Research Article
An exploratory study of investment
behaviour of investors
International Journal of Engineering
Business Management
Volume 9: 1–12
ª The Author(s) 2017
DOI: 10.1177/1847979017711520
journals.sagepub.com/home/enb
Mark KY Mak1 and WH Ip2
Abstract
The financial industry plays a significant role in Mainland Chinese and Hong Kong economies and has aroused increasing
managerial and academic interests in recent decades. Individual investors are becoming more cautious towards financial
investment which makes it difficult for financial service providers to formulate marketing strategies after experiencing
several financial crises. Prior research has suggested that financial investment behaviour would be affected by various
factors, including the demographic characteristics of individuals; however, they seldom study the differences in financial
investment behaviour between Mainland Chinese and Hong Kong investors or provide an easy-to-use approach for
practical usage. This exploratory study aims at filling the identified research gap by proposing linear regression models of
the financial investment behaviour of Mainland Chinese and Hong Kong investors. Based on the results of regression
analyses, (i) there exist significant differences in financial investment behaviour between Mainland Chinese and Hong Kong
investors, and (ii) investors’ psychological, sociological and demographic factors are significant predictors of their
investment behaviour/preferences. Thus, financial service providers are able to predict the investment behaviour/preference of its customers and formulate marketing and strategic decisions, such as customizing the financial investment
portfolio of customers on the basis of regression models built.
Keywords
Regression, exploratory study, investor behaviour, statistical analysis, financial industry
Date received: 24 January 2017; accepted: 12 April 2017
Introduction
As an international financial centre, Hong Kong offers a
variety of financial products, such as mutual funds, stocks
and bonds, for individual investors to invest. Due to the
close proximity, low tax rate, similarities of language and
culture and global access, Hong Kong remains the top offshore investment destination for Mainland Chinese investors.1 These facts encourage the financial industry in Hong
Kong to review marketing strategies for targeting this fastgrowing market segment, that is, Mainland Chinese investors investing in the offshore Hong Kong market.
At the same time, individual investors are becoming
more cautious towards financial investment and make it
difficult for financial service providers to formulate marketing strategies after experiencing several financial
crises.2 Indeed, financial service providers face the challenge of understanding the investment behaviour and
preferences of their customers for long-term benefits.2 In
order to have better market analysis and customer relationship management, various finance theories have been proposed by researchers.
Traditional finance theories assume that investment
behaviour is rational.3 However, well-known events such
as the financial tsunami between 2007 and 2008, displaying
apparently irrational behaviour, have caused a rethink in
1
Lerado Financial Group Company Limited, Hong Kong Island, Hong
Kong
2
Department of Industrial and Systems Engineering, The Hong Kong
Polytechnic University, Hung Hom, Kowloon, Hong Kong
Corresponding Author:
Mark KY Mak, Lerado Financial Group Company Limited, Hong Kong
Island, Hong Kong.
Email: brother820820@gmail.com
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage).
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International Journal of Engineering Business Management
the domain, and the emerging field of behavioural finance
has become a popular field of study in an attempt to explain
this irrational behaviour. Under the theory of behavioural
finance, researchers suggest that the investment behaviour
of individual investors in real life is influenced by a combination of specific psychological factors, such as overconfidence,4,5 representativeness4 and herding behaviour.6
The research focusing on psychological investment
behaviour, however, steers away from sociological factors
and personality traits. It seems investment behaviour is a
complicated domain that combines both rational and emotional elements rather than just one. Furthermore, behavioural finance is not purely based on psychological
factors but also sociological factors in the study of investment behaviour.7 Moreover, demographic factors such as
age and gender are also important in explaining investor
behaviour.8 Behavioural finance seems to explain reality
and to provide a better framework in the way the investors
behave. It is crucial to take psychological, sociological and
demographic factors into account to explore the major attributes of how investors behave.7,9
A review of the existing literature demonstrated that
researchers focus mainly on identifying factors influencing
investor behaviour and/or examining their impact on
investment decisions.10–12 Studies seldom investigated
how to predict investors’ preference based on the factors
influencing their behaviour. This gap is probably due to
researchers lacking access to the huge volumes of strictly
confidential financial transaction data required to draw
such conclusions from studying real behaviour.
In order to gain a deep understanding of investment behaviour of individual investors in Hong Kong and Mainland
China, statistical analyses are applied in this study, aimed at
identifying the differences in investment behaviour/preference between Mainland Chinese investors and Hong Kong
investors and explaining investment behaviour determined
by rational, emotional as well as demographic factors. Overall, several research questions are identified, including
What are the factors in the difference of the behaviour identified in the previous literature?
