International Research Journal of Finance and Economics

ISSN 1450-2887 Issue 50 (2010)

EuroJournals Publishing, Inc. 2010
http://www.eurojournals.com/finance.htm

Empirical Examination of Mutual Fund Performance

Eddy Junarsin
Faculty of Economics and Business, Universitas Gadjah Mada
E-mail: john.junarsin@fe.ugm.ac.id

Abstract
This study empirically examines the performance of mutual funds for the period of
1961-2009. In some tests, data available only cover year 2001 to year 2009. Two databases
are harnessed in analyzing the performance of mutual funds: (1) CRSP survivorship-biasfree mutual fund database and (2) CRSP main database. This research employs three main
approaches: (1) regressions to examine the relationship between fund return and actual
12b1 fee, management fee, expense ratio, turnover ratio, and age; (2) Jensens (1968) alpha
and Carharts (1997) four-factor model; (3) Grinblatt and Titmans (1993) measure; and (4)
Daniel et al.s (1997) and Wermers (2000) characteristic selectivity, characteristic timing,
and average style measures. Results provide evidence that gross return is positively related
to expense ratio, age, 12b1 fee, and management fee and negatively related to turnover
ratio. This finding is at odds with the evidence provided by Wermers (2000). During the
period of analysis, there were more mutual fund managers with significantly negative riskadjusted performances than those with significantly positive performances. This implies
that the majority of mutual fund managers do not have special capabilities of beating the
markets. Findings also show that Grinblatt and Titmans measures are significant but
negative for growth and growth and income funds. This implies that fund managers do not
have a special capability of outperforming benchmarks. Most of characteristic selectivity,
characteristic timing, and average style measures are insignificant, except the average style
for small-cap funds. Overall, the test results are not in favor of the assessment of fund
managers ability.
Keywords: Mutual fund performance, Jensens alpha, Carharts four-factor model,
characteristic selectivity, characteristic timing, and average style measures.
JEL Classifications codes: G10, G11, G23

1. Introduction
This paper emphasizes the holdings of domestic equity funds. According to Investment Company Fact
Book (2009), U.S. had the largest mutual fund market (51%) in the world in 2008. Of these U.S.
mutual funds, domestic equity funds had a share of 30%.

International Research Journal of Finance and Economics - Issue 50 (2010)

81

Figure 1: Shares of Total Net Assets of Mutual Funds

Source: Investment Company Fact Book (2009)

Figure 2: Shares of U.S. Mutual Funds

Source: Investment Company Fact Book (2009)

This research is aimed at empirically testing the performance of mutual funds for the period of
1961-2009. In some tests, data available only cover year 2001 to year 2009. Two databases are
harnessed in analyzing the performance of mutual funds: (1) CRSP survivorship-bias-free mutual fund
database and (2) CRSP main database. This paper employs three main approaches: (1) regressions to
examine the relationship between fund return and actual 12b1 fee, management fee, expense ratio,
turnover ratio, and age; (2) Jensens (1968) alpha and Carharts (1997) four-factor model; (3) Grinblatt
and Titmans (1993) measure; and (4) Daniel et al.s (1997) and Wermers (2000) characteristic
selectivity, characteristic timing, and average style measures.
Results provide evidence that gross return is positively related to expense ratio, age, 12b1 fee,
and management fee and negatively related to turnover ratio. This finding is at odds with the evidence
provided by Wermers (2000). During the period of analysis, there were more mutual fund managers
with significantly negative risk-adjusted performances than those with significantly positive
performances. This implies that the majority of mutual fund managers do not have special capabilities
of beating the markets. Findings also show that Grinblatt and Titmans measures are significant but

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International Research Journal of Finance and Economics - Issue 50 (2010)

negative for growth and growth and income funds. This implies that fund managers do not have a
special capability of outperforming benchmarks. Most of characteristic selectivity, characteristic
timing, and average style measures are insignificant, except the average style for small-cap funds.
Overall, the test results are not in favor of the assessment of fund managers ability.
The remainder of this paper is organized as follows. Section 2 presents literature review related
to mutual fund performance. Section 3 discusses research methods and results for model regressions.
Research methods and results for Jensens alpha and Carharts four-factor model are explained in
Section 4. Subsequently, Section 5 shows research methods and results for Grinblatt and Titmans
model and Daniel et al.s and Wermers measures. Eventually, Section 6 concludes.

2. Literature Review
A vast array of literature has tested and discussed mutual fund performance in various settings and
times. According to Jensen (1968), the concept of performance has at least two dimensions: (1) the
ability of the fund manager to increase returns through successful prediction of future prices and (2) the
ability of the fund manager to minimize the amount of insurable risk. Using 115 open-end mutual
funds for the period of 1955-1964, he finds that the fund managers, on average, were not able to
predict security prices well enough to outperform a buy-and-hold policy. Furthermore, they were also
not capable of performing significantly better than that expected from random chance.
Carhart (1997) used monthly data of diversified equity funds from January 1962 to December
1993, covering 1,892 diversified equity funds and 16,109 fund years. This research elaborates on shortterm persistence in equity mutual fund returns with common factors in stock returns and investment
costs. He provides evidence that buying last year's top-decile mutual funds and selling last year's
bottom-decile funds produces a return of eight percent annually. Of this spread, differences in the
market value and momentum of stocks held explain 4.6 percent, differences in transaction costs explain
1 percent, and differences in expense ratios explain 0.7 percent. Expense ratios, portfolio turnover, and
load fees are found to be negatively related to performance. This evidence suggests that: (1) investors
should avoid funds with persistently poor performance, (2) funds with high returns last year have
higher-than-average expected returns next year, but not in years afterwards, and (3) the investment
costs of expense ratios, transaction costs, and load fees have negative impacts on performance.
In another setting, Wermers (2000) merged CDA Investment Technologies database with CRSP
database to test mutual fund holdings and performance. He decomposed mutual fund returns and costs
into several components, finds that mutual funds, on average, hold stocks that outperform the market
index by 130 basis points annually, but their net returns underperform the market by 230 basis points.
Of this 2.3 percent difference in results, 0.7 percent is contributed by the underperformance of nonstock holdings and 1.6 percent is due to expenses and transaction costs. It is also found that highturnover funds, albeit with higher transactions costs, also hold stocks with significantly higher average
returns than do low-turnover funds.
Grinblatt and Titmans (1993) results seem to be more favorable towards the assessment of
fund managers abilities. Some fund managers are found to outperform benchmarks by two to three
percent. However, this study has caveats (Daniel et al. 1997) such as: (1) their benchmarks may not
account for anomalies such as size, book-to-market, and momentum factors; (2) the number of funds is
very limited. Hence, Daniel et al. (1997) and Wermers (2000) try to improve Grinblatt and Titmans
(1993) method by introducing a new approach to forming benchmarks by directly matching the
characteristics of the components stocks of a portfolio. They divide fund returns into: (1) characteristic
selectivity (CS); (2) characteristic timing (CT); and (3) average style (AS) measures. According to
Daniel et al. (1997), there are advantages of using mutual fund holdings to measure performance: (1)
using portfolio holdings of funds enable us to design benchmarks that capture investment styles, (2)
hypothetical returns generated from the portfolio holdings of funds exclude fees, expenses, and other
costs such that comparison with benchmarks will be more meaningful.

