Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
An Empirical Investigation of Mutual Fund Performance in
Different Economic Cycles under Alternative Fund Objectives
Giovanni Fernandez, Chaiyuth Padungsaksawasdi, Arun J. Prakash and
Therese E. Pactwa
In this paper, we investigate the abnormal returns of 13,232 mutual funds by applying
four well-known asset-pricing models, namely the CAPM, the three-moment CAPM,
the Fama and French (1993) three-factor, and the Carhart (1997) four-factor models
in different economic cycles, and under different fund objectives. Our results show
that the economic cycle does affect mutual fund performance especially, in bear
periods. However, the results from different fund objectives are inconclusive, implying
that abnormal returns are not objective-specific. Moreover, meta-analysis shows that
the abnormal returns are statistically significantly different across deciles and models,
meaning that each decile and model yields different abnormal returns.
JEL Classification Codes: G10 (General Financial Markets), G11 (Portfolio Choice;
Investment Decisions; Mutual Fund Performance), G12 (Asset Pricing)
Beginning with Sharpe (1964), there have been hundreds of empirical research
papers published in the literature regarding mutual fund performance. However, there
have been questions raised that investors still do not optimally allocate their funds to
those mutual fund managers who consistently outperform the market, nor do they use
any process to properly select the individual superior funds. While some studies have
shown that there exists a subset of managers who consistently produce positive
abnormal returns, or „alpha,‟ many investors do not truly know what types of mutual
funds fall into this category, and when to increase their asset allocations into these
particular mutual funds (Kosowski, Timmerman, Wermers and White, 2006). Mutual
funds have been (falsely) sold as the „cure-all‟ investment vehicle due to their instant,
low-cost diversification, and the implied superior skill of mutual fund managers.
However, for the managers or mutual funds which outperform the market, is this alpha
skill specific to a fund‟s objective, or is it due to economic cycles? In this study, we
attempt to answer these important questions.
____________________________________________________________
Giovanni Fernandez, Stetson University, DeLand, FL, E-mail: gfernan1@stetson.edu
Chaiyuth Padungsaksawasdi, Department of Finance, Thammasat Business School, Thammasat
University, Bangkok, Thailand, E-mail: chaiyuth@tbs.tu.ac.th
Arun J. Prakash, Florida International University, Miami, FL, (O) 305-348-3324,
E-mail: prakasha@fiu.edu,
Therese E. Pactwa, St. John‟s University, New York, NY, E-mail: pactwat@stjohns.edu
Page 1 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
The most important portfolio performance measure was given by Jensen (1968,
1969), and is known as Jensen‟s alpha. This tool is essentially a risk-adjusted
performance measure that represents the average return on a portfolio over and above
the rate of return predicted by the capital asset pricing model (CAPM), given the
portfolio's beta and the average market return. There have been other portfolio
performance measures that have appeared in the literature over the years, however
Jensen‟s alpha remains the preeminent measure due to its adaptability and
extendibility.1
Another empirical asset pricing model was introduced by Fama and French
(1993). This model is also a factor model, but adds two factors to the CAPM. These
factors are the “SMB” factor, which measures the difference between the return on a
portfolio of small cap stocks and that of large cap stocks, and the “HML” factor, which
measures the difference between the return on a portfolio of high book-to-market stocks
(value) and that of low book-to-market stocks (growth). Several studies have found that
the Fama-French (1993) three-factor (“FF3”) model displays a strong ability to capture
cross-sectional excess returns not explained by the CAPM (see, for example,
Jagannathan and Wang (1996)). However, there is still some predictable variability not
explained by the model.
The Carhart (1997) four-factor model (“F4”) includes the “PR1YR” (momentum)
factor, which is the difference between the return on a portfolio consisting of buying the
top performing stocks and selling the bottom performing stocks of the past two, to
twelve, months (Jegadeesh and Titman (1993)). The average pricing errors of the
CAPM and the FF3 are significantly greater than those of the F4 (Carhart (1997)).
Since mutual funds account for a substantial portion of individual investors‟
assets, it is vitally important to accurately describe the performance of these types of
investments. The recent literature investigates the performance of mutual funds by
employing the F4 model with monthly data (Kosowski et al. (2006)). In the past, the
methodology utilized in studying mutual funds involved studying past one-year returns,
and forming portfolios every year (see Elton, et al (1996), Carhart (1997), and
Kosowski, et al. (2006)). With the “lost decade” of portfolio appreciation, investors are
now updating their beliefs more frequently than in the past.2 Therefore, the methodology
used in the past does not accurately capture the landscape of the present. Rebalancing
mutual fund portfolios on an annual basis, the recent literature finds that persistence of
the top mutual fund decile exists.
More importantly, in practice, mutual funds report arithmetic mean returns, which
misleads investors to believe that the arithmetic returns are their earned benefits.
However, in reality, since their holding period returns are typically long term, investors
earn the geometric return (DeFuscso, et al (2007)). In addition, by mathematical
construction, the geometric mean is always less than or equal to the arithmetic mean,
which (when less) overstates the actual return earned by mutual fund investors. To
1
Other measures include those by Treynor (1965), Sharpe (1966, 1994, 2007), Fama (1972), Lo (2002), Treynor and
Black (1973), Goodwin (1998), Sortino and Price (1994), Grinblatt and Titman (1993), Daniel, Grinblatt, Titman, and
Wermers (1997), etc. Chapter 25 in Reilly and Brown (2012) provides a nice summary of the various measures.
2
The 2000s decade has been labeled as a “lost decade”for American consumers by several economists, because
there was “zero economics gains for the typical family.” For more, see: http://articles.washingtonpost.com/201001-02/business/36886520_1_decade-economists-households
Page 2 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
mitigate this risk, we use geometric mean returns as holding period returns rather than
arithmetic means to avoid overstating mutual fund performance while employing
monthly data in each model.
We employ the daily CRSP mutual fund database (which is free of survivorship
bias) from January 1999 to December 2009. The data consists of 13,232 mutual funds,
which include slightly over a third large-capitalization (“Large-Cap”) funds, about a third
midle-capitalization (“Mid-Cap”) funds, and slightly less than a third small-capitalization
(“Small-Cap”) funds. During this period, there were two stock market bubbles, followed
by severe recessions (as defined by the National Bureau of Economic Research). As
shown by Ferson and Schadt (1996) and Kosowski (2011), mutual fund abnormal
performance differs across economic cycles for various reasons, such as higher
redemptions and more pressure on fund managers during troubling times, and higher
mutual fund inflows and pressure from higher expectations during prospering times. As
a result, we break the data into periods of bull and bear markets to study mutual fund
performance. This should provide an answer to whether or not the behavior of the top
and bottom „alpha‟ (outperforming) funds changes during different economic cycles.
Furthermore, we study the abnormal returns by fund objectives and market
capitalizations, which allow us to test the existence of market anomalies (i.e., the size
effect and the value effect) in the stock market. Finally, we employ meta-analysis, which
tests the statistical differences in abnormal returns across portfolios, models, timeperiods, and objectives. This allows us to test whether or not our findings are
statistically significantly different from prior studies, along with testing for performance
differences during different economic conditions, under different objectives and, across
different pricing models.
The most important finding is that positive and negative abnormal returns are not
completely dependent on economic styles, or on fund objectives. Top performers and
worst performers continue to have statistically significant positive and negative returns
in all economic cycles and style objective subgroups, respectively. However, our metaanalysis demonstrates that the under and over-performances are statistically different
across bull markets, and between bear and bull markets. The abnormal returns are not
significantly different across bear markets. Furthermore, the meta-analysis of the style
objective subgroups demonstrates that while all bottom performers have statistically
significant negative abnormal returns, the returns are different across styles.
Our paper proceeds as follows. Section I discusses the relevant literature and
issues regarding mutual fund performance, while Section II describes the mutual fund
database that we utilized in our study. Section III describes the methodology used to
implement our analysis. Section V provides the empirical results, and Section VI
concludes our analysis.
I. Review of the Literature
Grinblatt and Titman (1992) created a multiple performance benchmark that is
formed based on the basis of securities characteristics to test how mutual fund
performance relates to past performance. They find persistence in differences in
performance, which is consistent with the ability of fund managers to earn abnormal
returns. Hendricks, Patel, and Zeckhauser (1993) find persistence in the performance of
Page 3 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
no-load, growth-oriented funds in the near term (one year). Poor performers also
persist, but this persistence is not attributable to known anomalies or survivorship bias.
Grinblatt and Titman (1993) introduce a new measure of portfolio performance that uses
portfolio holdings and does not require a benchmark. They find that aggressive growth
funds outperform from the years 1974 to 1984. Goetzman and Ibbotson (1994) find that
past performance and relative rankings are useful in predicting performance and
ranking, but they do not control for survivorship bias.3 (Brown et al. (1992) Brown and
Goetzman (1995) find that mutual fund performance persists, but is mostly attributed to
funds that lag the S&P 500. Also, poor performance increases the probability of
disappearance. Persistence is due to a common strategy amongst managers that is not
captured by standard stylistic categories or risk adjustment procedures. Elton, Gruber
and Blake (1996) find persistence in risk-adjusted returns. Using modern portfolio theory
techniques, rather than past rankings, improves the selection, and allows for the
construction of portfolios of funds that outperform. They construct a portfolio of actively
managed portfolios with the same risk as a portfolio of index funds, but with higher
mean returns.
In a groundbreaking paper, Carhart (1997) demonstrates that his four-factor
model almost completely explains the persistence in equity mutual funds‟ mean returns.
The best performing funds‟ results are mainly driven by momentum. On the other hand,
individual fund managers do not actually earn higher returns by pursuing the momentum
strategy. They typically are just holding these stocks and get „lucky‟ on the given year
when these stocks outperform. The persistence of the bottom decile in the performance
of funds is left unexplained. Carhart concludes that fund managers do not appear to
possess a unique skill.
Continuing with the theme of “luck,” Kosowski, Timmerman, Wermers, and White
(2006) show that after choosing the Carhart (1997) model as the best fit according to
standard model selection criteria,4 the alphas are non-normally distributed due to nonnormal individual funds alphas and heterogeneous risk taking among different funds.
Because of this, they examine mutual fund performance controlling for luck, without
imposing an ex ante parametric distribution from which funds returns are assumed to be
drawn. They compare the distribution of actual fund alphas with those that would be
expected after creating an empirical distribution using the bootstrap methodology. They
find that significantly more funds provide larger alphas than would be expected only
because of luck. Therefore, they conclude that a sizable minority of managers picks
stocks well enough to cover their expenses, and this performance persists. Barras,
Scalliet and Wermer (2010) find that 75% of mutual funds exhibit zero alpha, and that
almost none have positive alpha after 2006. Thus, they argue that almost all mutual
funds do not demonstrate positive abnormal returns.
3
Survivorship bias is an error resulting from only including firms that were not removed from the dataset during the
sample period. If survivorship bias is present, it can lead to false conclusions (mostly overoptimistic results).
4
Specifically, the Schwarz (1978) Information Criterion method.
Page 4 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
II. Data
The mutual fund data is from CRSP, which is free of survivorship bias.5 Daily
returns for active domestic equity funds are obtained from January 1999 through
December 2009, as well as each mutual fund‟s stated objective. There are a total of
13,232 funds, and an average of 3,989 mutual funds each month. These funds are
classified into nine groups by both the objective of the mutual fund and by the stock
market capitalization. These include: Large-Cap Core, Large-Cap Value, Large-Cap
Growth, Mid-Cap Core, Mid-Cap Value, Mid-Cap Growth, Small-Cap Core, Small-Cap
Value, and Small-Cap Growth. Appendix 1 presents the definition of these mutual fund
types. There are slightly over a third Large-Cap funds, about a third Mid-Cap funds, and
slightly less than a third Small-Cap funds. The other daily factor returns (SMB, HML,
and PR1YR) are also collected from CRSP.
III. Methodology
We begin by computing daily geometric mean returns each month from the daily
returns of each mutual fund, which represents the holding period returns of mutual fund
investors:
[∏
]
(1)
where
and
is the rate of return and the daily geometric mean return on portfolio i
for month t in excess of the daily one-month Treasury Bill geometric return, respectively.
We rank mutual fund performance based on the previous month‟s daily geometric
mean. The mutual funds are then grouped into deciles for each month, creating ten
equally weighted portfolios.6 This leads to each portfolio having 132 observations over
our entire sample. Therefore, there are a total of 1,320 observations for all portfolios. To
investigate the abnormal returns of mutual fund portfolios we employ ex-post versions of
both theoretical (the traditional CAPM and three-moment CAPM) and empirical asset
pricing models (the Fama-French (1993) three-factor and Carhart (1997) four-factor
models) as follows:
CAPM
(2)
(
)
3-Moment CAPM
(3)
(
)
3-Factor Model
4-Factor Model
(
(
)
)
(4)
(5)
5
Further details on this mutual fund database are available from CRSP:
http://www.crsp.com/products/documentation/crsp-survivor-bias-free-us-mutual-fund-guide-crspsift
6
As shown by Lessard (1976), Roll (1981), Grinblatt and Titman (1989), and Korajczyk and Sadka (2004) equally
weighted portfolios attain higher returns and volatility than value weighted portfolios.
Page 5 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
where
and
are the daily geometric mean returns of the one-month Treasury Bill
and on the CRSP equally weighted index portfolio for month t, respectively.
and
are the daily geometric mean returns on the size factor, the
value factor, and the momentum factor for month t, respectively.
is the error term of
portfolio i for month t.
IV. Results
4.1 Summary Statistics
We first call our attention to Table 2, which provides the factor summary
statistics. The first obvious observation is the difference between our excess monthly
returns when compared to those of the previous literature. With two recessions included
in our sample period, the worst performing portfolios of funds have much lower excess
returns than in the previous literature. The five lowest deciles exhibit negative excess
returns. Table 2 also shows more of what is evident of this past decade; mainly, the
monthly market excess return is negative. Since negative excess returns violate
assumptions underlying the theoretical models, we expect to find conflicting results
when compared to the previous literature.
Observing possible multicollinearity, it is evident from the correlation matrix that
the factors from our sample period are more highly correlated than those of the previous
literature (Carhart, 1997). The momentum factor is negatively correlated to the market
factor (-0.41899), and slightly positively correlated with the size factor (0.08765). This
can be explained by the flat decade of returns. With two short bull and bear markets,
contrarian strategies were profitable during these ten years (see Kim and Wei (2002)),
which implies that the market factor would be negatively correlated with a momentum
factor (i.e., positively correlated with a contrarian factor). The relationship between the
momentum factor and the size factor might have more to do with risk. Since returns
were highly volatile during this decade, and returns of small stocks tend to show
relatively more volatility than those of other stocks (Copeland and Copeland (1999)),
small stocks had amplified rallies during the bear and bull periods, leading to
momentum in these stocks. During the period examined, the value factor was now
negatively correlated to the size factor (-0.35588). This could be specifically due to the
two bubbles during this time period (technology and real estate/financial).
4.2 Overall Mutual Fund Performance
Next, we look at the overall mutual fund performance. We start this part of our
analysis by ranking mutual fund portfolios into ten deciles based on the previous months
returns. We then analyze the abnormal performance of each decile, using the CAPM,
three-moment CAPM, three-factor model, and four-factor model. Table 3 presents the
equally weighted mutual fund portfolios ranked by the previous month's geometric
excess returns over the entire period. Obviously (and evidenced in the table), mutual
fund performance differs significantly across deciles. The alphas, a measure of
abnormal return, monotonically increase from the worst performers in decile one
(significantly negative alphas) to best performers in decile ten (significantly positive
Page 6 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
alphas) in all four models. The alphas range from -0.255% to 0.001% for the CAPM, 0.209% to 0.118% for the three-moment CAPM, -0.258% to 0.113% for the three-factor
model, and -0.258% to 0.113% for the four-factor model.
The adjusted R2 values for each decile across models are almost indifferent,
which shows that the market risk premium is the most significant factor. (The adjusted
R2 does not automatically increase when more variables are added to a regression, as it
is adjusted for degrees of freedom). However, the adjusted R2 value in decile one (worst
performers) of the three-moment CAPM is approximately 2% larger than those of the
other models, and the adjusted R2 values in decile ten (top performers) of the threefactor and four-factor models are approximately 5% larger than the CAPM and threemoment CAPM. More importantly, the skewness variable in the three-moment CAPM is
more significant for poor performing mutual fund portfolios, which shows that poor
performing mutual funds load on negatively skewed stocks, causing the performance to
suffer. This can be evidenced by the t-statistic values of worst and top performers,
which are -4.89 and 0.36, respectively. This is expected since successful funds would
overweight on stocks that are positively skewed.
For the three-factor model, the size factor is less significant for worst performing
mutual fund portfolios (deciles one and two), implying that the worst performing mutual
fund managers do not exploit the size effect as successfully as those of the better
performing funds. The HML factor best captures the excess returns in the middle decile
groups. Surprisingly, the momentum factor does not play an important role in explaining
the excess mutual fund portfolio returns. Thus, our finding on the four-factor model
differs from Carhart's (1997) evidence that only poor performing funds show significantly
negative alphas, while the top performing funds show insignificant positive alphas.
In conclusion, the traditional CAPM is the best model to detect mutual fund
excess returns. Even though other factor loadings show significance, their explanatory
power is not as important during our sample period when compared to the previous
studies by Carhart (1997) and Kosowski et al. (2006). However, all performers do
overweight on small firms, while top and bottom performers steer clear of distinguishing
between growth or value stocks. During the period studied, the momentum trading
strategy does not play an important role in explaining the performance of mutual fund
portfolios. Our finding reinforces the fact that returns were flat over the past decade,
leading to the vanishing of momentum profits.
4.3 Mutual Fund Performance under Different Economic Cycles
Next, we look at mutual fund performance under different economic cycles.
Ferson and Schadt (1996) argue that it is logical to assume that varying economic
conditions affect mutual fund performance. Tables 4 and 5 show mutual fund
performance over bull and bear periods, respectively. The bull-bear periods are
determined by the National Bureau of Economic Research (NBER). 7 Over our entire
sample period from 1999 to 2009, there were two bull periods: years 1999 through 2000
(Table 4, Panel A), and years 2002 through 2007 (Table 4, Panel B). There were also
two bear periods: year 2001 (Table 5, Panel A) and years 2008 through 2009 (Table 5,
Panel B). In general, the mutual fund alphas are not statistically significantly different,
7
See: http://www.nber.org/cycles.html
Page 7 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
but are economically significantly different, during bull markets. The alphas of worst
(top) performing mutual fund portfolios over the 1999-2000 bull period, as shown in
Panel A of Table 4, for the CAPM, three-moment CAPM, three-factor, and four-factor
models, are -0.455% (0.243%), -0.297% (0.219%), -0.458% (0.233%), and -0.442%
(0.226%) respectively, Whereby those of over the 2002-2007 bull period, as shown in
Panel B of Table 4, are -0.162% (0.075%), -0.124% (0.075%), -0.164% (0.071%), and 0.166% (0.071%), respectively.
The market factor loadings are still positive, but are less significant than those
during the overall period (as provided in Table 3). Interestingly, the SMB factor over the
1999-2000 bull period is less significant than found in the overall sample in deciles 1 to
5, but is more significant than over the 2002-2007 bull period. This implies that that
during the 1999-2000 bull period, only successful managers loaded on small-cap
stocks. This is consistent with what occurred during the technology bubble, since the
successful technology firms of that time were young, small firms. The 2002 through
2007 bull period is less discriminating: both successful and poor performing funds all
loaded up on small-cap stocks.
Next, we turn our attention over to the HML factor loadings. Over both bull
periods, these loadings are less significant than those over the entire sample, meaning
that the value factor plays a smaller role in determining mutual fund performance than
found during the entire sample period. In addition, the momentum factor loadings are
still not significant, which implies that during up markets, mutual fund managers follow
