Spillover Effect in Islamic and Conventional Fund Family: Evidence
from Emerging Countries
JAMILEH ALI MUSTAFA1, ANAS AHMAD BANI ATTA1, AHMAD YAHIYA BANI AHMAD1,
MAHA SHEHADEH2, RIA AGUSTINA3
1Financial and Accounting Science Department, Faculty of Business,
Middle East University
JORDAN
2Finance and Banking Sciences Department,
Applied Sciences Privet University,
JORDAN
3Management Zakat and Waqf Department, Faculty of Business,
Stata Islamic University Of Raden Fatah,
INDONESIA
Abstract: - This study examines star and poor funds belonging to fund families in Saudi Arabia, Pakistan,
Indonesia, and Malaysia from 2007–2020. The analysis is divided into two parts. The first part examines how
Islamic and conventional star and poor funds contribute to the overall flow of their respective fund families.
Second, it examines and compares the spillover effect of Islamic and conventional star (poor) funds to peer
funds. These effects are estimated using pooled fixed- and random-effects regression analysis. Overall, we find
that having at least one-star fund leads to new money growth for families, whereas there is no effect from
having at least one poor fund. The presence of star (poor) funds has mixed effects on new money flow to peer
funds. To be precise, the spillover effect is found only in the presence of Islamic star funds. These findings
have important implications for investors because they mainly choose funds based on the reputation of the fund
family to which they belong, not on their fundamentals. This is especially pronounced in emerging markets,
where funds are young and have short track records, and so they provide little information to investors to make
sound investment decisions.
Key-Words: - Islamic finance, fund family, star fund, fund family flows, Islamic-focused family, emerging
countries
Received: November 3, 2022. Revised: April 15, 2023. Accepted: May 8, 2023. Published: May 15, 2023.
1 Introduction
Over the past decade, Islamic finance has grown
steadily at a pace of 10–12 percent per year and is
expected to grow to $3.8 trillion by 2024, [1]. This
growth is driven partly by the growing Muslim
population globally, which is forecasted to
constitute 29.7 percent of the global population in
2050. This projection places Islam as the second
largest religion after Christianity, [2].
Islamic assets under management (AUM) in
2008 was $802 million, increasing to $70.8 billion
in Q1/2020. A similar positive trend was shown by
Islamic funds, which increased from 802 to 1,535
during the same period, [1]. Equity funds constitute
the largest share of AUM, followed by money
market funds and commodity funds. By country,
Saudi Arabia and Malaysia have the two largest
shares of global AUM as of Q1/2017. Countries
with the most Islamic funds are Malaysia (371),
Saudi Arabia (350), Indonesia (155), and Pakistan
(83).
Despite this development, the Islamic fund
management industry is still a small niche within the
context of the global fund management industry. In
2019, the Islamic industry was valued at $56.1
billion, which was around 6.6 percent of the global
industry ($84.9 trillion), [3]. Nonetheless,
PricewaterhouseCoopers forecast that the Islamic
fund management industry will experience
accelerated growth due to higher participation of
pension funds, insurance firms, and high-net-worth
individuals, [3]. Other drivers include the increased
participation of institutional investors and non-
Muslim sustainable investors. Investment and asset
management firms should therefore exploit this
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Jamileh Ali Mustafa, Anas Ahmad Bani Atta,
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Maha Shehadeh, Ria Agustina
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opportunity and capture new demands for Islamic
funds.
As of 2020, Saudi Arabia and Malaysia house
the five largest Islamic asset management firms, [4].
These are NCB Capital (Saudi Arabia, AUM = $7.4
billion), Public Mutual (Malaysia, $7.2 billion),
Jadwa Asset Management (Saudi Arabia, $6.6
billion), CIMB Islamic (Malaysia, $4.9 billion), and
Samba Capital (Saudi Arabia, $3.1 billion). The
three largest firms in Pakistan rank below 20: Al
Meezan Investment (3.1 billion, 21st), NBP
Fullerton Asset Management ($3.3 billion, 31st),
and Alfalah GHP Investment ($1.8 billion, 35th).
Meanwhile, Mandiri Investasi ($1.2 billion) and
Trimegah Asset Management ($6.4 billion), the two
largest asset management firms in Indonesia, ranked
39th and 46th.
Some fund families are more capable of raising
capital from investors because of their reputation,
[5]. This reputation is earned from the historical
performance of their funds or fund families.
Investors expect reputable funds or fund families to
provide better returns. To attract investors, fund
families advertise their star funds to signal their
superior performance. They also tend to take
advantage of the spillover effect of their star funds
to improve the overall inflow of the family, [6]. But
it remains unclear whether star funds do improve
overall family flows and flows to peer funds
(spillover effect). In this paper, we examine whether
Islamic and conventional star funds improve overall
family flows. Most fund families have both Islamic
and conventional funds, but their characteristics and
underlying assumptions differ. Therefore, it is likely
that the size and significance of their contribution to
overall family flows differ, [7].
Previous studies have shown that fund
performance positively affects fund cash flow, but
little is known about whether the positive
performance of a fund can entice investors to
purchase its peers (i.e., other funds that belong to
the same family). It is also uncertain whether fund
families with a star fund perform better than those
without one. This study thus contributes novel
empirical evidence by analyzing the effects of star
funds at the fund family level. The findings have
important implications for investors because they
mainly choose funds based on the reputation of the
fund family to which they belong, not their
fundamentals. This is especially pronounced in
emerging markets, where funds are young and have
short track records, and so they provide little
information to investors to make sound investment
decisions. There is evidence that having at least one
superior-performing fund can produce a spillover
effect within a family [6], [8]. Superior past
performance, therefore, increases cash inflow into
both the fund and its family. Similar results are
found in SRI families, [5], [9], [10].
This paper examines the effect of star funds on
overall family flow and flows to their peer funds in
four emerging markets: Malaysia, Saudi Arabia,
Indonesia, and Pakistan. We further compare the
spillover effect of Islamic and conventional star
(poor) funds. These four markets are selected
because they have the largest Islamic AUM and
most Islamic funds as of Q1/2017, [11].
In the next section, we briefly present the Islamic
fund management industry in the four sample
countries. We then review related literature, explain
our methodology, and discuss the findings in the
following sections. The final section concludes.
2 Islamic Fund Management
Industries in Saudi Arabia,
Malaysia, Indonesia, and Pakistan
Saudi Arabia and Malaysia are at the forefront of
the emerging Islamic fund management industry.
They have made great efforts to increase awareness
of Islamic finance and Islamic asset management,
[12]. Both pioneers are trailed by Muslim and non-
Muslim majority countries. Ireland, the United
States, Luxembourg, Pakistan, Kuwait, and South
Africa are among the countries that have witnessed
the immense growth of the Islamic asset
management industry. Excluding Malaysia and
Saudi Arabia, Luxembourg, Mauritius, Ireland,
Kuwait, Pakistan, and the Cayman Islands are the
leading countries in terms of the number of Islamic
funds, [3].
2.1 Saudi Arabia
Saudi Arabia is the largest economy in the Middle
East, enabling the development of a large mutual
fund industry, [13]. It is currently the largest Islamic
financial market, housing diverse types of
institutions offering a broad range of financial
products. Its financial system is bank-centric, as 11
banks account for more than half of its financial
system’s assets. Most investment funds of the Gulf
Cooperation Council (GCC) are domiciled in the
country. The Capital Market Authority (CMA) of
Saudi Arabia reports that Saudi Arabia has 607
funds in 2020, compared to 270 in 2015. The asset
management industry was valued at $124.28 billion
in 2020 and $98 billion in 2015. The funds offer the
opportunity to invest in various asset classes from
local to international levels. As of 2020, Saudi
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Arabia has 41 fund management companies (FMC),
compared to 33 in 2015.
