Does Culture influence investors’ behavior and performance?

Does Culture influence investors’

behavior and performance?

Some

evidence from the setting of a game

environment

Master Thesis for MscBA Finance

2009

Yue Han

S1581201

Table of contents

Title and abstract………..3

I. Literature review and theoretical models…………..………7

A. Culture theory……… 7

B. Overconfidence………. 15

B.1. Overconfidence in psychology ………16

B.2. Overconfidence in finance………19

C. Hypotheses……….23

II. Data description and methodology……… ……….26

A. Hofstede cultural dimensions………...26

B. Trading data description………28

C. Overconfidence measures………..29

D. Return calculations………32

III. Results and analysis………...34

A. Results of test of hypotheses……….34

B. Regression results………...41

IV. Extended discussions………43

V Conclusion……...………45

Reference……….47

Does Culture influence investors’ behavior

and performance?

Some evidence from the

setting of a game environment

Yue Han

Abstract

In this research I study the culture influences on the investors’ trading practice. Specifically, I rely on Hofstede cultural framework, use the data from an investment game that contains trading information of Dutch and German investor, and employ the methods of independent sample t-test and the OLS regression. I find that (i) investors from more individualistic countries exhibits higher degree of overconfidence than investors from more collectivistic countries. (ii) The phenomenon of men being more overconfident than women is more pronounced in more masculine countries than in more feminine countries. (iii) The tracking error and volatility is lower for investors from countries with higher score on the dimension of Uncertainty Avoidance than investors from countries with lower score on this dimension. These results indicate that culture as modeled by Hofstdede does have influences on investors’ trading behavior and performance.

JEL classification : G15; Z10

Internationalization has been gradually taken place in the global financial markets. Individual investors, institutional investors, mutual funds managers, portfolio managers manage billions of dollars worth of assets in the global markets. It is generally believed that individual investors make their trading and investment decisions based on theories about capital markets and optimal portfolio allocation, therefore the practices of individual investing, to some extend, are expected to be similar across countries, methods such as technical analysis and fundamental analysis are indeed widely used.

With the development of financial literature, it is also discovered that factors such as age, experience, gender, social status, can affect the decision making process and investing performance; many researches have been done in this area (Powell and Ansic, 1997). However, so far, less attention has been paid on the influence of the cultural aspect in relation with individual trading behavior and performance. Understanding the culture differences that cannot be explained by pure economic reasoning is important.

them. There are four dimensions in his model, namely as Individualism, Power Distance, Masculinity and Uncertainty Avoidance. Individualism, deals with how the society viewing individual accomplishment versus collective accomplishment. In a country with a higher score on this dimension, more emphasis will be put on individual rights. Power Distance describes the degree of acceptance of inequality among people. A higher score on this dimension indicates that the society allows the distribution of wealth and power to grow unequally. Masculinity describes with how the society deals with stereotypical role that society assigned to male and female. A more masculine society places higher value on traditional masculine traits such as aggression and power. Last, Uncertainty Avoidance describes how people in a society deal with uncertainty of the future, a higher score on this dimension means that society has a low tolerance of the uncertainty. These dimensions are the core elements of Hofstede’s cultural framework. However, In order to discover the differences of investor behavior and performance in a financial context, simply compare the differences of the scores on these dimensions among countries are not enough, these cultural differences needs to be translated into behavioral finance context that are relevant for purpose of this study.

referred as the unbounded rationality, decision makers have complete information and perfect knowledge of the alternatives and consequences. However, these characteristics can hardly be accepted as a reference for the actual behavior of individuals in the real world. Bounded rationality theory says that the decision making process is subject to limitation, such as availability of information, under bounded rationality, people are intended rational but constrained by their cognitive capabilities, which can weaken their ability in making optimal decisions (Simon, 1957).

One example that people suffer from the above mentioned cognitive problems is the concept of overconfidence. Psychology literature suggests that people are likely to behave in an overconfident manner. This overconfident manner usually exists in two forms. One is that people overestimate the precision of their knowledge; the other is the overestimation of the ability to interpret the information and signals (Fischhoff et al, 1977). Odean (1999) test this theory and find out that people who are overconfident trade more frequently than those who are not, and this excessive trading actually reduce the return to the investors. The excessive trading caused by the overconfidence is because of the investors are too convinced of their own judgments and do not concern about the judgment from others, they believe their own action is less risky.

analyze the influences of the culture differences on investors’ trading practices. I argue that the culture difference will be mainly reflected by the difference of the degree of overconfidence between Dutch and German investors. To my knowledge, this study is the first research that analyzes the culture influences on trading behavior and performance in a game environment.

The rest of the paper will be organized as follows; in the next section, the relevant researches and the theoretical foundations of this paper regarding to culture and behavioral finance are discussed, and the hypotheses are established. In section II, the data and methodology of this paper are described. Test results and the analysis of the results are presented in section III. Section IV presents an extended discussion. Section V summarizes the findings of this paper.

I. Literature review and theoretical models

A. Culture theory

perfectly understand each other. Hall classifies these two forms of communication into high context culture and low context culture. Low context culture is the kind of culture that people focus on the explicit meaning of the written and spoken language, by simply read the words, people will have an accurate understanding of what is communicating. In high context, to understand each other, the interpretation must be done by including components that are surrounded the text.

However, the above mentioned studies are not useful to serve the purpose of quantifying and comparing different cultures at national level. This is exactly the advantages that Hofstede’s work can brings about. Hofstede defines culture as “the collective experience and the software of the mind that guides us in our daily interactions, people who lived under the same culture share similar value and norms, which separate one culture from another”(1980, p25). The central model of his theory is the Hofstede’s cultural dimension framework. In this framework, there are four dimensions of culture that is identified, and these dimensions are responsible for the difference between cultures that affect people’s value system and the pattern of behavior. They are Individualism, Power Distance, Uncertainty Avoidance and Masculinity. I will explain all four dimension explicitly.

