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Effect of Demographics on the Choice of Investments’

It has been observed that over the last decade the Income of the third world countries such as India, China and Indonesia has grown at a high pace. As the wealth of the people increases they will have confidence in the markets and start investing in financial products. This research paper deals with the investment decisions of all individuals across different income groups, age, gender etc. and tries to identify the affect of demographic factors on the decision making investors

The study aims to find out if the demographic factors of an individual namely his age, income, gender, savings, source of income and investment experience have any effect on the patterns of investment and hence affect his risk taking ability. Advanced quantitative techniques have been used to investigate the data and judgment has been given on the basis of statistical output.

The results would help the managers in the Wealth Management process in advising their clients better regarding investments that are most suitable according to their demographics and personality type. The study provides evidence that the investment choice depends on and is affected by the demographic variables.


India, China and Brazil showed the highest growth in the number of HNI’s in the year 2007 (The world wealth report 2008). The growth in the exposure that these markets have still remains untapped as they have only 3 percent exposure to equities. As the wealth of the people increases they will have confidence in the markets and start investing in financial products.

In the 1970s and early 1980s, researchers found enough evidences that the markets are efficient and investment decisions are taken rationally. However, over a period of time there have been major challenges to the rationality assumption. Such challenges, coming from behavioral finance, continue to advance the argument that the traditional finance theory’s predictive power is no match to what investors observe and experience in the markets, in reality. Behavioral finance is a new emerging science that exploits the irrational behavior of the investors. According to the behavioral economists, individuals do not function perfectly as the classical school opines. Weber (1999) makes the observation, “Behavioral finance closely combines individual behavior and market phenomena and uses the knowledge taken from both psychological field and financial theoryâ€?. The key result of a behavioral finance-enhanced relationship will be a portfolio to which the advisor can comfortably adhere while fulfilling the clients’ long-term goals. This result has obvious advantages which suggests that behavioral finance will continue to play an increasing role in Wealth Management

The study aims to find out if the demographic factors of an individual namely his age, income, gender, savings, source of income and investment experience have any effect on the patterns of investment and hence affect his risk taking ability. Quantitative techniques shall be used to investigate the data and the decision will be given on the basis of the analysis.

The results would help the people involved in the Wealth Management process in advising their clients better regarding investments that are most suitable according to their demographics and personality type.


The objective of this paper is to investigate how the investment choice is affected by the demographics of the investors, once we study the choice effecting variables, we will use past data and monitor what have been the returns achieved from such proportion of investments and we shall determine the ideal portfolio and mix in the portfolio. Such knowledge will be highly useful for financial advisors as it will help them to advise their clients regarding investments that are appropriate with respect to their demographic profiles.

Literature review

A number of studies have been conducted to study how risk tolerance varies with the individual demographics, such as, gender, age, education, income, etc. Most of these studies have, however, concentrated on exploring the gender differences in investment choice. Harlow and Keith (1990) found that women prefer low risk bets when asked to make choices in an experimental market environment, involving auctions and lotteries (Olsen and Cox, 2001).

Experimental evidence suggests that women may be more risk averse towards gamble (Hershey and Schoemaker, 1980). Large-scale one-on-one attitude surveys by the Investment Company Institute and SRI International in the year 1996 and 1997 respectively, also revealed that women tend to prefer lower risk assets than men. (Olsen and Cox, 2001). Women hold less risky assets than men (Jianakoplos and Bernasek, 1998) and they also choose less risky alternatives (Powll and Ansic, 1997). Women exhibited less risk-taking behavior than men in their most recent, largest and riskiest mutual fund investment decisions (Dwyer et al., 2002). Women are more risk averse than men in gambles, investment frames with possibility of loss and gamble frame with no losses (Eckel and Grossman, 2003).

Brynes and Miller (1999) have studied and investigated the relationship between risk and gender and concluded that women tend to take less risk than men (Olsen and Cox, 2001). Women are less likely to invest in riskier but high return assets than men (Mc Donald, 1997). However, the empirical investigation of gender difference in risk taking is inconclusive (Charness and Gneezy, 2004).