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International Research Journal of Finance and Economics - Issue 50 (2010)

3. Research Methods and Results: Regression Models

Firstly, I merge monthly_returns, monthly_nav, and monthly_tna files from the CRSP survivorshipbias-free mutual fund database. Lets label it as monthly_data. Subsequently, this merged data set
(monthly_data) is then merged with fund_fees file in order to get actual 12b1, management fee,
expense ratio, and turnover ratio data. Lets name this newly merged data set monthly_datafees. I
then define age as the difference (in days) between current report date and the first date of report for
each fund. For instance, if fund 1001 had its first-time data on March 31, 2000, then its age would be
30 days for the observation on April 30, 2000. Afterwards, I merge monthly_datafees data set with
fund_style file. For fund style, I utilize Lipper class, Lipper objective code, and Lipper asset code. I
name this merged data set monthly_datafeesstyle.
I make some adjustments to this data set. An observation is considered a missing value if
monthly net asset value (MNAV) is greater than equal 100 or less than equal 0, monthly total net asset
(MTNA) is greater than equal 20000 or less than equal 0, actual 12b1 fee is less than 0, expense ratio is
less than equal 0, management fee is greater than equal 10 or less than equal -5, turnover ratio greater
than equal 15 or less than 0, or monthly return greater than 1 or less than -1. I divide management fee
by 100 to get the decimal version of management fee. I then define gross return as the sum of monthly
return, actual 12b1 fee, and management fee. This is because net return has included actual 12b1 fee
and management fee. This data set has 36,605 funds and 3,237,544 fund-observations.
Table 1:

Descriptive Statistics of monthly_datafeesstyle Data Set

Mean
0.004469
12.65155
360.2326
0.004988
0.012561
0.004936
0.923675
2511.085
0.013967

Return
NAV
TNA
12b1
Expense ratio
Mgt. Fee
Turnover ratio
Age (days)
Gross ret.

Table 2:
Year
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976

Median
0.004379
10.72
45.8
0.0032
0.0115
0.0055
0.59
1826
0.014173

Std. Dev,
0.045094
9.720591
1208.832
0.003839
0.008713
0.005605
1.204755
2485.124
0.047792

Means of monthly_datafeesstyle Data Set Year by Year

No. of Fund
273
272
288
298
303
320
335
353
428
524
577
600
595
588
579
557

Return
-0.012595959
0.017454577
0.012579436
0.019106995
-0.005474054
0.029962105
0.015869484
-0.01248898
-0.006839271
0.01689344
0.009487659
-0.023524986
-0.022981178
0.026034984
0.020526071

NAV
12.85633466
11.15968301
12.30781267
13.38487259
14.04026835
12.94349191
13.84970005
13.82770826
12.26479617
9.87998653
11.31931735
12.30177855
10.28123161
8.209967055
8.76072074
9.938497984

TNA
81.82820513
88.70152063
104.5857706
123.0129362
158.1424582
165.2850163
191.7977443
195.8834947
154.6156346
144.5066692
152.5976007
161.2416038
136.9122062
104.037302
119.4122724
132.1690772

Expense Ratio
0.007093782
0.007206394
0.008019644
0.008820498
0.007947025
0.008153761
0.008222259
0.008635842
0.009017173
0.010145718
0.011275596
0.012336146
0.011797435
0.011845228
0.01257515
0.012550773

Turnover Ratio

Age (Days)

0.585847328
0.589307644
0.588081944
0.640993299
0.742985956
0.762852865
0.773015106
0.653778465
0.56830702
0.485682973
0.515702337

197.0745856
537.3509128
862.5274356
1187.008255
1477.953052
1738.425797
1918.40085
1915.809116
1909.285192
2045.225671
2280.963056
2565.144198
2853.925506
3082.980268
3359.625636

84
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

International Research Journal of Finance and Economics - Issue 50 (2010)

571
596
606
636
683
759
892
1,059
1,276
1,584
2,003
2,456
2,770
2,943
3,220
3,622
4,445
5,828
7,573
7,863
8,655
9,880
9,956
12,788
13,763
15,289
16,069
16,606
16,946
17,726
18,884
26,478
28,736

0.00201885
0.008180679
0.017356378
0.01848757
0.002444237
0.019501118
0.012743166
0.003934447
0.016344248
0.010756268
0.004011916
0.008711684
0.011681388
0.00108345
0.014708567
0.005128693
0.009562671
-0.001768652
0.013578799
0.007842108
0.009522667
0.007509006
0.012651439
0.000793263
-0.003582828
-0.009226458
0.016718449
0.00760745
0.005212196
0.008904169
0.005143265
-0.027096427
0.020084465

9.922153101
10.28304022
10.55980373
11.10243067
10.74809552
9.717237082
11.26373836
9.617101275
10.56213122
11.67490929
11.36698039
10.36064326
10.6959519
9.933226737
10.27607981
10.10970921
10.60667457
10.25165003
10.70277232
11.62991706
12.48538557
12.81341887
13.57235258
14.56794246
12.1984621
10.79003929
11.21903326
12.96458505
14.09245526
15.24765733
16.27283147
13.3539259
11.27608795

121.7826777
122.5375358
153.1578482
183.8013942
223.0024979
247.5984135
273.3021533
275.4328286
312.2885067
387.3238946
386.8708381
329.4970201
337.7742752
351.303972
363.140937
385.1695988
381.0684238
332.7100837
289.9045054
330.0496713
353.0970298
366.6398236
408.7402246
372.2626475
333.7266374
313.0167648
314.3717069
339.1611071
361.2789729
397.1222802
437.2329267
394.8157325
358.649392

0.011688126
0.011625177
0.011780509
0.01110206
0.010442698
0.010460021
0.010790139
0.00958346
0.009659913
0.009879217
0.010100736
0.010574835
0.011455109
0.011391303
0.011403683
0.011499136
0.010892936
0.01118969
0.011967993
0.012278511
0.012549166
0.012659983
0.01278583
0.013312704
0.013461089
0.013654937
0.013808396
0.013650441
0.013163512
0.012922019
0.012538943
0.012034894
0.011882862

0.52603031
0.458715854
0.558293248
0.627696262
0.740928008
0.72396373
0.82582443
0.846945869
0.792867324
0.905205047
0.925289044
1.007217453
0.884043665
0.869118179
0.898090006
0.627577971
0.630069823
0.693007392
0.770701921
0.766218399
0.772786323
0.823213972
0.909227136
0.956428363
1.064722743
1.107447513
1.039692266
1.017006748
0.939642445
0.888774963
0.883338774
0.902762723
0.988745232

3465.86839
3541.686006
3654.525461
3763.154118
3796.295668
3690.855386
3413.801589
3185.686944
2942.408883
2672.562194
2416.052005
2263.494503
2296.134649
2447.133271
2477.609095
2464.485308
2244.038642
1966.335678
1783.177451
1985.048198
2075.674735
2077.28856
2397.619434
2186.289798
2263.250434
2272.994626
2409.379065
2576.317835
2729.074585
2832.02218
2845.925467
2764.423787
2877.536516

Figure 3: Return and Expense Ratio Year by Year

Figure 3 exhibits that while return is fluctuating, expense ratio is relatively stable over time.

