Personality type investment intention

Financial Services Review 17 (2008) 219–236

Investment management and personality type
Cliff Mayfield, Grady Perdue,* Kevin Wooten
University of Houston—Clear Lake, Houston, TX 77058, USA

Abstract
We examine several psychological antecedents to both short-term and long-term investment
intentions, with specific focus on the Big Five personality taxonomy. The effects of specific personality
traits are evaluated using structural equation modeling (SEM). Our results indicate that individuals who
are more extraverted intend to engage in short-term investing, while those who are higher in neuroticism
and/or risk aversion avoid this activity. Risk adverse individuals also do not engage in long-term
investing. Individuals who are more open to experience are inclined to engage in long-term investing;
however, openness did not predict short-term investing. The implications of these findings are
discussed. © 2008 Academy of Financial Services. All rights reserved.
Keywords: Behavioral finance; intentions; investing; Big Five; personality

1. Introduction
Researchers across the past several decades have analyzed the behavior of investors and
have attempted to enhance our understanding of why people manage investments in different
ways. Today an extensive body of literature exists that seeks to explain how personal
characteristics influence the behavior of investors. If a common theme is present in this
literature, it is that personal characteristics influence investors’ perception of risk and their
willingness to assume risks. In turn the perception of risk determines investing behavior.
However, a prevailing question left unanswered is the extent to which individuals’ personal
characteristics influence their intentions about investing. If individuals’ investment intentions
are discernable, then educators and financial counselors would want to know if those
intentions are amendable.

* Corresponding author. Tel.: 1-281-283-3213; fax: 1-281-226-7334. E-mail
address: Perdue@uhcl.edu (G. Perdue)

220

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

1057-0810/08/$ – see front matter © 2008 Academy of Financial Services. All rights reserved.

The nature of risk and how individuals approach risk has been a developing discussion.
The expected utility approach of von Neumann and Morgenstern (1947) has provided the
foundation for the primary view of risk in economics and finance for many years. The main
concept in their model is that the maximization of expected utility is the sole factor in making
decisions. Extending their work Allais (1952) questions the exclusive use of the maximization
of expected utility as a single criterion when making a risky choice, raising the issue of a
person who could be faced with the trading off of expected return and the probability of
reaching a given goal. In similar fashion Markowitz (1952) proposes a two-criterion approach
when an investor is faced with the desire for higher returns but not wanting the uncertainty of
returns, which he perceives as risk. Many other researchers have extended this discussion.
The literature has developed into two schools of thought as researchers have sought to
explain the choices investors make about risk within their investments. One group of scholars
has used demographic features that relate the significance of gender, ethnicity, wealth,
income, and a variety of other factors to the explanation of investment management decisions.
The other group has its foundations in psychology, using investors’ psychological
characteristics to explain choices that are made concerning investment decisions.
Among what we describe as demographic studies, the implications of gender are most often
perceived by researchers as being important in explaining investor behavior. Bajtelsmit and
Bernasek (1996); Byrnes, Miller and Schafer (1999); Barber and Odean (2001); Felton,
Gibson and Sanbonmatsu (2003); Hallahan, Faff and McKenzie (2004); and Worthington
(2006) all reach the conclusion that gender plays an important role in general risk aversion.
Bajtelsmit, Bernasek and Jianakoplos (1999); Hariharan, Chapman and Domian (2000); and
Olsen and Cox (2001) arrive at the conclusion that women are not as likely to invest in higher
risk assets as men possessing similar significant personal characteristics.
Although an interesting array of demographic characteristics have been used to explain
what drives the investment behavior of individuals, the discussion continues in the literature
concerning the psychological antecedents that would accompany this human behavior. A
variety of studies have attempted to explore the psychological explanations for investor
behavior. For example Carducci and Wong (1998) find that persons with a Type A personality
are more willing to take higher levels of risk in all financial matters, though this may be
correlated to Type A persons tending to have higher levels of income (Thoresen and Low,
1990) than Type B individuals. There is also evidence (Wong and Carducci, 1991) of a desire
for “sensation seeking” by some persons in terms of their financial management.
But can investors assess risk with any accuracy when making investments? Hallahan, Faff
and McKenzie (2004) believe that individuals can self-assess their risk tolerance. Schooley
and Worden (1996) and Bailey and Kinerson (2005) conclude that there is a strong
relationship between self-assessed risk and investment behavior. Wa¨rneryd (1996) argues
that there is a relationship between the more specific investment risk attitude and the riskiness
inherent in investor portfolios. This research is confirmed by Keller and Siegrist (2006), who
find that one’s financial risk attitude has a positive influence on willingness to accept
investment risk and invest in stocks in one’s portfolio. However, research by Morse (1998)
concludes that individuals have difficulty perceiving the actual risk associated with the choice
of investments they face, and so have difficulty matching investments to their desired level of
risk exposure.

