Asia Pacific Management Review (2004) 9(3), 391-413
Demographic and Psychographic Profile of
Active and Passive Investors of KLSE:
A Discriminant Analysis
Ezlika Ghazali∗ and Md. Nor Othman∗∗
(received June 2003; revision received October 2003; accepted December 2003)
Abstract
The study attempts to delineate the demographic and lifestyle characteristics of active and
passive investors in Malaysia. The two groups are compared using eight demographic, five
psychographic, and five activity dimensions. The results indicate that there were significant
differences in terms of gender, age, occupation, monthly personal income and monthly household income between the two investor groups. In terms of psychographic dimensions, active
investors were more risk takers or innovative than passive investors. When the activity dimensions of the two groups were examined, significant differences were also found. Active
investors were found to be more knowledge-seekers, outdoor-lovers and outgoing/entertainment lovers when compared to passive investors. The application of discriminant analysis reveals that certain variables are relatively more important than others, in discriminating between
active and passive investors. With regards to level of importance, personal monthly income
ranks the highest, followed by occupation and outgoing/entertainment lover dimension of
activity items. Some marketing implications were also discussed.
Keywords: Consumer behavior; Psychographics; Investor profile; AIO inventory; Discriminant
analysis
1. Introduction
The stiff competition, growing complexity and sophistication of the
stock market have made stock broking a specialized profession. Globali
zation of markets presents considerable challenges and opportunities for
domestics and international marketers [1,17]. This will pose a threat to
securities industry in Malaysia. As such, the importance of marketing in
stock broking firms must not be taken for granted. According to Lee [9], the
effectiveness of a broker depends largely on the knowledge and understanding of the customers. This is because to the general public one stock
broking service is like another [12], and investors’ loyalty hinges primarily
∗
Department of Marketing and Information Systems, Faculty of Business & Accountancy,
University of Malaya, 50603 Kuala Lumpur, Malaysia. (Tel: 603-7967-3836, Fax: 603-79673810, Email: ezlika@um.edu.my)
∗∗
Dean of the Faculty of Business & Accountancy, University of Malaya, 50603 Kuala Lumpur,
Malaysia. (Tel: 603-7967-3800, Fax: 603-7967-3980, Email: mohdnor@.um.edu.my)
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on the qualities of the individual broker, not on the reputation of the firm [9].
Thus, by analyzing the psychographic characteristics of the individual investors, stockbrokers will be in a better position to understand the prospects
and identify new opportunities. The segmentation of potential clientele based
on knowledge of investors grouped in demographic factors (sex, age, income,
education and occupation), psychographic factors (social class, lifestyles)
and behavioral factors (active investors or passive investors) enables financial service marketer i.e. brokers to plan strategically and effectively.
Demographic variables have long been used as a foundation for segmenting the market for financial services [10,18]. The consumers within the
same demographic group can exhibit very different psychographic dimensions. According to Simon H. Friend [4], a marketing researcher for Survey
Research Malaysia, the Malaysian marketers can see their consumers in
‘black and white’ by using demographic variables. However, with psychographics, the marketers can see them in ‘colors’ [4]. The basic premise of
psychographics research is that the more you know and understand your
customers the more effectively you can communicate and market to them
[15].
However, very little empirical research exists pertaining to individual
investors and their psychographic profile in Malaysia. It would be interesting
to know the type of people making up Malaysian capital market.
The purpose of this study is to provide an insight into the profile of
active and passive investors in urban Malaysia, along with their demographic, lifestyle1 characteristics and activity participated. It is hoped that the
study will be able to discriminate between active and passive investors based
on these variables.
2. Literature Review
Numerous psychographic studies can be found in the literature. However, very few researches have been done on the demographic and psychographic profile of investor groups. In the subsequent sub-sections, past
literature on investor categories, the psychographics of investors and leisure
activities participated by investors will be discussed.
1
In this paper, the terms “lifestyle” and “psychographics” have been used interchangeably (see
Mowen, 2001)
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2.1 Investor Categories
Different researchers in the past had attempted to categorize investors in
different ways. At least three different ways of categorizing stock market
investors can be found. Barnewell [2], in her study, categorizes investors
into active and passive. The investment orientation of active investors is for
control, with 70 per cent of their investments in the higher risks and 30 per
cent in the lower risks assets. Passive investors, on the other hand, are noncontrol oriented with 70 per cent of their investment in the lower risks assets
and 30 per cent in higher risks. Both investor groups are classified using
focus group interviews [2].
Another way of categorizing investors is to look at the total investment
holdings the investors have. Based on this criterion, Warren et al. [18]
categorize investors into light and heavy. Light investors are those who have
US $30,000 or less in total investment holdings. Heavy investors, on the
other, are defined as those having a total investment holding of more than
US $30,000. Using a similar criterion, Lim [10], in a Malaysian study,
classifies individual investors also in terms of light and heavy investors.
Light investors are defined as those who have investment holdings of less
than RM20,000, while heavy investors are those with RM20,000 or more in
total investment holdings.
