




































 
 

 

69 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 7, No. 1, 69-77, 2020 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2020.71.69.77 

© 2020 by the authors; licensee Asian Online Journal Publishing Group 

    

 
 
 
The Influence of Demographic Factors on Investment Behaviour of Individual 
Investors: A Case Study of Edo State, Nigeria 

 
Agbo, Ezekiel1   

Abu, Prince Oshoke2   

 

 
( Corresponding Author) 

 
1,2Department of Economics, Faculty of Social Sciences, University of Benin, Benin City, Edo, Nigeria. 

 

 
Abstract 

This study empirically examines the influence of demographic factors on investment behaviour of 
individual investors using Edo state, Nigeria as its case study. Using the maximum likelihood 
method of estimation to estimate four multinomial logit equations, the results showed that 
educational level, occupation and marital status are the main demographic determinants of 
individual investors’ behaviour. Also, age and gender have strong influences on individual 
investor’s risk preference. Therefore, we recommend that it is pertinent that macroeconomic 
policies aimed at boosting investment should consider the expansionary effect of targeting civil 
servants and those in professional practice by providing them with investment incentives as these 
categories of persons have a much higher affinity for risk for investment purposes. 

 
Keywords: Demographic factors, Individual investors’ Behaviour, Multinomial Logit Equations. 

JEL Classification: G11; A31; C38; C39; G02; C40; D7. 
 

Citation | Agbo, Ezekiel; Abu, Prince Oshoke (2020). The Influence 
of Demographic Factors on Investment Behaviour of Individual 
Investors: A Case Study of Edo State, Nigeria. Economy, 7(1): 69-
77. 
History:  
Received: 7 April 2020 
Revised: 12 May 2020 
Accepted: 15 June 2020 
Published: 2 July 2020 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: Both authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 

1. Introduction ...................................................................................................................................................................................... 70 
2. Literature Review ............................................................................................................................................................................ 70 
3. Data and Estimation Methodology .............................................................................................................................................. 72 
4. Presentation and Analysis of Empirical Results ........................................................................................................................ 73 
5. Summary, Recommendations and Conclusions .......................................................................................................................... 75 
References .............................................................................................................................................................................................. 76 
 

 
 

 

 

 

 

 

 

http://crossmark.crossref.org/dialog/?doi=10.20448/journal.502.2020.71.69.77&domain=pdf&date_stamp=2017-01-14
http://creativecommons.org/licenses/by/3.0/
http://creativecommons.org/licenses/by/3.0/
https://www.asianonlinejournals.com/index.php/Economy/article/view/1826
https://orcid.org/0000-0002-7088-1179
https://orcid.org/0000-0002-7799-0634
https://www.asianonlinejournals.com/index.php/Economy/article/view/1826
https://orcid.org/0000-0002-7088-1179
https://orcid.org/0000-0002-7799-0634
https://www.asianonlinejournals.com/index.php/Economy/article/view/1826
https://orcid.org/0000-0002-7088-1179
https://orcid.org/0000-0002-7799-0634
https://www.asianonlinejournals.com/index.php/Economy/article/view/1826
https://orcid.org/0000-0002-7088-1179
https://orcid.org/0000-0002-7799-0634


Economy, 2020, 7(1): 69-77 

70 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Contribution of this paper to the literature 
This paper helps in analyzing the factors which influence formation of intention to invest and 
further direct towards investment in the investment behaviour of retail investors. It provides 
solid preference to retail investors on portfolio selection and other risk preferences. It also 
provides a hybrid model which justifies the extent upon which demographic factors influences 
the investment patterns of individual investors giving a particular location. 

 

1. Introduction 
1.1. Background of the Study 

This study analyzes the investment behavior of individual investors in Edo state Nigeria. Investment behavior 
refers to how investors judge, predict, analyze and review the procedures for decision making, which includes 
investment psychology, information gathering, defining and understanding, research and analysis. It is the 
employment of funds with the aim of earning income or capital appreciation (Pandey, 2001). Economic theories of 
investment behaviour are largely based on the belief that individuals behave in a rational manner and that all 
existing information is embedded in the investment decision process. This assumption is the crux of the efficient 
market hypothesis (Vijaya, 2016). But researchers questioning this assumption have uncovered evidence that 
rational behavior is not always as prevalent as we might believe. Behavioral finance models attempts to understand 
and explain how human emotions influence investors in their decision-making process. Furthermore, previous 
studies attempt to analyze the influence of demographic factors on the investment pattern of individual investors 
which has enhanced better understanding of why people manage investment in different ways, Debondt and Thaler 
(1995). This study examines the role of demographic factors as a differentiating and classifying factor of individual 
investors associated with their investment behaviour, the exposition to various investment avenues in their choice 
of portfolio selection and the level of information an individual investor has over his investments. This will enable 
financial advisors guide investors on the basis of their age, income, and risk tolerance. Earlier literature focused on 
the relationship between risk tolerance and demographics variables. Information on the nature of the relationship 
between demographic factors and individual investment behaviour will be of immense use to individual investors, 
financial experts, brokers and investment firms. 