Individualism

action to take when dealing with certain issues. In a more individualistic country, people are remotely integrated even within the family. They usually just take care of their own property and interests, and focus on their own desires rather than the group they belong to. On the other hand, in a more collectivistic country, people are encouraged to cooperate with their group members. The individual identity is not emphasized; the identity comes from what group they belong to. Any decisions made are expected to protect all the members within the group. Countries like UK and Netherlands are more individualistic countries; Pakistan and Indonesia are collectivistic countries.

Masculinity

Japan is a typical masculine country. On the other hand, in a more feminine society, it emphasizes on the traditional female role. Characteristics such as patience, compassion and empathy are encouraged. Instead of money, there are more concerns about people. Superhero type of character is not admired, and even look down to. The way people do business is more like a relationship than pure business clients. In addition, in different societies the values that men hold changes more rapidly than women, meaning that in more masculine countries , women are also more assertive aggressive than they are in feminine countries, but the degree is way smaller than the difference that men change from masculine to feminine countries. Netherlands and Denmark are some of the most feminine country in the world.

Power Distance

dimension such as demark and Sweden, people are more comfortable disagreeing with their superiors.

Uncertainty Avoidance

Uncertainty avoidance describes how the society deals with uncertainty about the future. In countries with a lower score on this dimension such as Denmark, people are generally less risk averse, more tolerant and comfortable with uncertainty, and they are more open minded towards difference beliefs and opinions. On the other hand, in countries with a higher score on this dimension such as Japanese and Germany, people have the urge to control the future; they are more stressed when dealing with uncertainty, more risk averse, and would like to work in predictable situations. The society emphasizes rules, laws, and regulations.

dimension of the model1. For example, the dimension of individualism has been studied extensively in details and merged with other subject. Oyserman et al (2002) suggests that Individualism are more likely to be found in western developed countries than in eastern developing countries, this can be explained by the civil liberation and Protestantism that makes the society emphasizing on the personal freedom and choice. Shafiro et al (2003) in their research study the effects of individualism and collectivism associated with gender aspects, using the sample that includes Ukrainian and American female workers. They find that although it is generally accepted that America as a country are more individualistic than Ukraine, the Ukrainian women are actually more individualistic than American women.

Beckmann (2008) is one of the first few researchers that apply more than one dimensions of Hofstede’s cultural framework in financial industry. He uses a questionnaire survey data consists portfolio managers from USA, Thailand, Japan, and Germany, to analyze their view and behavior. He argues that in countries with a higher score on the dimension of Power Distance, there should be more unequal distribution of power within this industry. The result shows that Thailand who has the highest score on this dimension has the least number of managers in the top position. Also, it suggests that age have a huge impact on the career advancement. People who are younger face way less opportunity to get promoted, and it is the older people who decide what kind of strategy and direction to take. In addition, the interaction between age and experience shows that not only older managers can be easily found in higher

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position, experience are actually less important comparing with age. As for the dimension of masculinity, the author argues that, the more masculine the country is, the more difficult to find women working as a portfolio manager, since this job is generally perceived as a traditional masculine job. In the result, it shows that all countries in his sample have fewer women than men in this business, and men manage significantly higher volumes and assets than women. This phenomenon is more pronounced in masculine countries than feminine countries. Japan, the most masculine country in the sample, has only 3% of women in this career. On the other end of the spectrum, Thailand, the most feminine country in the sample, has the largest number of women working as portfolio manager. When the authors take gender into account with respect to volume and assets being managed, it shows that in Japan, women manage significantly less volume and assets. For the dimension of Uncertainty Avoidance, the authors argue that Investors come from countries with a higher score on this dimension are more likely to stick to the benchmark for the sake of safety. They further argue that information plays an important role when dealing with uncertainty. When the degree of uncertainty avoidance is high, investors will put more effort and time in searching relevant information in order to minimize the risk associated with uncertainty. The test results confirm this speculation. In Japan, the country with the highest score on this dimension, investors spend the highest number of working hours on information seeking. While in the USA, the country with the lowest score in this dimension, investors spend the least time securing information.

in his study concludes that the way Hofstede establish the dimension indices is misleading, using these dimensions as explanatory variables will do more harm than good, he further claims that Hofstede’s methodology and theoretical basis is weak and are rarely used in anthropology and sociology which it suppose to be rooted in. in addition, Hofstede treats one country as a single culture, which fails to grasp the idea that variety of culture can exist in one country. In addition, he states that Hofstede seeing culture as being static, and ignores the fact that one culture can change over time. Lastly, the way Hofstede quantifies a complex culture characteristics into some stiff and inflexible indices is questionable, Inglehart and Baker (2000) also documents this problem in their research. They argue that economic development can have a huge impact on culture, therefore the way culture changes can be path dependent.

Holding these critics in mind, I rely on the Hofstede cultural framework as my central model. The purpose of this study is to discover the impacts of different culture have on the investors’ behavioral and performance. Therefore, these culture characteristics should be translated into behavioral aspect. After carefully review the Hofstde’s model, the key element in the context of behavioral finance that serves the purpose of this study, is overconfidence.

B. Overconfidence

The expression of overconfidence comes from psychology literature. The phenomenon has been recognized in varies occupations, for example, there is a large gap between the psychologist’s perception about their correctness and the actual diagnose outcome, this gap can go as large as 20 percent (Oskamp, 1965). Overconfidence in early studies is mainly about calibration and judging probability, and can be interpreted as similar to miscalibration (Fischhoff et al, 1977). In layman’s term, a good ability in calibration means people have correct and accurate judgment about how likely he would make mistakes in the process. Overconfidence is simply a specific type of miscalibration, it is the probability that the person considers to be correct that is larger than the true probability of the correctness.