Saudi Arabia established its Islamic mutual fund
industry in 1992. Its Islamic financial institutions
have begun offering various financial instruments to
better respond to the demands of citizens. Savings
and current accounts and financial deposits are
supplemented with investment programs and other
Islamic financial services that comply with Islamic
law.
2.2 Malaysia
The Malaysian Unit Trust Limited was established
in 1959, [14]. It only issued its first mutual fund in
1966 after being renamed Asia Unit Trust Berhad.
The mutual fund industry further grew with the
establishment of Permodalan Nasional Berhad
(PNB) in 1979 and the introduction of the Skim
Amanah Saham Nasional (ASN) fund in 1981. The
issuance of Amanah Saham Bumiputera (ASB) in
1991 further propelled the industry’s growth. In
1991, Tabung Ittikal, the first Islamic mutual fund,
was introduced by the Arab Malaysian Unit Trust
Berhad. The Development of the conventional and
Islamic mutual fund industries has been driven by
favorable legal and tax policies. The Securities
Commission (SC) reported that the mutual fund
industry in 2020 was valued at $188.91 billion in
2020 and $160.81 billion in 2015. During the same
period, the number of funds increased from 611 to
654. In 2019, 80 FMCs were operating in Malaysia,
compared to 51 in 2015. Malaysia currently ranks
second by contributing 31.7 percent, or $36.5
billion, to the global Islamic AUM with $36.5
billion.
2.3 Pakistan
ABAMCO Ltd. (now JS Investment Ltd.) launched
the first IMF in Pakistan in 2002. The net assets of
Pakistan’s IMF increased fifteen-fold between 2003
and 2008. However, the 2008 financial crisis and
less than complementary tax policies have severely
curbed subsequent growth. The mutual fund
industry was valued at $4 billion in 2020 and
doubled by over $2 billion in 2015. During the same
period, the number of funds increased from 221 to
255. Pakistan had 30 FMCs in 2019, increasing
from 22 in 2015. In 1995, Al Meezan Investment
Management Limited launched its first closed-end
fund. It became the first full-fledged Islamic-
compliant asset management firm in 2003,
subsequently launching its first Islamic fund. Gross
Islamic equity assets grew to $3.6 billion in 2008
from $800 million in 1996, [15]. Islamic equity
funds grew from 29 in 1996 to 232 in 2009.
Pakistan contributes 2.3 percent ($2.4 billion) to the
global Islamic AUM.
2.4 Indonesia
The Indonesian mutual fund industry was only
established in 1996 with 25 funds and an AUM of
$297.3 million, [16]. While there is now a range of
Shariah-compliant mutual funds based in Indonesia,
these are generally much younger and smaller than
the funds in Malaysia or Saudi Arabia, partly
because of an almost exclusive focus on local
investors. Currently, about 12 FMCs in Indonesia
offer Islamic mutual funds. Most of these are
financial institutions that are already active in other
types of Islamic products, [17]. According to the
Financial Services Authority of Indonesia, the asset
management industry stood at $38.98 billion in
2020, up 11.01 percent from $30.34 trillion at the
end of 2017. Indonesia had 86 FMCs at the end of
2019, compared to 77 FMCs as at the end of 2017.
At the end of August 2020, IMFs comprised 10.51
percent of the mutual fund industry, up from 10.24
percent in December 2017. The 210 Islamic mutual
funds, 28 of which were launched in 2020, have a
total net asset value of $2.05 billion or 6.31 percent
of the overall market.
3 Literature Review
Since the 1990s, there has been a growing body of
research on mutual fund performance, fund inflows,
and the behavior of mutual fund investors. Past fund
performance is an important determinant of investor
behavior; investors typically favor superior-
performing funds. There have also been studies on
how investors respond to expenses when investing
in mutual funds. Studies on fund performance and
flows in developed markets show that fund
performance and fund money inflow are positive
and asymmetric, [18], [19], [20], [21], [22]. Recent
research on fund performance and flow of corporate
bond funds in the US suggests that their flows
respond to their performance. However, sensitive
convexity is not found in the relationship between
both variables, [23]. Compared to conventional
funds, the flow-performance relationship in socially
responsible investment (SRI) funds is much weaker.
The loyalty of ethical and traditional investors is
also comparatively similar, [24].
Studies in emerging markets reveal a convex
relationship between fund flow and performance for
funds that invest in emerging market economies. In
other words, fund inflow increases when past
performance is positive, and vice versa, [25].
Islamic fund investors in Malaysia are more
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sensitive to poorly performing funds, suggesting
that they rationally select funds. The investors
demonstrate that they chase the best-performing
funds. Flow and performance relationship in Islamic
and conventional funds is also asymmetric and
convex, [26]. Flow-performance relationship in SRI
funds is asymmetric, as investors react less
aggressively to negative returns than positive
returns. There is also evidence for an asymmetric
flow-performance relationship in Sharia-compliant
funds, [27].
Scholars find empirical evidence for the positive
spillover effect within a family, [8], [28], [29].
Superior-performing (star) funds within a family can
help to increase the flows to peer funds. Conversely,
poorly performing funds do not attract funds into the
family. [16], show that the market share of families
can be increased with the possession of at least one-
a star fund.
Past studies have examined the spillover effect of
a fund using different methods and determinants,
[5], [6], [19], [28], [30], [31]. Some investigate
whether advertising produces a spillover effect on
the cash inflows of new funds, [32], [33], [34], [35].
There is evidence that star funds produce a spillover
effect by increasing cash inflows to non-star peer
funds, [8]. The authors define star funds as those
funds in the top 5 percent of family-adjusted return.
Flows to families with at least a single-star fund are
significantly higher than to families without such a
fund. Star funds contribute positively to their flows
and flow to peer funds. But similar evidence is not
found for low-ranking funds. Proportional treatment
of individual funds in a family is possible by
understanding the spillover effect in fund families.
[36], find that a superior-performing fund improves
the reputation of a fund family among investors.
This brings new cash inflows to the star fund and its
peers. Additionally, star funds increase the fund
family’s market share.
Other studies examine how star funds benefit
fund families. The majority of these studies report
that star funds attract more inflows to their families
by increasing flows to themselves and their peers.
[5], examine how star funds benefit Korean fund
families in 2001–2009. They find that families with
star funds are able to attract new flows better than
those without any. Star funds also increase new
flows to non-star peers and new funds in the family.
[37], estimate the effect of star and poor funds on
the flows of Islamic and conventional fund families
in Saudi Arabia, Malaysia, Indonesia, and Pakistan.
The results indicate that star funds are positively
related to family flows, and so fund families can
advertise their superior-performing funds to attract
more investments. Poor funds, on the other hand,
are not significantly related to the flows of the
overall sample and Islamic families. However, they
are negatively related to conventional families.
These findings suggest that investors of Islamic
families are more loyal because of the additional
moral and religious goals of their investments
compared to conventional family investors.
The flow-performance relationship for SRI
families with star funds is likewise similar. [6] find
that star funds in SRI families increase monthly cash
inflows to their peers. There is no evidence for the
negative spillover effect of poor funds.
This study is the first to compare the effect of
star funds on the flows of Islamic and conventional
families in emerging markets, with a high focus on
Islamic funds. It bridges the knowledge gap relating
to the spillover effect of star (poor) funds to their
peers, [6]. The findings have important implications
for fund family managers on how to increase new
inflows and for investors on how to select the best
funds.