In another study, Brandweek [3] reported a study by Yankelovich Partner of New York that categorizes investors into “Strugglers” and “Secures”.
“Strugglers” are those with median household income of U.S. $34,000 per
annum and mean investible assets of only U.S. $6,000, while “Secures” are
those having median household income of U.S. $75,000 per annum with
mean investible assets of U.S. $192,000. The study concludes that “Secures”
are future focused and possess money to burn, while “Strugglers” have low
assets to invest and limited financial savvy [3].
Based on Barnewell [2], the current study will categorize investors into
active and passive investors. However, unlike Barnewell [2], the present study will categorize them based on the frequency of transactions made by the
respondents over time. Barnewell [2] categorize the two investor groups
based on their control orientation and investment in risky or non-risky assets.
Definition of Barnewell [2] is not suitable because the current study look
into the behavior of individual investors as compared to Barnewell [2] who
look into institutional investors in which buying and selling for control is
more common.
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As such, our definition of active and passive investors basically refers to
whether a particular individual investor participated regularly in buying and
selling stock in the stock market. The more regular the transactions, the more
the investors is said to be active in the market. Passive investors are those
who do not participate regularly in the buying and selling of stock in the
market.
2.2 Investor Groups and Demographic Variables
Previous studies have found that active or heavy investors generally
have higher income when compared to passive or light investors [2,10,18].
In addition, Warren et al. [18] in their study, found that heavy investors tend
to reside in households with no children living at home or in households
with children of 18 years of age and older. They or their spouses tend to be
full-time homemakers and, in terms of education, have at least one to three
years of college. In Malaysia, Lim [10] found that heavy investors have a
greater tendency to be married but with no children or with children between
5 to 18 years of age. They also tend to have spouses who are employed fulltime. They also tend to be better educated than light investors. As for light
investors, both Lim [10] and Warren et al. [18] have found that most of them
are singles or widows or if married, still having children at home.
In terms of occupation, Barnewell [2], in his study, found that active
investors work either as small business owner, surgeon doctors, surgeon
dentists, independent CPA, independent lawyers or entrepreneurs. Passive
investors, on the other hand, are corporate executives, non-surgeon doctors,
non-surgeon dentists, CPA attached to big firms or lawyers attached to large
firms. However, Lim [10] found that there is no significant difference with
regards to the occupational characteristics of light and heavy investors. With
respect to ethnicity and age, Lim [10] found that heavy investors in Malaysia
are mostly Chinese who are elderly with an average age of 40.
Based on the literature discussed above, one might expect some differences in terms of demographic variables between active and passive investors.
H1: There will be some significant differences between active and passive investors along some demographic variables.
2.3 Psychographics of Investors
According to Mowen and Minor [11], psychographics analysis is a type
of consumer research that describes segments of consumers in terms of how
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they live, work, and play. Psychographics is employed to assess consumers’
lifestyles by analyzing their activities, interests, and opinions (AIO). Numerous studies have found that there are some relationships between brand or
product preferences and psychographics [19,20,21]. However, there is a
dearth of literature on the psychographics of investors. Previous studies on
individual investors have found that there are some relationships between
types of investors and a number of psychographic variables. Among the
psychographic variables examined in the previous studies related to investor
behavior are attitudes towards risk, self-confidence, conformist, communitymindedness, service volunteers, innovativeness, fashion/appearance consciousness, and credit user.
Past studies have found that active or heavy investors have a higher
level of self-confidence when compared to passive or light investors [2,10,
18]. In addition, Barnewell [2] also found that active investors tend to be
more risk takers when compared to passive investors. Passive investors, on
the other hand, tend to be more of risk avoiders. The study by Yankelovich
Partners of New York also conclude that ‘Strugglers’ are less inclined to
take risk, unsure on how to invest and are preoccupied with reducing their
debts. On the other hand, ‘Secures’ are those who accept risks for potential
rewards, information hungry and open to expert advise [3].
Warren et al. [18], in their study, found that light investors tend to be
more conformists and more inclined to be service volunteers when compared
to heavy investors. They also found that heavy investors are more nonconformists and less inclined to be service volunteers. Their results, however,
are somewhat different from those found by Lim [10]. Lim [10], in his study
on the Malaysian investors, found that heavy investors are more innovative
and adventurous in terms of choosing products or brands. They are more
fashion conscious when compared to light investors. In addition, Lim [10]
also found that heavy investors in Malaysia possess leadership quality and
are financially optimistic than light investors. In terms of credit usage,
Barnewell [2] found that active investors have a greater tendency to be credit
users when compared to the passive investors.
H2: There will be some differences between active and passive investors with respect to psychographic profile.
The psychographics profile of the investors will be determined after
factor analysis of psychographics items is made.