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

221

In a study that foreshadows the current study, Filbeck, Hatfield and Horvath (2005) use the
Myers-Briggs Type Indicator to assess risk tolerance differences between people with
different personality characteristics. From the discrete personality groupings in the
MyersBriggs, the researchers are able to establish behavioral linkages to risk tolerance. Their
findings confirm that personality type does explain some aspects of investment behavior.
Although some studies have sought to use specific measures of personality in explaining
investor behavior, this research adds to the literature by utilizing a personality framework
known as the Big Five (see Costa and McCrae, 1992a, 1995, 1997; Digman, 1997; Goldberg,
1992; McAdams, 1992). The personality taxonomy of the Big Five is generally considered
the most comprehensive and accepted, particularly for applied research (Barrick and Mount,
1991; Hogan and Hogan, 1991). The five dimensions (extraversion, agreeableness,
conscientiousness, emotional stability, and openness to experience) were derived from years
of statistical analysis and considered stable across situations and cross-culturally applicable.
We use portions of this taxonomy to test hypothesized models of how a person’s
psychological disposition may be related to one’s intentions about current and future investing
behavior.
As a result of the literature review, the present study undertakes two tasks. First we
undertake the examination of behavioral intentions as related to personal investment and
portfolio management. If behavioral intentions are good predictors of actual behavior and
these intentions can be changed by the formation of attitudes, subjective norms, and
perceptions of self-control, then they should be amenable to interventions from educators and
financial counselors. Thus, identifying the nature of behavioral intentions with respect to
personal finance is important. Second, given the paucity of literature examining risk
perceptions and personality factors as predictors of intentions, we set out to examine
prominent predictors of intentions, specifically with personal finance in mind.
This study is reported in five sections. After this introduction, the second section discusses
the theory of behavioral intentions. The third section presents the literature on the Big Five
and the methodology that is used to relate the Big Five and personality measures to investor
behavioral intentions. The fourth section reports the results of the study. A final segment
presents a concluding summary and offers suggestions for further research.

2.

Behavioral intentions: A model for use and exploration in investmentresearch

Behavioral intentions have been the topic of a large quantity of social and behavioral
science research over the past 35 years. Ajzen and Fishbein (1980) believe that behavioral
intentions are cognitive in nature, and act as a representation of a person’s readiness to engage
in a specific behavior. Behavioral intentions are hypothesized to be influenced by attitudes
toward a given behavior, subjective norms, and a perceived sense of behavioral control. This
theory contends that behavioral intentions are highly predictive of behavior. The current
revision of this theory is shown in Fig. 1.
According to the theory of planned behavior, the more favorable the attitude, the subjec-

222

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

Fig. 1. Theoretical model of the theory of planned behavior. Adapted from: Ajzen, I. (1991). The theory of planned
behavior. Organizational Behavior and Human Decision Processes, 50, 175–211.

tive norm, and the greater the perceived control, the greater the behavioral intentions will be.
Ajzen (1991, pp. 181–182) notes that:
As a general rule, the stronger the intention to engage in a behavior, the more likely should be
its performance. It should be clear, however, that a behavioral intention can find expression
in behavior only if the behavior in question is under volitional control.

Thus, the theory purports that behavioral intentions are highly related to targeted behaviors.
Of interest to this study are those efforts linking personality factors to behavioral intentions.
Several studies have investigated the relationship between personality and behavioral
intentions (de Bruijn, Kremers, de Vries, van Mechelen and Brug, 2007; Lauriola, Gioggi and
Saggino, 2001; Prislin and Kourlija, 1992). Generally, the results of these studies have been
inconclusive.
Related to personal finance, two studies have a direct bearing upon this study. Hessing,
Elffers and Weigle (1988) explore attitudes towards paying taxes with settling tax disputes
(e.g., tax evasion). They find attitudes toward taxes (i.e., intentions) and subjective norms are
highly correlated with self-reported behavior, but not with official tax documents. Bolton,
Cohen and Bloom (2006) investigate the effects of risk avoidance based upon behavioral
intentions. They find that a remedy message (media message for debt consolidation) actually
undermines bankruptcy risk perceptions and increases risky financial behavioral intentions as
credit card misuse increases. Thus, when risk is lowered through a remedy (e.g., debt
consolidation), there is an increase in alternative behavioral intentions for risk related financial
behavior (e.g., credit card abuse).
The available literature discussed above suggests that risk avoidance should be related to
behavioral intentions associated with personal finance. It is believed that the higher the level
of risk aversion, the lower the behavioral intentions should be to engage in planned portfolio
management. Thus, the following hypothesis is proposed:

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

223

Hypothesis 1: The greater the level of individuals’ risk aversion, the less likely will be their
intentions to engage in either short-term or long-term investing.
Support for this hypothesis is of theoretical and practical importance in that it would extend
the planned behavioral model to a social realm thus far unexplored, as well as provide
educators with grounds to assist others in more realistically assessing risk in the financial
marketplace.
The available literature also suggests that those with traits involving high degrees of
personal organization and high degrees of imagination and intellectual expression should be
predictive of intentions to attend and manage personal financial matters and investments. The
Big Five personality dimension, openness to experience, is characterized by both imagination
and intellectual expression (Costa and McCrae, 1992a). Thus, the following hypothesis is
proposed:
Hypothesis 2: The more that individuals are open to experience, the greater their intentions
to engage in short-term and long-term investing.
A final Big Five personality characteristic that may impact investment intentions is
conscientiousness. Conscientiousness is associated with strivings for achievement and
competence (Costa and McCrae, 1992a). This personality characteristic may serve as an
underlying determinant for why some individuals are more likely to use money as a tool to
influence and impress others (Lim and Teo, 1997). The final hypothesis is proposed:
Hypothesis 3: The more conscientious individuals are, the greater their intentions to engage
in short-term and long-term investing.
The exploration of the Big Five may further our understanding of how given personality
characteristics may be utilized to more effectively plan and manage personal finances, thereby
enhancing overall well-being.