neither momentum nor contrarian strategies, but stick to a buy-and-hold technique.
Focusing on the efficacy of the models, the R-squared values for all deciles and
models over the 1999-2000 bull period are smaller than those over the 2002-2007 bull
period, especially at the extreme levels. The difference in length of sample period (two
years versus six years) may be causing this issue. During both bull periods, the R 2
values of the three-factor and four-factor models are larger than those of the CAPM and
three-moment CAPM for the top performers but are smaller than those of worst
performers. Again, the difference could be attributed to sample size. For the middle
decile groups, the R2 values of the models are not much different.
It is natural to assume that during down markets, positive alphas should be
difficult to attain. However, we find that the dispersion in alphas (as presented in Panels
A and B of Table 5) does not, in general, drastically change from our entire sample to
bull market periods. The co-skewness factor plays a slightly less important role during
bear market periods than over the entire sample period. As shown in Panel A of Table
5, the SMB factor is not significant for all deciles over the 2001 bear period.
Interestingly, the momentum factor loading is negatively significant for better performing
funds during the 2001 bear market (Panel A), but positively significant for the worse
performing funds during the 2008-2009 bear market (Panel B). From this, we can
conclude that, during bear markets, funds that follow momentum strategies
underperform, while funds that follow contrarian strategies over-perform.
There are two possible explanations. First, stock prices do not follow either
momentum or contrarian movements during down markets, generally moving downward
regardless of their past performance. Therefore, managers trying to follow either
strategy would not be able to add any more alpha to their portfolios. Second, managers
tend to overreact to preserve capital, forcing them to abandon certain strategies they
Page 8 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
used during up markets. Since investment strategies do not assist managers, the
dispersion in mutual fund performance may be caused by differences in mutual fund
objectives.
Again, focusing on the efficacy of the models, the R2 values of all models during
the 2001 bear market period are smaller than those during the 2008-2009 bear period,
especially at the extreme levels. This potentially is from the smaller number of
observations. Nevertheless, the R2 values of all models during the bear periods are
larger than those of the bull periods, demonstrating that both the theoretical and the
empirical models perform better when stock markets are more volatile.
4.4 Mutual Fund Performance by Different Fund Objectives and Market
Capitalizations
Our next step is to examine whether mutual fund performance is consistent
among different fund objectives and market capitalizations. We categorize the funds by
market capitalization (large, mid, and small) with three fund objectives (core, growth,
and value). Tables 6, 7, and 8 and their corresponding Panels A, B, and C present
large, middle (“mid”), and small market capitalization (“cap”) mutual fund portfolios with
their corresponding core, growth, and value fund objectives.
In general, the abnormal returns of the large-cap funds (Table 6, Panels A, B,
and C) are consistent with the overall results. The relationship between excess returns
and the negative SMB factor is as expected (since SMB is a portfolio long small cap
stocks and short large cap stocks, a negative relationship between large cap stock
returns and the SMB factor is expected) for the large-cap core (LCC) and value (LCV)
subgroups. This result is not as strong for the large-cap growth (LCG) funds. The
relationship of the positive (negative) HML factor with the LCV (LCG) is also as
expected, since the HML is a portfolio long value stocks and short growth stocks.
(EXPLAIN) The momentum factor of LCG funds is positive but insignificant, while that of
LCV funds is negative and strongly significant. This implies that growth funds slightly
follow momentum strategies, while value funds do just the opposite.
For the mid-cap funds (Table 7, Panels A, B, and C), the efficacy of the models is
inferior to that of the large-cap funds, providing lower R2 values, especially for the worst
performers of the mid-cap value (MCV) funds. The co-skewness factors are negative
and strongly significant, especially for poor performing portfolios for the mid-cap core
(MCC) and MCV funds. The relationship between mid-cap fund excess returns and the
SMB factors for all models is positive and significant, demonstrating that these mutual
fund managers are not closely following their stated objectives, with mid-cap fund
managers buying small-cap stocks. The momentum factor is negatively significant for
the mid-cap growth (MCG) funds but positively significant for the MCV funds. This
opposes the results for large-cap funds, again exhibiting that mid-cap fund managers do
not invest according to their stated objectives.
For the small-cap funds (Table 8, Panel A, B, and C), like the mid-cap funds, the
efficacy of the models is also inferior to that of the large-cap funds. In general, the
relationships between returns and the size and value factors are as expected (by
construction, as stated above). However, the value factor of the small-cap core (SCC) is
positive and significant, demonstrating that these funds are deviating from their „core‟
Page 9 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
objective. The momentum factor of the small-cap growth (SCG) funds is positive and
significant while that of small-cap value (SCV) funds is negative and significant. This
shows that SCG funds follow the momentum strategy, whereas SCV funds follow the
contrarian strategy.
In conclusion, the abnormal returns detected by all of the models are not
economically different, but the factor loadings do play an important role for different fund
objectives. Specifically, the HML loadings of the MCC and SCC are more important than
that of the LCC. The results of the mid-cap funds contradict with our expectations.
Furthermore, the empirical models are superior to the theoretical models in explaining
the abnormal returns of style-objective funds (value and growth). Interestingly, while we
find that mid-cap and small-cap funds have slightly higher alphas than other subgroups,
we cannot conclude that a fund‟s objective determines dispersion in fund performance.
4.5 Meta-Analysis
While we observe some differences in performance when focusing on economic
cycles and fund objectives, we are further interested in whether the abnormal returns
are significantly different across subgroups for investor decision-making. As a
robustness check, we employ meta-analysis, which is a statistical procedure used to
compare several studies or results with the same hypothesis (Sheskin (2007)). Previous
studies have shown that the significance of alpha depends upon which model is
employed. Yet, no study exists to show whether the significance of alpha is
homogenous across models and deciles. The same level of statistical significance does
not mean, in fact, that the models perform equally well in detecting the abnormal
returns. To test whether there is any difference in the significance level of alpha
(abnormal returns) across models, and across deciles, we hypothesize as follows:
H0: The t-statistic numbers obtained for the k studies are consistent or indifferent
with one another.
H1: The t-statistic numbers obtained for the k studies are inconsistent or different
with one another.
To test the hypothesis above, the following equation is required:
∑(
̅ )
(6)
where and ̅ are the t-statistic for jth study and the average t-statistic values obtained
for the k studies, respectively. The statistic follows the chi-square distribution with n-1
degrees of freedom.
We start our analysis by comparing our results with Elton, Gruber and Blake
(1996) and Carhart (1997). The meta-analysis shows that our findings for the entire
sample differ significantly from those in the previous studies.8 This is expected since we
find that mutual fund alphas monotonically increase from negatively significant to
positively significant, while the prior literature concludes that overall mutual funds
8
For the sake of brevity, we do not include the results for the entire sample. These results are available from the
authors upon request.
Page 10 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
perform poorly. The more interesting findings are those found when studying our
subsamples.
Table 9 displays the results from the meta-analysis performed on our subsample
periods (bull and bear markets). Panel A shows the results when comparing the two bull
market periods (years 1999-2000, and 2002-2007), the two bear market periods (years
2001, and 2008-2009), and the more recent bull and bear market periods (years 20022007, and 2008-2009). Surprisingly, the performance of top and bottom performing
mutual funds differs significantly when comparing the two bull periods and (to a lesser
extent) when comparing the two bear periods. During the 2002-2007 bull market, mutual
fund alphas were more amplified for both the top and bottom performers than for those
in the first sub-period. Since the second bull market lasts longer than the first, the „good
and the bad‟ are further weeded out. Furthermore, the top and bottom performers show
significantly different alphas across the final bull and bear periods. This means that,
even after controlling for the risk factors of the given period, managers are able to better
perform during the bull periods than they are during the bear periods. While the overall
results are similar, this does demonstrate that the economic cycle does have an effect
on a manager‟s perceived skill. As expected, the performance of the ten portfolios for
each model shows significantly different results (Table 9, Panel B).
Table 10 presents the results of the meta-analysis performed on our subsample
of fund capitalizations and objectives. While bottom performers within large- and midcap funds perform differently across objectives (core, growth, and value, as shown in
Panel A), most other portfolios do not have significantly different abnormal returns.
Small-cap funds all perform relatively the same across fund objectives. This makes it
difficult for investors to determine in which objective to invest. Clearly, other factors play
an important role in distinguishing between the top and bottom performers.
Again, as expected, the results are significantly different across the ten portfolios
for each model, capitalization group, and objective group (Panel B). In Panel C, another
finding worthy of note is that, across the different capitalizations for the core objective,
the performance is significantly different for almost all deciles except for the top
performers. The same is only true for the growth subgroup‟s middle to worst performers
(when focusing on the CAPM and three-moment CAPM results). For the value
subgroup, this is only found for the worst performing decile utilizing the three-factor and
four-factor models.
On a final note, the worst performing funds generate different abnormal returns
across market capitalizations and objectives, while top performing funds do not. It is
also interesting to find that the meta-analysis results do not differ drastically across the
different models for value funds.
V. Conclusions and Suggestions for Further Research
In this paper, we utilized the daily CRSP mutual fund database to compute
excess geometric mean returns, which should be considered to be the holding period
returns by mutual fund investors. To investigate mutual fund performance, four wellknown asset pricing models were employed, namely the CAPM, the three-moment
CAPM, the three-factor model, and four-factor model. We examined various aspects of
mutual fund performance. First, we looked at whether or not mutual fund performances
Page 11 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
depend on economic cycles, and stated fund objectives. Then, we analyzed the efficacy
of the asset pricing models for mutual fund performance. As part of our analysis, we
employed meta-analysis to test the homogeneity of the mutual fund performances
across models and deciles.
Our results show that the dispersion of abnormal returns holds under different
economic conditions and over different style objectives. While the magnitude of positive
and negative performance slightly differs across subgroups, abnormal returns
monotonically increase from significantly negative to significantly positive in all
subgroups. While certain factors do assist in explaining portfolio excess returns, the
factors play a much more subdued role than found in previous studies. The theoretical
models performed well during this sample period. However, the empirical models
explained the excess returns of mutual funds by objective better than the theoretical
models.
Furthermore, the use of meta-analysis demonstrates that the abnormal
performance of mutual funds significantly differ between bull and bear periods. This
means that managers benefit from upward trending markets, but they are not as well
suited to counter downward trends. This is a disadvantage to investors since protection
on the downside is more important than overly achieving on the upside (Tversky and
Kahneman (1991)). The meta-analysis also demonstrates that while all bottom
performers have statistically significant negative returns, these returns significantly differ
by style objective. It is also important to note that mid-cap funds do not invest according
to their stated objective. We found that most mid-cap funds are heavily invested in
small-cap equities.
Using daily data rather than monthly data, we test mutual fund performance
using different models. We find that a certain subgroup outperforms during all subperiods and in all sub-samples. We conclude that mutual fund performance is managerspecific, and not market-cycle or objective specific. While the previous literature found
momentum to be an important determinant of mutual fund performance, the momentum
factor plays a much more subdued role in our sample. This finding agrees with the
overall story during our sample-period: no one strategy existed to assist managers in
avoiding the two devastating crashes. More research needs to be performed to
determine what allowed this subgroup to succeed. While knowing that certain managers
over-perform is important, it is of little use if investors cannot distinguish between
characteristics or strategies that make mutual fund managers successful.
References
Aragon, G.O., Ferson, W.E., 2006. Portfolio performance evaluation. Foundations and
Trends in Finance 2, 83-190.
Barras, Laurent, Olivier Scaillet, and Russ Wermers (2010) False Discoveries in Mutual
Fund Performance: Measuring Luck in Estimated Alphas, Journal of Finance, 65(1),
179-216.
Brown, S.J., Goetzmann, W.N., 1995. Performance persistence. Journal of Finance 50,
679-698.
Page 12 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Carhart, Mark M., 1997, “On Persistence in Mutual Fund Performance,” Journal of
Finance 52(1), 57-82.
Copeland, Maggie M., and Thomas E. Copeland,1999, “Market Timing: Style and Size
Rotation Using the VIX,” Financial Analysts Journal 55(2), 73-81.
Daniel, Kent, Mark Grinblatt, Sheridan Titman, and Russ Wermers, 1997, “Measuring
Mutual Fund Performance with Characteristics-Based Portfolios,” Journal of Finance
52(3), 1035-1058.
DeFusco, Richard A., Dennis W. McLeavey, Jerald E. Pinto, and David E. Runkle
(2007) Quantitative Investment Analysis (CFA Institute Investment Series).
Elton, Edwin J., Martin J. Gruber, and Christopher R, Blake, 1996, “The Persistence of
Risk-Adjusted Mutual Fund Performance,” Journal of Business 69(2), 133-157.
Fama, Eugene F., 1972, “Components of Investment Performance,” Journal of Finance
27(3), 551-657.
Fama, Eugene F., and Kenneth R. French, 1993, “Common Risk Factors in the Returns
on Stocks and Bonds,” Journal of Financial Economics 33(1), 3-56.
Ferson, Wayne E., and J. Lin, 2010. Alpha and performance measurement: The effect
of investor heterogeneity. University of Southern California. Working Paper.
Ferson, Wayne E., and Rudi W. Schadt, 1996, “Measuring Fund Strategy and
Performance in Changing Economic Conditions, Journal of Finance 52(2), 425-461.
Goetzmann, W.N., Ibbotson, R.G., 1994. Do winners repeat? Patterns in mutual fund
performance. Journal of Portfolio Management 20, 9-18.
Goodwin, Thomas H., 1998, “The Information Ratio,” Financial Analysts Journal 54(4),
34-43.
Grinblatt, M., Titman, S., 1989. Mutual fund performance: An analysis of quarterly
portfolio holdings. Journal of Business 62, 393-416.
Grinblatt, M., Titman, S., 1992. The persistence of mutual fund performance. Journal of
Finance 47, 1977-1984.
Grinblatt, Mark, and Sheridan Titman, 1993, “Performance Measurement without
Benchmarks: An Examination of Mutual Fund Returns,” Journal of Business 66(1),
47-68.
Hendricks, D., Patel, J., Zeckhauser, R., 1993. Hot hands in mutual funds: Short-run
persistence of relative performance, 1974-1988. Journal of Finance 48, 93-130.
Jegadeesh, N., Titman, S., 1993. Returns to buying winners and selling losers:
implications for market efficiency. Journal of Financa 48, 65-91.
Jensen, Michael C., 1968, “The Performance of Mutual Funds in the Period 1945-1964,”
Journal of Finance 23(2), 389-416.
Jensen, M.C., 1969. Risk, the pricing of capital assets, and the evaluation of investment
portfolios. Journal of Business 42, 167-247.
Kim, Woochan, and Shang-Jin Wei (2002) “Foreign Portfolio Investors before and
during a Crisis,” Journal of International Economics 56(1), 77-96.
Korajczyk, R. A., Sadka, R., 2004. Are momentum profits robust to trading costs?
Journal of Finance 59, 1039-1082.
Kosowski, R., 2011. Do mutual funds perform when it matters most to investors? US
mutual fund performance and risk in recessions and expansions. Quarterly Journal
of Finance, 1(3), 607-664.
Page 13 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Kosowski, R., Timmermann, A., Wermers, R., White, H., 2006. Can mutual fund "stars"
really pick stocks? New evidence from a bootstrap analysis. Journal of Finance
61(6), 2551-2595.
Kraus, A., Litzenberger, R., 1976. Skewness preference and the valuation of risk
assets. Journal of Finance 31, 1085-1100.
Lessard, Donald R., 1976, “World, Country and Industry Relationships in Equity
Returns,” Financial Analysts Journal 1(1), 32-38.
Lo, Andrew, 2002, “The Statistics of Sharpe Ratios,” Financial Analysts Journal 58(4),
36-52.
Reilly, Frank K., and Keith C. Brown, 2012, Investment Analysis & Portfolio
Management, South-Western Cengage Learning
Roll, Richard, 1978, “Ambiguity when Performance is Measured by the Securities
Market Line,” Journal of Finance 33(4), 1051-1069.
Roll, R., 1981. A possible explanation of the small firm effect. Journal of Finance 36,
879-888.
Schwarz, G.,1978, “Estimating the Dimension of a Model,” Annals of Statistics 6, 461464.
Sears, R.S., John Wei, K.C., 1985. Asset Pricing, Higher Moments, and the Market Risk
Premium: A Note. Journal of Finance 40, 1251-1253.
Sharpe, William F., 1964, “Capital Asset Prices: A Theory of Market Equilibrium under
Conditions of Risk,” Journal of Finance 19(3), 425-442.
Sharpe, William F.,1966, “Mutual Fund Performance,” Journal of Business 39(1 part 2),
119-138.
Sharpe, William F.,1994, “The Sharpe Ratio,” Journal of Portfolio Management, 21(1),
49-59.
Sharpe, William F.,2007, Investors and Markets: Portfolio Choices, Asset Prices, and
Investment Advice. Princeton, NJ: Princeton University Press.
Sheskin, D.J., 2007. Handbook of Parametric and Nonparametric Statistical Procedure
(Fourth Edition). Chapman & Hall/ CRC, Taylor & Francis Group, Boca Raton,
Florida.
Sortino, Frank A., and Lee N. Price, 1994, “Performance Measurement in a Downside
Risk Framework,” Journal of Investing 3(3), 59-65.
Treynor, Jack L., 1965, “How to Rate Management of Investment Funds,” Harvard
Business Review 43(1), 63-75.
Treynor, Jack L., and Fischer Black, 1973, “How to Use Security Analysis to Improve
Security Selection,” Journal of Business 46(1), 66-86.
Tversky, A., and D. Kahneman, 1991, Loss aversion in riskless choice: A referencedependent model, The Quarterly Journal of Economics 106, 1039-1061.
Page 14 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Appendix 1
Mutual Fund Type
Large-Cap Core (LCC)
Definition
At least 75% with market capitalization greater than 300% that of the middle
1000 of the S&P Super Composite 1500 and average price-to-cash flow, priceto-book, and three-year growth
Large-Cap Value (LCV)
At least 75% with market capitalization greater than 300% that of the middle
1000 of the S&P Super Composite 1500 and below average price-to-cash flow,
price-to-book, and three-year growth
Large-Cap Growth (LCG)
At least 75% with market capitalization greater than 300% that of the middle
1000 of the S&P Super Composite 1500 and above average price-to-cash flow,
price-to-book, and three-year growth
Mid-Cap Core (MCC)
At least 75% with market capitalization less than 300% that of the middle 1000
of the S&P Super Composite 1500 and average price-to-cash flow, price-tobook, and three-year growth
Mid-Cap Value (MCV)
At least 75% with market capitalization less than 300% that of the middle 1000
of the S&P Super Composite 1500 and below average price-to-cash flow, priceto-book, and three-year growth
Mid-Cap Growth (MCG)
At least 75% with market capitalization less than 300% that of the middle 1000
of the S&P Super Composite 1500 and above average price-to-cash flow, priceto-book, and three-year growth
Small-Cap Core (SCC)
At least 75% with market capitalization less than 250% that of the middle 1000
of the S&P Super Composite 1500 and average price-to-cash flow, price-tobook, and three-year growth
Small-Cap Value (SCV)
At least 75% with market capitalization less than 250% that of the middle 1000
of the S&P Super Composite 1500 and below average price-to-cash flow, priceto-book, and three-year growth
Small-Cap Growth (SCG)
At least 75% with market capitalization less than 250% that of the middle 1000
of the S&P Super Composite 1500 and above average price-to-cash flow, priceto-book, and three-year growth
Page 15 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 1 - Summary Statistics
This table presents the number of mutual funds over the period of 1999-2009, along with the number of
mutual funds by fund objective. The average number of funds column denotes the average number of
mutual funds in each month over the period of 1999-2009.
Portfolio
Number of
Average
Funds
Number of
Funds
Total
13,232
3988.7
LCC
2,617
764.84
LCV
1,440
414.12
LCG
2,220
651.61
MCC
1,013
268.97
MCV
835
231.8
MCG
1,399
455.67
SCC
1,620
496.22
SCV
857
261.83
SCG
1,231
443.64
Page 16 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 2 - Factor Summary Statistics
This table presents the correlation matrix between the different factors. MKT represents the market risk
premium. SMB, HML, and PR1YR are size, value, and momentum factors, respectively.
Factor
Correlation Matrix
Portfolio
MKT
SMB
HML
MKT
1.00000
SMB
0.28895
1.00000
HML
-0.22308
-0.35588
1.00000
PR1YR
-0.41899
0.08765
-0.07040
PR1YR
1.00000
Table 3 - Ranking Portfolios Employing Mean Excess Geometric Return
This table presents the equally weighted mutual fund portfolios, over the period of 19992009, sorted into deciles. The portfolio ranking is based on the previous month's
geometric mean excess return. MKT and SKEW represent the market risk premium and
the squared market risk premium (co-skewness), respectively. SMB, HML, and PR1YR
are the size, value, and momentum factors, respectively. The variables in the regression
equations are the daily geometric mean for each month. The equally weighted portfolio
reflects the disappearance of mutual funds over the sample period. Deciles one and ten
denote the best and worst performers of mutual fund portfolios, respectively. The
adjusted R2 and alpha () (abnormal return) are in percentages. The t-statistics are
shown in parentheses, with the italic and bold numbers indicating significance at the 5%
and 1% levels, respectively.
Page 17 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Port.
CAPM