One form of overconfidence is the illusion of control (Langer and Roth, 1975). People often believe that they have the ability to affect the how things happen when they just happen randomly. An example would be people sometimes believe that if the dice is throwing by himself or herself, the results will be more favorable to them than throwing by others. People consider it is their own efforts that make something favorable to them happen, not by luck.

and brown (1988) find that people tend to take credits for the success, and feel less responsible for the failure. He further states that people assign success to internal factors (intelligence, strength, etc), while failure to external traits (luck, unfavorable rule).

As psychological literature suggests, people often to consider themselves better than they actually are. In addition, people tend to have an idealistic self image, most people think they are better than the average person, and they consider themselves better than other people thinks of them. This is often termed as the better than average effect, which is rooted in miscalibraiton because there is no official benchmark for them to be compared from (Svenson, 1981). For example, in a famous experiment done by Svenson (1981), people are asked to rate their driving skill, most of the people consider they are better and less risky than the average driver, without knowing what skills does the average driver has in the group. Alicke (1995) further analyzes this problem and finds that instead of making people comparing themselves to an “average guy”, making them comparing themselves to an actual person, although any useful information are not told to the person, the degree of overconfidence decrease, and it will be further decreasing if the person has some kind of contact with the comparing person, even if it is just a visual image.

mechanism that our brain actively pick up the evidence who serve the motivation in the memory even if it is false, and ignores those memories that are not supporting the motivation. The judgment of confidence seems to only associate with how many supporting evidence there is regardless of many of those are not. Dunning (1989) in his research doubt whether the overconfidence as the form of better than average effect actually exists, because the characteristics that people are asked to compare to are usually vaguely defined, such as ‘better’, word like this can be interpreted in their own way. If the question and is defined clearly and does not subject to interpretation, the problem cease to exist. Gigerenzer et al (1991) further in his paper questions the existence of overconfidence in general. He states that people have good judgment of what they are knowledgeable, as long as it is presented in a natural environment. Often overconfidence is found that when people are asked to give answers or rate certain tasks under man-made situations, which makes the judgment appears more difficult than it actually is. Russo and Schoemaker (1992) in their study find out that there are two major elements that can affect the degree of overconfidence, one is the feedback time and the other is the easiness of the task. The later one are often termed as the hard easy effect, meaning that overconfidence are more easily to be found when the difficulty of a particular job is high, while under-confidence are more likely to occur when the job can be done quite easily.

Psychological literature starts to apply in economics and finance in 1970s2, but the study of overconfidence in the financial market is populated in 1990s. They are helpful in explaining some of the problems that can not be explained by the traditional economic theory, such as excessive trading volumes, mispricing of the stock. Studies of overconfidence in financial markets are done in forms of experiment, questionnaire, and data analyzing. Gervais and Odean (2001) argue that overconfident trader are often miscalibrated and earn lower profit due to the higher trading volume and volatility. This can be a dynamic process. When investors has a successful trade, they will trade even more aggressively, and the degree of overconfidence increase, otherwise it decreases, Deaves et al (2005) confirms this process but he suggests that the learning effect if weak. Chuang and lee (2006) in their paper using the data of U.S publicly listed companies, find that the overconfident traders are overact to private signals and under react to public signals.

In the financial market, there is usually transaction cost involved in trading; thus, investors should focus on increasing the returns at least to cover the transaction cost. But investors sometimes overestimate the precision of their information, although the stocks they bought outperform the stocks they sold, the earnings are not enough to cover the transaction cost, results in a loss. If this loss is larger than the transaction cost, the investors are not only overestimating the precision of their knowledge, they also overestimate their ability to interpret the information (Barber and Odean, 2001).

2

Odean(1999) uses a large brokerage account data, trying to find out if the stocks that investors bought outperform the stocks they sold, and if the earnings are enough to cover the transaction cost. The sample period is 2 years. The first hypotheses the author test is the return earned on the stocks investors bought is less than the return from the stocks that investors sold plus transaction cost, this is when overconfidence happens specifically in the domain of overestimation of the precision of their information. The second hypothesis is that the return investors earn on the stocks that they bought is less then the returns investors earn on the stock they sold, regardless of transaction cost. This happens if investors don’t have the ability to correctly interpret the information. In the result, it turns out that in all sample periods the return to the stock bought is less than the return to the stock sold. For a one year sample period the underperformance is 2.3 percentages lower. Obviously these investors did not make any money with their trades, but the author argues that it is possible that investors involved in trading not only for making profits. They might make a trade for the purpose of the liquidity, or to realize the losses from tax, or to rebalance their portfolios or there is a change in the risk preference of the investor.

involved in such trade. (2) Profitability is the only concern for sale, so no stocks are sold to realize tax loss. (3) To avoid the purpose of rebalancing the portfolio, the trades that only complete portfolio is sold are selected. (4) Only those sale and purchase are of similar sizes stocks are selected. Because size can be used as a proxy for risk, therefore the incentive of reducing risk preference to settle for lower return is minimized. After these alternative incentive are minimized, the investor are actually doing inferior than before, results in a even lower return.

The detection of overconfidence in stock market is not always easy, and the exact influence that overconfidence causes on trading profits is not always negative. Kyle and Wang (1997) state that some traders may seem like they are overconfident trader when market data displays certain characteristics such as short trading intervals, excessive volume, but this might due to the private information the trader have access to, which can lead to a higher profit. Benos (1998) also argues that being overconfident trader gives them a first mover advantage.

return of 11 percent, and those who ranked at the lowest turnover quintile have a mean return of 18%. These results clearly show that if the overconfidence is the drive for investors to trade, the investor who trade more actively receive a lower return than those who trade less.

