Investors’ Fund Selection Criteriashodhganga.inflibnet.ac.in/bitstream/10603/23447/9/09_chapter...

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Chapter V

Investors’ Fund Selection Criteria One of the main objectives of the study is to assess the perception of investors

regarding their fund selection criteria and to find, if any, the differences between retail

and non retail investors on the same. This chapter includes the analysis of the

importance of various fund selection constructs and comparison of retail and non

retail investors on the same. The comparison of various subsets of retail investors on

various constructs of fund selection criteria has also been discussed. The chapter is

divided into following sections

5.1 Importance of Fund Selection Criteria Constructs

5.1.1 Importance of Mutual Fund Schemes as Selection Criteria

5.1.2 Importance of Mutual Fund Companies as Selection Criteria

5.1.3 Importance of Investor Services as Selection Criteria

5.1.4 Behavioral Biasness as Selection Criteria

5.2 Comparison of Retail and Non Retail Investors

5.2.1 Comparison of Retail and Non Retail Investors on Mutual Fund Schemes Construct

5.2.2 Comparison of Retail and Non Retail Investors on Mutual Fund

Companies Construct

5.2.3 Comparison of Retail and Non Retail Investors on Investor Services Construct

5.2.4 Comparison of Retail and Non Retail Investors on Behavioral

Biases

5.3 Comparison of various subsets of Investors

5.3.1 Comparison of Investors categorized on the basis of Demographic Profile.

5.3.2 Comparison of Investors categorized on the basis of Economic Profile.

5.3.3 Comparison of Investors categorized on the basis of Purchase Behavior.

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5.3.4 Comparison of Investors categorized on the basis of Purchase Profile.

5.3.5 Comparison of Retail and Non Retail Investors on basis of their perception towards Objectives of Investing in Mutual Funds

5.3.6 Comparison of Retail and Non Retail Investors on basis of their

perception towards Advantages of Investing in Mutual Funds 5.1 Importance of Fund Selection Criteria Constructs

Campenhout (2007) and Capon et al (1996) argue that there is no

single theoretical framework for mutual fund selection criteria. As a result large

number of variables linked to fund selection grouped as various constructs have been

asked to the investors on five point importance (ranging from not at all important to

very important) and agreement scale (from strongly disagree to strongly agree). The

higher score on importance scale reflects the higher importance of that variable and

higher score on agreement scale reflects the higher agreement on that variable. The

various constructs that have been asked under the study are sources of information;

mutual fund schemes; mutual fund companies; investor services and behavioral

biases. Sources of information have been discussed at length in chapter IV. This

section deals with importance of mutual fund schemes as selection criteria (section

5.1.1); importance of mutual fund companies as selection criteria (section 5.1.2);

importance of investor services as selection criteria (section 5.1.3) and behavioral

biases as selection criteria (section 5.1.4).

5.1.1 Importance of Mutual Fund Schemes as Selection Criteria

Table 5.1 depicts the importance of selection criteria relating to mutual fund

schemes. Importance was asked for 20 variables on 5 point scale from not at all

important to very important. In terms of reliability analysis the Cronbach’s alpha for

the entire construct was high at 0.897 indicating high reliability for further analysis.

For the entire construct, the mean importance assigned by total investor base of 450

respondents was 3.67 (retail and non retail) with standard deviation of 0.66. Over all

non retail investors (M = 3.75, SD = 0.56) assigned higher importance to selection

criteria related to mutual fund schemes as compared to retail investors (M = 3.65, SD

= 0.67), but the difference between the two categories is not significant, U = -0.977,

p>0.05. Hence H0-2 is accepted. Over all the total sample base of 450 investors

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assigned highest importance to ‘return performance of the scheme’ (M = 4.02, SD =

1.28) and lowest importance to the ‘third value rankings of the scheme’ (M = 3.16,

SD = 1.19). Empirical research has established that mutual fund investors direct their

investments in top performing funds (Goriaev et al, 2002; Barber et al, 2005) and

further it has been widely reported that return performance of the scheme is one of the

important fund selection criteria (Capon et al, 1996; Rajeswari & Moorthy, 2002).

Table 5.1: Importance of Selection Criteria relating to Mutual Fund Schemes

Selection Criteria related to Mutual Fund Scheme

Retail Investors

Mean (SD) (N = 400)

Non Retail Investors Mean (SD)

(N = 50)

Total Sample Mean (SD) (N = 450)

Return performance of the scheme

3.96 (1.31) 4.48 (0.78) 4.02 (1.28)

Risk of the Scheme 3.90 (1.12) 4.22 (0.84) 3.93 (1.09) Number of assets in Fund’s Portfolio

3.64 (1.17) 3.78 (0.97) 3.66 (1.15)

Maturity profile of assets 3.69 (1.09) 3.96 (0.85) 3.72 (1.07) Quality of assets 3.95 (1.05) 4.14 (0.85) 3.97 (1.03) Reputation/Brand Name of the scheme

3.85 (1.10) 4.16 (0.91) 3.89 (1.08)

Reputation of Fund Manager 3.69 (1.19) 3.82 (1.02) 3.70 (1.17) Fund Size 3.59 (1.19) 3.80 (0.98) 3.62 (1.17) Age of the Fund 3.48 (1.17) 3.40 (1.03) 3.47 (1.16) Experience / Qualification of the Fund Manager

3.51 (1.26) 3.96 (0.87) 3.56 (1.23)

Investment Objective of the Scheme

3.77 (1.15) 3.80 (0.94) 3.77 (1.13)

Expense ratio of the Scheme 3.49 (1.12) 3.66 (1.18) 3.51 (1.13) Innovativeness in the Scheme 3.41 (1.20) 3.18 (1.08) 3.38 (1.19) Scheme rating 3.66 (1.17) 3.74 (1.12) 3.67 (1.16) Third Value Rankings of the Scheme

3.13 (1.20) 3.46 (1.01) 3.16 (1.19)

Investment Options 3.51 (1.15) 3.24 (1.00) 3.48 (1.14) Entry and Exit Loads 3.55 (1.15) 3.60 (1.22) 3.56 (1.16) Tax Benefits 3.87 (1.08) 3.74 (1.06) 3.86 (1.08) Minimum Initial Investment 3.46 (1.12) 3.16 (1.26) 3.43 (1.14) Growth Prospects 3.97 (1.03) 3.80 (1.24) 3.95 (1.06) For Entire Construct 3.65 (0.67) 3.75 (0.56) 3.67 (0.66)

(Tests of Difference between Retail and Non Retail investors) U(Z score) = -0.977, p exact = 0.328

Cronbach’s Alpha for Entire Construct = 0.897 Note 1. Importance was asked on 5 point scale from Not at all important to Very important 2. Shapiro Wilk test was used for establishing Normality. (* denotes non normality) 3. Mann Whitney Test was used for checking differences between retail and non retail investors

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Retail investors assigned highest importance to the ‘growth prospects of the

scheme’ (M = 3.97, SD = 1.03) followed by ‘return performance of the scheme’ (M =

3.96, SD = 1.31) and ‘quality of the assets’ (M = 3.95, SD = 1.05). Non retail

investors have different preferences as they assigned highest importance to the ‘return

performance of the scheme’ (M = 4.48, SD = 0.78) followed by ‘risk of the scheme’

(M = 4.22, SD = 0.84) and ‘reputation or brand name of the scheme’ (M = 4.16, SD =

0.91). The lowest importance was assigned to ‘third value rankings of the scheme’

(M = 3.13, SD = 1.20) by the retail investors and minimum initial investment (M =

3.16, SD = 1.26) by non retail investors.

Overall, in addition to returns, retail investors were more concerned with

qualitative parameters like growth prospects of the scheme and quality of assets. In

contrast, non retail investors were more concerned with the quantitative fund selection

criteria like return and risk performance and in addition they were also influenced by

brand name or reputation of the scheme. The study has found almost similar results as

reported by Sharma (2006) who argued that investment performance and reputation of

fund manager are one of the most important fund selection criteria. Interestingly,

retail investors assigned lowest importance to the third party rankings, which has

always been treated as important promotional means of the mutual fund schemes.

Further as expected minimum initial investment has little role to play in influencing

fund selection criteria of non retail investors

5.1.2 Importance of Mutual Fund Companies as Selection Criteria

Table 5.2 reflects the importance of selection criteria relating to mutual fund

companies. Importance of selection criteria was asked for 13 variables on 5 point

scale from not at all important to very important. The reliability analysis in terms of

Cronbach’s alpha was high at 0.844 indicating higher reliability. For the entire

construct, the mean importance assigned by total investor base of 450 respondents

was 3.58 with standard deviation of 0.67. Over all non retail investors (M = 3.66, SD

= 0.70) assigned higher importance to the construct as compared to retail investors (M

= 3.57, SD = 0.67), but the difference between the two is insignificant, U = -0.740,

p>0.05. Hence H0-3 accepted. Over all the highest importance was awarded to

‘reputation or brand name of AMC’ (M = 4.10, SD = 1.18) and lowest importance

was assigned to ‘intermediaries network’ (M = 3.21, SD = 1.20).

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Both retail (M = 4.05, SD = 1.22) and non retail investors (M = 4.52, SD =

0.64) assigned highest importance to reputation or brand name of asset management

company. The next in importance was assigned to ‘experience of asset management

company’ for both retail (M = 3.88, SD = 1.15) and non retail investors (M = 4.36,

SD = 0.74).

Table 5.2: Importance of Selection Criteria relating to Mutual Fund Companies

Selection Criteria related to Mutual Fund Company

Retail Investors M(SD)

(N = 400)

Non Retail Investors M (SD) (N = 50)

Total Sample M (SD)

(N = 450) Reputation / Brand Name of AMC

4.05 (1.22) 4.52 (0.64) 4.10 (1.18)

Experience of AMC 3.88 (1.15) 4.36 (0.74) 3.93 (1.12) Location of AMC in Investor’s city

3.29 (1.21) 3.18 (1.40) 3.28 (1.24)

Intermediaries Network 3.24 (1.15) 2.94 (1.55) 3.21 (1.20) Expertise of AMC in Managing Money

3.78 (1.00) 3.98 (1.05) 3.80 (1.01)

Infrastructure of AMC 3.36 (1.16) 3.12 (1.23) 3.33 (1.17) Customer Service Orientation of AMC

3.79 (1.04) 4.12 (1.02) 3.82 (1.04)

AMC’s Performance in other funds

3.70 (1.14) 4.14 (0.94) 3.75 (1.12)

Scope of AMC 3.46 (1.16) 3.86 (1.04) 3.51 (1.15) Fact that you own funds in the same AMC

3.33 (1.18) 3.22 (1.20) 3.32 (1.18)

AMC’s innovativeness in launching schemes

3.48 (1.11) 3.24 (1.09) 3.46 (1.11)

International Collaboration of AMC

3.40 (1.17) 3.10 (1.23) 3.36 (1.18)

Efficiency of Research wing of AMC

3.68 (1.18) 3.90 (1.03) 3.71 (1.16)

For Entire Construct 3.57 (0.67) 3.66 (0.70) 3.58 (0.67) (Tests of Difference between Retail and Non Retail Investors)

U(Z score) = -0.740, p exact = 0.459 Cronbach’s Alpha for Entire Construct = 0.844

Note 1. Importance was asked on 5 point scale from Not at all important to Very important 2. Shapiro Wilk test was used for establishing Normality (* denotes non normality) 3. Mann Whitney Test was used for checking differences between retail and non retail investors

The next variable in importance was different as retail investors assigned more

importance to ‘customer service orientation’ (M = 3.79, SD = 1.04) and non retail

investors assigned higher importance to ‘AMC’s performance in other funds’ (M =

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4.14, SD = 0.94). Both retail (M = 3.24, SD = 1.15) and non retail investors (M =

2.94, SD = 1.55) assigned lowest importance to ‘intermediaries network’.

Therefore for both retail and non retail investors, brand name or reputation and

experience of asset management company are important concerns. As expected, non

retail investors have more concern about the AMC’s performance in other funds and

retail investors are more concerned about customer services. The intermediary

network was not an important selection criteria for both categories of investors as for

one reason, there are other different avenues for the purchase of mutual funds like

online buying and secondly still for the Indian investor, mutual fund is a push

financial instrument, rather than a pull investment. As a result of this, investors don’t

bother about intermediaries’ network rather intermediaries seek the customers.

5.1.3 Importance of Investor Services as Selection Criteria

According to Jambodekar (1996), investor services are a major differentiating

factor in selection of mutual fund schemes so this section deals with several variables

in this regard. Table 5.3 highlights the importance of selection criteria relating to

investor services as provided by asset management company. The importance of the

selection criteria was asked for 13 variables on 5 point balanced scale from not at all

important to very important. The Cronbach’s alpha for the entire construct was high at

0.888 making data fit for further analysis. The total investor base of 450 respondents

assigned the mean importance at 3.67 with standard deviation of 0.75. Both the retail

(M = 3.67, SD = 0.76) and non retail investors (M = 3.67, SD = 0.65) assigned almost

equal importance to the selection criteria relating to the investor services, and there is

no significant difference between the two, U = -0.268, p>0.05. Hence H0-4 accepted.

Over all the highest importance was awarded to ‘well explained scheme

characteristics and risks in offer document’ (M = 3.94, SD = 1.16) and lowest

importance was assigned to ‘fringe benefits’ (M = 3.20, SD = 1.30).

Among the investor subsets, the retail investors assigned highest importance to

‘well explained scheme characteristics and risks in offer document’ (M = 3.92, SD =

1.17); followed by ‘wider investment management facilities’ (M = 3.88, SD = 1.08)

and ‘responsiveness to enquiry’ (M = 3.81, SD = 1.06). On the contrary, non retail

investors assigned highest importance to ‘efficiency and speed of investor’s grievance

handling’ (M = 4.22, SD = 0.95) followed by ‘prompt and transparent services’ (M =

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4.11, SD = 0.79) and ‘well explained scheme characteristics and risks in offer

document’ (M = 4.10, SD = 1.03). Both retail (M = 3.25, SD = 1.28) and non retail

investors (M = 2.80, SD = 1.44) assigned lowest importance to ‘fringe benefits’.

Table 5.3: Importance of Selection Criteria relating to Investor Services Selection Criteria related to Investor Services

Retail Investors Mean (SD) (N = 400)

Non Retail Investors

Mean (SD) (N = 50)


Well explained scheme characteristic and risks in offer document

3.92 (1.17) 4.10 (1.03) 3.94 (1.16)

Simple and well explained account statement

3.66 (1.30) 3.94 (1.05) 3.69 (1.28)

Easier investing process 3.72 (1.08) 3.88 (1.02) 3.74 (1.08) Multi channel investing avenues

3.56 (1.15) 3.40 (1.19) 3.54 (1.15)

Disclosure of NAV on every trading day

3.59 (1.19)

3.80 (1.14) 3.62 (1.19)

Efficiency and speed of Investor’s grievance handling

3.75 (1.17) 4.22 (0.95) 3.80 (1.16)

Fringe benefits 3.25 (1.28) 2.80 (1.44) 3.20 (1.30) Supporting AMC Staff 3.56 (1.17) 3.44 (1.10) 3.55 (1.16) Responsiveness to enquiry 3.81 (1.06) 3.96 (0.96) 3.83 (1.05) Well informed website 3.62 (1.15) 3.36 (1.15) 3.59 (1.15) Call centers and Toll free Nos 3.67 (1.10) 3.24 (1.18) 3.62 (1.11) Wider investment management facilities

3.88 (1.08) 3.46 (1.22) 3.83 (1.10)

Prompt and Transparent services

3.77 (1.11) 4.11 (0.79) 3.81 (1.09)



Note 1. Importance was asked on 5 point scale from Not at all important to Very important 2. Shapiro Wilk test was used for establishing Normality. (* denotes non normality) 3. Mann Whitney Test was used for checking differences between retail and non retail investors

There seems to be preference towards transparency and wider scope of services by

the retail investor and speed of services by the non retail investor. Fringe benefits are

not at all important as selection criteria for both retail and non retail investors as also

pointed out by Rajeswari & Moorthy (2002) and AMC’s should introspect on this

practice.

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5.1.4 Behavioral Biasness as Selection Criteria

Behavioral factors play a very predominant role in mutual fund purchase or

selection. In fact according to Campenhout (2007), behavioral framework is one of

the three frameworks which help in explaining fund selection.

Table 5.4: Behavioral Factors as Selection Criteria

Behavioural statements Retail Investors

Mean (SD) (N = 400)


Mean (SD) (N = 50)


Immediate historical performance of mutual fund strongly influences my / our buying behaviour

3.61 (1.31) 4.02 (1.18) 3.66 (1.30)

The fact that new fund offer is from very reputed asset management company, influences my / our buying behaviour

3.56 (1.18) 3.94 (1.09) 3.60 (1.18)

Historical performance is just a guiding factor. It doesn’t matter much to me / us in fund selection (Reverse)

2.70 (1.21) 1.48 (0.65) 2.56 (1.23)

If other mutual fund schemes of the asset management company are performing well and same AMC launches new fund offer, I / We will be inclined to buy the same

3.25 (1.10) 3.42 (1.12) 3.27 (1.11)

I / We buy mutual fund by understanding its stated investment objective (Reverse)

2.21 (1.00) 2.82 (1.41) 2.28 (1.07)

If my / our fund is performing well, I / We am / are inclined to remain invested in the same (Reverse)

2.63 (1.16) 3.06 (1.55) 2.68 (1.21)

If my / our fund is performing well, I / We will invest more in the same fund

2.21 (1.04) 2.02 (1.05) 2.18 (1.04)

If my / our fund is not performing well, most likely I / We will wait for its future performance

3.33 (1.16) 3.38 (1.06) 3.33 (1.15)

If my / our fund is not performing well, I / we will invest more in the same fund to average the purchase price

2.90 (1.33) 2.70 (1.29) 2.88 (1.33)

If my / our best researched fund has not performed according to the expectation, I / we am / are most likely to hold the same

3.22 (1.17) 3.26 (1.19) 3.22 (1.17)

Most of the times I / we hold my / our loosing funds and sell winning

3.34 (1.26) 3.06 (1.26) 3.30 (1.27)

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funds It becomes very difficult to believe that my / our decision to invest in the particular fund gets wrong

2.87 (1.24) 3.00 (1.27) 2.88 (1.24)

My / Our working in the particular industry influences my / our buying behaviour regarding a particular mutual fund scheme

3.13 (1.23) 2.72 (1.40) 3.08 (1.25)

If one of my funds say A, is at the same rate at which I / We purchased, I / We am / are not willing to replace this by fund B which is expected to return more

3.09 (1.15) 2.80 (1.30) 3.06 (1.17)

I / We buy mutual fund scheme as a part of my asset allocation process (Reverse)

2.34 (1.30) 2.10 (1.03) 2.31 (1.27)

I / We buy mutual funds only when there is some strong monetary incentive to do that (for example pass back of commission)

2.99 (1.43) 2.66 (1.32) 2.95 (1.42)

I / We buy mutual funds as a part of over all financial planning scenario (for example as a means of retirement planning) (Reverse)

2.43 (1.15) 2.06 (1.05) 2.39 (1.14)

I / We buy mutual fund scheme seeing its growth prospects, regardless of market conditions (Reverse)

2.40 (1.19) 2.12 (1.02) 2.37 (1.18)

I / We buy mutual fund schemes, seeing the growth prospects of market only

3.35 (1.11) 3.76 (1.09) 3.40 (1.12)

I / We buy mutual fund because the same company which sponsors AMC is also well respected in other verticals like insurance, banking etc.