Given the complexity and importance of investment decisions to individuals and the economy, there exists a 
mirage of theories and procedures; the expected utility theory, efficient market hypothesis, modern portfolio 
theories, prospect theory and mental Accounting. Many studies on this aspect has judiciously made use of 
behavioural finance to analyze the behavioural pattern of investor’s behaviour and specifically generalized 
assertions based on the cognitive and heuristic factors that affect individual investment behaviour. There exist a 
close association between individual investor behaviour and their own investment methods and also, each 
individual investor is different, some are more risk averse than others, and some have more resources than others. 
These premises beg the questions over the relevance of traditional theories like the prospect theory, mental 
accounting, efficient market hypothesis and modern portfolio theories (Kahneman & Tversky, 1979; Thaler, 1985; 
Von Neumann & Morgenstern, 1944). Individual investors are said to be influenced by some psychological biases. 
It is important to identify the most influential factors on investment behaviour. Despite the growing interests in 
this important and relatively new stream popularly known as behavioural finance, there are yet scanty scientific 
researches in this field especially in Nigeria. Therefore, this study represents one of such attempt to fill this gap by 
investigating the demographic factors influencing individual investment behaviour.  
 

1.2. Research Questions  
In other to attain the basic objectives of this study, the following research questions will be answered; 
i. To what extent do demographic factors impact on access to sufficient information by individual 

investors? 
ii. Do demographic factors have any impact on the risk appetite of individual investors? 
iii. The degree of impact of demographic factor on the investment avenue selection by individual 

investors? 
iv. Do demographic factors have any impact on the investment experience of individual investors?  

 

1.3. Objectives of the Study 
The general objective of this study is to determine the impact of demographic factors on individual investor 

behaviour with emphasis on risk appetite, availability of information, portfolio selection and investment experience. 
The specific objectives of this study are: 
i. To assess the influence of Demographic factors on the access to sufficient   information by individual 

investors. 
ii. To ascertain the influence of Demographic factors on the risk appetite of individual investors. 
iii. To establish the influence of Demographic factors on the investment avenue selection by individual 

investors. 
iv. To evaluate the influence of Demographic factors on the investment experience of individual investors. 

 

2. Literature Review 
Theories under behavioural finance seek to improve the standard theories of finance by introducing behavioural 

aspects to the investment decision making process.  Heuristic decision making process refers to rule of thumb 
which humans use to make decisions in complex, uncertain environments. Most times investors make decision with 
lax collection of information and objectivity and this involves the mix of mental, environmental and emotional 
factors. Some investors are overconfident of their ability to consistently time and beat the market, thus, they trade 
excessively, with trading costs denting profits (Tomola, 2013). Furthermore, these differences are most 
pronounced between single men and single women. Barber and Odean (2001) carried out a test on the prediction 



Economy, 2020, 7(1): 69-77 

71 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

that overconfident investors trade excessively by partitioning investors on gender. Their analysis showed that men 
tend to be more confident traders than women and thus trade more volumes than women. Consequently, their 
average returns are less than that of women. On the other hand, some investors place undue weight of decision 
making on the most available information. This leads to less return and sometimes poor results. This bias is the 
tendency for people to place greater importance on more recent data or experience. Shiller and Pound (1989) found 
that at the peak of the roaring 1980s Japanese bull market, only 14 percent of Japanese investors expected a crash 
and eventually after the crash, 32 percent said that they expected the crash. This illustrates the tendency for 
investors to become more optimistic when the market goes up and more pessimistic when it goes down and this 
tendency causes a good number of investors to consistently buy high and sell low. Kahneman and Tversky (1973) 
found that people usually forecast future uncertain events by focusing on recent history and pay less attention to 
the possibility that such short history could be generated by chance. Shefrin and Statman (1995) described what is 
known as the disposition effect where investors are disposed to selling the winners too early and to riding the 
losses too long. According to Odean (1998b) the disposition effect is consistent with the notion that realizing 
profits allows one to maintain self-esteem while incurring losses. Zoghalami and Matoussi (2009) carried out a 
survey to identify the psychological biases that influences the investor behaviour using a multinomial logit model. 
The Univariate and Multivariate analyses showed that investors’ behaviour in Tunisia was driven by various 
psychological factors such as precaution, under confidence, conservatism, under optimism and informational 
inferiority complex. Chandra and Kumar (2011) examined the extent to which psychological biases are responsible 
for individual investment behaviour using Principle Component Analysis. The results revealed some psychological 
axes, such as conservatism, under confidence, prudence, precautious attitude and informational asymmetry which 
have an influence on investor decision making. Le Phuoc and Doan (2011) investigated the behavioural factors 
influencing individual investors’ decisions at the Ho Chi Minh Stock Exchange using factor analysis. The results 
show that five behavioural factors such as herding, market, prospect, overconfidence -gamble’s fallacy and 
anchoring -ability bias affect the investment decisions of individual investors. 

