Investor Profiling Through Estimated Risk Tolerance Levels

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INVESTOR PROFILING THROUGH

ESTIMATED RISK TOLERANCE

LEVELS

SUBMITTED TO

PROF. SAPTARISHI PURKAYASTHA N. NISHCHALA SRIPATHI

FACULTY, IBS HYDERABAD ASSISTANT MANAGER, TATA AMC.

HYDERABAD

SUBMITTED BY

KARNA ABHITOSH SUMANKUMAR

09BSHYD0358

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Table of Contents

1. Abstract ............................................................................................................................... 3

2. Introduction ........................................................................................................................ 3

3. Literature Review ............................................................................................................... 4

4. Hypothesis Rationale .......................................................................................................... 7

5. Data and Sample ................................................................................................................. 8

6. Methodology ....................................................................................................................... 9

7. Data Analysis .................................................................................................................... 11

7.1 Sample Characteristics .................................................................................................. 11

7.2 Univariate Analysis ...................................................................................................... 12

7.3 Analysis of Covariance ................................................................................................. 13

7.4 Cluster Analysis ............................................................................................................ 14

8. Discussion and Conclusion .............................................................................................. 15

9. References ......................................................................................................................... 17

10. Annexure ......................................................................................................................... 19

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1. Abstract

This study presents evidence on the investors’ risk tolerance levels in the city of Hyderabad,

by using a questionnaire based survey method. The analysis indicates that men are more risk

tolerant than women and there is a tendency of an increase in risk averseness with an

increase in age. The analysis also indicates that there isn’t a very clear impact of the number

of dependents in the family on the risk tolerance of the investor, although the tolerance level

falls significantly if the number increases to four. There is also an indication that those

investors with higher income level appear to be more risk tolerant than otherwise. The most

important revelation in the study is related to the high risk tolerance observed in the young

migrant workforce present in Hyderabad, thus proving to be an attractive market segment

for Tata Mutual Fund.

2. Introduction

Mutual fund products can be differentiated on the basis of the risk attached to them. There

are products which can be classified as highly risky or moderately risky or having low risk.

But risk as a factor is always present in any financial product because the returns are

dependent on future market conditions, which can be volatile, and thus are difficult to

predict with accuracy.

It is also not easy to predict the risk tolerance of a potential investor. It has been found out in

past research work that risk is a factor which influences an individual’s decisions, including

financial and investments decisions (Lipe, 1998; and Yang and Qiu, 2005). Hence the link

between risk tolerance level and the choice for a particular financial or mutual fund product

is evident. The higher the risk tolerance of an individual, the higher financial risk he might

be willing to take. But the problem arises when a financial institution tries to estimate risk

tolerance levels in order to segment its existing and potential investors into a groups

showing similar behaviour. A situation can arise when the financial institution

underestimates or overestimates the risk taking ability of an investor and pushes a product

which is not suitable to that investor’s risk tolerance. Such mistakes might take a toll on the

relationship management of the financial institution and thereby impact its profitability and

business. On the other hand, if the institution is able to understand its investors’ risk

tolerance better, then it can offer the right products to the right segments and close the deal

with ease.

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This study was conducted by using a questionnaire based survey method and it was held in

the city of Hyderabad, since its people are the potential investors for Tata Mutual Fund

branch of Hyderabad. The basic objective of this study is to quantify the risk tolerance levels

of individuals who are well versed with financial products and investments and to group

them into heterogeneous segments showing similar risk taking ability. The area of this study

comes under behavioural finance. Behavioural finance is a separate branch of finance which

focuses on the individual attributes, psychological or otherwise, that shape the common

investment decisions (Ritter, 2003).

Further, this study tries to understand the impact of various factors such as sex, age, marital

status, number of dependents, native region and annual income on the risk tolerance level of

investors and also to understand the extent of the impact. I have not come across any other

study being done in Hyderabad which tries to quantify risk tolerance among investors and

analyze the impact of the “native region” variable. The variable of native region was chosen

keeping in mind the large number of migrant workforce present in Hyderabad. Such a type

of differentiation on the basis of demographics is helpful in further understanding and

ascertaining the investment behaviour of the migrant as well as the native population, by

comparing them.