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2.4 Leisure Activities Participated by Investors
Almost no studies can be found examining the types of leisure activities
participated by investors. The only known study on this area was conducted
by Yankelovich Partners of New York [3]. In their study, Yankelovich Partners found that ‘Strugglers’ tend to participate more in passive activities, like
watching television. They are seldom involved in surfing the net and tend to
avoid reading serious publications, such as business and finance. ‘Secures’
investors, on the other hand, are more inclined to watch television and participate in reading. In addition, they like to be involved in affluent activities,
such as dining-out, weekend getaways and gardening. They are also high
internet users and heavy newspaper and magazine readers, especially those
which are information intensive.
H3: There will be some different between active and passive investors
with respect to leisure activities participated.
The leisure activities profile will be developed only when the leisure
activity items were factor analyzed.
3. Research Methodology
This section outlines the methodology employed in the study. The study
was carried out by using the survey approach. This section provides a description of the design of the research instrument, and the sampling procedure
and data collection technique.
3.1 Research Instrument
The survey instrument was an eight-page questionnaire. The questions
relevant to this paper were found in four sections. The first section measured
the psychographic and lifestyle characteristics of the respondents. When developing the lifestyle questions in this section, the basic approach developed
by Wells and Tigert [20] and Plummer [16] was adopted. A total of 16
activity, interest and opinion (AIO) statements on a seven-point Likert-type
scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree) were used.
For each of the statements, respondents were required to indicate their level
of agreement to the statements. The statements measured constructs that
were deemed relevant to measuring investor lifestyles. Amongst the constructs measured were ‘credit user’, ‘risk taker’ and ‘self-confidence’. The
input for the lifestyle statements was derived from the AIO inventory developed by Wells and Tigert [20], as well as items developed by Plummer
[16] and Kinnear and Taylor [6]. In addition, several items were developed
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by the researchers based on the local applicability of the statements. Some
statements were modified to suit the local conditions. As an example, the
item “I like to fly” from Kinnear and Taylor [6] was restated to “I like to be
involved in risky sports, such as sky-diving, scuba-diving, etc.”
In the second section, issues related to non-work related activities were
measured. A total of 18 general statements regarding these activities were
given. The respondents were asked to indicate the extent to which they participated in these activities by circling the appropriate number: 5 = Regularly,
4 = Often, 3 = Sometimes, 2 = Almost Never, and 1 = Never. This section in
particular attempted to elicit some information on the subjects’ general afterwork activities and interests. Most of the items in this section were taken
from the study of Kamakura and Wedel [5]. Some items were restated or
changed. The item ‘Read magazines’ was expanded into four different items:
‘Read educational publications/magazines’; ‘Read computer or IT publications/magazines’; ‘Read finance publications/magazines’; and ‘Read business publications/magazines’. ‘Go to church’ was changed to ‘Go to mosque,
church or temple’ to reflect the local condition. The ‘Surf-the-net’ item was
added by the researcher to indicate a recent popular activity participated.
The third section was designed to measure how active the respondents
were in ‘playing shares’ in the Kuala Lumpur Stock Exchange (KLSE). To
measure this issue the following question was asked: “How many times in an
average month do you have your transaction done in the KLSE?” Six alternative responses were given, from ‘1 = Less than 1 time a month’, to ‘4 = 3
times a month’, and to ‘6 = More than 4 times a month’. Based on the
responses from this question, investor categories (i.e. active and passive investors) would be developed.
The last section was designed to collect the demographic information of
the respondents. Examples of the demographic variables measured were gender, ethnicity, marital status, employment status, education and income. The
variables were measured using a close-ended multiple-choice format.
Prior to the actual survey, a pilot test was conducted using 20 respondents. Based on the feedback obtained from these respondents, the final version of the questionnaire was developed. The questionnaire was produced in
two languages: English and Malay. The original English version of the questionnaire was translated into the Malay language, the national language of
Malaysia, using the back-to-back translation method [22].
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3.2 Sampling Procedure and Data Collection Method
The study was confined to the residents in the Kuala Lumpur and
Petaling Jaya areas. Respondents were from both sexes, aged 21 and above
and who invested in securities of public listed companies traded on the
Kuala Lumpur Stock Exchange (KLSE). To provide an adequate level of
confidence in the study, a sample size of 300 was targeted. The survey was
conducted over an eight-week period using self-administered drop-off method. The drop-off method was used by placing 50 copies of the questionnaires with eight stock-broking companies in the Kuala Lumpur and Petaling
Jaya areas. A key person, normally a senior remisier or dealer, in each of the
stock-broking companies was engaged to act as the contact person and the
distributing agent.
4. Research Results
This section presents the findings of the survey. It begins with a description of the general characteristics of the respondents. This is followed by
a demographic comparison between active and passive investors. The results
of the factor analysis on lifestyle and activity items are then presented. A
comparison is made between the two groups of investor in terms of their
lifestyle and activity profile. Finally, the results of stepwise discriminant
analysis are presented, using active and passive investors as the dependent
variable and the demographic characteristics, lifestyle profile and activity
participation as the independent variables.