4 Methodology
4.1 Data
We examine a sample of 70 fund families (503
funds) in Saudi Arabia (25), Malaysia (20),
Indonesia (14), and Pakistan (11). Data are collected
from Bloomberg. Following, [38], [39], the sample
is fund families whose assets are mostly in equities.
We include only equity funds, i.e., funds with at
least 60 percent of their portfolio in equity. Fund
families are divided into Islamic and conventional
using the 33 percent benchmark, a screening
methodology commonly applied by index providers.
A fund family whose conventional equity funds
make up more than 33 percent of its funds is
classified as a highly conventional mutual fund
family. We label such a fund as conventional-fund-
focused families (CFF). If they are less than 33
percent, the fund families are considered high
Islamic mutual fund families. We label such a fund
as Islamic-fund-focused families (IFF).
Following [34], [40], [41], family performance is
measured as its overall return. To be precise, it is
measured as the weighted average net asset value of
all equity funds in a given family. The sample
period is January 2007 to December 2020.
Performance is benchmarked against the FTSE All-
World Index, which provides the largest coverage of
global equity markets, [42]. The US 3-month T-bill
rate is the risk-free rate.
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Fund family performance is calculated using
Carhart’s four-factor model. Fund performance is
then ranked to identify star (top 5%) and poor funds
(bottom 5%), [5], [6]. The model is expressed as:
(1)
where is the monthly returns of a mutual fund
family (weighted net asset value of equity funds in a
fund family), is the risk-free rate, is the
return of the market benchmark, is the return
on the portfolio of small minus big stocks listed in
the respective benchmark in time t, is the
return on the portfolio of a high minus low book-to-
market stocks listed in the respective benchmark in
time t, and is the rate of return on the
portfolio of a high minus low momentum (prior 1-
year return) stocks in the respective benchmark in
time t. Table 1 shows the number of star and poor
funds in IFF and CFF across the sample period.
Values in parentheses are the total number of funds.
Table 1. Summary of star and poor funds in IFF and
CFF
Year
Star
Family
(Fund)
Poor
Family
(Fund)
Star
IFF
(Fund)
Star
CFF
(Fund)
Poor
IFF
(Fund)
Poor
CFF
(Fund)
2007
19 (28)
16 (25)
14 (19)
5 (9)
10 (15)
6 (10)
2008
20 (27)
16 (25)
14 (21)
6 (11)
10 (16)
6 (9)
2009
19 (27)
17 (25)
11 (17)
8 (10)
14 (19)
3 (6)
2010
19 (26)
17 (25)
11 (16)
8 (10)
15 (21)
2 (4)
2011
16 (27)
19 (26)
10 (16)
6 (11)
16 (21)
3 (5)
2012
19 (29)
18 (26)
12 (19)
7 (10)
13 (17)
5 (9)
2013
18 (25)
20 (28)
10 (15)
8 (10)
15 (20)
5 (8)
2014
20 (29)
19 (28)
14 (19)
6 (10)
14 (19)
5 (9)
2015
15 (25)
20 (29)
8 (15)
7 (10)
16 (22)
4 (7)
2016
17 (25)
16 (25)
9 (14)
8 (11)
13 (18)
3 (7)
2017
15 (25)
17 (25)
10 (17)
5 (8)
12 (16)
5 (9)
2018
18 (26)
18 (27)
11 (16)
7 (10)
14 (18)
4 (9)
2019
16 (24)
17 (26)
10 (16)
5 (9)
13 (16)
5 (8)
2020
17 (24)
17 (26)
11 (18)
6 (10)
14 (17)
6 (8)
New money growth is the dependent variable. It is
defined as the net growth of net total assets from
new external money. Three steps are followed to
obtain this rate, [5], [6]. First, fund inflow to each
fund is computed using Eq. (2). Total cash inflow
into the family is then calculated using Eq. (3). New
money growth rate for a fund family in month t can
then be calculated using Eq. (4):
(2)
=
(3)
(4)
where is the total net asset value of fund i at
period t; is the total net asset value of
fund i at period ; and is the raw return of
fund i at period t.
Four dummy variables are used to estimate the
spillover effect of Islamic and conventional star
(poor) funds:
1. ISF equals one if an IFF has at least one-star
fund;
2. IPF equals one if an IFF has at least one poor
fund;
3. CSF equals one if a CFF has at least one-star
fund; and
4. CPF equals one if a CFF has at least one poor
fund.
We also use five control variables: family age,
family size, number of funds, historical family
returns, and total family risk. Because the sample
covers four markets, we also use economic variables
as control variables. These are industry age, GDP
per capita, turnover ratio, and common law.
4.2 Model
The analysis comprises two parts. First, we estimate
the effect of star and poor funds on overall family
flow. Second, we compare the effect of Islamic and
conventional star (poor) funds on overall family
flow and flow to peer funds. The panel regression
model for the first analysis is expressed as:
(5)
where is new cash inflow into a fund
family at a time t;
is the returns of a
family at time t-1; is the number of
funds managed by the fund family; is
family age measured in log years; is fund
family size; is total family risk; is the
GDP per capita at time t; is the share
turnover ratio; is a dummy variable that
equals one for a common-law country; is
industry age at time t; is a dummy
variable whose value is one if fund family f has at
least one-star fund; is a dummy
variable whose value is one if fund family f has at
least one poor fund; and is the error term.
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A positive coefficient means that
fund family f earns more cash inflow because it has
at least a one-star fund. In contrast, if
is negative, there is less cash inflow
(or even cash outflow) because fund family f has at
least one poor fund.
The second part of the analysis is estimated using
the following panel regression model:
(6)
(7)
where is the cash flow growth
rate for fund family f in period t. This is the sum of
cash flows to all equity funds, except for Islamic
(conventional) star (poor) funds, in the fund family
in period t, divided by the sum of net asset values of
all funds in the family except for Islamic
(conventional) star (poor) funds in period t.
is the risk-adjusted return of family f,
excluding Islamic (conventional) star (poor) funds,
estimated using Carhart’s four-factor model.
is a dummy variable that equals
one if fund family f has at least one Islamic or
conventional star fund. is a
dummy variable that equals one if fund family f has
at least one Islamic or conventional poor fund.
is a constant term whose value is fixed for
fund family f and is an error term with
average and variance.
If () in Eq.
(6) is positive (negative) and statistically significant,
then Islamic star (poor) fund(s) produce a spillover
(reverse spillover) to peer funds. A similar
interpretation for conventional funds is given for a
positive (negative) and significant value of
() in Eq. (7).
5 Results and Discussion
5.1 Descriptive Statistics
Table 2 shows the results of the Breusch-
Pagan/Cook-Weisberg and variance inflation factor
(VIF) tests. The Breusch-Pagan/Cook-Weisberg test
identifies heteroscedasticity in the dataset. Because
prob > chi2 is > 0.05, there is constant variance in
the data and the absence of heteroscedasticity. The
computed VIFs are far below the threshold of 10,
indicating the absence of multicollinearity in the
data.
Table 3 shows the descriptive statistics for all
variables. There is positive new money growth for
the entire sample (M = 0.319). On average, the
sample families have been operating for 18 years. A
fund family has seven funds on average with a net
asset value of $1,850.6 million. The average total
risk is 0.330. All countries have negative returns for
the current month (M = -0.073) and the previous
month (M = -0.032). New money growth is positive
for Saudi Arabia (M = 0.236) and Malaysia (M =
0.763) but negative for Indonesia (M = -0.165) and
Pakistan (M = -0.315).
