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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
144
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
145
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 =
148
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)
Total Sample Mean (SD) (N = 450)
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)
For Entire Construct 3.67 (0.76) 3.67 (0.65) 3.67 (0.75) (Tests of Difference between Retail and Non Retail Investors)
U(Z score) = -0.268, p exact = 0.789 Cronbach’s Alpha for Entire Construct = 0.888
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)
Non Retail Investors
Mean (SD) (N = 50)
Total Sample Mean (SD) (N = 450)
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)
150
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)
For Entire Construct 2.93 (0.38) 2.87 (0.47) 2.92 (0.39) (Tests of Difference between Retail and Non Retail Investors)
U(Z score) = -0.322, p exact = 0.748 Cronbach’s Alpha for Entire Construct = 0.554
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
151
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).
152
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
153
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
154
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.
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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
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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
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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.
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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
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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
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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
Non 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
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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
(2.36%) item to item correlations are insignificant at 5% level of significance. The
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.
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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
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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)
3. Number of Insignificant Correlations (Significant at 5%)
4 2.36
4. Determinant Value 0.012 5. Kaiser Meyer Olkin Measure
(KMO) 0.814
6. Percent of residuals > 0.05 (abs)
47
7. Bartlett’s Test of Sphericity (χ2) df = 78
1950.514 (p Value = 0.000)
Individual Variables MSA Values . 0.5 to 0.7 (Mediocre) 0 0.00 0.7 to 0.8 (Good) 5 38.46 0.8 to 0.9 (Great) 7 53.84 > 0.9 (Superb) 1 7.70
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.
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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
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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
Constituent Variable Label Factor Loading
Factor Name Variance Explained
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
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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
*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)
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
considered for the study. Principal component analysis with Varimax rotation was
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),
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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)
3. Number of Insignificant Correlations (Significant at 5%)
0 0
4. Determinant Value 0.003 5. Percent of residuals > 0.05 (abs) 78.0 6. Kaiser Meyer Olkin Measure
(KMO) 0.888
7 Bartlett’s Test of Sphericity (χ2) df = 78
2604.071 (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) 9 69.23 > 0.9 (Superb) 4 30.77
‘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
Constituent Variable Label Factor Loading
Factor Name Variance Explained
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)
Factor Descriptive Statistics Mean (SD)
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
*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)
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
(10.00%) item to item correlations are insignificant at 5% level of significance. The
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
considered for the study. Principal component analysis with Varimax rotation was
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)
3. Number of Insignificant Correlations (Significant at 5%)
40 10
4. Determinant Value 0.005 5. Percent of residuals > 0.05 (abs) 44.0 6. Kaiser Meyer Olkin Measure
(KMO) 0.801
7. Bartlett’s Test of Sphericity (χ2) df = 190
2359.205 (p Value = 0.000)
Individual Variables MSA Values . 0.5 to 0.7 (Mediocre) 0 0.00 0.7 to 0.8 (Good) 9 45.00 0.8 to 0.9 (Great) 11 55.00 > 0.9 (Superb) 0 0.00
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
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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
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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
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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
186
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
Factor Descriptive Statistics Mean (SD)
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
Median 3.62 3.54 SD 0.68 0.64 Normality 0.98* 0.98
Investor Services Mean Importance
3.66 3.72 -0.684 0.491
Median 3.73 3.92 SD 0.75 0.81 Normality 0.98* 0.96*
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
Median 3.00 2.95 SD 0.39 0.30 Normality 0.98* 0.98
* 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.
191
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.