What are the major attributes to explain investment
behaviour?
How do the major attributes identified predict investors’ behaviour/preferences in Hong Kong and
Mainland China?
Literature review and hypotheses
development
Importance of understanding consumer behaviour
in financial market
In today’s increasingly competitive business environment,
a clear understanding of sophisticated consumer behaviour
is a key element for ensuring success. There are many
scholars who have examined the definition of consumer
behaviour. In general, consumer behaviour is the study of
customers and the processes they use to choose, consume
and dispose of products and services that satisfy their needs
and influence their experience.13
Understanding the underlying mechanisms that to lead
to these customers’ responses, therefore, helps business
organizations make better managerial decisions, regarding
providing the right product or service to their customers.14
An in-depth understanding of consumer behaviour further
helps business organizations to plan for the future buying
behaviour patterns of customers and formulate the appropriate marketing strategies in order to build long-term customer relationships.
In financial markets, investors are the customers or consumers. Exploring the behaviour of investors is therefore
important for financial institutions to devise appropriate
strategies and to market appropriate financial products or
offer new financial products to investors in order to better
satisfy their needs. To study investor behaviour, researchers have largely adopted the concept of behavioural finance
during the last decade.3
Overview of behavioural finance
Behavioural finance refers to the application of psychology
to finance.15 Behavioural finance offers an alternative tool
to study investor behaviour and the causes of market
anomalies. Scholars have applied behavioural finance to
explain financial market anomalies such as stock market
bubbles, overreaction and underreaction to new information16,17 that do not conform the traditional finance theory.
For example, Shefrin and Statman18 found that excessive
optimism creates speculative bubbles in financial markets.
Researchers also widely applied behavioural finance to
explain emotional investor behaviour in recent years.
Frankfurter and McGoun19 also indicated that psychology
and sociology is the essence of behavioural finance. However, according to the available literature described above,
researchers have emphasized the importance of psychological factors and overlooked other factors in the concept of
behavioural finance.
Other researchers support the view that sociological and
demographic factors are also important to explain investor
behaviour.7,8 Though some researchers have studied the
impacts of other factors such as gender or age on investment behaviour, these studies only explored the influences
with regard to investor behaviour but did not discuss any
findings about the financial decision-making process of
investors or predict their preference on financial products.
For example, Yang20 investigated, through case studies, the
influence of both gender and age differences towards financial investment behaviour in terms of overconfidence,
account-open time and trade frequency. These studies
within the field of behavioural finance provide evidence
Mak and Ip
that demographic factors such as age and gender should be
considered when studying investor behaviour.
Overall, in order to make the research closer to reality
and to better comprehend the way the investors behave, this
study took psychological, sociological and demographic
factors into account to explore how and why investors
behave differently. Identifying the major attributes to
explain investment behaviour by leveraging psychological,
sociological and demographic factors is thus essential for
this study in order to address the gap in knowledge.
Key attributes influencing financial
investment behaviour
In general, research in behavioural finance provides evidence that investors’ decisions are affected by behavioural
factors.21 Researchers found that investors do not behave in
a merely rational manner across financial markets and there
are a variety of factors influencing their decision-making in
investment; among those factors, psychological factors,
sociological factors and demographic factors are the major
elements.22–24 To study behaviour under more realistic
conditions and to better categorize the way investors
behave, this study identifies and evaluates the major attributes in the literature explaining investment behaviour
under three constructs, namely, the psychological factor,
sociological factor and demographic factor, and how these
attributes impact on the investors’ decision-making.
Key psychological attribute. Regarding the psychological factor, individual investors are driven by experience or
through an investment appraisal process to make investment decisions.25–27 Past experience, as a consequence,
affects investors’ risk perception in terms of attitude to risk
and risk tolerance.28 This is also supported by Byrne,29
indicating the positive correlation between investment
experience and risk. Researchers further pointed out that
accumulated investment experience significantly affects
investment decisions of individual investors in terms of
anchoring bias and overconfidence.30 These indicate that
the investment experience of individual investors forms a
strong basis for investment decisions and is therefore
included in this study by considering it as an important
psychological attribute that influences financial investment
behaviour.
Key demographic attributes. According to Maditinos et al.31
and Sadi et al.,32 the demographic factor is one of the
behavioural factors that plays a significant role in determining the behaviour and decisions of investors. For
example, demographic factors influence one’s choice of
investment products.33–35 Kabra et al.36 found that the
main factors affecting investment behaviour and investors’ decisions are age and gender. According to Huberman and Jiang,37 age and the amount of funds held tend to
indicate a negative correlation. Age is always an essential
3
Table 1. Factors and key attributes considered.