3.06 (1.30) 3.12 (1.41) 3.07 (1.32)



Note 1. Agreement was asked on 5 point scale from strongly disagree to strongly agree 2. Shapiro Wilk test was used for establishing Normality. (* denotes non normality) 3. Mann Whitney Test was used for checking differences between retail and non retail investors

Several behavioral biases have been recognized to influence investment

decisions. Notable among these are representativeness (Tversky & Kahneman, 1986);

disposition effect (Shefrin & Statman, 1985; Odean, 1998); cognitive dissonance

(Goetzman & Peles, 1997) and Endowment bias (Thaler et al, 1992). Table 5.4

represents the investor’s agreement on 20 behavioral statements asked on 5 point

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agreement scale running from strongly disagree to strongly agree. The higher score of

agreement indicates the bias behavior of the investor against the particular variable.

The reliability analysis in terms of Cronbach’s alpha was moderate for the construct

(Alpha = 0.554), indicating that further analysis can be carried out. Non retail

investors (M = 2.87, SD = 0.47) were less biased in their behavior as compared to the

retail investors (M = 2.93, SD = 0.38), but the difference between the two is found to

be insignificant, U = -0.322, p>0.05. Hence H0-5 is accepted.

Among the variables ‘the immediate historical performance of the mutual

fund’ biased the behavior of both retail (M = 3.61, SD = 1.31) and non retail mutual

fund investors (M = 4.02, SD = 1.18) in fund selection, but more strongly for the non

retail investor. In the similar manner, the variable ‘the fact that new fund offer is from

very reputed asset management company’ influenced the buying behavior strongly for

non retail investor (M = 3.94, SD = 1.09) as compared to retail investors (M = 3.56,

SD = 1.18). If some successful asset management company launches new fund offer,

investors are biased to buy it (M = 3.27, SD = 1.11) but non retail investors depicted

higher biasness (M = 3.42, SD = 1.12). Investors like to hold their fund, if it is not

performing well and wait for its future performance (M = 3.33, SD = 1.15), but this

behaviour has been found to be more predominant in non retail investors (M = 3.38,

SD = 1.06) as compared to retail investors (M = 3.33, SD = 1.16). They are also likely

to hold their best researched fund, in spite of its bad performance (M = 3.22, SD =

1.17) and most of the times investors are likely to hold their loosing funds and sell

their winning funds (M = 3.30, SD = 1.27) and retail investors have been more prone

to this behavior (M = 3.34, SD = 1.26). Investors are also likely to purchase mutual

funds seeing the growth prospects of market only instead of relying on virtues of the

scheme (M = 3.40, SD = 1.12), and again this behavioral bias has been more evident

in case of non retail investor (M = 3.76, SD = 1.09).

Investors remained unbiased to some of the behavioral tendencies, like they

are likely to invest more in their holding, which performs well (M = 2.18, SD = 1.04)

and the tendency was more evident in non retail investors (M = 2.02, SD = 1.05). At

the same time, investors buy mutual funds by understanding its stated investment

objective (M = 2.28, SD = 1.07) and retail investors were more peculiar about it (M =

2.21, SD = 1.00). Investors buy mutual funds as part of their asset allocation process

(M = 2.31, SD = 1.27).

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From the above results it is clear that non retail investors are found to be more

prone to representativeness as compared to retail investors. Representativeness was

evident from investor’s preference to immediate historical performance and role of

asset management company’s reputation in fund selection. Several researchers have

documented the evidence of representativeness (Barber et al, 2000) and it is observed

that more experienced investors are more inclined towards making trading mistakes

(Chen et al, 2004).

5.2 Comparison of Retail and Non Retail Investors

The most important objective of the study is comparison of the responses of

retail and non retail investors against different constructs of mutual fund selection

criteria. In addition to comparing on the constructs as whole in the previous section,

the study has taken the help of factor analysis technique to assess the difference

between the retail and non retail investors on various extracted factors from the

constructs. This section presents the results of the same. Specifically, the results of

comparison on extracted factors from mutual fund scheme construct are presented in

section 5.2.1; section 5.2.2 deals with comparison of retail and non retail investors on

extracted factors of mutual fund companies construct; section 5.2.3 deals with

comparison of retail and non retail investors on the basis of extracted factors of

investor services construct and section 5.2.4 deals with comparison of retail and non

retail investors on the basis of extracted factors of behavioral biases.

5.2.1 Comparison of Retail and Non Retail Investors on Mutual Fund Schemes Construct

One of the important constructs is the selection criteria relating to mutual fund

schemes. For determining broader selection criteria among mutual fund schemes

construct, factor analysis was applied on total investor base of 450 investors

(including both retail and non retail investors). This section includes factor analysis

on the variables relating to mutual fund schemes. All the variables are depicted in

Table 5.5. The details of the analysis are presented below. Twenty variables (X1 to

X20) were originally considered under the construct for mutual fund schemes and

factor analysis was applied on them to extract independent factors. The main variables

considered under the construct were return and risk performance of the scheme; asset

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profile of the scheme; fund and fund manager characteristics; ratings and investment

options.

Table 5.5: Selection Criteria related to Mutual Fund Schemes and respective labels used in Factor Analysis

S. No. Variable Label 1 Return performance of the scheme X1 2 Risk of the Scheme X2 3 Number of assets in Fund’s Portfolio X3 4 Maturity profile of assets X4 5 Quality of assets X5 6 Reputation/Brand Name of the scheme X6 7 Reputation of Fund Manager X7 8 Fund Size X8 9 Age of the Fund X9 10 Experience / Qualification of the Fund Manager X10 11 Investment Objective of the Scheme X11 12 Expense ratio of the Scheme X12 13 Innovativeness in the Scheme X13 14 Scheme rating X14 15 Third Value Rankings of the Scheme X15 16 Investment Options X16 17 Entry and Exit Loads X17 18 Tax Benefits X18 19 Minimum Initial Investment X19 20 Growth Prospects X20

Table 5.6 shows correlation between various variables considered for mutual

fund schemes. Perusal of correlation matrix reveals that ‘return performance of the

scheme’ (X1) and ‘risk performance of the scheme’ (X2) are correlated (r = 0.555);

similarly X2 is correlated with ‘number of assets in fund’s portfolio’ (X 3) (r = 0.537).

In turn the variable X3 is further correlated with ‘maturity profile of the assets’ (X4) (r

= 0.510) and ‘quality of assets’ (X5) (r = 0.518). All these variable show inter

correlations among themselves and seems to form a group. Similarly variables

‘reputation of fund manager’ (X7) is correlated with ‘experience or qualification of

fund manager’ (X10) (r = 0.525) and variables like ‘fund size’ (X8) is correlated with

‘age of the fund’ (X9) (r = 0.560).

Table 5.7 depicts the diagnostic parameters of factor analysis. There were 20

variables under study yielding 400 item to item correlations and out of the same 4

(1.00%) item to item correlations are insignificant at 5% level of significance. The

determinant value of item to item correlation matrix was 0.001, higher than the

required 0.00001, depicting feasibility of the factor analytic technique. The case to

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variable ratio was comfortable at 22.5 as compared to the required value of at least

five. Kaiser Meyer Olkin (KMO) measure of sampling adequacy was employed for

both the over all value and for individual variables. The overall KMO statistic was

0.898 (greater than the required 0.5) and all the variables have been classified as

‘great’ (50.00%) and ‘superb’ (50.00%) on the basis of individual KMO values,

depicting that factor analytic technique was feasible on the basis of sampling

adequacy. The test for identity matrix – Bartlett’s test of Sphericity is also highly

significant (χ2 = 3308.224, df = 190, P < 0.01), as a result correlation matrix is not an

identity matrix and contains enough variable to variable correlations for the factor

analysis technique. There were 41.00% residuals greater than the absolute value of

0.05 which is well below the mark of 50% indicating appropriateness of the factor

analysis technique.

Since all the variables depicted MSA values greater than 0.5, all the

variables were considered for the study. Principal component analysis with Varimax

rotation was applied to extract the factors for the construct. Table 5.8 depicts that the

construct of mutual fund schemes can be represented by four factors (Eigen value >

1.0) and the communality shows that the extracted factors explained 45.60 to 67.90

percent of the variance of the original input variables. All the variables with factor

loadings of more than 0.5 have been taken for the consideration. The factors have

been given appropriate names on the basis of constituent variables. The factor names,

their constituent variables, their factor loadings and the variance explained by the

factors have been summarized in Table 5.9. Four factors respectively explained

17.89%, 14.24%, 12.30% and 11.77% of variance. In total all the factors explained

56.20% of variance. The first and the most important factor consist of 7 variables

(X13, X10, X12, X9, X11, X7, X8). The factor loading of the variables in the first factor

ranged from 0.527 to 0.754. The factor explained 17.89% of variance with Eigen

value of 3.579 and therefore forms a very important factor in mutual fund scheme

construct. The factor has been named as ‘Managerial and intrinsic attributes’. The

factor represents fund managerial characteristics like ‘experience or qualification of

the manager’ and ‘reputation of the fund manager’. In addition it also represents

various intrinsic fund attributes like – ‘investment objective of the scheme’, ‘age of

the fund’ ; ‘size of the fund’, ‘expense ratio of the scheme’ and ‘innovativeness in the

scheme’. The factor basically is two dimensional in nature including the fund

managerial and intrinsic fund characteristics.

155

Table 5.6: Correlation Matrix for Variables related to Mutual Fund Schemes

Determinant = 0.001

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X1 1.000 .555 .416 .327 .364 .371 .233 .296 .266 .234 .241 .242 .068 .188 .346 .218 .211 .207 .099 .263 X2 .555 1.000 .537 .365 .457 .356 .312 .362 .304 .346 .290 .297 .177 .194 .236 .176 .241 .177 .077 .244 X3 .416 .537 1.000 .510 .518 .359 .429 .431 .384 .424 .441 .396 .367 .255 .216 .225 .333 .157 .288 .300 X4 .327 .365 .510 1.000 .491 .303 .358 .250 .206 .258 .302 .277 .161 .228 .166 .269 .245 .219 .246 .326 X5 .364 .457 .518 .491 1.000 .448 .461 .348 .345 .369 .401 .383 .334 .272 .325 .263 .390 .169 .258 .334 X6 .371 .356 .359 .303 .448 1.000 .408 .428 .351 .338 .209 .338 .275 .324 .313 .322 .378 .289 .275 .191 X7 .233 .312 .429 .358 .461 .408 1.000 .448 .350 .525 .432 .294 .255 .294 .259 .255 .305 .101 .172 .232 X8 .296 .362 .431 .250 .348 .428 .448 1.000 .560 .483 .318 .358 .295 .340 .290 .364 .343 .189 .183 .180 X9 .266 .304 .384 .206 .345 .351 .350 .560 1.000 .452 .372 .360 .390 .240 .288 .263 .312 .111 .180 .205 X10 .234 .346 .424 .258 .369 .338 .525 .483 .452 1.000 .393 .387 .357 .334 .239 .237 .299 .095 .149 .194 X11 .241 .290 .441 .302 .401 .209 .432 .318 .372 .393 1.000 .379 .352 .232 .274 .292 .272 .158 .191 .352 X12 .242 .297 .396 .277 .383 .338 .294 .358 .360 .387 .379 1.000 .422 .379 .225 .245 .395 .172 .209 .229 X13 .068 .177 .367 .161 .334 .275 .255 .295 .390 .357 .352 .422 1.000 .330 .107 .122 .285 .113 .231 .158 X14 .188 .194 .255 .228 .272 .324 .294 .340 .240 .334 .232 .379 .330 1.000 .403 .378 .323 .331 .228 .226 X15 .346 .236 .216 .166 .325 .313 .259 .290 .288 .239 .274 .225 .107 .403 1.000 .497 .360 .328 .349 .259 X16 .218 .176 .225 .269 .263 .322 .255 .364 .263 .237 .292 .245 .122 .378 .497 1.000 .411 .464 .365 .380 X17 .211 .241 .333 .245 .390 .378 .305 .343 .312 .299 .272 .395 .285 .323 .360 .411 1.000 .371 .411 .288 X18 .207 .177 .157 .219 .169 .289 .101 .189 .111 .095 .158 .172 .113 .331 .328 .464 .371 1.000 .430 .466 X19 .099 .077 .288 .246 .258 .275 .172 .183 .180 .149 .191 .209 .231 .228 .349 .365 .411 .430 1.000 .434 X20 .263 .244 .300 .326 .334 .191 .232 .180 .205 .194 .352 .229 .158 .226 .259 .380 .288 .466 .434 1.000

156

Table 5.7: Factor Analysis Diagnostics (Mutual fund Schemes Construct) S. No. Parameter Value Percentage

1. Case to variable ratio 22.5 2. Number of Item to Item

Correlations 400 ( 20

Variables)

3. Number of Insignificant Correlations (Significant at 5%)

4 1.00

4. Determinant Value 0.001 5. Percent of residuals > 0.05 (abs) 41.00 6. Kaiser Meyer Olkin Measure

(KMO) 0.898

7. Bartlett’s Test of Sphericity (χ2) df = 190

3308.224 (p Value =

0.000)

Individual Variables MSA Values . 0.5 to 0.7 (Mediocre) 0 0.00 0.7 to 0.8 (Good) 0 0.00 0.8 to 0.9 (Great) 10 50.00 > 0.9 (Superb) 10 50.00

Investors not only desire to take decisions on scheme characteristics but also

want to assess the attributes of the fund manager. The underlying meaning of the

factor points to the fact that investors want best from their fund, qualified and reputed

fund manager and in addition to desirable intrinsic fund characteristics. Moreover the

unidimensionality of the factor is established in the sense that experience fund

manager introduces innovation, maintains least expense ratio and optimal fund size

for achieving superior performance. Further these measures in turn help in creation of

reputation of fund manager.

In terms of the importance, second factor explained 14.24% of variance and

includes 5 variables (X2, X1, X3, X4, X5). Although the variable ‘number of assets in

the fund’s portfolio’ was cross loaded on Factor 1 and Factor 2, but the profile of the

variables was more related with 2nd factor. The factor loadings of the variables ranged

from 0.556 to 0.771. The Eigen value of the factor was 2.848 and is therefore an

important factor with in the construct of mutual fund schemes. The factor has been

named as ‘performance and asset profile’. The factor mainly represents the

performance of the scheme both from return and risk point of view, the variables are

commonly perceived to be major determinants of fund selection as observed by lot of

researchers. The factor also includes variables relating to the asset profile like

‘number of the assets in the portfolio’, ‘maturity profile of the assets’, and ‘quality of

157

the assets’ in the portfolio. Since the asset profile and its various other determinants

ultimately affects the performance of the scheme both from return and risk point of

view, the two dimensions are linked together.

Table 5.8: Principal Component Analysis with Varimax Rotation for Mutual Fund Schemes variables

Variable Factor 1 Factor 2 Factor 3 Factor 4 Communality X13 .754 -.066 -.024 .188 0.609

X10 .659 .238 .256 -.059 0.560

X12 .613 .152 .162 .191 0.461

X9 .603 .186 .351 -.063 0.526

X11 .559 .277 .008 .277 0.466

X7 .548 .354 .201 .040 0.467

X8 .527 .260 .502 -.091 0.606

X2 .185 .771 .178 -.008 0.660

X1 -.021 .751 .339 .009 0.679

X3 .494 .610 -.002 .222 0.665

X4 .226 .603 -.070 .383 0.566

X5 .434 .556 .090 .266 0.576

X15 .069 .176 .700 .241 0.584

X16 .109 .086 .628 .438 0.605

X14 .370 -.004 .523 .214 0.456

X6 .315 .360 .476 .089 0.464

X19 .171 -.010 .209 .721 0.593

X20 .111 .276 .080 .712 0.603

X18 -.067 .074 .440 .650 0.626

X17 .361 .091 .405 .412 0.472

Eigen Value 3.579 2.848 2.460 2.350

Variance Proportion

0.178 0.142 0.123 0.117

The third factor in terms of importance explained 12.30% of variance and

includes 4 variables (X15, X14, X16, X6). The factor has an Eigen value of 2.460 and is

important determinant in fund selection criteria with in the construct of mutual fund

schemes. The factor loadings of the variables ranged from 0.476 to 0.700. The factor

has been named as ‘third party assessment’ and reflects third party rankings, ratings

of the scheme in addition reputation or brand name of the scheme. In addition to third

party attributes (ranking, rating and brand name) the factor also reflects various

investment options.

158

Table 5.9: Factors’ Summary for Mutual Fund Schemes as Selection Criteria

Constituent Variable Label Factor Loading

Factor Name Variance Explained

by the Factor (%)

Innovativeness in the Scheme X13 .754

Managerial and Intrinsic

Attributes 17.89

Experience / Qualification of the Fund Manager

X10 .659

Expense ratio of the Scheme X12 .613 Age of the Fund X9 .603 Investment Objective of the Scheme

X11 .559

Reputation of Fund Manager X7 .548 Fund Size X8 .527 Risk of the Scheme X2 .771

Performance and Asset

Profile 14.24

Return performance of the scheme

X1 .751

Number of assets in Fund’s Portfolio

X3 .610

Maturity profile of assets X4 .603 Quality of assets X5 .556 Third Value Rankings of the Scheme

X15 .700

Third party assessment

12.30 Investment Options X16 .628 Scheme rating X14 .523 Reputation/Brand Name of the Scheme

X6 .476

Minimum Initial Investment X19 .721 Extrinsic Attributes

11.77 Growth Prospects X20 .712 Tax Benefits X18 .650

Overall the entire factor is associated with third party assessment. Investors

commonly use external information sources like magazines, newspapers, databases

and websites and read about the third party evaluation of the schemes. Investors base

their buying decision on third party ratings and take decisions accordingly. The factor

represents both the rational and non rational criteria for the buying decisions.