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International Research Journal of Finance and Economics - Issue 50 (2010)

Afterwards, I conduct two regressions using this data set. The first regression uses net return
while the second employs gross return. The regression formulae are shown below, respectively.
(1)
(2)
where i = fund i and t = month t.
Table 3:

Regression Results of monthly_datafeesstyle Data Set

Dependent Var.
Net ret.
Gross ret.

Parameters
Parameters

Intercept
0.001944
0.002150

exp_ratio
0.06335495
0.20281648

turn_ratio
-0.00004323
-0.00012853

age
0.0000001
0.0000001

actual_12b1

man_fee

0.64305429

0.97311

Numbers in bold are statistically significant

Regression results indicate that gross return is positively related to expense ratio, age, 12b1 fee,
and management fee and negatively related to turnover ratio. This finding is at odds with the evidence
provided by Wermers (2000).
Subsequently, I delete non-equity mutual funds and also non-U.S. funds. Therefore, in this
subsection of analysis, I only examine U.S. equity funds. This yields 18,401 funds and 1,109,349 fundobservations.
Table 4:

Descriptive Statistics of U.S. Equity Funds Data Set

Mean
0.002079
16.24867
315.4815
0.00609
0.01418
0.005346
0.945871
2331.427
0.013437

Return
NAV
TNA
12b1
Expense ratio
Mgt. Fee
Turnover ratio
Age
Gross ret.

Median
0.007672
13.1
33
0.005
0.0134
0.0067
0.64
1645
0.018734

Std. Dev,
0.058761
11.13576
1147.482
0.003551
0.010911
0.006433
1.211241
2553.961
0.058181

Compared to the total funds data set, U.S. equity funds data set has a slightly lower mean
return, higher NAV, higher 12b1 fee, higher expense ratio, higher management fee, higher turnover
ratio, and lower age.
Table 5:
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

Descriptive Statistics of U.S. Equity Funds Data Set Year by Year

No. of Fund
418
5152
6030
6972
7444
7674
8014
9208
10554
14896
16080

Ret.
0.084795
0.000701
-0.0073
-0.01971
0.024306
0.010094
0.006143
0.010606
0.005079
-0.03474
0.022877

NAV
20.80246
20.70079
15.60967
13.10229
13.59138
16.28326
17.79293
18.90742
19.67157
15.52127
12.6244

TNA
438.852
447.087
337.5072
269.7246
257.2878
301.4715
327.4008
350.8277
369.7504
296.2441
251.5638

12b1 Fee
0.006137
0.006226
0.006353
0.006395
0.006409
0.006347
0.006239
0.006148
0.006024
0.005835
0.005679

Expense Rat.
0.014944338
0.015025029
0.015156384
0.015549739
0.015859609
0.015706949
0.015008482
0.014406646
0.013653261
0.012778138
0.012494223

Mgt. Fee
0.005751
0.005922
0.005872
0.005599
0.005626
0.006032
0.006189
0.005769
0.005205
0.004696
0.004389

Turnover Rat.
0.947800877
0.981596055
1.121721707
1.126588344
1.015880092
0.960178466
0.941580978
0.867202308
0.851301228
0.854639334
0.944964923

Age (Days)
1900.513
1998.271
1970.869
1976.958
2114.757
2290.811
2423.438
2490.364
2485.266
2414.018
2543.626

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International Research Journal of Finance and Economics - Issue 50 (2010)

I then redo regressions (1) and (2) for the U.S. equity funds data set, and the results are as
follows.
Table 6:

Regression Results of U.S. Equity Funds Data Set

Dependent Var.
Net ret.
Gross ret.

Parameters
Parameters

intercept
0.000410
0.0005234

exp_ratio
0.0886434
0.184064

turn_ratio
-0.000358
-0.000441

age
0.0000002
0.0000001

actual_12b1

man_fee

0.764833933

0.972652771

Numbers in bold are statistically significant

Again, this study finds that gross return is significantly and positively related to expense ratio,
age, 12b1 fee, and management fee. Meanwhile, turnover ratio negatively affects the gross return.
These findings are similar to those for total funds data set.

4. Research Methods and Results: Jensens Alpha and Carharts Four-Factor

Model
Monthly risk-free rate, excess market return, small minus big (SML) factor, high minus low (HML)
factor, and momentum factor are gathered from Kenneth Frenchs data library on his website. French
reveals that the momentum factor is constructed monthly from six value-weighted portfolios formed
using independent sorts on size and prior return of NYSE, AMEX, and NASDAQ stocks. It is the
average of the returns on two high previous return portfolios (above 70th NYSE percentile) minus the
average of the returns on two low previous return portfolios (below 30th NYSE percentile).
I merge U.S. equity funds data set with Fama-Frenchs data set. Lets call it U.S. equity funds
with factors data set. I then define excess returns as:
(3)
where i = fund i and t = month t.
Since this analysis will only involve returns, I intend to conduct tests on portfolio level rather
than fund level. Hence, I then merge the U.S. equity funds with factors data set with crsp_portno_map
file. I then delete observations (redundant funds) referring to the same portfolio number in the same
date. This produces 5,550 portfolios and 261,251 portfolio-observations.
Table 7:

Year
2003
2004
2005
2006
2007
2008
2009

Descriptive Statistics of U.S. Equity Fund Portfolios Data Set Year by Year

Portfolio
1,239
2,444
2,456
2,703
3,019
4,751
5,160

Ret
0.0275
0.0100
0.0062
0.0105
0.0053
-0.0352
0.0218

NAV
15.8555
17.1983
18.8519
20.0170
21.0952
17.0853
13.9845

TNA
459.154
513.502
566.506
610.853
642.164
517.131
409.437

12b1
Fee
0.00384
0.00392
0.00393
0.00392
0.00394
0.00391
0.00387

Expens
e Rat.
0.0141
0.0139
0.0131
0.0127
0.0121
0.0114
0.0111

Mgt.
Fee
0.0058
0.0062
0.0065
0.0059
0.0054
0.0051
0.0047

Turnover
Rat.
1.0121
0.9777
0.9517
0.8744
0.8758
0.867
0.9656

Age
(Days)
3039.9
3197.6
3297.
3337.9
3246.8
3085.6
3055.3

Gross
Ret.
0.0364
0.0197
0.0164
0.0204
0.0149
-0.0253
0.0298

Jensens alpha is then estimated using the following regression:

(4)
where:
Rit = return on hedge fund i in month t,
Rft = risk-free rate in month t,
Rmt = return on market in month t,
e = error term,

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International Research Journal of Finance and Economics - Issue 50 (2010)

= intercept; this will be the measure of Jensens alpha,
= regression slope.