Table 2. Breusch-Pagan/ Cook-Weisberg and VIF
Test
Variables
VIF
1/ VIF
Heteroscedasticity
test
Family Age
1.40
0.7134
H0: Constant
variance
Prob > Chi2 =
0.092
Number of
Funds
1.30
0.7717
Family Size
1.14
0.8770
Past Flows
1.13
0.8866
Dummy
Star
-----
-----
Dummy
Poor
-----
-----
Past
Performance
1.04
0.9593
Total Risk
1.07
0.8954
Mean VIF
1.18
-----
Only Saudi Arabia has positive current (M =
0.157%) and previous month (M = 0.157%) returns.
Other countries experience negative returns for both
current and previous months. Malaysia has the least
negative current (M = -0.125) and lagged one-
month (M = -0.098) returns, while Pakistan has the
highest negative returns for both. Saudi Arabia at
the same time has the highest return volatility (M =
0.158). The second-most volatile market is
Indonesia (M = 0.07), followed by Pakistan (M =
0.06) and Malaysia (M = 0.05). In other words,
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Saudi Arabia has the highest risk among the sample
countries, while Malaysia has the lowest. Malaysia
has the oldest families (26 years), followed by
Indonesia (18 years), Saudi Arabia (14 years), and
Pakistan (11 years).
Malaysia and Pakistan have the highest average
number of funds (8), while Indonesia and Saudi
Arabia have six funds on average. While the
industry is the youngest in Pakistan, it follows an
aggressive strategy to introduce new funds to the
market, hence its high average. Consistent with its
age and number of funds, Malaysia’s fund family
size is the largest, with a total net asset value of
$2,267.2 million. Placing second is Saudi Arabia
($2,214.1 million), followed by Pakistan ($1,781
million), and Indonesia ($1,530 million). Saudi
Arabia has the lowest total risk (M = 0.259) while
Pakistan has the highest risk (M = 0.419), reflecting
recent regulations and policies concerning the
mutual fund industry.
Table 3 also reports new money growth for the
overall IFF and CFF sample and by country.
Overall, both IFF (M = 0.216) and CFF (M = 0.07)
have positive money growth, which means that they
receive positive net inflow during the sample period.
New money growth is larger for IFF, perhaps
because the market leaders, Malaysia and Saudi
Arabia, have a large number of IFFs and Islamic
funds. New money growth is positive for both IFF
(M = 0.762) and CFF (M = 0.321) in Malaysia.
Pakistan, however, reports negative new money
growth for both IFF (M = -0.240) and CFF (M = -
0.381). In other words, both receive negative net
inflow during the sample period. In Indonesia, new
money growth is positive for IFF (M = 0.107) and
negative for CFF (M = -0.14).
IFF has the highest returns in Saudi Arabia (M =
0.157). Malaysian IFF ranks second (M = -0.016),
followed by Indonesia (M = -0.106) and Pakistan
(M = -0.155). IFF is most mature in Malaysia (27
years), followed by Indonesia (19 years), Saudi
Arabia (14 years), and Pakistan (9 years). Malaysia
also has the most funds in IFF (9 funds), followed
by Pakistan (8), Saudi Arabia (6), and Indonesia (6).
Saudi Arabia leads in IFF asset value with $2,214
million, followed by Malaysia ($1,807 million),
Indonesia ($1,129 million), and Pakistan ($1,120
million). On average, lagged one-month flows are
highest in Pakistan (M = -0.02), then Malaysia (M =
-0.07), Indonesia (M = -0.36), and Saudi Arabia (M
= -0.58).
IFF has higher current month (M = -0.003) and
lagged one-month (M = 0.005) returns compared to
CFF (M = 0.147 and M = -0.147). While returns are
negative for the overall sample, IFF still performs
better than CFF. IFF (M = 17.73 years) is similar in
age to CFF (M = 17.76 years). The average number
of funds is relatively similar (IFF = 7.1, CFF = 7.2).
IFF has a larger net asset value (M = $1,522.5
million) than CFF (M = $1,270 million). On
average, IFF experiences lower money outflow (M
= -0.344) than CFF (M = -0.51).
IFFs report better monthly and lagged one-month
returns than CFFs in all four countries. IFFs are also
older than CFFs in Malaysia and Indonesia. CFFs
are four years older than IFFs in Pakistan. This
suggests the advantage of IFFs over CFFs in
Malaysia and Indonesia. IFFs are larger than CFFs
in Malaysia and Indonesia, while the opposite is true
in Pakistan. IFFs in Malaysia have an average of
nine funds, while CFFs are seven funds. The net
asset value of IFFs is $1,806 million while CFFs are
$971 million. In Indonesia, IFFs have more funds
(6) than CFFs (6). The net asset value of the former
is $1,129 million while the latter is $932 million.
Pakistani IFFs, on the other hand, have fewer funds
(8) than CFFs (8). IFFs also have a lower net asset
value ($1,120 million) than CFFs ($1,514 million).
Money outflow from IFFs is lower than CFFs for all
countries. Pakistani IFFs have the least outflow (M
= -0.019), followed by Malaysia (M = -0.069) and
Indonesia (M = -0.359). Overall, IFFs in Malaysia
and Saudi Arabia are superior to CFFs in most
variables, including performance.
5.2 Correlation
Table 4 shows the pairwise correlations between the
research variables and new money growth. Family
age, the number of funds, star dummy, and
historical returns correlate positively with new
money growth. This means that older families,
larger families, families with at least one-star fund,
and families with positive historical performance are
more likely to attract new inflows. Family size, total
risk, and poor dummy correlate negatively with new
money growth. This means that smaller families (in
terms of net asset value), riskier families, and
families with at least one poor fund are less likely to
attract new inflows. Family performance and past
family performance are also strongly and positively
correlated.