Kahneman and Tversky (1972) developed the prospect theories to discuss various states of mind that may 
influence an investor’s decision making process. Regret theory deals with the emotional reaction people experience 
after realizing they've made an error in judgment. Faced with the prospect of selling a stock, investors become 
emotionally affected by the price at which they purchased the stock. So, they avoid selling it as a way to avoid the 
regret of having made a bad investment, as well as the embarrassment of reporting a loss. Some investors avoid the 
possibility of feeling this regret by following the conventional wisdom and buying only stocks that everyone else is 
buying, rationalizing their decision with "everyone else is doing it" (Grable & Lytton, 1999b).  

There is a tendency to place particular events into mental compartments and the difference between these 
compartments sometimes impacts behaviour more than the events themselves. This is known as mental accounting. 
An interesting example of mental accounting is best illustrated by the hesitation to sell an unprofitable investment 
that once had gigantic profits. During an economic boom and bull market, people get accustomed to healthy, albeit 
paper gains. When the market correction deflates investor's net worth, they're more hesitant to sell at the smaller 
profit margin. They create mental compartments for the gains they once had, causing them to wait for the return of 
that gainful period (Thaler, 1999). More so, social environment influence people’s behavior by propelling 
conformity. Social influence has an immense power on individual judgment. When people are confronted with the 
judgment of a large group of people, they tend to change their wrong answers. They simply think that all the other 
people could not be wrong. Herd behavior may be the most generally recognized observation on financial markets 
in a psychological context. Even completely rational people can participate in herd behavior when they take into 
account the judgments of others, and even if they know that everyone else is behaving in a herd-like manner. 
Shiller, Robert, and John (1989b) show that even if people read a lot, their attention and actions appear to be more 
stimulated by interpersonal communications.  Moreover, there are market factors which influence the behaviour of 
sentimental and rational investors in different ways. External factors such as market information, price fluctuations 
and stock trends influences investors’ decision making.  More empirical studies show that investment avenues, 
functioning institutions, investors’ level of awareness, market conditions and demographic factors are among other 
factors that affect investors’ behavior. Bhushan and Medury (2013) examined the awareness level and investment 
behaviour of salaried individuals towards financial products. He found that individual investors are reasonably 
aware of investing their money in traditional and safe financial products whereas the awareness level of new age 
financial products among the population is low. Geetha and Vimala (2014) identified the popular perception of 
individual investors towards selected investment avenues and the predominant factors which influence individuals 
to go for savings. They found out that changes in demographic factor such as age, income, education, and 
occupation influence the investment avenue preference. Acha (2012) examined the behaviour of teachers towards 
savings and investment and to understand the resultant economic behaviour and its implications. They employed 
Chi square and Regression analysis. They discovered that Individual characteristics of teachers such as age, gender, 
marital status, lifestyle and family characteristics such as monthly family income, stage of family life cycle and 
upbringing status emerged as determinants of their savings and investment behaviour. Jain and Kushboo (2012) 
examined the association of demographic factors on investment choices using Chi Square test and the results show 
that the demographic profiles and personality type of the investors is closely associated with investment choices. 
Investors with higher income group prefer to invest in real estate and females prefer to invest in old products. 
Females were conservative while investing and males were aggressive. In the same vein, Chakraborty (2012) 
analyzed the investment pattern, saving objective and preferences of individual investor’s for various investment 
options available in India. They employed Chi -square, ANOVA, and factor analysis. The result showed that saving 
objective is influenced by demographic factors such as age, occupation and the income level of investors. Female 
investors tend to save more in a disciplined way than the male investors. It was concluded that women are risk 
averse indeed but save more than the male counterparts as the income level rises. Bahl (2012) carried out a study 
on the investment behavior among the working women in Punjab. They employed principal component analysis 
and discovered that working women invest their money in insurance plans. Kumari and Joseph (2014) investigated 
the influence of the financial literacy on individual investment decisions using Chi Square test. The found out that 



Economy, 2020, 7(1): 69-77 

72 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

apart from gender, there is a relationship between demographic factors and the level of financial literacy possessed 
by the respondents. Chitra and Sreedevi (2012) analyzed the influence of seven personality traits which includes 
emotional stability, extraversion, risk, return, agreeability, conscientiousness and reasoning on the choice of the 
investment pattern using Chi Square test. They found out that Personality traits of the investors have impact 
decisions making and also influence the method of investment. The study also found that the influence of 
personality traits on the investment decision is more compared to that of demographic variables.   

The review of literature shows that age, gender, and income are strong determinants of investment decisions. 
The literature also revealed that men tend to be less risk averse in their choice of portfolio selection compared to 
the female folks. This research however addresses the issue of individual investment decision making as well as 
how demographic factors such as age, education, occupation, family size, wealth status and gender affect their 
individual investment decision making in Benin metropolis of Edo state, Nigeria. 
 