Factor
Key attribute(s)
Psychological Investment experience
Demographic Age
Gender
Sociological Education level
Income level
Marital status
Review reference
sources
25–30
36,37,44
34,36,38–40,44
41–44
3,41,42,44
9,42
factor and has a significant relationship with investment
behaviour according to the literature and thus is included
in this study.
Gender is another crucial demographic attribute that
affects the investment decision-making process and
investor behaviour.38 Many researchers suggested that
there are gender differences in risk attitude and thus in
the choices of financial investment products.34,39 Many
existing studies supported that female investors are more
conservative than male investors when investing and
tend to show greater risk aversion than male investors.39,40 For financial service providers to offer financial products which are best suited for investors of
different genders, understanding the gender difference
in the investment behaviour of individuals is crucial and
thus is taken into account in this study.
Key sociological attributes. According to the extant literature,
education level,41,42 income level3,42 and marital status9,42 are found to be significant sociological attributes
determining investors’ behaviour and influencing their
investment decision. Al-Ajmi41 conducted an exploratory
study and concluded that income level and education level
are positively correlated with risk tolerance. Shaikh and
Kalkundrikar42 conducted an exploratory study and confirmed that income level, education level and marital status are factors affecting investors’ behaviour and
decision-making. Fares and Khami43 identified that the
education level of investors is statistically significant to
investment decision. Rizvi and Fatima3 also found a significant positive correlation between income and investment frequency. More studies revealing the significant
relevance of these sociological factors, including education level, income level and marital status, in investment
decisions and investor behaviour can be found in the literature.43,44 With the support of the literature review,
these three attributes, income level, education level and
marital status, are considered in this study.
Table 1 summarizes all the psychological, sociological
and demographic factors and attributes of behavioural
finance considered in this study.
The literature discussion above highlighted that financial investment behaviour is commonly influenced by
demographic, psychological and sociological factors, and
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International Journal of Engineering Business Management
the major attributes that explain investment behaviour are
age, income level, educational level, gender, investment
experience and marital status. However, little research
has been devoted to the behaviour of individual investors.45,46 Furthermore, Hong Kong is the top offshore
investment destination for the Mainland Chinese investors.
Investors in Hong Kong are mainly mixed, with Mainland
Chinese investors and local investors having different characteristics. In order to answer the third research question
identified in Introduction, the following hypotheses are
proposed:
H1: The investment behaviour/preference of the Mainland Chinese and Hong Kong investors, when considered together, can be predicted by the six attributes –
age, income level, educational level, gender, investment
experience and marital status.
H2: The fund share amount held by the Mainland Chinese and Hong Kong investors, when considered separately, can be predicted by the six attributes.
H3: The choice of country-specific financial investment
options selected by the Mainland Chinese and Hong
Kong investors, when considered separately, can be predicted by the six attributes.
Regression models and data
Regression models
To explore the problems in understanding financial
investment behaviour as well as to study the effects of
the six variables, as identified from literature review, on
investment behaviour of the Mainland Chinese and
Hong Kong investors, a quantitative interpretation of the
literature review was conducted for subsequent exploratory study. Based on the hypothesis, a table of variables
is created (Table 2) and three regression models are
constructed.
Model 1b
fundcurrencyT ¼ a þ b 1 ageT þ b 2 incomelevelT
þ b3 educationlevelT þ b4 genderT
þ b5 investmentexperienceT
þ b6 maritalstatusT
where the variable of the choice of country-specific financial investment options selected ( fundcurrencyT ) is a function of age ( ageT ), income level ( incomelevelT ), gender
( genderT ), educational level ( educationallevel T ), investment experience ( investmentexperienceT ) and marital status ( maritalstatusT ). a represents the regression constant.
bj ðj ¼ 1; 2 . . . ; 6Þ denotes the regression coefficients for
each independent variable.
The first regression model helps give a general picture
for understanding whether and how the six key attributes
identified affect and predict the investment behaviour of
both the Mainland Chinese and Hong Kong investors. In
order to have an in-depth examination on the differences in
investment behaviour between Mainland Chinese and
Hong Kong investors, the second and third models are
constructed to analyse how the six attributes identified
affect investors specifically in Mainland China and Hong
Kong in terms of the fund share amount held and choice of
country-specific financial investment option selected,
respectively.