3. Methodology
The present study uses structural equation modeling (SEM) that allows for the simultaneous
estimation and testing of the relationships of interest. In SEM causal processes are represented
by a series of structural equations that can be modeled graphically to aid in conceptualizing a
theoretical framework (Byrne, 2001).
For the purpose of this study, a survey was conducted of business school undergraduates
in an upper division commuter university located in an urban community. A total of 197
students participated, of which a total of 194 useable questionnaires were collected. The
students were asked to identify their age, gender, the previous number of years of investing
experience, and if they have ever taken a non-credit investments course. The survey was
administered in courses that are prerequisites to the university’s investments course.
The measures involving personality and risk avoidance that are used in this study are
frequently cited and well established in the management and industrial-organizational
psychology literature. We operationalize personality using two of the five measures from the
Big

224
Table 1
Trait

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236
Descriptions of the Big Five personality traits
Description

Neuroticism (N)

High scores indicate tenseness, moodiness, anxiety, and insecurity

Extraversion (E)

High scores indicate assertiveness, sociability, talkativeness, optimism, and
being upbeat and energetic
Openness (O)
High scores indicate an active imagination, aesthetic sensitivity, a
preference for variety, intellectual curiosity, and broad cultural interest
Agreeableness (A)
High scores indicate altruism, personal warmth, sympathy towards others,
helpfulness, and cooperation
Conscientiousness (C)
High scores indicate purposefulness, being strong willed, determination,
organization, reliability, and punctuality
Note. Adapted from Professional manual: Revised NEO personality inventory (NEO-PI-R) and NEO five-factor
inventory (NEO-FFI), by P.T. Costa and R.R. McCrae, 1992, Lutz, FL: Psychological Assessment Resources, Inc.,
and “The five factor model of personality and job performance in the European community,” by J.F. Salgado,
1997, Journal of Applied Psychology, 82, 30–43.

Five theory (Costa and McCrae, 1992a, 1995, 1997; Digman, 1997; Goldberg, 1992;
McAdams, 1992). Specifically, we use the NEO-FFI (Costa and McCrae, 2003). The NEOFFI is a 60-item inventory, which is a shortened version of the Big Five, using 12 items to
measure each of the five scales. Each item used a five-point scaled anchor, ranging from
strongly disagree, disagree, neutral, agree, to strongly agree. In keeping with the test manual,
numerous items are reversed scored to inhibit response bias. Shown in Table 1 are the
descriptions of the Big Five traits.
For measuring risk aversion we utilize measures that are developed by Gomez-Mejia and
Balkin (1989). This scale, which is based on the theoretical work of Slovic (1972) and the
operationalization of Gupta and Govndarajan (1984), uses four items with five-point
Likerttype response stems (strongly agree, agree, neutral, disagree, strongly disagree). These
items are reworded to make them situation specific to investment behavior and personal
finance. A high score would indicate a propensity to avoid the risk associated with a personal
investment. The measure with the reworded items is shown in the Appendix.
To measure the possible behavioral intentions associated with investment and personal
finance, exploratory items are constructed. The items for both short-term and long-term
intentions are shown in the Appendix. As illustrated they reflect behavioral intentions ranging
from tangible discrete actions to less tangible and more global intended behaviors.

4. Results
The descriptive statistics for the study variables are displayed in Table 2. The sample means
reported in the table for the personality and investment intention variables are based on a fivepoint scale (1 strongly disagree; 5 strongly agree). For non-credit courses taken in personal
finance or investments, respondents answered ‘yes’ if they took at least one non-credit course
related to investments and ‘no’ if they had never taken a course. The
Table 2

Summary descriptive and distributional statistics for items on the measurement models

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236
Questionnaire

Mean

SD

Kurtosis

Skewness

Risk aversion

2.80

.74

.39

.38

Openness

2.21

.46

.06

.11

Conscientiousness

2.51

.48

.32

.44

Neuroticism

1.64

.66

.82

Extraversion

2.51

.49

.15

.40

Agreeableness

2.51

.48

.19

.30

Short-term investment intentions

3.15

.73

.56

Long-term investment intentions

3.79

.69

.46

Years of financial experience
Less than 1 year
1 to 3 years

Percent

225

.60

.02
.52

64.5%
17.8%

3 to 5 years

5.9%

5 or more years

11.8%

Non-credit finance courses taken
Yes
No

9.6%
90.4%

Gender
Male
Female

41.3%
58.7%

Age
20
21 to 25

5.2%
46.1%

26 to 30

25.4%

31 to 40

15.5%

40

7.8%

majority of respondents had never taken a non-credit finance course. Despite being
undergraduates, survey participants represented a varied age group. Of this sample 5.2% are
20 years or younger, with 46.1% between ages 21 to 25, 25.4% between ages 26 to 30, 15.5%
between ages 31 to 40, and 7.8% over the age of 40. This sample consisted of 81 males
(41.3%) and 115 females (58.7%). All students were either juniors or seniors. Approximately
64.6% of the sample has had less than a year’s experience in personal investments, with
16.9% having 1 to 3 years, 6.6% with 3 to 5 years, and 18.3% having 5 or more years.