The other variables selected for the analysis are age, sex, marital status, number of

dependents and income. These have been chosen keeping in mind their assumed impact on

an individual’s financial decision making. A separate risk tolerance mean value for each and

every variable has been calculated for comparison and analysis.

The study finally concludes with suggestions of suitable Tata Mutual Fund products that

may be offered to an investor depending upon the segment he falls into.

3. Literature Review

Risk tolerance generally influences an investors’ decision to go or not to go for a particular

portfolio selection or for a particular stock or fund. But risk tolerance or the risk taking

appetite of an individual is not an easy thing to calculate. It is difficult to quantify such an

attitude which defines risk taking ability.

Hence, this very problem of calculating the risk tolerance level has attracted the attention of

many researchers, especially those belonging to developed western countries like the US,

where the financial system is comparatively well developed and the need to carry out such a

study was felt much earlier.

Although the core objective of my research project is to segment individuals into different

groups which have similar risk taking appetites but the first step to carry this forward was to

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calculate the risk tolerance scores of each and every respondent I survey. I needed to review

prior work done in the same area to identify a feasible method to calculate the said risk

tolerance scores. Fortunately I have come across many research papers, most belonging to

authors from developed countries that have used questionnaire approach for calculating the

risk tolerance levels of respondents. Moreover, the papers also discuss about the impact of

different variables on the risk taking ability of individual investors; which incidentally is the

second objective of my project.

The most important paper I have referred to is by Jasim Y. Al-Ajmi (2008). The paper

presents new evidence on the determinants of risk tolerance of individual investors in

Bahrain. On the basis of an analysis of close to 1,500 respondents, the findings indicate that

as investors, men have high propensity towards risk tolerance than women. Investors with

better level of education and wealth are more likely to seek risk than less educated and less

wealthy ones. The study also reports that investors’ risk tolerance declines when they have

more financial commitments as well as when they are approaching towards their retirement

age or are retired. That is, the effect of investor’s age on risk tolerance is complex, in

contrast to results reported elsewhere. Bahrainis are also found to be less risk tolerant than

non-Bahrainis. One of the most important implications of the results is that the investment

industry should not treat investors as one homogeneous group; therefore, men and women as

investors should be treated as separate market niches, each with its own needs and requiring

targeted marketing strategies. Investment companies and financial service marketers should

design investment programs to respond to the particular needs of women investors, men

investors, investors with particular education and age levels, wealthy investors, and

expatriate investors. The paper has used the questionnaire developed by Dow Jones and

Company which is found in the book Investments (Bodie et al, 2007). I will be using the

same scoring methodology given as a part of the said questionnaire itself.

This research paper becomes highly relevant to my project since the study in the paper was

conducted in the country of Bahrain. Bahrain too is an emerging nation, going through a

development phase, hence has similar socio-economic conditions to those prevalent in India.

Some of the research papers carrying out a similar study have used the financial

questionnaire and methodology developed by ProQuest risk profiling system. Ulla Y. Yip

(November 2000) examines the robustness of financial risk tolerance as a psychological

trait. One hundred and twenty-nine finance students each managed a portfolio on an on-line

trading simulation for eight weeks. Financial risk tolerance was measured three times - pre,

post and follow-up - and was found to be stable. The increase in financial experience and

knowledge, as well as the occurrence of a major stock market crash during the trading

period did not appear to affect the stability of risk tolerance. Generally males were found to

be more risk tolerant than females, this was also reflected in their trading strategies.

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However, this had no impact on their ability to obtain desired outcomes. It was concluded

that financial risk tolerance is better considered as a trait and not a state. Hence the paper

provides important insights about the psychological aspect of risk taking.

In the study carried out by Robert W. Faff, Terrance Hallahan and Michael D. McKenzie,

they analyze a large database of psychometrically derived financial risk tolerance scores

(RTS) and associated demographic information. They found that people’s self-assessed risk

tolerance generally accords with RTS. Further they found that gender, age, number of

dependents, marital status, income and wealth are significantly related to the RTS. Notably,

the relationship between age and risk tolerance exhibits a significant nonlinear structure in

their study.