While most research conducted prior to 1980 concluded that gender difference clearly exists, more recent research studies yield mixed results (Changanti and Parasuraman, 1996; and Powell and Ansic, 1997). Males and females are equally successful in taking decisions under conditions of risk (Hudgen and Fatkin, 1985). They are equally effective in the leadership role (Eagly et al., 1995) and are equally capable of processing and reacting to information (Stinerock et al., 1991).

As businessmen/women, many studies have found similar level of performance for women-owned business as those which are owned by men (Kalleberg and Leicht, 1991; and Fischer et al., 1993). In an abstract lottery choice, Schubert et al. (2000) framed choices as either potential gain, or potential loss. They found that women are more risk averse than men in domain of gain, while men are more risk averse than women in the frame of loss domain. Women fund mangers—both domestic and international—hold portfolios which are marginally riskier than those of men, and their returns also outperform those of men (Bliss and Potter, 2001). Women were found to be less risk averse than men when the gambles were framed as insurance (Duda et al., 2004). Although, the impact of gender on risk taking is significantly weakened when investor knowledge of financial markets and investments is controlled in the regression equation, the greater level of risk aversion among women, which is frequently documented in the literature, cannot be completely, explained by knowledge disparities (Dwyer et al., 2002).

In the Indian context, Gupta ( has indicated that from the angle of investor protection, the regulation of the new issue market is important for several reasons. The number of small investors in new issue market is massive. Most of new investors make their first entry into equity investments via the new issue market. So retaining common investor confidence in primary markets is important. Madhusoodan ( has indicated that in the Indian stock market, higher risk is not priced, hence investment in higher risk instruments is of no use. Kakati ( has indicated that Indian IPOs are under priced in the short run and overpriced in the long run. Selling after allotment, around the listing month, is the cause of major return differences between IPOs performance in the short run and long run. Gokaran has studied the financing patterns of the corporate growth in the country. The study indicated that equity markets suffer serious inadequacies as a mechanism for raising capital. Murali ( has indicated that new issues market (NIM) focuses on decreasing information asymmetry, easy accessibility of capital by large sections of medium and small enterprises, national level participation in promoting efficient investments, and increasing a culture of investments in productive sector. In order that these goals are achieved, a substantial level of improvement in the regulatory standards in India at the voluntary and enforcement levels is warranted. The most crucial steps to achieve these goals would be to develop measures to strengthen the new issues market.

To effectively and efficiently serve clients in today’s competitive industry, financial planners increasingly rely on information technology. The larger the financial planning firm, the more critical the use of information technology becomes as its applications extend to areas outside financial planning such as payroll, accounting, marketing, and operations. This article proposes the establishment of a new research discipline, financial planning informatics, which focuses on the development of technology tools to support the unique needs of financial planners. We live in the information age. Information is the result of processing, manipulating, and organizing data in a way that creates new knowledge (Rahman 2006).

A number of studies have been conducted to study how risk tolerance varies with the individual demographics, such as, gender, age, education, income, etc (Schooley & Worden, 1996; Shaw, 1996; Xiao & Noring, 1994; Watson and Naughton, 2007). Most of these studies have, however, concentrated on exploring the gender differences in investment choice. The impact of other demographic factors, such as, age, education, income, occupation and dependents on investment choice has not been investigated by many researchers. But whatever studies have been done suggest that they (other demographic factors) affect individual’s investment decisions.

Risk tolerance, a person’s attitude towards accepting risk, is an important concept which has implications for both financial service providers and consumers. For the latter, risk tolerance is one factor which may determine the appropriate composition of assets in a portfolio which is optimal in terms of risk and return relative to the needs of the individual (Droms, 1987). In fact, the well-documented home country bias of investors may be a manifestation of risk aversion on the part of investors (see Cooper, and Kaplanis, 1994 and Simons, 1999).

For fund managers, Jacobs and Levy (1996) argue that the inability to effectively determine investor risk tolerance may lead to homogeneity among investment funds. Further, Schirripa and Tecotzky (2000) argue that the standard Markowitz portfolio optimization process can be optimised by pooling groups of investors together with different attitudes to risk into a single efficient portfolio that maintains the groups average risk tolerance.

