25 (European Parliament; The Council, 2014).

Because of the failures in these measures, we attempted to examine if warning signals, issued before individual investors make their investment decision, influence individual investor trading behaviour in general, and also specified to overconfident investors. The standardized warning signals were also more specified, recommending the investors to not invest in risky assets (stocks). Therefore, an investment laboratory experiment was designed to observe the participants their investor behaviour. In short, the aim of this scientific research was to examine the impact of warning signals on individual investor’s trading behaviour.

First of all, a cohesive overview is given regarding investor behaviour including inter alia the development, biases, explanatory factors and different (sub)theories. After the literature review, an in-depth description of the investment laboratory experiment is discussed followed by an empirical analysis and the interpretation of its results Lastly, the discussion summarizes the main findings and leaves suggestions for further research.

2 Literature review

2.1 Traditional finance

The traditional approach of investor behaviour states that people, the so-called homo economicus, make decisions on a rational, balanced, emotionless, self-interested manner (Baker & Ricciardi, 2014). The traditional investor behaviour theory states investors have all the same information with the same expectations and act as one homogeneous group (Baker & Ricciardi, 2014). The traditional financial framework follows inter alia the Efficient Market Hypothesis and the Modern Portfolio Theory (Barberis & Thaler, 2003). The Efficient Market Hypothesis states that there will be no free lunch and the prices of securities are equal to their fundamental value, as they reflect all available information (De Man, 2005). The Modern Portfolio Theory, originally found by Harry Markowitz (1952), allows risk-averse investors to construct efficient portfolios maximizing their expected return for a given level of risk.

Unfortunate economic events such as recessions contradict the traditional approach of investor behaviour. Shiller (1987) his surveys explain that the crash in 1987 was not caused by financial factors such as earnings or interest rates but was as a result of irrational investor behaviour. Siegel (1992) also illustrates that the sky rocket stock prices in 1987 could not be explained by the changes in corporate profits or interest rates, but by the behaviour and sentiment of investors.

These examples are market imperfections where investors act irrational, nuancing the classical financial theory. The last 20 years, behavioural finance has become more popular due to its anomalies which contradict the traditional paradigm concerning the financial market (Ricciardi & Simon, 2000). This raises the question whether the classical financial theory is an incomplete explanation unable to give answer to the continuous changes of the financial market and its anomalies. Examples of such anomalies are the January effect, speculative market bubbles and crashes which deviate far from rational and logic behaviour. These unexplained phenomena have led to a switch from the classical financial theory to behavioural finance which shows (cognitive) psychology is a key factor why individual investors take particular decisions (Ricciardi & Simon, 2000).

2.2 Behavioural finance

The following two quotations illustrate the main differences between the traditional approach and behavioural finance:

“In contrast to the traditional homo economicus view, behavioural finance takes the real- world homo sapiens view: Real-world investors and traders make decisions based on bounded rationality, satisficing and emotion” (Baker & Ricciardi, 2014, p. 347).

“In deep contradiction to the classical paradigm, behavioural finance assumes that investors may be irrational in their reactions to new information and investment decisions” (Birău, 2012, p. 1).

Van Raaij (2016) explains that behavioural finance did not focus in the beginning on emotions and the sentiment of the investor, but gave its attention primarily to cognitive biases and heuristics. More descriptive models were used to develop behavioural economics and therefrom behavioural finance. “From a psychological perspective, economic psychology contributed to this development by studying economic behaviour of consumers, investors, and entrepreneurs” (van Raai, 2016, p. 2). Behavioural finance focuses on the thought of investors and their actions in the real world, rather than what rationally should be done. A point of criticism is that there is no overarching framework that links the explanations of individual investor’s behaviour. Therefore, insights in personality traits might be needed (van Raai, 2016).

Barberis and Thaler (2003) and Hirshleifer (2001) criticized behavioural finance referring to it as “model dredging”. “In other words, one can find a story to fit the facts to ex post explain some puzzling phenomenon” (Ritter, 2003, p. 5). Hirshleifer (2001, p. 1547) addressed the issue of when we would expect one behavioural bias to dominate others. He emphasized the salience effect which means that there is a tendency for people to excessively rely on the strength of information signals and under-rely on the weight of information signals.

2.2.1 Definitions

“Behavioural finance is the study of the influence of the psychological factors on financial markets evolution” (Birău, 2012, p. 1).

“Behavioural finance argues that some features of asset prices are most plausibly interpreted as deviations from fundamental value, and that these deviations are brought about by the presence of traders who are not fully rational” (Barberis & Thaler, 2003, p. 1054).

“Behavioral finance, a sub-field of behavioral economics, proposes psychology-based theories to explain stock market anomalies, such as severe rises or falls in stock price” (Kenton, 2018).

As this study is still developing and refining, there has been controversy regarding the bona fide definition (Ricciardi & Simon, 2000). Behavioural finance clarifies the decision-making process of individual investors being influenced by emotional factors. It tries to get a deeper look into their financial and investment decisions giving answers to what, why, and how they make such decisions.

Figure 1: Behavioural finance as an interdisciplinary science (Ricciardi & Simon, 2000)

As one can see on the figure above, behavioural finance attempts to combine social and psychological factors with traditional finance (as centrepiece) to give a better understanding regarding investment behaviour (Ricciardi & Simon, 2000). This is what makes behavioural finance an interdisciplinary science. These social and psychological factors do not only give a direction to the decision-making process of retail investors, but also to groups and entities (institutional investors). According to Statman (2004), individual investors are influenced by

psychological and behavioural factors during their decision-making process in terms of risk assessment and when they are affected by biases and heuristics (Ricciardi & Simon, 2000). In Thaler’s opinion, investors have not a rational view on economic factors and Olsen’s opinion is that investors indeed use psychological factors in their investment and financial decision- taking (Birău, 2012).

2.2.2 Building blocks

Behavioural finance is constructed through two building blocks: cognitive psychology and the limits of arbitrage (Ritter, 2003). Cognitive psychology studies the thinking process of people and the systematic errors of this process such as overconfidence (Ritter, 2003). Empirical studies show overconfident investors make mistakes by interpreting wrongly received information (Birău, 2012). Individual investors sometimes overreact at certain circumstances or act at their own irrational beliefs (Ritter, 2003). These irrational beliefs also referred as biases and heuristics are the source of cognitive distortions which are incorporated in the behavioural finance theory. Limitations of arbitrage address in guessing which situations arbitrage forces will have a good impact, and which situations they will not (Ritter, 2003).

2.2.3 Development

Behavioural finance started to develop when economist started to see the advantages of including psychological factors in economic theories (Shefrin, 2002). The Journal of Finance published in July 1985 two papers. One paper written by Werner De Bondt and Richard Thaler (1985) in which investors are described as people who seemed to overreact to both good and bad news. This led to under-priced securities which have lost in the past and overpriced securities which have won in the past. A second paper is written by Meir Statman and Hersh Shefrin (1999) regarding the disposition effect. Robert Olsen (1998) describes the ‘new paradigm’ or school of thought known as an effort to understand and predict systematic behaviour to make sure investors can make more correct investment decisions (Ricciardi & Simon, 2000). He states that behaviour finance has no cohesive theory yet, but he emphasizes the existence of many sub-theories (Ricciardi & Simon, 2000).

2.2.4 Theories 2.2.4.1 Cognitive dissonance theory

“Festinger’s theory of cognitive dissonance (Hunt, 1993) states that people feel rather an anxiety when they are confronted with conflicting beliefs” (Ricciardi & Simon, 2000, p. 4). People have the intention to reduce their inner conflict in two manners: the first approach is by changing feelings, opinions and past values. The second approach is by rationalizing or justifying the decisions we have taken so it seems the decisions have been taken from our personal values or viewpoints (Ricciardi & Simon, 2000).

2.2.4.2 Theory of regret

“The theory of regret states that an individual evaluates his or her expected reactions to a future event or situation” (Ricciardi & Simon, 2000, p. 5). For instance, when people have the choice between an unfamiliar brand or a familiar brand, an investor might consider the regret when investing in the unfamiliar brand, so he/she is more likely to invest in the familiar brand (Inman & McAlister, 1994).

The regret theory also fits within the scope of investor behaviour. Individual investors might refuse to sell a stock or mutual fund which value has been declined to avoid the negative feeling of regret by making a wrong investment reporting a loss. Furthermore, investors make the choice to follow the crowd and purchasing popular stocks (see more at Herd behaviour). This helps investors to ‘rationalize’ their investments if the value of the stocks declines. The negative emotions and feelings of the loss are reduced by the thought the whole group investors has lost their money on the same investment (Ricciardi & Simon, 2000).

2.2.4.3 Prospect theory versus the Expected Utility theory

The prospect theory is the most important theory in the scope of behavioural finance in case of modelling the preferences of investors (De Man, 2005). However, the major problem is that in most cases, people use systematically the ‘expected utility theory’. The prospect theory is therefore part of a countermovement which follows the ‘unaccepted utility theories’. The prospect theory describes the manner by which people take decisions under

circumstances of uncertainty because of their choices (Moyersoen, 2004). Kahneman and Tversky’s (1979) developed the prospect theory which states individual investors take a risk- averse position in situations of gains in wealth, but are loss-averse in situations of losses in wealth. “Where an agent’s utility is drawn as a function of changes in wealth, the shape of this function is such that, in the doing of wealth gains, an agent is risk-averse, while in the domain of losses, an agent prefers to gamble because this provides a higher expected utility compared to the case of a sure loss” (Baker & Ricciardi, 2014, p. 348). The negative feelings and emotions of getting a loss is more decisive than the happiness of equivalent gains, both scaled relative to a reference point (Baker & Ricciardi, 2014).

The prospect theory compared to the expected utility theory is more descriptive and focuses more on changes in wealth rather than the level of wealth. The theory also considers the phenomenon of framing by which an investor has two choices in his/her decision process, he/she can treat two different events separately (segregation) or as one (integration) (Ritter, 2003).

2.2.5 Heuristics and biases

Descriptive studies and experiments have been conducted by now, giving answers to why investors might be irrational distinguishing biases and heuristics. The focus of behavioural economics and finance now is the change in behaviour rather than how their mentality is constructed by perception, motivation, attitude and intention (van Raai, 2016). Behavioural economics intend to detect changes in economics. In this paradigm change, three stages could be distinguished (Kuhn, 1962; Lakatos, 1968; Van Raaij, 1985):

1. The neoclassical economic theory does not explain the anomalies, paradoxes and other discovered theoretical deviations. (Thaler, 1992). 2. Biases and heuristics give an answer to these deviations. An example of a successful partial theory is called the prospect theory (Kahneman and Tversky, 1979). 3. These biases and heuristics can be categorized in one covering (behavioural) economic theory. But this is not necessary as behavioural economics/finance can be a science with different partial theories (van Raai, 2016).

“A heuristic is a cognitive shortcut, rule of thumb, or quick and easy decision process simplifying decisions or substituting a difficult question with an easier one” (Kahneman, 2003) (van Raai, 2016, p. 4).

“A cognitive bias is a systematic (non-random) error in thinking, deviating from formal logic or accepted norms” (van Raai, 2016, p. 4).

A misunderstanding context can quickly lead to irrational decisions even with a rational thinking process. Individual investors can be biased by misunderstood information or by having a too strong conviction of an idea or opportunity. The context or mental frame an investor gives to the situation of taking a decision can also mislead your objective view (Saylor Academy, 2012).

In response to these biases and heuristics, a new term was introduced in behaviour finance called ‘bounded rationality’, a term associated with Herbert Simon (1947) (Ritter, 2003). In the psychology of judgment, the heuristics and biases in the process of decision making (Tversky and Kahneman, 1974) constitute critical building blocks together with mental frames. The problem is that mental frames are very hard to change. They are easy to manipulate and are socially shared (Ellul, 1965).

Cognitive psychology states that different patterns exist on how people behave. These anomalies are the theoretical cause of the existence of the behavioural finance theory and do influence the prices of investments which deviate from their fundamental value.

2.2.5.1 Heuristics

A synonym for heuristics are rules of thumb. It facilitates the decision process but sometimes it can support biases which leads to suboptimal decisions of investment (Ritter, 2003).

2.2.5.1.1 Representativeness

Representativeness is the tendency to classify a situation or phenomenon depending on a certain feature, under a rule or pattern (De Man, 2005). People tend to use this bias when they determine that the probability of one object is classified to a model. With this bias, this classification occurs with the percentage of features of the object that is contained by a model. Representativeness bias can be a good medium but in some cases, it can lead to high deviations in patterns which do not exist. New information to individual investors with representativeness could cause an overreaction. We have different biases classified in the representativeness heuristic:

- “The conjunction fallacy is the phenomenon which explains that the combination of two characteristics is more common than each of these characteristics alone” (Tentori K. et al., 2004, p. 470). - The law of small numbers / Gambler’s fallacy effect is the tendency to think that a small sample has the same characteristics as an entire population (Tverksy A. and Kahneman D., 1971). The law of small numbers leads to the ‘gambler’s fallacy effect’ (Rabin M., 2002). This means that people have the expectations that one event is followed by another event. - Base rate fallacy: the base rate neglect is the bias by which people don’t take into account base rate information and take irrational decisions. People will base their decisions on irrelevant information such as the extent of representativeness (Tversky A. and Kahneman D., 1973; Bar-Hillel M., 1980).