Rationality is reflected in the sense that the third party evaluates mutual funds

according to strict qualitative and quantitative criteria, which are employed both cross

sectionally and in time series. The evaluation may give an objective idea or

information to the investors for fund selection. But at the same time, this may bias the

159

decision making and investors may take decisions as influenced by behavioral bias of

representative and framing.

The last factor in terms of importance explained 11.77% of variance and

includes three variables (X19, X20, X18). The factor has an Eigen value of 2.350 and

factor loadings ranged from 0.650 to 0.721. The factor has been named as ‘Extrinsic

attributes’ and reflects the external characteristics and options of the scheme.

Specifically the factor includes variables like ‘minimum initial investment’, ‘growth

prospects of the scheme’ and ‘tax benefits associated with the scheme’. The factor

represents external facilities and options available with the scheme to the investors.

These facilities do influence the selection criteria of the investors.

The mutual fund schemes construct therefore can be represented by four

factors or components namely ‘managerial and intrinsic attributes’, ‘performance and

asset profile’, ‘third party assessment’ and ‘extrinsic attributes’. Notably the variable

‘entry and exit loads’ did not form the part of factor structure (Factor Loading =

0.412) and was not taken in consideration. The summated scales of all the factors

were created and are depicted in Table 5.10. There are four summated scales

corresponding to the four factors and the reliability analysis in terms of Cronbach’s

alpha has been reported for all the four factors. The highest reliability was observed

for ‘managerial and intrinsic attributes’ scale (alpha = 0.817) followed by

‘performance and asset profile’ (alpha = 0.803); ‘third party assessment’ (alpha =

0.705) and ‘extrinsic attributes’ (alpha = 0.704). The descriptive statistics in terms of

mean and standard deviation for all the summated scales is reported in Table 5.11.

Table 5.10: Reliability Analysis of Extracted Factors for Mutual Fund Schemes

Name of Factor Cronbach’s Alpha No of Items Managerial and intrinsic attributes

0.817 7

Performance and asset profile

0.803 5

Third party assessment 0.705 4 Extrinsic attributes 0.704 3

Table 5.11 depicts that both retail and non retail investors assigned importance

to the all extracted factors (all factors are found to be significantly different from

mean value of 3.0 at 1% level of significance). Non retail investors assigned higher

importance to ‘managerial and intrinsic attributes’ (M = 3.66, SD = 0.70) as compared

160

to the retail investors (M = 3.56, SD = 0.82), but the difference is not found to be

significant, t (448) = 0.146, p>0.05. In respect to ‘performance and asset profile’

factor, non retail investors assigned higher importance (M = 4.11, SD = 0.53) and

with a significant difference, t (79.33) = -4.265, p<0.01 from the retail investors (M =

3.83, SD = 0.87). With reference to factor of ‘third party assessment’, non retail

investors valued the construct more (M = 3.65, SD = 0.59) as compared to the retail

investors (M = 3.54, SD = 0.86), but the difference is insignificant, t (448) = -1.165,

p>0.05.

Table 5.11: Summated Scale Analysis for Extracted Factors of Mutual Fund Schemes (Comparison of Retail versus Non Retail Investors)

Factor Descriptive Statistics Mean (SD)

Anderson – Rubin Factor Scores

Mean (SD)

t Statistic

(df)

p

Retail Investors


Retail Non Retail

Managerial and Intrinsic attributes

3.56* (0.82)

3.66* (0.70)

0.002 (1.01)

-0.019 (0.85)

0.146 (448)

0.884

Performance and Asset Profile

3.83* (0.87)

4.11* (0.53)

-0.050 (1.02)

0.408 (0.66)

-4.265 (79.33)@

0.000

Third Party Assessment

3.54* (0.86)

3.65* (0.59)

-0.019 (1.02)

0.157 (0.80)

-1.165 (448)

0.244

Extrinsic Attributes

3.77* (0.85)

3.56* (0.97)

0.039 (0.97)

0.320 (1.17)

2.391 (448)

0.017

*Significant at 1% level of significance (significantly different from mean level of importance at 3.0) @Adjusted value of degree of freedom is taken due to significance of Levene test (Violation of assumption of equality of variances)

On the contrary, retail investors assigned higher importance to ‘extrinsic

attributes’ (M = 3.77, SD = 0.85) as compared to non retail investors (M = 3.56, SD =

0.97), and the difference is found to be significant, t (448) = 2.391, p<0.05. Hence

H0-2 is rejected against the factors of ‘performance and asset profile’ and ‘extrinsic

attributes’. Therefore it can be inferred that all the components of the mutual fund

schemes were important for the investors in their fund selection. Specifically

components were more important for non retail investors as compared to the retail

investors except the ‘extrinsic attributes’ which were more important for retail

investors. Also the construct of ‘performance and asset profile’ is significantly more

important for the non retail investors. The observation of non retail investors

161

assigning higher importance to performance and linked variables has also been

established by Sharma (2006).

5.2.2 Comparison of Retail and Non Retail Investors on Mutual Fund Companies Construct One of the other important constructs in mutual fund selection consists of

variables related to mutual fund companies. This section deals with the same. Asset

management companies are the originators of the scheme and play a vital role in the

investor’s fund selection criteria. In order to derive various components related to

construct of mutual fund companies, factor analysis was employed on various

variables relating to this construct. The technique was employed on 450 respondents

(both retail and non retail). All the variables along with their labels are depicted in

table 5.12. The details of the analysis are presented below.

Thirteen variables (X1 to X13) were originally considered under the construct

of mutual fund companies and factor analysis was applied on them to extract

independent factors. Table 5.13 shows the correlations between various variables

considered for mutual fund companies. Perusal of correlation matrix reveals that

‘reputation or brand name of AMC’ (X1) is correlated with ‘experience of AMC’ (X2)

(r = 0.593) and ‘location of AMC in investor’s city’ (X 3) is correlated with

‘intermediaries network’ (X4) (r = 0.530). This seems to group together. Similarly

‘AMC’s performance in other funds’ (X8) is correlated with ‘scope of AMC’ (X9) (r =

0.547) and variable X9 is further correlated with ‘fact that you own funds in same

AMC’ (X 10) (r = 0.547). These variables seem to form a separate group.

Table 5.12: Selection Criteria related to Mutual Fund Companies and respective labels used in Factor Analysis

S. No. Variable Label 1 Reputation / Brand Name of AMC X1 2 Experience of AMC X2 3 Location of AMC in Investor’s city X3 4 Intermediaries Network X4 5 Expertise of AMC in Managing Money X5 6 Infrastructure of AMC X6 7 Customer Service Orientation of AMC X7 8 AMC’s Performance in other funds X8 9 Scope of AMC X9 10 Fact that you own funds in the same AMC X10 11 AMC’s innovativeness in launching

schemes X11

12 International Collaboration of AMC X12 13 Efficiency of Research wing of AMC X13

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Table 5.14 depicts the diagnostic measures of factor analysis. There were 13

variables under study yielding 169 item to item correlations and out of these 4


determinant value of correlation matrix was 0.012, much higher than the required

value of 0.00001, depicting enough item to item correlations for feasibility of factor

analysis technique. The case to variable ratio was 34.6, which is much higher than the

required 5, and it depicts that factor analysis can be carried out on the basis of sample

size. The other measures of sampling adequacy, KMO statistic was 0.814 (higher than

the required 0.5). Further 38.46% of individual variable KMO measures have been

classified as good; 53.84% as great and 7.70% as superb. The test for identity matrix –

Bartlett’s test of Sphericity is also significant (χ2 = 1950.514, df = 78, p<0.01). There

were 47 per cent residuals greater than the absolute value of 0.05, which is below the

mark of 50% indicating appropriateness of the factor solution.

Since all the variables depicted MSA value greater than 0.5, so these were

considered for the study. Principal component analysis with Varimax rotation was

applied to extract the factors from the construct.

Table 5.15 depicts that the construct of mutual fund companies can be

represented by three factors (Eigen value > 1.00) and the communality summary

shows that the extracted factors explained 41.1% to 72.8% of the variance of original

input variables. All the variables which depicted factor loading of greater than 0.5

have been taken for consideration. The factors have been given appropriate names on

the basis of constituent variables. The factor names, their constituent variables, the

factor loadings and the variance explained by the factors have been summarized in

Table 5.16.

Three factors respectively explained 24.12%, 17.64% and 15.84% of total

variance. In total all the three factors explained 57.60% of variance. The first and the

most important factor consist of 7 variables (X13, X9, X8, X11, X12, X10 and X5). The

factor loadings of the variables ranged from 0.486 to 0.693. The factor has an Eigen

value of 3.135 and therefore it can be considered as the most important factor from

the mutual fund companies construct. The factor explained 24.12% of the variance

and has been named as ‘Innovativeness and Performance’. The factor represents two

dimensional nature of the construct one being the innovativeness of the mutual fund

company and other is the performance of the mutual fund company.

163

Table 5.13: Correlation Matrix for Variables related to Mutual Fund Companies

Determinant = 0.012

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X1 1.000 .593 .152 .161 .183 .193 .365 .236 .240 .136 .197 .145 .161 X2 .593 1.000 .276 .217 .341 .255 .387 .302 .264 .178 .311 .165 .328 X3 .152 .276 1.000 .530 .116 .326 .079 .069 .201 .242 .191 .286 .052 X4 .161 .217 .530 1.000 .265 .410 .132 .221 .280 .375 .349 .419 .164 X5 .183 .341 .116 .265 1.000 .312 .426 .331 .310 .165 .311 .214 .417 X6 .193 .255 .326 .410 .312 1.000 .275 .285 .382 .387 .328 .379 .190 X7 .365 .387 .079 .132 .426 .275 1.000 .422 .365 .216 .265 .109 .407 X8 .236 .302 .069 .221 .331 .285 .422 1.000 .547 .330 .285 .359 .394 X9 .240 .264 .201 .280 .310 .382 .365 .547 1.000 .547 .441 .430 .278 X10 .136 .178 .242 .375 .165 .387 .216 .330 .547 1.000 .467 .416 .239 X11 .197 .311 .191 .349 .311 .328 .265 .285 .441 .467 1.000 .450 .472 X12 .145 .165 .286 .419 .214 .379 .109 .359 .430 .416 .450 1.000 .421 X13 .161 .328 .052 .164 .417 .190 .407 .394 .278 .239 .472 .421 1.000

164

Table 5.14: Factor Analysis Diagnostics (Mutual Fund Companies Construct)

S. No. Parameter Value Percentage 1. Case to variable ratio 34.6 2. Number of Item to Item

Correlations 169 (13

Variables)


4 2.36

4. Determinant Value 0.012 5. Kaiser Meyer Olkin Measure

(KMO) 0.814

6. Percent of residuals > 0.05 (abs)

47


1950.514 (p Value = 0.000)


Innovativeness is reflected by various variables like ‘AMC’s innovativeness in

launching scheme’; ‘efficiency of research wing of AMC’ (research here depicts the

research in management of assets and / or related to development of new product),

‘International collaboration of AMC’ (also depicts intention to launch new products in

collaboration with international mutual fund companies with greater expertise and

sophistication; this also indirectly reflects relation to innovation) and ‘scope of AMC’.

The performance dimension is reflected by the variables like ‘AMC’s performance in

other funds’; and ‘Expertise of AMC in managing money’. One of the variable is

indirectly related to the construct characteristics, ‘fact that you own funds in the same

AMC’, now if any investor is having funds in the same AMC and wishes to invest

more in the same AMC in some other funds, he is most likely biased in his decision

by AMC’s performance or by some of its innovation in launching the new product or

both. Two of the variables namely ‘international collaboration of AMC’ and ‘fact that

you own funds in the same AMC’ depicted cross loadings with second factor of

‘Location and infrastructure’. Although these two variables could be linked with

second factor indirectly, but at the same time they also depicted linkages with the first

factor not only on the basis of meaning but also because of their higher loadings with

the first factor.

165

Table 5.15: Principal Component Analysis with Varimax Rotation for Mutual Fund Companies Variables

Variable Factor 1 Factor 2 Factor 3 Communality X13 .693 -.057 .250 0.546

X9 .686 .286 .124 0.568

X8 .683 .032 .255 0.533

X11 .630 .322 .101 0.511

X12 .580 .482 -.090 0.577

X10 .571 .469 -.078 0.552

X5 .486 .048 .415 0.411

X3 -.092 .809 .194 0.700

X4 .189 .782 .095 0.656

X6 .354 .548 .171 0.455

X2 .154 .219 .810 0.728

X1 .041 .165 .792 0.656

X7 .475 -.087 .600 0.593

Eigen Value 3.135 2.293 2.059

Variance Proportion

0.241 0.176 0.158

In terms of importance, the second factor explained 17.64% of the variance

and consists of three variables (X3, X4 and X6). The factor loadings ranged from 0.548

to 0.809 and the factor has an Eigen value of 2.293. The factor has been named as

‘Location and infrastructure’ and consists of variables like ‘location of AMC in

investor’s city’, ‘infrastructure of AMC’ and ‘intermediaries network’. The factor is

one dimensional in meaning and reflects the infrastructural strength of asset

management company, in terms of its wider reach through branches (location of AMC

in investors city); intermediaries (intermediaries network) and better infrastructure.

Although the variable of location of AMC in investor’s city is more redundant now

due to online presence of almost every AMC, still investors would like to personally

visit the branch and they find physical presence of the branch in their city to be easier,

comfortable and confidence building.

The last factor in terms of importance explained 15.84% of variance with an

Eigen value of 2.059. The factor consists of three variables (X2, X1 and X7) and the

factor loadings ranged from 0.600 to 0.810. The factor has been named as ‘experience

and reputation’ and includes variables namely ‘experience of AMC’, ‘reputation or

brand name of AMC’ and ‘customer service orientation of AMC’. The factor reflects

166

the ability of asset management company in terms of its experience and its reputation.

One of the variables, which were indirectly related, is ‘Customer service orientation’.

Table 5.16: Factors’ Summary for Mutual Fund Companies as Selection Criteria



by the Factor (%)

Efficiency of Research wing of AMC

X13 .693 Innovativeness

and Performance

24.12

Scope of AMC X9 .686 AMC’s Performance in Other funds

X8 .683

AMC’s innovativeness in launching schemes

X11 .630

International Collaboration of AMC

X12 .580

Fact that you own funds in the same AMC

X10 .571

Expertise of AMC in Managing Money

X5 .486

Location of AMC in Investor’s city

X3 .809 Location

and Infrastructure 17.64 Intermediaries Network X4 .782

Infrastructure of AMC X6 .548 Experience of AMC X2 .810 Experience

and Reputation 15.84

Reputation / Brand Name of AMC

X1 .792

Customer Service Orientation of AMC

X7 .600

Although the variable was cross loaded with first factor but because of its

meaning and higher magnitude of factor loading it has been retained with the third

factor. Customer service orientation itself indirectly speaks of experience in terms of

customer or grievance handling, which becomes a major decision point for the

investor with reference to scheme selection. Further reputation of the asset

management company is build not only on the basis of performance of its schemes,

but also on the basis of how effectively it handles its customers.

Thus the broader construct of asset management companies can be explained

by three factors namely – ‘innovativeness and performance’, ‘location and

infrastructure’, and ‘experience and reputation’. This study therefore adds to the

results obtained by earlier studies in this regard. The major factors observed in these

167

studies are – infrastructure and reputation (Rajeswari & Moorthy, 2002); reputation

and competent performance (Ranganathan, 2006). The summated scales of all the

three factors were created and depicted in table 5.17. There are three summated scales

corresponding to the three factors and the reliability analysis in terms of Cronbach’s

alpha has been reported for all the three scales. The highest reliability was observed

for ‘innovativeness and infrastrucure’ (alpha = 0.807); ‘experience and reputation’

(alpha = 0.712); followed by ‘location and infrastructure’ (alpha = 0.687). The

descriptive statistics in terms of means and standard deviations for all the summated

scales is reported in Table 5.18.

Table 5.18 depicts that retail investors assigned importance to all the

components of mutual fund companies construct (as the mean importance of every

extracted construct is significantly different from value of 3.00 at 1% level of

significance).

Table 5.17: Reliability Analysis of Extracted Factors for Mutual Fund Companies

Name of Factor Cronbach’s Alpha No of Items Innovativeness and Performance

0.807 7

Location and Infrastructure 0.687 3 Experience and Reputation 0.712 3

The non retail investors, assigned higher importance to the construct of

‘innovativeness and performance’ and ‘experience and reputation’ but not to the

‘location and infrastructure’. In terms of highest and lowest importance to the

components, both retail and non retail investors assigned highest importance to

‘experience and reputation’ and lowest importance to ‘location and infrastructure’.

The Anderson Rubin (AR) scores have been computed against the summated scales

and the hypotheses tests have been conducted against AR scores.

Non retail investors assigned higher importance to ‘innovativeness and

performance’ (M = 3.63, SD = 0.74) as compared to the retail investors (M = 3.55, SD

= 0.78) but the difference is not found to be significant, t (448) = -0.699, p>0.05. In

contrary, retail investors assigned higher importance to ‘location and infrastructure’

(M = 3.30, SD = 0.91) as compared to non retail investors (M = 3.08, SD = 1.17) and

the difference is found to be significant, t (57.40) = 2.088, p<0.05.