4.1 Characteristics of the Respondents
From a total of 300 returned questionnaires, 245 were useable for analysis. The male respondents outnumbered female by almost 3:1. The gender
proportion seemed to match those obtained by Lim [10] and KLSE [7]. The
dominance of males in share-ownership could be explained by the fact that,
in Malaysia, men tended to have a more risk-oriented personality as well as
having a higher income than women.
An equal percentage of respondents (28.3 per cent) fell in the 26-29 and
30-35 age groups. A smaller proportion of respondents were in the 36-39
years and 40-49 years age groups. Almost two-thirds of the respondents
were married. Slightly more than a third of the respondents were singles. In
terms of ethnic group, Malays comprised the largest group (48.8 per cent),
followed by the Chinese (40.6 per cent) and Indians (8.6 per cent).
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When monthly personal income was examined 36.5 per cent of the respondents were in the RM2,000-RM3,999 income group. They were followed
by those earning RM4,000-RM5,999 (24.1 per cent), RM6,000-RM7,999
(15.4 per cent), and less than RM2,000 income groups (12.4 per cent). In
terms of monthly household income, 29.7 per cent of the respondents had a
monthly household income of RM2,000–RM4,999, followed by those in the
RM5,000–RM7,999 income category (25.9 per cent). Those respondents
with a monthly household income of RM10,000–RM12,999 represented
13.4 per cent of the total.
In terms of occupation, respondents in the ‘administrative/managerial’
position and ‘professionals’ (doctors, engineers, etc.) made up the majority
of the respondents representing 26.2 per cent and 23.4 per cent of the total
respectively. Those in the ‘sales/marketing’ made up 15.6 per cent and ‘business owners’ consisted of 11.9 per cent. With regards to educational background, the majority of the respondents (69.0 per cent) had a university or
professional degree. Those with a college diploma represented 15.9 per cent
of the respondents, followed by 9.4 per cent having studied only up to SPM/
SPVM/O-level.
4.2 Active Versus Passive Investors
After examining the frequency distribution of the responses on the number of times in an average month the respondents made their transactions in
the KLSE, the study decided to divide active and passive investors based on
the following: active investors were those who transacted in the share market
at least 3 times in an average month. Passive investors, on the other hand,
were those who transacted in the share market, less than 3 times in an
average month. Based on this categorization, the study found that 37.5 per
cent of the respondents were active and 62.5 per cent of them were passive.
4.3 Active and Passive Investors: A Demographic Comparison
Using the Chi-square analysis, significant differences (at p < .05) were
found between active and passive investors in five different demographic
variables: gender, age, occupation, personal monthly income, and household
monthly income. Table 1 presents the results of the analyses. Due to missing
data, the total number of both categories might not be consistent.
In terms of gender, the study found that, in general, male respondents
tended to be more active than female respondents.
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Ezlika Ghazali et al
Table 1 Active and Passive Investors: A Demographic Comparisona
Active Investors
Passive Investors
N
%
N
%
Gender (χ2 significant, p=0.003)
Male
Female
75
15
43.4
22.4
98
52
56.6
77.6
Age (χ2 significant, p=0.011)
25 and below
26-35
36 and above
10
40
40
50.0
30.1
46.0
10
93
47
50.0
69.9
54.0
48
34
7
41.0
35.1
28.0
69
63
18
59.0
64.9
72.0
28
61
33.3
39.4
56
94
66.7
60.6
21
31
16
18
37.5
31.0
32.0
64.3
35
69
34
10
62.5
69.0
68.0
35.7
Personal Monthly Income (χ2 significant, p=0.000)
32
RM3,999 and Below
RM4,000 to 5,999
20
RM6,000 and Above
38
27.6
35.7
59.4
54
36
26
72.4
64.3
40.6
Ethnicity (χ2 not significant, p=0.401)
Malay
Chinese
Indian and Others
Marital Status (χ2 not significant, p=0.358)
Single
Married
Occupation (χ2 significant, p=0.011)
Professional
Admin/Managerial/Sales/Marketing
Technical/Supervisor/Teacher/Lecturer
Own Business
Household Monthly Income (χ2 significant, p=0.022)
24
28.6
60
RM4,999 and Below
RM5,000 to 9,999
32
37.6
53
RM10,000 and above
33
50.8
32
Education Level (χ2 not significant, p=0.293)
31
42.5
42
Non-Degree
Degree
59
35.3
108
Note: aDue to missing data, the total number of both categories might not be consistent.
71.4
62.4
49.2
57.5
64.7
In terms of age, investors in the ‘25 and below’ and ’36 and above’ age
groups tended to be more active than those in the 26-35 age group. In terms
of ethnicity, no significant difference between the three ethnic groups was
found. This result differed from those obtained by Lim [10] and Osman [14].
The two studies reported that Chinese were more heavy or active investors in
the stock market than the other ethnic groups. When marital status was ex400
Asia Pacific Management Review (2004) 9(3), 391-413
amined, no significant difference was found between the two investor groups.
This too contradicts the findings of Lim [10] on heavy and light investors.
Lim [10] found that heavy investors tended to be married.
