Fund Family Performance:
Evidence from Emerging Countries
AHMAD YAHIYA BANI AHMAD
Financial and Accounting Science Department, Faculty of Business,
Middle East University,
JORDAN
ANAS AHMAD BANI ATTA
Financial and Accounting Science Department, Faculty of Business,
Middle East University,
JORDAN
MAHA SHEHADEH
Finance and Banking Sciences Department,
Applied Sciences Privet University,
JORDAN
HAIDER MOHAMMED ALI BANIATA
School of Business,
Jadara University,
JORDAN
LAITH YOUSEF BANI HANI
Department of Commerce and Management Studies,
Andhra University,
INDIA
Abstract: - This study examines fund family performance, in terms of selectivity and market timing skills of
fund family managers, in Saudi Arabia, Malaysia, Indonesia, and Pakistan from 2007–2021. Selectivity skills
are measured using excess returns, Sharpe ratio, Treynor ratio, Jensen’s alpha, and Carhart’s four-factor model,
whereas market timing ability is measured using the Treynor-Mazuy and Henriksson-Merton models. The
analysis is carried out on three levels of sample: by entire sample, by country, and by Islamic vs conventional
families. The findings evince the good selectivity but poor timing skills of family managers. A novel
contribution of this study is that family managers of Islamic and conventional families have different selectivity
and timing skills, which can be attributed to the different goals of each type of family.
Keywords: Islamic Finance, Family Performance, Jensen’s Alpha, Four-Factor Model, Selectivity Skills,
Market Timing Ability.
Received: October 14, 2022. Revised: April 2, 2023. Accepted: April 25, 2023. Published: May 11, 2023.
1 Introduction
A collection of mutual funds offered by the same
issuer, typically an asset management company
(AMC), [1], sold under a common brand name, and
promoted via a common promotion and distribution
channel is known as a family of funds, [2]. Three
rationales underlie family-level analysis, [3]. First, a
fund family enables economies of scale in servicing,
promoting, and distributing funds. Second, a fund
family has the flexibility to reallocate its resources
to capitalize on market opportunities. Third, the
selectivity skills of family managers are peroxide by
the family’s reputation. Therefore, more reputable
families would assure investors that their managers
possess proficient selection and monitoring skills.
The increasing importance of fund family
analysis can be seen from reports classifying and
presenting fund family data and research (e.g.,
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Morningstar and Barron’s). These reports dedicate
at least one page to each fund family, pairing
qualitative and quantitative research with standard
managerial criteria. These reports aim to make
family data more accessible, verify the data
provided by the families, eliminate information
asymmetry, provide confidence to investors that the
families are acting in their best interests, and
improve the financial experience of investors. A
Morningstar study shows that positive-rated families
have positive historical returns for investors, as
measured by the Morningstar Risk-Adjusted
Success Ratio (MRAR). MRAR measures the
percentage of funds in a fund family that survives
within a certain period and has an MRAR superior
to the MRAR of a median fund in the same
category. The study maintains that fund families
with a positive MRAR can build and maintain long-
term trust and investor experience.
The global mutual fund industry had a total
asset under management (AUM) of $79.2 trillion in
2017, a 12% growth from $71 million in 2016.
Continuous growth is expected, eventually tripling
by 2025, [4].
Mutual funds are available in most markets
globally. They pool investor funds and allocate them
into a basket of securities, typically capital market
and money market instruments. This allows
investors to diversify without additional
administration, information collection, and
monitoring costs, among others, [5]. More than half
of current mutual funds are equity funds, followed
by fixed-income and real estate, and private equity
funds.
This study is motivated by the prevalence of
fund families. Examining the performance of
member funds allows the identification of high-
performing funds and among them star fund(s). The
overall performance of a fund family is altogether
different from the performance of individual funds,
considering that families offer diverse funds with
different objectives and strategies, [6], 7]. The
decision to invest in a fund depends on the attribute
of its family and the skill of family managers.
Member funds that perform well create a good
reputation for the family and signal the superior
skills of their managers, [8]. This paper thus
examines whether fund families can outperform
market benchmarks and whether their managers
demonstrate good market timing and selectivity
skills.
This study contributes evidence on fund family
performance to the literature, which has mostly
focused on individual fund performance. The
distinct characteristics of fund families as mentioned
above mean that member funds cannot be treated
like individual funds. Additionally, most investors
use a top-down approach when making investment
decisions: They will first select a family before
determining which member funds to invest in.
2 Literature Review
Fund families are investment firms that manage and
operate a variety of mutual funds. Virtually all
mutual funds are related to a family. Because of this
nature, issues related to mutual funds should be
studied at the family level.
Fund families offer a diverse set of funds with
diverse objectives and strategies to meet the
dissimilar objectives of investors and enable them to
diversify with funds that belong to the same family.
There is growing research on the influence of fund
families on member funds’ attributes. A fund family
has its own objective, such as maximizing profits
from member funds. To achieve this objective, it
devises different strategies to attract the maximum
amount of investment. Larger families enjoy the
benefits of economies of scale (higher returns at
lower costs), which are realized by learning from
experience and continuous improvement of
operating efficiency, [9], 10].
