Confidence (144. Info and Decision Making)

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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# Springer Science+Business Media, LLC 2012

Abstract This study identifies how investors’ confidence and information gathering abilityaffect their decision making by using the investment theory on crystallized and fluid intelli-gences. We adopt the Rasch model to analyze latent and unobservable factors that causedifficulties in investment participation for investors in Taiwan. These investors are moreconfident in technical analysis but less confident in trading regulations. Further, they findmedia and professional sources difficult to trust, but professional advice is very accessible.

Lower income significantly influences investors’ confidence and their information-gatheringability just as youth significantly contributes to more confidence. But gender and maturity

significantly contribute to factors that concern their information gathering ability. Regionaldemographic 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 informationgathering ability, thus indicating that investors’ confidence enhances the development of theability 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 fromasymmetric information. Asymmetric information is characterized by incomplete informa-tion that often limits investment participation (Díaz 2009) and can result in individual

investors making biased investment decisions. Consequently, individual investors can en-counter higher transaction costs when they search for better information (Rapp and Aubert

J Financ Serv ResDOI 10.1007/s10693-012-0144-0

A. S. Yang (*)Institute of International Management, National Cheng Kung University, Tainan, Taiwan,Republic of Chinae-mail: [email protected]



2011). However, investment knowledge and best practices are a form of accumulatedinformation 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 individualinvestors’ 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 withothers and therefore benefit from lower transaction costs for investment decisions (Bailey et al. 2003). Those investors who possess increased knowledge of investment practices gener-ally experience lower transaction costs associated with investment analyses and informationsearches. They can also adopt less active participatory roles as investors while being able tomore 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 despitethe 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 onfindings of observable causes of investment difficulties, such as a reliance on financialintermediaries or public information (Campbell 2006; Liang and Wang 2007; Koehler andMercer 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 rarelydiscussed in this literature. Investors who are unfamiliar with investment products andwho lack the knowledge to make appropriate investment decisions might require moreeducation to develop investment acceptance and intentions (Campbell 2006; Wang et al.2006). These investors might also adopt risk aversion behaviors, or they might excludethemselves from market participation to minimize losses due to a lack of knowledge (Plathand Stevenson 2005; Benartzi and Thaler 2002; Gomes and Michaelides 2005). Becausethese difficulties are not easy to discover or identify, an empirical study to investigate andidentify them should lead to a better understanding of what investors face during thedecision-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 individualinvestors face with respect to financial decision making. We attempt to identify thedifficulties, ranked by order of importance, that cause obstacles and prevent individualinvestors from participating in financial markets. Our study focuses on analyzing thesedifficulties in relation to self-confidence and with respect to gaining access to informa-tion that leads to investment participation. Contrary to methods used in the literaturethat identify the relation between various personal attributes and their effects on

investment behavior, we propose using the Rasch measurement model to rank thedifficulties of various tasks involved in the investment decision-making process. TheRasch 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 conve-nience of using a small sample size to obtain reliable and valid results, but also theidentification of respondents’ abilities in relation to the tasks (Jackson et al. 2002).

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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 analysisrelated to the comprehension of prospectuses, research reports, and analyses regarding thedetermination of holding periods. In contrast, investors’ confidence is lowest with regard to

trading regulations that include such things as buying and selling limitations, tax calcula-tions, and risk minimization.We also find that individual investors’ information gathering relies mostly on interper-

sonal sources that involve professional assistance consisting of investment consultations,asset allocation analyses, and portfolio management courses. However, individual investorsfind 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 indicatesignificant variations in the level of confidence and the capability to gather information.

Furthermore, youth significantly contributes to increased confidence, while maturity signif-icantly contributes to an increased ability to gather information. The investor ’s gender alsoshows significance for information gathering that leads to investment participation. Simi-larly, 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 havehigher levels of confidence and information gathering than investors in the other tworegions.