3. Data and Estimation Methodology 
3.1. Data 

Preliminary scanning of various secondary data sources preceded primary data collection. The primary data 
investigation proceeded on the framed objectives of the present study. The research instrument consisted of a 
structured questionnaire which was used to collect first hand responses from individual investors in Edo state. This 
primary data has been put to further statistical analysis so as to find some useful information and generate 
inferences related to the objective of the study. The data was collected by the way of personal discussion for 
designing the questionnaires. Questionnaires were sent to approximately 230 respondents on the basis of 
convenience sampling. The responses obtained from the exercise were coded and analyzed. Questionnaires 
consisted of demographic information of individual respondent such as name, gender, age group, education, income 
group, occupation, family size. In line with the research topic, the study involves individual investors from selected 
areas in Benin metropolis, Edo State, Nigeria. The selected areas are Ugbowo, New Benin, Ring road, Ikpoba Hill, 
and Sapele Road. 
 

 
Table-1.Descriptions of Variables 

Variable code Variable names Descriptions 

AGE Age of the respondents  Age is the most investigated demographic factor among all. It is largely 
accepted that the risk behaviour of an individual depends on his/her age. 
Older individuals tend to be less risk tolerant than younger individuals, 
probably because older individuals have less time to meet their goals and 
objectives. 

DOM Where respondents are 
domiciled 

A home or residence of the respondent. The place he lives also influences 
his investment pattern. 

EDU Educational attainment 
of the respondents 

Education refers to facts, skills and knowledge that have been learned. 
The Level of education encourages an individual to assume higher level 
financial risk and investment opportunities. Similarly, other studies 
found that the increased levels of education are associated with an 
increased level of investment 

FSIZ Family size of the 
respondents  

Family size includes being single, married, divorced and married with 
children as well. family size influences the nature and the amount of 
investment 

FTYP Family type of the 
respondents 

Family type includes both nuclear and extended family. This also, to a 
significant extent influence the behavioural pattern of the individual 
investor 

GEND Gender of the 
respondents 

Gender depicts both male and female. Research shows that gender has 
greater influence on the investment pattern of individual investors 

IEXP Investment experience of 
the respondents 

Investment experience explains how long an investor has been trading in 
a particular place and on a particular stock 

MAST Marital status of the 
respondents 

Marital status implies whether an investor is married, single or divorced. 
It is believed that married investors are more averse to high financial risk 
because they have more financial commitments and a larger number of 
dependents thereby affecting their investment pattern. 

OCC Occupation of the 
respondents 

Occupation refers to the principal activity which someone engages in to 
meet requirements for their livelihood (Grable & Lytton, 1999b; Grable 
& Lytton, 1999a). An investor may be working in the private sector or 
the public sector or be self-employed. 

INVA Investment avenue 
selection of the 
respondents 

This refers to the investment avenues available to the respondent and the 
investment choice of an individual investor. 

REL Religion of the 
respondents 

Religion is a particular system of faith and worship eg Islam, Christian, 
Hindu e.t.c. religious believes influence investors performance 

RSID Residence of the 
respondents 

Residence depicts where an investor lives or resides. It could be rural or 
urban which also determine the investor’s choice of investment. 

RAPP Risk appetite of the 
respondents 

Investors level of risk tolerance also affect his investment behaviour 

SINFO Sufficient information 
available to the 
respondents 

Sufficient information refers to the avenues of information an investor is 
exposed to. Where and how his information sources educates him on a 
portfolio investment. 

WEALTH Wealth status of the 
respondents 

Wealth refers to riches, valuable material possessions. The wealth of an 
individual investor determines the level of investment and his behaviour 
towards diversification of portfolios. 



Economy, 2020, 7(1): 69-77 

73 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

3.2. Estimation Methodology 
The estimation method used in this study involves the multinomial logit model which provides the opportunity 

to trace the effect of variables of different measurement scales on nominal scale dependent variables (Brooks, 2008). 
The coefficients are interpreted as the log transformation of the odds ratio in favor of the dependent variable 
(Brooks, 2006; Gujarati, 2004). Other attendant tests associated with the multinomial logit model are the Pseudo 
R2 and the likelilhood Ratio Chi2 tests which test for the goodness of fit of the multinomial logit specification 
(Brooks, 2006). 

The empirical model used can be specified as follows: 

                                                                    
                                           

                                                                              
                                 

                                                                   
                                           

                                                                              
                                

Where                          indicates that individual behaviour of the ith respondent is proxied by the 
availability of sufficient information, investment experience, portfolio investment selection preference and risk 
appetite of the ith respondent. On sufficient information, the availability of education to persons in younger 
generations tend to expose them to the ease of access to information unlike those of the older generations 
considering that education itself evolves over time as well. 
 

4. Presentation and Analysis of Empirical Results 
The purpose of this section is to present the summary analysis of variables and estimated results of the 

multinomial logit models. Univariate data was generated from the questionnaire upon which inferences were drawn 
for the multinomial logit equation. Consequently, the multinomial logit models are estimated with the aid of the 
maximum likelihood method of estimation and the log-odds ratio are interpreted towards tracing the impact of 
demographic variables on of individual investor behaviour. 
 

Table-2.Descriptive summary of ages and family size of respondents. 