3-Moment Model
Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
5
6
7
8
9
10
-0.255
1.420
(-16.93)
(22.50)
-0.127
1.137
(-16.18)
(34.36)
-0.086
1.080
(-15.11)
(45.39)
-0.054
1.040
(-12.29)
(56.30)
-0.028
1.009
(-7.25)
(63.11)
-0.003
0.989
(-0.88)
(63.44)
0.002
0.976
(5.17)
(55.16)
0.049
0.969
(9.12)
(43.32)
0.080
0.965
(11.30)
(32.57)
0.001
0.970
(12.60)
(24.29)
79.41
90.01
94.02
96.03
96.82
96.85
95.87
93.47
89.00
81.80
3-Factor Model
2

4-Factor Model
Adj.
MKT
SMB
HML
R
-0.209
1.287
-2.442
(-12.50)
(20.04)
(-4.89)
-0.115
1.101
-0.669
(-12.32)
(30.69)
(-2.40)
-0.076
1.051
-0.529
(-11.34)
(40.95)
(-2.65)
-0.046
1.017
-0.422
(-8.91)
(51.11)
(-2.73)
-0.022
0.992
-0.319
(-4.79)
(57.19)
(-2.36)
0.001
0.977
-0.222
(0.20)
(57.17)
(-1.67)
0.024
0.969
-0.132
(4.77)
(49.56)
(-0.87)
0.049
0.967
-0.047
(7.67)
(39.01)
(-0.24)
0.079
0.966
0.021
(9.29)
(29.43)
(0.08)
0.118
0.977
0.125
(10.21)
(22.08)
(0.36)
82.50
90.36
94.28
96.22
96.92
96.89
95.86
93.43
88.91
81.68
2
Adj.