168

Table 5.18: Summated Scale Analysis for Extracted Factors of Mutual Fund Companies (Comparison of Retail versus Non Retail Investors) Factor Descriptive Statistics

Mean (SD) Anderson – Rubin

Factor Scores Mean (SD)

t Value

p Value

Retail Investors

Non Retail

Investors

Retail Non Retail

Innovativeness and Performance

3.55* (0.78)

3.63* (0.74)

-0.011 (1.01)

0.093 (0.83)

-0.699 (448)

0.485

Location and Infrastructure

3.30* (0.91)

3.08 (1.17)

0.040 (0.96)

-0.324 (1.18)

2.088 (57.40

) @

0.041

Experience and Reputation

3.90* (0.91)

4.33* (0.64)

-0.617 (1.01)

0.493 (0.73)

-4.787 (74.22

)@

0.000


Further non retail investors assigned higher importance to the factor of

‘experience and reputation’ (M = 4.33, SD = 0.64) as compared to retail investors (M

= 3.90, SD = 0.91) and the difference is found to be significant, t (74.22) = -4.787,

p<0.01. Hence H0-3 rejected against the factors of ‘location and infrastructure’ and

‘experience and reputation’. The results are important from the point of view of

segmentation as retail investors are more biased and provide more importance to the

location and infrastructural issues and less importance to the experience and

reputation as compared to non retail investors.

5.2.3 Comparison of Retail and Non Retail Investors on Investor Services Construct

This section deals with the comparison of retail and non retail investors on the

construct comprising variables related to investor services. Asset management

companies provide most of the investor services. Therefore most of the variables

relate to services as provided by asset management companies and not the other

intermediaries. To further define the construct in terms of various sub components

factor analysis was employed on related variables. The factor analytic technique was

169

employed on 450 respondents (both retail and non retail). All the variables along with

their labels are depicted in table 5.19. The details of the analysis are presented below.

Thirteen variables (X1 to X13) were originally considered under the construct

of investor services and factor analysis was applied on them to extract independent

factors. Table 5.20 shows the correlations between various variables considered for

investor services. Perusal of correlation matrix reveals that ‘well explained scheme

characteristics and risks in offer document’ (X1) is correlated with ‘simple and well

explained account statement’ (X2) (r = 0.648) and ‘easier investing process’ (X3) (r =

0.524). Variable X2 is further correlated with X3 (r = 0.736), ‘efficiency and speed of

investor’s grievance handing’ (X6) (r = 0.539) and ‘responsiveness to enquiry’ (X9) (r

= 0.549). So these variables (X1, X2, X3, X6 and X9) seem to group together. In

addition there are evidences of higher correlations between ‘supporting AMC staff’

(X8) and ‘responsiveness to enquiry’ (X9) (r = 0.578). Variable X8 is further correlated

with ‘well informed website’ (X10) (r = 0.512). Variables X9 and X10 are also

significantly correlated (r = 0.571). The other group of variables like ‘well informed

website’ (X10), ‘call centers and toll free numbers’ (X11) and ‘wider investment

management facilities’ (X12) also depicts higher item to item correlations.

Table 5.19: Selection criteria related to Investor Services and respective labels

used in Factor Analysis

S. No. Variable Label 1 Well explained scheme characteristic and risks

in offer document X1

2 Simple and well explained account statement X2 3 Easier investing process X3 4 Multi channel investing avenues X4 5 Disclosure of NAV on every trading day X5 6 Efficiency and speed of Investor’s grievance

handling X6

7 Fringe benefits X7 8 Supporting AMC Staff X8 9 Responsiveness to enquiry X9 10 Well informed website X10 11 Call centers and Toll free Numbers X11 12 Wider investment management facilities X12 13 Prompt and Transparent services X13

Table 5.21 depicts the diagnostic measures of factor analysis. There were 13

variables under study yielding 169 item to item correlations and out of these none of

170

the correlation is insignificant at 5% level of significance. The determinant value of

correlation matrix was 0.003 much higher than the required value of 0.00001,

depicting enough item to item correlations for feasibility of factor analysis technique.

The case to variable ratio was 34.6, which is much higher than the required 5, and it

depicts that factor analysis can be carried out on the basis of sample size. The other

measures of sampling adequacy, KMO was at 0.888 (higher than the required 0.5).

Further 69.23% of individual variable KMO measures have been classified as great

and 30.77% as superb. The test for identity matrix – Bartlett’s test of Sphericity is

also significant (χ2 = 2604.071, df = 78, p<0.01). There were 40% residuals greater

than the absolute value of 0.05, which is well below the mark of 50% indicating

appropriateness of the factor solution.

Since all the variables depicted MSA value greater than 0.5, these were


applied to extract the factors from the construct. Table 5.22 depicts that the construct

of investor services can be represented by three factors (Eigen value > 1.00) and the

communality summary shows that the extracted factors explained 47.7% to 80.30% of

the variance of original input variables. All the variables which depicted factor

loading of greater than 0.5 have been taken for consideration. The factors have been

given appropriate names on the basis of constituent variables. The factor names, their

constituent variables, the factor loadings and the variance explained by the factors

have been summarized in table 5.23.

Three factors respectively explained 27.96%, 24.94% and 9.45% of total

variance. In total all the three factors explained 62.35% of variance. The first and the

most important factor consist of 6 variables (X10, X9, X11, X8, X12 and X13). The factor

loadings of the variables ranged from 0.523 to 0.796. The factor has an Eigen value of

3.635 and therefore it can be considered as the most important factor within investor

services construct. The factor explained 27.96% of the variance and has been named

as ‘Responsiveness’. The factor represents ability and effectiveness of asset

management company in providing constant feedback, information and response to

the investors. The factor includes variables like ‘well informed website’ (representing

online media to provide accurate, latest and reliable information), ‘responsiveness to

enquiry’ (reflecting the speed and orientation towards making response to investor’s

enquiry), ‘call centers and toll free numbers’ (again representing another media for

providing information to the investors),

171

Table 5.20: Correlation Matrix for Variables related to Investor Services

Determinant = 0.003

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X1 1.000 .648 .524 .367 .356 .308 .129 .279 .468 .355 .373 .439 .362 X2 .648 1.000 .736 .482 .365 .539 .145 .490 .549 .467 .353 .441 .489 X3 .524 .736 1.000 .526 .389 .482 .234 .426 .446 .428 .360 .418 .427 X4 .367 .482 .526 1.000 .470 .472 .279 .290 .282 .336 .307 .392 .340 X5 .356 .365 .389 .470 1.000 .325 .222 .179 .193 .154 .192 .295 .347 X6 .308 .539 .482 .472 .325 1.000 .262 .458 .384 .292 .244 .273 .415 X7 .129 .145 .234 .279 .222 .262 1.000 .308 .148 .171 .362 .245 .209 X8 .279 .490 .426 .290 .179 .458 .308 1.000 .578 .512 .450 .382 .420 X9 .468 .549 .446 .282 .193 .384 .148 .578 1.000 .571 .441 .440 .389 X10 .355 .467 .428 .336 .154 .292 .171 .512 .571 1.000 .549 .488 .406 X11 .373 .353 .360 .307 .192 .244 .362 .450 .441 .549 1.000 .566 .441 X12 .439 .441 .418 .392 .295 .273 .245 .382 .440 .488 .566 1.000 .566 X13 .362 .489 .427 .340 .347 .415 .209 .420 .389 .406 .441 .566 1.000

172

Table 5.21: Factor Analysis Diagnostics (Investor Services Construct)

S. No. Parameter Value Percentage 1. Case to variable ratio 34.6 2. Number of Item to Item

Correlations 169 (13 Variables)


0 0


(KMO) 0.888

7 Bartlett’s Test of Sphericity (χ2) df = 78

2604.071 (p Value = 0.000)


‘supporting AMC staff’ (depicting the ability of the staff for responsiveness), ‘wider

investment management facilities’ (reflects options available with the investors in

regard to various avenues and facilities for investment, this variable is indirectly

Table 5.22: Principal Component Analysis with Varimax Rotation for Investor

Service Variables

Variable Factor 1 Factor 2 Factor 3 Communality X10 .796 .150 .022 0.657

X9 .759 .268 -.146 0.669

X11 .729 .090 .380 0.684

X8 .696 .218 .164 0.559

X12 .639 .294 .234 0.549

X13 .523 .406 .197 0.477

X5 -.039 .726 .274 0.603

X4 .159 .718 .279 0.619

X3 .402 .717 -.036 0.677

X2 .502 .716 -.197 0.803

X6 .266 .628 .149 0.487

X1 .426 .592 -.208 0.576

X7 .188 .163 .827 0.746

Eigen Value 3.635 3.243 1.229

Variance Proportion

0.279 0.249 0.094

173

linked to the factor in terms that higher investment facilities may be supposed to

increase responsiveness), ‘prompt and transparent services’ (depicting the speed of

responsiveness).

In terms of importance, the second factor explained 24.94% of the variance

and consists of six variables (X5, X4, X3, X2, X6 and X1). The factor loadings ranged

from 0.592 to 0.726 and the factor has an Eigen value of 3.243. The factor has been

named as ‘Adequate disclosures and easiness in investing’ and consists of variables

like ‘disclosure of NAV on every trading day’ (directly linked with disclosures

dimension), multi channel investing avenues (linked with easiness in investing),

‘simple and well explained accounting statement’ (reflecting disclosure), ‘efficiency

Table 5.23: Factors’ Summary for Investor Services as Selection Criteria



by the Factor (%)

Well informed website X10 .796 Responsiveness

27.96

Responsiveness to enquiry X9 .759 Call centers and Toll free Numbers

X11 .729

Supporting AMC Staff X8 .696 Wider investment management facilities

X12 .639

Prompt and Transparent services

X13 .523

Disclosure of NAV on every trading day

X5 .726 Adequate

Disclosures and Easiness in Investing

24.94

Multi channel investing avenues

X4 .718

Easier investing process X3 .717 Simple and well explained account statement

X2 .716

Efficiency and speed of Investor’s grievance handling

X6 .628

Well explained scheme characteristic and risks in offer document

X1 .592

Fringe benefits X7 .827 Fringe benefits 9.45

and speed of investor’s grievance handling’ (indirectly related to easier investing

process, as grievance handling is one of the issues of entire investing process) and

174

‘well explained scheme characteristic and risks in offer document’ (reflecting

disclosures).

The last factor in terms of importance explained 9.45% of variance with an

Eigen value of 1.229. This is the single variable factor and the variable included is

‘Fringe Benefits’. The factor loading has been observed to be 0.827 and the factor has

been named on the basis of its single constituent, and is named as ‘Fringe Benefits’.

The factor reflects various benefits in the form of various frills like insurance, credit

cards etc which are available with some of the mutual fund schemes.

Thus the broader construct of investor services can be explained by three

factors namely – ‘responsiveness’, ‘adequate disclosures and easiness in investing’,

and ‘fringe benefits’. The findings are in addition to the earlier studies (for example

Rajeswari & Moorthy, 2002) that have highlighted the role of disclosures and fringe

benefits only. The summated scales of all the three factors were created and depicted

in table 5.24. There are three summated scales corresponding to the three factors and

the reliability analysis in terms of Cronbach’s alpha has been reported for all the three

scales. The highest reliability was observed for ‘responsiveness’ (alpha = 0.847);

followed by ‘adequate disclosures and easier investing process’ (alpha = 0.839). Third

factor, being the single variable factor, reliability analysis has not been performed for

the same. The descriptive statistics in terms of means and standard deviations for all

the summated scales is reported in table 5.25.

Table 5.24: Reliability Analysis of Extracted Factors for Investor Services

Name of Factor Cronbach’s Alpha No of Items Responsiveness 0.847 6 Adequate Disclosures and Easiness in investing

0.839 6

Fringe benefits NA 1

Table 5.25 depicts that both retail investors and non retail investors assigned

importance to ‘responsiveness’ and ‘disclosures in easiness in investing’. While retail

investors assigned importance to ‘Fringe benefits’ the same was not important

selection criteria for the non retail investors.

The Anderson Rubin (AR) scores were computed against the summated scales

and the hypotheses tests have been conducted against AR scores. Non retail investors

assigned higher importance to ‘adequate disclosures and easier investing process’ (M

175

= 3.89, SD = 0.70) as compared to retail investors (M = 3.70, SD = 0.89) and the

difference is found to be significant, t (448) = -2.5050, p<0.05. On the contrary, retail

investors assigned greater importance to the factor ‘fringe benefits’ (M = 3.25, SD =

1.28) as compared to non retail investors (M = 2.80, SD = 1.44) and the difference is

found to be significant, t (448) = 3.108, p<0.01. With reference to the factor of

‘responsiveness’ the difference between retail investors (M = 3.72, SD = 0.85) and

non retail investors (M = 3.59, SD = 0.68) is not found to be significant, t (69.85) =

1.832, p >0.05. The results are in contrast to what is documented by Sharma (2006)

and are important from the point of view of segmentation as retail investors accord

more importance to the fringe benefits issues and less importance to the adequate

disclosures and easier investing process as compared to non retail investors. Hence

H0-4 rejected against the factors of ‘adequate disclosures and easiness in investing’

and ‘fringe benefits’.

Table 5.25: Summated Scale Analysis for Extracted Factors of Investor Services (Comparison of Retail versus Non Retail Investors)


Anderson – Rubin

Factor Scores Mean (SD)

t Statistic

(df)

p Value

Retail Investors

Non Retail

Investors

Retail Non Retail

Responsiveness 3.72* (0.85)

3.59* (0.68)

0.024 (1.02)

-0.200 (0.78)

1.832 (69.85)@

0.071

Adequate Disclosures and Easiness in Investing

3.70* (0.89)

3.89* (0.70)

-0.041 (1.01)

0.335 (0.81)

-2.505 (448)

0.013

Fringe Benefits 3.25* (1.28)

2.80 (1.44)

0.050 (0.98)

-0.415 (1.07)

3.108 (448)

0.002


5.2.4 Comparison of retail and non retail investors on behavioral biases Behavioral biases play an important role in influencing the fund selection and

purchase. One of the aims of the study is to assess difference, if any, between retail

and non retail investors on various constructs of fund selection. This section deals

176

with the study of differences between the retail and non retail investors regarding

behavioral biases. Factor analysis was applied to total investor base of 450 investors

(including both retail and non retail investors). All the variables are depicted in table

5.26. The details of the analysis are presented below.

Twenty variables (X1 to X20) were originally considered under the construct

for behavioral biases and factor analysis was applied on them to extract independent

factors. Variables for the behavioral biases actually represent different behaviors as

established by the empirical research on financial behavior. The main variables which

were included relate to representativeness, cognitive dissonance, overconfidence and

framing. Table 5.27 shows correlation between various variables considered under the

construct.

Perusal of correlation matrix reveals that ‘immediate historical performance of

the mutual fund strongly influences buying behavior’ (X 1) is significantly correlated

with the variable, ‘the fact that the new fund offer is from very reputed asset

management company, influences buying behavior’ (X2) (r = 0.523). Similarly the

variable ‘if my / our fund is not performing well, I / we will invest more in the same

fund to average the purchase price’ (X9) is significantly correlated with ‘if my / our

best researched fund has not performed according to the expectations, I / We are most

likely to hold the same’ (X10) (r = 0.454). Further variable X10 is significantly

correlated with variable ‘most of the times I / We hold my / our loosing funds and sell

winning funds’ (X11) (r = 0.420). These variables seem to group together as single sub

construct. In another subset of item to item correlation matrix, variable ‘I / We buy

mutual fund scheme as part of my / our asset allocation process’ (X15) is significantly

correlated with ‘I / We buy mutual funds as a part of overall financial planning

scenario’ ( X17) (r = 0.498). In turn, X17 is significantly correlated with the variable ‘I

/ We buy mutual fund scheme seeing its growth prospects, regardless of market

conditions’ (X18) (r = 0.506). These variables also seem to group together.

177

Table 5.26: Selection criteria related to Behavioral factors and respective labels used in Factor Analysis

S. No. Variable Label 1 Immediate historical performance of mutual fund strongly influences my/our buying behaviour X1 2 The fact that new fund offer is from very reputed asset management company, influences my/our buying behaviour X2 3 Historical performance is just a guiding factor. It doesn’t matter much to me/us in fund selection X3 4 If other mutual fund schemes of the asset management company are performing well and same AMC launches new

fund offer, I/We will be inclined to buy the same X4

5 I/We buy mutual fund by understanding its stated investment objective X5 6 If my/our fund is performing well, I/We am/are inclined to remain invested in the same X6 7 If my/our fund is performing well, I/We will invest more in the same fund X7 8 If my/our fund is not performing well, most likely I/We will wait for its future performance X8 9 If my/our fund is not performing well, I/we will invest more in the same fund to average the purchase price X9 10 If my/our best researched fund has not performed according to the expectation, I/We am/are most likely to hold the

same X10

11 Most of the times I/We hold my/our loosing funds and sell winning funds X11 12 It becomes very difficult to believe that my/our decision to invest in the particular fund gets wrong X12 13 My/Our working in the particular industry influences my buying behaviour regarding a particular mutual fund

scheme X13

14 If one of my/our funds say A, is at the same rate at which I/we purchased, I/We am/are not willing to replace this by fund B which is expected to return more

X14

15 I/We buy mutual fund scheme as a part of my/our asset allocation process X15 16 I/We buy mutual funds only when there is some strong monetary incentive to do that (for example pass back of

commission) X16

17 I /We buy mutual funds as a part of over all financial planning scenario (for example as a means of retirement planning)

X17

18 I/We buy mutual fund scheme seeing its growth prospects, regardless of market conditions X18 19 I/We buy mutual fund schemes, seeing the growth prospects of market only X19 20 I/We buy mutual fund because the same company which sponsors AMC is also well respected in other verticals like

insurance, banking etc. X20

178

Table 5.28 depicts the diagnostic parameters of factor analysis. There were 20

variables under study yielding 400 item to item correlations and out of the same 40


determinant value of item to item correlation matrix was 0.005, higher than the

required 0.00001, depicting feasibility of the factor analytic technique. The case to

variable ratio was comfortable at 22.5 as compared to the required value of at least

5.00. Kaiser Meyer Olkin measure of sampling adequacy was employed for both the

over all value and for individual variables. The overall KMO statistics was 0.801

(greater than the required 0.5) and all the variables have been classified as ‘good’

(45.00%) and ‘great’ (55.00%) on the basis of individual KMO values, depicting that

factor analytic technique is feasible on the basis of sampling adequacy.

The test for identity matrix – Bartlett’s test of Sphericity is also highly

significant (χ2 = 2359.205, df = 190, P < 0.01), as a result correlation matrix is not an

identity matrix and contains enough variable to variable correlations for the factor

analysis technique. There were 44% residuals greater than the absolute value of 0.05

which is well below the mark of 50% indicating appropriateness of the factor analysis

solution.

