J Financ Serv Res (2013) 44:303–329
DOI 10.1007/s10693-012-0144-0
Decision Making for Individual Investors:
A Measurement of Latent Difficulties
Ann Shawing Yang
Received: 8 June 2011 / Revised: 30 May 2012 / Accepted: 10 June 2012 /
Published online: 5 July 2012
# Springer Science+Business Media, LLC 2012
Abstract This study identifies how investors’ confidence and information gathering ability
affect their decision making by using the investment theory on crystallized and fluid intelligences. We adopt the Rasch model to analyze latent and unobservable factors that cause
difficulties in investment participation for investors in Taiwan. These investors are more
confident in technical analysis but less confident in trading regulations. Further, they find
media and professional sources difficult to trust, but professional advice is very accessible.
Lower income significantly influences investors’ confidence and their information-gathering
ability just as youth significantly contributes to more confidence. But gender and maturity
significantly contribute to factors that concern their information gathering ability. Regional
demographic differences show variations in decision making regarding investment preferences,
while increasing income encourages investment diversification through multiple decisions.
This study identifies a strong correlation between investors’ confidence and their information
gathering ability, thus indicating that investors’ confidence enhances the development of the
ability to gather investment information.
Keywords Decision making . Individual investors . Rasch measurement . Cognitive ability
JEL Classification G11
1 Introduction
Individual investors often face difficulties in investment decision making that derives from
asymmetric information. Asymmetric information is characterized by incomplete information that often limits investment participation (Díaz 2009) and can result in individual
investors making biased investment decisions. Consequently, individual investors can encounter higher transaction costs when they search for better information (Rapp and Aubert
A. S. Yang (*)
Institute of International Management, National Cheng Kung University, Tainan, Taiwan,
Republic of China
e-mail: annsyang@mail.ncku.edu.tw
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2011). However, investment knowledge and best practices are a form of accumulated
information that can only be learned through experience (Lease et al. 1974; Hong et al.
2004; Grinblatt and Keloharju 2009; and Glaser and Weber 2009). Because individual
investors’ rely on past experiences, they can experience a gradual overestimation of their
investment skills that can often result in investment losses (Grinblatt and Keloharju 2009;
Glaser and Weber 2009).
The investment participation of individual investors is also related to personal attributes
and socialization behavior. Active investors might seek out information by socializing with
others and therefore benefit from lower transaction costs for investment decisions (Bailey et
al. 2003). Those investors who possess increased knowledge of investment practices generally experience lower transaction costs associated with investment analyses and information
searches. They can also adopt less active participatory roles as investors while being able to
more accurately determine the amount of their investments (Rapp and Aubert 2011).
Therefore, individual investors with less knowledge are more prone to mistakes in their
decision making than are those with better, more comprehensive knowledge (Cohn et al.
1975; Schlarbaum et al. 1978; Grinblatt and Keloharju 2009). Thus, knowledge acquisition
becomes an important task for investors’ decision making and market participation despite
the possible difficulties.
However, scant literature exists about the latent difficulties that cause low levels of
investor participation or high transaction costs. The literature that does exist focuses on
findings of observable causes of investment difficulties, such as a reliance on financial
intermediaries or public information (Campbell 2006; Liang and Wang 2007; Koehler and
Mercer 2009; Capon et al. 1996; Nofsinger 2001). Latent difficulties that are not as easily
observed, such as specific tasks related to investment decisions or analyses, are rarely
discussed in this literature. Investors who are unfamiliar with investment products and
who lack the knowledge to make appropriate investment decisions might require more
education to develop investment acceptance and intentions (Campbell 2006; Wang et al.
2006). These investors might also adopt risk aversion behaviors, or they might exclude
themselves from market participation to minimize losses due to a lack of knowledge (Plath
and Stevenson 2005; Benartzi and Thaler 2002; Gomes and Michaelides 2005). Because
these difficulties are not easy to discover or identify, an empirical study to investigate and
identify them should lead to a better understanding of what investors face during the
decision-making process. Thus, this study serves as an extension to the literature on
identifying tasks related to investment decisions that present obstacles to investment
participation.
Using data on Taiwanese investors, we investigate the difficulties that individual
investors face with respect to financial decision making. We attempt to identify the
difficulties, ranked by order of importance, that cause obstacles and prevent individual
investors from participating in financial markets. Our study focuses on analyzing these
difficulties in relation to self-confidence and with respect to gaining access to information that leads to investment participation. Contrary to methods used in the literature
that identify the relation between various personal attributes and their effects on
investment behavior, we propose using the Rasch measurement model to rank the
difficulties of various tasks involved in the investment decision-making process. The
Rasch measurement model is a unidimensional measurement scale that efficiently and
precisely measures responses and transforms the raw data into linear units (Moral et al.
2006). The advantages of the Rasch measurement model include not only the convenience of using a small sample size to obtain reliable and valid results, but also the
identification of respondents’ abilities in relation to the tasks (Jackson et al. 2002).
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305
We find that most investors are confident about technical analysis but are not confident
with regard to trading regulations. Investors’ confidence is highest in technical analysis
related to the comprehension of prospectuses, research reports, and analyses regarding the
determination of holding periods. In contrast, investors’ confidence is lowest with regard to
trading regulations that include such things as buying and selling limitations, tax calculations, and risk minimization.
We also find that individual investors’ information gathering relies mostly on interpersonal sources that involve professional assistance consisting of investment consultations,
asset allocation analyses, and portfolio management courses. However, individual investors
find media sources, such as newspapers, magazines, the Internet, TV, radio, and financial
planners, difficult to trust.
Researchers find that lower income significantly influences both the level of confidence
and the level of information gathering. Marital status and educational background indicate
significant variations in the level of confidence and the capability to gather information.
Furthermore, youth significantly contributes to increased confidence, while maturity significantly contributes to an increased ability to gather information. The investor’s gender also
shows significance for information gathering that leads to investment participation. Similarly, regional differences have an effect as investors in the northern region of Taiwan
participate more than those in the middle and southern regions. Northern investors have
higher levels of confidence and information gathering than investors in the other two
regions.
The results from this empirical study contribute to the literature by advancing the
comprehension of the underlying difficulties that investors face during the process of
investing. The findings from the results provide solutions to resolve or minimize such
difficulties.
The paper is organized as follows. Section 2 presents a discussion of related literature on
investors’ decision making. Section 3 presents the Rasch model as well as the layout of this
study’s methods and procedures. Section 4 provides the results of the study. Section 5
contains a summary and conclusions.
2 Literature
2.1 Empirical literature
Slovic (1972) was the first to propose research on decision making for financial investments
by focusing on investors’ behavior related to information processing and strategy selection.
Although investors can adopt several methods to gather investment information, they
primarily rely on the collection of information via an intermediary, such as subscriptions
to financial periodicals, personal connections, or managers. Information collection conducted through these sources allows investors to devote less time to the decision-making
process (Lease et al. 1974; Capon et al. 1996). Because the visibility and duration of
information or news influences investment decision making (Nofsinger 2001), good news
can then encourage investors to purchase, while bad news can cause investors to sell. Social
interactions among interest groups, friends, or word-of-mouth serve as common types of
information sources (Hong et al. 2004; Hoffmann and Broekhuizen 2010). Higher levels of
social interactions not only increase investment participation, but also generate more information gathering (Hong et al. 2004; Loibl and Hira 2009). Investors that gather information
via the Internet generally show a greater knowledge of investments compared to those who
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rely on the personal services of a financial planner (Pellinen et al. 2011). Furthermore, access
to the Internet encourages households to increase their level of participation in online trading
(Bogan 2008). While knowledge about transaction costs and related online trading discounts
are more apparent in households already participating in the stock market, high costs
associated with market transactions, information, and access prohibit households without
prior trading experience from participating in the market (Bogan 2008). Therefore, information gathering related to investment purposes varies with availability, channel access, and
source dependency.