In an interesting study carried out by researchers (Diane K. Schooley, Ph.D., CCM, and

Debra Drecnik Worden, Ph.D., September 2003) they focused on the generation X, those

born in 1964 through 1980 and have tried to understand their tolerance for risk. This study

explores Gen Xers' risk tolerance by evaluating their propensity for risk-taking, as well as

their attitude toward risk, their capacity for risk and their knowledge of risk. The results

from the study indicated that an evaluation of this generation's risk tolerance through the

four components of Cordell’s Risk PACK yields significant information for financial

planners. In terms of the percentage of financial assets allocated to equities, this generation

of investors in the Suri'cy of Consumer Finances exhibits a low propensity for risk taking,

relative to other generations. Their low propensity for risk taking is also consistent with their

perceived capacity for risk—evidenced by very short-term financial planning horizons and

with their phase in the life cycle. The study further states that what financial planners should

realize is that Generation X appears to lack financial knowledge. Gen Xers seem to lack a

clear understanding of the risk/return trade-off and the appropriate asset allocations required

to meet financial goals. While they claim to be concerned about the adequacy of their

retirement income, they are not deliberate in addressing this long-term goal. It is possible

that this disconnect between target planning horizon and savings goal is because Gen Xers

lack the discipline to defer consumption to save for retirement. The results of this study

cannot distinguish whether the dissonance between planning horizon and goals is due to lack

of financial knowledge or to a lack of discipline. But regardless of the explanation, an

educational opportunity for financial planners undoubtedly exists. Another relevant research

paper is by James E. Corter and Yuh-Jia Chen (2005) in which the authors investigate a new

instrument designed to assess investment risk tolerance, the Risk Tolerance Questionnaire

(RTQ). RTQ scores were positively correlated with scores on two other investment risk

measures, but were not correlated with a measure of sensation-seeking (Zuckerman, 1994),

suggesting that investment risk tolerance is not explainable by a general cross-domain

appetite for risk. Importantly, RTQ scores were positively correlated with the riskiness of

respondents’ actual investment portfolios, meaning that investors with high risk-tolerance

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score tend to have higher-risk portfolios. Finally, respondents with relatively more

investment experience had more risk-tolerant responses and higher-risk portfolios than less

experienced investors.

But it was also important for me understand if the risk tolerance level of an individual

influences the level of risk he/she decides while making investments. Robert Masters (1989)

concludes in his paper that there between the risk-taking propensity and the level of risk

taken by an individual investor. The author found out the greater the investors knowledge

about investments, the greater the willingness to take risk. The study also indicates that

while gender has little influence on the risk taking, occupation and marital status do. Since

this paper states that there is in fact a relationship, hence I can move forward with my

project without having lingering doubts in my head regarding the relationship between risk

taking attitude and the actual investment decisions made by any investor.

The literature survey has been of great help for me, especially by searching the questionnaire

and the risk scoring methodology (Jasim Y. Al-Ajmi, 2008). It has helped me to move ahead

in the project after sorting out one of the biggest challenges of quantifying the degree of risk

tolerance of any respondent. The project is different than the earlier work done since it

basically tries to achieve the objective of segmenting individual investors on the basis of

certain easily identifiable variables. And the biggest point of difference is the socio cultural

and economic conditions prevalent in India. These factors could lead to a significant

difference in risk taking attitude of Indians (people from the city of Hyderabad) and those

belonging to the western countries where most of the earlier studies were conducted.

4. Hypothesis Rationale

A hypothesis has been formed to study that when only one variable among sex, age, marital

status, number of dependents, native region and income is allowed to vary then what is the

role played by the other constant variables in shaping the risk tolerance of the individual.

This hypothesis has been formed in order to check if the results of the univariate analysis

regarding the risk tolerance levels are robust or not.

The following are the hypothesis statements:

• Null Hypothesis: When only one variable among sex, age, marital status, number of

dependents, native region and income is allowed to vary there is no significant role

played by the other constant variables in influencing the risk tolerance.