Since all the variables depicted MSA values greater than 0.5, these were


applied to extract the factors for the construct. Table 5.29 depicts that the construct of

behavioral factors can be represented by six factors (Eigen value > 1.0) and the

communality shows that the extracted factors explained 43.50 to 74.30 percent of the

variance of the original input variables. All the variables with factor loadings of more

than 0.5 have been taken for the consideration. The factors have been given

appropriate names on the basis of constituent variables. The factor names, their

constituent variables, their factor loadings and the variance explained by the factors

have been summarized in Table 5.30.

Six factors respectively explained 11.98%, 10.71%, 10.65%, 9.36%, 8.85%

and 8.45% of variance. In total all the factors explained 60.00% of variance. The first

and the most important factor consist of 4 variables (X17, X18, X15 and X7). Although

variable X7 was having cross loading (Factor loading = -0.47) with factor 4, but was

retained with this factor due to its meaning, sense and higher magnitude of factor

loading. The factor loading of the variables in the first factor ranged from 0.533 to

179

Table 5.27: Correlation Matrix for Variables related to Behavioral Biasness

Determinant = 0.005

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X1 1.000 .523 -.177 .270 -.158 -.094 -.281 .236 .165 .233 .164 .301 .226 .123 -.446 .137 -.307 -.295 .223 .227 X2 .523 1.000 -.115 .321 -.192 -.151 -.192 .360 .039 .242 .099 .202 .253 .236 -.379 .105 -.230 -.186 .159 .206 X3 -.177 -.115 1.000 -.242 .024 .223 .067 -.056 -.114 -.158 -.164 -.201 -.135 -.159 .104 -.136 .132 .220 -.175 -.173 X4 .270 .321 -.242 1.000 -.129 -.106 -.014 .164 .205 .133 .227 .306 .353 .219 -.056 .220 -.031 -.170 .101 .177 X5 -.158 -.192 .024 -.129 1.000 .359 .382 -.235 -.124 -.172 -.118 -.118 -.363 -.256 .240 -.082 .308 .285 -.148 -.145 X6 -.094 -.151 .223 -.106 .359 1.000 .299 -.188 -.191 -.111 -.135 -.181 -.220 -.189 .157 -.265 .166 .170 -.125 -.284 X7 -.281 -.192 .067 -.014 .382 .299 1.000 -.150 -.069 -.142 -.037 -.050 -.138 -.042 .429 .083 .339 .275 -.159 -.112 X8 .236 .360 -.056 .164 -.235 -.188 -.150 1.000 .283 .298 .210 .205 .222 .269 -.254 .080 -.225 -.111 .168 .225 X9 .165 .039 -.114 .205 -.124 -.191 -.069 .283 1.000 .454 .395 .276 .333 .195 -.078 .103 -.107 -.077 .217 .300 X10 .233 .242 -.158 .133 -.172 -.111 -.142 .298 .454 1.000 .420 .258 .256 .237 -.128 .064 -.132 -.073 .138 .260 X11 .164 .099 -.164 .227 -.118 -.135 -.037 .210 .395 .420 1.000 .289 .292 .228 .003 .223 -.006 .029 .162 .299 X12 .301 .202 -.201 .306 -.118 -.181 -.050 .205 .276 .258 .289 1.000 .419 .395 -.240 .204 -.136 -.161 .186 .273 X13 .226 .253 -.135 .353 -.363 -.220 -.138 .222 .333 .256 .292 .419 1.000 .400 -.134 .135 -.216 -.291 .095 .250 X14 .123 .236 -.159 .219 -.256 -.189 -.042 .269 .195 .237 .228 .395 .400 1.000 -.035 .326 -.086 -.124 .121 .282 X15 -.446 -.379 .104 -.056 .240 .157 .429 -.254 -.078 -.128 .003 -.240 -.134 -.035 1.000 .085 .498 .392 -.204 -.098 X16 .137 .105 -.136 .220 -.082 -.265 .083 .080 .103 .064 .223 .204 .135 .326 .085 1.000 .048 .001 .136 .326 X17 -.307 -.230 .132 -.031 .308 .166 .339 -.225 -.107 -.132 -.006 -.136 -.216 -.086 .498 .048 1.000 .506 -.207 -.085 X18 -.295 -.186 .220 -.170 .285 .170 .275 -.111 -.077 -.073 .029 -.161 -.291 -.124 .392 .001 .506 1.000 -.303 -.213 X19 .223 .159 -.175 .101 -.148 -.125 -.159 .168 .217 .138 .162 .186 .095 .121 -.204 .136 -.207 -.303 1.000 .442 X20 .227 .206 -.173 .177 -.145 -.284 -.112 .225 .300 .260 .299 .273 .250 .282 -.098 .326 -.085 -.213 .442 1.000

180

Table 5.28: Factor Analysis Diagnostics (Behavioral biases)

S. No. Parameter Value Percentage 1. Case to Variable ratio 22.5 2. Number of Item to Item

Correlations 400 ( 20 Variables)


40 10


(KMO) 0.801


2359.205 (p Value = 0.000)


0.733. The factor explained 11.98% of variance with Eigen value of 2.397 and

therefore forms a very important factor in behavioral bias construct. The factor has

been named as ‘Planning and Rationality’.

The factor reflects the rational and unbias behavior of the investors and

includes the variables like ‘buying mutual funds as part of overall financial planning

scenario’ (reflects the desirable and planned behavior on part of the investors);

‘buying mutual fund schemes seeing their growth prospects, regardless of the market

conditions’ (reflecting another desirable and un-bias behavior by the investors);

‘buying mutual fund schemes as part of asset allocation process’ (again a planned

behavior that is desirable) and variable ‘if fund is performing well, investing more in

the same fund’ (from the point of view of behavioral scientists, this is again a

desirable behavior as the action increases wealth and discourages selling winning

investments). This factor therefore reflects the rational and planned attitude of the

investors and is highly desirable trait that should be present in all investors.

The second factor in terms of importance explained 10.71% of variance and

has the Eigen value of 2.142. The factor consists of 4 variables (X4, X13, X12 and X14)

and has been named as ‘Endowment’. The factor reflects the endowment attitude of

the investors, which means that investors value their holdings more as compared to

the other similar alternatives as they feel more emotionally attached to their holdings.

181

Table 5.29: Principal Component Analysis with Varimax Rotation for Behavioral Variables Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Communality

X17 .733 -.067 -.049 -.151 .160 -.035 0.594

X18 .721 -.287 .081 .016 .098 -.228 0.671

X15 .651 .013 -.026 -.470 .061 -.045 0.652

X7 .533 .190 -.076 -.206 .424 -.026 0.549

X4 .014 .689 .056 .237 .030 .063 0.540

X13 -.201 .633 .322 .020 -.333 -.127 0.671

X12 -.096 .596 .288 .155 -.050 .120 0.488

X14 .140 .514 .175 .147 -.419 .080 0.517

X3 .236 -.442 -.019 .114 -.108 -.396 0.435

X9 -.081 .151 .769 -.058 -.059 .146 0.649

X10 -.073 .085 .753 .201 -.039 .041 0.623

X11 .133 .229 .661 .042 -.062 .188 0.548

X2 -.120 .233 .008 .815 -.083 .050 0.743

X1 -.327 .237 .090 .662 .091 .193 0.656

X8 -.043 .005 .396 .521 -.272 .013 0.504

X5 .324 -.102 -.112 -.071 .721 .090 0.601

X6 .102 -.082 -.036 .004 .688 -.303 0.583

X19 -.284 -.036 .182 .085 .011 .712 0.629

X20 .013 .119 .289 .139 -.215 .693 0.644

X16 .396 .316 -.085 .141 -.336 .500 0.646

Eigen Value 2.397 2.142 2.132 1.874 1.771 1.689

Variance Proportion

0.119 0.107 0.106 0.093 0.088 0.084

182

Table 5.30: Factors’ Summary for Behavioral Biases as Selection Criteria Constituent Variable Label Factor

Loading Factor Name Variance

Explained by the Factor (%)

I/We buy mutual funds as a part of over all financial planning scenario (for example as a means of retirement planning)

X17 .733 Planning and Rationality

11.98

I/We buy mutual fund scheme seeing its growth prospects, regardless of market conditions

X18 .721

I/We buy mutual fund scheme as a part of my/our asset allocation process

X15 .651

If my/our fund is performing well, I/We will invest more in the same fund

X7 .533

If other mutual fund schemes of the asset management company are performing well and same AMC launches new fund offer, I/We will be inclined to buy the same

X4 .689

Endowment

10.71

My/our working in the particular industry influences my/our buying behaviour regarding a particular mutual fund scheme

X13 .633

It becomes very difficult to believe that my/our decision to invest in the particular fund gets wrong

X12 .596

If one of my/our funds say A, is at the same rate at which I/We purchased, I/We am/are not willing to replace this by fund B which is expected to return more

X14 .514

If my/our fund is not performing well, I/We will invest more in the same fund to average the purchase price

X9 .769 Cognitive

Dissonance

10.65 If my/our best researched fund has not performed according to the expectation, I/We am/are most likely to hold the same

X10 .753

Most of the times I/We hold my/our loosing funds and sell winning funds

X11 .661

183

The fact that new fund offer is from very reputed asset management company, influences my/our buying behaviour

X2 .815 Representativeness

9.36 Immediate historical performance of mutual fund strongly influences my/our buying behaviour

X1 .662

If my/or fund is not performing well, most likely I/We will wait for its future performance

X8 .521

I/We buy mutual fund by understanding its stated investment objective X5 .721 Objectivity 8.85 If my/our fund is performing well, I/We am/are inclined to remain

invested in the same X6 .688

I/We buy mutual fund schemes, seeing the growth prospects of market only

X19 .712 External

Stimulants

8.45 I/We buy mutual fund because the same company which sponsors AMC is also well respected in other verticals like insurance, banking etc.

X20 .693

I/We buy mutual funds only when there is some strong monetary incentive to do that (for example pass back of commission)

X16 .500

184

The factor consists of variables like ‘if other mutual fund schemes of the asset

management company are performing well and same AMC launches new fund offer,

there is an inclination to buy the same’. Although in sense of wording, this variable

points more to the behavior of representativeness heuristic, but also in turn points out

to endowment behavior as the investors who are holding the funds of one asset

management company feel attached to the same and are inclined to buy other schemes

of the same asset management company, provided other conditions remain the same.

The other variables this factor includes are of ‘working in particular industry

influences buying behavior regarding a particular mutual fund scheme’ (this also

signifies the endowment attitude, as by working in a particular industry not only

provides the knowledge of that industry but also investors start feeling attached to it

and gets influenced in their buying decisions). The next variable which is present in

this factor is direct outcome of the endowment behavior ‘it becomes very difficult to

believe that decision to invest in particular fund gets wrong’. The last variable which

is present in the factor is again a direct interpretation of endowment behavior, the

variable is ‘if one of my / our funds say A, is at the same rate at which I / We

purchased, I / we are not willing to replace this by fund B which is expected to return

more’.

The third factor in terms of importance explained 10.65% of variance and has

an Eigen value of 2.132. The factor consists of three variables and has been named as

‘Cognitive dissonance’. The factor consists of three variables (X9, X10 and X11) and

the factor loadings ranged from 0.661 to 0.769. The cognitive dissonance bias reveals

that investors get uncomfortable in accepting their mistakes and act accordingly. The

factor consists of variables like – ‘if my / our fund is not performing well, I / We will

invest more in the same fund to average the purchase price’ reveals the outcome of

cognitive dissonance bias since the fact that the fund is not performing well but there

is bias to not to accept the mistake. The second variable is also a direct outcome of the

cognitive dissonance bias, the variable is, ‘if my / our best researched fund has not

performed according to the expectation, I / We are most likely to hold the same’. The

third variable, ‘most of the times I/We hold my /our loosing funds and sell winning

funds’ is also directly related to cognitive dissonance bias, as investors gets very

uncomfortable to see their loosing funds and accepting their mistakes thereof.

The fourth factor in terms of importance explained 9.36% of variance and has

an Eigen value of 1.874. The factor consists of three variables (X2, X1 and X8), the

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factor loadings of which ranged from 0.521 to 0.815. The factor has been named as

representativeness heuristic or bias and reflects the representative attitude of the

investors. The representative behavior means that investor takes short cuts to invest

and the decision is influenced by past memory, similar events or success of brand

names in other contexts etc. The main variables that is included in this factor is ‘that

fact that new fund offer is from very reputed asset management company, influences

buying behavior’ (this variable reflects the representative attitude in terms that asset

management company being the very reputed one, becomes representative of its

future launches and the investors gets influenced by the past successes or brand name

of asset management company – but the future successes of the same AMC in other

schemes is totally random and same success may be repeated or not). The second

variable is also an direct outcome of the representativeness bias, the variable is

‘immediate historical performance of mutual fund strongly influences my / our buying

behavior’ (here also the past results become representative of future successes and the

investor purchases on the basis of that – but again future performance is a random

event and may or may not depend on past performance of the fund). The third variable

that is included in this factor is ‘if my fund is not performing well, most likely I will

wait for its future performance’. Although this variable is more indicative of cognitive

dissonance but is included in this factor on the basis of higher magnitude of factor

loading with this factor as compared to cognitive dissonance factor.

The fifth factor in terms of importance explained 8.85% of variance and has an

Eigen value of 1.771. The factor consists of two variables (X5 and X6) and the factor

loadings ranged from 0.688 to 0.721. The factor has been named as ‘objectivity’ and

reflects the un-bias behavior of investors in taking their investing decisions. The

variables included in the factor are – ‘I / We buy mutual fund by understanding its

stated investment objective’ and ‘If my / our fund is performing well, I / we are

inclined to remain invested in the same’. Both the variables are indicative of

objectivity of the investor in terms of their investment in mutual funds and display the

required behavior.

The last factor in terms of importance explained 8.45% of variance and has an

Eigen value of 1.689. The factor consists of three variables (X19, X20 and X16) and the

factor loadings of the variable ranged from 0.500 to 0.712. The factor has been named

as ‘External stimulants’. The factor reflects the changing behavior of the investor in

presence of external stimulants and their decision making in light of that. The external

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stimulants can be growth prospects of the market; likelihood of the AMC to emerge

stronger because of its strong presence in other verticals; some strong monetary

incentives. The factor includes variables like – ‘I / we buy mutual fund schemes,

seeing the growth prospects of market only’; ‘I / We buy mutual fund because the

same company which sponsors AMC is also well respected in other verticals like

insurance, banking etc’ and ‘I / We buy mutual funds only when there is some strong

monetary incentive to do that’. All variables are indicative of external stimulants or

influencers in fund selection.

Thus the investor’s behavioral biases can be represented by six broad

categories of ‘planning and rationality’; ‘endowment’; ‘cognitive dissonance’;

‘representativeness’; ‘objectivity’; and ‘external stimulants’. Although several

behavioral biases have been depicted to influence fund selection like

representativeness, cognitive dissonance and endowment (for example Tversky &

Kahnaman, 1986; Goetzman & Peles, 1997), what research lacks is the clear and

concrete evidence on desirable behaviors shown by the investors, like planning and

rationality and objectivity. This research demonstrates the existence of the same. The

summated scales of all the constructs were created and reliability analysis has been

performed. Table 5.31 depicts the Cronbach alpha for reliability analysis of the

summated scales that have been generated as the result of factor analysis procedure.

The highest Cronbach alpha observed was for ‘planning and rationality’ (alpha =

0.734); followed by ‘cognitive dissonance’ (alpha = 0.685); ‘endowment’ (alpha =

0.684); ‘representativeness’ (alpha = 0.641); ‘objectivity’ (alpha = 0.526) and

‘external stimulants’ (alpha = 0.556).

Table 5.31: Reliability Analysis of Extracted Factors for Behavioral Factors

Name of Factor Cronbach’s Alpha No of Items Planning and Rationality 0.734 4 Endowment 0.684 4 Cognitive Dissonance 0.685 3 Representativeness 0.641 3 Objectivity 0.526 2 External Stimulants 0.556 3

Further an attempt was made to study the differences between retail and non

retail investors on the basis of extracted components. Anderson Rubin scores were

computed for the extracted factors for each of the investor subset (retail and non

187

retail) and ‘t’ tests have been employed on the same. The results are presented in table

5.32.

Overall, both retail investors and non retail investors have been found to be

planned and rational (as observed from their individual means, and significantly

different from neutral 3.0 towards disagreement). Similarly although retail investors

have been found to be objective, but non retail investors are neutral in this regard.

Further retail investors depicted the biases of endowment, cognitive dissonance,

representativeness, and take decisions under the influence of external stimulants. On

the contrary, non retail investors were found to be neutral towards endowment bias,

cognitive dissonance and external stimulants but were found biased by

representativeness.

Retail investors were less planned and rational in their decision making (M =

2.34, SD = 0.87) as compared to the non retail investors (M = 2.07, SD = 0.80) and

the difference between the two is significant, t (448) = 3.268, p<0.01. With reference

to endowment bias and cognitive dissonance bias, retail investors were more biased as

compared to non retail investors but the difference between the two is not significant.

Table 5.32: Comparison of Retail and Non Retail Investors on the basis of Behavioral biases


Anderson – Rubin Factor

Scores Mean (SD)

t Statistic

(df)

p Value

Retail Investors

Non Retail

Investors

Retail Non Retail

Planning and Rationality$

2.34* (0.87)

2.07* (0.80)

0.053 (1.00)

-0.431 (0.84)

3.268 (448)

0.001

Endowment 3.08* (0.84)

2.98 (1.00)

-0.015 (0.98)

0.126 (1.11)

-0.948 (448)

0.343

Cognitive Dissonance

3.15* (0.98)

3.00 (1.01)

0.022 (1.01)

-0.177 (0.90)

1.332 (448)

0.183

Representativeness 3.50* (0.93)

3.78* (0.84)

-0.021 (1.02)

0.170 (0.79)

-1.549@ (70.80)

0.126

Objectivity$ 2.42* (0.87)

2.94 (1.31)

-0.091 (0.90)

0.733 (1.34)

-4.208@ (54.70)

0.000

External Stimulants

3.13* (0.94)

3.18 (0.96)

-0.051 (0.99)

0.415 (0.94)

-3.150 (448)

0.002

*Significant at 1% level of significance (significantly different from mean level of importance at 3.0) @Adjusted value of degree of freedom is taken due to significance of Levene test (Violation of assumption of equality of variances) $For the score of planning and rationality and Objectivity, fewer score signify higher attribute

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As far as representativeness is concerned, non retail investors were more biased as

compared to the retail investors, but again the difference between the two is not

significant. Non retail investors have been found to be less objective (M = 2.94, SD =

1.31) as compared to the retail investors (M = 2.42, SD = 0.87) and the difference

between the two is significant, t (54.70) = -4.208, p<0.01. Further, non retail

investors were more influenced by external stimulants (M = 3.18, SD = 0.96) as

compared to the retail investors (M = 3.13, SD = 0.94) and the difference between the

two is found to be significant, t (448) = -3.150, p<0.01. Hence H0-5 rejected against

the factors of ‘planning and rationality’, ‘objectivity’ and ‘external stimulants’.