Table 5.34: Investors’ Importance of Selection Criteria Constructs
(Investors grouping on the basis of Investor’s Age)
Selection Criteria Constructs
Parameter Investors (< = 31 years)
(N = 266)
Investors (> 31 years) (N = 134)
t/ U Statisti
(Z Score)
p Value
Sources of Information
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
Median 3.65 3.80 SD 0.71 0.58 Normality 0.97* 0.99
Mutual Fund Companies
Mean Importance
3.56 3.59 -0.16 0.870
Median 3.69 3.54 SD 0.69 0.64 Normality 0.97* 0.98*
Investor Services Mean Importance
3.63 3.77 -1.61 0.107
Median 3.77 3.77 SD 0.78 0.74 Normality 0.97* 0.97*
H Statistic χ2 (3) 59.477 72.962 p Value 0.000 0.000 Behavioural biasness
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*
* 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) 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
192
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)
Selection Criteria Constructs
Parameter Investors (Grad. and Less)
(N = 212)
Investors (Post Graduation
and More) (N = 188)
U (Z Score)
P Value
Sources of Information
Mean Importance
3.21 3.08 -1.67 0.094
Median 3.18 3.12 SD 0.69 0.77 Normality 0.99 0.98*
Mutual Fund Schemes
Mean Importance
3.58 3.73 -2.11 0.035
Median 3.60 3.80 SD 0.72 0.61 Normality 0.98* 0.98*
Mutual Fund Companies
Mean Importance
3.55 3.60 -0.39 0.693
Median 3.62 3.62 SD 0.68 0.66 Normality 0.98* 0.99
Investor Services
Mean Importance
3.61 3.75 -1.89 0.059
Median 3.54 3.92 SD 0.77 0.75 Normality 0.98* 0.97*
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
Median 3.00 2.95 SD 0.34 0.41 Normality 0.98* 0.97*
* 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.
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.
Table 5.36: Investors’ Importance of Selection Criteria Constructs
(Investors grouping on the basis of Investor’s Marital Status)
Selection Criteria Constructs
Parameter Investors (Single)
(N = 180)
Investors (Married) (N = 220)
t/ U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
3.25 3.08 2.34 t (398)
0.02
Median 3.24 3.09 SD 0.68 0.76 Normality 0.99 0.99
Mutual Fund Schemes
Mean Importance
3.57 3.72 -2.13 0.03
Median 3.60 3.80 SD 0.71 0.63 Normality 0.97* 0.98*
Mutual Fund Companies
Mean Importance
3.56 3.58 -0.17 0.868
Median 3.69 3.62 SD 0.68 0.66 Normality 0.97* 0.98
Investor Services Mean Importance
3.66 3.69 -0.35 0.729
Median 3.77 3.77 SD 0.78 0.75 Normality 0.97 0.97*
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
Median 2.95 3.00 SD 0.34 0.41 Normality 0.98* 0.97*
* 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.
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)
Selection Criteria Constructs
Parameter Investors (Service)
(N = 275)
Investors (Non
Service) (N = 125)
U Statistic (Z Score)
p Value
Sources of Information
Mean Importance
3.14 3.18 -0.01 0.989
Median 3.18 3.12 SD 0.74 0.71 Normality 0.98* 0.97*
Mutual Fund Schemes
Mean Importance
3.71 3.53 -2.44 0.015
Median 3.80 3.55 SD 0.65 0.71 Normality 0.97* 0.98
Mutual Fund Companies
Mean Importance
3.59 3.53 -0.98 0.327
Median 3.69 3.54 SD 0.66 0.70 Normality 0.98* 0.98*
Investor Services Mean Importance
3.73 3.54 -2.54 0.011
Median 3.77 3.46 SD 0.77 0.73 Normality 0.97* 0.97*
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
Median 2.95 3.00 SD 0.40 0.31 Normality 0.97* 0.99
* 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.