The results from this empirical study contribute to the literature by advancing thecomprehension of the underlying difficulties that investors face during the process of

investing. The findings from the results provide solutions to resolve or minimize suchdifficulties.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 thisstudy’s methods and procedures. Section 4 provides the results of the study. Section 5contains 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 subscriptionsto financial periodicals, personal connections, or managers. Information collection con-ducted 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 newscan then encourage investors to purchase, while bad news can cause investors to sell. Socialinteractions 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 infor-mation gathering (Hong et al. 2004; Loibl and Hira 2009). Investors that gather informationvia 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, accessto the Internet encourages households to increase their level of participation in online trading(Bogan 2008). While knowledge about transaction costs and related online trading discountsare 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, infor-mation gathering related to investment purposes varies with availability, channel access, andsource 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 socialnetworks, and sufficient investment knowledge generally tend to diversify investments(Hoffmann and Broekhuizen 2010; Capon et al. 1996). Similarly, mature investors residingin 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 andthat 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 counterpartswho trade less frequently. Therefore, our conclusion is that demographic profiles related toage, marital status, income, and gender provide preliminary and knowledgeable insights intoinvestors’ trading behavior and ability.

Furthermore, investment skills that focus on the technicalities of risk and return analysesinfluence and contribute to decision making. But investors’ decision making, from the

perspective of utility, is determined by risk preferences (Dorn and Huberman 2010). Thedecision-making process, therefore, consists of technical analysis, risk and return evaluation,and performance surveillance (Radcliffe 1990). Investors’ preferences toward median port-

folios 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 skillsthrough 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 decisionmaking 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 problemsolving in math (Primi et al. 2010). Crystallized intelligence refers to an individual’s basicknowledge 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 ratesand thus gain from portfolio compositions. However, the development of crystallized intelli-gence 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 byclaiming to possess better investment skills that lead to better performances (Glaser and Weber 2007). Cognitive ability or fluid intelligence, which greatly determines one’s intellectualefficiency or intellectual quality (IQ), reaches its maximum level at an early adult age andremains stable throughout middle and old ages (Facon 2008). Thus, investors’ risk preferenceslargely influence decision making for successful portfolio diversification, whereas tradingexperience 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 easilyobserved. The Rasch measurement model allows for the separation of the tasks andindividuals to ensure that all tasks provide the same measurement outcome with regard todifficulty 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, andthe 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 andLinacre 2005). This theory has been used extensively, perhaps most notably in the analysisof data from functional assessment instruments in rehabilitation medicine (Massof andFletcher 2001) and psychometric methods (Garamendi et al. 2006). The model’s principalfunction 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 isdependent 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 beclarified in settings where “θn” represents the individual’s ability and “ bi” represents thetask ’s level of difficulty:

P 1 θn; bijð Þ ¼ eθnbi

1 þ eθnbi: ð1Þ

The probability that an investor n reports that he or she cannot achieve task i with ease is:

P 0 θn; bijð Þ ¼ 1 P 1 θn; bijð Þ ¼ 11 þ eθnbi: ð2Þ

The odds that an investor n reports that he or she can achieve task i with ease are:

P 1 θn; bijð Þ

P 0 θn; bijð Þ ¼ eθnbi

; ð3Þ

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and the log of the odds ratio, or the “logit ”, is

ln P 1 θn; bijð Þ

P 0 θn; bijð Þ ¼ θn bi; 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 difficultythat a respondent might experience in financial decision making, the Rasch model canidentify 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 therespondent 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 reliabilityindicates 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. Theindividual separation index estimates how well individuals can be differentiated on themeasured variable. The task separation index estimates how well tasks can be differentiatedon the measured variable. The latter two indices are quantified in standard error units (Bondand 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 tothe 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 easilyobserved 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 mapto identify the levels of difficulties experienced by different individuals for their uniquestrategy development. Our empirical study uses the Rasch model to focus on individualinvestors 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 previouslyunobserved or hidden due to the generalization of investors. Fisher (2009) recommendsthe 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 toidentify ownership patterns in the financial assets of households, and they find that theapplication 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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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 financialdecision 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 designedon 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 theinformation-weighted fit mean squares (Infit MNSQ) when the task ’s values are outside therange of 0.8 – 1.20 (Garamendi et al. 2006). Survey tasks that meet the correlation betweenthe data and the model are identified as latent and viable variables. These variables remain inthe 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.) with17 tasks. Using the confidence scale, respondents are asked to rate the level of difficulty theyface regarding technical analyses, trading regulations, and factors relating to investors’