5.3 Comparison of various subsets of Investors

The profile of retail investors varied on account of several demographic,

economic criteria. They were also different on basis of their mutual fund purchase

behavior and purchase profile. Also they had different attitude regarding the

objectives and advantages of mutual funds. This section deals with the comparison of

various subsets of investors, categorized on various basis, with reference to their

attitude towards fund selection. Section 5.3.1 deals with comparison of investors on

the basis of demographic profile, section 5.3.2 deals with comparison of investors on

the basis of economic profile, section 5.3.3 deals with comparison of investors on the

basis of purchase behavior, section 5.3.4 deals with comparison of investors on the

basis of purchase profile, section 5.3.5 deals with comparison of investors on the basis

of their attitude towards objectives of investing in mutual funds and finally section

5.3.6 deals with comparison of investors on the basis of their attitude towards

advantages of investing in mutual funds.

5.3.1 Comparison of Investors categorized on the basis of Demographic Profile.

Several demographic criteria were enquired from the retail investors

specifically the variables asked were – gender, age, educational qualification, marital

status and occupation at the time of study. The results of comparison of investors

categorized on these variables are presented in this section.

The respondents in the study have been assessed on importance of fund

selection criteria on the basis of gender. In the sample base of 400 retail investors, 328

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were males and 72 were females. Both males and females assigned although different

importance to various selection criteria constructs, but the difference is found to be

insignificant. Table 5.33 depicts the results.

Overall both males and females assigned different importance to the various

constructs of selection criteria and the difference is found to be significant, H (3) =

104.852, p<0.01 (for males) and H (3) = 23.148, p<0.01 (for females). Males assigned

highest importance to mutual fund schemes (M = 3.67, SD = 0.66) and lowest

importance to sources of information (M = 3.15, SD = 0.74). In contrast females

assigned highest importance to investor services (M = 3.72, SD = 0.81) and lowest

importance to sources of information (M = 3.16, SD = 0.66).

Males assigned higher importance to constructs of mutual fund schemes (M =

3.67, SD = 0.66) and the construct of mutual fund companies (M = 3.59, SD = 0.68)

as compared to females (M = 3.59, SD = 0.74; M = 3.48, SD = 0.64) but the

difference between the two is insignificant. On the contrary females assigned more

importance to sources of information (M = 3.16, 0.66); and investor services (M =

3.72, SD = 0.81) as compared to the respective figures for males (M = 3.15, SD =

0.74; M = 3.66, SD = 0.75) but the difference between the two constructs is

insignificant.

Females were found to more behaviorally biased (M = 2.95, Mdn = 2.95, SD =

0.30) as compared to males (M = 2.92, Mdn = 3.00, SD = 0.39) but the difference

between the two is found to be insignificant, U = -0.212, p>0.05. This analysis

therefore depicts that gender has no role to play in mutual fund selection. Hence H0-6

accepted with respect to gender of respondents.

The average age of investors in the sample was 31 years. The attempt was

made to categorize the investors into two categories namely – investors who are less

than or equal to 31 years old and other category of the investors who are more than

31 years, and the differences between the two categories were further assessed in

terms of their importance of fund selection criteria. Table 5.34 presents the results.

Both the categories assigned different importance to the selection constructs

and the difference(s) are found to be significant. Further both the categories valued

the construct of investor services the most and construct of sources of information the

least.

Investors who were less than or equal to 31 years old assigned higher

importance (M = 3.21, Mdn = 3.18, SD = 0.69) to the construct of sources of

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information as compared to their counterparts (M = 3.04, Mdn = 3.03, SD = 0.80) and

the difference between the two is significant, t (234.97) = 2.05, p < 0.05.

Table 5.33: Investors’ Importance of Selection Criteria Constructs

(Investors grouping on the basis of Gender)

Selection Criteria Constructs

Parameter Investors (Males)

(N = 328)

Investors (Females) (N = 72)

U Statistic (Z score)

p Value

Sources of Information

Mean Importance

3.15 3.16 -0.102 0.919

Median 3.18 3.18 SD 0.74 0.66 Normality 0.99* 0.99

Mutual Fund Schemes

Mean Importance

3.67 3.59 -0.763 0.445

Median 3.70 3.65 SD 0.66 0.74 Normality 0.98* 0.95*

Mutual Fund Companies

Mean Importance

3.59 3.48 -1.318 0.188


Investor Services Mean Importance

3.66 3.72 -0.684 0.491


H Statistic χ2 (3) 104.852 23.148 p Value 0.000 0.000 Behavioural biasness

Mean Agreement

2.92 2.95 -0.212 0.832


* Significant at 5% Note:

1. Importance was asked on 5-point scale from Not at all important to Very important 2. Shapiro-Wilk Test was used for checking Normality. (* depicts non normality) 3. Tests across the rows depict differences in types of investors and across the columns depict

differences between the constructs for a particular group of investors.

Further with reference to construct of mutual fund scheme, the investors who

were more than 31 years old assigned higher importance (M = 3.76, Mdn = 3.80, SD

= 0.58) to the construct as compared to the others (M = 3.60, Mdn = 3.65, SD = 0.71)

and at significant difference, U = -1.96, p<0.05.

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With respect to the construct of mutual fund companies and investor services,

although investors who were more than 31 years old assigned higher importance than

their counterparts, but no significant difference was found between them at 5% level

of significance.


(Investors grouping on the basis of Investor’s Age)


Parameter Investors (< = 31 years)

(N = 266)

Investors (> 31 years) (N = 134)

t/ U Statisti

(Z Score)

p Value


Mean Importance

3.21 3.04 2.05 t (234.9)$

0.041

Median 3.18 3.03 SD 0.69 0.80 Normality 0.99 0.98

Mutual Fund Schemes

Mean Importance

3.60 3.76 -1.96 0.049



Mean Importance

3.56 3.59 -0.16 0.870



3.63 3.77 -1.61 0.107



Mean Agreement

2.94 2.92 -0.92 0.358

Median 2.95 3.02 SD 0.34 0.44 Normality 0.99 0.93*


1. Importance was asked on 5-point scale from Not at all important to Very important 2. Shapiro-Wilk Test was used for checking Normality. (* depicts non normality) Tests across

the rows depict differences in types of investors and across the columns depict differences between the constructs for a particular group of investors.

$ Adjustment of degrees of freedom due to violation of homoscedasticity

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Also as far as behavioral bias is concerned, although investors who were less

than 31 years old appeared to be more biased (M = 2.94, SD = 0.34) than the other

investors (M = 2.92, SD = 0.44), but again the difference is not significant, U = -0.92,

p>0.05. From the above analysis it is clear that age plays role in selection criteria,

while relatively young investors’ value sources of information more, for the other

category variables linked to mutual fund schemes are important. Hence H0-6 rejected

with respect to age of the respondent for the constructs of sources of information and

mutual fund schemes. These findings are in contrast to the findings of Singh and

Chander (2006) who argued that age is only found to be associated with the investor’s

perception but it does not play role in fund selection.

On the basis of education also, two categories were created namely investors

who are graduate and less and the second category of investors who are postgraduate

and more. Out of the 400 retail investors, 212 investors were graduates and less as

compared to the 188 investors who have attained the post graduation qualification and

more. Further an attempt was made to assess the difference between two categories on

account of their importance for mutual fund selection criteria and the results are

presented in table 5.35. Both the categories assigned different importance scores to

the different constructs of mutual fund selection and at the same time, both valued

investor services construct the most and sources of information construct the least.

For the sources of information construct, graduates and less assigned higher

importance as compared to the second category, but the difference is not significant.

With reference to the construct of mutual fund schemes, post graduates and more

assigned higher importance (M = 3.73, Mdn = 3.80, SD = 0.61) as compared to

graduates and less (M = 3.58, Mdn = 3.60, SD = 0.72) and the difference is found to

be significant, U = -2.11, p<0.05. Regarding the other constructs of mutual fund

companies and investor services, post graduates and more assigned more importance

to the constructs as compared to the investors who are graduates and less, but the

difference between the two categories is not found to be significant.

Further, investors who are graduates and less depicted more behavioral

biasness (M = 2.96, SD = 0.34) with respect to their fund selection, as compared to

the other investors who are postgraduates and more (M = 2.88, SD = 0.41), but the

difference is not found to be significant, U = -1.69, p>0.05. Hence H0-6 rejected with

respect to education of respondents for the construct of mutual fund schemes. As a

result education has limited role to play in fund selection criteria and investors who

193

have higher education consider mutual fund schemes as an important selection

criteria, significantly important than the investors who were not so highly educated.

Table 5.35: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Investor’s Education)


Parameter Investors (Grad. and Less)

(N = 212)

Investors (Post Graduation

and More) (N = 188)

U (Z Score)

P Value


Mean Importance

3.21 3.08 -1.67 0.094


Mutual Fund Schemes

Mean Importance

3.58 3.73 -2.11 0.035



Mean Importance

3.55 3.60 -0.39 0.693


Investor Services

Mean Importance

3.61 3.75 -1.89 0.059


H Statistic χ2(3)

40.000 92.063

p Value 0.000 0.000 Behavioural biasness

Mean Agreement

2.96 2.88 -1.69 0.09





Marriage of a person may change his orientation towards saving or investment

and therefore may lead to change his fund selection criteria. Thus the hypothesis was

194

formed in order to see the influence of marriage on fund selection behavior of the

investors. Table 5.36 presents the results. Out of the 400 retail investors, 180 investors

were single and 220 investors were married at the time of study.


(Investors grouping on the basis of Investor’s Marital Status)


Parameter Investors (Single)

(N = 180)

Investors (Married) (N = 220)

t/ U Statistic

(Z Score)

p Value


Mean Importance

3.25 3.08 2.34 t (398)

0.02


Mutual Fund Schemes

Mean Importance

3.57 3.72 -2.13 0.03



Mean Importance

3.56 3.58 -0.17 0.868



3.66 3.69 -0.35 0.729


H Statistic χ2(3) 35.307 96.471 p Value 0.000 0.000 Behavioral Biasness

Mean Agreement

2.95 2.91 -0.39 0.698





Both the categories of single and married investors assigned different

importance to the constructs of selection criteria and at significant difference. While

195

single investors assigned highest importance to the construct of investor services (M =

3.66, SD = 0.78) the married investors assigned highest importance to the construct of

mutual fund schemes (M = 3.72, SD = 0.63). Both the categories assigned lowest

importance to the selection criteria related to sources of information construct.

With reference to the construct of sources of information, single investors

assigned higher importance (M = 3.25.Mdn = 3.24, SD = 0.68) to the construct as

compared to the married investors (M = 3.08, Mdn = 3.09, SD = 0.76) and the

difference is found to be significant, t (398) = 2.34, p<0.05. On the contrary, married

investors assigned higher importance (M = 3.72, Mdn = 3.80, SD = 0.63) to the

construct of mutual fund schemes as compared to single investors (M = 3.57, Mdn =

3.60, SD = 0.71) and the difference is also found to be significant, U = -2.13, p<0.05.

Regarding the other constructs of mutual fund companies and investor services,

although married investors assigned higher importance yet the difference with the

perception of the single investors is not found to be significant.

Further, married investors were found to less behaviorally biased (M = 2.91,

SD = 0.41) as compared to their single counterparts (M = 2.95, SD = 0.34) but the

difference is not found to be significant, U = -0.39, p>0.05. Interestingly marriage

therefore has a role to play in selection criteria especially related to the constructs of

sources of information and mutual fund schemes. It seems that after marriage the

importance of sources of information decreases, while the importance of mutual fund

schemes construct increases. Hence H0-6 rejected with respect to marital status of

respondents against the construct of sources of information and mutual fund schemes.

The occupation of investors may influence their fund selection criteria. For

example investors working as service men may be different than those who are

running their own business in their selection criteria. Total sample of 400 retail

investors is broadly categorized into two occupations – service and non service and

attempt was made to differentiate between the two on account of mutual fund

selection criteria. Table 5.37 presents the results. Both the categories of service and

non service investors valued the constructs differently and the difference is found to

be significant. Further both the service class and non service class investors also

assigned highest importance to the investor services construct and lowest importance

to sources of information construct.

Non service class investors assigned higher importance to sources of

information construct as compared to service class investors, but the difference is

196

found to be insignificant. With reference to the construct of mutual fund schemes,

service class investors valued the construct more (M = 3.71, Mdn = 3.80, SD = 0.65)

as compared to their counterparts (M = 3.53, Mdn = 3.55, SD = 0.71) and at

significant difference, U = -2.44, p<0.05.

Table 5.37: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Investor’s Occupation)


Parameter Investors (Service)

(N = 275)

Investors (Non

Service) (N = 125)

U Statistic (Z Score)

p Value


Mean Importance

3.14 3.18 -0.01 0.989


Mutual Fund Schemes

Mean Importance

3.71 3.53 -2.44 0.015



Mean Importance

3.59 3.53 -0.98 0.327



3.73 3.54 -2.54 0.011


H Statistic χ2(3) 112.504 21.788 p Value 0.000 0.000 Behavioral biasness

Mean Agreement

2.90 2.97 -1.12 0.263





There is no significant difference observed between the service class and non

service class investors in regard to the fund selection related to mutual fund

197

companies construct. Service class investors assigned higher importance to the

selection criteria construct of investor services (M = 3.73, Mdn = 3.77, SD = 0.77) as

compared to non service class investors (M = 3.54, Mdn = 3.46, SD = 0.73) and the

difference is found to be significant, U = -2.54, p<0.05.

Further non service class investors seem to be more behaviorally biased (M =

2.97, Mdn = 3.00, SD = 0.31) in relation to their fund selection, as compared to

service class investors (M = 2.90, Mdn = 2.95, SD = 0.40), but the difference is not

significant, U = -1.12, p>0.05. Hence H0-6 is rejected with respect to occupation of

retail investors against the constructs of mutual fund schemes and investor services.

Investors’ occupation therefore has a role to play in fund selection and those

who are in service valued construct of mutual fund schemes and investor services

more as compared to investors in other occupation. These results are also in addition

to the findings of Singh & Chander (2006).

5.3.2 Comparison of Investors categorized on the Basis of Economic Profile.

Investors were further enquired on their economic profile in terms of their

individual annual income and individual annual saving. This section deals with the

comparison of subsets of investors on the basis of their economic profile with

reference to their attitude towards fund selection criteria

It is hypothesized that the level of annual income of an investor may influence

his selection criteria. Accordingly the total sample base of 400 retail investors is

divided into two categories namely – investors whose annual income is less than Rs

2.0 lacs and the other category of investors whose annual income is greater than or

equal to Rs 2.0 lacs. Further they have been compared on the basis of different

selection criteria constructs. Table 5.38 presents the results. In this case also both the

categories assigned different importance to the various constructs and the difference is

found to be significant. Further investors who earn less valued mutual fund companies

construct the most and sources of information the least, on the contrary those who

earn more assigned highest importance to the investor services construct and lowest

importance to the sources of information construct.

Although the first category assigned higher importance to sources of

information construct but the difference with the second category is not significant.

198

Investors who earn more than or equal to Rs 2.0 lacs per annum valued construct of

mutual fund schemes more (M = 3.73, Mdn = 3.80, SD = 0.64) as compared to their

counterparts (M = 3.51, Mdn = 3.60, SD = 0.71) and the difference between the two is

found to be significant, U = -2.75, p<0.01. Although mutual fund schemes construct is

influenced by the annual income of the individual investors, yet there is no significant

difference observed between the two categories for the construct of mutual fund

companies. Those investors who earn greater than or equal to Rs 2.0 lac per annum

assigned higher importance to the construct of investor services (M = 3.78, Mdn =

3.77, SD = 0.72) as compared to the others (M = 3.47, Mdn = 3.46, SD = 0.81) and

the difference between the two categories is found to be significant, U = -3.71,

p<0.01.

Both the categories of the investors seem to be equally biased in their behavior

towards their fund selection and the difference between the two is insignificant, U = -

0.81, p>0.05. Hence H0-6 rejected with respect to annual income of respondents

against the constructs of mutual fund schemes and investor services. This analysis

reflects that as the annual income increases, investors become more cautious in their

fund selection criteria especially related to mutual fund schemes and investor services.

Since annual individual income plays part in influencing fund selection

criteria, individual annual savings can also help in deciding about the selection, as the

funds have to be invested out of savings only. To test this hypothesis, the total sample

base of 400 retail investors is categorized into two categories of investors who have

less than Rs 1.0 lacs annual saving and the other category of investors who have more

than or equal to Rs 1.0 lac annual saving. The results are presented in Table 5.39. As

similar to the annual income, here also both the categories of the investors valued

constructs differently and the difference is found to be significant.

The later category assigned higher importance to the sources of information

(M = 3.23, Mdn = 3.24, SD = 0.73) as compared to the former category (M = 3.10,

Mdn = 3.00, SD = 0.73) and the difference is significant, U = -2.12, p<0.05. Investors

who save more than or equal to Rs 1.0 lacs annually valued the construct of mutual

fund schemes more (M = 3.80, Mdn =3.90, SD = 0.62) as compared to their

counterparts (M = 3.55, Mdn = 3.60, SD = 0.69) and the difference between the two is

found to be significant, U = -3.62, p<0.01. Similarly for the construct of mutual fund

companies, investors who save more assigned higher importance (M = 3.67, Mdn =

199

3.69, SD = 0.66) as compared to their counterparts (M = 3.50, Mdn = 3.62, SD =

0.67) and at significant difference, U = -2.32, p<0.05.