Fund family behavior and strategies have
gathered the interest of scholars, (e.g., [11]–[14]),
while others have examined how families influence
their member funds, [15]. When there is an
opportunity to generate substantial returns, for
instance capturing an emergent investor or market
segment, fund families will issue new funds, even if
they are already managing high-performing funds,
[11]. Despite this strategy, investors may still prefer
to diversify their investments across different
families to reduce portfolio risk, [15] examine the
risk impact of restricting investments in a single
fund family. They find that funds with similar
objectives are more closely correlated with those of
the same family compared to those of other families.
Most likely, this is caused by the tendency of sibling
funds to hold similar stocks and be exposed to
similar risk factors. Therefore, they suggest
diversifying mutual fund investment across multiple
families.
[13], [14], and, [16], examine how performance
is transferred between member funds. To do so, a
fund family reallocates resources to member funds
that are more likely to increase their overall value,
[13], revealing that in the US, fund heterogeneity is
correlated with between-fund competition between
and within families. Fund families employ the
category proliferation strategy to meet investors’
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diverse needs. The strategy correlates positively
with fund differentiation but not fund performance.
This suggests the independence of a fund from
sibling funds.
Fund family performance also affects their
members, [14], revealing that some member funds
demonstrate persistent performance within their
families, and it is linked to family size. This implies
some autonomy exercised by the family to unevenly
allocate resources among its members. It also
supports the hypothesis that resource allocation in
fund families is performance-based, not needs-
based. This conclusion is supported by, [16].
Analyzing US funds in 1991–2001, [16], find that
families increase their overall return through
strategic allocation (and reallocation) of resources to
member funds. The superior performance of funds
with good historical performance or high fees comes
at the expense of low-value funds. These results, in
sum, demonstrate how families create distortions in
delegated asset management.
The competitive and strategic behaviors of fund
families influence the risk-taking behavior and
performance persistence of member funds.
Analyzing US and European funds in 1999–2009,
[17], find that fund families are not necessarily
superior in performance compared to individual
funds. However, the future portfolio performance of
family funds and non-family funds, estimated based
on past performance, is significantly different. The
risk-taking behavior of a fund until the end of the
year is also influenced by its mid-year ranking in its
own family and sector.
Another form of resource allocation of fund
families is the assignment and coordination of fund
managers, [18], hypothesize that manager placement
strategies are related to market efficiency. Their
analysis of US funds in 1991–2010 reveals that to
turn around the performance of less efficient funds,
fund families are likely to assign skilled managers to
them. The apparent objective of this intervention is
the maximization of the families’ overall value, not
the investment value of investors.
Portfolio performance and investment behavior
of member funds are also influenced by the trading
desk efficiency of fund families. Trading costs and
portfolio liquidity can be reduced with more
efficient trading desks while increasing fund
performance and trading rate, [3].
The number of funds managed by a fund family
affects its AUM. In Pakistan, [19], examined
whether the issuance of new funds and growing
family size affect the AUM of the fund family. The
evidence suggests that the effect is positive and
significant. Moreover, as funds grow in number and
size, so does the fund family.
Some studies explore the behavior of member
fund managers and within-firm competition, [20],
21]. They find evidence that fund managers compete
with their peers to rank higher in the family. Such
competition is more prevalent in larger firms, but
teams in those firms compete less.
Synthesizing the studies above, there appears to
be a tendency for investors to react asymmetrically
to fund performance. Fund inflow to funds with
superior performance is much higher than fund
outflow from funds with poor performance. This
convex relationship suggests that a fund family will
have a larger AUM with a single-star fund and some
low-performing funds rather than with several funds
of average performance. An important corollary of
this conclusion is that fund families focus on
producing star funds rather than maintaining several
average-performing funds.
The studies reviewed above are mostly from
developed markets. However, similar research in
developing and emerging markets is still nascent.
There is evidence in the Malaysian market that
diversifying funds across fund families will reduce
portfolio risk rather than investing in several funds
of a single family. The returns of funds from a
single family are correlated higher than those from
multiple families. This is likely because sibling
funds share similar information and investing
strategies, [22]. Fund families in Malaysia also
demonstrate good selectivity skills but poor timing
ability. Additionally, these skills vary among the
managers of member funds, [23].
Our review reveals clear gaps in the literature
related to the fund family performance. Past studies
have primarily concentrated on fund performance
and fund family characteristics and their effect on
fund performance. This study bridges the identified
gap by contributing novel evidence on fund family
performance, in terms of security selection and
market timing skills of family managers, in
emerging markets.
3 Methodology
3.1 Data
A sample of 70 families, for a total of 503 funds, is
collected from Bloomberg. These families operate
in Saudi Arabia (25), Malaysia (20), Indonesia (14),
and Pakistan (11). Islamic families are distinguished
from conventional funds using the 33% rule, i.e.,
one-third of the family must be made up of Islamic
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funds. Otherwise, the family is classified as a
conventional family.
The sample period is January 2007–December 2018.
To benchmark fund family performance, we use two
global indices. Islamic families are benchmarked
against the FTSE Global Islamic Index. Both
Islamic and conventional families are measured
against the FTSE All-World Index since it is the
largest market capitalization index of developed and
emerging markets, [24]. The risk-free rate,
following past mutual fund studies, is the 3-month
T-bill rate. Performance is peroxide by monthly
returns:
 
 (1)
where i is the index and t is the period.
3.2 Selectivity Skills Models
A common measure of fund performance is
selectivity models. We use four selectivity models:
Sharpe ratio, Treynor ratio, Jensen’s measure, and
Carhart’s four-factor model. These models measure
performance as either raw returns, excess returns, or
risk-adjusted measures. Family performance is
measured as the weighted average performance of
its member funds, [8], [25].