2.2.5.1.2 Anchoring

People start forming their expectations by an initial value which is often arbitrary and is often a suggestion of others (De Man, 2005). The second stage is to deviate from this value. The problem is that investors by anchoring grasp themselves at the intrinsic value and don’t deviate that much from the value. An example is that if a prediction is made in the first stage of a decision process, further predictions are put less weight on or do not count anymore.

2.2.5.1.3 Affect heuristic

Kahneman (1972) defined this heuristic as the fact that the decision maker choses what to do depends on what seems the best in their opinion and take confidence in what they feel. This is in contrast to what rational the best solution or decision should be. This heuristic is mainly driven by emotions. Kahneman (2013) states the existence of elements by which some ‘somatic labels’ are categorized. When the labels are positive, people tend to choose for this decision but if negative, people are not interested for this decision.

2.2.5.1.4 Framing

“Framing is the notion that how a concept is presented to individuals matters” (Ritter, 2003, p. 431). The way a problem is presented to someone, has a huge influence on the solution or the result. It is an important factor because investors tend to be risk averse in a situation of profits and risk tolerant in a situation of loss. For example, people are more attractive to discounts than they don’t have to pay surcharge (Ritter, 2003). Framing has an influence on your risk tolerance. This means that when an investor is loss averse, the context determines if he is willing to take risk or not to avoid a loss. Framing is close the ‘prospect theory’ of Tversky and Kahneman (1979), or the fact that the preference of decisions is related to the circumstances.

2.2.5.1.5 Mental accounting

The chain of cognitive operations used by people to formulate, organize and evaluate a financial activity (Thaler R.H., 1999). It is the way decisions are made and evaluated. Decisions that should be made together are taken sometimes separately. For example, when people go to a restaurant, they order food which they would not buy at home because it is too expensive in a store. (De Man, 2005). Narrow framing is one of the important characteristics of mental accounting. This means that people lay their focus too much on what will happen in the close future, but do not take into account what will happen in the long run.

2.2.5.1.6 Herd behaviour

Herd behaviour is the phenomenon by which investors or analysts follow the meaning, vision or behaviour of the majority instead of forming their own. This results in predictions which are a small variety of the market average and equal the advice of analysts. Herd behaviour is one of the main factors of price fluctuations, by which the prices deviate a lot from their fundamental value. Trends will also be strengthened by herd behaviour. This bias could result in overreaction, or in the worst case in bubbles and crashes. Another notion is that young investors are more vulnerable to herd behaviour than older investors (De Man, 2005).

2.2.5.1.7 Conservatism

This bias explains people might underreact on changes and is the opposite of the representativeness bias. People underestimate new information and put less weight on it. Anchoring is one of the main factors which causes conservatism. Risk aversion and laziness are also important factors for conservatism. In contrast, ‘base rate neglect’ is a bias which does not take into account the ‘base rate’. However after a time, people will adjust to the changes and might even overreact to the changes and doesn’t take into account the long- term consequences.

2.2.5.1.8 Disposition effect

“The disposition effect is an empirically observed phenomenon in which investors sell winning shares too quickly and hold losing shares too long” (Baker & Ricciardi, 2014, p. 348). The disposition effect is strengthened by regret aversion as it rises the tendency to keep losers too long also called entrapment. Psychology explains that individual investors can easily put the losses they get for selling shares back of their mind. They want to keep their ‘winners’ safe by selling the shares. It is also an indication of ‘loss aversion’.

The two factors why investors sell their investments too soon are:

- The fear of losing money when there is profit, or the strong loss aversion of the investor because he wants to keep the investments in case of a loss.

- The fact that investors attach less value to supplementary profits and losses.

A study of Odean (1998) show that individual investors sell only 9.8 % of their losses. This in contrast with 14.8 % of their profits.

2.2.5.2 Biases 2.2.5.2.1 Excessive optimism

This bias means that investors tend to estimate the outcomes of events excessively positive. Good performance will be underestimated and bad performance will be overestimated. Weinstein (1980) has studied excessive optimism among college students. He asked them to estimate the chance a chain of events would happen to them. This chain of events was a mix between positive events and negative events. This study confirmed that college students estimated that the positive circumstances would rather happen to them rather than to their fellow students. The opposite was also true, students thought negative happenings would rather happen to their fellow students than to themselves (Helewaut & De Schrijver, 2014).

2.2.5.2.2 Overconfidence

“Can be usefully defined as the tendency to overestimate the probability of achieving one’s objectives as a result of a presumptuous belief in one’s abilities or attributes as they may be used to bring about a particular outcome” (François-Heude & Fabre, 2009, p. 80). Overconfidence is also sex determined because man seem to be more overconfident than woman (Barber & M. Odean, 2001). Overconfident investors tend to trade more compared to rational investors which also results in lower expected utilities. Investor’s confidence intervals, where they estimate their performance between a lower and upper boundary, are notably smaller. In a study of Alpert and Riaffa (1982), investors had to estimate 90 % confidence interval of the containing the number of points of the Dow Jones. Only 60% of the investors seem to correctly estimate the confidence interval. Another issue is that people will not belief that a situation will take place or are sure a certain event will happen, even if this belief is unjustified. They belief having the necessary knowledge and bad results are caused due to bad luck or external forces which are beyond their control. This is called the attribution

theory. Good performance is the result of their own internal factors whereas the cause of bad performance lays external (Miller & Ross, 1975).

Overconfidence can result in an overreaction (or the opposite) when people are exposed to new information. The difference between excessive optimism and overconfidence is small but exist. People can be pessimists but have a confident view on their future (Helewaut & De Schrijver, 2014).

Schaefer et al. (2004) examined if there existed an association amongst the Big five personality factors and overconfidence. They concluded that confidence has a positive correlation with extraversion and openness to experience and no correlation was found with neuroticism, agreeableness and conscientiousness. Another personal trait, not part of the big five which has a big influence on overconfidence is narcissism. The two elements defining narcissism are an inflated positive view of oneself and a self-regulatory strategy to maintain and enhance this self-view (Morf and Rhodewalt 2001).

Overconfidence is first determined by the ex-ante assumptions of one’s beliefs in its abilities. This means the skewed first impression of the probability of one’s distribution regarding its abilities. Secondly, overconfidence is determined post ante to unjustified certainty or the skewed second impression of the deviation of one’s prediction regarding to a certain outcome or result. (François-Heude & Fabre, 2009).

In our experiment, we made use of two out of the three kinds of overconfidence with each its different measuring method:

1. Miscalibration (or overprecision) type of overconfidence. For instance, investors are asked the question what they think their minimum and maximum stock performance will be with a 90 percent certainty. If their real stock performance is below or above their minimum/maximum guess, they are considered overconfident. Typically, overconfident subjects provide too narrow intervals not containing the correct answer (Alpert & Raiffa, 1982).

2. Better-than-average (or overplacement) type of overconfidence. When individuals assess their relative skill, they tend to overstate their acumen relative to the average individual (Larwood and Whittaker (1977); Svenson (1991); Alicke (1985)). Individual investors have the tendency to link their success to their abilities and their actions, and their bad outcomes to bad luck. This phenomenon is called the misattribution bias, closely related to the better-than-average effect.

3. The third type of overconfidence is called overestimation. People appear to overestimate their abilities and performance, but also their chance of success and their level of control (Moore & Healy, 2008).

Behavioural studies show there is a positive relation between an overconfident and optimistic person. Overconfidence and optimism bias are the most quoted biases of the cognitive biases because they have a link with foremost all other biases and they include both the internal and external dimensions of risk perception (Ricciardi & Simon, 2000).

2.2.5.2.3 Confirmation bias

Confirmation bias is the tendency people have to search actively at information which confirms their existing expectations (Wason, P.C., 1966; Mynatt C.R. et al., 1977,). People interpret information as if it confirms their expectations and ignore information which contradicts their expectations. Sometimes they also misinterpret this information so that it confirms their expectations (Barberis N. and Thaler R.H., 2001).

2.2.5.2.4 Illusion of control

This is the bias by which the decision maker wants to have too much control on the outcome. An example is the case by which a manager wants to keep all the activities in-house instead of outsourcing some activities to reduce costs. The difference between illusion of control and excessive optimism is that people have the expectation, independent of the source, to have

a positive performance whereas the source of the positive outcome can be found by the individual itself when he/she is biased with illusion of control (Helewaut & De Schrijver, 2014)

2.2.5.2.5 Hindsight bias

This is a tendency by which people belief they predicted a situation that has happened. This results in a too optimistic preposition about the future (De Man, 2005).

2.2.5.2.6 Self-attribution bias.

People with the self-attribution bias belief their success is caused by their own skills. This is in contrast with failure which they attribute to bad luck or other external factors. If this bias is repeatable, people will have the tendency to overestimate their own talents (De Man, 2005).

2.2.6 Limitations arbitrage

Arbitrage is the process by which investors can make profit due to price fluctuations on different financial markets. By buying an effect on the one market, and selling this effect on the other market with a higher price, profits can be make caused by exchange rate fluctuations between the two different markets. This transaction has no risk, because of the sale of this effect with a higher price. Arbitrage takes care that the market is in balance because it removes these price differences after a while. This due to the fact that the prices rise in the market where the effect is extensively purchased and declining prices in the market where this effect is sold. Ritter (2003) states that there are limits regarding this investment strategy. There are two kinds of events:

- Events with low frequencies. This results in non-rational decisions on arbitrage and people cannot predict financial market. - Events with high frequencies. Arbitrage works by these events rational, and people know how to handle due to these high frequencies.

2.2.7 Explanatory factors investor behaviour

2.2.7.1 Risk tolerance

Table 1: Investment goals per age stage (Saylor Academy, 2012)

During these age stages, the risk tolerance of investors develops from risk averse to risk tolerant. The older an investor becomes, the closer he/she comes to retirement and the more risk of losing wage, the less risk you want to take. The later the age stage, the more important your investments will become, because it can be your only source of income when retiring (Saylor Academy, 2012).

2.2.7.2 Wealth

Studies have shown that the level of wealth can influence the decision-making process of investors. Investors tend to be less risk averse when they have created their wealth “actively” perhaps they have more confidence in making more wealth. Passively wealth creators are afraid to lose wealth which cannot be replaced (Saylor Academy, 2012).

2.2.7.3 Personal traits

“The primary emotions that determine risk-taking behaviour are not greed and fear, but hope and fear, as psychologist Lola Lopes pointed out in 1987” (Saylor Academy, 2012). Second, as there exist different types of investors, all the investors tend to make repeatedly the same errors. This means that practitioners (portfolio managers, financial planners and advisors, investors etcetera), the group people examined in behavioural finance, have the same psychological traits in common. Many personality traits influence investment behaviour, including whether you generally are (Saylor Academy, 2012):

- Confident or anxious - Deliberate or impetuous - Organized or sloppy - Rebellious or conventional - Abstract or linear thinker

The Five-factor model is a good approach to determine the personality of individual investors and are the bases of most other personality traits. It exists of five important personality factors (Baker & Ricciardi, 2014):

- Extraversion - Agreeableness - Conscientiousness - Neuroticism - Openness to experience/intellect

Personality traits have a huge impact on investor behaviour, specific on the investment decisions and the outcomes. Durand, Newby, Peggs, and Siekierka ( 2013) claim that prices in financial markets are “auctionlike” and disparate strands of behavioural finance might be brought together by understanding the personality traits of the marginal price setter. Another study of Soane and Chmiel (2005) also shows that risk-taking behaviour can be

affected by personality traits. Different individual dispositional motivation causes a wide scope in different risk-taking behaviour. Personality traits determines the goals and the affirmative risk tolerance. Nicholson, Soane, Fenton-O’Creevy, and Willman (2005) report the risk-taking process in six decision dimensions, including career and finance. These dimensions are positively correlated with extraversion and openness to experience, and negatively associated with neuroticism, agreeableness, and conscientiousness.

One of the first steps that can be taken to avoid pitfalls in making decisions is by understanding your own personality. When a person is aware of his own biased behaviour or thought, he is able to take more control of his thoughts, actions and disruptive moods, in other words, he can improve his own ability of self-regulation (Goleman 1998). Statman and Wood (2004) also claim that having an insight in investors’ personalities can help advisors with building portfolios and realize better performances. Pompain (2012, p.11) maintains that “if you can identify what basic type of investor you are, and then diagnose your unique irrational behaviour, you will be in a much better position to overcome these behaviours and, ultimately reach your financial goals”.

2.2.7.4 Genes

Genes are also one of the factors to determine investor’s decisions. Cesarini et al. (2010) explains that almost a quarter of the deviation in exposure to risk is caused by investors’ genetic variation. The genetic basis for personality is as well evidence that variance in financial decision-making might have a genetic basis suggesting an association between personality traits and financial decision-making.

2.2.7.5 Sex (or gender)

Barber and Odean (2001) did a study on comparing the performance between men and woman. This study followed two observations: (1) men tend to be more prone to overconfidence than woman (Deaux and Farris, 1977), and (2) models that assume that overconfident investors tend to predict investors will trade aggressive and thus more excessively. Barber and Odean (2001) documented that by combining these two observations,

men tended to trade more excessively compared to women and that their overconfident investor behaviour would damage their performance. They made their conclusions by looking at the turnover rate; the turnover rate of men are 80 percent which was a lot higher than women with a turnover rate of 50 percent. The excessive trading affected their return; while both sexes had a low return, the return of men was even lower than the women’s return. Neither men nor women seem to have stock selection ability so this gave rise in a tendency by which men trade more aggressively and hence leading to higher trading costs pulling down their performance.