When the occupational status of the respondents was examined, a significant difference was found. Higher proportions of active investors owned
business and were professionals when compared to passive investors. This
result was consistent with the findings of [2] who found that active investors
tended to be small business owners, entrepreneurs and professionals. However, the result of the current study differed from the result of Lim [10] on
light and heavy investors. Lim [10] found that there was no significant
difference in terms of occupational status between light and heavy investors.
As expected, considerably higher number of active investors among
those with higher personal and household income. This finding was somewhat similar to the findings of Lim [10] as well as Warren et al. [18]. Lim
[10] found that more than half of the heavy investors were from the highest
household income category (above RM5,000), whereas more than half of the
light investors came from the lowest household income category (less than
RM1,000).
No significant difference was found between the two investor groups
with regards to educational level. This showed that education had no bearing
on whether a person would be active or passive investor. This result was
somewhat consistent with the finding of Lim [10].
4.4 Factor Analysis
Factor analysis was performed on the 16 AIO (psychographic) statements and 18 activity statements to identify the underlying dimensions measured by the statements. The analysis was also done to determine whether
the data could be condensed or summarized into smaller set of factors or
dimensions.
4.5 Factor Analysis of the AIO Statements
The principal components analysis performed extracted five factors
having eigenvalues greater than 1.0. The five factors accounted for 62.0 per
cent of the total variance. The orthogonal Varimax rotational approach was
subsequently applied on the unrotated factors to obtain simpler and more
meaningful factor solutions. Only items with factor loadings of 0.40 and
above were considered as significant in interpreting the factors.
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Table 2 Factor Loadings of AIO Items and Alpha Scores of Each Factor
Loadingsa
Item/Factor
Factor I: Self-confident/Independent
I think I have more self-confidence than most
people
I think I have a lot of personal ability
I am more independent than most people
I like to be considered as a leader
Factor II: Careful Spender
I use credit card for the unexpected only
I like to pay cash for everything I buy
I buy many things with a credit card or charge card
Factor III: Risk Oriented/Innovative
I often try new and different things
I like to involve in risky sports like sky-diving,
rock-climbing or horse-riding
Taking chances can be fun
I often try new brands
Factor IV: Debt Avoider
To buy anything (other than a house or car) on
credit is unwise
It is good to have a charge account
A person should not buy unless has cash
Factor V: This item was droppedc
I like stimulation and changes
Buying shares is too risky
I often try new and different things
Alpha
Scoresb
0.805
0.859
0.815
0.499
0.657
0.769
-0.738
0.465
0.489
0.7755
0.6093
0.6281
0.715
0.803
0.706
0.5925
-0.622
0.709
0.781
0.565
0.602
0.3495
Note: aOnly items with factor loadings greater or equal to 0.40 are shown; bThe component items
of each factor were tested for internal consistency reliability using Cronbach’s coefficient alpha;
c
Due to low Alpha Score, Factor V was dropped from further analysis.
Table 2 presents the derived factor analysis solutions. Factors I, II and
III were all loaded with four items, explaining 16.2 per cent, 12.2 per cent
and 12.2 per cent of the variance respectively. Factors IV and V, comprising
three loaded-items each, explained 11.6 per cent and 9.8 per cent of the
variance respectively.
The component items of each factor were tested for internal consistency
reliability using Cronbach’s coefficient alpha (see again Table 2). Except for
Factor V, the alpha scores of the other four factors were between 0.59 to
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0.78. According to Nunnally [13], these values were quite acceptable in an
exploratory research. Due to the low alpha score, Factor V will be dropped
from further analysis.
When analyzing the items in the factors, some interpretable dimensions
can be identified (see again Table 2). Factor I, labeled “Self-Confident/Independent”, depicts investors who are strong believers in their personal
ability and leadership capability. They tend to be very independent in nature
and have high confidence in themselves. The “Careful Spender” (Factors II),
reflects individuals who tend to avoid unnecessary credit purchases especially using credit cards. They also exhibit lower leadership capability.
Factor III, called “Risk Oriented/Innovative”, portrays investors who are
innovative enough to try new and different things in life and who like experimenting new brands in the market. They are also risk takers by nature,
love to take chances and tend to be involved in risky sports. Factor IV,
named “Debt Avoider”, typifies individuals having negative attitudes towards credit, in general, and credit card in particular.
4.6 Factor Analysis of the Activity Items
Factor analysis was also performed on the 18 activity items. The principal components analysis performed extracted five factors with eigenvalues greater than 1.0. The five factors accounted for 60.0 per cent of the
total variance explained. The factor matrix was later rotated using the Varimax rotated factor solutions. Only items with factor loadings of 0.40 and
above in the rotated factor matrix were considered as significant in interpreting the factors.
Factors I, II and III were all loaded with five items, explaining 15.6,
12.3 and 12.3 per cent of the variance, respectively. Factors IV and V,
comprised of two and four items, explaining 10.6 and 9.1 per cent of the
variance respectively. Table 3 shows the rotated factor matrix. For further
analysis, only factors with at least three items loaded would be included. As
such, Factor IV was dropped from further analysis.