I delete regressions with observations fewer than 30. Results show that most fund portfolios
have insignificant Jensens alphas. 440 fund portfolios experience negative and significant Jensens
alphas whereas only 138 fund portfolios earn positive and significant Jensens alphas.
Table 8:

Jensens Alpha
Mean Alpha
-0.000147009
-0.004921943
0.005653849

Not significant
Negative and significant
Positive and significant

Freq.
2512
440
138

Meanwhile, Carharts (1997) four-factor model is formulated as follows:

(5)
where:
Rit = return on hedge fund i in year t,
Rft = risk-free rate in year t,
Rmt = return on market in year t,
SMB = returns on small-sized portfolios minus those on big-sized portfolios,
HML = returns on high book-to-market portfolios minus those on low book-to-market
portfolios,
Momentum = momentum factor,
= intercept; this will be the measure of hedge fund manager skills,
1, 2, 3, 4 = regression slopes,
e = error term.
Table 9:

Four-Factor Model Regression Parameters

Not significant alpha

Negative and significant alpha
Positive and significant alpha

Mean Alpha
-0.00008.724
-0.00417607
0.004897149

Market Beta
0.936252599
0.947584238
0.911491006

SMB Beta
0.171640932
0.164037567
0.075210578

HML Beta
-0.086797563
0.059233587
-0.129955285

Momentum Beta
0.018179953
0.00183735
0.066952895

Freq.
4226
1173
151

Results in Table 9 imply that 1,173 fund portfolios have negative and significant alphas while
151 fund portfolios enjoy positive and significant alphas, whereas the other fund portfolios are not
significant. Accordingly, during the period of analysis, there were more mutual fund managers with
significantly negative risk-adjusted performances than those with significantly positive performances.
This implies that the majority of mutual fund managers do not have special capabilities of beating the
markets.

5. Research Methods: Grinblatt and Titmans Model and Daniel et al.s and
Wermers Measures
I collect monthly data on permno, stock price, capitalization, return, and exchange code from CRSP
main database from 2003 to 2009. Firstly, I use only stocks traded on the NYSE to create size ranks
based on capitalization. This ranking process is conducted every year from July to June the next year,
similar to Fama and French (1993). Next, the quintile ranks formulated using NYSE stocks are then
applied to all stocks in my CRSP data set. Of each size rank each year, I then create quintile ranks

88

International Research Journal of Finance and Economics - Issue 50 (2010)

based on prior year return (return in month t-13). Accordingly, I now have 25 stock portfolios based on
size and prior-year-return ranks.
From the CRSP Mutual Fund database, I get portno, report date, security rank, effective date,
percentage of a security held relative to total net assets (percent_tna), number of security shares held in
the portfolio (nbr_shares), market value of each security held, and CRSP company key from holdings
file. I define January, February, March as quarter 1; April, May, June as quarter 2; and July, August,
September as quarter 3; and October, November, December as quarter 4. I use report date instead of
effective date. If a portno reported twice in an assigned quarter, then I use the latest report month. For
instance, if a portno A provided reports in February and March 2003, and both months are included
into quarter 1, then I use the report in March. This holdings file is then merged with holdings_co_info
file to get additional variables such as security name, cusip number, permno, and ticker symbol. Since I
use monthly data, I assume that the stock holdings hold for the whole next quarter until a new report is
submitted. For instance, the stock holdings in March 2004 held until a new report appeared in June
2004.
I also make use of monthly_return file to get each funds monthly returns, and get styles from
fund_style file. The styles that I use are taken from Lipper asset code and Lipper objective code. I then
merge the holdings file with monthly_return and fund_style files using crsp_portno_map file as the
intermediary. The merging process is similar to that reported in the previous section.
I exclude fund portfolios with Lipper asset code other than EQ. This purports to analyze
equity funds only. Furthermore, I also exclude international fund portfolios, which have Lipper class
codes of 'CH', 'CN', 'DM', 'EM', 'EMD', 'EU', 'GFS', 'GH', 'GL', 'GLCC', 'GLCG', 'GLCV', 'GLI',
'GMLC', 'GMLG', 'GMLV', 'GNR', 'GRE', 'GS', 'GSMC', 'GSME', 'GSMG', 'GSMV', 'GTK', 'GX', 'IF',
'ILCC', 'ILCG', 'ILCV', 'IMLC', 'IMLG', 'IMLV', 'INI', 'IRE', 'IS', 'ISMC', 'ISMG', 'ISMV', 'JA', 'LT',
'PC', 'XJ'. Finally, I only include observations with Lipper objective codes of G, SC, and GI. G is
growth fund, SC is small-cap fund, and GI is growth and income fund. Eventually, I merge CRSP
mutual fund data set with CRSP main data set by permno.
Grinblatt and Titmans (1993) measure is as follows:
(6)
where:
GTt = GT measure in month t,
wj,t-1 = portfolio weight on stock j in month t-1,
Rjt = return on stock j in month t,
wj,t-13 = portfolio weight on stock j in month t-13.
Weight in this study is calculated as follows:
(7)
where wjt = portfolio weight on stock j in month t.
Based on this approach, the benchmark used is the current return earned by the portfolio formed
or held 13 months ago. I then average the GT measure across all fund portfolios, within the same style,
for a particular month. Subsequently, I time-series average the GT measure across all months to
observe the statistical significance.
Daniel et al. (1997) divide fund returns into: (1) characteristic selectivity (CS); (2)
characteristic timing (CT); and (3) average style (AS) measures. CS uses the return on a portfolio of
stocks matched to a fund portfolios holdings each month along the dimensions of market
capitalization (representing size) and prior year return. CS is calculated as follows:
(8)

89

International Research Journal of Finance and Economics - Issue 50 (2010)

where:

CSt = CS measure in month t,

wj,t-1 = portfolio weight on stock j in month t-1,
Rjt = return on stock j in month t,
Rt (bj,t-1) = return in month t on portfolio of stocks b in which stock j belongs to in month t-1.
I subsequently average the CS measure across all fund portfolios, within the same style, for a
particular month. Finally, I time-series average the CS measure across all months to observe the
statistical significance. If CS is not statistically different from zero, that means the performance of a
fund portfolio can be replicated by buying stocks with similar capitalization and prior year return
characteristics to the stocks that the fund portfolio holds. On the other hand, CS is significantly
positive, this implies that the fund manager has a selectivity ability.
A fund manager is considered capable of earning additional performance if size or momentum
(prior year return) strategy has time-varying expected returns that the fund manager can exploit by
adjusting his or her portfolio weights (Daniel et al. 1997). For instance, if Fund Portfolio A increases
its weight on big size stocks at the beginning of a month when size effect is strong, then the manager is
said to have a timing capability. This ability is measured by CT:
(9)
where:
CTt = CT measure in month t,
wj,t-1 = portfolio weight on stock j in month t-1,
wj,t-13 = portfolio weight on stock j in month t-13,
Rt (bj,t-1) = return in month t on portfolio of stocks b in which stock j belongs to in month t-1,
Rt (bj,t-13) = return in month t on portfolio of stocks b in which stock j belongs to in month t-13.
I then average the CT measure across all fund portfolios, within the same style, for a particular
month. Afterwards, I time-series average the CT measure across all months to observe the statistical
significance.
The other measure is AS, which indicates returns earned due to the tendency to hold stocks
with certain characteristics.
(10)
where:
ASt = AS measure in month t,
wj,t-13 = portfolio weight on stock j in month t-13,
Rt (bj,t-13) = return in month t on portfolio of stocks b in which stock j belongs to in month t-13.
The table below shows the number of fund portfolios in my sample based on each investment
style.
Table 10: Number of Fund Portfolios Based on Each Style
Year
G
2004
457
2005
550
2006
584
2007
613
2008
666
2009
843
G = growth funds; SC = small-cap funds; GI = growth and income funds

SC
250
300
328
351
378
464

GI
222
259
263
265
268
344

90

International Research Journal of Finance and Economics - Issue 50 (2010)

Table 11:
Year
2004

2005

2006

2007

2008

2009

Month

Growth Funds: Mean Across All Funds for a Particular Month

Freq.

Size Style

Momentum Style

Hypothetical Ret.