MKT
SMB
HML
PR1YR
-0.258
1.378
0.180
-0.011
-0.003
(-16.94)
(18.30)
(1.93)
(-0.12)
(-0.05)
-0.130
1.096
0.131
0.022
-0.029
(-16.52)
(28.12)
(2.73)
(0.47)
(-1.07)
-0.088
1.053
0.099
0.049
-0.024
(-15.67)
(37.82)
(2.87)
(1.49)
(-1.22)
-0.057
1.023
0.076
0.068
-0.020
(-13.15)
(48.06)
(2.90)
(2.68)
(-1.37)
-0.030
0.998
0.072
0.081
-0.016
(-8.35)
(-8.35)
(3.25)
(3.76)
(-1.29)
-0.006
0.978
0.081
0.082
-0.012
(-1.77)
(56.41)
(3.77)
(3.95)
(-1.01)
0.018
0.963
0.111
0.085
-0.005
(4.63)
(49.49)
(4.64)
(3.65)
(-0.38)
0.044
0.950
0.160
0.085
0.002
(8.98)
(39.13)
(5.35)
(2.94)
(0.12)
0.074
0.935
0.229
0.082
0.011
(11.58)
(29.58)
(5.85)
(2.18)
(0.49)
0.113
0.926
0.316
0.064
0.024
(13.29)
(22.05)
(6.10)
(1.27)
(0.83)
R
-0.258
1.380
0.179
-0.010
(-17.01)
(20.92)
(1.96)
(-0.12)
-0.130
1.115
0.121
0.028
(-16.54)
(32.54)
(2.57)
(0.60)
-0.088
1.069
0.091
0.054
(-15.67)
(43.59)
(2.68)
(1.64)
-0.057
1.037
0.069
0.072
(-13.14)
(55.21)
(2.68)
(2.86)
-0.030
1.009
0.067
0.084
(-8.36)
(63.68)
(3.05)
(3.93)
-0.006
0.987
0.076
0.084
(-1.79)
(64.70)
(3.65)
(4.10)
0.018
0.966
0.110
0.086
(4.64)
(56.68)
(4.67)
(3.73)
0.044
0.948
0.161
0.085
(9.02)
(44.61)
(5.50)
(2.96)
0.074
0.927
0.232
0.080
(11.63)
(33.47)
(6.08)
(2.15)
0.113
0.909
0.325
0.059
(13.33)
(24.66)
(6.39)
(1.18)
Page 18 of 36
79.79
90.35
94.28
96.31
97.19
97.29
96.55
94.70
91.34
86.05
R2
79.63
90.36
94.30
96.33
97.20
97.29
96.53
94.65
91.28
86.01
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 4 Ranking Portfolios over Bull Periods
Panels A and B provide mutual fund performance over the bull market periods from 1999-2009.. The definitions of variables are the same as in Table 3. The t-statistics are shown in
parentheses, with the italic and bold numbers indicating significance at the 5% and 1% levels, respectively.
Panel A: Years 1999-2000
Port.
CAPM
3-Moment Model
3-Factor Model
4-Factor Model

Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
5
6
7
8
9
10
-0.455
1.399
(-11.00)
(7.83)
-0.213
0.990
(-9.02)
(9.69)
-0.135
0.941
(-8.66)
(13.96)
-0.077
0.904
(-7.35)
(19.97)
-0.030
0.886
(-3.85)
(26.13)
0.013
0.885
(1.72)
(27.13)
0.058
0.903
(5.67)
(20.60)
0.106
0.932
(6.93)
(14.06)
0.165
0.971
(7.23)
(9.86)
0.243
1.047
(7.37)
(7.37)
72.41
80.14
89.40
94.54
96.74
96.97
94.85
89.53
80.70
69.85
2

Adj.
MKT
SMB
HML
R
-0.297
1.198
-8.904
(-4.23)
(6.83)
(-2.65)
-0.149
0.908
-3.607
(-3.45)
(8.38)
(-1.74)
-0.107
0.905
-1.611
(-3.59)
(12.18)
(-1.13)
-0.077
0.905
0.024
(-3.78)
(17.61)
(0.02)
-0.047
0.907
0.930
(-3.17)
(24.52)
(1.32)
-0.013
0.918
1.464
(-0.99)
(27.78)
(2.32)
0.027
0.943
1.740
(1.46)
(20.66)
(1.99)
0.074
0.974
1.844
(2.55)
(13.48)
(1.33)
0.133
1.011
1.796
(3.03)
(9.21)
(0.85)
0.219
1.077
1.323
(3.42)
(6.71)
(0.43)
78.34
81.81
89.53
94.28
96.84
97.47
95.46
89.89
80.47
68.69
2
Adj.

MKT
SMB
HML
PR1YR
-0.442
1.396
0.245
-0.018
-0.187
(-9.71)
(5.04)
(1.10)
(-0.06)
(-1.03)
-0.203
0.984
0.136
-0.034
-0.135
(-7.88)
(6.28)
(1.08)
(-0.20)
(-1.31)
-0.128
0.977
0.097
0.022
-0.010
(-7.56)
(9.49)
(1.18)
(0.19)
(-1.47)
-0.072
0.968
0.065
0.059
-0.072
(-6.57)
(14.47)
(1.22)
(0.80)
(-1.63)
-0.028
0.968
0.063
0.095
-0.46
(-3.66)
(20.59)
(1.66)
(1.86)
(-1.48)
0.013
0.967
0.076
0.112
-0.028
(1.74)
(21.28)
(2.08)
(2.25)
(-0.94)
0.054
0.986
0.117
0.143
-0.005
(5.42)
(16.16)
(2.38)
(2.15)
(-0.12)
0.099
1.011
0.185
0.184
0.020
(6.93)
(11.60)
(2.64)
(1.93)
(0.36)
0.153
1.026
0.270
0.211
0.050
(7.60)
(8.37)
(2.74)
(1.58)
(0.62)
0.226
1.048
0.375
0.213
0.086
(8.20)
(6.25)
(2.78)
(1.16)
(0.78)
R
-0.458
1.377
0.145
0.034
(-10.66)
(4.98)
(0.72)
(0.11)
-0.215
0.970
0.064
0.003
(-8.68)
(6.09)
(0.55)
(0.02)
-0.137
0.967
0.044
0.049
(-8.31)
(9.15)
(0.57)
(0.43)
-0.078
0.961
0.027
0.079
(-7.26)
(13.83)
(0.54)
(1.05)
-0.032
0.963
0.038
0.108
(-4.28)
(19.95)
(1.10)
(2.08)
0.011
0.964
0.061
0.119
(1.51)
(21.33)
(1.86)
(2.45)
0.054
0.985
0.114
0.145
(5.86)
(16.60)
(2.65)
(2.26)
0.101
1.013
0.196
0.178
(7.65)
(11.91)
(3.18)
(1.94)
0.158
1.031
0.297
0.197
(8.42)
(8.56)
(3.40)
(1.52)
0.233
1.056
0.421
0.189
(9.07)
(6.38)
(3.51)
(1.06)
Page 19 of 36
70.90
78.79
88.52
94.33
97.08
97.43
95.85
92.42
87.27
81.96
R2
70.99
79.52
89.16
94.77
97.25
97.42
95.63
92.08
86.87
81.60
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Years 2002-2007
Port.
1
2
3
4
5
6
7
8
9
10
CAPM
3-Moment Model
Adj.

MKT
-0.162
1.239
(-18.47)
(23.61)
-0.091
1.107
(-15.01)
(30.41)
-0.064
1.066
(-12.86)
(35.90)
-0.043
1.038
(-9.98)
(40.45)
-0.025
1.023
(-6.35)
(43.43)
-0.008
1.011
(-2.10)
(44.26)
0.009
1.002
(2.36)
(42.11)
0.028
0.999
(6.28)
(38.11)
0.049
0.993
(9.48)
(32.41)
0.075
0.992
R2
88.68
92.86
94.78
95.84
96.37
96.50
96.15
95.34
93.66
Adj.

MKT
SKEW
-0.124
1.160
-3.863
(-11.42)
(24.14)
(-4.97)
-0.066
1.054
-2.557
(-8.64)
(31.15)
(-4.67)
-0.045
1.027
-1.911
(-7.03)
(36.28)
(-4.17)
-0.028
1.008
-1.482
(-4.97)
(40.21)
(-3.66)
-0.014
0.999
-1.151
(-2.55)
(42.31)
(-3.01)
0.008
0.993
-0.887
(0.15)
(42.32)
(-2.34)
0.016
0.988
-0.651
(2.82)
(39.67)
(-1.61)
0.032
0.989
-0.467
(5.14)
(35.65)
(-1.04)
0.051
0.987
-0.292
(7.00)
(30.26)
(-0.55)
0.991
-0.060
(25.83)
(-0.10)
91.46
3-Factor Model
R2
91.55
94.50
95.77
96.46
96.75
96.71
96.23
95.34
93.60
91.34
4-Factor Model
Adj.

MKT
SMB
HML
-0.164
1.194
0.249
0.018
(-19.33)
(22.60)
(3.27)
(0.20)
-0.093
1.077
0.183
0.042
(-16.14)
(29.89)
(3.53)
(0.67)
-0.066
1.045
0.135
0.044
(-13.73)
(35.09)
(3.15)
(0.85)
-0.045
1.023
0.107
0.048
(-10.70)
(39.46)
(2.88)
(1.08)
-0.027
1.010
0.097
0.048
(-6.98)
(42.44)
(2.83)
(1.17)
-0.010
0.998
0.098
0.053
(-2.68)
(43.70)
(2.99)
(1.35)
0.007
0.987
0.116
0.060
(1.96)
(42.44)
(3.45)
(1.50)
0.025
0.979
0.144
0.070
(6.27)
(39.41)
(4.03)
(1.63)
0.046
0.966
0.191
0.081
(10.13)
(34.54)
(4.75)
(1.68)
0.071
0.957
0.242
0.089
(13.83)
(29.87)
(5.24)
(1.61)
R2
89.99
93.89
95.41
96.29
96.76
96.94
96.79
96.34
95.39
94.09
Adj.

MKT
SMB
HML
PR1YR
-0.166
1.236
0.225
-0.012
0.067
(-19.35)
(20.08)
(2.89)
(-0.13)
(1.31)
-0.095
1.107
0.166
0.020
0.049
(-16.24)
(26.42)
(3.13)
(0.31)
(1.39)
-0.067
1.071
0.120
0.025
0.042
(-13.87)
(30.95)
(2.75)
(0.48)
(1.43)
-0.046
1.046
0.095
0.032
0.036
(-10.87)
(34.72)
(2.48)
(0.70)
(1.44)
-0.028
1.028
0.087
0.035
0.029
(-7.12)
(37.06)
(2.47)
(0.83)
(1.26)
-0.011
1.013
0.090
0.043
0.024
(-2.83)
(37.92)
(2.67)
(1.05)
(1.06)
0.007
1.001
0.108
0.050
0.023
(1.75)
(36.77)
(3.13)
(1.21)
(0.99)
0.024
0.993
0.136
0.060
0.022
(6.00)
(34.10)
(3.70)
(1.35)
(0.91)
0.045
0.979
0.184
0.072
0.021
(9.80)
(29.81)
(4.43)
(1.43)
(0.77)
0.071
0.968
0.235
0.081
0.018
(13.45)
(25.68)
(4.94)
(1.41)
(0.56)
0.075
(12.42)
(27.60)
(8.71)
Page 20 of 36
R2
90.09
93.98
95.48
96.35
96.79
96.95
96.79
96.33
95.36
94.03
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 5 - Ranking Portfolios over Bear Periods : This table shows the mutual fund performance over the bear market period. The definitions of variables are the same as in Table 3. The t-statistics are
shown in parentheses, with the italic and bold numbers indicating significance at the 5% and 1% levels, respectively. Panel A: Year 2001
Port.
CAPM
3-Moment Model
3-Factor Model
4-Factor Model

Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
-0.349
1.605
(-6.68)
(10.11)
-0.180
1.299
(-6.00)
(14.20)
-0.119
1.190
(-4.94)
(16.24)
-0.073
1.116
90.19
94.80
95.98
96.31
2

Adj.
MKT
SMB
HML
R
-0.261
1.566
-4.527
(-2.90)
(9.83)
(-1.18)
-0.130
1.277
-2.619
(-2.50)
(13.93)
(-1.18)
-0.087
1.175
-1.652
(-2.04)
(15.56)
(-0.90)
-0.055
1.108
-0.941
90.55
95.00
95.91
2
Adj.