226

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

Shown in Table 3 is the correlation matrix for both the demographic variables and the
primary variables of interest. Consistent with the literature we find that a significant
relationship exists between gender and investment intentions, with males reporting more
intentionality to engage in both short-term (r -.244; p .01) and long-term (r -.147; p .05)
personal investing. Interestingly, we find that individuals with greater prior experience in
managing their personal finances respond more favorably to having intentions to attend to
their short-term (r 0.152; p .05), but not their long-term investments (r 0.119). This suggests
that although an individual may have investment experience, he may not have a long-term
financial perspective. The number of non-credit finance courses taken was related to shortterm (r 0.169; p .01), but not long-term intentions (r 0.047). As shown in Table 3, those
individuals who are low in risk aversion and those who are more open to

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

227

228

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

experiences have a greater likelihood of attending to both their short-term investments (risk
aversion r -.456; openness r 0.189) and long-term investments (risk aversion r -.352;
openness r 0.272), and all correlations were significant at the p .01 level.
Although formal hypotheses were not established, there are some significant relationships
between both the personality traits of neuroticism and extraversion and short-term investment
intentions. Anxious individuals reported less intentionality to engage in short-term investing
(r -.186; p .05), whereas extraverted individuals expressed greater short-term intentions (r
0.221; p .01). Because anxious individuals tend to experience more insecurity, it is plausible
that they would be less inclined to engage in short-term investing. Conversely, extraverted
individuals, who are optimistic and outgoing, can be seen as more likely to consult a financial
advisor or take the initiative to begin investing on their own. However, neither neuroticism
nor extraversion had any impact, on long-term investment intentions. The personality
dimension agreeableness, as one might expect, had no effect on investment intentions.
Because these are post hoc findings, further research should examine the generalizability of
the relationships between these personality traits and investment intentions.
To test our hypothesized relationships between the three personality traits (i.e., risk
aversion, conscientiousness, and openness to experience) and investment intentions, we
evaluated two full structural equation models using AMOS 7.0. One advantage of using
structural equation modeling is that the analysis allows for the simultaneous evaluation of
both the accuracy of the measures (i.e., estimation of the factor loadings and error variance
that is associated with each question used for assessing our variables), and testing of the
strength of the relationships between our latent variables of interest. In the model depicted in
Fig. 2, the measurement portion of the model is represented by the latent variables in circles
(e.g., risk aversion, conscientiousness), the items associated with the actual questions on the
survey instrument used to assess the variables (e.g., Item 1, Item 2, Item 3, Item 4), the item’s
respective factor loading (i.e., the number corresponding to the arrow from each latent
variable to its items), and the error variance represented in the ovals. For example, there are
four questions used to assess the variable risk aversion and they are represented by the
rectangles labeled Item 1 through Item 4. In the case of risk aversion, the factor loading (i.e.,
path coefficient) for Item 1 is 0.69 and is the extent to which Item 1 loads on to risk aversion.
Although there is no established criterion, generally a factor loading of 0.40 or higher is
considered meaningful (Ford et al., 1986). The error variance (i.e., measurement error)
associated with each particular item is indicated by the number in the ovals. Item 1 for risk
aversion has an error variance of 0.53. In other words, 47% of the variance in respondent’s
scores on the question, “I am not willing to take risk when choosing a stock or investment,”
is explained by our latent variable, “risk,” whereas 53% is explained by other factors. The
accuracy of the measurement for each latent variable can be assessed by evaluating the factor
loadings and error variances for the remaining items in the model.
The second portion of the structural equation model is the structural component. The
structural component is represented by the path(s) between our latent variables (e.g., the arrow
between risk aversion and short-term intentions), which illustrate our hypothesized
relationships. For example, in Fig. 2 the standardized path coefficient between risk aversion

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

229

Fig. 2. Short-term investment intentions. (Unstandardized regression coefficients are shown in parentheses.)

and short-term investment intentions is -.60, which means that if risk aversion increases by
one standard deviation, short-term intentions decreases by 0.60 standard deviations. The
unstandardized path coefficients are also reported in parentheses; however, these statistics do
not correct for differences of scale and therefore unstandardized path coefficients cannot
easily be compared.
The first of our structural equation models (Fig. 2) examines how well risk aversion,
conscientiousness, and openness to experience predict short-term investment intentions and
the second model (Fig. 3) tests how well these same variables do at predicting long-term
investment intentions. In accordance with common psychometric methods, the measures for
each variable are factor analyzed a priori to determine which items adequately capture their
intended construct. As a result, the number of items for each of the Big Five personality
variables is reduced for the evaluation of each model. Both short-term and long-term
investment models are statistically overidentified, a requirement of structural equation
modeling, and select error terms between the items for the predictors are allowed to covary.