3.2.1 Raw Returns and Excess Returns
The raw returns of a fund family are the weighted
average raw returns of its member firms. It is
measured as:

 
(2)
where the weight of fund i is calculated by the TNA
of fund i divided by the TNA of the family and n is
the number of funds in the family.
Excess returns are measured as:
   (3)
where is the raw family return of the family
over the period t and  is the risk-free return over
period t.
3.2.2 Sharpe Ratio (1966)
Sharpe ratio ranks mutual fund performance. It is
formulated as:
 
 (4)
Where  is a fund family returns in period t,
 is the risk-free return, and  is the standard
deviation of mean excess family returns.
3.2.3 Treynor Ratio (1965)
Treynor ratio simply replaces the standard deviation
in the Sharpe ratio with a beta to measure systematic
risk. It is computed as:

 
 (5)
Where is the beta coefficient in period t. It
measures the sensitivity between excess returns and
a market benchmark.
3.2.4 Single-factor CAPM model (Jensen, 1968)
Jensen’s alpha is a measure of risk-adjusted
abnormal performance in the market by capturing
the abnormal excess returns of a fund family, [26].
Jensen’s alpha determines whether a fund family is
over performing or otherwise. A positive and
significant alpha indicates the over performance of a
family fund, which is attributed to the manager’s
stock selection ability. It is computed as:
  
   (6)
where captures any excess returns above a
market benchmark and  is the term error.
3.2.5 Carhart’s four-factor Model (1997)
Extending Fama and French’s three-factor model, it
is formulated as:
   
   
(7)
where  is the difference in return between a
small-cap portfolio and a large-cap portfolio at
period t;  is the difference in return between a
portfolio of high-book-to-market stock and a low-
book-to-market stock at period t; and  is the
difference in return between high and low
momentum (lagged one year return) at time t.
3.3 Market Timing Ability Models
Treynor–Mazuy (TM) (1966) and Henriksson–
Merton (HM) (1981) models are used to measure
the market timing skills of fund family managers.
The models measure the skill of family managers in
timing capital shifts between risky and less risky
securities in anticipation of future market
trajectories. While fund family performance is
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partially determined by market conditions, skilled
managers are able to time entry or exit the market,
thereby maximizing returns and minimizing losses.
3.3.1 TM Model (1966)
Market timing skills in the TM model are estimated
by the square of market returns:
 󰇛
󰇜  (8)
Where  indicates market timing. A positive and
significant coefficient indicates the ability of the
fund family to forecast market trajectories and
respond to them in a timely manner.
3.3.2 HM Model (1981)
   
󰇛 󰇜  (9)
Where is the market timing coefficient, is a
dummy variable that takes a value of one if market
returns are positive.
4 Results and Discussions
4.1 Descriptive Statistics
Table 1 presents the descriptive statistics for
monthly family returns, market benchmarks, and
other risk factors. The data have negative skewness,
positive kurtosis, and non-normal distribution.
Overall family returns are positive (M = 0.0920),
while Islamic and conventional funds have similar
negative returns.
Table 2 shows Pearson’s correlation coefficients
between the variables. This examination is carried
out to detect multicollinearity. Fund family returns
are weakly correlated with the market benchmarks.
The benchmarks themselves are weakly correlated
to each other. The highest correlation is between T-
bill and FTSE Islamic returns (r = 0.305). Thus,
multicollinearity is not an issue.
4.2 Selectivity Skills
4.2.1 Entire Sample
Raw return, excess return, Sharpe ratio, and
Treynor ratio
Table 3 presents the results. Fund family
performance is compared to the FTSE All-World
Index and FTSE Global Islamic Index. The mean
monthly raw returns of the fund families are 0.07%.
Islamic (0.03%) and conventional (0.04%) funds
have almost similar raw returns. The excess returns
of the families (i.e., subtracting the risk-free rate
from raw returns) remain positive (0.009%), but the
excess returns of Islamic and conventional funds are
negative. Family raw and excess returns are above
both market benchmarks. They are also less volatile,
with a beta below the market (< 1). Taken together,
these results suggest that fund families provide
higher returns at lower total risk, perhaps because of
their diversification strategy.
The Sharpe ratio compares mean excess returns
to total risk (standard deviation). In other words, it
measures the amount of reward received when
taking an additional risk. Fund families have a
positive Sharpe ratio, while both market
benchmarks have negative ratios. This means that
investing in fund families provides better returns,
relative to the risks undertaken, than Islamic and
conventional equity investments. The Treynor ratio
is positive for both market benchmarks, indicating
the overperformance and effective diversification
strategies of fund families.
The findings of these relative performance
measures indicate the superior performance of fund
families over market benchmarks, attributed to the
diversification strategies of the families. Consistent
with the modern portfolio theory (MPT), given a
market risk level, investors can diversify their
portfolio to generate maximum returns at the lowest
possible risk. These relative measures, however, are
limited in that they only rank a fund relative to their
peers; they do not provide any significant statistical
or economic meaning. The next section, therefore,
analyzes fund family performance relative to market
benchmark returns using several performance
models.