psychology. Similarly, respondents use the information scale to rate the difficulties theyencounter when gathering information through media sources and interpersonal sources andto rate their fixed costs associated with that type of information gathering. The responses arescored 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 theRasch model. Therefore, the estimated parameters and the model-fit statistics can becalibrated 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 confidenceand 8 tasks related to information. A standard error-type stopping rule is used in the ratingscale 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 tothe inability to predict the task ’s difficulty and the individual’s ability (Soutar andCornish-Ward 1997). Tasks unfitted to the unidimensional scale are eliminated due tothe inability to significantly improve the scale (Ganglmair and Lawson 2003). There-fore, eliminated tasks are viewed as irrelevant in influencing or affecting an individual ’sability to perform a task. For example, Soutar and Cornish-Ward (1997) eliminate thesavings account task and other property tasks to obtain a good fit for the unidimen-sional Rasch model and thus identify financial asset-ownership patterns. To identify

adoption difficulties for mobile banking, Yang (2009a) eliminates tasks relating tomobile-banking, transaction-reply fees, and customer service. The Rasch model usesthe 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 areacceptable 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 areeliminated (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 Raschmodel (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. Theyalso clearly note the latent difficulties not easily detected. Furthermore, these remainingtasks 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 andFletcher 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 responsechoices for rating-scale tasks ranging from 1 (strongly disagree) to 5 (strongly agree).Reliability estimates that consist of an individual’s reliability, the individual separationindex, 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 toidentify 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 therespondents (Heesch et al. 2006). Finally, the differential item function (DIF) iscalculated 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 excluderespondents working in the financial services industries as they are more apt and resourcefulat 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 arenot 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 anincreasing need to invest. Respondents with and without investment experiences are com-

pared to identify latent difficulties toward investing that might serve as a barrier in devel-oping 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 anindividual’s wealth (Lease et al. 1974). While most respondents were reluctant to disclosetheir 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 thestudies were being conducted by scholars for non-commercial use and that their anonymitywould be preserved.

Respondents were solicited at bank (headquarter and branches) and ATM (automaticteller machines) locations where financial transactions could be conducted. Every tenthcustomer 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 askedto participate. Taiwan’s financial deregulation in 2001 under the Financial Holding CompanyAct allows banking institutions to conduct investment and security related services andoperations in the form of a financial holding company (Yang 2009b).

Once an individual was solicited, we determined any possible conflicts or poorlycompleted 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 responsesand excluded from the survey analysis. Therefore, out of the 1,077 respondents, weidentified and eliminated 313 respondents with invalid survey responses that resulted in764 valid respondents. Thus, the survey is measurable on a rating scale and meets thegeneralizability characteristic to allow any group of individuals to provide a response to anytask (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 monthlyincome of NT$38,5601 (SD024,275). Sample respondents in the central region of Taiwanare 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, 26of 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 astheir most preferred investment, while the central and southern regions show that 48.7 % and45.9 % invest in insurance respectively. The second and third preferred investments in thenorthern 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 mutualfunds and 11.3 % have real estate holdings. Thus, the evidence shows that investors in thenorthern 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) andSierminska et al. (2006) on the regional influences on invested assets.

1 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.