MKT
SMB
HML
PR1YR
-0.360
1.468
0.361
-0.298
0.124
(-5.75)
(4.52)
(1.12)
(-0.88)
(0.48)
-0.201
1.137
0.226
-0.041
-0.040
(-5.77)
(6.28)
(1.26)
(-0.22)
(-0.28)
-0.141
1.012
0.140
0.045
-0.110
(-5.25)
(7.24)
(1.01)
(0.31)
(-0.98)
-0.096
0.931
0.085
0.106
-0.156
(-4.26)
(7.93)
(0.73)
(0.87)
(-1.66)
-0.060
0.853
0.042
0.162
-0.199
(-2.98)
(8.12)
(0.40)
(1.48)
(-2.37)
-0.028
0.802
0.010
0.165
-0.215
(-1.49)
(8.28)
(0.11)
(1.64)
(-2.78)
0.004
0.748
0.011
0.195
-0.240
(0.21)
(7.46)
(0.11)
(1.86)
(-3.00)
0.039
0.692
0.010
0.226
-0.276
(1.74)
(5.97)
(0.09)
(1.87)
(-2.99)
0.017
0.259
-0.316
(0.12)
(1.74)
(-2.77)
R
-0.369
1.368
0.303
-0.211
(-6.47)
(5.81)
(1.07)
(-0.78)
-0.119
1.170
0.245
-0.069
(-6.32)
(9.00)
(1.57)
(-0.46)
-0.134
1.101
0.192
-0.032
(-5.19)
(10.34)
(1.51)
(-0.26)
1.057
0.159
-0.003
(10.68)
(1.34)
(-0.02)
-0.046
1.014
0.136
0.023
(-1.91)
(10.07)
(1.13)
(0.20)
-0.013
0.976
0.112
0.015
(-0.52)
(9.73)
(0.94)
(0.13)
0.021
0.942
0.125
0.026
(0.80)
(8.71)
(0.96)
(0.21)
0.058
0.916
0.142
0.032
(1.93)
(7.34)
(0.95)
(0.22)
0.102
0.895
0.167
0.038
96.03
90.35
95.32
96.21
96.27
R2
89.32
94.71
96.19
96.94
-0.085
(-3.37)
(16.96)
(-1.39)
(15.90)
(-0.56)
(-3.57)
5
6
7
8
-0.035
1.049
(-1.65)
(16.18)
-0.004
1.007
(-0.17)
(15.94)
0.031
0.971
(1.39)
(14.22)
0.070
0.947
95.95
95.84
94.81
-0.030
1.047
-0.272
(-0.76)
(15.01)
(-0.16)
-0.009
1.009
0.272
(-0.23)
(14.86)
(0.17)
0.021
0.976
0.556
(0.50)
(13.33)
(0.31)
0.956
0.986
(11.43)
(0.49)
0.944
1.432
92.90
95.52
95.39
94.30
92.32
95.64
95.31
94.19
92.02
97.24
97.46
97.09
95.99
0.051
(2.71)
(12.04)
(1.08)
9
0.116
0.932
90.00
0.089
89.31
88.74
0.080
93.86
0.639
(3.79)
(10.00)
(1.59)
(9.59)
(2.86)
(0.60)
(6.05)
(0.94)
(2.90)
(0.22)
(4.46)
10
0.176
0.930
(4.82)
(8.38)
86.29
0.138
0.947
1.956
(2.10)
(8.13)
(0.69)
85.54
0.160
0.878
0.195
0.030
(3.75)
(4.98)
(0.92)
(0.15)
Page 21 of 36
84.51
0.136
0.596
0.030
0.274
-0.348
(3.82)
(3.24)
(0.16)
(1.42)
(-2.37)
90.17
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Years 2008-2009
Port.
1
2
3
4
5
6
7
8
9
10
CAPM
3-Moment Model
Adj.

MKT
-0.269
1.386
(-8.73)
(15.62)
-0.117
1.118
(-8.45)
(27.98)
-0.080
1.082
(-6.40)
(30.02)
-0.052
1.056
(-4.50)
(31.68)
-0.028
1.034
(-2.53)
(32.85)
-0.005
1.015
(-0.47)
(32.92)
0.018
1.001
(1.62)
(32.07)
0.41
0.992
(3.66)
(30.39)
0.068
0.982
(5.66)
(28.44)
0.102
0.976
(7.99)
(26.49)
R2
91.35
97.14
97.51
97.76
97.91
97.92
97.81
97.57
97.23
96.82
3-Factor Model
Adj.

MKT
SKEW
-0.162
1.141
-2.465
(-6.95)
(18.62)
(-6.92)
-0.094
1.066
-0.528
(-5.40)
(23.21)
(-1.98)
-0.060
1.036
-0.464
(-3.79)
(24.90)
(-1.92)
-0.034
1.014
-0.417
(-2.31)
(26.24)
(-1.86)
-0.012
0.999
-0.353
(-0.87)
(26.92)
(-1.64)
0.008
0.986
-0.293
(0.55)
(26.65)
(-1.37)
0.027
0.979
-0.224
(1.89)
(25.66)
(-1.01)
0.049
0.975
-0.172
(3.20)
(24.16)
(-0.73)
0.074
0.968
-0.144
(4.55)
(22.56)
(-0.58)
0.106
0.967
-0.097
(6.09)
(21.02)
(-0.36)
R2
97.24
97.48
97.78
97.98
98.06
98.00
97.81
97.52
97.14
96.69
4-Factor Model
Adj.

MKT
SMB
HML
-0.283
1.305
0.502
0.032
(-9.38)
(12.79)
(1.97)
(0.18)
-0.125
1.075
0.288
0.007
(-9.83)
(24.96)
(2.67)
(0.10)
-0.088
1.037
0.268
0.024
(-7.82)
(27.33)
(2.82)
(0.35)
-0.059
1.012
0.249
0.028
(-5.75)
(29.03)
(2.85)
(0.45)
-0.034
0.993
0.231
0.029
(-3.51)
(29.97)
(2.78)
(0.48)
-0.012
0.974
0.228
0.030
(-1.24)
(30.18)
(2.82)
(0.53)
0.011
0.958
0.237
0.032
(1.11)
(29.72)
(2.93)
(0.56)
0.034
0.946
0.256
0.031
(3.46)
(28.47)
(3.08)
(0.51)
0.060
0.933
0.279
0.033
(5.83)
(26.94)
(3.22)
(0.53)
0.093
0.921
0.312
0.039
(8.80)
(25.74)
(3.48)
(0.60)
R2
92.15
97.72
98.10
98.31
98.41
98.44
98.40
98.27
98.09
97.95
Adj.

MKT
SMB
HML
PR1YR
-0.259
1.410
0.465
0.124
0.163
(-8.54)
(13.09)
(1.95)
(0.71)
(2.05)
-0.108
1.148
0.262
0.072
0.114
(-10.82)
(32.31)
(3.34)
(1.24)
(4.36)
-0.074
1.097
0.247
0.077
0.094
(-7.90)
(33.01)
(3.36)
(1.43)
(3.84)
-0.048
1.062
0.231
0.072
0.078
(-5.20)
(32.43)
(3.19)
(1.35)
(3.22)
-0.025
1.033
0.217
0.064
0.062
(-2.68)
(30.85)
(2.93)
(1.17)
(2.52)
-0.005
1.004
0.217
0.057
0.046
(-0.51)
(28.86)
(2.83)
(1.00)
(1.81)
0.015
0.978
0.230
0.049
0.030
(1.46)
(26.79)
(2.85)
(0.82)
(1.11)
0.036
0.954
0.254
0.037
0.011
(3.27)
(24.67)
(2.97)
(0.58)
(0.38)
0.058
0.926
0.282
0.027
-0.011
(5.12)
(22.98)
(3.16)
(0.41)
(-0.36)
0.088
0.898
0.019
-0.034
(7.74)
(22.27)
(0.29)
(-1.16)
0.320
(3.59)
Page 22 of 36
R2
93.23
98.80
98.88
98.85
98.75
98.60
98.41
98.19
98.00
97.98
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 6 - Ranking Large Capitalization Mutual Fund Portfolio with Different Objectives
This table presents large capitalization mutual fund performance classified by fund objectives. The definitions of variables are the same as in Table 3. The
t-statistics are shown in parentheses, with the italic and bold numbers indicating significance at the 5% and 1% levels, respectively.
Panel A: Large Capitalization Core Equity
Port.
CAPM

3-Moment Model
Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
5
6
-0.149
1.107
(-18.99)
(33.53)
-0.068
0.962
(-15.06)
(50.48)
-0.047
0.948
(-10.54)
(50.85)
-0.032
0.937
(-7.30)
(50.19)
-0.021
0.931
(-4.73)
(49.85)
-0.011
0.923
89.55
95.11
95.18
95.05
94.99
94.91
3-Factor Model
2

4-Factor Model
Adj.
MKT
SMB
HML
R
-0.118
1.016
-1.663
(-14.50)
(32.42)
(-6.83)
-0.063
0.945
-0.312
(-11.55)
(45.40)
(-1.93)
-0.044
0.940
-0.149
(-8.22)
(45.66)
(-0.93)
-0.031
0.934
-0.070
(-5.79)
(45.15)
(-0.44)
-0.021
0.931
0.008
(-3.93)
(45.02)
(0.05)
-0.012
0.927
0.074
92.27
95.21
95.17
95.02
94.95
94.87
2
Adj.

MKT
SMB
HML
PR1YR
-0.148
1.178
-0.144
0.068
0.049
(-19.52)
(31.27)
(-3.09)
(1.51)
(1.88)
-0.067
1.003
-0.139
0.050
0.005
(-16.95)
(51.35)
(-5.74)
(2.15)
(0.34)
-0.045
0.984
-0.144
0.048
-0.006
(-12.07)
(53.19)
(-6.29)
(2.16)
(-0.47)
-0.031
0.971
-0.148
0.047
-0.011
(-8.32)
(53.34)
(-6.56)
(2.16)
(-0.87)
-0.019
0.962
-0.145
0.044
-0.015
(-5.18)
(52.59)
(-6.43)
(2.03)
(-1.14)
-0.009
0.950
-0.142
0.041
-0.018
(-2.31)
(51.60)
(-6.25)
(1.85)
(-1.42)
0.001
0.942
-0.137
0.039
-0.020
(0.37)
(50.69)
(-5.96)
(1.77)
(-1.52)
0.012
0.934
-0.126
0.036
-0.018
(3.27)
(49.85)
(-5.43)
(1.62)
(-1.36)
0.026
0.925
-0.115
0.032
-0.017
(6.63)
(47.51)
(-4.76)
(1.40)
(-1.22)
0.047
0.912
-0.092
0.031
-0.016
(10.58)
(41.70)
(-3.42)
(1.20)
(-1.03)
R
-0.148
1.144
-0.127
0.058
(-19.29)
(34.21)
(-2.75)
(1.29)
-0.067
1.000
-0.137
0.049
(-17.00)
(58.42)
(-5.80)
(2.13)
0.045
0.988
-0.146
0.049
(-12.12)
(60.93)
(-6.52)
(2.24)
-0.031
0.979
-0.151
0.049
(-8.35)
(61.19)
(-6.87)
(2.28)
-0.019
0.972
-0.150
0.047
(-5.20)
(60.36)
(-6.77)
(2.18)
-0.009
0.963
-0.149
0.044
90.35
96.45
96.71
96.73
96.65
R2
90.53
96.42
96.69
96.73
96.66
96.55
96.52
7
8
9
10
(-2.37)
(49.41)
-0.000
0.916
(-0.11)
(49.27)
0.011
0.911
(2.45)
(49.92)
0.024
0.904
(5.62)
(49.34)
0.046
0.895
(9.81)
(45.91)
94.88
95.00
94.89
94.15
(-2.22)
(44.83)
(0.46)
-0.003
0.923
0.126
(-0.53)
(44.91)
(0.79)
0.007
0.920
0.176
(1.41)
(45.75)
(1.12)
0.020
0.917
0.251
(3.79)
(45.66)
(1.61)
0.038
0.917
0.387
(6.96)
(43.31)
(2.35)
94.86
95.01
94.95
94.34
(-2.34)
(59.22)
(-6.63)
(2.02)
0.001
0.955
-0.144
0.043
(0.33)
(58.17)
(-6.34)
(1.95)
0.012
0.947
-0.132
0.040
(3.23)
(57.24)
(-5.79)
(1.79)
0.026
0.936
-0.120
0.036
(6.59)
(54.59)
(-5.09)
(1.55)
0.047
0.923
-0.098
0.035
(10.56)
(47.97)
(-3.69)
(1.34)
Page 23 of 36
96.41
96.31
95.97
94.87
96.44
96.33
95.99
94.88
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Large Capitalization Growth Equity
Port.
1
2
3
4
5
6
7
8
9
10
CAPM
3-Moment Model
Adj.