Single-factor model (Jensen, 1958)
Table 4 presents the results of the single-factor
model against Islamic and conventional
benchmarks. Jensen’s alpha indicates the monthly
abnormal returns of fund families. Jensen’s alpha of
the families against FTSE Global Islamic is 0.19%
and against FTSE All-World is 0.20%. These
indicate the superior performance of the families
over both benchmarks, consistent with results in the
previous section.
The adjusted R2 of the fund families for FTSE
Global Islamic is 0.81 and FTSE All-World 0.85.
The high R2 values suggest that family managers
passively follow the market, but they are unable to
perform well. Perhaps due to stricter rules in Islamic
investments, which may inhibit performance, the
alpha of the Islamic benchmark is lower than its
conventional counterpart.
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Four-Factor Model (Carhart, 1997)
Data for the four-factor model are not readily
available, so we collect them from Fama and
French’s website. Monthly returns are computed
based on the FTSE All-World Index.
Table 5 presents the results using the FTSE All-
World as the market benchmark. The fund families
outperform the market benchmark (α = 0.20, p <
0.05) and have lower risks (β = -0.14, p < 0.1). This
result supports the single-factor model. Fund
families prefer smaller (SMB = -0.034, p < 0.05) and
growth-oriented stocks (HML = -0.05, p < 0.05).
This preference allows them to outperform the four-
factor benchmarks. Moreover, fund families
diversify to remove unsystematic risk, leaving only
market risk.
Table 1. Descriptive statistics
Fund Family
FTSE Islamic
SMB
HML
MOM
TB
Mean
0.0920
-0.0615
-0.0803
0.0461
-0.0185
0.0647
Median
0.1319
-0.0630
0.1323
-0.1564
0.2093
0.0630
Maximum
0.6932
0.4582
0.1835
0.4296
0.2093
0.1385
Minimum
-0.5834
-0.4537
-0.4057
-0.1564
-0.9254
0.0183
Std. Dev.
0.0371
0.0569
0.2810
0.2674
0.5194
0.0303
Skewness
-1.1581
-1.9824
-0.2192
0.6690
-0.6446
0.5210
Kurtosis
25.763
24.796
-1.8810
-1.4830
-1.5201
1.4891
Table 2. Correlation matrix
Fund Family
FTSE Islamic
FTSE All World
SMB
HML
MOM
TB
Fund Family
1.0000
FTSE Islamic
-0.0301
1.0000
FTSE All
World
-0.0832
0.0854
1.0000
SMB
0.0020
0.0006
0.1491
1.0000
HML
0.0076
0.0508
0.1277
0.0637
1.0000
MOM
-0.0103
-0.0675
-0.1613
-0.0551
-0.418
1.0000
TB
0.0449
-0.3046
-0.1087
-0.0071
-0.025
0.0148
1.000
Table 3. Mean raw returns and excess returns
Fund Family
FTSE Islamic
FTSE All World
Mean raw returns
0.0740
0.0032
0.0023
Mean excess returns
0.0093
-0.0615
-0.0824
Std. Dev
0.0317
0.0569
0.0465
Sharpe ratio
0.3936
-1.0812
-1.3424
FTSE Global Islamic Index
Beta
0.1307
1.0000
-
Treynor
0.0711
-
-
FTSE All World Index
Beta
0.1166
-
1.0000
Treynor
0.0797
-
-
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Table 4. Single-factor model
FTSE Global Islamic
FTSE All World
α
β
Adj.
α
β
Adj.
Coeff
0.1942
-0.2435
0.81
0.2025
-0.1067
0.85
SE
0.0072
0.0801
-
0.0077
0.0916
-
p
0.0002
0.0023
-
0.0001
0.2441
-
Table 5. Carhart’s four-factor model
Coef
SE
p
Alpha
0.2011
-0.0079
0.0001
Market
-0.1400
0.0974
0.0505
SMB
-0.0336
0.0023
0.0488
HML
-0.0538
0.0022
0.0015
MOM
-0.0022
0.0017
0.2042
Adj.
-
0.88
-
Table 6. Sharpe ratio by country
Country
Measurement
Fund Family
FTSE Islamic
FTSE All World
Saudi Arabia
Panel A: Mean raw, mean excess return, and Sharpe ratio
Mean raw returns
0.5372
0.0032
0.0023
Mean excess returns
0.4675
-0.0664
-0.0673
SD
0.0419
0.0569
0.0465
Sharpe ratio
0.7554
-1.1673
-1.4479
Panel B: Beta and Treynor ratio using FTSE Islamic as benchmark
Beta
-0.1592
1.0000
--
Treynor
0.9360
--
--
Panel C: Beta and Treynor ratio using FTSE All world as benchmark
Beta
0.0748
--
1.0000
Treynor
0.0247
--
--
Malaysia
Panel A: Mean raw, mean excess return, and Sharpe ratio
Mean raw returns
0.2445
0.0032
0.0023
Mean excess returns
0.1749
-0.0264
-0.0273
SD
0.0367
0.0569
0.0465
Sharpe ratio
0.0466
-0.4636
-0.5868
Panel B: Beta and Treynor ratio using FTSE Islamic as benchmark
Beta
0.0264
1.0000
--
Treynor
0.5646
--
--
Panel C: Beta and Treynor ratio using FTSE All world as benchmark
Beta
0.2632
--
1.0000
Treynor
0.0567
--
--
Indonesia
Panel A: Mean raw, mean excess return, and Sharpe ratio
Mean raw returns
0.2395
0.0032
0.0023
Mean excess returns
0.1680
-0.0682
-0.0691
SD
0.0424
0.0569
0.0465
Sharpe ratio
0.3467
-1.1992
-1.4869
Panel B: Beta and Treynor ratio using FTSE Islamic as benchmark
Beta
-0.2390
1.0000
--
Treynor
-0.7030
--
--
Panel C: Beta and Treynor ratio using FTSE All world as benchmark
Beta
0.3782
--
1.0000
Treynor
0.0443
--
--
Pakistan
Panel A: Mean raw, mean excess return, and Sharpe ratio
Mean raw returns
0.1370
0.0032
0.0023
Mean excess returns
0.0281
-0.1056
-0.1066
SD
0.0215
0.0569
0.0465
Sharpe ratio
0.0425
-1.8569
-2.2915
Panel B: Beta and Treynor ratio using FTSE Islamic as benchmark
Beta
-0.2134
1.0000
--
Treynor
-0.1316
--
--
Panel C: Beta and Treynor ratio using FTSE All world as benchmark
Beta
-0.3881
--
1.0000
Treynor
-0.0724
--
--
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DOI: 10.37394/23207.2023.20.88
Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
957
Volume 20, 2023
Table 7. Single-factor model by country
Country
FTSE Global Islamic
FTSE All World
α
β
Adj.