230

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

Fig. 3. Long-term investment intentions. (Unstandardized regression coefficients are shown in parentheses.)

5. Personality and short-term intentions
For the model predicting short-term investment intentions (see Fig. 2), the majority of the
fit indices we examine indicate good model fit. The exception is the 2 statistic (2 (141) 183.63,
p .009); however, 2 can be spuriously high when data becomes increasingly non-normal
(West, Finch and Curran, 1995). Using Marsh and Hocevar’s (1985) rule of thumb, dividing
2
by its degrees of freedom yielded a value of 1.30, which indicates reasonable model fit. The
Goodness of Fit Index (GFI) and Comparative Fit Index (CFI) indices were 0.91 and 0.95,
respectively. The root mean square error of approximation is 0.04, with a 90% confidence
interval of 0.02 to 0.06, and the p value test of close fit is equal to 0.85. All the fit indices
indicate that our observed data fits the hypothesized model well. Closer inspection of the path
coefficients in the model, shown in Fig. 2, indicate that risk aversion is the only significant
predictor of short-term intentions (r -.60; p .001). The multiple R2 of the model is 0.44 and
the residual variance (.56) is reported in Fig. 2 as per convention. Otherwise stated, individuals
that scored higher on their aversion to risk are less likely to engage in short-term investing,
and this relationship explains 44% of the variance in our criterion. In the behavioral sciences,
explaining 9% or more of the total variance is generally regarded as a medium effect and

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

231

explaining 25% or more of the variance is a large effect (Cohen, 1977). Whether individuals
were conscientious or open to experience did not play a role in determining their short-term
investment intentions.

6. Personality and long-term intentions
The model for predicting long-term investment intentions is presented in Fig. 3. Similar to
short-term investment intentions, all fit indices point toward moderately good model fit with
the exception of the 2 statistic, (2 (142) 187.98, p .006). 2 divided by its degrees of freedom
yielded 1.32, indicating reasonable model fit. The remaining indices are as follows: GFI 0.91,
CFI 0.94, RMSEA 0.04 (90% CI 0.023 – 0.057, p value 0.81). As in our short-term
investment model, risk aversion (r -.41, p .001) contributes significantly to the variance
explained in long-term intentions. However, unlike in our short-term investment model,
openness to experience (r 0.25, p .05) also significantly contributes to long-term intentions.
The multiple R2 for our long-term intentions model is 0.30.

7. Conclusion
Although this study was largely exploratory, a few conclusions can be made. First,
statistically and methodologically, the use of the Big Five taxonomy is useful. Although there
have been hundreds of studies utilizing the Big Five, the current study may be among the first
to utilize this commonly accepted approach to personality and trait measurement in
conjunction with personal finance. Even though the Big Five factors have come under
criticism by some scholars (Block, 1995; Previn, 1994), the overall stability and validity of
the method has more recently been greatly supported (Barbaranelli and Caprara, 2000;
DeYoung, 2006; DeYoung, Quilty and Peterson, 2007). This study thus extends the utility of
the Big Five model as a viable approach for examining economic behavior.
The finding that males report greater intentions for both short-term and long-term
investment activity than females is consistent with the findings of other researchers
(Bajtelsmit, Bernasek and Jianakopolus, 1999; Hariharan, Chapman and Domain, 2000; Olsen
and Cox, 2001). The association in this study between gender and risk aversion as related to
investment was not significant, but generally supportive of the view that females are more
risk averse in investment matters. The extent to which investment intentions are related in any
way to knowledge of investments (Chen and Volpe, 1998; Volpe, Chen and Pavlicko, 1996)
or investor overconfidence (Barber and Odean, 2000; Bhandari and Deaves, 2006) is not
addressed in this study. Thus, future studies should also examine the differential effects of
these considerations.
The exploratory effort in the present research to establish a measure of investment
intentions is important at several levels. First, it is an extension of the general model of
planned behavior (Ajzen, 1991) to yet another area of social behavior, in this case financial
planning and investment. Given the prominence of this theory in broad areas of social
behavior, this is a rich paradigm for interdisciplinary contributions. Of particular importance
is the finding that investment intentions are comprised of two separate components (shortterm