Decision making for investment purposes also varies according to demographic profiles
(Cohn et al. 1975; Campbell 2006; Gomes and Michaelides 2005; Benartzi and Thaler
2002). For example, married investors are more risk adverse than single investors (Cohn et
al. 1975). Wealth and education have a positive correlation with investment participation
(Campbell 2006). Consistent with these findings, lower income prevents investors from
participating in investment markets, and higher income encourages investors to accumulate
holdings of financial assets (Soutar and Cornish-Ward 1997; Gomes and Michaelides 2005).
Mature male investors in the northeastern US with higher levels of education, strong social
networks, and sufficient investment knowledge generally tend to diversify investments
(Hoffmann and Broekhuizen 2010; Capon et al. 1996). Similarly, mature investors residing
in northern Europe also show more diversified and larger financial asset holdings (Bijmolt et
al. 2004). However, female investors of the same age with more education and greater
wealth make investing decisions that are more risky (Cesarini et al. 2010). Barber and Odean
(2001) find that male investors are more overconfident than their female counterparts and
that the latter, with varying maturities, prefer less risky investment decisions than the former
group. Furthermore, male investors, married or single, generally tend to receive lower
investment returns due to excessive trading when compared to their female counterparts
who trade less frequently. Therefore, our conclusion is that demographic profiles related to
age, marital status, income, and gender provide preliminary and knowledgeable insights into
investors’ trading behavior and ability.
Furthermore, investment skills that focus on the technicalities of risk and return analyses
influence and contribute to decision making. But investors’ decision making, from the
perspective of utility, is determined by risk preferences (Dorn and Huberman 2010). The
decision-making process, therefore, consists of technical analysis, risk and return evaluation,
and performance surveillance (Radcliffe 1990). Investors’ preferences toward median portfolios are generally adopted to minimize mistakes in decision making related to risk
premiums and to increase portfolio efficiency (Benartzi and Thaler 2002). Investors refer
to management quality, market status, and stock price trends of listed companies for
investment decisions (Clark-Murphy and Soutar 2004). They also develop trading skills
through the technical analysis of financial assets and portfolios to assist them in their
decision making, thus increasing their confidence in investing.
2.2 Theoretical literature
The objectives of this study are to examine whether the latent difficulties in investment decision
making can be built on the theoretical foundations of fluid and crystallized intelligences. Cattell
(1971) propose that fluid and crystallized intelligences are critical determinants in learning.
Fluid intelligence refers to an individual’s analytical and organizational skills such as problem
solving in math (Primi et al. 2010). Crystallized intelligence refers to an individual’s basic
knowledge acquired through education and training, that is, comprehension ability (Schweizer
and Koch 2002). For investment decision making, Goetzmann and Kumar (2008) find that
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skilled investors adopt an under-diversification strategy to actively create high turnover rates
and thus gain from portfolio compositions. However, the development of crystallized intelligence is dependent on fluid intelligence, which changes according to experiences and personal
background (Schweizer and Koch 2002; Primi et al. 2010). Individuals with a higher level of
subjectively assessed intelligence tend to be more overconfident and have personality traits that
reflect extraversion (Chamorro-Premuzic et al. 2005). Thus, investors who are active market
participants generally develop increased confidence toward investment decision making by
claiming to possess better investment skills that lead to better performances (Glaser and Weber
2007). Cognitive ability or fluid intelligence, which greatly determines one’s intellectual
efficiency or intellectual quality (IQ), reaches its maximum level at an early adult age and
remains stable throughout middle and old ages (Facon 2008). Thus, investors’ risk preferences
largely influence decision making for successful portfolio diversification, whereas trading
experience leads to better portfolio management (Dorn and Huberman 2005).
3 Methodology
This study applies the Rasch measurement model to identify the likelihood or probability of
experiencing difficulties related to investment skills and environments that are not easily
observed. The Rasch measurement model allows for the separation of the tasks and
individuals to ensure that all tasks provide the same measurement outcome with regard to
difficulty levels even if applied to different groups of respondents (Tesio 2003).
Georg Rasch (1960) provides a dichotomous model to measure the subject’s ability and
the task’s difficulty through the formulation of a relation between ability and difficulty, and
the probability of success. The Rasch model is an example of the Objective Measurement
Theory in that ability and difficulty are measured in “logits” (log-odds units) (Bezruczko and
Linacre 2005). This theory has been used extensively, perhaps most notably in the analysis
of data from functional assessment instruments in rehabilitation medicine (Massof and
Fletcher 2001) and psychometric methods (Garamendi et al. 2006). The model’s principal
function is to confirm that differently weighted response scales are required for different
tasks to provide a valid scale. It also suggests that the validity of the task’s inclusion is
dependent on an assessment of the task’s difficulty across individuals.
If respondents believe a task related to investors’ confidence or information gathering
ability can be achieved with ease, we assign a score of one to that task or zero otherwise. The
probability that an investor n responds that he or she can achieve task i with ease can now be
clarified in settings where “θn” represents the individual’s ability and “bi” represents the
task’s level of difficulty:
Pð1jθn ; bi Þ ¼
eθn bi
:
1 þ eθn bi
ð1Þ
The probability that an investor n reports that he or she cannot achieve task i with ease is:
Pð0jθn ; bi Þ ¼ 1 Pð1jθn ; bi Þ ¼
1
:
1 þ eθn bi
ð2Þ
The odds that an investor n reports that he or she can achieve task i with ease are:
Pð1jθn ; bi Þ
¼ eθn bi ;
Pð0jθn ; bi Þ
ð3Þ
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and the log of the odds ratio, or the “logit”, is
ln
Pð1jθn ; bi Þ
¼ θn bi ;
Pð0jθn ; bi Þ
ð4Þ
which isolates the parameters that are of interest to us.
Plainly stated, the power of the Rasch model (Massof and Fletcher 2001) is that it
provides estimates of variables of interest on an interval scale. That is, for any difficulty
that a respondent might experience in financial decision making, the Rasch model can
identify the level of that difficulty. The model also provides an objective assessment of
the validity and reliability of an instrument using standard criteria. The reliability of the
respondent refers to a person’s expected rank ordering on the construct if the same sample of
individuals is given another set of tasks measuring the same construct. The task’s reliability
indicates the replicability of the task placements (i.e., logit parameter and difficulty level)
should the same tasks be given to another sample with comparable ability levels. The
individual separation index estimates how well individuals can be differentiated on the
measured variable. The task separation index estimates how well tasks can be differentiated
on the measured variable. The latter two indices are quantified in standard error units (Bond
and Fox 2001). The application of the Rasch model provides an indication of the task’s order
and the task’s fit as well as an assessment of the validity of its survey (Bond 2004).
The task’s order addresses the difficulty level of the tasks, and the fit of the variables to
the Rasch model helps determine the validity of the individual and the task estimations
(Oreja-Rodríguez and Yanes-Estevez 2007).
The Rasch model contributes by emphasizing the identification of latent or not easily
observed difficulties for an individual to perform a task on a unidimensional scale (Ewing et
al. 2005; Soutar and Cornish-Ward 1997). Thus, the model provides an individual-task map
to identify the levels of difficulties experienced by different individuals for their unique
strategy development. Our empirical study uses the Rasch model to focus on individual
investors with tasks related to investors’ confidence and information gathering ability (task)
for a personal customization of financial services. Such specialization or individualization of
investors can assist in providing more in-depth information that has been previously
unobserved or hidden due to the generalization of investors. Fisher (2009) recommends
the use of the Rasch model in finance-related studies for a uniform metric with an invariance
and traceability in measurement specifications to develop uniform metrology standards.
Within the finance literature, Soutar and Cornish-Ward (1997) apply the Rasch model to
identify ownership patterns in the financial assets of households, and they find that the
application of the Rasch model is useful in developing unidimensional scales. In other
words, the order of financial asset acquisition is evaluated on an individual basis and on a per
asset basis. Yang (2009a) uses the Rasch model to analyze the difficulty in the adoption of
mobile banking for various banking transactions for individual respondents. Pellinen et al.
(2011) uses the Rasch model to analyze mutual fund investors’ behavior. Bijmolt et al.