• Alternate Hypothesis: When only one variable among sex, age, marital status,

number of dependents, native region and income is allowed to vary there is a

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significant role played by the other constant variables in influencing the risk

tolerance.

Earlier in the project I was inclined to test whether the independent variables I have chosen

have an influence on the risk tolerance. But the ample evidence reported in the past literature

regarding the influence of variables like sex, age, income, marital status already reports the

same. Hence, instead of testing the influence of the independent variables as a whole I have

tested them in order to see if they support the univariate results regarding risk tolerance. As I

mentioned earlier there is a lot of literature which suggests the influence of variables like

age, sex, marital status and wealth on the risk tolerance of an individual. In the United

States, Bruce and Johnson (1994) find that women take less investment risk. Jianakoplos and

Barnesek (1998) report results lend further support to the hypothesis that a far lower

percentage of women than men are willing to take any financial risk at all. Bajtelsmit and

Bernasek (1996) find sex as the third most important factor in determinants of investors’ risk

attitude. Lewellen et al. (1977) find that sex was the third most important determinant of

investor style (after age and income).

Age is found to be the most important determinant of investor style, Lewellen et al. (1977).

Many researchers support the notion that young people are less risk averse than elder people

in the same task context. As people get older, they rebalance their portfolios in favor of

fixed income securities at the expense of common stock [see Bodie et al. (1992), Bodie and

Crane (1997)] and Strong and Taylor (2001)].

As far as income is concerned Grable and Lytton (1999) find that wealth is one determinant

of investors’ risk attitude. Their results show that there is a positive risk seeking and wealth;

that is, wealthy investors are likely to hold a higher proportion of their portfolios in risky

assets. These results lend further support to the expected utility theory advanced by

Friedman (1974), Lewellen, Lease and Schlarbaum (1975), Blume (1978), Riley and Chow

(1992), Cohn, et al. (1996), Huang and Litzenberger (1988), and Bernheim et al. (2001).

Robert Masters (1989) concluded that marital status does have an influence on the risk

taking ability of the investors. “Number of dependents” and “native region” are the

variables which have not been tested or researched on a wide basis earlier but I still have

included them based on the context of my study.

5. Data and Sample

Primary data was collected for analysis in this study. The data has been collected in the form

of responses to the questionnaire from the people of Hyderabad. The sampling methodology

used is convenience sampling. The population of the study has been people who have prior

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knowledge of making investments, and who are currently residing in Hyderabad. A sample

was selected from the population and a total of 234 respondents were interviewed, which

forms the sample size of the study. Normal distribution for the sample has been assumed.

Responses from the 234 questionnaires were coded and analyzed.

The city areas having a high number of migrant populations were visited to get the responses

from the migrant workforce. Retired and senior citizens were searched and approached in

their homes and in community centers.

In order to test the reliability of the questionnaire instrument the cronbach alpha was

calculated on the first 30 respondents and the result came out as 0.692 which was considered

as acceptable to move ahead with the survey. Later, I again calculated the cronbach alpha of

the responses of the total 234 respondents before running any test on the data. The result

came out as 0.736 which shows good internal consistency of the responses to the test

instrument and validates the use of the questionnaire.

6. Methodology

� In order to quantify the qualitative aspect of the degree of risk tolerance a

questionnaire survey has been used (Jasim Y. Al-Ajmi, 2008).

� Convenient sampling was used to collect the responses.

� The questionnaire is divided into two segments; the first part includes questions to

ascertain the details such as age, income, marital status etc of the respondent.

� The second part constitutes a risk quiz (part B in Appendix), which is being used to

assign risk-taking propensity scores to each of the respondents on the basis of their

answers to the questions.

� The said questionnaire has been developed by Dow Jones and Company and found in

the book Investments (Bodie, Kane and Marcus, 2002). The risk quiz consists of

total 9 questions or cases in which respondents have to select one of three possible

answers.

