Are Competence and Overconfidence related to Psychological Traits?
Alexandre Zanetta (UCB)
Benjamin Miranda Tabak (UCB)
Resumo
Este trabalho demonstra que a ilusão de competência e o excesso de confiança estão
relacionados com traços psicológicos. Apresentamos novos resultados que ligam traços
psicológicos individuais a ocorrência da ilusão de competência e do excesso de
confiança. Focamos no grau de impulsividade, traços de personalidade, conhecimento,
inteligência, sexo e idade. Ilusão de competência e excesso de confiança estão mais
presentes em homens jovens, que são menos impulsivos. Além disso, os indivíduos que
são mais abertos à experiência, conscientes e emocionalmente estáveis são mais
propensos ao efeito competência. No caso de excesso de confiança, instabilidade
emocional mostrou-se dominante.
Palavras-Chaves: autoconfinaça excessiva; ilusão de competência; cinco grandes
fatores; traços de personalidade; escore de impulsividade.
Abstract
This paper shows that competence and overconfidence are related to psychological
traits. We present novel results that link individual psychological traits to the illusion of
competence and overconfidence. We focus on the degree of impulsiveness, personality
traits, knowledge, intelligence, gender and age. Illusion of competence and
overconfidence are shown to be more present in younger men who are less impulsive.
Furthermore, individuals that are more open to experience, conscientious and
emotionally stable are more prone to the competence effect. In the case of
overconfidence, emotional instability proved to be dominant.
Keywords: overconfidence; illusion of competence; five big factors, personality
traits; impulsiveness score.
JEL Codes: C91; D81.
Área 7 - Microeconomia, Métodos Quantitativos e Finanças
1. INTRODUCTION
The primary goal of this study is to investigate whether there is some kind of relation
between two theories – overconfidence and competence effect, which have been well
established in the literature – and psychological traits (Heath and Tversky, 1991; Fox
and Tversky, 1995; Gleser and Weber, 2007; Barber and Odean, 2001; Graham et al.,
2009; Bruno et al., 2010).
Our main hypothesis is that an individual’s psychological traits can be the key to the
identification of the standard type of agent who experiences the two types of cognitive
errors addressed here. Mapping individual traits that lead to cognitive errors may help
elucidate the personal aspects that influence the decision-making process and cause
agents to make biased decisions.
It is important to understand the behavior of agents because it determines market
behavior, which influences individual behavior. So, there exists a link between
individual and collective actions. Given this hypothesis, it is of paramount importance
to identify the determinants of individual behavior in order to understand how the
economy works and how contagion occurs (Lea et al., 1987).
In general, individuals prefer to trust their own judgment, which is subjective, rather
than bet on a lottery, which has an objective probability, even when the probability of
winning is the same for both bets. This behavior is known as the competence effect. An
individual’s knowledge of, and competence in, a given topic is a determinant factor for
defining his/her behavior towards uncertain events. The more competent in a given
topic an individual believes he/she is, the greater the chance that he/she will bet on
his/her own judgment. This occurs because agents inflate subjective probabilities,
which become greater than an objective probability of the same value (Fox and
Tversky, 1995; Heath and Tversky, 1991).
Agents overvalue their predictive abilities and believe they can identify future events,
even when their predictions are about uncertain events. This cognitive illusion is known
as overconfidence (Tversky and Kahneman, 1974; Fischhof et al., 1977; Sustein, 1998).
Overconfidence has a direct adverse effect on investors, as they tend to trade more often
than do more rational agents. Men show more overconfidence and this reduces
investment portfolio profitability because of the increase in trading cost and frequency,
affecting more severely those investors who have a larger asset portfolio and those who
are better educated (Gleser and Weber, 2007; Barber and Odean, 2001; Graham et al.,
2009).
There is a group of traits, with their respective intensity, which defines an individual,
distinguishing him/her from others. Personality traits include emotional stability,
conscientiousness, extraversion, openness to experience and level of socialization or
agreeableness (Allport, 1961; McCrae and Costa, 1989; Guilford, 1975; McAdams,
1992).
The big five model results are compatible with the application of other models that map
personality traits, and is one of the best currently available models for the identification
of personality traits (Briggs, 1992; Goldberg, 1992).
2
The use of personality traits in economics is not novel as they have already been
utilized to understand and explain economic issues such as the participation of women
in certain job markets and income determinants and differences (Rosenbloom et al.,
2008; Semykina and Linz, 2007; Palifka, 2009). Maybery et al. (2005) investigate the
relation between personality and the heterogeneous behavior of agents.