The author performs an additional test by dividing the sample into two groups, men and women. In psychology literature, there are theories suggest that men tend to be more overconfident than women, assume they have access to the same knowledge base, and this difference in overconfidence is especially pronounced in certain activity (Beyer, 1990). Beyer (1990) further argues that the clarity of feedback mechanism also affects the degree of overconfidence between men and women. If the feedback mechanism is unequivocal and immediately available, there is not much difference between genders when they estimating their own ability. However, women often underestimate their ability when the feedback is rather vague and difficult to interpret. Stock market is one typical place that the feedback is often ambiguous. Therefore, the author expects that men are often overconfident in the financial market than women. The results in Odean (1998b) paper clearly support this speculation. He finds that men trade 40 percent more frequent than women, which leads to a 0.9 percent lower return, the difference of the degree of overconfidence between men and women is even larger if they are both single.

Without looking the exact scores of the two countries that Hofstede gives on each dimension, people may think there should not be much differences since both Netherlands and Germany are Western European countries, but after examining the exact score closely from the figure 1 in the section II, it can be seen that except for the dimension of Power Distance, all the indicators are significantly different. Since the score on dimension of Power Distance of two countries are rather similar, and this dimension mainly describes the people’s attitude towards inequality and wealth distribution, which may not be applicable to the purpose of this study. The focus for this study lies on the rest of the dimensions, which are Individualism, Masculinity, and Uncertainty Avoidance.

show that in more individualistic countries, people tend to consider themselves as having above average ability in many areas, while in collectivistic countries; people are more adaptive and try to accommodate themselves to the social norms. Zuckerman (1979) further suggests that in individualistic countries, people are more concerned about their self-esteem and ego, and are more likely to protect them in the form of assigning success to their own ability and failure to other factors. Therefore, in this study, it is reasonable to expect that investors in more individualistic countries tend to be more overconfident. Hence, my first hypothesis is

H1: Investors in countries with a high score on individualism (Netherlands) exhibit higher degree of overconfidence than the investors in countries (Germany) with a lower score on individualism.

countries ,women are also more assertive aggressive than they are in feminine countries, but the degree of changes is way smaller than the difference between men changes in feminine and masculine countries. Therefore I would expect that the degree of overconfidence of men to women is larger in more masculine countries than in more feminine countries. Hence, my second hypothesis is:

H2: The phenomenon of men being more overconfident than women will be more pronounced in the countries with a higher score on masculinity (Germany) than the country with a lower score on this dimension (Netherlands).

The dimension of Uncertainty Avoidance reflects how a society deals with uncertainty about the future. A higher score on this dimension meaning that people in this society generally have low tolerance for uncertainty, are more risk averse, and try to minimize the uncertainty. Therefore, my third hypothesis is very straight forward

H3: The tracking error to the official benchmark is lower and the volatility of the return is lower for the investors from countries with a higher score on the dimension of Uncertainty Avoidance (Germany), than investors from countries with a lower score on this dimension (Netherlands)

A. Hofstded’s cultural dimensions

The scores on Hofstede dimension used in this paper come from a large survey that was carried out by Hofstede on more than 80000 IBM employees in 72 countries from 1967 to 1973. The survey contains a series of question that were designed to find out people’s attitude towards each cultural framework dimension. The mean scores are calculated from the survey for each dimension, and factor analyses are performed to analyze these mean scores, then final score on each dimension is constructed from the factors. Figure 1 shows the scores of Netherlands and Germany on each dimension according to Hofstede model.

Figure. 1. score on each dimension of Hofstede cultural framework of Netherlands and Germany. Individlaism (IDV), Power Distance (PDI), Masculinity (MAS), Uncertainty Avoidance (UAI).

Table.1. score on each dimension of Hofstede cultural framework of Netherlands, Germany and other countres (Hofstede. 2001, 499).

Country PDI IDV MAS UAI Netherlands 38 80 14 53 Germany 35 67 66 65 Other countries Austrlia 36 90 61 51 Belgium 65 75 54 94 Brazil 69 38 49 79 China 80 20 66 30 Denmark 18 74 16 23 Finland 33 63 26 59 France 68 71 43 86 Germany 35 67 66 65 Greece 60 35 57 112 India 77 48 56 40 Italy 50 76 70 75 Jamaica 45 39 68 13 Japan 54 46 95 92 Mexico 81 30 69 82 Russia 93 39 36 95 South Africa 49 65 63 49 United states 40 91 62 46

versus Netherlands of 14; the society of Germany has much more masculinity characteristics than Netherlands. Finally, Germany has a higher score on uncertainty avoidance than Netherlands, indicating a lower tolerance for the uncertainty.

B. Trading data description

This study makes use of a very unique data set. It is from an investment game called Fortis OBAM Challenge that was used to promote the Fortis OBAM fund. In the year of 2007, Fortis, a company that is specialized in insurance, banking, and investment, initiated a campaign to promote their Fortis OBAM fund. There is an investment game that is carried out as part of this promotion. People are invited to participate in this game and most of the participants are Dutch and German. Every participant is given the amount of 1 billion euros to invest in any stocks they like. The goal is to beat the OBAM fund itself, and the final winner will win a certain prize. The game is hosted by websiteXpress, a web based game development firm in the Netherlands; its main product is myWallet, an internet based software that can be used by individual investors or professionals as portfolio management and monitoring tool. The way it works is similar to an online broker, which allows participants to manage multiple portfolios with different investing instruments such as stocks, options and other derivatives.

length of this game is six months, during the game, more than 3000 portfolios has been created. There are 992 valid investors are recorded in the data set. In addition, some complementary information such as gender is stored for each participant.