� Alternatives are weighted between 1 and 3. The degree of a respondent’s risk

aversion is calculated by adding the weights of the answers. The respondents scoring

between 9 and 14 points are assumed to be conservative investors. Those who score

between 15 and 21 points have been classified as moderate investors, and those who

score between 22 and 27 points have been classified as of having above-average risk

tolerance. This scoring methodology has being used in quantifying the hitherto

qualitative variable of risk-taking ability.

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� The questionnaire has been successfully tested for reliability and internal consistency

by using Cronbach alpha test in the SPSS 13.0 software before running any

statistical test on it. The result of the same is shown in data analysis section.

� A descriptive analysis of the data has been run in the SPSS software to find out the

mean values of the risk scores for each category of independent variables like those

of men and women, those of married and unmarried etc.

� Further, the analysis of covariance (ANCOVA) has been calculated as a part of the

regression analysis to test the robustness of the descriptive results and to find out the

impact or influence of constant independent variables on the dependent variable (risk

tolerance level) when only one variable among them is allowed to vary and others

are held constant.

� Finally, a cluster analysis has been run on the collected data.

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7. Data Analysis

7.1 Sample Characteristics

Table 1: Descriptive summary of the sample characteristics

Variables Frequency Risk Tolerance

Sex

Male

Female

140

94

1.84

1.70

Age

18-28

29-30

40-50

51-61

62-73

74 and above

58

55

47

38

27

9

2.05

1.87

1.68

1.71

1.52

1.11

Marital Status

Married

Unmarried

184

50

1.70

2.08

Number of Dependents

0

1

2

3

4

73

66

65

24

6

1.82

1.76

1.80

1.83

1.17

Native Region South India

Gujarat

North India

West India (excluding

Gujarat)

North East

East India (excluding North

East)

89

37

35

26

21

26

1.74

1.89

1.83

1.77

1.67

1.81

Annual Income (in INR)

0-2,49,9999

2,50,000- 4,99,999

5,00,000-7,49,999

7,50,000- 9,99,999

10,00,000- 12,49,999

12,50,000 and above

98

88

30

11

4

3

1.62

1.83

2.07

1.91

2.25

1.67

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Table 1 gives the characteristics of the sample including the frequency of respondents and

mean risk score values of each and every variable. Further, I am reporting the results of

univariate analysis, analysis of covariance and cluster analysis.

7.2 Univariate analysis

The results in table 1 show that men are more risk tolerant than women. Although both the

average man (mean of 1.84) and woman (mean of 1.70) can be classified into as moderate

risk takers, yet there is a statistical difference between their mean risk tolerance scores. This

result is in agreement to several research studies done earlier.

Moving further it is observed that in general the risk tolerance is decreasing with an increase

in the age. There is a significant fall in the risk tolerance level when moving from the

younger to the older respondents. Respondents belonging to the youngest age bracket of 18-

28 years showed the highest risk tolerance (mean of 2.05) and in general the tolerance level

appears to have been reduced with increasing age. The reasons for low risk tolerance among

older respondents can be attributed to the shorter life period they are expecting to live. They

might not be willing to risk money for they also don’t expect to make up for possible losses

through long term future earnings. They also expect to have a shorter investment horizon in

sight so they might believe in short term fixed returns rather than long term returns which

are risky in nature.

It has been found out that married people (1.70) show less risk tolerance than unmarried

ones (2.08). It is not difficult to understand the reasons behind this as most of the unmarried

respondents are young, who have already showed a penchant for higher risk taking and

many of the married respondents belong to the higher age group brackets which have shown

a tendency for lesser risk tolerance.

The effect of number of dependents has not been very clear and straightforward. It has been

found out that the risk tolerance of those respondents having 2 dependents was more than

those having a single dependent. And further, the risk tolerance of those having 3

dependents was more than those having 2 of them. But it is also observed that when the

number of dependents reaches 4 there is a drastic fall in the risk tolerance level, thus

suggesting that increasing number of dependents has a significant effect on risk tolerance

after reaching a particular level. It can be said that with more number of dependents the

financial obligations of the investor increases, thus he might not be willing to risk his assets

as there are many dependents for him to take care of.