MKT
-0.199
1.378
(-12.28)
(20.30)
-0.096
1.121
(-11.80)
(32.77)
-0.067
1.091
(-9.18)
(35.40)
-0.048
1.074
(-6.88)
(36.70)
-0.031
1.059
(-4.59)
(37.57)
-0.015
1.048
(-2.25)
(37.67)
0.000
1.038
(0.03)
(37.19)
0.017
1.031
(2.42)
(35.63)
0.036
1.026
(4.92)
(33.22)
0.063
1.024
(7.71)
(30.01)
R2
75.83
89.12
90.53
91.13
91.50
91.54
91.34
90.64
89.38
87.29

3-Factor Model
Adj.
MKT
SKEW
-0.173
1.303
-1.390
(-9.01)
(17.71)
(-2.43)
-0.095
1.117
-0.080
(-9.60)
(29.47)
(-0.27)
-0.069
1.095
0.061
(-7.72)
(32.06)
(0.23)
-0.051
1.083
0.162
(-6.05)
(33.45)
(0.64)
-0.035
1.072
0.235
(-4.35)
(34.44)
(0.97)
-0.020
1.064
0.291
(-2.55)
(34.72)
(1.22)
-0.006
1.056
0.342
(-0.78)
(34.44)
(1.44)
0.009
1.053
0.399
(1.11)
(33.17)
(1.62)
0.027
1.052
0.486
(3.09)
(31.17)
(1.85)
0.052
1.056
0.589
(5.34)
(28.38)
(2.04)
R2
76.71
89.04
90.46
91.09
91.50
91.58
91.41
90.76
89.58
87.59
4-Factor Model
Adj.

MKT
SMB
HML
-0.187
1.310
-0.090
-0.517
(-12.82)
(20.64)
(-1.03)
(-6.04)
-0.088
1.084
-0.099
-0.350
(-13.53)
(38.46)
(-2.54)
(-9.21)
-0.059
1.059
-0.100
-0.323
(-10.34)
(42.34)
(-2.91)
(-9.57)
-0.040
1.044
-0.102
-0.309
(-7.41)
(44.28)
(-3.15)
(-9.73)
-0.023
1.029
-0.092
-0.298
(-4.48)
(45.32)
(-2.94)
(-9.74)
-0.008
1.017
-0.079
-0.289
(-1.51)
(44.87)
(-2.54)
(-9.47)
0.007
1.004
-0.065
-0.282
(1.29)
(43.47)
(-2.04)
(-9.06)
0.023
0.993
-0.047
-0.284
(4.13)
(40.92)
(-1.40)
(-8.68)
0.042
0.983
-0.030
-0.289
(6.96)
(37.16)
(-0.82)
(-8.08)
0.068
0.975
-0.003
-0.293
(9.89)
(32.37)
(-0.08)
(-7.22)
Page 24 of 36
R2
80.99
93.36
94.39
94.82
95.04
94.96
94.66
94.07
92.98
91.09
Adj.

MKT
SMB
HML
PR1YR
-0.188
1.383
-0.127
-0.495
0.106
(-13.04)
(19.42)
(-1.44)
(-5.83)
(2.14)
-0.088
1.103
-0.108
-0.344
0.028
(-13.59)
(34.49)
(-2.74)
(-9.02)
(1.26)
-0.060
1.075
-0.109
-0.318
0.024
(-10.38)
(37.86)
(-3.09)
(-9.37)
(1.19)
-0.040
1.059
-0.110
-0.305
0.021
(-7.44)
(39.51)
(-3.31)
(-9.53)
(1.13)
-0.024
1.045
-0.100
-0.293
0.024
(-4.52)
(40.58)
(-3.14)
(-9.54)
(1.31)
-0.008
1.033
-0.088
-0.285
0.024
(-1.55)
(40.23)
(-2.76)
(-9.28)
(1.35)
0.007
1.022
-0.074
-0.277
0.026
(1.26)
(39.07)
(-2.29)
(-8.86)
(1.43)
0.023
1.013
-0.057
-0.278
0.029
(4.11)
(36.87)
(-1.67)
(-8.48)
(1.51)
0.042
1.005
-0.041
-0.282
0.032
(6.96)
(33.58)
(-1.11)
(-7.89)
(1.54)
0.068
1.002
-0.017
-0.285
0.039
(9.92)
(29.42)
(-0.40)
(-7.02)
(1.63)
R2
81.51
93.39
94.41
94.83
95.07
94.99
94.71
94.13
93.05
91.20
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel C: Large Capitalization Value Equity
Port.
1
2
3
4
5
6
7
8
9
10
CAPM
3-Moment Model
Adj.

MKT
-0.137
1.035
(-11.52)
(20.66)
-0.067
0.901
(-7.96)
(25.47)
-0.045
0.879
(-5.59)
(25.78)
-0.030
0.862
(-3.79)
(26.17)
-0.016
0.851
(-2.13)
(26.62)
-0.004
0.843
(-0.56)
(26.84)
0.008
0.837
(1.03)
(26.88)
0.020
0.831
(2.72)
(26.63)
0.035
0.821
(4.72)
(26.13)
0.055
0.811
(7.36)
(25.83)
R2
76.48
83.18
83.52
83.92
84.38
84.60
84.63
84.38
83.88
83.56

3-Factor Model
Adj.
MKT
SKEW
-0.101
0.928
-1.971
(-7.61)
(18.26)
(-4.99)
-0.056
0.869
-0.579
(-5.59)
(22.50)
(-1.93)
-0.038
0.857
-0.412
(-3.86)
(22.85)
(-1.41)
-0.024
0.846
-0.295
(-2.56)
(23.28)
(-1.05)
-0.012
0.839
-0.230
(-1.29)
(23.74)
(-0.84)
-0.001
0.832
-0.191
(-0.07)
(23.98)
(-0.71)
0.011
0.829
-0.154
(1.17)
(24.05)
(-0.57)
0.022
0.825
-0.114
(2.48)
(23.87)
(-0.42)
0.035
0.821
-0.004
(3.91)
(23.58)
(-0.02)
0.001
0.818
0.133
(5.81)
(23.54)
(0.49)
R2
80.13
83.52
83.64
83.93
84.35
84.54
84.55
84.29
83.76
83.47
4-Factor Model
Adj.

MKT
SMB
HML
-0.142
1.158
-0.225
0.433
(-16.35)
(30.65)
(-4.33)
(8.50)
-0.070
1.002
-0.206
0.332
(-13.66)
(45.08)
(-6.74)
(11.06)
-0.048
0.979
0.198
0.331
(-10.22)
(47.64)
(-6.99)
(11.95)
-0.033
0.958
-0.186
0.329
(-7.37)
(49.56)
(-6.98)
(12.62)
-0.020
0.944
-0.169
0.328
(-4.54)
(50.44)
(-6.57)
(12.99)
-0.008
0.932
-0.158
0.327
(-1.82)
(50.60)
(-6.22)
(13.15)
0.004
0.925
-0.151
0.325
(0.95)
(49.76)
(-5.89)
(12.96)
0.016
0.917
-0.140
0.327
(3.71)
(47.80)
(-5.28)
(12.65)
0.031
0.905
-0.128
0.327
(6.73)
(44.68)
(-4.58)
(11.98)
0.051
0.888
-0.107
0.318
(10.08)
(40.47)
(-3.52)
(10.74)
Page 25 of 36
R2
87.89
94.02
94.62
95.01
95.19
95.22
95.07
94.69
93.98
92.78
Adj.

MKT
SMB
HML
PR1YR
-0.142
1.156
-0.225
0.432
-0.002
(-16.28)
(26.82)
(-4.22)
(8.40)
(-0.05)
0.069
0.975
-0.193
0.324
-0.039
(-13.82)
(39.19)
(-6.27)
(10.89)
(-2.25)
-0.048
0.950
-0.184
0.322
-0.042
(-10.39)
(41.58)
(-6.50)
(11.81)
(-2.61)
-0.032
0.929
-0.171
0.320
-0.042
(-7.50)
(43.41)
(-6.48)
(12.53)
(-2.82)
-0.019
0.916
-0.155
0.319
-0.041
(-4.60)
(44.22)
(-6.06)
(12.91)
(-2.87)
-0.007
0.906
-0.145
0.319
-0.039
(-1.80)
(44.29)
(-5.72)
(13.05)
(-2.70)
0.004
0.899
-0.137
0.317
-0.039
(1.04)
(43.54)
(-5.38)
(12.86)
(-2.74)
0.017
0.889
-0.126
0.319
-0.041
(3.87)
(41.78)
(-4.77)
(12.54)
(-2.74)
0.032
0.872
-0.111
0.317
-0.047
(7.02)
(39.09)
(-4.04)
(11.90)
(-3.04)
0.051
0.852
-0.088
0.607
-0.054
(10.51)
(35.33)
(-2.95)
(10.66)
(-3.21)
R2
87.87
94.20
94.85
95.27
95.44
95.45
95.31
94.95
94.35
93.27
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 7 Ranking Mid Capitalization Mutual Fund Portfolio with Different Objectives
This table presents mid capitalization mutual fund performance classified by fund objectives. The definitions of variables are the same as in Table 3. The tstatistics are shown in parentheses, with the italic and bold numbers indicating significance at the 5% and 1% levels, respectively.
Panel A: Mid Capitalization Core Equity
Port.
CAPM

3-Moment Model
Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
5
6
7
8
9
10
-0.208
1.444
(-12.38)
(20.51)
-0.078
1.124
(-8.87)
(30.31)
-0.044
1.083
(-5.42)
(32.13)
-0.020
1.066
(-2.56)
(32.38)
-0.000
1.059
(-0.04)
(-32.06)
0.016
1.057
(2.02)
(31.63)
0.032
1.055
(3.88)
(30.17)
0.050
1.045
(5.60)
(27.61)
0.073
1.037
(7.46)
(25.41)
0.104
1.036
(9.24)
(21.81)
76.22
87.51
88.73
88.89
88.69
88.41
87.41
85.32
83.11
78.36
3-Factor Model
2

4-Factor Model
Adj.
MKT
SMB
HML
R
-0.135
1.234
-3.866
(-8.06)
(19.12)
(-7.71)
-0.058
1.065
-1.080
(-5.70)
(27.16)
(-3.55)
-0.027
1.035
-0.882
(-2.89)
(28.76)
(-3.15)
-0.005
1.023
-0.792
(-0.57)
(28.94)
(-2.89)
0.013
1.019
-0.733
(1.44)
(28.59)
(-2.65)
0.028
1.022
-0.653
(2.99)
(28.15)
(-2.32)
0.043
1.023
-0.593
(4.37)
(26.80)
(-2.00)
0.060
1.017
-0.515
(5.57)
(24.49)
(-1.60)
0.079
1.018
-0.351
(6.75)
(22.60)
(-1.00)
0.107
1.028
-0.135
(7.82)
(19.55)
(-0.33)
83.59
88.53
89.45
89.48
89.18
88.79
87.69
85.50
83.11
78.21
2
Adj.

MKT
SMB
HML
PR1YR
-0.223
1.507
0.388
0.366
0.149
(-14.69)
(20.05)
(4.18)
(4.08)
(2.84)
-0.087
1.096
0.366
0.103
0.061
(-12.74)
(32.27)
(8.71)
(2.54)
(2.58)
-0.053
1.058
0.360
0.115
0.059
(-9.02)
(36.52)
(10.06)
(3.33)
(2.92)
-0.030
1.039
0.369
0.121
0.059
(-5.45)
(38.66)
(11.12)
(3.77)
(3.14)
0.010
1.034
0.376
0.130
0.062
(-1.91)
(39.27)
(11.56)
(4.14)
(3.40)
0.006
1.036
0.375
0.131
0.066
(1.13)
(38.50)
(11.29)
(4.08)
(3.54)
0.022
1.033
0.384
0.119
0.072
(3.89)
(36.16)
(10.89)
(3.50)
(3.62)
0.041
1.016
0.402
0.103
0.073
(6.26)
(31.62)
(10.12)
(2.68)
(3.24)
0.063
1.002
0.406
0.080
0.072
(8.51)
(27.36)
(8.98)
(1.83)
(2.80)
0.095
0.992
0.443
0.053
0.078
(10.58)
(22.36)
(8.07)
(1.00)
(2.51)
R
-0.222
1.405
0.439
0.336
(-14.24)
(20.70)
(4.70)
(3.67)
-0.087
1.055
0.387
0.091
(-12.41)
(34.54)
(9.19)
(2.20)
-0.052
1.018
0.381
0.103
(-8.70)
(38.83)
(10.54)
(2.92)
-0.029
0.999
0.390
0.109
(-5.20)
(40.88)
(11.57)
(3.31)
-0.010
0.991
0.398
0.117
(-1.76)
(41.16)
(11.98)
(3.61)
0.007
0.990
0.398
0.117
(1.17)
(40.10)
(11.70)
(3.52)
0.023
0.983
0.409
0.104
(3.80)
(37.44)
(11.30)
(2.95)
0.041
0.966
0.427
0.088
(6.11)
(33.00)
(10.58)
(2.23)
0.063
0.953
0.431
0.066
(8.36)
(28.84)
(9.46)
(1.47)
0.095
0.939
0.470
0.037
(10.42)
(23.59)
(8.55)
(0.69)
Page 26 of 36
80.11
92.38
93.87
94.48
94.58
94.31
93.61
92.09
90.02
86.31
R2
81.15
92.70
94.21
94.84
94.99
94.78
94.16
92.64
90.52
86.86
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Mid Capitalization Growth Equity
Port.
CAPM

3-Moment Model
Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
5
6
7
8
9
10
-0.244
1.623
(-11.53)
(18.28)
-0.111
1.354
(-7.55)
(21.97)
-0.071
1.307
(-5.25)
(23.00)
-0.043
1.284
(-3.33)
(23.74)
-0.019
1.269
(-1.48)
(23.41)
0.003
1.255
(0.22)
(23.17)
0.024
1.246
(1.82)
(22.60)
0.047
1.229
(3.55)
(22.03)
0.075
1.213
(5.56)
(21.29)
0.113
1.202
(7.68)
(19.50)
71.77
78.63
80.12
81.12
80.67
80.35
79.55
78.71
77.53
74.33
3-Factor Model
2

4-Factor Model
Adj.
MKT
SMB
HML
R
-0.211
1.526
-1.773
(-8.41)
(15.85)
(-2.37)
-0.101
1.324
-0.546
(-5.69)
(19.48)
(-1.03)
-0.063
1.284
-0.425
(-3.87)
(20.45)
(-0.87)
-0.037
1.267
-0.318
(-2.37)
(21.18)
(-0.68)
-0.014
1.253
-0.281
(-0.89)
(20.90)
(-0.60)
0.007
1.243
-0.229
(0.46)
(20.72)
(-0.49)
0.027
1.237
-0.166
(1.70)
(20.26)
(-0.35)
0.049
1.225
-0.084
(3.04)
(19.81)
(-0.18)
0.076
1.212
-0.003
(4.60)
(19.21)
(-0.01)
0.110
1.211
0.156
(6.18)
(17.73)
(0.29)
72.74
78.64
80.08
81.04
80.58
80.23
79.41
78.55
77.36
74.15
2
Adj.