R2
α
β
Adj. R2
Saudi Arabia
Coeff
0.4714
0.0594
0.55
0.4904
0.3397
0.61
SE
0.0120
0.1327
-
0.0131
0.1530
--
p
0.0018
0.6540
-
0.0028
0.0264
--
Malaysia
Coeff
0.0177
0.0319
0.53
0.0271
0.2666
0.57
SE
0.0075
0.1188
--
0.0078
0.1433
--
p
0.0362
0.7878
--
0.0048
0.0630
--
Indonesia
Coeff
0.0163
-0.0750
0.66
0.0232
0.2030
0.67
SE
0.0162
0.1785
--
0.0092
0.1675
--
p
0.0005
0.6740
--
0.0115
0.2255
--
Pakistan
Coeff
0.0147
-0.0313
0.60
0.0157
-0.1062
0.63
SE
0.0325
0.2650
--
0.0373
0.3137
--
p
0.4470
0.9057
--
0.6536
0.7348
--
Table 8. Carhart’s four-factor model by country
Country
α
Market
SMB
HML
MOM
Adj.
Saudi Arabia
Coeff
0.4912
0.0227
0.0271
0.0426
0.0070
0.81
SE
0.0137
0.1535
0.0294
0.0345
0.0176
-
p
0.0000
0.0356
0.0357
0.2160
0.6909
-
Malaysia
Coeff
0.2890
0.2580
0.0125
0.0342
0.0420
0.74
SE
0.0084
0.1435
0.0246
0.0288
0.0147
-
p
0.0006
0.0240
0.0115
0.2351
0.4400
-
Indonesia
Coeff
0.2008
0.0313
0.0254
-0.0044
-0.0245
0.70
SE
0.0188
0.2114
0.0389
0.0456
0.0233
-
p
0.0000
0.0174
0.0132
0.9238
0.2943
-
Pakistan
Coeff
0.0125
-0.1311
0.0224
0.0923
-0.0037
0.71
SE
0.0382
0.3144
0.0598
0.0705
0.0360
-
p
0.7442
0.6768
0.7078
0.1905
0.9175
-
4.2.2 By Country
Table 6 presents the results by country. Their
performance is compared to two market
benchmarks. The findings indicate that the families
outperform and have lower risks than the
benchmarks. These results suggest that fund
families provide higher returns at lower total risk,
perhaps because of their diversification strategy.
Saudi Arabia performs better than other
countries as it has the highest raw returns (M =
0.538) and excess returns (M = 0.47). Ranking
second is Malaysia, followed by Indonesia and
Pakistan. The families have positive Sharpe ratios,
but the benchmarks have negative ratios for all
countries. This suggests that fund families provide
better investment returns than both benchmarks.
Saudi Arabia has the highest Sharpe ratio (0.76),
followed by Indonesia (0.35), Malaysia (0.05), and
Pakistan (0.04). Saudi Arabia likewise has the
highest positive Treynor ratio (0.94) when
performance is compared against the Islamic
benchmark. It is followed by Malaysia (0.56). Both
Indonesia (-0.70) and Pakistan (-0.13) have a
negative Treynor ratio.
When measured against the conventional
benchmark, the ratios of all countries, except
Pakistan, are positive. In this case, Malaysia, not
Saudi Arabia, ranks first in terms of performance.
Indonesia also performs better than Saudi Arabia.
Single-factor Model (Jensen, 1968)
Table 7 the results of the single-factor model against
Islamic and conventional benchmarks. Jensen’s
alphas of all the fund families are positive regardless
of benchmark, suggesting their superior
performance over the benchmarks. Similar to the
previous results, Saudi Arabia performs best,
irrespective of benchmarks. Malaysia ranks second,
followed by Indonesia and Pakistan. Fund family
performance is better when measured against the
conventional benchmark as opposed to the Islamic
benchmark. This is perhaps due to stricter rules in
Islamic investments, which may inhibit
performance.
Four-factor model (Carhart, 1997)
Table 8 presents the results of the four-factor model
using FSTE All-World as the market benchmark.