CS

CT

AS

GT
-0.001627328

10

298

2.85593625

1.418632115

0.012313585

0.000359085

0.448687335

0.008680332

11

240

2.780604447

2.240117746

0.029412678

-0.004464185

3.24273016

0.032407628

0.00112773

12

398

2.935873056

1.751989612

0.025923803

-0.002976422

2.170412644

0.028802728

-0.001820733

310

2.703022211

1.972838232

-0.013478884

-0.000750097

-2.058699431

-0.012834372

0.000606208

232

2.693404493

1.697025525

0.015413183

0.000367114

1.150573039

0.017649149

0.000718775

329

3.068990524

1.964607182

-0.00893856

-0.002317876

-1.390109978

-0.006765334

-0.000150841

288

2.664124454

1.376466412

-0.011931324

0.002010567

-2.119613663

-0.015522054

-0.001420633

243

2.678078594

1.323887298

0.02779729

0.000382911

1.812421108

0.023890196

-0.000318228

398

3.208453864

1.938633835

0.006532886

-0.007807275

0.408651636

0.006860571

0.002011838

336

2.895970388

1.904251879

0.030926938

-0.001068187

2.93648849

0.029798279

0.001578398

332

2.769103077

1.111253093

-0.002459782

-0.003651506

-0.874320025

-0.000556218

0.000351357

433

3.100147033

1.718593456

0.009100826

-0.000775915

0.135207419

0.007686948

0.001631511

10

343

2.765042147

1.853324414

-0.005288651

0.000660098

-1.629693103

-0.008947812

-0.001013073

11

325

2.532760175

1.584506074

0.024182953

-0.001001222

1.884212328

0.023211385

-0.001318596

12

384

2.815291067

2.16100576

0.003328627

-0.001181693

-0.135022531

0.004687421

-0.000514556

334

2.512402615

1.888815941

0.020125924

-0.006473865

3.115773251

0.031773786

0.000703364

318

2.595562203

1.223459903

0.001670326

0.008574563

-1.39408226

-0.009441687

0.000160814

420

2.85019147

1.873573532

0.009746569

-0.003872202

1.484216812

0.012660775

-1.50323E-05

383

2.540967706

1.250527389

0.007295277

-0.000652991

0.004385948

0.006621355

-0.000341463

357

2.456910718

1.145098254

-0.015844876

0.000374212

-2.981220793

-0.019212277

0.001129957

453

2.873879563

2.234859921

0.001170433

-0.00304371

-0.595760838

0.002688181

-0.000268536

422

2.641585403

1.601910809

-0.000538306

-0.000171785

-1.538702076

-0.00623847

1.32018E-05

385

2.549360298

1.968935626

0.01604684

-0.000960952

0.941413212

0.014636283

-0.002430708

463

2.809287992

1.524627932

0.016399466

0.009664484

1.302115553

0.020570577

0.000133023

10

421

2.566440625

1.617935793

0.019175017

-0.001935065

1.387982951

0.020130082

-0.001001235

11

331

2.565546421

1.412243482

0.013802559

0.000877483

0.411317765

0.011276726

-0.000462558

12

417

3.03586328

2.392186842

0.006284383

0.000923628

-0.263079471

0.003804022

0.000696002

352

2.759706414

1.579171468

0.014931175

-0.002212856

0.995868409

0.015963227

8.27831E-05

318

2.697142822

1.875126449

-0.007612856

-0.004976851

-0.930018219

-0.004526034

-0.001537055

464

2.986082196

1.748695336

0.008297392

0.000944449

0.230406417

0.007886358

0.001269573

421

2.654539987

1.709896479

0.025420488

-0.000188862

1.523035361

0.023294148

0.000475798

392

2.598846869

1.62474888

0.023600646

-6.64899E-05

1.202900511

0.019861251

0.000222316

498

3.022061663

1.153390211

-0.007046513

5.48335E-05

-1.629503892

-0.010674464

7.01566E-05

431

2.736234252

1.535013284

-0.013090805

0.004485645

-2.652241141

-0.017624487

0.001565906

418

2.594490558

1.49549983

0.01078434

0.003406027

0.055270647

0.006774338

0.00070172

501

2.906404014

1.9995984

0.027999565

0.001390063

1.533164008

0.023325068

0.000176548

10

426

2.755142

1.910072277

0.021420136

0.00277393

0.811863439

0.013840713

0.00209106

11

390

2.735879929

2.006919653

-0.02235064

-0.001293136

-3.517901525

-0.024164741

8.32073E-05

12

452

3.099368736

2.140292789

0.001006818

0.0001305

-1.614568721

-0.007682724

0.004343013

440

2.79963341

1.688251335

-0.037004523

-0.007348481

-4.612994549

-0.034185793

-0.007806448
0.002107779

388

2.628692067

1.817585818

-0.010741004

-0.004146712

-2.143256394

-0.012252804

507

2.910666942

1.449259415

-0.001832931

0.003474167

-1.809662595

-0.010670971

0.002020978

482

2.550640701

1.676381475

0.032940906

0.000533269

3.194100427

0.032521265

-0.001068578

434

2.541693003

2.038550389

0.016965976

-0.001077849

1.995480356

0.016839589

0.000798369

524

3.08553207

2.401178172

-0.045879431

-0.000272195

-8.563707093

-0.056287022

0.009346232

459

2.912256953

1.484847989

0.004132195

0.013266854

0.252333851

-0.002284384

-0.005128016

406

2.852810021

1.313355837

0.013672097

0.002909592

1.088646182

0.006893546

0.000639435

504

3.238243743

2.102115727

-0.062226536

0.012327127

-11.27139095

-0.071007556

-0.013489475

10

348

2.818542546

2.26633686

-0.076003296

0.001558149

-13.27198689

-0.083530684

-0.007296101

11

271

2.747997753

1.853744016

-0.05893031

-0.016043448

-2.858886544

-0.037324615

-0.02568203

12

416

3.265212573

1.425556767

0.00747608

-0.01972927

0.643345619

0.014871025

-0.010226546

331

2.404635473

1.832905328

-0.02402494

0.020718092

-3.22110681

-0.038336486

-0.002293091

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International Research Journal of Finance and Economics - Issue 50 (2010)

2

332

2.385976364

1.346877291

-0.04024123

0.009043065

-3.922634162

-0.050506252

0.002142363

608

3.10971774

2.156473074

0.067931673

-0.009930709

9.76431559

0.066322582

-0.011288597

570

2.828300988

2.191141376

0.082658335

-0.002302383

11.28858362

0.057753071

-0.016213057

548

2.734574369

2.517257522

0.037756827

-0.003323799

3.711527979

0.035805859

-0.007606457

688

4.20374233

3.533821616

0.008510996

0.001737747

-0.367844967

0.001131215

0.001919966

634

3.893750476

1.958656511

0.068651748

0.010963957

8.719526793

0.058552678

-0.006436943

540

2.970010821

2.203879304

0.024444175

-0.002066129

2.763517622

0.035202284

0.003115447

654

3.300760902

2.648497082

0.030720986

-0.007979351

7.091316622

0.048394207

-0.024914276

10

231

4.028575493

2.115838116

-0.01349709

0.003000546

-8.291599911

-0.026019502

-0.001533894

11

165

3.695642006

1.272732619

0.043145379

-0.000471039

6.418261856

0.048486357

-0.004624882

12

59

8.286728321

4.335821558

0.023510091

-0.05384732

20.9495887

0.070727917

-0.003673713

(11)
where:
wjt = portfolio weight on stock j in month t,
Size rankjt = size rank which stock j belongs to in month t.
(12)
where:
wjt = portfolio weight on stock j in month t,
Prior-year-return rankjt = prior-year-return rank which stock j belongs to in month t.
(13)
where:
wjt = portfolio weight on stock j in month t,
Retjt = return on stock j in month t.
Size style in Table 11 is cross-funds average for a particular month of each funds size style.
Momentum style in Table 11 is cross-funds average for a particular month of each funds momentum
style. Hypothetical return in Table 11 is cross-funds average for a particular month of each funds
hypothetical return.
Tables 12 and 13 show the means across all funds for a particular month for small-cap funds
and growth and income funds, respectively.

92

International Research Journal of Finance and Economics - Issue 50 (2010)

Table 12: Small-Cap Funds: Mean Across All Funds for a Particular Month
Year
2004

2005

2006

2007

2008

2009

Month
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12

Freq.
184
155
222
195
146
199
170
145
213
193
189
251
201
190
216
196
195
257
240
210
247
231
219
271
250
195
241
219
196
282
251
230
281
248
236
283
244
227
279
269
244
305
282
252
297
248
222
274
209
172
257
208
206
316
297
273
383
359
292
354
98
72
23

Size Style
1.721407626
1.631434671
1.858070705
1.705992493
1.680101549
1.949411843
1.741439212
1.700978384
2.020142891
1.746403404
1.729344949
1.950516987
1.690453622
1.609799696
1.882128425
1.64892922
1.61578853
1.863940003
1.659942595
1.666139962
1.893343002
1.675893248
1.590366484
1.726026006
1.572897901
1.647205893
1.917576848
1.731132703
1.726451517
1.833379219
1.631332316
1.620836203
1.920872793
1.66382728
1.63077359
1.809780343
1.713017918
1.712875606
1.921304339
1.681531172
1.63065414
1.827825621
1.585971483
1.591808543
2.067176092
1.691629116
1.663128772
2.033720111
1.968723347
1.947506091
1.962064173
1.175848559
1.099506153
1.741133676
1.548384184
1.758426374
3.888134784
4.324125823
2.746518442
4.70971936
1.838710793
1.469584974
1.772116684

Momentum Style
1.1843168
1.880812976
1.773021367
1.55352413
1.617630017
1.755521479
1.455129821
1.191549398
1.765734398
1.917546432
1.040137752
1.526639696
1.828648218
1.53931693
2.286911331
1.704232377
1.098725942
1.831131567
1.20101368
0.979775461
2.212699166
1.80768363
1.920891472
1.381757283
1.447810581
1.212750968
2.113588615
1.380568136
1.985710617
1.590218846
1.770160662
1.367821173
1.092936681
1.421531524
1.20292685
1.809032016
1.572031382
1.910062446
1.977975545
1.576678738
1.740649525
1.76338454
1.704152431
1.791152779
2.555869303
1.457287383
1.037864918
2.116967621
2.322086471
2.035186873
1.552557586
1.352737482
1.018765917
1.939828046
1.99022866
2.377459598
5.210549945
3.116768711
2.855455541
5.462290987
1.438648845
0.764226332
1.224329897

Hypothetical Ret.
0.014983891
0.040974442
0.022831466
-0.013076554
0.015701484
-0.008344845
-0.022183587
0.035719016
0.024214141
0.037389649
-0.002215944
0.009308202
-0.011073388
0.025714964
0.001997711
0.040862494
0.003317708
0.028895089
0.00502495
-0.020126683
0.002612236
-0.013321462
0.014010852
0.00992771
0.024062334
0.01734206
0.004805689
0.013873039
0.002303444
0.011489215
0.014815779
0.026134235
-0.003140831
-0.023130966
0.012438667
0.015882234
0.018619299
-0.028186116
0.002638935
-0.027723716
-0.009792372
0.004990661
0.030035549
0.02665829
-0.05019459
0.020318217
0.024286703
-0.04569239
-0.129169421
-0.069847894
0.04982983
-0.028271031
-0.033038761
0.069933547
0.120878516
0.031245661
0.02527546
0.139814828
0.026559341
0.087153037
-0.026842645
0.018003271
0.046564591