Some observations from the data need to be excluded. Since the aim of this study is to compare the investing behavior and performance of Dutch and German investors, those who are not from these two countries are excluded, there are 56 of them. This leaves 936 valid investors, 521 of them are Dutch and 415 are German. In addition, there are certain rules that need to be met according to the game. One of them is that investors can not maintain in the complete cash position. So I exclude those investors who are not up to this requirement for more than half period of the game. The game lasts for 6 months, (130 trading days); those who remain cash position for more than 65 days are excluded. There are 300 of them. Furthermore, one participant, according to a staff who administrate the game, is suspicious of working in the exchange or having the equipment allowing him reaching updated stock prices 10 minutes faster than any other participants, is excluded. So we end up with 635 investors.

C. Overconfidence measures

what is generally considered as the typical measure for the overconfidence, the turnover ratio on a daily basis. It was proposed by Barber and Odean (2001). To calculate daily turnover ratio for each investor, I first calculate the daily sales turnover and daily purchase turnover. Daily sales turnover is calculate as the value of the stock sold on that day divided by the total value of the portfolio that the investor held at the beginning of the day, which can be taken from the closing value of the whole portfolio at the previous day. Then, I calculate the daily purchase turnover. It is calculated by using the value of the stock purchased on that day divided by the total value of the portfolio at the end of the day. This end of the day value is the closing value of the portfolio in the same day. Then, I add daily sales turnover and daily purchase turnover together and weighted by one half to get the total turnover for each investor. Finally, I divide total turnover by the number of trading days (130) to arrive at the average daily turnover ratio for every investor. The formula is as follow:

T Vat Vpt Vat st V TRd T t / ) 5 . 0 * / 5 . 0 * / ( 1 + =

∑

TRd=Daily turnover ratio, if annualized, then simply multiply the number

of trading days in a year.

Vst: Value of the stock sold on day t

Vpt: Value of the stock purchased on day t

The second overconfidence measure I use in this paper is trading frequency. It is proposed by Perttunen and Tyynelä (2003). This measure is calculated simply by

adding the number of days that an investor executes a purchase order and the number of days that an investor executes a sale order together and divides this number by the number of trading days. The authors state that this measure of overconfidence has some advantages over the traditional daily turnover ratio. As I’ve discussed in the second chapter, overconfident trader trade more than they need to, because they either overestimated the precision of their information and signal or overestimate the ability to interpret information, so they believe by making more trades they will have a higher return. With daily turnover ratio, investor could trade aggressively and frequently but still end up with very low turnover ratio if the trades they make are small in values. This way of trading still could qualify as an aggressive overconfident trader. Trading frequency captures this flaw. The formula for trading frequency is:

T Tp Ts

TF =( + )/ (2)

TF: Trading frequency of an investor

Ts: Number of days that investor executes a sales order

Tp: Number of days that investor executes a purchase order

D. Return calculation

The returns I use in this study are all gross returns. The game system records all the investors’ stockholdings on a daily basis. If a transaction takes place, the transaction price will be recorded, and return is calculated using this price. If no transaction is made, then the closing price is used. So the return of every individual stock will be calculated and the return of each investor is calculated by adding up the weighted return of each stock. The formula for calculating daily return for investors is as follow:

∑

Rt: Daily return for an investor in day t

Pit: Beginning-of-day market value for the holding of stock i in day t divided by

the beginning-of-day market value of all stocks held by an investor. Rit: Gross daily return for stock I in day t.

St: Number of stocks held by investor in day t.

year. This would allow us to see the two investing approach on the same investor. Unfortunately, due to some technical data limitation of the game, and the fact that the length of the game is rather short (130 trading days), define a very short lived passive portfolio benchmark might not be that useful. The benchmark I use in this paper is the return of OBAM fund, since the purpose of the game is to beat this fund, the abnormal return is defined as the investor’s return in excess of the return of OBAM fund.

Rbt Rt

Rat= − (4)

Rat: The abnormal return of an investor in day t

Rt: Daily return for an investor in day t

Rbt: Daily return for the benchmark index(OBAM, this case) in day t

In addition, the formula for calculating Tracking error is as follow:

∑

σ : Daily tracking error for an investor Rt: Daily return for an investor in day t

Rbt: Daily return for the benchmark index(OBAM, this case) in day t

The formula for calculating return volatility to an investor is as follow: 2 1 ) ( ) 1 ( 1

_∑

σ : Return volatility for an investor in day t

Rt: Daily return for an investor in day t

Rm: Average return to an investor

T: number of trading days

III. Results and analysis

A. results of tests of Hypotheses

Table 2 descriptive statistics.

Whole Sample Dutch Germany Number of investors 635 341 294 Average number of stocks 41.8 44.4 38.7 Median average number of stocks 35.6 38.7 32.5 Mean portfolio value (in millions) 969.2 966 973 Median portfolio value (in millions) 969.2 966 973

mean portfolio value of Dutch investors are actually lower. From this simple descriptive statistics, a first impression can be formed that there may be some kind of overconfidence associated with Dutch investors and that hurts their performance. In addition, the test for skewness, and kurtosis are performed. The results are presented in the appendix table 1 and table 2. It can be seen from the tables that most of the variables suffer from Skewness and Kurtosis problems, so the assumption of normal distribution might be violated. For the purpose of this study, this is not really an issue, since the method employed in this paper is the independent sample t-test. This test can be used without the assumption of normality if the sample size is sufficiently large (larger than 30). In addition, central limit theorem states that, when the sample size is large enough, the distribution can be considered as approximately normally distributed (Rice, 1995). Previous researchers that study the issue of overconfidence also employ the independent sample t-test to their sample under these premises (Barber and Odean, 2001, Perttunen and Tyynelä, 2003). Therefore, with the large sample size in this paper, the results generated by the independent sample t-test can be considered as reliable.

sample t-test, which is the most appropriate method for the purpose of this study. It is widely used in the situation that needs to determine if the means of two independent groups are statistically different from each other. The null hypothesis of this test is that the means of two samples are the same; any difference is just due to sample errors. It is employed in almost all the previous researches that study the differences in the degree of overconfidence between varies groups. (Barber and Odean, 2001, Perttunen and Tyynelä, 2003). I divide the sample according to the nationality into Dutch and German investors. Then I calculate the average turnover ratio, trading frequency and abnormal returns for each investor. Last, I calculate the differences of these measures between Dutch and German investors.