The component items of each factor were tested for internal consistency
reliability using Cronbach’s coefficient alpha (see again Table 3). As suggested by Nunnally [13], the alpha scores of 0.70 to 0.79 were acceptable in
an exploratory research. Factor V had also been dropped due to low alpha
score of 0.36. When analyzing the items in the factors, some interpretable
dimensions can be identified (see again Table 3).
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Table 3 Factor Loadings of General After-Work-Activity Items
and Alpha Scores of Each Factor
Loadingsa
Items/Factors
Factor I: Knowledge Seeker
Read education publications/magazines
Read computer/IT
publications/magazines
Read finance publications/magazines
Read business publications/magazines
Surf the Net
Factor II: Outdoor Lover
Read business publications/magazines
Go to beach or country side
Weekend getaways
Travel overseas on vacations
Go to parties or social functions
Factor III: Outgoing/Entertainment
Lover
Go to parties or social functions
Dine out
Go out with friends
Watch TV
Go to live music shows
Factor IV: This item was droppedc
Practice dangerous sports
Camping
Factor V: This item was droppedd
Give donation to charity
Go to mosque/church/temple
Gardening
Watch TV
Alpha
Scoresb
0.698
0.762
0.773
0.766
0.625
0.406
0.546
0.585
0.739
0.560
0.592
0.692
0.716
0.537
0.555
0.764
0.770
0.600
0.723
0.433
0.435
0.7943
0.7007
0.7475
-
0.3577
Note: aOnly items with factor loadings greater or equal to 0.40 are shown; bThe component items
of each factor were tested for internal consistency reliability using Cronbach’s coefficient alpha.;
c
Due to less than three items loaded, Factor IV was dropped from further analysis; dDue to low
Alpha Score, Factor V was dropped from further analysis.
Factor I, labeled “Knowledge Seeker”, displays individual shareowners
who enjoy spending their time after work reading informative magazines or
publications, especially related to business, finance, computer or information
technology and education. They also surf the net frequently. The “Outdoor
Lover” (Factor II), portrays those who love traveling during their free time.
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In general, they travel out of town almost every weekend, go to the beach or
countryside, travel overseas on vacation and also go to parties and social
functions. Besides traveling, they also read business magazines or publications. Factor III, labeled “Outgoing/Entertainment Lover”, depicts socially
active contemporary individuals who enjoy city life. They regularly dine out
and take pleasure in hanging out with friends. They are modern individuals
who enjoy good live music shows besides parties and social functions. They
also love watching TV.
4.7 Active and Passive Investors: Comparing Psychographic and Activity
Dimensions
An analysis of the lifestyle characteristics and leisure activity participated of the two groups of investors along the seven dimensions portrayed
by the survey data was carried out through a comparison of the mean values
for the two groups using t-tests. Table 4 presents the results of the compareson. The differences in group means were statistically significant in three out
of the seven dimensions compared. The dimensions were ‘risk-oriented/innovative’, ‘knowledge seeker’, ‘outdoor lover’ and ‘outgoing/entertainment
lover’. No significant differences were found between the two groups of
investor in ‘self-confident/independent’, ‘careful spender’, and ‘debt avoider’ dimensions.
From the table, a profile of active and passive investors can be drawn up.
Specifically, active investors were more inclined to take risks and more
innovative when compared to the passive investors. As compared to earlier
findings, this result closely resembled Barnewell [2] and Warren et al. [18],
but contradicts the findings of Lim [10]. Lim [10], in his study on heavy and
light investors, found that there was no significant difference between the
two groups of investors in terms of risk-orientation dimension.
Compared to passive investors, the study revealed that active investors
were knowledge-oriented and often read financial and business publications.
They also frequently surfed the net. Furthermore, they tended to engage in
affluent activities, such as dining out and traveling on vacations relative to
passive investors. These match the findings in the US [3] that active investors tend to have more positive attitude towards gaining knowledge, more
socially active, and love entertainment as well as outdoor activities.
Interestingly, the profile of the Malaysian investors in the stock market
does not entirely resemble that of the Americans. In fact, there are some differences in the psychographic dimensions of Malaysian and American inves-
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Table 4 Comparing the Mean Scores of Lifestyle and Activity Dimensionsa
Active
Investors
Passive
Investors
Significanceb
Self-confident/Independent
21.64
21.08
0.325
Careful Spender
16.73
15.81
0.185
Risk Oriented/Innovative
18.66
17.07
0.008
Debt Avoider
11.57
11.49
0.892
Outdoor Lover
18.08
15.82
16.91
14.71
0.017
0.004
Outgoing/Entertainment Lover
16.79
15.96
0.037
Dimensions
Psychographics:
Leisure Activity:
Knowledge Seeker
Note: a Higher scores represent greater agreement with the attributes; b Level of significance using
T-Test.
tors. In terms of credit user and self-confidence, for example, the profile of
the Malaysian investors found in this study contradicts Barnewell’s [2] study.