MKT
SMB
HML
PR1YR
-0.241
1.492
0.368
-0.517
0.064
(-13.26)
(16.58)
(3.31)
(-4.81)
(1.03)
-0.112
1.233
0.445
-0.390
0.074
(-11.21)
(24.94)
(7.28)
(-6.60)
(2.14)
-0.073
1.198
0.440
-0.357
0.083
(-8.48)
(28.19)
(8.38)
(-7.04)
(2.79)
-0.045
1.183
0.426
-0.340
0.087
(-5.66)
(30.27)
(8.82)
(-7.28)
(3.21)
-0.022
1.169
0.451
-0.323
0.094
(-2.84)
(30.85)
(9.62)
(-7.15)
(3.55)
-0.000
1.158
0.459
-0.313
0.097
(-0.02)
(30.78)
(9.87)
(-6.98)
(3.71)
0.021
1.146
0.472
-0.312
0.097
(2.65)
(29.78)
(9.93)
(-6.80)
(3.63)
0.043
1.128
0.484
-0.300
0.096
(5.42)
(28.39)
(9.85)
(-6.33)
(3.48)
0.072
1.117
0.481
-0.288
0.101
(8.26)
(26.05)
(9.08)
(-5.62)
(3.38)
0.109
1.106
0.484
-0.291
0.102
(10.55)
(21.66)
(7.68)
(-4.77)
(2.86)
R
-0.241
1.448
0.391
-0.530
(-13.24)
(18.30)
(3.58)
(-4.97)
-0.111
1.182
0.470
-0.405
(-11.01)
(26.83)
(7.74)
(-6.81)
-0.072
1.141
0.468
-0.374
(-8.21)
(29.78)
(8.86)
(-7.24)
-0.044
1.124
0.456
-0.358
(-5.40)
(31.56)
(9.30)
(-7.45)
-0.021
1.105
0.483
-0.343
(-2.64)
(31.74)
(10.07)
(-7.30)
0.000
1.091
0.492
-0.333
(0.06)
(31.46)
(10.30)
(-7.13)
0.021
1.080
0.506
-0.332
(2.62)
(30.49)
(10.37)
(-6.96)
0.044
1.062
0.517
-0.320
(5.28)
(29.16)
(10.30)
(-6.51)
0.072
1.048
0.516
-0.308
(8.02)
(26.73)
(9.55)
(-5.83)
0.109
1.036
0.520
-0.312
(10.34)
(22.47)
(8.17)
(-5.01)
Page 27 of 36
79.83
90.16
91.86
92.63
92.83
92.76
92.42
91.84
90.43
87.08
R2
79.83
90.42
92.27
93.13
93.42
93.41
93.08
92.49
91.15
87.76
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel C: Mid Capitalization Value Equity
Port.
1
2
3
4
5
6
7
8
9
10
CAPM
3-Moment Model
Adj.

MKT
-0.237
1.199
(-7.45)
(8.96)
-0.078
0.995
(-7.75)
(23.44)
-0.046
0.964
(-4.85)
(24.42)
-0.022
0.939
(-2.42)
(24.40)
-0.003
0.919
(-0.36)
(24.25)
0.015
0.902
(1.71)
(23.76)
0.034
0.895
(3.74)
(23.47)
0.052
0.895
(5.75)
(23.39)
0.074
0.894
(7.87)
(22.53)
0.107
0.903
(10.36)
(20.85)
R2
37.72
80.72
81.96
81.94
81.75
81.14
80.76
80.65
79.46
76.80

3-Factor Model
Adj.
MKT
SKEW
-0.144
0.928
-4.979
(-4.04)
(6.77)
(-4.68)
-0.050
0.912
-1.519
(-4.38)
(20.84)
(-4.47)
-0.022
0.895
-1.272
(-2.03)
(21.67)
(-3.96)
-0.000
0.875
-1.176
(-0.02)
(21.60)
(-3.74)
0.016
0.862
-1.046
(1.56)
(21.40)
(-3.34)
0.033
0.851
-0.937
(3.12)
(20.91)
(-2.96)
0.050
0.849
-0.834
(4.62)
(20.64)
(-2.61)
0.065
0.857
-0.697
(6.04)
(20.58)
(-2.15)
0.085
0.862
-0.586
(7.54)
(19.84)
(-1.73)
0.114
0.884
-0.358
(9.13)
(18.48)
(-0.96)
R2
46.33
83.17
83.80
83.58
83.07
82.21
81.59
81.18
79.77
76.79
4-Factor Model
Adj.

MKT
SMB
HML
-0.264
1.277
0.426
1.011
(-9.18)
(10.18)
(2.46)
(5.98)
-0.091
1.040
0.185
0.506
(-13.05)
(34.09)
(4.39)
(12.30)
-0.058
1.008
0.184
0.494
(-9.58)
(-9.58)
(5.01)
(13.79)
-0.035
0.982
0.485
0.491
(-6.08)
(39.06)
(5.35)
(14.49)
-0.016
0.962
0.184
0.498
(-2.86)
(39.45)
(5.48)
(14.85)
0.002
0.941
0.204
0.489
(0.42)
(38.76)
(6.10)
(14.95)
0.021
0.930
0.214
0.485
(3.64)
(37.59)
(6.26)
(14.54)
0.039
0.926
0.228
0.474
(6.63)
(36.01)
(6.44)
(13.68)
0.061
0.918
0.250
0.464
(9.35)
(32.40)
(6.39)
(12.15)
0.09
0.919
0.280
0.455
(11.96)
(27.13)
(5.99)
(9.95)
Page 28 of 36
R2
50.66
91.03
92.65
93.07
93.21
93.07
92.70
92.15
90.57
87.24
Adj.

MKT
SMB
HML
PR1YR
-0.264
1.258
0.436
1.006
-0.028
(-9.13)
(8.78)
(2.46)
(5.88)
(-0.29)
-0.091
1.029
0.190
0.503
-0.16
(-13.00)
(29.60)
(4.43)
(12.10)
(-0.67)
-0.058
0.992
0.192
0.490
-0.023
(-9.56)
(32.85)
(5.13)
(13.57)
(-1.09)
-0.035
0.963
0.195
0.486
-0.027
(-6.06)
(33.81)
(5.53)
(14.27)
(-1.36)
-0.016
0.940
0.195
0.482
-0.031
(-2.84)
(34.14)
(5.73)
(14.65)
(-1.64)
0.003
0.916
0.217
0.482
-0.036
(0.47)
(33.52)
(6.42)
(14.76)
(-1.91)
0.021
0.903
0.228
0.477
-0.040
(3.73)
(32.49)
(6.63)
(14.38)
(-2.09)
0.039
0.897
0.243
0.465
-0.043
(6.77)
(31.08)
(6.81)
(13.51)
(-2.12)
0.061
0.887
0.265
0.455
-0.046
(9.51)
(27.86)
(6.75)
(11.97)
(-2.07)
0.094
0.866
0.307
0.439
-0.078
(12.40)
(23.16)
(6.65)
(9.83)
(-3.00)
R2
50.31
90.99
92.66
93.11
93.30
93.21
92.89
92.36
90.80
87.99
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 8 - Ranking Small Capitalization Mutual Fund Portfolio with Different Objectives
This table presents small capitalization mutual fund performance classified by fund objectives. The definitions of variables are the same as in Table 3. The
t-statistics are shown in parentheses, with the italic and bold numbers indicating significance at the 5% and 1% levels, respectively.
Panel A: Small Capitalization Core Equity
Port.
CAPM

3-Moment Model
Adj.
MKT
2

Adj.
MKT
SKEW
R
1
2
3
4
5
6
7
8
9
10
-0.214
1.428
(-11.22)
(17.81)
-0.084
1.157
(-7.33)
(24.13)
-0.049
1.114
(-4.47)
(24.27)
-0.025
1.092
(-2.33)
(23.96)
-0.006
1.079
(-0.58)
(23.73)
0.010
1.067
(0.94)
(23.55)
0.028
1.060
(2.54)
(23.21)
0.048
1.057
(4.23)
(22.29)
0.074
1.051
(6.06)
(20.62)
0.108
1.050
(8.12)
(18.80)
70.71
81.61
81.78
81.40
81.10
80.86
80.41
79.10
76.40
72.89
3-Factor Model
2

4-Factor Model
Adj.
MKT
SMB
HML
R
-0.153
1.250
-3.266
(-7.29)
(15.48)
(-5.20)
-0.062
1.094
-1.168
(-4.62)
(21.26)
(-2.92)
-0.030
1.059
-1.009
(-2.33)
(21.37)
(-2.62)
-0.008
1.043
-0.906
(-0.65)
(21.09)
(-2.36)
0.010
1.033
-0.847
(0.74)
(20.88)
(-2.20)
0.025
1.023
-0.813
(1.98)
(20.73)
(-2.12)
0.041
1.020
-0.727
(3.17)
(20.44)
(-1.87)
0.060
1.023
-0.627
(4.39)
(19.65)
(-1.55)
0.083
1.023
-0.500
(5.67)
(18.22)
(-1.15)
0.115
1.030
-0.364
(7.15)
(16.68)
(-0.76)
75.60
82.62
82.57
82.03
81.64
81.37
80.78
79.32
76.46
72.80
2
Adj.

MKT
SMB
HML
PR1YR
-0.233
1.379
0.670
0.284
0.097
(-14.42)
(17.21)
(7.07)
(2.97)
(1.73)
-0.101
1.068
0.667
0.252
0.023
(-16.45)
(35.18)
(17.78)
(6.95)
(1.11)
0.066
1.025
0.664
0.270
0.019
(-12.79)
(40.01)
(20.96)
(8.82)
(1.09)
-0.043
1.001
0.670
0.281
0.014
(-8.82)
(41.53)
(22.48)
(9.75)
(0.83)
-0.024
0.984
0.678
0.287
0.008
(-5.25)
(43.30)
(24.15)
(10.59)
(0.52)
-0.008
0.973
0.679
0.290
0.008
(-1.72)
(43.94)
(24.81)
(10.98)
(0.53)
0.010
0.963
0.680
0.281
0.009
(2.11)
(41.73)
(23.84)
(10.17)
(0.53)
0.030
0.956
0.695
0.267
0.010
(5.72)
(36.79)
(21.63)
(8.61)
(0.57)
0.056
0.944
0.719
0.247
0.015
(8.70)
(29.77)
(18.34)
(6.52)
(0.68)
0.090
0.931
0.756
0.216
0.016
(11.62)
(24.23)
(15.93)
(4.71)
(0.61)
R
-0.233
1.312
0.733
0.264
(-14.27)
(18.49)
(7.50)
(2.76)
-0.101
1.052
0.675
0.247
(-16.41)
(39.47)
(18.34)
(6.86)
-0.066
1.012
0.671
0.266
(-12.75)
(44.88)
(21.58)
(8.74)
-0.043
0.992
0.675
0.278
(-8.81)
(46.84)
(23.12)
(9.73)
-0.024
0.979
0.681
0.286
(-5.25)
(49.10)
(24.80)
(10.63)
-0.008
0.967
0.682
0.289
(-1.72)
(49.83)
(25.48)
(11.03)
0.010
0.958
0.683
0.279
(2.13)
(47.31)
(24.48)
(10.21)
0.030
0.949
0.698
0.265
(5.75)
(41.64)
(22.23)
(8.63)
0.056
0.934
0.724
0.244
(8.73)
(33.56)
(18.87)
(6.50)
0.090
0.920
0.762
0.213
(11.67)
(27.30)
(16.40)
(4.69)
Page 29 of 36
79.35
94.87
96.04
96.39
96.73
96.84
96.53
95.65
93.67
91.13
R2
79.67
94.88
96.05
96.38
96.71
96.82
96.51
95.63
93.64
91.09
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Small Capitalization Growth Equity
Port.
1
2
3
4
5
6
7
8
9
10
CAPM
3-Moment Model
Adj.

MKT
-0.240
1.641
(-10.25)
(16.70)
-0.111
1.358
(-7.30)
(21.25)
-0.073
1.317
(-5.02)
(21.68)
-0.045
1.297
(-3.15)
(21.67)
-0.021
1.289
(-1.50)
(21.73)
0.001
1.284
(0.09)
(21.49)
0.025
1.280
(1.70)
(21.12)
0.049
1.268
(3.29)
(20.32)
0.077
1.255
(4.98)
(19.39)
0.114
1.241
(7.11)
(18.40)
R2
67.97
77.48
78.16
78.15
78.24
77.87
77.26
75.88
74.11
72.04

3-Factor Model
Adj.
MKT
SKEW
-0.202
1.531
-2.028
(-7.30)
(14.39)
(-2.45)
-0.099
1.322
-0.673
(-5.38)
(18.77)
(-1.23)
-0.062
1.287
-0.563
(-3.56)
(19.20)
(-1.08)
-0.036
1.272
-0.460
(-2.11)
(19.24)
(-0.90)
-0.014
1.267
-0.395
(-0.81)
(19.33)
(-0.78)
0.008
1.266
-0.331
(0.44)
(19.16)
(-0.64)
0.029
1.266
-0.245
(1.67)
(18.88)
(-0.47)
0.052
1.257
-0.188
(2.91)
(18.20)
(-0.35)
0.079
1.248
-0.135
(4.25)
(17.40)
(-0.24)
0.113
1.244
0.057
(5.82)
(16.65)
(0.10)
R2
69.17
77.56
78.19
78.12
78.18
77.77
77.13
75.71
73.92
71.83
4-Factor Model
Adj.