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Table 2 compares the effects of income on the wealth portfolio composition of the 738respondents 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 stocksat 63.1 %, insurance at 59 %, mutual funds at 41 %, and real estate at 20.5 %. The extremelyhigh-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

Table 1 Descriptive Statistics of Study Sample: (a) Regional demographics. Table 1 shows the descriptivestatistics of all respondents by regions. Approximately 26 persons out of a total of 764 persons earn income, but do not participate in investing

Demographic characteristics All Regions Northern 224

persons (29.3 %)

Central 80

persons (10.5 %)

Southerna 460

persons (60.2 %)

Persons % Persons % Persons % Persons %

Gender

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 24.6d. above 51 107 14 31 13.8 17 21 59 12.8

Mean/S.D. 37.7/11.3 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 455 59.6 126 56.3 26 69.1 273 59.5

Education

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

Monthly Income

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 130 17 48 21.4 18 22.2 64 13.9

d. Above 70001 69 9 21 9.4 6 7.4 42 9.2

Mean/S.D. 38560/24275 41295/24119 42222/23452 36580/24340

Wealth Portfolio

a. Time deposits 643 86.8 190 88 65 83.3 388 87.4

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

a Includes the eastern region and the off-island regions

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income leads to an increase in investing in other types of assets. This finding is consistent withBijmolt 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. TheTSI represents the estimate of the spread or the separation of the tasks. The ISI represents anestimate of the spread of the individuals (Franchignoni et al. 2010). The investors’ confi-dence 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 theinvestors’ confidence scale of 2.88. These values measured for validity correspond toDuncan et al. (2003) that recommend values greater than three for the TSI and greater thantwo for the ISI. The reliability measures for investor confidence and information gatheringare 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 between0.99 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 Raschmodel 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 andvalidity analyses by providing in-depth analyses of an individual’s ability and a task ’sdifficulty as identified on a measurement scale. The identification of an individual and atask ’s separation is not only important for medical decisions, but also for the customizationof financial services on an individual basis. Conrad et al. (2006) analyze problems in moneymismanagement represented by respondents who are unable to improve their financial

Table 2 Descriptive Statistics of Study Sample: (b) Wealth portfolio. Table 2 shows the descriptive statisticsof 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 TimeDeposits Mutual Funds Stocks Insurance Real Estate

Person Person % Person % Person % Person % Person %

a. Below 30000 283 246 86.9 89 31.4 72 25.4 99 35 13 4.6

b. 30001 – 50000 264 241 91.3 122 46.2 132 50 117 44.3 34 12.9

c. 50001 – 70000 122 102 83.6 50 41 77 63.1 72 59 25 20.5

d. Above 70001 69 54 78.3 41 59.4 40 58 31 44.9 17 24.6

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situation by saving money, paying bills, budgeting, and making financial decisions; and thusthe authors adopt the separation indices. Yang (2009a) adopts the TSI and ISI values fromDuncan 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 gatheringability as they apply to decision making are listed according to the raw scores of theindividual 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

Table 3 Task Estimates and Fit Statistics for 764 Investors. This table shows the level of difficulties for thequestionnaire task in Part 1, Investor Confidence, and Part 2, Information Gathering. Each respondent selects aresponse according to a difficulty level on a five-point Likert scale. Mean values of difficulty levels arecalculated according to individual tasks via Infit MNSQ and Outfit MNSQ. Task and person are bothidentified and analyzed via the Rasch measurement. Validity and reliability of questionnaire are shown by

the separation index and reliability valuesTask no. Raw score Estimate logits Error Infit MNSQ Outfit MNSQ

Part 1 Investor Confidence

12 1730 0.31 0.06 1.05 1.01

2 1752 0.23 0.06 1.17 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 1.116 1894 −0.27 0.06 0.90 0.89

9 1903 −0.30 0.06 0.91 0.91

5 1913 −0.33 0.06 0.98 1.00

Mean 0 0.06 1.00 0.99

Std. Dev. 0.23 0 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 1986 0.20 0.05 0.89 0.89