In Barnewell’s study, the American active investors were found to have
significantly higher self-confidence and were more inclined to use credit
than passive investors. However, in some aspects, the findings of the current
study were similar to the findings of Lim [10]. Lim [10] found out that
heavy and light investors did not differ in terms of the credit-user dimension.
4.8 Active and Passive Investors: Stepwise Discriminant Analysis
As mentioned earlier, one of the primary objectives of this study was to
identify variables that distinguish active and passive investors and to determine the degree of importance of these variables. While the test of significance of the differences between the mean values of the characteristics
provides an initial insight into the differences between the two groups, it
fails to recognize the weights of each variable that best discriminate between active individual investors and passive individual investors.
The stepwise discriminant analysis was performed to provide an initial
insight into the discriminating power of the variables. Fifteen variables have
been included, i.e., eight demographic variables, four psychographics factors
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Table 5 Stepwise Discriminant Analysis
Function
Eigenvalue
1
0.127
Canonical
Correlation
0.336
Step
Entered
1
2
3
D6: Personal Monthly Income
D5: Occupation
A5: Outgoing/Entertainment Lover
Group Centroids
Active
Investors
-0.275
Passive
Investors
Actual
Group
0.458
Active
Investors
Passive
Investors
Wilk's
Lambda
0.887
Wilk’s
Lambda
0.931
0.906
0.887
Significance
0.000
Significance
0.000
0.000
0.000
Classification Result
Predicted Group Membership
Passive
Active Investors
Investors
52.2 percent
47.8 percent
32.7 percent
67.3 percent
61.7 percent of original grouped cases
correctly classified.
and three activity factors. Table 5 shows the summary results of the stepwise
discriminant analysis.
Only three out of fifteen variables were able to significantly discriminate
between the two groups of investors. Two were demographic variables and
one activity factor. The first to enter the analysis was personal monthly
income, indicating the significant of having money above all in becoming an
active investor. This was followed by occupation. And the third to enter the
analysis was outgoing/entertainment lover, the only one psychographics or
activity dimension. The canonical correlation was 0.336, when squared
implied that this model could explain 13.39 percent of the variance in the
dependent variable investor category. The Eigenvalue and Wilk's Lambda
were moderate at about 0.13 and 0.89 respectively. However the classification matrix showed that predictive accuracy of the function was 61.7 percent
between the active and passive investors (see again Table 5).
The discriminant functions for the three variables entered were all highly significant (p=0.000) indicating that the variables discriminated between
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the groups to be significant. The ranking of the independent variables (personal monthly income, occupation and Outgoing/Entertainment Lover)
screened by stepwise discriminant method in terms of their relative discriminatory powers was based on each of their absolute sizes of loadings and
weights.
The Structure Matrix (see Table 6) reveals that both Personal Monthly
Income and Occupation discriminate the most between the investor group,
with weights of 0.766 and 0.550 respectively. The Stepwise Discriminant
procedure further compute that the third and the last variable that discriminate the most between the investor groups is “Outgoing/Entertainment
Lover” with discriminant loading of 0.43. All other remaining variables not
accounted in the Stepwise analysis have minimal separation and relatively
high dependency among each other.
The three most important discriminating variables between active and
passive investors were then compared using group means. Table 7 provided
the summary results of the analysis. From Table 7, the group mean of
‘Personal Monthly Income’ for active investors was higher than passive investors with respective values of 3.30 and 2.58. 42.2 percent of active
investors earned RM6,000 and above monthly as compared to passive investors which only 17.8 percent. The result concurred with Lim [10] but differed from Barnewell’s [2] and Warren et al. [18], whose analysis assigned
no discriminating power to monthly personal income variable. The situation
appeared different in the Malaysian context where levels of per- sonal
income were found to be very important in discriminating the two groups of
individual investors.
The demographic variable, “Occupation” entered by stepwise analysis
was ranked the second most important discriminating variable. The group
means for occupation were 3.64 and 3.05 for active and passive investors
respectively (see again Table 7). The majority of active investors owned
business as compared to passive. The third most important variable was
“Outgoing/Entertainment Lover”. The group means scores for the activity
profile were 16.79 for active investors as compared to passive investors,
15.96. The survey data suggested that active investor spent more time (as
compared to passive investors) in reading business, financial, and IT publications, besides affluent activities such as dining out, weekend getaways,
hanging out with friends, going to parties or social functions and attending
live music shows. These seem to match the findings on the US investors by
Yakelovich Partner of New York as reported by Brandweek [3].
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Table 6 Standardized Canonical Discriminant Function Coefficient and
Structure Matrix (Stepwise Analysis)
Variables screened
by Stepwise
procedure
Coefficient
Personal Monthly
Income
0.776
Occupation
0.550
Outgoing/Entertainment Lover
0.432
Variables
Personal Monthly Income
Occupation
Household Monthly Income
Outdoor Lover
Outgoing/Entertainment
Lover
Adventurous/Risk Taker
Knowledge Seeker
Careful Spender
Risk Oriented/Innovative
Age
Gender
Educational Attainment
Self-confidence/Independ.