232

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

and long-term). The extent to which behavioral intentions are highly predictive of eventual
behavior needs to be tested in the context of manifest financial and investment behavior.
Future research needs to determine if those who truly differ in openness to experience and
risk aversion do in fact differ in investment intentions and their subsequent investment
behaviors and decisions. Thus, a complete test of the planned behavior model is still required.
The finding in the present study that suggests personality influences or contributes to the
intentions of investors is not without practical applications. To the extent that education and
financial counselors are able to profile those who differ in personality and subsequent
intentions, much promise may be in store relative to educative interventions. Reviews of the
behavioral intention literature (Armitage and Conner, 2001) and of attitudinal change (Rydell
and McConnell, 2006) suggest that attitudes and subsequent behavior are indeed malleable.
Thus, by use of direct influence, education, cognitive reframing, and interventions aimed at
creating a different social reality for those with a low degree of openness and a high degree
of risk avoidance, a better financial future seems achievable.
The results of this study also extend the work of Filbeck et al. (2005) who found that
individuals who differ in personality traits, as measured by the Myers-Briggs Type Indicator,
vary in their risk tolerance as framed in terms of expected utility theory (i.e., variance and
skew acceptable to a given rate of return). The significant and negative correlation reported
in this study between the personality trait of openness and investment specific risk aversion
is contrary to the finding by Filbeck et al. (2005). In the Filbeck et al. (2001) study, those who
scored higher in the traits of thinking (objective decision making), judging (organization and
order), and sensing (concrete and practical), reported increased risk tolerance. Thus, our study
suggests that those who are creative and non-traditional in their experiences may consider
greater investment risk, whereas the findings for Filbeck et al. (2005) suggest that those who
are objective, orderly, and concrete may have the greater risk tolerance toward their
investments. The results of this study also differ from that of Filbeck et al. (2005) to the extent
that they report the trait of extraversion had no measurable impact upon risk tolerance. In our
study, extraversion predicted short-term investment intentions; as well, extraversion was
negatively, but not significantly related to investment specific risk avoidance.
Literature involving general personality research, the Big Five, and broad areas of
functioning and mental health is supportive of our general findings (McAdams, 2006; Rand
2006; Weiner and Greene, 2008). Specific to this study, the meta-analysis results of
ConnorSmith and Falchsbart (2007) involving Big Five traits and coping tend to be
supportive. Connor-Smith and Falshsbart (2007) found extraversion and conscientiousness
was predictive of concrete problem solving and cognitive structuring as coping strategies. In
our study, extraversion and conscientiousness were both positively related to short-term
investment intentions, and significantly so for extraversion. That the meta-analysis results
from Zhao and Seibert (2006) involving entrepreneurial behavior was predicted by
conscientiousness and openness to experience is also of interest. In the current study, both
short-term and long-term intentions are predicted by openness, and a positive trend was found
for conscientiousness. One possible explanation for the difference between our study and that
of Filbeck et al. (2005) involves the items used in the questionnaires to assess risk. The items
used by Filbeck et al. (2005) required the respondent to calculate the odds and allocate
percentages from their portfolio. The questionnaire used in the present study involved a
general measure of risk in investment behavior such as having a preference for lower risk with

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

233

less return. Thus, not only how risk was theoretically operationalized, but how risk was
measured (allocations vs. intentions) may account for the difference between the two studies.
It is clear that additional research needs to be done in this area to clarify these relationships
and their potential implications (e.g., investor education and counseling).
The financial services industry has had a long history of educating investors for the purpose
of creating wealth, resulting in a strong economy and society. It is now time to identify specific
individuals (personality types) and specific interventions (change and education programs)
aimed at those considered “at risk” for not attending to their financial needs.

Appendix

Big five personality measures (revised scales)
Neuroticism
1.
2.
3.
4.
5.

I often feel inferior to others.
When I’m under a great deal of stress, sometimes I feel like I’m going to pieces.
I often feel tense and jittery.
Sometimes I feel completely worthless.
Too often, when things go wrong, I get discouraged and feel like giving up.

Extraversion
1.
2.
3.
4.

I really enjoy talking to people.
I often feel as if I’m bursting with energy.
I am a cheerful, high-spirited person.
I am a very active person.

Openness to experience
1. I am intrigued by the patterns I find in art and nature.
2. I often try new and foreign foods.
3. I have little interest in speculating on the nature if the universe or the human condition.1
4. I have a lot of intellectual curiosity.
5. I often enjoy playing with theories or abstract ideas.
Agreeableness
1.
2.
3.
4.

I often get into arguments with my family and co-workers.1
Some people think I’m selfish and egotistical.1
Some people think of me as cold and calculating.1
I generally try to be thoughtful and considerate.

234

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

Conscientiousness
1.
2.
3.
4.
5.

I keep my belongings neat and clean.
I’m pretty good about pacing myself so as to get things done on time.
I waste a lot of time before settling down to work.1
Sometimes I’m not as dependable or reliable as I should be.1
I never seem to be able to get organized.1 1 Items are reverse scored.

Risk aversion
1. I am not willing to take risk when choosing a stock or investment.
2. I prefer a low risk/high return investment with a steady performance over an investment
that offers higher risk/higher return.
3. I prefer to remain with an investment strategy that has known problems rather than take
the risk trying a new investment strategy that has unknown problems, even if the new
investment strategy has great returns.
4. I view risk in investment as a situation to be avoided at all cost.
Items used to measure investment intentions
Short-term investment intentions
1.
2.
3.
4.
5.

I intend to invest in an IRA every year.
I intend to put at least half of my investment money into the stock market.
I intend to engage in portfolio management activities at least twice per week.
I intend to perform my own investment research instead of using outside advice.
I intend to compare my portfolio performance to that of professional managers.