Individuals build two types of reasoning, one called automatic and one known as
reflective. Impulsiveness can be defined as a decision that is taken by an individual
based on automatic reasoning, without reflection about the issue (Chaiken and Trope,
1999; Doob, 1993).
Depending on the situation, automatic reasoning can produce a better response than
reflective reasoning, but in some situations, the former is not easily controlled and is
brought into action without interference of human wishes and can become
uncontrollable in certain cases. Automatic reasoning is inherent to human beings since
the beginning of times. For further details, see Lawrence and Stanford (1999) and
Gerbin, Ahadi and Patton (1987).
Some studies use impulsiveness as a way to try to understand and explain economic
issues such as the determinants of investor behavior. Because of that this approach has
been widely acknowledged and has therefore gained significant importance in the
understanding of consumers’ decision-making process (Hunter and Kemp, 2004;
Foxall, 2003; Lai, 2010; Billieux et al., 2008).
We used the academic performance of participants as a way to measure their level of
intelligence and knowledge. This variable was used as a proxy for knowledge and
intelligence because, even though it is not perfect, it is widely available and
contemplates important aspects observed in the analyzed characteristic.
The impulsiveness and knowledge and intelligence variables are important
characteristics that provide good information about how agents have been employing
their rational capacity. Less impulsive individuals, who make their decisions more
conscientiously and have a better academic performance, can be considered to be more
rational. Finally, we also test whether gender and age differences have any influence on
illusion of knowledge and overconfidence.
2. EXPERIMENT DESIGN AND PROCEDURES
Three questionnaires were sequentially handed over to be answered individually. The
first one included questions on personal data, personality traits and impulsiveness of
participants. The second one simulated some situations and prompted participants to
give their opinions about which result was correct and to what extent they found their
choice to be correct. The third questionnaire requested that participants choose between
betting on the ticked answer and betting on a lottery with the same winning probability.
Details about the questionnaires used are shown in the Appendix.
The questions about the identification of personality traits were extracted and adapted
from Goldeberg (1992). The questions about impulsiveness were based on the Barratt
Impulsiveness Scale. The questionnaire was extracted and adapted from Patton,
Stanford and Barratt (1995).
3
To capture the aspects about participants’ individual intelligence, we used the index
calculated by the Catholic University of Brasilia known as the Academic Performance
Index, which is the result of the arithmetic mean of the grades obtained by students
throughout their academic life. And finally, the questionnaire to measure the
competence effect and overconfidence was extracted from Heathy and Tversky (1991).
First part: data collection and mapping of personality traits and impulsiveness.
To capture information on the personality traits of participants, we used a structured
questionnaire containing five questions for each of the five traits. The questionnaire on
impulsiveness contains a total of 30 questions.
Second part: questionnaires on finance.
The identification of cognitive failure related to overconfidence was based on a
question that requested that the respondent predict his/her performance in the
experiment compared to the group as a whole.
Each question on finances contained two alternatives (a correct one and an incorrect
one), and each participant could check only one. The participant ascribed the
probability of getting the correct answer to each question. This probability should fall
within the 50-100% interval. Extreme probabilities (50% and 100%) mean “guess at”
and “certainty about” the chosen answer, respectively.
Values smaller than 50% were not allowed as there were only two possible answers.
The assumption that the probability of getting the correct answer is 50% implies that
the probability that the unchosen answer is correct will be greater than 50%.
Questions without a correct answer were added to this questionnaire. Getting the correct
answer relied on the knowledge about the occurrence of future events, which meant a
high level of uncertainty.
Third part: bet on the lottery versus on his/her own answer.
In this part, the participant was given the opportunity to choose, for each answered
question, between betting on his/her answer or on a lottery with objective probability
equal to the subjective probability attributed by him/her to the question.
At the end of the experiment, we randomly chose 10% of the participants, played the
game, and changed the result into a monetary prize. The individual prize corresponded
to R$ 40.00 (approximately US 23.5), totaling R$ 520.00 in the whole experiment.
Since this was a game, the randomly chosen participant could win or not the prize. If
he/she lost it, another participant was randomly chosen until all prizes were awarded.
3. RESULTS
3.1. Competence effect
For the competence effect to occur, it is expected that the higher the subjective
probability attributed individually, the greater the tendency of individuals to bet on their
own answer. The hypothesis can be written as:
H0:
Ha: Correlation > 0, implying presence of competence effect.