Table 5.38: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Investor’s Annual Income)


Parameter Investors (Less than or equal to 2.0 Lac Rs)

(N = 139)

Investors (Greater

than 2.0 Lac Rs)

(N = 261)

t /U

p Value


Mean Importance

3.17 3.14 0.35 t (398)

0.729


Mutual Fund Schemes

Mean Importance

3.51 3.73 -2.75 0.006



Mean Importance

3.49 3.61 -1.48 0.138



3.47 3.78 -3.71 0.000


H Statistic χ2(3) 19.942 116.30 p Value 0.000 0.000 Behavioural biasness

Mean Importance

2.93 2.92 -0.81 0.416





Same results are evident for the construct of investor services as those investors

who save more valued the construct more (M = 3.81, Mdn = 3.92, SD = 0.68) as

200

compared to those investors who save less (M = 3.58, Mdn = 3.69, SD = 0.81) and the

difference between the two is found to be significant, U = -2.87, p<0.01.

Table 5.39: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Investor’s Annual Saving)


Parameter Investors (Less than or equal to 1.0 Lac Rs)

(N = 239)

Investors (Greater

than 1.0 Lac Rs)

(N = 161)

U Statistic

(Z score)

p Value


Mean Importance

3.10 3.23 -2.12 0.034


Mutual Fund Schemes

Mean Importance

3.55 3.80 -3.62 0.000



Mean Importance

3.50 3.67 -2.32 0.020



3.58 3.81 -2.87 0.004


H Statistic χ2(3) 108.331 67.891 p Value 0.000 0.000 Behavioural biasness

Mean Importance

2.92 2.94 -1.24 0.216



1. Importance was asked on 5-point scale from Not at all important to Very important 2. Shapiro-Wilk Test was used for checking Normality. (* depicts non normality) 3. Tests across the rows depict differences in types of investors and across the columns

depict differences between the constructs for a particular group of investors. Investors who save more seem to be slightly more behaviorally biased (M =

2.94, Mdn = 3.05, SD = 0.39) as compared to others (M = 2.92, Mdn = 2.95, SD =

201

0.37), but the difference between the two categories is found to be insignificant, U = -

1.24, p>0.05. Hence H0-6 rejected with respect to individual annual savings against the

constructs of sources of information, mutual fund schemes, mutual fund companies

and investor services. As the savings of the investor increases, the liklehood of that

savings going to be invested in financial instrument also increases, as a result higher

saving may act as important determinant of fund selection criteria. This analysis

reflects that individual annual savings acts as a major influencer in fund selection

criteria among economic variables and with increase in annual individual savings the

investors become more conscious and consider almost every construct to be important

in their fund selection. The results are directly in accordance with the findings of

Capon et al (1996).

5.3.3 Comparison of investors categorized on the basis of purchase behavior Investors depict different behavior in context to purchase of mutual funds.

They may have preference for certain kind of mutual fund category (like equity or

debt or balanced), or may choose to invest in some particular plan (like dividend or

growth or dividend reinvestment); or in habit of investing either through lumpsum or

using systematic investment plan (SIP); or prefer to buy from some preferred avenue

like from asset management company or through broker or through bank or may be

online buying. This section discusses the importance of mutual fund selection criteria

by comparing different subsets of investors created on the basis of their purchase

behavior.

On several parameters, retail investors have been compared. Two classes of

investors are created on the basis of their most preferred type of scheme. The two

classes created are equity investors and non equity investors. Table 5.40 depicts the

differences between equity and non equity investors against different constructs

related to the mutual fund selection. Non equity investors assigned higher importance

to sources of information (M = 3.27, SD = 0.78) as compared to equity investors (M =

3.12, SD = 0.71) but the difference is found to be non significant t (398) = -1.75, p

exact > 0.05. Similarly non equity investors assigned higher importance to construct

of mutual fund schemes (M = 3.73, SD = 0.61) as compared to equity investors (M =

3.64, SD = 0.70) and the difference is found to be non significant (U = -0.83, p>0.05).

202

Table 5.40: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Type of Asset)


Parameter Investors (Equity) (N = 303)

Investors (Non

Equity) (N = 97)

t / U p Value


Mean Importance

3.12 3.27 -1.75 (t Value)

0.080


Mutual Fund Schemes

Mean Importance

3.64 3.73 -0.83 0.408



Mean Importance

3.57 3.60 -0.30 0.762



3.69 3.64 -0.58 0.563


F/ H Statistic χ2 (3), F(3,384)

108.331 (H Statistic)

7.718 (F Value)4


Mean Agreement

2.89 3.04 -3.08 0.002




differences between the constructs for a particular group of investors. 4. Post Hoc test (Scheffe) was applied to see the differences among the constructs. Two groups

of selection criteria constructs - (Sources of information) and (Mutual fund schemes, Mutual fund companies and Investor services) are formed.

Regarding the construct relating to mutual fund companies, non equity

investors assigned higher importance (M = 3.60, SD = 0.68) as compared to equity

investors (M = 3.57, SD = 0.67) and again the difference is found to be non

significant (U = -0.30, p>0.762). Equity investors assigned higher importance to

203

selection criteria linked to investor services (M = 3.69, SD = 0.78) as compared to non

equity investors (M = 3.64, SD = 0.75) but the difference is found to be non

significant (U = -0.58, p>0.05).

Equity investors assigned different importance to different constructs and the

difference is found to be significant, H(3) = 108.331, p<0.01. They assigned highest

importance to the construct of investor services (M = 3.69, SD = 0.78) and lowest

importance to the sources of information (M = 3.12, SD = 0.71). Non equity investors

also assigned different importance to the constructs related to mutual fund selection

criteria and difference is found to be significant F(3,384) = 7.718, p< 0.01. They

assigned highest importance to the construct related to mutual fund schemes (M =

3.73, SD = 0.61) and lowest importance to the sources of information (M = 3.27, SD

= 0.78). Post Hoc Scheffe Test was employed to see the formation of homogenous

groups among the non equity investors regarding their importance of selection criteria

at 5% level of significance. Two homogenous subsets of selection criteria are formed

namely (sources of information) and (constructs of mutual fund schemes, mutual fund

companies and investor services).

Table 5.40 also depicts that non equity investors (M = 3.04, SD = 0.32) have

been more biased in their behavioral tendencies with reference to mutual fund

selection, as compared to equity investors (M = 2.89, SD = 0.39) and the difference is

found to be significant, U = -3.08, p<0.01. The type of fund category doesn’t have a

predominant role to play in the fund selection, except significant difference is

observed between equity and non equity investor on account of behavioral biasness.

Hence H0-6 is rejected with respect to fund buying preference against the constructs of

behavioral biasness.

Retail investors have been classified on the basis of their preference for the

dividend schemes. There were 157 investors who have higher preference for investing

in dividend schemes as compared to 243 investors who had higher preference for non

dividend schemes. An attempt was made to compare dividend preferring investors

with the others regarding their fund selection criteria and the results are presented in

Table 5.41. Investors preferring non dividend schemes assigned higher importance to

the sources of information (M = 3.22, Mdn = 3.24, SD = 0.73) as compared to

investors who preferred to invest in dividend paying schemes (M = 3.06, Mdn = 3.00,

SD = 0.74) and the difference is found to be significant, U = -2.39, p<0.05.

204

Table 5.41: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Scheme Choice)


Parameter Investors (Dividend) (N = 157)

Investors (Non

Dividend) (N = 243)

t / U p Value


Mean Importance

3.06 3.22 -2.39 0.016


Mutual Fund Schemes

Mean Importance

3.61 3.69 -0.97 0.328



Mean Importance

3.43 3.66 -3.25$ t (293.9)

0.001



3.62 3.72 -1.35 0.177



Mean Agreement

2.94 2.92 -0.08 0.930




depict differences between the constructs for a particular group of investors. $ Adjustment of degrees of freedom due to violation of homogeneity of varainces

Similarly investors preferring non dividend schemes assigned higher

importance to the selection criteria linked to mutual fund schemes (M = 3.69, Mdn =

3.75, SD = 0.65) as compared to the investors preferring dividend paying schemes (M

= 3.61, Mdn = 3.65, SD = 0.72), but the difference is not found to be significant, U = -

0.97, p>0.05.

205

For the construct relating to mutual fund companies, investors preferring non

dividend schemes assigned higher importance (M = 3.66, Mdn = 3.69, SD = 0.63) as

compared to investors preferring dividend paying schemes (M = 3.43, Mdn = 3.46,

SD = 0.73) and the difference is found to be significant, t (293.9) = -3.25, p<0.01.

Similarly investors preferring non dividend schemes assigned higher importance to

the selection criteria linked to investor services (M = 3.72, Mdn = 3.77, SD = 0.77) as

compared to the investors preferring dividend paying schemes (M = 3.62, Mdn =

3.77, SD = 0.76), but the difference is not found to be significant, U = -1.35, p>0.05.

Hence H0-6 rejected with respect to dividend preference of the schemes against the

constructs of sources of information and mutual fund companies.

Investors preferring dividend paying schemes assign different importance to

the various constructs of mutual fund selection criteria and the difference is

significant, H(3) = 53.81, p<0.01. They assigned highest importance to investor

services (M = 3.62, SD = 0.76) and lowest to sources of information (M = 3.06, SD =

0.74). Similarly investors who normally prefer non dividend paying schemes assigned

different importance to the various constructs of mutual fund selection criteria and the

difference is significant, H(3) = 76.52, p<0.01. They assigned highest importance to

investor services (M = 3.72, SD = 0.77) and lowest to sources of information (M =

3.22, SD = 0.73). Overall analysis depicts that investors preferring dividend schemes

valued the construct of mutual fund companies less as compared to investors

preferring non dividend schemes

Since last few years, mutual fund industry has greatly emphasized on adoption

of systematic investment plans or SIPs. The main virtues highlighted are regular

investing so that timing of the market does not become a constraint. In addition

product penetration goes deeper especially among the salaried people as the small

amount is invested at regular intervals. An attempt has been made to differentiate

among the investors who mostly prefer lumpsum investing against the investors who

prefer investing through SIP. In the survey of 400 retail investors, 169 investors

mostly preferred investment through lumpsum investing and 231 investors have given

their desirable preference as investing through SIP.

Table 5.42 depicts the comparison of investors preferring lumpsum investing

with investors preferring investing through SIP with reference to various constructs of

selection criteria.

206

Table 5.42: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Investing Frequency)


Parameter Investors (Lumpsum investing) (N = 169)

Investors (SIP

Investing) (N = 231)

U Statistic

(Z Score)

p Value


Mean Importance

3.18 3.14 -0.15 0.883


Mutual Fund Schemes

Mean Importance

3.55 3.74 -3.33 0.001



Mean Importance

3.58 3.57 -0.08 0.934



3.59 3.74 -2.09 0.036


H Statistic χ2 (3) 40.128

94.917

P Value 0.000 0.000 Behavioural factors

Mean Importance

3.00

2.88 -3.50 0.000




depict differences between the constructs for a particular group of investors.

Investors investing through lumpsum investing assigned higher importance to

sources of information construct (M = 3.18, Mdn = 3.12, SD = 0.73) as compared to

the investors preferring SIP (M = 3.14, Mdn = 3.18, SD = 0.74) but the difference is

not found to be significant, U = -0.15, P>0.05. Investors preferring through SIP

assigned higher importance to the construct of mutual fund schemes (M = 3.74, Mdn

= 3.90, SD = 0.71) as compared to the investors preferring lumpsum investing (M =

207

3.55, Mdn = 3.55, SD = 0.61) and the difference is found to be significant, U = -3.33,

p<0.01. Regarding the selection criteria related to mutual fund companies, investors

(M = 3.58, Mdn = 3.62, SD = 0.64) preferring lumpsum investing assigned slightly

higher importance as compared to the investors preferring SIP (M = 3.57, Mdn = 3.69,

SD = 0.70) and the difference is insignificant, U = -0.08, p>0.05. With reference to

investor services, investors preferring SIP (M = 3.74, Mdn = 3.92, SD = 0.77)

assigned higher importance as compared to investors preferring lumpsum investing

(M = 3.59, Mdn = 3.62, SD = 0.76) and the difference is found to be significant, U = -

2.09, p<0.05.

Investors preferring to invest through lumpsum investing seem to be more

behaviorally biased (M = 3.00, Mdn = 3.05, SD = 0.39) as compared to investors

preferring SIP (M = 2.88, Mdn = 2.95, SD = 0.36) and the difference is significant, U

= -3.50, P<0.01. Hence H0-6 rejected with respect to dividend preference against the

constructs of mutual fund schemes, investor services and behavioral factors. This may

be due to the reason that investing through SIP includes discipline and patience and

therefore keep away the behavioral biases.

In addition, investors preferring lumpsum investing perceived the importance

of various selection criteria constructs differently and significantly, H(3) = 40.128,

p<0.01. They assigned highest importance to the investor services and lowest

importance to sources of information construct in their fund selection behavior.

Similarly investors preferring SIP investing assigned significantly different

importance to fund selection criteria constructs, H(3) = 94.917, p<0.01. They assigned

equal importance to construct(s) relating to mutual fund schemes and investor

services and least important to the sources of information construct. Therefore for SIP

investors constructs of mutual fund schemes and investor services are more important

as compared to others due to need to for constant and regular investment process.

Investors can buy the mutual funds from different sources and this may

influence their selection criteria. Accordingly the total sample base of 400 retail

investors is categorized into three categories on the basis of their buying sources. The

three categories formed are – buying from asset management company, buying from

individual financial agent or individual broker and the third category is buying from

other means (like buying from bank or online buying). The three categories have been

further compared with each other on the basis of the selection criteria. The results are

presented in Table 5.43.

208

Investors belonging to all the three categories of purchase avenue assigned

different importance scores and the difference is significant. Those who prefer to buy

from asset management company assigned highest importance to the construct of

investor services. On the contrary those who prefer to buy from broker or individual

financial agent assigned highest importance to mutual fund schemes. Further all the

three categories assigned lowest importance to the sources of information construct

All the three categories assigned different importance to the construct of

sources of information, with buying from other means assigning highest importance

(M = 3.22, SD = 0.74) and buying from AMC the lowest importance (M = 3.09, SD =

0.79), the difference between the three categories is found to be insignificant. Those

investors who buy from AMC valued the construct of mutual fund schemes the most

(M = 3.74, SD = 0.67) as compared to others and the difference between the three

categories is found to be significant, H(2) = 9.14, p<0.01. For the construct of mutual

fund companies, those who prefer to buy from AMC valued the construct most (M =

3.62, SD = 0.72) as compared to the others but the difference between the three is not

found to be significant, H (2) = 3.82, p>0.05. Those investors who prefer to buy from

AMC assigned highest importance to the construct of investor services (M = 3.81, SD

= 0.81) as compared to their counterparts and the difference between the three groups

is found to be significant, H(2) = 9.78, p<0.01. For behavioral biasness towards the

fund selection, no significant difference has been observed in the three categories, H

(2) = 1.00, p>0.05. Hence H0-6 rejected with respect to preference for purchase avenue

against the constructs of mutual fund schemes and investor services.

Loads are highly critical determinants of the mutual fund performance. Many

of the empirical researches point out abnormal performance before the loads and

under performance after the loads have been considered. An attempt has been made to

assess the difference between investors who are aware of the loads with the investors

who are not aware, with reference to the fund selection criteria and the results are

presented in Table 5.44

209

Table 5.43: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of buying source) Selection Criteria Constructs Parameter Investors

(Buying from AMC)

(N = 158)

Investors (Buying from

IFA) (N = 143)

Investors (Buying from Other

Means) (N = 99)

H Statistic χ2 (2)

p Value

Sources of Information Mean 3.09 3.18 3.22 0.92 0.63 Median 3.12 3.18 3.18 SD 0.79 0.65 0.74 Normality 0.98* 0.98 0.98

Mutual Fund Schemes Mean 3.74 3.69 3.47 9.14 0.01 Median 3.83 3.75 3.40 SD 0.67 0.63 0.72 Normality 0.97* 0.98 0.96*

Mutual Fund Companies Mean 3.62 3.61 3.46 3.82 0.15 Median 3.69 3.62 3.46 SD 0.72 0.60 0.69 Normality 0.98* 0.98* 0.98

Investor Services Mean 3.81 3.62 3.55 9.78 0.008 Median 3.96 3.62 3.69 SD 0.81 0.73 0.73 Normality 0.96* 0.98* 0.97*

H Statistic χ2 (3) 74.413 47.199 14.028 p Value 0.000 0.000 0.000 Behavioural biasness Mean 2.91 2.92 2.96 1.00 0.606

Median 2.95 3.00 3.00 SD 0.40 0.37 0.37 Normality 0.97* 0.97 0.98


1. Importance was asked on 5-point scale from Not at all important to Very important 2. Shapiro-Wilk Test was used for checking Normality. (* depicts non normality)

210

Investors who were aware about the loads assigned higher importance (M = 3.18,

Mdn = 3.18, SD = 0.71) to sources of information as compared to the investors who were

not aware (M = 2.97, Mdn = 3.00, SD = 0.89) and the difference is found to be

significant, t (398) = 1.97, p<0.05.


(Investors grouping on the basis of Awareness regarding Loads) Selection Criteria Constructs

Parameter Investors (Aware) (N = 348)

Investors (Not Aware)

(N = 52)

t / U Statistic

(Z Score)

p Value


Mean Importance

3.18 2.97 1.97 t (398)

0.049


Mutual Fund Schemes

Mean Importance

3.66 3.60 -0.11 0.911



Mean Importance

3.59 3.43 -1.24 0.215



3.68 3.63 -0.35 0.726



Mean Importance

2.93 2.95 -0.61 0.540





211

Aware investors further assigned higher importance to mutual fund schemes (M =

3.66, Mdn = 3.70, SD = 0.65) construct as compared to investors who were not aware (M

= 3.60, Mdn = 3.70, SD = 0.85), but the difference is not found to be significant, U = -

0.11, p>0.05. Regarding the construct linked to mutual fund companies, load aware

investors assigned higher importance (M = 3.59, Mdn = 3.62, SD = 0.67) in comparison

to the investors who were not aware (M = 3.43, Mdn = 3.62, SD = 0.73) but the

difference is not found to be significant, U = -1.24, p>0.05. With reference to investor

services, investors who were aware about the loads (M = 3.68, Mdn = 3.77, SD = 0.76)

assigned higher importance as compared to investors who were not aware (M = 3.63,

Mdn = 3.77, SD = 0.80) and the difference is found to be insignificant, U = -0.35, p>0.05.