On average, Saudi Arabian fund families
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Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
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Volume 20, 2023
outperform the market benchmark (α = 0.49, p <
0.05) and have lower risks (β = 0.02, p < 0.05).
Malaysia and Indonesia show similar results.
However, the alpha is not significant in Pakistan,
which means that the fund families are unable to
outperform the four-factor benchmarks. Excepting
Pakistan, the fund families in all countries prefer
smaller stocks. The HML and MOM are not
significant for any country.
4.2.3 Islamic vs Conventional Families
Raw return, excess return, the Sharpe ratio, and the
Treynor ratio
Table 9 presents the results of Islamic VS
Conventional Families. Islamic families have a
mean raw return of 0.30%, while conventional
families have 0.17%. The mean excess returns (i.e.,
raw returns less the risk-free rate) of both families
remain positive (Islamic: 0.24%, conventional:
0.10%). These results suggest that both types of
families outperform the market benchmarks. Islamic
families also have higher mean raw and excess
returns and a lower beta than conventional families.
Islamic families are therefore a more attractive
investment since it provides more returns at lower
risks.
The Sharpe ratio of Islamic families (0.45) is
higher than conventional families (0.22) and the
market benchmarks. Likewise, Islamic families have
a higher Treynor ratio than conventional families,
regardless of the benchmarks used.
We conclude that measures of relative performance
show the superior performance of Islamic families
over conventional families and market benchmarks.
One reason for these results is that the sample
period is during a bearish market, which favors
Islamic funds due to their lower risks. In a bearish
market, Islamic funds outperform their conventional
counterparts, [27], 28].
Single-factor model (Jensen, 1968)
Table 10 results of the single-factor model against
Islamic and conventional benchmarks. Islamic
families have a Jensen’s alpha of 0.22% against the
Islamic benchmark and 0.23% against the
conventional benchmark. Both alphas of Islamic
families are greater than those of conventional
families. All alphas are also significantly different
from zero. Briefly, Islamic families significantly
outperform conventional families.
The adjusted R2 of Islamic (conventional) families is
0.67 (0.71) against the Islamic benchmark and 0.65
(0.77) against the conventional benchmark. The
conventional benchmark appears to be biased
toward conventional families. The high R2 values
suggest that family managers passively follow the
market, but they are unable to perform well.
Expenses and fees may contribute to
underperformance, [29].
Four-factor model (Carhart, 1997)
Table 11 results of the four-factor model using the
FTSE All-World Index as the market benchmark.
Islamic families have a positive and significant
alpha (α = 0.23, p < 0.05), which means that, on
average, they outperform the four-factor
benchmarks. Additionally, Islamic families (β = -
0.2, p > 0.05) have lower risk than conventional
families (β = -0.04, p > 0.05). These results are
consistent with the single-factor model.
Conventional families also outperform the market
benchmark (α = 0.14, p < 0.05). Nonetheless, their
performance still trails Islamic families.
Both Islamic (SMB = 0.02, p > 0.05) and
conventional families (SMB = 0.03, p > 0.05) prefer
smaller stocks, perhaps because they are easier to
manage. Preference for growth-to-value stocks is
also demonstrated by Islamic (HML = -0.05, p <
0.05) and conventional families (HML = -0.04, p <
0.05) to attract investors who prefer long-term and
growth investments. These results support, [30], 31].
MOM is not significant for both. These preferences
allow Islamic families to outperform conventional
families and four-factor benchmarks.
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DOI: 10.37394/23207.2023.20.88
Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
959
Volume 20, 2023
Table 9. Mean Raw Returns, Mean Excess Returns, Sharpe Ratio, and Treynor Ratios (Islamic VS
Conventional Families)
Country
Measure
Fund Family
FTSE Islamic
FTSE All
World
Islamic Family
Panel A: Mean raw, mean excess return, and Sharpe ratio
Mean raw returns
0.3082
0.0032
0.0023
Mean excess returns
0.2446
-0.0603
-0.0612
Std. Dev
0.4424
0.0568
0.0464
Sharpe ratio
0.4510
-1.0610
-1.3177
Panel B: Beta and Treynor ratio using FTSE Islamic as benchmark
Beta
-0.1975
1.0000
--
Treynor
-0.2748
--
--
Panel C: Beta and Treynor ratio using FTSE All world as benchmark
Beta
0.1517
--
1.0000
Treynor
1.6122
--
--
Conventional
Family
Panel A: Mean raw, mean excess return, and Sharpe ratio
Mean raw returns
0.1764
0.0032
0.0023
Mean excess returns
0.1065
-0.0668
-0.0676
Std. Dev
0.4646
0.0569
0.0465
Sharpe ratio
0.2291
-1.1731
-1.4550
Panel B: Beta and Treynor ratio using FTSE Islamic as benchmark
Beta
-0.1606
1.0000
--
Treynor
-0.4086
--
--
Panel C: Beta and Treynor ratio using FTSE All world as benchmark
Beta
-0.1100
--
1.0000
Treynor
-0.9677
--
--
Table 10. Single-factor model (Islamic vs conventional)
Country
FTSE Global Islamic
FTSE All World
α
β
Adj. R2
α
β
Adj. R2
Islamic Family
Coeff
0.2257
-0.3128
0.67
0.2330
-0.1899
0.65
SE
0.0081
0.0918
-
0.0088
0.1054
--
p
0.0000
0.0007
-
0.0000
0.0716
--
Conventional family
Coeff
0.0928
-0.2052
0.71
0.1042
-0.0334
0.75
SE
0.0139
0.1454
--
0.0150
0.1657
--
p
0.0000
0.1583
--
0.0000
0.8401
--
Table 11. Four-factor model (Islamic vs conventional)
Country
α
Market
SMB
HML
MOM
Adj.