Model 2a
fundholdCN ¼ a þ b 1 ageCN þ b2 incomelevelCN
þ b3 educationlevelCN þ b4 genderCN
þ b5 investmentexperienceCN
þ b6 maritalstatusCN
Model 2b
fundholdHK ¼ a þ b1 ageHK þ b2 incomelevelHK
þ b3 educationlevelHK þ b4 genderHK
þ b5 investmentexperienceHK
þ b6 maritalstatusHK
Model 1a
fundholdT ¼ a þ b 1 ageT þ b2 incomelevelT
þ b 3 educationlevelT þ b4 genderT
þ b 5 investmentexperienceT
þ b 6 maritalstatusT
where the variable of the fund share amount held
( fundholdT ) is a function of age ( ageT ), income level
( incomelevelT ), gender ( genderT ), educational level
( educationallevel T ), investment experience (invertmentexperience T ) and marital status ( maritalstatusT ). a
represents the regression constant. bj ðj ¼ 1; 2 . . . ; 6Þ
denotes the regression coefficients for each independent
variable.
Model 3a
fundcurrencyCN ¼ a þ b1 ageCN þ b 2 incomelevelCN
þ b3 educationlevelCN þ b4 genderCN
þ b5 investmentexperienceCN
þ b6 maritalstatusCN
Model 3b
fundcurrencyHK ¼ a þ b1 ageHK þ b2 incomelevelHK
þ b3 educationlevelHK þ b4 genderHK
þ b5 investmentexperienceHK
þ b6 maritalstatusHK
Mak and Ip
5
Table 2. Notation of variables.
Notation
Representation Notation
fundholdT
The fund share
amount held
by investors
fundholdCN
fundcurrencyT
The choice of
countryspecific
financial
investment
options
selected by
investors
fundcurrencyCN
ageT
Age of
investors
ageCN
incomelevelT
Income level of incomelevelCN
investors
educationlevelT
Education level
of investors
educationlevelCN
genderT
Gender of
investors
genderCN
investmentexperienceT Investment
experience
of investors
investmentexperienceCN
maritalstatusT
Marital status
of investors
maritalstatusCN
a
Regression
constant
bj
ðj ¼ 1; 2 . . . ; 6Þ
Data
To analyse how the major attributes identified under demographic, psychological and sociological constructs affect
investors in both Hong Kong and Mainland China, financial transaction data and investors’ characteristics are collected and analysed to support this research. The data of
customers from 2012 to 2014 were collected from a financial services provider listed in the Hong Kong Stock
Exchange. In recent years, the number of its customers
from Mainland China has been considerably increasing,
and thus the institution desires to learn about the investment
behaviour of mainlanders and understand the differences in
investment preference between mainland Chinese and
Hong Kong investors. This is the reason why the institution
supports this research by providing the confidential data
Representation
Notation
fundholdHK
The fund share
amount held by
Mainland
Chinese
investors
fundcurrencyHK
The choice of
country-specific
financial
investment
options selected
by Mainland
Chinese
investors
Age of Mainland
Chinese
investors
Income level of
Mainland
Chinese
investors
Education level of
Mainland
Chinese
investors
Gender of
Mainland
Chinese
investors
Investment
experience of
Mainland
Chinese
investors
Marital status of
Mainland
Chinese
investors
Regression
coefficient
ageHK
incomelevelHK
Representation
The fund share
amount held
by Hong Kong
investors
The choice of
countryspecific
financial
investment
options
selected by
Hong Kong
investors
Age of Hong
Kong
investors
Income level of
Hong Kong
investors
educationlevelHK
Education level
of Hong Kong
investors
genderHK
Gender of Hong
Kong
investors
investmentexperienceHK Investment
experience of
Hong Kong
investors
maritalstatusHK
Marital status of
Hong Kong
investors
about its customers and makes this research possible by
overcoming the obstacle – failing to access to the huge
volume of financial transaction data that is confidential.
As the research aims to explore the individual investor
behaviour, a relative large sample size is recommended in
this kind of exploratory research for generating valid
results. As mentioned by Saunders et al.,47 a larger sample
size can help produce more reliable results as the samples
can be more representative. In this research, 142,496 samples were collected from a financial services provider listed
in the Hong Kong Stock Exchange, of which 87,057 samples were from Mainland Chinese investors and 55,439
were from Hong Kong investors.
Table 3 provides descriptive statistics for investors’
characteristics.
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International Journal of Engineering Business Management
Table 3. Descriptive statistics.