Table. 3. Difference of trading activities between Dutch and German investors. P-values are in parenthesis.

Whole sample Dutch German Difference Number of observations 635 316 263

Average turnover ratio 0.0077 0.0086 0.0066 0.002 (0.035) Trading frequency 0.16 0.17 0.14 0.03 (0.028) Abnormal returns 0.00028 0.00019 0.00037 -0.00018 (0.000)

daily basis. From the table it can be seen that Dutch investors has higher turnover ratios than German investors, Dutch investors turn their portfolio 0.86% daily or 215% annually, and German investors turn their portfolio 0.0066 daily or 165% annually. The difference of turnover ratio between investors is statically significant at 5% confidence level. Trading frequency can be simply interpreted as how many days investors trade in a year, the results are similar to turnover ratio. Dutch investors trade more frequently than German investors, and the difference is statistically significant at 5% confidence level

For the variable of abnormal returns, it can be seen that the returns to Dutch investors are lower than the returns to German investors, the difference in returns are highly significant at 5% confidence level. Thus, although Dutch investors trade more frequently then German investors, they actually earns a lower return, this may indicate that more trading hurt their performance. Therefore, the results are concord with my first hypothesis, people in countries with a higher score on individualism tend to think they are better than others, and this leads to higher degree of overconfidence, that drive investors to trade more frequently.

Table. 4. Difference of trading activities between male and female investors in Netherlands.

P-values are in parenthesis.

Dutch investors Whole sample Men Women Difference Number of observations 341 316 25

Average turnover ratio 0.00864 0.0086 0.0087 -0.0001 (0.975) Trading frequency 0.17 0.17 0.21 -0.04 (0.208) Abnormal returns 0.00019 0.00021 -0.00003 0.00024 (0.143)

Table. 5. Difference of trading activities between male and female investors in Germany.

P-values are in parenthesis.

German investors Whole sample Men Women Difference Number of observations 294 263 31

Table 4 and 5 presents the test results for each country. In the sample that only contains Dutch investors, both turnover ratio and trading frequency of men are lower than women, but the differences in these measures are not statistically significant, indicating that there is no real difference in the degree of overconfidence between men and women. While in the sample of German investors, the turnover ratio and trading frequency are both higher for men than women, and the differences in the measures are statistically significant at 5% confidence level, indicating a higher degree of overconfidence displayed by men.

The third hypotheses states that investors in countries with a higher score on Uncertainty Avoidance will have a lower tracking error and lower volatilities comparing with investors in countries with a lower score on this dimension. To test this hypothesis, I calculate the daily tracking errors to the benchmark OBAM fund, daily volatilities of the returns to investors, and the differences of these measures for each investor from both countries.

Table. 6. Difference of trading activities between Dutch and German investors. P-values are in parenthesis.

Whole sample Dutch German Difference Number of observations 635 316 263

Tracking error 0.240 0.0242 0.0236 0.0006 (0.036) Volatility 0.016 0.0163 0.0154 0.0009 (0.031)

B. Regression results

To confirm the results of the above tests, I run a regression analysis on the data set. I use the whole sample data to run regression. First I use turnover ratio and trading frequency as the dependent variable, and gender dummy (men=1) and nationality dummy (Dutch=1) as the independent variable.

Table. 7. Regression results. Effects of nationality and gender variable on turnover ratio and trading frequency. Nationality dummy (Dutch=1), Gender dummy (Men=1) P-values are in parenthesis. *** significant at 1% level, ** significant at 5% level.

Dependent variables

Independent variables Turnover ratio Trading frequency Intercept 0.005 0.135 (0.002) (0.000)*** Nationality 0.002 0.031 0.046 (0.030)** Gender 0.002 0.004 0.04 (0. 878)

investors. The statistics are not significant for gender; this is also in a way explained by the tests I run for the masculinity. The significance of degree of overconfidence of German male investors is in a way canceled out by the opposite Dutch investors.

To confirm the test results from the third hypothesis, I use tracking error and volatility as the dependent variable, and nationality dummy (Dutch=1) and gender dummy (men=1) as the independent variable.

Table. 8. Regression results. Effects of nationality and gender variable on tracking error and volatility. Nationality dummy (Dutch=1), Gender dummy (Men=1) P-values are in parenthesis. *** significant at 1% level, ** significant at 5% level.

Dependent variables

Independent variables Tracking error Volatility Intercept 0.024 0.016 (0.000) (0.000)*** Nationality 0.001 0.001 0.036 (0.030) ** Gender -0.001 -0.001 0.294 (0.275)

volatility in their portfolio than German investors. Again, the statistics are not significant for gender dummy variable.