27 2022 0.10 0.05 0.92 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

Mean 0 0.05 0.99 0.99

Std. Dev. 0.23 0 0.10 0.009

Item reliability 0.94 Item separation index 4.11 Item Infit MNSQ 0.99 Item Infit Zstd -0.1

Person reliability 0.89 Person separation index 2.82 Person Infit MNSQ 0.99 Person Infit Zstd -0.3

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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. Alltasks from the investors’ confidence scale and information gathering scale show infit andoutfit 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 hold-ing 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 regulationsthat 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 makingaccording 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 interper-sonal 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 moredifficulty for investors in terms of their use of references in decision making. Informationsources 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 bydifficulty levels. The higher a task is located up the vertical axis, the more difficult the task isfor 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 1bshows that investors find consultation services as the least difficult information source for reliable decision making, while they feel the greatest difficulty is related to receivinginvestment 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 fromleast to most can be observed for average measures and threshold calibrations for both theinvestors’ confidence and the information gathering scales. Average measure and thresholdcalibrations 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 theexistence of noise in the measurement process (Bond and Fox 2001). The Infit MNSQ andthe outfit MNSQ for the rating scale categories 1 to 5 in both the investors’ confidence andinformation gathering scales are all less than two, which is consistent with a noiselessmeasurement process for the Rasch model.

The probability of responses to any particular category is illustrated to indicate likelyresponses when considering the investor ’s ability and the task ’s difficulty (Fig. 2). The threshold

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calibration values from Table 4 also serve to identify the location of intersections of the categoryresponses in Fig. 2. For example, the investors’ confidence scale shows that the first threshold

(a) Investor-Confidence Scale

(b) Information-Gathering Scale

Fig. 1 The Person-Item Map for Investor Decision Making comprises (a) the Investor-confidence scale – topfigure and (b) the Information-gathering scale – bottom figure. Figures 1a and 1b show the number of personscapable of completing a task on the left side. Task difficulties are ranked on the right-hand side by difficultylevels. 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 limitationsthe 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 withinformation from financial planners

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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 scaleis close to 0.5 or 50 % (see the y-axis) with a corresponding threshold calibration of −3.93 (seethe 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 inthe responses of various subject groups. Table 5 shows the variations in responses related to thelevels of difficulty experienced for tasks on the investors’ confidence and information gatheringscales. On both scales, investors with an income above or below NT$30,000 experience themost 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 taskslisted on the investors’ confidence scale. In contrast, investors under the age of 37 and over theage 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 highschool education experience more investing difficulties than those with university degrees.Additionally, single investors experience fewer investing difficulties than those who are marriedfor 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 abil-ity 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 todetermine whether the correlation between different outcome measures exists (Hawthorne et al. 2011).

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


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


1 580 −2.57 1.18 1.12 NONE

2 2643 −1.37 0.93 0.94 −3.483 2033 −0.33 0.91 0.92 −0.55

4 1418 0.79 0.95 0.98 0.55

5 175 1.95 1.21 1.13 3.49

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coefficient assists in determining the correlations of dimensions or scales (Ghanem et al.2010). The investors’ confidence dimension and the information gathering dimensionshow a Spearman’s correlation coefficient value of 0.667, thus demonstrating highcorrelation. Investors who are more confident seem to more easily engage in informa-tion gathering.

Part 1 – Investor Confidence

Part 2 – Information Gathering

Fig. 2 Probability Curves for Investor Decision-Marking Rating Scale. The above figures show individualsof different capabilities and compares each person’s probability of executing the task represented by a task bycalculating the difference in value for the customer-minus-situation difficulty. For example, when a person’sability 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)

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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 tothe 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 urbanizationis 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 northernregion have a measured value of 0.33 and experience less difficulty than those in the southernregion, 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, consis-tently experiences less difficulty than the other two regions. The northern region experiences theleast difficulty with regard to decision making based on advertisements (task 24) compared tothe 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