CS
-0.000375377
-0.002066379
-0.002884431
0.000831169
0.000388436
0.000953459
-0.000389592
0.001883294
-0.000678247
0.003129881
-0.000297372
-0.000382376
-0.002467186
-0.003294543
-0.002305955
0.000956692
0.003481302
0.002571703
0.001305777
-0.00024828
0.005259
-0.001114753
-0.003823374
0.004676574
-0.00221393
0.001238005
0.000586899
-0.001355959
0.000312325
0.003022272
-0.000936903
0.001541306
0.002212695
0.004935805
0.001018502
0.001375404
0.001391345
0.001212731
0.00176411
-0.003974474
-0.000538536
0.002451993
0.00378919
-0.000995656
0.003371932
0.005162937
0.00121481
0.012563929
0.005451651
-0.004331691
-0.007306973
0.02799263
0.002250826
-0.0103004
-0.024555802
-0.008988247
-0.008180406
0.008598807
-0.02135169
-0.012639377
-0.000628354
-0.002669719
-0.003732314

CT
1.96470822
10.27846194
4.597581906
-4.578246223
2.566663511
-3.542043338
-6.568479287
6.743080731
3.71692733
8.557998205
-1.897126031
-0.019320814
-3.000929279
4.598276936
-0.488331685
7.474230178
-0.863115129
5.100427946
-0.418201415
-6.085295202
-1.836754241
-4.062445516
2.685219037
1.68131968
4.781322227
1.686631306
0.301116929
1.299883493
-0.867781283
0.779284726
1.783760934
3.178623428
-1.991921394
-7.05813286
1.262675209
0.699477524
1.619458521
-8.772109778
-2.084689262
-7.087066042
-4.171422633
-1.444004058
4.154406098
4.611480849
-14.01626989
4.805041963
4.506647992
-16.21775139
-19.07467636
-7.426926268
3.748757331
-6.229168
-7.164605128
18.2793088
35.2847588
7.165112369
5.033397296
34.65319042
9.153621828
41.92818234
-14.34502106
4.842576676
19.59138272

AS
0.01129068
0.040679726
0.023288796
-0.015892234
0.016120172
-0.013304249
-0.026095231
0.030762235
0.014823696
0.03449737
-0.005676959
0.003162912
-0.012819655
0.026929627
0.001469657
0.038423751
-0.003099076
0.024067346
0.001340872
-0.025286983
-0.005169068
-0.017461213
0.015209717
0.012516647
0.024569849
0.012125645
0.003484657
0.012552351
-0.001410332
0.007539656
0.012889781
0.020431762
-0.007758576
-0.028430068
0.008675736
0.008764883
0.011648274
-0.036225637
-0.007696821
-0.030362996
-0.01655716
-0.005107905
0.023874592
0.025044442
-0.064095609
0.016615595
0.018481663
-0.059451634
-0.076335661
-0.039848125
0.028099585
-0.040980496
-0.051764095
0.069635948
0.1241564
0.030079056
-0.005271349
0.148600184
0.046003762
0.115466321
-0.03613706
0.021823835
0.057614726

GT
-0.001188806
-0.002192748
-0.002455669
0.001944736
0.000731785
-0.000567798
0.000431038
-0.000905774
0.000872456
0.000313183
0.000137721
0.002020644
-0.000848135
-0.001985357
-0.000779169
0.000730517
8.23925E-05
-0.000666031
0.000288831
0.000557185
0.000955977
0.000120427
-0.002927169
-0.000778946
-0.001483263
-0.002153199
0.000354859
-0.000492327
-0.000785442
0.001642335
-0.000456538
-0.001192708
-0.000173792
0.000889636
-0.000630657
0.002226887
0.002093249
0.001013131
0.001961802
-0.005431534
0.000433091
0.000478206
0.000533508
-0.003434884
0.00208475
0.004526156
-0.001638007
-0.004517351
0.002775025
-0.035970142
0.007555214
0.005572376
0.013195231
-0.015544651
-0.040696009
-0.014997029
0.077689712
-0.057931531
-0.01364686
-0.013377805
0.000222145
3.92035E-05
-0.013081874

93

International Research Journal of Finance and Economics - Issue 50 (2010)

Table 13: Growth and Income Funds: Mean Across All Funds for a Particular Month
Year
2004

2005

2006

2007

2008

2009

Month

Freq.

Size Style

Momentum Style

Hypothetical Ret.