 Age Famsiz 

Mean 35.35 3.408284 
Std. Dev. 9.693675 2.543465 
Median 34 3 
Variance 93.96734 6.469217 
Skewness 0.3861829 0.7243132 
Kurtosis 2.543977 2.776672 

                                                        
On the summary analysis of the ages and family size of the respondents, it is observed that the mean age is 35 

years approximately while the standard deviation of the respondents’ ages stood at 9.69 years. The median age 
stood at 34 and the distribution of the ages appear roughly normal as the skewness stood at 0.39 approximately 
and the kurtosis stood at 2.54 approximately. The average family size is 3 persons while the standard deviation 
stood at 3 persons as well approximately. The distribution of the family size is also normal as the skewness 
measure stood at 0.72 approximately and the kurtosis stood at 2.78 approximately. 
 

4.1.  Multivariate Analysis of the Effect of Demographic Factors on Individual Investors’ Behaviour 
In this section the impact of demographic variables on investment behaviour is estimated in the Table 3. 

According to the results for dependent variables are availed towards proxing investment behaviour. On 
commencing with Investment Avenue, the regression results show that the selected demographic variables do not 
have individual statistical significance as far as impacting on the investment avenue score of the respondents is 
concerned. However the overall fit is quite good given the r-square of 23.32% and an F-statistic of 1.92 which has 
corresponding probability value of 0.011 and signifies overall statistical significance at the 5% level. 

On investment experience, it is seen that the age, occupation of the respondent, level of education, religion, 
wealth and area domiciled have profound impacts on the investment experience of the respondent. Older 
respondents tend to have lower scores on investment experience and this is buttressed by the negative impact 
coefficient of age which stands at -0.01 and it statistically significant at the 5% level. Students tend to have higher 
investment experience scores significantly as the average score rose by significantly at the 5% level by 0.26 in the 
event that the respondent is a student. Oddly graduates and non-graduates have statistically higher experience 
scores compared to other educational level categories but the former is higher of the two with an incremental 
coefficient of 0.23 while corresponding to the later is an incremental coefficient of 0.17. On the role of religion in 
boosting investment experience, it is seen that those who are traditional worshippers have statistically significant 
incremental coefficient of 0.46 at the 5% level. Expectedly the poor have declining investment experience with the 
incremental coefficient corresponding to the poor being -0.80 but oddly it is also noticed that though the 
incremental coefficients corresponding to middle and upper class are statistically insignificant they are both 
negative suggesting that even respondents in the upper and middle class also record declining investment 
experience. 
 
 
 
 

 



Economy, 2020, 7(1): 69-77 

74 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Table-3.Estimation of the impact of demographic variables on investment behavior. 

Variables Investment avenue 
score 

Average 
Investment 

experience score 

Average Risk 
appreciation score 

Average Sufficient 
information score 

Age 0.0058889 
(0.884) 

-0.0068436 
(0.039)** 

0.0709893 
(0.084)* 

0.0029575 
(0.665) 

Family Size -0.283794 
(0.135) 

0.0090255 
(0.558) 

0.031547 
(0.869) 

-0.0296202 
(0.354) 

Occupation     
Civil Service 0.247558 

(0.771) 
0.487772 
(0.481) 

0.4309967 
(0.615) 

0.3294126 
(0.023)** 

Professional -0.835623 
(0.407) 

0.0073239 
(0.929) 

-0.9034317 
(0.375) 

0.5414113 
(0.002)*** 

Student 1.149127 
(0.425) 

0.2609866 
(0.027)** 

1.091467 
(0.452) 

-0.0985877 
(0.684) 

Level of Education     
Graduate 0.3098806 

(0.772) 
0.2279279 
(0.010)** 

-1.705027 
(0.116) 

0.7206735 
(0.000)*** 

Non-Graduate 0.3136206 
(0.783) 

0.1721544 
(0.065)* 

-2.314451 
(0.045)** 

0.8952685 
(0.000)*** 

Pgd -0.4258915 
(0.725) 

0.1229341 
(0.214) 

-1.671154 
(0.173) 

0.4366116 
(0.034)** 

Undergraduate -0.3415789 
(0.843) 

-0.062652 
(0.656) 

-2.860398 
(0.102) 

0.8256724 
(0.005)*** 

Religion     
Muslim 1.52553 

(0.252) 
-0.0309057 

(0.775) 
0.9455773 

(0.481) 
-0.2338419 

(0.298) 
Traditional -2.12368 

(0.381) 
0.4627366 
(0.020)** 

-0.201613 
(0.934) 

0.0907639 
(0.824) 

Family Type     
Nuclear 0.4503012 

(0.726) 
0.1048985 

(0.316) 
0.0810123 

(0.950) 
-0.1236719 

(0.567) 
Gender     
Male -0.7716139 

(0.156) 
0.0609012 

(0.170) 
-0.1712963 

(0.754) 
0.0532794 

(0.561) 
MARITAL STATUS     

Married -0.1323605 
(0.905) 

0.0042499 
(0.962) 

0.1816268 
(0.871) 

0.2580045 
(0.169) 

Single -1.170756 
(0.359) 

-0.0757217 
(0.466) 

1.225243 
(0.342) 

0.1886892 
(0.381) 