4
When we compare quantitative variables, we use Pearson’s correlation coefficient,
which, according to Malhotra and Birks (2007), should be used to measure the intensity
and direction of the linear association between two variables of this type. For
qualitative variables, we adopted Cramer’s V correlation coefficient, which is more
appropriate to deal with indicator variables.
We calculated the correlation coefficient for all participants and we set those with a
positive correlation apart from those with a negative or zero correlation. At a 10%
significance level, we found that 53% of participants had a positive correlation.
For indicative signs of the presence of the competence effect to exist, it is necessary
that the higher the average probability the higher the percentage of choosing one’s own
answer. The hypothesis can be written as:
H0:
Ha: Correlation > 0, implying presence of competence effect.
We found a directly proportional correlation of 35.6% (p=0.000) between the average
probability and the percentage related to choosing one’s own answer. Thus, the higher
the probability of getting the correct answer to a given question, the greater the
tendency of an individual to bet on his/her own answer.
From this part onwards, we split participants into different groups, categorizing them
according to several characteristics. Whenever we split participants into two groups, in
order for the competence effect to occur, we expected that the higher the subjective
probability individually attributed in a given group, the greater the tendency of
individuals to bet on their own answer.
In each group, we calculated the correlation coefficient for all participants and we
separated those with a positive correlation from those with a negative or zero
correlation.
First, we categorized participants according to gender to verify whether the competence
effect was stronger in the group comprised of men only or in that made up of women
only.
At a 5% significance level, we could reject that 68% of male participants with a
positive correlation was equal to 50% (p=0.038). We did not find significant results for
the group comprised of women only.
We also measured the mean linear association of both groups to check whether there
was some relation between the attributed probability and the choice of one’s own
answer. For the group of men, we found a statistically significant positive correlation
coefficient of 55.2% (p=0.0001), whereas for the group of women, the correlation
coefficient was not significant (p=0.111). This way, men were more vulnerable to the
competence effect than were women.
We categorized participants according to age to check whether the presence of the
competence effect was stronger in the group comprised of younger individuals only or
in that made up of older individuals.
From this part on, we used the median to split the group into two. In each group, we
calculated the correlation coefficient between the subjective probability individually
attributed and the tendency of individuals to bet on their own answer. We separated
those with a positive correlation from those with a negative or zero correlation.
5
At a 10% significance level, we found that 53% of younger participants had a positive
correlation, whereas older ones had a positive correlation in only 49% of the cases. For
the younger group, we obtained a statistically significant correlation coefficient of
43.7% (p=0.006). For the older group, the correlation coefficient was not significant
(p=0.252). Younger individuals were more vulnerable to the competence effect than
older ones.
In this part, we categorized participants according to their academic performance to
verify whether the competence effect was stronger among those with a better
performance or in the group comprised only of individuals with a poorer performance.
At a 10% significance level, we found that 58% of participants with a better
performance had a positive correlation and that 60% of those with a poorer performance
had a negative correlation.
For those with a better performance, we obtained a statistically significant positive
correlation coefficient of 34.7% (p=0.030). For those with a poorer performance, we
did not find a significant correlation coefficient (p=0.147).
Individuals with a better performance were more vulnerable to the competence effect
than those with a poorer performance. Again, we split participants according to their
impulsiveness score to determine whether the presence of the competence effect was
stronger in the group with more impulsive individuals or in that made up only of less
impulsive individuals.
At a 10% significance level, 54% of less impulsive participants and only 47% of more
impulsive ones had a positive correlation. For the group with a higher score, we
obtained a statistically significant correlation coefficient of 22.5% (p=0.100). For the
group with a lower score, we found a more significant positive correlation coefficient of
30.7% (p=0.036). Individuals with a lower impulsiveness score were more vulnerable
to the competence effect than those with a higher score.
We also assessed academic performance and the impulsiveness score and we used this
association as strong characteristics that are observed in more rational individuals.
We separated individuals with a lower impulsiveness score and with a higher academic
performance from the others. This group showed greater positive intensity regarding
those aspects required for individuals to base their decisions on rationality: higher level
of knowledge and larger use of their reflective capacity, making more conscientious
choices.
At a 10% significance level, we found that 53% of participants with more rational traits
had a positive correlation. At this same significance level, we found that 64% of
participants with less rational characteristics had a negative correlation.
For the group with more rational characteristics, the positive correlation coefficient
corresponded to 32.3% (p=0.103). In the group with less rational individuals, the
correlation coefficient was not significant either (p=0.212).