(2004) apply the Rasch model to find latent factors that influence the ownership of financial
products for consumers in various countries.
3.1 Survey design
For our study, in determining survey tasks, we consider the related literature on investors’
confidence and information gathering, including Lease et al. (1974), Hong et al. (2004),
Loibl and Hira (2009), and Clark-Murphy and Soutar (2004). Drafts of the survey were
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309
pretested on experts and scholars with more than 10 years of experience in financial asset
investments. The survey was then modified on the basis of their feedback to make it
understandable for a broad range of individuals with varying experiences in financial
decision making. Additionally, a random pretest of our survey was distributed to the general
public to identify problematic questions. Thus, the final version of our survey was designed
on the basis of input from experts, scholars, and the general public.
3.2 Scale adjustment (refinement)
The Rasch measurement system requires a preliminary adjustment of scale according to the
information-weighted fit mean squares (Infit MNSQ) when the task’s values are outside the
range of 0.8–1.20 (Garamendi et al. 2006). Survey tasks that meet the correlation between
the data and the model are identified as latent and viable variables. These variables remain in
the Rasch ordinal scale to represent the respondents’ ability and the task’s difficulty (Ewing
et al. 2005). The survey used in this study consists of 37 tasks, specifically an investors’
confidence scale (Confidence) with 20 tasks and an information gathering scale (Info.) with
17 tasks. Using the confidence scale, respondents are asked to rate the level of difficulty they
face regarding technical analyses, trading regulations, and factors relating to investors’
psychology. Similarly, respondents use the information scale to rate the difficulties they
encounter when gathering information through media sources and interpersonal sources and
to rate their fixed costs associated with that type of information gathering. The responses are
scored on the following five-point Likert scale: “strongly disagree” (1), “disagree” (2),
“neutral” (3), “agree” (4), and “strongly agree” (5). Participant responses to the 37 tasks
are analyzed using WINSTEPS (Linacre 2006) that is an interactive computer program that
estimates θn for each investor and for each task i in logit units. WINSTEPS deals with
polytomous responses by applying the Masters–Andrich modification (Masters 1982) to the
Rasch model. Therefore, the estimated parameters and the model-fit statistics can be
calibrated via a joint maximum-unconditional-likelihood estimating procedure (Wright
1996). A total of 19 tasks are eliminated due to their misfit: 11 tasks related to confidence
and 8 tasks related to information. A standard error-type stopping rule is used in the rating
scale and for the partial credit tasks (Smith and Smith 2004). Thus, the modified scale for
investors’ decision making is reduced from 37 tasks to 18 tasks.
Survey tasks meeting the uniformity requirement compose a scale to identify the
order of importance to fit the Rasch model (Soutar and Cornish-Ward 1997; Ganglmair
and Lawson 2003; Yang 2009a). Survey tasks unfitted to the scale are eliminated due to
the inability to predict the task’s difficulty and the individual’s ability (Soutar and
Cornish-Ward 1997). Tasks unfitted to the unidimensional scale are eliminated due to
the inability to significantly improve the scale (Ganglmair and Lawson 2003). Therefore, eliminated tasks are viewed as irrelevant in influencing or affecting an individual’s
ability to perform a task. For example, Soutar and Cornish-Ward (1997) eliminate the
savings account task and other property tasks to obtain a good fit for the unidimensional Rasch model and thus identify financial asset-ownership patterns. To identify
adoption difficulties for mobile banking, Yang (2009a) eliminates tasks relating to
mobile-banking, transaction-reply fees, and customer service. The Rasch model uses
the invariant characteristic for tasks and individuals (Drehmer et al. 2000; Fisher 2009)
to determine via the fit statistics those that are unacceptable from those that are
acceptable to the Rasch model (Ewing et al. 2005). Bias is indicated when a task or
a individual shows a misfit to the Rasch model, and unfitted tasks or individuals are
eliminated (Drehmer et al. 2000). This elimination process continues until all tasks and
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individuals are fit for scaling to meet the invariance and unidimensionality of the Rasch
model (Drehmer et al. 2000; Ewing et al. 2005; Conrad et al. 2006; Fisher 2009;
Ganglmair and Lawson 2003).
The remaining tasks, with consultations from investment professionals, are capable of
representing issues concerning investment decision making. They show no duplications or
repeated content that might represent redundancy in the survey’s task constructions. They
also clearly note the latent difficulties not easily detected. Furthermore, these remaining
tasks pinpoint the hidden difficulties that investors are unlikely to openly admit.
3.3 Scale examination
The raw scores are the first indication of the measurement of difficulty in the Rasch model.
That is, an individual’s ability (θn) to respond to a task with ease (or the task’s difficulty (bi))
is calculated according to responses on an ordinal scale. The conversion of raw scores into
logits, or odds ratios, provides evidence of the fit of the data to the Rasch model (Massof and
Fletcher 2001). Threshold calibration is used as the second step to identify the respondents’
likelihood of experiencing difficulty for tasks according to their own ability. Category
probability curves are constructed to show a graphical representation of response
choices for rating-scale tasks ranging from 1 (strongly disagree) to 5 (strongly agree).
Reliability estimates that consist of an individual’s reliability, the individual separation
index, a task’s reliability, and the task separation index are further analyzed. The fit
statistics composed of the mean square, infit, and outfit values are also computed to
identify abnormal responses to tasks and people. The task characteristic is also studied
to identify the probability of discrepancies in responses according to the ability of the
respondents (Heesch et al. 2006). Finally, the differential item function (DIF) is
calculated to identify potential differences in the response patterns of sample subgroups
(Kahler and Strong 2006; Pallant and Tennant 2007).
3.4 Survey implementation
We use this survey instrument to collect data from banking customers in three regions
(northern, central, and southern) of Taiwan. The survey aims to collect data on investment
difficulties for individual investors that seek to gather information and improve investment
techniques and skills for better investing. We refer to Pellinen et al. (2011) to exclude
respondents working in the financial services industries as they are more apt and resourceful
at investing. We refer to Bogan (2008) to focus on the general public with and without
investment experiences as we analyze their hidden or latent investment difficulties that are
not easily identified or observed. In particular, according to Grinblatt and Keloharju (2009),
we focus on investors with college degrees who are employed and who tend to show an
increasing need to invest. Respondents with and without investment experiences are compared to identify latent difficulties toward investing that might serve as a barrier in developing investors’ confidence and information gathering ability. Regional representativeness is
carefully identified because it influences the use of financial services and products (Bijmolt
et al. 2004; Sierminska et al. 2006).
We distributed surveys on-site and in person. Respondents were not asked to provide
names or contact information as anonymity is a necessity for surveys relating to an
individual’s wealth (Lease et al. 1974). While most respondents were reluctant to disclose
their true holdings (Soutar and Cornish-Ward 1997; Campbell 2006), a range of assets were
provided for a general background of the respondents’ wealth. Respondents could choose to
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disclose part or all of their asset holdings by selecting one or more responses. However,
respondents were willing to provide complete responses when they were informed that the
studies were being conducted by scholars for non-commercial use and that their anonymity
would be preserved.
Respondents were solicited at bank (headquarter and branches) and ATM (automatic
teller machines) locations where financial transactions could be conducted. Every tenth
customer entering the bank branches or waiting at ATM machines was invited to be a survey
participant. If our invitation to participate was declined, the next “tenth” customer was asked
to participate. Taiwan’s financial deregulation in 2001 under the Financial Holding Company
Act allows banking institutions to conduct investment and security related services and
operations in the form of a financial holding company (Yang 2009b).
Once an individual was solicited, we determined any possible conflicts or poorly
completed surveys through the Rasch model with the WINSTEPS (Linacre 2006) software.
Using the process outlined in subsection 3.2, respondents that provided illegible answers,
omitted questions, or contradictory responses were identified as invalid survey responses
and excluded from the survey analysis. Therefore, out of the 1,077 respondents, we
identified and eliminated 313 respondents with invalid survey responses that resulted in
764 valid respondents. Thus, the survey is measurable on a rating scale and meets the
generalizability characteristic to allow any group of individuals to provide a response to any
task (Drehmer et al. 2000; Ewing et al. 2005).