Type of Residence     
Private 1.298355 

(0.175) 
-0.000023 

(1.000) 
1.045269 
(0.278) 

0.3484334 
(0.032)** 

Public House 0.2354237 
(0.852) 

0.0873943 
(0.396) 

0.1750204 
(0.891) 

0.4896353 
(0.023)** 

Other Public House 4.814284 
(0.106) 

-0.2632284 
(0.277) 

-1.91209 
(0.523) 

0.9468903 
(0.060)* 

Wealth     
Middle Class 1.298355 

(0.202) 
-0.1251845 

(0.115) 
0.2703981 

(0.783) 
0.1398761 

(0.394) 
Poor 0.2354237 

(0.234) 
-0.8069388 
(0.001)*** 

2.702752 
(0.363) 

0.3342726 
(0.501) 

Upper Class 4.814284 
(0.315) 

-0.0104996 
(0.858) 

-1.178627 
(0.106) 

0.2485318 
(0.042) 

Domicile     
Semi Urban -1.463273 

(0.600) 
-0.8025914 
(0.001)*** 

2.635205 
(0.349) 

0.3348713 
(0.476) 

Urban -0.474283 
(0.868) 

-0.8226538 
(0.001)*** 

2.917259 
(0.312) 

0.5752249 
(0.233) 

_Cons 7.596091 
(0.066)* 

2.857784 
(0.000)*** 

3.892432 
(0.348) 

0.3005168 
(0.664) 

Summary Measures and Diagnostics 
R-square 0.2332 0.2406 0.1438 0.2537 

Adjusted R-square 0.1116 0.1202 0.0080 0.1353 
F-stat 1.92 2.00 1.06 2.14 

Prob. F-stat 0.0113** 0.0075*** 0.3984 0.0036*** 
Note: *indicates 10% statistical significance, **indicates 5% statistical significance, ***indicates 1% statistical significance, values in ( ) are 
probability values. 

 
Older respondents though scoring low on investment experience have a greater affinity for risk taking as older 

respondents have their risk appreciation score rising incrementally by 0.07 significantly at the 10% level. Non-
graduates also appear to have a comparatively lower affinity for risk taking as their risk appreciation score declines 
by -2.31 significantly at the 5% level. All other variables record statistically insignificant impact on the degree of 
risk appreciation of the respondent. The fit of the risk appreciation score equation is quite low standing at 14.38% 
and the adjusted r-square being 0.8% and corroborating the poorness of the fit is the F-statistic which posts a value 
of 1.06 and a probability value of 0.3984. 



Economy, 2020, 7(1): 69-77 

75 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

While age and family size have no significant impact on the sufficient information score, it is observed that civil 
servants and professionals have their sufficient information scores rising by 0.33 and 0.54 respectively. The level of 
education of the respondents goes a long way in equipping the respondents with adequate information as the 
incremental coefficients for graduates stood at 0.72, for post graduate students 0.43 and for undergraduates 0.83 
but as it turns out even non-graduates also have access to sufficient information with an incremental score of 0.90. 
Of all the proxies of investment behaviour, the access to sufficient information responds to the type of residence of 
the respondent. The results show that respondents who reside in private residence have the sufficient information 
score rising incrementally by 0.35, while for those in public houses 0.49 and for those in other forms of public 
houses the figure stood at 0.95. the fit of the sufficient information score equation is quite sound as the F-test for 
overall significance posts a statistic of 2.14 and a probability value of 0.0036 which shows that the overall 
regression is significant at the 1% level. The r-square shows that 25.37% of the systematic variation in the 
sufficient information score is explained by the regressors and after adjusting for degree of freedom the adjusted r-
square shows that 13.53% is the explained variation. 
 

4.2. Implications of Findings 
Featuring prominently among the demographic factors with regards to its significant impact on individual 

investor behaviour are the educational level, the occupation and marital status of the respondents. It is clear that 
persons in the civil service according to the results have higher affinity for taking risks than persons in other 
occupations and these same category of persons, persons in the civil service, show more tendency to access 
information bordering on their investments. However these same persons, persons in the civil service as well as 
those in professional practice, have the tendency to record just moderate levels of experience in investing. 
Providing some justification for this discovery are the findings of Burman, Dur, and Van (2012) who are of the view 
that with an increase in the tenure of civil servants and as a result of their beliefs that their services are merit goods 
they tend to take on more risky stances and this could be reflected even in the investment behaviour of individual 
investors who turn out to be civil servants. The findings of this study as well as those of Burman et al. (2012) fly in 
the face of those of Tucker (1988) who concluded that persons in the civil service tend to be less motivated to take 
risks compared to entrepreneurs in the private sector. 

On the role of education in individual investor behaviour, this study arrives at the finding that education does 
not really matter as far as investment experience is concerned. This is due to the fact that both graduates and non-
graduates do not differ in their limited investment experience. This shows that education does not really account 
significantly for suitable individual investor behaviour and this is due to the fact that educational attainment in this 
part of the world is only but a rationing mechanism for assigning jobs to school leavers and does not necessarily 
imply entrepreneurial advance (Todaro & Smith, 2009). Finally it is seen that the marital status of persons also 
count towards the investment experience of the persons under consideration and this is anchored on the view of 
Barber and Odean (2001)that marriage has the effect of stifling beneficial individual investor behaviour and hence 
divorcees or single person are better poised to be active individual investors. 
 