Although a number of factors have been proposed and tested, a brief survey of the results reveals a distinct lack of consensus. First, it is generally thought that risk tolerance decreases with age (see Wallach and Kogan 1961; McInish 1982; Morin and Suarez 1983; and Palsson 1996) although this relationship may not necessarily be linear (see Riley and Chow 1992; Bajtelsmit and VanDerhai 1997). Intuitively this result can be explained by the fact that younger investors have a greater (expected) number of years to recover from the losses that may be incurred with risky investments. Interestingly, there is some suggestion that biological changes in enzymes due to the aging process may be responsible (see Harlow and Brown, 1990). More recent research however, reveals evidence of a positive relationship or fails to detect any impact of age on risk tolerance (see Wang and Hanna 1997; Grable and Joo 1997; Grable and Lytton 1998, Hanna, Gutter and Fan, 1998; Grable 2000, Hariharan, Chapman and Domian, 2000; and Gollier and Zeckhauser, 2002).

A second demographic which is frequently argued to determine risk tolerance is gender and Bajtelsmit and Bernasek (1996), Palsson (1996), Jianakoplos and Bernasek (1998), Bajtelsmit, Bernasek and Jianakoplos (1999), Powell and Ansic (1997), and Grable (2000) find support for the notion that females have a lower preference for risk than males. Grable and Joo (1999) and Hanna, Gutter and Fan (1998) however, find that gender is not significant in predicting financial risk tolerance.

Education is a third factor which is thought to increase a person’s capacity to evaluate risks inherent to the investment process and therefore endow them with a higher financial risk tolerance (see Baker and Haslem, 1974; Haliassos and Bertaut, 1995; Sung and Hanna, 1996). Shaw (1996) derives a model which suggests an element of circularity in this argument however, as the relative risk aversion of an individual is shown to determine the rate of human capital acquisition.

Income and wealth are two related factors which are hypothesised to exert a positive relationship on the preferred level of risk (see Friedman 1974; Cohn, Lewellen, Lease and Schlarbaum 1975; Blume 1978; Riley and Chow 1992; Grable and Lytton 1999; Schooley and Worden 1996; Shaw 1996; and Bernheim et al, 2001). For the latter, however, the issue is not clear cut. On the one hand, wealthy individuals can more easily afford to incur the losses resulting from a risky investment and their accumulated wealth may even be a reflection of their preferred level of risk. Alternatively, wealthy people may be more conservative with their money while people with low levels of personal wealth may view risky investments as a form of lottery ticket and be more willing to bear the risk associated with such payoffs. This argument is analogous to Bowman’s (1982) proposition that troubled firms prefer and seek risk.

Investigation of the investment decisions made by married individuals presents a unique challenge to researchers as the investment portfolio of the couple may reflect the combined risk preferences of the couple (Bernasek and Shwiff, 2001). The available evidence suggests that single investors are more risk tolerant (Roszkowski, Snelbecker and Leimberg, 1993) although some research has failed to identify any significant relationship (McInish, 1982; Masters, 1989; and Haliassos and Bertaut, 1995).


The study employs primary data collected by communicating with the respondents with the help of a structured questionnaire. Before undertaking the survey, a pilot test of the questionnaire was done with 40 respondents. Their views were incorporated in the final questionnaire and desired results were obtained. The study is based on responses obtained from the respondents belonging to a wide cross section. The total sample consisted of about 150 people, Males/Females from Salaried/ Self Employed, were split from different Age groups of Less than 35, 35-45, 45 and above.

Investment Experience (Measured in the No of years) and the savings of Individuals post investment was also observed.

The study employed non-probabilistic sampling method to select the respondents. The sampling method used can best be described as a mix of judgmental and convenient sampling.

The questionnaire (Annexure) consists of a risk profiling exercise combined with the demographic characteristics required about the investor. Later a combination of cluster analysis along with a couple of other tests like LOGIT, PROBIT Etc will be used.


The risk taking ability of the respondents was found by looking at the patterns and similarities that could be found and understood in the data. Techniques of Regression and Logit tests are used. Then the demographic characteristics of the people to their risk taking ability and any similar patterns are also identified.

From the final questionnaire we got to know the risk profile, demographic profile, choice of investments, other habits and observations etc. Later any patterns and similarities were looked at in the data. The analysis was done using Logit tests identifying probabilities, Multi logistic regression, Man- Whitney U test and chi square.

The following hypotheses were formulated to study whether the choice of Investment depends upon variables, such as, gender, age, income, educational qualification and occupation. The hypotheses are stated as follows:

Ho.1: There is no significant difference between the males and females in their choice of investment avenues.