2.2.8 Individual investors

Financial economists have a different vision on individuals and institutions (Kaniel, Saar & Titman, 2008). Institutions are seen as informed, but individuals are seen to be biased and are often thought of as the proverbial noise traders in the sense of Kyle (1985) or Black (1986). When investors are risk-averse, they tend to invest more in bonds or low-risk assets. When investors are risk-seeking, they look for risky assets hoping to realize a higher return. A study was conducted to examine the relation between the buying and selling by individuals and their returns using a data set by the NYSE. The data set was modelled with evidence of the NYSE’s Consolidated Equity Audit Trail Data (CAUD) files that contain detailed information on all orders (buy or sell) on the exchange market. This data was constructed with the aggregated volume of the aggregated buy and sell of each asset on each day. These orders could be used as a measure of individual trading behaviour. In our experiment, we used this also this type of measure to determine individual trading behaviour.

2.2.9 Financial education to help investors make decisions.

Financial education could possibly help people to make better financial decisions (Mandell, 2001; Lusardi and Mitchell, 2014), but others such as Willis (2011) concluded that financial education switches investor’s confidence in to overconfidence but have no effect on the performance of their financial behaviour. Another solution for making investors less biased is to provide investment advice by experts and analysts. Nonetheless, biases and heuristics could be there automatically or could be a habit, even by experts. These phenomena led to

errors when they were studied in discrete separate situations. But in practice, investors get continuously feedback regarding their gains and losses. This feedback allows them to correct their biases and to make sure they disappear after a while. So, the impact of warning signals and financial education on investor’s biases could show mixed results. In ‘debiasing’ programs, biases are errors to be eliminated to get unbiased decision-making (Fishhoff, 1982). These trainings can be positive and eliminate the biases, but sometimes investors prefer to use heuristics which are ‘fast and frugal’ rather than analysing and comparing every solution to make sure they pick the best option available (Arkes, 1991).

2.3 Warning signals

Consumer organisations want to know the reason why consumers (retail investors) invest, why they save their money and most importantly why not. From an investor point of view, this might protect them from irrational sellers and from themselves also referred as dysfunctional decision behaviour. Financial institutions are selling financial products and service together with the necessary and personal advice towards consumers (van Raai, 2016).

This advice should be in line with consumer protection so that this advice is less sales-driven and more consumer-centric. The idea is that products should be offered in line with the needs of people in the short and long term, and are safe within different economic conditions. The task of sellers of financial products such as investment firms is to care that investors don’t invest in securities who are for instance too risky according their risk profile. Some financial products can in the short term be profitable, but can under undesirable economic conditions be unprofitable in the long term.

It is written in disclosure requirements that investors have to be informed about the independency agents have on the intermediary and the profit of sold products. If a commission is incorporated in the income of an agent. The advice will be less biased if the income is based on the hours, agents spent more time to give clients proper advice. Studies shows that clients do not think further on the given advice, but follow it blindly. That’s why strict regulation is needed to protect investors. Clients who possess less financial knowledge should get more information which is more accessible to grasp. Financial advisers should also

take into account client’s risk tolerance and the time they want to spend on their money management.

Sellers of financial products have to take into account the important factors : on the one hand they are imposed to their sales targets and on the other hand they are the clients confidential adviser for assisting and advising clients and have to care about their finances in the best way. This care is their moral and legal obligation to completely inform their clients and help them understanding what the profits, costs and risks of securities are. Their important factor is the client’s interest, and not their own interest (of profit). Duty of care is necessary during the whole life cycle of holding a security e.g. possessing the insurance policy, mortgage, or other financial products. General duty of care is obligated for the whole content of the portfolio, including all financial products. When clients do not take into account the long-term negative outcomes in the process of decision taking financial institutions should warn them for not buying at all the securities. Consumer adjusted advice costs extra time and effort for both parties but it may result in more loyalty and positive recommendations toward other (potential) clients (van Raai, 2016).

A a protection for investor’s own biased behaviour and to minimalize market failures, The (EC) Directive on Markets in Financial Instruments (MiFID) has been created. This with the objective to make sure the (retail) investor’s investment process occurs on an informed bases. The MiFID includes that investment firms have to inform their retail clients and should be informed about their retail clients and their financial products. This concept is influenced by the underlying philosophy that the quality of financial markets and products is hardly to determine ex ante. European securities regulation want to ensure investors do not make wrong investment decisions caused by a lack of asymmetric information. This concept would improve the trust in the (financial) markets, investor’s capital allocation, better informed stock market prices and liquidity. But, this concept has a positive side and a negative one. On the one hand, it is the objective for a better informed decision-process, but on the other hand, it causes a boundary in the decision process. This boundary affects that the investment process by the fact that once an investor has taken his/her investment decision, this question will not further be questioned by the regulation of the law. Professor Louis Loss expression that every “investor has the right to make a fool of himself” (Klöhn, Preventing Excessive Retail

Investor Trading under MiFID, 2009, p. 439) is the base of European securities regulation and private law as well. Or in short, this is the foundation of private autonomy (Klöhn, Preventing Excessive Retail Investor Trading under MiFID, 2009)).

Due to the financial crisis ‘2007-2009) and the fact that investors were still biased in their decision-process, since 3 January 2018, an updated version of MiFID has entered into force, named MiFID II. The principles are the same, but are stricter now due to the proven defaults or limitations of the regulation. Some examples of important changes (Febelfin, 2019) :

- Stricter rules for the sale of complex financial products - Stricter procedures which are needed to have a better fit of the financial products with the right clients. - Independent investment advices - Better provision of information

To take care of the protection of financial clients and in line with the MiFID II- regulation system each investment firm has to collect the necessary information about the knowledge and experience of each (potential) client who wants to make use of investment services . Each financial institution is obliged to send a warning signal to its (potential) client when a financial product does not fit within their scope of financial knowledge and experience (Europees Parlement & Raad, 2014).

3 Methodology

Based on the literature review, the following hypothesis was derived: “Warning signals do have a positive (negative) impact on individual investor’s trading behaviour.” Individual investor’s trading behaviour will be measured in various ways because there is simply not one single method to measure this type of behaviour. By means of 7 approaches in total, this empirical research will attempt to answer our research question.

By creating a controlled financial, virtual environment, the aim of this empirical research was to examine the impact of warning signals, acting as a treatment or condition, on individual investor’s trading behaviour such as excessive trading based on several measures. On the one hand, the intent of this experiment was to capture the difference of individual investor’s trading behaviour between the two independent samples. On the other hand, the aim was to explain the level of individual investor’s trading behaviour, as possible heterogeneity effects could take place among both independent samples.

The structure of this subsection is the following: firstly, the set-up of this investment laboratory experiment; followed by breaking down the data set extracted from the experiment; thirdly, the build-up of the appropriate econometric model; fourthly an interpretation of the obtained empirical results and lastly discussing the added value and limitations of this experiment.

3.1 Set-up investment laboratory experiment (experimental design)

This investment laboratory experiment was inspired by P. Branas-Garza and A. Cabrales (2016) their academic handbook: Experimental Economics; Volume II, Economic Applications. We are fully aware that following assumptions and simplifications deviate far from reality. However, an econometric model should not be judged by its assumptions, but by its predictive power (Friedman, 1953). Nota bene that besides individual investor’s trading behaviour (see more

at Data), everything else was kept as constant as possible by (successfully) randomizing the experiment.

All participants were asked to put together a portfolio containing a small number of assets that they traded over a fixed period of time. With given cash of 10 000 EUR, participants could buy and/or sell assets during five trading periods. Each trading period was equal to one trading year (252 days). Participants could make their capital allocation decision by determining the proportion they wanted to invest in risky and risk-free assets. The risky assets consisted of nine stocks with all having an equal risk-return trade-off, as the risk-free asset contained a treasury note in the form of a zero-coupon bond. Therefore, historical holding period returns (of the stocks) were irrelevant for future investment decisions. The risk-free asset could be considered as a cash equivalent or money market instrument. Note that no transaction costs or taxes were charged on all buy and sale transactions. Short-selling and taking out a loan were not allowed either. Lastly, no dividends were disbursed by all stocks. The reason for these assumptions is to maintain the simplicity in this experiment within our scope of education.

Individual investor’s trading behaviour was measured by looking at the total number of transactions, total transaction volume, invested proportion per asset class (risky, risk-free and cash) and by measuring possible overconfident behaviour. To capture the two types of overconfidence, the participants were asked to forecast the lowest and highest bound of their portfolio performance (miscalibration type) with 90% certainty and how they considered their performance compared to others (better-than-average type). Other standard information such as age, gender and level of risk aversion was requested to observe possible heterogeneity within our two (homogeneous) samples of participants.

This experimental between-subjects design has been fully constructed in Microsoft Excel using VBA to program the necessary message boxes and macros. Please notice that two different Excel files were created representing the control and treatment group, whereas only the latter would receive a warning signal each trading period. Clear instructions were provided to the participants to avoid at all cost any form of deception.

3.1.1 Sample of participants

Power analyses learned that a sample size of 70 to 90 participants was needed to guarantee the external validity of this investment laboratory experiment. The target group of participants for this experiment were financial professionals active in the Belgian banking sector. Most participants were employed at Belgium’s major banks and several other niche players. The banks preferred to stay anonymous for commercial purposes and total anonymity was guaranteed for all participants. In total, over 90 participants were randomly gathered and assigned to a group to increase both external and internal validity of this experiment (Campbell & Stanley, 1963). 56% of the participants were men, as 44% were women. The average age of our participants was 36 years with a standard deviation of 12 years. A restriction of our sample was the simple fact that only (financial) professionals were targeted; students, unemployed and pensioners were left out of the scope. Most participants were approached face-to-face or via phone, after which they received an e-mail provided with an Excel file and the necessary instructions. Over 95% of the participants returned the Excel. Because of unsuccessfully completing the experiment, a few cases were removed (data cleaning), leaving 81 participants (see Table 2) for the dataset. It took approximately 3 weeks to collect all the data, starting from the end of April. Lastly, there were no financial incentives, as we fully counted on the voluntariness of the participants.

Warning signal (treatment group) N= 41 No warning signal (control group N= 40 Table 2: Number of participants per group

3.1.2 Pricing process of assets

3.1.2.1 Risky assets

As mentioned ut supra, the risky assets consisted of nine stocks, all having the same risk- return trade-off: an average HPR of 8% and a 20% stock volatility per annum (trading period). Underlying rationale for this is to eliminate the price effect wherefore investment decisions

cannot influence the price of the risky assets, as future expectations are homogeneous of nature. Therefore, for all nine stocks, random price paths were simulated in a risk-neutral world (Hull, 2015). This means that each current share price for each trading period is equal to the last share price multiplied by the discount rate considering following input parameters ut infra:

Initial stock price (푺퐭) Randomly assigned to the nine stocks Annual stock volatility (σ) 20% Holding period return per annum (퐫) 8% Time interval between trading periods (Δt) 1 trading year Time to maturity per annum (T) 252 days Table 3: Input parameters to simulate random price paths (own content)

Based on the input parameters above, the following equation was used to simulate the stock prices (Hull, 2015):

σ2 (r− ).Δt+σ.ε√Δt 2 푆t+Δt = 푆t. 푒

Figure 2: Simulation random price paths stocks (Hull, 2015)

ε√Δt represents the Wiener process, also considered as a particular type of the Markov process. “A Markov process is a stochastic process where only the current value of the variable is relevant for predicting the future. The past history of the variable and the way that the present has emerged from the past are irrelevant” (Hull, 2015,p.324). This definition refers to to the elimination of the price effect and the fact that the stock price follows a random and unpredictable walk. Furthermore, this risk-neutral assumption also implies that there are no investment opportunities or as Milton Friedman (1975) would say: “There’s no such thing as a free lunch”.

The random deviation ε was calculated by drawing a random sample from a standard normal distribution with a mean and standard deviation equal to respectively 0 and 1. A combination of two Excel formulas was used to obtain ε:

− The RAND function which draws uniform, random samples from [0;1] − The NORMINV (inverse) function (x, µ, σ) which calculates N-1(x)

In the table below, all simulated stock prices can be retrieved for 11 years (trading periods). Note that the stock price at trading period 1 can be found at Year T, as the stock price at trading period 2 can be found at Year T+1 etcetera. The stock prices from Year T-5 to Year T- 1 merely served as additional information for the participants. Please also notice that the names of all nine stocks has been chosen totally random by using an online corporate name generator to give our experiment a more realistic touch. Hence, the names should not influence the participant’s investment decisions, as they are in no sense related to existing stocks. The figure below shows how the formula was logged in the formula bar of Microsoft Excel.