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)
Selection Criteria Constructs
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
Sources of Information
Mean Importance
3.17 3.14 0.35 t (398)
0.729
Median 3.24 3.18 SD 0.74 0.72 Normality 0.99 0.99
Mutual Fund Schemes
Mean Importance
3.51 3.73 -2.75 0.006
Median 3.60 3.80 SD 0.71 0.64 Normality 0.97* 0.98*
Mutual Fund Companies
Mean Importance
3.49 3.61 -1.48 0.138
Median 3.62 3.62 SD 0.68 0.67 Normality 0.97* 0.99
Investor Services Mean Importance
3.47 3.78 -3.71 0.000
Median 3.46 3.77 SD 0.81 0.72 Normality 0.98 0.97*
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
Median 2.95 3.00 SD 0.34 0.40 Normality 0.98 0.96*
* 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.
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)
Selection Criteria Constructs
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
Sources of Information
Mean Importance
3.10 3.23 -2.12 0.034
Median 3.00 3.24 SD 0.73 0.73 Normality 0.99 0.97*
Mutual Fund Schemes
Mean Importance
3.55 3.80 -3.62 0.000
Median 3.60 3.90 SD 0.69 0.62 Normality 0.98* 0.98*
Mutual Fund Companies
Mean Importance
3.50 3.67 -2.32 0.020
Median 3.62 3.69 SD 0.67 0.66 Normality 0.98* 0.98
Investor Services Mean Importance
3.58 3.81 -2.87 0.004
Median 3.69 3.92 SD 0.81 0.68 Normality 0.98* 0.96*
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
Median 2.95 3.05 SD 0.37 0.39 Normality 0.98 0.93*
* 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. 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)
Selection Criteria Constructs
Parameter Investors (Equity) (N = 303)
Investors (Non
Equity) (N = 97)
t / U p Value
Sources of Information
Mean Importance
3.12 3.27 -1.75 (t Value)
0.080
Median 3.12 3.24 SD 0.71 0.78 Normality 0.99 0.99
Mutual Fund Schemes
Mean Importance
3.64 3.73 -0.83 0.408
Median 3.70 3.70 SD 0.70 0.61 Normality 0.98* 0.99
Mutual Fund Companies
Mean Importance
3.57 3.60 -0.30 0.762
Median 3.62 3.62 SD 0.67 0.68 Normality 0.98* 0.99
Investor Services Mean Importance
3.69 3.64 -0.58 0.563
Median 3.77 3.62 SD 0.78 0.75 Normality 0.97* 0.98
F/ H Statistic χ2 (3), F(3,384)
108.331 (H Statistic)
7.718 (F Value)4
p Value 0.000 0.000 Behavioural biasness
Mean Agreement
2.89 3.04 -3.08 0.002
Median 3.31 3.05 SD 0.39 0.32 Normality 0.98* 0.98
* 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. 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)
Selection Criteria Constructs
Parameter Investors (Dividend) (N = 157)
Investors (Non
Dividend) (N = 243)
t / U p Value
Sources of Information
Mean Importance
3.06 3.22 -2.39 0.016
Median 3.00 3.24 SD 0.74 0.73 Normality 0.98 0.99*
Mutual Fund Schemes
Mean Importance
3.61 3.69 -0.97 0.328
Median 3.65 3.75 SD 0.72 0.65 Normality 0.97* 0.99*
Mutual Fund Companies
Mean Importance
3.43 3.66 -3.25$ t (293.9)
0.001
Median 3.46 3.69 SD 0.73 0.63 Normality 0.99 0.99
Investor Services Mean Importance
3.62 3.72 -1.35 0.177
Median 3.77 3.77 SD 0.76 0.77 Normality 0.98* 0.97*
H Statistic χ2 (3) 53.81 76.52 p Value 0.000 0.000 Behavioural biasness
Mean Agreement
2.94 2.92 -0.08 0.930
Median 2.95 3.00 SD 0.34 0.41 Normality 0.98 0.97*
* 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. $ 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)
Selection Criteria Constructs
Parameter Investors (Lumpsum investing) (N = 169)
Investors (SIP
Investing) (N = 231)
U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
3.18 3.14 -0.15 0.883
Median 3.12 3.18 SD 0.73 0.74 Normality 0.98 0.98*
Mutual Fund Schemes
Mean Importance
3.55 3.74 -3.33 0.001
Median 3.55 3.90 SD 0.61 0.71 Normality 0.98 0.96*
Mutual Fund Companies
Mean Importance
3.58 3.57 -0.08 0.934
Median 3.62 3.69 SD 0.64 0.70 Normality 0.98 0.98*
Investor Services Mean Importance
3.59 3.74 -2.09 0.036
Median 3.62 3.92 SD 0.76 0.77 Normality 0.98 0.97*
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
Median 3.05 2.95 SD 0.39 0.36 Normality 0.96* 0.98*
* 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.