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Table 3. Descriptive statistics
All
High IMF family
High CMF family
Equality of
variance
Equality of
means
N
Mean
SD
N
Mean
SD
N
Mean
SD
t-stat
p
t-stat
p
Saudi Arabia
N. Fund
162
--
--
162
--
--
--
--
--
--
--
--
--
N. fam
25
--
--
25
--
--
--
--
--
--
--
--
--
Fam Ret
25
0.157
0.158
25
0.157
0.158
--
--
--
--
--
--
--
N.M.G
25
0.236
1.753
25
0.236
1.753
--
--
--
--
--
--
--
Fam age
25
13.86
2.267
25
13.86
2.267
--
--
--
--
--
--
--
Fund/fam
25
6.48
2.777
25
6.48
2.777
--
--
--
--
--
--
--
Fam size
25
2214.1
410.4
25
2214.1
410.4
--
--
--
--
--
--
--
P. return
25
0.156
0.158
25
0.156
0.158
--
--
--
--
--
--
--
Total risk
25
0.259
0.221
25
0.259
0.221
--
--
--
--
--
--
--
Malaysia
N. Fund
170
--
--
88
--
--
82
--
--
--
--
--
--
N. fam
20
--
--
11
--
--
9
--
--
--
--
--
--
Fam Ret
20
-0.125
0.045
11
-0.016
0.044
9
-0.044
0.048
0.112
0.456
1.893
0.031
N.M.G
20
0.563
1.613
11
0.762
1.487
9
0.321
1.161
3.623
0.001
4.793
0.000
Fam age
20
26.4
1.408
11
26.83
1.792
9
25.1
1.153
2.215
0.014
1.508
0.067
Fund/fam
20
8.5
2.682
11
8.866
2.372
9
7.4
2.437
-1.24
0.134
-2.93
0.002
Fam size
18
2267.2
582.7
10
1806.9
548.5
9
971.1
151.1
11.27
0.000
11.38
0.000
P. return
20
-0.125
0.045
10
-0.125
0.044
9
-0.124
0.048
0.360
0.359
2.029
0.022
Total risk
20
0.335
0.147
11
0.327
0.157
9
0.344
0.134
-0.88
0.189
-0.63
0.026
Indonesia
N. Fund
83
--
--
50
--
--
33
--
--
--
--
--
--
N. fam
14
--
--
8
--
--
6
--
--
--
--
--
--
Fam Ret
14
-0.133
0.067
8
-0.106
0.072
6
-0.147
0.054
3.854
0.000
3.777
0.000
N.M.G
13
-0.037
1.438
8
0.107
1.402
6
-0.140
1.383
2.280
0.012
3.434
0.000
Fam age
14
17.78
2.120
8
18.94
2.995
6
15.7
2.746
2.026
0.022
3.780
0.000
Fund/fam
14
5.929
1.036
8
6
1.253
6
5.80
0.403
1.032
0.152
1.884
0.032
Fam size
12
1530.3
218.4
7
1129.1
144.7
6
931.9
143.1
9.314
0.000
7.19
0.000
P. return
14
-0.132
0.066
8
-0.125
0.071
6
-0.145
0.055
3.863
0.000
3.742
0.000
Total risk
14
0.378
0.177
8
0.396
0.182
6
0.355
0.169
0.497
0.068
0.048
0.015
Pakistan
N. Fund
87
--
--
44
--
--
43
--
--
--
--
--
--
N. fam
11
--
--
6
--
--
5
--
--
--
--
--
--
Fam Ret
11
-0.193
0.055
6
-0.155
0.047
5
-0.163
0.061
1.899
0.030
1.917
0.030
N.M.G
10
-0.315
0.755
6
-0.240
0.720
5
-0.381
0.833
1.661
0.049
2.886
0.002
Fam age
11
10.77
3.328
6
9.33
2.428
5
12.50
3.922
-2.95
0.002
-4.65
0.000
Fund/fam
10
7.909
3.269
6
7.50
4.750
5
8.40
3.585
-1.21
0.115
-0.59
0.278
Fam size
10
1780.8
554.2
6
1119.6
536.7
5
1514
555
7.621
0.000
8.429
0.000
P. return
11
-0.164
0.055
6
-0.157
0.047
5
-0.176
0.062
1.497
0.068
1.449
0.076
Total risk
11
0.419
0.269
6
0.429
0.280
5
0.406
0.257
0.498
0.310
-0.49
0.311
All countries
N. Fund
502
--
--
344
--
--
158
--
--
--
--
--
--
N. fam
70
--
--
50
--
--
20
--
--
--
--
--
--
Fam Ret
70
-0.073
1.235
50
-0.003
0.184
20
-0.147
0.057
1.096
0.000
2.340
0.000
N.M.G
70
0.319
0.083
50
0.216
1.472
20
0.066
1.417
5.028
0.000
5.597
0.000
Fam age
70
17.74
1.421
50
17.73
1.421
20
17.76
2.455
-2.26
0.012
-1.97
0.000
Fund/fam
68
7.183
2.453
49
7.164
2.296
20
7.200
2.721
-3.58
0.000
-3.12
0.000
Fam size
68
1850.6
430.5
50
1522.5
446.2
19
1270
419.5
32.42
0.000
43.83
0.000
P. return
70
-0.032
0.051
49
-0.005
0.184
20
-0.147
0.058
3.051
0.000
2.697
0.000
Total risk
70
0.330
0.211
50
0.317
0.221
20
0.363
0.183
-2.19
0.014
-4.97
0.000
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Table 4. Correlation matrix
New
money
growth
Family
returns
Family
age
Number
of funds
Family
size
D star
D poor
Past
returns
Total
risk
New money growth
1.000
Family returns
0.210
1.000
Family age
0.199
0.1177
1.000
Number of funds
0.217
-0.135
0.187
1.000
Family size
-0.080
0.0858
-0.203
0.345
1.000
D star
0.714
0.1866
0.060
0.190
-0.056
1.000
D poor
-0.043
-0.1842
0.075
0.192
-0.038
-0.010
1.000
Past returns
0.152
0.9266
-0.124
-0.141
0.073
0.029
-0.150
1.000
Total risk
-0.032
-0.021
-0.117
-0.150
-0.019
-0.003
0.011
-0.227
1.000
6 Empirical Results
6.1 Part One: Effect of Star (Poor) Funds
on Overall Fund Family Flows
6.1.1 Overall Family
In this section, we examine the effect of having at
least one star (poor) fund on overall family flow.
We estimate this effect by regressing new money
growth on the dummy variables of the star fund and
poor fund. Table 5 shows the regression results for
all countries and by country.
Past family returns and lagged family returns are
positively and significantly related to new money
growth. These support evidence in the literature on
the positive performance-flow relationship at the
fund and family levels, [8], [31], [43], [44], [45],
[46], [47], [48], [49], [50], [51], [52], [53]. Because
new money growth is positively linked to the
strategy of generating a star fund, we conclude that
investing in a fund family implies that high-risk
investment yields high returns. The dummy star
variable positively and significantly affects new
money growth. In other words, star fund(s) can
increase the new inflow of funds to the family.
However, the dummy poor variable is not
significant, suggesting that poor fund(s) does not
lead to family outflows. According to, [31], [44],
fund performance does not lead to money outflows
from the funds, likely due to the cognitive
dissonance of investors. Our results also support,
[5], [8] at the family level.
Country-level analysis shows that past family
returns are positive and significant predictors of new
money growth for all countries. The dummy star
variable is likewise positive and significant,
indicating that star funds increase the overall
inflows of fund families in all four markets. Saudi
Arabia has the largest coefficient (B = 0.972, p <
0.05), while Pakistan has the smallest (B = 0.644, p
< 0.05). Indonesia (B = 0.912, p < 0.05) and
Malaysia (B = 0.922, p < 0.05) have comparable
coefficients. This means that having at least one-star
fund increases fund family inflows by 0.644–0.972
units in those respective countries.
Except for Indonesia, the dummy star coefficient
is not significant for all countries. The variable is
negative and significant for Indonesia, B = - 0.264, p
< 0.05. This means that having at least one poor
fund can lead to money outflows from the fund
family. A likely reason for this is because
Indonesian investors are still unsophisticated, and so
they quickly withdraw their investments in losing
funds. In contrast, poor funds in the three other
sample countries do not lead to family outflows.
This means that investors in those markets do not
withdraw their investments from poor families.
6.1.2 IFF vs. CFF
This section compares the effect of star (poor) funds
on the overall outflows of IFF and CFF. Table 5
presents the results. The results for Saudi Arabia are
similar to the results in the previous section, seeing
that Islamic funds comprise more than two-thirds of
their fund family portfolios. Therefore, the focus
will be given only to the three other sample
countries.
Star funds have a positive and significant effect
on new money growth for both IFF (B = 0.981, p <
0.05) and CFF (B = 0.324, p < 0.05), which means
that their presence increases inflows to both types of
families. Both IFF and CFF can advertise their star
funds to attract more inflows. While poor funds
negatively affect new money growth, this
relationship is not significant. These results are
consistent with the previous section. Investors of
both IFF and CFF, in general, do not withdraw their
investments from families with poor funds.