Figure 3: Equation simulation stock price of Bonous at year T (own content)

Table 4: Simulation random price paths (own content) (own price paths random 4:Table Simulation

3.1.2.2 Risk-free asset

The risk-free asset was in this experiment a treasury note in the form of a zero-coupon bond with the following properties: a time-to-maturity of five years (t) and a yield-to-maturity of 3% (y, continuous compounding). As there were no coupon payments, the bond was issued below par value (Present Value) and the bond was reimbursed at par value (Face Value). The present value is calculated by discounting the face value at a discount rate (YTM) of 3% for five years giving us the following equations:

푃푉 = 퐹푉. 푒(−푦.푡) 퐹푉 = 푃푉. 푒(푦.푡)

Figure 4: Bond pricing process (Bodie, Kane, & Marcus, 2017)

The table ut infra presents the bond prices (PV’s) for each trading period with their respective face values. For instance, the present value of trading period 1 can be retrieved at Year T, presuming a face value of 1000 EUR. The present value of trading period can be retrieved at Year T+1 and so on. Nota bene that the present value grows each trading period at the discount rate (e(yt)), assuming there are no other factors such as a possible correlation to the stocks to influence the bond price. Moreover, it’s no use to calculate the face value of T+5, as there are only 5 trading periods.

Year Present Value Face Value T €860,71 €1000 T+1 €886,71 €1030,45 T+2 €913,93 €1061,84 T+3 €941,76 €1094,17 T+4 €970,45 €1127,50 T+5 €1000 N/A Table 5: Pricing zero-coupon bonds (own content)

3.1.3 Course of the experiment 3.1.3.1 Introduction

The introduction (see Figure 5) served mainly to give answer to the participants to the following three questions: − WHAT is this experiment about? − WHY is this experiment conducted? − HOW does this experiment work?

Figure 5: Experiment explained: what-why-how (own content)

Moreover, their gender, age and level of risk aversion was asked to provide data supporting our control variables.

3.1.3.2 Financial suitability test and trading periods

Participants first needed to fill in a financial suitability test in the form of a questionnaire including 20 questions measuring their knowledge and investor experience. Entering the first trading period (and those thereafter), participants of only the treatment group received a warning signal (see Figure 6) notifying that the risky assets were not financially suitable

according to their knowledge and investor experience. Even if they managed to score the highest possible result, they would still receive the warning signal.

Figure 6: Warning signal in Microsoft Excel (own content)

Thereafter, the participants from both control and treatment group were asked to construct a portfolio (see Table 6) where they could decide to buy and/or sell assets (taking into account the initial given cash of 10 000 EUR) they regarded necessary for their investment strategy. It speaks for itself that in the first trading period only purchases could be made, as there were no initial holdings in the portfolio. Each trading period, participants could find back the current bond price and its properties, the evolution of the stocks over the last five trading periods and obviously the current share price as well. Additional information such as the yield per share were available per trading period, more profoundly discussed below under Financial ratios.

Table 6: Investment decision form (own content)

Please note that by making purchases (and later on sales as well), participants could see immediately all changes for their portfolio based on the current share price of that trading period (See tables 7 and 8).

Table 7: Overview portfolio template trading period 1 (own content)

Table 8: Overview portfolio value template trading period 1 (own content)

Afterwards, the participants were asked the following two questions before they could move on to the next trading period (see figure 7): − How do you position your portfolio performance compared to other participants (best to worst)? − Forecast your portfolio performance in an interval with a certainty of 90%.

Figure 7: Questions to observe overconfidence (own content)

After completing all previous mentioned tasks, the participant might proceed to the next trading period, where he/she would find the new portfolio value caused by the market fluctuations of the assets. From that time forward, the participant had to repeat the same process all over again till he/she reached the end of the fifth trading period.

3.1.3.3 Final results

Eventually after having completed all five trading periods, each participant could see his/her final results including the past stock and bond prices of all trading periods and ultimately an overview of his/her portfolio. Afterwards, the participants were thanked to contribute to this investment laboratory experiment.

3.1.4 Financial ratios

Following financial ratios were used in a prior, similar experiment that seemed relevant to include as well in this investment laboratory experiment (Sinnaeve & Vandekerckhove, 2018). All financial ratios served merely as additional information for participants to make their investment decisions each trading period.

3.1.4.1 Yield portfolio

∑(퐶푎푠ℎ; 푅𝑖푠푘푦 푎푠푠푒푡푠; 푅𝑖푠푘 푓푟푒푒 푎푠푠푒푡푠) − €10 000 Yield portfolio = 푖 €10 000 Figure 8: Yield portfolio (own content)

The equation above shows that the yield of the portfolio at some point in time (𝑖) was equal to the current portfolio value, being the sum of all (remaining) cash, risky and risk-free assets being held in the portfolio, minus the initial given cash of 10 000 EUR of which the result was divided by 10 000 EUR. The yield of the portfolio was expressed in %.

3.1.4.2 Yield per stock (in portfolio)

Before the yield per stock could be calculated, the average purchase price had to be computed:

푃푖,푡. 푄푖,푡 + 퐴푃푃푖,푡−1. 퐻푖,푡−1 퐴푣푒푟푎푔푒 푝푢푟푐ℎ푎푠푒 푝푟𝑖푐푒푖,푡 = 푄푖,푡 + 푄푖,푡−1 Figure 9: Average share price (own content)

Knowing that:

푃푖,푡: the purchase price of stock 𝑖 in trading period 푡 (e.g. Bonous in trading period 2)

푄푖,푡: the number of stocks 𝑖 purchased in trading period 푡

퐴푃푃푖,푡−1: the average purchase price of stock 𝑖 in trading period 푡 (e.g. Bonous in trading period 1)

퐻푖,푡−1: the number of stocks 𝑖 held in the portfolio at trading period 푡

N.B: the average purchase price of trading period 1 was equal to the current purchase price (at year T), nonetheless how many stocks were purchased during that trading period. This can be explained by the simple fact that there were no initial holdings, as all participants started with a cash amount of 10 000 EUR.

푃푖,푡 − 퐴푃푃푖,푡 푌𝑖푒푙푑 푝푒푟 푠푡표푐푘 (𝑖푛 푝표푟푡푓표푙𝑖표)푖,푡 = 퐴푃푃푖,푡 Figure 10: Yield per stock in portfolio (own content)

Thereafter, the yield per stock (expressed in %) could be calculated, as the equation ut supra shows. Note well that the yield per stock (in portfolio) was not calculated if there were no stocks 𝑖 held in the last and/or current trading period. If last period no stocks 𝑖 were purchased, it goes without saying that there is no yield. Same rationale holds true for the current trading period, as all stocks were sold or no stocks were purchased at all. Following

this reasoning, it explains why the yield was not computed either for the first trading period. Applying this in Microsoft Excel, a combination of an IF function and OR function was used. Breaking down Figure 11, gives us the following information:

− Table8[@Number]: Number of stocks Bonous in portfolio at trading period 1 (퐻푖,푡−1)

− [@Number]: Number of stocks Bonous in portfolio at trading period 2 (퐻푖,푡)

− [@[Share/ Bond price]]: Purchase price of Bonous in trading period 2 (푃푖,푡)

− P35: Average purchase price of Bonous in trading period 2 (퐴푃푃푖,푡)

Figure 11: Equation yield per stock of Bonous at trading period 2 (own content)

3.1.4.3 Average stock price

∑푃푖,푡 퐴푣푒푟푎푔푒 푠푡표푐푘 푝푟𝑖푐푒 = 푛 Figure 12: Average stock price (own content)

The average price of stock 𝑖 (expressed in EUR) at trading period 푡 was nothing more than the arithmetic average of last 5 trading periods (푛) using the AVERAGE() function in Microsoft Excel, as you can see in Figure 12.

3.1.4.4 Evolution stock price relative to the last trading period

푃푖,푡 − 푃푖,푡−1 퐸푣표푙푢푡𝑖표푛 푠푡표푐푘 푝푟𝑖푐푒 푟푒푙푎푡𝑖푣푒 푡표 푡ℎ푒 푙푎푠푡 푡푟푎푑𝑖푛푔 푝푒푟𝑖표푑 = 푃푖,푡 Figure 13: Evolution stock price relative to the last trading period (own content)

The evolution of the stock price relative to the last trading period (in %) was not hard either to compute, as you can see in Figure 13.

3.1.5 Microsoft Excel VBA and macros

“Visual Basic for Applications runs as an internal programming language in Microsoft Office (MS Office, Office) applications such as Access, Excel, PowerPoint, Publisher, Word, and Visio.” (Kenton, 2019). In order to avoid possible pitfalls of our experiment and cheating behaviour of our participants, several message boxes and macros were programmed. In the appendices, the syntax of VBA used in this experiment can be retrieved.

3.1.5.1 Message boxes

3.1.5.1.1 Message box 1: “You have filled in the financial suitability test, you may proceed to the next trading period. Good luck!”

After filling in the financial suitability test, the participants received a notification in the form of a pop-up window allowing them to proceed to the first trading period (see Figure 14). Participants were not able to proceed until they filled in all 20 questions via an “If Then” statement by the Visual Basic Editor of Microsoft Excel.

Figure 14: Message box financial suitability test (own content)

Each completed question resulted in a corresponding cell in Excel with a value of 1, in contradiction to the questions left blank leading to a corresponding cell value of 0. Thereafter all Excel cells were multiplied together, only allowing the participants to proceed if the value of the product was equal to 1.

3.1.5.1.2 Message box 2: “Based on the financial suitability test, we strongly recommend you not to invest in the risky assets, being the 9 stocks.”

As mentioned ut supra, the treatment group received each period a warning regardless their score on the financial suitability test. As participants were obliged to close the pop-up window before they could continue, it was assured that they perceived and received the warning signal which was a crucial element of our research (see Figure 15).

Figure 15: Message box warning signal (own content)

3.1.5.1.3 Message box 3: “Insufficient funds.”

Since participants had only received 10 000 EUR in cash and could not borrow, a notification popped up if their total purchases (in EUR) exceeded their remaining cash balance (see Figure 16). An “If Then” statement via VBA was here used as well.

Figure 16: Message box insufficient funds (own content)

3.1.5.1.4 Message box 4: “You haven’t purchased sufficient stocks/bonds to sell this number of stocks/bonds.”

Another possible pitfall is that participants, perhaps by accident, would attempt to sell more stocks or bonds than they had in their actual portfolio. In that case, they would receive a notification telling them they had not sufficient stocks or bonds in their current portfolio to sell the requested amount (see Figure 17). An “If Then” statement via VBA was here used as well.

Figure 17: Message box sales exceeding purchases (own content)

Each sale transaction per asset (stock/bond) that did not exceed their current portfolio including possible purchases resulted in a corresponding cell in Excel with a value of 0, in contradiction to sale transactions exceeding the current amount of that asset held in the portfolio leading to a corresponding cell value of 1 (see Figure 18). Thereafter, all Excel cells were added up together, only allowing the participants to proceed if the value of the sum was equal to 0.

Figure 18: IF function in Excel comparing sales with current portfolio and purchases

3.1.5.1.5 Message box 5: “You’ve completed the Xth trading period, you may proceed to the Yth trading period.”

After completing a trading period, participants received a notification mentioning they could proceed to the next trading period (see Figure 9). Therefore, they had to answer the two questions at the very bottom, capturing two types of overconfidence, before they could move

on. It stands to reason, it was not required to make additional investment decisions since holding your current portfolio is also an investment strategy. An “If Then” statement via VBA was here used as well.

Figure 19: Message box completing the first trading period (own content)

For the first question (see Figure 19), same rationale was used as in the questions of the financial suitability test (Message box 1). The second question (see Figure 20) was a combination of following functions: − IF() function − OR() function − ISBLANK() function

Figure 20: IF function in Excel checking for blank cells (own content)

If left blank, the corresponding cell value would be 0, in the other case 1. Hereafter both Excel cells were multiplied together, only allowing the participants to proceed to the next trading period if the value of the product was equal to 1.

3.1.5.2 Macros 3.1.5.2.1 Macro 1: Unhiding next trading period

Primo, closing previous mentioned pop-up window (see Figure 19), the experiment was programmed to unhide the next Excel sheet or trading period.

3.1.5.2.2 Macro 2: Locking last trading period(s)

Secondo, last trading period was locked when participants moved on to the subsequent trading period to avoid cheating behaviour. Nonetheless, participants could consult at any time previous trading periods for further investment decisions, only this time in read mode.

3.1.6 Limitations investment laboratory experiment

Firstly, typical point of criticism on this investment laboratory experiment could be that there was a lack of realism (Falk & Heckman, 2009). On the one hand, having no transaction costs, taxes, borrowing possibilities, dividend-paying stocks, a limited asset choice set limited the realism of this experiment. On the other hand, it was earlier discussed that underlying rationale for this was to maintain the simplicity in this experiment within our scope of education and an experimental design should not be judged by its assumptions but by its predictive power (Friedman, 1953).

Second concern might be that the sample of participants was too specific, as only financial professionals of the Belgian banking sector were involved. This could be an indication to doubt the external validity of the experiment. In addition, there might be not enough observations either to obtain powerful statistical results. Nonetheless, a skewed distribution was assumed with at least 40 participants per group (Mcclave, Benson, Sincich, & Knypstra, 2014).

Thirdly, the money at stake could be criticized because the initial given cash wash only 10 000 EUR which is relatively small (Falk & Heckman, 2009). Therefore, subjects would maybe not take the experiment too seriously whereby they would take more risk than their usual risk appetite.