Mainland Chinese
Hong Kong
Amount of fund share
held
Frequency Percentage Mean
Marital status
Gender
Age
Educational level
Household’s net
worth
Divorced
Married
Single
Total
Female
Male
Total
0–24
25–29
30–34
35–39
40–44
45–49
50–54
55 and over
Total
Elementary
Junior
Senior and above
Total
Below
HKD100,000
HKD100,000–
300,000
HKD300,000–
500,000
HKD500,000–
1M
HKD1M and
above
Total
1736
48,423
36,898
87,057
57,294
29,763
87,057
3991
24,607
20,773
15,977
10,148
6420
3651
1490
87,057
191
9763
77,103
87,057
25,091
2.0
55.6
42.4
100.0
65.8
34.2
100.0
4.6
28.3
23.9
18.4
11.7
7.4
4.2
1.7
100.0
0.2
11.2
88.6
100.0
28.8
33,214
38.2
35.57
8019
9.2
5813
Variance
320.20
536,282.26
121.76 2,213,504.50
29.81
12,524.81
86.74 1,250,327.26
90.61 1,843,410.28
79.29
108,577.21
86.74 1,250,327.26
20.96
3214.61
31.96
18,667.56
47.04
18,838.95
73.83
51,271.84
126.30
117,385.82
220.76
261,766.61
460.34 28,281,137.04
97.32
202,074.94
86.74 1,250,327.26
657.67 1,895,934.40
144.14 10,512,259.97
78.06
74,801.48
86.74 1,250,327.26
27.31
19,121.63
Amount of fund
share held
Frequency Percentage Mean
Variance
1156
14,703
39,580
55,439
24,337
31,102
55,439
4564
19,558
14,085
8144
4340
1886
2467
395
55,439
442
25,639
29,358
55,439
37,751
2.1
26.5
71.4
100.0
43.9
56.1
100.0
8.2
35.3
25.4
14.7
7.8
3.4
4.4
0.7
100.0
0.8
46.2
53.0
100.0
68.1
6033.15
12,508
22.6
41.66
38,737.45
58.43
18,073.38
4150
7.5
38.00
4321.43
6.7
110.23
73,910.99
491
0.9
66.01
13,164.51
14,920
17.1
86.74
1,250,327.26
539
1.0
87,057
100.0
306.67
7,151,080.17
55,439
100.0
Assumption analysis
To ensure the validity of the regression models, several
requirements in applying multiple regression models,
including linearity, multivariate normality, homogeneity
of variance and multicollinearity, are tested. As mentioned by Poole and O’Farrell 48 and Antonakis and
Dietz,49 the models are only valid when these requirements are tested and satisfied. Before conducting the
actual regression analyses, preliminary analyses are conducted to ensure the requirements for the regression
models are fulfilled.
Linearity test. Figure 1 shows the significance of the linear
relationship for model 1a. From Figure 1, the p value for all
variables are <0.0001, indicating that significant linear
relationships between all independent variables and dependent variable exist. Thus, the assumption of linearity for
regression is fulfilled.
15.82
311.31
51.09 156,248.17
29.08
13,370.21
34.64
51,089.72
27.71
3330.20
40.07
88,395.31
34.64
51,089.72
16.74
767.42
23.04
1942.98
31.00
4086.63
38.87
16,685.62
55.78
94,869.81
158.18 1,133,002.77
30.64
3402.27
61.51
15,648.61
34.64
51,089.72
40.64
6357.03
27.36
17,769.82
40.91
80,777.85
34.64
51,089.72
24.11
1857.15
555.36 3,906,787.08
34.64
51,089.72
Multivariate normality test. Figure 2 shows the histogram of
the standardized residuals for model 1a. According to Stevens,50 if residuals fit a normal curve, multivariate normality is not a problem. The histogram of the residuals of
model 1a shows a symmetrical bell-shape and fairly normal
distribution. Thus, the assumption of multivariate normality is fulfilled.
Homogeneity of variance. Figure 3 shows the scatter plot of
the residuals for model 1a. According to Osborne and
Waters,51 if residuals scatter randomly and close to the zero
axis, homogeneity of variance is not a problem. Homogeneity of variance is not a problem for model 1a in our study
as an almost horizontal band of points is scattered around
and close to the zero axis, as shown in Figure 3. Thus, the
assumption of homogeneity of variance is fulfilled.
Multicollinearity. Figure 4 shows the values of the coefficients of determination (R2) and variance inflation factors
Mak and Ip
7
Figure 1. IBM SPSS Statistics (SPSS) output for multiple correlation coefficient.
Figure 3. SPSS output for analysis of residuals.
Figure 2. SPSS output for histogram of residuals.