Country analysis shows that IFF and CFF
results in Malaysia and Pakistan are consistent with
the overall sample analysis. Star fund positively
influences new money growth in Malaysian IFF (B
= 0.812, p < 0.05) and CFF (B = 0.67, p < 0.05) and
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Pakistani IFF (B = 0.458, p < 0.05) and CFF (B =
0.725, p < 0.05). In Indonesia, a star fund in a CFF
is positively related to its new money growth (B =
0.721, p < 0.05), while a poor fund is negatively
related to its new money growth (B = -0.005, p <
0.05).
Table 5. Regression Estimates
Variables
All MF family
IMF family
CMF family
Difference
Saudi Arabia
Constant
-0.383
(0.129)
-0.383
(0.129)
---
---
Family Age
0.034
(0.049)*
0.034
(0.049)*
---
---
Number fund
0.077
(0.022)*
0.077
(0.022)*
---
---
Family Size
0.234
(0.186)
0.234
(0.186)
---
---
Dummy Star
0.972
(0.000)**
0.972
(0.000)**
---
---
Dummy Poor
-0.535
(0.620)
-0.535
(0.620)
---
---
Past Family Returns
0.163
(0.018)**
0.163
(0.018)**
---
---
Total Risk
-0.502
(0.003)**
-0.502
(0.003)**
---
---
GDP per Capita
0.3061
(0.829)
0.3061
(0.829)
---
---
Turnover Ratio
0.2477
(0.315)
0.2477
(0.315)
---
---
Common Law
0.1547
(0.321)
0.1547
(0.321)
---
---
Industry Age
0.5558
(0.237)
0.5558
(0.237)
---
---
Prob > F
0.0000
0.0000
---
---
Adjusted.
0.62
0.62
---
---
Malaysia
Constant
-0.592
(0.010)
-0.556
(0.014)
0. 687
(0.442)
-0.830
(0.000)
Family Age
0.019
(0.000)**
0.058
(0.001)**
-0.007
(0.587)
0.012
(0.002)**
Number fund
0.045
(0.794)
-0.028
(0.546)
0.053
(0.024)**
-0.004
(0.874)
Family Size
0.253
(0.365)
0.622
(0.009)**
0.077
(0.319)
0.841
(0.001)**
Dummy Star
0.922
(0.000)**
0.812
(0.005)**
0.670
(0.037)**
0.0696
(0.000)**
Dummy Poor
-0.017
(0.153)
-0.016
(0.580)
-0.015
(0.375)
-0.041
(0.730)
Past Family Returns
0.564
(0.0501)*
0.244
(0.045)*
0.443
(0.031)*
0.612
(0.412)*
Total Risk
-0.323
(0.410)
-0.016
(0.981)
-0.043
(0.016)**
-0.015
(0.031)*
GDP per Capita
0.7503
(0.563)
0.5213
(0.403)
0.3201
(0.096)
----
Turnover Ratio
0.3604
(0.729)
0.2019
(0.522)
0.6702
(0.626)
----
Common Law
0.0325
(0.020)*
0.3505
(0.408)
0.1932
(0.020)*
----
Industry Age
0.6330
(0.794)
-0.3311
(0.574)
0.3290
(0.653)
----
Prob > F
0.0011
0.0000
0.0001
0.0000
Adjusted.
0.74
0.67
0.66
0.59
Indonesia
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Variables
All MF family
IMF family
CMF family
Difference
Constant
-0.943
(0.017)
-0.796
(0.044)
-0.078
(0.000)
-0.224
(0.004)
Family Age
0.024
(0.000)**
0.048
(0.000)**
0.023
(0.218)
0.011
(0.002)*
Number fund
0.074
(0.170)
0.150
(0.013)*
0.056
(0.461)
0.038
(0.400)
Family Size
0.896
(0.424)
0.727
(0.732)
0.664
(0.000)*
0.079
(0.009)*
Dummy Star
0.912
(0.000)**
0.710
(0.000)**
0.721
(0.000)**
0.729
(0.000)**
Dummy Poor
-0.264
(0.018)*
-0.495
(0.527)
-0.005
(0.033)*
-0.069
(0.688)
Past Family Returns
0.319
(0.002)**
0.850
(0.022)**
0.636
(0.006)**
0.644
(0.038)**
Total Risk
-0.091
(0.002)*
-0.436
(0.445)
-0.808
(0.000)*
-0.243
(0.001)*
GDP per Capita
0.9152
(0.562)
0.8451
(0.296)
0.4584
(0.256)
----
Turnover Ratio
0.5770
(0.030)*
0.5732
(0.019)*
0.6810
(0.047)*
----
Common Law
-0.0216
(0.914)
-0.2243
(0.732)
-0.1922
(0.617)
----
Industry Age
-0.7288
(0.356)
-0.8058
(0.431)
-0.8010
(0.531)
----
Prob > F
0.0001
0.0005
0.0000
0.0000
Adjusted.
0.63
0.55
0.57
0.52
Pakistan
Constant
-0.750
(0.248)
-0.407
(0.169)
-0.444
(0.618)
-0.079
(0.889)
Family Age
-0.005
(0.601)
0.043
(0.081)
-0.027
(0.007)*
0.083
(0.002)*
Number fund
0.012
(0.412)
-0.036
(0.116)
0.002
(0.956)
0.003
(0.934)
Family Size
0.908
(0.362)
0.471
(0.405)
0.629
(0.172)
0.074
(0.307)
Dummy Star
0.644
(0.000)**
0.458
(0.010)**
0.725
(0.000)**
0.571
(0.000)**
Dummy Poor
-0.074
(0.587)
-0.204
(0.439)
-0.152
(0.326)
-0.197
(0.343)
Past Family Returns
0.454
(0.023)*
0.316
(0.010)*
0.591
(0.048)*
0.551
(0.005)*
Total Risk
-0.248
(0.277)
-0.484
(0.152)
-0.086
(0.765)
-0.337
(0.023)*
GDP per Capita
-0.4694
(0.734)
-0.4434
(0.734)
-0.4694
(0.734)
----
Turnover Ratio
-0.2089
(0.241)
-0.5219
(0.287)
-0.2839
(0.4221)
----
Common Law
-0.0177
(0.510)
-0.1701
(0.351)
-0.2307
(0.470)
----
Industry Age
0.5946
(0.489)
0.1856
(0.549)
0.6546
(0.631)
----
Prob > F
0.0000
0.0000
0.0001
0.0012
Adjusted.
0.62
0.59
0.69
0.56
All countries
Constant
-0.466
(0.068)
-0.684
(0.360)
-0.525
(0.206)
-0.061
(0.633)
Family Age
0.014
(0.000)*
0.027
(0.002)**
-0. 021
(0.532)
0.020
(0.000)*
Number fund
0.012
(0.027)*
0.003
(0.046)*
-0.014
(0.620)
0.028
(0.079)
Family Size
0.386
(0.422)
0.034
(0.979)
0.433
(0.028)*
0.387
(0.001)**
Dummy Star
0. 940
(0.000)**
0.981
(0.000)**
0.324
(0.000)**
0.763
(0.000)**
Dummy Poor
-0.172
(0.521)
-0.280
(0.209)
-0.078
(0.584)
-0.307
(0.056)
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.95
Jamileh Ali Mustafa, Anas Ahmad Bani Atta,
Ahmad Yahiya Bani Ahmad,
Maha Shehadeh, Ria Agustina
E-ISSN: 2224-2899
1052
Volume 20, 2023
Variables
All MF family
IMF family
CMF family
Difference
Past Family Returns
0.149
(0.042)**
0.242
(0.025)**
0. 107
(0.046)**
0.040
(0.004)**
Total Risk
-0.018
(0.010)*
-0.189
(0.049)*
-0.469
(0.241)
-0.085
(0.044)*
GDP per Capita
0.3811
(0.068)
-0.2850
(0.518)
0.4054
(0.301)
----
Turnover Ratio
-0.0072
(0.050)*
-0.0152
(0.033)*
-0.2336
(0.321)
----
Common Law
0.0228
(0.794)
0.0893
(0.044)*
0.0149
(0.921)
----
Industry Age
0.9616
(0.148)
-0.5878
(0.234)
-0.4001
(0.893)
----
Prob > F
0.0000
0.0000
0.0000
0.0000
Adjusted.