IV. Extended discussions

especially relevant in this study, because it is a simulated investment game. There is no real loss to this game besides the time they invested in. Although there is a prize for the final winner, and this could be the motivation for people to trade to win, I speculate that there exist a certain number of people that are in this game solely for the purpose of entertaining themselves. In addition, people in a simulated environment might behave differently than they do in the real world. I believe this line of reasoning will not invalid the results of this paper, since whatever influences this ‘game mentality’ have on people’s behavior, it should affect them in the same direction. Therefore, although the general degree of overconfidence of the whole sample might be amplified due to the relaxed game mentality, it is not likely to affect differences between the measures.

addition, it is also reasonable to believe that some of the participants actually have skills. For example, in the data section I exclude one participant that is suspicious of working in the exchange. This could imply that at least some of the participants are not overconfident, and may actually possess the skill and information. Their way of trading is simply aggressive, resulted in higher overconfidence measure. However, the test results of lower return associated with higher overconfidence measure means that either this type of participants are too few to make a statistically significant effect or the skill they believe they have, is an illusion, they are just by definition, overconfidence.

V. Conclusion.

investors from Netherlands, a country with a higher score on the dimension of individualism, are more overconfident than investors from Germany, the country with a lower score on this dimension. (ii) The phenomenon of men being more overconfidence is more pronounced in Germany, the country with a higher score on the dimension of masculinity than investors from Netherlands, the country with a lowers score on this dimension. And (iii), investors from Netherlands, the country with a lower score on the dimension of uncertainty avoidance, have higher tracking error to the benchmark and higher volatility of the portfolio than investors from Germany, the country with a higher score on this dimension. What I find in this study indicate that culture difference as modeled by Hofstede, does have influences on investors trading practice. One potential limitation with this study is the choice of return benchmark, because of data constraint and short length of the game period, the widely used passive buy-and-hold portfolio benchmark can not be constructed. This changes the way abnormal returns are calculated, since by simply comparing the return over the benchmark OBAM, it is not able to see and compare the two trading approach on the same investors, which is what overconfidence is defined in Barber and Odean’s (2000, 2001) study, but I argue that this will not affect the reliability of the findings of this paper since the core measures for the trading activity such as the turnover ratio and trading frequency are not affected by the choice of benchmark.

References

Alicke M.D., Klotz M.L., Breitenbecher D.L., Yurak T.J., Vredenburg D.S. (1995),

Personal Contact, Individuation and the Better-Than-Average Effect, “Journal of

Personality and Social Psychology”, Vol. 68, No. 5, pp. 804–825.

Barber B., Odean T. (2000), Trading Is Hazardous to Your Wealth: The Common

Stock Investment Performance of Individual Investors, “Journal of Finance”, Vol. 55,

No. 2, pp. 773–806.

Barber B., Odean T. (2001), Boys Will Be Boys: Gender, Overconfidence, and

Common Stock Investment, “The Quarterly Journal of Economics”, Vol. 116, No. 1,

pp. 261–292.

Baskerville R.F. (2003), Hofstede never studied culture, “Accounting, Organizations and Society”, Vol. 28, pp.1–14.

Beckmann D., Lukas M., Megumi S. (2008), Does Culture Influence Asset Managers’

Views and Behavior?, “Journal of Economic Behavior and Organization”, Vol. 67, pp.

624-643.

Beyer P. (1990), Gender Differences in the Accuracy of Self-Evaluations of

Performance, “Journal of Personality and Social Psychology”, Vol. 59, No. 5, pp.

960–970.

Brealey, R.A., Myers, S.C., Allen F. (2006), Corporate Finance, Irwin/McGraw Hill, Chicago.

Chuang W.-I., Lee B.-S. (2006), An empirical evaluation of the overconfidence

hypothesis, “Journal of Banking and Finance”, Vol. 30, No. 9, pp. 2489–2515.

Church A. T., Marcia S. K., Alice M. D. (2006), Implicit theories and self-perceptions

of traitedness across cultures: Toward integration of cultural and trait psychology perspectives, “Journal of Cross-Cultural Psychology” 37, 694-716.

Deaves R., Luders E., Schroder M. (2005), The Dynamics of Overconfidence:

Evidence from Stock Market Forecasters, “Discussion Paper”, No. 05-83.

Dunning D., Meyerowitz J.A., Holzberg A.D. (1989), Ambiguity and Self-Evaluation:

The Role of Idiosyncratic Trait Definitions in Self-Serving Assessments of Ability,

Appropriateness of Extreme Confidence, “Journal of Experimental Psychology”, Vol.

3, No. 4, pp. 552–564.

Fischhoff B., Koriat A., Lichtenstein S. (1980), Reasons for Confidence, “Journal of Experimental Psychology: Human Learning and Memory”, Vol. 6, No. 2, pp. 107–118.

Gervais S., Odean T. (2001), Learning to Be Overconfident, “The Review of Financial Studies”, Vol. 14, No. 1, pp. 1–27.

Gigerenzer G., Hoffrage U., Kleinbolting H. (1991), Probabilistic Mental Models: A

Brunswikian Theory of Confidence, “Psychological Review”, Vol. 98, No. 4, pp.

506–528.

Hall, E. T. (1976), Beyond Culture. New York: Doubleday

Hall, E. T. (2000), Context and meaning. In L. A. Samovar & R. E. Porter (Eds.), Intercultural Communication: A Reader, 9th ed. Belmont, CA: Wadsworth Publishing Co.

Hofstede, G.H. (1980), Culture’s Consequences: International Differences in

Work-Related Values. Sage Publications, Beverly Hills, CA.

Hofstede, G.H. (2001), Culture’s Consequences: Comparing Values, Behaviors,

Institutions, and Organizations Across Nations. Sage Publications, Thousand Oaks,

CA.

Inglehart, R., Baker,W.E. (2000), Modernization, cultural change, and the persistence

of traditional values, “American Sociological Review” Vol. 65, pp. 19–51.

Keown, Charles F. (1989), Risk perceptions of Hong Kongese vs. Americans, “Risk Analysis” Vol. 9, pp. 401-405.