(VIF) calculated for model 1a. According to Hart and
Sailor,52 if tolerance (T), which is defined as T ¼ 1 R2, is below 0.20, the multicollinearity problem is a
severe problem. Furthermore, the attributes are moderately correlated if the value of VIF is between 1 and 5.53
In our study, correlations are at acceptable levels as the
T values for the six key attributes are over 0.20 and the
VIF values are between 1 and 5. These indicate low
multicollinearity and thus the assumption of multicollinearity is fulfilled.
Having fulfilled all the requirements, the multiple
regression model 1a is confirmed to be valid and actual
regression analysis is then conducted. Similar assumption analyses as discussed for model 1a are also conducted for models 1b, 2a, 2b, 3a and 3b, and all the
requirements are fulfilled. In the next section, the results
are reported.
Results and analysis
With the help of the 142,496 observation samples, regression analyses are conducted in this section to assess the
relationships between the key attributes identified and the
investment behaviour/preferences between Mainland Chinese and Hong Kong investors.
Tables 4 and 5 show the results for the standardized
coefficients and adjusted R2.
Effects of key attributes on investment
behaviour/preference
From the regression results of model 1a (Table 4), all
the six key attributes identified have a significant (at
0.01 level) effect on the fund share amount held when
Mainland Chinese and Hong Kong investors are considered together. Thus, age, income level, education level,
gender, investment experience and marital status are
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International Journal of Engineering Business Management
Figure 4. SPSS output for the measure of tolerance.
Table 4. Results of regression analyses for models 1 and 2.
Model 1a
age
incomelevel
educationlevel
gender
investment
experience
maritalstatus
R2
0.022***
0.273***
0.013***
0.012***
0.014***
Model 1b
Model 2a
Model 2b
0.025*** 0.030***
0.024***
0.030***
0.277***
0.241***
0.014***
0.016*** 0.007
0.004
0.011***
0.021***
0.119*** 0.023*** 0.035***
0.013*** 0.020*** 0.021***
0.074
0.018
0.075
0.015***
0.060
Effects of key attributes on the fund share
amount held
*Statistical significance at the 0.1 level.
**Statistical significance at the 0.05 level.
***Statistical significance at the 0.01 level.
Table 5. Results of regression analyses for model 3.
age
incomelevel
educationlevel
gender
investmentexperience
maritalstatus
R2
choice of the country-specific financial investment option
selected by Mainland Chinese and Hong Kong investors.
By leveraging the results shown in Table 4, it is concluded that the financial investment behaviour/preference
of Mainland Chinese and Hong Kong investors, when considered together, are inseparable with regard to their demographic factor (i.e. age), psychological factor (i.e.
investment experience) and sociological factor (i.e. income
level, education level and marital status).
Model 3a
Model 3b
0.015***
0.020***
0.006
0.003
0.140***
0.014***
0.021
0.027***
0.032***
0.015***
0.011***
0.121***
0.005
0.006
*Statistical significance at the 0.1 level.
**Statistical significance at the 0.05 level.
***Statistical significance at the 0.01 level.
statistically significant predictors of the fund share
amount held by Mainland Chinese and Hong Kong
investors.
From the regression results of model 1b (Table 4), five
out of the six key attributes, excluding gender (p value ¼
0.223 > 0.1), identified have a significant (at 0.01 level)
effect on the choice of the country-specific financial investment option selected when Mainland Chinese and Hong
Kong investors are considered together. Thus, only age,
income level, education level, investment experience and
marital status are statistically significant predictors of the
From the regression results of model 2a (Table 4), all the
six key attributes identified have a significant (at 0.01
level) effect on the fund share amount held by Mainland
Chinese investors. Thus, it is confirmed that age, income
level, education level, gender, investment experience and
marital status are statically significant predictors of the
fund share amount held by Mainland Chinese investors.
From the regression results of model 2b (Table 4), five
out of the six key attributes, excluding education level (p
value ¼ 0.107 > 0.1), identified have a significant (at 0.01
level) effect on the fund share amount held by Hong Kong
investors. Thus, only age, income level, gender, investment
experience and marital status are statistically significant
predictors of the fund share amount held by Hong Kong
investors.
By leveraging the results shown in Table 4, it is concluded that the fund share amount held by Mainland Chinese and Hong Kong investors, when considered together,
are inseparable with their demographic factor (i.e. age and
gender), psychological factor (i.e. investment experience)
and sociological factor (i.e. income level and marital status). The top three most significant attributes are age,
income level and investment experience. The standardized
coefficient of age is 0.030 for Mainland Chinese investors and 0.024 for Hong Kong investors. The standardized
coefficient of income level is 0.277 for Mainland Chinese
Mak and Ip
9
Table 6. Impacts of key attributes.