One of the most revealing and important results comes from the native region

differentiation. It has been found out that the migrant population in Hyderabad has shown a

high penchant for risk taking. With Gujaratis topping the list (mean of 1.89) and North

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Indians coming second (mean of 1.83) the migrants have shown high risk taking

preferences. South Indians, who constitute the native population of Hyderabad showed

lower risk tolerance and are second last in the list (mean of 1.74). However it is seen that

migrants from North East have scored the lowest in terms of risk tolerance (mean of 1.67).

The reasons for the high risk tolerance among the migrant population can be that most of

them belong to the younger age groups and don’t have many dependents to take care of, they

also appeared to more knowledgeable about the financial products and markets. South

Indians might have shown lesser risk tolerance owing to the fact that many of the

respondents belonging to the older age groups were from the south, thus it might have pulled

down the mean risk value of the entire sample.

It was also observed that there is a general trend for people having higher income to score

more on risk tolerance. The reason can be that people with higher amount of disposable

income can feel more secured while making financial investments as possible losses due to

risk would also not affect the general living standard of the investor. And he might be able

to absorb the loss better than a person having low income.

7.3 Analysis of Covariance

I performed the general linear model of analysis of covariance in order to test the robustness

of the univariate results and also to know whether the results can be attributed to the

relationship between the independent variables. The test was performed six times. Each

time one determinant of risk (sex, age, marital status, no. of dependents, native region and

age) was considered as a fixed factor and the other determinants (independent variables)

were considered as covariates. The result of the test is shown in table 3.

Table 3: Summary results of Analysis of Covariance

Variables R Square F Statistics

Sex

Age

Marital Status

Number of Dependents

Native Region

Income

28.6%

31.2%

28.6%

32.3%

29.1%

29.4%

1.958*

5.469*

0.691*

4.108*

0.752*

3.998*

*Significant at less than 0.05 level

The results of analysis of covariance further lend support and validation to the univariate

results as it can be seen that when only one determinant of risk allowed to vary the other

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determinants have a significant effect in determining the risk tolerance. Here we can see that

when sex is considered as a fixed factor and only it is allowed the other factors (covariates)

explain 28.6% of the change in risk tolerance.

Hence the results lend further support to the univariate results where it was found that men

are more risk tolerant than women. And that unmarried people are more risk tolerant than

the married ones etc.

The result also enables me to reject the null hypothesis that there is no dependence of the

independent variables like sex, age, marital status, number of dependents, native region and

income on the risk tolerance of the investors when only one among them is allowed to very

and other are kept constant. Because it is quite evident that all the variables or all the

determinants play a significant role in shaping the risk tolerance level of an investor.

7.4 Cluster analysis

I ran cluster analysis to find out the number of heterogeneous segments in the sample

showing similar characteristics and the also find the dominant factors of each of those

segments. I report the results of the cluster analysis and its interpretation.

After running hierarchical clustering on the data, it was found out that there are three

possible clusters emerging from the sample. Further, k-means clustering was run by

indicating the number of clusters as three and the following results were obtained.

Table 3: Final Cluster Centers

Cluster

1 2 3 Sex 1.48 1.44 1.32

Age 4.67 1.88 2.34

Marital Status 1.00 1.20 1.36

No. of dependents 1.97 2.41 2.29

Native region 1.42 4.79 1.76

Income 1.33 1.54 2.52

Risk score 1.47 1.74 2.01

Table 4: Number of Cases

Cluster 1 60.000

2 80.000

3 94.000

Valid 234.000

Missing .000

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Table 4 indicates that among the three clusters the first cluster has 60 cases, second one has

80 and the third one has 94. Table 4 indicates that the dominating variable in the first cluster

is age (highest center value). The dominating variable in the second cluster is native region

and the variables of dominance in the third cluster are income and age (with the highest and

second highest values respectively).

These three clusters can be analyzed by focusing on the dominating variables thereof. The

three clusters can be classified as:

Investors close to retirement or those who are already retired (age): This cluster might be

majorly consisted of those investors who are close to retirement or are already retired and

who have very less risk tolerance compared to the younger age groups.

Migrant investors in Hyderabad (native region): This cluster might be majorly consisted of

the migrant investors residing in Hyderabad. These investors in general show high tolerance

for financial risk. They generally belong to the younger age groups.