5. Summary, Recommendations and Conclusions 
5.1.  Summary 

This study investigates the role of demographic factors on individual investor behaviour in Edo state Nigeria 
with emphasis on these five areas vis-a-visUgbowo, New Benin, Ring road, Ikpoba Hill, and Sapele Road and more 
specifically this study seeks to investigate the impact of demographic factors on the access to information by 
individual investors, risk appetite of individual investors, portfolio investment selection by individual investors and 
investment experience of individual investors. With these specific investor behaviors this study sets out a 
theoretical framework based on the role of demographic factors such as age, wealth, religion, occupation, family 
size, gender, marital status, education attainment, family type, residence of respondents and the area where 
respondents are domiciled. Four multinomial logit equations are estimated to detect the role of these demographic 
factors on individual investor behavior to capture the four objectives. The results from the study showed that the 
educational level, the occupation and marital status of the respondents are the main determinants of the individual 
investor behaviour. The results agree that respondents in the civil service and in professional practice had more 
access to information and were willing to take up portfolios with high risk.  

On the role of education in individual investor behaviour, this study arrives at the finding that education does 
not really matter as far as investment experience is concerned as being a graduate does not distinguish them from 
non-graduates as they both have limited knowledge of investment. Finally it is seen that the marital status of 
persons also count towards the investment experience of the persons under consideration as marriage has the effect 
of stifling beneficial individual investor behaviour and hence divorcees or single person are better poised to be 
active individual investors. 
 

5.2.  Recommendations 
Based on the findings the study, the following recommendations are made: 

i. Civil servants and those in the professional practice are poised to be risk loving and this can be exploited 
by policy makers seeking to spur investment in Nigeria by ensuring that civil servants are given further 
incentive to further encourage risk taking. Civil servants should be given special attention while ensuring 
that their earnings provide a veritable source of capital for investing while the government sets out policies 
towards encouraging their investment via targeted subsidies, selective tax waivers and special 
interventions aimed at raising the capital base of investing civil servants. 

ii. Curriculum and instructional reforms have to be embarked upon in Nigeria universities to ensure that 
graduates have the necessary technical know-how necessary to spur them in their investment and enable 
them have profound knowledge of investments and investment procedures. This is to ensure that the 
education which graduates spend at least 4 years amassing does not amount to a waste and mere academic 
exercise with little or no potential towards wealth creation in the country. Such reforms should include: 



Economy, 2020, 7(1): 69-77 

76 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

a. Ensuring that the curriculum in Nigerian universities are in tune with the realities of the Nigerian business 
environment and society 

b. Introducing and laying emphasis on pragmatic disciplines capable of raising the psychomotor abilities of 
graduates in addition to their cognitive abilities – this is necessary towards ensuring that graduate appreciate 
the practical aspects of their discipline and not become mere opportunistic academics. 

c. Encouraging start-ups among young graduates by supplying them the necessary incentives such as finance 
and training towards enabling them become employers of labor and not seekers of scarce jobs. 

iii. While investor behaviour among married couples may be stunted due to the activities of raising a family, 
the synergistic effects of two business oriented home makers can be the difference. However in the case of 
single parents, divorcees or single persons effort should be made to ensure that they productively engaged 
towards availing investment opportunities since these category of persons are better poised to be active 
individual investors. 

iv. Further research should be carried out on the nature of identifying the close relationship between 
demographic factors and investment behaviour of individual investors. The point is that this proposal, on 
the one hand, can help individuals to take different issues into considerations before taking investment 
decisions, and on the other hand, make investment advisors be more effective when offering different 
investment alternatives to individuals in order to ensure that its customers have positive impression about 
the investment experience and ultimately, to make its financial and investing techniques effective in 
practice. 

 

5.3. Conclusions 
The study concludes that demographic variables intervene in the investment style of investors. However, the 

profound significant variables are the occupations and marital status of investors, whilst education plays a poor role 
in spurring individual investment. The later conclusion is anchored on the fact that educational institutions in the 
country are fast becoming centers for educational attainment with dismal levels of human capital. It behooves the 
educational and macroeconomic policy makers to boost the human capital quality stemming from educational 
institutions across the country. It is also pertinent that macroeconomic policies aimed at boosting investment 
consider the expansionary effect of targeting civil servants and those in professional practice by providing them 
with investment incentives as these categories of persons have a much higher affinity for risk for investment 
purposes. 
 