0.72
0.69
0.68
0.63
This suggests that poor funds lead to money
outflows from CFF. These results are similar to
those of the overall Indonesian sample. Indonesian
investors are perhaps still unsophisticated and seek
to dispose of poor funds. In the case of Indonesian
IFF, the results are similar to Malaysia and Pakistan:
the star fund positively affects new money growth
(B = 0.71, p < 0.05) but the poor fund has no
significant relationship with it.
6.1.3 Economic Variables
Four economic variables are used as control
variables: GDP per capita (economic development),
share turnover ratio (financial development),
common law (investor protection), and industry age
(mutual fund industry development). Fund families
are more likely to invest in equity markets of
countries with higher economic development,
familiarity, and investor protection due to their
lower fixed costs. Funds that are more mature and
have more experience investing in a given market
also enjoy lower fixed costs, [16].
Economic development is correlated to income
per capita, education, and skills. More developed
economies also have more advanced sectors and
innovation and investment opportunities. Investors
in such economies are also more sophisticated,
which means that they closely monitor fund and
family performance, even exerting pressure on
performance management. Our results indicate that
GDP per capita is not significantly related to the
new money growth of IFF, CFF, and the overall
sample. This suggests that a more developed
economy is not necessarily associated with
additional family inflows or outflows.
More developed financial markets have
performance advantages because they are more
liquid and have lower transaction costs. However,
financial development is not related to new money
growth in Saudi Arabia, Malaysia, and Pakistan.
This suggests that more liquid markets do not
necessarily attract additional inflows into fund
families. In contrast, financial development has a
positive influence on new money growth of overall
fund families, IFF, and CFF in Indonesia. A 1
percent increase in share turnover ratio leads to an
increase in overall family flows by 0.03 percent, in
IFF flows by 0.01 percent, and in CFF flows by 0.04
percent.
Regulations and policies influence investor
behavior. Poor protection in a given market will
make them reluctant to invest in it. Markets with
weaker security for investors have fewer debts and
less developed stock markets. Legal system quality
is critical for contract enforcement, and it signals the
attitude of the attitude towards business. In
Malaysia, the common law dummy variable has a
positive influence on new money growth (B = 0.03,
p < 0.05), suggesting that legal origin leads to more
family inflows. However, this relationship is not
significant in the remaining sample countries. This
means that legal origin has no impact on the family
flow in these countries.
The mutual fund industry is a rapidly developing
financial intermediary. As an industry becomes
older, investors will be more experienced. The
greater the investment in mutual funds, the more
experienced managers will be, [16]. The mutual
fund industry becomes more efficient as it increases
in age, which may attract more investors. Our
results, however, show that industry age is not a
significant predictor of new money growth. In other
words, it does not affect fund family flows.
6.2 Part Two: Spillover Effect of Islamic
and Conventional Star (Poor) Funds
In this section, we examine whether having at least
one star (poor) fund can attract new inflows into
peer funds. The main explanatory variables are the
Islamic star fund (ISF) and poor fund (IPF)
conventional star fund (CSF) and poor fund (CPF).
The dependent variable is new money growth.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.95
Jamileh Ali Mustafa, Anas Ahmad Bani Atta,
Ahmad Yahiya Bani Ahmad,
Maha Shehadeh, Ria Agustina
E-ISSN: 2224-2899
1053
Volume 20, 2023
6.2.1 Spillover Effect of Islamic Star (Poor)
Funds
Most fund families have a combined portfolio of
Islamic and conventional funds. Examining Islamic
mutual funds is of great importance to Muslim
investors. Islamic star funds are important to attract
Muslim investors, as they are averse to funds that
contravene Islamic laws. Socially conscious non-
Muslim investors are also interested in Islamic
funds as their goals generally overlap with those of
socially responsible funds. Islamic funds are
therefore expected to have a positive spillover effect
on their peer funds.
Table 6 reports the regression results. The ISF
dummy variable is positive and significant, which
means that the Islamic star fund produces a spillover
effect to other funds within the same family. The
star funds attract additional flows to non-star peers.
On average, this spillover effect is 0.78 percent
higher than IFF without a star fund. This result is
consistent with, [8] (conventional funds) and, [6]
(SRI funds). By having a superior-performing fund,
IFFs signal their ability to generate profits for
Islamic investors. These investors place their
investments in non-star funds because they expect
the families to produce similar positive returns as
the star funds.
We then estimate whether Islamic poor funds
would lead to outflows from peer funds. We find
that the IPF dummy variable is negative but not
significant. This means that the negative spillover
effect of IPF is not meaningful. Islamic poor funds
do not affect the flow of peer funds. Similar results
are shown in the country analysis. ISF has a
significant positive effect on new money growth of
peer funds, while IPF has a non-significant effect. In
other words, ISF attracts new inflows to peer funds
in all four countries, whereas IPF does not drive
investors to dispose of their holdings in other funds.
6.2.2 Spillover Effect of Conventional Star
(Poor) Funds
Table 6 presents the regression results for CFFs.
The CSF (CPF) dummy variable equals one if there
is at least one star (poor) conventional fund in the
family. Similar to ISF, CSF has a significant
positive effect on the new money growth of peer
funds. This supports [8], [47]. The results are also
similar for the country analysis.
In contrast to IPF, CPF is a negative predictor of
new money growth of peer funds. This means that
investors generally withdraw their holdings in poor
funds to minimize losses. It follows that, on
average, poor performance leads to family outflows.
A possible explanation of this behavior is that
conventional fund investors can move their capital
to other conventional and Islamic funds, and so they
seek to maximize their returns. Muslim investors,
however, are restricted to only Islamic funds. This
finding contradicts, [5], who find that the poor
performance of conventional funds does not lead to
fundamental outflows.
7 Conclusion
This study contributes empirical evidence on the
effect of Islamic star (poor) funds on overall flows
to the fund family and peer funds (spillover effect)
in four Muslim-majority countries. These countries
are selected because they have the most Islamic
mutual funds in terms of quantity and asset size
globally. We present two novel contributions. First,
we find that families operating mostly Islamic funds
(IFF) outperform those operating mostly
conventional funds (CFF). Second, we compare the
spillover effects of Islamic and conventional star
(poor) funds to peer funds. The findings are
important because investors typically base their
decisions on the reputation of fund families, not
fund fundamentals. This is especially true in
emerging markets, where funds are young and have
short track records, providing little information to
investors to make sound investment decisions, [5],
[6].
Overall, we find that having at least-one star
fund leads to new money growth for fund families,
whereas no effect is found for having at least one
poor fund. In other words, star funds increase new
inflows to fund families, but poor funds do not lead
to funding family outflows. Country analysis reveals
a similar effect in all countries except Indonesia,
where poor funds lead to family outflows. Taken
together, these results indicate that fund family
investors are generally sophisticated and perseverant
in realizing their gains.
The spillover effect from Islamic star (poor)
funds to peer funds is asymmetric. Specifically, the
spillover effect is only found in the presence of an
Islamic star fund. Islamic star funds thus contribute
positively to peer inflows. This result is consistent
with [8], [9]. More importantly, Islamic poor funds
do not lead to outflows from peer funds, suggesting
that Islamic investors are less sensitive to poor
performance and more loyal to Islamic funds.