3.5 Sample descriptive statistics
In Table 1, the sample population surveyed is composed of 45.7 % men and 54.3 % women,
and their mean age is 37.7 years old (standard deviation (SD)011.3). The majority of
respondents are married, have a college education (53.1 %), and earn a mean monthly
income of NT$38,5601 (SD024,275). Sample respondents in the central region of Taiwan
are slightly older than those in the northern and southern regions with a mean age of 41 years
(SD010.76). Respondents in the central region generally earn a higher monthly income
(mean042,222; SD023,452), have received a college education, and have held a bank
account longer (mean07.79 years; SD03.63).
Table 1 shows the regional demographics of the wealth portfolios of the 764 individuals, 26
of whom earn an income but do not participate in investing. Of the respondents, 86.8 %
participate in time deposits as savings, 40.9 % have mutual funds, 43.4 % have stocks, 42.9 %
have insurance, and 12 % invest in real estate. All regions show a relatively high savings rate of
approximately 83 %. Besides savings, the northern region shows that 50.9 % have stocks as
their most preferred investment, while the central and southern regions show that 48.7 % and
45.9 % invest in insurance respectively. The second and third preferred investments in the
northern region are mutual funds at 43.1 % and insurance at 35.2 %. In the central region, 41 %
have stocks and 35.9 % have mutual funds; while in the southern region, 41 % have mutual
funds and 11.3 % have real estate holdings. Thus, the evidence shows that investors in the
northern region prefer stock-related investments more so than investors in the central and
southern regions who show a greater preference toward investments characterized as long term,
such as insurance. This preference corresponds to the findings of Bijmolt et al. (2004) and
Sierminska et al. (2006) on the regional influences on invested assets.
The currency in Taiwan is the New Taiwan Dollar and abbreviated as “NT$”. The exchange rate between
New Taiwan Dollar and U Dollar is about NT$30: US$1 as of May 30, 2012. A mean monthly income of NT
$38,560, therefore, equals US$1,285.
1
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Table 1 Descriptive Statistics of Study Sample: (a) Regional demographics. Table 1 shows the descriptive
statistics of all respondents by regions. Approximately 26 persons out of a total of 764 persons earn income,
but do not participate in investing
All Regions
Northern 224
persons (29.3 %)
Central 80
persons (10.5 %)
Southerna 460
persons (60.2 %)
Persons
%
Persons
%
Persons
%
Persons
%
a. Men
349
45.7
100
44.6
30
37
219
47.7
b. Women
415
54.3
124
55.4
51
63
240
52.3
Age
a. 18–29
256
33.5
76
33.9
18
22.2
162
35.3
b. 30–41
219
28.7
74
3.3
20
24.6
125
27.2
c. 42–50
182
23.8
43
19.2
26
32.1
113
24.6
d. above 51
107
14
31
13.8
17
21
59
12.8
Mean/S.D.
37.7/11.3
Demographic characteristics
Gender
37.3/11.76
41.1/10.76
36.6/10.79
Marital Status
a. Single
309
40.4
98
43.8
25
30.9
186
40.5
b. Married
Education
455
59.6
126
56.3
26
69.1
273
59.5
a. High school and below
253
33
51
22.7
24
29.6
178
38.9
b. College
406
53.1
125
55.8
48
59.3
233
50.8
c. Masters/Ph.D.
105
13.8
48
21.4
9
11.1
48
10.5
a. Below 30000
293
38.4
66
29.4
20
24.6
207
45.1
b. 30001–50000
272
35.6
89
39.7
37
45.7
146
31.8
c. 50001–70000
d. Above 70001
130
69
17
9
48
21
21.4
9.4
18
6
22.2
7.4
64
42
13.9
9.2
Mean/S.D.
38560/24275
41295/24119
a. Time deposits
643
86.8
190
88
65
83.3
388
b. Mutual funds
303
40.9
93
43.1
28
35.9
182
41
c. Stocks
321
43.4
110
50.9
32
41
179
10.3
d. Insurance
318
42.9
76
35.2
38
48.7
204
45.9
e. Real estates
89
12
27
12.5
12
15.4
50
11.3
Monthly Income
42222/23452
36580/24340
Wealth Portfolio
a
87.4
Includes the eastern region and the off-island regions
Table 2 compares the effects of income on the wealth portfolio composition of the 738
respondents who participate in investing. We find that the group with a monthly income
below NT$30,000 prefer bank deposits (86.9 %) over all other types of investments by a
significant margin. The income group between NT$30,001 and NT$50,000 also shows a
preference for bank deposits. In the higher income group that earns between NT$50,001 and
NT$70,000, the concentrations begin to narrow as deposits are at 83.6 %, followed by stocks
at 63.1 %, insurance at 59 %, mutual funds at 41 %, and real estate at 20.5 %. The extremely
high-income group with income above NT$70,001 invests mostly in deposits (78.3 %),
followed by mutual funds at 59.4 %, stocks at 58 %, insurance at 44.9 %, and real estate at
24.6 %. In sum, we find a decrease in deposits with increasing income, while an increase in
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Table 2 Descriptive Statistics of Study Sample: (b) Wealth portfolio. Table 2 shows the descriptive statistics
of all respondents by regions. Approximately 26 persons out of a total of 764 persons earn income, but do not
participate in investing. Table 2 shows the wealth portfolios by income categories of the 738 persons whom
participate in investing
Wealth Portfolio/Income All
Time
Deposits
Mutual Funds Stocks
Person Person
%
Person
a. Below 30000
283
246
86.9
89
b. 30001–50000
264
241
91.3
122
c. 50001–70000
122
102
83.6
50
d. Above 70001
69
54
78.3
41
%
Insurance
Person
Real Estate
Person
%
%
Person
%
31.4
72
25.4
99
35
13
4.6
46.2
132
50
117
44.3
34
12.9
41
77
63.1
72
59
25
20.5
59.4
40
58
31
44.9
17
24.6
income leads to an increase in investing in other types of assets. This finding is consistent with
Bijmolt et al. (2004) that argue the direct influence of income affects wealth accumulation.
4 Results
4.1 Goodness of fit
The influences on the investors’ decision making that stems from the investors’ confidence
and information gathering ability are first identified in the task separation index (TSI) for
task difficulties and the individual separation index (ISI) for investor ability in Table 3. The
TSI represents the estimate of the spread or the separation of the tasks. The ISI represents an
estimate of the spread of the individuals (Franchignoni et al. 2010). The investors’ confidence scale shows a slightly lower TSI at 3.71 than that of the information gathering scale at
4.11. Conversely, the information gathering scale shows a lower ISI of 2.82 than that of the
investors’ confidence scale of 2.88. These values measured for validity correspond to
Duncan et al. (2003) that recommend values greater than three for the TSI and greater than
two for the ISI. The reliability measures for investor confidence and information gathering
are 0.93 and 0.94 respectively. These values confirm the survey’s unidimensionality with
reliability measures greater than 0.5 (Oreja-Rodríguez and Yanes-Estevez 2007). The Infit
MNSQ, as an indicator of goodness-of-fit (Pickard et al. 2006), shows values that rang between
0.99 and 1 for both the investor’s confidence and information gathering ability. The ideal infit
MNSQ value is one to accept fit statistics for the Rasch measurement model (Duncan et al.
2003). These values strongly indicate that the overall validity of our model is acceptable.
While the majority of finance studies on decision making apply the Rasch model without
specifying the individual and task separation indices (Soutar and Cornish-Ward 1997;
Pellinen et al. 2011; Bijmolt et al. 2004; Ham and Kleiner 2007), the validity of the Rasch
model provides insights into an individual’s ability and a task’s difficulty to distinguish the
appropriateness of a certain task for a particular individual (Conrad et al. 2006; Yang 2009a).