Table 5 Task Characteristics. This table shows variations in the task responses of respondents according todemographic characteristics. Variations in task responses are significant for income level, age, and gender

Characteristic Persons Average p-value

Investor confidence Info.gathering Investor confidence Info.gathering Investor confidence Info.gathering

Gender Men 349 349 −1.3009 −0.4979 0.120 0.091*

Women 415 415 −1.6432 −0.7867

Marital Status No 309 309 −0.7720 −0.1800 0.000*** 0.000***

Yes 455 445 −1.1371 −0.5727

Income Level (1) <NT$30,000 293 293 −1.3911 −0.7411 0.005*** 0.035**

>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$ 50,000 199 199 −1.5882 −0.5435Education high school 253 253 −1.3806 −0.7779 0.000*** 0.000***

Bachelor 406 406 −0.7822 −0.2035

Master/Ph.D. 105 105 −0.8482 −0.3504

Age (1) <25 yrs. 256 256 −1.1049 −0.5750 0.247 0.833

>26 yrs. 508 508 −1.6793 −0.6681

Age (2) <29 yrs. 329 329 −1.0560 −0.5291 0.136 0.253

>30 yrs. 435 435 −1.8127 −0.7180

Age (3) <33 yrs. 408 408 −1.1403 −0.5104 0.071* 0.275

>34 yrs. 356 356 −

1.8840 −

0.8201Age (4) <37 yrs. 254 254 −2.0335 −0.5104 0.711 0.097*

>38 yrs. 510 510 −1.2146 −0.8201

Note: * p<0.1, ** p<0.05, *** p<0.01

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region (measured value of −0.50) with decision making via a financial planner (task 33), whilethe 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 southernregion 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. Thesymbols (diamond with long dashed lines), (square with solid lines), and

(triangle with short dashed lines) represent the northern, central, and southern regionsrespectively. Variations in responses to difficulties related to investors’ confidence and infor-mation gathering ability are indicated through task 4 (the investors’ confidence scale) andthrough tasks 24, 29, and 30 (the information gathering scale).

Table 6 Investor Confidence – Regional Comparisons. This table shows the differences in responses tovarious tasks in northern, central, and southern regions. Each task response is analyzed by the respondent ’sgeographical 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 DIF contrast S.E. t Prob.

Northern Central Southern

2 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 – −0.36 0.22 −1.63 0.1037

– −0.03 0.08 −0.11 0.20 −0.53 0.5947

0.33 – 0.08 0.25 0.13 1.86 0.0635*

5 −

0.33 −

0.24 0.09 0.21 0.43 0.6672 – −0.24 −0.34 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

−0.29 – −0.27 −0.01 0.13 −0.08 0.9333

9 −0.25 −0.27 – −0.02 0.21 −0.09 0.9256

– −0.27 −0.32 0.05 0.20 0.24 0.8129

−0.25 – −0.32 0.07 0.13 0.52 0.6013

10 0.06 0.05 – −0.02 0.22 −0.08 0.9328

– 0.05 0.15 −0.11 0.20 −0.53 0.5990

0.06 – 0.15 −0.09 0.13 −0.69 0.4909

11 0.02 0.19 – 0.17 0.22 0.79 0.4276

– 0.19 0.17 0.02 0.21 0.10 0.9239

0.02 – 0.17 −0.15 0.13 −1.17 0.2415

12 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

14 −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

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4.7 Robustness check

We also conduct a DIF test between the 738 experienced investors and 26 non-experienced investors on the 18 tasks for the investors’ confidence and informationgathering scales (see Table 8). After a careful review of the data for the 764 respond-ents, we find that the 26 individuals with no experience want to start investing in thenear future. We also find for the investors’ confidence scale no significant differences indifficulty levels for the two groups of investors. For the information gathering scale, wefind 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-

Table 7 Information Gathering – Regional Comparisons. This table shows the differences in responses tovarious tasks in northern, central, and southern regions. Each task response is analyzed by the respondent ’sgeographical region. Tasks 24, 29, and 33 show significant regional effects on the difficulty level amongst northern, central, and southern regions

Task no. Measure DIF contrast S.E. t Prob.