In contrast, the spillover effect from the
conventional star (poor) funds to peer funds is
symmetric. In other words, the spillover effect is
found in the presence of stars and poor funds. Star
funds produce a positive spillover effect, attracting
more inflows to peer funds. Conversely, poor funds
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.95
Jamileh Ali Mustafa, Anas Ahmad Bani Atta,
Ahmad Yahiya Bani Ahmad,
Maha Shehadeh, Ria Agustina
E-ISSN: 2224-2899
1054
Volume 20, 2023
lead to outflows from peer funds. Conventional
investors are more sensitive to poor performance,
likely because they can quickly shift their capital to
other investments. On the other hand, Islamic
investors are limited in their choice of investment,
as it must be compliant with the rules of Shariah,
and so their capital movement is more restricted.
Two general recommendations are proposed. First,
because of the importance and advantages of
funding families, future works may extend this
research to other countries, especially emerging
countries. They may also consider investigating
Islamic funds in non-Muslim-majority countries.
Table 6. Spillover effect
Variables
All MF family
IMF family
CMF family
Difference
Saudi Arabia
Constant
-0.383
(0.129)
-0.383
(0.129)
---
---
Family Age
0.034
(0.049)*
0.034
(0.049)*
---
---
Number fund
0.077
(0.022)*
0.077
(0.022)*
---
---
Family Size
0.234
(0.186)
0.234
(0.186)
---
---
Dummy Star
0.972
(0.000)**
0.972
(0.000)**
---
---
Dummy Poor
-0.535
(0.620)
-0.535
(0.620)
---
---
Past Family Returns
0.163
(0.018)**
0.163
(0.018)**
---
---
Total Risk
-0.502
(0.003)**
-0.502
(0.003)**
---
---
Prob > F
0.0000
0.0000
---
---
Adjusted.
0.62
0.62
---
---
Malaysia
Constant
-0.592
(0.010)
-0.556
(0.014)
0. 687
(0.442)
-0.830
(0.000)
Family Age
0.019
(0.000)**
0.058
(0.001)**
-0.007
(0.587)
0.012
(0.002)**
Number fund
0.045
(0.794)
-0.028
(0.546)
0.053
(0.024)**
-0.004
(0.874)
Family Size
0.253
(0.365)
0.622
(0.009)**
0.077
(0.319)
0.841
(0.001)**
Dummy Star
0.922
(0.000)**
0.812
(0.005)**
0.670
(0.037)**
0.0696
(0.000)**
Dummy Poor
-0.017
(0.153)
-0.016
(0.580)
-0.015
(0.375)
-0.041
(0.730)
Past Family Returns
0.564
(0.0501)*
0.244
(0.045)*
0.443
(0.031)*
0.612
(0.412)*
Total Risk
-0.323
(0.410)
-0.016
(0.981)
-0.043
(0.016)**
-0.015
(0.031)*
Prob > F
0.0011
0.0000
0.0001
0.0000
Adjusted.
0.74
0.67
0.66
0.59
Indonesia
Constant
-0.943
(0.017)
-0.796
(0.044)
-0.078
(0.000)
-0.224
(0.004)
Family Age
0.024
(0.000)**
0.048
(0.000)**
0.023
(0.218)
0.011
(0.002)*
Number fund
0.074
(0.170)
0.150
(0.013)*
0.056
(0.461)
0.038
(0.400)
Family Size
0.896
(0.424)
0.727
(0.732)
0.664
(0.000)*
0.079
(0.009)*
Dummy Star
0.912
(0.000)**
0.710
(0.000)**
0.721
(0.000)**
0.729
(0.000)**
Dummy Poor
-0.264
(0.018)*
-0.495
(0.527)
-0.005
(0.033)*
-0.069
(0.688)
Past Family Returns
0.319
(0.002)**
0.850
(0.022)**
0.636
(0.006)**
0.644
(0.038)**
Total Risk
-0.091
(0.002)*
-0.436
(0.445)
-0.808
(0.000)*
-0.243
(0.001)*
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.95
Jamileh Ali Mustafa, Anas Ahmad Bani Atta,
Ahmad Yahiya Bani Ahmad,
Maha Shehadeh, Ria Agustina
E-ISSN: 2224-2899
1055
Volume 20, 2023
Variables
All MF family
IMF family
CMF family
Difference
Prob > F
0.0001
0.0005
0.0000
0.0000
Adjusted.
0.63
0.55
0.57
0.52
Pakistan
Constant
-0.750
(0.248)
-0.407
(0.169)
-0.444
(0.618)
-0.079
(0.889)
Family Age
-0.005
(0.601)
0.043
(0.081)
-0.027
(0.007)*
0.083
(0.002)*
Number fund
0.012
(0.412)
-0.036
(0.116)
0.002
(0.956)
0.003
(0.934)
Family Size
0.908
(0.362)
0.471
(0.405)
0.629
(0.172)
0.074
(0.307)
Dummy Star
0.644
(0.000)**
0.458
(0.010)**
0.725
(0.000)**
0.571
(0.000)**
Dummy Poor
-0.074
(0.587)
-0.204
(0.439)
-0.152
(0.326)
-0.197
(0.343)
Past Family Returns
0.454
(0.023)*
0.316
(0.010)*
0.591
(0.048)*
0.551
(0.005)*
Total Risk
-0.248
(0.277)
-0.484
(0.152)
-0.086
(0.765)
-0.337
(0.023)*
Prob > F
0.0000
0.0000
0.0001
0.0012
Adjusted.
0.62
0.59
0.69
0.56
All countries
Constant
-0.466
(0.068)
-0.684
(0.360)
-0.525
(0.206)
-0.061
(0.633)
Family Age
0.014
(0.000)*
0.027
(0.002)**
-0. 021
(0.532)
0.020
(0.000)*
Number fund
0.012
(0.027)*
0.003
(0.046)*
-0.014
(0.620)
0.028
(0.079)
Family Size
0.386
(0.422)
0.034
(0.979)
0.433
(0.028)*
0.387
(0.001)**
Dummy Star
0. 940
(0.000)**
0.981
(0.000)**
0.324
(0.000)**
0.763
(0.000)**
Dummy Poor
-0.172
(0.521)
-0.280
(0.209)
-0.078
(0.584)
-0.307
(0.056)
Past Family Returns
0.149
(0.042)**
0.242
(0.025)**
0. 107
(0.046)**
0.040
(0.004)**
Total Risk
-0.018
(0.010)*
-0.189
(0.049)*
-0.469
(0.241)
-0.085
(0.044)*
Prob > F
0.0000
0.0000
0.0000
0.0000
Adjusted.
0.72
0.69
0.68
0.63
When comparing markets, it is important to
account for regulatory differences between mutual
fund industries of developed and emerging countries
and Muslim-majority and non-Muslim-majority
countries. Second, future works may consider
investigating other types of mutual funds, for
instance, balanced funds and fixed-income funds.
Other fund family attributes can also be included,
subject to data availability.
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Contribution of Individual Authors to the
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Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
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Scientific Article or Scientific Article Itself
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and Applied Science Private University for the
financial support granted to cover the publication
fee of this article.
Conflict of Interest
The authors have no conflict of interest to declare.
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.95
Jamileh Ali Mustafa, Anas Ahmad Bani Atta,
Ahmad Yahiya Bani Ahmad,
Maha Shehadeh, Ria Agustina
E-ISSN: 2224-2899
1058
Volume 20, 2023