Long-term investment intentions
1. I intend to save at least 10% of my gross earnings for investing/saving/retirement
purposes.
2. I intend to have a portfolio that focuses on multiple asset classes (i.e., stocks, bonds,
cash, real estate, etc.).
3. I intend to take an investments course.
4. I intend to manage my portfolio for maximum gross return rather than tax and cost
efficiency.
5. I intend to invest some money in long-term assets where my money will be tied up
andinaccessible for years.

References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50,
179–211.

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

235

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ:
Prentice-Hall.
Allais, P. M. (1953). Le Comportment de l’Homme Rationnel devant Le Risque: Critique des Postulats et Axiomes
de L’Ecole Americaine. Econometrica, 21, 503–546.
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behavior: A meta-analytic review. British
Journal of Social Psychology, 40, 471–499.
Bailey, J. J., & Kinerson, C. (2005). Regret Avoidance and Risk Tolerance. Financial Counseling and Planning,
16, 23–28.
Bajtelsmit, V. L., & Bernasek, A. (1996). Why do women invest differently than men? Financial Counseling and
Planning, 7, 1–10.
Bajtelsmit, V. L., Bernasek, A., & Jianakoplos, N. A. (1999). Gender differences in defined contribution decisions.
Financial Services Review, 8, 1–10.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment.
The Quarterly Journal of Economics, 116, 261–292.
Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A metaanalysis. Personnel Psychology, 44, 1–26
Bhandari, G., & Deaves, R. (2006). The Demographics of Overconfidence. The Journal of Behavioral Finance, 7,
5–11.
Block, J. (1995). A contrarian view of the five factor approach to personality description. Psychological Bulletin,
117, 238–246.
Bolton, L. E., Cohen, J. B., & Bloom, P. N. (2006). Does marketing products as remedies create a “get out of jail
free card?” Journal of Consumer Research, 33, 71–81.
Byrne, B. M. (2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and
Programming. Mahwah, NH: Lawrence Erlbaum Associates, Inc.
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis.
Psychological Bulletin, 125, 367–383.
Carducci, B. J., & Wong, A. S. (1998). Type A and risk taking in everyday money matters. Journal of Business
and Psychology, 12, 355–359.
Chen, H., & Volpe, R. (1998). An analysis of personal financial literacy among college students. Financial Services
Review, 7, 107–128.
Cohen, J. (1977). Statistical Power Analysis for the Behavioral Sciences (rev. ed.). Hillsdale, NJ, England:
Lawrence Erlbaum Associates, Inc.
Connor-Smith, J. K., & Flachsbart, C. (2007). Relations between personality and coping: A meta-analysis. Journal
of Applied Psychology, 93, 1080–1107.
Costa, P. T., & McCrae, R. R. (1992a). Normal personality assessment in clinical practice: The NEO Personality
Inventory. Psychological Assessment, 4, 5–13.
Costa, P. T., & McCrae, R. R. (1992b). Professional Manual: Revised NEO Personality Inventory (NEO-PI-R)
and NEO five-Factor Inventory (NEO-FFI). Lutz, FL: Psychological Assessment Resources, Inc.
Costa, P. T., & McCrae, R. R. (1995). Domains and facets: Hierarchical personality assessment using the revised
Neo Personality Inventory. Journal of Personality Assessment, 64, 21–50.
Costa, P. T., & McCrae, R. R. (1997). Stability and change in personality assessment: The revised NEO Personality
Inventory in the year 2000. Journal of Personality Assessment, 68, 86–94.
Costa, P. T., & McCrae, R. R. (2003). NEO-FFI: NEO Five Factor Inventory. Lutz, FL: Psychological Assessment
Resources, Inc.
De Boele, R. (2000). The Big Five Personality Factors: The Psychological Approach to Personality. Seattle,
WA: Hogrefe & Huber.
de Bruijn, G., Kremers, S. P. J., de Vries, H., van Mechelen, W., & Brug, J. (2007). Associations of
socialenvironmental and individual-level factors with adolescent soft drink consumption: Results from the
SMILE study. Health Education Research, 22, 227–237.