Fund share amount held
Factor
Attribute
Psychological
Demographic
Investment experience
Age
Gender
Education level
Income level
Marital status
Sociological
Choice of the country-specific
financial investment option selected
Mainland Chinese
investors
Hong Kong
investors
Mainland Chinese
investors
Hong Kong
investors
0.023
0.030
0.011
þ0.016
þ0.277
0.021
0.035
þ0.024
þ0.021
N/A
þ0.241
þ0.015
þ0.140
þ0.015
N/A
N/A
0.020
þ0.014
þ0.121
0.027
þ0.011
þ0.015
þ0.032
N/A
þ: relationship in the positive direction; : relationship in the negative direction; N/A: absence of a significant relationship.
investors and 0.241 for Hong Kong investors. The standardized coefficient of investment experience is 0.023 for
Mainland Chinese investors and 0.035 for Hong Kong
investors. In spite of the differences in the magnitude of
the three most significant attributes, the directions of the
relationship are similar, except for age. For example,
income level has a positive effect on the fund share amount
held by Mainland Chinese and Hong Kong investors. However, age has a negative effect on the fund share amount
held by Mainland Chinese investors but a positive effect on
Hong Kong investors. In other words, younger Mainland
Chinese and older Hong Kong investors tend to hold a
higher fund share.
Effects of key attributes on the choice of the countryspecific financial investment option selected
From the regression results of model 3a (Table 5), four out
of six key attributes, excluding education level (p value ¼
0.141 > 0.1) and gender (p value ¼ 0.325 > 0.1), identified
have a significant (at 0.01 level) effect on the choice of the
country-specific financial investment option selected by
Mainland Chinese investors. Thus, it is confirmed that age,
income level, investment experience and marital status are
statically significant predictors of the choice of the countryspecific financial investment option selected by Mainland
Chinese investors.
From the regression results of model 3b (Table 5), five
out of the six key attributes, excluding marital status (p
value ¼ 0.127 > 0.1), identified have a significant (at
0.01 level) effect on the choice of the country-specific
financial investment option selected by Hong Kong investors. Thus, only age, income level, education level, gender
and investment experience are statistically significant predictors of the choice of the country-specific financial
investment option selected by Hong Kong investors.
By leveraging the results shown in Table 5, it is concluded that the choice of the country-specific financial
investment options selected by Mainland Chinese and
Hong Kong investors, when considered together, are
closely correlated with the demographic factor (i.e. age),
psychological factor (i.e. investment experience) and
sociological factor (i.e. income level and marital status).
The three most significant attributes are the age, income
level and investment experience. The standardized coefficient of age is 0.015 for Mainland Chinese investors and
0.027 for Hong Kong investors. The standardized coefficient of income level is 0.020 for Mainland Chinese
investors and 0.032 for Hong Kong investors. The standardized coefficient of investment experience is 0.140 for
Mainland Chinese investors and 0.121 for Hong Kong
investors.
Investment experience has a positive effect on the
choice of country-specific financial investment options
selected by both Mainland Chinese and Hong Kong investors. On the contrary, age and income level have different
effects on the choice of the country-specific financial
investment options selected by Mainland Chinese and
Hong Kong investors. For example, younger Mainland Chinese investors and older Hong Kong investors tend to have
the same choice of the country-specific financial investment option.
Table 6 summarizes the impacts of key factors and attributes on the investment behaviour of Mainland Chinese
and Hong Kong investors. The table summarizes the relationship (direction and magnitude) between the key attributes, the fund share amount held and the choice of the
country-specific financial investment options selected by
investors.
Discussion
Practical and strategic importance of this research
With reference to the results of regression models 1 (Table
4), 2 (Table 4) and 3 (Table 5), there exist significant differences in the financial investment behaviour/preference,
in terms of the fund share amount held and the choice of the
country-specific financial investment option selected,
between Mainland Chinese and Hong Kong investors. For
example, the impact of age on the fund share amount held
10
International Journal of Engineering Business Management
by and choice of the country-specific financial investment
option selected by Mainland Chinese and Hong Kong
investors is opposite. Similarities between the investment
behaviour of Mainland Chinese and Hong Kong investors
are also found. Particularly, the three most significant attributes, age, income level and investment experience, influencing investment behaviour for both Mainland Chinese
and Hong Kong investors are the same, though the influence may in the opposite directions. Also, income level has
a positive effect, while investment experience has a negative effect on the fund share amount held by investors.