Islamic Family
Coeff
0.2354
-0.2029
0.0257
-0.0474
0.0158
0.77
SE
0.0092
0.1055
0.0214
0.0251
0.0129
-
p
0.0000
0.0546
0.2310
0.0492
0.2189
-
Conventional
Family
Coeff
0.1398
-0.0442
0.0310
-0.0440
-0.0119
0.73
SE
0.0157
0.1660
0.0359
0.0420
0.0215
-
p
0.0000
0.7902
0.9769
0.0257
0.5797
-
Table 12. TM and HM, sample
FTSE Global Islamic
FTSE All-World
α
Gamma\Delta
Adj. R2
α
Gamma\Delta
Adj. R2
Panel A: Market timing measure; Treynor-Mazuy model
Coeff
0.1953
-0.1292
0.74
0.1933
-0.3466
0.92
SE
0.0075
0.2547
-
0.0082
0.0870
-
p
0.1914
0.6118
-
0.5611
0.0006
-
Panel B: Market timing measure; Hendrickson-Merton model
Coeff
0.1935
-0.0807
0.76
0.1986
-0.3068
0.79
SE
0.0072
0.1190
-
0.0079
0.1609
-
p
0.0002
0.4980
-
0.0001
0.0311
-
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Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
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Volume 20, 2023
4.3 Market Timing Ability
4.3.1 Entire Sample
Treynor-Mazuy and Hendrickson-Merton
Table 12 presents the TM and HM results for the
entire sample, estimated using ordinary least squares
(OLS). Panel A shows that according to the TM
model, fund families have poor timing ability and
good selectivity skills irrespective of the
benchmarks used. The alphas are positive while
gammas are negative. Panel B shows the results of
the HM model. Similarly, they indicate that family
managers have poor timing skills but good
selectivity skills, regardless of the benchmarks used.
The alphas are positive while gammas are negative.
Because both models produce similar results, and
because these results are also supported by the
single- and four-factor models, it is strongly
suggested that family managers are skilled in
selecting securities. These selectivity skills are
perhaps enabled by the diversification and
investment opportunities of fund families. But the
managers do not possess adequate timing skills.
Their ability is perhaps hampered by the quantity
and diversity of member funds in a family, which
causes management and monitoring of funds to be
more difficult.
4.3.2 By Country
Treynor-Mazuy model (TM)
Table 13 presents the by-country TM results
estimated using OLS. Similar to the previous
section, the fund families of all countries
demonstrate good selectivity skills against both
Islamic and conventional benchmarks, as all alphas
are positive. Excepting Indonesia, all countries have
poor timing skills when measured against both
benchmarks.
Hendrickson-Merton model (HM)
Table 14 presents the by-country HM results
estimated using OLS. The results generally support
the TM model. All countries have positive alphas
against both benchmarks, indicating their good
security selection skills. Saudi Arabia and Malaysia
have negative deltas, suggesting their poor market
timing skills. Indonesia and Pakistan have good
market timing skills as indicated by their positive
deltas.
Table 13. TM by country
Country
FTSE Global Islamic
FTSE All World
Alpha
Gamma
Adj.
Alpha
Gamma
Adj.
Saudi Arabia
Coeff
0.4728
-0.1807
0.66
0.4991
-1.5384
0.68
SE
0.0123
0.3960
-
0.0156
1.4976
--
p
0.0005
0.6480
-
0.0001
0.3043
--
Malaysia
Coeff
0.0204
-0.6200
0.71
0.0237
-2.3097
0.72
SE
0.0080
0.3625
--
0.0079
1.5346
--
p
0.0106
0.0873
--
0.0027
0.1324
--
Indonesia
Coeff
0.1623
0.0395
0.59
0.2120
0.4160
0.61
SE
0.0178
0.5419
--
0.0196
2.0396
--
p
0.0002
0.9417
--
0.0003
0.2363
--
Pakistan
Coeff
0.0262
-0.2062
0.68
0.0061
-0.7841
0.71
SE
0.0331
0.8704
--
0.0609
3.5697
--
p
0.4290
0.8127
--
0.9190
0.8261
--
Table 14. HM by country
Country
FTSE Global Islamic
FTSE All World
Alpha
Delta
Adj.
Alpha
Delta
Adj.
Saudi Arabia
Coeff
0.4715
-0.0073
0.62
0.5075
-0.0385
0.61
SE
0.0120
0.1846
-
0.0147
0.2912
--
p
0.0001
0.9682
-
0.0009
0.0112
--
Malaysia
Coeff
0.0184
-0.2281
0.57
0.0237
-0.1927
0.60
SE
0.0080
0.2309
--
0.0084
0.3688
--
p
0.0211
0.3233
--
0.0047
0.6012
--
Indonesia
Coeff
0.1609
0.1893
0.68
0.2054
0.1126
0.71
SE
0.0164
0.2404
--
0.0194
0.3345
--
p
0.0004
0.4311
--
0.0002
0.7363
--
Pakistan
Coeff
0.0251
0.0959
0.49
0.0085
0.3381
051
SE
0.0325
0.2746
--
0.0491
0.4269
--
p
0.4409
0.7268
--
0.8614
0.4284
--
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Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
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Table 15. TM (Islamic vs conventional)
Country
FTSE Global Islamic
FTSE All World
α
Gamma
Adj. R2
α
Gamma
Adj.