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
* 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)
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.
Table 5.44: Investors’ Importance of Selection Criteria Constructs
(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
Sources of Information
Mean Importance
3.18 2.97 1.97 t (398)
0.049
Median 3.18 3.00 SD 0.71 0.89 Normality 0.99 0.97
Mutual Fund Schemes
Mean Importance
3.66 3.60 -0.11 0.911
Median 3.70 3.70 SD 0.65 0.85 Normality 0.98* 0.95*
Mutual Fund Companies
Mean Importance
3.59 3.43 -1.24 0.215
Median 3.62 3.62 SD 0.67 0.73 Normality 0.99* 0.95*
Investor Services Mean Importance
3.68 3.63 -0.35 0.726
Median 3.77 3.77 SD 0.76 0.80 Normality 0.98* 0.96
H Statistic χ2 (3) 105.921 20.267 p Value 0.000 0.000 Behavioural biasness
Mean Importance
2.93 2.95 -0.61 0.540
Median 2.95 3.00 SD 0.37 0.81 Normality 0.98* 0.97
* 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.
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)
Selection Criteria Constructs
Parameter Investors (Concentrated
Portfolio) (N = 296)
Investors (Diverse
Portfolio) (N = 104)
t / U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
3.14 3.20 -0.74 t (398)
0.461
Median 3.15 3.18 SD 0.75 0.68 Normality 0.99 0.99
Mutual Fund Schemes
Mean Importance
3.67 3.61 -1.07 0.286
Median 3.75 3.62 SD 0.69 0.62 Normality 0.97* 0.98
Mutual Fund Companies
Mean Importance
3.59 3.53 -1.43 0.152
Median 3.69 3.54 SD 0.70 0.59 Normality 0.98* 0.98
Investor Services Mean Importance
3.70 3.60 -1.43 0.152
Median 3.77 3.46 SD 0.79 0.72 Normality 0.97* 0.98
H Statistic χ2 (3) 102.04 24.126 p Value 0.000 0.000 Behavioural biasness
Mean Importance
2.91 2.98 -1.44 0.149
Median 2.95 3.00 SD 0.39 0.33 Normality 0.98* 0.95*
* 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.
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)
Selection Criteria Constructs
Parameter Investors (Concentrated
Portfolio) (N = 227)
Investors (Diverse
Portfolio) (N = 173)
U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
3.14 3.17 -0.97 0.330
Median 3.12 3.18 SD 0.71 0.79 Normality 0.99 0.98*
Mutual Fund Schemes
Mean Importance
3.55 3.79 -3.12 0.002
Median 3.60 3.85 SD 0.71 0.61 Normality 0.98* 0.98*
Mutual Fund Companies
Mean Importance
3.51 3.65 -1.65 0.098
Median 3.62 3.62 SD 0.69 0.65 Normality 0.98* 0.98
Investor Services Mean Importance
3.60 3.77 -2.16 0.030
Median 3.69 3.77 SD 0.79 0.73 Normality 0.97* 0.97*
H Statistic χ2 (3)
57.816 71.727
p Value 0.000 0.000 Behavioural biasness
Mean Importance
2.95 2.91 -0.62 0.536
Median 3.00 2.95 SD 0.37 0.39 Normality 0.98* 0.95*
* 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.