Investors with good income but from young to middle aged groups (income and age):

This cluster might be majorly consisted of investors who earn medium to high level of

income but they are in the young to middle aged stages of their lives with many being

married and having dependents to take care of. Hence they show moderate level of risk

tolerance and form the major part of the entire sample.

8. Discussion and Conclusion

The purpose of this study was to quantify the qualitative aspect of risk tolerance and use this

quantification to segment potential investors into heterogeneous groups. Also, I wanted to

examine if the six determinant variables used in the study have an influence on the risk

tolerance of the investors, and to find out if there is a relationship among them (change this).

I used a questionnaire based survey method to collect the primary, used several statistical

procedures and also ran a cluster analysis to meet the objectives of my study. The results of

the entire study show that:

1. Male investors have higher risk tolerance than their female counterparts. 2. Young investors have higher risk tolerance than the older ones. 3. Married investors are more risk averse than unmarried ones. 4. There is significant fall in the risk tolerance of investors when their dependents

increase by a high number. 5. Young migrant workforce of Hyderabad shows a penchant for high risk tolerance. 6. Risk tolerance tends to increase with an increase in income.

When I analyze the clusters formed by the cluster analysis, the most important implication

of the results is regarding the high risk tolerance levels shown by the segment of young

migrant investors in Hyderabad. Most of this population works either in IT/ITES companies

or in the services sector and are in general earning medium levels of income. They appear to

be an attractive market segment to pitch equity related mutual fund products, which are

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suitable to their risk taking appetite. Products like Tata Pure Equity fund, Tata P/E fund,

Tata Infrastructure fund and Tata Equity Opportunities fund can prove to be suitable for this

segment.

For the segment having many married and middle aged investors who generally have

dependents in their family and show a moderate level of risk appetite, funds like Tata Tax

Saving Fund (which provides tax benefits) and Tata Balanced Fund (which invests 35-45%

of its corpus in fixed return debt instruments) can prove to be the most suitable.

The segment consisting of mostly past investors who are either retired or are nearing

retirement, although not a very a promising market segment because of their low risk

tolerance levels, can still be turned into investors by offering them suitable low risk attached

products like Tata Monthly Income fund, Tata Liquid fund and Tata Floater fund which

have majority of investments in good quality debt instruments.

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10. References

1. Al Ajmi, J. Y., 2008. Risk Tolerance of Individual Investors in an Emerging Market.

International Research Journal of Finance and Economics, ISSN 1450-2887 Issue 17.

2. Schooley D K, Ph.D., CCM, and Worden D. D., September 2003. Ph.D., Generation

X: Understanding Their Risk Tolerance and Investment Behavior. Journal of

Financial Planning.

3. Corter J. E. and Chen Y. J., 2006. Do Investment Risk Tolerance Attitudes predict

Portfolio Risk?. Journal of Business and Psychology, Vol. 20, No. 3, Spring 2006.

4. Yip U. Y., November 2000. Financial Risk Tolerance: A state or a Trait. A thesis

submitted in partial fulfillment of the requirements for the degree of Master of

Psychology (Organisational) of The University of New South Wales.

5. Faff R. W. Hallahan T. and McKenzie M. D. An Empirical Investigation of

Personal Financial Risk Tolerance. Department of Accounting and Finance, Monash

University.

6. Bodie, Kane and Marcus, 2002. Investments. Tata Mc-Graw Hill publication. Fifth

edition.

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Page | 19

11. Annexures

Cronbach’s alpha output

Pilot test:

Reliability Statistics

Cronbach's Alpha N of Items

.697 9

On the entire sample:

Reliability Statistics

Cronbach's Alpha N of Items

.736 9

Output of Analysis of Covariance:

Sex:

Tests of Between-Subjects Effects Dependent Variable: Risk_score

Source Type III Sum of Squares df Mean Square F Sig.