The results obtained were insufficient to determine that individuals with more rational
characteristics are more vulnerable to the competence effect than others. We
categorized participants according to the intensity displayed by each personality trait to
verify whether the competence effect was stronger in the group comprised only of those
with greater intensity or in that made up of those with a smaller intensity .
6
At a 10% significance level, we found that 53% of participants who were more open to
experience had a positive correlation. At this same significance level, we observed that
only 49% of participants who were more closed to experience had a positive
correlation.
For the group of those more open to experience, there was a statistically significant
positive correlation coefficient of 41.9% (p=0.008). For those more closed to
experience, the correlation coefficient was not significant (p=0.200). Individuals more
open to experience were more vulnerable to the competence effect than those who were
less open to it.
We separated participants according to the conscientiousness trait to verify whether the
competence effect was stronger than in the group made up only of more conscientious
individuals or in that made up only of less conscientious individuals.
At a 10% significance level, we found that 53% and 56% of more conscientious and
less conscientious participants, respectively, had a positive correlation. For the group of
more conscientious individuals, we obtained a statistically significant positive
correlation coefficient of 46.2% (p=0.002). For the group of less conscientious
individuals, the correlation coefficient could not be considered to be different from zero
(p=0.378).
More conscientious individuals were more vulnerable to the competence effect than less
conscientious ones. For the emotional stability trait, we verified whether the presence of
the competence effect was stronger in the group made up only of more emotionally
stable individuals or in that comprised only of less emotionally stable ones. At a 10%
significance level, we found that only 47% of more emotionally stable participants had
a positive correlation whereas 54% of less emotionally stable participants showed the
same type of correlation.
For the group of more emotionally stable individuals, we obtained a statistically
significant positive correlation coefficient of 51.4% (p=0.001). For the group of less
emotionally stable individuals, the results were not significant (p=0.471). More
emotionally stable participants were more vulnerable to the competence effect than
those who were less emotionally stable. There was no statistical significance for any
other personality trait.
In what follows, we check the intensity at which this deviation affects both groups.
First, we verified whether the average subjective probability can be considered to be
different across the groups. After that, we checked whether the percentage related to the
choice of one’s own answer is different across these groups.
We used Student’s t test in all cases described in the present study in order to compare
two population means from two samples. This test is used to check whether the means
of two populations are significantly different from each other. We did not consider the
normality of the sample because according to Myers and Well (2003) the parametric
Student’s t test is robust to type I error, even when the distributions are flat or biased.
7
Men attributed a mean probability of correct answers of 76.02% compared to 68.79%
by women (p=0.003). Therefore, we can say that men tend to attribute more subjective
probability to getting their answers correct than do women. In 64.83% of the cases, men
preferred to bet on their own choices whereas women had such preference in only
54.06% of the cases (p=0.030). This way, it is possible to say that men tend to prefer to
bet on their own choice than do women.
The group made up of more sociable participants attributed an average probability of
67.87% to getting the correct answer compared to 72.98% by the least sociable ones.
The percentage values can be considered statistically different from each other
(p=0.074).
In 64.29% of the cases, the most sociable individuals preferred to bet on their own
choices whereas the least sociable had such preference in 53.98% of the cases
(p=0.067). Thus, we can state that the most sociable ones prefer to bet on their own
answers than do the least sociable ones.
For the remaining groups, the differences were not statistically significant. In what
follows, we show a table that summarizes the significant results obtained for the
competence effect.