References 
Acha, A. (2012). Saving and investment behaviour of teachers – An empirical study. International Journal of Physical and Social Sciences, 2(8), 

263-286. 
Bahl, S. (2012). Investment behaviour of working women of Punjab. Arth Prabhand: A Journal of Economics and Management, 1(6), 21-35. 
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of 

Economics, 116(1), 261-292.Available at: https://doi.org/10.1162/003355301556400. 
Bhushan, P., & Medury, Y. (2013). Gender differences in investment behaviour among employees. Asian Journal of Research in Business 

Economics and Management, 3(12), 147-157. 
Brooks, C. (2006). Introductory econometrics for Finance (2nd ed., Vol. 1, pp. 32-34). UK: Cambridge University Press. 
Brooks, C. (2008). Introductory econometrics for finance (2nd ed., Vol. 1, pp. 32-34). UK: Cambridge University Press. 
Burman, M., Dur, R., & Van, D. B. (2012). Public sector employees: Risk averse and altruistic? (pp. 4-6). CESIFO Working Paper No. 3851 

Category 13: Behavioural Economics June 2012. 
Chakraborty, S. (2012). A study of saving and investment behaviour of individual households – An empirical evidence from Orissa. TIJ's 

Research Journal of Economics & Business Studies, 2(1), 24-34. 
Chandra, A., & Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear transformation approach. MPRA 

Paper No.29722. 
Chitra, K., & Sreedevi, R. (2012). Does personality traits influence the choice of investment. The IUP Journal of Behavioural Finance, 8(2), 47-

57. 
Debondt, M., & Thaler, H. (1995). Financial decision making in markets and firms: A behavioural perspective. Handbook in Operations 

Research and Management Science, 9(13), 45-52. 
Geetha, S., & Vimala, K. (2014). Perception of household individual investors towards selected financial investment avenues (with reference 

to investors in Chennai city). Procedia Economics and Finance, 11, 360-374.Available at: https://doi.org/10.1016/s2212-
5671(14)00204-4. 

Grable, J. E., & Lytton, R. H. (1999b). Assessing financial risk tolerance: Do demographic, socioeconomic, and attitudinal factors work. 
Family Relations and Human Development/Family Economics and Resource Management Biennial, 27(3), 80–88. 

Grable, J. E., & Lytton, R. H. (1999a). Financial risk tolerance revisited: The development of a risk assessment instrument. Financial Services 
Review, 8(3), 163-181.Available at: https://doi.org/10.1016/s1057-0810(99)00041-4. 

Gujarati, D. (2004). Basic econometrics (2nd ed., Vol. 2, pp. 56-72). New York: McGraw Hill Book Co. 
Jain, & Kushboo. (2012). The effect of demographics on investment choice: An empirical study of investors in Rajasthan. Journal of 

Management and Science, 1(2), 111-130.Available at: https://doi.org/10.26524/jms.2012.13. 
Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430-454.Available 

at: https://doi.org/10.1016/0010-0285(72)90016-3. 
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237-251. 
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 363-391. 
Kumari, R., & Joseph, M. (2014). Family burden on parents of the children with cerebral palsy; effectiveness of the family centered psycho 

social intervention programme. IOSR Journal of Humanities and Social Science, 19(5), 56-59.Available at: 
https://doi.org/10.9790/0837-19515659. 

Le Phuoc, L., & Doan, T. T. H. (2011). Behavioral factors influencing individual investors´ decision-making and performance: A survey at the Ho Chi 
Minh stock exchange. Unpublished Master's Thesis. Umea university, Umea, Northern Sweden.    

Odean, T. (1998b). Are investors reluctant to realize their losses? Journal of Finance, 53(3), 1775−1798. 
Pandey, I. M. (2001). Corporate dividend policy and behaviour: The Malaysian experience. IIMA Working Paper No. 2001-11-01. 
Shefrin, H., & Statman, M. (1995). Making sense of beta, size, and book-to-market. The Journal of Portfolio Management, 21(2), 26-

34.Available at: https://doi.org/10.3905/jpm.1995.409506. 
Shiller, R. J., & Pound, J. (1989). Survey evidence on diffusion of interest and information among investors. Journal of Economic Behavior & 

Organization, 12(1), 47-66.Available at: https://doi.org/10.3905/jpm.1995.409506. 
Shiller, R., Robert, J., & John, P. (1989b). Survey evidence on the diffusion of interest and information among investors. Journal of Economic 

Behavior and Organization, 12, 47-66. 



Economy, 2020, 7(1): 69-77 

77 
© 2020 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199-214.Available at: 
https://doi.org/10.1287/mksc.4.3.199. 

Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12(3), 183-206. 
Todaro, M., & Smith, S. (2009). Economic development (12th ed.). New York: New York University. 
Tomola, M. (2013). Factors influencing investment decisions in capital market: A study of individual investors in Nigeria. Organization & 

Markets in Emerging Economies, 4(1), 15-28. 
Tucker, D. (1988). An introspection of investors psychology. Indian Journal of Commerce and Management Studies, 7(1), 23-25. 
Vijaya, E. (2016). An Empirical analysis on behavioural pattern of Indian individual equity investors. Osmani University, Hyderabad, 

Telangana, India, 9(13), 45-52. 
Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behaviour. Princeton: Princeton University Press. 
Zoghalami, F., & Matoussi, H. (2009). A survey of Tunisian investors. International Research Journal of Finance and Economics, 6(3), 66-81. 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. 
Any queries should be directed to the corresponding author of the article. 

 