CS

CT

AS

GT
-0.000966808

10

144

3.195768036

1.568641342

0.01315093

0.000744673

0.791523753

0.009124081

11

112

3.145305765

2.404543841

0.030812551

-0.005138954

3.18387105

0.035667653

3.73135E-05

12

190

3.374575538

1.991410173

0.026630434

-0.004362265

2.266484319

0.032541297

-0.001749315

156

3.113451739

2.442544311

-0.012073146

0.001540217

-2.179071383

-0.014959189

0.000593553

108

3.213953112

1.919472057

0.020221611

0.003357393

1.306846283

0.019504687

0.000667288

158

3.501149607

2.242364159

-0.009230459

-0.001036279

-3.217358938

-0.006665584

0.000790032

134

3.186604415

1.469862343

-0.011615417

0.004285136

-7.003237095

-0.01674794

-0.001377981

110

3.154021369

1.453869142

0.023442265

-0.008239318

4.483544701

0.027161704

-0.000227282

178

3.593198874

2.025048414

0.006913514

-0.008218224

0.662728021

0.007265314

0.001950584

151

3.316636171

2.120967331

0.026674958

-0.007717354

4.269037879

0.033491608

-0.000401762

139

3.212462192

1.320900572

-0.003397178

-0.004429982

-0.976582002

0.00016111

0.000171252

193

3.513967453

1.9921065

0.008565731

-0.001729761

0.162571339

0.008218286

0.00139464

10

148

3.194823417

1.925090217

-0.006204189

0.000302546

-1.747592468

-0.009284805

-0.003032559

11

138

2.966171893

1.782126548

0.02261095

-0.004520651

1.534067306

0.025303166

-0.001800741

12

175

3.233156384

2.40914573

0.004815721

0.000381471

-0.027013073

0.004766327

0.000509854

150

2.882082343

2.142969378

0.022610443

-0.005999135

6.418470097

0.033228558

-7.97428E-05

134

2.980883793

1.398313403

0.003946609

0.012132609

-1.729057744

-0.012273674

-0.000821295

191

3.183481661

2.104225931

0.010211272

-0.003495267

3.090590718

0.012886572

-0.000603322

180

2.927749887

1.398759778

0.013755413

0.005227241

0.279587093

0.007969292

-0.001236414

163

2.827362518

1.358907119

-0.016415121

0.002347438

-5.778540433

-0.021647347

-0.000554405

210

3.264571391

2.357749818

0.002399649

-0.002321698

-0.901152421

0.003645953

-7.04681E-06

193

3.086777079

1.808300069

0.007050718

0.008612376

-2.588669725

-0.007305182

-0.00044125

172

2.99681674

2.169337716

0.016036451

-0.003554554

1.859369595

0.017186844

-0.002155167

206

3.248491357

1.712620946

0.018368241

0.00868119

1.763140974

0.023002178

0.000223905

10

187

2.9688969

1.807969339

0.020036992

-0.002891866

1.871886102

0.022302628

-0.001347959

11

139

2.913476948

1.558520351

0.012333229

-0.001369295

0.751406933

0.013442108

-0.000824516

12

184

3.240212923

2.454499749

0.012893602

0.007404066

0.100580783

0.004130899

-0.000147098

155

3.023084553

1.690954068

0.012720394

-0.006105359

1.895309091

0.017155445

7.10342E-05

133

2.963099

1.971466114

-0.011588867

-0.00798993

-1.087804924

-0.005113013

-0.001548833

195

3.401156611

2.010435057

0.00850082

0.000467391

0.736459136

0.008781433

0.000870986

172

3.03928049

1.89480331

0.027848606

-0.000403866

3.571406435

0.027303335

-0.000350421

160

3.067440459

1.984320998

0.026599397

0.000286726

4.070768514

0.02307251

0.000454133

213

3.369104247

1.309231308

-0.010856674

-0.001734058

-2.382394178

-0.012099939

9.10747E-05

176

3.262722231

1.79826943

-0.024009964

-0.002596653

-6.099090779

-0.021355782

0.001390831

169

3.046931217

1.862452871

0.010381173

0.001615062

0.463080322

0.007971165

-0.000480667

203

3.365320666

2.272909947

0.023901781

-0.005995815

2.186778308

0.026705382

0.000216019

10

190

3.191187682

2.169393269

0.01503866

-0.004997996

1.470740634

0.015725633

0.001539649

11

172

3.091257895

2.212719924

-0.02386778

-0.000840757

-4.725079532

-0.025912495

0.001104344

12

205

3.380579498

2.198997455

-0.003401252

-0.002798529

-2.15850708

-0.010821493

0.005111294

196

3.044910971

1.987581156

-0.02967091

0.003682677

-6.041465278

-0.037990338

-0.004654172
0.004119964

172

2.954988573

1.971965077

-0.015616245

-0.007786824

-3.113101356

-0.014214106

202

3.318019609

1.56155144

-0.003028134

0.002567539

-2.232398469

-0.011882182

0.002380393

190

2.994669912

1.922737559

0.036261573

-0.000985849

4.280922208

0.037662851

-0.000914013

171

3.027924539

2.407161026

0.014483092

-0.006222576

3.135210634

0.019191876

0.001222261

217

3.552676727

2.738163283

-0.061258255

-0.007646912

-11.80485906

-0.066531883

0.013685028

186

3.216979508

1.519138139

0.008322321

0.019048753

0.945194657

-0.001963651

-0.006757328

168

3.152607213

1.328774432

0.014260554

0.001582977

2.092591724

0.00862404

-0.001042497

201

3.483488085

2.21138241

-0.061359332

0.016598925

-14.23284292

-0.077944686

-0.021900595

10

137

3.155175135

2.37719872

-0.091783733

0.002988599

-8.243031966

-0.084700162

-0.003286186

11

126

3.11094796

1.929978174

-0.069012079

-0.019174301

-4.077586835

-0.043597627

-0.030949629

12

174

3.567452689

1.522201409

0.010820318

-0.020473804

1.372786509

0.01986215

-0.007836707

139

2.711836051

2.114128707

-0.040198977

0.008185323

-5.392396657

-0.050521769

-0.002657098

94

International Research Journal of Finance and Economics - Issue 50 (2010)

2

134

2.623152839

1.602639978

-0.055098111

0.000726691

-6.437122055

-0.059837818

0.005191441

230

3.218261073

2.230958638

0.06963942

-0.010298303

11.5928163

0.069487363

-0.013304505

221

2.912264577

2.267909282

0.081307337

-0.008772994

15.17383635

0.06391341

-0.015633332

210

3.03387734

2.777210207

0.041260807

-0.003000023

6.405650952

0.040760891

-0.00816884

291

7.486823306

6.08233021

0.021736462

0.010968185

-0.325101064

0.001657662

0.00216492

275

5.80797204

2.850384315

0.100376719

0.016252096

16.17160019

0.075740016

-0.000143919

222

4.133339165

3.063999918

0.040278804

0.003153573

6.186976654

0.037736882

-0.012569178

261

4.372471465

3.55018221

0.032509467

-0.018334619

10.1654095

0.054896224

-0.004515784

10

94

3.144030141

1.635090395

-0.009383549

0.003986377

-6.435703708

-0.020032267

-0.000461384

11

61

2.364978195

0.824593711

0.027205985

-0.000862772

1.599544394

0.031512236

-0.002809911

12

11

2.259836598

1.241299572

0.016692057

-0.004904193

0.46226262

0.024055956

-0.019070379

Table 14: Significances of CS, CT, AS, and GT Measures

G
SC
GI

CS
-0.00101
-0.00026
-0.00095

CT
0.339842
1.96513
0.283147

AS
0.005487
0.0081
0.00507

GT
-0.0019
-0.00172
-0.0021

Numbers in bold are statistically significant

Table 14 indicates that GT measures are significant but negative for G and GI funds. This
implies that fund managers do not have a special capability of outperforming benchmarks. Most of CS,
CT, and AS measures are insignificant, except AS for SC funds. Overall, the test results are not in
favor of the assessment of fund managers ability.

Conclusion
This study purports to empirically examine the performance of mutual funds for the period of 19612009. Three main approaches are utilized: (1) regressions to examine the relationship between fund
return and actual 12b1 fee, management fee, expense ratio, turnover ratio, and age; (2) Jensens (1968)
alpha and Carharts (1997) four-factor model; (3) Grinblatt and Titmans (1993) measure; and (4)
Daniel et al.s (1997) and Wermers (2000) characteristic selectivity, characteristic timing, and average
style measures.
Results provide evidence that gross return is positively related to expense ratio, age, 12b1 fee,
and management fee and negatively related to turnover ratio. This finding is at odds with the evidence
provided by Wermers (2000). During the period of analysis, there were more mutual fund managers
with significantly negative risk-adjusted performances than those with significantly positive
performances. This implies that the majority of mutual fund managers do not have special capabilities
of beating the markets. Findings also show that Grinblatt and Titmans measures are significant but
negative for growth and growth and income funds. This implies that fund managers do not have a
special capability of outperforming benchmarks. Most of characteristic selectivity, characteristic
timing, and average style measures are insignificant, except the average style for small-cap funds.
Overall, the test results are not in favor of the assessment of fund managers ability.

International Research Journal of Finance and Economics - Issue 50 (2010)

95

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