Northern Central Southern

24 0.41 −0.01 – −0.41 0.19 −2.17 0.0309**

– −0.01 0.18 −0.19 0.18 −1.05 0.2953

0.41 – 0.18 0.23 0.12 1.89 0.0592*

25 −0.46 −0.25 – 0.21 0.19 1.10 0.2709

– -0.25 -0.30 0.05 0.18 0.30 0.7636

−0.46 – −0.30 −0.16 0.12 −1.32 0.1887

27 0.04 −

0.04 −

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

29 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 – 0.31 −0.18 0.12 −1.53 0.1265

30 0.23 0.23 – 0.00 0.19 0.02 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

32 −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

33 −0.19 −0.09 – 0.10 0.19 0.55 0.5846

– −0.09 −0.50 0.41 0.18 2.32 0.0207**

−0.19 – −0.50 0.31 0.12 2.58 0.0101**

35 −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

36 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

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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 toinvestment participation. The investigation is conducted via the Rasch measurement modelwith Cattell’s (1971) investment theory to identify the level of difficulty experienced byindividual investors in relation to their confidence and information gathering ability. Thisstudy differs from previous research in that it identifies specific tasks related to decisionmaking 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 interper-sonal sources such as consulting services, advertisements, and asset allocation analysesaccessible 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).


N

C

S

-0.4

-0.3

-0.2

-0.1

0

0.10.2

0.3

0.4

task 2 task 4 task 5 task 6 task 9 task 10 task 11 task 12 task 14

Logit

N

C

S


N

C

S

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

task 24 task 25 task 27 task 29 task 30 task 32 task 33 task 35 task 36

Logit

N

C

S

Fig. 3 Differential Item Functioning: Investor Confidence and Information Gathering. The above figuresshow graphical representations of the differential item functioning results in Table 7. Task numbers and logit values of various regions are identified accordingly

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Investors’ confidence is greatest with respect to technical analysis that leads to anunderstanding of the prospectus, symbols and abbreviations, and to making a determi-nation as to investment holding periods. However, investors apparently find themselvesless confident when attempting to understand trading regulations such as those requiringan 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 difficultiesexperienced in the decision making necessary for investment participation. Our findings

show that the individual’

s attributes such as income, age, gender, marital status, andeducational background can significantly affect the level of difficulty experienced ininvestment decision making. In particular, educational background, marital status, andincome level determine the level of difficulty experienced by investors. For example,investors with a high school education experience more difficulties with investing thanthose with university degrees at the bachelors, masters, or doctoral levels. Singleinvestors also experience fewer investment difficulties than those who are married.

Table 8 DIF analysis on Investor Experience. This table shows the differences in response to various tasksfor 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 experiencedinvestors. For the Information-gathering scale, task 30 shows a significant difference in difficulty levels

amongst non-experienced investors and experienced investorsTask no. Measure DIF contrast S.E. t Prob.

No experience 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 −0.40 −0.12 −0.29 0.32 −0.90 0.3660

9 −0.05 −0.39 0.34 0.34 1.00 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 −0.35 −0.32 −0.02 0.31 −0.08 0.9374

27 0.15 0.06 0.09 0.30 0.29 0.7741

29 0.27 0.21 0.06 0.31 0.20 0.838130 −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

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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 invest-ment decisions, a finding that is consistent with those of Campbell ( 2006), Gomes and

Michaelides (2005), and Cohn et al. (1975), Hoffmann and Broekhuizen (2010), andCesarini et al. (2010). Furthermore, our study’s results show that respondents who aremore mature in age tend to possess a greater ability to gather appropriate informationfor investment decision making, a finding that corresponds to that of Facon (2008) onfluid intelligence and cognitive ability.