MKT
SMB
HML
-0.245
1.402
0.765
-0.423
(-13.32)
(17.48)
(6.92)
(-3.91)
-0.120
1.158
0.745
-0.224
(-14.21)
(31.43)
(14.67)
(-4.52)
-0.082
1.121
0.750
-0.192
(-11.29)
(35.26)
(17.12)
(-4.48)
-0.055
1.100
0.753
-0.192
(-8.10)
(37.38)
(18.56)
(-4.84)
-0.031
1.092
0.749
-0.194
(-4.70)
(38.20)
(18.99)
(-5.03)
-0.008
1.086
0.756
-0.195
(-1.29)
(38.01)
(19.20)
(-5.06)
0.015
1.079
0.759
-0.202
(2.19)
(36.54)
(18.65)
(-5.08)
0.039
1.065
0.773
-0.197
(5.24)
(33.06)
(17.40)
(-4.54)
0.066
1.051
0.784
-0.196
(8.03)
(29.14)
(15.78)
(-4.03)
0.104
1.036
0.778
-0.205
(10.97)
(25.07)
(13.66)
(-3.67)
Page 30 of 36
R2
80.80
93.27
94.62
95.25
95.45
95.45
95.14
94.21
92.77
90.55
Adj.

MKT
SMB
HML
PR1YR
-0.246
1.488
0.721
-0.397
0.126
(-13.52)
(16.51)
(6.48)
(-3.69)
(2.00)
-0.121
1.208
0.719
-0.209
0.074
(-14.59)
(29.49)
(14.20)
(-4.27)
(2.61)
-0.083
1.172
0.724
-0.177
0.075
(-11.72)
(33.45)
(16.73)
(-4.23)
(3.05)
-0.055
1.156
0.725
-0.175
0.082
(-8.57)
(36.18)
(18.35)
(-4.59)
(3.67)
-0.031
1.150
0.719
-0.177
0.084
(-5.04)
37.26)
(18.87)
(-4.79)
(3.90)
-0.009
1.145
0.726
-0.177
0.087
(-1.46)
(37.33)
(19.14)
(-4.83)
(4.07)
0.014
1.141
0.728
-0.184
0.090
(2.22)
(35.99)
(18.59)
(-4.86)
(4.08)
0.038
1.130
0.740
-0.180
0.096
(5.44)
(32.57)
(17.25)
(-4.29)
(3.96)
0.066
1.120
0.749
-0.175
0.101
(8.33)
(28.65)
(15.50)
(-3.75)
(3.72)
0.103
1.099
0.747
-0.186
0.092
(11.21)
(24.05)
(13.22)
(-3.40)
(2.90)
R2
81.24
93.56
94.95
95.67
95.90
95.94
95.67
94.80
93.43
91.07
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel C: Small Capitalization Value Equity
Port.
1
CAPM
3-Moment Model
Adj.

MKT
-0.179
1.210
(-9.97)
(16.05)
-0.072
0.988
(-5.73)
(18.84)
-0.041
0.955
(-3.30)
(18.50)
-0.018
0.934
(-1.48)
(18.30)
0.000
0.920
(0.03)
(18.16)
0.018
0.912
(1.47)
(18.14)
0.036
0.906
(3.01)
(18.18)
0.054
0.904
(4.59)
(18.11)
0.075
0.898
(6.31)
(17.91)
0.104
0.899
(8.57)
(17.68)
R2
66.21

3-Factor Model
Adj.
MKT
SKEW
-0.098
0.975
-4.308
(-5.58)
(14.39)
(-8.18)
-0.040
0.897
-1.665
(-2.82)
(16.33)
(-3.90)
-0.013
0.875
-1.463
(-0.93)
(16.00)
(-3.44)
0.006
0.865
-1.270
(0.41)
(15.81)
(-2.99)
0.021
0.859
-1.130
(1.51)
(15.71)
(-2.66)
0.037
0.856
-1.015
(2.58)
(15.72)
(-2.40)
0.053
0.856
-0.912
(3.74)
(15.79)
(-2.16)
0.070
0.860
-0.802
(4.90)
(15.77)
(-1.89)
0.088
0.860
-0.686
(6.16)
(15.64)
(-1.60)
0.113
0.871
-0.510
(7.78)
(15.55)
(-1.17)
R2
77.58
4-Factor Model
Adj.

MKT
SMB
HML
-0.206
1.203
0.652
0.802
(-16.59)
(22.22)
(8.74)
(10.98)
-0.094
0.984
0.526
0.653
(-14.33)
(34.56)
(13.41)
(17.02)
-0.063
0.953
0.524
0.664
(-10.58)
(36.80)
(14.69)
(19.01)
-0.040
0.933
0.524
0.664
(-7.13)
(37.94)
(15.46)
(20.04)
-0.022
0.919
0.523
0.664
(-4.00)
(38.54)
(15.91)
(20.65)
-0.005
0.908
0.524
0.655
(-0.83)
(38.46)
(16.10)
(20.56)
0.014
0.890
0.526
0.642
(2.53)
(37.88)
(16.06)
(20.04)
0.033
0.895
0.530
0.630
(5.76)
(36.11)
(15.51)
(18.86)
0.054
0.884
0.536
0.613
(8.88)
(33.54)
(14.75)
(17.23)
0.082
0.877
0.553
0.587
(12.51)
(30.56)
(13.99)
(15.18)
R2
84.30
Adj.

MKT
SMB
HML
PR1YR
-0.206
1.207
0.650
0.803
0.006
(-16.52)
(19.53)
(8.51)
(10.88)
(0.15)
-0.093
0.950
0.543
0.643
-0.050
(-14.49)
(29.80)
(13.79)
(16.90)
(-2.24)
-0.063
0.917
0.543
0.653
-0.053
(-10.76)
(31.85)
(15.26)
(18.99)
(-2.66)
-0.040
0.894
0.543
0.653
-0.056
(-7.27)
(32.95)
(16.20)
(20.15)
(-2.98)
-0.022
0.878
0.543
0.651
-0.059
(-4.07)
(33.59)
(16.82)
(20.87)
(-3.26)
-0.004
0.865
0.546
0.542
-0.062
(-0.79)
(33.59)
(17.14)
(20.86)
(-3.47)
0.014
0.855
0.548
0.629
-0.065
(2.74)
(33.13)
(17.18)
(20.39)
(-3.63)
0.033
0.846
0.554
0.615
-0.071
(6.15)
(31.57)
(16.73)
(19.22)
(-3.79)
0.054
0.833
0.562
0.598
-0.075
(9.41)
(29.17)
(15.92)
(17.52)
(-3.76)
0.083
0.819
0.583
0.570
-0.085
(13.29)
(26.48)
(15.24)
(15.42)
(-3.93)
R2
84.18
2
3
4
5
6
7
8
9
10
72.99
72.27
71.82
71.50
71.46
71.54
71.39
70.93
70.40
75.65
74.40
73.44
72.77
72.47
72.33
71.95
71.27
70.48
Page 31 of 36
92.84
93.72
94.12
94.33
94.33
94.18
93.66
92.76
91.51
93.06
94.00
94.46
94.73
94.78
94.69
94.26
93.44
92.37
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Table 9 - Meta-Analysis for Different Economic Cycles
The objective of the meta-analysis is to test whether the significance of statistics for several studies is homogenous. The bull periods are years 1999-2000
and 2002-2007, and the bear periods are years 2001 and 2008-2009. The bull and bear periods columns compare the latest bull (2002-2007) and bear
(2008-2009) periods. This table presents the meta-analysis of mutual fund performance over economic cycles. The statistic follows the chi-square
distribution with n-1 degrees of freedom. The null hypothesis is that the t-statistic values for several studies are consistent. The italic and bold numbers
indicate significance at the 5% and 1% levels, respectively.
Port.
Panel A: Comparison over Different Time Periods
Bull Periods
Bear Periods
Bull and Bear Periods
CAPM
3-Moment
3-Factor
4-Factor
CAPM
3-Moment
3-Factor
4-Factor
CAPM
3-Moment
3-Factor
4-Factor
1
27.900
25.848
37.584
46.465
2.101
8.201
4.234
3.892
47.434
9.990
49.501
58.428
2
17.940
13.468
27.826
34.945
3.001
4.205
6.160
12.751
21.517
5.249
19.908
14.688
3
8.820
5.917
14.688
19.908
1.066
1.531
3.458
3.511
20.866
5.249
17.464
17.820
4
3.458
0.708
5.917
9.245
0.638
0.423
2.376
0.442
15.015
3.538
12.251
16.074
5
3.125
0.192
3.645
5.986
0.387
0.006
1.280
0.045
7.296
1.411
6.020
9.857
6
7.296
0.650
8.778
10.442
0.045
0.304
0.259
0.480
1.328
0.080
1.037
2.691
7
5.478
0.925
7.605
6.734
0.026
0.966
0.048
0.781
0.274
0.432
0.361
0.042
8
0.211
3.354
0.952
0.432
0.451
2.247
1.170
1.170
3.432
1.882
3.948
3.726
9
2.531
7.880
1.462
2.420
1.748
4.381
4.410
2.464
7.296
3.001
9.245
10.951
10
12.751
13.992
11.329
13.781
5.024
7.960
12.751
7.683
9.812
3.432
12.650
16.302
Page 32 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Comparison across Deciles
3-Moment
3-Factor
4-Factor
CAPM
Bull Periods
Year 1999-2000
523.911
89.856
572.901
472.507
Year 2002-2007
1048.119
433.306
1190.443
1169.110
Year 2001
155.806
27.668
130.577
120.915
Year 2008-2009
312.581
168.901
383.720
353.227
Bear Periods
Table 10 - Meta-Analysis for Different Fund Objectives
This table presents the meta-analysis of mutual fund performance by fund objectives. The statistic follows the chi-square distribution with n-1 degrees of
freedom. The null hypothesis is that the t-statistic values for several studies are consistent. The italic and bold numbers indicate significance at the 5% and
1% levels, respectively.
Page 33 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel A: Market Capitalization Across Objectives
Port.
Large-Cap
CAPM
3-Moment
Medium-Cap
3-Factor
4-Factor
CAPM
3-Moment
3-Factor
Small-Cap
4-Factor
CAPM
3-Moment
3-Factor
4-Factor
1
33.801
26.524
20.988
20.995
13.891
11.793
14.362
16.672
0.861
1.961
5.659
4.740
2
25.261
18.468
7.738
7.046
1.012
1.153
2.177
1.871
1.675
3.457
3.060
2.437
3
13.080
11.386
2.264
1.893
0.171
1.695
0.964
0.583
1.543
3.463
2.448
2.062
4
7.348
7.560
0.615
0.483
0.480
3.022
0.426
0.192
1.395
3.202
1.422
1.385
5
4.277
5.503
0.319
0.259
1.143
3.815
0.678
0.577
1.186
2.793
0.785
0.792
6
2.049
3.627
0.352
0.300
1.852
4.498
0.641
0.666
0.969
2.437
0.396
0.460
7
0.773
2.252
0.474
0.430
2.650
5.239
0.819
0.910
0.881
2.287
0.093
0.226
8
0.055
1.037
0.406
0.374
3.022
5.207
0.927
0.929
0.901
2.137
0.177
0.256
9
0.447
0.392
0.070
0.088
3.038
4.630
0.955
0.875
0.999
1.968
0.412
0.602
10
3.512
1.389
0.238
0.263
3.623
4.369
1.667
2.245
1.118
2.002
1.189
2.428
Page 34 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel B: Comparison Across Deciles
CAPM
3-Moment
3-Factor
4-Factor
Core
743.721
410.615
885.711
892.907
Growth
435.523
241.723
619.167
626.069
Value
306.730
163.055
692.613
718.352
Core
459.282
260.245
678.720
714.704
Growth
326.015
193.429
585.927
608.146
Value
359.958
203.433
671.688
686.762
Small-Cap
331.617
190.660
897.955
900.352
Growth
274.700
161.745
743.131
786.841
Value
289.640
154.024
878.423
929.419
Large-Cap
Medium-Cap
Core
Page 35 of 36
Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Panel C: Objectives Across Market Capitalizations
Port.
Core
Growth
Value
CAPM
3-Moment
3-Factor
4-Factor
CAPM
3-Moment
3-Factor
4-Factor
CAPM
3-Moment
3-Factor
4-Factor
1
35.137
31.350
16.901
16.471
2.107
1.505
0.144
0.115
8.439
6.412
35.458
35.264
2
33.480
27.805
12.472
10.579
12.792
11.064
5.684
6.030
3.032
3.857
0.820
1.114
3
21.321
21.138
9.499
8.011
10.934
10.741
4.975
5.301
2.731
4.381
0.513
0.755
4
15.740
17.891
7.722
6.615
8.849
9.711
3.935
4.304
2.699
5.152
0.941
1.197
13.170
17.045
8.005
7.284
6.407
8.170
2.559
2.644
2.650
5.322
1.471
1.630
6
10.465
15.268
7.019
6.768
3.864
6.000
1.445
1.474
3.110
5.830
2.520
2.587
7
8.246
13.047
6.023
6.195
2.002
4.051
0.921
1.013
3.932
6.427
3.655
3.702
8
4.989
9.193
4.925
5.078
0.700
2.327
0.852
1.162
4.674
6.610
4.495
4.664
9
1.846
4.487
2.616
2.618
0.250
1.249
0.756
1.191
4.961
6.715
3.903
3.974
10
1.478
0.408
0.938
0.721
0.229
0.355
0.589
0.832
4.556
5.575
3.247
4.031
5
Page 36 of 36