Investors who were not aware about the loads seems to be more behaviorally

biased (M = 2.95, Mdn = 3.00, SD = 0.81) as compared to investors who were aware of

the loads (M = 2.93, Mdn = 2.95, SD = 0.37) but the difference is found to be

insignificant, U = -0.61, P>0.05. Hence H0-6 rejected with respect to awareness of loads

against the constructs of sources of information.

An attempt was also made to see the relative importance of various fund selection

constructs with in one group of investors. Investors who were aware of loads assigned

different and significant importance to various selection criteria constructs, H(3) =

105.921, p<0.01. Similarly investors who were not aware of the loads assigned different

importance measures to the selection criteria and the difference is found to be significant,

H(3) = 20.267, p<0.01. Further both the categories assigned highest importance to the

investor services construct and lowest importance to the construct of sources of

information

From the above analysis, therefore it is clear that loads play a role in fund

selection behavior and is especially related to the construct of sources of information,

where load aware investors assign significantly higher importance

5.3.4 Comparison of investors categorized on the basis of purchase profile

As different investors show different purchase behavior, their profiles are also

different. The study has attempted to assess the mutual fund purchase profile in terms of

212

mutual fund investment, number of schemes in mutual fund portfolio, number of asset

management companies in the portfolio and investing experience. On the basis of

investment amount in portfolio, the investors have been classified as retail and non retail

investors and comparison of them in the context of fund selection criteria is presented in

other sections. This section deals with the comparison of investors classified on the basis

of scheme diversification (number of schemes in the portfolio), AMC diversification

(number of mutual fund companies in the portfolio) and investing experience (in terms of

years of investing experience)

Portfolio diversification reduces risk and increases stability in the return as

propounded by many of the financial researchers. Investors on the same lines like to add

number of mutual fund schemes in their portfolio. Study has categorized mutual fund

investors into two categories on the basis of their scheme diversification. Investors

having less than 5 mutual fund schemes in their portfolio are said to maintain

concentrated portfolio and greater than or equal to 5 schemes are said to maintain

diversified portfolio. Study has made an attempt to differentiate both kind of investors

(investors maintaining concentrated portfolios versus investors maintaining diversified

portfolio) on account of fund selection criteria, and the results are presented in Table

5.45.

An attempt has been made to study the relative importance of various constructs

of selection criteria in the subsets created on the basis of scheme diversification. Different

categories of investors assigned different importance scores and are observed at

significant difference. Those investors who were having concentrated portfolio assigned

highest importance to construct of investor services and those investors who were

maintaining diversified portfolio assigned highest importance to mutual fund schemes.

Further both the categories assigned lowest importance to construct of sources of

information.

Diversified portfolio investors’ valued sources of information more (M = 3.20,

Mdn = 3.18, SD = 0.68) as compared to investors who maintained concentrated portfolios

(M = 3.14, Mdn = 3.15, SD = 0.75), but the difference is found to be insignificant, t (398)

= -0.74, p>0.05

213

Table 5.45: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Scheme Diversification)


Parameter Investors (Concentrated

Portfolio) (N = 296)

Investors (Diverse


t / U Statistic

(Z Score)

p Value


Mean Importance

3.14 3.20 -0.74 t (398)

0.461


Mutual Fund Schemes

Mean Importance

3.67 3.61 -1.07 0.286



Mean Importance

3.59 3.53 -1.43 0.152



3.70 3.60 -1.43 0.152



Mean Importance

2.91 2.98 -1.44 0.149





For the construct of mutual fund schemes, concentrated portfolio investors

assigned higher importance (M = 3.67, Mdn = 3.75, SD = 0.69) as compared to the

diversified portfolio investors (M = 3.61, Mdn = 3.62, SD = 0.62), but again the

difference is found to be insignificant, U = -1.07, p>0.05. Concentrated portfolio

214

investors (M = 3.59, Mdn = 3.69, SD = 0.70) also assigned higher importance to the

construct of mutual fund companies as compared to the diversified portfolio investors (M

= 3.53, Mdn = 3.54, SD = 0.59), but again the difference is found to be insignificant, U =

-1.43, p>0.05.

With reference to the construct of investor services, the concentrated portfolio

investors assigned higher importance (M = 3.70, Mdn = 3.77, SD = 0.79) as compared to

the investors who preferred to maintain diversified portfolios (M = 3.60, Mdn = 3.46, SD

= 0.72), but the difference is found to be insignificant, U = -1.43, p>0.05. Investors who

maintained diversified portfolios are more behaviorally biased (M = 2.98, Mdn = 3.00,

SD = 0.33) as compared to the investors who preferred concentrated portfolio (M = 2.91,

Mdn = 2.95, SD = 0.39), but the difference between the two is found to be insignificant,

U = -1.44, p>0.05. Hence H0-6 accepted with respect to mutual fund scheme

diversification.

Diversification also relates to how investors diversify their investments among

various asset management companies. Since different asset management companies

follow different investing strategies and try to maintain consistent behavior among their

schemes, it becomes prudent enough to diversify among various asset management

companies. Study has attempted to classify retail investors on the basis of how they

diversify among asset management companies. Study has defined two categories namely

– concentrated portfolio investors (who diversify their investments in less than 3 AMC’s)

and diversified portfolio investors (who diversify their investments among three or more

than three asset management companies). Further attempt was made to differentiate these

two categories on the basis of their importance of fund selection criteria. The results of

the same are presented in Table 5.46.

Different categories of investors (categorized according to AMC diversification)

assigned different importance scores. While the investors who maintained concentrated

portfolios assigned highest importance to the construct of investor services, the

diversified investors assigned highest importance to the mutual fund schemes. In addition

both the categories assigned lowest importance to the construct of sources of information.

In every construct of fund selection, diversified portfolio investors accorded higher

importance as compared to the concentrated investors.

215

Table 5.46: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of AMC Diversification)


Parameter Investors (Concentrated


Investors (Diverse


U Statistic

(Z Score)

p Value


Mean Importance

3.14 3.17 -0.97 0.330


Mutual Fund Schemes

Mean Importance

3.55 3.79 -3.12 0.002



Mean Importance

3.51 3.65 -1.65 0.098



3.60 3.77 -2.16 0.030


H Statistic χ2 (3)

57.816 71.727


Mean Importance

2.95 2.91 -0.62 0.536





For sources of information construct, diversified portfolio investors assigned

higher importance (M = 3.17, Mdn = 3.18, SD = 0.79) as compared to the investors who

216

prefer to maintain concentrated portfolios (M = 3.14, Mdn = 3.12, SD = 0.71) but the

difference is found to be insignificant, U = -0.97, p>0.05.

With reference to the construct of mutual fund schemes, diversified portfolio

investors assigned higher importance (M = 3.79, Mdn = 3.85, SD = 0.61) as compared to

the concentrated portfolio investors (M = 3.55, Mdn = 3.60, SD = 0.71) and the

difference is found to be significant, U = -3.12, p<0.01. Further, diversified portfolio

investors assigned more importance to the selection criteria related to mutual fund

companies (M = 3.65, Mdn = 3.62, SD = 0.65) as compared to the concentrated portfolio

investors (M = 3.51, Mdn = 3.62, SD = 0.69) but the difference is found to be

insignificant, U = -1.65, p>0.05. Diversified portfolio investors valued investor services

more (M = 3.77, Mdn = 3.77, SD = 0.73) as compared to the concentrated portfolio

investors (M = 3.60, Mdn = 3.69, SD = 0.79) and at the significant difference, U = -2.16,

p<0.05.

Concentrated portfolio investors have been more behaviorally biased (M = 2.95,

Mdn = 3.00, SD = 0.37) with reference to their fund selection criteria as compared to

diversified portfolio investors (M = 2.91, Mdn = 2.95, SD = 0.39), but the difference is

insignificant, U = -0.62, p>0.05. Hence H0-6 rejected with respect to AMC diversification

against the constructs of mutual fund schemes and investor services.

The analysis on categories of investors on the basis of diversification trends

presents an interesting picture. While the diversification at the scheme level is not a

discriminating factor, the diversification at AMC level is as more diverse investors value

the construct of mutual fund schemes and investor services more as compared to the

investors who do not maintain adequate AMC diversification.

Experience of the investors helps him in fund selection as selection criteria may

change or become more stabilized with time and experience. Study has attempted to

assess the experience in years and classified the investors into two categories namely –

investors with less experience (less than 2 years) and investors with higher experience

(equal to or greater than 2 years). Further an attempt is made to assess the difference

between two categories regarding their fund selection criteria (Table 5.47).

Both less and more experience investors assigned different importance scores to

various constructs and the difference is observed at significant level. Further both the

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categories assigned highest importance to the construct of investor services and lowest

importance to the construct of sources of information

Overall more experienced investors assigned higher importance to the selection

criteria constructs. More experienced investors assigned higher importance to the sources

of information construct (M = 3.20, Mdn = 3.24, SD = 0.72) as compared to the less

experienced investors (M = 3.12, Mdn = 3.03, SD = 0.74) and the difference is found to

be significant, U = -2.07, p<0.05. Similar results are evident in case of selection criteria

related to mutual fund schemes as experienced investors assigned higher importance (M

= 3.86, Mdn = 3.90, SD = 0.55) in comparison to the less experienced investors (M =


p<0.01.

Regarding the construct related to mutual fund companies, here again more

experienced investors assigned higher importance (M = 3.70, Mdn = 3.69, SD = 0.58) as

compared to less experienced investors (M = 3.47, Mdn = 3.54, SD = 0.73) and at

significant difference, U = -3.09, p<0.01. Selection criteria related to investor services

were also valued more by the experienced investors (M = 3.88, Mdn = 4.00, SD = 0.65)

as compared to the less experienced investors (M = 3.50, Mdn = 3.54, SD = 0.82) and at

highly significant difference, U = -4.64, p<0.01. Further both the experienced and less

experienced investors were almost equally behaviorally biased towards their mutual fund

selection with almost no significant difference, U = -0.74, p>0.05. Hence H0-6 rejected

with respect to investing experience against the constructs of sources of information,

mutual fund schemes, mutual fund companies and investor services.

Investing experience has proved to be the major role playing factor in

determination of fund selection criteria. With more experience, learning and

sophistication occurs and investors start valuing higher almost every selection criteria.

5.3.5 Comparison of Retail and Non Retail Investors on basis of their Perception towards Objectives of Investing in Mutual Funds

There are certain objectives behind the investment and different investors have

different objectives. Study has made investors to rate different investment objectives on

the importance scale. Further an attempt was made to categorize investors on the basis of

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how important they feel for the investment objectives. On the basis of mean score of the

importance of the investment objectives, two categories have been created namely –

investors for whom investment objectives are not important and the second category of

the investors for whom the investment objectives are important. An attempt is also made

Table 5.47: Investors’ Importance of Selection Criteria Constructs (Investors grouping on the basis of Investing Experience)


Parameter Investors (Less

Experience) (N = 214)

Investors (More

Experience) (N = 186)

U Statistic

(Z Score)

p Value


Mean Importance

3.12 3.20 -2.07 0.038


Mutual Fund Schemes

Mean Importance

3.48 3.86 -5.64 0.000



Mean Importance

3.47 3.70 -3.09 0.002



3.50 3.88 -4.64 0.000



Mean Importance

2.93 2.93 -0.74 0.462



1. Importance was asked on 5-point scale from Not at all important to Very important 2. Shapiro-Wilk Test was used for checking Normality. (* denotes non normality) 3. Tests across the rows depict differences in types of investors and across the columns depict


219

to differentiate between the two categories on the basis of selection criteria related to

mutual funds selection. Results are presented in Table 5.48

The importance assigned by both the categories of investors to different

constructs of fund selection is different at significant level. Further both the categories

assigned highest importance to the construct of investor services and lowest importance

to the construct of sources of information.

In all the constructs of mutual fund selection, investors for whom investment

objectives were important assigned higher importance to the various constructs as

compared to the investors for whom investment objectives were not important. With

reference to sources of information as selection criteria, investors for whom objectives of

investment were important assigned higher importance (M = 3.38, Mdn = 3.38, SD =

0.72) as compared to investors for whom investment objectives were not important (M =

2.99, Mdn = 3.00, SD = 0.69) and the difference between the two is found to be

significant, U = -5.19, p<0.01. Similar results are evident for the construct of mutual fund

schemes. Here also the investors for whom investment objectives were important

assigned higher importance to the construct (M = 3.91, Mdn = 4.10, SD = 0.66) as

compared to their counterparts (M = 3.47, Mdn = 3.52, SD = 0.62) and the difference is

found to be significant, U = -6.65, p<0.01.

With reference to the selection criteria relating to mutual fund companies,

investors for whom investment objectives were important assigned higher importance (M

= 3.83, Mdn = 3.85, SD = 0.64) to the construct as compared to the others (M = 3.38,

Mdn = 3.38, SD = 0.63), and the difference is found to be significant, U = -6.78, p<0.01.

For construct related to investor services, investors for whom investment objectives were

important, assigned higher importance to the construct (M = 4.00, Mdn = 4.11, SD =

0.69) as compared to the others (M = 3.44, Mdn = 3.46, SD = 0.73) and difference is

found to be significant, U = -7.25, p<0.01.

Investors for whom objectives of investment were important were more

behaviorally biased (M = 3.03, Mdn = 3.10, SD = 0.37) as compared to the other

investors (for whom objectives of investment were not important) (M = 2.85, Mdn =

2.90, SD = 0.67) and the difference between the two is found to be significant, U = -4.73,

p<0.01. Hence H0-6

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Table 5.48: Investors’ Importance of Selection Criteria Constructs (Grouping on the basis of Objectives of Investing in Mutual Funds)


Parameter Investors (Objectives

are not important) (N = 232)

Investors (Objectives

are important) (N = 168)

U Statistic

(Z Score)

p Value


Mean Importance

2.99 3.38 -5.19 0.000


Mutual Fund Schemes

Mean Importance

3.47 3.91 -6.65 0.000



Mean Importance

3.38 3.83 -6.78 0.000



3.44 4.00 -7.25 0.000



Mean Importance

2.85 3.03 -4.73 0.000





rejected with respect to importance of objectives of investing in mutual funds against all

selection criteria constructs.

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Investors who feel that by investing in mutual funds, their investment objectives

are fulfilled and investing objectives are important value all the constructs at significantly

higher level as compared to the investors who think otherwise. This again points out

towards increasing sophistication level and simultaneously higher importance scores to

all the constructs.

5.3.6 Comparison of Retail and Non Retail Investors on basis of their Perception towards Advantages of Investing in Mutual Funds

Investing in mutual funds suffice different advantages for investors, who in turn

have different importance rating to advantages. This study has assessed the importance of

investment advantages, as reported by the investors and has tried to categorize the

investors in two categories – namely the investors for whom the mutual fund investing is

advantageous and the second category of investors for whom the mutual fund investing is

not advantageous. The two categories have been further analysed on their scores to

different selection criteria and the results are depicted in Table 5.49

The different categories of investors grouped on the basis of importance of

advantages of mutual fund investing differently valued all the constructs of mutual fund

selection and the difference is observed at significant level. The investors for whom

mutual fund investing is not advantageous assigned highest importance to mutual fund

schemes and their counterparts assigned highest importance to the investor services.

Further both the categories assigned lowest importance to the construct of sources of

information.

In all the constructs related to mutual fund selection, investors in the first category

assigned higher importance as compared to their counterparts. Against the construct of

sources of information, the investors for whom the mutual fund investing was

advantageous assigned higher importance (M = 3.41, Mdn = 3.59, SD = 0.82) as

compared to others (M = 2.95, Mdn = 3.00, SD = 0.57) and the difference between the

two is found to be significant, U = -6.77, p<0.01. Similar results are evident for construct

related to mutual fund schemes, where the first category assigned higher importance (M =

4.04, Mdn = 4.15, SD = 0.57) as compared to their counterparts in the second category

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(M = 3.34, Mdn = 3.40, SD = 0.58) and with the significant difference, U = -10.81,

p<0.01.

Table 5.49: Investors’ Importance of Selection Criteria Constructs (Grouping on the basis of Advantages of Investing in Mutual Funds)


Parameter Investors (Mutual fund investing is

not advantageous)

(N = 223)

Investors (Mutual fund investing is

advantageous) (N = 177)

U Statistic

(Z Score)

p Value


Mean Importance

2.95 3.41 -6.77 0.000


Mutual Fund Schemes

Mean Importance

3.34 4.04 -10.81 0.000



Mean Importance

3.29 3.93 -9.62 0.000


Investor Services

Mean Importance

3.32 4.12 -10.56 0.000


H Statistic χ2 (3)

63.682 93.436


Mean Importance

2.86 3.01 -4.48 0.000





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With reference to the construct related to mutual fund companies, the investors

for whom the mutual fund investing was advantageous assigned higher importance (M =

3.93, Mdn = 4.00, SD = 0.58) as compared to others (M = 3.29, Mdn = 3.38, SD = 0.61)

and the difference is significant, U = -9.62, p<0.01. The same is for the construct of

investors services, where the investors in the former category valued construct more (M =

4.12, Mdn = 4.23, SD = 0.62) as compared to the investors in the second category (M =


p<0.01.

The investors for whom mutual fund investing was advantageous depicted more

behavioral biasness (M = 3.01, Mdn = 3.05, SD = 0.40) as compared to the other

investors for whom the mutual fund investing was not advantageous (M = 2.86, Mdn =

2.90, SD = 0.35) and difference is found to be significant, U = -4.48, p<0.01. Hence H0-6

rejected with respect to importance of advantages of investment in mutual funds against

all the constructs.

The mutual fund investing advantage also presents a similar picture as that of

mutual fund objectives, and in this case also as the mutual fund investing becomes more

advantageous, the investors assign higher importance to all the constructs of fund

selection

Concludingly this chapter pointed out the major differences between retail and

non retail investors. Although on whole construct basis no significant difference has been

observed between retail and non retail mutual fund investors against all of the constructs

tested. Instead significant differences have been found with respect to factors of

‘performance and asset profile’; ‘extrinsic attributes’, ‘location and infrastructure’;

‘experience and reputation’; ‘adequate disclosures and easiness in investing’; ‘fringe

benefits’; ‘planning and rationality’; ‘objectivity’ and ‘external stimulants’.

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