Fourthly, the possibility exist this experiment could suffer from the Hawthorne effects where participants behave differently based on the fact they perceive they are being observed. At

the same time, being observed is not an exclusive property of laboratory experiment while many decisions in the real world are observed as well (Falk & Heckman, 2009).

Lastly, self-selection of participants could bias the results and hence questioning the generalization of the empirical evidence (external validity). Although, if randomization appears to be successful, the selection effect should as good as fully be eliminated.

3.2 Data 3.2.1 Description variables 3.2.1.1 Individual investor’s trading behaviour

3.2.1.1.1 Total transactions (TT)

Firstly, individual investor’s trading behaviour was measured by looking at the number of purchase and sale transactions for each trading period. In other words, there was a maximum of 20 (purchase and sale) transactions per trading period except for the first trading period (10). That totals a maximum of 90 transactions per participant. An arguable concern might be that there occurs to be double-counting and where working with the net transaction could be a way out. For instance, a participant purchases X and sells Y stocks of Bonous in trading period 1. Total transactions would be two in contradiction with the net transaction that would only count as one. Nevertheless, purchase and sale transactions are both of a different nature and count therefore as two separate investment decisions. Expectations are that warning signals could positively influence the number of total transactions (hence less transactions) and thus could reduce excessive trading.

3.2.1.1.2 Turnover rate total transaction volume (TTTV)

The total transactions only measure the number of transactions and do not take into consideration the transaction volume. Therefore, it seemed useful to include as well the turnover rate as a second measure for individual investor’s trading behaviour. The invested amount (in EUR) of all purchase and sale transactions of all trading periods was added up, after which the outcome was divided by the initial given cash of 10 000 EUR. Expectations are

that warning signals could positively influence the turnover rate of the total transaction volume (hence lower turnover rate) and thus could reduce excessive trading.

3.2.1.1.3 Proportion per asset class (PR, PRF & PC)

Furthermore, it could be interesting to examine if the warning signals (treatment) would impact the participants their capital allocation decision. Hence, it appeared to be interesting to take a closer look at the average proportion (expressed in %), through all trading periods, held/invested in cash, stocks (risky asset) and bonds (risk-free asset). Expectations are that warning signals could positively influence the capital allocation decision and thus could reduce excessive risk-taking.

3.2.1.1.4 Overconfidence (Overconf_1 & Overconf_2)

As earlier discussed in the literature review, there are several types of overconfidence where two of them were measured in this experiment.

The better-than-average effect or also called overplacement effect is the strong belief that one is better than the median person (Barber & M. Odean, The Behavior of Individual Investors, 2013). At the end of each trading period, participants had to compare their portfolio performance relative to the portfolio performance of other participants. The participants could rank themselves in following quartiles:

− Top 0-25% investors. − Between the best 25-50% investors. − Between the 50-75% investors. − Between the 75%-100% investors.

Participants that ranked them more than once incorrectly over the median yield, were considered overconfident investors. In other words, if the participant showed overconfident behaviour on a regular basis (at least two trading periods or 40% of the time), they were subject to the better-than-average effect. Note that it is statistically impossible that the

majority of participants performed above or below the median yield contrary to the average yield which is the case with skewed distributions (Moore & Healy, 2008). In the table below, one can see the median yield for each trading period of both the control and treatment group.

Trading Trading Trading Trading Trading period 1 period 2 period 3 period 4 period 5 Control group 27,00% 17,50% 17,50% 24,00% 23,00% Treatment group 15,00% 19,00% 14,00% 22,00% 28,00% Table 9: Median yield through different trading periods among different groups

Second type of overconfidence is called miscalibration or overprecision where the individual strongly believes that he/she knows more than he/she actually does (Barber & M. Odean, The Behavior of Individual Investors, 2013). Each trading period, participants had to estimate an interval of their portfolio performance with a certainty (or probability) of 90%. In other words, a well-calibrated participant would estimate minimum 4 out of the 5 times a correct interval containing the actual portfolio performance. Overconfident investors will typically provide too narrow intervals, often not containing their portfolio performance (Alpert & Raiffa, 1982). Participants who were not able to estimate the interval at least 4 out of the 5 times correctly were considered overconfident.

Do not confuse miscalibration with the third type of overconfidence: overestimation, the tendency to overestimate one’s actual ability (Barber & M. Odean, The Behavior of Individual Investors, 2013). If for example the participant managed to realize a higher return that lies outside his/her interval, he/she was still considered overconfident. This type of overconfident behaviour aimed for how well the participant could estimate their portfolio performance, using a confidence interval, regardless a high (positive) or low (negative) yield. Finally, expectations are that warning signals could positively influence the overconfidence bias and thus could reduce the impact of the better-than-average and miscalibration effect.

3.2.1.2 Type of group (Group_dummy)

A dummy variable was created to distinguish participants belonging to the control (0) and treatment (1) group. Note that this will be the only independent variable for the regression models, as all other variables below are used as control variables.

3.2.1.3 Investor knowledge and experience (IKE)

As discussed in the Course of the experiment, participants had to fill in a financial suitability test before they could proceed to the first trading period. The test contained 20 questions whereof 17 were measuring their investor knowledge and 3 gauging for their investor experience. Participants were given a score regarding their investor knowledge and experience depending on the number of correct answers they gave on each of those 20 questions. The questions were collected from a questionnaire of a Belgian major bank containing over 150 questions (Anonymous, 2019). The bank here too preferred to stay anonymous for commercial purposes. A sample of 20 questions was taken based on the criterion that the average individual investor should pass the test. Note that this questionnaire in practice is partially used to determine an individual investor’s risk profile.

3.2.1.4 Level of risk aversion (A)

The level of risk aversion or (risk tolerance) or ‘price of risk’ was calculated as a function of the risk premium (or excess return) and the volatility of a risky asset (Bodie, Kane, & Marcus, 2017):

퐸(푟) − 푟푓 퐴 = 𝜎2 Figure 20: level of risk aversion (Bodie, Kane, & Marcus, 2017)

Knowing that:

퐴: Level of risk aversion 퐸(푟): Expected rate of return on the risky asset

푟푓: Risk-free rate of return 𝜎: Volatility risky asset

Therefore, participants were asked the following question: “Suppose that a risky asset (for instance a stock) is known to have an annual average stock volatility of 20%, knowing that the rate of return of a risk-free asset (for instance a bond) is equal to 2%. Which average rate of return (in %) would you like to realize on the risky asset to compensate the risk you are taking?”

Following the literature, an average individual investor would answer 10 % resulting in a level of risk aversion of 2. In other words, the investors would like to realize a 2% risk premium

(퐸(푟)-푟푓) per unit risk (𝜎).

3.2.1.5 Age and gender

Finally, age (expressed in years) and gender were included as well to observe possible heterogeneity within our treatment and control group, besides the impact of warning signals.

3.2.2 Extraction empirical data

A separate Excel sheet was created to extract all the empirical data out of the different versions of the experiment. As retrieving all the necessary data manually would be a time- consuming job, a new macro was created and programmed in Excel to facilitate the extraction. The syntax can also here be found back in the appendices. The extraction was nothing more than retrieving all the variables mentioned above for each participant.

3.2.3 Descriptive statistics

The tables ut supra permit to look more in-depth at the basic features of this dataset including inter alia measures of central tendency and dispersion. First of all, the total sample is discussed after which the dataset is separately analysed comparing the control versus the treatment group

3.2.3.1 Total sample

Variable Mean Median Minimum Maximum SD Skewness Ex. Kurt. TT 19,9010 18,0000 1,0000 84,0000 14,5780 1,5257 3,5307 TTTV 2,6379 2,0958 0,5223 6,9302 1,6322 1,0037 0,1307

PR 0,6345 0,6904 0,0000 0,9998 0,3059 -0,5058 -0,9937

PRF 0,2860 0,1846 0,0000 0,9497 0,2682 0,7543 -0,4494

PC 0,0795 0,0201 0,0001 0,5568 0,1254 1,9863 3,4051 IKE 0,5136 0,5000 0,0000 0,9500 0,2174 -0,0027 -0,4111

A 2,0648 1,7500 -0,50 9,5000 1,5237 2,1265 6,6914 Age 35,8890 33,0000 22,0000 62,0000 12,2300 0,5780 -1,0279 Table 10: Descriptive statistics total sample

The average number of total transactions (TT) is equal to 20 which is far under the maximum of 90 transactions. The turnover rate of the total transaction volume (TTTV) was on average 2,64 or in other words: 26 400 EUR was on average invested through the five trading periods, considering the initial given cash of 10 000 EUR. If one looks at the coefficient of variation or also known as the relative standard deviation, the turnover rate of the total transaction volume (CV=0,6187) seems to be less volatile compared to the number of total transactions (CV=0,7325). Put another way, the latter appears to have a greater dispersion among all participants.

The capital allocation proportions are on average the following: 63,45% in risky assets (PR) and 28,60% in risk-free assets (PRF). The remaining 7,95% was held in cash (PC) which could indicate irrationality among the investors, as the risk-free asset could be considered as a cash equivalent or money market instrument.

The average participant passed the financial suitability test (IKE, mean of 51,36%) which could point to a proper difficulty level to measure the investor his/her knowledge and experience. The average level of risk aversion (A) was 2,06 which is by approximation in line what was

presumed (≈2). With a minimum and maximum age of respectively 22 and 62 years, both younger and older participants were included in the experiment.

In addition, most distributions regarding the variables above are positively skewed to the right

except for Pr, PRF and the age of the investors. The Excess Kurtosis implies none of the variables have a normal distribution referring to the tailedness of their probability distribution.

Overconf_1 Frequency Percentage frequency Cumulative percentage frequency Not overconfident 55 67,90% 67,90% Overconfident 26 32,10% 100% Total 81 100% Table 11: Frequency table better-than-average effect (total sample)

Overconf_2 Frequency Percentage frequency Cumulative percentage frequency Not overconfident 36 44,44% 44,44% Overconfident 45 55,56% 100% Total 81 100% Table 12: Frequency table miscalibration effect (total sample)

Gender Frequency Percentage frequency Cumulative percentage frequency Women 36 44,44% 44,44% Men 45 55,56% 100% Total 81 100% Table 13: Frequency table gender (total sample)

To delve into the dichotomous variables, one should look at the frequency tables above. Generally speaking, it turns out that only the minority of participants (32,10%) was subject to the better-than-average effect. This is in contrast with the miscalibration effect where a small majority of participants (55,56%) seemed to be overconfident. At last, more men (55,56%) than women (44,44%) participated in this experiment.

3.2.3.2 Two independent samples

The tables ut infra show the descriptive statistics of both the control and treatment group. First remarkable note is that there were on average nearly 10 transactions less by those that received a warning signal (24,90 versus 15,02). The turnover rate was more than 60% lower for participants belonging to the treatment group. Hence, the treatment group (20 271 EUR) invested on average 12 369 EUR or 1,24 times less than the control group (32 640 EUR). On the one hand, the control group (75,87%) invests on average almost 25% more in risky assets in comparison with the treatment group (51,34%). On the other hand, the treatment group (39,38%) invests significantly more in risk-free assets compared to the control group (17,54%). Further examination will point out if this tendency is due to the impact of the warning signals or other control variables such as the level of risk aversion. The proportion held in cash is more or less the same which in theory should be close to zero assuming fully rationality among the individual investors.

The average passing grade of the financial suitability test was for both groups close to 50%, confirming a proper difficulty level as mentioned earlier. Same can be said regarding the average level of risk aversion (≈2) and the average age of investors (≈36). In the treatment group (46,34%), there were slightly more female participants than in the control group (42,50%). Overall, the distribution of gender across both groups was broadly the same (see more at Table 15). These controls seem to point to two homogeneous samples and thus successful randomization.

Variable Mean Median Minimum Maximum SD Skewness Ex. Kurt. TT 24,9000 23,0000 5,0000 84,0000 16,0880 1,4144 2,8998 15,0240 12,0000 1,0000 52,0000 11,1300 1,3048 1,8744 TTTV 3,2640 3,2127 0,5223 6,9302 1,7692 0,3798 -0,7703 2,0271 1,7725 0,7957 6,5161 1,2254 2,0118 4,3394

PR 0,7587 0,8339 0,2106 0,9998 0,2369 -0,8872 -0,4984 0,5134 0,5243 0.0000 0,9998 0,3193 -0,0263 -1,2307

PRF 0,1754 0,0963 0,0000 0,6172 0,1921 0,9662 -0,4956 0,3938 0,3610 0,0000 0,9497 0,2893 0,3022 -0,9753

PC 0,0659 0,0188 0,0001 0,4503 0,1116 2,2776 4,5013 0,0928 0,0201 0,0002 0,5568 0,1377 1,7394 2,4752 IKE 0,5213 0,5500 0,1000 0,9500 0,2069 -0,0870 -0,6002 0,5061 0,5000 0,0000 0,9500 0,2295 0,07614 -0,3174 A 2,0437 2,0000 0,0000 7,0000 1,3020 1,7600 3,8891 2,0854 1,5000 -0,5000 9,500 1,7290 2,1804 6,6051 Age 35,725 30,5000 22,0000 62,0000 12,7760 0,5091 -1,2019 36,0490 34,0000 22,0000 60,0000 11,8300 0,6669 -0,8262 Table 14: Descriptive statistics control and treatment group

Gender Frequency Percentage Frequency Cumulative Frequency Percentage Woman 17 19 42,50% 46,34% 42.50% 46,34% Man 23 22 57,50% 53,66% 100% 100% Total 40 41 100% 100% Table 15: Frequency table gender (control and treatment group)

Starting with the better-than-average effect, the number of overconfident investors dropped with nearly 50% after receiving a warning signal (see more at Table). The question therefore arises whether this shift is due to the treatment effect. Secondly concerning the miscalibration effect, the number of ‘not overconfident’ investors doubled in absolute terms (see more at Table). Same question should be asked regarding the cause of this shift. Note well that the minority of both groups, referring to the better-than-average effect, was found to be overconfident in contrast to the miscalibration effect.