Therefore, the ISI and TSI serve as extensions to the commonly adopted reliability and
validity analyses by providing in-depth analyses of an individual’s ability and a task’s
difficulty as identified on a measurement scale. The identification of an individual and a
task’s separation is not only important for medical decisions, but also for the customization
of financial services on an individual basis. Conrad et al. (2006) analyze problems in money
mismanagement represented by respondents who are unable to improve their financial
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Table 3 Task Estimates and Fit Statistics for 764 Investors. This table shows the level of difficulties for the
questionnaire task in Part 1, Investor Confidence, and Part 2, Information Gathering. Each respondent selects a
response according to a difficulty level on a five-point Likert scale. Mean values of difficulty levels are
calculated according to individual tasks via Infit MNSQ and Outfit MNSQ. Task and person are both
identified and analyzed via the Rasch measurement. Validity and reliability of questionnaire are shown by
the separation index and reliability values
Task no.
Raw score
Estimate logits
Error
Infit MNSQ
Outfit MNSQ
Part 1 Investor Confidence
12
2
1730
1752
0.31
0.23
0.06
0.06
1.05
1.17
1.01
1.20
4
1775
0.15
0.06
0.99
0.97
11
1781
0.12
0.06
0.89
0.87
10
1784
0.11
0.06
0.97
0.96
14
1825
−0.03
0.06
1.10
1.11
6
1894
−0.27
0.06
0.90
0.89
9
1903
−0.30
0.06
0.91
0.91
5
Mean
1913
−0.33
0
0.06
0.06
0.98
1.00
1.00
0.99
0.23
0
Std. Dev.
0.09
0.10
Item reliability 0.93
Item separation index 3.71
Item Infit MNSQ 1.00
Item Infit Zstd -0.1
Person’s reliability 0.89
Person separation index 2.88
Person Infit MNSQ 0.99
Person Infit Zstd -0.3
Part 2 Information Gathering
30
1957
0.28
0.05
0.95
0.98
24
1978
0.22
0.05
1.19
1.17
29
27
1986
2022
0.20
0.10
0.05
0.05
0.89
0.92
0.89
0.91
36
2023
0.09
0.05
0.92
0.93
32
2086
−0.08
0.05
1.05
1.02
35
2093
−0.10
0.05
0.90
0.90
25
2179
−0.34
0.05
1.05
1.06
33
2188
−0.37
0.05
1.08
1.09
0
0.05
0.99
0.99
Mean
Std. Dev.
Item reliability 0.94
0.23
0
Item separation index 4.11
0.10
Item Infit MNSQ 0.99
0.009
Item Infit Zstd -0.1
Person reliability 0.89
Person separation index 2.82
Person Infit MNSQ 0.99
Person Infit Zstd -0.3
situation by saving money, paying bills, budgeting, and making financial decisions; and thus
the authors adopt the separation indices. Yang (2009a) adopts the TSI and ISI values from
Duncan et al. (2003) for mobile banking difficulties to identify those transactions (tasks)
most likely to cause difficulties for individual consumers (individuals).
4.2 Difficulties in tasks for investor confidence and information gathering
Detailed identification of the effects of investors’ confidence and information gathering
ability as they apply to decision making are listed according to the raw scores of the
individual tasks in Table 3. Tasks are ranked in an ascending hierarchy from least to most
difficult. A task with a lower raw score is an easier task for the majority of respondents. A
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315
task with a higher estimate logit value is also an easier task. Linacre and Wright (1994)
recommend that the appropriate fit for the data to the model is between 0.6 and 1.40. All
tasks from the investors’ confidence scale and information gathering scale show infit and
outfit values between 0.9 and 1.19 and 0.87 and 1.20 respectively.
The investors’ confidence scale reflects the hierarchy of difficulty. Table 3 shows that
individual tasks related to technical analysis, including understanding the prospectus (task
12), comprehending symbols and abbreviations (task 2), and determining investment holding periods (task 4), are perceived as the least difficult tasks. Task estimated logit values for
the above are 0.31, 0.23, and 0.15 respectively. Individual tasks related to trading regulations
that include buying and selling limitations (task 5), tax calculations (task 9), and investment
risk minimization (task 6) are identified as the most difficult tasks for decision making
according to the investors’ confidence scale. Estimated logit values for these tasks are −0.33,
−0.30, and −0.27 respectively.
Table 3 also shows the relation between investors’ decision making and information
gathering ability. Investors’ information gathering ability is primarily conducted via interpersonal sources such as consulting services (task 30 with a logit value of 0.28), advertisements
(task 24 with a logit value of 0.22), and asset allocation analyses (task 29 with a logit value of
0.20). However, information gathering via media sources and financial planners creates more
difficulty for investors in terms of their use of references in decision making. Information
sources viewed as insufficient for decision making include visits to financial planners (task 33),
newspaper and magazine investment articles (task 25), and Internet investment information
(task 35). Their estimated logit values are −0.37, −0.34, and −0.10, respectively.
Figure 1 provides a graphical presentation of task difficulty as estimated for the investors’
confidence scale and the information gathering scale. Figure 1’s individual-task map for
investors’ decision making comprises Fig. 1a, the investors’ confidence scale, and Fig. 1b,
the information gathering scale. Figures 1a and b show the number of individuals capable of
completing a task on the left side. Task difficulties are ranked on the right-hand side by
difficulty levels. The higher a task is located up the vertical axis, the more difficult the task is
for investors. Figure 1a shows that investors find understanding the prospectus to be the least
difficult, while buying and selling limitations are the most difficult to understand. Figure 1b
shows that investors find consultation services as the least difficult information source for
reliable decision making, while they feel the greatest difficulty is related to receiving
investment information from financial planners.
4.3 Difficulties in task categories for investor confidence and information gathering
The fit of response categories from 1 (strongly disagree) to 5 (strongly agree) is further
analyzed to assess the quality of the rating scale in Table 4. An orderly fit of values from
least to most can be observed for average measures and threshold calibrations for both the
investors’ confidence and the information gathering scales. Average measure and threshold
calibrations for the investors’ confidence scale range from −3.36 to 2.37 and −3.93 to 3.88,
respectively, and those of the information gathering scale range from −2.57 to 1.95 and
−3.48 to 3.49 respectively. The Outfit MNSQ with values greater than two also indicate the
existence of noise in the measurement process (Bond and Fox 2001). The Infit MNSQ and
the outfit MNSQ for the rating scale categories 1 to 5 in both the investors’ confidence and
information gathering scales are all less than two, which is consistent with a noiseless
measurement process for the Rasch model.
The probability of responses to any particular category is illustrated to indicate likely
responses when considering the investor’s ability and the task’s difficulty (Fig. 2). The threshold
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J Financ Serv Res (2013) 44:303–329
(a) Investor-Confidence Scale
(b) Information-Gathering Scale
Fig. 1 The Person-Item Map for Investor Decision Making comprises (a) the Investor-confidence scale – top
figure and (b) the Information-gathering scale – bottom figure. Figures 1a and 1b show the number of persons
capable of completing a task on the left side. Task difficulties are ranked on the right-hand side by difficulty
levels. The higher a task is located up the vertical axis, the less difficult the task for investors. Figure 1a shows
that investors find understanding the prospectus as the least difficult, but find buying and selling limitations
the most difficult for decision making. Figure 1b shows that investors find consultation services as the most
accessible information source for decision making, but feel the most difficulty for decision making comes with
information from financial planners
calibration values from Table 4 also serve to identify the location of intersections of the category
responses in Fig. 2. For example, the investors’ confidence scale shows that the first threshold
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317
Table 4 Diagnostics for the Investor Decision-Making Rating Scale. This table reports the likely responses of
respondents on a five-point Likert scale. The majority of respondents chose 2 (disagree) for tasks in Investor
Confidence and Information Gathering that might cause difficulties in investment decision making
Category
label
Observed
counts
Average
measure
Infit
MNSQ
Outfit
MNSQ
Threshold
calibration
Part 1 Investor Confidence
1
813
−3.36
1.11
1.06
NONE
2
3077
−1.90
0.96
0.98
−3.93
3
1924
−0.64
0.92
0.92
−0.78
4
801
0.68
0.96
0.99
0.84
5
72
2.37
1.20
1.13
3.88
Part 2 Information Gathering
1
580
−2.57
1.18
1.12
NONE
2
3
2643
2033
−1.37
−0.33
0.93
0.91
0.94
0.92
−3.48
−0.55
4
1418
0.79
0.95
0.98
0.55
5
175
1.95
1.21
1.13
3.49
calibration value is −3.39, a value that corresponds to the intersection of categories 1 and 2.