8
Table 1 – Summary of competence effect results
Whole group
Academic Performance
Age
Openness to Experience
Conscientiousness
Emotional Stability
Corr(i)(SP,OA)>0 in 53% of cases
(p=0.100)
Corr(T)(SP,OA)=37%
(p=0.000)
Group (+)
Group (-)
Corr(i)(SP,OA)>0 in 58% of
cases
(p=0.100)
Corr(T)(SP,OA)=35%
(p=0.030)
Corr(i)(SP,OA)>0 in 53% of
cases
(p=0.100)
Corr(T)(SP,OA)=44%
(p=0.006)
Corr(i)(SP,OA)>0 in 53% of
cases
(p=0.100)
Corr(T)(SP,OA)=42%
(p=0.008)
Corr(i)(SP,OA)>0 in 53% of
cases
(p=0.100)
Corr(T)(SP,OA)=46%
(p=0.002)
Corr(i)(SP,OA)>0 in 47% of
cases
(p=0.100)
Corr(T)(SP,OA)=51%
(p=0.001)
Corr(i)(SP,OA)>0 in 40% of
cases
(p=0.100)
Corr(T)(SP,OA)=20%
(p=0.147)
Corr(i)(SP,OA)>0 in 49% of
cases
(p=0.100)
Corr(T)(SP,OA)=11%
(p=0.252)
Corr(i)(SP,OA)>0 in 49% of
cases
(p=0.100)
Corr(T)(SP,OA)=14%
(p=0.200)
Corr(i)(SP,OA)>0 in 56% of
cases
(p=0.100)
Corr(T)(SP,OA)=6%
(p=0.378)
Corr(i)(SP,OA)>0 in 54% of
cases
(p=0.100)
Corr(T)(SP,OA)=-1%
(p=0.471)
SP = 67.87%
SP = 72.98%
(p=0.074)
Level of Socialization
OA = 64.29%
OA = 53.98%
(p=0.067)
Gender
Male
Female
Corr(i)(SP,OA)>0 in 68% of
cases
(p=0.100)
Corr(T)(SP,OA)=55%
(p=0.000)
Corr(i)(SP,OA)>0 in 41% of
cases
(p=0.100)
Corr(T)(SP,OA)=18%
(p=0.111)
SP = 76.02%
SP = 68.79%
(p=0.003)
OA = 64.83%
OA = 54.06%
(p=0.033)
Where:
SP
= Subjective probability (probability of correct answers attributed by the
participant)
OA
= Bet on one’s own answer (percentage of questions in which the participant
preferred to bet on his/her own answers)
Corr(i) = Individual correlation
Corr(T) = Total correlation
9
3.2. Overconfidence
To estimate self-confidence, we built an index that shows the difference between the
probability ascribed to correct answers subtracted from the percentage of actual correct
answers. For the overconfidence effect to exist, we expect the mean self-confidence
index to be greater than zero. The larger the value obtained for the self-confidence
index, the larger the presence of overconfidence. We can write the hypothesis as:
H0:
Mean self-confidence index
Ha:
Mean self-confidence index > 0, implying presence of overconfidence.
By using a 5% significance level, we cannot reject that the mean value of 4.25% for the
self-confidence index shown by participants is smaller than or equal to zero. When the
data were submitted to normality tests, they passed the Kolmogorov-Smirnov test with
Lilliefors significance correction (K-S:p=0.200) and the Shapiro-Wilk test (SW:p=0.455).
Therefore, we found evidence of the overconfidence effect in the made up of all
participants in this experiment. We compared the self-confidence index with the stated
self-confidence reported by participants in order to check whether there was some kind
of association between the declared and observed indices.
In order for an association to be found between the two, those individuals who believed
they would outperform their peers are expected to have a higher self-confidence index.
We can write the hypothesis as:
H0:
Correlation = 0, implying absence of association between the stated and
observed self-confidence.
Ha:
Correlation
observed self-confidence.
The presence of directly proportional correlation between the self-confidence index and
the stated self-confidence was equal to 21.4% (p=0.048). In this case, those who
considered themselves to be self-confident had a greater probability to show cognitive
error.
We categorized participants according to gender to verify whether overconfidence was
stronger in the group made up only of men or in that comprised only of women. For the
group of men, at a 5% significance level, we cannot reject that a mean value equal to
7.95% shown by participants is smaller than or equal to zero (K-S: p=0.200; S-W:
p=0.912).
For the group of women, even at a 10% significance level, we cannot reject that the
mean value of the self-confidence index is different from zero. By comparing the values
obtained for the two groups, we can reject that the mean self-confidence indices
between men and women are equal to each other (p=0.024). Thus, the group comprised
only of men had a positive and higher overconfidence index than that of the group of
women.
We stratified participants by age to verify whether overconfidence was stronger in the
group of younger individuals or in that made up only of older individuals.
10
For the group of younger participants, at a 5% significance level, we cannot reject that
the mean value of 5.33% for the self-confidence index shown by participants is smaller
than or equal to zero (K-S: p=0.200; S-W: p=0.246).
For the group of older individuals, even at a 10% significance level, we cannot reject
that the mean value of 1.61% for the self-confidence index shown by participants is
equal to zero (K-S: p=0.200; S-W: p=0.774).
We found evidence of the overconfidence effect only in the group constituted of
younger participants. The values presented by the two groups can be considered
statistically different from each other (p=0.031). The group made up of younger
individuals had a positive and higher self-confidence index than that of the group of
older individuals.
We obtained a negative correlation coefficient of 20.6% between age and selfconfidence, which is statistically different from zero (p=0.089). The older the
participant, the smaller the self-confidence index.
Taking academic performance into consideration, we checked whether the presence of
overconfidence was stronger in the group made up only of individuals with a better
academic performance or in that which included only those individuals with a poorer
performance.