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
217
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 =
3.48, Mdn = 3.45, SD = 0.72) and the difference is found to be significant, U = -5.64,
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
218
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)
Selection Criteria Constructs
Parameter Investors (Less
Experience) (N = 214)
Investors (More
Experience) (N = 186)
U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
3.12 3.20 -2.07 0.038
Median 3.03 3.24 SD 0.74 0.72 Normality 0.99* 0.97*
Mutual Fund Schemes
Mean Importance
3.48 3.86 -5.64 0.000
Median 3.45 3.90 SD 0.72 0.55 Normality 0.99 0.97*
Mutual Fund Companies
Mean Importance
3.47 3.70 -3.09 0.002
Median 3.54 3.69 SD 0.73 0.58 Normality 0.99* 0.99
Investor Services Mean Importance
3.50 3.88 -4.64 0.000
Median 3.54 4.00 SD 0.82 0.65 Normality 0.98* 0.97*
H Statistic χ2 (3) 39.162 104.899 p Value 0.000 0.000 Behavioural biasness
Mean Importance
2.93 2.93 -0.74 0.462
Median 2.95 3.00 SD 0.37 0.40 Normality 0.99 0.94*
* 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. (* denotes 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.
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
220
Table 5.48: Investors’ Importance of Selection Criteria Constructs (Grouping on the basis of Objectives of Investing in Mutual Funds)
Selection Criteria Constructs
Parameter Investors (Objectives
are not important) (N = 232)
Investors (Objectives
are important) (N = 168)
U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
2.99 3.38 -5.19 0.000
Median 3.00 3.38 SD 0.69 0.72 Normality 0.98* 0.99
Mutual Fund Schemes
Mean Importance
3.47 3.91 -6.65 0.000
Median 3.52 4.10 SD 0.62 0.66 Normality 0.98* 0.95*
Mutual Fund Companies
Mean Importance
3.38 3.83 -6.78 0.000
Median 3.38 3.85 SD 0.63 0.64 Normality 0.99 0.96*
Investor Services Mean Importance
3.44 4.00 -7.25 0.000
Median 3.46 4.11 SD 0.73 0.69 Normality 0.98* 0.95*
H Statistic χ2 (3) 71.233 71.086 p Value 0.000 0.000 Behavioural biasness
Mean Importance
2.85 3.03 -4.73 0.000
Median 2.90 3.10 SD 0.67 0.37 Normality 0.97* 0.97*
* 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. (* denotes 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.
rejected with respect to importance of objectives of investing in mutual funds against all
selection criteria constructs.
221
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
222
(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)
Selection Criteria Constructs
Parameter Investors (Mutual fund investing is
not advantageous)
(N = 223)
Investors (Mutual fund investing is
advantageous) (N = 177)
U Statistic
(Z Score)
p Value
Sources of Information
Mean Importance
2.95 3.41 -6.77 0.000
Median 3.00 3.59 SD 0.57 0.82 Normality 0.99 0.96*
Mutual Fund Schemes
Mean Importance
3.34 4.04 -10.81 0.000
Median 3.40 4.15 SD 0.58 0.57 Normality 0.97* 0.94*
Mutual Fund Companies
Mean Importance
3.29 3.93 -9.62 0.000
Median 3.38 4.00 SD 0.61 0.58 Normality 0.98* 0.97*
Investor Services
Mean Importance
3.32 4.12 -10.56 0.000
Median 3.31 4.23 SD 0.68 0.62 Normality 0.98 0.94*
H Statistic χ2 (3)
63.682 93.436
p Value 0.000 0.000 Behavioural biasness
Mean Importance
2.86 3.01 -4.48 0.000
Median 2.90 3.05 SD 0.35 0.40 Normality 0.98* 0.95*
* 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. (* denotes 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.
223
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 =
3.32, Mdn = 3.31, SD = 0.68) and the difference is found to be significant, U = -4.48,
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’.