Therefore, the probability of a category 2 (disagree) response for the investors’ confidence scale
is close to 0.5 or 50 % (see the y-axis) with a corresponding threshold calibration of −3.93 (see
the x-axis). The first intersection of probability curves for the information gathering scale is
approximately 0.5 or 50 % with a threshold calibration of −3.48.
4.4 The task’s characteristics based on demographic backgrounds
The analysis of task characteristic curves serves to identify the probability of discrepancies in
the responses of various subject groups. Table 5 shows the variations in responses related to the
levels of difficulty experienced for tasks on the investors’ confidence and information gathering
scales. On both scales, investors with an income above or below NT$30,000 experience the
most significant level of difficulty, followed by those with an income above or below NT
$50,000. Investors under the age of 33 and over the age of 34 experience difficulties with tasks
listed on the investors’ confidence scale. In contrast, investors under the age of 37 and over the
age of 38 experience difficulties with tasks on the information gathering scale. The gender of
the investors also influences the difficulty levels of tasks on the information gathering scale. For
both the investors’ confidence and the information gathering scales, investors with a high
school education experience more investing difficulties than those with university degrees.
Additionally, single investors experience fewer investing difficulties than those who are married
for both the investors’ confidence and the information gathering scales.
4.5 Spearman’s correlation coefficient between investor confidence and information gathering
Dimensional correlations between investors’ confidence and information gathering ability are conducted via Spearman’s correlation coefficient.2 The Spearman’s correlation
2
To compare each respondent’s reaction in different dimensions, the relationship of different dimensions can
be examined (Ghanem et al. 2010; Gorter et al. 2009). The Spearman correlation coefficient is used to
determine whether the correlation between different outcome measures exists (Hawthorne et al. 2011).
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Part 1 – Investor Confidence
Part 2 – Information Gathering
Fig. 2 Probability Curves for Investor Decision-Marking Rating Scale. The above figures show individuals
of different capabilities and compares each person’s probability of executing the task represented by a task by
calculating the difference in value for the customer-minus-situation difficulty. For example, when a person’s
ability for performing the task is higher than the situation difficulty measure for a task of about 4 to 5 logits,
there is a more than 80 % probability that the person will respond with a 5 (strongly agree)
coefficient assists in determining the correlations of dimensions or scales (Ghanem et al.
2010). The investors’ confidence dimension and the information gathering dimension
show a Spearman’s correlation coefficient value of 0.667, thus demonstrating high
correlation. Investors who are more confident seem to more easily engage in information gathering.
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319
Table 5 Task Characteristics. This table shows variations in the task responses of respondents according to
demographic characteristics. Variations in task responses are significant for income level, age, and gender
Characteristic
Persons
p-value
Average
Investor
Info.
Investor
Info.
Investor
Info.
confidence gathering confidence gathering confidence gathering
Gender
Marital Status
Men
349
349
−1.3009
−0.4979
Women
415
415
−1.6432
−0.7867
No
309
309
−0.7720
−0.1800
Yes
455
445
−1.1371
−0.5727
0.120
0.091*
0.000***
0.000***
0.005***
0.035**
<NT$30,000
293
293
−1.3911
−0.7411
>NT$ 30,000
471
471
−1.5464
−0.6010
Income Level (2) <NT$ 50,000
565
565
−1.4511
−0.6939
0.01**
0.021**
>NT$ 50,000
high school
199
253
199
253
−1.5882
−1.3806
−0.5435
−0.7779
0.000***
0.000***
Bachelor
406
406
−0.7822
−0.2035
0.247
0.833
0.136
0.253
Income Level (1)
Education
Age (1)
Age (2)
Age (3)
Age (4)
Master/Ph.D.
105
105
−0.8482
−0.3504
<25 yrs.
256
256
−1.1049
−0.5750
>26 yrs.
508
508
−1.6793
−0.6681
<29 yrs.
329
329
−1.0560
−0.5291
>30 yrs.
435
435
−1.8127
−0.7180
<33 yrs.
>34 yrs.
408
356
408
356
−1.1403
−1.8840
−0.5104
−0.8201
0.071*
0.275
0.711
0.097*
<37 yrs.
254
254
−2.0335
−0.5104
>38 yrs.
510
510
−1.2146
−0.8201
Note: * p<0.1, **p<0.05, ***p<0.01
4.6 Differential item functioning of investor confidence and information gathering
The regional differences from the degree of urbanization in the task’s difficulty that relate to
the investors’ confidence and information gathering ability are compared via the DIF
(Tables 6 and 7). The northern region has the capital city where the degree of urbanization
is the highest, followed by the central (semi-urbanized) and southern (least urbanized) regions.
Task 4 (investment holding period) on the investors’ confidence scale reflects the difference
perceived for responses vis-à-vis regional differences. For this task, investors in the northern
region have a measured value of 0.33 and experience less difficulty than those in the southern
region, with a measured value of 0.08. All other tasks on the investors’ confidence scale appear
across all three regions, with varying levels of difficulty, but they are nonsignificant. However,
the information gathering scale indicates that investors in all three regions experience slight
differences in decision making with regard to investment participation in tasks related to
advertisements (task 24), asset allocation analysis (task 29), and financial planning (task 33).
The northern region, ranging from −0.19 to −0.41 according to the measured values, consistently experiences less difficulty than the other two regions. The northern region experiences the
least difficulty with regard to decision making based on advertisements (task 24) compared to
the central region (measured value of −0.01) and the southern region (measured value of 0.18).
The northern region also experiences less difficulty (measured value of −0.19) than the southern
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Table 6 Investor Confidence – Regional Comparisons. This table shows the differences in responses to
various tasks in northern, central, and southern regions. Each task response is analyzed by the respondent’s
geographical region. The majority of tasks show nonsignificant influences from the regional effect except for
task 4 for which the regional effect influences the difficulty level between northern and southern regions
Task no.
Measure
Northern
Central
DIF contrast
S.E.
t
Prob.
Southern
0.17
0.16
–
−0.01
0.22
−0.05
0.9575
–
0.16
0.27
−0.12
0.21
−0.58
0.5653
0.17
–
0.27
−0.11
0.13
−0.81
0.4204
4
0.33
–
−0.03
−0.03
–
0.08
−0.36
−0.11
0.22
0.20
−1.63
−0.53
0.1037
0.5947
0.33
–
0.08
0.0635*
5
−0.33
−0.24
–
−0.24
−0.34
2
0.25
0.13
1.86
0.09
0.21
0.43
0.6672
0.11
0.20
0.52
0.6022
−0.33
–
−0.34
0.01
0.13
0.10
0.9213
6
−0.29
−0.13
–
0.15
0.22
0.71
0.4768
–
−0.13
−0.27
0.14
0.20
0.70
0.4815
9
−0.29
−0.25
–
−0.27
−0.27
–
−0.01
−0.02
0.13
0.21
−0.08
−0.09
0.9333
0.9256
0.8129
10
11
12
14
–
−0.27
−0.32
0.05
0.20
0.24
−0.25
–
−0.32
0.07
0.13
0.52
0.6013
0.06
0.05
–
−0.02
0.22
−0.08
0.9328
0.5990
–
0.05
0.15
−0.11
0.20
−0.53
0.06
–
0.15
−0.09
0.13
−0.69
0.4909
0.02
0.19
–
0.17
0.22
0.79
0.4276
–
0.02
0.19
–
0.17
0.17
0.02
−0.15
0.21
0.13
0.10
−1.17
0.9239
0.2415
0.32
0.34
–
0.02
0.22
0.10
0.9243
–
0.34
0.30
0.04
0.21
0.20
0.8413
0.32
–
0.30
0.02
0.13
0.15
0.8772
−0.02
-0.06
–
−0.04
0.22
−0.21
0.8372
–
−0.06
−0.03
−0.03
0.20
−0.16
0.8707
-0.02
–
−0.03
0.01
0.13
0.09
0.9306
Statistically significant at *P<0.1
region (measured value of −0.50) with decision making via a financial planner (task 33), while
the central region experiences less difficulty (measured value of −0.09) than the southern region
(measured value of −0.5) on decision making via a financial planner (task 33). The southern
region experiences less difficulty (measured value of 0.31) than the central region (measure
value of −0.17) with decision making based on an asset allocation analysis (task 29). Figure 3
provides a graphical representation of the differential item functioning for the three regions. The
(diamond with long dashed lines),
(square with solid lines), and
symbols
(triangle with short dashed lines) represent the northern, central, and southern regions
respectively. Variations in responses to difficulties related to investors’ confidence and information gathering ability are indicated through task 4 (the investors’ confidence scale) and
through tasks 24, 29, and 30 (the information gathering scale).