We found a positive correlation coefficient of 28.6% between academic performance
and the self-confidence index, which was statistically different from zero (p=0.028).
The better the academic performance, the higher the self-confidence index.
By using a 5% significance level for the group with better academic performance, we
could not reject that the mean value of 9.55% for the self-confidence index is smaller
than or equal to zero (K-S: p=0.200; S-W: p=0.455).
For the group with poorer academic performance, even at a 10% significance level, we
could not reject that the mean value of -2.49% for the self-confidence index was equal
to zero (K-S: p=0.200; S-W: p=0.977).
We observed indicative signs of the overconfidence effect only in the group comprised
of individuals with a better academic performance. The values obtained for both groups
can be regarded as statistically different from each other (p=0.001). The group of
individuals with better academic performance had a positive overconfidence index that
is larger than that of the group with a poorer performance.
We categorized participants according to the impulsiveness score to verify whether
overconfidence was stronger in the group of more impulsive individuals or in that of
less impulsive participants. No significant correlation coefficient was found between
the impulsiveness score and the self-confidence index (p=0.668).
For less impulsive individuals, even at a 10% significance level, we cannot reject that
the mean value of 3.73% for the self-confidence index is smaller than or equal to zero
(K-S: p=0.200; S-W: p=0.522).
For more impulsive individuals, even at a 10% significance level, we cannot reject that
the mean value of 2.93% for the self-confidence index is equal to zero (K-S: p=0.200;
S-W: p=0.335). Therefore, both more and less impulsive individuals did not show the
overconfidence effect.
11
Afterwards, we categorized participants according to rational characteristics to check
whether overconfidence was stronger in the group of individuals with a better academic
performance associated with a low impulsiveness score or in the group made up of
those with a poorer performance associated with a higher level of impulsiveness.
We found a positive correlation coefficient of 24.2% between rational characteristics
and the self-confidence index that was statistically different from zero (p=0.045). The
more rational the participant, the higher the self-confidence index.
For those with more rational characteristics, at a 5% significance level, we could reject
that the mean value of 12.32% for the self-confidence index was equal to zero (K-S:
p=0.200; S-W: p=0.093).
As to the group of less rational individuals, even at a 10% significance level, we cannot
reject that the mean value of 2.19% for the self-confidence index is equal to zero (K-S:
p=0.200; S-W: p=0.126).
We found evidence of the overconfidence effect only in the group of more rational
individuals. The values obtained for both groups can be considered statistically
different from each other (p=0.020).
The group of more rational individuals had a positive overconfidence index that was
greater than that of the group of less rational individuals. The participants were
categorized according to the “openness to experience” trait to verify whether
overconfidence was stronger in the group of individuals that were more open to
experience or in that in which they were closed to experience.
We obtained a correlation coefficient of 24% between the “openness to experience”
trait and the self-confidence index, which was statistically different from zero
(p=0.047).
We calculated the mean self-confidence index for both groups and compared their
results. By adopting a 5% significance level for the group of individuals who were more
open to experience, we could reject that the mean value of 5.77% for the selfconfidence index was equal to zero (K-S: p=0.188; S-W: p=0.018).
For the group of individuals who were more closed to experience, even by adopting a
10% significance level, we could not reject that the mean value of 1.23% for the selfconfidence index is equal to zero (K-S: p=0.200; S-W: p=0.873).
We found evidence of the overconfidence effect only in the group of individuals who
were more open to experience. The values obtained for both groups cannot be regarded
as statistically different (p=0.176).
The “openness to experience” trait was not a good criterion to determine the presence
of the overconfidence effect. We categorized the participants according to the
“conscientiousness” trait to check whether overconfidence was stronger in the group of
more conscientious individuals than in the group of less conscientious ones.
The “conscientiousness” trait was not strong enough to determine the presence of the
overconfidence effect. To analyze the “emotional stability” trait, we categorized the
participants in order to verify whether overconfidence was stronger in the group of
more emotionally stable individuals or in the group of less emotionally stable ones. We
observed no evidence of the overconfidence effect in the group of more emotionally
stable individuals.
12
We found evidence of the overconfidence effect only in the group of less emotionally
stable individuals. The value obtained for both groups could be regarded as statistically
different from each other (p=0.024). The group of less emotionally stable individuals
had a positive overconfidence index that was greater than that of more emotionally
stable individuals.
We categorized the participants according to the “extraversion” trait to check whether
overconfidence was stronger in the group of more extroverted individuals or in the
group of more introverted ones.