Kluckhohn, F.R. and Strodtbeck, F.L. (1961), Variations in value orientations. Greenwood, Westport, CT.

Kyle A.S., Wang F.A. (1997), Speculation Duopoly with Agreement to Disagree: Can

Overconfidence Survive the Market Test?, “Journal of Finance”, Vol. 52, No.5, pp.

2073–2090.

Langer E.J., Roth J. (1975), Heads I Win, Tails It’s Chance: The Illusion of Control

as a Function of the Sequence of Outcomes in a Purely Chance Task, “Journal of

Linton, R. (1945). The Cultural Background of Personality. New York/London. Appleton-Century.

March, J. G.. (1978), Bounded rationality, ambiguity, and the engineering of choice. “Journal of Economics”, Vol. 9, No. 2, pp. 758-608.

Markus, H, and Kitayama, S. (1991), Culture and the self: Implications for cognition,

emotion, and motivation, “Psychological Review”, Vol. 98, pp. 224-253.

Merritt, A. (2000), Culture in the cockpit: do Hofstede’s dimensions replicate? “Journal of Cross-Cultural Psychology”, Vol. 31, pp. 283–301.

Nesdale, D., Naito, M. (2005), Individualism-collectivism and the attitudes to school

bullying of Japanese and Australian students, “Journal of Cross-Cultural Psychology”

Vol. 36, pp. 537–556.

Odean T. (1998b) Volume, volatility, price and profit when all trades are above

average, “Journal of Finance” Vol. 53, pp. 1887-1934

Odean T. (1999), Do Investors Trade Too Much?, “American Economic Review”, Vol. 89, No. 5, pp. 1279–1298.

Oskamp S. (1965), Overconfidence in Case-Study Judgments, “Journal of Consulting Psychology”, Vol. 29, No. 3, pp. 261–265.

Oysermann, D., Coon, H.M., Kemmelmeier, M. (2002), Rethinking individualism and

collectivism: evaluation of theoretical assumptions and meta-analyses.

“Psychological Bulletin” Vol. 128, pp. 3–72.

Perttunen J & Tyynelä M. (2003), Trading behaviour of Finnish households: activity,

performance and overconfidence, “Finnish Journal of Business” Vol. 52, pp. 157-178.

Powell, M., Ansic, D. (1997). Gender differences in risk behaviour in financial

decision-making: An experimental analysis, “Journal of Economic Psychology”, Vol.

18, No. 6, pp. 605–628.

Rice, J. (1995), Mathematical Statistics and Data Analysis (Second ed.), Duxbury Press.

Russo J., Schoemaker P. (1992), Managing Overconfidence, “Sloan Management Review” Vol. 33, No. 2, pp. 7-17.

Shafiro, M.V., Himelein, M.J., Best, D.L. (2003), Differences in

Psychology”, Vol. 34, pp. 297–303.

Shefrin, H. M. and M. Stateman. (1984), Explaining investor preference for cash

dividends, “Journal of Financial Economics” Vol. 13, pp.253-282.

Skala, D. (2008). Overconfidence in Psychology and Finance - An Interdisciplinary

Literature Review. “Bank i Kredyt”, No. 4, pp. 33-50, 2008.

Simon, H.A. (1957), Administrative Behavior, Free Press/MacMillan, New York. Shuper, P.A., Sorrentino, R.M., Otsubo,Y., Hodson, G.,Walker, A.M. (2004), A theory

of uncertainty orientation. “Journal of Cross-Cultural Psychology” Vol. 35, pp.

460–480.

Svenson O. (1981), Are We All Less Risky and More Skilful Than Our Fellow

Drivers?, “Acta Psychologica”, Vol. 47, No. 2, pp. 143-48.

Triadis, H.C. (1995). Individualism and Collectivism. Westview Press, Boulder, CO. Taylor S., Brown J.D. (1988), Illusion and Well-Being: A Social Psychological

Perspective on Mental Health, “Psychological Bulletin”, Vol. 103, No. 2, pp.

193–210.

Trompenaars, F., Hampden-Turner, C. (1997), Riding the Waves of Culture. London, UK: Nicholas Brealey Publishing

Tversky, A., Kahneman, D. (1974), Judgment under uncertainty: Heuristics and

biases. “Science”, Vol. 185, pp. 1124-1131.

Weinstein N.D. (1980), Unrealistic Optimism About Future Life Events, “Journal of Personality and Social Psychology”, Vol. 39, No. 5, pp. 806–820.

Zuckerman, M. (1979), Attribution of success and failure revisited, or: The

motivational bias is alive and well in attribution theory, ‘Journal of Personality” Vol.

Appendix

Table 1. Descriptive statistics of skewness and kurtosis of Dutch investors

Descriptive Statistics 341 .00864596 6.790 .132 60.322 .263 341 .16963680 1.948 .132 3.760 .263 341 .02425220 3.287 .132 12.850 .263 341 .00019138 -.325 .132 3.545 .263 341 .01629513 2.656 .132 8.596 .263 341 turn over ratio

trading frequency tracking error

mean abnormal return Volatility

Valid N (listwise)

Statistic Statistic Statistic Std. Error Statistic Std. Error

N Mean Skewness Kurtosis

Table 2. Descriptive statistics of skewness and kurtosis of German investors

Descriptive Statistics 294 .00664350 6.755 .142 63.554 .283 294 .13851386 2.336 .142 5.827 .283 294 .01544772 2.705 .142 10.083 .283 294 .02362793 3.414 .142 16.304 .283 294 .00037215 -.324 .142 1.454 .283 294 turn over ratio

trading frequency Volatility

tracking error

mean abnormal return Valid N (listwise)

Statistic Statistic Statistic Std. Error Statistic Std. Error