Corrected Model 18.436(a) 6 3.073 15.160 .000

Intercept 10.069 1 10.069 49.680 .000

Age 3.799 1 3.799 18.744 .000

Marital_Status .140 1 .140 .691 .407

No.of_dependents .581 1 .581 2.867 .092

Native_region .430 1 .430 2.123 .147

Income 3.564 1 3.564 17.583 .000

Sex .397 1 .397 1.958 .163

Error 46.009 227 .203

Total 804.000 234

Corrected Total 64.444 233

a R Squared = .286 (Adjusted R Squared = .267)

Page | 20

Age:




Intercept 8.259 1 8.259 41.512 .000

Marital_Status .080 1 .080 .400 .528

No.of_dependents .165 1 .165 .828 .364

Native_region .481 1 .481 2.418 .121

Income 3.309 1 3.309 16.630 .000

Sex .224 1 .224 1.128 .289

Age 5.440 5 1.088 5.469 .000

Error 44.367 223 .199

Total 804.000 234



Marital Status:




Intercept 25.310 1 25.310 124.877 .000

No.of_dependents .581 1 .581 2.867 .092

Native_region .430 1 .430 2.123 .147

Income 3.564 1 3.564 17.583 .000

Sex .397 1 .397 1.958 .163

Age 3.799 1 3.799 18.744 .000

Marital_Status .140 1 .140 .691 .407

Error 46.009 227 .203

Total 804.000 234



Page | 21

Number of Dependents:


Source Type III Sum of Squares Df Mean Square F Sig.


Intercept 8.443 1 8.443 41.320 .000

Native_region .694 1 .694 3.397 .067

Income 3.011 1 3.011 14.735 .000

Sex .248 1 .248 1.215 .271

Age 4.010 1 4.010 19.622 .000

Marital_Status .309 1 .309 1.515 .220

Error 46.590 228 .204

Total 804.000 234



Native Region:




Intercept 10.743 1 10.743 52.460 .000

Income 3.403 1 3.403 16.617 .000

Sex .388 1 .388 1.892 .170

Age 3.821 1 3.821 18.658 .000

Marital_Status .099 1 .099 .482 .488

No.of_dependents .712 1 .712 3.476 .064

Native_region .770 5 .154 .752 .585

Error 45.669 223 .205

Total 804.000 234



Page | 22

Income: Tests of Between-Subjects Effects Dependent Variable: Risk_score



Intercept 16.391 1 16.391 80.345 .000

Sex .381 1 .381 1.868 .173

Age 3.911 1 3.911 19.172 .000

Marital_Status .118 1 .118 .577 .448

No.of_dependents .535 1 .535 2.621 .107

Native_region .562 1 .562 2.752 .099

Income 4.078 5 .816 3.998 .002

Error 45.494 223 .204

Total 804.000 234



Output of Cluster analysis:

Iteration History(a)

Iteration

Change in Cluster Centers

1 2 3

1 2.317 2.536 2.850

2 .202 .062 .193

3 .040 .128 .291

4 .060 .176 .321

5 .200 .245 .392

6 .091 .200 .183

7 .000 .047 .043

8 .000 .031 .028

9 .000 .097 .082

10 .000 .000 .000

a Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change for any center is .000. The current iteration is 10. The minimum distance between initial centers is 6.708.

Page | 23

Final Cluster Centers

Cluster

1 2 3

Sex 1.48 1.44 1.32

Age 4.67 1.88 2.34

Marital_Status 1.00 1.20 1.36

No.of_dependents 1.97 2.41 2.29

Native_region 1.42 4.79 1.76

Income 1.33 1.54 2.52

Risk_score 1.47 1.74 2.01

Number of Cases in each Cluster

Cluster 1 60.000

2 80.000

3 94.000

Valid 234.000

Missing .000

ANOVA

Cluster Error

F Sig. Mean Square df Mean Square df

Risk_score 5.517 2 .231 231 23.862 .000

Sex .571 2 .239 231 2.396 .093

Age 148.627 2 .888 231 167.323 .000

Marital_Status 2.407 2 .149 231 16.116 .000

No.of_dependents 3.529 2 1.154 231 3.058 .049

Native_region 265.655 2 .802 231 331.096 .000

Income 32.986 2 .722 231 45.715 .000

The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.

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