We obtained a correlation coefficient of 25.0% between the “extraversion” trait and the
self-confidence index, which was statistically different from zero (p=0.038). We
calculated the mean self-confidence index for both groups and compared their results.
By adopting a 10% significance level for the group of extroverted individuals, we could
reject that the mean value of 4.55% for the self-confidence index is equal to zero (K-S:
p=0.200; S-W: p=0.128). For the group of introverted individuals, even after adopting a
10% significance level, we could not reject that the mean value of 2.35% of the selfconfidence index is equal to zero (K-S: p=0.200; S-W: p=0.983).
We found evidence of the overconfidence effect only in the group of extroverted
individuals. The values obtained for both groups cannot be considered to be statistically
different from each other (p=0.515). The “extraversion” trait does not seem to be an
important criterion for determining the presence of the overconfidence effect.
Finally, we categorized the participants according to the level of socialization in order
to verify whether overconfidence was stronger in the group of more sociable
individuals than in the group of less sociable ones. No correlation coefficient was found
between the level of socialization and the self-confidence index (p=0.930).
There was evidence of the overconfidence effect only in the group of less sociable
individuals. The values obtained for both groups cannot be considered to be statistically
different (p=0.414).
The level of socialization trait was not strong enough to determine the presence of the
overconfidence effect. Table 2 summarizes the significant results observed for the
overconfidence effect.
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Table 2 – Summary of overconfidence effect results
Description
Whole group
Academic Performance
(AP)
Age
(A)
Openness to Experience
(OE)
Extraversion
(E)
Academic Performance and
Impulsiveness
(APxI)
Gender
(G)
Self-Confidence Index
(SCI)
SCI = 4.25%
(p=0.005)
Corr(SCI,SO)=21.4%
(p=0.048)
Group (+)
Group (-)
SCI = 9.55%
SCI = 0%
(p=0.050)
(p=0.100)
Different means (p=0.001)
Corr(SCI,AP)=28.6%
(p=0,028)
SCI = 0%
SCI = 5.33%
(p=0.100)
(p=0.050)
Different means (p=0.031)
Corr(SCI,A)=-20.6%
(p=0.089)
SCI = 5.77%
SCI = 0%
(p=0.050)
(p=0.100)
Different means (p=0.176)
Corr(SCI,OE)=24.0%
(p=0.047)
SCI = 4.55%
SCI = 0%
(p=0.100)
(p=0.100)
Equal means (p=0.515)
Corr(SCI,E)=25.0%
(p=0.038)
AP (+) e I(-)
AP (-) e I(+)
SCI = 12.32%
SCI = 0%
(p=0.050)
(p=0.100)
Different means (p=0.020)
Corr(SCI,APxI)=24.2%
(p=0.045)
Male
Female
SCI = 7.95%
SCI = 0%
(p=0.050)
(p=0.100)
Different means (p=0.024)
Where:
SO = Stated Overconfidence
SCI = Mean probability of getting a correct answer – Percentage of questions answered
correctly (Self-Confidence Index)
4. FINAL REMARKS
We found evidence that people with important rational characteristics also have
illusions of overconfidence and competence, at least in the same intensity as those with
less rational characteristics.
Male and younger individuals are more vulnerable to the influence of these two types of
cognitive biases. The information gathered by our research group helped us outline a
profile of individuals who are influenced by these two types of biases. Even though the
study of the characteristics at issue in our paper is far from exhaustive, these
characteristics are strong enough to determine the presence of biases and to identify
personal aspects that may help distinguish individuals in a larger group.
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For the competence effect, we found that young men are affected more often, showing
low impulsiveness and personal characteristics such as adventurous, focused and
resilient.
More sensible individuals preferred to bet on their own answers. However, they
ascribed a lower probability to getting the correct answers than did challenging
individuals. In terms of overconfidence, younger men are more prone to make such a
mistake, showing characteristics that are inherent to rationality, which was addressed
herein because of the low impulsiveness score, and also showing more emotional
reactions.
The reactive characteristic of the emotional stability trait indicates people who easily
experience negative emotions, such as anger, depression, stress, and frustrations. The
highest self-confidence index was observed in individuals who show important rational
characteristics. These individuals had approximately three times as high an index than
the mean value found for all participants.
5. REFERENCES
Allport, G.W. (1961) Pattern and growth in personality. New York: Holt, Rinehart, &
Winston.