In view of the similarities in investment behaviour for
both Mainland Chinese and Hong Kong investors, financial
service providers can utilize the findings to design and
promote different financial investment products based on
the demographic, psychological and sociological attributes,
particularly income level and investment experience, of
individual investors from Mainland Chinese and Hong
Kong. The following targeted marketing strategy can be
formulated:
Target group 3: Hong Kong investors aged 45–49
The results of regression analyses of models 2a and 2b
(Table 4) indicate that age is a statistically significant predictor for the fund share amount held by both Mainland
China and Hong Kong investors. However, the results indicate that younger investors from Mainland China and older
investors from Hong Kong hold a higher fund share. The
results of descriptive analysis (Table 3) further identified
that Hong Kong investors aged between 45 and 49 show
great enthusiasm for investment and hold the highest fund
share on average (i.e. 158.18 unit). Thus, Hong Kong investors aged between 45 and 49 should be the key age group
for development and they are investors of great potential.
Financial service providers should then invest more money
in advertising and strengthen their products, as well as
designing higher yield funds so as to attract these enthusiastic and high purchasing power Hong Kong investors aged
between 45 and 49 to invest more in terms of frequency and
money. In the following subsection, limitations of this
research are discussed.
Target group 1: High-income investors
Limitations
From the regression results of models 2a and 2b (Table
4), income level has a significant (at 0.01 level) and positive effect (0.277, 0.241) on the fund share amount held by
both Mainland Chinese and Hong Kong investors. Financial service providers should therefore invest more money
in advertising and strengthening their products, as well as
designing a wider range of financial investment portfolios
so as to attract these high-income investors to invest.
Target group 2: Less experienced investors
From the regression results of models 2a and 2b
(Table 4), investment experience has a significant (at
0.01 level) and negative effect (0.023, 0.035) on the
fund share amount held by both Mainland Chinese and
Hong Kong investors. The possible reasons are listed
below. As individual investors become more experienced, they become more conservative and show less
enthusiasm in fund investment. Therefore, high-risk
funds with a potential of offering higher returns are only
attractive to less experienced investors. Financial service
providers should then design higher yield funds and
allocation funds so as to raise the investment interests
of investors with less investment experience for the
highest profits.
Furthermore, in view of the differences in investment
behaviour between Mainland Chinese and Hong Kong
investors, financial service providers can utilize the findings to design and promote different financial investment
products based on the demographic attribute of age. The
following strategy can be formulated for financial service
providers to better tackle Mainland Chinese and Hong
Kong investors:
In this research, financial transaction data and investors’
characteristics are collected from a single case company.
Despite the fact that 142,496 samples were collected and
used in the regression analyses, the empirical results may
not represent fully the financial investment behaviour or
investment preferences of all Mainland Chinese and Hong
Kong investors, given the limited number of case companies. In the future, more financial transaction data and
investors’ characteristics should be collected from other
Hong Kong-based financial service providers to make the
results more generalized and convincing.
Conclusions
The financial industry plays a significant role in the Mainland China and Hong Kong economies and has aroused
increasing managerial and academic interest in recent
decades. Unfortunately, after the financial crisis of 2008
and the global crisis of 2009, investors are becoming more
cautious towards investments, especially in high-risk financial products. Furthermore, Hong Kong is the top offshore
investment destination for the Mainland Chinese investors.
Investors in Hong Kong are mainly mixed, with Mainland
Chinese investors and local investors having different characteristics. These make it more difficult for financial service providers to understand customers’ financial
investment behaviour and investment preferences.
Attempting to address the real-world challenges and
research gap, this study has (i) empirically identified that
demographic, psychological and sociological factors cause
different investment behaviour and (ii) identified that the
major attributes that explain and predict investment
Mak and Ip
behaviour/preferences of Mainland Chinese and Hong
Kong investors are age, income level, educational level,
gender, investment experience and marital status. Regression analyses and data provided by one of the Asia’s leading financial service providers were used to help the
financial industry formulate strategic and marketing
strategies.
This exploratory study helps to fill the identified
research gap and enable financial service providers to better understand their customers’ financial investment behaviour and investment preferences from the perspective of
investors’ characteristics. With the huge volumes of confidential transaction data and investors’ characteristics available, the research results are believed to be able to reflect
the real behaviour of individual investors from Mainland
China and Hong Kong and can offer financial service providers a foundation for sustainable strategies formulation.
In future research, it is suggested to extend the regression
results to build a data mining model to market the most
appropriate products to individual investors from Mainland
Chinese and Hong Kong and to gain a better understanding
of their financial investment behaviour in an effective and
efficient manner.
Acknowledgements
The authors thank the editors and reviewers for their valuable
comments and suggestions that have improved the quality of the
article. The authors would like to thank the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University for their support in this work.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of this article.
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