J Financ Serv Res (2013) 44:303–329
321
Table 7 Information Gathering – Regional Comparisons. This table shows the differences in responses to
various tasks in northern, central, and southern regions. Each task response is analyzed by the respondent’s
geographical region. Tasks 24, 29, and 33 show significant regional effects on the difficulty level amongst
northern, central, and southern regions
Task no.
Measure
Northern
24
25
27
29
30
32
33
35
36
DIF contrast
Central
S.E.
t
Prob.
Southern
0.41
−0.01
–
−0.41
0.19
−2.17
–
−0.01
0.18
−0.19
0.18
−1.05
0.2953
0.41
–
0.18
0.23
0.12
1.89
0.0592*
−0.46
–
−0.25
-0.25
–
-0.30
0.21
0.05
0.19
0.18
1.10
0.30
0.2709
0.7636
−0.46
–
−0.30
0.04
−0.04
0.0309**
−0.16
0.12
−1.32
0.1887
−0.08
0.19
−0.43
0.6709
–
−0.04
0.15
−0.18
0.18
−1.04
0.3000
0.04
–
0.15
−0.10
0.12
−0.86
0.3909
0.12
−0.17
–
−0.29
0.19
−1.53
0.2172
–
-0.17
0.31
−0.47
0.18
−2.68
0.0075**
0.12
0.23
–
0.23
0.31
–
−0.18
0.00
0.12
0.19
−1.53
0.02
0.1265
0.9822
–
0.23
0.32
−0.09
0.18
−0.49
0.6241
0.23
–
0.32
−0.09
0.12
−0.76
0.4462
−0.08
0.04
–
0.12
0.19
0.65
0.5178
–
0.04
−0.11
0.15
0.18
0.85
0.3966
−0.08
–
−0.11
0.03
0.12
0.23
0.8202
−0.19
−0.09
–
0.10
0.19
0.55
0.5846
–
−0.19
−0.09
–
−0.50
−0.50
0.41
0.31
0.18
0.12
2.32
2.58
0.0207**
0.0101**
−0.12
0.02
–
0.13
0.19
0.71
0.4799
–
0.02
−0.12
0.13
0.18
0.75
0.4532
−0.12
–
−0.12
0.00
0.12
−0.01
0.9891
0.05
0.26
–
0.21
0.19
1.07
0.2852
–
0.26
0.09
0.17
0.18
0.97
0.3312
0.05
–
0.09
−0.03
0.12
−0.26
0.7920
Statistically significant at *P<0.1 and **P<0.05
4.7 Robustness check
We also conduct a DIF test between the 738 experienced investors and 26 nonexperienced investors on the 18 tasks for the investors’ confidence and information
gathering scales (see Table 8). After a careful review of the data for the 764 respondents, we find that the 26 individuals with no experience want to start investing in the
near future. We also find for the investors’ confidence scale no significant differences in
difficulty levels for the two groups of investors. For the information gathering scale, we
find only one task (consulting services) with a significant difference in difficulty levels.
Thus, our results indicate that it was appropriate to select the experienced investors for
the difficulty identification over the non-experienced investors. The omission of non-
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J Financ Serv Res (2013) 44:303–329
Part 1 Investor Confidence
Logit
0.4
N
0.3
0.2
S
0.1
N
C
S
0
C
-0.1
-0.2
-0.3
-0.4
task 2
task 4
task 5
task 6
task 9
task 10
task 11
task 12
task 14
Part 2 Information Gathering
Logit
0.6
N
0.4
0.2
S
0
C
N
C
S
-0.2
-0.4
-0.6
task 24 task 25 task 27 task 29 task 30 task 32 task 33 task 35 task 36
Fig. 3 Differential Item Functioning: Investor Confidence and Information Gathering. The above figures
show graphical representations of the differential item functioning results in Table 7. Task numbers and logit
values of various regions are identified accordingly
experienced investors shows no effects for our analysis of the difficulties in investors’
decision making.
5 Conclusions
This empirical research attempts to identify difficulties in decision making with regard to
investment participation. The investigation is conducted via the Rasch measurement model
with Cattell’s (1971) investment theory to identify the level of difficulty experienced by
individual investors in relation to their confidence and information gathering ability. This
study differs from previous research in that it identifies specific tasks related to decision
making in financial markets and compares the variations in the levels of difficulty by
individual investors and regions.
With respect to information gathering ability, we determine that investors find interpersonal sources such as consulting services, advertisements, and asset allocation analyses
accessible and relatively effortless. However, information provided by financial planners,
publicized in newspapers or magazines, or on the Internet cause considerable difficulties for
investors with regard to their decision making. This finding is contrary to the findings of
Lease et al. (1974), Capon et al. (1996), Nofsinger (2001), and Loibl and Hira (2009).
J Financ Serv Res (2013) 44:303–329
323
Table 8 DIF analysis on Investor Experience. This table shows the differences in response to various tasks
for investors without trading experience (26 persons) and investors with trading experience (738 persons) for
the Investor-confidence scale and the Information-gathering scale. For the Investor-confidence scale, no task
shows any significant differences in difficulty levels amongst non-experienced investors and experienced
investors. For the Information-gathering scale, task 30 shows a significant difference in difficulty levels
amongst non-experienced investors and experienced investors
Task no.
Measure
No experience
DIF contrast
S.E.
t
Prob.
With experience
Investor-Confidence Scale – Investor Experience
2
0.03
0.25
−0.22
0.33
−0.66
0.5079
4
−0.21
−0.18
−0.03
0.33
−0.09
0.9276
5
−0.26
−0.24
−0.02
0.32
−0.05
0.9579
6
9
−0.40
−0.05
−0.12
−0.39
−0.29
0.34
0.32
0.34
−0.90
1.00
0.3660
0.3179
10
0.16
0.25
−0.09
0.34
−0.26
0.7912
11
0.46
0.09
0.37
0.34
1.09
0.2752
12
0.42
0.13
0.29
0.33
0.85
0.3939
14
−0.09
0.21
−0.30
0.32
−0.94
0.3494
Information-Gathering Scale – Investor Experience
24
0.48
0.15
0.33
0.31
1.05
0.2931
25
27
−0.35
0.15
−0.32
0.06
−0.02
0.09
0.31
0.30
−0.08
0.29
0.9374
0.7741
29
0.27
0.21
0.06
0.31
0.20
0.8381
30
−0.42
0.32
−0.74
0.30
−2.46
0.0139**
32
−0.06
−0.14
0.09
0.30
0.29
0.7728
33
0.12
−0.27
0.39
0.30
1.28
0.2021
35
−0.19
−0.12
−0.07
0.30
−0.22
0.8225
36
0.01
0.12
−0.11
0.31
−0.37
0.7148
Statistically significant at **P<0.05
Investors’ confidence is greatest with respect to technical analysis that leads to an
understanding of the prospectus, symbols and abbreviations, and to making a determination as to investment holding periods. However, investors apparently find themselves
less confident when attempting to understand trading regulations such as those requiring
an understanding of the buying and selling limitations, tax calculations, and risk
minimization. This result corresponds to those of Clark-Murphy and Soutar (2004)
and Benartzi and Thaler (2002).