Barber, B.M.; Odean, T. (2001). Boys Will be Boys: Gender, Overconfidence, and
Common Stock Investment. Quarterly Journal of Economics. 116:261-292
Billieux, J.; Rochat, L.; Rebetez, M.M.L.; Van Der Linden, M. (2008) Are all facets of
impulsivity related to self-reported compulsive buying behavior? Personality and
Individual Differences. 44(6): 1432-1442.
Briggs, S. R. (1992) Assessing the Five-Factor Model of personality description.
Journal of Personality. 60: 253-293.
Bruno, M.; Matsushita, R.; Da Silva, S. (2010) Risk Seeking Behavior of Preschool
Children in a Gambling Task. Journal of Economic Psychology. 32:In press.
Chaiken, S.; Trope, Y. (1999) Dual Process Theories in Social Psychology. Nova York:
Guilford.
Doob, L. (1993) Hesitation: impulsivity and reflection. In McCown, W.; Johnson, J.L.;
Shure M.B. The Impulsive Client – Theory, Research and Treatment. Washington:
American Psychological Association.
Fischhof, B. et al. (1977) Knowing with uncertainty: The appropriateness of extreme
confidence. Journal of Experimental Psychology. 3: 522-564.
Fox, C.; Tversky, A. (1995) Ambiguity Aversion and Comparative Ignorance.
Quarterly Journal of Economics. 110: 585-603.
Foxall, G.R. (2003) The behavior analysis of consumer choice: An introduction to the
special issue. Journal of Economic Psychology. 24(5) 581-588.
Gerbing, D.W., Ahadi, S.A. Patton, J.H. (1987) Toward a conceptualization of
impulsivity: a comparison across the behavioral and self-report domains. Multivariate
Behavioral Research. 22:357-379.
Gleser, M.; Weber, M. (2007) Overconfidence and trading volume. The GENEVA Risk
and Insurance Review. 32:1-36.
15
Goldberg, L. R. (1992) The development of markers for the Big-Five factor structure.
Psychological Assessment, 4: 26-42.
Graham, J.R.; Harvey, C.R.; Huang, H. (2009) Investor competence, trading frequency.
Management Science. 55(7): 1094-1106.
Guilford, J.P. (1975) Factors and factors of personality. Psychological Bulletin, 82:
802-814.
Hunter, K.; Kemp S.(2004) The personality of e-commerce investors. Journal of
Economic Psychology. 25(4): 529-537.
Heath, C.; Tversky, A. (1991) Preference and Belief: Ambiguity and competence in
choice under uncertainty. Journal of Risk and Uncertainty. 4: 5-28.
Lai, C.W. (2010) How financial attitudes and practices influence the impulsive buying
behavior of college and university students. Social Behavior and Personality: an
international journal. 38(3):373-380.
Lawrence, J.B.; Stanford, M.S. (1999) Impulsivity and time of day: effects on
performance and cognitive tempo. Personality and Individual Differences. 26:199207.
Lea, S. E.G.; Tarpy, R. M.; Webley, P. (1987) The individual in the economy.
Cambridge: Cambridge University Press.
Malhotra, N.K.; Birks, D.F.(2007) Marketing Research: An Applied Approach. 3rd Ed.
Prentice-Hall, Harlow.
Maybery, D.; Crase, L.; Gullifer, C.(2005). Categorising farming values as economic,
conservation and lifestyle. Journal of Economic Psychology. 26(1): 59-72
McAdams, D.P. (1992) The Five-factor Model in personality: A critical appraisal.
Journal of Personality. 60: 329-361.
McCrae, R.R.; Costa, P.T. (1989) More reasons to adopt the Five-Factor Model.
American Psychologist. 44: 451-452.
Myers e Well (2003)
Palifka, B.J. (2009) Personality and income in Mexico: Supervisor assessments vs. selfassessments. Journal of Economic Psychology. 30(1):92-106.
Patton, J.H.; Stanford, M.S, Barratt, E.S. (1995) Factor structure of Barratt
impulsiveness scale. J Clin Psychol. 51:768–74.
Rosenbloom, J.L.; Ash, R.A., Dupont, B.; Coder, L. (2008) Why are there so few
women in information technology? Assessing the role of personality in career
choices. Journal of Economic Psychology. 29(4): 543-554.
Semykina, A.; Linz, S.J. (2007) Gender differences in personality and earnings:
Evidence from Russia. Journal of Economic Psychology. 28(3): 387-410.
Sunstein, C.R. (1998), "Selective Fatalism". Journal of Legal Studies. 27: 799-823.
Tversky, A.; Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.
Science. 185: 1124-1131.
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