236

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

DeYoung, C. G. (2006). Higher order factors of the Big Five in a multi-informant sample. Journal of Personality
and Social Psychology, 91, 1138–1151.
DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five.
Journal of Personality and Social Psychology, 93, 880–896.
Digman, J. M. (1997). Higher order factors of the Big Five. Journal of Personality and Social Psychology, 73,
1246–1256.
Felton, J., Gibson, B., & Sanbonmatsu, D. M. (2003). Preference for Risk in Investing as a Function of Trait
Optimism and Gender. The Journal of Behavioral Finance, 4, 33–40.
Filbeck, G., Hatfield, P., & Horvarth, P. (2005). Risk Aversion and Personality Type. The Journal of Behavioral
Finance, 6, 170–180.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and
Research. Reading, MA: Addison-Wesley.
Ford, J. K., MacCallum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied
psychology: A critical review and analysis. Personnel Psychology, 39, 291–314.
Goldberg, L. R. (1992). Development of markers for the Big Five factor structure. Psychological Assessment, 4,
26–42.
Gomez-Mejia, I. R., & Balkin, D. (1989). Effectiveness and aggregate compensation strategies. Industrial
Relations, 28, 431–445.
Gupta, A. K., & Govndarajan, V. (1984). Business unit strategy, managerial characteristics, and business unit
effectiveness as strategy implementation. Academy of Management Journal, 27, 25–41.
Hallahan, T. A., Faff, R. W., & McKenzie, M. D. (2004). An empirical investigation of personal financial risk
tolerance. Financial Services Review, 13, 57–78.
Hariharan, G., Chapman, K. S., & Domian, D. L. (2000). Risk tolerance and asset allocations for investors nearing
retirement. Financial Services Review, 9, 159–170.
Hessing, D. J., Elffers, H., & Weigel, R. H. (1988). Exploring the limits of self-reported and reasoned action: An
investigation of the psychology of tax evasion behavior. Journal of Personality and Social Psychology, 54,
405–413.
Hogan, R., & Hogan, J. (1991). Personality and status. In D. G. Gilbert & J. J. Connolly (Eds.), Personality, Social
Skills, and Psychopathology: An Individual Differences Approach (pp. 137–154). New York: Plenum
Press.
Keller, C., & Siegrist, M. (2006a). Investing in stocks: The influence of financial risk attitude and values-related
money and stock market attitudes. Journal of Economic Psychology, 27, 285–303.
Lauriola, M., Gioggi, A., & Saggino, A. (2001). The Big Five and the intention to choose a university faculty: An
exploratory study of high school students. Rassegna di psicologia, 18, 133–141.
Lim, V. K. G., & Teo, T. S. H. (1997). Sex, money and financial hardship: An empirical study of attitudes towards
money among undergraduates in Singapore. Journal of Economic Psychology, 18, 369–386.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, VII, 77–91.
Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept:
First- and higher-order factor models and their invariance across groups. Psychological Bulletin, 97, 562–582.
McAdams, D. P. (1992). The five–factor model of personality: A critical appraisal. Journal of Personality, 60,
329–361.
McAdams, D. P. (2006). The Person: A New Introduction to Personality Psychology (4th ed). Hoboken, NJ: John
Wiley & Sons.
McCrae, R. R., & Costa, P. T. (2007). Brief versions of the NEO-PI-3. Journal of Individual Differences, 28 (3),
116–128.
Morse, W. C. (1998). Risk taking in Personal Investments. Journal of Business and Psychology, 13, 281–288.
Olsen, R. A., & Cox, C. M. (2001). The influence of gender on the perception and response to investment risk:
The case of professional investors. The Journal of Psychology and Financial Markets, 2, 29–36.
Previn, L. A. (1994). A critical analysis of current trail theory. Psychological Inquiry, 5, 103–113.

C. Mayfield, G. Perdue, K. Wooten / Financial Services Review 17 (2008) 219–236

237

Prislin, R., & Kourlija, N. (1992). Predicting behavior of high and low self-monitors: An application of the theory
of planned behavior. Psychological Reports, 70, 1131–1138.
Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attendance change: A system of
reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008.
Salgado, J. F. (1997). The five factor model of personality and job performance in the European community.
Journal of Applied Psychology, 82, 30–43.
Schooley, D. K., & Worden, D. D. (1996). Risk aversion measures: Comparing attitudes and asset allocation.
Financial Services Review, 5, 87–99.
Slovic, P. (1972). Information processing, situational specificity, and generality of risk taking behavior. Journal
of Personality and Social Psychology, 22, 128–134.
Steiger, J. H.(1990). Structural model evaluation and modification: An interval estimation approach. Multivariate
Behavioral Research, 25, 173–180.
Thoresen, C. E., & Low, K. G. (1990). Women and the Type A behavior pattern: Review and commentary. Journal
of Social Behavior and Personality, 5, 117–133.
Volpe, R. P., Chen, H., & Pavlicko, J. J. (1996). Investment literacy among college students: A survey. Financial
Practice and Education, 6, 86–94.
Von Neumann, J., & Morgenstern, O. (1947). Theory of Games and Economic Behavior. Princeton: Princeton
University Press.
Wa¨rneryd, K. E. (1996). Risk attitude and risky behavior. Journal of Economic Psychology, 17, 749–770.
Weiner. I. B. (2008). Handbook of Personality Assessment. Hoboken, NJ: John Wiley & Sons.
West, S. G., Finch, F. J., & Curran, P. J. (1995). Structural equation modeling with non-normal variables. Problems
and remedies. In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp.
56–75). Thousand Oaks, CA: Sage.
Wong, A., & Carducci, B. J. (1991). Sensation seeking and financial risk taking in everyday money matters.
Journal of Business and Psychology, 5, 525–530.
Worthington, A. C. (2006). Predicting financial literacy in Australia. Financial Services Review, 15, 59–79.
Zhao, H., & Seibert, S. (2006). The Big Five personality dimensions and entrepreneurial status: A meta-analytical
review. Journal of Applied Psychology, 91, 259–271.

…