Ho.2: There is no significant difference among the investors belonging to different age groups in their choice of investment avenues.

Ho.3: There is no significant difference between the investors of different occupations in their choice of investment avenues.

Ho.4: There is no significant difference between the investors having different investment experience in their choice of investment avenues.

Ho.5: There is no significant difference between the investors having different savings post investment in their choice of investment avenues.

Logit Regression

Using the data, we have calculated if the respondent is a risk taking or a risk averse investor. His risk taking behavior is taken as a Dependent variable. The various independent variables include Age, Gender, No of dependents, Income; savings post investments, investment experience etc. The model studies the change in the dependent variable due to change in all these independent variables.

We use ungrouped method of Logit regression as we observe that these variables are independent and are not very much correlated with each other; hence they show lesser chance of hetroscedasticity with each other.

Wald statistic (test) was used to test the significance of individual logistic regression coefficients for each independent variables ( that is to test the null hypothesis in logistic regression that a particular logit coefficient is zero). It is the ratio of the unstandardised logit coefficient to its standard error. The Wald statistic and its corresponding p probability level is part of the SPSS output. The independents may be dropped from the equations when their effect is not significant by the Wald statistic. We observe that the regression equation is significant at 10% with Wald value of 2.959.

It was observed that among the independent variables the Age, gender and Investment experience are considered to be significant with a Wald value of 18.571, 3.47, 3.457 respectively they are also significant as they fall in significance level of 10%. However No of dependents, the Income and savings post investment are not significant enough and they are not at a significant level too with more than 10% significance level.

It is observed that the number of dependents or siblings of a person does not define his risk taking ability and capacity, same is the reason for the person being salaried or being self employed for his living. There is no pattern observed for the level of savings that person has after his investment habits. Hence it can be said that the risk taking capacity can be mainly judged by his Age, Gender and Investment experience.

The logit can be converted easily into an odds ratio simply by using an exponential function. The original odds are multiplied by e to the bth power, where b is the logistic regression coefficient, when the given independent increases by one unit. The ratio of odds ratio of the independent is the ratio of the relative importance of the independent variables on the dependent variables. The value of ratio for income 1.083 . Hence a unit change in income affects the change in risk taking ability by 1.083

Further in the regression equation the variable Age is highly significant with the score of 21.443 in the equation, so is gender and investment experience. The equation has a overall statistics of 28.953 with a appropriate significance level.

R Square in logistic regression

R2 – measures attempts to measure strength of association. For small examples, for instance, an R2 – like measure might be high when the goodness of fit was unacceptable by model chi- square or some other test.

Cox and Snell R square is used to in the interpretation of multiple R square based on the likelihood, but the value lesser than1 is, the better. Here the value is 0.230. Nagelkerke’s R2 divides Cox and Snell’s R2 by its maximum in order to achieve a measure that ranges from 0 to 1. Therefore Nagelkerke’s R2 which is here 0.310 will normally be higher than the Cox and Snell measure but will tend to run lower than the corresponding OLS R2 which is 133.048. Nagelkerke’s R2 is the most-reported of the R-squared estimates.


The insight of how an investment choice gets affected by the demographic variables helps the financial advisors to advise their clients better. The clients, on the other hand, on being advised regarding the investments that suit their profile, will not only rate such an advice higher but will also appreciate it. This study thus, will certainly improve the mutual trust between the advisor and his client. Similar studies with diverse samples will help in understanding the investment psychology better.

From the research we observe that the risk taking ability can be mainly judged by his Age, Gender and Investment experience. That is if the person falls in a specific age category, the financial planner cab be readily prepared for the desires level of risky portfolio to be offered to the client. It has been noticed from the data that mostly people with high age are risk adverse on the contrary young people like to take very high risks and invest in aggressive stocks and speculative instruments. Men have been observed to be more risk taking and aggressive than most females. And people who have experience of trading in the financial markets also determine the level of risk they like to take.

It is observed that the no of dependents or siblings that a person does not define his risk taking ability and capacity initially we thought that people who have more no of siblings would like to take less risk however same has not been observed in this case, same is the case for the person being salaried or being self employed for his living. Similarly no pattern has been observed for the level of savings that person has after his investment habits and the level of risk that he like to take.

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