In the case of regional comparisons with regard to the degrees of urbanization, thecentral region (semi-urbanized) experiences the greatest level of difficulty in determininginvestment holding periods and in determining a need for and conducting asset alloca-tion analyses. Contrary to the findings of Cheng and Lai (2005), investors in thesouthern region (least urbanized) are relatively capable of and comfortable with con-

ducting their own asset allocation analyses. The southern region experiences greater difficulty than the central and northern regions in terms of investing with the assistanceof a financial planner. Corresponding to the findings of Capon et al. (1996), thenorthern 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 difficultyacross various regions with different degrees of urbanization for subsample groups withdifferential task functioning. Additionally, individual attributes that could serve asindicators for significant levels of difficulty experienced by investors are also identifiedthrough task characteristics. Rather than focusing on the identification of the relations

between factors associated with investment preferences or investment behavior, thisstudy investigates the latent factors that affect investors in the process of investment

decision making. The application of the Rasch measurement model assists in pinpoint-ing the unobservable difficulties investors might face before, during, and after theinvestment decision-making process. The model also highlights the discrepancies inthe levels of difficulty experienced by segments of investors according to their individ-ual attributes.

The results of this study suggest that government agencies might encourage investors’education through financial service providers. This method might include providing freecourses related to trading limitations, tax calculations, and the enforcement of strict regu-lations on the publicity on investment information in newspapers and magazines. For the

development of investors’

confidence, financial service providers might consider offeringmore advanced trading courses or seminars for a specific fee. The government might alsoimpose 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 indifferent regions, individualized financial services might be provided in relation to asset allocation assistance, honest advertising, trustworthy financial planners, and investment

J Financ Serv Res



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 withcertain income levels and work experience tend to have greater investment needs thanothers.

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 the37 tasks of the survey questionnaire. Respondents were asked to select the most appropriate response on afive-point Likert scale. This questionnaire is categorized into investor-confidence and information-gatheringdimensions by asking “how difficult ” a task is for execution

Structure category Task no. Task description

Part 1 Investor Confidence 1 I feel investment terminologies are complex, making investingdifficult for me.

2 I feel investment symbols and abbreviations written in English;making investing difficult for me.

3 I feel investment return calculations are complex; makinginvesting difficult for me.

4 I feel investment holding periods are diversified; makinginvesting difficult for me.

5 I feel buying and selling limitations for investing are overlycomplex; making investing difficult for me.

6 I feel investment risk evaluations have no guarantees; makinginvesting difficult for me.

7 I feel dividend payment calculations are complex; makinginvesting 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 investingdifficult 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; makinginvesting difficult for me.

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Table 9 (continued)

Structure category Task no. Task description

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 fixedamounts; making investing difficult for me.

18 I feel I can only invest on an occasional basis; makinginvesting difficult for me.

19 I feel I can only invest on an irregular basis; makinginvesting difficult for me.

20 I feel I do not have any fixed investment plans, makinginvesting difficult for me.

Part 2 Information Gathering 21 I feel I have not heard of any investment products; makinginvesting 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 fromadvertisements; making investing difficult for me.

25 I feel I cannot understand newspapers and magazines oninvestment news; making investing difficult for me.

26 I feel I do not have recommendations from friends andrelatives; 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; makinginvesting difficult for me.

29 I feel I do not have asset allocation analysis; makinginvesting difficult for me.

30 I feel I do not have consulting services; making investingdifficult for me.

31 I feel I do not have relevant product information; makinginvesting difficult for me.

32 I feel I can not invest by TV or radio; making investingdifficult for me.

33 I feel I can not invest by a financial planner ’s visit; makinginvesting difficult for me.

34 I feel I can not invest by telephone; making investingdifficult for me.

35 I feel I can not invest by the Internet; making investingdifficult for me.

36 I feel I can not invest through the Mail; making investingdifficult for me.

37 I feel I can only invest by physical presence at servicecounters.

J Financ Serv Res



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Confidence (144. Info and Decision Making)