R2
Islamic Family
Coeff
0.2249
0.0131
0.62
0.2217
1.6670
0.63
SE
0.0085
0.2881
-
0.0093
1.0136
--
p
0.0000
0.7203
-
0.0000
0.3210
--
Conventional Family
Coeff
0.0978
-0.5500
0.77
0.1030
-0.4206
0.75
SE
0.0146
0.4809
--
0.0157
1.6263
--
p
0.0000
0.2528
--
0.0000
0.7959
--
Table 16. HM (Islamic vs conventional)
Country
FTSE Global Islamic
FTSE All World
α
Delta
Adj. R2
α
Delta
Adj. R2
Islamic Family
Coeff
0.2254
0.0432
0.62
0.2266
-0.5583
0.61
SE
0.0082
0.1369
-
0.0090
0.1886
--
p
0.0000
0.7521
-
0.0000
0.3121
--
Conventional Family
Coeff
0.0914
0.1536
0.57
0.1036
-0.0512
0.60
SE
0.0140
0.2112
--
0.0153
0.2708
--
p
0.0000
0.4669
--
0.0000
0.8501
--
4.3.3 Islamic Vs Conventional
Treynor-Mazuy model (TM)
Table 15 presents the results for the analysis of
security selection and market timing ability for the
Treynor-Mazuy model (TM) using ordinary least
square (OLS), for Islamic and conventional families.
The results show that both Islamic and conventional
families exhibit security selection coefficients
significantly different from zero irrespective of the
benchmarks used. While both Islamic and
conventional families exhibit coefficients
insignificantly different from zero irrespective of the
benchmarks used. Nevertheless, there is evidence
that Islamic families have better security selection
and poor market timing ability than conventional
families. In conclusion, both Islamic and
conventional families have good selectivity skills,
while both have poor market timing ability, with a
relative advantage for Islamic families over
conventional families.
Hendrickson-Merton model (HM)
Table 16 presents the results for the analysis of
security selection and market timing ability for fund
families using the Hendrickson-Merton model (HM)
and ordinary least square (OLS), for Islamic and
conventional families. The results are similar to the
results of the Treynor-Mazuy model (TM) analysis.
Islamic and conventional families exhibit security
selection coefficients significantly different from
zero irrespective of the benchmarks used. While
both Islamic and conventional families exhibit
coefficients insignificantly different from zero
irrespective of the benchmarks used. Alpha is
positive for both Islamic and conventional families
whether used Islamic or conventional market
benchmarks. Similar to the results of the TM model,
Islamic families have better security selection and
poor market timing ability than conventional
families.
5 Conclusion
This study contributes novel evidence on fund
family performance to the literature. We conclude
with two important findings. First, fund family
managers possess good security selection skills,
benefitting from data and research available in fund
families, in addition to diversification and
investment opportunities. However, their market
timing skills are poor, likely because fund families
have diverse and numerous member funds, which
restrict the managers from effectively projecting
market trajectories and timing their entry or exit.
Second, fund families work towards creating a star
fund and then issue new funds to improve their
overall performance.
These findings have important implications for
fund managers and investors. Managers can gain an
advantage over their peers by improving their
market timing skills. Investors should allocate their
capital to well-managed funds, i.e., those whose
managers select securities and time markets well.
Identifying such funds will enable investors to gain
higher returns at lower total risk. The findings may
also aid investors in making correct investment
decisions, considering that they mostly employ the
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Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
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Volume 20, 2023
top-down approach when making investment
choices.
The findings are likewise useful for
academicians and regulators. They provide
important empirical evidence on fund family
performance and fund manager skills in emerging
markets.
We propose two recommendations based on the
findings. First, empirical evidence on fund family
performance is still lacking. The advantages of fund
families—research and data support, more extensive
networks, and diversification opportunities, among
others—may enable them to perform better than
standalone funds. We, therefore, recommend
focusing on fund family performance and how to
fund family characteristics influence the
performance of member funds and itself. Second,
we encourage scholars to focus their research on
emerging and developing markets, such as the
Middle East, South Asian, and Southeast Asian
countries. Past studies are primarily concentrated in
developed markets, and so their conclusions may
not be readily generalizable to developing and
emerging markets due to differences in market
characteristics and culture, among others.
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Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
963
Volume 20, 2023
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
-Anas Ahmad Bani Atta contributed on the
Conceptualization, Methodologyand Formal
Analysis and Supervision.
-Ahmad Yahiya Bani Ahmad contributed on the
Investigation, the Resources and the Writing,
Review & Editing.
-Maha Shehadeh contributed on the Resources and
Visualization.
-Haider Mohammed Ali Baniata contributed on the
Resources.
-Laith Yousef Bani Hani contributed on the Writing
of the Original Draft as well as the Review &
Editing.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The authors are grateful to Middle East University
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.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.88
Ahmad Yahiya Bani Ahmad,
Anas Ahmad Bani Atta, Maha Shehadeh,
Haider Mohammed Ali Baniata,
Laith Yousef Bani Hani
E-ISSN: 2224-2899
964
Volume 20, 2023