Overconf_1 Frequency Percentage frequency Cumulative percentage frequency Not overconfident 23 32 57,50% 78,05% 57,50% 78,05% Overconfident 17 9 42,50% 21,95% 100% 100% Total 40 41 100% 100% Table 16: Frequency table better-than-average effect (control and treatment group)

Overconf_2 Frequency Percentage frequency Cumulative percentage frequency Not overconfident 12 24 30,00% 58,54% 30,00% 58,54%

Overconfident 28 17 70,00% 41,46% 100% 100% Total 40 41 100% 100% Table 17: Frequency table miscalibration effect (control and treatment group)

3.3 Econometric model 3.3.1 Nonparametric tests for independent samples

As a preliminary test or manipulation check, the difference of individual investor’s trading behaviour could be observed by comparing the medians or frequencies (see Figure) across both independent samples to evaluate the treatment effect (warning signals). To obtain results guaranteeing the internal and external validity, successful randomization was required (Mcclave, Benson, Sincich, & Knypstra, 2014). This means that participants were randomly gathered and allocated to a control or treatment group, by which in theory it should be possible to measure (unbiasedly) the causal effect of warning signals on individual investor’s trading behaviour. This means that there should be no omitted variable bias, as warning signals are considered independent of all other variables.

퐻0: 휂푐 − 휂푡 = 0

퐻푎: 휂푐 − 휂푡 ≠ 0 Figure 21: Hypothesis testing quantitative variables

퐻0: 𝜌푐 − 𝜌푡 = 0

퐻푎: 𝜌푐 − 𝜌푡 ≠ 0 Figure 22: Hypothesis testing qualitative variables

It would be a little too short-sighted to assume a distributional type of the empirical data such as the normal distribution. In addition, normality tests learned that none of the dependent variables contained a normal distribution. For this end, a Shapiro-Wilk test was used in SPSS all significantly indicating non-normality (see more at Appendices). It goes without saying that it is impossible for dummy variables (types of overconfidence) to have a normal distribution, as they only can take two values. On top of that, the descriptive statistics discussed before came to a similar conclusion. Besides, working with a non-parametric test could slightly ease the assumptions (e.g. no transaction costs, borrowing possibilities etc.). Therefore, the Mann-

Whitney U-test, also known as the Wilcoxon rank-sum test, was used as a baseline for the dependent, quantitative variables (TT, TTTV, PR, PRF, and PC). Note that this test assumes the distribution of the variables to be the same for both the control and treatment group. The dichotomous variables namely both types of overconfidence were measured by Fisher’s exact test, an alternative for smaller frequencies instead of the Chi-square test of independence. Both tests were computed in SPSS.

3.3.2 Regression models

Despite (successful) randomization, it might be interesting to include several control variables. Including more regressors could lead to improved efficiency and thus a higher explanatory power. Secondly, randomization could have gone wrong and should therefore be tested by regressing the type of group (Group_dummy) on other control variables. A third reason for adding more regressors (interaction effects) was to detect possible heterogeneity in the treatment, as the impact of warning signals might not be the same for all participants. The purpose here is to clarify the level of individual investor’s trading behaviour besides the difference between the control and treatment group (see Figure 23). All models were computed in Gretl.

N.B.: *, ** and *** refer to a significance level of respectively 10%, 5% and 1%.

퐻0: 훽 = 0

퐻푎: 훽 ≠ 0 Figure 23: Hypothesis testing regression models

A Classical Linear Regression Model (OLS) was used for the dependent, quantitative variables, as a Logit (or Probit) model was preferred for the dummy variables. An important note to be made regarding the dummy variables is that the linear probability model was not considered, since probabilities might not lie within the range [0,1] (Koop, 2006). Note that neither the Logit or Probit model was preferred, as the estimation method is very similar. In addition, each model was tested for econometric pitfalls cross-sectional data are subject to: the omitted variable bias, multicollinearity and heteroscedasticity. The omitted variable was taken care of by including sufficient controls besides the independent variable (Type of

group), based on economic theories and own insights. Additionally, individual t-tests were executed checking for relevant explanatory variables. And to top it off, successful randomization should guarantee the absence of the omitted variable bias. By taking a look at the Spearman’s rank correlation coefficients, Variation Inflation Factors and Belsley-Kuh- Welsch collinearity diagnostics, possible multicollinearity could be detected. The treatment variable (Type of group) is likely to affect the variance of individual investor’s trading behaviour which could cause heteroscedasticity. If so, White's heteroscedasticity consistent standard errors or using a GLS model could be a way out (Koop, 2006).

3.4 Empirical results 3.4.1 Testing randomization (Group_dummy)

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE SLOPE AT MEAN Const 0,1408 1,0875 0,897 −0,0679 IKE −0,2717 1,0690 0,799 0,0047 A 0,0187 0,1571 0,905 0,0003 Age 0,0013 0,0196 0,946 −0,0282 Gender −0.1128 0,4840 0,816 McFadden R2= 0,0017 Adj. R2= -0,0873 Number of ‘correctly predicted’ cases: 41 (50,6%) 0,996

Table 18: Logit model testing randomization (N=81)

Since the type of group is a dummy variable, a logit model was used to test whether the randomization was successful. The dependent variable here is the type of group, distinguishing the participants belonging to the control and treatment group. Control variables are investor knowledge & experience, level of risk aversion, age and gender. None of them are independent variables, since the aim of this test was to check whether the control variables are significantly different across the control and treatment group. In the blink of an eye, one can see that none of the control variables are significantly related to the type of group. The Likelihood ratio test points out this model has no explanatory power (P- value=0,966), since the McFadden or pseudo R2 is notably low. No multicollinearity was detected with the highest VIF being 1,17 which is far below the limit of 10. Moreover, it is

reasonable to assume homoscedasticity for limited dependent variable models. Finally, a residual variance analysis showed that there were no outliers.

3.4.2 Preliminary testing results 3.4.2.1 Total Transactions (TT)

Hypothesis: “Warning signals do have an impact on the total number of transactions.”

Mean Rank Sum of Ranks No warning signal 49,63 1985 Warning signal 32,59 1336 Total 3321 Two-sided P-value 0,001*** Table 19: Wilcoxon rank-sum test total transactions (N=81)

The table above shows that the sum of ranks (and mean rank) regarding the total transactions of the control group is significantly different (and higher) relative to the sum of ranks (and mean rank) of the treatment group (P-value=0,001***) at a significance level of 1%. In other words: “We are 99% confident that warning signals do have a (positive) impact on the total number of transactions and hence on individual investor’s trading behaviour.”

3.4.2.2 Turnover rate Total Transaction Volume (TTTV)

Hypothesis: “Warning signals do have an impact on the turnover rate regarding the total transaction volume.”

Mean Rank Sum of Ranks No warning signal 50,13 2005 Warning signal 32,10 1316 Total 3321 Two-sided P-value 0,001*** Table 20: Wilcoxon rank-sum test turnover rate total transaction volume (N=81)

The table above shows that the sum of ranks (and mean rank) regarding the turnover rate of the total transaction volume of the control group is significantly different (and higher) relative to the sum of ranks (and mean rank) of the treatment group (P-value=0,001***) at a significance level of 1%. In other words: “We are 99% confident that warning signals do have

a (positive) impact on the turnover rate regarding the total transaction volume and hence on individual investor’s trading behaviour.”

3.4.2.3 Proportion per asset class (RR, RRF & RC)

Hypothesis: “Warning signals do have an impact on the proportion invested in risky assets.”

Mean Rank Sum of Ranks No warning signal 50,00 2000,00 Warning signal 32,22 1321,00 Total 3321,00 Two-sided P-value 0,001*** Table 21: Wilcoxon rank-sum test proportion risky assets (N=81)

The table above shows that the sum of ranks (and mean rank) regarding the proportion invested in risky assets of the control group is significantly different (and higher) relative to the sum of ranks (and mean rank) of the treatment group (P-value=0,001***) at a significance level of 1%. In other words: “We are 99% confident that warning signals do have a (positive) impact on the proportion invested in risky assets and hence on individual investor’s trading behaviour.”

Hypothesis: “Warning signals do have an impact on the proportion invested in risk-free assets.”

Mean Rank Sum of Ranks No warning signal 31,93 1277,00 Warning signal 49,85 2044,00 Total 3321,00 Two-sided P-value 0,001*** Table 22: Wilcoxon rank-sum test proportion risk-free assets (N=81)

The table above shows that the sum of ranks (and mean rank) regarding the proportion invested in risk-free assets of the control group is significantly different (and lower) relative to the sum of ranks (and mean rank) of the treatment group (P-value = 0,001***) at a significance level of 1%. In other words: “We are 99% confident that warning signals do have

a (positive) impact on the proportion invested in risk-free assets and hence on individual investor’s trading behaviour.”

Hypothesis: “Warning signals do have an impact on the proportion held in cash.”

Mean Rank Sum of Ranks No warning signal 38,90 1556,00 Warning signal 43,05 1756,00 Total 3321,00 Two-sided P-value 0,427 Table 23: Wilcoxon rank-sum test proportion cash (N=81)

The Mann-Whitney U-test or Wilcoxon rank-sum test shows that there was no significant relation regarding the proportion held in cash across both groups (P-value = 0,427 > 0,10). The sum of ranks (and mean rank) are nearly the same. In other words: “Warning signals do not have an impact on the proportion held in cash and hence on individual investor’s trading behaviour.”

3.4.2.4 Overconfidence 3.4.2.4.1 Better-than-average effect (Overconf_1)

Hypothesis: “Warning signals do have an impact on the better-than-average effect among individual investors.”

No warning signal Warning signal Total Not overconfident 23 32 55 Overconfident 17 9 26 Total 40 41 81 Table 24: Contingency table better-than-average effect

Fisher’s exact test, based on the contingency table above, proves there is a significant association between the type of group a participant belonged to and presence of overconfident behaviour (better-than-average effect) at a significance level of 10% (P-value = 0,059*). In other words: “We are 90% confident that the warning signals do have an impact on the better-than-average effect and hence on individual investor’s trading behaviour.”

3.4.2.4.2 Miscalibration effect (Overconf_2)

Hypothesis: “Warning signals do have an impact on the miscalibration effect among individual investors.”

No warning signal Warning signal Total Not overconfident 12 24 36 Overconfident 28 17 45 Total 40 41 81 Table 25: Contingency table miscalibration effect

Fisher’s exact test, based on the contingency table above, proves there is a significant association between the type of group a participant belonged to and the presence of overconfident behaviour (miscalibration effect) at a significance level of 5% (P-value = 0,014**). In other words: “We are 95% confident that the warning signals do have an impact on the miscalibration effect and hence on individual investor’s trading behaviour.”

3.4.3 Multiple regression analyses 3.4.3.1 Total Transactions

Hypothesis: “Warning signals do have a positive (negative) impact on the total number of transactions.”

Group_dummy IKE A Age Gender TT TT -0,365*** 0,052 0,108 -0,278** 0,209* 1,000 Table 26: Spearman’s correlation matrix total transactions, warning signals and controls

Spearman’s Rho illustrate in the table above that three variables seem to have an association with the total number of transactions. The type of group and age appear to be negatively correlated with the total number of transactions (P-value= 0,001; P-value= 0,012). This could be a first indication that warning signals could impact the total number of transactions. But one should be aware to not confuse causality with correlation. Furthermore, gender seems to have a positive correlation of 0,209 with the total number of transactions (P-value=0,062). This could mean that men trade more excessively than women. Same note concerning causality versus correlation can be made here.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE Const 25,8180 6,2637 0,001*** Group_dummy −9,0739 2,5125 0,001*** IKE 7,0209 6,0484 0,250 A 0,0946 0,8814 0,915 Age −0,2088 0,1098 0,061* Gender 2,0730 2,7387 0,452 R2= 0,2309 Adj. R2= 0,1782 0,001*** Table 27: CLRM model total transactions (N=79)

After using the F-test, the CLRM model turns out to have an explanatory power (R2) of 23,09% (P-value = 0,001***) This means that 23,09% of the variance of the total transactions can be explained by the included variables. The dependent variable is the total transactions, as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender.

Moreover, two variables seem to have a significant relation with the total number of transactions. Primo, there is a strong, negative relation between the group a participant belonged to and the total number of transactions confirming the first approach of the hypothesis (P-value=0,001***). Ceteris paribus, individual investors that received several warning signals will carry out 9 purchase and/or sale transactions less in comparison with individual investors that received none. This implies that warning signals could reduce excessive trading and thus have a positive impact on individual investor’s trading behaviour. Secondo, a negative link was found between the age and the total transactions (P- value=0,061*). If an individual investor grows five years older, keeping all other included variables constant, he/she will carry out one purchase or sale transaction less.