Marital Status
Ethnicity
Variables
ranked by
size of
correlation
within
function
0.761
0.510
0.479
0.427
0.316
0.309
0.237
0.234
0.207
0.190
-0.179
0.166
0.128
0.080
0.037
Table 7 Stepwise Variables and the Respective Group Means
Stepwise Variables
Group Means
Active Investors
Passive Investors
3.30
3.64
16.79
2.58
3.05
15.96
Personal Monthly Income
Occupation
Outgoing/Entertainment Lover
The finding however contradicted with that of Lim [10] whose analysis
found that the light investors enjoyed spending their evenings out rather than
staying at home when compared to heavy investors.
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5. Discussions
This paper is one of the first few attempts to examine the lifestyles of
individual investors in Malaysia. It can contribute to our understanding of
investors’ lifestyle and its relationship with share purchasing.
The study found that active and passive investors are indeed different
demographically and psychographically in a number of areas. Demographically, as income increases the incidence of buying shares actively increases among individual investors in Malaysia. In terms of other demographic characteristics, active investors are predominantly male, middle age
group, working as professionals, having managerial positions or running
own businesses. However, the two groups, active and passive investors, do
not differ significantly in three demographic characteristics, namely race,
marital status and education.
With regards to psychographic characteristics, the study found that
individual active investors in general can be considered to be more riskoriented and innovative. Active investors also have a positive attitude towards gaining knowledge in areas related to business, finance, computer or
education. In addition, they tend to be relatively more socially and physically active, outgoing and outdoor-oriented. Active investors have money
to travel and like to have fun. In contrast, passive investors appear to be the
opposite in terms of the aforementioned characteristics. These findings have
some resemblance to the findings of Yankelovich Partners of New York [3],
which reveal that the financially secure investors tend to be active investors.
In addition, they are mostly information hungry and open to expert advice.
The study found that active investors are more inclined to watch television, more involved in reading and surfing for information in the Internet.
They are also more involved in affluent activities, such as, dining-out and
weekend getaways. In addition, active investors’ preferences are usually
information intensive and they are heavy newspaper and magazine readers
with content preference towards news, business, finance, computer and education. Thus, in terms of leisure activities, Malaysian active investors are
somewhat similar to those investors in New York [3]. However, it is interesting to note further that, active investors in Malaysia are also entertainment
lovers and lead a more socially and physically active life when compared to
passive investors.
The application of discriminant analysis reveals that certain variables are
relatively more important than others, in discriminating between active and
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passive investors. With regards to level of importance, personal monthly
income ranks the highest, followed by occupation and outgoing/entertainment lover dimension of activity items. These override other variables as
significant influence of becoming active or passive investors.
5.1 Implications of the Study
The results of the study reveal that there are some differences between
active and passive investors in terms of demographic, psychographic and
leisure activity dimensions. These findings can have important implications
to marketers, specifically stockbrokers and remisiers. The study found that
demographic characteristics, such as gender, age, monthly personal income,
monthly household income and occupation, are more important than ethnicity, marital status and educational background in delineating whether the
investors are going to be active or passive. As such, to be more prospectconscious, a broker or remisier has to depend on these characteristics to
influence the investors. Different marketing strategies have to be utilized to
reach different groups of investors.
The result of the study reveals that female investors tend to be more
passive than males in terms of investment. This can be explained by the fact
that females tend to have risk-averse personality, do less investment research
than males, and are a little unsure about investing their assets [9]. The roles
of remisiers, brokers or financial analysts are important to motivate this
segment. In particular, they must be able to give sound, professional advice
to female investors.
Communication with passive investors should be kept simple to alleviate
confusion. This is due to the fact that passive investors, in general, tend to
avoid serious publications, such as business and finance. The results also
show that, in general, passive investors use the internet as a source of information less frequently than their active counterparts. Thus, to communicate with this segment, traditional media such as television, newspaper,
and magazine, will be important. Remisiers and brokers have a vital role to
play, especially in assisting passive investors in their investment decisions as
the findings indicate that they are risk-averse.
The findings of the lifestyle profile and after-work activities of the two
groups, for instance, further reveal the importance of publications as a
source of information for analysis and investment. In particular, active
investors are information-intensive. In the future, it is expected that the
importance of the internet would increase with investors getting more
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dependent on the net for information. Malaysian stockbrokers must realize
that internet and e-commerce represent a very important opportunity in the
future. Brokers that ignore this most important development do so at their
own peril.
This study further helps to verify the use of demographics in distinguishing between the two types of investors. It also suggests that the failure in
using psychographics as variables for segmentation limits the opportunity
for further segmentation and blurs some real differences between individual
investors and their financial services needs. Therefore, both psychographic
and demographic information should be employed to segment investors.
Financial services marketers equipped with this type of information would
be in a much better position to predict the needs of their prospective clients
in order to improve customer delight. A delight experience goes beyond such
to request the firm being held the best interest of customers at heart [7].
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