Further, we find that demographic profiles directly influence the level of difficulties
experienced in the decision making necessary for investment participation. Our findings
show that the individual’s attributes such as income, age, gender, marital status, and
educational background can significantly affect the level of difficulty experienced in
investment decision making. In particular, educational background, marital status, and
income level determine the level of difficulty experienced by investors. For example,
investors with a high school education experience more difficulties with investing than
those with university degrees at the bachelors, masters, or doctoral levels. Single
investors also experience fewer investment difficulties than those who are married.
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J Financ Serv Res (2013) 44:303–329
Investors with incomes less than NT$30,000 show a more significant effect on their
confidence than those with incomes between NT$30,000 and NT$50,000. The latter
group of investors shows greater ability in information gathering when making investment decisions, a finding that is consistent with those of Campbell (2006), Gomes and
Michaelides (2005), and Cohn et al. (1975), Hoffmann and Broekhuizen (2010), and
Cesarini et al. (2010). Furthermore, our study’s results show that respondents who are
more mature in age tend to possess a greater ability to gather appropriate information
for investment decision making, a finding that corresponds to that of Facon (2008) on
fluid intelligence and cognitive ability.
In the case of regional comparisons with regard to the degrees of urbanization, the
central region (semi-urbanized) experiences the greatest level of difficulty in determining
investment holding periods and in determining a need for and conducting asset allocation analyses. Contrary to the findings of Cheng and Lai (2005), investors in the
southern region (least urbanized) are relatively capable of and comfortable with conducting their own asset allocation analyses. The southern region experiences greater
difficulty than the central and northern regions in terms of investing with the assistance
of a financial planner. Corresponding to the findings of Capon et al. (1996), the
northern region with a higher level of urbanization experiences the lowest level of
difficulty associated with investment decision making.
Furthermore, the Spearman’s correlation coefficient is highly correlated to the investors’
confidence and information gathering dimensions. This correlation corresponds to Schweizer
and Koch (2002) and Primi et al. (2010) on the reliance of crystallized intelligence (investors’
confidence dimension) for the better development of fluid intelligence (information gathering
dimension).
This study contributes to the literature by providing a ranking of the tasks, from least
difficult to most difficult, associated with the decision making regarding investment
participation. We use a task and individual separations to identify the level of difficulty
across various regions with different degrees of urbanization for subsample groups with
differential task functioning. Additionally, individual attributes that could serve as
indicators for significant levels of difficulty experienced by investors are also identified
through task characteristics. Rather than focusing on the identification of the relations
between factors associated with investment preferences or investment behavior, this
study investigates the latent factors that affect investors in the process of investment
decision making. The application of the Rasch measurement model assists in pinpointing the unobservable difficulties investors might face before, during, and after the
investment decision-making process. The model also highlights the discrepancies in
the levels of difficulty experienced by segments of investors according to their individual attributes.
The results of this study suggest that government agencies might encourage investors’
education through financial service providers. This method might include providing free
courses related to trading limitations, tax calculations, and the enforcement of strict regulations on the publicity on investment information in newspapers and magazines. For the
development of investors’ confidence, financial service providers might consider offering
more advanced trading courses or seminars for a specific fee. The government might also
impose more regulations on Internet trading publicity and promotions to better protect
investors, while an overall improvement in the job qualifications and general image of
financial planners is definitely in order. With regard to various degrees of urbanization in
different regions, individualized financial services might be provided in relation to asset
allocation assistance, honest advertising, trustworthy financial planners, and investment
J Financ Serv Res (2013) 44:303–329
325
technique training. The government might assist by loosening the trading regulations and
providing a more secure financial trading environment with respect to qualified financial
planners, credible advertising content, and trading channels.
This study samples investors in various regions of Taiwan with different degrees of
urbanization, work experience, and income levels to represent the generalized segment of
investors who participate in financial markets. However, a better classification of investor
segmentation and extended international locations could be compared. Nevertheless, our
selected sample group is representative of the wider segment of investors, as investors with
certain income levels and work experience tend to have greater investment needs than
others.
Acknowledgments The author is grateful to Haluk Ünal (the Editor-in-Chief), David K. Musto (the editor)
and an anonymous referee for their helpful comments and suggestions that have significantly improved this
paper. The author thanks the National Science Council (NSC) of Taiwan, ROC, for financially supporting this
research under the grant NSC97-2410-H-006-094-SSS.
Appendix
Table 9 Tasks in the 37-task Questionnaire for the Investor Decision-Making Survey. This table presents the
37 tasks of the survey questionnaire. Respondents were asked to select the most appropriate response on a
five-point Likert scale. This questionnaire is categorized into investor-confidence and information-gathering
dimensions by asking “how difficult” a task is for execution
Structure category
Part 1 Investor Confidence
Task no.
1
2
Task description
I feel investment terminologies are complex, making investing
difficult for me.
I feel investment symbols and abbreviations written in English;
making investing difficult for me.
3
I feel investment return calculations are complex; making
investing difficult for me.
4
I feel investment holding periods are diversified; making
investing difficult for me.
5
I feel buying and selling limitations for investing are overly
complex; making investing difficult for me.
6
I feel investment risk evaluations have no guarantees; making
investing difficult for me.
7
I feel dividend payment calculations are complex; making
investing difficult for me.
8
I feel trading confirmation and procedures are complicated;
making investing difficult for me.
9
I feel investment tax calculations are complex; making investing
difficult for me.
10
I feel investment rights and responsibilities are complicated;
making investing difficult for me.
11
I feel financial statements are hard to understand; making
investing difficult for me.
12
I feel the prospectus is too technical; making investing difficult
for me.
13
I feel industrial analysis reports are too technical; making
investing difficult for me.
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J Financ Serv Res (2013) 44:303–329
Table 9 (continued)
Structure category
Task no.
14
I feel macroeconomic analysis reports are too technical; making
investing difficult for me.
15
I feel I do not have sufficient capital; making investing
difficult for me.
16
I feel I can only borrow to invest; making investing difficult
for me.
17
I feel I can only invest on a periodic basis with fixed
amounts; making investing difficult for me.
I feel I can only invest on an occasional basis; making
investing difficult for me.
18
Part 2 Information Gathering
Task description
19
I feel I can only invest on an irregular basis; making
investing difficult for me.
20
I feel I do not have any fixed investment plans, making
investing difficult for me.
21
I feel I have not heard of any investment products; making
investing difficult for me.
22
I feel I am only aware of very few investment products;
making investing difficult for me.
23
I feel I cannot request or gather investment information;
making investing difficult for me.
24
I feel I cannot make investment decisions from
advertisements; making investing difficult for me.
I feel I cannot understand newspapers and magazines on
investment news; making investing difficult for me.
25
26
I feel I do not have recommendations from friends and
relatives; making investing difficult for me.
27
I feel I am not participating in portfolio management courses;
making investing difficult for me.
28
I feel I am not attending investment seminars; making
investing difficult for me.
29
I feel I do not have asset allocation analysis; making
investing difficult for me.
30
I feel I do not have consulting services; making investing
difficult for me.
31
I feel I do not have relevant product information; making
investing difficult for me.
I feel I can not invest by TV or radio; making investing
difficult for me.
32
33
I feel I can not invest by a financial planner’s visit; making
investing difficult for me.
34
I feel I can not invest by telephone; making investing
difficult for me.
35
I feel I can not invest by the Internet; making investing
difficult for me.
36
I feel I can not invest through the Mail; making investing
difficult for me.
37
I feel I can only invest by physical presence at service
counters.
J Financ Serv Res (2013) 44:303–329
327
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