Two outliers were removed, being the maximum values (52 & 84) of the treatment and control group, to avoid possible bias of the estimated coefficients (see Table 28). No multicollinearity was detected with the highest VIF being 1,179 which is still far below the limit of 10. White’s test showed there was no heteroscedasticity (P-value = 0,724).

Warning signal (treatment group) N= 40 No warning signal (control group) N= 39 Table 28: Number of participants per group after removing outliers (TT)

3.4.3.2 Turn-over rate Total Transaction Volume

Hypothesis: “Warning signals do have a positive (negative) impact on the turnover rate regarding the total transaction volume.”

Group_dummy IKE A Age Gender TTTV TTTV -0,385*** -0,013 0,137 -0,319*** 0,181 1,000 Table 29: Spearman’s correlation matrix turnover rate total transaction volume, warning signals and controls

Spearman’s Rho illustrate in the table above that two variables seem to have a strong association with the total number of transactions. The type of group and age appear to be negatively correlated with the total number of transactions (P-value= 0,001; P-value= 0,004). This could be a first indication that warning signals could impact the turnover rate regarding the turnover rate of the total transaction volume. But one should be aware to not confuse causality with correlation.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE Const 4,1320 0,6904 0,001*** Group_dummy −1,3277 0,2812 0,001*** IKE 0,8143 0,6766 0,233 A −0,0296 0,0975 0,762 Age −0,0391 0,0122 0,002*** Gender 0,1133 0,3083 0,714 R2= 0,3443 Adj. R2= 0,2988 0,001*** Table 30: CLRM model turnover rate total transaction volume (N=78)

After using the F-test, the CLRM model turns out to have an explanatory power (R2) of 34,43% (P-value = 0,001***) This means that, with 99% confidence, 34,43% of the variance of the turnover rate regarding the total transaction volume can be explained by the included variables. The dependent variable is the turnover rate regarding the total transaction volume,

as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender.

Moreover, two variables seem to have a strong, significant relation with the turnover rate regarding the total transaction volume. Primo, there is a strong, negative relation between the group a participant belonged to and the turnover rate regarding the total transaction volume confirming the second approach of the hypothesis (P-value=0,001***). Ceteris paribus, individual investors that received several warning signals will invest 13 277 EUR or 1,33 times less in comparison with individual investors that received none. This implies that warning signals could reduce excessive trading and thus could have a positive impact on individual investor’s trading behaviour. Secondo, a negative link was found between the age and the total transactions (P-value=0,002*). If an individual investor grows five years older, keeping all other included variables constant, he/she will invest 1955 EUR or 0,1955 times less.

Three outliers were removed, inter alia being the maximum values (6,52 & 6,93) of the treatment and control group, to avoid possible bias of the estimated coefficients (see Table 31). No multicollinearity was detected with the highest VIF being 1,191 which is still far below the limit of 10. White’s test showed there was no heteroscedasticity (P-value = 0,577).

Warning signal (treatment group) N= 39 No warning signal (control group N= 39 Table 31: Number of participants per group after removing outliers (TTTV)

3.4.3.3 Proportion per asset class

Hypothesis: “Warning signals do have a positive (negative) impact on the proportion invested in risky assets.”

Group_dummy IKE A Age Gender PR

PR -0,380*** -0,002 -0,168 -0,063 0,172 1,000 Table 32: Spearman’s correlation matrix proportion risky assets, warning signals and controls

Spearman’s Rho illustrate in the table above only one variable seems to have a strong association with the proportion invested in risky assets. The type of group appears to be negatively correlated with the proportion invested in risky assets (P-value= 0,001). This could be a first indication that warning signals could impact the proportion invested in risky assets. But one should be aware to not confuse causality with correlation.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE Const 0,7832 0,1573 0,001*** Group_dummy −0,2420 0,0630 0,001*** IKE −0,0837 0,1510 0,581 A −0,0124 0,0222 0,576 Age −0,0004 0,0028 0,893 Gender 0,1007 0,0685 0,146 R2= 0,1973 Adj. R2= 0,1438 0,005*** Table 33: CLRM model proportion risky assets (N=81)

After using the F-test, the CLRM model turns out to have an explanatory power (R2) of 14,38% (P-value = 0,005***) This means that, with 99% confidence, 14,38% of the variance of the proportion invested in risky assets can be explained by the included variables. The dependent variable is the proportion invested in risky assets, as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender.

Moreover, only one variable seems to have a significant relation with the proportion invested in risky asset. There appears to be a strong, negative relation between the group a participant belonged to and proportion invested in risky assets confirming the third approach of the hypothesis (P-value = 0,001***). Ceteris paribus, individual investors that received several warning signals will invest 24,20% less in risky assets compared to individual investors that received none. This implies that warning signals could reduce excessive risk-taking and thus could have a positive impact on individual investor’s trading behaviour.

There were no outliers and no multicollinearity was detected with the highest VIF being 1,171 which is still far below the limit of 10. White’s test showed there was no heteroscedasticity (P-value = 0,545).

Hypothesis: “Warning signals do have a positive (negative) impact on the proportion invested in risk-free assets.”

Group_dummy IKE A Age Gender PRF

PRF 0,385*** 0,050 0,145 0,161 -0,137 1,000 Table 34: Spearman’ correlation matrix risk-free assets, warning signals and controls

Spearman’s Rho illustrate in the table above only one variable seems to have a strong association with the proportion invested in risk-free assets. The type of group appears to be negatively correlated with the proportion invested in risky assets (P-value= 0,001). This could be a first indication that warning signals could impact the proportion invested in risk-free assets. But one should be aware to not confuse causality with correlation.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE Const 0,0290 0,1367 0,833 Group_dummy 0,2163 0,0547 0,001*** IKE 0,1187 0,1313 0,369 A 0,0165 0,0193 0,395 Age 0,0025 0,0024 0,310 Gender −0,0641 0,0595029 0,285 R2=0,2113 Adj. R2=0,1587 0,003*** Table 35: CLRM model proportion risk-free assets (N=81)

After using the F-test, the CLRM model turns out to have an explanatory power (R2) of 21,13% (P-value = 0,003***) This means that, with 99% confidence, 21,13% of the variance of the proportion invested in risk-free assets can be explained by the included variables. The dependent variable is the proportion invested in risk-free assets, as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender.

Moreover, only one variable seems to have a significant relation with the proportion invested in risk-free assets. There appears to be a strong, positive relation between the group a participant belonged to and proportion invested in risk-free assets confirming the fourth approach of the hypothesis (P-value=0,001***). Ceteris paribus, individual investors that received several warning signals will invest 21,63% more in risk-free assets compared to individual investors that received none. This implies that warning signals could reduce excessive risk-taking and thus could have a positive impact on individual investor’s trading behaviour.

Hypothesis: “Warning signals do have a positive (negative) impact on the proportion held in cash.”

Group_dummy IKE A Age Gender RC

RC 0,089 0,092 0,074 -0,069 -0,032 1,0000 Table 36: Spearman’s correlation matrix proportion cash, warning signals and controls

Spearman’s Rho shows that are no significant correlations between the proportion invested in cash and the other variables.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE Const 0,0529 0,0532 0,323 Group_dummy 0,0251 0,0199 0,211 IKE 0,0390 0,0486 0,425 A 0,0001 0,0070 0,983 Age −0,0010 0,0010 0,297 Gender 0,0120 0,0219 0,585 R2= 0,0625 Adj. R2= -0,0035 0,457 Table 37: CLRM model proportion cash (N=77)

The F-test points out this CLRM model has no explanatory power (P-value=0,4567) and therefore is not usable. Consequently, none of the variables are significantly related with the

proportion held in cash. This makes sense, since its only goal was to provide the participants a starting amount of cash, namely 10 000 EUR. The dependent variable is the proportion invested in risk-free assets, as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender. As this model is not valuable, it is no use to test for multicollinearity or heteroscedasticity. Mark well that initially three outliers were left out (see Table 38) before the regression model was estimated.

Warning signal (treatment group) N= 39 No warning signal (control group N= 38

Table 38: Number of participants per group after removing outliers (PC)

3.4.3.4 Overconfidence 3.4.3.4.1 Better-than-average effect

Hypothesis: “Warning signals do have a positive (negative) impact on the miscalibration effect among individual investors.”

Group_dummy IKE A Age Gender Overconf_1 Overconf_1 -0,220** 0,212* 0,015 -0,243** 0,083 1,000 Table 39: Spearman’s correlation matrix better-than-average effect, warning signals and controls

Spearman’s Rho illustrate in the table above that three variables seem to have an association with the better-than-average effect. The type of group and age appear to be negatively correlated with the better-than-average effect (P-value= 0,048; P-value=0,029). This could be a first indication that warning signals could impact the better-than-average effect. But one should be aware to not confuse causality with correlation. Furthermore, the investor his/her knowledge and experience seems to have a positive correlation of 0,212 with the better-than- average effect (P-value=0,058). Same note concerning causality versus correlation can be made here.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE SLOPE AT MEAN Const 0,6425 1,2737 0,614 Group_dummy −1,0054 0,5196 0,053* −0,2063 IKE 2,1006 1,2679 0,098* 0,4344 A −0,1673 0,1928 0,385 −0,0346 Age −0,0478 0,0245 0,051* −0,0099 Gender −0,0686 0,5484 0,900 −0,0142 McFadden R2=0,1127 Adj. R2= -0,0052 Number of ‘correctly predicted’ cases: 41 (50,6%) 0,043** Table 40: Logit model better-than-average effect (N=81)

After using the Likelihood ratio test, the logit model turns out to have explanatory power and is therefore usable (P-value = 0,043**) This means that in 50,6% of cases, the included variables will be able to correctly estimate whether the participant is subject to the better- than-average effect. The dependent variable is the better-than-average effect, as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender.

Moreover, three variables seem to have a significant relation with the better-than-average effect. Primo, there is a negative relation between the group a participant belonged to and the better-than-average effect confirming the sixth approach of the hypothesis (P-value = 0,053*). The interpretation of is not as straightforward as an OLS model, for which one should look at the slopes of the mean. Ceteris paribus, individual investors that received several warning signals are 20,63% less likely to be subject to the better-than-average effect in comparison with investors that received none. This implies that warning signals could reduce overconfident behaviour and thus could have a positive impact on individual investor’s trading behaviour. Secondo, there seems to be a positive relation between the investor his/her knowledge and experience and the better-than-average effect (P-value = 0,098*). If an investor scored 5% better (hence one correct answer) on the financial suitability test, considering all other variables constant, he/she has 43,44% less chance to be subject to the better-than-average effect. Tertio, a negative link was found between the age and the better- than-average effect (P-value=0,051*). If an individual investor grows 1 years older, keeping

all other included variables constant, he/she will be 0,99% less prone to the better-than- average effect.

No outliers were removed and no multicollinearity was detected with the highest VIF being 1,171 which is still far below the limit of 10. It stands to reason to assume homoscedasticity for this model.

3.4.3.4.2 Miscalibration effect

Hypothesis: “Warning signals do have a positive (negative) impact on the miscalibration effect among individual investors.”

Group_dummy IKE A Age Gender Overconf_2 Overconf_2 -0,287*** -0,068 -0,157 -0,032 0,350*** 1,000 Table 41: Spearman’s correlation matrix miscalibration effect, warning signals and controls

Spearman’s Rho illustrate in the table above that two variables seem to have a strong association with the miscalibration effect. The type of group appears to be negatively correlated with the miscalibration effect (P-value = 0,009). This could be a first indication that warning signals could impact the miscalibration effect. But one should be aware to not confuse causality with correlation. Furthermore, gender seems to have a positive correlation of 0,209 with the total number of transactions (P-value = 0,062). This could mean that men are more prone to the miscalibration effect than women and thus more likely to be overconfident. Same note concerning causality versus correlation can be made here.

VARIABLE COEFFICIENT STANDARD ERROR P-VALUE SLOPE AT MEAN Const 0,3382 1,2517 0,787 Group_dummy −1,3769 0,5246 0,009*** −0,3249 IKE −1,5241 1,1859 0,199 −0,3733 A −0,0410 0,1864 0,826 −0,010 Age 0,0139 0,0229 0,544 0,0034 Gender 1,8253 0,5801 0,002*** 0,4241 McFadden R2= 0,1757 Adj. R2= 0,0679 Number of ‘correctly predicted’ cases = 56 (69,1%) 0,002*** Table 42: Logit model miscalibration effect

After using the Likelihood ratio test, the logit model turns out to have explanatory power and is therefore usable (P-value = 0,002***) This means that in 69,1% of cases, the included variables will be able to correctly estimate whether the participant is subject to the miscalibration effect. The dependent variable is the miscalibration effect, as the independent variable is the type of group. The control variables are as usually the investor knowledge and experience, the level of risk aversion, the age and gender.

Moreover, two variables seem to have a strong significant relation with the miscalibration effect. Primo, there is a negative relation between the group a participant belonged to and the miscalibration effect confirming the seventh approach of the hypothesis (P-value = 0,009***). Ceteris paribus, individual investors that received several warning signals are 32,49% less likely to be subject to the miscalibration effect in comparison with investors that received none. This implies that warning signals could reduce overconfident behaviour and thus could have a positive impact on individual investor’s trading behaviour. Secondo, there seems to be a positive relation between the gender and the miscalibration effect (P-value = 0,002***). Keeping all other variables constant, male investors are 42,41% more prone to the better-than-average effect compared to female investors. This is in line with what Barber & Odean (2001) found before: men tend to be more overconfident than women.

4 Discussion

Overall, the empirical evidence resulting from this investment laboratory experiment provide some useful insights regarding individual investor’s trading behaviour. Six out of the seven approaches measuring investor behaviour were able to answer our research question: “What is the impact of warning signals on individual investor’s trading behaviour?”.

Warning signals seem to have a strong, positive impact on the total number of transactions, turnover rate regarding the total transaction volume and the proportion invested in risky and risk-free assets also known as the capital allocation decision. Moreover, overconfident behaviour (better-than-average and miscalibration effect) could be reduced as well by means of warning signals. It does not appear to impact the proportion held in cash which makes sense as it merely serves as a mean to finance their investment decisions. These results imply that warning signals could reduce excessive trading and risk-taking. In addition, we also looked at other variables that might explain individual investor’s trading behaviour. Older investors seem to trade less and hence are slightly less prone to overconfidence regarding the better-than-average effect. As Barber & Odean (2001) pointed out before, this experiment proves here as well that men seem to be more overconfident (miscalibration effect) than women.

Referring to the limitations of this investment laboratory experiment (See 3.1.6.), a lack of realism and the fact that the money at stake is too small might have been the major constraints of this experiment (Falk & Heckman, 2009). Therefore, participants did most likely not take the experiment seriously which could have resulted in excessive trading and risk- taking which is exactly what was examined. So, working with (real-time) market data could open doors for further research. Another concern could be that the pool of participants was too specific and small, so focussing on a broader population could be way out which would help as well to increase the external validity of the results. As there were no financial incentives and were entirely dependent on the voluntariness of our participants, the self-

selection bias could be a valid argument. Nevertheless, the participants were gathered as randomly as possible after which they were randomly assigned to a group.

It might be interesting to include even more regressors, allowing the regression models to estimate interaction effects or in other terms to detect possible heterogeneity in the treatment effect. For instance, will warning signals impact more men than woman their investor behaviour and to what extent? Same can be said concerning the age, level of risk aversion and the knowledge/experience of the individual investor. Further, it could be valuable as well to extend this CLRM model to a panel data model, working for instance with an individual effects model. This would allow to study the impact of warning signals across different trading periods and might be able to spot a difference of impact between for example the first and last trading period. Last suggestion is to extend the Logit model to a Tobit model regarding the limited dependent variable models. On the one hand, it would be possible to determine whether an individual investor is overconfident. On the other hand, it would also be possible to measure to what extent an investor is overconfident.

In the context of MiFID II, our research could imply that warning signals might reduce investor biases and heuristics if issued before the transaction is executed. Investment firms could thereby help to improve the allocation efficiency resulting in more efficient capital allocation decisions by individual investors (Klöhn, 2009). Considering the limitations of this experiment, further research is required to confirm or to dispute our results.

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6 Appendices

6.1 Financial suitability test

1. I hold a corporate bond on a securities account. In that case…

o I am co-owner of the corporation who issued the bond. o I am creditor of the issuer of the bond. o the issuer determines the maturity date. o I don’t know the answer.

2. A financial product with a leverage is a product…

o whereof the value can increase or decrease faster than the value of the underlying asset in which it refers to. o whereof the value is exclusively determined by the value of the underlying asset in which it refers to. o whereof the purchase grants me access to acquiring other financial products. o I don’t know the answer.

3. The main feature of a perpetual is…

o The issuer by definition never pays the invested amount back. o Mostly, the prospectus provides an option for the issuer to prematurely pay back on a contractual, defined date. o A perpetual has by definition no maturity date and is only issued in American Dollars (USD) o I don’t know the answer.

4. What is the credit risk of a treasury note? Will you lose the invested amount if the issuer goes bankrupt?

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o Yes, I have lost the entire invested amount. o No, because the reimbursement is always fully guaranteed (by a third party, the government, the deposit and guarantee fund). o Yes, but I carry only the risk if my investment falls outside the conditions of the deposit and guarantee fund. o I don’t know the answer.

5. You possess a savings insurance of branch 21 (Tak 21). Which statement is correct? o I borrow my money to an insurance company. Hence, I am a creditor. o I invest my money in a savings product in the form of an insurance contract. o I invest my money in investment funds in the form of an insurance contract. o I don’t know the answer.

6. Do fluctuations on the financial markets impact the value of a product with a predetermined reimbursement minimum? o No, the value is still the same thanks to the predetermined reimbursement minimum. o No, this product is not listed on the exchange market. o Yes, in case of sale before maturity date the predetermined is not valid whereby the value can’t fluctuate. o I don’t know the answer.

7. Can you sell investment insurances (without capital protection) in the short run? o No, I have to wait till maturity date. o Yes, that’s possible against the valid asset value. o Yes, I can sell immediately against minimally the initial asset value. o I don’t know the answer.

8. If a stock disburses a dividend, does the dividend remain constant or does it vary year to year?

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o The dividend is determined in the statutes of the corporation. o The dividend will increase each year in line with the inflation. o The dividend fluctuates year to year, depending on the dividend policy of the corporation. o I don’t know the answer.

9. You owe a real estate certificate. Which statement is correct? o I am directly legal co-owner of the property. o I am creditor. I receive on a yearly basis a coupon and at the moment of sale a part of the sale revenues. o All revenues are capitalized and will be disbursed in the future at the moment of sale. o I don’t know the answer.

10. The major difference between a call warrant and a put warrant is that… o a call warrant speculates on the increase of the underlying asset, as a put warrant speculates on the decrease of the underlying asset. o the premium of a call warrant is higher than a premium of a put warrant. o a call warrant has always a higher exercise price than a put warrant. o I don’t know the answer.

11. Who decides whether (convertible) bonds can be converted into a predetermined number of shares? o The investor decides this. Because of this extra choice possibility for the investor, the interest compensation is usually lower than normal bonds (OLO’s). o The corporation decides this. Because of this extra right for the corporation, the interest compensation is usually higher than normal bonds (OLO’s). o This is determined by the analysts of the bank where I bought my convertible bonds. They have the necessary expertise.

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o I don’t know the answer.

12. An ETF (Exchange-Traded Fund) has the intention to copy the (share) price movement of an underlying asset or index. This implies that o the manager (promotor) of the ETF replicates exactly the underlying asset/index. The deviation between the ETF and the underlying is determined by the management fee(s). o the manager of the ETF has a certain freedom to replicate the underlying. This might lead to significant differences between the ETF and the underlying. o the manager ought to follow the (share) price movement of the index, but the way in which he attempts to realize this is not alike for all ETFs. o I don’t know the answer.

13. Which of the statements below about turbo’s (speeders) is correct? o A long turbo speculates on a decrease of the underlying asset. o A short turbo speculates on an increase of the underlying asset. o A turbo is a derivative product with a leverage effect. A limited change in (share) price movement of the underlying asset could lead to substantial gains or losses. o I don’t know the answer.

14. Which is the main risk when you invest in a commodity tracker, that denominates in EUR? o I carry a risk: the value of the tracker tracks the value of commodities in which the tracker invests and hence is subject to price movements of the commodities. Prices of commodities can fluctuate substantially influenced by its supply and demand. o I carry a risk: the value of the tracker can vary due to market fluctuations but is designed in a way that the tracker will always outperform the market wherefore I am protected from fluctuations of the commodities.

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o I don’t carry a risk: the value of a tracker is stable and the invested amount is guaranteed. o I don’t know the answer.

15. The buffer between the stop-loss and funding level (financieringsniveau) serves to: o when the stop-loss level is breached, the turbo will be put on hold and offers the investor optionally the remaining amount. o compensate the costs of the issuer. o guarantee a minimum amount for the investor. o I don’t know the answer.

16. There is 10 000 EUR that I want to invest in shares. I risk, with a decrease of the share, as much as I would invest this amount in call options. Which statement is correct? o Yes, both investment strategies hold the same risk when the share decreases in price. o No, in case of call options, the expected increase of the share must unfold itself during the maturity of the option. If the share price decreases below the exercise price, the option will become worthless. Therefore, options are riskier. o The purchased shares can be held till the stock exchange would increase, as the same counts for call options. There, both investment strategies have the same risk. o I don’t know the answer.

17. Which of the following option strategies is a covered short strangle? o I purchase now half of the intended shares and I simultaneously purchase a call and put option for each share. o I purchase now half of the intended shares and write a call option on the purchased shares, I also a writ a put option for the remaining intended shares. o I purchase the intended position of shares upon which I write a call option (for each share) resulting in premiums to finance put options (for each share). o I don’t know the answer.

18. Have you carried out at minimum 3 purchase or sale transactions in any of the following types of investments over the past 4 years with a minimum of 5000 EUR? o No, I haven’t carried out any of such transactions. o Yes I have carried out such transactions (multiple answers are possible): ▪ Treasury notes, savings insurance (Tak 21), pension planning (Pensioensparen) or fixed deposit (Termijnrekening). ▪ Bonds, bond funds, funds or investment insurances (with capital protection) or with a predetermined reimbursement minimum. ▪ Shares, equity funds or investment insurances (without capital protection). ▪ (Reversed) convertible bonds, perpetual bonds, structured bonds, complex funds, listed ETFs, warrants, commodity trackers, turbo’s etc.

19. What’s your current position on your securities account? o No, I am currently not holding any investments o Yes, I am holding several investments: ▪ I hold investments that I barely follow. ▪ I hold investments in bonds, bond funds, funds or investment insurances with capital protection) or with a predetermined reimbursement minimum which I follow or discuss with my advisor. ▪ I hold investments in individual shares, real estate certificates, equity funds or investment insurances (without capital protection) which I follow or discuss with my advisor on a regular basis. ▪ I hold investments in (reversed) convertible bonds, perpetual bonds, structured bonds, complex funds, listed ETFs, warrants, commodity trackers, turbo’s which I follow or discuss with my advisor on a very frequent basis.

20. What’s your experience concerning options?

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o I have opened/closed at least 3 times an option position, totalling a minimum of 250 EUR, during the last 36 months (except for rolling option positions) within financial institutions.

o I have opened/closed less than 3 times an option position, totalling a minimum of 250 EUR, during the last 36 months (except for rolling option positions) within financial institutions.

6.2 Snapshots experiment 6.2.1 Introduction

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6.2.2 Financial suitability test

6.2.3 Trading period 1

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6.2.4 Trading period 2-5

6.2.5 Final results

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6.3 Spearman’s correlation matrix (all variables)

6.4 Syntax VBA 6.4.1 Warning signal & Locking last trading period

Private Sub Workbook_SheetActivate(ByVal Sh As Object)

On Error Resume Next If Sh.Name = "Trading Period 1" Then

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MsgBox "Based on the financial suitability test, we strongly recommend you not to invest in the risky assets, being the 9 shares." End If

If Sh.Name = "Trading Period 2" Then MsgBox "Based on the financial suitability test, we strongly recommend you not to invest in the risky assets, being the 9 shares." Sheets("Trading Period 1").Protect DrawingObjects:=True, Contents:=True, Scenarios:=True End If

If Sh.Name = "Trading Period 3" Then MsgBox "Based on the financial suitability test, we strongly recommend you not to invest in the risky assets, being the 9 shares." Sheets("Trading Period 2").Protect DrawingObjects:=True, Contents:=True, Scenarios:=True End If

If Sh.Name = "Trading Period 4" Then MsgBox "Based on the financial suitability test, we strongly recommend you not to invest in the risky assets, being the 9 shares." Sheets("Trading Period 3").Protect DrawingObjects:=True, Contents:=True, Scenarios:=True End If

If Sh.Name = "Trading Period 5" Then MsgBox "Based on the financial suitability test, we strongly recommend you not to invest in the risky assets, being the 9 shares." Sheets("Trading Period 4").Protect DrawingObjects:=True, Contents:=True, Scenarios:=True End If

If Sh.Name = "Final Results" Then Sheets("Trading Period 5").Protect DrawingObjects:=True, Contents:=True, Scenarios:=True End If

End Sub

6.4.2 Message box 1 & Unhiding next trading period

Private Sub Worksheet_SelectionChange(ByVal Target As Range)

If Range("L323").Value <> 0 Then

MsgBox "You have filled in the financial suitability test, you may proceed to the first trading period. Good luck!"

Call Unhide_T1

Sheets("Trading Period 1").Unprotect

End If

End Sub

6.4.3 Message box 2,3 and 4 & Unhiding next trading period (e.g. Trading Period 2)

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Private Sub Worksheet_SelectionChange(ByVal Target As Range)

If Range("J37").Value < 0 Then MsgBox "Insufficient Funds"

If Range("P32").Value <> 0 Then MsgBox "You haven't purchased sufficient stocks/bonds to sell this number of stocks/bonds."

If Range("P52").Value <> 0 Then

MsgBox "You've completed the second trading period, you may proceed to the third trading period."

Call Unhide_T3

Sheets("Trading Period 3").Unprotect

End If

End Sub

6.5 Syntax SPSS (Normality and non-parametric tests)

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